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1. Identification and Analysis of Neutrophil Extracellular Trap-Related Genes in Osteoarthritis by Bioinformatics and Experimental Verification

Author

Luan T, Yang X, Kuang G, Wang T, He J, Liu Z, Gong X, Wan J, and Li K

Subjects
neutrophil extracellular traps, osteoarthritis, immune infiltration, tlr7, machine learning algorithms, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract

Tiankuo Luan,1,* Xian Yang,2,* Ge Kuang,1 Ting Wang,3 Jiaming He,1 Zhibo Liu,3 Xia Gong,1 Jingyuan Wan,2 Ke Li4 1Department of Anatomy, Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of Pharmacology, Chongqing Medical University, Chongqing, People’s Republic of China; 3Department of Orthopedics, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 4Department of Orthopedics, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jingyuan Wan; Ke Li, Email jywan@cqmu.edu.cn; chrisli8929@live.cnBackground: Osteoarthritis (OA) is a common joint disease with long-term pain and dysfunction that negatively affects the quality of life of patients. Neutrophil extracellular traps (NETs), consisting of DNA, proteins and cytoplasm, are released by neutrophils and play an important role in a variety of diseases. However, the relationship between OA and NETs is unclear.Methods: In our study, we used bioinformatics to explore the relationship between OA and NETs and the potential biological markers. GSE55235, GSE55457, GSE117999 and GSE98918 were downloaded from the Gene Expression Omnibus (GEO) database for subsequent analysis.After differential analysis of OA expression matrices, intersection with NET-related genes (NRGs) was taken to identify Differentially expressed NRGs (DE-NRGs) in OA processes. Evaluation of immune cell infiltration by ssGSEA and CIBERSORT algorithm. The GSVA method was used to analyze the activity changes of Neutrophils pathway, Neutrophil degranulation and Neutrophil granule constituents pathway.Results: Based on RandomForest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) learning algorithms, five core genes (CRISPLD2, IL1B, SLC25A37, MMP9, and TLR7) were identified to construct an OA-related nomogram model for predicting OA progression. ROC curve results for these genes validated the nomogram’s reliability. Correlation analysis, functional enrichment, and drug predictions were performed for the core genes. TLR7 emerged as a key focus due to its high importance ranking in RF and SVM-RFE analyses. Gene Set Enrichment Analysis (GSEA) revealed a strong association between TLR7 and the Neutrophil extracellular trap pathway. Expression of core genes was demonstrated in mice OA models and human OA samples. TLR7 expression in ATDC5 cell line was significantly higher than control after TNFα induction, along with increased IL6 and MMP13.Conclusion: TLR7 may be related to NETs and affects OA.Keywords: neutrophil extracellular traps, osteoarthritis, immune infiltration, TLR7, machine learning algorithms

Published
2023

2. Detection of promoter hypermethylation of the CpG island of E-cadherin in gastric cardiac adenocarcinoma

Author

Guo W, Dong Z, Guo Y, Kuang G, Yang Z, and Chen Z

Subjects
E-cadherin, methylation, gastric cardiac adenocarcinoma, Medicine
Abstract

Abstract Aim Abnormal hypermethylation of CpG islands associated with tumor suppressor genes can lead to transcriptional silencing in neoplasia. The aim of this study was to investigate the promoter methylation and expression of E-cadherin gene in gastric cardiac adenocarcinoma (GCA). Methods A nested MSP approach, immunohistochemistry method and RT-PCR were used respectively to examine the methylation status of the 5' CpG island of E-cadherin, its protein expression and mRNA expression in tumors and corresponding normal tissues. Results E-cadherin was methylated in 63 of 92 (68.5%) tumor specimens, which was significantly higher than that in corresponding normal tissues (P < 0.001). Methylation frequencies of stage III and IV tumor tissues was significantly higher than that in stage I and II tumor tissues (P = 0.01). Methylation status of poor differentiation group was significantly higher than moderate and poor-moderate differentiation groups (P < 0.01). By immunostaining 51 of 92 tumor tisssues demonstrated heterogeneous, positive immunostaining of tumor tissues (44.6%), significantly different from matched normal tissues (P < 0.001). Positive immunostaining of stage III and IV tumor tissues was significantly lower than stage I and II tumor tissues (P < 0.01). Poor differentiation group was also significantly lower than moderate and poor-moderate differentiation groups (P < 0.05). 80 percent of tumor tissues with E-cadherin gene methylated showed inactivated mRNA expression. Conclusions High methylation status of the 5' CpG island of E-cadherin gene may be one of the mechanisms in the development of gastric cardiac adenocarcinoma.

Published
2009
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3. Design of a 42 T Resistive Magnet at the CHMFL

Author

Fang, Z., Li, J., Qian, X. X., Wang, Z. J., Su, J., Zhou, Y., Zhang, Y., Chen, S. Y., and Kuang, G. L.

