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As a type of central nervous system tumor, meningioma usually compresses the nerve center due to its local expansion, further causing neurological deficits. However, there are limited therapeutic approaches for meningiomas. ITF2357, a potent class I and II histone deacetylase inhibitor (HDACi), has been shown to inhibit cell proliferation, promote apoptosis, and block the cell cycle in various sarcoma cells, including glioblastoma and peripheral T-cell lymphoma. Here, we investigated the potential role of ITF2357 on meningioma cancer cells (IOMM-Lee cells). First, we demonstrated that the half-maximal inhibitory concentration (IC50) of ITF2357 was 1.842 μM by MTT assay. In addition, ITF2357 effectively inhibited the proliferation and colonization ability of IOMM-Lee cells. Flow cytometry analysis showed that ITF2357 induced G0/G1 and G2/M phase cell cycle arrest and cell apoptosis. Mechanically, the RNA sequencing data revealed that ITF2357 could affect the PI3K-Akt signaling pathway and the cell cycle progression. Furthermore, the expression levels of Akt, PI3K, p-Akt, and p-PI3K were determined by western blotting. Collectively, our data revealed that ITF2357 induces G0 G1 and G2/M phase arrest and apoptosis by inhibiting hyperactivation of the PI3K-Akt pathway, ultimately inhibiting cell viability and proliferation of meningioma cells, which developed a new approach to the treatment of meningioma.

Novel thermo-reversible hydrogels that undergo gelation in feedback to external stimuli have numerous applications in the food, biomedical, and functional materials fields. Muscle myofibrillar protein (MP) has long been known for thermally irreversible gelation. Once the reversible gelation of MP is achieved, its scope for research and application will expand.The work reported here achieved, for the first time, a thermo-reversible MP gelation by elaborate deamidation using protein glutaminase (PG). The protein concentration and PG reaction time within windows of 1.0-2.5% and 8 h or 12 h were observed to be vital for creating thermo-reversible gels. The gel strength increased with protein concentration. The gel displayed a perforated lamellar microstructure, which resulted in a high water-holding capacity. The rheological results revealed the thermo-reversibility of the gel was robust for up to five cycles of heating and cooling. The thermally reversible gelation is closely related to the reversible assembly between individual α-helix and helical coiled coil. Hydrophobic interactions proved to be predominantly involved in the formation and stabilization of the gel network structure.This work increases the scope of research into the thermo-responsive behavior of MP-based gel. It can foster advances in research into the applications of muscle proteins and into the use of PG as a novel ingredient in the food industry. © 2022 Society of Chemical Industry.

Colorectal cancer is one of the most common malignancies causing the majority of cancer-related deaths. There is an urgent need to develop new anticancer modalities. Recently, efforts have been made to turn clinically approved drugs into anticancer agents in specific tumor microenvironments via NPs. Disulfiram (DSF) as an effective copper (Cu

As a type of central nervous system tumor, meningioma usually compresses the nerve center due to its local expansion, further causing neurological deficits. However, there are limited therapeutic approaches for meningiomas. ITF2357, a potent class I and II histone deacetylase inhibitor (HDACi), has been shown to inhibit cell proliferation, promote apoptosis and block the cell cycle in a variety of sarcoma cells, including glioblastoma and peripheral T-cell lymphoma. Here, we investigated the antitumor potential of ITF2357 on meningioma cells (IOMM). First, we demonstrated that the half-maximal inhibitory concentration (IC50) of ITF2357 was 1.842 µg/ml by MTT assay. In addition, ITF2357 effectively inhibited the proliferation and colonization ability of IOMM cells. Flow cytometry analysis showed that ITF2357 induced G0/G1 cell cycle arrest and cell apoptosis. Mechanically, the RNA sequencing data revealed that ITF2357 could affect the PI3K-Akt signaling pathway and the cell cycle progression. Furthermore, the expression level of cyclin B1, cyclin D1, and CDK1 was determined by western blotting. Collectively, our data revealed that ITF2357 inhibited cell viability and proliferation of meningioma cells by inducing G0/G1 phase arrest and apoptosis, and inhibiting cell cycle-related proteins (CDK/cyclin B1/cyclin D1), which developed a new approach to the treatment of meningioma.

