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An Xin,Xing Guannan,Wang Jing,Tian Yanhong,Liu Yunfang,Wan Qiong 한국탄소학회 2021 Carbon Letters Vol.31 No.4
The carbon spheres (CSs) synthesized by an ultrasonic-spray pyrolysis method were activated for supercapacitor electrode. There are plenty of cracks on the surface of the activated carbon spheres (ACSs), which expend with increasing the activation temperature and activator dosage. The specifc capacitance of ACSs increases with the activation temperature and activator dosage and reach to maximal value at certain conditions. Importantly, the ACS sample activated at relatively low activation temperature (600 °C) and 7 of mass ratio of KOH to CSs has the highest specifc capacitance (about 209 F g−1 at 50 mA g−1 of current density) and indicates the excellent cycling stability after 1000 consecutive charge–discharge cycles. Furthermore, the graphene sheets could be found in the samples that were activated at 1000 °C. And the electrode prepared by the sample has the very low series resistance because of the excellent conductivity of the formed graphene sheets.
( Jing Liu ),( Guilian Yang ),( Xing Gao ),( Zan Zhang ),( Yang Liu ),( Xin Yang ),( Chunwei Shi ),( Qiong Liu ),( Yanlong Jiang ),( Chunfeng Wang ) 한국미생물 · 생명공학회 2019 Journal of microbiology and biotechnology Vol.29 No.1
The lactic acid bacteria species Lactobacillus plantarum (L. plantarum) has been used extensively for vaccine delivery. Considering to the critical role of dendritic cells in stimulating host immune response, in this study, we constructed a novel CD11c-targeting L. plantarum strain with surface-displayed variable fragments of anti-CD11c, single-chain antibody (scFv-CD11c). The newly designed L. plantarum strain, named 409-aCD11c, could adhere and invade more efficiently to bone marrow-derived DCs (BMDCs) in vitro due to the specific interaction between scFv-CD11c and CD11c located on the surface of BMDCs. After incubation with BMDCs, the 409-aCD11c strain harboring a eukaryotic vector pValac-GFP could lead to more efficient expression of GFP compared with wild-type strains shown by flow cytometry analysis, indicating the enhanced translocation of pValac-GFP from L. plantarum to BMDCs. Similar results were also observed in an in vivo study, which showed that oral administration resulted in efficient expression of GFP in both Peyer’s patches (PP) and mesenteric lymph nodes (MLNs) within 7 days after the last administration. In addition, the CD11c-targeting strain significantly promoted the differentiation and maturation of DCs, the differentiation of IL-4+ and IL-17A+ T helper (Th) cells in MLNs, as well as production of B220+ IgA+ B cells in the PP. In conclusion, this study developed a novel DC-targeting L. plantarum strain which could increase the ability to deliver eukaryotic expression plasmid to host cells, indicating a promising approach for vaccine study.
Mechanical evaluation of polymer microneedles for transdermal drug delivery: In vitro and in vivo
Rui Xuan Liu,Yu Ting He,Ling Liang,Liu Fu Hu,Yue Liu,Rui-xing Yu,Bo Zhi Chen,Yong Cui,Xin Dong Guo 한국공업화학회 2022 Journal of Industrial and Engineering Chemistry Vol.114 No.-
In this study, we reported two types of PMNs based on polylactic acid (PLA) and polyvinyl alcohol (PVA),respectively. Parafilm M film, porcine skin, and rats’ models were operated to evaluate the mechanicalproperties in vitro and in vivo to find optimal parameters for efficient insertion. Insertion depth was measuredusing Digital Force Gauge by changing insertion force and speed, respectively. Results showed thatincreasing the insertion force and speed used for PMNs application led to a significant increase in thedepth of insertion. A force of 18 N under a speed of 330 mm/min was the optimal condition for insertingPMNs array into ParafilmM film and porcine skin. In addition, PLA-MNs exhibited higher robustness andenhanced homogeneity in insertion depth compared with PVA-MNs, but PVA-MNs were able to reachmuch deeper insertion depth. Moreover, Sprague Dawley (SD) rat experiments confirmed the effectivenessof optimal insertion parameters for transdermal drug delivery. This study illustrated not only thedevelopment of novel PMNs but also the mechanical evaluation for the design of PMNs.
