Enhanced photoelectric properties of PbSnSe thin films via quick oxygen ion-implantation sensitization

Journal of Materials Science - Although chalcogenide materials continue to generate considerable interest due to great potentials for various optoelectronic devices, annealing for a long time in...     

Automated segmentation of brain tumor based on improved U-Net with residual units

Multimedia Tools and Applications - The study presents a new approach to automate segmentation of clinically significant brain tumor and, to a certain extent, addresses two major issues associated...     

Ratiometric Detection of Glutathione Based on Disulfide Linkage Rupture between a FRET Coumarin Donor and a Rhodamine Acceptor

Abnormal levels of glutathione, a cellular antioxidant, can lead to a variety of diseases. We have constructed a near-infrared ratiometric fluorescent probe to detect glutathione concentrations in biological samples. The probe consists of a coumarin donor, which is connected through a disulfide-tethered linker to a rhodamine acceptor. Under the excitation of the coumarin donor at 405 nm, the probe shows weak visible fluorescence of the coumarin donor at 470 nm and strong near-infrared fluorescence of the rhodamine acceptor at 652 nm due to efficient Forster resonance energy transfer (FRET) from the donor to the acceptor. Glutathione breaks the disulfide bond through reduction, which results in a dramatic increase in coumarin fluorescence and a corresponding decrease in rhodamine fluorescence. The probe possesses excellent cell permeability, biocompatibility, and good ratiometric fluorescence responses to glutathione and cysteine with a self-calibration capability. The probe was utilized to ratiometrically visualize glutathione concentration alterations in HeLa cells and Drosophila melanogaster larvae.     

电动汽车用动力电池振动疲劳测试方法开发

动力电池是当前新能源汽车中的核心零件,其性能,安全,可靠与耐久性直接影响整个车辆安全耐久性能,而电池系统的抗振动冲击特性是产品开发设计过程中需要满足的重要指标.文中着重阐述动力电池振动疲劳的测试验证策略与具体实施指导,为电池系统研发和测试工作提供重要参考.     

Bee Bread Ameliorates Vascular Inflammation and Impaired Vasorelaxation in Obesity-Induced Vascular Damage Rat Model: The Role of eNOS/NO/cGMP-Signaling Pathway

Obesity and hyperlipidemia are major risk factors for developing vascular diseases. Bee bread (BB) has been reported to exhibit some biological actions, including anti-obesity and anti-hyperlipidemic. This study aims to investigate whether bee bread can ameliorate vascular inflammation and impaired vasorelaxation activity through eNOS/NO/cGMP pathway in obese rats. Forty male Sprague-Dawley rats were randomly divided into four groups (n = 10/group), namely: control (normal group), obese rats (OB group), obese rats treated with bee bread (0.5 g/kg/day, OB/BB group) and obese rats treated with orlistat (10 mg/kg/day, OB/OR group). The latter three groups were given a high-fat diet (HFD) for 6 weeks to induced obesity before being administered with their respective treatments for another 6 weeks. After 12 weeks of the total experimental period, rats in the OB group demonstrated significantly higher Lee obesity index, lipid profile (total cholesterol, triglyceride, low-density lipoprotein), aortic proinflammatory markers (tumor necrosis factor-α, nuclear factor-κβ), aortic structural damage and impairment in vasorelaxation response to acetylcholine (ACh). Bee bread significantly ameliorated the obesity-induced vascular damage manifested by improvements in the lipid profile, aortic inflammatory markers, and the impaired vasorelaxation activity by significantly enhancing nitric oxide release, promoting endothelial nitric oxide synthase (eNOS) and cyclic guanosine monophosphate (cGMP) immunoexpression. These findings suggest that the administration of bee bread ameliorates the impaired vasorelaxation response to ACh by improving eNOS/NO/cGMP-signaling pathway in obese rats, suggesting its vascular therapeutic role.     

One step method of structure engineering porous graphitic carbon nitride for efficient visible-light photocatalytic reduction of Cr(Ⅵ)

Porous g-C3N4 nanosheets (PCN) were prepared by the nickel-assisted one-step thermal polymerization method.Hydrogen (H2) which was produced by the reaction between nickel (Ni) foam and ammonia (NH3) defined the structure and properties of PCN.During the formation of PCN,the participation of H2 not only enhanced the spacing between layers but also boosted the specific surface area that more active sites were exposed.Additionally,H2 promoted pores formation in the nanosheets,which was beneficial to the transfer of photons through lamellar structure and improved the absorption efficiency of visible light.Remarkably,the obtained PCN possessed better Cr(Ⅵ) photocatalytic reduction efficiency than pure g-C3N4.The reaction rate constant (k) of PCN (0.013 min-1) was approximately twice that of bare g-C3N4 (0.007 min-1).Furthermore,the effects of original pH and concentration of Cr(Ⅵ)-containing solution on removal efficiency of Cr(Ⅵ) were explored.A possible photocatalytic mechanism was proposed based on the experiments of radical scavengers and photoelectrochemical characterizations.     

Reconstructing gene regulatory networks with a memetic-neural hybrid based on fuzzy cognitive maps

Reconstructing gene regulatory networks (GRNs) plays an important role in identifying the complicated regulatory relationships, uncovering regulatory patterns in cells, and gaining a systematic view for biological processes. In order to reconstruct large-scale GRNs accurately, in this paper, we first use fuzzy cognitive maps (FCMs), which are a kind of cognition fuzzy influence graphs based on fuzzy logic and neural networks, to model GRNs. Then, a novel hybrid method is proposed to reconstruct GRNs from time series expression profiles using memetic algorithm (MA) combined with neural network (NN), which is labeled as MANN_FCM-GRN. In MANN_FCM-GRN, the MA is used to determine regulatory connections in GRNs and the NN is used to determine the interaction strength of the regulatory connections. In the experiments, the performance of MANN_FCM-GRN is validated on both synthetic data and the benchmark dataset DREAM3 and DREAM4. The experimental results demonstrate the efficacy of MANN_FCM-GRN and show that MANN_FCM-GRN can reconstruct GRNs with high accuracy without expert knowledge. The comparison with existing algorithms also shows that MANN_FCM-GRN outperforms ant colony optimization, non-linear Hebbian learning, and real-coded genetic algorithms.