Influence of miR155 on allergic conjunctivitis in mice via regulation of NF-κB signal pathway

Purpose: To investigate the effect of miR-155 on allergic conjunctivitis (AC) in mice, and to elucidate the mechanism of action. Methods: Sixty (60) Balb/c mice were randomly divided into three groups with 20 mice per group. Ovalbumin (OVA) was used to induce experimental model of AC in mice. Mice in the AC+miR-155 siRNA group were given miR-SiRNA once daily for 2 weeks before inducing AC. The expressions of miR-155 in conjunctival tissue of the control and AC groups were assayed with reverse transcriptionpolymerase chain reaction (RT-PCR). In addition, anti-OVA IgE antibody, eotaxin, IL-13 and IFN-γ levels were determined using ELISA (enzyme-linked immunosorbent assay). The regulatory effect of miR-155 on the NF-κB signal pathway in mice conjunctiva tissue with AC was determined using immunoblotting. Results: Higher miR-155 expression was seen in serum of AC group than in that of control group (p < 0.05). Inhibition of miR-155 mitigated AC-induced pathological injury, reduced infiltration of eosinophils, lowered serum levels of anti-AVO IgE antibody eotaxin and Il-13, and increased IFN-γ level (p < 0.05). Phosphorylation of P65 of conjunctiva tissue of AC mice was blocked after inhibition of miR-155. Conclusion: The inhibition of miR-155 ameliorates AC in mice most likely via a mechanism related to the inhibition of phosphorylation of P65. This provides a theoretical basis for new drug research and development

Semi-Supervised Semantic Segmentation of Remote Sensing Images Based on Dual Cross-Entropy Consistency

Semantic segmentation is a growing topic in high-resolution remote sensing image processing. The information in remote sensing images is complex, and the effectiveness of most remote sensing image semantic segmentation methods depends on the number of labels; however, labeling images requires significant time and labor costs. To solve these problems, we propose a semi-supervised semantic segmentation method based on dual cross-entropy consistency and a teacher–student structure. First, we add a channel attention mechanism to the encoding network of the teacher model to reduce the predictive entropy of the pseudo label. Secondly, the two student networks share a common coding network to ensure consistent input information entropy, and a sharpening function is used to reduce the information entropy of unsupervised predictions for both student networks. Finally, we complete the alternate training of the models via two entropy-consistent tasks: (1) semi-supervising student prediction results via pseudo-labels generated from the teacher model, (2) cross-supervision between student models. Experimental results on publicly available datasets indicate that the suggested model can fully understand the hidden information in unlabeled images and reduce the information entropy in prediction, as well as reduce the number of required labeled images with guaranteed accuracy. This allows the new method to outperform the related semi-supervised semantic segmentation algorithm at half the proportion of labeled images

Learning to Zoom and Unzoom

Many perception systems in mobile computing, autonomous navigation, and AR/VR face strict compute constraints that are particularly challenging for high-resolution input images. Previous works propose nonuniform downsamplers that "learn to zoom" on salient image regions, reducing compute while retaining task-relevant image information. However, for tasks with spatial labels (such as 2D/3D object detection and semantic segmentation), such distortions may harm performance. In this work (LZU), we "learn to zoom" in on the input image, compute spatial features, and then "unzoom" to revert any deformations. To enable efficient and differentiable unzooming, we approximate the zooming warp with a piecewise bilinear mapping that is invertible. LZU can be applied to any task with 2D spatial input and any model with 2D spatial features, and we demonstrate this versatility by evaluating on a variety of tasks and datasets: object detection on Argoverse-HD, semantic segmentation on Cityscapes, and monocular 3D object detection on nuScenes. Interestingly, we observe boosts in performance even when high-resolution sensor data is unavailable, implying that LZU can be used to "learn to upsample" as well.Comment: CVPR 2023. Code and additional visuals available at https://tchittesh.github.io/lzu

The elasticity of tobacco demand in Australia

This paper examines the elasticity of demand of tobacco products in Australia from 2000 to 2011. The hypothesis is that the demand for cigarettes is inelastic. The alternate hypothesis is that the demand for cigarettes is elastic. The hypothesis implies that increasing tobacco tax decreases government tax revenue, while the opposite is true for a decrease in tobacco tax. This paper obtains data mainly from Australian Bureau of Statistics and Cancer Council Victoria. We find an increase in the excise rate and government revenue from tobacco products, therefore implying that the demand of tobacco products in Australia is inelastic. We find further support of this finding by examining factors such as the age and income structure of the population