Mapping of QTLs controlling content of fatty acid composition in rapeseed (Brassica napus)
Xing Ying Yan,Jia Na Li,Rui Wang,Meng Yan Jin,Li Chen,Wei Qian,Xin Na Wang,Lie Zhao Liu 한국유전학회 2011 Genes & Genomics Vol.33 No.4
The improvement of fatty acid composition is one of the major goals of breeding in rapeseed (Brassica napus). The aim of this study was to provide more information on the genetic determination of fatty acid composition by investigating quantitative trait loci (QTLs). The study was based on two-year of field trials (in 2006 and 2007) with a population of recombinant inbred lines (RILs), which originated from a cross between GH06 and P174. The level of erucic acid (C22:1) was significantly negatively correlated with those of palmitic acid (C16:0), oleic acid (C18:1), linoleic acid (C18:2), linolenic acid (C18:3), and eicosenoic acid (C20:1) in both years. A total of 40 QTLs for six fatty acids were detected and most of them were clustered on linkage groups N8, N9, and N13. These results suggested strongly that there were significant correlations between the levels of fatty acid components and would be useful for the future improvement of breeding programs focused on fatty acids in rapeseed.
Use of deep learning in nano image processing through the CNN model
Xing, Lumin,Liu, Wenjian,Liu, Xiaoliang,Li, Xin,Wang, Han Techno-Press 2022 Advances in nano research Vol.12 No.2
Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.
Xin-gang Lu,Yan-ping Ma,Xing-hua Liu 한국원예학회 2012 Horticulture, Environment, and Biotechnology Vol.53 No.5
Fuji’ apple fruit harvested 10 days before normal harvest (H1) and at normal harvest (H2) were untreated or treated with 1 ㎕·L-1 1-methylcyclopropene (1-MCP) and stored at 0 for up to 30 weeks. Fruits from H1 were firmer and had higher titratable acidity (TA) but lower soluble solids concentrations than those from H2. 1-MCP treatment delayed loss of firmness and TA in fruit from both harvests during storage. Superficial scald incidence was decreased by 1-MCP treatment, but flesh browning was unaffected. H2 fruit had higher total phenolics, flavonoid, and glutathione content as well as total antioxidant activity than H1 fruit at harvest and throughout storage. 1-MCP treated fruit tended to have higher levels of these constituents than untreated fruit in peel, but not in flesh tissues. These results suggest that fruit harvested at the mature stage have better integral quality with 1-MCP treatment.
Structural time-varying damage detection using synchrosqueezing wavelet transform
Liu, Jing-Liang,Wang, Zuo-Cai,Ren, Wei-Xin,Li, Xing-Xin Techno-Press 2015 Smart Structures and Systems, An International Jou Vol.15 No.1
This paper proposed a structural time-varying damage detection method by using synchrosqueezing wavelet transform. The instantaneous frequencies of a structure with time-varying damage are first extracted using the synchrosqueezing wavelet transform. Since the proposed synchrosqueezing wavelet transform is invertible, thus each individual component can be reconstructed and the modal participation factor ratio can be extracted based on the amplitude of the analytical signals of the reconstructed individual components. Then, the new time-varying damage index is defined based on the extracted instantaneous frequencies and modal participation factor ratio. Both free and forced vibrations of a classical Duffing nonlinear system and a simply supported beam structure with abrupt and linear time-varying damage are simulated. The proposed synchrosqueezing wavelet transform method can successfully extract the instantaneous frequencies of the damaged structures under free vibration or vibration due to earthquake excitation. The results also show that the defined time-varying damage index can effectively track structural time-varying damage.
Application of machine learning and deep neural network for wave propagation in lung cancer cell
Xing, Lumin,Liu, Wenjian,Li, Xin,Wang, Han,Jiang, Zhiming,Wang, Lingling Techno-Press 2022 Advances in nano research Vol.13 No.3
Coughing and breath shortness are common symptoms of nano (small) cell lung cancer. Smoking is main factor in causing such cancers. The cancer cells form on the soft tissues of lung. Deformation behavior and wave vibration of lung affected when cancer cells exist. Therefore, in the current work, phase velocity behavior of the small cell lung cancer as a main part of the body via an exact size-dependent theory is presented. Regarding this problem, displacement fields of small cell lung cancer are obtained using first-order shear deformation theory with five parameters. Besides, the size-dependent small cell lung cancer is modeled via nonlocal stress/strain gradient theory (NSGT). An analytical method is applied for solving the governing equations of the small cell lung cancer structure. The novelty of the current study is the consideration of the five-parameter of displacement for curved panel, and porosity as well as NSGT are employed and solved using the analytical method. For more verification, the outcomes of this reports are compared with the predictions of deep neural network (DNN) with adaptive optimization method. A thorough parametric investigation is conducted on the effect of NSGT parameters, porosity and geometry on the phase velocity behavior of the small cell lung cancer structure.