Search

Background Antiviral intervention in hepatitis B virus (HBV)-infected pregnant women can effectively reduce mother-to-child transmission. However, the immunological characteristics of pregnant women with chronic HBV infection and the effects of antiviral intervention during pregnancy on maternal immune response remain unknown. We aimed to investigate these effects by comparing mothers who received antiviral intervention during pregnancy with those who did not. Methods Pregnant women positive for hepatitis B surface antigen and hepatitis B e-antigen (HBsAg+ HBeAg+) were enrolled at delivery, including 34 received prophylactic antiviral intervention during pregnancy (AVI mothers) and 15 did not (NAVI mothers). T lymphocyte phenotypes and functions were analysed using flow cytometry. Results At delivery, maternal regulatory T cell (Treg) frequency in AVI mothers was significantly higher than that in NAVI mothers (P + T cells in AVI mothers displayed a decreased ability to secrete IFN-γ (P = 0.005) and IL-21 (P = 0.043), but an increased ability to secrete IL-10 and IL-4 (P = 0.040 and P = 0.036), which represented a higher Treg frequency, enhanced Th2 response and suppressed Th1 response. Treg frequency among AVI mothers was correlated negatively with serum HBsAg and HBeAg levels. After delivery, the ability of CD4+ T cells or CD8+ T cells to secrete IFN-γ or IL-10 was similar and no significant difference in Treg frequency was found between the two groups. Conclusions Prophylactic antiviral intervention during pregnancy has an effect on T cell immunity in pregnant women, which was characterised by increased maternal Treg frequency, enhanced Th2 response and suppressed Th1 response at delivery.

Cigarette smoke (CS)-caused ferroptosis was involved in the pathogenesis of COPD, but the role of ferroptosis in lung epithelial injury and inflammation is not clear. Rats were treated with CS or CUR and BEAS-2B cells were exposed to CS extract (CSE), ferrostatin-1 (Fer-1), deferoxamine (DFO), or CUR to detect reactive oxygen species (ROS) accumulation, lipid peroxidation, iron overload, and ferroptosis-related protein, which were the characteristic changes of ferroptosis. Compared with the control group, CSE-treated BEAS-2B cells had more cell death, higher cytotoxicity, and lower cell viability. The infiltration of inflammatory cell around the bronchi in the CS group of rats was more than that in the normal group. Meanwhile, CSE/CS elevated the levels of interleukin-6 and tumor necrosis factor-α in BEAS-2B cells and bronchoalveolar lavage fluid of rats. Besides, accumulative ROS and depleted glutathione was observed in vitro. In BEAS-2B cells and lung tissues of rats, CSE/CS increased malondialdehyde and iron; down-regulated solute carrier family 7, glutathione peroxidase 4, and ferritin heavy chain levels; and up-regulated transferrin receptor level. These changes were rescued by pretreatment of Fer-1 or DFO in vitro, and mitigated by CUR in vitro and in vivo. Collectively, this study reveals that ferroptosis was involved in lung epithelial cell injury and inflammation induced by CS, and CUR may alleviate CS-induced injury, inflammation, and ferroptosis of lung epithelial cell.

Shulei Sun, Yuehao Shen, Jie Wang, Jinna Li, Jie Cao, Jing Zhang Department of Respiratory and Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin 300052, People’s Republic of ChinaCorrespondence: Jing ZhangDepartment of Respiratory and Critical Care Medicine, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, People’s Republic of ChinaTel +86-22-60361612Fax +86-22-60361720Email tjzyyzhangjing@163.comPurpose: Autophagy plays essential roles in the development of COPD. We aim to identify and validate the potential autophagy-related genes of COPD through bioinformatics analysis and experiment validation.Methods: The mRNA expression profile dataset GSE38974 was obtained from GEO database. The potential differentially expressed autophagy-related genes of COPD were screened by R software. Then, protein–protein interactions (PPI), correlation analysis, gene-ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied for the differentially expressed autophagy-related genes. Finally, RNA expression of top five differentially expressed autophagy-related genes was validated in blood samples from COPD patients and healthy controls by qRT-PCR.Results: A total of 40 differentially expressed autophagy-related genes (14 up-regulated genes and 26 down-regulated genes) were identified between 23 COPD patients and 9 healthy controls. The PPI results demonstrated that these autophagy-related genes interacted with each other. The GO and KEGG enrichment analysis of differentially expressed autophagy-related genes indicated several enriched terms related to autophagy and mitophagy. The results of qRT-PCR showed that the expression levels of HIF1A, CDKN1A, BAG3, ERBB2 and ATG16L1 in COPD patients and healthy controls were consistent with the bioinformatics analysis results from mRNA microarray.Conclusion: We identified 40 potential autophagy-related genes of COPD through bioinformatics analysis. HIF1A, CDKN1A, BAG3, ERBB2 and ATG16L1 may affect the development of COPD by regulating autophagy. These results may expand our understanding of COPD and might be useful in the treatment of COPD.Keywords: autophagy, COPD, bioinformatics analysis, gene expression omnibus dataset

