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37 results on '"Zhang, Hengmin"'

1. Nonconvex ${{L_ {{1/2}}}} $-Regularized Nonlocal Self-similarity Denoiser for Compressive Sensing based CT Reconstruction

Author

Li, Yunyi, Jiang, Yiqiu, Zhang, Hengmin, Liu, Jianxun, Ding, Xiangling, and Gui, Guan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Compressive sensing (CS) based computed tomography (CT) image reconstruction aims at reducing the radiation risk through sparse-view projection data. It is usually challenging to achieve satisfying image quality from incomplete projections. Recently, the nonconvex ${{L_ {{1/2}}}} $-norm has achieved promising performance in sparse recovery, while the applications on imaging are unsatisfactory due to its nonconvexity. In this paper, we develop a ${{L_ {{1/2}}}} $-regularized nonlocal self-similarity (NSS) denoiser for CT reconstruction problem, which integrates low-rank approximation with group sparse coding (GSC) framework. Concretely, we first split the CT reconstruction problem into two subproblems, and then improve the CT image quality furtherly using our ${{L_ {{1/2}}}} $-regularized NSS denoiser. Instead of optimizing the nonconvex problem under the perspective of GSC, we particularly reconstruct CT image via low-rank minimization based on two simple yet essential schemes, which build the equivalent relationship between GSC based denoiser and low-rank minimization. Furtherly, the weighted singular value thresholding (WSVT) operator is utilized to optimize the resulting nonconvex ${{L_ {{1/2}}}} $ minimization problem. Following this, our proposed denoiser is integrated with the CT reconstruction problem by alternating direction method of multipliers (ADMM) framework. Extensive experimental results on typical clinical CT images have demonstrated that our approach can further achieve better performance than popular approaches., Comment: Preprint submitted to Journal of The Franklin Institute. Corresponding Author: yunyili@hnust.edu.cn, guiguan@njupt.edu.cn

Published
2022

3. Learning Semantically Enhanced Feature for Fine-Grained Image Classification

Author

Luo, Wei, Zhang, Hengmin, Li, Jun, and Wei, Xiu-Shen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter. Unlike previous methods that rely on complex part localization modules, our approach learns fine-grained features by enhancing the semantics of sub-features of a global feature. Specifically, we first achieve the sub-feature semantic by arranging feature channels of a CNN into different groups through channel permutation. Meanwhile, to enhance the discriminability of sub-features, the groups are guided to be activated on object parts with strong discriminability by a weighted combination regularization. Our approach is parameter parsimonious and can be easily integrated into the backbone model as a plug-and-play module for end-to-end training with only image-level supervision. Experiments verified the effectiveness of our approach and validated its comparable performance to the state-of-the-art methods. Code is available at https://github.com/cswluo/SEF, Comment: Accepted by IEEE Signal Processing Letters. 5 pages, 4 figures, 4 tables

Published
2020
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4. Learning with Inadequate and Incorrect Supervision

Author

Gong, Chen, Zhang, Hengmin, Yang, Jian, and Tao, Dacheng

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Practically, we are often in the dilemma that the labeled data at hand are inadequate to train a reliable classifier, and more seriously, some of these labeled data may be mistakenly labeled due to the various human factors. Therefore, this paper proposes a novel semi-supervised learning paradigm that can handle both label insufficiency and label inaccuracy. To address label insufficiency, we use a graph to bridge the data points so that the label information can be propagated from the scarce labeled examples to unlabeled examples along the graph edges. To address label inaccuracy, Graph Trend Filtering (GTF) and Smooth Eigenbase Pursuit (SEP) are adopted to filter out the initial noisy labels. GTF penalizes the l_0 norm of label difference between connected examples in the graph and exhibits better local adaptivity than the traditional l_2 norm-based Laplacian smoother. SEP reconstructs the correct labels by emphasizing the leading eigenvectors of Laplacian matrix associated with small eigenvalues, as these eigenvectors reflect real label smoothness and carry rich class separation cues. We term our algorithm as `Semi-supervised learning under Inadequate and Incorrect Supervision' (SIIS). Thorough experimental results on image classification, text categorization, and speech recognition demonstrate that our SIIS is effective in label error correction, leading to superior performance to the state-of-the-art methods in the presence of label noise and label scarcity.

