46 research outputs found

    Viscum album extract suppresses cell proliferation and induces apoptosis in bladder cancer cells

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    Purpose: To evaluate the effect of Viscum album (VA) extract on the progression of bladder cancer (BC) and its effect on the proliferation and apoptosis of T24 and J82 bladder cancer cells. Methods: 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay (MTT assay) was conducted to examine the proliferation of bladder cancer cells. Flow cytometry (FCM) was employed to assess changes in the cell cycle of bladder cancer cells. The expression levels of proliferating cell nuclear antigen (PCNA), CLND1 (cyclin D1), p21, and p27 in control and VA extract-treated (100, 200, or 300 μg/mL) T24 and J82 cells were measured by immunoblot assay. The effects of VA extract on T24 or J82 cell apoptosis were evaluated using FCM. Immunoblot assay was performed to evaluate Bcl2, Bax, and cleaved caspase 3 expression in control or VA extract-treated bladder cancer cells. In addition, the effect of VA extract on Axl-AKT pathways was also evaluated by immunoblot assay. Results: Viscum album extract treatment significantly blocked bladder cancer cell proliferation and induced cell cycle arrest. In addition, VA extract stimulated bladder cancer cell apoptosis. Moreover, this study found that VA extract suppressed Axl-AKT pathways in bladder cancer. Conclusion: Viscum album extract exerts anti-proliferation and pro-apoptosis effects on bladder cancer cells. These abilities render Viscum album extract as promising agent in bladder cancer treatment

    Simulation analysis of the conveyor chain system based on MATLAB

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    Abstract. In the aluminium ingot casting machine conveyor chain system, the vibration of the production process caused by the transport chain crawl and polygon effect produces water ripple, which directly affect the quality of the products. So this article combined with the MATLAB simulation technology to analyze the signal which affect the conveyor chain stability. And using its powerful graphics functions and mathematical functions to further improve the stability of conveyor chain provides reference

    A Lightweight Recurrent Aggregation Network for Satellite Video Super-Resolution

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    Intelligent processing and analysis of satellite video has become one of the research hotspots in the representation of remote sensing, and satellite video super-resolution (SVSR) is an important research direction, which can improve the image quality of satellite video. However, existing approaches for SVSR often underutilize a notable advantage inherent to satellite video, the presence of extensive sequential imagery capturing a consistent scene. Presently, the majority of SVSR methods merely harness a limited number of adjacent frames for enhancing the resolution of individual frames, thus resulting in suboptimal information utilization. In response, we introduce the recurrent aggregation network for satellite video superresolution (RASVSR). This innovative framework leverages a bidirectional recurrent neural network to propagate extracted features from each frame across the entire video sequence. It relies on an alignment method based on optical flow and deformable convolution (DCN) to realize the alignment of the features, and a temporal feature fusion module to realize effective feature fusion over time. Notably, our research underscores the positive influence of employing lengthier image sequences in SVSR. In the context of RASVSR, with better alignment and fusion, we make the perceptual field of each frame spanning 100 frames of the video, thus, acquiring richer information, and information between different images can be complementary. This strategic approach culminates in superior performance compared with alternative methods, as evidenced by a noteworthy 1.15 dB improvement in PSNR, with very few parameters

    Perceptual evaluation of light field image

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    Recently, light field image has attracted wide attention. However, much less work has been conducted on the perceptual evaluation of light field image. In this work, we create the first windowed 5 degree of freedom light field image database (Win5-LID) based on stereoscopic display, which provides windowed 5 DOF experience and all the depth cues of light field image. The database consists of light field images with representative compression and reconstruction artifacts. We assume that the light field quality is not only affected by sub-views quality but also depth cues. Picture quality and overall quality are then evaluated and the results validate our assumption. Finally, the performance of existing image quality metrics is analyzed on our database. The results indicate that the performance of the state-of-the-art image quality metrics remains to be improved

    Atomic Oxygen SAO, AO and QBO in the Mesosphere and Lower Thermosphere Based on Measurements from SABER on TIMED during 2002–2019

