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Genome-wide identification, characterization and expression analysis of the BMP family associated with beak-like teeth in Oplegnathus

Yuting Ma, Yuting Ma, Yongshuang Xiao, Yongshuang Xiao, Yongshuang Xiao, Zhizhong Xiao, Zhizhong Xiao, Zhizhong Xiao, Zhizhong Xiao, Zhizhong Xiao, Yanduo Wu, Yanduo Wu (+13 others)
2022 Frontiers in Genetics  
For example, it promotes the proliferation, survival, invasion and cancer stem cell properties of hepatocellular carcinoma (HCC) cells (Li et al., 2013) .  ...  reference genome accession codes used for analysis in this study are as follows: O. fasciatus (NCBI accession code: PRJNA393383) and O. punctatus (CNGB accession code: CNP0001488) (Xiao et al., 2019; Li  ... 
doi:10.3389/fgene.2022.938473 pmid:35923711 pmcid:PMC9342863 doaj:e20c5b01cedd4778a44f17f9120d911b fatcat:v3yym2kmlnfbdhuyehkcylomxq

Auxin Response Factor2 (ARF2) and Its Regulated Homeodomain Gene HB33 Mediate Abscisic Acid Response in Arabidopsis

Li Wang, Deping Hua, Junna He, Ying Duan, Zhizhong Chen, Xuhui Hong, Zhizhong Gong, Li-Jia Qu
2011 PLoS Genetics  
Chuanyou Li and Lijia Qu for providing plant materials. Author Contributions Conceived and designed the experiments: LW DH JH ZC ZG. Performed the experiments: LW DH JH YD ZC XH.  ... 
doi:10.1371/journal.pgen.1002172 pmid:21779177 pmcid:PMC3136439 fatcat:44kjqushybcapgw5vzwlrppbva

Learning without Forgetting [article]

Zhizhong Li, Derek Hoiem
2017 arXiv   pre-print
When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task
more » ... ta to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.
arXiv:1606.09282v3 fatcat:gg6qxupkunc7nfxkm2zytzm22y

Biased Estimates of Advantages over Path Ensembles [article]

Lanxin Lei and Zhizhong Li and Dahua Lin
2019 arXiv   pre-print
The estimation of advantage is crucial for a number of reinforcement learning algorithms, as it directly influences the choices of future paths. In this work, we propose a family of estimates based on the order statistics over the path ensemble, which allows one to flexibly drive the learning process, towards or against risks. On top of this formulation, we systematically study the impacts of different methods for estimating advantages. Our findings reveal that biased estimates, when chosen
more » ... opriately, can result in significant benefits. In particular, for the environments with sparse rewards, optimistic estimates would lead to more efficient exploration of the policy space; while for those where individual actions can have critical impacts, conservative estimates are preferable. On various benchmarks, including MuJoCo continuous control, Terrain locomotion, Atari games, and sparse-reward environments, the proposed biased estimation schemes consistently demonstrate improvement over mainstream methods, not only accelerating the learning process but also obtaining substantial performance gains.
arXiv:1909.06851v1 fatcat:z3bngk6oircmlj6covxtidkd7y

The Arabidopsis Nodulin Homeobox Factor AtNDX Interacts with AtRING1A/B and Negatively Regulates Abscisic Acid Signaling

Yujuan Zhu, Xiaoying Hu, Ying Duan, Shaofang Li, Yu Wang, Amin Ur Rehman, Junna He, Jing Zhang, Deping Hua, Li Yang, Li Wang, Zhizhong Chen (+6 others)
2020 The Plant Cell  
ACKNOWLEDGMENTS We thank Zhen Li at the China Agricultural University for performing LC-MS/MS analysis.  ...  DOI 10.1105/tpc.19.00604 ; originally published online January 9, 2020; 2020;32;703-721 Plant Cell Song, Qianwen Sun, Shuhua Yang and Zhizhong Gong Zhang, Deping Hua, Li Yang, Li Wang, Zhizhong Chen, Chuanyou  ...  Li, Baoshan Wang, Chun-Peng Yujuan Zhu, Xiaoying Hu, Ying Duan, Shaofang Li, Yu Wang, Amin Ur Rehman, Junna He, Jing Regulates Abscisic Acid Signaling The Arabidopsis Nodulin Homeobox Factor AtNDX Interacts  ... 
doi:10.1105/tpc.19.00604 pmid:31919300 pmcid:PMC7054043 fatcat:ghqgoinemzbhvnpqhffrupstny

Representation Consolidation for Training Expert Students [article]

