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1,100 results on '"ZHANG Jingjie"'

1. Chemical constituents of Pteris ensiformis from Guizhou

Author

XIAO Li, SHEN Linyan, ZHANG Jingjie, HE Kang, YE Jianghai, ZHAO Chenliang, ZHANG Qilong, and ZOU Juan

Subjects
pteris ensiformis, chemical constituent, structural identification, anti-tumor activity, anticoagulant activity, Botany, QK1-989
Abstract

To study the chemical constituents of Pteris ensiformis, silica gel, gel, MCI, C18 and other column chromatography were used for separation and purification, and their structures were identified by 1H-NMR, 13C-NMR, MS, IR and othe spectral data; the anti-tumor and anti-coagulation activities of some monomers were screened by MTS, APTT, PT and TT. The results were as follows: (1) A total of 15 compounds were isolated from P. ensiformis, the compounds were 2-hydroxy-acetylpyrrole (1), N-(3-carboxypropyl)-2-acetylpyrrole (2), 3-hydroxy-2-methylpyridine (3), N-methylhydroxylamine (4), pterosin S 13-O-glucoside (5), obtupterosin C (6), ent-11α-hydroxy-15-oxokauran-19-oic acid (7), ent-11α-hydroxy-15-oxokaur-16-en-19-oic acid (8), β-sitosterol (9), ent-11α-hydroxy-15-oxokaur-16-en-19-oic acid-O-glucopyranoside (10), 5, 5′-dibutoxy-2, 2′-bifuran (11), 5, 5′-di (2-ethyl-hexyloxy)-2, 2′-bifuran (12), (-)-loliolide (13), succinic acid (14), fumaric acid (15). Compound 1 is a new natural product of pyrrole alkaloids. Compounds 1-7, 10-15 were isolated from P. ensiformis for the first time, and compounds 1, 3, 4 were isolated from Pteris for the first time. (2) The results of activity test showed that compounds 1, 2, 3, 5, 6 and 10 inhibited the growth of tumor cells HL-60, A549, SMMC-7721, MDA-MB-231 and SW480 in vitro at a concentration of 40 μmol·L-1, At the concentration of 2.0 mmol·L-1, compounds 1, 2, 3 and 6 shortened APTT and compounds 1, 5 and 6 prolonged PT. The study enriches the chemical constituents of P. ensiformis from Guizhou and provides a material basis for the development of anti-tumor drugs.

Published
2024
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4. MolCAP: Molecular Chemical reActivity pretraining and prompted-finetuning enhanced molecular representation learning

Author

Wang, Yu, Zhang, JingJie, Jin, Junru, and Wei, Leyi

Subjects
Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Molecular representation learning (MRL) is a fundamental task for drug discovery. However, previous deep-learning (DL) methods focus excessively on learning robust inner-molecular representations by mask-dominated pretraining framework, neglecting abundant chemical reactivity molecular relationships that have been demonstrated as the determining factor for various molecular property prediction tasks. Here, we present MolCAP to promote MRL, a graph pretraining Transformer based on chemical reactivity (IMR) knowledge with prompted finetuning. Results show that MolCAP outperforms comparative methods based on traditional molecular pretraining framework, in 13 publicly available molecular datasets across a diversity of biomedical tasks. Prompted by MolCAP, even basic graph neural networks are capable of achieving surprising performance that outperforms previous models, indicating the promising prospect of applying reactivity information for MRL. In addition, manual designed molecular templets are potential to uncover the dataset bias. All in all, we expect our MolCAP to gain more chemical meaningful insights for the entire process of drug discovery.