Abstract

The High Magnetic Field Laboratory of the Chinese Academy of Sciences (CHMFL) is now upgrading its power supply system and cooling system, so further improvement of the magnetic field of water-cooled magnets will become possible. The design of a new 32-mm bore resistive magnet, which will generate 42 T with 30+ MW, has been completed. The magnet consists of four elongated-hole coils and two circular-hole coils, and all the coils are electrically connected in series. The design of this magnet follows the developed new technologies for the hybrid insert, which generated 34.22 T in 11 T superconducting background field. Details of the coil design and structural and mechanical design are presented.

Published
2024
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4. Test Operations of the Upgraded Hybrid Magnet at CHMFL

Author

Fang, Z., Chen, W. G, Huang, P. C, Chen, Zhiyou, Ding, H. W, Zhao, H., Qian, X. X, Jiang, S. L, Zhang, Y., and Kuang, G. L

Abstract

High Magnetic Field Laboratory of Chinese Academy of Sciences (CHMFL) has upgraded the 40 T hybrid magnet built in October 2016. The latest test operation of the upgraded CHMFL hybrid magnet was completed in August 2022, and the maximum central field arrived at 45.22 T in the center of a 32 mm clear bore, which is the highest steady magnetic field by user magnet in the world. The upgraded hybrid magnet consists of a Florida-Bitter resistive magnet producing 34.22 T central field and a superconducting magnet producing 11 T central field and providing a room-temperature bore of 800 mm. The enhanced performance of the upgraded CHMFL hybrid magnet is mainly attributed to the new design of the resistive insert coils, and the outer superconducting magnet still generated steadily a central field of 11 T without change. This article mainly describes the commissioning and test operating results of the upgraded CHMFL hybrid magnet.

Published
2024
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7. Informative feature disentanglement for unsupervised domain adaptation

Author

Deng, W. (Wanxia), Zhao, L. (Lingjun), Liao, Q. (Qing), Guo, D. (Deke), Kuang, G. (Gangyao), Hu, D. (Dewen), Pietikäinen, M. (Matti), Liu, L. (Li), Deng, W. (Wanxia), Zhao, L. (Lingjun), Liao, Q. (Qing), Guo, D. (Deke), Kuang, G. (Gangyao), Hu, D. (Dewen), Pietikäinen, M. (Matti), and Liu, L. (Li)

Abstract

Unsupervised Domain Adaptation (UDA) aims at learning a classifier for an unlabeled target domain by transferring knowledge from a labeled source domain with a related but different distribution. The strategy of aligning the two domains in latent feature space via metric discrepancy or adversarial learning has achieved considerable progress. However, these existing approaches mainly focus on adapting the entire image and ignore the bottleneck that occurs when forced adaptation of uninformative domain-specific variations undermines the effectiveness of learned features. To address this problem, we propose a novel component called Informative Feature Disentanglement (IFD), which is equipped with the adversarial network or the metric discrepancy model, respectively. Accordingly, the new network architectures, named IFDAN and IFDMN, enable informative feature refinement before the adaptation. The proposed IFD is designed to disentangle informative features from the uninformative domain-specific variations, which are produced by a Variational Autoencoder (VAE) with lateral connections from the encoder to the decoder. We cooperatively apply the IFD to conduct supervised disentanglement for the source domain and unsupervised disentanglement for the target domain. In this way, informative features are disentangled from the domain-specific details before the adaptation. Extensive experimental results on three gold-standard domain adaptation datasets, e.g., Office31, Office-Home and VisDA-C, demonstrate the effectiveness of the proposed IFDAN and IFDMN models for UDA.

Published
2022

8. Domain knowledge powered two-stream deep network for few-shot SAR vehicle recognition

Author

Zhang, L. (Linbin), Leng, X. (Xiangguang), Feng, S. (Sijia), Ma, X. (Xiaojie), Ji, K. (Kefeng), Kuang, G. (Gangyao), Liu, L. (Li), Zhang, L. (Linbin), Leng, X. (Xiangguang), Feng, S. (Sijia), Ma, X. (Xiaojie), Ji, K. (Kefeng), Kuang, G. (Gangyao), and Liu, L. (Li)

Abstract

Synthetic aperture radar (SAR) target recognition faces the challenge that there are very little labeled data. Although few-shot learning methods are developed to extract more information from a small amount of labeled data to avoid overfitting problems, recent few-shot or limited-data SAR target recognition algorithms overlook the unique SAR imaging mechanism. Domain knowledge-powered two-stream deep network (DKTS-N) is proposed in this study, which incorporates SAR domain knowledge related to the azimuth angle, the amplitude, and the phase data of vehicles, making it a pioneering work in few-shot SAR vehicle recognition. The two-stream deep network, extracting the features of the entire image and image patches, is proposed for more effective use of the SAR domain knowledge. To measure the structural information distance between the global and local features of vehicles, the deep Earth mover’s distance is improved to cope with the features from a two-stream deep network. Considering the sensitivity of the azimuth angle in SAR vehicle recognition, the nearest neighbor classifier replaces the structured fully connected layer for K -shot classification. All experiments are conducted under the configuration that the SARSIM and the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset work as a source and target task, respectively. Our proposed DKTS-N achieved 49.26% and 96.15% under ten-way one-shot and ten-way 25-shot, whose labeled samples are randomly selected from the training set. In standard operating condition (SOC) as well as three extended operating conditions (EOCs), DKTS-N demonstrated overwhelming advantages in accuracy and time consumption compared with other few-shot learning methods in K -shot recognition tasks.