Monodehydroascorbate reductase (MDHAR), a key enzyme to reduce monodehydroascorbate (DHA) to ascorbic acid (AsA), plays an important role in maintaining the level of ascorbic acid in plant cells. It helps to remove reactive oxygen species and regulate cellular redox homeostasis. In this study, we cloned a MDHAR gene from a sugar beet M14 line (BvM14). The full-length BvM14-MDHAR was 1737 bp, and its ORF contained 1059 bp encoding the MDHAR of 352 amino acids. In addition, we expressed the coding sequence of BvM14-MDHAR in Escherichia coli and purified the BvM14-MDHAR protein with high enzymatic activity. Quantitative real-time PCR analysis revealed that the BvM14-MDHAR was up-regulated in the BvM14 roots under salt stress. To investigate the functions of the BvM14-MDHAR, it was constitutively expressed in Arabidopsis thaliana. The transgenic plants exhibited an obvious salt stress tolerance phenotype, as evidenced by longer roots, higher fresh weight and chlorophyll contents, as well as higher AsA/DHA levels than wild-type (WT) seedlings under salt stress. In addition, the overexpression seedlings showed higher activities of MDHAR and dehydroascorbate reductase (DHAR) and decreased cell membrane damage compared to WT. The results showed that the BvM14-MDHAR confers salt tolerance through its activity in redox regulation. It is a potential target for enhancing crop salt stress tolerance through genetic engineering and molecular breeding effort.

Background Soil salinity is one of the most detrimental abiotic stresses that limit crop production and threaten global food security. The responsiveness of stomatal guard cells to various stimuli is important for plants to balance transpirational water loss and carbon dioxide (CO2) intake for photosynthesis. It is important to identify differences in proteomic changes under salt stress and to understand the mechanisms underlying salt stress-induced stomatal movement in sugar beet guard cells through proteomic analysis. Results In this study, we observed stomatal closure and changes in ascorbate peroxidase activities during short-term salt stress treatment of sugar beet monosomic addition line M14. We analyzed proteomic changes in guard cells in response to the salt stress using an iTRAQ-based quantitative proteomic approach. A total of 142 proteins were differentially changed in guard cells by the salt stress treatment. They include several ribosomal proteins, metabolic enzymes and photosystem-related proteins. Gene ontology (GO) annotation of the differentially abundant proteins highlights most of the proteins are related to binding activity, response to stimuli, and glutathione metabolism, phenylpropanoid biosynthesis and carbon fixation pathways. Many of the proteins were targeted to the chloroplast, nucleus and cell wall. They may play an important role in the process of stomata closure. In addition, several antioxidant enzymes (e.g., peroxidase, ascorbate peroxidase and dehydroascorbate reductase) involved in the regulation of ROS homeostasis were identified. Transcriptional levels of 16 differential proteins involved in stress responses did not correlate well with the protein levels, indicating different regulatory mechanisms in guard cells. Conclusions The proteomics results have revealed interesting mechanisms underlying the sugar beet guard cell response to salt stress, which have not been reported before. The knowledge may facilitate molecular breeding and/or engineering efforts toward enhancing crop stress tolerance while not compromising yield.

Background Salt stress is often associated with excessive production of reactive oxygen species (ROS). Oxidative stress caused by the accumulation of ROS is a major factor that negatively affects crop growth and yield. Root is the primary organ that senses and transmits the salt stress signal to the whole plant. How oxidative stress affect redox sensitive proteins in the roots is not known. Results In this study, the redox proteome of sugar beet M14 roots under salt stress was investigated. Using iTRAQ reporters, we determined that salt stress caused significant changes in the abundance of many proteins (2305 at 20 min salt stress and 2663 at 10 min salt stress). Using iodoTMT reporters, a total of 95 redox proteins were determined to be responsive to salt stress after normalizing again total protein level changes. Notably, most of the differential redox proteins were involved in metabolism, ROS homeostasis, and stress and defense, while a small number play a role in transport, biosynthesis, signal transduction, transcription and photosynthesis. Transcription levels of 14 genes encoding the identified redox proteins were analyzed using qRT-PCR. All the genes were induced by salt stress at the transcriptional level. Conclusions Based on the redox proteomics results, we construct a map of the regulatory network of M14 root redox proteins in response to salt stress. This study further refines the molecular mechanism of salt resistance at the level of protein redox regulation.

Science and technology innovation are crucial components underpinning agriculture. We constructed an evaluation framework including 4 pillars and 21 indicators, taking 31 provinces in China as examples to examine the level of agriculture science and technology development from a regional perspective. We found that there is an obvious gap between east and west nationwide, and that only about half of the provinces have reached the high and medium levels. It was worth noting that the innovation conditions in Shanghai and Beijing presented huge advantages, of vital importance to a first-class talent team, a complete innovation system, a stable and prosperous market, and active exchanges and cooperation. In addition, to maximize the transformation of agricultural science and technology achievements into real productivity, local government should also strengthen the construction of agricultural research and innovation platforms, technology transfer, and transformation of results. The findings advance understanding of the strengths and weaknesses of the evaluation subjects’ agricultural science and technology development from a regional perspective and are expected to provide some basis for the government and stakeholders to make relevant decisions.

