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Article ; Online: Neutrophil CC1 plays a protective role in orthotopic liver transplantation: views from the perspective of natural compounds.

Fang, Yafei / Hu, Qinghua

2023 Volume 21, Issue 4, Page(s) 241–242

MeSH term(s)	Humans ; Liver Transplantation ; Neutrophils ; Liver ; Reperfusion Injury
Language	English
Publishing date	2023-04-20
Publishing country	China
Document type	Journal Article
ZDB-ID	2192577-X
ISSN	1875-5364 ; 2095-6975 ; 1672-3651
ISSN (online)	1875-5364
ISSN	2095-6975 ; 1672-3651
DOI	10.1016/S1875-5364(23)60432-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A hybrid denoising approach for PPG signals utilizing variational mode decomposition and improved wavelet thresholding.

Hu, Qinghua / Li, Min / Jiang, Linwen / Liu, Mei

Technology and health care : official journal of the European Society for Engineering and Medicine

2024

Abstract: Background: Photoplethysmography (PPG) signals are sensitive to motion-induced interference, leading to the emergence of motion artifacts (MA) and baseline drift, which significantly affect the accuracy of PPG measurements.: Objective: The objective ... ...

Abstract	Background: Photoplethysmography (PPG) signals are sensitive to motion-induced interference, leading to the emergence of motion artifacts (MA) and baseline drift, which significantly affect the accuracy of PPG measurements. Objective: The objective of our study is to effectively eliminate baseline drift and high-frequency noise from PPG signals, ensuring that the signal's critical frequency components remain within the range of 1 ∼ 10 Hz. Methods: This paper introduces a novel hybrid denoising method for PPG signals, integrating Variational Mode Decomposition (VMD) with an improved wavelet threshold function. The method initially employs VMD to decompose PPG signals into a set of narrowband intrinsic mode function (IMF) components, effectively removing low-frequency baseline drift. Subsequently, an improved wavelet thresholding algorithm is applied to eliminate high-frequency noise, resulting in denoised PPG signals. The effectiveness of the denoising method was rigorously assessed through a comprehensive validation process. It was tested on real-world PPG measurements, PPG signals generated by the Fluke ProSim™ 8 Vital Signs Simulator with synthesized noise, and extended to the MIMIC-III waveform database. Results: The application of the improved threshold function let to a substantial 11.47% increase in signal-to-noise ratio (SNR) and an impressive 26.75% reduction in root mean square error (RMSE) compared to the soft threshold function. Furthermore, the hybrid denoising method improved SNR by 15.54% and reduced RMSE by 37.43% compared to the improved threshold function. Conclusion: This study proposes an effective PPG denoising algorithm based on VMD and an improved wavelet threshold function, capable of simultaneously eliminating low-frequency baseline drift and high-frequency noise in PPG signals while faithfully preserving their morphological characteristics. This advancement establishes the foundation for time-domain feature extraction and model development in the domain of PPG signal analysis.
Language	English
Publishing date	2024-02-20
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1159961-3
ISSN	1878-7401 ; 0928-7329
ISSN (online)	1878-7401
ISSN	0928-7329
DOI	10.3233/THC-231996
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 3846: Show issues

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Article ; Online: Layer-Specific Knowledge Distillation for Class Incremental Semantic Segmentation.

Wang, Qilong / Wu, Yiwen / Yang, Liu / Zuo, Wangmeng / Hu, Qinghua

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

2024 Volume 33, Page(s) 1977–1989

Abstract: Recently, class incremental semantic segmentation (CISS) towards the practical open-world setting has attracted increasing research interest, which is mainly challenged by the well-known issue of catastrophic forgetting. Particularly, knowledge ... ...