Published
2022

9. Importance-aware information bottleneck learning paradigm for lip reading

Author

Sheng, C. (Changchong), Liu, L. (Li), Deng, W. (Wanxia), Bai, L. (Liang), Liu, Z. (Zhong), Lao, S. (Songyang), Kuang, G. (Gangyao), Pietikäinen, M. (Matti), Sheng, C. (Changchong), Liu, L. (Li), Deng, W. (Wanxia), Bai, L. (Liang), Liu, Z. (Zhong), Lao, S. (Songyang), Kuang, G. (Gangyao), and Pietikäinen, M. (Matti)

Abstract

Lip reading is the task of decoding text from speakers’ mouth movements. Numerous deep learning-based methods have been proposed to address this task. However, these existing deep lip reading models suffer from poor generalization due to overfitting the training data. To resolve this issue, we present a novel learning paradigm that aims to improve the interpretability and generalization of lip reading models. In specific, a Variational Temporal Mask (VTM) module is customized to automatically analyze the importance of frame-level features. Furthermore, the prediction consistency constraints of global information and local temporal important features are introduced to strengthen the model generalization. We evaluate the novel learning paradigm with multiple lip reading baseline models on the LRW and LRW-1000 datasets. Experiments show that the proposed framework significantly improves the generalization performance and interpretability of lip reading models.

Published
2022

11. SAR Target Recognition Based on Model Transfer and Hinge Loss with Limited Data

Author

Q. H. (Qishan He), Zhao, L. (Lingjun), Kuang, G. (Gangyao), and Liu, L. (Li)

Subjects
SAR-ATR, Generative adversial network, Transfer learning
Abstract

Convolutional neural networks have made great achievements in field of optical image classification during recent years. However, for Synthetic Aperture Radar automatic target recognition (SAR-ATR) tasks, the performance of deep learning networks is always degraded by the insufficient size of SAR images, which cause both severe over-fitting and low-capacity feature extraction model. On the other hand, models with high feature representation ability usually lose anti-overfitting capability to a certain extent, while enhancing the network’s robustness leads to degradation in feature extraction capability. To balance above both problems, a network with model transfer using the GAN-WP and non-greedy loss is introduced in this paper. Firstly, inspired by the Support Vector Machine’s mechanism, multi-hinge loss is used during training stage. Then, instead of directly training a deep neural network with the insufficient labeled SAR dataset, we pretrain the feature extraction network by an improved GAN, called Wasserstein GAN with gradient penalty and transfer the pre-trained layers to an all-convolutional network based on the fine-tune technique. Furthermore, experimental results on the MSTAR dataset illustrate the effectiveness of the proposed new method, which additional shows the classification accuracy can be improved more largely than other method in the case of sparse training dataset.

Published
2021

12. Transferable discriminative feature mining for unsupervised domain adaptation

Author

Zhao, L. (Lingjun), Deng, W. (Wanxia), Kuang, G. (Gangyao), Hu, D. (Dewen), Liu, L. (Li), Zhao, L. (Lingjun), Deng, W. (Wanxia), Kuang, G. (Gangyao), Hu, D. (Dewen), and Liu, L. (Li)

Abstract

Unsupervised Domain Adaptation (UDA) aims to seek an effective model for unlabeled target domain by leveraging knowledge from a labeled source domain with a related but different distribution. Many existing approaches ignore the underlying discriminative features of the target data and the discrepancy of conditional distributions. To address these two issues simultaneously, the paper presents a Transferable Discriminative Feature Mining (TDFM) approach for UDA, which can naturally unify the mining of domain-invariant discriminative features and the alignment of class-wise features into one single framework. To be specific, to achieve the domain-invariant discriminative features, TDFM jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data, and discriminative clustering of unlabeled target data. It then conducts the class-wise alignment by decreasing intra-class variations and increasing inter-class differences across domains, encouraging the emergence of transferable discriminative features. When combined, these two procedures are mutually beneficial. Comprehensive experiments verify that TDFM can obtain remarkable margins over state-of-the-art domain adaptation methods.

Published
2021

13. Deep ladder-suppression network for unsupervised domain adaptation

Author

Deng, W. (Wanxia), Zhao, L. (Lingjun), Kuang, G. (Gangyao), Hu, D. (Dewen), Pietikäinen, M. (Matti), Liu, L. (Li), Deng, W. (Wanxia), Zhao, L. (Lingjun), Kuang, G. (Gangyao), Hu, D. (Dewen), Pietikäinen, M. (Matti), and Liu, L. (Li)

