Academic literature on the topic 'Xi tong gong cheng'

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Journal articles on the topic "Xi tong gong cheng"

1

Gao, Bao-Xi, Gong Cheng, and Lea Ziskind-Conhaim. "Development of Spontaneous Synaptic Transmission in the Rat Spinal Cord." Journal of Neurophysiology 79, no. 5 (1998): 2277–87. http://dx.doi.org/10.1152/jn.1998.79.5.2277.

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Gao, Bao-Xi, Gong Cheng, and Lea Ziskind-Conhaim. Development of spontaneous synaptic transmission in the rat spinal cord. J. Neurophysiol. 79: 2277–2287, 1998. Dorsal root afferents form synaptic connections on motoneurons a few days after motoneuron clustering in the rat lumbar spinal cord, but frequent spontaneous synaptic potentials are detected only after birth. To increase our understanding of the mechanisms underlying the differentiation of synaptic transmission, we examined the developmental changes in properties of spontaneous synaptic transmission at early stages of synapse formation. Spontaneous postsynaptic currents (PSCs) and tetrodotoxin (TTX)-resistant miniature PSCs (mPSCs) were measured in spinal motoneurons of embryonic and postnatal rats using whole cell patch-clamp recordings. Spontaneous PSC frequencies were higher than mPSC frequencies in both embryonic and postnatal motoneurons, suggesting that even at embryonic stages, when action-potential firing rate was low, presynaptic action potentials played an important role in triggering spontaneous PSCs. After birth, the twofold increase in spontaneous PSC frequency was attributed to an increase in action-potential–independent quantal release rather than to a higher rate of action-potential firing. In embryonic motoneurons, the fluctuations in peak amplitude of spontaneous PSCs were normally distributed around single peaks with modal values similar to those of mPSCs. These data indicated that early in synapse differentiation spontaneous PSCs were primarily composed of currents generated by quantal release. After birth, mean mPSC amplitude increased by 50% but mean quantal current amplitude did not change. Synchronous, multiquantal release was apparent in postnatal motoneurons only in high-K+ extracellular solution. Comparison of the properties of miniature excitatory and inhibitory postsynaptic currents (mEPSCs and mIPSCs) demonstrated that mean mEPSC frequency was higher than mIPSC frequency, suggesting that either excitatory synapses outnumbered inhibitory synapses or that the probability of excitatory transmitter release was higher than the release of inhibitory neurotransmitters. The finding that mIPSC duration was several-fold longer than mEPSC duration implied that despite their lower frequency, inhibitory currents could modulate motoneuron synaptic integration by shunting incoming excitatory inputs for prolonged time intervals.
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2

Cho, Byong-han. "The Revival of Statecraft Current in the Qian-Jia(乾嘉) Era: The Tong Cheng Archaic Prose School(桐城古文派) and The Chang Zhou Gong Yang School(常州公羊學派) at the Height of Emperical Research's Prosperty". Journal of Ming-Qing Historical Studies 6 (30 квітня 1997): 119–78. http://dx.doi.org/10.31329/jmhs.1997.04.6.119.

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3

Xu, Binghe, Qingyuan Zhang, Xichun Hu, et al. "Abstract GS1-06: A randomized control phase III trial of entinostat, a once weekly, class I selective histone deacetylase inhibitor, in combination with exemestane in patients with hormone receptor positive advanced breast cancer." Cancer Research 82, no. 4_Supplement (2022): GS1–06—GS1–06. http://dx.doi.org/10.1158/1538-7445.sabcs21-gs1-06.

