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Wang Y, Zhu Y, Luo L, He J. Q-space imaging based on Gaussian radial basis function with Laplace regularization. Magn Reson Med 2024; 92:128-144. [PMID: 38361281 DOI: 10.1002/mrm.30049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/21/2024] [Accepted: 01/24/2024] [Indexed: 02/17/2024]
Abstract
PURPOSE To introduce the diffusion signal characteristics presented by spherical harmonics (SH) basis into the q-space imaging method based on Gaussian radial basis function (GRBF) to robustly reconstruct ensemble average diffusion propagator (EAP) in diffusion MRI (dMRI). METHODS We introduced the Laplacian regularization of the signal into the dMRI imaging method based on GRBF, and derived the relevant indicators of microstructure imaging and the orientation distribution function (ODF) providing fiber bundle direction information based on EAP. In addition, this method is combined with a multi-compartment model to calculate the diameter of fiber bundle axons. The evaluation of the results included qualitative comparisons and quantitative assessments of the signal fitting. RESULTS The results show that the proposed method achieves the more significant accuracy improvement in reconstructing signal. Meanwhile, ODFs estimated by the proposed method show the sharper profiles and less spurious peaks, even under the sparse and noisy conditions. In the 36 sets of axon diameter estimation experiments, 34 and 30 sets of results showed that the proposed method reduced the mean and SD of axon diameter estimates, respectively. Moreover, compared with the current state-of-the-art method, the mean and SD of axon diameter estimated by the proposed method are mostly lower, with 32 and 29 of 36 groups. CONCLUSION The proposed method outperforms the GRBF regarding signal fitting and the estimation of the EAP and ODF with multi-shell sparse samples. Moreover, it shows the potential to recover important features of microstructures with less uncertainty by using proposed method together with multi-compartment models.
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Affiliation(s)
- Yuanjun Wang
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yuemin Zhu
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Lingli Luo
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Jianglin He
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai, China
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Carvajal Rico J, Alaeddini A, Faruqui SHA, Fisher-Hoch SP, Mccormick JB. A Laplacian regularized graph neural network for predictive modeling of multiple chronic conditions. Comput Methods Programs Biomed 2024; 247:108058. [PMID: 38382304 DOI: 10.1016/j.cmpb.2024.108058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 01/25/2024] [Accepted: 02/02/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND AND GOALS One of the biggest difficulties facing healthcare systems today is the prevalence of multiple chronic diseases (MCC). Mortality and the development of new chronic illnesses are more likely in those with MCC. Pre-existing diseases and risk factors specific to the patient have an impact on the complex stochastic process that guides the evolution of MCC. This study's goal is to use a brand-new Graph Neural Network (GNN) model to examine the connections between specific chronic illnesses, patient-level risk factors, and pre-existing conditions. METHODS We propose a graph neural network model to analyze the relationship between five chronic conditions (diabetes, obesity, cognitive impairment, hyperlipidemia, and hypertension). The proposed model adds a graph Laplacian regularization term to the loss function, which aims to improve the parameter learning process and accuracy of the GNN based on the graph structure. For validation, we used historical data from the Cameron County Hispanic Cohort (CCHC). RESULTS Evaluating the Laplacian regularized GNN on data from 600 patients, we expanded our analysis from two chronic conditions to five chronic conditions. The proposed model consistently surpassed a baseline GNN model, achieving an average accuracy of ≥89% across all combinations. In contrast, the performance of the standard model declined more markedly with the addition of more chronic conditions. The Laplacian regularization provided consistent predictions for adjacent nodes, beneficial in cases with shared attributes among nodes. CONCLUSIONS The incorporation of Laplacian regularization in our GNN model is essential, resulting in enhanced node categorization and better predictive performance by harnessing the graph structure. This study underscores the significance of considering graph structure when designing neural networks for graph data. Future research might further explore and refine this regularization method for various tasks using graph-structured data.
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Affiliation(s)
- Julian Carvajal Rico
- Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX, 78249, United States of America
| | - Adel Alaeddini
- Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX, 78249, United States of America.
