Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data ...Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data resolved profiles.Hourly speciated VOC data measured in Shijiazhuang,China from May to September 2021 were used to conduct study.The mean VOC concentration in the daytime and at nighttime were 32.8 and 36.0 ppbv,respectively.Alkanes and aromatics concentrations in the daytime(12.9 and 3.08 ppbv)were lower than nighttime(15.5 and 3.63 ppbv),whereas that of alkenes showed the opposite tendency.The concentration differences between daytime and nighttime for alkynes and halogenated hydrocarbonswere uniformly small.The reactivities of the dominant species in factor profiles for gasoline emissions,natural gas and diesel vehicles,and liquefied petroleum gas were relatively low and their profiles were less affected by photochemical losses.Photochemical losses produced a substantial impact on the profiles of solvent use,petrochemical industry emissions,combustion sources,and biogenic emissions where the dominant species in these factor profiles had high reactivities.Although the profile of biogenic emissions was substantially affected by photochemical loss of isoprene,the low emissions at nighttime also had an important impact on its profile.Chemical losses of highly active VOC species substantially reduced their concentrations in apportioned factor profiles.This study results were consistent with the analytical results obtained through initial concentration estimation,suggesting that the initial concentration estimation could be the most effective currently availablemethod for the source analyses of active VOCs although with uncertainty.展开更多
CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs a...CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs and diseases can enhance our understanding of diseases and provide new strategies and tools for early diagnosis,treatment,and disease prevention.However,existing models have limitations in accurately capturing similarities,handling the sparse and noise attributes of association networks,and fully leveraging bioinformatical aspects from multiple viewpoints.To address these issues,this study introduces a new non-negative matrix factorization-based framework called NMFMSN.First,we incorporate circRNA sequence data and disease semantic information to compute circRNA and disease similarity,respectively.Given the sparse known associations between circRNAs and diseases,we reconstruct the network to complete more associations by imputing missing links based on neighboring circRNA and disease interactions.Finally,we integrate these two similarity networks into a non-negative matrix factorization framework to identify potential circRNA-disease associations.Upon conducting 5-fold cross-validation and leave-one-out cross-validation,the AUC values for NMFMSN reach 0.9712 and 0.9768,respectively,outperforming the currently most advanced models.Case studies on lung cancer and hepatocellular carcinoma show that NMFMSN is a good way to predict new associations between circRNAs and diseases.展开更多
Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were freque...Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were frequently observed during the heating season.Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM_(2.5) as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities.Secondary nitrate had the highest contribution for Beijing(37.3%),and residential heating/biomass burning was the largest for Baoding(27.1%).Secondary nitrate,mobile,biomass burning,district heating,oil combustion,aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period(2019.01.10–2019.08.22)(Mann-Whitney Rank Sum Test P<0.05).In case of Baoding,soil,residential heating/biomass burning,incinerator,coal combustion,oil combustion sources showed significant differences.The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients.Conditional Bivariate Probability Function(CBPF)was used to identify the inflow directions for the sources,and joint-PSCF(Potential Source Contribution Function)was performed to determine the common potential source areas for sources affecting both cities.These models facilitated a more precise verification of city-specific influences on PM_(2.5) sources.The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities.展开更多
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 smar...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.展开更多