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Abstract Background Entinostat is a novel, potent, once weekly, orally bioavailable, class I selective histone deacetylase (HDAC) inhibitor. In a previous Phase II study, the combination of entinostat with exemestane showed significant improvement of overall survival in patients with advanced hormone receptor (HR) positive breast cancer. To verify and further confirm the benefit of HDAC inhibitor in combination with exemestane we designed a randomized, controlled trial to assess the efficacy and safety in a larger population of Chinese patients with advanced, HR positive breast cancer. Methods We carried out the randomized, double-blind, placebo-controlled, Phase III trial at 35 sites in China. Eligible patients were women (aged ≥18 years) with HR positive, human epidermal growth factor receptor-2 (HER2) negative breast cancer, whose disease had relapsed or progressed after at least one endocrine therapy (either in advanced or metastatic or adjuvant setting), and who had at least one measurable lesion, adequate organ function, ECOG performance status of 0-1, and adequate haematological and biochemical parameters. Patients were randomly assigned (2:1) via an interactive web-response system to orally take 5 mg entinostat or placebo. Both groups received oral administration of 25 mg exemestane daily. Randomization was stratified according to previous usage of CDK4/6 (yes vs no), fulvestrant (yes vs no), chemotherapy (yes vs no), and the presence of visceral metastases (yes vs no). Patients, investigators, study site staff, and the sponsor were masked to treatment assignment. The primary endpoint was Independent Radiographic Committee (IRC)-assessed progression free survival (PFS). Efficacy and safety analyses were done in all patients who received at least one dose of any study treatment. The study has reached the required number of events for final analysis of the primary endpoint. The trial is no longer enrolling patients, but follow-up for investigation of overall survival is ongoing. This study was registered with ClinicalTrials.gov with the number of NCT03538171. Results From April 16th, 2019 to May 13th, 2020, 354 patients were enrolled and randomly assigned as 235 to the entinostat group and 119 to the placebo group. IRC-assessed median PFS was 6.32 months (95% CI 5.30-9.11) in the entinostat group and 3.72 months (95% CI 1.91-5.49) in the placebo group (HR 0.74 [95% CI 0.57-0.96]; p<0.001). The most common Grade 3 or 4 adverse events in the entinostat group vs placebo group were neutropenia (103 [43.8%] vs 119 [0.8%] ), thrombocytopenia (20 [8.5%] vs 1 [0.8%]), and leucopenia (15 [6.4%] vs 0). Serious adverse events occurred in 28 out of 235 patients (11.9%) in the entinostat group and 11 out of 119 patients (9.2%) in the placebo group. Conclusions Entinostat and exemestane combination treatment significantly improved PFS compared with exemestane alone in patients with advanced, HR positive, HER2 negative breast cancer that progressed after previous endocrine therapy. Entinostat and exemestane combination was generally tolerated and can offer meaningful clinical benefit in these patients with unmet medical need. This phase III trial was sponsored by Taizhou EOC Pharma Co., Ltd. Citation Format: Binghe Xu, Qingyuan Zhang, Xichun Hu, Qing Li, Tao Sun, Wei Li, Quchang Ouyang, Jingfen Wang, Zhongsheng Tong, Min Yan, Huiping Li, Xiaohua Zeng, Changping Shan, Xian Wang, Xi Yan, Jian Zhang, Yue Zhang, Jiani Wang, Liang Zhang, Ying Lin, Jifeng Feng, Qianjun Chen, Jian Huang, Yongkui Lu, Hongsheng Li, Jinsheng Wu, Jing Cheng, Yanrong Hao, Cuizhi Geng, Min Lu, Yanping Li, Xi Chen, Lihua Song, Xueying Wu, Changlu Hu, Xinhong Wu, Xiaojia Wang, Yueyin Pan, Yuehong Cui, Guohua Yu, Sanyuan Sun. A randomized control phase III trial of entinostat, a once weekly, class I selective histone deacetylase inhibitor, in combination with exemestane in patients with hormone receptor positive advanced breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr GS1-06.
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4

Thinh, Nguyen Hong, Tran Hoang Tung, and Le Vu Ha. "Depth-aware salient object segmentation." VNU Journal of Science: Computer Science and Communication Engineering 36, no. 2 (2020). http://dx.doi.org/10.25073/2588-1086/vnucsce.217.

