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Background: The incidence of cerebral ischemia disease leading cause of death in human population worldwide. Treatment of cerebral ischemia remains a clinical challenge for researchers and mechanisms of cerebral ischemia remain unknown. During the cerebral ischemia, inflammatory reaction and oxidative stress plays an important role. The current investigation scrutinized the neuroprotective and anti-inflammatory role of pterostilbene against cerebral ischemia in middle cerebral artery occlusion (MCAO) rodent model and explore the underlying mechanism.Methods: The rats were divided into following groups viz., normal, sham, MCAO and MCAO + pterostilbene (25 mg/kg) group, respectively. The groups received the oral administration of pterostilbene for 30 days followed by MCAO induction. The neurological score, brain water content, infarct volume and Evan blue leakage were estimated. Hepatic, renal, heart, inflammatory cytokines and inflammatory mediators were estimated.Results: Pterostilbene treatment significantly (p < 0.001) improved the body weight and suppressed the glucose level and brain weight. Pterostilbene significantly (p < 0.001) reduced the hepatic, renal and heart parameters. Pterostilbene significantly (p < 0.001) decreased the level of glutathione (GSH), glutathione peroxidase (GPx), superoxide dismutase (SOD) and decreased the level of malonaldehyde (MDA), 8-Hydroxy-2′-deoxyguanosine (8-OHdG). Pterostilbene significantly (p < 0.001) inflammatory cytokines and inflammatory parameters such as cyclooxygenase-2 (COX-2), inducible nitric oxidase synthase (iNOS) and prostaglandin (PGE2). Pterostilbene significantly (p < 0.001) down-regulated the level of metalloproteinases (MMP) such as MMP-2 and MMP-9. Pterostilbene suppressed the cellular swelling, cellular disintegration, macrophage infiltration, monocyte infiltration and polymorphonuclear leucocyte degranulation in the brain.Conclusion: In conclusion, Pterostilbene exhibited the neuroprotective effect against cerebral ischemia in rats via anti-inflammatory mechanism.

Objectives: To investigate the effect of purmorphamine (PUR), a Shh co-receptor Smoothened (Smo) agonist, on postoperative cognitive dysfunction (POCD) rat models.Methods: Eighteen-month-old male S...

Background The association between abnormal gut microbiome composition and depression is well established. However, the composition and functional capacity of the gut microbiota regarding depressed women has been poorly addressed. Methods Stool samples from 62 female patients with major depressive disorder (MDD) and 46 healthy controls (Con) were analyzed by 16S rRNA gene sequencing; Twenty fecal samples from the patient group and 21 fecal samples from the Con group were further analyzed by shotgun metagenomic sequencing. Psychiatric symptoms and psychological, social, and professional functioning was also assessed. Results Phylum Bacteroidetes, proteobaeteria, and Fusobacteria were greatly enriched in patients with MDD, while the Firmicutes and Actinobacteria phyla were consistently higher in Con. Notably, 18 microbial markers were identified on a random forest model and achieve an area under the curve of 0.92 between patients with MDD and the Con group. Forty-five species and their associated function were identified with statistically significant differences between patients with MDD and the Con group. Limitations The number of recruited samples, especially samples enrolled for shotgun metagenomic sequencing was relatively small, and the stool samples were collected only at baseline, making it difficult to establish a causal association between changes in gut microbiota compositions and disease remission. Conclusions This study characterizes the gut microbiota and their related function in female MDD. The gut microbiota-based biomarkers may be helpful in diagnosis and the altered gut microbial metabolites may contribute to the pathogenesis of MDD in women, representing potential microbial targets.