Abstract

Unsupervised domain adaptation (UDA) aims at learning a classifier for an unlabeled target domain by transferring knowledge from a labeled source domain with a related but different distribution. Most existing approaches learn domain-invariant features by adapting the entire information of the images. However, forcing adaptation of domain-specific variations undermines the effectiveness of the learned features. To address this problem, we propose a novel, yet elegant module, called the deep ladder-suppression network (DLSN), which is designed to better learn the cross-domain shared content by suppressing domain-specific variations. Our proposed DLSN is an autoencoder with lateral connections from the encoder to the decoder. By this design, the domain-specific details, which are only necessary for reconstructing the unlabeled target data, are directly fed to the decoder to complete the reconstruction task, relieving the pressure of learning domain-specific variations at the later layers of the shared encoder. As a result, DLSN allows the shared encoder to focus on learning cross-domain shared content and ignores the domain-specific variations. Notably, the proposed DLSN can be used as a standard module to be integrated with various existing UDA frameworks to further boost performance. Without whistles and bells, extensive experimental results on four gold-standard domain adaptation datasets, for example: 1) Digits; 2) Office31; 3) Office-Home; and 4) VisDA-C, demonstrate that the proposed DLSN can consistently and significantly improve the performance of various popular UDA frameworks.

Published
2021

14. Informative class-conditioned feature alignment for unsupervised domain adaptation

Author

Deng, W. (Wanxia), Cui, Y. (Yawen), Liu, Z. (Zhen), Kuang, G. (Gangyao), Hu, D. (Dewen), Pietikäinen, M. (Matti), Liu, L. (Li), Deng, W. (Wanxia), Cui, Y. (Yawen), Liu, Z. (Zhen), Kuang, G. (Gangyao), Hu, D. (Dewen), Pietikäinen, M. (Matti), and Liu, L. (Li)

Abstract

The goal of unsupervised domain adaptation is to learn a task classifier that performs well for the unlabeled target domain by borrowing rich knowledge from a well-labeled source domain. Although remarkable breakthroughs have been achieved in learning transferable representation across domains, two bottlenecks remain to be further explored. First, many existing approaches focus primarily on the adaptation of the entire image, ignoring the limitation that not all features are transferable and informative for the object classification task. Second, the features of the two domains are typically aligned without considering the class labels; this can lead the resulting representations to be domain-invariant but non-discriminative to the category. To overcome the two issues, we present a novel Informative Class-Conditioned Feature Alignment (IC2FA) approach for UDA, which utilizes a twofold method: informative feature disentanglement and class-conditioned feature alignment, designed to address the above two challenges, respectively. More specifically, to surmount the first drawback, we cooperatively disentangle the two domains to obtain informative transferable features; here, Variational Information Bottleneck (VIB) is employed to encourage the learning of task-related semantic representations and suppress task-unrelated information. With regard to the second bottleneck, we optimize a new metric, termed Conditional Sliced Wasserstein Distance (CSWD), which explicitly estimates the intra-class discrepancy and the inter-class margin. The intra-class and inter-class CSWDs are minimized and maximized, respectively, to yield the domain-invariant discriminative features. IC2FA equips class-conditioned feature alignment with informative feature disentanglement and causes the two procedures to work cooperatively, which facilitates informative discriminative features adaptation. Extensive experimental results on three domain adaptation datasets confirm the superiority of

Published
2021

15. Joint clustering and discriminative feature alignment for unsupervised domain adaptation

Author

Deng, W. (Wanxia), Liao, Q. (Qing), Zhao, L. (Lingjun), Guo, D. (Deke), Kuang, G. (Gangyao), Hu, D. (Dewen), Liu, L. (Li), Deng, W. (Wanxia), Liao, Q. (Qing), Zhao, L. (Lingjun), Guo, D. (Deke), Kuang, G. (Gangyao), Hu, D. (Dewen), and Liu, L. (Li)

Abstract

Unsupervised Domain Adaptation (UDA) aims to learn a classifier for the unlabeled target domain by leveraging knowledge from a labeled source domain with a different but related distribution. Many existing approaches typically learn a domain-invariant representation space by directly matching the marginal distributions of the two domains. However, they ignore exploring the underlying discriminative features of the target data and align the cross-domain discriminative features, which may lead to suboptimal performance. To tackle these two issues simultaneously, this paper presents a Joint Clustering and Discriminative Feature Alignment (JCDFA) approach for UDA, which is capable of naturally unifying the mining of discriminative features and the alignment of class-discriminative features into one single framework. Specifically, in order to mine the intrinsic discriminative information of the unlabeled target data, JCDFA jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data, and discriminative clustering of unlabeled target data, where the classification of the source domain can guide the clustering learning of the target domain to locate the object category. We then conduct the cross-domain discriminative feature alignment by separately optimizing two new metrics: 1) an extended supervised contrastive learning, i.e., semi-supervised contrastive learning 2) an extended Maximum Mean Discrepancy (MMD), i.e., conditional MMD, explicitly minimizing the intra-class dispersion and maximizing the inter-class compactness. When these two procedures, i.e., discriminative features mining and alignment are integrated into one framework, they tend to benefit from each other to enhance the final performance from a cooperative learning perspective. Experiments are conducted on four real-world benchmarks ( e.g., Office-31, ImageCLEF-DA, Office-Home and VisDA-C). All the results demonstrate that our JCDFA can obtain rem