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Object segmentation is an important task which is widely employed in many computer vision applications such as object detection, tracking, recognition, and retrieval. It can be seen as a two-phase process: object detection and segmentation. Object segmentation becomes more challenging in case there is no prior knowledge about the object in the scene. In such conditions, visual attention analysis via saliency mapping may offer a mean to predict the object location by using visual contrast, local or global, to identify regions that draw strong attention in the image. However, in such situations as clutter background, highly varied object surface, or shadow, regular and salient object segmentation approaches based on a single image feature such as color or brightness have shown to be insufficient for the task. This work proposes a new salient object segmentation method which uses a depth map obtained from the input image for enhancing the accuracy of saliency mapping. A deep learning-based method is employed for depth map estimation. Our experiments showed that the proposed method outperforms other state-of-the-art object segmentation algorithms in terms of recall and precision. KeywordsSaliency map, Depth map, deep learning, object segmentation References[1] Itti, C. Koch, E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on pattern analysis and machine intelligence 20(11) (1998) 1254-1259.[2] Goferman, L. Zelnik-Manor, A. Tal, Context-aware saliency detection, IEEE transactions on pattern analysis and machine intelligence 34(10) (2012) 1915-1926.[3] Kanan, M.H. Tong, L. Zhang, G.W. Cottrell, Sun: Top-down saliency using natural statistics, Visual cognition 17(6-7) (2009) 979-1003.[4] Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, H.-Y. Shum, Learning to detect a salient object, IEEE Transactions on Pattern analysis and machine intelligence 33(2) (2011) 353-367.[5] Perazzi, P. Krähenbühl, Y. Pritch, A. Hornung, Saliency filters: Contrast based filtering for salient region detection, in: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, IEEE, 2012, pp. 733-740.[6] M. Cheng, N.J. Mitra, X. Huang, P.H. Torr, S.M. Hu, Global contrast based salient region detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 37(3) (2015) 569-582.[7] Borji, L. Itti, State-of-the-art in visual attention modeling, IEEE transactions on pattern analysis and machine intelligence 35(1) (2013) 185-207.[8] Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps, arXiv preprint arXiv:1312.6034.[9] Li, Y. Yu, Visual saliency based on multiscale deep features, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 5455-5463.[10] Liu, J. Han, Dhsnet: Deep hierarchical saliency network for salient object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 678-686.[11] Achanta, S. Hemami, F. Estrada, S. Susstrunk, Frequency-tuned saliency detection model, CVPR: Proc IEEE, 2009, pp. 1597-604.Fu, J. Cheng, Z. Li, H. Lu, Saliency cuts: An automatic approach to object segmentation, in: Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, IEEE, 2008, pp. 1-4Borenstein, J. Malik, Shape guided object segmentation, in: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 1, IEEE, 2006, pp. 969-976.Jiang, J. Wang, Z. Yuan, T. Liu, N. Zheng, S. Li, Automatic salient object segmentation based on context and shape prior., in: BMVC. 6 (2011) 9.Ciptadi, T. Hermans, J.M. Rehg, An in depth view of saliency, Georgia Institute of Technology, 2013.Desingh, K.M. Krishna, D. Rajan, C. Jawahar, Depth really matters: Improving visual salient region detection with depth., in: BMVC, 2013.Li, J. Ye, Y. Ji, H. Ling, J. Yu, Saliency detection on light field, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2806-2813.Koch, S. Ullman, Shifts in selective visual attention: towards the underlying neural circuitry, in: Matters of intelligence, Springer, 1987, pp. 115-141.Laina, C. Rupprecht, V. Belagiannis, F. Tombari, N. Navab, Deeper depth prediction with fully convolutional residual networks, in: 3D Vision (3DV), 2016 Fourth International Conference on, IEEE, 2016, pp. 239-248.Bruce, J. Tsotsos, Saliency based on information maximization, in: Advances in neural information processing systems, 2006, pp. 155-162.Ren, X. Gong, L. Yu, W. Zhou, M. Ying Yang, Exploiting global priors for rgb-d saliency detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 