The diagnosis of multi-fault in wind turbine gearbox based on vibration signal processing is considered challenging as the collected measurements from acceleration transducers are often a non-linear mixture of signals induced from an unknown number of sources, i.e. an underdetermined blind source separation (UBSS) problem. In this study, a novel UBSS-based algorithmic solution is proposed to address this technical challenge from two aspects: source number estimation and source signal recovery. The former is realised based on the empirical mode decomposition and singular value decomposition (SVD) joint approach; and for the latter, the observed vibration signals are transformed to the time-frequency domain using short-time Fourier transform to obtain the sparse representation of the signals. The fuzzy C-means clustering and l 1 norm decomposition are carried out to estimate the mixing matrix and recover the source signals, respectively. The performance of proposed solution is extensively assessed through experiments based on simulation, testbed and realistic wind farm measurements for a range of fault scenarios for both linear and non-linear scenarios. The numerical result clearly confirms the effectiveness of the proposed algorithmic solution for non-linear multi-fault diagnosis of wind turbines.

The signal processing-based bearing fault diagnosis in wind turbine gearbox is considered challenging as the vibration signals collected from acceleration transducers are, in general, a mixture of signals originating from an unknown number of sources. Even worse, the source number is often larger than the number of installed sensors, and hence the fault characterisation is effectively an under-determined blind source separation problem. In this study, a novel sparse component analysis-based algorithmic solution is proposed to address this technical challenge from two aspects: source number estimation and source signal recovery, to enable accurate and efficient bearing fault diagnosis. The source number estimation is implemented based on the empirical mode decomposition and singular value decomposition joint approach. The observed signals are transformed to the time-frequency domain using short-time Fourier transform to obtain the sparse representation of the signals. The fuzzy C-means clustering and l 1 norm decomposition methods are used to estimate the mixing matrix and recover the source signals, respectively. The proposed solution is assessed through simulation experiments for scenarios of linearly and non-linearly mixed bearing vibration signals, and the numerical result confirms the effectiveness of the proposed algorithmic solution.

Antipsychotic agents have been reported to promote hippocampal neurogenesis and improve cognitive deficits; yet, the molecular mechanisms underlying these actions remain unclear. In the present study, we used a murine model of schizophrenia induced by 5-day intraperitoneal injection with the non-competitive N-methyl-d-aspartate receptor antagonist MK801 (0.3mg/kg/day) to assess cognitive behavioral deficits, changes in Notch signaling, and cellular proliferation in the hippocampus of adult male C57BL/6 mice after 2-week administration of risperidone (Rip, 0.2mg/kg/day) or vehicle. We then utilized in vivo stereotaxic injections of a lentivirus expressing a short hairpin RNA (shRNA) for Notch1 into the dentate gyrus to examine the role of Notch1 in the observed actions of Rip. We found that Rip ameliorated cognitive deficits and restored cell proliferation in MK801-treated mice in a manner associated with the up-regulation of Notch signaling molecules, including Notch1, Hes1, and Hes5. Moreover, these effects were abolished by pretreatment with Notch1 shRNA. Our results suggest that the ability of Rip to improve cognitive function in schizophrenia is mediated in part by Notch signaling.

Localization and navigation algorithm is the key technology to determine whether or not an AGV (automatic guided vehicle) can run normally. In this paper, we summarize the popular navigation technologies first and then focus on the positioning principle of Nav200 which is adopted in our AGV system. Besides that, the map building method and the layout of the reflective board is also introduced briefly. This paper introduced two navigation methods. The traditional navigation method only uses the sensor data and the electronic map to guide AGV. To improve positioning accuracy, we use the Kalman filter to minimize the error of localization sensor. At last some simulation work was done, the results shows that the localization accuracy was improved by adopting Kalman filter algorithm.

In this paper the predictive control of switching system is introduced to the DC/AC converter. Our aim is to control the output power of converter closer to the reference. By considering the working status of DC/AC converter is actually switched among several definite subsystems, the switching system model of DC/AC converter is constructed. The tracking problem of converter output power can be changed to be that of its three-phase output currents. Based on the switching system model of converter, the three-phase output currents of those subsystems are predicted and the switching command is determined by examining the discrepancies between the three-phase predictive currents corresponding to each subsystem and the reference ones. Simulations demonstrate that our control scheme for DC/AC converter connected with ultra-capacitor has great advantages in output power tracking and sinusoidal output current waveforms.