Abstract	Recently, class incremental semantic segmentation (CISS) towards the practical open-world setting has attracted increasing research interest, which is mainly challenged by the well-known issue of catastrophic forgetting. Particularly, knowledge distillation (KD) techniques have been widely studied to alleviate catastrophic forgetting. Despite the promising performance, existing KD-based methods generally use the same distillation schemes for different intermediate layers to transfer old knowledge, while employing manually tuned and fixed trade-off weights to control the effect of KD. These KD-based methods take no consideration of feature characteristics from different intermediate layers, limiting the effectiveness of KD for CISS. In this paper, we propose a layer-specific knowledge distillation (LSKD) method to assign appropriate knowledge schemes and weights for various intermediate layers by considering feature characteristics, aiming to further explore the potential of KD in improving the performance of CISS. Specifically, we present a mask-guided distillation (MD) to alleviate the background shift on semantic features, which performs distillation by masking the features affected by the background. Furthermore, a mask-guided context distillation (MCD) is presented to explore global context information lying in high-level semantic features. Based on them, our LSKD assigns different distillation schemes according to feature characteristics. To adjust the effect of layer-specific distillation adaptively, LSKD introduces a regularized gradient equilibrium method to learn dynamic trade-off weights. Additionally, our LSKD makes an attempt to simultaneously learn distillation schemes and trade-off weights of different layers by developing a bi-level optimization method. Extensive experiments on widely used Pascal VOC 12 and ADE20K show our LSKD clearly outperforms its counterparts while achieving state-of-the-art results.
Language	English
Publishing date	2024-03-18
Publishing country	United States
Document type	Journal Article
ISSN	1941-0042
ISSN (online)	1941-0042
DOI	10.1109/TIP.2024.3372448
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Multi-Task Credible Pseudo-Label Learning for Semi-Supervised Crowd Counting.

Zhu, Pengfei / Li, Jingqing / Cao, Bing / Hu, Qinghua

IEEE transactions on neural networks and learning systems

2023 Volume PP

Abstract: As a widely used semi-supervised learning strategy, self-training generates pseudo-labels to alleviate the labor-intensive and time-consuming annotation problems in crowd counting while boosting the model performance with limited labeled data and massive ...

Abstract	As a widely used semi-supervised learning strategy, self-training generates pseudo-labels to alleviate the labor-intensive and time-consuming annotation problems in crowd counting while boosting the model performance with limited labeled data and massive unlabeled data. However, the noise in the pseudo-labels of the density maps greatly hinders the performance of semi-supervised crowd counting. Although auxiliary tasks, e.g., binary segmentation, are utilized to help improve the feature representation learning ability, they are isolated from the main task, i.e., density map regression and the multi-task relationships are totally ignored. To address the above issues, we develop a multi-task credible pseudo-label learning (MTCP) framework for crowd counting, consisting of three multi-task branches, i.e., density regression as the main task, and binary segmentation and confidence prediction as the auxiliary tasks. Multi-task learning is conducted on the labeled data by sharing the same feature extractor for all three tasks and taking multi-task relations into account. To reduce epistemic uncertainty, the labeled data are further expanded, by trimming the labeled data according to the predicted confidence map for low-confidence regions, which can be regarded as an effective data augmentation strategy. For unlabeled data, compared with the existing works that only use the pseudo-labels of binary segmentation, we generate credible pseudo-labels of density maps directly, which can reduce the noise in pseudo-labels and therefore decrease aleatoric uncertainty. Extensive comparisons on four crowd-counting datasets demonstrate the superiority of our proposed model over the competing methods. The code is available at: https://github.com/ljq2000/MTCP.
Language	English
Publishing date	2023-02-08
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2023.3241211
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Exploring Diverse Representations for Open Set Recognition

Wang, Yu / Mu, Junxian / Zhu, Pengfei / Hu, Qinghua

2024

Abstract: Open set recognition (OSR) requires the model to classify samples that belong to closed sets while rejecting unknown samples during test. Currently, generative models often perform better than discriminative models in OSR, but recent studies show that ... ...

Abstract	Open set recognition (OSR) requires the model to classify samples that belong to closed sets while rejecting unknown samples during test. Currently, generative models often perform better than discriminative models in OSR, but recent studies show that generative models may be computationally infeasible or unstable on complex tasks. In this paper, we provide insights into OSR and find that learning supplementary representations can theoretically reduce the open space risk. Based on the analysis, we propose a new model, namely Multi-Expert Diverse Attention Fusion (MEDAF), that learns diverse representations in a discriminative way. MEDAF consists of multiple experts that are learned with an attention diversity regularization term to ensure the attention maps are mutually different. The logits learned by each expert are adaptively fused and used to identify the unknowns through the score function. We show that the differences in attention maps can lead to diverse representations so that the fused representations can well handle the open space. Extensive experiments are conducted on standard and OSR large-scale benchmarks. Results show that the proposed discriminative method can outperform existing generative models by up to 9.5% on AUROC and achieve new state-of-the-art performance with little computational cost. Our method can also seamlessly integrate existing classification models. Code is available at https://github.com/Vanixxz/MEDAF. Comment: 9 pages, 4 figures. Accepted to AAAI 2024
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2024-01-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Calcium sensing receptor: A promising therapeutic target in pulmonary hypertension.

