Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.

Graph Regularized L_p Smooth Non-negative Matrix Factorization for Data Representation

This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods.

Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration

This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorization by total variation constraint and graph regularization.The main contributions of our work are the following.First,total variation is incorporated into NMF to control the diffusion speed.The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information.Second,we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power.Third,the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given.Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.

Obtaining Profiles Based on Localized Non-negative Matrix Factorization

Nonnegative matrix factorization (NMF) is a method to get parts-based features of information and form the typical profiles. But the basis vectors NMF gets are not orthogonal so that parts-based features of information are usually redundancy. In this paper, we propose two different approaches based on localized non-negative matrix factorization (LNMF) to obtain the typical user session profiles and typical semantic profiles of junk mails. The LNMF get basis vectors as orthogonal as possible so that it can get accurate profiles. The experiments show that the approach based on LNMF can obtain better profiles than the approach based on NMF. Key words localized non-negative matrix factorization - profile - log mining - mail filtering CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373066, 60303024), National Grand Fundamental Research 973 Program of China (2002CB312000), National Research Foundation for the Doctoral Program of Higher Education of China (20020286004).Biography: Jiang Ji-xiang (1980-), male, Master candidate, research direction: data mining, knowledge representation on the Web.

High Quality Audio Object Coding Framework Based on Non-Negative Matrix Factorization

Object-based audio coding is the main technique of audio scene coding. It can effectively reconstruct each object trajectory, besides provide sufficient flexibility for personalized audio scene reconstruction. So more and more attentions have been paid to the object-based audio coding. However, existing object-based techniques have poor sound quality because of low parameter frequency domain resolution. In order to achieve high quality audio object coding, we propose a new coding framework with introducing the non-negative matrix factorization(NMF) method. We extract object parameters with high resolution to improve sound quality, and apply NMF method to parameter coding to reduce the high bitrate caused by high resolution. And the experimental results have shown that the proposed framework can improve the coding quality by 25%, so it can provide a better solution to encode audio scene in a more flexible and higher quality way.

A novel trilinear decomposition algorithm:Three-dimension non-negative matrix factorization

Non-negative matrix factorization （NMF） is a technique for dimensionality reduction by placing non-negativity constraints on the matrix. Based on the PARAFAC model, NMF was extended for three-dimension data decomposition. The three-dimension nonnegative matrix factorization （NMF3） algorithm, which was concise and easy to implement, was given in this paper. The NMF3 algorithm implementation was based on elements but not on vectors. It could decompose a data array directly without unfolding, which was not similar to that the traditional algorithms do, It has been applied to the simulated data array decomposition and obtained reasonable results. It showed that NMF3 could be introduced for curve resolution in chemometrics.

Prognostic model for prostate cancer based on glycolysis-related genes and non-negative matrix factorization analysis

Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential.Methods:First,gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO),and glycolysis-related genes were obtained from the Molecular Signatures Database(MSigDB).Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained,which were used in nonnegative matrix factorization(NMF)to identify clusters.The correlation between clusters and clinical features was discussed,and the differentially expressed genes(DEGs)between the two clusters were investigated.Based on the DEGs,we investigated the biological differences between clusters,including immune cell infiltration,mutation,tumor immune dysfunction and exclusion,immune function,and checkpoint genes.To establish the prognostic model,the genes were filtered based on univariable Cox regression,LASSO,and multivariable Cox regression.Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model.A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting.Result:The genes obtained from MSigDB were enriched in glycolysis functions.Two clusters were identified by NMF analysis based on 272 glycolysis-related genes,and a prognostic model based on DEGs between the two clusters was finally established.The prognostic model consisted of LAMPS,SPRN,ATOH1,TANC1,ETV1,TDRD1,KLK14,MESP2,POSTN,CRIP2,NAT1,AKR7A3,PODXL,CARTPT,and PCDHGB2.All sample,training,and test cohorts from The Cancer Genome Atlas(TCGA)and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups.The area under the ROC curve showed great performance of this prognostic model.Conclusion:A prognostic model based on glycolysis-related genes was established,with great performance and potential significance to the clinical application.

