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Prostate cancer (PCa) is one of the most frequent cancers in men, and its biomolecular targets have been extensively studied. This study aimed to analyze the expression of toll-like receptor 9 (TLR9) and vascular endothelial growth factor C (VEGF-C) and the clinical value of the coexpression of TLR9 and VEGF-C in PCa. We retrospectively evaluated 55 patients with clinically localized, intermediate-risk, or high-risk PCa who underwent laparoscopic radical prostatectomy (LRP) and extended pelvic lymph node dissection (ePLND) without neoadjuvant hormonal therapy at a single institution from June 2013 to December 2016. In all 55 patients, the median number of lymph nodes (LNs) resected was 23 (range: 18-31), and a total of 1269 LNs were removed, of which 78 LNs were positive. Seventeen patients had positive LNs, with a positive rate of 30.9%. In addition, the immunohistochemical results in the above patients revealed that high TLR9 expression was correlated with higher Gleason score (GS) (P = 0.049), increased LN metastasis (P = 0.004), and more perineural invasion (PNI) (P = 0.033). Moreover, VEGF-C expression was associated with GS (P = 0.040), pathological stage (pT stage) (P = 0.022), LN metastasis (P = 0.003), and PNI (P = 0.001). Furthermore, a significant positive correlation between TLR9 and VEGF-C was found (P < 0.001), and the TLR9/VEGF-C phenotype was associated with LN metastasis (P = 0.047). Collectively, we propose that TLR9 stimulation may promote LN metastasis in PCa cells through the upregulation of VEGF-C expression, thereby affecting the prognosis of PCa patients. Therefore, these markers may serve as valuable targets for the treatment of PCa.

In order to solve the problem related to adaptive energy management strategies based on driving condition identification being difficult to be applied to a real hybrid electric vehicle (HEV) controller, this paper proposes an energy management strategy by combining the driving condition identification algorithm based on genetic optimized K-means clustering algorithm (KGA-means), and the equivalent consumption minimization strategy (ECMS). The simulation results show that compared with ECMS, the energy management strategy proposed in this article drives the engine working point closer to the best efficiency curve, and smooths out the state of charge (SOC) change and better maintains the SOC in a highly efficient area. As a result, the vehicle fuel consumption reduces by 6.84%.

Ischemic stroke is a common type of cerebrovascular event and also the leading cause of disability. Post-stroke cognitive impairment occurs frequently in stroke survivors. Shexiang Baoxin Pill (SBP) is a proprietary Chinese medicine, initially used to treat cardiovascular diseases. Herein, we aim to explore the effects of SBP on oxygen glucose deprivation and reoxygenation (OGD/R) in neuronal cells (CATH.a) and cerebral ischemia/reperfusion injury induced post-stroke cognitive impairment in middle cerebral artery occlusion (MCAO) rat model. MCAO rats received two doses of oral SBP treatment (28 or 56 mg/kg) after 1 h of operation and once daily for 2 weeks continuously. Behavioral tests, immunoblotting, and immunofluorescence were examined after 14 days. Current data suggest that SBP enhanced cell viability and downregulated apoptosis via activating the PI3K/Akt signaling pathway in CATH. a cells. Furthermore, 14 days of SBP treatment promoted the recovery of learning and locomotor function in the MCAO rats. SBP up-regulated the expression of p-Akt, p-GSK3β, as well as the expression of NMDAR1, PSD-95, and AMPAR. Also, SBP down-regulated the expression of p-CaMKII. These results indicated that long-term SBP treatment might be a potential option for cognitive impairment induced by the ischemic stroke.

In this paper, we construct new exact solutions of some (2+1)-dimensional Burgers-type systems by using corresponding Cole–Hopf-type transformations. The obtained linear equations show that abundant (2+1)-dimensional soliton structures can be constructed for the physical quantity W = λ ( ln f ) xy by selecting appropriate parameters. In particular, new meshy soliton structures and interactions are revealed for the first time.