25-32.Fang, J. Wang, M. Narwaria, P. Le Callet, W. Lin, Saliency detection for stereoscopic images., IEEE Trans. Image Processing 23(6) (2014) 2625-2636.Hou, L. Zhang, Saliency detection: A spectral residual approach, in: Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, IEEE, 2007, pp. 1-8.Guo, Q. Ma, L. Zhang, Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform, in: Computer vision and pattern recognition, 2008. cvpr 2008. ieee conference on, IEEE, 2008, pp. 1-8.Fang, W. Lin, B.S. Lee, C.T. Lau, Z. Chen, C.W. Lin, Bottom-up saliency detection model based on human visual sensitivity and amplitude spectrum, IEEE Transactions on Multimedia 14(1) (2012) 187-198.Lang, T.V. Nguyen, H. Katti, K. Yadati, M. Kankanhalli, S. Yan, Depth matters: Influence of depth cues on visual saliency, in: Computer vision-ECCV 2012, Springer, 2012, pp. 101-115.Zhang, G. Jiang, M. Yu, K. Chen, Stereoscopic visual attention model for 3d video, in: International Conference on Multimedia Modeling, Springer, 2010, pp. 314-324.Wang, M.P. Da Silva, P. Le Callet, V. Ricordel, Computational model of stereoscopic 3d visual saliency, IEEE Transactions on Image Processing 22(6) (2013) 2151-2165.Peng, B. Li, W. Xiong, W. Hu, R. Ji, Rgbd salient object detection: A benchmark and algorithms, in: European Conference on Computer Vision (ECCV), 2014, pp. 92-109.Wu, L. Duan, L. Kong, Rgb-d salient object detection via feature fusion and multi-scale enhancement, in: CCF Chinese Conference on Computer Vision, Springer, 2015, pp. 359-368.Xue, Y. Gu, Y. Li, J. Yang, Rgb-d saliency detection via mutual guided manifold ranking, in: Image Processing (ICIP), 2015 IEEE International Conference on, IEEE, 2015, pp. 666-670.Katz, A. Adler, Depth camera based on structured light and stereo vision, uS Patent App. 12/877,595 (Mar. 8 2012).Chatterjee, G. Molina, D. Lelescu, Systems and methods for determining depth from multiple views of a scene that include aliasing using hypothesized fusion, uS Patent App. 13/623,091 (Mar. 21 2013).Matthies, T. Kanade, R. Szeliski, Kalman filter-based algorithms for estimating depth from image sequences, International Journal of Computer Vision 3(3) (1989) 209-238.Y. Schechner, N. Kiryati, Depth from defocus vs. stereo: How different really are they?, International Journal of Computer Vision 39(2) (2000) 141-162.Delage, H. Lee, A.Y. Ng, A dynamic bayesian network model for autonomous 3d reconstruction from a single indoor image, in: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 2, IEEE, 2006, pp. 2418-2428.Saxena, M. Sun, A.Y. Ng, Make3d: Learning 3d scene structure from a single still image, IEEE transactions on pattern analysis and machine intelligence 31(5) (2009) 824-840.Hedau, D. Hoiem, D. Forsyth, Recovering the spatial layout of cluttered rooms, in: Computer vision, 2009 IEEE 12th international conference on, IEEE, 2009, pp. 1849-1856.Liu, S. Gould, D. Koller, Single image depth estimation from predicted semantic labels, in: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, IEEE, 2010, pp. 1253-1260.Ladicky, J. Shi, M. Pollefeys, Pulling things out of perspective, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 89-96.K. Nathan Silberman, Derek Hoiem, R. Fergus, Indoor segmentation and support inference from rgbd images, in: ECCV, 2012.Liu, J. Yuen, A. Torralba, Sift flow: Dense correspondence across scenes and its applications, IEEE transactions on pattern analysis and machine intelligence 33(5) (2011) 978-994.Konrad, M. Wang, P. Ishwar, 2d-to-3d image conversion by learning depth from examples, in: Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, IEEE, 2012, pp. 16-22.Liu, C. Shen, G. Lin, Deep convolutional neural fields for depth estimation from a single image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5162-5170.Wang, X. Shen, Z. Lin, S. Cohen, B. Price, A.L. Yuille, Towards unified depth and semantic prediction from a single image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2800-2809.Geiger, P. Lenz, C. Stiller, R. Urtasun, Vision meets robotics: The kitti dataset, International Journal of Robotics Research (IJRR).Achanta, S. Süsstrunk, Saliency detection using maximum symmetric surround, in: Image processing (ICIP), 2010 17th IEEE international conference on, IEEE, 2010, pp. 2653-2656.E. Rahtu, J. Kannala, M. Salo, J. 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5