Objective: To discuss the expression and significance of angiostatin, vascular endothelial growth factor and matrix metalloproteinase-9 in the brain tissue of diabetic rats with ischemia reperfusion. Methods: A total ...Objective: To discuss the expression and significance of angiostatin, vascular endothelial growth factor and matrix metalloproteinase-9 in the brain tissue of diabetic rats with ischemia reperfusion. Methods: A total of 60 male Wistar rats were randomly divided into the normal group, sham group, diabetic cerebral infarction group and single cerebral infarction group according to the random number table, with 15 rats in each group. The high sucrose diet and intraperitoneal injection of streptozotocin were performed for the modeling of diabetic rats, while the thread-occlusion method was employed to build the model of cerebral ischemia reperfusion. The immunohistochemical staining was performed to detect the expression of angiostatin, vascular endothelial growth factor(VEGF) and matrix metalloproteinase-9(MMP-9) in the brain tissue. Results: The expression of angiostatin after the reperfusion in the brain tissue of rats in the single cerebral infarction group and diabetic cerebral infarction group was increased 6 h after the reperfusion, reached to the peak on 1 d and then decreased gradually. The expression of angiostatin in the diabetic cerebral infarction group 6 h, 1 d, 3 d and 7 d after the reperfusion was significantly higher than that in the single cerebral infarction group(P<0.05). VEGF began to be increased 1 h after the reperfusion in the single cerebral infarction group and diabetic cerebral infarction group, reached to the peak at 6 h and then decreased gradually. The expression of VEGF in the diabetic cerebral infarction group at each time point after the reperfusion was significantly lower than that in the single cerebral infarction group(P<0.05). MMP-9 began to be be increased 1 h after the reperfusion in the single cerebral infarction group and diabetic cerebral infarction group, reached to the peak on 1 d and then decreased gradually. The expression of MMP-9 in the diabetic cerebral infarction group at each time point after the reperfusion was significantly higher than that in the single cerebral infarction group(P<0.05). Conclusions: The high glucose environment in which the diabetic cerebral infarction is occurred is to induce the formation of MMP-9 at first and then activate and increase the expression of angiostatin. Afterwards, the expression of VEGF is inhibited, resulting in the poor angiogenesis after cerebral infarction, which thus makes the injury of brain tissue after cerebral infarction even worse than the non-diabetes mellitus.展开更多
The constrained weighted-non-negative matrix factorization(CW-NMF)hybrid receptor model was applied to study the influence of steelmaking activities on PM_(2.5)(particulate matter with equivalent aerodynamic diameter ...The constrained weighted-non-negative matrix factorization(CW-NMF)hybrid receptor model was applied to study the influence of steelmaking activities on PM_(2.5)(particulate matter with equivalent aerodynamic diameter less than 2.5μm)composition in Dunkerque,Northern France.Semi-diurnal PM_(2.5)samples were collected using a high volume sampler in winter 2010 and spring 2011 and were analyzed for trace metals,water-soluble ions,and total carbon using inductively coupled plasma–atomic emission spectrometry(ICP-AES),ICP-mass spectrometry(ICP-MS),ionic chromatography and micro elemental carbon analyzer.The elemental composition shows that NO_(3)^(-),SO_(4)^(2-),NH_4~+and total carbon are the main PM_(2.5)constituents.Trace metals data were interpreted using concentration roses and both influences of integrated steelworks and electric steel plant were evidenced.The distinction between the two sources is made possible by the use Zn/Fe and Zn/Mn diagnostic ratios.Moreover Rb/Cr,Pb/Cr and Cu/Cd combination ratio are proposed to distinguish the ISW-sintering stack from the ISW-fugitive emissions.The a priori knowledge on the influencing source was introduced in the CW-NMF to guide the calculation.Eleven source profiles with various contributions were identified:8 are characteristics of coastal urban background site profiles and 3 are related to the steelmaking activities.Between them,secondary nitrates,secondary sulfates and combustion profiles give the highest contributions and account for 93%of the PM_(2.5)concentration.The steelwork facilities contribute in about 2%of the total PM_(2.5)concentration and appear to be the main source of Cr,Cu,Fe,Mn,Zn.展开更多
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 informatio...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.展开更多
A current problem in diet recommendation systems is the matching of food preferences with nutritional requirements,taking into account individual characteristics,such as body weight with individual health conditions,s...A current problem in diet recommendation systems is the matching of food preferences with nutritional requirements,taking into account individual characteristics,such as body weight with individual health conditions,such as diabetes.Current dietary recommendations employ association rules,content-based collaborative filtering,and constraint-based methods,which have several limitations.These limitations are due to the existence of a special user group and an imbalance of non-simple attributes.Making use of traditional dietary recommendation algorithm researches,we combine the Adaboost classifier with probabilistic matrix factorization.We present a personalized diet recommendation algorithm by taking advantage of probabilistic matrix factorization via Adaboost.A probabilistic matrix factorization method extracts the implicit factors between individual food preferences and nutritional characteristics.From this,we can make use of those features with strong influence while discarding those with little influence.After incorporating these changes into our approach,we evaluated our algorithm’s performance.Our results show that our method performed better than others at matching preferred foods with dietary requirements,benefiting user health as a result.The algorithm fully considers the constraint relationship between users’attributes and nutritional characteristics of foods.Considering many complex factors in our algorithm,the recommended food result set meets both health standards and users’dietary preferences.A comparison of our algorithm with others demonstrated that our method offers high accuracy and interpretability.展开更多
