Synthesis and protective effect of pyrazole conjugated imidazo[1,2-a]pyrazine derivatives against acute lung injury in sepsis rats via attenuation of NF-κB, oxidative stress and apoptosis

The current work was conducted to elucidate the pharmacological effect of pyrazole-conjugated imidazo[1,2-a]pyrazine derivatives against acute lung injury in rats in sepsis and their mechanism of action. Various pyrazole-conjugated imidazo[1,2-a]pyrazine derivatives have been synthesized in a straightforward synthetic route. They exhibited a diverse range of inhibitory activity against NF-ĸB with IC50 ranging from 1 to 94 µmol L–1. Among them, compound 3h [(4-(4-((4-hydroxyphenyl)sulfonyl)phenyl)-5-(4-methoxyphenyl)-4,5-dihydro-1H-pyrazol-1-yl)(8-(methylamino)imidazo[1,2-a]pyrazin-2-yl)methanone] was identified as the most potent NF-κB inhibitor with IC50 of 1.02 µmol L–1. None of the synthesized compounds was found cytotoxic to normal cell-line MCF-12A. The pharmacological activity of the most potent NF-ĸB inhibitor 3h was also investigated in cecal ligation and puncture (CLP)-induced sepsis injury of the lung in rats. Compound 3h was administered to rats after induction of lung sepsis, and various biochemical parameters were measured. Results suggested that compound 3h significantly reduced lung inflammation and membrane permeability, as evidenced by H&E staining of lung tissues. It substantially reduced the generation of pro-inflammatory cytokines (TNF-α, IL-1B, IL-6) and oxidative stress (MPO, MDA, SOD). It showed attenuation of NF-ĸB and apoptosis in Western blot and annexin-PI assay, resp. Compound 3h also reduced the production of bronchoalveolar lavage fluid from the lung and provided a protective effect against lung injury. Our study showed the pharmacological significance of pyrazole-conjugated imidazo[1,2-a]pyrazine derivative 3h against acute lung injury in sepsis rats

Multipole plasmon resonances in self-assembled metal hollow-nanospheres

MOST of China under the 973 programs [2009CB930704]; National Natural Science Foundation of China [61106118]; Science and Technology Project of Fujian Province of China [2013H0046]; Natural Science Foundation of Fujian Province of China [2011J01362]; Fundamental Research Funds for the Central Universities [2011121026]Recently, multipole plasmonic mode resonances in metal hollow structures, such as dipole, quadrupole, and octupole modes, have been widely investigated by researchers with the aim for potential applications in bio-sensing, fluorescence, nanolasers or nonlinear nano-photonics. Here, in this work, the multipole plasmon resonances in self-assembled metal hollow-nanospheres (HNSs) are theoretically and experimentally demonstrated and the hot spots originating from the higher order mode plasmonic resonance and interparticle coupling effect are proposed to be used for Raman scattering enhancements. Dipole, quadrupole, octupole and hexadecapole mode plasmonic resonances were clearly resolved in the extinction spectra of these Ag HNS arrays showing good agreement with the theoretical simulation results. Strong regular hot spots were obtained around the surface and in the gaps of the Ag HNSs through the higher order mode plasmonic resonances and corresponding interparticle coupling effect between the HNSs. Maximum local field intensity was accomplished by optimizing the size of as well as the coupling distance between the HNSs and then it was applied to SERS sensing. Raman mapping also demonstrated these self-assembled plasmonic cavity arrays to be a stable and uniform SERS-active substrate

Correlation Tracking via Spatial-Temporal Constraints and Structured Sparse Regularization