Alzheimer’s disease classification based on sparse functional connectivity and non-negative matrix factorization

A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the non-negative adaptive sparse representation(NASR)method is applied to compute the sparse functional connectivity among brain regions based on functional magnetic resonance imaging(fMRI)data for feature extraction.Afterwards,the sparse non-negative matrix factorization(sNMF)method is adopted for dimensionality reduction to obtain low-dimensional features with straightforward physical meaning.The experimental results show that the proposed framework outperforms the competing frameworks in terms of classification accuracy,sensitivity and specificity.Furthermore,three sub-networks,including the default mode network,the basal ganglia-thalamus-limbic network and the temporal-insular network,are found to have notable differences between the AD patients and the healthy subjects.The proposed framework can effectively identify AD patients and has potentials for extending the understanding of the pathological changes of AD.

Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

Data is humongous today because of the extensive use of World WideWeb, Social Media and Intelligent Systems. This data can be very important anduseful if it is harnessed carefully and correctly. Useful information can beextracted from this massive data using the Data Mining process. The informationextracted can be used to make vital decisions in various industries. Clustering is avery popular Data Mining method which divides the data points into differentgroups such that all similar data points form a part of the same group. Clusteringmethods are of various types. Many parameters and indexes exist for the evaluationand comparison of these methods. In this paper, we have compared partitioningbased methods K-Means, Fuzzy C-Means (FCM), Partitioning AroundMedoids (PAM) and Clustering Large Application (CLARA) on secure perturbeddata. Comparison and identification has been done for the method which performsbetter for analyzing the data perturbed using Extended NMF on the basis of thevalues of various indexes like Dunn Index, Silhouette Index, Xie-Beni Indexand Davies-Bouldin Index.

Clustering Student Discussion Messages on Online Forumby Visualization and Non-Negative Matrix Factorization

The use of online discussion forum can?effectively engage students in their studies. As the number of messages posted on the forum is increasing, it is more difficult for instructors to read and respond to them in a prompt way. In this paper, we apply non-negative matrix factorization and visualization to clustering message data, in order to provide a summary view of messages that disclose their deep semantic relationships. In particular, the NMF is able to find the underlying issues hidden in the messages about which most of the students are concerned. Visualization is employed to estimate the initial number of clusters, showing the relation communities. The experiments and comparison on a real dataset have been reported to demonstrate the effectiveness of the approaches.

Unsupervised Multi-Level Non-Negative Matrix Factorization Model: Binary Data Case

Rank determination issue is one of the most significant issues in non-negative matrix factorization (NMF) research. However, rank determination problem has not received so much emphasis as sparseness regularization problem. Usually, the rank of base matrix needs to be assumed. In this paper, we propose an unsupervised multi-level non-negative matrix factorization model to extract the hidden data structure and seek the rank of base matrix. From machine learning point of view, the learning result depends on its prior knowledge. In our unsupervised multi-level model, we construct a three-level data structure for non-negative matrix factorization algorithm. Such a construction could apply more prior knowledge to the algorithm and obtain a better approximation of real data structure. The final bases selection is achieved through L2-norm optimization. We implement our experiment via binary datasets. The results demonstrate that our approach is able to retrieve the hidden structure of data, thus determine the correct rank of base matrix.

Quercetin exerts anti-inflammatory effects via inhibiting tumor necrosis factor-α-induced matrix metalloproteinase-9 expression in normal human gastric epithelial cells

BACKGROUND Gastric injury is the most common digestive system disease worldwide and involves inflammation,which can lead to gastric ulcer or gastric cancer(GC).Matrix metallopeptidase-9[MMP-9(gelatinase-B)]plays an important role in inflammation and GC progression.Quercetin and quercetin-rich diets represent potential food supplements and a source of medications for treating gastric injury given their anti-inflammatory activities.However,the effects and mechanisms of action of quercetin on human chronic gastritis and whether quercetin can relieve symptoms remain unclear.AIM To assess whether tumor necrosis factor-α(TNF-α)-induced MMP-9 expression mediates the anti-inflammatory effects of quercetin in normal human gastric mucosal epithelial cells.METHODS The normal human gastric mucosa epithelial cell line GES-1 was used to establish a normal human gastric epithelial cell model of TNF-α-induced MMP-9 protein overexpression to evaluate the antiinflammatory effects of quercetin.The cell counting Kit-8 assay was used to evaluate the effects of varying quercetin doses on cell viability in the normal GES-1 cell line.Cell migration was measured using Transwell assay.The expression of proto-oncogene tyrosine-protein kinase Src(cSrc),phospho(p)-c-Src,extracellular-signal-regulated kinase 2(ERK2),p-ERK1/2,c-Fos,p-c-Fos,nuclear factor kappa B(NF-κB/p65),and p-p65 and the effects of their inhibitors were examined using Western blot analysis and measurement of luciferase activity.p65 expression was detected by immunofluorescence.MMP-9 m RNA and protein levels were measured by quantitative reverse transcription polymerase chain reaction(q RT–PCR)and gelatin zymography,respectively.RESULTS q RT-PCR and gelatin zymography showed that TNF-αinduced MMP-9 m RNA and protein expression in a dose-and time-dependent manner.These effects were reduced by the pretreatment of GES-1 cells with quercetin or a TNF-αantagonist(TNFR inhibitor)in a dose-and timedependent manner.Quercetin and TNF-αantagonists decreased the TNF-α-induced phosphorylation of c-Src,ERK1/2,c-Fos,and p65 in a dose-and time-dependent manner.Quercetin,TNF-αantagonist,PP1,U0126,and tanshinone IIA(TSIIA)reduced TNF-α-induced c-Fos phosphorylation and AP-1–Luciferase(Luc)activity in a dose-and time-dependent manner.Pretreatment with quercetin,TNF-αantagonist,PP1,U0126,or Bay 11-7082 reduced TNF-α-induced p65 phosphorylation and translocation and p65–Luc activity in a dose-and timedependent manner.TNF-αsignificantly increased GES-1 cell migration,and these results were reduced by pretreatment with quercetin or a TNF-αantagonist.CONCLUSION Quercetin significantly downregulates TNF-α-induced MMP-9 expression in GES-1 cells via the TNFR-c-Src–ERK1/2 and c-Fos or NF-κB pathways.

