Kaempferol, also known as kaempferol-3 or kaempferide, is a flavonoid compound that naturally occurs in tea, as well as numerous common vegetables and fruits, including beans, broccoli, cabbage, gooseberries, grapes, kale, strawberries, tomatoes, citrus fruits, brussel sprouts, apples and grapefruit. The present review mainly summarizes the application of kaempferol in treating diseases and the underlying mechanisms that are currently being studied. Due to its anti-inflammatory properties, it may be used to treat numerous acute and chronic inflammation-induced diseases, including intervertebral disc degeneration and colitis, as well as post-menopausal bone loss and acute lung injury. In addition, it has beneficial effects against cancer, liver injury, obesity and diabetes, inhibits vascular endothelial inflammation, protects the cranial nerve and heart function, and may be used for treating fibroproliferative disorders, including hypertrophic scar.

To study the effect of fine particle size and volume concentration on the performance of solid-liquid two-phase centrifugal pump, the mixture multiphase flow model, RNG k-ε turbulence model, and SIMPLEC algorithm were used to simulate the two-phase flow of the centrifugal pump. The effects of particle size and volume concentration on internal pressure distribution, solid volume distribution, and external characteristics were analyzed. The results show that under the design discharge conditions, with the increase of particle size and volume concentration, the internal pressure of the flow field will decrease, and the volume fraction of solid phase in the impeller passage will also decrease as a whole. The solid particles gradually migrate from the suction surface to the pressure surface, and the particles in the volute channel are mainly concentrated in the flow channel near the outlet side of the volute. With the increase of particle size and volume concentration, the negative pressure value at the inlet of centrifugal pump increases, the total pressure difference at the inlet and outlet decreases, and the head and efficiency decrease accordingly.

In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN models have their own advantages, i.e., each of them puts forward many effective operations and achieves good prediction results in the real applications. If users can effectively utilize and combine these excellent operations integrating the advantages of existing models, then they may obtain more effective STGCN models thus create greater value using existing work. However, they fail to do so due to the lack of domain knowledge, and there is lack of automated system to help users to achieve this goal. In this paper, we fill this gap and propose Auto-STGCN algorithm, which makes use of existing models to automatically explore high-performance STGCN model for specific scenarios. Specifically, we design Unified-STGCN framework, which summarizes the operations of existing architectures, and use parameters to control the usage and characteristic attributes of each operation, so as to realize the parameterized representation of the STGCN architecture and the reorganization and fusion of advantages. Then, we present Auto-STGCN, an optimization method based on reinforcement learning, to quickly search the parameter search space provided by Unified-STGCN, and generate optimal STGCN models automatically. Extensive experiments on real-world benchmark datasets show that our Auto-STGCN can find STGCN models superior to existing STGCN models used for search space construction, which demonstrates the effectiveness of our proposed method.

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Background: PTEN is a multifunctional tumor suppressor gene mutating at high frequency in a variety of cancers. However, its expression in pan-cancer, correlated genes, survival prognosis, and regulatory pathways are not completely described. Here, we aimed to conduct a comprehensive analysis from the above perspectives in order to provide reference for clinical application. Methods: we studied the expression levels in cancers by using data from TCGA and GTEx database. Obtain expression box plot from UALCAN database. Perform mutation analysis on the cBioportal website. Obtain correlation genes on the GEPIA website. Construct protein network and perform KEGG and GO enrichment analysis on the STRING database. Perform prognostic analysis on the Kaplan-Meier Plotter website. We also performed transcription factor prediction on the PROMO database and performed RNA-RNA association and RNA-protein interaction on the RNAup Web server and RPISeq. The gene 3D structure, protein sequence and conserved domain were obtained in NCBI respectively.Results: PTEN was underexpressed in all cancers we studied. It was closely related to the clinical stage of tumors, suggesting PTEN may involved in cancer development and progression. The mutations of PTEN were present in a variety of cancers, most of which were truncation mutations and missense mutations. Among cancers (KIRC, LUAD, THYM, UCEC, Gastric Cancer, Liver Cancer, Lung Cancer, Breast Cancer), patients with low expression of PTEN had a shorter OS time and poorer OS prognosis. The low expression of PTEN can cause the deterioration of RFS in certain cancers (TGCT, UCEC, LIHC, LUAD, THCA), suggesting that the expression of PTEN was related to the clinical prognosis. Our study identified genes correlated with PTEN and performed GO enrichment analysis on 100 PTEN-related genes obtained from the GEPIA website. Conclusions: The understanding of PTEN gene and the in-depth exploration of its related regulatory pathways may provide insight for the discovery of tumor-specific biomarkers and clinical potential therapeutic targets.

Machine learning algorithms have made remarkable achievements in the field of artificial intelligence. However, most machine learning algorithms are sensitive to the hyper-parameters. Manually optimizing the hyper-parameters is a common method of hyper-parameter tuning. However, it is costly and empirically dependent. Automatic hyper-parameter optimization (autoHPO) is favored due to its effectiveness. However, current autoHPO methods are usually only effective for a certain type of problems, and the time cost is high. In this paper, we propose an efficient automatic parameter optimization approach, which is based on the mapping from data to the corresponding hyperparameters. To describe such mapping, we propose a sophisticated network structure. To obtain such mapping, we develop effective network constrution algorithms. We also design strategy to optimize the result futher during the application of the mapping. Extensive experimental results demonstrate that the proposed approaches outperform the state-of-theart apporaches significantly.

The major criteria to distinguish conscious Artificial Intelligence (AI) and non-conscious AI is whether the conscious is from the needs. Based on this criteria, we develop ConsciousControlFlow(CCF) to show the need-based conscious AI. The system is based on the computational model with a short-term memory (STM) and long-term memory (LTM) for consciousness and the hierarchy of needs. To generate AI based on real needs of the agent, we developed several LTMs for special functions such as feeling and sensor. Experiments have demonstrated that the agents in the proposed system behave according to the needs, which coincides with the prediction.