This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed ...This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.展开更多
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 factorizati...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.展开更多
Describing matrix–fracture interaction is one of the most important factors for modeling natural fractured reservoirs.A common approach for simulation of naturally fractured reservoirs is dual-porosity modeling where...Describing matrix–fracture interaction is one of the most important factors for modeling natural fractured reservoirs.A common approach for simulation of naturally fractured reservoirs is dual-porosity modeling where the degree of communication between the low-permeability medium(matrix)and high-permeability medium(fracture)is usually determined by a transfer function.Most of the proposed matrix–fracture functions depend on the geometry of the matrix and fractures that are lumped to a factor called shape factor.Unfortunately,there is no unique solution for calculating the shape factor even for symmetric cases.Conducting fine-scale modeling is a tool for calculating the shape factor and validating the current solutions in the literature.In this study,the shape factor is calculated based on the numerical simulation of fine-grid simulations for single-phase flow using finite element method.To the best of the author’s knowledge,this is the first study to calculate the shape factors for multidimensional irregular bodies in a systematic approach.Several models were used,and shape factors were calculated for both transient and pseudo-steady-state(PSS)cases,although in some cases they were not clarified and assumptions were not clear.The boundary condition dependency of the shape factor was also investigated,and the obtained results were compared with the results of other studies.Results show that some of the most popular formulas cannot capture the exact physics of matrix–fracture interaction.The obtained results also show that both PSS and transient approaches for describing matrix–fracture transfer lead to constant shape factors that are not unique and depend on the fracture pressure(boundary condition)and how it changes with time.展开更多
Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials （LFPs） based on sparse non negative matrix factor...Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials （LFPs） based on sparse non negative matrix factorization （SNMF）. SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four SpragueDawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the powerincreased LFP components were selected as working memoryrelated features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory.展开更多
The effect of vascular endothelial growth factor （VEGF） overexpression on matrix metalloproteinase-2 （MMP-2） in nasopharyngeal carcinoma （NPC） cells in vitro and the possible mechanism involved were investigated...The effect of vascular endothelial growth factor （VEGF） overexpression on matrix metalloproteinase-2 （MMP-2） in nasopharyngeal carcinoma （NPC） cells in vitro and the possible mechanism involved were investigated, and the correlation between the expression of VEGF and MMP-2 in NPC evaluated. The NPC cells were transfected with PAd-trackVEGF165 plasmid. The expression levels of VEGF and MMP-2 mRNA and protein in NPC cells were detected by semi-quantitative RT-PCR and Western blot respectively. It was found that the expression of VEGF and MMP-2 mRNA and protein was significantly increased in NPC cells after transfection of VEGF 165. It was concluded that the expression of VEGF was correlated to the in vitro invasion of NPC cells, and the induction of MMP-2 by VEGF was a key process of NPC cell invasion.展开更多
Traditional data driven fault detection methods assume that the process operates in a single mode so that they cannot perform well in processes with multiple operating modes. To monitor multimode processes effectively...Traditional data driven fault detection methods assume that the process operates in a single mode so that they cannot perform well in processes with multiple operating modes. To monitor multimode processes effectively,this paper proposes a novel process monitoring scheme based on orthogonal nonnegative matrix factorization(ONMF) and hidden Markov model(HMM). The new clustering technique ONMF is employed to separate data from different process modes. The multiple HMMs for various operating modes lead to higher modeling accuracy.The proposed approach does not presume the distribution of data in each mode because the process uncertainty and dynamics can be well interpreted through the hidden Markov estimation. The HMM-based monitoring indication named negative log likelihood probability is utilized for fault detection. In order to assess the proposed monitoring strategy, a numerical example and the Tennessee Eastman process are used. The results demonstrate that this method provides efficient fault detection performance.展开更多