Published
2019

9. Unified Framework for Faster Clustering via Joint Schatten p-Norm Factorization With Optimal Mean

Author

Zhang, Hengmin, Zhao, Jiaoyan, Zhang, Bob, Gong, Chen, Qian, Jianjun, and Yang, Jian

Abstract

To enhance the effectiveness and efficiency of subspace clustering in visual tasks, this work introduces a novel approach that automatically eliminates the optimal mean, which is embedded in the subspace clustering framework of low-rank representation (LRR) methods, along with the computationally factored formulation of Schatten $p$ -norm. By addressing the issues related to meaningful computations involved in some LRR methods and overcoming biased estimation of the low-rank solver, we propose faster nonconvex subspace clustering methods through joint Schatten $p$ -norm factorization with optimal mean (JS $p$ NFOM), forming a unified framework for enhancing performance while reducing time consumption. The proposed approach employs tractable and scalable factor techniques, which effectively address the disadvantages of higher computational complexity, particularly when dealing with large-scale coefficient matrices. The resulting nonconvex minimization problems are reformulated and further iteratively optimized by multivariate weighting algorithms, eliminating the need for singular value decomposition (SVD) computations in the developed iteration procedures. Moreover, each subproblem can be guaranteed to obtain the closed-form solver, respectively. The theoretical analyses of convergence properties and computational complexity further support the applicability of the proposed methods in real-world scenarios. Finally, comprehensive experimental results demonstrate the effectiveness and efficiency of the proposed nonconvex clustering approaches compared to existing state-of-the-art methods on several publicly available databases. The demonstrated improvements highlight the practical significance of our work in subspace clustering tasks for visual data analysis. The source code for the proposed algorithms is publicly accessible at https://github.com/ZhangHengMin/TRANSUFFC.

Published
2024
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14. Proprotein Convertase Subtilisin/Kexin 9 (PCSK9) Promotes Macrophage Activation via LDL Receptor-Independent Mechanisms

Author

Katsuki, Shunsuke, primary, K. Jha, Prabhash, additional, Lupieri, Adrien, additional, Nakano, Toshiaki, additional, Passos, Livia S.A., additional, Rogers, Maximillian A., additional, Becker-Greene, Dakota, additional, Le, Thanh-Dat, additional, Decano, Julius L., additional, Ho Lee, Lang, additional, Guimaraes, Gabriel C., additional, Abdelhamid, Ilyes, additional, Halu, Arda, additional, Muscoloni, Alessandro, additional, V. Cannistraci, Carlo, additional, Higashi, Hideyuki, additional, Zhang, Hengmin, additional, Vromman, Amélie, additional, Libby, Peter, additional, Keith Ozaki, C., additional, Sharma, Amitabh, additional, Singh, Sasha A., additional, Aikawa, Elena, additional, and Aikawa, Masanori, additional

Published
2022
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17. Systems Approach to Discovery of Therapeutic Targets for Vein Graft Disease: PPARα Pivotally Regulates Metabolism, Activation, and Heterogeneity of Macrophages and Lesion Development

Author

Decano, Julius L., primary, Singh, Sasha A., additional, Gasparotto Bueno, Cauê, additional, Ho Lee, Lang, additional, Halu, Arda, additional, Chelvanambi, Sarvesh, additional, Matamalas, Joan T., additional, Zhang, Hengmin, additional, Mlynarchik, Andrew K., additional, Qiao, Jiao, additional, Sharma, Amitabh, additional, Mukai, Shin, additional, Wang, Jianguo, additional, Anderson, Daniel G., additional, Ozaki, C. Keith, additional, Libby, Peter, additional, Aikawa, Elena, additional, and Aikawa, Masanori, additional

Published
2021
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23. Abstract 329: Systems-based Target Discovery Reveals PPARa as a Key Metabolic Regulator for Macrophage Activation in Vein Graft Disease