| | - Syed Hasib Akhter Faruqui
- Department of Engineering Technology, Sam Houston State University, Huntsville, Tx, 77341, United States of America
| | - Susan P Fisher-Hoch
- School of Public Health Brownsville, The University of Texas Health Science Center at Houston, Houston, TX, 78520, United States of America
| | - Joseph B Mccormick
- School of Public Health Brownsville, The University of Texas Health Science Center at Houston, Houston, TX, 78520, United States of America
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Ding Y, Lei X, Liao B, Wu FX. MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations. Brief Bioinform 2022; 23:6552270. [PMID: 35323901 DOI: 10.1093/bib/bbac079] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/07/2022] [Accepted: 02/15/2022] [Indexed: 01/20/2023] Open
Abstract
MOTIVATION MicroRNAs (miRNAs), as critical regulators, are involved in various fundamental and vital biological processes, and their abnormalities are closely related to human diseases. Predicting disease-related miRNAs is beneficial to uncovering new biomarkers for the prevention, detection, prognosis, diagnosis and treatment of complex diseases. RESULTS In this study, we propose a multi-view Laplacian regularized deep factorization machine (DeepFM) model, MLRDFM, to predict novel miRNA-disease associations while improving the standard DeepFM. Specifically, MLRDFM improves DeepFM from two aspects: first, MLRDFM takes the relationships among items into consideration by regularizing their embedding features via their similarity-based Laplacians. In this study, miRNA Laplacian regularization integrates four types of miRNA similarity, while disease Laplacian regularization integrates two types of disease similarity. Second, to judiciously train our model, Laplacian eigenmaps are utilized to initialize the weights in the dense embedding layer. The experimental results on the latest HMDD v3.2 dataset show that MLRDFM improves the performance and reduces the overfitting phenomenon of DeepFM. Besides, MLRDFM is greatly superior to the state-of-the-art models in miRNA-disease association prediction in terms of different evaluation metrics with the 5-fold cross-validation. Furthermore, case studies further demonstrate the effectiveness of MLRDFM.
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Affiliation(s)
- Yulian Ding
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, S7N 5A9, Saskatchewan, Canada
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, 620 West Chang'an Avenue, 710119, Shaanxi, China
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, 99 Longkun South Road, 571158, Hainan, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, S7N 5A9, Saskatchewan, Canada.,Department of Mechanical Engineering and Department of Computer Science, University of Saskatchewan, 57 Campus Drive, S7N5A9, Saskatchewan, Canada
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Song W, Wang W, Dai DQ. Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data. Brief Bioinform 2021; 23:6381248. [PMID: 34607358 DOI: 10.1093/bib/bbab398] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/30/2021] [Accepted: 09/01/2021] [Indexed: 12/13/2022] Open
Abstract
The discovery of cancer subtypes has become much-researched topic in oncology. Dividing cancer patients into subtypes can provide personalized treatments for heterogeneous patients. High-throughput technologies provide multiple omics data for cancer subtyping. Integration of multi-view data is used to identify cancer subtypes in many computational methods, which obtain different subtypes for the same cancer, even using the same multi-omics data. To a certain extent, these subtypes from distinct methods are related, which may have certain guiding significance for cancer subtyping. It is a challenge to effectively utilize the valuable information of distinct subtypes to produce more accurate and reliable subtypes. A weighted ensemble sparse latent representation (subtype-WESLR) is proposed to detect cancer subtypes on heterogeneous omics data. Using a weighted ensemble strategy to fuse base clustering obtained by distinct methods as prior knowledge, subtype-WESLR projects each sample feature profile from each data type to a common latent subspace while maintaining the local structure of the original sample feature space and consistency with the weighted ensemble and optimizes the common subspace by an iterative method to identify cancer subtypes. We conduct experiments on various synthetic datasets and eight public multi-view datasets from The Cancer Genome Atlas. The results demonstrate that subtype-WESLR is better than competing methods by utilizing the integration of base clustering of exist methods for more precise subtypes.
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Affiliation(s)
- Wenjing Song
- Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Weiwen Wang
- Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Dao-Qing Dai
- Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China
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Gu Y, Li K. A Transfer Model Based on Supervised Multi-Layer Dictionary Learning for Brain Tumor MRI Image Recognition. Front Neurosci 2021; 15:687496. [PMID: 34122003 PMCID: PMC8193061 DOI: 10.3389/fnins.2021.687496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 04/19/2021] [Indexed: 11/30/2022] Open
Abstract
Artificial intelligence (AI) is an effective technology for automatic brain tumor MRI image recognition. The training of an AI model requires a large number of labeled data, but medical data needs to be labeled by professional clinicians, which makes data collection complex and expensive. Moreover, a traditional AI model requires that the training data and test data must follow the independent and identically distributed. To solve this problem, we propose a transfer model based on supervised multi-layer dictionary learning (TSMDL) for brain tumor MRI image recognition in this paper. With the help of the knowledge learned from related domains, the goal of this model is to solve the task of transfer learning where the target domain has only a small number of labeled samples. Based on the framework of multi-layer dictionary learning, the proposed model learns the common shared dictionary of source and target domains in each layer to explore the intrinsic connections and shared information between different domains. At the same time, by making full use of the label information of samples, the Laplacian regularization term is introduced to make the dictionary coding of similar samples as close as possible and the dictionary coding of different class samples as different as possible. The recognition experiments on brain MRI image datasets REMBRANDT and Figshare show that the model performs better than competitive state of-the-art methods.