Salt stress can cause cellular dehydration, which induces oxidative stress by increasing the production of reactive oxygen species (ROS) in plants. They may play signaling roles and cause structural damages to the cells. To overcome the negative impacts, the plant ROS scavenging system plays a vital role in maintaining the cellular redox homeostasis. The special sugar beet apomictic monosomic additional M14 line (BvM14) showed strong salt stress tolerance. Comparative proteomics revealed that six antioxidant enzymes (glycolate oxidase (GOX), peroxiredoxin (PrxR), thioredoxin (Trx), ascorbate peroxidase (APX), monodehydroascorbate reductase (MDHAR), and dehydroascorbate reductase3 (DHAR3)) in BvM14 were responsive to salt stress. In this work, the full-length cDNAs of genes encoding these enzymes in the redox system were cloned from the BvM14. Ectopic expression of the six genes reduced the oxidative damage of transgenic plants by regulating the contents of hydrogen peroxide (H2O2), malondialdehyde (MDA), ascorbic acid (AsA), and glutathione (GSH), and thus enhanced the tolerance of transgenic plants to salt stress. This work has charecterized the roles that the antioxidant enzymes play in the BvM14 response to salt stress and provided useful genetic resources for engineering and marker-based breeding of crops that are sensitive to salt stress.

This paper develops a novel off-policy game Q-learning algorithm to solve the anti-interference control problem for discrete-time linear multi-player systems using only data without requiring system matrices to be known. The primary contribution of this paper lies in that the Q-learning strategy employed in the proposed algorithm is implemented in an off-policy policy iteration approach other than on-policy learning due to the well-known advantages of off-policy Q-learning over on-policy Q-learning. All of the players work hard together for the goal of minimizing their common performance index meanwhile defeating the disturbance that tries to maximize the specific performance index, and finally they reach the Nash equilibrium of the game resulting in satisfying disturbance attenuation condition. In order to find the solution to the Nash equilibrium, the anti-interference control problem is first transformed into an optimal control problem. Then an off-policy Q-learning algorithm is proposed in the framework of typical adaptive dynamic programming (ADP) and game architecture, such that control policies of all players can be learned using only measured data. Comparative simulation results are provided to verify the effectiveness of the proposed method.

In this paper, a data-driven optimal control method based on adaptive dynamic programming and game theory is presented for solving the output feedback solutions of the $H_\infty $ control problem for linear discrete-time systems with multiple players subject to multi-source disturbances. We first transform the $H_\infty $ control problem into a multi-player game problem following the theoretical solutions according to game theory. Since the system state may not be measurable, we derive the output feedback based control policies and disturbances through mathematical operations. Considering the advantages of off-policy reinforcement learning (RL) over on-policy RL, a novel off-policy game Q-learning algorithm dealing with mixed competition and cooperation among players is developed, such that the $H_\infty $ control problem can be finally solved for linear multi-player systems without the knowledge of system dynamics. Moreover, rigorous proofs of algorithm convergence and unbiasedness of solutions are presented. Finally, simulation results demonstrated the effectiveness of the proposed method.

This paper presents a novel off-policy game Q-learning algorithm to solve $H_\infty $ control problem for discrete-time linear multi-player systems with completely unknown system dynamics. The primary contribution of this paper lies in that the Q-learning strategy employed in the proposed algorithm is implemented in an off-policy policy iteration approach other than on-policy learning, since the off-policy learning has some well-known advantages over the on-policy learning. All of players struggle together to minimize their common performance index meanwhile defeating the disturbance that tries to maximize the specific performance index, and finally they reach the Nash equilibrium of game resulting in satisfying disturbance attenuation condition. For finding the solution of the Nash equilibrium, $H_\infty $ control problem is first transformed into an optimal control problem. Then an off-policy Q-learning algorithm is put forward in the typical adaptive dynamic programming (ADP) and game architecture, such that control policies of all players can be learned using only measured data. More importantly, the rigorous proof of no bias of solution to the Nash equilibrium by using the proposed off-policy game Q-learning algorithm is presented. Comparative simulation results are provided to verify the effectiveness and demonstrate the advantages of the proposed method.

This paper develops a novel Proportional-Integral-Derivative (PID) tuning method for multi-agent systems with a reinforced self-learning capability for achieving the optimal consensus of all agents. Unlike the traditional model-based and data-driven PID tuning methods, the developed PID self-learning method updates the controller parameters by actively interacting with unknown environment, with the outcomes of guaranteed consensus and performance optimization of agents. Firstly, the PID control-based consensus problem of multi-agent systems is formulated. Then, finding the PID gains is converted into solving a nonzero-sum game problem, thus an off-policy Q-learning algorithm with the critic-only structure is proposed to update the PID gains using only data, without the knowledge of dynamics of agents. Finally, simulations are given to verify the effectiveness of the proposed method.