With the aging of the global population, accumulating interest is focused on manipulating the fundamental aging-related signaling pathways to delay the physiological aging process and eventually slow or prevent the appearance or severity of multiple aging-related diseases. Recently, emerging evidence has shown that RNA modifications, which were historically considered infrastructural features of cellular RNAs, are dynamically regulated across most of the RNA species in cells and thereby critically involved in major biological processes, including cellular senescence and aging. In this review, we summarize the current knowledge about RNA modifications and provide a catalog of RNA modifications on different RNA species, including mRNAs, miRNAs, lncRNA, tRNAs, and rRNAs. Most importantly, we focus on the regulation and roles of these RNA modifications in aging-related diseases, including neurodegenerative diseases, cardiovascular diseases, cataracts, osteoporosis, and fertility decline. This would be an important step toward a better understanding of fundamental aging mechanisms and thereby facilitating the development of novel diagnostics and therapeutics for aging-related diseases.

User influence is a very important factor for microblog user recommendation in mobile social network. However, most existing user influence analysis works ignore user’s temporal features and fail to filter the marketing users with low influence, which limits the performance of recommendation methods. In this paper, a Tensor Factorization based User Cluster (TFUC) model is proposed. We firstly identify latent influential users by neural network clustering. Then, we construct a features tensor according to latent influential user’s opinion, activity, and network centrality information. Furthermore, user influences are predicted by the latent factors resulting from the temporal restrained CP decomposition. Finally, we recommend microblog users considering both user influence and content similarity. Our experimental results show that the proposed model significantly improves recommendation performance. Meanwhile, the mean average precision of TFUC outperforms the baselines with 3.4% at least.

A prediction model based on the principal component analysis (PCA) and back propagation (BP) neural network is proposed for BOF end-point phosphorus content, based on the characters of BOF metallurgical process and production data. PCA is used to reduce dimensionality of the factors influencing end-point phosphorus content, and eliminate the correlations among the factors, and then the obtained principal components are used as BP neural network input vectors. The combined PCA-BP neural network model is trained and tested by history data, and is further compared with multiple linear regression (MLR) model and BP neural network model. The results of the comparison show that the PCA-BP neural network model has the highest prediction accuracy and PCA improved the generalization capability. Finally, online prediction system of BOF end-point phosphorus content based on PCA and BP neural network is developed and applied in actual productive process. Field application results indicate that the hit rate of end-point phosphorus content is 96.67%, 93.33% and 86.67% respectively when prediction errors are within ±0.007%, ±0.005% and ±0.004%. The combined PCA-BP neural network model has achieved the effective prediction for end-point phosphorus content, and provided a good reference for end-point control and judgment of quick direct tapping of BOF.

Given advantages of artificial intelligent technology, the genetic algorithm (GA) and back propagation (BP) neural network is used to construct time series model for recognizing temperature change waveform of single thermocouple in mold breakout process. Based on breakout mechanism, the logic rules are used to construct spatial model of multi-thermocouples for identifying two-dimensional (2D) propagation behavior of the sticker. Time series model based on GA-BP neural network and spatial model based on logic rules form a new breakout prediction method. And the simulation and field test of this method are carried out for validating its performance. Simulation results of time series model show that the GA-BP neural network has better recognition precision than BP neural network for sticking temperature pattern of single thermocouple. And simulation results of spatial model show that it can predict all stickers accurately and timely, with no missed alarm and false alarm. Furthermore, field test results show that this breakout prediction method has detection ratio of 100% and a lower false alarm frequency (0.1365% times/heat), which is better than actual breakout prediction system used in continuous casting production. So the combined method of GA-BP neural network and logic rules is feasible and effective in breakout prediction and can be used in more intelligently industrial process.

User influence analysis in social media has attracted tremendous interest from both the sociology and social data mining. It is becoming a hot topic recently. However, most approaches ignore the temporal characteristic that hidden behind the comments and articles of users. In this paper, we introduce a Tensor Factorization based on User Cluster (TFUC) model to predict the ranking of users’ influence in micro blogs. Initially, TFUC obtain an influential users cluster by neural network clustering algorithm. Then, TFUC choose influential users to construct tensor model. A time matrix restrain TFUC expect CP decomposition and ranked users by their influence score that obtained from predicted tensor at last. Our experimental results show that the MAP of TFUC is higher than existing influence models with 3.4% at least.

In an urban rail transit system, issues regarding headway regulation have aroused wide attention. The assurance of headway regularity can decrease train delay times and average passenger waiting times. An integrated control method is proposed to optimize train headway by adjusting the train arrival time at stations. The adjustment of train arrival time is achieved by using an analytical method, and then the speed profile for each train is calculated by a suboptimal method, which has been applied in a practical system. Through simulation, the CPU time for calculating optimal train arrival time and speed profile is analyzed, respectively. The analysis demonstrates that the proposed method satisfies the real-time requirements for solving the headway regulation problem. By adopting the proposed method, the average passenger waiting time and the energy consumption can be decreased. In particular, the proposed method has better performance when the dispatch headway is large.