This paper considers a problem of unsupervised spectral unmixing of hyperspectral data. Based on the Linear Mixing Model （ LMM）, a new method under the framework of nonnegative matrix fac- torization （NMF） is prop...This paper considers a problem of unsupervised spectral unmixing of hyperspectral data. Based on the Linear Mixing Model （ LMM）, a new method under the framework of nonnegative matrix fac- torization （NMF） is proposed, namely minimum distance constrained nonnegative matrix factoriza- tion （MDC-NMF）. In this paper, firstly, a new regularization term, called endmember distance （ED） is considered, which is defined as the sum of the squared Euclidean distances from each end- member to their geometric center. Compared with the simplex volume, ED has better optimization properties and is conceptually intuitive. Secondly, a projected gradient （PG） scheme is adopted, and by the virtue of ED, in this scheme the optimal step size along the feasible descent direction can be calculated easily at each iteration. Thirdly, a finite step （ no more than the number of endmem- bers） terminated algorithm is used to project a point on the canonical simplex, by which the abun- dance nonnegative constraint and abundance sum-to-one constraint can be accurately satisfied in a light amount of computation. The experimental results, based on a set of synthetic data and real da- ta, demonstrate that, in the same running time, MDC-NMF outperforms several other similar meth- ods proposed recently.展开更多
An image fusion method combining complex contourlet transform(CCT) with nonnegative matrix factorization(NMF) is proposed in this paper.After two images are decomposed by CCT,NMF is applied to their highand low-freque...An image fusion method combining complex contourlet transform(CCT) with nonnegative matrix factorization(NMF) is proposed in this paper.After two images are decomposed by CCT,NMF is applied to their highand low-frequency components,respectively,and finally an image is synthesized.Subjective-visual-quality of the image fusion result is compared with those of the image fusion methods based on NMF and the combination of wavelet /contourlet /nonsubsampled contourlet with NMF.The experimental results are evaluated quantitatively,and the running time is also contrasted.It is shown that the proposed image fusion method can gain larger information entropy,standard deviation and mean gradient,which means that it can better integrate featured information from all source images,avoid background noise and promote space clearness in the fusion image effectively.展开更多
Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the di...Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.展开更多
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 ...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.展开更多
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(...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.展开更多
基金supported by the National Key R&D Program of China(No.2023YFC3705801)the National Natural Science Foundation of China(No.42177085).
文摘Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data resolved profiles.Hourly speciated VOC data measured in Shijiazhuang,China from May to September 2021 were used to conduct study.The mean VOC concentration in the daytime and at nighttime were 32.8 and 36.0 ppbv,respectively.Alkanes and aromatics concentrations in the daytime(12.9 and 3.08 ppbv)were lower than nighttime(15.5 and 3.63 ppbv),whereas that of alkenes showed the opposite tendency.The concentration differences between daytime and nighttime for alkynes and halogenated hydrocarbonswere uniformly small.The reactivities of the dominant species in factor profiles for gasoline emissions,natural gas and diesel vehicles,and liquefied petroleum gas were relatively low and their profiles were less affected by photochemical losses.Photochemical losses produced a substantial impact on the profiles of solvent use,petrochemical industry emissions,combustion sources,and biogenic emissions where the dominant species in these factor profiles had high reactivities.Although the profile of biogenic emissions was substantially affected by photochemical loss of isoprene,the low emissions at nighttime also had an important impact on its profile.Chemical losses of highly active VOC species substantially reduced their concentrations in apportioned factor profiles.This study results were consistent with the analytical results obtained through initial concentration estimation,suggesting that the initial concentration estimation could be the most effective currently availablemethod for the source analyses of active VOCs although with uncertainty.