BackgroundTo improve the quality of plan for the radiation treatment of advanced left breast cancer by introducing the auxiliary structures (ASs) which are used to spare the regions with no intact delineated structures adjacent to the target volume.MethodsCT data from 20 patients with left-sided advanced breast cancer were selected. An AS designated as A1 was created to spare the regions of the aorta, pulmonary artery, superior vena ava, and contralateral tissue of the upper chest and neck, and another, designated as A2, was created in the regions of the cardia and fundus of the stomach, left liver lobe, and splenic flexure of the colon. IMRT and VMAT plans were created for cases with and without the use of the AS dose constraints in plan optimization. Dosimetric parameters of the target and organs at risk (OARs) were compared between the separated groups.ResultsWith the use of AS dose constraints, both the IMRT and VMAT plans were clinically acceptable and deliverable, even showing a slight improvement in dose distribution of both the target and OARs compared with the AS-unused plans. The ASs significantly realized the dose sparing for the regions and brought a better conformity index (p < 0.05) and homogeneity index (p < 0.05) in VMAT plans. In addition, the volume receiving at least 20 Gy (V20) for the heart (p < 0.05), V40 for the left lung (p < 0.05), and V40 for the axillary-lateral thoracic vessel juncture region (p < 0.05) were all lower in VMAT plans.ConclusionThe use of the defined AS dose constraints in plan optimization was effective in sparing the indicated regions, improving the target dose distribution, and sparing OARs for advanced left breast cancer radiotherapy, especially those that utilize VMAT plans.

Background: Salt stress is a major abiotic stress that limits plant growth, development and productivity. Studying the molecular mechanisms of salt stress tolerance may help to enhance crop productivity. Sugar beet monosomic addition line M14 exhibits tolerance to salt stress. Results: In this work, the changes in the BvM14 proteome and redox proteome induced by salt stress were analyzed using a multiplex iodoTMTRAQ double labeling quantitative proteomics approach. A total of 80 proteins were differentially expressed under salt stress. Interestingly, 42 potential redox-regulated proteins showed differential redox change under salt stress. A large proportion of the redox proteins were involved in photosynthesis, ROS homeostasis and other pathways. For example, ribulose bisphosphate carboxylase/oxygenase activase changed in its redox state after salt treatments. In addition, three redox proteins involved in regulation of ROS homeostasis were also changed in redox states. Transcription levels of eighteen differential proteins and redox proteins were profiled. Conclusions: The results showed involvement of protein redox modifications in BvM14 salt stress response and revealed the short-term salt responsive mechanisms. The knowledge may inform marker-based breeding effort of sugar beet and other crops for stress resilience and high yield.

Background Salt stress is a major abiotic stress that limits plant growth, development and productivity. Studying the molecular mechanisms of salt stress tolerance may help to enhance crop productivity. Sugar beet monosomic addition line M14 exhibits tolerance to salt stress. Results In this work, the changes in the BvM14 proteome and redox proteome induced by salt stress were analyzed using a multiplex iodoTMTRAQ double labeling quantitative proteomics approach. A total of 80 proteins were differentially expressed under salt stress. Interestingly, A total of 48 redoxed peptides were identified for 42 potential redox-regulated proteins showed differential redox change under salt stress. A large proportion of the redox proteins were involved in photosynthesis, ROS homeostasis and other pathways. For example, ribulose bisphosphate carboxylase/oxygenase activase changed in its redox state after salt treatments. In addition, three redox proteins involved in regulation of ROS homeostasis were also changed in redox states. Transcription levels of eighteen differential proteins and redox proteins were profiled. (The proteomics data generated in this study have been submitted to the ProteomeXchange and can be accessed via username: reviewer_pxd027550@ebi.ac.uk, password: q9YNM1Pe and proteomeXchange# PXD027550.) Conclusions The results showed involvement of protein redox modifications in BvM14 salt stress response and revealed the short-term salt responsive mechanisms. The knowledge may inform marker-based breeding effort of sugar beet and other crops for stress resilience and high yield.

This paper studies the event-triggered sliding mode control problem for singular systems subject to the unknown nonlinear function and the exogenous disturbance. For saving the communication resources, a new adaptive event-triggering communication scheme (AETCS) is designed, which scheme uses the information on the nonlinear function part. Secondly, for the error system, we provide a novel integral sliding surface, which makes it beneficial to construct a new augmented delay system model by utilizing a delay system method. Furthermore, the sliding mode control (SMC) method for the error system is applied to compensate the unknown nonlinearity by using its estimate and match the exogenous disturbance by its upper bound. According to the Lyapunov function theory, stability criteria are got on the basis of LMIs. Moreover, two novel event-triggered adaptive sliding mode controllers based on RBF neural network are designed such that reachability conditions are obtained, and the asymptotic stability of singular systems with the H

This paper aims at using an off-policy integral reinforcement learning (IRL) algorithm to solve the linear quadratic tracking (LQT) control problem of completely unknown continuous-time systems, such that an arbitrary higherorder polynomial signal can be followed via an optimal approach. Firstly, a linear continuous-time system with unknown model matrices is introduced with a target of tracking the reference signal with higher-order polynomials. Secondly, based on the knowledge of the on-policy IRL, an off-policy IRL algorithm is used to solve the derived iterative Bellman equation and update the control policy resulting in the optimal control policy. Finally, a simulation example is given to show the efficiency of the proposed approach.