Author

Decano, Julius, primary, Zhang, Hengmin, additional, Singh, Sasha, additional, Mlynarchik, Andrew, additional, Mojcher, Alexander, additional, Wang, Jianguo, additional, Lee, Lang Ho, additional, Halu, Arda, additional, Higashi, Hideyuki, additional, Mattson, Peter, additional, Qiao, Jiao, additional, Choi, Jung, additional, Creager, Michael, additional, Libby, Peter, additional, Anderson, Daniel G, additional, Ozaki, C. Keith, additional, Aikawa, Elena, additional, and Aikawa, Masanori, additional

Published
2018
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25. Macrophage Notch Ligand Delta-Like 4 Promotes Vein Graft Lesion Development

Author

Koga, Jun-ichiro, primary, Nakano, Toshiaki, additional, Dahlman, James E., additional, Figueiredo, Jose-Luiz, additional, Zhang, Hengmin, additional, Decano, Julius, additional, Khan, Omar F., additional, Niida, Tomiharu, additional, Iwata, Hiroshi, additional, Aster, Jon C., additional, Yagita, Hideo, additional, Anderson, Daniel G., additional, Ozaki, C. Keith, additional, and Aikawa, Masanori, additional

Published
2015
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26. Angiopoietin Like Protein 2 (ANGPTL2) Promotes Adipose Tissue Macrophage and T lymphocyte Accumulation and Leads to Insulin Resistance

Author

Sasaki, Yusuke, primary, Ohta, Masayuki, additional, Desai, Dhruv, additional, Figueiredo, Jose-Luiz, additional, Whelan, Mary C., additional, Sugano, Tomohiro, additional, Yamabi, Masaki, additional, Yano, Wataru, additional, Faits, Tyler, additional, Yabusaki, Katsumi, additional, Zhang, Hengmin, additional, Mlynarchik, Andrew K., additional, Inoue, Keisuke, additional, Mizuno, Ken, additional, and Aikawa, Masanori, additional

Published
2015
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28. Dentin Glycoprotein

Author

Yamakoshi, Yasuo, primary, Hu, Jan C.-C., additional, Fukae, Makoto, additional, Zhang, Hengmin, additional, and Simmer, James P., additional

Published
2005
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31. Activation of the Pro-survival Phosphatidylinositol 3-Kinase/AKT Pathway by Transforming Growth Factor-β1 in Mesenchymal Cells Is Mediated by p38 MAPK-dependent Induction of an Autocrine Growth Factor

Author

Horowitz, Jeffrey C., primary, Lee, Daniel Y., additional, Waghray, Meghna, additional, Keshamouni, Venkateshwar G., additional, Thomas, Peedikayil E., additional, Zhang, Hengmin, additional, Cui, Zongbin, additional, and Thannickal, Victor J., additional

Published
2004
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32. Abstract 13820: Systems Biology of Arteriovenous Fistula to Explore Novel Mechanistic Insights and Therapeutic Targets

Author

Decano, Julius L, Bueno, Caue Gasparotto A, Quieroz Do-Amaral, Gabriela A, Lee, LangHo A, Arda, Halu A, Zhang, Hengmin, Ozaki, C. Keith, Singh, Sasha A, Aikawa, Elena, and Aikawa, Masanori

Abstract

Introduction:Hemodialysis access by arteriovenous fistulas (AVF) remain the primary lifeline for chronic renal failure patients. AVF failure rates, however, are unacceptably high with no effective medical therapy and underlying mechanisms remain unclear. By closely mimicking the clinical AVF creation in mice, we have taken a proteomics-assisted systems approach to target discovery for AVF failure.Methods:We created AVF in Ldlr-/- mice via surgical anastomosis between the distal end of the left external jugular vein (EJV) and the post arteriotomy lateral aspect of the left mid-common carotid artery. Functional fistulas validated by Color Doppler (Vevo2100, Fujifilm-Visualsonics) at 1-week post-op were used for this study. AVF tissues and their paired right non-surgically modified EJVs (normal veins) were harvested at 2, 4, and 6-weeks post-surgery (n=3-4). Each AVF and EJV tissue was divided into 3 equal lengths, and each segment was then processed for histology and LC-MS/MS. Pathway enrichment and network analysis of the proteome provided biological insights on temporal and regional changes during the arterialization of AVF. Putative key regulators of the AVF disease were identified through a multi-step network objects prioritization strategy.Results:As early as 2 weeks after surgery, proteins related to macrophage immune response and cytoskeleton protein regulation increased in the proximal third of the venous segment (to the anastomosis) relative to the middle and distal third segments. At 4 weeks, these increases in predominant proteins moved to the middle third contiguous segment, with ~50% of this proteome remaining elevated in the proximal third while ~50% decline in abundance. Interactome analysis of this spatiotemporal movement of the AVF proteome from proximal to distal portions over time, accompanied by changes in Doppler and histologic features, revealed putative key regulators in the process of AVF failure (e.g., EGF receptor, karyopherins ?1 and ?2, ELAV-like protein 1, FAM21A).Conclusions:We propose that the first ever network analysis of the AVF proteome revealed early pathologic tissue changes in a mouse model and identified potential therapeutic targets for this devastating maladaptive venous disease.