Zhang, Jiwei / Li, Qinli / Liao, Pu / Xiao, Rui / Zhu, Liping / Hu, Qinghua

Life sciences

2024 Volume 340, Page(s) 122472

Abstract: Pulmonary hypertension (PH) is characterized by elevation of pulmonary arterial pressure and pulmonary vascular resistance. The increased pulmonary arterial pressure and pulmonary vascular resistance due to sustained pulmonary vasoconstriction and ... ...

Abstract	Pulmonary hypertension (PH) is characterized by elevation of pulmonary arterial pressure and pulmonary vascular resistance. The increased pulmonary arterial pressure and pulmonary vascular resistance due to sustained pulmonary vasoconstriction and pulmonary vascular remodeling can lead to right heart failure and eventual death. A rise in intracellular Ca
MeSH term(s)	Animals ; Humans ; Calcium ; Cell Proliferation ; Cells, Cultured ; Hypertension, Pulmonary/therapy ; Hypoxia ; Lung ; Myocytes, Smooth Muscle ; Pulmonary Artery ; Receptors, Calcium-Sensing/metabolism ; Vascular Remodeling
Chemical Substances	Calcium (SY7Q814VUP) ; Receptors, Calcium-Sensing
Language	English
Publishing date	2024-01-28
Publishing country	Netherlands
Document type	Journal Article ; Review
ZDB-ID	3378-9
ISSN	1879-0631 ; 0024-3205
ISSN (online)	1879-0631
ISSN	0024-3205
DOI	10.1016/j.lfs.2024.122472
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Article ; Online: Autoencoder in Autoencoder Networks.

Zhang, Changqing / Geng, Yu / Han, Zongbo / Liu, Yeqing / Fu, Huazhu / Hu, Qinghua

IEEE transactions on neural networks and learning systems

2024 Volume 35, Issue 2, Page(s) 2263–2275

Abstract: Modeling complex correlations on multiview data is still challenging, especially for high-dimensional features with possible noise. To address this issue, we propose a novel unsupervised multiview representation learning (UMRL) algorithm, termed ... ...

Abstract	Modeling complex correlations on multiview data is still challenging, especially for high-dimensional features with possible noise. To address this issue, we propose a novel unsupervised multiview representation learning (UMRL) algorithm, termed autoencoder in autoencoder networks (AE2-Nets). The proposed framework effectively encodes information from high-dimensional heterogeneous data into a compact and informative representation with the proposed bidirectional encoding strategy. Specifically, the proposed AE2-Nets conduct encoding in two directions: the inner-AE-networks extract view-specific intrinsic information (forward encoding), while the outer-AE-networks integrate this view-specific intrinsic information from different views into a latent representation (backward encoding). For the nested architecture, we further provide a probabilistic explanation and extension from hierarchical variational autoencoder. The forward-backward strategy flexibly addresses high-dimensional (noisy) features within each view and encodes complementarity across multiple views in a unified framework. Extensive results on benchmark datasets validate the advantages compared to the state-of-the-art algorithms.
Language	English
Publishing date	2024-02-05
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2022.3189239
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Genome-wide investigation of

Zhang, Yu / Hu, Qinghua / Zhai, Xinyu / Tu, Zhonghua / Wang, Jing / Wang, Minxin / Li, Huogen

AoB PLANTS

2024 Volume 16, Issue 2, Page(s) plae008

Abstract: The plant-specific SQUAMOSA promoter-binding protein-like (SPL) transcription factors play a pivotal role in various developmental processes, including leaf morphogenesis and vegetative to reproductive phase transition. ...

Abstract	The plant-specific SQUAMOSA promoter-binding protein-like (SPL) transcription factors play a pivotal role in various developmental processes, including leaf morphogenesis and vegetative to reproductive phase transition.
Language	English
Publishing date	2024-02-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2555823-7
ISSN	2041-2851
ISSN	2041-2851
DOI	10.1093/aobpla/plae008
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The identification and functional characterization of the LcMCT gene from Liriodendron chinense reveals its potenatial role in carotenoids biosyanthesis.