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Affiliation(s)
- Yi Gu
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Kang Li
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
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Zhao J, Ma Y, Liu L. Microbiome Data Analysis by Symmetric Non-negative Matrix Factorization With Local and Global Regularization. Front Mol Biosci 2021; 8:643014. [PMID: 33987200 PMCID: PMC8111298 DOI: 10.3389/fmolb.2021.643014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/22/2021] [Indexed: 12/03/2022] Open
Abstract
A network is an efficient tool to organize complicated data. The Laplacian graph has attracted more and more attention for its good properties and has been applied to many tasks including clustering, feature selection, and so on. Recently, studies have indicated that though the Laplacian graph can capture the global information of data, it lacks the power to capture fine-grained structure inherent in network. In contrast, a Vicus matrix can make full use of local topological information from the data. Given this consideration, in this paper we simultaneously introduce Laplacian and Vicus graphs into a symmetric non-negative matrix factorization framework (LVSNMF) to seek and exploit the global and local structure patterns that inherent in the original data. Extensive experiments are conducted on three real datasets (cancer, cell populations, and microbiome data). The experimental results show the proposed LVSNMF algorithm significantly outperforms other competing algorithms, suggesting its potential in biological data analysis.
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Affiliation(s)
- Junmin Zhao
- School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan, China
| | - Yuanyuan Ma
- School of Computer and Information Engineering, Anyang Normal University, Anyang, China
| | - Lifang Liu
- School of Education, Anyang Normal University, Anyang, China
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Abstract
In pharmaceutical sciences, a crucial step of the drug discovery is the identification of drug-target interactions (DTIs). However, only a small portion of the DTIs have been experimentally validated. Moreover, it is an extremely laborious, expensive, and time-consuming procedure to capture new interactions between drugs and targets through traditional biochemical experiments. Therefore, designing computational methods for predicting potential interactions to guide the experimental verification is of practical significance, especially for de novo situation. In this article, we propose a new algorithm, namely Laplacian regularized Schatten p-norm minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for new targets where there are no known interactions. Specifically, we first take advantage of the drug and target similarity information to dynamically prefill the partial unknown interactions. Then based on the assumption that the interaction matrix is low-rank, we use Schatten p-norm minimization model combined with Laplacian regularization terms to improve prediction performance in the new drug/target cases. Finally, we numerically solve the LRSpNM model by an efficient alternating direction method of multipliers algorithm. We evaluate LRSpNM on five data sets and an extensive set of numerical experiments show that LRSpNM achieves better and more robust performance than five state-of-the-art DTIs prediction algorithms. In addition, we conduct two case studies for new drug and new target prediction, which illustrates that LRSpNM can successfully predict most of the experimental validated DTIs.
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Affiliation(s)
- Gaoyan Wu
- The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
| | - Mengyun Yang
- School of Science, Shaoyang University, Shaoyang, China
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, Virginia, USA
| | - Jianxin Wang
- The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
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Tang C, Zhou H, Zheng X, Zhang Y, Sha X. Dual Laplacian regularized matrix completion for microRNA-disease associations prediction. RNA Biol 2019; 16:601-611. [PMID: 30676207 PMCID: PMC6546388 DOI: 10.1080/15476286.2019.1570811] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2018] [Revised: 11/30/2018] [Accepted: 01/03/2019] [Indexed: 01/21/2023] Open
Abstract
Since lots of miRNA-disease associations have been verified, it is meaningful to discover more miRNA-disease associations for serving disease diagnosis and prevention of human complex diseases. However, it is not practical to identify potential associations using traditional biological experimental methods since the process is expensive and time consuming. Therefore, it is necessary to develop efficient computational methods to accomplish this task. In this work, we introduced a matrix completion model with dual Laplacian regularization (DLRMC) to infer unknown miRNA-disease associations in heterogeneous omics data. Specifically, DLRMC transformed the task of miRNA-disease association prediction into a matrix completion problem, in which the potential missing entries of the miRNA-disease association matrix were calculated, the missing association can be obtained based on the prediction scores after the completion procedure. Meanwhile, the miRNA functional similarity and the disease semantic similarity were fully exploited to serve the miRNA-disease association matrix completion by using a dual Laplacian regularization term. In the experiments, we conducted global and local Leave-One-Out Cross Validation (LOOCV) and case studies to evaluate the efficacy of DLRMC on the Human miRNA-disease associations dataset obtained from the HMDDv2.0 database. As a result, the AUCs of DLRMC is 0.9174 and 0.8289 in global LOOCV and local LOOCV, respectively, which significantly outperform a variety of previous methods. In addition, in the case studies on four significant diseases related to human health including Colon Neoplasms, Kidney neoplasms, Lymphoma and Prostate neoplasms, 90%, 92%, 92% and 94% out of the top 50 predicted miRNAs has been confirmed, respectively.