Implicit Neural Deformation for Sparse-View Face Reconstruction

In this work, we present a new method for 3D face reconstruction from sparse-view RGB images. Unlike previous methods which are built upon 3D morphable models (3DMMs) with limited details, we leverage an implicit representation to encode rich geometric features. Our overall pipeline consists of two major components, including a geometry network, which learns a deformable neural signed distance function (SDF) as the 3D face representation, and a rendering network, which learns to render on-surface points of the neural SDF to match the input images via self-supervised optimization. To handle in-the-wild sparse-view input of the same target with different expressions at test time, we propose residual latent code to effectively expand the shape space of the learned implicit face representation as well as a novel view-switch loss to enforce consistency among different views. Our experimental results on several benchmark datasets demonstrate that our approach outperforms alternative baselines and achieves superior face reconstruction results compared to state-of-the-art methods.Comment: 10 pages, 6 figures, The 30th Pacific Conference on Computer Graphics and Applications. Pacific Graphics(PG) 202

Surface display of heterologous proteins in Bacillus thuringiensis using a peptidoglycan hydrolase anchor

<p>Abstract</p> <p>Background</p> <p>Previous studies have revealed that the lysin motif (LysM) domains of bacterial cell wall-degrading enzymes are able to bind to peptidoglycan moieties of the cell wall. This suggests an approach for a cell surface display system in Gram-positive bacteria using a LysM-containing protein as the anchoring motif. In this study, we developed a new surface display system in <it>B. thuringiensis </it>using a LysM-containing peptidoglycan hydrolase, endo-<it>β</it>-<it>N</it>-acetylglucosaminidase (Mbg), as the anchor protein.</p> <p>Results</p> <p>Homology searching in the <it>B. thuringiensis </it>YBT-1520 genome revealed a putative peptidoglycan hydrolase gene. The encoded protein, Mbg, exhibited substantial cell-wall binding capacity. The deduced amino acid sequence of Mbg was structurally distinguished as an N-terminal domain with two tandemly aligned LysMs and a C-terminal catalytic domain. A GFP-fusion protein was expressed and used to verify the surface localization by Western blot, flow cytometry, protease accessibility, SDS sensitivity, immunofluorescence, and electron microscopy assays. Low-level constitutive expression of Mbg was elevated by introducing a sporulation-independent promoter of <it>cry3Aa</it>. Truncated Mbg domains with separate N-terminus (Mbgn), C-terminus (Mbgc), LysM₁, or LysM₂were further compared for their cell-wall displaying efficiencies. The Mbgn moiety contributed to cell-wall anchoring, while LysM₁was the active domain. Two tandemly repeated Mbgns exhibited the highest display activity, while the activity of three repeated Mbgns was decreased. A heterologous bacterial multicopper oxidase (WlacD) was successfully displayed onto the surface of <it>B. thuringiensis </it>target cells using the optimum (Mbgn)₂anchor, without radically altering its catalytic activity.</p> <p>Conclusion</p> <p>Mbg can be a functional anchor protein to target different heterologous proteins onto the surface of <it>B. thuringiensis </it>cells. Since the LysM domain appears to be universal in Gram-positive bacteria, the strategy presented here could be applicable in other bacteria for developing this type of system.</p

Clustering Analysis of User Loyalty Based on K-means

In recent years, the rise of machine learning has made it possible to further explore large data in various fields. In order to explore the attributes of loyalty of public transport travelers and divide these people into different clustering clusters, this paper uses K-means clustering algorithm (K-means) to cluster the holding time, recharge amount and swiping frequency of bus travelers. Then we use Kernel Density Estimation Algorithms (KDE) to analyze the density distribution of the data of holding time, recharge amount and swipe frequency, and display the results of the two algorithms in the way of data visualization. Finally, according to the results of data visualization, the loyalty of users is classified, which provides theoretical and data support for public transport companies to determine the development potential of users