"International Stroke Conference 2013 Abstract Graders." Stroke 44, suppl_1 (2013). http://dx.doi.org/10.1161/str.44.suppl_1.aisc2013.

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Alex Abou-Chebl, MD Michael Abraham, MD Joseph E. Acker, III, EMT-P, MPH Robert Adams, MD, MS, FAHA Eric Adelman, MD Opeolu Adeoye, MD DeAnna L. Adkins, PhD Maria Aguilar, MD Absar Ahmed, MD Naveed Akhtar, MD Rufus Akinyemi, MBBS, MSc, MWACP, FMCP(Nig) Karen C. Albright, DO, MPH Felipe Albuquerque, MD Andrei V. Alexandrov, MD Abdulnasser Alhajeri, MD Latisha Ali, MD Nabil J. Alkayed, MD, PhD, FAHA Amer Alshekhlee, MD, MSc Irfan Altafullah, MD Arun Paul Amar, MD Pierre Amarenco, MD, FAHA, FAAN Sepideh Amin-Hanjani, MD, FAANS, FACS, FAHA Catherine Amlie-Lefond, MD Aaron M. Anderson, MD David C. Anderson, MD, FAHA Sameer A. Ansari, MD, PhD Ken Arai, PhD Agnieszka Ardelt, MD, PhD Juan Arenillas, MD PhD William Armstead, PhD, FAHA Jennifer L. Armstrong-Wells, MD, MPH Negar Asdaghi, MD, MSc, FRCPC Nancy D. Ashley, APRN,BC, CEN,CCRN,CNRN Stephen Ashwal, MD Andrew Asimos, MD Rand Askalan, MD, PhD Kjell Asplund, MD Richard P. Atkinson, MD, FAHA Issam A. Awad, MD, MSc, FACS, MA (hon) Hakan Ay, MD, FAHA Michael Ayad, MD, PhD Cenk Ayata, MD Aamir Badruddin, MD Hee Joon Bae, MD, PhD Mark Bain, MD Tamilyn Bakas, PhD, RN, FAHA, FAAN Frank Barone, BA, DPhil Andrew Barreto, MD William G. Barsan, MD, FACEP, FAHA Nicolas G. Bazan, MD, PhD Kyra Becker, MD, FAHA Ludmila Belayev, MD Rodney Bell, MD Andrei B. Belousov, PhD Susan L. Benedict, MD Larry Benowitz, PhD Rohit Bhatia, MBBS, MD, DM, DNB Pratik Bhattacharya, MD MPh James A. Bibb, PhD Jose Biller, MD, FACP, FAAN, FAHA Randie Black Schaffer, MD, MA Kristine Blackham, MD Bernadette Boden-Albala, DrPH Cesar Borlongan, MA, PhD Susana M. Bowling, MD Monique M. B. Breteler, MD, PhD Jonathan Brisman, MD Allan L. Brook, MD, FSIR Robert D. Brown, MD, MPH Devin L. Brown, MD, MS Ketan R. Bulsara, MD James Burke, MD Cheryl Bushnell, MD, MHSc, FAHA Ken Butcher, MD, PhD, FRCPC Livia Candelise, MD S Thomas Carmichael, MD, PhD Bob S. Carter, MD, PhD Angel Chamorro, MD, PhD Pak H. Chan, PhD, FAHA Seemant Chaturvedi, MD, FAHA, FAAN Peng Roc Chen, MD Jun Chen, MD Eric Cheng, MD, MS Huimahn Alex Choi, MD Sherry Chou, MD, MMSc Michael Chow, MD, FRCS(C), MPH Marilyn Cipolla, PhD, MS, FAHA Kevin Cockroft, MD, MSc, FACS Domingos Coiteiro, MD Alexander Coon, MD Robert Cooney, MD Shelagh B. Coutts, BSc, MB.ChB., MD, FRCPC, FRCP(Glasg.) Elizabeth Crago, RN, MSN Steven C. Cramer, MD Carolyn Cronin, MD, PhD Dewitte T. Cross, MD Salvador Cruz-Flores, MD, FAHA Brett L. Cucchiara, MD, FAHA Guilherme Dabus, MD M Ziad Darkhabani, MD Stephen M. Davis, MD, FRCP, Edin