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Sparse data assessed the association of apolipoprotein A1 (ApoA1) and high density lipoprotein cholesterol (HDL-C) with inflammation. We investigated this association in a hospital-based cross-sectional pilot study that included 7296 patients with coronary artery disease (CAD). In multivariate analysis, negative associations of ApoA1 and HDL-C with C-reactive protein (CRP), high sensitivity CRP (hsCRP), and tumor necrosis factor-α (TNF-α) were shown. The corresponding CRP, hsCRP, and TNF-α values were 5.28 (vs 11.70 mg/L), 4.50 (vs 11.50 mg/L), and 7.68 (vs 10.90 pg/mL) for ApoA1, and 7.13 (vs 10.60 mg/L), 6.27 (vs 9.19 mg/L), and 8.11 (vs 11.86 pg/mL) for HDL-C in the fourth quartiles compared with the first quartiles. ApoA1/HDL-C ratio was inversely associated with hsCRP and interleukin-6 (IL-6). No significant associations of ApoA1 and HDL-C with IL-6 and IL-8, and of ApoA1/HDL-C ratio with CRP, IL-8, and TNF-α were observed. In path analyses, there was no evidence of mediating effects of body mass index on the "ApoA1 and HDL-C-inflammation" relationship. Generally, our study of CAD patients identified graded and inverse associations of ApoA1, HDL-C, and ApoA1/HDL-C ratio with inflammatory marker (CRP, hsCRP, IL-6, IL-8, or TNF-α) levels. [ABSTRACT FROM AUTHOR]

Scarce data examined the associations of circulating calcium (Ca) and magnesium (Mg) with high-sensitivity C-reactive protein (hsCRP) in humans. We conducted a hospital-based cross-sectional study to evaluate the associations of serum Ca, Mg, and Ca/Mg ratio with hsCRP and examine the mediating roles of body mass index (BMI). A total of 7203 patients with coronary artery disease (CAD) (average age: 61.0 years) were included. Serum concentrations of Ca, Mg, and hsCRP were measured. The multivariate analysis of covariance was used to determine Ca- and Mg-hsCRP associations. Serum Ca and Mg were inversely associated with hsCRP, while no significant association between Ca/Mg ratio and hsCRP was detected. After adjustment for age and sex, higher Ca and Mg concentrations were associated with lower hsCRP (quintile5 [Q5] vs. Q1: 6.35 vs. 11.88 mmol/L for Ca; Q4 vs. Q1: 4.61 vs. 6.44 mmol/L for Mg). The multivariate-adjusted analysis found that serum Ca and Mg were inversely associated with hsCRP levels (Q5 vs. Q1: 6.69 vs. 11.33 mmol/L for Ca; Q4 vs. Q1: 4.65 vs. 6.32 mmol/L for Mg). Similar findings were observed in the stratified analyses by sex (men and women) and BMI (< 28 and ≥ 28 kg/m2). In path analysis, BMI had no mediating effects on the Ca- or Mg-hsCRP associations. Generally, our study showed significant inverse associations of serum Ca and Mg with hsCRP in CAD patients. Our findings provided further support for the anti-inflammatory effects of Ca and Mg in CAD patients. [ABSTRACT FROM AUTHOR]

In this paper, the observer-based proportional-integral-derivative (PID) control problem is investigated for a class of actuator-saturated systems affected by multiplicative noises under the binary encoding scheme (BES). The communication between the sensors and the observers is implemented via a shared network, in which the measurement signals are transmitted by the BES. During the transmission, random bit errors may occur in the bit string due to the channel noises. The aim of this paper is to design an observer-based PID controller such that the closed-loop system is exponentially mean-square ultimately bounded with the consideration of random bit errors, actuator saturations and multiplicative noises. Sufficient conditions are presented to guarantee the expected performance of the resulted tracking error, and the controller gain matrices are obtained by solving a set of matrix inequalities. Finally, the effectiveness of the proposed PID control scheme is verified by a simulation example. [ABSTRACT FROM AUTHOR]

Apolipoproteins exert a key role on glucose metabolism; however, scarce data have examined the relationship between apolipoproteins and glycated haemoglobin (HbA1c) in Chinese adults. This study determined the cross-sectional and longitudinal associations of serum Apolipoprotein A1 (ApoA1), Apolipoprotein B (ApoB) and the ApoB/A1 ratio with HbA1c in Chinese adults. A total of 1448 subjects (584 men and 864 women) aged 54.8 years were included in a baseline survey, and the concentrations of Apo and HbA1c were measured. A total of 826 participants were followed up approximately once after 3.94 ± 0.62 years. In cross-sectional analysis, serum ApoA1 was inversely associated with HbA1c, while ApoB and the ApoB/A1 ratio were positively associated with HbA1c. After further adjusting for the potential covariates, a higher ApoA1 was associated with lower HbA1c (Quartile 4 [Q4] vs. Q1 = 5.673% vs. 5.796%, P-trend = 0.014). In contrast, positive association of ApoB concentration and the ApoB/A1 ratio with HbA1c level were showed (Q4 vs. Q1 = 5.805% vs. 5.589% for ApoB; Q4 vs. Q1 = 5.841% vs. 5.582% for ApoB/A1 ratio). The longitudinal results showed no significant associations of ApoA1, ApoB levels and the ApoB/A1 ratio with HbA1c changes (all P-trends > 0.05). Path analysis suggested that body mass index did not have mediating effect on Apo-HbA1c association. Our findings revealed that higher ApoA1, lower ApoB concentrations and the ApoB/A1 ratio were associated with lower HbA1c level in Chinese adults. [ABSTRACT FROM AUTHOR]

