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- 저자 : Yunpeng Bai (검색결과 5 건)
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Zhi Li,Yunpeng Zhong,Danfeng Bai,Miaomiao Lin,Xiujuan Qi,Jinbao Fang 한국원예학회 2020 Horticulture, Environment, and Biotechnology Vol.61 No.5
Kiwifruit ( Actinidia sp.) vines are poorly adapted to waterlogging stress. Actinidia valvata is more tolerant against waterloggingstress than Actinidia deliciosa , and the latter contains some common rootstocks that are frequently used in kiwifruit cultivation. Little is known about the responses of A. valvata genotypes against waterlogging stress and during post-waterloggingrecovery. Here, we compared physiological traits of three A. valvata genotypes (KR1, KR3, and KR5) during waterloggingstress and recovery. Kiwifruit vines displayed water loss, a decline in the net photosynthetic rate, and inhibited shoot elongationduring waterlogging. These three genotypes could endure long-term waterlogging owing to their unique root systemconfi gurations as well as by sustaining carbohydrate reserves in the roots. Feeder roots of KR1 vines were damaged earlierand lost water more quickly than the other genotypes. Under the same stress, KR3 formed adventitious roots more rapidly,while KR5 had an improved ability to control water loss in above-ground tissues. After reoxygenation, growth of vineswas partially recovered due to water loss control, photosynthetic recovery, and carbohydrate replenishment. KR3 and KR5recovered their growth earlier and replenished more carbohydrates than KR1 after re-aeration. During waterlogging, both therelative water content and carbohydrate levels of vines can limit the recovery effi ciency after re-aeration. Our results revealedmutual and distinct responses of diff erent A. valvata genotypes during waterlogging stress and recovery and provided moreinsight into the physiological basis of their adaptation to waterlogging stress.
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Qiang Zhao,Yunpeng Bai,Dan Liu,Nan Zhao,Huiyuan Gao,Xiaozhe Zhang 고려인삼학회 2020 Journal of Ginseng Research Vol.44 No.5
Background: Peptides have diverse and important physiological roles in plants and are ideal markers for species identification. It is unclear whether there are specific peptides in Panax quinquefolius L. (PQ). The aims of this study were to identify Quinetides, a series of diverse posttranslational modified native peptides of the ribonuclease-like storage protein (ginseng major protein), from PQ to explore novel peptide markers and develop a new method to distinguish PQ from Panax ginseng. Methods: We used different fragmentation modes in the LTQ Orbitrap analysis to identify the enriched Quinetide targets of PQ, and we discovered Quinetide markers of PQ and P. ginseng using ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry analysis. These “peptide markers” were validated by simultaneously monitoring Rf and F11 as standard ginsenosides. Results: We discovered 100 Quinetides of PQ with various post-translational modifications (PTMs), including a series of glycopeptides, all of which originated from the protein ginseng major protein. We effectively distinguished PQ from P. ginseng using new “peptide markers.” Four unique peptides (Quinetides TP6 and TP7 as markers of PQ and Quinetides TP8 and TP9 as markers of P. ginseng) and their associated glycosylation products were discovered in PQ and P. ginseng. Conclusion: We provide specific information on PQ peptides and propose the clinical application of peptide markers to distinguish PQ from P. ginseng.
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Gasoline Desulfurization by Catalytic Alkylation over Methanesulfonic Acid
Wu, Xiaolin,Bai, Yunpeng,Tian, Ying,Meng, Xuan,Shi, Li Korean Chemical Society 2013 Bulletin of the Korean Chemical Society Vol.34 No.10
Methanesulfonic acid (MSA) was used as catalyst to remove trace organic sulfur (thiophene) from Fluid Catalytic Cracking gasoline (FCC) via alkylation with olefins. The reactions were conducted in Erlenmeyer flask equipped with a water-bath under atmospheric pressure. The influence of the temperature, the reaction time, and the mass ration of MSA were investigated. After a 60 min reaction time at 343 K, the thiophene conversion of 98.7% was obtained with a mass ration of MSA to oil of 10%. The catalyst was reused without a reactivation treatment, and the thiophene conversion reached 92.9% at the third time. The method represents an environmentally benign route to desulfur, because MSA could easily be separated from the reaction mixture via decantation and it could be reused.
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Gasoline Desulfurization by Catalytic Alkylation over Methanesulfonic Acid
Xiaolin Wu,Yunpeng Bai,Ying Tian,Xuan Meng,Li Shi 대한화학회 2013 Bulletin of the Korean Chemical Society Vol.34 No.10
Methanesulfonic acid (MSA) was used as catalyst to remove trace organic sulfur (thiophene) from Fluid Catalytic Cracking gasoline (FCC) via alkylation with olefins. The reactions were conducted in Erlenmeyer flask equipped with a water-bath under atmospheric pressure. The influence of the temperature, the reaction time, and the mass ration of MSA were investigated. After a 60 min reaction time at 343 K, the thiophene conversion of 98.7% was obtained with a mass ration of MSA to oil of 10%. The catalyst was reused without a reactivation treatment, and the thiophene conversion reached 92.9% at the third time. The method represents an environmentally benign route to desulfur, because MSA could easily be separated from the reaction mixture via decantation and it could be reused.
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Residual current fault type recognition based on S3VM and KNN cooperative training
Zhang, Xiangke,Wang, Yajing,Dou, Zhenhai,Wang, Wei,Bai, Yunpeng The Korean Institute of Power Electronics 2022 JOURNAL OF POWER ELECTRONICS Vol.22 No.11
It is difficult to detect the residual current of specific fault types in low-voltage distribution networks, which results in few labeled residual current samples. Thus, it is difficult to recognize the fault types of residual current. To solve this problem, a cooperative training classification model based on an improved squirrel search algorithm (ISSA) for a semi-supervised support vector machine (S3VM) and the k-nearest neighbor (KNN) is proposed (ISSA-S3VM-KNN). First, the residual current is decomposed into k intrinsic mode functions (IMFs) by variational mode decomposition (VMD), and the characteristic parameters of the IMFs are extracted to obtain a characteristic dataset for establishing a classification model. Second, to solve the problem where it is difficult to the select parameters (such as the penalty factors, slack variables and kernel function) of a S3VM, an ISSA parameter optimization method is proposed to self-adaptively select the optimal combination of parameters for the S3VM. Finally, the KNN is used to verify the classification results of an ISSA-S3VM through cooperative training, which further improves the classification accuracy of the S3VM for unlabeled residual current samples. Classification results of measured and simulation data show that the classification accuracy of the ISSA-S3VM-KNN is higher than that of the SVM-BPNN, WE-AE-BPNN, and PSO-SVM. The ISSA-S3VM-KNN provides a certain theoretical basis for achieving fast and accurate residual current fault type recognition.
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