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Additional file 2: Table S2. The results of mNSS score.

Additional file 5: Table S5. The number of Nissl bodies.

Additional file 12: Table S11. The level of inflammatory factors.

Additional file 8: Figure S1. Related western blot data in triplicate.

Additional file 6: Table S6. The percentage of apoptotic cells.

Additional file 3: Table S3. The results of TTC staining

Additional file 4: Table S4. Protein level of NSE (pg/ml).

Additional file 9: Table S8. The number of GFAP+ and IBA-1+ cells.

Additional file 7: Table S7. The level of apoptosis related protein.

Cardiac remodeling is a self-regulatory response of the myocardium and vasculature under the stressful condition. Cardiomyocytes (CMs), vascular smooth muscle cells (VSMCs), endothelial cells (ECs), and cardiac fibroblasts (CFs) are all involved in this process, characterized by change of morphological structures and mechanical/chemical activities as well as metabolic patterns. Despite current development of consciousness, the control of cardiac remodeling remains unsatisfactory, and to further explore the underlying mechanism and seek the optimal therapeutic targets is still the urgent need in clinical practice. It is now emerging that long noncoding RNAs (lncRNAs) play key regulatory roles in these adverse responses: lncRNA TUG1, AK098656, TRPV1, GAS5, Giver, and Lnc-Ang362 have been indicated in hypertension-related vascular remodeling, H19, TUG1, UCA1, MEG3, APPAT, and lincRNA-p21 in atherosclerosis (AS), and HIF1A-AS1 and Lnc-HLTF-5 in aortic aneurysm (AA). In addition, Neat1, AK139328, APF, CAIF, AK088388, CARL, MALAT1, HOTAIR, XIST, and NRF are involved in postischemia myocardial remodeling, while Mhrt, Chast, CHRF, ROR, H19, Plscr4, and MIAT are involved in myocardial hypertrophy, and MALAT1, wisper, MEG3, and H19 are involved in extracellular matrix (ECM) reconstitution. Signaling to specific miRNAs by acting as endogenous sponge (ceRNA) was the main form that regulates the target gene expression during cardiac remodeling. This review will underline the updates of lncRNAs and lncRNA-miRNA interactions in maladaptive remodeling and also cast light on their potential roles as therapeutic targets, hoping to provide supportive background for following research.

This paper presented a new approach called bi-inverted Gaussian model to calculated the diagnostic characteristic parameters of vegetation spectral. And used the parameters calculated from Hyperion image to make water content mapping. Using laboratory experiment measuring data, the relationships between absorption depth and the vegetation water content (VWC) were calculated. between absorption depth and VWC was 0.868 and the RMSE was 0.798. The correlations between them were higher than other vegetation indices.

Endmember extraction plays an important role in spectral unmixing. Traditional endmember extraction algorithms, such as EEAs, only use spectral information to get the endmember, but ignore the spatial characteristic of the remote sensing image. Because of this, the algorithms are susceptible to the noise and anomaly image, which reduces the accuracy of endmember extraction. Focusing on this problem, a new EEA (OSP-LSC) combining subspace projection and local spatial information is proposed. Based on the theory of convex simplex, the algorithm sequentially extracts the endmembers by combining the subspace projection and simplex volume analysis. During the extracting process, the local spectral similarity constraint is used to improve the robustness to the noise and anomaly pixel, which also avoids to the huge computational cost caused by global spatial information. Furthermore, the simplex volume calculation is free of dimensionality which may cause the possible loss of original information, The experimental results on synthetic and real hyperspectral image show that the experimental results on synthetic and real hyperspectral image show that the proposed algorithm can improve the accuracy of the endmember extraction, and is more robust to the noise and anomaly pixels compared to the spectral based EEAs.

Composites of styrene butadiene rubber (SBR), hydrogenated acrylonitrile–butadiene rubber (HNBR) and silica were prepared. Sulfur (S), dicumyl peroxide (DCP) and a combination of S and DCP (M) were used as curing agents, respectively. The morphology of the composites with different blend ratio was examined to correlate with observations on mechanical properties by scanning electron microscopy. The effects of blend ratio and curing systems on the curing characteristics and mechanical properties, such as stress–strain behavior, tensile strength, elongation at break and hardness of SBR/HNBR/Silica composites, were studied. Composites prepared by M curing systems showed comparatively better mechanical properties, wet traction and rolling resistance than S and D curing systems. The tensile strength, tear strength, and elongation at break were improved by adding HNBR for M curing systems. The wet traction of the vulcanizates containing HNBR was better than that of the vulcanizates without HNBR. A relatively uniform dispersion of silica was observed for SBR/HNBR/silica compositions compared with SBR/silica composites.

Low-viscosity novel bismaleimide was mixed with surface-modified Si 3N4 nanoparticles to form nanocomposites using high shear and ultrasonic process. Impact strength, flexural strength, and storage modulus of the nanocomposites with various contents of Si3N4 nanoparticles were investigated. The mechanical properties were optimal with 3.0% mass fraction of surface-modified Si3N4 nanoparticles. In comparison to the neat bismaleimide resin the impact and flexural strengths of the nanocomposites increased by 52.3% and 20.9%, respectively, and the storage modulus showed a 54% increase at room temperature while the glass-transition temperature of nanocomposites was slightly decreased with the addition of Si3N4 nanoparticles. Surface-modified Si3N 4 nanoparticles as fillers reinforced the toughness of the bismaleimide resin, which was confirmed by scanning electronic microscope.