Published
2019
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35. LRR for Subspace Segmentation via Tractable Schatten-$p$ Norm Minimization and Factorization.

Author

Zhang H, Yang J, Shang F, Gong C, and Zhang Z

Abstract

Recently, nuclear norm-based low rank representation (LRR) methods have been popular in several applications, such as subspace segmentation. However, there exist two limitations: one is that nuclear norm as the relaxation of rank function will lead to the suboptimal solution since nuclear norm-based minimization subproblem tends to the over-relaxations of singular value elements and treats each of them equally; the other is that solving LRR problems may cause more time consumption due to involving singular value decomposition of the large scale matrix at each iteration. To overcome both disadvantages, this paper mainly considers two tractable variants of LRR: one is Schatten-p norm minimization-based LRR (i.e., SpNM_LRR) and the other is Schatten-p norm factorization-based LRR (i.e., SpNFLRR) for p=1, 2/3 and 1/2. By introducing two or more auxiliary variables in the constraints, the alternating direction method of multiplier (ADMM) with multiple updating variables can be devised to solve these variants of LRR. Furthermore, both computational complexity and convergence property are given to evaluate nonconvex multiblocks ADMM algorithms. Several experiments finally validate the efficacy and efficiency of our methods on both synthetic data and real world data.

Published
2018
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36. Dentin glycoprotein: the protein in the middle of the dentin sialophosphoprotein chimera.

Author

Yamakoshi Y, Hu JC, Fukae M, Zhang H, and Simmer JP

Subjects
Acetylglucosamine chemistry, Amino Acid Sequence, Amino Acids chemistry, Animals, Asparagine chemistry, Carbohydrate Sequence, Carbohydrates chemistry, Chromatography, Chromatography, High Pressure Liquid, Chromatography, Ion Exchange, Durapatite chemistry, Durapatite pharmacology, Edetic Acid chemistry, Electrophoresis, Gel, Two-Dimensional, Electrophoresis, Polyacrylamide Gel, Glucose chemistry, Guanidine chemistry, Ions, Mannose chemistry, Mass Spectrometry, Metalloendopeptidases chemistry, Molecular Sequence Data, NF-kappa B metabolism, Oligosaccharides chemistry, Oxygen metabolism, Peptide-N4-(N-acetyl-beta-glucosaminyl) Asparagine Amidase chemistry, Phosphoproteins chemistry, Polysaccharides chemistry, Protein Binding, Protein Conformation, Protein Structure, Tertiary, Recombinant Fusion Proteins chemistry, Sequence Homology, Amino Acid, Structure-Activity Relationship, Swine, Time Factors, Tooth embryology, Sialoglycoproteins chemistry, Sialoglycoproteins physiology
Abstract