Published
2021

16. Deep ladder reconstruction-classification network for unsupervised domain adaptation

Author

Deng, W. (Wanxia), Su, Z. (Zhuo), Qiu, Q. (Qiang), Zhao, L. (Lingjun), Kuang, G. (Gangyao), Pietikäinen, M. (Matti), Xiao, H. (Huaxin), Liu, L. (Li), Deng, W. (Wanxia), Su, Z. (Zhuo), Qiu, Q. (Qiang), Zhao, L. (Lingjun), Kuang, G. (Gangyao), Pietikäinen, M. (Matti), Xiao, H. (Huaxin), and Liu, L. (Li)

Abstract

Unsupervised Domain Adaptation aims to learn a classifier for an unlabeled target domain by transferring knowledge from a labeled source domain. Most existing approaches learn domain-invariant features by adapting the entire information of each image. However, forcing adaptation of domain-specific components can undermine the effectiveness of learned features. We propose a novel architecture called Deep Ladder Reconstruction-Classification Network (DLaReC) which is designed to learn cross-domain shared contents by suppressing domain-specific variations. The DLaReC adopts an encoder with cross-domain sharing and a target-domain reconstruction decoder. The encoder and decoder are connected with residual shortcuts at each intermediate layer. By this means, the domain-specific components are directly fed to the decoder for reconstruction, relieving the pressure to learn domain-specific variations at later layers of the shared encoder. Therefore, DLaReC allows the encoder to focus on learning cross-domain shared representations and ignore domain-specific variations. DLaReC is implemented by jointly learning three tasks: supervised classification of the source domain, unsupervised reconstruction of the target domain and cross-domain shared representation adaptation. Extensive experiments on Digit, Office31, ImageCLEF-DA and Office-Home datasets demonstrate the DLaReC outperforms state-of-the-art methods on the whole. The average accuracy on the Digit datasets, for instance, is improved from 95.6% to 96.9%. In addition, the result on Amazon → Webcam obtains significant improvement, i.e., from 91.1% to 94.7%.

Published
2021

19. A conceptual design of model coil for the 40-T hybrid magnet superconducting outsert

Author

Tan, Y., Chen, W., Pan, Y., Wang, F., Chen, Z., Zhu, J., and Kuang, G.

Subjects
Electrical conduits -- Composition, Electrical conduits -- Usage, Niobium -- Electric properties, Niobium -- Magnetic properties, Superconducting magnets -- Design and construction, Tin alloys -- Electric properties, Tin alloys -- Magnetic properties, Business, Electronics, Electronics and electrical industries
Published
2009

20. A class imbalance loss for imbalanced object recognition

Author

Zhang, L. (Linbin), Zhang, C. (Caiguang), Quan, S. (Sinong), Xiao, H. (Huaxin), Kuang, G. (Gangyao), Liu, L. (Li), Zhang, L. (Linbin), Zhang, C. (Caiguang), Quan, S. (Sinong), Xiao, H. (Huaxin), Kuang, G. (Gangyao), and Liu, L. (Li)

Abstract

The class imbalance problem exists widely in vision data. In these imbalanced datasets, the majority classes dominate the loss and influence the gradient. Hence, these datasets have a significantly negative impact on the performance of many state-of-the-art methods. In this article, we propose a class imbalance loss (CI loss) to handle this problem. To distinguish imbalanced datasets in accordance with the extent of imbalance, we also define an imbalance degree that works as a decision index factor in the CI loss. Because the minority classes with fewer samples probably lose chances in descending the gradient in the training process, CI loss is introduced to make these minority classes descend further than the majority classes. In view of the imbalanced distribution of data in few-shot learning, a method for generating an imbalanced few-shot learning dataset is presented in this article. We conducted a large number of experiments in the MiniImageNet dataset, which showed the effectiveness of an algorithm for model-agnostic metalearning for rapid adaptation with CI loss. In the problem of detecting 15 ship categories, our loss function is transplanted to a rotational region convolutional neural network detection method and a cascade network architecture and achieves higher mean average precision than focal loss and cross-entropy loss. In addition, the Mixed National Institute of Standards and Technology dataset and the Moving and Stationary Target Acquisition and Recognition dataset are sampled to imbalance datasets to verify the effectiveness of CI loss.

Published
2020

23. Research Progress of EPR Spectrometer Under High Frequency and High Field

Author

KONG Lingwen, KUANG Guangli, and WU Xiangyang

Subjects
electron paramagnetic resonance (epr), epr spectrometer, review, high frequency and high field, Electricity and magnetism, QC501-766
Abstract

Electron paramagnetic resonance (EPR) is a measurement method to research the microscopic information of magnetic materials. Because early EPR studies were limited by magnetic field strength and microwave frequency, some microscopic information of materials could not be clearly displayed. In recent years, with the development of high magnetic field technology and microwave technology, continuous wave electron paramagnetic resonance (cw-EPR) spectrometer and pulsed electron paramagnetic resonance (pulsed EPR) spectrometer have been fully used under high frequency and high field, and the sensitivity, spectral resolution and other performance indicators of the spectrometer have also been improved. This paper mainly introduces the principle and structure of EPR spectrometer under high frequency and high field, its development history and research status both domestically and internationally, and the latest application in related fields.