Pipeline fault diagnosis is of practical necessity for the stable operation of large-scale oil and gas transportation systems. Despite the remarkable achievements of existing intelligent fault diagnosis methods, these studies have encountered noticeable performance degradation when directly applied in real industrial scenarios due to the absence of sufficient and balanced datasets. To this end, this paper proposes a novel multi-generator switching adversarial network (MGSAN) to augment the pipeline data under different health states, especially faulty ones. First, a multi-mode generator is designed to produce various candidate solutions, where a parameter-sharing scheme is employed to ensure consistency among modes at the parameter level. After that, a mode switching strategy is presented to maintain a proper tradeoff between the generator and the discriminator, which is capable of changing the state of the generator from one mode to another according to the performance (i.e., quality and diversity) of the candidate solutions. Finally, extensive experimental results on visualization and quantitative evaluation show that the MGSAN algorithm has more stable behavior and stronger exploration capabilities than well-known GAN variants. Additionally, the proposed MGSAN algorithm is successfully exploited to improve the accuracy of pipeline fault diagnosis. • A multi-mode generator is proposed to provide sufficient gradients. • A mode-switching strategy is designed to keep the generator in the optimal mode. • The proposed MGSAN algorithm is applied to address the class imbalance issue. [ABSTRACT FROM AUTHOR]

This paper is concerned with the non-fragile state estimation (NFSE) problem for a class of delayed genetic regulatory networks (GRNs) under stochastic sampling mechanisms. The measurement output is sampled before being transmitted to the estimator where the sampling period switches between two different values in a random way. By transforming the sampling periods into bounded time-delays, the issue of designing a non-fragile state estimator based on stochastically sampled measurements is converted into the NFSE problem for GRNs with multiple probabilistic interval delays. Then, by constructing a Lyapunov–Krasovskii functional and employing the Gronwall's inequality together with Jensen's inequality, a sufficient criterion is obtained to ensure the exponential mean-square stability of the corresponding estimation error dynamics. Furthermore, the desired non-fragile estimator gain is obtained via solving a convex optimization problem. Finally, the validity of the developed stochastic sampled-data NFSE scheme is verified via a numerical example. [ABSTRACT FROM AUTHOR]

Objectives: Fruit intake may influence gestational diabetes mellitus (GDM) risk. However, prospective evidence remains controversial and limited. The current study aimed to investigate whether total fruit and specific fruit intake influence GDM risk.Design: A prospective cohort study was conducted. Dietary information was collected by a 3-d 24-h dietary recall. All participants underwent a standard 75-g oral glucose tolerance test at 24-28 gestational weeks. Log-binomial models were used to estimate the association between fruit intake and GDM risk, and the results are presented as relative risks (RR) and 95 % CI.Setting: Southwest China.Participants: Totally, 1453 healthy pregnant women in 2017.Results: Total fruit intake was not associated with lower GDM risk (RR of 1·03 (95 % CI 0·83, 1·27) (Ptrend = 0·789)). The RR of GDM risk was 0·73 for the highest anthocyanin-rich fruit intake quartile compared with the lowest quartile (95 % CI 0·56, 0·93; Ptrend = 0·015). A higher grape intake had a linear inverse association with GDM risk (Q4 v. Q1: RR = 0·65; 95 % CI 0·43, 0·98; Ptrend = 0·044), and after further adjustment for anthocyanin intake, the inverse association tended to be non-linear (Q4 v. Q1: RR = 0·65; 95 % CI 0·44, 0·98; Ptrend = 0·079). However, we did not find an association between glycaemic index-grouped fruit, glycaemic load-grouped fruit or other fruit subtype intake and GDM risk.Conclusions: In conclusion, specific fruit intake (particularly anthocyanin-rich fruit and grapes) but not total fruit intake was inversely associated with GDM risk. [ABSTRACT FROM AUTHOR]

Seed number per pod (SNPP) and seed weight (SW) are two components of seed yield in rapeseed (Brassica napus). Here, a natural population of rapeseed was employed for genome-wide association analysis for SNPP and SW across multi-years. A total of 101 and 77 SNPs significantly associated with SNPP and SW with the phenotypic variances (R2) ranging from 1.35 to 29.47% and from 0.78 to 34.58%, respectively. And 43 and 33 homologs of known genes from model plants were located in the 65 and 49 haplotype blocks (HBs) for SNPP and SW, respectively. Notably, we found 5 overlapping loci and 3 sets of loci with collinearity for both SNPP and SW, of which 4 overlapping loci harbored the haplotypes with the same direction of genetic effects on SNPP and SW, indicating high possibility to simultaneously improve SNPP and SW in rapeseed. Our findings revealed both overlapping and independent loci controlling seed number per pod and seed weight in rapeseed. [ABSTRACT FROM AUTHOR]