Ice storage air-conditioning system can bring benefits to power supplier and consumers for its advantage of shifting power consumption at peak hours during day to the off-peak hours at night. In this paper, we adopted an improved particle swarm optimization algorithm to develop an optimal control strategy for ice storage air-conditioning system with the aim of minimizing operation cost subject to various operational constrains. Comparing with the usual chiller-priority and ice-storage-priority control strategy, the proposed control scheme can not only meet the building cooling load but also achieve the minimum operation cost.

Sirtuin 1 (Sirt1) is a class III histone deacetylase involved in neuroprotection induced by hyperbaric oxygen preconditioning (HBO-PC) in animal models of ischemia. However, the underlying mechanisms remain to be illustrated. In the present study, rats exposed to middle cerebral artery occlusion (MCAO) were used to establish an ischemic stroke model. The infarct volume ratio, neurobehavioral score, and expressions of Sirt1, nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase 1 (HO-1), and superoxide dismutase 1 (SOD1) were evaluated at 7 days after reperfusion, and the level of malondialdehyde (MDA) was used to assess oxidative stress. HBO-PC increased the expression of Sirt1 and reduced infarct volume ratio and neurobehavioral deficit in MCAO rats. Meanwhile, HBO-PC also increased expression of Nrf2, HO-1, and SOD1 and decreased MDA content. Furthermore, either Sirt1 or Nrf2 knockdown by short interfering RNA (siRNA) inhibited the expression of Nrf2, HO-1, and SOD1 and eliminated the neuroprotective effects of HBO-PC. Taken together, the results suggest that the Nrf2/antioxidant defense pathway is involved in the long lasting neuroprotective effects of Sirt1 induced by HBO-PC against transient focal cerebral ischemia.

In this paper, we exploited the short-term electricity price forecasting issue by introducing a global search mechanism based on the improved particle swarm optimization (MPSO) algorithm for the neural network training. The proposed MPSO algorithm is used for the initial weights and threshold of BP neural network in the process of optimization. We then proposed a novel short-term electricity price forecasting model based on MPSO-BP neural network. The paper provides a number of examples of bidding model of the California electricity market to forecasting market clear price using BP neural network trained by MPSO. Through the comparative study of the conventional BP neural network and the proposed MPSO-BP neural network, the proposed method demonstrates improved performance in finding the optimal solution with excellent convergence time for all the simulated scenarios.

The gradually extensive penetration of small-scale distributed renewable generators in existing medium-voltage power distribution networks highlights many technical challenges which call for urgent solutions from power utilities. This paper attempts to optimize the power factor of distributed generators (DGs) integrated in distribution networks and presents a novel algorithmic solution. With the aim of minimizing power loss whilst maintaining the node voltage, the problem is formulated with a mathematical model elaborating the DGs and a set of constraints in distribution networks and addressed through adopting an extended particle swarm optimization (PSO) approach. The suggested algorithm is assessed through numerical simulation experiments with the IEEE 33-bus system and the outcome shows that the optimization algorithm can effectively reduce the power loss and promote the node voltages across the overall distribution network.

Fault feature extraction method based on Empirical Mode Decomposition (EMD) and fault diagnosis model based on Least Squares Support Vector Machines (LSSVM) were proposed after typical faults in drive train for wind turbines being analyzed. An experiment was designed to verify the validity of feature extraction method and the intelligent diagnosis model. The results showed that EMD can effectively extract fault characteristics of the drive train in wind turbines, the classification speed and diagnosis accuracy of LSSVM classifier based on radial basis function are better than the SVM, BPNN and other classifiers which are commonly used in practice.