Published
2023
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24. The promoting effect of modified Dioscorea pills on vascular remodeling in chronic cerebral hypoperfusion via the Ang/Tie signaling pathway

Author

Kuang Guiying, Shu Zhigang, Zhu Chunli, Li Hongbing, and Zhang Cheng

Subjects
modified dioscorea pills, chronic cerebral hypoperfusion, ang/tie signaling pathway, microvessel density, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

The objective of this study was to investigate the effect of modified Dioscorea pills (MDP) on microcirculatory remodeling in the hippocampus of rats with chronic cerebral hypoperfusion (CCH) through the angiopoietin (Ang)/tyrosine kinase receptor tyrosine kinase with immunoglobulin-like and EGF-like domains (Ang receptor) 2 (Tie-2) signaling pathways, which may underlie the cognitive improvement observed in CCH rats.

Published
2023
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28. Effect of progressive substitution of Bi3+ by La3+ on the structural, magnetic, and transport properties of Bi0.6Ca0.4MnO3.

Author

Zhang, R. R., Kuang, G. L., Yin, L. H., and Sun, Y. P.

Subjects
*MANGANESE oxides, *PEROVSKITE, *ELECTRONICS, *MAGNETORESISTANCE, *PHASE separation method (Engineering)
Abstract

Structural, magnetic, and electronic transport properties of Bi0.6-xLaxCa0.4MnO3 (0≤x≤0.6) manganites have been studied systematically. The parent compound exhibits robust charge ordered antiferrromagnetic (COAFM) phase with TCO∼293 K and AFM Néel temperature TN∼149 K. The sample with x=0.1 show a M(T) curve of 'arch bridge' form between 142 and 253 K, which is attributed to the AFM peak embedded in the broad transition of the CO. For the middle doping samples (0.2≤x≤0.4), although the CO peaks disappear as a result of the suppression of the long range CO state, AFM superexchange coupling has been strengthened with the increasing of La doping level. The Bi0.1La0.5Ca0.4MnO3 sample shows interesting phenomena such as magnetization steps and magnetic memory effect. The resistivity of the samples exhibits insulating behavior in our measured temperature range for x≤0.4. While both x=0.5 and x=0.6 compounds exhibit metal-insulator transition. Furthermore, large low field magnetoresistance (MR) and hysteresis appears only in the MR(H) curve of x=0.5 sample. These results are discussed in terms of the charge localization caused by the lattice distortion due to the character of the 6s2 lone pair of Bi3+ ions. [ABSTRACT FROM AUTHOR]

Published
2010
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29. Effect of progressive substitution of Bi3+ by La3+ on the structural, magnetic, and transport properties of Bi0.6Ca0.4MnO3.

Author

Zhang, R. R., Kuang, G. L., Yin, L. H., and Sun, Y. P.

Subjects
MANGANESE oxides, PEROVSKITE, ELECTRONICS, MAGNETORESISTANCE, PHASE separation method (Engineering)
Abstract

Structural, magnetic, and electronic transport properties of Bi0.6-xLaxCa0.4MnO3 (0≤x≤0.6) manganites have been studied systematically. The parent compound exhibits robust charge ordered antiferrromagnetic (COAFM) phase with TCO∼293 K and AFM Néel temperature TN∼149 K. The sample with x=0.1 show a M(T) curve of 'arch bridge' form between 142 and 253 K, which is attributed to the AFM peak embedded in the broad transition of the CO. For the middle doping samples (0.2≤x≤0.4), although the CO peaks disappear as a result of the suppression of the long range CO state, AFM superexchange coupling has been strengthened with the increasing of La doping level. The Bi0.1La0.5Ca0.4MnO3 sample shows interesting phenomena such as magnetization steps and magnetic memory effect. The resistivity of the samples exhibits insulating behavior in our measured temperature range for x≤0.4. While both x=0.5 and x=0.6 compounds exhibit metal-insulator transition. Furthermore, large low field magnetoresistance (MR) and hysteresis appears only in the MR(H) curve of x=0.5 sample. These results are discussed in terms of the charge localization caused by the lattice distortion due to the character of the 6s2 lone pair of Bi3+ ions. [ABSTRACT FROM AUTHOR]

Published
2010
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32. Exosome-Derived miRNAs as Potential Biomarkers for Prostate Bone Metastasis

Author

Lu Z, Hou J, Li X, Zhou J, Luo B, Liang S, Lo RK, Wong TM, and Kuang GM

Subjects
prostate cancer, bone metastasis, exosomes, bioinformatics analysis, mirnas, Medicine (General), R5-920
Abstract