Seed number per pod (SNPP) and seed weight (SW) are two components of seed yield in rapeseed (Brassica napus). Here, a natural population of rapeseed was employed for genome-wide association analysis for SNPP and SW across multi-years. A total of 101 and 77 SNPs significantly associated with SNPP and SW with the phenotypic variances (R2) ranging from 1.35 to 29.47% and from 0.78 to 34.58%, respectively. And 43 and 33 homologs of known genes from model plants were located in the 65 and 49 haplotype blocks (HBs) for SNPP and SW, respectively. Notably, we found 5 overlapping loci and 3 sets of loci with collinearity for both SNPP and SW, of which 4 overlapping loci harbored the haplotypes with the same direction of genetic effects on SNPP and SW, indicating high possibility to simultaneously improve SNPP and SW in rapeseed. Our findings revealed both overlapping and independent loci controlling seed number per pod and seed weight in rapeseed. [ABSTRACT FROM AUTHOR]

In this paper, the H ∞ filtering problem is addressed for a class of nonlinear stochastic discrete-time systems subject to Round-Robin protocol, measurement outliers, randomly occurring parameter uncertainties and randomly occurring nonlinearities. First of all, the considered system is influenced by the parameter uncertainties, nonlinear dynamics and external disturbances. Next, mutually independent Bernoulli-distributed white sequences are employed to describe the phenomena of randomly occurring parameter uncertainties and nonlinearities, respectively. Besides, the Round-Robin protocol is introduced to prevent the occurrence of data collisions during the signal transmission. Moreover, a filter with adaptive saturation is constructed to weaken the impact of the measurement outliers. Through constructing a suitable Lyapunov function, sufficient conditions are established to ensure that the error system dynamics is exponentially mean-square stable and satisfies the H ∞ performance. Subsequently, the filter gains are designed by solving a set of linear matrix inequalities. Finally, a simulation example is given to show the effectiveness of the filtering scheme proposed in this paper. [ABSTRACT FROM AUTHOR]

The distributed state estimation problem is examined for a kind of nonlinear time-varying stochastic systems through sensor networks (SNs) with randomly switching topologies as well as redundant channels. The random switches of the topologies for SNs are governed by Markovian jumping parameters and the redundant channels are introduced to help improve the capability of the network communication. We are interested in designing distributed state estimators so that the estimation error dynamics is confirmed to reach a prescribed level of average ${H}_{\infty }$ performance in terms of a finite horizon. Through intensive stochastic analysis, we acquire some sufficient conditions that guarantee the existence of the expected state estimators whose gain parameters are obtained recursively by means of the solution to a series of matrix inequalities. A numerical simulation is carried out to illustrate the validity of the developed state estimation algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the event-triggered non-fragile state estimation problem is considered for a class of uncertain discrete-time neural networks with random delays and state-dependent stochastic disturbances. The norm-bounded uncertainty goes into all the parameters of the addressed neural network model. A Bernoulli stochastic variable is introduced to characterize the probability distribution of the occurrence of the time delays. The phenomenon of estimator gain variations is considered to account for the potential implementation error of the state estimator. An event-triggered mechanism is proposed with hope to mitigate the network load and orchestrate the bandwidth utilization. We aim to design a non-fragile state estimator such that the error dynamics of the neuron state estimation is globally asymptotically stable in the mean square. By means of the Lyapunov stability theory, delay-distribution-dependent sufficient conditions are established to ensure the existence of the desired event-triggered non-fragile state estimators and the estimator parameters are then derived in terms of the solution to a set of linear matrix inequalities. An illustrative example is utilized to show the usefulness and applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Scarce data explored the associations of apolipoproteins with hemoglobin glycation index (HGI) and triglyceride-glucose (TyG) index. This study determined associations of serum apolipoproteinA1 (ApoA1) and high density lipoprotein cholesterol (HDL-C) with HGI and TyG index in coronary artery disease (CAD) patients. A total of 10,803 CAD patients were included in this cross-sectional pilot study. Serum concentrations of ApoA1 and HDL-C were measured. Analyses of covariance were used to compare the mean differences in glucose metabolism indices (e.g., HGI, TyG index, hemoglobin glycation [HbA1c], fasting blood glucose [FBG]) among the quartiles of ApoA1, HDL-C and HDL-C/ApoA1 ratio. In multivariate analysis, higher ApoA1, HDL-C and HDL-C/ApoA1 ratio were associated with significantly lower HGI (Quartile [Q]4 vs. Q1: −0.032 % vs. 0.017 % for ApoA1; −0.072 % vs. 0.079 % for HDL-C; −0.083 % vs. 0.085 % for HDL-C/ApoA1 ratio). Intermediate ApoA1 level was inversely associated with TyG index (Q2 vs. Q1: 296.278 vs. 306.794). The mean TyG index were significantly decreased with increased HDL-C and HDL-C/ApoA1 ratio (Q4 vs. Q1: 298.584 vs. 309.221 for HDL-C; 300.405 vs. 315.218 for HDL-C/ApoA1 ratio). Moreover, the inverse associations of ApoA1, HDL-C and HDL-C/ApoA1 ratio with HbA1c and FBG also were observed. In path analysis, the associations of HDL-C and HDL-C/ApoA1 ratio with TyG index were mediated by obesity. This study provided further support for the hypoglycemic effects of ApoA1 and HDL-C in patients with CAD. Replication of these findings is warranted in further longitudinal studies in different populations. • Assessing the associations of serum ApoA1, HDL-C with HGI, TyG index firstly • Examining the mediating effects of BMI on these associations firstly • ApoA1 and HDL-C were valid indices for HGI and TyG index modulation. • ApoA1 and HDL-C exerted hypoglycemic effects by HGI and TyG index. [ABSTRACT FROM AUTHOR]

