Academic literature on the topic 'Guo ji qi ye'

The aim of this paper is to define the institutional difference between the aristocratic lineage ruling the service fief of the Western Zhou era and the royal dynasty, reigning over the independent state of the Eastern Zhou era. Different approaches to the genesis of the Qin State are discussed in this paper: the archaeological approach and the “Zhou fiefdom” approach. The first one lacks data directly describing the political process. The problem of the second one is its being based practically on one written source that postdates the events described by 500 years. Therefore, to escape the failures of these methods, the author developed a specific approach that would both deal with political and institutional data on the one side while using data from different sources contemporary to the events in question. Data explicated from Bu Qi gui, Qin gong zhong and Guo ji zi Bai pan – three inscriptions on the bronze vessels dating between IX and VII centuries BC was scrutinized and compared. Two of them were cast by Qin rulers and the third describes the events leading to the creation of the Qin domain. Comparing information of these sources with the data from Sima Qian’s Shi ji allows to determine the precise moment of the Qin domain being transformed into the Qin State and show the institutional innovations that went along with this process. The interior political change of this time is described (i.e. the political crisis of royal inheritance) as well as the exterior change in Qin’s place inside the hierarchy of Zhou domains, particularly the changing relations between the Qin State and the domain of Xiao Guo. This clarified the place that the process of territorial expansion had in this transformation. As a hypothesis, the author built a model presenting the properties distinguishing a service fief and an independent state.

Background: To explore the prescription rules of famous ancient physicians in the treatment of threatened miscarriage. Methods: Traditional Chinese Medicine (TCM) prescriptions for threatened miscarriage were screened out of Fu Ren Da Quan Liang Fang by Ziming Chen, Yi Zong Jin Jian by Qian Wu, and Fu Qing Zhu Nv Ke by Qingzhu Fu. Data were standardized and analyzed through the TCM inheritance auxiliary platform. Results: A total of 29 prescriptions for threatened miscarriage were screened. Dang Gui, E Jiao, Gan Cao, Chuan Xiong, Bai Shao were the top five frequently prescribed Chinese herbs. The common herb–herb combinations used by Ziming Chen contained E Jiao, Dang Gui, Chuan Xiong, Ai Ye, Cong Bai, and Sang Ji Sheng. Ren Shen, Gan Cao, and Bai Zhu were the common herbal groups used by Qingzhu Fu. Huang Qi, Shu Di Huang, Bai Shao, Dang Gui, and Gan Cao were one of Qian Wu’s core prescriptions, with Dang Gui and Chuan Xiong being the others. According to the analysis of four Qi, five flavors, and meridian tropism of the prescriptions, herbs with the warm nature, or with the sweet, pungent, bitter flavors topped the list of application. The top six meridian tropisms of high-frequency herbs were: liver, spleen, lung, kidney, heart, and stomach meridian. Conclusion: Based on the principle of restoring the balance within the organs and enriching Qi and blood, clinical treatment of threatened miscarriage involves invigorating the Chong and Ren channels, nourishing Yin, dispelling cold and wind, generating and activating blood, regulating and harmonizing Qi.

