Circular RNA hsa_circ_0000317 inhibits non-small cell lung cancer progression through regulating microRNA-494-3p/phosphatase and tensin homolog deleted on chromosome 10 axis

Background: Circular RNA (circRNA), a group of non-coding RNA, is pivotal in the progression of various cancers, including Non-Small Cell Lung Cancer (NSCLC). Some circRNAs have been reported to be implicated in the progression of NSCLC, however, the function and molecular mechanism of hsa_circ_0000317 (circ_0000317) in NSCLC have not been fully understood. Methods: The significantly differentially expressed circRNA in NSCLC tissues, circ_0000317, was screened out by microarray. Circ_0000317, microRNA(miR)-494-3p and Phosphatase and Tensin Homolog Deleted on Chromosome 10 (PTEN) expressions in NSCLC tissues were respectively probed by quantitative real-time polymerase chain reaction and western blot assay. MTT and Transwell assays were adopted to examine the growth, migration, and invasion of NSCLC cells. Bioinformatics, luciferase reporter gene assay, RNA immunoprecipitation, and RNA pull-down assay were conducted to probe the relationships among circ_0000317, miR-494-3p, and PTEN. Results: Circ_0000317 expression level was reduced in NSCLC tissues and cell lines. Circ_0000317 expression in NSCLC patients was associated with TNM stage and lymphatic metastasis. Circ_0000317 overexpression restrained the proliferation, migration, and invasion of NSCLC cells, but co-transfection of miR-494-3p mimics partially reversed this effect. In addition, circ_0000317, was identified as a competitive endogenous RNA, which could sponge miR-494-3p to increase PTEN expression and activate PI3K/AKT pathway. Conclusion: Circ_0000317, inhibits NSCLC progression via modulating miR-494-3p/PTEN/PI3K/AKT pathway

Research Advances in Structural Properties and Metabolism of Milk Fat Globule Membrane

Breast milk is the safest and most perfect natural food for infant growth and development. As one of the most important components in breast milk, the milk fat globule membrane (MFGM) is a 3-layer membrane structure surrounding milk fat globules (MFG). This unique structure not only maintains the stability of milk but also plays an important role in the digestive and metabolic processes of infants. In this article, we introduce the reader to the composition and structural specificity of MFGM, review the sequential digestion of MFGM depending on several enzymes in the mouth, stomach and intestine of healthy infants, and elaborate on the interaction mechanism between MFGM and various enzymes, in order to provide a reference for relevant studies

A Review of Fundamental Optimization Approaches and the Role of AI Enabling Technologies in Physical Layer Security

With the proliferation of 5G mobile networks within next-generation wireless communication, the design and optimization of 5G networks are progressing in the direction of improving the physical layer security (PLS) paradigm. This phenomenon is due to the fact that traditional methods for the network optimization of PLS fail to adapt new features, technologies, and resource management to diversified demand applications. To improve these methods, future 5G and beyond 5G (B5G) networks will need to rely on new enabling technologies. Therefore, approaches for PLS design and optimization that are based on artificial intelligence (AI) and machine learning (ML) have been corroborated to outperform traditional security technologies. This will allow future 5G networks to be more intelligent and robust in order to significantly improve the performance of system design over traditional security methods. With the objective of advancing future PLS research, this review paper presents an elaborate discussion on the design and optimization approaches of wireless PLS techniques. In particular, we focus on both signal processing and information-theoretic security approaches to investigate the optimization techniques and system designs of PLS strategies. The review begins with the fundamental concepts that are associated with PLS, including a discussion on conventional cryptographic techniques and wiretap channel models. We then move on to discuss the performance metrics and basic optimization schemes that are typically adopted in PLS design strategies. The research directions for secure system designs and optimization problems are then reviewed in terms of signal processing, resource allocation and node/antenna selection. Thereafter, the applications of AI and ML technologies in the optimization and design of PLS systems are discussed. In this context, the ML- and AI-based solutions that pertain to end-to-end physical layer joint optimization, secure resource allocation and signal processing methods are presented. We finally conclude with discussions on future trends and technical challenges that are related to the topics of PLS system design and the benefits of AI technologies