This paper focuses on the filtering problem for nonlinear systems over sensor networks whose topologies are changeable subject to Round-Robin (RR) protocol in the finite horizon case. The phenomenon of switching topologies for sensor networks is considered due to the weak connections between the node and its neighbors. The Round-Robin communication strategy is employed to save the limited bandwidth and reduce the network resource consumption. It is our target to design distributed filters so that the dynamics of augmented error system satisfies the given level of average H ∞ performance. We get the sufficient conditions in order to guarantee the existence of the desired distributed filters whose parameter matrices would be attained by a recursive method in terms of a set of matrix inequalities. In the end, a numerical simulation is exploited to describe the validity of the raised filter design technique. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the second most common neurodegenerative disorder characterized by the progressive loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNpc). Rupture of lysosome is a major cellular stress condition leading to cell death in PD. We have previously shown that environmental oxidative toxins could impair autophagic flux and lysosomal functions in PD. Poloxamer 188 (P188) is an amphipathic polymer which has cytoprotective effect in traumatic brain injury and stroke. But whether Dyrk1A could rescue lysosome malfunction-mediated DA neuron death and α-synuclein aggregation in PD is still unknown. In the present study, MPTP mice models and MPP+-treated SH-SY5Y cells were used for study, and we found that P188 rescued MPP+-induced lysosomal dysfunction and impaired autophagy flux in mild MPP+-treated SH-SY5Y cells. P188 administration significantly restored lysosomal membrane integrity and prevented cathepsins leakage from the lysosomes into the cytoplasm, which triggered caspase-dependent apoptotic cell death in sub-acute MPTP mouse model and MPP+-treated SH-SY5Y cells. Furthermore, P188 ameliorated α-synuclein accumulation and behavioral impairment in chronic MPTP mouse model with MPTP and probenecid treatment. P188 could alleviate MPTP-induced DA neurons damage by restoring lysosome function. • Poloxamer 188 rescues MPP+-induced lysosomal dysfunction and impaired autophagy flux in vitro. • Poloxamer 188 reduces lysosomal membrane permeabilization-mediated cell apoptosis in vitro. • Poloxamer 188 alleviates dopaminergic neurons damage in MPTP mouse model. • Poloxamer 188 blunts α-synuclein accumulation and behavioral impairment in MPTP mouse model. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with the local design of the distributed H∞‐consensus filtering problem for a class of discrete time‐varying systems subject to both multiplicative noises and deception attacks over sensor networks. The target plant and the measuring sensors are disturbed by multiplicative noises with known statistics. The malicious signal involved in deception attacks is constrained by a specific sector‐like bounded condition, which reflects certain tolerable bound on the difference between the attack signal and the true signal. Attention is paid to the design of filter gains for guaranteeing a desirable filtering performance that simultaneously characterizes the filtering accuracy and the consensus requirement. To handle the proposed filtering problem, the supply rate function is firstly chosen for each node and then the dissipation matrix is constructed as a column substochastic matrix based on the stochastic vector dissipation theory. Subsequently, sufficient conditions by means of recursive linear matrix inequalities are presented for each node such that the filtering error and the consensus error satisfy the desirable H∞‐consensus performance index over a finite horizon. Finally, an illustrative simulation is presented to demonstrate the effectiveness of the proposed filter strategy. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we analyze the finite‐horizon fault estimation issue for a kind of time‐varying nonlinear systems with imperfect measurement signals under the stochastic communication protocol (SCP). The imperfect measurements result from randomly occurring sensor nonlinearities obeying sensor‐wise Bernoulli distributions. The Markov‐chain‐driven SCP is introduced to regulate the signal transmission to alleviate the communication congestion. The aim of the considered issue is to propose the design algorithm of a group of time‐varying fault estimators such that the estimation error dynamics satisfies both the H∞ and the finite‐time boundedness (FTB) performance requirements. First, sufficient conditions are set up to guarantee the existence of the satisfactory H∞ FTB fault estimators through intensive stochastic analyses and matrix operations. Then, the gains of such fault estimators are explicitly parameterized by resorting to the solution to recursive linear matrix inequalities. Finally, the correctness of the devised fault estimation approach is demonstrated by a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, the passivity analysis problem is investigated for a class of discrete-time stochastic neural networks (DSNNs) with randomly occurring mixed time delays (ROMDs). The mixed delays comprise time-varying discrete delays, infinite-distributed delays as well as finite-distributed delays. A set of Bernoulli-distributed white sequences is used to account for the random nature of the occurrence of the mixed time delays. In addition, stochastic disturbances are taken into consideration to describe the state-dependent noises caused possibly by electronic devices and hardware implementation of neural networks. By using a combination of Lyapunov-Krasovskii functional, free-weighting matrix approach and stochastic analysis technique, we establish sufficient conditions guaranteeing the passivity performance of the underlying DSNNs. Furthermore, a delay-dependent robust passivity criterion is presented to deal with the parameter uncertainties in the DSNNs with ROMDs. A simulation example is provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract This paper is concerned with the state estimation problem for a class of Markovian jumping neural networks (MJNNs) with sensor nonlinearities, mode-dependent time delays and stochastic disturbances subject to the Round-Robin (RR) scheduling mechanism. The system parameters experience switches among finite modes according to a Markov chain. As an equal allocation scheme, the RR communication protocol is introduced for efficient usage of limited bandwidth and energy saving. The update matrix method is adopted to deal with the periodic time-delays resulting from the RR protocol. The objective of the addressed problem is to construct a state estimator for the MJNNs such that the dynamics of the estimation error is exponentially ultimately bounded in the mean square with a certain upper bound. Sufficient conditions are established for the existence of the desired state estimator by resorting to a combination of the Lyapunov stability theory and the stochastic analysis technique. Furthermore, the estimator gain matrices are characterized in terms of the solution to a convex optimization problem. Finally, a numerical simulation example is exploited to demonstrate the effectiveness of the proposed estimator design strategy. [ABSTRACT FROM AUTHOR]