An integral reinforcement learning (IRL) algorithm is used for solving the optimal tracking control problem for partial unknown continuous-time systems that try to chase a polynomial reference signal. By using IRL to get the control input of the Bellman equation derived from the problem, the approximate controller is able to get without sufficient system dynamics. Firstly, the LQT problem is formulated. An augmented vector is defined, and an algebraic Riccati equation is obtained based on the dynamic programming method. Then, employing IRL yields the iterative Bellman equation and policy updating expression. And the approximate optimal tracking control policy is finally solved, under which the reference signal with higherorder polynomials and unknown model parameters can be tracked by a linear system with partial known model parameters, meanwhile the specific performance can be minimized. Finally, by a simulation example, the efficiency of the provided method is confirmed.

In this paper, we study the adaptive optimal consensus control of leaderless multi-agent systems (MASs) with heterogeneous dynamics. First, the consensus control problem is converted into a graphical minimax game problem and the corresponding algebraic Riccati equation (ARE) for each agent is obtained. Then, an on-policy reinforcement learning algorithm is proposed to online learn the optimal control policy without requiring the system dynamics. A certain rank condition is established to guarantee the convergence of the proposed online learning algorithm to the unique solution of the ARE. Finally, the effectiveness of the proposed algorithm is demonstrated through a numerical simulation.

This paper takes two-player systems as an example to study the robust optimal tracking control problem for linear discrete-time (DT) multi-player systems with constant uncertainty. To this end, by using adaptive dynamic programming (ADP) method and game theory, the optimal feedback control problem of the dynamic aspect was translated into a two-player cooperative game problem. Thus, we developed a novel off-policy cooperative game Q-learning algorithm first to learn the feedback controllers through the measured data along the system trajectories. Then the steady-state control inputs can be obtained by utilizing the Lagrange equation and correlative parameters learned from the proposed algorithm. Finally, the control inputs of the linear DT systems with uncertainty can be calculated by combining the feedback controllers and the steady-state control inputs. Simulation results are given to verify the effectiveness of the proposed method.

ICS are anti-inflammatory agents which have been suggested to benefit people with worsening symptoms of COPD, by improving lung function, reducing exacerbation of disease, and enhancing overall quality of life. This systematic review and meta-analysis explored the association of the risk of pneumonia in COPD patients that were undergoing treatment using ICS alone or together with LABAs or LAMAs. PubMed, Cochrane Library and EMBASE were systematically searched through August 1, 2019; only double-blinded randomized controlled trials were eligible for this study. Eighteen randomized controlled trials were included. ICS treatment was linked to increased pneumonia incidence (RR, 1.47; 95% CI, 1.26-1.71

In this paper, an off-policy game Q-learning algorithm is proposed for solving linear discrete-time non-zero sum multi-player game problems. Unlike the existing Q-learning methods for solving the Riccati equation by on-policy learning approaches for multi-player games, an off-policy game Q-learning method is developed for achieving the Nash equilibrium of multiple players. To this end, first, a non-zero sum game problem is formulated, and the value function and the Q-function defined according to each-player individual performance index are rigorously proved to be linear quadratic forms. Then, based on the dynamic programming and Q-learning methods, an off-policy game Q-learning algorithm is developed to find the control policies for multi-player games, such that the Nash equilibrium is reached under the learned control policies. The merit of this paper lies in that the proposed algorithm does not require the system model parameters to be known a priori and fully utilizes measurable data to learn the Nash equilibrium solution. Moreover, there is no bias of Nash equilibrium solution when implementing the proposed off-policy game Q-learning algorithm even though probing noises are added to control policies for maintaining the persistent excitation condition. While bias of the Nash equilibrium solution could be produced if on-policy game Q-learning is employed. This is another contribution of this paper.

This paper studies the operational optimal control problem for the industrial flotation process, a key component in the mineral processing concentrator line. A new model-free data-driven method is developed here for real-time solution of this problem. A novel formulation is given for the optimal selection of the process control inputs that guarantees optimal tracking of the operational indices while maintaining the inputs within specified bounds. Proper tracking of prescribed operational indices, namely concentrate grade and tail grade, is essential in the proper economic operation of the flotation process. The difficulty in establishing an accurate mathematic model is overcome, and optimal controls are learned online in real time, using a novel form of reinforcement learning we call interleaved learning for online computation of the operational optimal control solution. Simulation experiments are provided to verify the effectiveness of the proposed interleaved learning method and to show that it performs significantly better than standard policy iteration and value iteration.