Dentin sialophosphoprotein (DSPP) is a major secretory product of odontoblasts and is critical for proper dentin formation. DSPP is believed to be processed into only two structural/functional domains: dentin sialoprotein (DSP) and dentin phosphoprotein (DPP). Here we report the isolation and characterization of a third domain of DSPP, designated dentin glycoprotein (DGP). DGP was isolated from a guanidine/EDTA extract of porcine tooth dentin by ion exchange, hydroxyapatite affinity, size exclusion, and RP-HPL chromatography. Endoproteinase lysine C digestion products of DGP were characterized by Edman sequencing and mass spectrometry. The porcine DGP backbone is the 81-amino acid segment of DSPP (Ser392 to Gly472) between the DSP and DPP domains. DGP has four phosphorylated serine residues (Ser453, Ser455, Ser457, and Ser462) and one glycosylated asparagine (Asn397). There are no other post-translational modifications. DGP is a stains-all positive protein with an apparent molecular mass on SDS-PAGE of 19 kDa, which is reduced by glycopeptidase A digestion to 16 kDa. A variety of glycans can be linked to Asn397. All are complex biantennary structures with a common N-linked pentasaccharide core (mannose3-N-acetylglucosamine2), most with a fucosyl residue on the innermost N-acetylglucosamine. The alpha1-3 and alpha1-6 arms are always galactose beta1-4 N-acetylglucosamine beta1-2 mannose, and either or both arms can be unsialidated or monosialidated. The calculated monoisotopic molecular masses of the different glycosylated forms of the DGP phosphoprotein are: unsialidated 10,523 and 10,670, monosialidated 10,815 and 10,961, and disialidated 11,106, and 11,252 Da, with the disialidated forms being the most abundant.

Published
2005
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37. Activation of the pro-survival phosphatidylinositol 3-kinase/AKT pathway by transforming growth factor-beta1 in mesenchymal cells is mediated by p38 MAPK-dependent induction of an autocrine growth factor.

Author

Horowitz JC, Lee DY, Waghray M, Keshamouni VG, Thomas PE, Zhang H, Cui Z, and Thannickal VJ

Subjects
Apoptosis, Blotting, Western, Caspase 3, Caspases metabolism, Cell Division, Cell Survival, Cells, Cultured, Culture Media, Conditioned pharmacology, Cytokines metabolism, DNA, Single-Stranded metabolism, Densitometry, Dose-Response Relationship, Drug, Enzyme Activation, Enzyme Inhibitors pharmacology, Enzyme-Linked Immunosorbent Assay, Fibroblasts metabolism, Growth Substances metabolism, Humans, Imidazoles pharmacology, Lung pathology, Microscopy, Fluorescence, Models, Biological, Mutation, Phenotype, Phosphorylation, Plasmids metabolism, Pyridines pharmacology, Signal Transduction, Time Factors, Transforming Growth Factor beta1, Transforming Growth Factor beta2, p38 Mitogen-Activated Protein Kinases, Mesoderm metabolism, Mitogen-Activated Protein Kinases metabolism, Phosphatidylinositol 3-Kinases metabolism, Transforming Growth Factor beta metabolism
Abstract

Transforming growth factor-beta1 (TGF-beta1) is a multifunctional cytokine involved in differentiation, growth, and survival of mesenchymal cells while inhibiting growth/survival of most other cell types. The mechanism(s) of pro-survival signaling by TGF-beta1 in mesenchymal cells is unclear. In this report, we demonstrate that TGF-beta1 protects against serum deprivation-induced apoptosis of mesenchymal cells isolated from patients with acute lung injury and of normal human fetal lung fibroblasts (IMR-90). TGF-beta receptor(s)-activated signaling in these cells involves rapid activation of the Smad and p38 MAPK pathways within minutes of TGF-beta1 treatment followed by a more delayed activation of the pro-survival phosphatidylinositol 3-kinase-protein kinase B (PKB)/Akt pathway. Pharmacological inhibition of p38 MAPK with SB203580 or expression of a p38 kinase-deficient mutant protein inhibits TGF-beta1-induced PKB/Akt phosphorylation. Conditioned medium from TGF-beta1-treated cells rapidly induces PKB/Akt activation in an SB203580- and suramin-sensitive manner, suggesting p38 MAPK-dependent production of a secreted growth factor that activates this pro-survival pathway by an autocrine/paracrine mechanism. Inhibition of the phosphatidylinositol 3-kinase-PKB/Akt pathway blocks TGF-beta1-induced resistance to apoptosis. These results demonstrate the activation of a novel TGF-beta1-activated pro-survival/anti-apoptotic signaling pathway in mesenchymal cells/fibroblasts that may explain cell-specific actions of TGF-beta1 and provide mechanistic insights into its pro-fibrotic and tumor-promoting effects.

Published
2004
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