The distributed control of complex and dynamic manufacturing systems is a challenging task and the multi-agent system (MAS) becomes a suitable solution due to its flexibility in both distributing and integrating software functionalities across the controlled system. This paper investigates the potentials of improving agent message delivery by enhancing the underlying communication network with IP QoS mechanisms using service differentiation and early congestion avoidance techniques. The extensive simulation experiments demonstrated the effectiveness of our approach in providing small latency, small jitter and improved throughputs in ACL message delivery, whilst without deteriorating other data applications between distributed controllers.

This paper presents a novel design solution of digital micro-ohmmeter based on the micro-resistance measurements. Through the system analysis, two key factors which have impacts on the measurement accuracy are identified: constant-current source circuit and A/D converter. Therefore, we suggest a design approach to improve the common constant-current source circuit to obtain enhanced system linearity, stability, precision and robustness and use high resolution A/D converter to ensure converting accurately. The proposed design approach is assessed and demonstrates that the load disturbance can be limited to 0.05%, and the measurement precision could reach 0.2% ranging from 20mΩ to 20Ω and 1.0% below 20mΩ.

A combination approach based on real option and CAPM model is presented for illuminating the value of staging investment in early-stage high-technology growth-oriented companies. Process of stage investment has been simulated by Cossin's mix option model, and supplemented investment triggers and investment cost have been obtained. Compared with one-off investment, it is evident that stage investment cost is lower than that of one-off investment, as well as risk.

This paper proposes a new approach for multi-objective robust control. The approach extends the standard generalized l2 (Gl2) and generalized H2 (GH2) conditions to a set of new linear matrix inequality (LMI) constraints based on a new stability condition. A technique for variable parameterization is introduced to the multi-objective control problem to preserve the linearity of the synthesis variables. Consequently, the multi-channel multi-objective mixed Gl2/GH2 control problem can be solved less conservatively using computationally tractable algorithms developed in the paper.

Introduction: Botulinum toxin type A (BTX-A) has been reported to be effective for the therapy for migraine. The purpose of this study was to investigate the effect of BTX-A on the immunoreactive levels of calcitonin gene-related peptide (CGRP) and substance P (SP) in the jugular plasma and medulla oblongata of migraine in rats induced by nitroglycerin (NTG), and then to evaluate and compare the effectiveness of fixed (muscle)-sites and acupoint-sites injection of BTX-A for migraine therapy of patients in a randomly controlled trial extending over four months. Materials and Methods: Rats with NTG-induced migraine were subcutaneously injected with vehicle or BTX-A (5 U/kg or 10 U/kg bodyweight). CGRP- and SP-like immunoreactivity (CGRP-LI and SP-LI) were determined by radioimmunoassay. In clinical trials, sixty patients respectively received BTX-A (2.5 U each site, 25 U per patient) at fixed-sites (group F, n = 30) including occipitofrontalis, corrugator supercili, temporalis and trapezius or at acupoint-sites (group A, n = 30) including EX-HN3, EX-HN5, GV20, GB8, GB20 and BL10. Results: Local BTX-A injection suppressed NTG-induced CGRP-LI and SP-LI levels in jugular plasma and oblongata. BTX-A injection for both groups with migraine significantly reduced the attack frequency, intensity, duration and associated symptoms. The efficacy of BTX-A for migraine in group A (93% improvement) was more significant than that in group F (83% improvement) (P < 0.01). Conclusions: The evidence that BTX-A decreases NTG-induced CGRP-LI and SP-LI levels in trigeminovascular system suggests that BTX-A attenuates migraine by suppression of neuropeptide release. BTX-A injections for migraine at acupoint-sites and fixed-sites are effective. Acupoint-sites BTX-A administration shows more efficacy for migraine than fixed-sites application.