This paper constructs a conceptual model of how group-buying servicescape affect consumers’ purchase intention and discusses the regulating effects of price discount and time pressure. This research uses a survey approach to collect data, as a result, we collected 506 valid questionnaires. The results show that aesthetic appeal, information exchange, interpersonal interaction and perceived security have a positive effect on positive emotion; layout and functionality, information exchange and interpersonal interaction have a positive effect on virtual touch. Positive emotion and virtual touch can promote the consumer’s purchase intention, and time pressure and price discount play a regulatory function. The results of this study have an important reference value for group-buying operators to use servicescape to attract and retain consumers.

In this study, firstly, based on related researches about consumer online purchasing behavior before, we proposed that there were some perceived factors that influence perceived value which has a direct impact on consumer's online purchasing decision-making. Secondly, on the analysis for the antecedent of consumer perceived factors with online shopping, we proposed the definition of online shopping capability of consumer and try to explore what factors would affect consumers' perceived value when they are online shopping in the perspective of consumer's online shopping capability and their different influences on consumer purchasing behavior. We designed a questionnaire and obtained 1359 valid data from our investigation. Data analysis indicates that besides online shopping capability of consumers, there are other four significant common factors such as trust, perceived risk, perceived benefit and perceived cost, have significant influences on consumers' perceived value. The causal relation among online shopping capability of consumers, the other four perceived factors and perceived value and consumer online purchasing behavior are verified using confirmatory factor analysis. We explored the differences and their changing way among different kinds of consumers. Discussions for the results indicated that consumers with different shopping experiences have different online shopping capabilities and significant differences on the other four perceived factors.

The use of chlorophyll fluorescence measurements and RGB intensity values to noninvasively evaluate changes of the internal chemical parameters (total flavonoid, total phenols, ABTS cation and DPPH radical scavenging activities and sugars) was investigated during all the development stages (ST) and the last four development stages of mulberry fruits in this study. The appropriately fitted relationships in this study revealed that chlorophyll fluorescence and RGB intensity have high correlations with the internal chemical parameters of mulberry fruits. Especially, each of the internal chemical parameters has higher correlations with chlorophyll fluorescence and RGB intensity during the period of ST4–ST7 than during the period of ST1–ST7 respectively. High correlations between chlorophyll fluorescence and the internal chemical parameters were found during the period of ST4–ST7, with R 2 ranged from 0.82 to 0.94. RGB intensity values showed fine correlations with the internal chemical parameters with R 2 ranged from 0.93 to 0.97 during the period of ST4–ST7. Therefore, chlorophyll fluorescence and RGB intensity values may be potential methods for quality evaluation of mulberry fruits during development, however, it is of great importance to select the most adequate parameter for the evaluation of different chemical components.

Based on the research of the existing literature, this article especially added some factors, such as operating leverage and sustainable growth rate; to evaluate the Guangdong listed small and medium-sized enterprise sustainable development ability. This article selects the 2013 annual report data from the GTA CSMAR Database and uses the factor analysis and the cluster analysis of SPSS22.0 multivariate statistical methods. Respectively, it evaluates the Guangdong listed small and medium-sized enterprises(SMEs) sustainable development ability according to single factor score and comprehensive factor score. In final, According to the findings, we put forward the corresponding suggestions and countermeasures.

Based on the theory of planned behavior (TPB) and ethics theory, this article especially added some variables, such as value-added service, to explore the influence factors that consumers use pirated software. The model was examined by using the structural equation model (SEM). The results showed that “sharing” of cultural value and perceived usefulness influence the attitude toward pirated software of individuals, and then affect piracy intention. However, social ethical climate and value-added service had no obvious effect in the produce process of piracy intention. In final, According to the findings, we put forward the corresponding suggestions and countermeasures.

The optimization of train trajectory plays a critical role in the improvement of operational performance of trains and has received intensive attention in recent years. However, the accurate calculation of operational cost of trains, such as time and energy, needs the establishment of corresponding effective formulas, which has not been investigated sufficiently in the existing literature. This paper studies an optimal control problem of train trajectory design and a new modeling approach which provides accurate formulas as for the calculation of time and energy are provided. By using this method, the optimization problem can be transformed into a multi-stage decision process, and hence can be resolved by the famous dynamic programming. Finally, the simulation results demonstrate the effectiveness of the proposed method in obtaining the optimal trajectory of a real subway line with the minimum cost of time and energy, as well as reaching a relative high level of riding comfort.