FRACP, FAHA Deidre De Silva, MBBS, MRCP Amir R. Dehdashti, MD Gregory J. del Zoppo, MD, MS, FAHA Bart M. Demaerschalk, MD, MSc, FRCPC Andrew M. Demchuk, MD Andrew J. DeNardo, MD Laurent Derex, MD, PhD Gabrielle deVeber, MD Helen Dewey, MB, BS, PhD, FRACP, FAFRM(RACP) Mandip Dhamoon, MD, MPH Orlando Diaz, MD Martin Dichgans, MD Rick M. Dijkhuizen, PhD Michael Diringer, MD Jodi Dodds, MD Eamon Dolan, MD, MRCPI Amish Doshi, MD Dariush Dowlatshahi, MD, PhD, FRCPC Alexander Dressel, MD Carole Dufouil, MD Dylan Edwards, PhD Mitchell Elkind, MD, MS, FAAN Matthias Endres, MD Joey English, MD, PhD Conrado J. Estol, MD, PhD Mustapha Ezzeddine, MD, FAHA Susan C. Fagan, PharmD, FAHA Pierre B. Fayad, MD, FAHA Wende Fedder, RN, MBA, FAHA Valery Feigin, MD, PhD Johanna Fifi, MD Jessica Filosa, PhD David Fiorella, MD, PhD Urs Fischer, MD, MSc Matthew L. Flaherty, MD Christian Foerch, MD Gregg C. Fonarow, MD, FAHA Andria Ford, MD Christine Fox, MD, MAS Isabel Fragata, MD Justin Fraser, MD Don Frei, MD Gary H. Friday, MD, MPH, FAAN, FAHA Neil Friedman, MBChB Michael Froehler, MD, PhD Chirag D. Gandhi, MD Hannah Gardener, ScD Madeline Geraghty, MD Daniel P. Gibson, MD Glen Gillen, EdD, OTR James Kyle Goddard, III, MD Daniel A. Godoy, MD, FCCM Joshua Goldstein, MD, PhD, FAHA Nicole R. Gonzales, MD Hector Gonzalez, PhD Marlis Gonzalez-Fernandez, MD, PhD Philip B. Gorelick, MD, MPH, FAHA Matthew Gounis, PhD Prasanthi Govindarajan, MD Manu Goyal, MD, MSc Glenn D. Graham, MD, PhD Armin J. Grau, MD, PhD Joel Greenberg, PhD, FAHA Steven M. Greenberg, MD, PhD, FAHA David M. Greer, MD, MA, FCCM James C. Grotta, MD, FAHA Jaime Grutzendler, MD Rishi Gupta, MD Andrew Gyorke, MD Mary N. Haan, MPH, DrPH Roman Haberl, MD Maree Hackett, PhD Elliot Clark Haley, MD, FAHA Hen Hallevi, MD Edith Hamel, PhD Graeme J. Hankey, MBBS, MD, FRCP, FRCP, FRACP Amer Haque, MD Richard L. Harvey, MD Don Heck, MD Cathy M. Helgason, MD Thomas Hemmen, MD, PhD Dirk M. Hermann, MD Marta Hernandez, MD Paco Herson, PhD Michael D. Hill, MD, MSc, FRCPC Nancy K. Hills, PhD, MBA Robin C. Hilsabeck, PhD, ABPP-CN Judith A. Hinchey, MD, MS, FAHA Robert G. Holloway, MD, MPH William Holloway, MD Sherril K. Hopper, RN Jonathan Hosey, MD, FAAN George Howard, DPH, FAHA Virginia J. Howard, PhD, FAHA David Huang, MD, PhD Daniel Huddle, DO Richard L. Hughes, MD, FAHA, FAAN Lynn Hundley, RN, MSN, ARNP, CCRN, CNRN, CCNS Patricia D. Hurn, PhD, FAHA Muhammad Shazam Hussain, MD, FRCPC Costantino Iadecola, MD Rebecca N. Ichord, MD M. Arfan Ikram, MD Kachi Illoh, MD Pascal Jabbour, MD Bharathi D. Jagadeesan, MD Vivek Jain, MD Dara G. Jamieson, MD, FAHA Brian T. Jankowitz, MD Edward C. Jauch, MD, MS, FAHA, FACEP