Chemical composition of the essential oil of whole plant of Elsholtizia dense Benth and its anti-tumor effect on human hepatoma cells

Purpose: To determine the chemical components of the essential oil of Elsholtizia dense in Sichuan Province and evaluate the effect of the oil on human hepatoma cells (SMMC-7721) in vitro.Methods: The essential oil was extracted using the modified steam-distillation  extraction method, and its chemical components were determined by gas  chromatography-mass spectrometry (GC-MS). The effect of the essential oil on proliferation of SMMC-7721 cells was studied by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay, with L02 and HeLa cells serving as control groups.Results: GC-MS results show that the essential oil of E. dense contains 40  components. Thirty seven components were identified and accounted for 98.39 % of the essential oil. The two main components were rosefuran epoxide (53.12 %) and 2-ethyl imidazole (29.8 %). The oil significantly inhibited cell proliferation in a concentration- and time-dependent manner (p < 0.05). SMMC-7721 cells were more inhibited than L02 and HeLa cells by the oil, with half maximal inhibitory concentration (IC50) values of 26.23 and 25.46 μg/mL after 8-h and 24-h treatments, respectively.Conclusion: Out of the 40 chemical components of the essential oil of E. dense, rosefuran epoxide and 2-ethyl imidazole were the most abundant. The oil has a significant anti-tumor effect on SMMC-7721 cells, and thus has a potential to be developed as an anti-liver cancer drug.Keywords: Medicinal herb, Elsholtizia dense Benth, Essential oil, Rosefuran epoxide, 2-Ethyl imidazole, Anti-tumor activit

Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA

Text-based visual question answering (TextVQA) faces the significant challenge of avoiding redundant relational inference. To be specific, a large number of detected objects and optical character recognition (OCR) tokens result in rich visual relationships. Existing works take all visual relationships into account for answer prediction. However, there are three observations: (1) a single subject in the images can be easily detected as multiple objects with distinct bounding boxes (considered repetitive objects). The associations between these repetitive objects are superfluous for answer reasoning; (2) two spatially distant OCR tokens detected in the image frequently have weak semantic dependencies for answer reasoning; and (3) the co-existence of nearby objects and tokens may be indicative of important visual cues for predicting answers. Rather than utilizing all of them for answer prediction, we make an effort to identify the most important connections or eliminate redundant ones. We propose a sparse spatial graph network (SSGN) that introduces a spatially aware relation pruning technique to this task. As spatial factors for relation measurement, we employ spatial distance, geometric dimension, overlap area, and DIoU for spatially aware pruning. We consider three visual relationships for graph learning: object-object, OCR-OCR tokens, and object-OCR token relationships. SSGN is a progressive graph learning architecture that verifies the pivotal relations in the correlated object-token sparse graph, and then in the respective object-based sparse graph and token-based sparse graph. Experiment results on TextVQA and ST-VQA datasets demonstrate that SSGN achieves promising performances. And some visualization results further demonstrate the interpretability of our method.Comment: Accepted by TIP 202

Column-Spatial Correction Network for Remote Sensing Image Destriping

The stripe noise in the multispectral remote sensing images, possibly resulting from the instrument instability, slit contamination, and light interference, significantly degrades the imaging quality and impairs high-level visual tasks. The local consistency of homogeneous region in striped images is damaged because of the different gains and offsets of adjacent sensors regarding the same ground object, which leads to the structural characteristics of stripe noise. This can be characterized by the increased differences between columns in the remote sensing image. Therefore, the destriping can be viewed as a process of improving the local consistency of homogeneous region and the global uniformity of whole image. In recent years, convolutional neural network (CNN)-based models have been introduced to destriping tasks, and have achieved advanced results, relying on their powerful representation ability. Therefore, to effectively leverage both CNNs and the structural characteristics of stripe noise, we propose a multi-scaled column-spatial correction network (CSCNet) for remote sensing image destriping, in which the local structural characteristic of stripe noise and the global contextual information of the image are both explored at multiple feature scales. More specifically, the column-based correction module (CCM) and spatial-based correction module (SCM) were designed to improve the local consistency and global uniformity from the perspectives of column correction and full image correction, respectively. Moreover, a feature fusion module based on the channel attention mechanism was created to obtain discriminative features derived from different modules and scales. We compared the proposed model against both traditional and deep learning methods on simulated and real remote sensing images. The promising results indicate that CSCNet effectively removes image stripes and outperforms state-of-the-art methods in terms of qualitative and quantitative assessments