Secure State Estimation of Cyber-Physical System under Cyber Attacks: <i>Q</i>-Learning vs. SARSA

This paper proposes a reinforcement learning (RL) algorithm for the security problem of state estimation of cyber-physical system (CPS) under denial-of-service (DoS) attacks. The security of CPS will inevitably decline when faced with malicious cyber attacks. In order to analyze the impact of cyber attacks on CPS performance, a Kalman filter, as an adaptive state estimation technology, is combined with an RL method to evaluate the issue of system security, where estimation performance is adopted as an evaluation criterion. Then, the transition of estimation error covariance under a DoS attack is described as a Markov decision process, and the RL algorithm could be applied to resolve the optimal countermeasures. Meanwhile, the interactive combat between defender and attacker could be regarded as a two-player zero-sum game, where the Nash equilibrium policy exists but needs to be solved. Considering the energy constraints, the action selection of both sides will be restricted by setting certain cost functions. The proposed RL approach is designed from three different perspectives, including the defender, the attacker and the interactive game of two opposite sides. In addition, the framework of Q-learning and state–action–reward–state–action (SARSA) methods are investigated separately in this paper to analyze the influence of different RL algorithms. The results show that both algorithms obtain the corresponding optimal policy and the Nash equilibrium policy of the zero-sum interactive game. Through comparative analysis of two algorithms, it is verified that the differences between Q-Learning and SARSA could be applied effectively into the secure state estimation in CPS

Secure State Estimation of Cyber-Physical System under Cyber Attacks: Q-Learning vs. SARSA

This paper proposes a reinforcement learning (RL) algorithm for the security problem of state estimation of cyber-physical system (CPS) under denial-of-service (DoS) attacks. The security of CPS will inevitably decline when faced with malicious cyber attacks. In order to analyze the impact of cyber attacks on CPS performance, a Kalman filter, as an adaptive state estimation technology, is combined with an RL method to evaluate the issue of system security, where estimation performance is adopted as an evaluation criterion. Then, the transition of estimation error covariance under a DoS attack is described as a Markov decision process, and the RL algorithm could be applied to resolve the optimal countermeasures. Meanwhile, the interactive combat between defender and attacker could be regarded as a two-player zero-sum game, where the Nash equilibrium policy exists but needs to be solved. Considering the energy constraints, the action selection of both sides will be restricted by setting certain cost functions. The proposed RL approach is designed from three different perspectives, including the defender, the attacker and the interactive game of two opposite sides. In addition, the framework of Q-learning and state&ndash;action&ndash;reward&ndash;state&ndash;action (SARSA) methods are investigated separately in this paper to analyze the influence of different RL algorithms. The results show that both algorithms obtain the corresponding optimal policy and the Nash equilibrium policy of the zero-sum interactive game. Through comparative analysis of two algorithms, it is verified that the differences between Q-Learning and SARSA could be applied effectively into the secure state estimation in CPS

Effect of grain size on fatigue strength of 304 stainless steel

In this study, three types of 304 stainless steel samples with different strengths were prepared by refining the grain size through rolling. The microstructure of the samples was observed by electron microscopy. The influence of grain size on the static tensile properties and fatigue strength of the material is mainly attributed to changes in the plastic deformation fracture mechanism and micro-deformation mechanism. In addition, a new fatigue strength prediction model is proposed based on the influence of tensile strength and work-hardening capacity. Compared with the staircase method and Basquin formula models, the proposed model maintains the accuracy of fatigue strength prediction while reducing the cost of fatigue experiments. This provides a new approach for predicting the fatigue strength of specific materials and improving anti-fatigue design capabilities

Functional Diversity of Soil Microorganisms and Influencing Factors in Three Typical Water-Conservation Forests in Danjiangkou Reservoir Area

