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Background: Surgical resection and chemotherapy are the primary treatment options for cervical cancer; however, efficacy of chemotherapy drugs is limited by drug resistance. There is an urgent need to find new compounds. Gambogic acid lysinate (GAL), a new compound made from gambogic acid and lysine, has good anti-tumor activity, however, the effect of GAL on cervical cancer remains undetermined. Objective: The present study sought to explore the anti-tumor activity of GAL in SiHa cells. Methods: Cell viability was detected by means of an MTT assay, a cell growth curve was drawn with Microsoft Excel 2010, the cell cycle and cell apoptosis were evaluated by flow cytometry, and Western blotting was employed to explore the mechanism of GAL. Additionally, the in vivo anti-tumor activity of GAL was studied through a xenograft tumor model in nude mice. Results: GAL inhibited the proliferation of both SiHa cells (IC50 was 0.83 μmol/l and 0.77 μmol/l respectively for 48 h and 72 h) and HeLa cells (IC50 did not reach). In SiHa cells, GAL (1 and 2 μmol/l) inhibited cell proliferation and 2 μmol/l GAL could also induce cell apoptosis and decrease the number of S phase. Both 1 and 2 μmol/l GAL inhibited SiHa cells invasion and increased the number of G0/G1 phase. The results of Western blot assay demonstrated that P53 and P21 were involved in SiHa cells S phase arrest and BCL-2 and BAX were involved in SiHa cells apoptosis. In vivo study showed that the growth of SiHa cell xenograft tumors was inhibited via cell apoptosis induced by GAL (2.5 mg/kg body weight), however, GAL (2.5 mg/kg body weight) had no significant effect on weight gain of mice. Conclusion: GAL induced SiHa cells apoptosis by BCL-2 and BAX pathway and SiHa cells S phase arrest by P53 and P21 pathway in vitro and inhibited the growth of SiHa cell xenograft tumors.

The rough set has been proven to be a powerful tool for dealing with uncertainty and vagueness in different fields. The hesitant fuzzy rough set is a generalization of rough sets to solve more complex problems. Since the existing hesitant fuzzy rough sets don’t satisfy the inclusive property, a novel hesitant fuzzy rough set based on the dual score functions is proposed. Then four generalized hesitant fuzzy rough set about the novel one are presented. Next, the lower approximation distribution reductions can be obtained by the discernibility matrix. Moreover, it is discovered that finding the lower approximation distribution reductions of a hesitant fuzzy decision system is equivalent to finding the minimal transversals of its hypergraph. An improved algorithm for hesitant fuzzy decision systems based on hypergraphs is presented to accelerate the reduction process. Finally, The hybrid data of Hepatitis C Virus from UCI is used as an instance background for data analyses to reflect the feasibility of the proposed algorithm.

In order to solve the problems of low power transfer efficiency (PTE) and limited distance in magnetic coupling resonant wireless power transfer (MCR-WPT) technology. In this paper, based on the magnetic field control ability of metamaterials (MMs), the way to improve the performance of MCR-WPT systems is studied. First, the influence of MMs on the coupling of MCR-WPT system is theoretically analyzed by establishing an equivalent circuit model. Through a series of simulations and experiments, the relationships between the PTE and the array and placement of the MM slab are investigated. The results demonstrate that when one MM slab is placed in the middle, near the Tx coil or the Rx coil, the optimal PTE can be obtained by inserting one slab with $6 \times 6$ , $1 \times 1$ , and $6 \times 6$ arrays, respectively. Moreover, the systems with multilayer MM slabs are also studied. The measured PTEs on one, two, and three layers of MM slabs can increase by 20.6%, 29.3%, and 22.6%, respectively. The efficiency improvement capability of one MM slab is better than three slabs but worse than two slabs. This paper discusses the application of MMs in MCR-WPT systems, which has a certain reference significance.

Under the joint action of dynamic and static loads, the connecting bolts of the top cover of the pump turbine are prone to cumulative damage in local areas such as threads, resulting in cracks or development, and fatigue fracture may occur in severe cases, which will cause serious safety accidents. Therefore, it is necessary to carry out accurate fatigue life estimation analysis for connecting bolts, which has always been a hot spot and difficulty in the industry. This paper introduces several commonly used fatigue life analysis methods of parts, combined with the force characteristics and structural characteristics of the connecting bolts of the top cover, and analyzes and discusses the fatigue life analysis methods suitable for the top cover bolts. Finally, it is believed that the application of stress-strain field strength method can better describe the actual state of the bolt danger area, and the current damage state has an influence on the amount of damage generated by further loading, and the fatigue life analysis of connecting bolts can be combined with the stress-strain field strength method and variable damage linear cumulative damage theory, and the fatigue life and residual fatigue life can be estimated in the design stage or after the phased service of the bolt.