Discriminative correlation filter (DCF) has achieved promising performance in visual tracking for its high efficiency and high accuracy. However, DCF trackers usually suffer from some challenges, such as boundary effects and appearance changes. In this paper, we propose a novel correlation tracking method via spatial-temporal constraints and structured sparse regularization. Firstly, we introduce the background-aware selection strategy to extract real negative examples, and penalize the filter coefficients close to the boundary locations for spatial protection, both of which can alleviate the boundary effects. Secondly, we restrict the filters with structured sparse regularization to handle the local appearance changes, and exploit temporal consistent constraint on the filters to address the global appearance changes. Finally, we employ the alternative direction method of multipliers to optimize our correlation tracking model. In our optimization framework, we combine grayscale, color names, histogram of orientation gradient with deep features for appearance learning to improve the discrimination. Meanwhile, we penalize spatial constraint and structured sparse regularization alternatively based on occlusion detection to enhance processing efficiency. The qualitative and quantitative experiments are conducted on the OTB dataset. Experimental results demonstrate that the proposed tracker has better performance than other state-of-the-art trackers

Evaluation of Electric Vehicle Integrated Charging Safety State Based on Fuzzy Neural Network

Electric vehicles have been promoted worldwide because of their high energy efficiency and low pollution. However, frequent charging safety accidents have to a certain extent restricted the development of electric vehicles. Therefore, it is extremely important to accurately evaluate the safety state of EV charging. The paper presents an integrated safety assessment method for electric vehicle charging safety based on fuzzy neural network. The integrated fault model was established by analyzing the correlation between truck–pile–grid. Then the integrated evaluation index was analyzed and sorted out, and the comprehensive fuzzy evaluation method used to evaluate. Following this, the improved GA_BP neural network algorithm was used to calculate the weight. Compared with the evaluation effect before and after the improvement, the simulation results show that the GA_BP neural network has higher accuracy and smaller error than the ordinary BP neural network. Finally, the feasibility and effectiveness of the evaluation method was verified by a case study

A Green Demand-Responsive Airport Shuttle Service Problem with Time-Varying Speeds

This study proposes a multiobjective mixed integer linear programming (MOMILP) model for a demand-responsive airport shuttle service. The approach aims to assign a set of alternative fuel vehicles (AFVs) located at different depots to visit each demand point within the specified time and transport all of them to the airport. The proposed model effectively captures the interactions between path selection and environmental protection. Moreover, users with flexible pick-up time windows, the time-varying speed of vehicles on the road network, and the limited fuel for the route duration are also fully considered in this model. The work aims at simultaneously minimizing the operating cost, vehicle fuel consumption, and CO2 emissions. Since this task is an NP-hard problem, a heuristic-based nondominated sorting genetic algorithm (NSGA-II) is also presented to find Pareto optimal solutions in a reasonable amount of time. Finally, a real-world example is provided to illustrate the proposed methodology. The results demonstrate that the model not only selects an optimal depot for each AFV but also determines its route and timetable plan. A sensitivity analysis is also given to assess the effect of early/late arrival penalty weights and the number of AFVs on the model performance, and the difference in quality between the proposed and traditional models is compared

Magnesium Lithospermate B Downregulates the Levels of Blood Pressure, Inflammation, and Oxidative Stress in Pregnant Rats with Hypertension

Background. Magnesium lithospermate B (MLB) was shown to suppress oxidative stress and reduce hypertension, but the role of MLB in pregnancy-induced hypertension (PIH) remains unknown. The objective of this study was to demonstrate the effects of MLB on rats with PIH. Methods. A total of 40 pregnant SD rats were selected, and 30 rats were orally given NG-nitro-L-arginine methyl ester (L-NAME, 60 mg/kg/day) to establish PIH rat models. Rats were equally divided into four groups: control, PIH, 5 mg/kg MLB, and 10 mg/kg MLB. MLB was consecutively administered into PIH rats for one week. The effects of MLB on mean arterial blood pressure (MAP), urine protein level, inflammation, and oxidative stress together with angiogenesis were analyzed. Results. MLB prevented the elevation in MAP and urine protein levels induced by L-NAME. The activities of inflammatory cytokines were highly increased in serum and placental tissues of PIH rats, while cotreatment with MLB partially reversed the activities of these cytokines. MLB also recovered the expression of reactive oxygen species (ROS) in plasma of PIH rats together with levels of oxidative stress and antioxidant capacity in the placenta of PIH rats. The decreased expressions of vascular endothelial growth factor (VEGF), endothelial nitric oxide synthase (eNOS), and NO observed in PIH rats were increased by MLB. In addition, 10 mg/kg MLB exhibited higher protective effects as compared to lower doses of 5 mg/kg. Conclusion. This study demonstrated that pretreatment with MLB decreased MAP, inflammation, and oxidative stress in rats with gestational hypertension

