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2,121 results on '"Cao, Fang"'

4. Dual-quartet phosphorescent emission in the open-shell M1Ag13 (M = Pt, Pd) nanoclusters

Author

Cao Fang, Chang Xu, Wei Zhang, Meng Zhou, Dong Tan, Lixia Qian, Daqiao Hu, Shan Jin, and Manzhou Zhu

Subjects
Science
Abstract

Abstract Dual emission (DE) in nanoclusters (NCs) is considerably significant in the research and application of ratiometric sensing, bioimaging, and novel optoelectronic devices. Exploring the DE mechanism in open-shell NCs with doublet or quartet emissions remains challenging because synthesizing open-shell NCs is difficult due to their inherent instability. Here, we synthesize two dual-emissive M1Ag13(PFBT)6(TPP)7 (M = Pt, Pd; PFBT = pentafluorobenzenethiol; TPP = triphenylphosphine) NCs with a 7-electron open-shell configuration to reveal the DE mechanism. Both NCs comprise a crown-like M1Ag11 kernel with Pt or Pd in the center surrounded by five PPh3 ligands and two Ag(SR)3(PPh3) motifs. The combined experimental and theoretical studies revealed the origin of DE in Pt1Ag13 and Pd1Ag13. Specifically, the high-energy visible emission and the low-energy near-infrared emission arise from two distinct quartet excited states: the core-shell charge transfer and core-based states, respectively. Moreover, PFBT ligands are found to play an important role in the existence of DE, as its low-lying π* levels result in energetically accessible core-shell transitions. This novel report on the dual-quartet phosphorescent emission in NCs with an open-shell electronic configuration advances insights into the origin of dual-emissive NCs and promotes their potential application in magnetoluminescence and novel optoelectronic devices.

Published
2024
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5. BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors

Author

Wantlin, Kathryn, Wu, Chenwei, Huang, Shih-Cheng, Banerjee, Oishi, Dadabhoy, Farah, Mehta, Veeral Vipin, Han, Ryan Wonhee, Cao, Fang, Narayan, Raja R., Colak, Errol, Adamson, Adewole, Heacock, Laura, Tison, Geoffrey H., Tamkin, Alex, and Rajpurkar, Pranav

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Medical data poses a daunting challenge for AI algorithms: it exists in many different modalities, experiences frequent distribution shifts, and suffers from a scarcity of examples and labels. Recent advances, including transformers and self-supervised learning, promise a more universal approach that can be applied flexibly across these diverse conditions. To measure and drive progress in this direction, we present BenchMD: a benchmark that tests how well unified, modality-agnostic methods, including architectures and training techniques (e.g. self-supervised learning, ImageNet pretraining),perform on a diverse array of clinically-relevant medical tasks. BenchMD combines 19 publicly available datasets for 7 medical modalities, including 1D sensor data, 2D images, and 3D volumetric scans. Our benchmark reflects real-world data constraints by evaluating methods across a range of dataset sizes, including challenging few-shot settings that incentivize the use of pretraining. Finally, we evaluate performance on out-of-distribution data collected at different hospitals than the training data, representing naturally-occurring distribution shifts that frequently degrade the performance of medical AI models. Our baseline results demonstrate that no unified learning technique achieves strong performance across all modalities, leaving ample room for improvement on the benchmark. Code is released at https://github.com/rajpurkarlab/BenchMD.

Published
2023

8. Deep Learning Compiler Load Balancing Optimization Method for Model Training

Author

WANG Li, GAO Kai, ZHAO Yaqian, LI Rengang, CAO Fang, GUO Zhenhua

Subjects
model training, compiler optimization, load balancing, hierarchical scheduling, automatic pipelining, Electronic computers. Computer science, QA75.5-76.95
Abstract