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    Using version 1.0 of TIMED/SABER nighttime O(3P) density data in the mesosphere and lower thermosphere (MLT) retrieved from 2.0 and 1.6 μm radiances, we conducted a study on the semiannual oscillation (SAO), annual oscillation (AO) and quasi-biennial oscillation (QBO) of the atomic oxygen volume mixing ratio at 96 km, from 40° S to 40° N, for 2002–2019. We first analyzed the altitude profiles of the atomic oxygen volume mixing ratio and kinetic temperature, and chose to study the daily average of the atomic oxygen volume mixing ratio at 96 km. For the analysis of SAO and AO, we fitted two sinusoidal functions with periods of 6 and 12 months to the daily mean atomic oxygen volume mixing ratio to obtain the annual and semiannual amplitude. The SAO amplitudes had two peaks of 1.68 × 10−3 and 1.63 × 10-3 at about 25° S and 25° N, and displayed a clear hemispheric symmetry. The AO amplitude increased with the latitude and showed distinct minima (valleys) of 3.36 × 10−4 around the equator, as well as a clear hemispheric asymmetry. The correlation coefficient between the atomic oxygen volume mixing ratio QBO with equatorial stratospheric QBO was higher in the tropics than the mid latitudes

    3D Component Segmentation Network and Dataset for Non-Cooperative Spacecraft

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    Spacecraft component segmentation is one of the key technologies which enables autonomous navigation and manipulation for non-cooperative spacecraft in OOS (On-Orbit Service). While most of the studies on spacecraft component segmentation are based on 2D image segmentation, this paper proposes spacecraft component segmentation methods based on 3D point clouds. Firstly, we propose a multi-source 3D spacecraft component segmentation dataset, including point clouds from lidar and VisualSFM (Visual Structure From Motion). Then, an improved PointNet++ based 3D component segmentation network named 3DSatNet is proposed with a new geometrical-aware FE (Feature Extraction) layers and a new loss function to tackle the data imbalance problem which means the points number of different components differ greatly, and the density distribution of point cloud is not uniform. Moreover, when the partial prior point clouds of the target spacecraft are known, we propose a 3DSatNet-Reg network by adding a Teaser-based 3D point clouds registration module to 3DSatNet to obtain higher component segmentation accuracy. Experiments carried out on our proposed dataset demonstrate that the proposed 3DSatNet achieves 1.9% higher instance mIoU than PointNet++_SSG, and the highest IoU for antenna in both lidar point clouds and visual point clouds compared with the popular networks. Furthermore, our algorithm has been deployed on an embedded AI computing device Nvidia Jetson TX2 which has the potential to be used on orbit with a processing speed of 0.228 s per point cloud with 20,000 points

    3D printed porous β-Ca2SiO4 scaffolds derived from preceramic resin and their physicochemical and biological properties

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    Silicate bioceramic scaffolds are of great interest in bone tissue engineering, but the fabrication of silicate bioceramic scaffolds with complex geometries is still challenging. In this study, three-dimensional (3D) porous β-Ca2SiO4 scaffolds have been successfully fabricated from preceramic resin loaded with CaCO3 active filler by 3D printing. The fabricated β-Ca2SiO4 scaffolds had uniform interconnected macropores (ca. 400 μm), high porosity (>78%), enhanced mechanical strength (ca. 5.2 MPa), and excellent apatite mineralization ability. Importantly, the results showed that the increase of sintering temperature significantly enhanced the compressive strength and the scaffolds sintered at higher sintering temperature stimulated the adhesion, proliferation, alkaline phosphatase activity, and osteogenic-related gene expression of rat bone mesenchymal stem cells. Therefore, the 3D printed β-Ca2SiO4 scaffolds derived from preceramic resin and CaCO3 active fillers would be promising candidates for bone tissue engineering

    3D Component Segmentation Network and Dataset for Non-Cooperative Spacecraft

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    Spacecraft component segmentation is one of the key technologies which enables autonomous navigation and manipulation for non-cooperative spacecraft in OOS (On-Orbit Service). While most of the studies on spacecraft component segmentation are based on 2D image segmentation, this paper proposes spacecraft component segmentation methods based on 3D point clouds. Firstly, we propose a multi-source 3D spacecraft component segmentation dataset, including point clouds from lidar and VisualSFM (Visual Structure From Motion). Then, an improved PointNet++ based 3D component segmentation network named 3DSatNet is proposed with a new geometrical-aware FE (Feature Extraction) layers and a new loss function to tackle the data imbalance problem which means the points number of different components differ greatly, and the density distribution of point cloud is not uniform. Moreover, when the partial prior point clouds of the target spacecraft are known, we propose a 3DSatNet-Reg network by adding a Teaser-based 3D point clouds registration module to 3DSatNet to obtain higher component segmentation accuracy. Experiments carried out on our proposed dataset demonstrate that the proposed 3DSatNet achieves 1.9% higher instance mIoU than PointNet++_SSG, and the highest IoU for antenna in both lidar point clouds and visual point clouds compared with the popular networks. Furthermore, our algorithm has been deployed on an embedded AI computing device Nvidia Jetson TX2 which has the potential to be used on orbit with a processing speed of 0.228 s per point cloud with 20,000 points
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