Expression of matrix metalloproteinase-1 and tumor necrosis factor-α in ulcerative colitis

AIM： To examine the expression of matrix metalloproteinase-1 （MMP-1） and tumor necrosis factor-α （TNF-~~） in the colon mucosa of patients with ulcerative colitis （UC）.METHODS： Reverse transcription-polymerase chain reaction （RT-PCR） and immunohistochemistry were used to examine the expression of MMP-1 and TNF-α at both mRNA and protein levels in the colon mucosa of patients with UC. Correlation between MMP-1 and TNF-α and their correlation with the severity of the disease were also analyzed statistically.RESULTS： The expression of MMP-1 and TNF-α in the ulcerated and inflamed colon mucosa of patients with UC was significantly higher than that in the non-inflamed mucosa of normal controls at both mRNA and protein levels. Furthermore, the expression of MMP-1 and TNF-α in the ulcerated area was significantly higher than that in the inflamed area of patients with UC （0.9797 ± 0.1433 vs 0.6746 ± 0.0373, 0.8669 ± 0.0746 vs 0.5227 ± 0.0435, P 〈 0.05）. There was no statistically significant difference in the non-inflamed area of normal controls. There was a significant correlation between MMP-1 and TNF-c~ expression （0.9797 ± 0.1433 vs 0.8669 ± 0.0746, P 〈 0.05）, the correlating factor was 0.877. MMP-1 and TNF-α showed a significant correlation with the severity of the disease （0.0915 ：e 0.0044 vs 0.0749 vs 0.0032, 0.0932 ± 0.0019 vs 0.0724 ± 0.0043, P 〈 0.05）, their correlating factors were 0.942 and 0.890, respectively.CONCLUSION： Excessively expressed MMP-1 directly damages the colon mucosa by degrading extracellular matrix （ECM） in patients with UC. While damaging colon mucosa, excessively expressed TNF-α stimulates MMPs secreting cells to produce more MMP-1 and aggravates the mucosa damage. MMP-1 promotes secretion of TNF-α in a positive feedback manner to cause further injury in the colon mucosa. MMP-1 and TNF-α correlate well with the severity of the disease, and therefore, can be used clinically as biological markers to judge the severity of UC.

Isolation of Whole-plant Multiple Oscillations via Non-negative Spectral Decompositio

Constrained spectral non-negative matrix factorization(NMF)analysis of perturbed oscillatory process control loop variable data is performed for the isolation of multiple plant-wide oscillatory sources.The technique is described and demonstrated by analyzing data from both simulated and real plant data of a chemical process plant. Results show that the proposed approach can map multiple oscillatory sources onto the most appropriate control loops,and has superior performance in terms of reconstruction accuracy and intuitive understanding compared with spectral independent component analysis(ICA).