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Affiliation(s)
- Chang Tang
- School of Computer Science, China University of Geosciences, Wuhan, China
| | - Hua Zhou
- Department of Hematology, The Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an, China
| | - Xiao Zheng
- Wuhan University of Technology Hospital, Wuhan University of Technology, Wuhan, China
| | - Yanming Zhang
- Department of Hematology, The Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an, China
| | - Xiaofeng Sha
- Department of Oncology, Huai’an Hongze District People’s Hospital, Huai’an, China
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Ma Y, Hu X, He T, Jiang X. Hessian regularization based symmetric nonnegative matrix factorization for clustering gene expression and microbiome data. Methods 2016; 111:80-84. [PMID: 27339941 DOI: 10.1016/j.ymeth.2016.06.017] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2016] [Accepted: 06/19/2016] [Indexed: 10/21/2022] Open
Abstract
Nonnegative matrix factorization (NMF) has received considerable attention due to its interpretation of observed samples as combinations of different components, and has been successfully used as a clustering method. As an extension of NMF, Symmetric NMF (SNMF) inherits the advantages of NMF. Unlike NMF, however, SNMF takes a nonnegative similarity matrix as an input, and two lower rank nonnegative matrices (H, HT) are computed as an output to approximate the original similarity matrix. Laplacian regularization has improved the clustering performance of NMF and SNMF. However, Laplacian regularization (LR), as a classic manifold regularization method, suffers some problems because of its weak extrapolating ability. In this paper, we propose a novel variant of SNMF, called Hessian regularization based symmetric nonnegative matrix factorization (HSNMF), for this purpose. In contrast to Laplacian regularization, Hessian regularization fits the data perfectly and extrapolates nicely to unseen data. We conduct extensive experiments on several datasets including text data, gene expression data and HMP (Human Microbiome Project) data. The results show that the proposed method outperforms other methods, which suggests the potential application of HSNMF in biological data clustering.
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Affiliation(s)
- Yuanyuan Ma
- School of Information Management, Central China Normal University, Wuhan 430079, China.
| | - Xiaohua Hu
- School of Computer, Central China Normal University, Wuhan 430079, China.
| | - Tingting He
- School of Computer, Central China Normal University, Wuhan 430079, China.
| | - Xingpeng Jiang
- School of Computer, Central China Normal University, Wuhan 430079, China.
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Zhang Z, Yan S, Zhao M. Similarity preserving low-rank representation for enhanced data representation and effective subspace learning. Neural Netw 2014; 53:81-94. [PMID: 24561453 DOI: 10.1016/j.neunet.2014.01.001] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2013] [Revised: 10/12/2013] [Accepted: 01/02/2014] [Indexed: 10/25/2022]
Abstract
Latent Low-Rank Representation (LatLRR) delivers robust and promising results for subspace recovery and feature extraction through mining the so-called hidden effects, but the locality of both similar principal and salient features cannot be preserved in the optimizations. To solve this issue for achieving enhanced performance, a boosted version of LatLRR, referred to as Regularized Low-Rank Representation (rLRR), is proposed through explicitly including an appropriate Laplacian regularization that can maximally preserve the similarity among local features. Resembling LatLRR, rLRR decomposes given data matrix from two directions by seeking a pair of low-rank matrices. But the similarities of principal and salient features can be effectively preserved by rLRR. As a result, the correlated features are well grouped and the robustness of representations is also enhanced. Based on the outputted bi-directional low-rank codes by rLRR, an unsupervised subspace learning framework termed Low-rank Similarity Preserving Projections (LSPP) is also derived for feature learning. The supervised extension of LSPP is also discussed for discriminant subspace learning. The validity of rLRR is examined by robust representation and decomposition of real images. Results demonstrated the superiority of our rLRR and LSPP in comparison to other related state-of-the-art algorithms.
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Affiliation(s)
- Zhao Zhang
- School of Computer Science and Technology, Soochow University, Suzhou 215006, PR China; Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Shuicheng Yan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Mingbo Zhao
- Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.
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