David Jeck, MD Sayona John, MD Karen C. Johnston, MD, FAHA S Claiborne Johnston, MD, FAHA Jukka Jolkkonen, PhD Stephen C. Jones, PhD, SM, BSc Theresa Jones, PhD Anne Joutel, MD, PhD Tudor G. Jovin, MD Mouhammed R. Kabbani, MD Yasha Kadkhodayan, MD Mary A. Kalafut, MD, FAHA Amit Kansara, MD Moira Kapral, MD, MS Navaz P. Karanjia, MD Wendy Kartje, MD, PhD Carlos S. Kase, MD, FAHA Scott E. Kasner, MD, MS, FAHA Markku Kaste, MD, PhD, FESO, FAHA Prasad Katakam, MD, PhD Zvonimir S. Katusic, MD Irene Katzan, MD, MS, FAHA James E. Kelly, MD Michael Kelly, MD, PhD, FRCSC Peter J. Kelly, MD, MS, FRCPI, ABPN (Dip) Margaret Kelly-Hayes, EdD, RN, FAAN David M. Kent, MD Thomas A. Kent, MD Walter Kernan, MD Salomeh Keyhani, MD, MPH Alexander Khalessi, MD, MS Nadia Khan, MD, FRCPC, MSc Naim Naji Khoury, MD, MS Chelsea Kidwell, MD, FAHA Anthony Kim, MD Howard S. Kirshner, MD, FAHA Adam Kirton, MD, MSc, FRCPC Brett M. Kissela, MD Takanari Kitazono, MD, PhD Steven Kittner, MD, MPH Jeffrey Kleim, PhD Dawn Kleindorfer, MD, FAHA N. Jennifer Klinedinst, PhD, MPH, MSN, RN William Knight, MD Adam Kobayashi, MD, PhD Sebastian Koch, MD Raymond C. Koehler, PhD, FAHA Ines P. Koerner, MD, PhD Martin Köhrmann, MD Anneli Kolk, PhD, MD John B. Kostis, MD Tobias Kurth, MD, ScD Peter Kvamme, MD Eduardo Labat, MD, DABR Daniel T. Lackland, BA, DPH, FAHA Kamakshi Lakshminarayan, MD, PhD Joseph C. LaManna, PhD Catherine E. Lang, PT, PhD Maarten G. Lansberg, MD, PhD, MS Giuseppe Lanzino, MD Paul A. Lapchak, PhD, FAHA Sean Lavine, MD Ronald M. 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Meyer, MD Robert Mikulik, MD, PhD James M. Milburn, MD Kazuo Minematsu, MD, PhD J Mocco, MD, MS Yousef Mohammad, MD MSc FAAN Mahendranath Moharir, MD, MSc, FRACP Carlos A. Molina, MD Joan Montaner, MD PhD Majaz Moonis, MD, MRCP Christopher J. Moran, MD Henry Moyle, MD, PhD Susanne Muehlschlegel, MD, MPH Susanne Muehlschlegel, MD, MPH Yuichi Murayama, MD Stephanie J. Murphy, VMD, PhD, DACLAM, FAHA Fadi Nahab, MD Andrew M. Naidech, MD, MPh Ashish Nanda, MD Sandra Narayanan, MD William Neil, MD Edwin Nemoto, PhD, FAHA Lauren M. Nentwich, MD Perry P. Ng, MD Al C. Ngai, PhD Andrew D. Nguyen, MD, PhD Thanh Nguyen, MD, FRCPC Mai Nguyen-Huynh, MD, MAS Raul G. Nogueira, MD Bo Norrving, MD Robin Novakovic, MD Thaddeus Nowak, PhD David Nyenhuis, PhD Michelle C. Odden, PhD Michael O'Dell, MD Christopher S. Ogilvy, MD Jamary Oliveira-Filho, MD, PhD Jean Marc Olivot, MD, PhD Brian O'Neil, MD, FACEP Bruce Ovbiagele, MD, MSc, FAHA Shahram Oveisgharan, MD Mayowa Owolabi, MBBS,MWACP,FMCP Aditya S. 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Dissertations / Theses on the topic "Xi tong gong cheng"