基金the Gansu Province Industrial Support Plan(No.2023CYZC-25)Natural Science Foundation of Gansu Province(No.23JRRA770)the National Natural Science Foundation of China(No.62162040)。
文摘CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs and diseases can enhance our understanding of diseases and provide new strategies and tools for early diagnosis,treatment,and disease prevention.However,existing models have limitations in accurately capturing similarities,handling the sparse and noise attributes of association networks,and fully leveraging bioinformatical aspects from multiple viewpoints.To address these issues,this study introduces a new non-negative matrix factorization-based framework called NMFMSN.First,we incorporate circRNA sequence data and disease semantic information to compute circRNA and disease similarity,respectively.Given the sparse known associations between circRNAs and diseases,we reconstruct the network to complete more associations by imputing missing links based on neighboring circRNA and disease interactions.Finally,we integrate these two similarity networks into a non-negative matrix factorization framework to identify potential circRNA-disease associations.Upon conducting 5-fold cross-validation and leave-one-out cross-validation,the AUC values for NMFMSN reach 0.9712 and 0.9768,respectively,outperforming the currently most advanced models.Case studies on lung cancer and hepatocellular carcinoma show that NMFMSN is a good way to predict new associations between circRNAs and diseases.
基金supported by the National Institute of Environmental Research(NIER)funded by the Ministry of Environment(No.NIER-2019-04-02-039)supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry&Technology Institute(KEITI)funded by the Ministry of Environment(MOE).
文摘Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were frequently observed during the heating season.Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM_(2.5) as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities.Secondary nitrate had the highest contribution for Beijing(37.3%),and residential heating/biomass burning was the largest for Baoding(27.1%).Secondary nitrate,mobile,biomass burning,district heating,oil combustion,aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period(2019.01.10–2019.08.22)(Mann-Whitney Rank Sum Test P<0.05).In case of Baoding,soil,residential heating/biomass burning,incinerator,coal combustion,oil combustion sources showed significant differences.The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients.Conditional Bivariate Probability Function(CBPF)was used to identify the inflow directions for the sources,and joint-PSCF(Potential Source Contribution Function)was performed to determine the common potential source areas for sources affecting both cities.These models facilitated a more precise verification of city-specific influences on PM_(2.5) sources.The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities.
基金Supported by Shaanxi Provincial Overall Innovation Project of Science and Technology,China(Grant No.2013KTCQ01-06)
文摘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.