This study determined the associations of apolipoprotein A1 (ApoA1), high-density lipoprotein cholesterol (HDL-C), and HDL-C/ApoA1 ratio with fasting blood glucose (FBG) and evaluated the mediating effects of high-sensitivity C-reactive protein (hsCRP) and body mass index (BMI). A cross-sectional study with 4805 coronary artery disease (CAD) patients was performed. In multivariable analyses, higher ApoA1, HDL-C, and HDL-C/ApoA1 ratio were associated with significantly lower FBG level (Q [quartile] 4 vs Q1: 5.67 vs 5.87 mmol/L for ApoA1; 5.64 vs 5.98 mmol/L for HDL-C; 5.63 vs 6.01 mmol/L for HDL-C/ApoA1 ratio). Moreover, inverse associations of ApoA1, HDL-C, and HDL-C/ApoA1 ratio with abnormal FBG (AFBG) were found with odd ratios (95% confidence interval) of .83 (.70–.98), .60 (.50–.71), and .53 (.45–.64) in Q4 compared with Q1. Path analyses indicated that “ApoA1 (or HDL-C)-FBG” associations were mediated by hsCRP and “HDL-C-FBG” association was mediated by BMI. Our data suggested that higher ApoA1, HDL-C, and HDL-C/ApoA1 ratio were favorably associated with a lower FBG level in CAD patients and these associations might be mediated by hsCRP or BMI. Collectively, higher concentrations of ApoA1, HDL-C, and HDL-C/ApoA1 ratio might decrease the risk of AFBG. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed filtering problem for a class of time-varying systems subject to deception attacks and event-triggering protocols. Due to the bandwidth limitation, an event-triggered communication strategy is adopted to alleviate the data transmission pressure in the algorithm implementation process. The partial nodes-based filtering problem is considered, where only a partial of nodes can measure the information of the plant. Meanwhile, the measurement information possibly suffers the deception attacks in the transmission process. Sufficient conditions can be established such that the error dynamics satisfies the prescribed average performance constraints. The parameters of designed filters can be calculated by solving a series of recursive linear matrix inequalities. A simulation example is presented to demonstrate the effectiveness of the proposed filtering method in this paper. [ABSTRACT FROM AUTHOR]

This paper designs the event-triggered non-fragile state estimator for a class of time-delayed complex networks with randomly occurring sensor saturations (ROSSs) and estimator gain variations on the basis of measurements from partial nodes. Both the time-varying state delays and the stochastic disturbances are considered in the network model. A Bernoulli-distributed white sequence is utilized to reflect the phenomenon of ROSSs. Two sequences of Gaussian distributed random variables combined with the multiplicative norm-bounded uncertainties are used to characterize the randomly occurring gain variations in the estimators. An event generator function is employed to regulate the transmission of data from the sensor to the estimator. The aim of this paper is to design an exponentially ultimately bounded state estimator in mean square through measurement outputs from a partial of network nodes under the event-triggered mechanism. With the help of Lyapunov-Krasovskii functional and stochastic analysis techniques, sufficient conditions are acquired for the existence of the desired state estimator which ensures that the estimation error dynamics is exponentially ultimately bounded in mean square, and then the estimator gain matrices can be computed via the software Matlab. A simulation example is provided to demonstrate the effectiveness of the proposed state estimation method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the Tobit Kalman filtering problem for a class of linear discrete-time system with random parameters. The elements of both the system matrix and the measurement matrix are allowed to be random variables in order to reflect the reality. The information matrix is employed to 1) derive the covariance between any two random variables; and 2) establish a novel weighting covariance formula to address the quadratic terms associated with the random matrices. A set of Bernoulli random variables is introduced to govern the censoring phenomenon on the measurement output. The conditional expectation, as a basic tool, is utilized to deal with the dependence among the random variables. Within the framework of the traditional Kalman filtering, the proposed filtering algorithm includes the information from both the random parameters and the censored measurements. A simulation example is presented to illustrate the effectiveness and applicability of the designed algorithm. [ABSTRACT FROM AUTHOR]