Shexiang Baoxin Pill (SBP), a formulated traditional Chinese medicine (TCM), has been widely used to treat cardiovascular diseases for years. This herbal mixture has been shown to promote differentiation of cultured neuronal cells. Here, we aimed to investigate the effects of SBP in attenuating cognitive impairment in APP/PS1 transgenic mice. Ethanol and water extracts of SBP, denoted as SBP and SBP , were standardized and applied onto cultured rat pheochromocytoma PC12 cells. The potential effect of SBP extract in attenuating the cognitive impairments in APP/PS1 transgenic mice was shown by following lines of evidence: (i) inhibition of Aβ fibril formation, (ii) suppression of secretions of cytokines, and (iii) improvement of behavioral tests by Morris water maze. SBP and SBP inhibited the formation of β-amyloid fibrils and protected the Aβ-induced cytotoxicity in cultured PC12 cells. In APP/PS1 transgenic mice, the treatment of SBP inhibited expressions of NO, NOS, AChE, as well as aggregation of Aβ. Besides, the levels of pro-inflammatory cytokines were suppressed by SBP treatment in the transgenic mice. Importantly, the behavioral tests by Morris Water maze indicated that SBP attenuated cognitive impairments in APP/PS1 transgenic mice. The current result has supported the notion that SPB might ameliorate the cognitive impairment through multiple targets, suggesting that SBP could be considered as a promising anti-AD agent.

Background: Shexiang Baoxin Pill (SBP) is a well-known composite formula of traditional Chinese medicine (TCM), which is commonly used today in treating cardiovascular diseases. SBP consists of seven materials thereof, including Moschus , extract of Ginseng Radix et Rhizoma , Bovis Calculus Artifactus , Cinnamomi Cortex , Styrax , Bufonis Venenum , and Borneolum Syntheticum . Here, we are investigating the potential roles of SBP in inducing neuron differentiation, i.e., seeking possible application in neurodegenerative diseases.Methods: Water and ethanol extracts of SBP, denoted as SBPwater and SBPEtOH, respectively, as well as its individual herbal materials, were standardized and applied onto cultured rat pheochromocytoma PC12 cells. The potential effect of SBP extracts in neuronal differentiation was suggested by following parameters: (i) induction of neurite outgrowth of PC12 cells, (ii) increase of neurofilament expression, and (iii) activation of transcription of neurofilament.Results: The treatments of SBPwater and SBPEtOH, or extracts from individual herbal materials, with or without low concentration of nerve growth factor (NGF), could potentiate the differentiation of cultured PC12 cells. The differentiation was indicated by increase of neurite outgrowth, as well as expression of neurofilaments. In addition, application of H89, a protein kinase A (PKA) inhibitor, suppressed the SBP-induced neurofilament expressions, as well as the phosphorylation of cAMP-responsive element binding protein (CREB) in cultures.Conclusion: SBP is proposed to possess trophic activity in modulating neuronal differentiation of PC12 cells, and this induction is shown to be mediated partly by a cAMP-PKA signaling pathway. These results indicate the neurite-promoting SBP could be useful in developing potential drug in treating or preventing neurodegenerative diseases.

As an effective reinforcement learning (RL) algorithm, Q-learning has been applied to energy management strategy of hybrid electric vehicle (HEV) in recent years. In the existing literatures, the values of three hyperparameters based on Q-learning are all given in advance, which are respectively exploratory rate ε, discount factor γ and learning rate α. However, different values of hyperparameters will influence on fuel economy of the vehicle and offline computation speed. In this paper, it is proposed that the method of optimization on hyperparameters adapting to driving cycle. Firstly, the mathematical model between three hyperparameters and iteration times is established based on inherent regularity of hyperparameters influencing on vehicle performance respectively. Secondly, it is determined that the optimal changing index k_index of iteration number based on Q-learning corresponding to typical driving cycles. Finally, the simulation model of Yubei District in Chongqing is constructed based on the method of Kullback-Leibler (KL) driving cycle identification. The simulation results indicate that equivalent fuel consumption of the proposed strategy is reduced by 0.4 % and the offline operation time is reduced by 6 s. It can be concluded that the proposed strategy can not only improve fuel economy of the vehicle, but also accelerate the computation speed.