Abstract Background: Natural killer (NK) cells exhibit anti-tumor activity in a non-antigen-specific manner without causing graft-versus-host disease. T cell and cord blood NK cells expressing chimeric antigen receptor (CAR) targeting CD19 have demonstrated remarkable clinical efficacies against B cell lymphomas (Maude et al, N Engl J Med 2018; Neelapu et al, N Engl J Med 2017; Liu et al, N Engl J Med 2020). Celularity has developed a platform for the expansion and differentiation of human placental CD34 + stem cells towards NK cells. The introduction of CD19 CAR enables generation of CAR19-CYNK cells that can be used as an off-the-shelf, cryopreserved, allogeneic cell therapy for CD19 + B cell malignancies. Reported here are the in vitro and in vivo results evaluating anti-tumor activity of CAR19-CYNK against CD19 + B cell malignancies. Methods: CAR19-CYNK cells were generated by retroviral transduction of human placental CD34 + cells with an anti-CD19 CAR (CD19scFv-CD28CD3ζ, Sorrento Therapeutics), followed by culture expansion in the presence of cytokines. CD19 CAR expression and phenotype of CAR19-CYNK cells were characterized by flow cytometry using the following surface markers: CD56, CD3, CD226, CD16, CD11a, CD94, NKG2D, NKp30, NKp44, NKp46. The in vitro anti-tumor activity of CAR19-CYNK against the B cell lymphoma cell lines, Daudi and Nalm-6, was assessed at various effector to target (E:T) ratios using a flow cytometry-based cytotoxicity assay and multiplex Luminex analysis for cytokine profiling. Non-transduced (NT) NK cells were used as control. In vivo efficacy of CAR19-CYNK was assessed using a disseminated B-cell lymphoma xenograft model in B-NDG-hIL15 mice. B-NDG-hIL15 mice lack T, B, and NK cells and are transgenic for human IL-15 to support CAR19-CYNK persistence and maturation. Luciferase expressing Daudi cells (3×10 6) were intravenously (IV) injected on Day 0 three days after the mice were preconditioned with a myeloablative dose of busulfan to allow for better tumor cell engraftment. CAR19-CYNK cells (1x10 7) were IV injected on Day 7. Tumor burden was assessed weekly by bioluminescence imaging (BLI) and the mice were followed for assessment of their survival (n=5 mice per group). Results: Placental CD34 + cells were genetically modified using a retroviral vector and achieved an average of 29.2% ± 12.4% (range 17.5% to 50.1%; n=5 donor lots) CD19 CAR expression on CAR19-CYNK cells at the end of 35-day culture. The average fold expansion of CAR19-CYNK was 6186 ± 2847 with the range of 2692 to 10626 (n=5 donor lots). Post-thaw evaluation of CAR19-CYNK (n=5 donor lots) revealed 93.8 ± 3.9% of CD56 +CD3 - NK cells, and transduction of CD19 CAR on CYNK did not significantly alter NK cell phenotype based on various activation and lineage markers (CD226, CD16, CD11a, CD94, NKG2D, NKp30, NKp44, NKp46). CAR19-CYNK displayed enhanced in vitro cytotoxicity against lymphoma cell lines, Daudi and Nalm-6, compared to that of NT NK cells. At the E:T ratio of 10:1, CAR19-CYNK (n=5 donor lots) elicited significant increased cytotoxicity against Nalm-6 compared to that of NT NK cells, with 75.9 ± 14.8% vs. 0.00 ± 0.00% at 24h (p<0.005). Under the same condition, CAR19-CYNK (n=4 donor lots) showed higher cytotoxicity against Daudi compared to that of NT NK cells with 23.6 ± 18.9% vs. 4.9 ± 4.0%. When cocultured with tumor cell lines, CAR19-CYNK showed increased secretion of the proinflammatory cytokines GM-CSF (p<0.05 for both Nalm-6 and Daudi), IFN-g (p<0.05 for Nalm-6), and TNF-a compared to that of NT NK cells at an E:T ratio of 1:1 for 24h. To evaluate the in vivo efficacy of CAR19-CYNK, a disseminated Daudi xenograft B-NDG-hIL15 model was used. CAR19-CYNK treated mice demonstrated a significant survival benefit with a median survival of 39 days versus a median survival of 28 days for the vehicle treated group (p<0.05). Conclusions: In summary, we have successfully established a process for generating CAR19-CYNK cells from human placental CD34 + cells. CAR19-CYNK demonstrated enhanced in vitro cytotoxicity against CD19 + B cell malignancies and in vivo survival benefit in a disseminated lymphoma xenograft B-NDG-hIL15 model. Further development of CAR19-CYNK for CD19 + B cell malignancies is warranted. Disclosures Gergues: Celularity Inc: Current Employment, Current equity holder in publicly-traded company. Raitman: Celularity Inc.: Current Employment, Current equity holder in publicly-traded company. Gleason: Celularity Inc.: Current Employment, Current equity holder in publicly-traded company. Rousseva: Celularity Inc.: Current Employment, Current equity holder in publicly-traded company. He: Celularity Inc.: Current Employment, Current equity holder in publicly-traded company. Van Der Touw: Celularity Inc.: Current Employment, Current equity holder in publicly-traded company. Ye: Celularity Inc.: Current Employment, Current equity holder in publicly-traded company. Kang: Celularity Inc.: Current Employment, Current equity holder in publicly-traded company. Zhang: Sorrento Therapeutics Inc.: Current Employment, Current equity holder in publicly-traded company. Pai: Sorrento Therapeutics Inc.: Current Employment, Current equity holder in publicly-traded company. Guo: Sorrento Therapeutics Inc.: Current Employment, Current equity holder in publicly-traded company. Ji: Sorrento Therapeutics Inc.: Current Employment, Current equity holder in publicly-traded company. Hariri: Celularity Inc.: Current Employment, Current equity holder in publicly-traded company. Zhang: Celularity Inc.: Current equity holder in publicly-traded company, Ended employment in the past 24 months.

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. 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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. Heikkilä, Segmenting salient objects from images and videos, in: Computer Vision-ECCV 2010, Springer, 2010, pp. 366-37.