Background A newly developed dietary inflammatory index (DII) to evaluate the inflammatory potential of diets was published recently. Many studies have investigated the link between diet-related inflammation and human cancer risk, but the results remain controversial. Objective We sought to determine the dose-response relation between DII and human cancer risk based on published epidemiologic literature. Design To summarize evidence, we performed a dose-response meta-analysis to investigate the association between DII and cancer incidence. We systematically searched PubMed, Embase, Web of Science, and the Cochrane library up to 5 November 2017. After data extraction, pooled RRs were calculated and dose-response analyses were performed using a restricted cubic spline model with 4 knots. Subgroup analyses, sensitivity analyses, and tests for publication bias were also performed. Results In all, 44 high-quality studies with 1,082,092 participants were included. The results showed that an elevated DII (continuous-RR: 1.13; 95% CI: 1.09, 1.16; category DIIhighest vs lowest-RR: 1.58; 95% CI: 1.45, 1.72) independently indicated higher cancer risk except for lung cancer and Australian studies. A linear dose-response relation between DII and overall cancer risk was found, with an 8.3% increase in the risk of cancer per DII score. The pooled RR of DII and cancer risk was 1.86 (95% CI: 1.63, 2.13) from 30 case-control studies but was lower in 14 prospective cohorts (RR: 1.29; 95% CI: 1.19, 1.40). The sensitivity analysis and Egger's test supported the main results. Conclusions Our analysis indicated that higher DII is significantly correlated with cancer risk. More prospective studies with large sample sizes, involving more ethnic groups and different cancer types, are required in the future. This review was registered with PROSPERO as CRD42017077075.

The entrance of Tianshan tunnel was constructed in residual slope accumulation of gravelly soil while the massif at the entrance was considered as a potential landslide mass. In this context, landslide is very likely to occur to cause engineering accidents under the effects of tunneling construction disturbance and rainfall infiltration. By analyzing the engineering geological and hydrogeological conditions of the tunnel, the researchers discussed the original construction scheme of the tunnel entrance and decided to optimize the construction technology of the tunnel entrance. During the construction design, the stability of side and face-upward slope of the entrance was calculated first, which reaches a conclusion that the side and face-upward slope probably landslide towards the free face induced by rainstorms. Next, the potential landslide mass at side and face-upward slope of the tunnel entrance was optimized and governed by using the method of lengthening umbrella arches and increasing reverse pressure by filling soil. Finally, the “three-bench seven-step” method was utilized to replace the center diagram method and the reasonability of the excavation method was verified by comparison and analysis using three-dimensional numerical simulation. Thus, the construction efficiency can be improved on the precondition of guaranteeing the safety.

The tracking control of H∞ dynamic output feedback is proposed for the fuzzy networked systems of the same category, in which each system is discrete-time nonlinear and is missing measurable data. In other words, the loss of data packet occurs randomly in both the uplink and the downlink. The independent variables that are called the Bernoulli random variables are considered to design the loss of data packets. The method of parallel distributed compensation (PDC) in terms of the T-S fuzzy model is applied to investigate the dynamic controller of tracking control on the systems. Then, it is presented that the analytical H∞ performance of the output error between the reference model and the fuzzy model for the closed-loop system containing dynamic output feedback controller is proven. Furthermore, the achieved sufficient conditions in terms of LMIs ensure that the closed-loop system is stochastically stable in the H∞ sense. Finally, a numerical system is offered to show the effectiveness of the established technique.

The idea of network-induced delay and packet disordering processing unit (DDPU) using fuzzy rules is presented for the first time to handle the network-induced delay and packet disordering synchronously in T–S fuzzy descriptor systems communicated by network. Particularly, there are two crucial variables in DDPU, which come from the delays of two packets arriving at actuator successively, to characterize the levels of those two network problems, respectively. Under the assumptions of bounded network-induced delay and bounded survival interval of a packet, these two variables are then served as premise variables of the DDPU module. Then, it is quite reasonable that four IF–THEN fuzzy rules are employed, provided that every fuzzy premise variable is described by two fuzzy sets. Furthermore, considering that the derivative of time-varying input delay is 1, a new Lyapunov–Krasovskii functional is designed for T–S fuzzy descriptor systems with input delay. A sufficient condition for the admissibility problem of T–S fuzzy descriptor systems is obtained in terms of strict linear matrix inequalities (LMIs). Finally, example results are presented to illustrate the usefulness and effectiveness of the proposed fuzzy DDPU method.

In this paper, the model-based fuzzy control application for double-layer operation systems with uncertainty is addressed. Due to the multiple-time scales of complex industrial systems, as well as the inborn uncertainties of the system, the challenge of feedback control is presented. A new approach based on a class of fuzzy models with uncertainty fulfilling the condition of sector bound for the operation process system is proposed. First, a fuzzy model of the system is considered in terms of the system's described dynamical characteristics, and the PI fuzzy controller is then employed for the model, which will ensure controlled plant tracking set-points. Second, with different sampling rates, a dual-layer model combining PI fuzzy controller and dynamical fuzzy output feedback controller are presented. Third, the S-procedure is applied to derive the feedback controller, which can simultaneously guarantee the mean value of steady-state error between the realistic and target operational index is zero and the stability of the dual closed-loop control system is established. Finally, significant simulations have been executed to show the effectiveness of the proposed approach.

