367 research outputs found
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
Convolutional Initialization for Data-Efficient Vision Transformers
Training vision transformer networks on small datasets poses challenges. In
contrast, convolutional neural networks (CNNs) can achieve state-of-the-art
performance by leveraging their architectural inductive bias. In this paper, we
investigate whether this inductive bias can be reinterpreted as an
initialization bias within a vision transformer network. Our approach is
motivated by the finding that random impulse filters can achieve almost
comparable performance to learned filters in CNNs. We introduce a novel
initialization strategy for transformer networks that can achieve comparable
performance to CNNs on small datasets while preserving its architectural
flexibility.Comment: 14 pages, 9 figures, 8 table
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 COâ‚‚ Reduction in Aqueous Electrolytes
We report the light-induced modification of catalytic selectivity for photoelectrochemical COâ‚‚ 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 COâ‚‚ 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 COâ‚‚ reduction over hydrogen evolution in aqueous electrolytes. Specifically, we observed that plasmon-driven COâ‚‚ 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 COâ‚‚ reduction in aqueous media
COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks
In this paper, we consider the problem of change detection (CD) with two
heterogeneous remote sensing (RS) images. For this problem, an unsupervised
change detection method has been proposed recently based on the image
translation technique of Cycle-Consistent Adversarial Networks (CycleGANs),
where one image is translated from its original modality to the modality of the
other image so that the difference map can be obtained by performing
arithmetical subtraction. However, the difference map derived from subtraction
is susceptible to image translation errors, in which case the changed area and
the unchanged area are less distinguishable. To overcome the above shortcoming,
we propose a new unsupervised copula mixture and CycleGAN-based CD method
(COMIC), which combines the advantages of copula mixtures on statistical
modeling and the advantages of CycleGANs on data mining. In COMIC, the
pre-event image is first translated from its original modality to the
post-event image modality. After that, by constructing a copula mixture, the
joint distribution of the features from the heterogeneous images can be learnt
according to quantitive analysis of the dependence structure based on the
translated image and the original pre-event image, which are of the same
modality and contain totally the same objects. Then, we model the CD problem as
a binary hypothesis testing problem and derive its test statistics based on the
constructed copula mixture. Finally, the difference map can be obtained from
the test statistics and the binary change map (BCM) is generated by K-means
clustering. We perform experiments on real RS datasets, which demonstrate the
superiority of COMIC over the state-of-the-art methods
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