This study addresses the distributed H∞ and H2 consensus control problems for multi‐agent systems with general integrator dynamics and undirected topology. First, based on the frequency domain description of linear multi‐agent systems, a sufficient and necessary condition is imposed on each controller to achieve consensus. Second, both H∞ and H2 optimal controllers are computed analytically according to the corresponding performance indices. The novel controllers not only can optimise the reference tracking performance but also has a simple tuning way to trade off the nominal performance and robustness. Finally, by application to the formation control of autonomous surface vessels shows the effectiveness of the proposed two control strategies. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed state estimation problem for a class of time-varying systems over sensor networks. An event-triggered communication scheme is utilized to save the constrained computation resource and network bandwidth while preserving the desired performance. The measurements on each node are transmitted to the estimators only when a certain triggering condition is satisfied. Moreover, in order to improve the reliability of data transmission services, we exploit redundant communication channels during the transmission process. The purpose of this paper is to design a set of time-varying state estimators such that the dynamics of the state estimation error satisfies the average H ∞ performance constraints. The specific gains of the estimator can be obtained by calculating a series of recursive linear matrix inequalities (RLMIs). Finally, a simulation example is presented to show the effectiveness of the state estimation method proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the H ∞ state estimation problem is investigated for a class of uncertain discrete-time neural networks subject to infinitely distributed delays and fading channels. Randomly occurring uncertainties (ROUs) are introduced to reflect the random nature of the network condition fluctuations, and the channel fading phenomenon is considered to account for the possibly unreliable network medium on which the measurement signal is transmitted. A set of Bernoulli-distributed white sequences are employed to govern the ROUs and the L -th Rice fading model is utilized where channel coefficients are mutually independent random variables with certain probability density function on [ 0 , 1 ] . We aim to design a state estimator such that the dynamics of the estimation error is asymptotically stable while satisfying the prescribed H ∞ performance constraint. By adopting the Lyapunov–Krasovskii functional and the stochastic analysis theory, sufficient conditions are established to ensure the existence of the desired state estimators and the explicit expression of such estimators is acquired. A simulation example is provided to verify the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a general event-triggered framework is set up to deal with the variance-constrained H ∞ control problem for a class of discrete time-varying systems with randomly occurring saturations, stochastic nonlinearities and state-multiplicative noises. Based on the relative error with respect to the measurement signal, an event indicator variable is introduced and the corresponding event-triggered scheme is proposed in order to determine whether the measurement output is transmitted to the controller or not. The stochastic nonlinearities under consideration are characterized by statistical means which can cover several classes of well-studied nonlinearities. A set of unrelated random variables is exploited to govern the phenomena of randomly occurring saturations, stochastic nonlinearities and state-dependent noises. The purpose of the addressed multiobjective control problem is to design a set of time-varying output feedback controller such that, over a finite horizon, the closed-loop system achieves both the prescribed H ∞ noise attenuation level and the state covariance constraints. A recursive matrix inequality approach is developed to derive the sufficient conditions for the existence of the desired finite-horizon controllers, and the analytical characterization of such controllers is also given. Simulation studies are conducted to demonstrate the effectiveness of the developed controller design scheme. [ABSTRACT FROM AUTHOR]

In this paper, the non-fragile state estimation problem is investigated for a class of continuous neural networks with time-delays and nonlinear perturbations. The estimator to be designed is of a simple linear structure without requiring the exact information of the activation functions or the time-delays, and is therefore easy to be implemented. Furthermore, the designed estimator gains are allowed to undergo multiplicative parameter variations within a given range and the non-fragility is guaranteed against possible implementation errors. The main purpose of the addressed problem is to design a non-fragile state estimator for the recurrent delayed neural networks such that the dynamics of the estimation error converges to the equilibrium asymptotically irrespective of the admissible parameter variations with the estimator gains. By employing a combination of the Lyapunov functionals and the matrix analysis techniques, sufficient conditions are established to ensure the existence of the desired estimators and the explicit characterization of such estimators are then given via solving a linear matrix inequality. Finally, a simulation example is used to illustrate the effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed fault estimation problem for a class of uncertain stochastic systems over sensor networks. The norm-bounded uncertainty enters into the system in a random way governed by a set of Bernoulli distributed white sequence. The purpose of the addressed problem is to design distributed fault estimators, via available output measurements from not only the individual sensor, but also its neighbouring sensors, such that the fault estimation error converges to zero exponentially in the mean square while the disturbance rejection attenuation is constrained to a give level by means of theperformance index. Intensive stochastic analysis is carried out to obtain sufficient conditions for ensuring the exponential stability as well as prescribedperformance for the overall estimation error dynamics. Simulation results are provided to demonstrate the effectiveness of the proposed fault estimation technique in this paper. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of designing a non-fragile state estimator for a class of uncertain discrete-time neural networks with time-delays. The norm-bounded parameter uncertainties enter into all the system matrices, and the network output is of a general type that contains both linear and nonlinear parts. The additive variation of the estimator gain is taken into account that reflects the possible implementation error of the neuron state estimator. The aim of the addressed problem is to design a state estimator such that the estimation performance is non-fragile against the gain variations and also robust against the parameter uncertainties. Sufficient conditions are presented to guarantee the existence of the desired non-fragile state estimators by using the Lyapunov stability theory and the explicit expression of the desired estimators is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is given to demonstrate the effectiveness of the proposed design approach. [ABSTRACT FROM AUTHOR]