This study investigates sliding mode control (SMC) for non-linear networked control systems (NCSs) subject to packet disordering as well as external disturbances. The main objectives of the proposed method are to predict packet disordering and to stabilise the NCSs in case of the unknown packet disordering in the future. Firstly, linearisation of non-linear systems and the technology of adopting the newest control input with a stochastic parameter are employed to model the system as a linear Markovian jumping system. Secondly, with the application of a time series prediction model, the phenomenon of disordering better under the novel measurement is portrayed. Then, robust H ∞ SMC is designed by solving the linear matrix inequalities (LMIs). Finally, examples with sampled disordering packets are simulated to illustrate the effectiveness and advantages of the proposed method.

In this paper, an off-policy Q-learning method is proposed to solve the linear quadratic tracking problem of discrete-time system based on the output feedback of the system when the system model parameters are unknown. First, a linear discrete-time system with unknown parameters in the system matrix is given. Then, based on the Q-learning method and dynamic programming, an off-policy Q-learning algorithm without knowing system model parameters is proposed, such that the optimal controller is designed to obtain the control strategy which uses the system output data to learn the output feedback data driven optimal tracking control for linear discrete time systems with output feedback. Finally, the simulation results verify the effectiveness of the method.

In this paper, a novel off-policy Q-learning is developed for solving optimal tracking problem of cracking outlet temperature (COT) of SC-1 ethylene cracking furnace, using only the measured data along the system trajectories. This paper takes the outlet temperature of ethylene cracking furnace as the background, taking the state space model as the basis, and combines the data-driven off-policy Q-learning algorithm. A novel off-policy Q-learning algorithm is presented by introducing behavior control policy and combining dynamic programming with Q-learning, such that the optimal tracking controller gain is learned with no need of knowledge of system dynamics enabling the tracking of the target. Simulation results are given to verify the effectiveness of the proposed method.

Immature immune system and immune tolerance induced by exposure to HBeAg in utero and/or shortly after infection in newborns were reportedly the causes of chronic HBV infection. To investigate the effect of maternal-derived HBeAg on neonatal T cell immunity, we analysed and compared T cell phenotypes and functions among neonates born to HBsAg+ /HBeAg+ mothers (HBeAg+ neonates), HBsAg+ /HBeAg- mothers (HBeAg- neonates) and healthy control mothers (HC neonates), using flow cytometry. The results showed that neonatal T cell phenotypes were similar regardless of HBeAg exposure. Upon anti-CD3 and anti-CD28 stimulation in HBeAg+ neonates, CD4+ T cell production of IFN-γ (P < .05) was significantly enhanced, while CD8+ T cells secreted significantly more IL-2 compared with those in HBeAg- and HC groups (P < .05). Moreover, similar levels of IFN-γ and IL-10 were observed in the culture supernatant after stimulation with rHBsAg, rHBcAg or rHBeAg among HBeAg+ , HBeAg- and HC neonates, whereas HBeAg+ neonates produced more TNF-α than HBeAg- neonates upon stimulation with rHBcAg. In conclusion, the results indicated that the HBsAg+ /HBeAg+ maternal environment did not influence the phenotypes of cord blood T cells but boosted neonatal non-specific Th1-type cytokine production.

This article presents a novel technique to achieve plant-wide performance optimization for large-scale unknown industrial processes by integrating the reinforcement learning method with the multiagent game theory. A main advantage of this technique is that plant-wide optimal performance is achieved by a distributed approach where multiple agents solve simplified local nonzero-sum optimization problems so that a global Nash equilibrium is reached. To this end, first, the plant-wide performance optimization problem is reformulated by decomposition into local optimization subproblems for each production index in a multiagent framework. Then, the nonzero-sum graphical game theory is utilized to compute the operational indices for each unit process with the purpose of reaching the global Nash equilibrium, resulting in production indices following their prescribed target values. The stability and the global Nash equilibrium of this multiagent graphical game solution are rigorously proved. The reinforcement learning methods are then developed for each agent to solve the nonzero-sum graphical game problem using data measurements available in the system in real time. The plant dynamics do not have to be known. Finally, the emulation results are given to show the effectiveness of the proposed automated decision algorithm by using measured data from a large mineral processing plant in Gansu Province, China.

Consider the inherent existence of unmodeled dynamics when identifying system models, it is worth investigating control to guarantee the robust stability of the systems. This paper focuses on robust control for two-player time-invariant difference game with uncertain unmodeled dynamics by using adaptive learning way. To this end, the optimization control problem for two-player linear difference games with bounded unmodeled dynamics is formulated first. Then, dynamic programming combined with game theory and adaptive critic learning are employed for the purpose of finding the stabilizing control polices, such that an adaptive learning method is developed for stabilizing the closed-loop two-player dynamics with unmodeled dynamics. The robust stabilization of the uncertain two-player game systems is rigorously proved. Simulations are given to show the effectiveness of the proposed method.

