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701 results on '"Cong Hao"'

103. Identification and verification of low-tillering QTL based on a new model of genetic analysis in wheat.

Author

ZHANG Zhi-Yuan, ZHOU Jie-Guang, LIU Jia-Jun, WANG Su-Rong, WANG Tong-Zhu, ZHAO Cong-Hao, YOU Jia-Ning, DING Pu-Yang, TANG Hua-Ping, LIU Yan-Lin, JIANG Qian-Tao, CHEN Guo-Yue, WEI Yu-Ming, and MA Jian

Abstract

Effective tiller number (ETN) directly affects panicle number and is closely related to wheat grain yield. Mining quantitative trait loci (QTLs) associated with wheat tiller number and analyzing the correlation between tiller number and other important agronomic traits can provide the theoretical basis for molecular breeding. In this study, we first proposed and established a novel genetic analysis framework of "multiple environmental assessments-depth analysis of individual traits-comprehensive evaluation of various traits-friendly marker development-verification in different backgrounds". Using this approach, low-tillering QTLs were identified and validated base on an F6 recombinant inbred line population (MC population) derived from the low-tillering plant msf and Chuannong 16 (CN16), phenotypic data of effective tillers from multiple environments, and a 16K chip-based constructed genetic linkage map. QTL mapping results showed that there were four QTLs controlling tillering on chromosomes 1A, 5A, and 6D, respectively. Qltn.sau-MC-1A was a stable and major low-tillering QTL explaining 13.39%--60.40% of the phenotypic variation rate, and its positive allele was from msf. Phenotypic analysis showed that ETN of the lines carrying the positive allele of Qltn.sau-MC-1A was significantly less than those with the negative alleles. Correlation analysis showed that ETN had a significantly positive correlation with plant height (PH), and a significantly negative correlation with thousand kernel weight (TKW), kernel number per spike (KNPS), kernel weight per spike (KWPS), and flag leaf width (FLW), but no significantly correlation with flag leaf length (FLL) and anthesis date (AD). Genetic analysis showed that positive allele of Qltn.sau-MC-1A had a significant effect on increasing KNPS, KWPS, and TKW, but delaying AD. Validation results in different backgrounds suggested that the ETN of lines carrying positive alleles from Qltn.sau-MC-1A could be significantly decreased. Collectively, we established a new genetic mapping approach and further used it to identify and validate a major QTL controlling low-tiller number, Qltn.sau-MC-1A, which laid a foundation for further fine mapping and understanding the mechanism of tiller formation. [ABSTRACT FROM AUTHOR]

Published
2024
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108. The Sixth Visual Object Tracking VOT2018 Challenge Results.

Author

Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman P. Pflugfelder, Luka Cehovin Zajc, Tomás Vojír, Goutam Bhat, Alan Lukezic, Abdelrahman Eldesokey, Gustavo Fernández, álvaro García-Martín, álvaro Iglesias-Arias, A. Aydin Alatan, Abel González-García, Alfredo Petrosino, Alireza Memarmoghadam, Andrea Vedaldi, Andrej Muhic, Anfeng He, Arnold W. M. Smeulders, Asanka G. Perera, Bo Li 0114, Boyu Chen, Changick Kim, Changsheng Xu, Changzhen Xiong, Cheng Tian, Chong Luo, Chong Sun, Cong Hao, Daijin Kim 0001, Deepak Mishra 0002, Deming Chen, Dong Wang 0004, Dongyoon Wee, Efstratios Gavves, Erhan Gundogdu, Erik Velasco-Salido, Fahad Shahbaz Khan, Fan Yang 0035, Fei Zhao, Feng Li 0031, Francesco Battistone, George De Ath, Gorthi R. K. Sai Subrahmanyam, Guilherme Sousa Bastos, Haibin Ling, Hamed Kiani Galoogahi, Hankyeol Lee, Haojie Li, Haojie Zhao, Heng Fan 0001, Honggang Zhang 0002, Horst Possegger, Houqiang Li, Huchuan Lu, Hui Zhi, Huiyun Li, Hyemin Lee, Hyung Jin Chang, Isabela Drummond, Jack Valmadre, Jaime Spencer Martin, Javaan Singh Chahl, Jin Young Choi 0002, Jing Li 0036, Jinqiao Wang, Jinqing Qi, Jinyoung Sung, Joakim Johnander, João F. Henriques, Jongwon Choi, Joost van de Weijer 0001, Jorge Rodríguez Herranz, José M. Martínez 0001, Josef Kittler, Junfei Zhuang, Junyu Gao 0002, Klemen Grm, Lichao Zhang, Lijun Wang, Lingxiao Yang, Litu Rout, Liu Si, Luca Bertinetto, Lutao Chu, Manqiang Che, Mario Edoardo Maresca, Martin Danelljan, Ming-Hsuan Yang 0001, Mohamed H. Abdelpakey, Mohamed S. Shehata, Myunggu Kang, Namhoon Lee, Ning Wang 0020, Ondrej Miksik, Payman Moallem, Pablo Vicente-Moñivar, Pedro Senna, Peixia Li, Philip H. S. Torr, Priya Mariam Raju, Ruihe Qian, Qiang Wang 0051, Qin Zhou, Qing Guo 0005, Rafael Martin Nieto, Rama Krishna Sai Subrahmanyam Gorthi, Ran Tao 0004, Richard Bowden, Richard M. Everson, Runling Wang, Sangdoo Yun, Seokeon Choi, Sergio Vivas, Shuai Bai, Shuangping Huang, Sihang Wu, Simon Hadfield, Siwen Wang, Stuart Golodetz, Ming Tang 0001, Tianyang Xu, Tianzhu Zhang, Tobias Fischer 0001, Vincenzo Santopietro, Vitomir Struc, Wei Wang 0335, Wangmeng Zuo, Wei Feng 0005, Wei Wu 0021, Wei Zou, Weiming Hu, Wengang Zhou, Wenjun Zeng 0001, Xiaofan Zhang 0002, Xiaohe Wu, Xiao-Jun Wu 0001, Xinmei Tian 0001, Yan Li 0014, Yan Lu 0001, Yee Wei Law, Yi Wu 0001, Yiannis Demiris, Yicai Yang, Yifan Jiao, Yuhong Li, Yunhua Zhang, Yuxuan Sun, Zheng Zhang 0022, Zheng Zhu, Zhenhua Feng 0001, Zhihui Wang 0001, and Zhiqun He

