Identifying biomarkers for evaluating wound extent and age in the contused muscle of rats using microarray analysis: a pilot study

Wound age estimation is still one of the most important and significant challenges in forensic practice. The extent of wound damage greatly affects the accuracy and reliability of wound age estimation, so it is important to find effective biomarkers to help diagnose wound degree and wound age. In the present study, the gene expression profiles of both mild and severe injuries in 33 rats were assayed at 0, 1, 3, 24, 48, and 168 hours using the Affymetrix microarray system to provide biomarkers for the evaluation of wound age and the extent of the wound. After obtaining thousands of differentially expressed genes, a principal component analysis, the least absolute shrinkage and selection operator, and a time-series analysis were used to select the most predictive prognostic genes. Finally, 15 genes were screened for evaluating the extent of wound damage, and the top 60 genes were also screened for wound age estimation in mild and severe injury. Selected indicators showed good diagnostic performance for identifying the extent of the wound and wound age in a Fisher discriminant analysis. A function analysis showed that the candidate genes were mainly related to cell proliferation and the inflammatory response, primarily IL-17 and the Hematopoietic cell lineage signalling pathway. The results revealed that these genes play an essential role in wound-healing and yield helpful and valuable potential biomarkers for further targeted studies.

Research on complex attribute big data classification based on iterative fuzzy clustering algorithm

In order to overcome the low classification accuracy of traditional methods, this paper proposes a new classification method of complex attribute big data based on iterative fuzzy clustering algorithm. Firstly, principal component analysis and kernel local Fisher discriminant analysis were used to reduce dimensionality of complex attribute big data. Then, the Bloom Filter data structure is introduced to eliminate the redundancy of the complex attribute big data after dimensionality reduction. Secondly, the redundant complex attribute big data is classified in parallel by iterative fuzzy clustering algorithm, so as to complete the complex attribute big data classification. Finally, the simulation results show that the accuracy, the normalized mutual information index and the Richter’s index of the proposed method are close to 1, the classification accuracy is high, and the RDV value is low, which indicates that the proposed method has high classification effectiveness and fast convergence speed.

A Novel Three-stage Feature Fusion Methodology and its Application in Degradation State Identification for Hydraulic Pumps

The performance of feature is essential to the degradation state identification for hydraulic pumps. The initial feature set extracted from the vibration signal of the hydraulic pump is often high-dimensional and contains redundant information, which undermines the effectiveness of the feature set. The novel three-stage feature fusion scheme proposed in this paper aims to enhance the performance of the original features extracted from the vibration signal. First, sparse local Fisher discriminant analysis (SLFDA) performs intra-set fusion within the two original feature sets, respectively. SLFDA has a good effect on samples with intra-class multimodality, and the feature set fused by it has obvious multivariate normal distribution characteristics, which is conducive to the next fusion. Second, our modified intra-class correlation analysis (MICA) is used to fuse two feature sets in the second stage. MICA is a CCA (Canonical correlation analysis) -based method. A new class matrix is used to modify the covariance matrix between two feature sets, which allows MICA to conveniently inherit the discriminating structure while fusing features. Finally, we propose a feature selection algorithm based on kernel local Fisher discriminant analysis (KLFDA) and kernel canonical correlation analysis (KCCA) to select the desired features. This algorithm based on Max-Relevance and Min-Redundancy (mRMR) framework solves the problem that CCA cannot properly evaluate the correlation between features and the class variable, as well as accurately evaluates the correlation among features. Based on the experimental data, the proposed method is compared with several popular methods, and the feature fusion methods used in some previous studies related to the fault diagnosis of rotating machinery are compared with it as well. The results show that the fusion effectiveness of our method is better than other methods, which obtains higher recognition accuracy.

Information-based summary statistics for spatial genetic structure inference

Inference of spatial patterns of genetic structure often relies on parameter estimation and model evaluation using a set of summary statistics (SS) that summarise the information present in the data. An important subset of these SS is best described as diversity indices, which are based on information theory principles that can be classified as belonging to three different ‘families’ encompassing a spectrum of information measures, qH. These include the richness family of order q = 0, ArSS; the Shannon family of order q = 1, HSS; and the heterozygosity family of order q = 2, HeSS. Although commonly used by ecologists, the Shannon family has been rather neglected by population geneticists and evolutionary biologists. However, recent population genetic studies have advocated their use, yet the power of these SS for spatial structure discrimination has not been systematically assessed.In this study, we performed a comprehensive assessment of the three families of SS, as well as a fourth family consisting of SS belonging to the Shannon family but expressed in terms of Hill numbers , for spatial structure inference using simulated microsatellites data under typical spatial scenarios. To give an unbiased evaluation, we used three machine learning methods, Kernel Local Fisher discriminant analysis (KLFDA), random forest classification (RFC), and deep neural network (DL), to test the performance of different SS to discriminate between spatial scenarios, and then identified the most informative metrics for discriminatory power.Results showed that the SS family of order q = 1 expressed in terms of Hill numbers, , outperformed the other two families (ArSS, HeSS) as well as the untransformed Shannon entropy (HSS) family. Jaccard dissimilarity (J) and its Mantel’s r showed the highest discriminatory power to discriminate all spatial scenarios, followed by Shannon differentiation ΔD and its Mantel’s r.Information-based summary statistics, especially the diversity of order q = 1 and Shannon differentiation measures, can increase the power of spatial structure inference. In addition, different sets of SS provide complementary power for discriminating between spatial scenarios.

Revisiting the performance of PCA versus FDA versus Simple Projection for image recognition

In this paper, our main aim is to show a better dimension reduction process of high dimensional image data sets from several existing techniques. To verify it we start with most useful singular value decomposition to reduce the dimensionality of data to incorporate principal components. On the other hand, we classify data in advance to work out Fisher’s discriminant. From many real-world examples, we set a very well-known paradigm of analysis using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) or Fisher Discriminant Analysis (FDA) and Simple Projection (SP) to recognize people from their facial images. We consider that we have some images of known people that can be used to compare and recognize new images (of the same set of face images). Moreover, we show graphical and tabular representation for average performance of correct recognition as well as analyze the effectiveness of three different machine learning techniques.