基金supported by Shandong Science and Technology Development Plan Project(No.Y2006C02)
文摘Objective: To discuss the expression and significance of angiostatin, vascular endothelial growth factor and matrix metalloproteinase-9 in the brain tissue of diabetic rats with ischemia reperfusion. Methods: A total of 60 male Wistar rats were randomly divided into the normal group, sham group, diabetic cerebral infarction group and single cerebral infarction group according to the random number table, with 15 rats in each group. The high sucrose diet and intraperitoneal injection of streptozotocin were performed for the modeling of diabetic rats, while the thread-occlusion method was employed to build the model of cerebral ischemia reperfusion. The immunohistochemical staining was performed to detect the expression of angiostatin, vascular endothelial growth factor(VEGF) and matrix metalloproteinase-9(MMP-9) in the brain tissue. Results: The expression of angiostatin after the reperfusion in the brain tissue of rats in the single cerebral infarction group and diabetic cerebral infarction group was increased 6 h after the reperfusion, reached to the peak on 1 d and then decreased gradually. The expression of angiostatin in the diabetic cerebral infarction group 6 h, 1 d, 3 d and 7 d after the reperfusion was significantly higher than that in the single cerebral infarction group(P<0.05). VEGF began to be increased 1 h after the reperfusion in the single cerebral infarction group and diabetic cerebral infarction group, reached to the peak at 6 h and then decreased gradually. The expression of VEGF in the diabetic cerebral infarction group at each time point after the reperfusion was significantly lower than that in the single cerebral infarction group(P<0.05). MMP-9 began to be be increased 1 h after the reperfusion in the single cerebral infarction group and diabetic cerebral infarction group, reached to the peak on 1 d and then decreased gradually. The expression of MMP-9 in the diabetic cerebral infarction group at each time point after the reperfusion was significantly higher than that in the single cerebral infarction group(P<0.05). Conclusions: The high glucose environment in which the diabetic cerebral infarction is occurred is to induce the formation of MMP-9 at first and then activate and increase the expression of angiostatin. Afterwards, the expression of VEGF is inhibited, resulting in the poor angiogenesis after cerebral infarction, which thus makes the injury of brain tissue after cerebral infarction even worse than the non-diabetes mellitus.
基金financially supported by the Nord-Pas-de-Calais Region Councilthe Ministry of Higher Education and Research+1 种基金the European Regional Development FundsAdib Kfoury acknowledges the“Pole Metropolitain Cote d'Opale”(PMCO)for its PhD financial support
文摘The constrained weighted-non-negative matrix factorization(CW-NMF)hybrid receptor model was applied to study the influence of steelmaking activities on PM_(2.5)(particulate matter with equivalent aerodynamic diameter less than 2.5μm)composition in Dunkerque,Northern France.Semi-diurnal PM_(2.5)samples were collected using a high volume sampler in winter 2010 and spring 2011 and were analyzed for trace metals,water-soluble ions,and total carbon using inductively coupled plasma–atomic emission spectrometry(ICP-AES),ICP-mass spectrometry(ICP-MS),ionic chromatography and micro elemental carbon analyzer.The elemental composition shows that NO_(3)^(-),SO_(4)^(2-),NH_4~+and total carbon are the main PM_(2.5)constituents.Trace metals data were interpreted using concentration roses and both influences of integrated steelworks and electric steel plant were evidenced.The distinction between the two sources is made possible by the use Zn/Fe and Zn/Mn diagnostic ratios.Moreover Rb/Cr,Pb/Cr and Cu/Cd combination ratio are proposed to distinguish the ISW-sintering stack from the ISW-fugitive emissions.The a priori knowledge on the influencing source was introduced in the CW-NMF to guide the calculation.Eleven source profiles with various contributions were identified:8 are characteristics of coastal urban background site profiles and 3 are related to the steelmaking activities.Between them,secondary nitrates,secondary sulfates and combustion profiles give the highest contributions and account for 93%of the PM_(2.5)concentration.The steelwork facilities contribute in about 2%of the total PM_(2.5)concentration and appear to be the main source of Cr,Cu,Fe,Mn,Zn.
文摘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.
基金This work was supported in part by the National Natural Science Foundation of China(51679105,51809112,51939003,61872160)“Thirteenth Five Plan”Science and Technology Project of Education Department,Jilin Province(JJKH20200990KJ).