There are two empirical studies in this paper. In the first study we investigated what were the important factors which would impact on the perceived factors of consumers’ shopping online in China through interviewing, email and internet investigation, and obtained three important factors which were products, websites and personal factors based on 1075 valid respondents. In the second study we designed a questionnaire with 29 items used to measure the three kinds of influence factors verified in the first study and 792 valid data were collected. The differences between consumers with different shopping experiences and genders were compared and tested according to statistical and ANOVA analysis. The results indicate that: shopping experiences are important influent factors which will cause the differences of consumers’ perceived factors in the process of shopping online; consumers’ attitudes for online shopping would gradually be changed as the times of their shopping experience add up; there are significant differences about products factors between consumers both with different shopping experiences and with different genders.

The influence of perceived risk on customers’ purchasing behavior still plays an important role in the overall process of business to customer(B2C) E-commerce, though the extent to which it does so varies according to the characteristics of consumers. In this study, perceived risk dimensions affecting customers’ purchasing behavior and their structural relationships were investigated through a customer survey and statistical analysis using the methods of confirmatory factor analysis and structural equation model. The result shows that the five independent dimensions, perceived health risk, perceived quality risk, perceived time risk, perceived delivery risk and perceived after-sale risk affect significantly customers’ purchasing behavior, while perceived privacy risk, perceived social risk and perceived economic risk are the less relevant factors.

The dimension structure of consumers' perceived risk (CPR) for the overall process of B2C E-Commerce was empirical researched in this paper. Based on a thorough review of prior researches and interviews with experienced customers, the basic CPR elements for the overall process of B2C E-Commerce were explored. Then, a formal measure was developed after a pilot survey and preliminary statistics, and the data were collected through some formal surveys. 32 items were used to test the risks in the phases of the overall process of B2C and 388 respondents were surveyed by the questionnaire. Finally, eight risk common factors were explored through exploratory factor analysis, in which only 30 items were kept in. Eight dimensions of consumers' perceived risk were ascertained by confirmatory factor analysis, i.e. perceived health risk, perceived quality risk, perceived privacy risk, perceived economic risk, perceived time risk, perceived social risk, perceived delivery risk and perceived after-sale risk. The cumulative variance rate is 88.627%, ail the Cronbach's α of the eight constructs are greater than 0.8, and the construct validities are acceptable.

Manufacturing industry is one of the most important industries in China; Guangdong plays a decisive role in the development of Chinese industry. There are a lot of listed enterprises coming from the manufacturing industry of Guangdong. In this paper, we have comparison analysis for the competitiveness of important manufacture industries in Guangdong based on 89 listed enterprises. Firstly, a dimensionless optimization method is used to analyze the original data from year 2000 to 2007 for 89 enterprises. Secondly, we evaluate competitive abilities of listed enterprises. Thirdly, we use statistic method to count the average and annual values of industries based on the classifying the listed enterprises and provide the comparison analysis for competitiveness of industries of Guangdong. The results in the paper illustrate that there are not obvious difference in average level for the competitive abilities among manufacture industries, but the changes of the competitiveness among different industries are upward annually in a convergence trend.

Credit risk assessment of companies has been an important part of the study on risk management for a long time, especially for the default risk of companies with a high liability-to-asset rate. In this paper, we use factor analysis method to establish a logistic regression model and make an empirical analysis on the credit risk of listed companies of Capital-Intensive Industries. The results show that long-term pay ability, cash flow factor, profitability, and sales ability have the greatest influence on credit risk assessment, and the next come the short-term liquidity and asset-liability. By the inspection of testing samples, it is proved that this Logistic model is effective for credit risk assessment because of its high accuracy and reliable recognition, good prediction and generalization ability.

With the rapid development of information and communication technology, the decision makers of firms are facing many kinds of IS/IT projects investment opportunities, so they need to provide their preferences over a set of decision alternatives in order to maximize investment value of the projects. Considering the special characteristics of IS/IT projects, this paper develops a two-stage investment game model between two competitive firms on IS/IT projects. This model integrates the ideas of real option theory, game theory, NPV and dynamic programming method. At first, the investment thresholds are derived to divide the areas of market demand. Then the fully characterized equilibrium strategies for both firms are proposed based on these areas. The research in this paper provides a new analytical framework on the investment decision-making for IS/IT projects.