Inflexible combined heat and power (CHP) plants and uncertain wind power production result in excess power in distribution networks, which leads to inverse power flow challenging grid operations. Power-to-X facilities such as electrolysers and electric boilers can offer extra flexibility to the integrated energy system. In this regard, we aim to jointly determine the optimal Power-to-X facility sizing and integrated energy system operations in this study. To account for wind power uncertainties, a distributionally robust chance-constrained model is developed to characterize wind power uncertainties using ambiguity sets. Linear decision rules are applied to analytically express real-time recourse actions when uncertainties are exposed, which allows the propagation of wind power uncertainties to gas and heat systems. Accordingly, the developed three-stage distributionally robust chance-constrained model is converted into a computationally tractable single-stage mixed-integer conic model. A case study validates the effectiveness of introducing the electrolyser and electric boiler into the integrated energy system, with respect to the decreased system cost, expanded CHP plant flexibility and reduced inverse power flow. The developed distributionally robust optimization model exhibits better effectiveness and robustness compared to a chance-constrained optimization model assuming wind forecast errors follow Gaussian distribution. Detailed profit analysis reveals that although the overall system cost is minimized, the profit is distributed unevenly across various stakeholders in the system.

Introduction The electric drive system is usually composed of an electric motor and gear transmission system, and widely used for industrial applications. However, the dynamic behaviors of the coupled electromechanical system are still not well understood, especially when there are faults in the system. Purpose The faults such as gear spalling fault of the mechanical system or inter-turn short-circuit fault of electrical device will threaten the operation safety of the electric vehicle. Thus, the faulty dynamic analysis for the electric drive system is crucial for avoiding fatal catastrophes. Method In this paper, the permeance network motor model with inter-turn short-circuit fault and the dynamic planetary gear transmission model considering spalling faults are proposed, respectively. The electromechanical dynamic model integrating two models mentioned above is employed to acquire the fault characteristics. Then, the vibration characteristics of the electric drive system with and without gear fault or inter-turn fault under the effects of time-varying mesh stiffness, slot effect and magnetic saturation are distinguished at different operation conditions. Results The results show that the inter-turn short-circuit fault can trigger conspicuous harmonics within the low frequency domain of the mesh force components, which will predicatively obstruct the detection or diagnosis of the spalling fault. Moreover, the vibration characteristics and vibration mechanism concerning these faults are revealed by the frequency and statistical analysis, which facilitates the condition monitoring for the gear transmission system when the tooth spalling and inter-turn faults are coupled. Conclusion The research results bring theoretical reference for the dynamic analysis and vibration-based condition monitoring of the vehicles’ integrated electric drive system.

In this paper, we deal with the zeros of the q -shift difference-differential polynomials [ P ( f ) ∏ j = 1 d f ( q j z + c j ) s j ] ( k ) − α ( z ) and ( P ( f ) ∏ j = 1 d [ f ( q j z + c j ) − f ( z ) ] s j ) ( k ) − α ( z ) , where P ( f ) is a nonzero polynomial of degree n , q j , c j ∈ C ∖ { 0 } ( j = 1 , … , d ) are constants, n , d , s j ( j = 1 , … , d ) ∈ N + and α ( z ) is a small function of f . The results of this paper are an extension of the previous theorems given by Chen and Chen and Qi. We also investigate the value sharing for q -shift difference polynomials of entire functions and obtain some results which extend the recent theorem given by Liu, Liu and Cao. MSC: 39A50, 30D35.