AIM: To study the metabolic profiling of serum samples from compensated and decompensated cirrhosis patients. METHODS: A pilot metabolic profiling study was conducted using three groups: compensated cirrhosis patients...AIM: To study the metabolic profiling of serum samples from compensated and decompensated cirrhosis patients. METHODS: A pilot metabolic profiling study was conducted using three groups: compensated cirrhosis patients (n = 30), decompensated cirrhosis patients (n = 30) and healthy controls (n = 30). A 1H nuclear magnetic resonance (NMR)-based metabonomics approach was used to obtain the serum metabolic profiles of the samples. The acquired data were processed by multivariate principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA). RESULTS: The OPLS-DA model was capable of distinguishing between decompensated and compensated cirrhosis patients, with an R2Y of 0.784 and a Q2Y of 0.598. Twelve metabolites, such as pyruvate, phenylala-nine and succinate, were identified as the most influential factors for the difference between the two groups. The validation of the diagnosis prediction showed that the accuracy of the OPLS-DA model was 85% (17/20). CONCLUSION: 1H NMR spectra combined with pattern recognition analysis techniques offer a new way to diagnose compensated and decompensated cirrhosis in the future.展开更多
In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-s...In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） al.gorithm is used to build soft-senor models for some secondary variables.then, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.展开更多
Massive human interference in natural ecosystems is resulting in a few ＂winners＂ and many ＂losers＂. However, the drivers of this winner-loser replacement pattern remain poorly understood. The aim of the study repo...Massive human interference in natural ecosystems is resulting in a few ＂winners＂ and many ＂losers＂. However, the drivers of this winner-loser replacement pattern remain poorly understood. The aim of the study reported here was to identify winners among the tree flora of Xishuangbanna and compare their functional traits, specific leaf area （SLA）, wood density （WD）, seed mass （SM） and maximum height （MH） with previously identified losers （i.e., endangered species）. Fifteen native tree species were identified as winners from expert opinion, plot-based surveys of secondary forests and plotless surveys along roads. Twelve endangered tree species for which trait information could be obtained were used for comparison. Traits were compared with a Wilcoxon rank-sum test. Winners had significantly higher SLA, but lower WD. SM and MH did not differ significantly between groups. When the effects of phylogeny were removed by using phylogenetic generalized least squares, the difference in SLA became marginally insignificant. Principal component analysis resulted in two overlapping groups, showing that the selected traits were insufficient to distinguish winners and losers. Our results suggest that the ＂few winners, many losers＂ paradigm applies to trees in Xishuangbanna, with15 species accounting for most trees in the disturbed habitats sampled.展开更多
We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis(PCA) subspace, and then we employ an L_1 reg...We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis(PCA) subspace, and then we employ an L_1 regularization to restrict the sparsity of the residual term, an L_2 regularization term to restrict the sparsity of the representation coefficients, and an L_2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.展开更多
文摘AIM: To study the metabolic profiling of serum samples from compensated and decompensated cirrhosis patients. METHODS: A pilot metabolic profiling study was conducted using three groups: compensated cirrhosis patients (n = 30), decompensated cirrhosis patients (n = 30) and healthy controls (n = 30). A 1H nuclear magnetic resonance (NMR)-based metabonomics approach was used to obtain the serum metabolic profiles of the samples. The acquired data were processed by multivariate principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA). RESULTS: The OPLS-DA model was capable of distinguishing between decompensated and compensated cirrhosis patients, with an R2Y of 0.784 and a Q2Y of 0.598. Twelve metabolites, such as pyruvate, phenylala-nine and succinate, were identified as the most influential factors for the difference between the two groups. The validation of the diagnosis prediction showed that the accuracy of the OPLS-DA model was 85% (17/20). CONCLUSION: 1H NMR spectra combined with pattern recognition analysis techniques offer a new way to diagnose compensated and decompensated cirrhosis in the future.
基金Supported by the National Natural Science Foundation of China （No.60421002） and the New Century 151 Talent Project of Zhejiang Province.
文摘In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） al.gorithm is used to build soft-senor models for some secondary variables.then, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.
基金supported by the 1000 Talents Program(WQ20110491035)
文摘Massive human interference in natural ecosystems is resulting in a few ＂winners＂ and many ＂losers＂. However, the drivers of this winner-loser replacement pattern remain poorly understood. The aim of the study reported here was to identify winners among the tree flora of Xishuangbanna and compare their functional traits, specific leaf area （SLA）, wood density （WD）, seed mass （SM） and maximum height （MH） with previously identified losers （i.e., endangered species）. Fifteen native tree species were identified as winners from expert opinion, plot-based surveys of secondary forests and plotless surveys along roads. Twelve endangered tree species for which trait information could be obtained were used for comparison. Traits were compared with a Wilcoxon rank-sum test. Winners had significantly higher SLA, but lower WD. SM and MH did not differ significantly between groups. When the effects of phylogeny were removed by using phylogenetic generalized least squares, the difference in SLA became marginally insignificant. Principal component analysis resulted in two overlapping groups, showing that the selected traits were insufficient to distinguish winners and losers. Our results suggest that the ＂few winners, many losers＂ paradigm applies to trees in Xishuangbanna, with15 species accounting for most trees in the disturbed habitats sampled.
基金supported by the National Natural Science Foundation of China(No.61401425)
文摘We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis(PCA) subspace, and then we employ an L_1 regularization to restrict the sparsity of the residual term, an L_2 regularization term to restrict the sparsity of the representation coefficients, and an L_2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.
