Fasudil ameliorated liposaccharide-induced acute kidney injury in mice by inhibiting NLRP3

Purpose: To determine the influence of fasudil on LPS-mediated acute kidney injury (AKI) in mice.Methods: Healthy C57 mice (n = 140) of largely similar weight were used in this study. They were assigned to a treatment group (n = 40), a model group (n = 50), and a blank control group (n = 50). Mice in treatment and model groups were injected with lipopolysaccharide (LPS). In the treatment group, each mouse was injected intravenously with fasudil daily before the establishment of the mouse model of AKI. All mice were sacrificed 6 h after establishing the AKI model. Portions of the kidney from mice were used for preparation of tissue homogenates, while the remaining portions were subjected to primary culture. Transformed C3H Mouse Kidney-1 (TCMK1) and mesangial cells from mouse glomeruli (SV40-MES-13) cells were used for assays of cell growth and apoptosis. Blood samples were alsocollected from the mice. Thereafter, the levels of blood urea nitrogen (BUN) and creatinine (Cr) in kidney homogenates of the three groups were determined. Moreover, levels of NLRP3, nuclear factor kappa-B (NF-κB), toll-like receptor 4 (TLR4), tumor necrosis factor-α (TNF-α), interleukin (IL)-6, and IL-1β in the homogenates and blood were assayed. Cell growth and apoptosis were also measured.Results: The treatment group and model group showed higher levels of BUN and Cr than the control group, with a higher level observed in model mice than in the treatment mice. There were significantly higher relative levels of NF-κB, NLRP3 and TLR4 in treatment and model groups than in controls, with a higher level observed in model mice than in treatment mice. There were significantly higher concentrations of inflammatory factors in treatment and model mice groups than in control mice, with higher levels observed in model mice than in treatment mice. The TCMK1 and SV40-MES-13 cells in the two groups showed slower cell growth and stronger apoptosis than those in control group (p < 0.05).Conclusion: Fasudil relieved LPS-mediated AKI in mice by suppressing TLR4/NF-κB signal pathway and lowering NLRP3. Thus, fasudil has potential as a new adjunctive agent for the treatment of AKI

An Analysis of the Equal Opportunity of Graduate Students in China’s First-class Universities Based on the Enrollment Policy

Based on the theory of social justice in New Ethics and Rawls’s theory of justice, this paper discussed the connotation of equal opportunity for postgraduate entrance in china. Taking China’s 42 first-class universities as an example, the practice level of equal opportunity was evaluated in postgraduate entrance, from two dimensions, including academic degree postgraduate and professional degree postgraduate, through constructed the opportunity inequality index. Based on the two kinds of causes and three concrete manifestations of unequal opportunities, the quantitative analysis is carried out through the introduction of the unequal contribution degree. In the end, some suggestions are put forward to promote the equal opportunity of postgraduate entrance, such as “reducing discriminatory conditions of applicants, opening enrollment requirements”, “increasing the number of univesities that have the candidates who exempted from unified-examination, and setting up reasonable enrollment plan of this type candidates”. Keywords: postgraduate enrollment policy, equal opportunity, first-class university DOI: 10.7176/PPAR/9-3-04 Publication date:March 31st 201

A Filter Algorithm with Inexact Line Search

A filter algorithm with inexact line search is proposed for solving nonlinear programming problems. The filter is constructed by employing the norm of the gradient of the Lagrangian function to the infeasibility measure. Transition to superlinear local convergence is showed for the proposed filter algorithm without second-order correction. Under mild conditions, the global convergence can also be derived. Numerical experiments show the efficiency of the algorithm

MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images

For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction. However, they suffer degraded CD performance on small changed areas due to the simple single-scale integration of deep CNNs and transformer modules. To address this issue, we propose a hybrid network based on multi-scale CNN-transformer structure, termed MCTNet, where the multi-scale global and local information are exploited to enhance the robustness of the CD performance on changed areas with different sizes. Especially, we design the ConvTrans block to adaptively aggregate global features from transformer modules and local features from CNN layers, which provides abundant global-local features with different scales. Experimental results demonstrate that our MCTNet achieves better detection performance than existing state-of-the-art CD methods

Robust Point Cloud Processing through Positional Embedding

End-to-end trained per-point embeddings are an essential ingredient of any state-of-the-art 3D point cloud processing such as detection or alignment. Methods like PointNet, or the more recent point cloud transformer -- and its variants -- all employ learned per-point embeddings. Despite impressive performance, such approaches are sensitive to out-of-distribution (OOD) noise and outliers. In this paper, we explore the role of an analytical per-point embedding based on the criterion of bandwidth. The concept of bandwidth enables us to draw connections with an alternate per-point embedding -- positional embedding, particularly random Fourier features. We present compelling robust results across downstream tasks such as point cloud classification and registration with several categories of OOD noise.Comment: 18 pages, 13 figures, 5 table

Optical Excitation of a Nanoparticle Cu/p-NiO Photocathode Improves Reaction Selectivity for CO₂ Reduction in Aqueous Electrolytes

We report the light-induced modification of catalytic selectivity for photoelectrochemical CO₂ reduction in aqueous media using copper (Cu) nanoparticles dispersed onto p-type nickel oxide (p-NiO) photocathodes. Optical excitation of Cu nanoparticles generates hot electrons available for driving CO₂ reduction on the Cu surface, while charge separation is accomplished by hot-hole injection from the Cu nanoparticles into the underlying p-NiO support. Photoelectrochemical studies demonstrate that optical excitation of plasmonic Cu/p-NiO photocathodes imparts increased selectivity for CO₂ reduction over hydrogen evolution in aqueous electrolytes. Specifically, we observed that plasmon-driven CO₂ reduction increased the production of carbon monoxide and formate, while simultaneously reducing the evolution of hydrogen. Our results demonstrate an optical route toward steering the selectivity of artificial photosynthetic systems with plasmon-driven photocathodes for photoelectrochemical CO₂ reduction in aqueous media