Zhizhong Li, Avinash Ravichandran, Charless Fowlkes, Marzia Polito, Rahul Bhotika, Stefano Soatto
2021 arXiv   pre-print
Traditionally, distillation has been used to train a student model to emulate the input/output functionality of a teacher. A more useful goal than emulation, yet under-explored, is for the student to learn feature representations that transfer well to future tasks. However, we observe that standard distillation of task-specific teachers actually *reduces* the transferability of student representations to downstream tasks. We show that a multi-head, multi-task distillation method using an
more » ... ed proxy dataset and a generalist teacher is sufficient to consolidate representations from task-specific teacher(s) and improve downstream performance, outperforming the teacher(s) and the strong baseline of ImageNet pretrained features. Our method can also combine the representational knowledge of multiple teachers trained on one or multiple domains into a single model, whose representation is improved on all teachers' domain(s).
arXiv:2107.08039v1 fatcat:eep5rmzstvdq5ckggtxdiiom2q

ViM: Out-Of-Distribution with Virtual-logit Matching [article]

Haoqi Wang, Zhizhong Li, Litong Feng, Wayne Zhang
2022 arXiv   pre-print
Mathematically, let the i-th logit of x be l i , and then the score is ViM(x) = e α √ x T RR T x C i=1 e li + e α √ x T RR T x . (7) This equation reveals that two factors affect the ViM score: if its  ...  Apply the function t(x) = − ln 1 x − 1 to the ViM score, then we have an equivalent expression α x P ⊥ − ln C i=1 e li . (8) The first term is the virtual logit in Eq. ( 5 ) while the second term is the  ... 
arXiv:2203.10807v1 fatcat:lxobxw4w3faolekxccv4zrvfa4

Disruption of the cellulose synthase gene, AtCesA8/IRX1, enhances drought and osmotic stress tolerance in Arabidopsis

Zhizhong Chen, Xuhui Hong, Hairong Zhang, Youqun Wang, Xia Li, Jian-Kang Zhu, Zhizhong Gong
2005 The Plant Journal  
Two allelic Arabidopsis mutants, leaf wilting 2-1 and leaf wilting 2-2 (lew2-1 and lew2-2 ), were isolated in a screen for plants with altered drought stress responses. The mutants were more tolerant to drought stress as well as to NaCl, mannitol and other osmotic stresses. lew2 mutant plants accumulated more abscisic acid (ABA), proline and soluble sugars than the wild type. The expression of a stress-inducible marker gene RD29A, a proline synthesis-related gene P5CS (pyrroline-5-carboxylate
more » ... nthase) and an ABA synthesis-related gene SDR1 (alcohol dehydrogenase/reductase) was higher in lew2 than in the wild type. Map-based cloning revealed that the lew2 mutants are new alleles of the AtCesA8/IRX1 gene which encodes a subunit of a cellulose synthesis complex. Our results suggest that cellulose synthesis is important for drought and osmotic stress responses including drought induction of gene expression.
doi:10.1111/j.1365-313x.2005.02452.x pmid:15998313 fatcat:ynizqi4cwzgkbe2enme2fla5qe

Regularizing Reasons for Outfit Evaluation with Gradient Penalty [article]

Xingxing Zou, Zhizhong Li, Ke Bai, Dahua Lin, Waikeung Wong
2020 arXiv   pre-print
Li et al. [18] used annotated quality scores to supervise the grading of outfits. Han et al. [11] used Bi-LSTM, and Visileva et al. [33] improved on it by considering type information.  ... 
arXiv:2002.00460v1 fatcat:xtzcotgtebgnng5pu7wcvzsipa

ABA-Mediated ROS in Mitochondria Regulate Root Meristem Activity by Controlling PLETHORA Expression in Arabidopsis

Li Yang, Jing Zhang, Junna He, Yingying Qin, Deping Hua, Ying Duan, Zhizhong Chen, Zhizhong Gong, Hao Yu
2014 PLoS Genetics  
Acknowledgments We thank Jian-Kang Zhu for critically reading the manuscript and Chuanyou Li for providing relative materials. Author Contributions  ... 
doi:10.1371/journal.pgen.1004791 pmid:25522358 pmcid:PMC4270459 fatcat:hw552bdvfbgvnn3hj7vzprfu3u

Degradation of the ABA co-receptor ABI1 by PUB12/13 U-box E3 ligases

Lingyao Kong, Jinkui Cheng, Yujuan Zhu, Yanglin Ding, Jingjing Meng, Zhizhong Chen, Qi Xie, Yan Guo, Jigang Li, Shuhua Yang, Zhizhong Gong
2015 Nature Communications  
Acknowledgements We thank Dr Libo Shan (Texas A&M University) for providing pub12 and pub13 mutant seeds, and Dr Zhen Li (China Agricultural University) for LC-MS/MS analysis.  ... 
doi:10.1038/ncomms9630 pmid:26482222 pmcid:PMC4667695 fatcat:sawztp4265gwppmvlarur2lgxu

