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您的检索式：作者名="Zhong Fengqi"
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| 1 | Application of a new metallography technique to cast iron显示文摘 | Zhou Jiyang Zhong Fengqi Schmitz | 1993 | Prakt Metallography1993，30，： | 1 |
| 2 | Origin of abnormal high pressure and its relationship with hydrocarbon accumulation in the Dina 2 Gas Field, Kuqa Depression显示文摘Based on distribution of formation pressure by indirect estimation and formation testing,this study investigates origin of abnormal high pressure in the Dina 2 Gas Field in the Kuqa Depression in combination with the latest research findings.Contribution of major overpressure mechanisms to this gas field is estimated,and generation of the abnormal high pressure as well as its relationship with natural gas accumulation is explored.Disequilibrium compaction,tectonic stress,and overpressure transfer are the major overpressure mechanisms.Overpressure transfer resulted from vertical opening of faults and folding is the most important cause for the overpressure.Gas accumulation and abnormal high pressure generation in the reservoirs of the Dina 2 Gas Field show synchroneity.During the early oil-gas charge in the Kangcun stage,the reservoirs were generally normal pressure systems.In the Kuqa deposition stage,rapid deposition caused disequilibrium compaction and led to generation of excess pressure(approximately 5-10 MPa)in the reservoirs.During the Kuqa Formation denudation stage to the Quaternary,reservoir overpressure was greatly increased to approximately 40-50 MPa as a result of vertical pressure transfer by episodic fault activation,lateral overpressure transfer by folding and horizontal tectonic stress due to intense tectonic compression.The last stage was the major period of ultra-high pressure generation and gas accumulation in the Dina 2 Gas Field. | Fengqi Zhang Zhenliang Wang Hongli Zhong Yubin Song Weiming Liu Chi Wei | 2016 | Petroleum Research2016，1，1： | 1 |
| 3 | A novel FZD6 mutation revealed the cause of cleft lip and/or palate in a Chinese family显示文摘Cleft lip and/or palate(CL/P)is a most common craniofacial birth defect which has multifactorial etiology.In our study,we aimed to discover the underlying etiological gene variation in a Chinese family diagnosed as non-syndromic CL/P(NSCL/P).The blood sample of the proband and her parents were detected by whole exome sequencing.The Mendelian inheritance pattern,allele frequency,variation location,function analysis and literature search were applied to filtrate and screen the mutation.Besides,the candidates were confirmed by Sanger sequencing.We meanwhile explored the conservative analysis and protein homology simulation.As a result,a start-lost mutation c.1A>GAtg/Gtg in the Frizzled-6(FZD6)gene predicting p.Met1 was detected.The variation has not been reported before and was predicted to be harmful.The alteration caused missing of two starting amino acids that are evolutionarily conserved for FZD6 protein.Moreover,the specific structure of the mutant protein obviously changed according to the results of the homologous model.In conclusion,the results suggest c.1A>GAtg/Gtg in the FZD6(NM_001164616)might be the genetic etiology for non-syndromic CL/P in this pedigree.Furthermore,this finding provided new etiologic information,supplementing the evidence that FZD6 is a strong potential gene for CL/P. | Jieni Zhang Huaxiang Zhao Wenbin Huang Fengqi Song Wenjie Zhong Mengqi Zhang Yunfan Zhang Zhibo Zhou Jiuxiang Lin Feng Chen | 2020 | Genes & Diseases2020，7，3： | 0 |
| 4 | Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning显示文摘Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security.While the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown.In this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in 2012.We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012.Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era. | Renhai Zhong Yue Zhu Xuhui Wang Haifeng Li Bin Wang Fengqi You Luis F.Rodríguez Jingfeng Huang K.C.Ting Yibin Ying Tao Lin | 2023 | Fundamental Research2023，3，6： | 0 |
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