In this paper, the non-fragile state estimation problem is investigated for a class of discrete-time neural networks subject to Markovian jumping parameters and time delays. In terms of a Markov chain, the mode switching phenomenon at different times is considered in both the parameters and the discrete delays of the neural networks. To account for the possible gain variations occurring in the implementation, the gain of the estimator is assumed to be perturbed by multiplicative norm-bounded uncertainties. We aim to design a non-fragile state estimator such that, in the presence of all admissible gain variations, the estimation error converges to zero exponentially. By adopting the Lyapunov–Krasovskii functional and the stochastic analysis theory, sufficient conditions are established to ensure the existence of the desired state estimator that guarantees the stability of the overall estimation error dynamics. The explicit expression of such estimators is parameterized by solving a convex optimization problem via the semi-definite programming method. A numerical simulation example is provided to verify the usefulness of the proposed methods. [ABSTRACT FROM AUTHOR]

The associations of levels of apolipoprotein A1 (ApoA1) and apolipoprotein B and ApoB/A1 ratio and risk of a first stroke have not been reliably documented. We performed a meta-analysis to summarize the relationships and confirmed them in a case-control study. We identified relevant publications in PubMed and Embase databases up to June 1, 2015. A Dersimonian-Laird random effects model was used to compute summary relative risks (RRs) and 95 % confidence intervals (CIs). A case-control study was conducted in a southern Chinese population. We included 8 cohort and 4 case-control studies (222,774 subjects; 10,032 first stroke events) in the meta-analysis. Reduced ApoA1 level and increased ApoB level and ApoB/A1 ratio was associated with a first stroke in cohort studies (RR 0.86 [95 % CI 0.79-0.94], 1.66 [1.62-1.69], and 1.66 [1.63-1.70], respectively) and reduced ApoA1 level and increased ApoB/A1 ratio in case-control studies (0.68 [0.47-0.99] and 1.76 [1.50-2.06], respectively). When stratified by stroke type in cohort studies, the RR for ischemic stroke was 0.83 (0.76-0.90), 1.36 (1.32-1.40), and 1.38 (1.35-1.42) for the 3 factors, respectively. In our case-control study (1013 cases; 1029 controls), the OR for a first ischemic stroke was 0.83 (0.74-0.92), 1.33 (1.18-1.48) and 2.10 (1.76-2.51), respectively, with increased ApoA1 level associated with hemorrhagic stroke (1.37 [1.06-1.78]). Meta-analysis suggests that reduced ApoA1 level and increased ApoB level and ApoB/A1 ratio are risk factors for a first ischemic but not hemorrhagic stroke. Elevated ApoA1 level may be a risk factor for a first hemorrhagic stroke. [ABSTRACT FROM AUTHOR]

Background: Supplementation with B vitamins for stroke prevention has been evaluated over the years, but which combination of B vitamins is optimal for stroke prevention is unclear. We performed a network meta-analysis to assess the impact of different combinations of B vitamins on risk of stroke. Methods: A total of 17 trials (86 393 patients) comparing 7 treatment strategies and placebo were included. A network meta-analysis combined all available direct and indirect treatment comparisons to evaluate the efficacy of B vitamin supplementation for all interventions. Results: B vitamin supplementation was associated with reduced risk of stroke and cerebral hemorrhage. The risk of stroke was lower with folic acid plus vitamin B6 as compared with folic acid plus vitamin B12 and was lower with folic acid plus vitamin B6 plus vitamin B12 as compared with placebo or folic acid plus vitamin B12. The treatments ranked in order of efficacy for stroke, from higher to lower, were folic acid plus vitamin B6 > folic acid > folic acid plus vitamin B6 plus vitamin B12 > vitamin B6 plus vitamin B12 > niacin > vitamin B6 > placebo > folic acid plus vitamin B12. Conclusions: B vitamin supplementation was associated with reduced risk of stroke; different B vitamins and their combined treatments had different efficacy on stroke prevention. Folic acid plus vitamin B6 might be the optimal therapy for stroke prevention. Folic acid and vitamin B6 were both valuable for stroke prevention. The efficacy of vitamin B12 remains to be studied. [ABSTRACT FROM AUTHOR]

