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 pr...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.展开更多
AIM： To explore a feasible method on the establishment of an animal model of conjunctivochalasis （CCH）. METHODS： Twelve clean-grade New Zealand white rabbits were divided into four groups （n=3/group）： the contr...AIM： To explore a feasible method on the establishment of an animal model of conjunctivochalasis （CCH）. METHODS： Twelve clean-grade New Zealand white rabbits were divided into four groups （n=3/group）： the control group （one received no interventions, and the others underwent subconjunctival injection of sterile water）, the matrix metalloproteinases （MMPs） group （administered subconjunctival injection of MMP-3）, the aging group （administered subcutaneous injection of D-galactose）, the tumor necrosis factor-α （TNF-α） solution group （administered eye drops of TNF-α）. Anterior segment photography, conjunctival tissue light microscopy and transmission electron microscopy （TEM） were performed after 12wk. RESULTS： Among all groups, the MMPs group had the following changes： the looser connection between the inferior bulbar conjunctiva and sclera; the more disordered collagen fibers （Trichrome staining） and the broken elastic fibers （Aldehyde-fuchsin staining）; the focal necrosis of fibroblasts （TEM）. CONCLUSION： Administration of MMPs may be a feasible method for the establishment of an animal model of CCH.展开更多
The germinal matrix being an accumulation of immature blood vessels in the premature infant brain is known to be the main cause of the intracranial hemorrhage. To investigate the injuring mechanism to the blood vessel...The germinal matrix being an accumulation of immature blood vessels in the premature infant brain is known to be the main cause of the intracranial hemorrhage. To investigate the injuring mechanism to the blood vessels of the germinal matrix, a modeling scenario that consists of three basic steps is proposed. First, the cerebral blood flow that depends on autoregulation, CO2 reactivity, and variations of intracranial pressure is modeled. Second, the chaotic blood vessel network of the germinal matrix is generated, and blood pressures in the vessels of this network are computed dependent on the outcome of the first step. In the third step, the pressures computed on the second step are used in finite element simulations of separate blood vessels of the germinal matrix to detect critical values for vessels impairment.展开更多
The characterization of reinforcement in 15% SiC particles reinforced AI matrix composites processed by powder metallurgy route was studied by statistical method. During the analysis, a new approach for the estimation...The characterization of reinforcement in 15% SiC particles reinforced AI matrix composites processed by powder metallurgy route was studied by statistical method. During the analysis, a new approach for the estimation of the characterization of reinforcement was presented. The mathematic software MATLAB was used to calculate the area and perimeter of reinforcement, in which the image processing technique was applied. Based on the calculation, the fractal dimension, shape factor, reinforcement size distribution and reinforcement distribution were investigated. The results show that the reinforcement shape is similar to rectangle; the reinforcement size distribution is broad with the＇ range of 1-12 μm; the topography of reinforcement is smooth; and the reinforcement distribution is inhomogeneous. Furthermore, the cell model based on the statistical characterization was established and tested.展开更多
Environmental problems from heavy metals(HMs)attract global attention.Accurately identifying sources and quantitatively evaluating ecological risks are keys for HMs pollution prevention.Dongting Lake in China was inve...Environmental problems from heavy metals(HMs)attract global attention.Accurately identifying sources and quantitatively evaluating ecological risks are keys for HMs pollution prevention.Dongting Lake in China was investigated through integrated methods like positive matrix factorization and Nemerow integrated risk index to examine spatial distribution,contamination characteristics,pollution sources,and the contribution of each source and pollutant to the ecological risk of 14 HMs in its surface sediments.Results showed that the mean concentrations of HMs were 0.82-9.44 times greater than the corresponding background values.The spatial distribution of HMs varied significantly,with high values of As,Cd,Mn,Pb,Sn,Tl and Zn concentrated in the sediments from Xiangjiang inlet and Yangtze outlet;Co,Cr,Cu,Ni and V in the Lishui sediments;Hg and Sb in the sediments from Yuanjiang and Zishui inlets,respectively.The accumulation of HMs was affected by five sources:mercury mining and atmospheric deposition(F1)(17.99%),urban domestic sewage and industrial sewage discharge(F2)(24.44%),antimony ore mining and smelting(F3)(6.50%),non-ferrous metal mining and extended processing industrial sources(F4)(15.72%),and mixed sources mainly from natural sources and agricultural sources(F5)(35.35%).F1 and F2 were identified as priority pollution sources;Cd,Hg,Tl,Sb and As,especially Cd and Hg,posed relatively high ecological risks and were prioritized HMs for control.展开更多
文摘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.