This paper proposes a robust autoencoder with Wasserstein distance metric to extract the linear separability features from the input data. To minimize the difference between the reconstructed feature space and the original feature space, using Wasserstein distance realizes a homeomorphic transformation of the original feature space, i.e., the so-called the reconstruction of feature space. The autoencoder is used for features extraction of linear separability in the reconstructed feature space. Experiment results on real datasets show that the proposed method reaches up 0.9777 and 0.7112 on the low-dimensional and high-dimensional datasets in extracted accuracies, respectively, and also outperforms competitors. Results also confirm that compared with feature metric-based methods and deep network architectures-based method, the linear separabilities of those features extracted by distance metric-based methods win over them. More importantly, the linear separabilities of those features obtained by evaluating distance similarity of the data are better than those obtained by evaluating feature importance of data. We also demonstrate that the data distribution in the feature space reconstructed by a homeomorphic transformation can be closer to the original data distribution.

Secondary hydrocarbon migration is an important aspect of oil-gas accumulation research. While previous studies have relied on geological and fluid geochemical characteristics to predict migration direction, these results are often limited by the number of samples. In recent years, basin simulation has emerged as a valuable tool in hydrocarbon migration research due to its extensive algorithms and adaptable modeling capabilities. It has obvious technical advantages especially for resource evaluation in areas with a lower exploration degree and scarce data. The deep oil and gas in the Cambrian petroleum system in the Tarim Basin is a deep hydrocarbon challenging area in China. There has been no breakthrough in exploration due to the large burial depth ( generally > 6000 m ) and the long accumulation and transformation processes. Therefore, predicting the secondary migration of Cambrian oil and gas has become the key to solving this problem. In this study, first, the thermal evolution of the Lower Cambrian source rocks in the Tarim Basin was recovered, and the three thermal evolution models were developed. The secondary migration process of the Cambrian petroleum system was restored using the geologic model of the source rocks and paleotectonic evolution and the latest fluid potential information. By comparing the simulation results, four secondary migration models of the ultradeep oil-gas migration and accumulation were developed: multisource, multiphase, multidirectional accumulation; multisource, multiphase, single-directional accumulation; single-source, multiphase, multidirectional accumulation; and single-source, multiphase, single-directional accumulation. The fluid potential simulation results indicate that the Cambrian oil and gas have salient inheritance characteristics. The dominant migration channels in the uplift and slope are beneficial to oil-gas migration and accumulation, and the Katake uplift and the west Bachu uplift have multisource charging accumulation. The east Bachu uplift and the Tabei uplift are oil-gas accumulation zones that are beneficial to the lower petroleum system due to the continuous charging of a single petroleum system. This fluid potential simulation provides a new solution for studying the secondary migration of deep oil and gas. It provides an important reference for studying hydrocarbon accumulation in deep and ultradeep areas.

Pore structure, especially pore shape and size, is a key factor affecting reservoir permeability. There is a poor correlation between permeability and porosity when the pore structure is complex, which leads to the low accuracy of permeability prediction by using porosity. In view of the preceding problems, six factors related to pore structure are summarized from rock physical models and entitled seismic pore structure factors (SPSFs), and the sensitivity of SPSF to permeability is analyzed with the data of two groups of clastic rock cores. The results find that the shear Lee factor is most sensitive to permeability, followed by the shear flexibility factor. In addition, there are good linear correlations between the shear Lee factor and pore size and between the shear flexibility factor and pore aspect ratio. Therefore, the combination of the shear Lee factor and pore aspect ratio can better characterize pore structure, and thus reservoir permeability. A seismic permeability prediction method based on pore aspect ratio, the shear Lee factor, and lithofacies is developed and applied to the Jurassic tight sandstone reservoirs of the Sichuan Basin. The results find that the permeability prediction accuracy is within half an order of magnitude, which is five times higher than the single-factor method of the shear flexibility factor. Compared with porosity, permeability distribution is more consistent with the measured productivity, which confirms the effectiveness of the method. It also provides a new way to predict the spatial distribution of reservoir permeability.