ANTI-TUMOUR EFFECTS OF POLYSACCHARIDES ISOLATED FROM ARTEMISIA ANNUA L BY INDUCING CELL APOPTOSIS AND IMMUNOMODULATORY ANTI-HEPATOMA EFFECTS OF POLYSACCHARIDES

Background: It is well known that various polysaccharides present anti-tumour effects by inducing cell apoptosis and immunomodulation. However, it is still unclear about the roles of polysaccharides isolated from Artemisia apiacea (HQG) to hepatoma and its underlying mechanism. The objective of the study was to examine the anti-hepatoma effects of HQG and its related mechanism. Materials and Methods: HQG was prepared in house and the quality and purity were confirmed by infra-red spectrum and gel permeation chromatography (GPC). Tumour-bearing mice induced by injection of mouse hepatoma H22 cells were used to evaluate the tumour growth inhibition by HQG administration. Cell immunostaining, JC1 staining and flow cytometer were performed to examine the cell apoptosis, mitochondrial membrane potential change and immunomodulation in response to HQG treatment. Results: HQG treatment inhibited hepatoma growth in tumour-bearing mice. Cell apoptosis rate of human hepatoma 7402 cells and of the cells from ascites in tumour-bearing mice was increased after HQG treatment. Mitochondrial membrane potential in human hepatoma 7402 cells was decreased after HQG treatment. CD4+ and CD8+ T- lymphocytes subpopulation was increased while the ratio of CD4+/ CD8+ decreased in tumour-bearing mice after HQG administration. IFN-γ and IL-4 secretion was increased in spleen lymphocytes in tumour-bearing mice after HQG administration. Conclusion: The study concluded that polysaccharides isolated from Artemisia apiacea (HQG) can inhibit hepatoma cell growths by facilitating cell apoptosis and immuno-defence

Joint Beamforming and Antenna Movement Design for Moveable Antenna Systems Based on Statistical CSI

This paper studies a novel movable antenna (MA)-enhanced multiple-input multiple-output (MIMO) system to leverage the corresponding spatial degrees of freedom (DoFs) for improving the performance of wireless communications. We aim to maximize the achievable rate by jointly optimizing the MA positions and the transmit covariance matrix based on statistical channel state information (CSI). To solve the resulting design problem, we develop a constrained stochastic successive convex approximation (CSSCA) algorithm applicable for the general movement mode. Furthermore, we propose two simplified antenna movement modes, namely the linear movement mode and the planar movement mode, to facilitate efficient antenna movement and reduce the computational complexity of the CSSCA algorithm. Numerical results show that the considered MA-enhanced system can significantly improve the achievable rate compared to conventional MIMO systems employing uniform planar arrays (UPAs) and that the proposed planar movement mode performs closely to the performance upper bound achieved by the general movement mode

A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm

Labeled data in insufficient amounts and missing categories are two observable features for high maneuvering target trajectory data. However, the existing research achievements are insufficient for solving these two problems simultaneously during data classification. This study proposed a two-stage semi-supervised trajectory data classification algorithm. By pre-training the autoencoder and combining it with the Siamese network, a two-stage joint training was formed, which enabled the model to deal with missing categories by clustering and maintaining the classification ability under the missing label categories. The experimental simulation results showed that the performance of this algorithm was better than the classical semi-supervised algorithm label propagation and transferred learning when the amount of various labeled data was as low as 1–5. The two-stage training model also had a good effect on the problem of missing categories. When 75% of the types were missing, the purity could still reach 82%, which was about eight percentage points higher than the directly trained network. When two problems appeared simultaneously, compared with the directly trained network, the performance improved by about three percentage points on average, and the purity was consistently higher than the clustering results. In summary, this algorithm was more tolerant of the problems of labeled datasets, so it was more practical

The Impact of Partial Balance of Imbalanced Dataset on Classification Performance

The imbalance of network data seriously affects the classification performance of algorithms. Most studies have only used a rough description of data imbalance with less exploration of the specific factors affecting classification performance, which has resulted in difficulty putting forward targeted solutions. In this paper, we find that the impact of medium categories on classification performance cannot be ignored, and therefore propose the concept of partial balance, consisting of Class Number of Partial Balance (β) and Balance Degree of Partial Samples (μ). Combined with Global Slope (α), a parameterized model is established to describe the difference of imbalanced datasets. Experiments are performed on the Moore Dataset and CICIDS 2017 Dataset. The experiment’s results on Random Forest, Decision Tree and Deep Neural Network show increasing α is a conducive step in the performance improvement of minority classes and overall classes. When β of dominant categories increases, that of inferior classes decreases, which results in a decrease in the average performance of minority classes. The lower μ is, the closer the sample size of medium classes is to the minority classes, and the better the average performance is. Based on the conclusions, we propose and verify some basic strategies by various classical algorithms

Combined soft templating with thermal exfoliation toward synthesis of porous g-C3N4 nanosheets for improved photocatalytic hydrogen evolution

Insufficient active sites and fast charge carrier recombination are detrimental to photocatalytic activity of graphitic carbon nitride (g-C3N4). In this work, a combination of pore creating with thermal exfoliation was employed to prepare porous g-C3N4 nanosheets for photocatalytic water splitting into hydrogen. Hexadecyl trimethyl ammonium chloride (CTAC) as the soft template promoted the formation of porous g-C3N4 during the thermal condensation of melamine. On further post-synthesis calcination, the porous g-C3N4 aggregates were exfoliated into discrete nanosheets, accompanied by an increase in specific surface area and defects. Optimal porous g-C3N4 nanosheets achieved 3.6 times the photocatalytic hydrogen evolution rate for bulk counterpart. The enhanced photocatalytic activity may be ascribed to TCN-1%CTAC has larger specific surface area, stronger optical absorption intensity and higher photogenerated electron–hole separation efficiency. The external quantum efficiency of TCN-1%CTAC was measured to be 3.4% at 420 nm. This work provides a simple combinatorial strategy for the preparation of porous g-C3N4 nanosheets with low cost, environmental friendliness and enhanced photocatalytic activity

Upcycling of Cr-Containing Sulfate Waste into Efficient FeCrO₃/Fe₂O₃ Catalysts for CO₂ Hydrogenation Reaction

Upcycling Cr-containing sulfate waste into catalysts for CO2 hydrogenation reaction benefits both pollution mitigation and economic sustainability. In this study, FeCrO3/Fe2O3 catalysts were successfully prepared by a simple hydrothermal method using Cr-containing sodium sulfate (Cr-SS) as a Cr source for efficient conversion and stable treatment of Cr. The removal rate of Cr in Cr-SS can reach 99.9% at the optimized hydrothermal conditions. When the synthesized catalysts were activated and used for the CO2 hydrogenation reaction, a 50% increase in CO2 conversion was achieved compared with the catalyst prepared by impregnation with a comparable amount of Cr. According to the extraction and risk assessment code (RAC) of the Reference Office of the European Community Bureau (BCR), the synthesized FeCrO3/Fe2O3 is risk-free. This work not only realizes the detoxification of the Cr-SS but transfers Cr into stable FeCrO3 for application in a catalytic field, which provides a strategy for the harmless disposal and resource utilization of Cr-containing hazardous waste