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Identification and Mapping of a Thermo-Sensitive Genic Self-Incompatibility Gene in Maize
Xin Ge Lin,Hui Ling Xie,Zhang Ying Xi,Yan Min Hu,Guang Yuan Zhao,Liu Jing Duan,Zong You Hao,Zong Hua Liu,Ji Hua Tang 한국유전학회 2009 Genes & Genomics Vol.31 No.3
In this study, we describe a novel ecological self-incompatibility (SI) line HE97 in maize. The main environmental factors influencing the inbred line characteristics were identified through field sowing trials during a two-year study period (2001 and 2002). The results showed that daily minimum temperature had the greatest effect on floral morphology and breeding system of the SI line. In staminate floret differentiation, when the daily minimum temperature exceeded 24℃, the line exhibited complete self-compatibility; however SI was observed when the daily minimum temperature was below 20℃. Therefore, we characterized the line as exhibiting thermo-sensitive genic self-incompatibility (TGSI). A set of F2 and F2:3 populations, derived from the inbred lines HE97 and Z58, were evaluated for two years to elucidate the TGSI line patterns of inheritance. Classical genetic analyses and QTL mapping results revealed that HE97 self-incompatibility was governed by a single allele, named here as tgsi1. The tgsi1 gene was mapped to chromosome 2 between SSR markers nc131 and bnlg1633, with a distance of 2.40 cM from nc131 and 2.44 cM from bnlg1633.
Li, Hui-Yan,Ge, Xin,Huang, Guang-Ming,Li, Kai-Yu,Zhao, Jing-Quan,Yu, Xi-Miao,Bi, Wen-Si,Wang, Yu-Lin Asian Pacific Journal of Cancer Prevention 2012 Asian Pacific journal of cancer prevention Vol.13 No.7
Aim: Platinum agents have shown to be effective in the treatment of colorectal cancer. We assessed whether single nucleotide polymorphisms (SNPs) in GSTP1, ERCC1 Asn118Asn and ERCC2 Lys751Gln might predict the overall survival in patients receiving oxaliplatin-based chemotherapy in a Chinese population. Methods: SNPs of GSTP1, ERCC1 Asn118Asn and ERCC2 Lys751Gln in 335 colorectal cancer patients were assessed using TaqMan nuclease assays. Results: At the time of final analysis on Nov. 2011, the median follow-up period was 37.7 months (range from 1 to 60 months). A total of 229 patients died during follow-up. Our study showed GSTP1 Val/Val (HR=0.44, 95% CI=0.18-0.98), ERCC1 C/C (HR=0.20, 95% CI=0.10-0.79) and ERCC2 G/G (HR=0.48, 95% CI=0.19-0.97) to be significantly associated with better survival of colorectal cancer. GSTP1 Val/Val, ERCC1 C/C and ERCC2 G/G were also related to longer survival among patients with colon cancer, with HRs (95% CIs) of 0.41 (0.16-0.91), 0.16 (0.09-0.74) and 0.34 (0.16-0.91), respectively. Conclusion: GSTP1, GSTP1, ERCC1 Asn118Asn and ERCC2 Lys751Gln genotyping might facilitate tailored oxaliplatin-based chemotherapy for colorectal cancer patients.
Puling Liu,Xiao-dong Yao,Guang-yan Ge,Zhengchun Du,Xiaobing Feng,Jianguo Yang 한국정밀공학회 2021 International Journal of Precision Engineering and Vol.22 No.2
This paper proposes a novel dynamic linearization modeling method for machine tool thermal errors based on data-driven control theory, with improved accuracy and robustness under various practical working conditions of machine tool. The nonlinear, quasi-static and pseudo-hysteric characteristics of the machine tool temperature field are identified as the main causes for poor robustness in conventional thermal error mathematical models. The theoretical and practical difficulties in applying conventional modeling approaches based on the model-based control theory are demonstrated using two types of CNC machine tools as examples. The data-driven control theory is applied to dynamic linearization modeling and the developed data model has shown significant improvement over the general dynamic model in terms of model accuracy and robustness. The feasibility and effectiveness of the proposed dynamic linearization modeling method has been verified using two experiments, demonstrating excellent robustness and ability to adapt to various machining conditions and to improve machining accuracy.
Xiao-dong Yao,Zheng-Chun Du,Guang-yan Ge,Jian-Guo Yang 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.1
In this work, a novel machine tool thermal error modeling method based on dynamic temperature gradient is proposed, and a thermal error compensation method based on unfalsified control is developed. The dynamic temperature gradient is used to optimize the locations of temperature measuring points on the machine tool. Real-time compensation for the thermal error can be achieved using the developed compensation method by establishing the correlations between dynamic temperature gradient and thermal error in the machine tool. Different from traditional model-based methods, the developed compensation approach does not rely on an accurate model of the thermal error but instead uses online input/output data to adaptively select the best controller at any moment, thereby improving thermal error prediction accuracy and robustness. The effectiveness of the developed thermal error compensation method is demonstrated on a turning center, where the spindle thermal error is compensated during the manufacturing of 120 inner bore parts and 120 shaft parts. After compensation using the proposed approach, thermal errors are reduced from 27 µm to 9 µm for the inner bore parts and from 31 µm to 11 µm for the shaft parts, respectively.