Top, the horizontal axis represents haplotypes and the vertical axis represents the phenotypic values of seed number per pod and seed weight. 3 Genetic effects on seed number per pod and seed weight among haplotypes in overlapping association regions in a natural population of rapeseed Linkage disequilibrium analysis and haplotype analysis of 5 associated regions (a-e). [Extracted from the article]

In this paper, a general event-triggered framework is developed to deal with the finite-horizon H\infty filtering problem for discrete time-varying systems with fading channels, randomly occurring nonlinearities and multiplicative noises. An event indicator variable is constructed and the corresponding event-triggered scheme is proposed. Such a scheme is based on the relative error with respect to the measurement signal in order to determine whether the measurement output should be transmitted to the filter or not. The fading channels are described by modified stochastic Rice fading models. Some uncorrelated random variables are introduced, respectively, to govern the phenomena of state-multiplicative noises, randomly occurring nonlinearities as well as fading measurements. The purpose of the addressed problem is to design a set of time-varying filter such that the influence from the exogenous disturbances onto the filtering errors is attenuated at the given level quantified by a H\infty-norm in the mean-square sense. By utilizing stochastic analysis techniques, sufficient conditions are established to ensure that the dynamic system under consideration satisfies the H\infty filtering performance constraint, and then a recursive linear matrix inequality (RLMI) approach is employed to design the desired filter gains. Simulation results demonstrate the effectiveness of the developed filter design scheme. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the event-triggered distributed state estimation problem for a class of uncertain stochastic systems with state-dependent noises and randomly occurring uncertainties over sensor networks. An event-triggered communication scheme is proposed in order to determine whether the measurements on each sensor should be transmitted to the estimators or not. The norm-bounded uncertainty enters into the system in a random way. Through available output measurements from not only the individual sensor but also its neighbouring sensors, a sufficient condition is established for the desired distributed estimator to ensure that the estimation error dynamics are exponentially mean-square stable. These conditions are characterized in terms of the feasibility of a set of linear matrix inequalities, and then the explicit expression is given for the distributed estimator gains. Finally, a simulation example is provided to show the effectiveness of the proposed event-triggered distributed state estimation scheme. [ABSTRACT FROM AUTHOR]

This paper deals with the finite-horizon reliable H ∞ output feedback control problem for a class of discrete time-varying systems with randomly occurring uncertainties (ROUs), randomly occurring nonlinearities (RONs) as well as measurement quantizations. Both the deterministic actuator failures and probabilistic sensor failures are considered in order to reflect the reality. The actuator failure is quantified by a deterministic variable varying in a given interval and the sensor failure is governed by an individual random variable taking value on [ 0 , 1 ] . Both the nonlinearities and the uncertainties enter into the system in random ways according to Bernoulli distributed white sequences with known conditional probabilities. The main purpose of the problem addressed is to design a time-varying output feedback controller over a given finite horizon such that, in the simultaneous presence of ROUs, RONs, actuator and sensor failures as well as measurement quantizations, the closed-loop system achieves a prescribed performance level in terms of the H ∞ -norm. Sufficient conditions are first established for the robust H ∞ performance through intensive stochastic analysis, and then a recursive linear matrix inequality approach is employed to design the desired output feedback controller achieving the prescribed H ∞ disturbance rejection level. A numerical example is given to demonstrate the effectiveness of the proposed design scheme. [ABSTRACT FROM AUTHOR]

This paper is concerned with the finite-horizon estimation problem of randomly occurring faults for a class of nonlinear systems whose parameters are all time-varying. The faults are assumed to occur in a random way governed by two sets of Bernoulli distributed white sequences. The stochastic nonlinearities entering the system are described by statistical means that can cover several classes of well-studied nonlinearities. The aim of the problem is to estimate the random faults, over a finite horizon, such that the influence from the exogenous disturbances onto the estimation errors is attenuated at the given level quantified by an H ∞ -norm in the mean square sense. By using the completing squares method and stochastic analysis techniques, necessary and sufficient conditions are established for the existence of the desired finite-horizon H ∞ fault estimator whose parameters are then obtained by solving coupled backward recursive Riccati difference equations (RDEs). A simulation example is utilized to illustrate the effectiveness of the proposed fault estimation method. [ABSTRACT FROM AUTHOR]

SUMMARY This paper is concerned with the distributed [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed filtering problem for a class of uncertain stochastic systems with fading channels over sensor networks. The norm-bounded uncertainty enters into the system in a random way, and the Bernoulli distributed white sequence is introduced to govern the random occurrences of such uncertainties. The lossy sensor network suffers from the phenomenon of fading measurements that are described by a modified łth-order Rice fading model in which the channel coefficients can have any probability density function on the interval [0, 1]. Through available output measurements from not only the individual sensor but also its neighboring sensors, a sufficient condition is established for the desired distributed filter to ensure that the filtering dynamics is exponentially mean-square stable and the prescribedperformance constraint is satisfied. The distributed filter gains are characterized by solving an auxiliary convex optimization problem. Finally, a simulation example is provided to show the effectiveness of the proposed filtering scheme. [ABSTRACT FROM PUBLISHER]