Local inhibition of matrix metalloproteinases reduced M2 macrophage activity and impeded recovery in spinal cord transected rats after treatment with fibroblast growth factor-1 and nerve grafts

Alternatively activated macrophages （M2 macrophages） promote central nervous system regeneration. Our previous study demonstrated that treatment with peripheral nerve grafts and fibroblast growth factor-1 recruited more M2 macrophages and improved partial functional recovery in spinal cord transected rats. The migration of macrophages is matrix metalloproteinase （MMP） dependent. We used a general inhibitor of MMPs to influence macrophage migration, and we examined the migration of macrophage populations and changes in spinal function. Rat spinal cords were completely transected at Ts, and 5 mm of spinal cord was removed （group T）. In group R, spinal cord-transected rats received treatment with fibroblast grow th factor- 1 and peripheral nerve grafts. In group RG, rats received the same treatment as group R with the addition of 200 μM GM6001 （an MMP inhibitor） to the fibrin mix. We found that MMP-9, but not MMP- 2, was upregulated in the graft area of rats in group R. Local application of the MMP inhibitor resulted in a reduction in the ratio of arginase-1 （M2 macrophage subset）/inducible nitric oxide synthase-postive cells. When the MMP inhibitor was applied at 8 weeks postoperation, the partial functional recovery observed in group R was lost. This effect was accompanied by a decrease in brain-derived neurotrophic factor levels in the nerve graft. These results suggested that the arginase-1 positive population in spinal cord transected rats is a migratory cell population rather than the phenotypic conversion of early iNOS^＋ cells and that the migration of the arginase-1^＋ population could be regulated locally. Simultaneous application of MMP in- hibitors or promotion of MMP activity for spinal cord injury needs to be considered if the coadministered treatment involves M2 recruitment.

Methanol extract of Codium fragile inhibits tumor necrosis factor-ɑ-induced matrix metalloproteinase-9 and invasiveness of MDA-MB-231 cells by suppressing nuclear factor-κB activation

Objective:To evaluate whether the methanol extract of Codium fragile(MECF) regulates tumor necrosis factor-α(TNF-α)-induced invasion of human breast cancer MDA-MB-231 cells by suppressing matrix metalloproteinase-9(MMP-9).Methods:Reverse transcriptionpolymerase chain reaction(RT-PCR) and western blot analysis were performed to analyze the expression of MMP-9 and nuclear factor-κB(NF-κB) subunits,p65 and p50,and IκB in MDA-MB-231 cells.3-(4,5-Dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide(MTT) assay was used for cell viability.MMP-9 activity and invasion were measured by gelatin zymography and a matrigel invasion assay,respectively.NF- κB activity was measured by an electrophoretic mobility shift assay and luciferase activity.Results:MECF had no effects on cell viability up to a concentration of 100 μg/mL in human breast cancer MDA-MB-231 cells regardless of the presence of TNF-α.MDA-MB-231 cells that were stimulated with TNF-α showed a marked increase of invasion compared to the untreated control,whereas pretreatment with MECF downregulated the TNF-α-induced invasion of MDA-MB-231 cells.Additionally,zymography,western blot analysis,and reverse transcriptase-polymerase chain reaction(RT-PCR) confirmed that MECF decreased TNF-α-induced MMP-9 expression and activity which is a key regulator for cancer invasion.According to an electrophoretic morbidity shift assay,pretreatment with MECF in MDA-MB-231 cells significantly decreased the TNF-α-induced DNA-binding activity of nuclear factor- κB(NF- κB),which is an important transcription factor for regulating cancer invasion-related genes such as MMP-9.Furthermore,treatment with MECF sustained the expression of p65 and p50 in response to TNF-α in the cytosolic compartment.The luciferase assay demonstrated that MECF attenuated TNF-α-induced NF- κB luciferase activity.Conclusion:MECF exhibited its antiinvasive capability by downregulating TNF-α-induced MMP-9 expression,resulting from the suppression of NF- κB activity in the human breast cancer cell line MDA-MB-231.

Effects of Heparin on Transforming Growth Factor-β_1 and Extracellular Matrix Components in the Glomeruli of Diabetic Rats

The effects of heparin on the expression of transforming growth factor-β 1 (TGF-β 1) and two extracellular matrix components laminin (LN) and fibronectin (FN) in diabetic rat glomeruli were investigated. Twenty-six rats were randomly divided into control group (C, n=8), diabetic group (D, n=9), and diabetes+heparin group (DH, n=9). After 8-week therapy of heparin (200 U once daily by abdominal injection), TGF-β 1, LN and FN expression in glomeruli was detected by immunohistochemical method. The results showed that the expression levels of TGF-β 1, LN and FN were higher in group D than in group C. It was found that heparin could reduce 24-h urinary albumin excretion and inhibit overexpression of TGF-β 1, LN and FN in glomeruli of diabetic rats. It suggested that the inhibitory effect of heparin on diabetic glomerular sclerosis was at least partly related with the inhibition of TGF-β 1 expression.