Zhenquan Lu,1,&ast; Jian Hou,1,&ast; Xiao Li,2 Jun Zhou,2 Bingfeng Luo,1 Songwu Liang,1 Richard K Lo,1 Tak Man Wong,2 Guan-Ming Kuang2 1Department of Urology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong Province, People’s Republic of China; 2Department of Orthopaedics and Traumatology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Guan-Ming Kuang, Email kuanggm@hku-szh.orgAbstract: Purpose: The purpose of this study was to identify the potential exosome-derived microRNAs (miRNAs) related to prostate cancer (Pca) bone metastasis.Methods: Two datasets were collected. One dataset was from the authors’ institute, for which two groups of 10 patients each were designed: in the first one, the patients had early-stage localised Pca without bone metastasis, and in the other, the patients presented with Pca with bone metastasis. Then, the miRNA expression profiles of the blood exosomes were obtained and analysed. The other dataset was a public dataset of the miRNA expression transcriptome (GSE26964), which was downloaded from Gene Expression Omnibus (GEO). The results of both datasets were jointly analysed and the most bone-metastatic-related differentially expressed miRNAs (diff-miRNAs) were identified and further validated. Finally, a series of bioinformatics analyses were performed and the relationship between target genes of the diff-miRNAs and the pathogenesis and progression of bone metastasis of Pca were studied.Results: From the authors’ dataset, in all, 313 diff-miRNAs were identified, of which 205 were up-regulated while 108 were down-regulated. From the GSE26964 dataset, 107 diff-miRNAs were found, of which 44 were up-regulated and 63 were down-regulated. Taking the intersection of the results of both datasets, four diff-miRNAs were identified: hsa-miR-125a-3p, hsa-miR-330-3p, hsa-miR-339-5p and hsa-miR-613. In all, 94 target genes of the four diff-miRNAs were predicted. After considering the intersection of the results from the GSE32269 dataset, we obtained 25 target genes. Although either positive or negative correlations were found among the diff-miRNAs with some of the target genes, there is a lack of evidence on how such correlations regulate the development and promotion of Pca bone metastasis.Conclusion: Hsa-miR-125a-3p, hsa-miR-330-3p, hsa-miR-339-5p and hsa-miR-613 are potential biomarkers for Pca bone metastasis.Keywords: prostate cancer, bone metastasis, exosomes, bioinformatics analysis, miRNAs

Published
2022

33. A Non-contact Apparatus for Measuring Longitudinal Critical Current of REBCO Tapes Using the Magnetic Circuit Method.

Author

Jiang, D. H., Tan, Y. F., Chen, Z. Y., Chen, W. G., Kuang, G. L., and Zou, G. H.

Subjects
CRITICAL current measurement, FINITE element method, PROPORTIONALITY (Ethics), MAGNETIC flux, SEMICONDUCTOR devices
Abstract

This paper details the design and construction of a non-contact apparatus that can measure the longitudinal critical current of REBCO tapes based on magnetic circuit method. A 3D finite element model was proposed to study the relation between Ic (critical current of the REBCO tape) and BH (magnetic field at the Hall sensor). The proportionality coefficient of Ic and BH was obtained by using the said model. Several experiments on the REBCO tapes were conducted to determine the repeatability, reliability, and accuracy of the apparatus. Four parameters (average value, minimum value, the range, and the coefficient of critical current variation (COV)) were used to determine the repeatability and accuracy of the apparatus. It was observed that transmission speed affects test results because of relaxation of the magnetic flux. Another tape was tested at different transmission speeds to study the relation between BH and decay time. From the results, a principle formula was established to explain the relation between BH and decay time, and it was shown that the test results also take the same form as the principle formula. [ABSTRACT FROM AUTHOR]

Published
2019
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35. Study on Multi-source Data Fusion Framework Based on Graph

Author

KUANG Guang-sheng, GUO Yan, YU Xiao-ming, LIU Yue, CHENG Xue-qi

Subjects
fusion representation, multi-source, graph fusion, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

When analyzing various data in a given task,most of current researches only analyze single-source data and lack me-thods applied to multi-source data.But now data are becoming more abundant,therefore,this paper proposes a multi-source data fusion framework for fusing data from multiple network platforms.The data of the same platform contains text and various attri-butes,and there are also great differences in content and form among data of different platforms.Most existing network information mining methods only use part of the data in the same platform for analysis,and even ignore the interaction between the data of different platforms.Therefore,this paper proposes a data fusion framework,which can not only use more features of the same platform to improve the performance of a single platform,but also fuse the data features of different platforms to complement each other,thereby improving the performance of multiple platforms.This paper uses the task of event classification,and the abundant features effectively improve the F1 value,which verifies the effectiveness of the proposed multi-source data framework.

Published
2021
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37. Inhibition effects of acridone on the growth of breast cancer cells in vivo.

Author

XIA, Y.-F., CHU, H.-J., KUANG, G.-F., JIANG, G.-J., and CHE, Y.-C.