As a key part of the forest ecosystem, soil microorganisms play extremely important roles in maintaining the ecological environment and the security of water quality in reservoir areas. However, it is not clear whether there are differences in the functional diversity of soil microorganisms in different types of water-conservation forests in reservoir areas, and which factors affect the functional diversity of soil microorganisms. In our study, the Biolog-Eco microplate technique was used to analyze the carbon source metabolic characteristics of soil microbial communities in three typical water-conservation forests and a non-forest land: Pinus massoniana-Quercus variabilis mixed forest (MF), Pinus massoniana forest (PF), Quercus variabilis forest (QF) and non-forest land (CK). The results showed that the average well color development (AWCD), the Shannon diversity index (SDI) and the richness index (S) of the three forest lands was significantly greater than that of the non-forest land (p PF > MF), but there was no significant difference among different types of forests. The microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) of QF and PF were higher than those of MF and CK, but the microbial biomass C/N ratio (MBC/MBN) was lower. The variance partitioning analysis (VPA) showed that 86.4% of the variation was explained by plant (community) diversity, soil physical and chemical properties and soil microbial biomass, which independently explained 10.0%, 28.9%, and 14.9% of the variation, respectively. The redundancy analysis (RDA) showed that total phosphorus (TP), microbial biomass carbon (MBC), total nitrogen (TN), number of plant species (Num) and alkali-hydro nitrogen (Wn) were the key factors affecting the functional diversity of soil microorganisms. This study confirmed that forest ecosystem is better than non-forest land in maintaining soil microbial function diversity. Moreover, Quercus variabilis forest may be a better stand type in maintaining the diversity of soil microbial functions in the study area

Prediction model of low cycle fatigue life of 304 stainless steel based on genetic algorithm optimized BP neural network

The low cycle fatigue life of 304 stainless steel is an essential basis for safety assessment. Usually, there is a complex nonlinear relationship between fatigue life and influencing factors, which is difficult to be predicted by traditional fatigue life models. Based on this, the BP algorithm and genetic optimization algorithm (GA) for the fatigue life prediction problem of 304 stainless steel is proposed. Based on the existing large amount of test data, the fatigue life of 304 stainless steel material is predicted by using BP and GA-BP learning models. The results show that the GA-BP prediction model is more flexible, the correlation coefficient R reaches 0.98158, the prediction data are within the 2 times error limit and closer to the ideal line, and the model prediction is better

Response of Functional Diversity of Soil Microbial Community to Forest Cutting and Regeneration Methodology in a Chinese Fir Plantation

With the expansion of pure forest planting area and the increase in the number of rotations used, soil activity and plant productivity have significantly reduced. The functional diversity of soil microorganisms plays a vital role in forest health and the long-term maintenance of productivity. Though the optimization of forest cutting and regeneration methodologies is necessary to improve the functional diversity of soil microorganisms, the effects of harvest residual treatment on the functional diversity of soil microorganisms remain unclear. During the period 2018&ndash;2020, we designed four harvest residual treatments&mdash;reference (RF), residual burning (RB), crushing and mulching (MT), and no residuals (NR)&mdash;to determine soil physical and chemical properties. We also used microbial biomass (MB) to evaluate the diversity in carbon source metabolism of soil microorganisms through Biolog microplate technology, and discussed the response mechanism of microbial functional diversity to the different forest cutting and regeneration methodologies used in Chinese fir plantations. The results indicated that RB significantly increased the carbon metabolic capacity of the microbial community, the community richness, and its dominance compared to RF, MT, and NR; however, they also showed that it decreased the uniformity of the soil microbial community. NR showed a poor carbon utilization capacity for microorganisms compared to RF and MT, while MT significantly increased the utilization capacity of carbohydrate and amino acid carbon compared with RF. Soil nutrients were the main driving factors of soil microbial carbon metabolic activity, and the different responses of microbial functional diversity to various forest cutting and regeneration methodologies were mainly due to the variation in the nutrient inputs of harvest residues. This study provides a practical basis for enhancing the functional diversity of soil microorganisms in plantations through the management of harvest residues