Progressive amorphization of GeSbTe phase-change material under electron beam irradiation

Fast and reversible phase transitions in chalcogenide phase-change materials (PCMs), in particular, Ge-Sb-Te compounds, are not only of fundamental interests, but also make PCMs based random access memory (PRAM) a leading candidate for non-volatile memory and neuromorphic computing devices. To RESET the memory cell, crystalline Ge-Sb-Te has to undergo phase transitions firstly to a liquid state and then to an amorphous state, corresponding to an abrupt change in electrical resistance. In this work, we demonstrate a progressive amorphization process in GeSb2Te4 thin films under electron beam irradiation on transmission electron microscope (TEM). Melting is shown to be completely absent by the in situ TEM experiments. The progressive amorphization process resembles closely the cumulative crystallization process that accompanies a continuous change in electrical resistance. Our work suggests that if displacement forces can be implemented properly, it should be possible to emulate symmetric neuronal dynamics by using PCMs

Neuroprotective effect of thiamine triethylorthoformate conjugate against Parkinson disease in a mouse model

Purpose: To investigate the effect of thiamine triethylorthoformate conjugate (TTO) on Parkinson disease (PD) in vitro and in vivo in a mice model. Methods: The effect of TTO on behavioural changes in PD mouse model was studied using pole, traction and swimming tests. Astrocyte proliferation after TTO treatment was assessed using 3 (4, 5 dimethyl 2 thi¬azolyl) 2, 5 diphenyl 2 H tetrazolium bromide (MTT) assay. Apoptosis was determined with flow cytometry using Annexin V Fluorescein isothiocyanate kit. Results: Treatment of PD mice with TTO led to a decrease in climbing time, increase in suspension score and enhancement of swimming score, when compared to the untreated group (p < 0.05). Treatment of astrocytes with TTO prior to MPP incubation significantly increased proliferation (p < 0.05). Apoptosis induction in astrocytes by MPP was attenuated by pre-treatment with TTO. Pre-treatment of astrocytes with 10 µM TTO markedly reduced JNK activation, when compared to astrocytes incubated with MPP alone (p < 0.05). Up-regulation of Bax and down-regulation of Bcl 2 by MPP in astrocytes were attenuated by pre-treatment with TTO. MPP-induced up-regulation of cleaved caspase 3 was suppressed in astrocytes by TTO pre-treatment (p < 0.05). Conclusion: Treatment with TTO prevents MPP+ -induced neuronal damage in vitro in astrocytes and in vivo in mice. The neuro-protective effect of TTO involves down-regulation of JNK activation, inhibition of caspase-3 level, decrease in Bax and increase in Bcl-2 expression. Thus, TTO has a potential for use in the treatment of Parkinson’s disease

Enhanced electrochemical properties of LiFePO4 by Mo-substitution and graphitic carbon-coating via a facile and fast microwave-assisted solid-state reaction

A composite cathode material for lithium ion battery applications, Mo-doped LiFePO4/C, is obtained through a facile and fast microwave-assisted synthesis method. Rietveld analysis of LiFePO4-based structural models using synchrotron X-ray diffraction data shows that Mo-ions substitute onto the Fe sites and displace Fe-ions to the Li sites. Supervalent Mo6+ doping can act to introduce Li ion vacancies due to the charge compensation effect and therefore facilitate lithium ion diffusion during charging/discharging. Transmission electron microscope images demonstrate that the pure and doped LiFePO4 nanoparticles were uniformly covered by an approximately 5 nm thin layer of graphitic carbon. Amorphous carbon on the graphitic carbon-coated pure and doped LiFePO4 particles forms a three-dimensional (3D) conductive carbon network, effectively improving the conductivity of these materials. The combined effects of Mo-doping and the 3D carbon network dramatically enhance the electrochemical performance of these LiFePO4 cathodes. In particular, Mo-doped LiFePO4/C delivers a reversible capacity of 162 mA h g(-1) at a current of 0.5 C and shows enhanced capacity retention compared to that of undoped LiFePO4/C. Moreover, the electrode exhibits excellent rate capability, with an associated high discharge capacity and good electrochemical reversibility