Matrix dimensionality reduction for mining typical user profiles

Recently clustering techniques have been used to automatically discover typical user profiles. In general, it is a challenging problem to design effective similarity measure between the session vectors which are usually high-dimensional and sparse. Two approaches for mining typical user profiles, based on matrix dimensionality reduction, are presented. In these approaches, non-negative matrix factorization is applied to reduce dimensionality of the session-URL matrix, and the projecting vectors of the user-session vectors are clustered into typical user-session profiles using the spherical k -means algorithm. The results show that two algorithms are successful in mining many typical user profiles in the user sessions.

AAV-mediated expression of p65shRNA and bone morphogenetic protein 4 synergistically enhances chondrocyte regeneration

BACKGROUND:Adeno-associated virus(AAV)gene therapy has been proven to be reliable and safe for the treatment of osteoarthritis in recent years.However,given the complexity of osteoarthritis pathogenesis,single gene manipulation for the treatment of osteoarthritis may not produce satisfactory results.Previous studies have shown that nuclear factorκB could promote the inflammatory pathway in osteoarthritic chondrocytes,and bone morphogenetic protein 4(BMP4)could promote cartilage regeneration.OBJECTIVE:To test whether combined application of AAV-p65shRNA and AAV-BMP4 will yield the synergistic effect on chondrocytes regeneration and osteoarthritis treatment.METHODS:Viral particles containing AAV-p65-shRNA and AAV-BMP4 were prepared.Their efficacy in inhibiting inflammation in chondrocytes and promoting chondrogenesis was assessed in vitro and in vivo by transfecting AAV-p65-shRNA or AAV-BMP4 into cells.The experiments were divided into five groups:PBS group;osteoarthritis group;AAV-BMP4 group;AAV-p65shRNA group;and BMP4-p65shRNA 1:1 group.Samples were collected at 4,12,and 24 weeks postoperatively.Tissue staining,including safranin O and Alcian blue,was applied after collecting articular tissue.Then,the optimal ratio between the two types of transfected viral particles was further investigated to improve the chondrogenic potential of mixed cells in vivo.RESULTS AND CONCLUSION:The combined application of AAV-p65shRNA and AAV-BMP4 together showed a synergistic effect on cartilage regeneration and osteoarthritis treatment.Mixed cells transfected with AAV-p65shRNA and AAV-BMP4 at a 1:1 ratio produced the most extracellular matrix synthesis(P<0.05).In vivo results also revealed that the combination of the two viruses had the highest regenerative potential for osteoarthritic cartilage(P<0.05).In the present study,we also discovered that the combined therapy had the maximum effect when the two viruses were administered in equal proportions.Decreasing either p65shRNA or BMP4 transfected cells resulted in less collagen II synthesis.This implies that inhibiting inflammation by p65shRNA and promoting regeneration by BMP4 are equally important for osteoarthritis treatment.These findings provide a new strategy for the treatment of early osteoarthritis by simultaneously inhibiting cartilage inflammation and promoting cartilage repair.

Matrix metalloproteinases contribute to kidney fibrosis in chronic kidney diseases

Matrix metalloproteinases(MMPs) are members of the neutral proteinase family. They were previously thought to be anti-fibrotic because of their ability to degrade and remodel of extracellular matrix. However, recent studies have shown that MMPs are implicated in initiation and progression of kidney fibrosis through tubular cell epithelial–mesenchymal transition(EMT) as well as activation of resident fibroblasts, endothelial-mesenchymal transition(Endo MT) and pericyte-myofibroblast transdifferentiation. Interstitial macrophage infiltration has also been shown to correlate with the severity of kidney fibrosis in various chronic kidney diseases. MMPs secreted by macrophages, especially MMP-9, hasbeen shown by us to be profibrotic by induction of tubular cells EMT. EMT is mainly induced by transforming growth factor-β(TGF-β). However, MMP-9 was found by us and others to be up-regulated by TGF-β1 in kidney tubular epithelial cells and secreted by activated macrophages, resulting in EMT and ultimately kidney fibrosis. Therefore, MMP-9 may serve as a potential therapeutic target to prevent kidney fibrosis in chronic kidney disease. This review, by a particular focus on EMT, seeks to provide a comprehensive understanding of MMPs, especially MMP-9, in kidney fibrosis.