Published
2018
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122. Pedagogical Principle Based E-learning Exploration: A Case of Construction Mediation Training

Author

Qu, Yingying, Yu, Zhiwen, Cong, Hao, Hao, Tianyong, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Huang, Tien-Chi, editor, Lau, Rynson, editor, Huang, Yueh-Min, editor, Spaniol, Marc, editor, and Yuen, Chun-Hung, editor

Published
2017
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127. Targeted isolation of prenylated indole alkaloids from the marine-derived fungus Penicillium janthinellum HK1‑6 using molecular networking

Author

Bao-Cong Hao, Yao-Yao Zheng, Zhong-Hui Li, Cai-Juan Zheng, Chang-Yun Wang, and Min Chen

Subjects
Organic Chemistry, Plant Science, Biochemistry, Analytical Chemistry
Abstract

Four prenylated indole alkaloids (1−4) were targeted isolated from the mangrove rhizosphere soil-derived fungus Penicillium janthinellum HK1-6 by using molecular networking strategies. Among them, the planar structure and relative configuration of notoamide X (1) were elucidated by detailed analysis of the spectroscopic data especially the NOESY spectrum for the first time and its absolute configuration was determined by ECD spectrum. Furthermore, curated molecular networks of MS/MS data were generated with GNPS which allowed highlighting six prenylated indole alkaloids (5, 6, 8, 9, 11, 12) that had not previously been identified in this fungus and two (7, 10) that had never been observed in any fungus. The MS/MS fragmentation pathway of these prenylated indole alkaloids was summarized.

Published
2023
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145. PAF-FHE: Low-Cost Accurate Non-Polynomial Operator Polynomial Approximation in Fully Homomorphic Encryption Based ML Inference

Author

Jingtian Dang, Jianming Tong, Anupam Golder, Arijit Raychowdhury, Cong Hao, and Tushar Krishna

Abstract

Machine learning (ML) is getting more pervasive. Wide adoption of ML in healthcare, facial recognition, and blockchain involves private and sensitive data. One of the most promising candidates for inference on encrypted data, termed Fully Homomorphic Encryp-tion (FHE), preserves the privacy of both data and the ML model. However, it slows down plaintext inference by six magnitudes, with a root cause of replacing non-polynomial operators with latency-prohibitive 27-degree Polynomial Approximated Function (PAF). While prior research has investigated low-degree PAFs, naive stochastic gradient descent (SGD) training fails to converge on PAFs with degrees higher than 5, leading to limited accuracy compared to the state-of-the-art 27-degree PAF. Therefore, we propose four training techniques to enable convergence in the post-approximation model using PAFs with an arbitrary degree, including (1) Dynamic Scaling (DS) and Static Scaling (SS) to enable minimal approximation error during approximation, (2) Coefficient Tuning (CT) to obtain a good initial coefficient value for each PAF, (3) Progressive Approximation (PA) to simply the two-variable regression optimization problem into single-variable for fast 1 and easy convergence, and (4) Alternate Training (AT) to retraining the post-replacement PAFs and other linear layers in a decoupled divide-and-conquer manner. A combination of DS/SS, CT, PA, and AT enables the exploration of accuracy-latency space for FHE-domain ReLU replacement. Leveraging the proposed techniques, we propose a systematic approach (PAF-FHE) to enable low-degree PAF to demonstrate the same accuracy as SotA high-degree PAFs. We evaluated PAFs with various degrees on different models and variant datasets, and PAF-FHE consistently enables low-degree PAF to achieve higher accuracy than SotA PAFs. Specifically, for ResNet-18 under the ImageNet-1k dataset, our spotted optimal 12-degree PAF reduces 56% latency compared to the SotA 27-degree PAF with the same post-replacement accuracy (69.4%). While as for VGG-19 under the CiFar-10 dataset, optimal 12-degree PAF achieves even 0.84% higher accuracy with 72% latency saving. Our code is open-sourced at: https://github.com/TorchFHE/PAF-FHE

Published
2023
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