文摘A current problem in diet recommendation systems is the matching of food preferences with nutritional requirements,taking into account individual characteristics,such as body weight with individual health conditions,such as diabetes.Current dietary recommendations employ association rules,content-based collaborative filtering,and constraint-based methods,which have several limitations.These limitations are due to the existence of a special user group and an imbalance of non-simple attributes.Making use of traditional dietary recommendation algorithm researches,we combine the Adaboost classifier with probabilistic matrix factorization.We present a personalized diet recommendation algorithm by taking advantage of probabilistic matrix factorization via Adaboost.A probabilistic matrix factorization method extracts the implicit factors between individual food preferences and nutritional characteristics.From this,we can make use of those features with strong influence while discarding those with little influence.After incorporating these changes into our approach,we evaluated our algorithm’s performance.Our results show that our method performed better than others at matching preferred foods with dietary requirements,benefiting user health as a result.The algorithm fully considers the constraint relationship between users’attributes and nutritional characteristics of foods.Considering many complex factors in our algorithm,the recommended food result set meets both health standards and users’dietary preferences.A comparison of our algorithm with others demonstrated that our method offers high accuracy and interpretability.
基金supported by Project of Chongqing Science and Technology Bureau (cstc2022jxjl0005)。
文摘This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.
基金supported by the National Natural Science Foundation of China(61702251,41971424,61701191,U1605254)the Natural Science Basic Research Plan in Shaanxi Province of China(2018JM6030)+4 种基金the Key Technical Project of Fujian Province(2017H6015)the Science and Technology Project of Xiamen(3502Z20183032)the Doctor Scientific Research Starting Foundation of Northwest University(338050050)Youth Academic Talent Support Program of Northwest University(360051900151)the Natural Sciences and Engineering Research Council of Canada,Canada。
文摘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.
文摘Describing matrix–fracture interaction is one of the most important factors for modeling natural fractured reservoirs.A common approach for simulation of naturally fractured reservoirs is dual-porosity modeling where the degree of communication between the low-permeability medium(matrix)and high-permeability medium(fracture)is usually determined by a transfer function.Most of the proposed matrix–fracture functions depend on the geometry of the matrix and fractures that are lumped to a factor called shape factor.Unfortunately,there is no unique solution for calculating the shape factor even for symmetric cases.Conducting fine-scale modeling is a tool for calculating the shape factor and validating the current solutions in the literature.In this study,the shape factor is calculated based on the numerical simulation of fine-grid simulations for single-phase flow using finite element method.To the best of the author’s knowledge,this is the first study to calculate the shape factors for multidimensional irregular bodies in a systematic approach.Several models were used,and shape factors were calculated for both transient and pseudo-steady-state(PSS)cases,although in some cases they were not clarified and assumptions were not clear.The boundary condition dependency of the shape factor was also investigated,and the obtained results were compared with the results of other studies.Results show that some of the most popular formulas cannot capture the exact physics of matrix–fracture interaction.The obtained results also show that both PSS and transient approaches for describing matrix–fracture transfer lead to constant shape factors that are not unique and depend on the fracture pressure(boundary condition)and how it changes with time.
基金supported by the National Natural Science Foundation of China (61074131 and 91132722)the Doctoral Fund of the Ministry of Education of China (21101202110007)
文摘Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials （LFPs） based on sparse non negative matrix factorization （SNMF）. SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four SpragueDawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the powerincreased LFP components were selected as working memoryrelated features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory.
基金This project was supported by grants from National Excellent Young Scientists Foundation of China (No.39925035) the Major Clinical Project of Ministry of Health (No.22012332).
文摘The effect of vascular endothelial growth factor （VEGF） overexpression on matrix metalloproteinase-2 （MMP-2） in nasopharyngeal carcinoma （NPC） cells in vitro and the possible mechanism involved were investigated, and the correlation between the expression of VEGF and MMP-2 in NPC evaluated. The NPC cells were transfected with PAd-trackVEGF165 plasmid. The expression levels of VEGF and MMP-2 mRNA and protein in NPC cells were detected by semi-quantitative RT-PCR and Western blot respectively. It was found that the expression of VEGF and MMP-2 mRNA and protein was significantly increased in NPC cells after transfection of VEGF 165. It was concluded that the expression of VEGF was correlated to the in vitro invasion of NPC cells, and the induction of MMP-2 by VEGF was a key process of NPC cell invasion.