With the intermittency and uncertainty of wind power, two-time-scale maximal power fluctuation restrictions (MPFRs) are set by electrical company for the combined power of the wind farm and the hybrid energy storage system (HESS): the maximal fluctuation of the combined power in any 1- and 30-min time window must be kept within γ1min% and γ30-min% of the wind farm rated power, respectively. Under this circumstance, an approach of improved first-order low-pass filter (IFLF) is proposed, which could adjust the time constant in any second if necessary when the fluctuation during the latest two seconds is exceeding a certain valve. Then a strategy to manage the power sharing between the battery and the super-capacitor at every second is described, which is based on Particle Swarm Optimization (PSO) algorithm and consider the constraints for the state of charge (SOC) of battery and the change times between battery charging and discharging. Simulation results demonstrate that the proposed control strategy is effective both in mitigating the wind power fluctuation to satisfy the MPFRs with lower HESS capacity and prolonging the lifetime of battery.

Our work is to establish an equivalent model for a 200MW offshore wind farm under construction and research the improvement of power quality at point of common connection (PCC) on PSCAD/EMTDC. Considering that the wind turbines in the different regions of wind farm are clustered into groups according to the received wind speed, the semi-aggregated equivalent model, not the aggregation into a single wind generator, is applied to represent the collective response of the offshore wind farm based on the paralleled transformation of internal collector network. Since high voltage alternative current (HVAC) is the better solution for short-distance transmission system connecting the offshore wind farm to the on-land electrical grid, static var compensation (SVC) is employed to compensate the reactive power generated by the submarine cables and improve the power quality. Simulation results show that the semi-aggregated equivalent model for offshore wind farm is reasonable and effective, and SVC can compensate the reactive power and enhance the power quality at PCC as well.

A hybrid energy storage system (HESS), including battery energy storage system (BESS) and super-capacitors energy storage system (SCESS), is adopted to smooth the fluctuation of wind power, with the aim of making it satisfy the certain power quality demands at two different time scales. Based on the predictive output power of wind farm (WF), particle swarm optimization (PSO) algorithm is utilized to determine the optimal desired power schedule for WF, which can reduce the capacity demand of HESS since the desired power schedule is closer to the wind output power while satisfying the desirable smoothing requirements. In the control strategy of BESS, the reduction of state transition of BESS is considered in the objective function, which can help to extend the lifetime of BESS. Simulation results demonstrate the feasibility and effectiveness of the proposed HESS scheduling strategy.

Tumor suppressor p53, known as 'the guardian of the genome', has the ability to prevent the emergence of transformed cells by the induction of cell cycle arrest and apoptosis. Otherwise, there were researches about the function of p53, such as NDA repair, regulating metabolism and maternal reproduction in recent years. Furthermore, there was a new function for p53 in antiviral apoptosis mentioned in the research, Integration of interferon-alpha/beta signaling to p53 responses in tumour suppression and antiviral defense. In order to define the antiviral function of p53, many target genes has been defined, such as IRF9, IRF5, ISG15 and TLR3. All of these implied there must be a complex mechanism for role of p53 in antiviral innate immunity, adaptive immunity and inflammation.

From the perspective of entrepreneurs, the article set up multi-dimensional screening model between venture capitalists and entrepreneurs, showed multi-dimensional adverse selection that venture capitalists may hiding level of value-adding inputs to induced, improved that venture capitalists equity proportion would be affected positively by value-adding inputs cost, fund and information rents, venture capitalists degree of value-adding implementation would be affected directly by venture capitalists efficiency, if efficiency be low, venture capitalists value-adding implementation degree would be distorted downward, and degree of distortion would be affected by the probability distribution of venture capitalists types, information rents.

Along with the concept of 1œsmart home1 developed in recent years, utilize the energy with a more intelligent and efficient manner is one of its key issues. In this paper, we exploit the home energy management system and present a design solution of wireless based power outlet and control terminal which could measure the energy consumption and carry out control actions with adopted strategies through the wireless-based home communication network. The suggested solution can greatly improve the energy utilization efficiency at home with greatly flexibility.

Multiplicity fluctuation of the target‐associated protons emitted in the interactions in a wide range of projectile energies from 500 A MeV to 60 A GeV is investigated in the framework of two‐dimensional scaled factorial moment methodology. The evidence of non‐statistical multiplicity fluctuation is found in our data sets.