基金Supported by the Key Medical Discipline Project of Shanghai Municipal Health Bureau-Ophthalmology(No.ZK2015A20)the Health System Independent Innovation Science Foundation of Shanghai Putuo District(No.2015PTKW001)Plateau Science,Integrated Traditional Chinese and Western Medicine,Shanghai University of Traditional Chinese Medicine
文摘AIM： To explore a feasible method on the establishment of an animal model of conjunctivochalasis （CCH）. METHODS： Twelve clean-grade New Zealand white rabbits were divided into four groups （n=3/group）： the control group （one received no interventions, and the others underwent subconjunctival injection of sterile water）, the matrix metalloproteinases （MMPs） group （administered subconjunctival injection of MMP-3）, the aging group （administered subcutaneous injection of D-galactose）, the tumor necrosis factor-α （TNF-α） solution group （administered eye drops of TNF-α）. Anterior segment photography, conjunctival tissue light microscopy and transmission electron microscopy （TEM） were performed after 12wk. RESULTS： Among all groups, the MMPs group had the following changes： the looser connection between the inferior bulbar conjunctiva and sclera; the more disordered collagen fibers （Trichrome staining） and the broken elastic fibers （Aldehyde-fuchsin staining）; the focal necrosis of fibroblasts （TEM）. CONCLUSION： Administration of MMPs may be a feasible method for the establishment of an animal model of CCH.
文摘The germinal matrix being an accumulation of immature blood vessels in the premature infant brain is known to be the main cause of the intracranial hemorrhage. To investigate the injuring mechanism to the blood vessels of the germinal matrix, a modeling scenario that consists of three basic steps is proposed. First, the cerebral blood flow that depends on autoregulation, CO2 reactivity, and variations of intracranial pressure is modeled. Second, the chaotic blood vessel network of the germinal matrix is generated, and blood pressures in the vessels of this network are computed dependent on the outcome of the first step. In the third step, the pressures computed on the second step are used in finite element simulations of separate blood vessels of the germinal matrix to detect critical values for vessels impairment.
文摘The characterization of reinforcement in 15% SiC particles reinforced AI matrix composites processed by powder metallurgy route was studied by statistical method. During the analysis, a new approach for the estimation of the characterization of reinforcement was presented. The mathematic software MATLAB was used to calculate the area and perimeter of reinforcement, in which the image processing technique was applied. Based on the calculation, the fractal dimension, shape factor, reinforcement size distribution and reinforcement distribution were investigated. The results show that the reinforcement shape is similar to rectangle; the reinforcement size distribution is broad with the＇ range of 1-12 μm; the topography of reinforcement is smooth; and the reinforcement distribution is inhomogeneous. Furthermore, the cell model based on the statistical characterization was established and tested.
基金financially supported by the Key Research and Development Program of Hunan Province,China(No.2023SK2006)the Natural Science Foundation of Hunan Province,China(No.2023JJ50057)+2 种基金the Science and Technology Plan Project of Geological Bureau of Hunan Province,China(No.HNGSTP202411)the Open Project of Key Laboratory of the Ministry of Natural Resources,China(No.BL202105)the Natural Science Foundation of Changsha City,China(No.kq2202090)。
文摘Environmental problems from heavy metals(HMs)attract global attention.Accurately identifying sources and quantitatively evaluating ecological risks are keys for HMs pollution prevention.Dongting Lake in China was investigated through integrated methods like positive matrix factorization and Nemerow integrated risk index to examine spatial distribution,contamination characteristics,pollution sources,and the contribution of each source and pollutant to the ecological risk of 14 HMs in its surface sediments.Results showed that the mean concentrations of HMs were 0.82-9.44 times greater than the corresponding background values.The spatial distribution of HMs varied significantly,with high values of As,Cd,Mn,Pb,Sn,Tl and Zn concentrated in the sediments from Xiangjiang inlet and Yangtze outlet;Co,Cr,Cu,Ni and V in the Lishui sediments;Hg and Sb in the sediments from Yuanjiang and Zishui inlets,respectively.The accumulation of HMs was affected by five sources:mercury mining and atmospheric deposition(F1)(17.99%),urban domestic sewage and industrial sewage discharge(F2)(24.44%),antimony ore mining and smelting(F3)(6.50%),non-ferrous metal mining and extended processing industrial sources(F4)(15.72%),and mixed sources mainly from natural sources and agricultural sources(F5)(35.35%).F1 and F2 were identified as priority pollution sources;Cd,Hg,Tl,Sb and As,especially Cd and Hg,posed relatively high ecological risks and were prioritized HMs for control.