基金Supported by the National Natural Science Foundation of China(61374140,61403072)
文摘Traditional data driven fault detection methods assume that the process operates in a single mode so that they cannot perform well in processes with multiple operating modes. To monitor multimode processes effectively,this paper proposes a novel process monitoring scheme based on orthogonal nonnegative matrix factorization(ONMF) and hidden Markov model(HMM). The new clustering technique ONMF is employed to separate data from different process modes. The multiple HMMs for various operating modes lead to higher modeling accuracy.The proposed approach does not presume the distribution of data in each mode because the process uncertainty and dynamics can be well interpreted through the hidden Markov estimation. The HMM-based monitoring indication named negative log likelihood probability is utilized for fault detection. In order to assess the proposed monitoring strategy, a numerical example and the Tennessee Eastman process are used. The results demonstrate that this method provides efficient fault detection performance.
基金Supported by the National Natural Science Foundation of China （ No. 60872083 ） and the National High Technology Research and Development Program of China （No. 2007AA12Z149）.
文摘This paper considers a problem of unsupervised spectral unmixing of hyperspectral data. Based on the Linear Mixing Model （ LMM）, a new method under the framework of nonnegative matrix fac- torization （NMF） is proposed, namely minimum distance constrained nonnegative matrix factoriza- tion （MDC-NMF）. In this paper, firstly, a new regularization term, called endmember distance （ED） is considered, which is defined as the sum of the squared Euclidean distances from each end- member to their geometric center. Compared with the simplex volume, ED has better optimization properties and is conceptually intuitive. Secondly, a projected gradient （PG） scheme is adopted, and by the virtue of ED, in this scheme the optimal step size along the feasible descent direction can be calculated easily at each iteration. Thirdly, a finite step （ no more than the number of endmem- bers） terminated algorithm is used to project a point on the canonical simplex, by which the abun- dance nonnegative constraint and abundance sum-to-one constraint can be accurately satisfied in a light amount of computation. The experimental results, based on a set of synthetic data and real da- ta, demonstrate that, in the same running time, MDC-NMF outperforms several other similar meth- ods proposed recently.
基金Supported by National Natural Science Foundation of China (No. 60872065)
文摘An image fusion method combining complex contourlet transform(CCT) with nonnegative matrix factorization(NMF) is proposed in this paper.After two images are decomposed by CCT,NMF is applied to their highand low-frequency components,respectively,and finally an image is synthesized.Subjective-visual-quality of the image fusion result is compared with those of the image fusion methods based on NMF and the combination of wavelet /contourlet /nonsubsampled contourlet with NMF.The experimental results are evaluated quantitatively,and the running time is also contrasted.It is shown that the proposed image fusion method can gain larger information entropy,standard deviation and mean gradient,which means that it can better integrate featured information from all source images,avoid background noise and promote space clearness in the fusion image effectively.
基金supported by the National Natural Science Foundation of China(No.51877013),(ZJ),(http://www.nsfc.gov.cn/)the Natural Science Foundation of Jiangsu Province(No.BK20181463),(ZJ),(http://kxjst.jiangsu.gov.cn/)sponsored by Qing Lan Project of Jiangsu Province(no specific grant number),(ZJ),(http://jyt.jiangsu.gov.cn/).
文摘Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.
基金This work is supported by the Chinese Outstanding Youth Foundation (No. 69925308) Program for Changjiang Scholars and Innovative Research Team in University.
基金supported by the National Science Council(102-2320-B-324-001),Chinaupported by grants from Taipei Veterans General Hospital(V103E6-001&V104E6-001)by grants(MOST 104-2314-B-010-012-MY3,MOST 105-2314-B-010-013-MY2 and MOST 106-2632-B-324-001)from the Ministry of Science and Technology in Taiwan,China
文摘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.
基金supported by Basic Science Research Program(2015R1D1A1A01060538)through the National Research Foundation of Korea(NRF)funded from the Ministry of Education,Science and Technology of Korea
文摘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.