The Performance and Reaction Mechanism of Untreated Steel Slag Used as a Microexpanding Agent in Fly Ash-Based Geopolymers

Steel slag is an industrial by-product of the steelmaking process, which is under-utilized and of low value due to its characteristics. Alkali-activated technology offers the possibility of high utilization and increased value of steel slag. A geopolymer composition was composed of steel slag, fly ash, and calcium hydroxide. Four experimental groups utilizing steel slag to substitute fly ash are established based on varying replacement levels: 35%, 40%, 45%, and 50% by mass. The final samples were characterized by compressive strength tests, and Fourier-transform infrared spectroscopy measurements, thermogravimetric measurements, scanning electron microscopy with energy dispersive spectroscopy, X-ray diffraction, and mercury intrusion porosimetry were used to investigate the chemical composition and microstructure of the final products. Higher steel slag/fly ash ratios lead to a lower bulk density and lower compressive strength. The compressive strength ranges from 3.7 MPa to 5.6 MPa, and the bulk density ranges from 0.85 g/cm3 to 1.13 g/cm3. Microstructural and energy-dispersive X-ray spectroscopy analyses show that the final geopolymer products were a type of composite consisting of both calcium aluminate silicate hydrate and sodium aluminate silicate hydrate, with the unreacted crystalline phases acting as fillers

Data_Sheet_1_Severity-dependent functional connectome and the association with glucose metabolism in the sensorimotor cortex of Parkinson's disease.docx

Functional MRI studies have achieved promising outcomes in revealing abnormal functional connectivity in Parkinson's disease (PD). The primary sensorimotor area (PSMA) received a large amount of attention because it closely correlates with motor deficits. While functional connectivity represents signaling between PSMA and other brain regions, the metabolic mechanism behind PSMA connectivity has rarely been well established. By introducing hybrid PET/MRI scanning, the current study enrolled 33 advanced PD patients during medication-off condition and 25 age-and-sex-matched healthy controls (HCs), aiming to not only identify the abnormal functional connectome pattern of the PSMA, but also to simultaneously investigate how PSMA functional connectome correlates with glucose metabolism. We calculated degree centrality (DC) and the ratio of standard uptake value (SUVr) using resting state fMRI and 18F-FDG-PET data. A two-sample t-test revealed significantly decreased PSMA DC (PFWE 0.44). In summary, we identified disease severity-dependent PSMA functional connectome which in addition uncoupled with glucose metabolism in PD patients. The current study highlighted the critical role of simultaneous PET/fMRI in revealing the functional-metabolic mechanism in the PSMA of PD patients.</p

Tunable broadband transmissive terahertz cross-polarization converter enabled by a hybrid metal-graphene metasurface

Graphene has shown potential in terahertz (THz) polarization modulation due to highly tunable optoelectronic properties, fast photoelectric response, and ease of integration. However, the performance of polarization converters based on graphene metasurfaces is often limited by the achievable carrier mobility of large-area graphene. In this paper, a flexible and tunable broadband transmissive THz cross-polarization converter based on a hybrid metal-graphene metasurface is proposed. It is composed of two metal grating layers with a graphene-loaded 45-degree antenna array sandwiched between them. The THz response of the antenna can be tuned by adjusting the graphene Fermi level, which further alters the cross-polarization conversion efficiency (CPCE) of the device. The average CPCE can be continuously tuned from 80.3% to 4.5% within a broadband from 0.6 to 2.0 THz, and the average modulation depth of the whole band is 94.2%. The mechanisms of this highly efficient polarization conversion and dynamic modulation are explained with a transfer matrix method and an equivalent circuit model. Furthermore, the proposed structure has a low requirement on the graphene mobility, which is only 500 cm2/(V·s) here. This work provides a new approach to highly efficient tunable cross-polarization conversion over a broadband in THz, which will promote the application of graphene-based polarization modulators in THz sensing, imaging and communication