Published
2023

5. Finite Element Simulation of Cutting Temperature Distribution in Coated Tools During Turning Processes

Author

Zhang, Jingjie, Fan, Guanghui, Zhang, Liwei, Fan, Lili, Zhang, Guoqing, Meng, Xiangfei, Qi, Yu, Li, Guangchen, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Wang, Bing, editor, Hu, Zuojin, editor, Jiang, Xianwei, editor, and Zhang, Yu-Dong, editor

Published
2024
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10. Locally anisotropic covariance functions on the sphere

Author

Cao, Jian, Zhang, Jingjie, Sun, Zhuoer, and Katzfuss, Matthias

Subjects
Statistics - Methodology
Abstract

Rapid developments in satellite remote-sensing technology have enabled the collection of geospatial data on a global scale, hence increasing the need for covariance functions that can capture spatial dependence on spherical domains. We propose a general method of constructing nonstationary, locally anisotropic covariance functions on the sphere based on covariance functions in R^3. We also provide theorems that specify the conditions under which the resulting correlation function is isotropic or axially symmetric. For large datasets on the sphere commonly seen in modern applications, the Vecchia approximation is used to achieve higher scalability on statistical inference. The importance of flexible covariance structures is demonstrated numerically using simulated data and a precipitation dataset.

Published
2022

11. Walsh Diffusions as Time Changed Multi-parameter Processes

Author

Bayraktar, Erhan, Zhang, Jingjie, and Zhang, Xin

Subjects
Mathematics - Probability, 60J60, 60G60
Abstract

Inspired by allocation strategies in multi-armed bandit model, we propose a pathwise construction of Walsh diffusions. For any infinitesimal generator on a star shaped graph, there exists a unique time change associated with a multi-parameter process such that the time change of this multi-parameter process is the desired diffusion. The time change has an interpretation of time allocation of the process on each edge, and it can be derived explicitly from a family of equations., Comment: Typos corrected and some lemmata whose are added for additional clarification

Published
2022

41. Conditional Automated Channel Pruning for Deep Neural Networks

Author

Liu, Yixin, Guo, Yong, Liu, Zichang, Liu, Haohua, Zhang, Jingjie, Chen, Zejun, Liu, Jing, and Chen, Jian

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Model compression aims to reduce the redundancy of deep networks to obtain compact models. Recently, channel pruning has become one of the predominant compression methods to deploy deep models on resource-constrained devices. Most channel pruning methods often use a fixed compression rate for all the layers of the model, which, however, may not be optimal. To address this issue, given a target compression rate for the whole model, one can search for the optimal compression rate for each layer. Nevertheless, these methods perform channel pruning for a specific target compression rate. When we consider multiple compression rates, they have to repeat the channel pruning process multiple times, which is very inefficient yet unnecessary. To address this issue, we propose a Conditional Automated Channel Pruning(CACP) method to obtain the compressed models with different compression rates through single channel pruning process. To this end, we develop a conditional model that takes an arbitrary compression rate as input and outputs the corresponding compressed model. In the experiments, the resultant models with different compression rates consistently outperform the models compressed by existing methods with a channel pruning process for each target compression rate.

Published
2020
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42. Minimum and maximum conductance of a thin film layer bridged interface: the role of anharmonicity and layer thickness

Author

Zhang, Jingjie, Rastgarkafshgarkolaei, Rouzbeh, Polanco, Carlos A., Le, Nam Q., Esfarjani, Keivan, Norris, Pamela M., and Ghosh, Avik W.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Computational Physics
Abstract

We study the role of anharmonicity at interfaces with an added intermediate layer designed to facilitate interfacial phonon transport. Our results demonstrate that while in the harmonic limit the bridge may lower the conductance due to fewer available channels, anharmonicity can strongly enhance the thermal conductance of the bridged structure due to added inelastic channels. Moreover, we show that the effect of anharmonicity on the conductance can be tuned by varying temperature or the bridge layer thickness, as both parameters change the total rate of occurrence of phonon-phonon scattering processes. Additionally, we show that the additive rule of thermal resistances(Ohms law) is valid for bridge layer thickness quite shorter than the average bulk MFP, beyond the regime it would be expected to fail.

Published
2019

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