Hu, Qinghua / Zhang, Yu / Tu, Zhonghua / Wen, Shaoying / Wang, Jing / Wang, Minxin / Li, Huogen

Gene

2024 Volume 902, Page(s) 148180

Abstract: Terpenoids are not only important component of plant floral scent, but also indispensable elements in the formation of floral color. The petals of Liriodendron chinense are rich in tetraterpene carotenoids and release large amounts of volatile ... ...

Abstract	Terpenoids are not only important component of plant floral scent, but also indispensable elements in the formation of floral color. The petals of Liriodendron chinense are rich in tetraterpene carotenoids and release large amounts of volatile monoterpene and sesquiterpene compounds during full blooming stage. However, the mechanism of terpenoid synthesis is not clear in L. chinense. In this study, we identified a LcMCT gene and characterized its potential function in carotenoids biosynthesis. A total of 2947 up-regulated differentially expressed genes (DEGs) were discerned from the transcriptomic data of L. chinense petals, with a significant enrichment of DEGs related to plant hormone signal transduction and terpenoid backbone biosynthesis. After comprehensive analysis on these DEGs, the LcMCT gene was selected for subsequent function characterization. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) results showed that LcMCT was expressed at the highest level in the petals during full blooming stage, suggesting a possible role in carotenoids biosynthesis and volatile terpenoid biosynthesis. Subcellular localization showed that the LcMCT protein was localized in the chloroplast. Overexpression of LcMCT in Arabidopsis thaliana affected the expression levels of MEP pathway genes. Moreover, the MCT enzyme activity and carotenoids contents in transgenic A. thaliana were increased by 69.27% and 15.57%, respectively. These results suggest that LcMCT promotes the biosynthesis of terpenoid precursors via the MEP pathway. Our work lays a foundation for exploring the mechanism of terpenoid synthesis in L. chinense.
MeSH term(s)	Carotenoids ; Liriodendron/genetics ; Liriodendron/metabolism ; Terpenes/metabolism ; Transcriptome ; Gene Expression Profiling ; Gene Expression Regulation, Plant
Chemical Substances	Carotenoids (36-88-4) ; Terpenes
Language	English
Publishing date	2024-01-20
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	391792-7
ISSN	1879-0038 ; 0378-1119
ISSN (online)	1879-0038
ISSN	0378-1119
DOI	10.1016/j.gene.2024.148180
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Zs.A 1357: Show issues

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Article ; Online: Multiview Deep Subspace Clustering Networks.

Zhu, Pengfei / Yao, Xinjie / Wang, Yu / Hui, Binyuan / Du, Dawei / Hu, Qinghua

IEEE transactions on cybernetics

2024 Volume PP

Abstract: Multiview subspace clustering aims to discover the inherent structure of data by fusing multiple views of complementary information. Most existing methods first extract multiple types of handcrafted features and then learn a joint affinity matrix for ... ...

Abstract	Multiview subspace clustering aims to discover the inherent structure of data by fusing multiple views of complementary information. Most existing methods first extract multiple types of handcrafted features and then learn a joint affinity matrix for clustering. The disadvantage of this approach lies in two aspects: 1) multiview relations are not embedded into feature learning and 2) the end-to-end learning manner of deep learning is not suitable for multiview clustering. Even when deep features have been extracted, it is a nontrivial problem to choose a proper backbone for clustering on different datasets. To address these issues, we propose the multiview deep subspace clustering networks (MvDSCNs), which learns a multiview self-representation matrix in an end-to-end manner. The MvDSCN consists of two subnetworks, i.e., a diversity network (Dnet) and a universality network (Unet). A latent space is built using deep convolutional autoencoders, and a self-representation matrix is learned in the latent space using a fully connected layer. Dnet learns view-specific self-representation matrices, whereas Unet learns a common self-representation matrix for all views. To exploit the complementarity of multiview representations, the Hilbert-Schmidt independence criterion (HSIC) is introduced as a diversity regularizer that captures the nonlinear, high-order interview relations. Because different views share the same label space, the self-representation matrices of each view are aligned to the common one by universality regularization. The MvDSCN also unifies multiple backbones to boost clustering performance and avoid the need for model selection. Experiments demonstrate the superiority of the MvDSCN.
Language	English
Publishing date	2024-03-22
Publishing country	United States
Document type	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2024.3372309
Database	MEDical Literature Analysis and Retrieval System OnLINE

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