Abstract

OBJECTIVE: To investigate the anti-tumor effect of acridone against breast cancer in vivo and provide a therapeutic agent for treatment of breast cancer. MATERIALS AND METHODS: The nude mice xenografted tumor model was established by MCF-7 cells. The mice were randomly divided into four groups. The mice in each group (n=6) were intraperitoneally injected with 0.1 mg/kg saline (low-dose), 0.5 mg/kg (middle-dose) and 1.0 mg/kg (high-dose) of acridone for 21 days, respectively. At the end of the animal experiment, the weight of tumors was recorded to calculate the tumor inhibition rate. The serum hormone levels in peripheral blood were determined using ELISA. Hematoxylin and eosin (HE) staining was used to analyze the histopathological changes. The expression of ABCG2 protein and mRNA were determined by Western blot and RT-PCR, respectively. RESULTS: The inhibition rates of tumor growth in the high-dose, middle-dose, and low-dose groups were 29.18%, 17.21%, and 4.27%, respectively. Compared with control and lowdose group, the tumors growth rate in high-dose and middle-dose groups were decreased significantly. Histologically, the tumors were inhibited in the growth rate, the tissue structure was broken. Estrogen in all groups with acridone treatment decreased, the progesterone in high-dose and middle-dose groups increased remarkably. The expression of ABCG2 protein and ABCG2 mRNA decreased after treatment with acridone. CONCLUSIONS: We showed that acridone could induce cell apoptosis, inhibited ABCG2 (ATP-binding cassette sub-family G member 2) protein and adjusted hormone level. The results suggested that acridone could serve as a chemotherapeutic agent for treatment of breast cancer in vivo. [ABSTRACT FROM AUTHOR]

Published
2018

39. Statistical Modeling Methods of Single-channel Complex-valued SAR Images for Ship Detection

Author

LENG Xiangguang, JI Kefeng, XIONG Boli, and KUANG Gangyao

Subjects
single-channel sar image, ship detection, complex-valued information, statistical modeling, Electricity and magnetism, QC501-766
Abstract

Synthetic Aperture Radar (SAR), which features rich imaging modes, wide coverage, and high resolution, is an effective technique for long-term, dynamic, and large-scale monitoring of the ocean. Under the assumption of fully developed speckle, traditional ship detection methods in single-channel SAR images focus mainly on amplitude information. Since conventional assumptions are not strictly true in high-resolution situations, this prevents the full investigation of phase or complex-valued information in single-channel SAR images. In this paper, with a focus on ship detection applications, we categories the methods used in the statistical modeling of single-channel complexvalued SAR images as amplitude-, phase-, or complex-valued-based. After providing a brief overview of amplitude statistical modeling methods, we focus on phase and complex-valued statistical modeling methods of single-channel SAR images, describing their modeling processes and parameter estimation methods. We then present the results of our recent ship detection research based on complex-valued statistical information in single-channel SAR images and make suggestions regarding future research.

Published
2020
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40. FTACMT study protocol: a multicentre, double-blind, randomised, placebo-controlled trial of faecal microbiota transplantation for autism spectrum disorder

Author

Li Ling, Wei Wei, Li Lin, Li Ke, Ye Chen, Li Ning, Wang Yu, Zhang Xin, Yang Rong, Zhang Xueying, Cui Jiaqu, Chen Qiyi, Qin Huanlong, Du Yasong, Zhao Xiaoxin, Lu Jubao, Lv Xiaoqiong, Ma Chunlian, Chen Shidong, Kuang Guifang, Zhao Dongmei, Fang Shuanfeng, Zhang Xujing, Yang Binrang, Wang Yanxia, Yuan Song, Zhou Xiang, Zhang Beihua, Jiang Lin, Ji Hong, Yan Yinmei, Yan Peihua, Huang Linsheng, Zhang Shaoyi, Ma Chenhuan, Miao Yun, Ling Wenqi, Geng Bojing, Feng Xueying, Li Huilin, Liu Liyan, Wu Weijia, Li Dexin, and Jiang Qianru

Subjects
Medicine
Published
2022
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41. Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network

Author

Nuobei Xie, Kuang Gong, Ning Guo, Zhixing Qin, Zhifang Wu, Huafeng Liu, and Quanzheng Li

Subjects
Dynamic PET, Parametric imaging, Direct reconstruction, Convolutional neural network, Temporal non-local, Low dose, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Parametric imaging based on dynamic positron emission tomography (PET) has wide applications in neurology. Compared to indirect methods, direct reconstruction methods, which reconstruct parametric images directly from the raw PET data, have superior image quality due to better noise modeling and richer information extracted from the PET raw data. For low-dose scenarios, the advantages of direct methods are more obvious. However, the wide adoption of direct reconstruction is inevitably impeded by the excessive computational demand and deficiency of the accessible raw data. In addition, motion modeling inside dynamic PET image reconstruction raises more computational challenges for direct reconstruction methods. In this work, we focused on the 18F-FDG Patlak model, and proposed a data-driven approach which can estimate the motion corrected full-dose direct Patlak images from the dynamic PET reconstruction series, based on a proposed novel temporal non-local convolutional neural network. During network training, direct reconstruction with motion correction based on full-dose dynamic PET sinograms was performed to obtain the training labels. The reconstructed full-dose /low-dose dynamic PET images were supplied as the network input. In addition, a temporal non-local block based on the dynamic PET images was proposed to better recover the structural information and reduce the image noise. During testing, the proposed network can directly output high-quality Patlak parametric images from the full-dose /low-dose dynamic PET images in seconds. Experiments based on 15 full-dose and 15 low-dose 18F-FDG brain datasets were conducted and analyzed to validate the feasibility of the proposed framework. Results show that the proposed framework can generate better image quality than reference methods.

Published
2021
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