Salinity is a major abiotic stress affecting plant growth, development and agriculture productivity. Understanding the molecular mechanisms of salt stress tolerance will provide valuable information for effective crop engineering and breeding. Sugar beet monosomic addition line M14 obtained from the intercross between Beta vulgaris L. and Beta corolliflora Zoss exhibits tolerance to salt stress. In this study, the changes in the M14 proteome and phosphoproteome induced by salt stress were analyzed. We report the characteristics of the M14 plants under 0, 200, and 400mM NaCl using label-free quantitative proteomics approaches. Protein samples were subjected to total proteome profiling using LC-MS/MS and phosphopeptide enrichment to identify phosphopeptides and phosphoproteins. A total of 2182 proteins were identified and 114 proteins showed differential levels under salt stress. Interestingly, 189 phosphoproteins exhibited significant changes at the phosphorylation level under salt stress. Several signaling components associated with salt stress were found, e.g. 14-3-3 and mitogen-activated protein kinases (MAPK). Fifteen differential phosphoproteins and proteins involved in signal transduction were tested at the transcriptional level. The results revealed the short-term salt responsive mechanisms of the special sugar beet M14 line using label-free quantitative phosphoproteomics.Sugar beet monosomic addition line M14 is a special germplasm with salt stress tolerance. Analysis of the M14 proteome and phosphoproteome under salt stress has provided insight into specific response mechanisms underlying salt stress tolerance. Reversible protein phosphorylation regulates a wide range of cellular processes such as transmembrane signaling, intracellular amplification of signals, and cell-cycle control. This study has identified significantly changed proteins and phosphoproteins, and determined their potential relevance to salt stress response. The knowledge gained can be potentially applied to improving crop salt tolerance.

This study proposes a novel sampling and control strategy to find a suboptimal sampling period sequence and control input sequence, such that a quadratic cost function of state and control input of a networked control system (NCS) with packet disordering is minimised. First, a discrete-time system model of the NCS with packet disordering, transmission delay and packet loss in terms of displacement values of packets is put forward. Second, a linear quadratic regulation (LQR) problem of the NCS is formulated, showing that the optimal controller depends on sampling period and quality of services (QoS) of networks. Interactive effects between sampling period and QoS of networks pose a challenge in solving the LQR problem of the NCS. To overcome this difficulty, different from traditional transmission-delay-based or packet-loss-based sampling scheme, a novel packet-disordering-based sampling period selection scheme is proposed. Furthermore, an algorithm is presented to find a suboptimal solution to the LQR problem in this study. Finally, simulation results demonstrate the effectiveness of the proposed approach.

Both maturation delay for prey and gestation delay for predator are considered.Positivity of solutions and uniform persistence of system are investigated.Combined dynamic effects of double time delays and economic interest are studied.Existence and control for singularity induced bifurcation are investigated.Local stability analysis and properties of Hopf bifurcation are studied. A differential-algebraic prey-predator system with commercial harvesting on predator is proposed, where maturation delay for prey and gestation delay for predator are considered. Since commercial harvesting is dynamically influenced by variation of economic interest, we will investigate combined dynamic effects of double time delays and economic interest on population dynamics. Positivity of solutions and uniform persistence of system are studied. In the absence of time delay, by taking economic interest as bifurcation parameter, existence of singularity induced bifurcation is investigated based on differential-algebraic system theory. State feedback controllers are designed to eliminate singularity induced bifurcation and stabilize the proposed system around corresponding interior equilibrium. In the presence of double time delays, by analyzing associated characteristic transcendental equation, it is found that interior equilibrium loses local stability when double time delays cross corresponding critical values. According to Hopf bifurcation theorem for functional differential equation, existence of Hopf bifurcation is investigated as local stability switches. Based on normal form theory and center manifold theorem, directions of Hopf bifurcation and stability of the bifurcating periodic solutions are studied. Numerical simulations are carried out to show consistency with theoretical analysis.

This paper presents a model-free optimal approach based on reinforcement learning for solving the output regulation problem for discrete-time systems under disturbances. This problem is first broken down into two optimization problems: 1) a constrained static optimization problem is established to find the solution to the output regulator equations (i.e., the feedforward control input) and 2) a dynamic optimization problem is established to find the optimal feedback control input. Solving these optimization problems requires the knowledge of the system dynamics. To obviate this requirement, a model-free off-policy algorithm is presented to find the solution to the dynamic optimization problem using only measured data. Then, based on the solution to the dynamic optimization problem, a model-free approach is provided for the static optimization problem. It is shown that the proposed algorithm is insensitive to the probing noise added to the control input for satisfying the persistence of excitation condition. Simulation results are provided to verify the effectiveness of the proposed approach.