Total Synthesis of Cordycepin

Qihuan Li, Ruchun Yang, Zhizhong Ruan, Tao Hu, Haixin Ding, Qiang Xiao
2013 Youji huaxue  
Cordycepin has a large spectrum of biological and pharmaceutical activities, which is beneficial to human health in a number of aspects. In present paper, two total synthetic routes are developed to provide high quality product of cordycepin using D-glucose or D-xylose as the starting materials. The key step of the total synthesis is deoxygenation of 3-OH using Barton-McCombie reaction to afford 3-deoxyribose. Cordycepin is obtained in 8 steps and 7 steps with 37% and 40% overall yield, respectively. Its purity is above 98.5%.
doi:10.6023/cjoc201303009 fatcat:ae57q4dk45fctmenyreuu64wku

Primitive Fitting Based on the Efficient multiBaySAC Algorithm

Zhizhong Kang, Zhen Li, Duccio Rocchini
2015 PLoS ONE  
Although RANSAC is proven to be robust, the original RANSAC algorithm selects hypothesis sets at random, generating numerous iterations and high computational costs because many hypothesis sets are contaminated with outliers. This paper presents a conditional sampling method, multiBaySAC (Bayes SAmple Consensus), that fuses the BaySAC algorithm with candidate model parameters statistical testing for unorganized 3D point clouds to fit multiple primitives. This paper first presents a statistical
more » ... esting algorithm for a candidate model parameter histogram to detect potential primitives. As the detected initial primitives were optimized using a parallel strategy rather than a sequential one, every data point in the multiBaySAC algorithm was assigned to multiple prior inlier probabilities for initial multiple primitives. Each prior inlier probability determined the probability that a point belongs to the corresponding primitive. We then implemented in parallel a conditional sampling method: BaySAC. With each iteration of the hypothesis testing process, hypothesis sets with the highest inlier probabilities were selected and verified for the existence of multiple primitives, revealing the fitting for multiple primitives. Moreover, the updated version of the initial probability was implemented based on a memorable form of Bayes' Theorem, which describes the relationship between prior and posterior probabilities of a data point by determining whether the hypothesis set to which a data point belongs is correct. The proposed approach was tested using real and synthetic point clouds. The results show that the proposed multi-BaySAC algorithm can achieve a high computational efficiency (averaging 34% higher than the efficiency of the sequential RANSAC method) and fitting accuracy (exhibiting good performance in the intersection of two primitives), whereas the sequential RANSAC framework clearly suffers from over-and under-segmentation problems. Future work will aim at further optimizing this strategy through its application to other problems such as multiple point cloud co-registration and multiple image matching.
doi:10.1371/journal.pone.0117341 pmid:25781620 pmcid:PMC4363901 fatcat:mjvbhz4nlzbt7iyatibcimsz2a

Policy Continuation with Hindsight Inverse Dynamics [article]

Hao Sun, Zhizhong Li, Xiaotong Liu, Dahua Lin, Bolei Zhou
2019 arXiv   pre-print
Solving goal-oriented tasks is an important but challenging problem in reinforcement learning (RL). For such tasks, the rewards are often sparse, making it difficult to learn a policy effectively. To tackle this difficulty, we propose a new approach called Policy Continuation with Hindsight Inverse Dynamics (PCHID). This approach learns from Hindsight Inverse Dynamics based on Hindsight Experience Replay, enabling the learning process in a self-imitated manner and thus can be trained with
more » ... ised learning. This work also extends it to multi-step settings with Policy Continuation. The proposed method is general, which can work in isolation or be combined with other on-policy and off-policy algorithms. On two multi-goal tasks GridWorld and FetchReach, PCHID significantly improves the sample efficiency as well as the final performance.
arXiv:1910.14055v2 fatcat:3bzktgmmrvbmjdlug6lfnpft5u

Multi-scale 2D Representation Learning for weakly-supervised moment retrieval [article]

Ding Li, Rui Wu, Yongqiang Tang, Zhizhong Zhang, Wensheng Zhang
2021 arXiv   pre-print
Video moment retrieval aims to search the moment most relevant to a given language query. However, most existing methods in this community often require temporal boundary annotations which are expensive and time-consuming to label. Hence weakly supervised methods have been put forward recently by only using coarse video-level label. Despite effectiveness, these methods usually process moment candidates independently, while ignoring a critical issue that the natural temporal dependencies between
more » ... candidates in different temporal scales. To cope with this issue, we propose a Multi-scale 2D Representation Learning method for weakly supervised video moment retrieval. Specifically, we first construct a two-dimensional map for each temporal scale to capture the temporal dependencies between candidates. Two dimensions in this map indicate the start and end time points of these candidates. Then, we select top-K candidates from each scale-varied map with a learnable convolutional neural network. With a newly designed Moments Evaluation Module, we obtain the alignment scores of the selected candidates. At last, the similarity between captions and language query is served as supervision for further training the candidates' selector. Experiments on two benchmark datasets Charades-STA and ActivityNet Captions demonstrate that our approach achieves superior performance to state-of-the-art results.
arXiv:2111.02741v1 fatcat:fmvmp2k3xvcjlp3d6fahgiqkiq
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