1

Li, Ke. "Shi du cha ju yu xi tong you hua Zhongguo xian dai hua jin cheng zhong de qu yu jing ji /." Beijing : Zhongguo she hui ke xue chu ban she, 2000.

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Wong, Lai-wing. "The application of systemic functional linguistics to the teaching of evaluative writing at matriculation level Xi tong gong neng yu yan xue zai yu ke ping lun xie zuo jiao xue de ying yong /." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37609531.

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Ho, Miu-chun Michelle. "The application of systemic functional linguistics to teaching individual brief narrative speaking to junior secondary students Xi tong gong neng yu yan xue zai chu zhong ji shi duan jiang jiao xue de ying yong /." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37520350.

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Gu, Donghui. "Shanghai xia gang zhi gong yan jiu she hui zhi chi xi tong, ge ren hui ying yu zai jiu ye = A study of the unemployed in Shanghai : social support systems, individual responses & reemployment /." online access from Digital dissertation consortium, 2000. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?9984698.

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Tong, Wun-sing. "The application of systemic functional grammar in Chinese practical compositions : the teaching of news reporting = Xi tong gong neng yu yan xue zai shi yong wen jiao xue shang de ying yong yan jiu - yi xin wen gao xie zuo jiao xue wei li /." Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25755559.

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Tse, Hue-ying. "The inheritance of modern Cantonese opera from traditional Chinese opera an examination of the different types of role in Tang Di-sheng's (1917-1959) scripts = Dang dai yue ju dui chuan tong xi qu zhi cheng chuan : cong Tang Disheng (1917-1959) ju ben kan xing dang yi shu de yi yi /." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43085878.

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Books on the topic "Xi tong gong cheng"

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Xi tong gong cheng. Zhejiang jiao yu chu ban she, 1986.

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Xi tong gong cheng yu hang tian xi tong gong cheng guan li. Zhongguo yu hang chu ban she, 2007.

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yong, Lin fu, and Sun kai, eds. Xi tong gong cheng yin lun. 2nd ed. Qing hua da xue chu ban she, 2009.

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bian, Zhao Yong Zhu, ed. Xi tong gong cheng dao lun. 2nd ed. Hua xue gong ye chu ban she, 2013.

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Jun shi xi tong gong cheng. Guo fang gong ye chu ban she, 2014.

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1963-, Zhang Xiaodong, and Zhang Baodong active 2006, eds. An quan xi tong gong cheng. Zhongguo ji liang chu ban she, 2006.

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jun, Wang yu, Han ke qi, Cai qing xiang, Li ke min, and ZHANG YU XIANG, eds. Cai kuang xi tong gong cheng. Zhong guo kuang ye da xue chu ban she, 2000.

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Huan jing xi tong gong cheng. Guo fang gong ye chu ban she, 2008.

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lin, Wang fu. Nong ye xi tong gong cheng. Zhong guo nong ye chu ban she, 2006.

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Qi ye xi tong gong cheng. Hebei ke xue ji shu chu ban she, 1985.

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