For computing-intensive artificial intelligence (AI) training tasks, the computational graph is more complex, and data loading, task division of the computational graph, and load balancing of task scheduling have become the key factors affecting the computing performance. This paper proposes three optimization methods to make the task scheduling of model training in deep learning compilers reach the load balance state. Firstly, the load balance between CPU and back-end computing devices is realized by automatically establishing an efficient pipeline for data loading and model training, which improves the overall energy efficiency of the system. Secondly, the layered optimization technology of computational graph is used to realize the load balance of computational graph when the back-end devices are scheduling. Finally, this paper improves the resource utilization of back-end devices by automatically establishing efficient pipeline between layers. Experimental results show that the proposed optimization method achieves the system load balancing in the process of automatically mapping the training tasks to underlying hardware devices. Compared with traditional deep learning frameworks and compilers such as TensorFlow, nGraph, etc., this paper achieves 2%~10% performance improvement in the training of different AI models, and the overall power consumption of the training system can be reduced by more than 10%.

Published
2024
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32. Phase I study of a non-S2P SARS-CoV-2 mRNA vaccine LVRNA009 in Chinese adults

Author

Chen, Gui-Ling, Yu, Xu-Ya, Luo, Li-Ping, Zhang, Fan, Dai, Xia-Hong, Li, Nan, Shen, Zhen-Wei, Wu, Kai-Qi, Lou, Dan-Feng, Peng, Cong-Gao, Jin, Ting-Han, Huang, Yu-Mei, Shao, Xi, Liu, Qi, Jiang, Qi, Guo, Tong, Cao, Fang, Zhu, Jing-Rui, Wu, Xiao-Hong, Pei, Rong-Juan, Deng, Fei, Jiang, Guo-Ping, Li, Yu-Hua, Gao, Hai-Nv, He, Jian-Xing, Zhong-Chen, Peng, Yu-Cai, and Li, Lan-Juan

Published
2023
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39. Extraction and identification of new flavonoid compounds in dandelion Taraxacum mongolicum Hand.-Mazz. with evaluation of antioxidant activities

Author

Rong Wang, Weihua Li, Cao Fang, Xinxin Zheng, Chao Liu, and Qing Huang

Subjects
Medicine, Science
Abstract

Abstract Due to the interest in the potential pharmacological application of dandelion, the chemical constituents and activities of Taraxacum mongolicum Hand.-Mazz were studied. Box–Behnken response surface methodology was employed to optimize the protocol for extraction of flavonoid from dandelion. The molecular structures of different flavonoid compounds were acquired and analyzed by liquid chromatography-mass spectrometry (LC–MS) and nuclear magnetic resonance (NMR) spectroscopy. Several major flavonoid compounds were isolated and purified, namely, hesperetin-5′-O-β-rhamnoglucoside, hesperetin-7-glucuronide, kaempferol-3-glucoside, baicalein, hyperseroside, which were extracted for the first time from dandelion. Hesperetin-5′-O-β-rhamnoglucoside was identified as a new type of flavonoid that had never reported in the literature. This new flavonoid has outstanding antioxidant activity, as shown by its IC50 value (8.72 mg/L) for scavenging DPPH free radicals. The determination of the structure-related antioxidant activities could be interpreted based on DFT calculations. As such, we have not only illustrated the rich flavonoid contents in Taraxacum mongolicum Hand.-Mazz, but also revealed new types of flavonoid compounds in dandelion in terms of structure and antioxidant properties.

Published
2023
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49. Blood–Brain Barrier Mechanisms in Stroke and Trauma

Author

Li, Wenlu, Cao, Fang, Takase, Hajime, Arai, Ken, Lo, Eng H., Lok, Josephine, Barrett, James E., Editor-in-Chief, Flockerzi, Veit, Editorial Board Member, Frohman, Michael A., Editorial Board Member, Geppetti, Pierangelo, Editorial Board Member, Hofmann, Franz B., Editorial Board Member, Kuner, Rohini, Editorial Board Member, Michel, Martin C., Editorial Board Member, Page, Clive P., Editorial Board Member, Wang, KeWei, Editorial Board Member, Cader, Zameel, editor, and Neuhaus, Winfried, editor

Published
2022
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