Expression of Ets-1, Ang-2 and maspin in ovarian cancer and their role in tumor angiogenesis

<p>Abstract</p> <p>Background</p> <p>Various angiogenic regulators are involved in angiogenesis cascade. Transcription factor Ets-1 plays important role in angiogenesis, remodeling of extracellular matrix, and tumor metastasis. Ets-1 target genes involved in various stages of new blood vessel formation include angiopoietin, matrix metalloproteinases (MMPs) and the protease inhibitor maspin.</p> <p>Methods</p> <p>We used immunohistochemistry (IHC) to detect the expression of Ets-1, angiopoietin-2 (Ang-2) and maspin in ovarian tumor and analyzed the relationship between the expression of these proteins and the clinical manifestation of ovarian cancer.</p> <p>Results</p> <p>Ets-1 expression was much stronger in ovarian cancer compared to benign tumors, but had no significant correlation with other pathological parameters of ovarian cancer. However, Ang-2 and maspin expression had no obvious correlation with pathological parameters of ovarian cancer. Ets-1 had a positive correlation with Ang-2 which showed their close relationship in angiogenesis. Although microvessel density (MVD) value had no significant correlation with the expression of Ets-1, Ang-2 or maspin, strong nuclear expression of maspin appeared to be correlated with high grade and MVD.</p> <p>Conclusions</p> <p>The expression of Ets-1, Ang2 and maspin showed close relationship with angiogenesis in ovarian cancer and expression of maspin appeared to be correlated with high grade and MVD. The mechanisms underlying the cross-talk of the three factors need further investigations.</p

Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery.

As hyperspectral imagery (HSI) contains rich spectral and spatial information, a novel principal component analysis (PCA) and segmented-PCA (SPCA)-based multiscale 2-D-singular spectrum analysis (2-D-SSA) fusion method is proposed for joint spectral–spatial HSI feature extraction and classification. Considering the overall spectra and adjacent band correlations of objects, the PCA and SPCA methods are utilized first for spectral dimension reduction, respectively. Then, multiscale 2-D-SSA is applied onto the SPCA dimension-reduced images to extract abundant spatial features at different scales, where PCA is applied again for dimensionality reduction. The obtained multiscale spatial features are then fused with the global spectral features derived from PCA to form multiscale spectral–spatial features (MSF-PCs). The performance of the extracted MSF-PCs is evaluated using the support vector machine (SVM) classifier. Experiments on four benchmark HSI data sets have shown that the proposed method outperforms other state-of-the-art feature extraction methods, including several deep learning approaches, when only a small number of training samples are available

A dynamic neighborhood learning-based gravitational search algorithm

Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named Kbest, which stores those superior agents after fitness sorting in each iteration. Since the global property of Kbest remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence. To address these problems, in this paper, we propose a dynamic neighborhood learning (DNL) strategy to replace the Kbest model and thereby present a DNL-based GSA (DNLGSA). The method incorporates the local and global neighborhood topologies for enhancing the exploration and obtaining adaptive balance between exploration and exploitation. The local neighborhoods are dynamically formed based on evolutionary states. To delineate the evolutionary states, two convergence criteria named limit value and population diversity, are introduced. Moreover, a mutation operator is designed for escaping from the local optima on the basis of evolutionary states. The proposed algorithm was evaluated on 27 benchmark problems with different characteristic and various difficulties. The results reveal that DNLGSA exhibits competitive performances when compared with a variety of state-of-the-art M-HS algorithms. Moreover, the incorporation of local neighborhood topology reduces the numbers of calculations of gravitational force and thus alleviates the high computational cost of GSA

A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging.

As a cutting-edge technique for denoising and feature extraction, singular spectrum analysis (SSA) has been applied successfully for feature mining in hyperspectral images (HSI). However, when applying SSA for in situ feature extraction in HSI, conventional pixel-based 1-D SSA fails to produce satisfactory results, while the band-image-based 2D-SSA is also infeasible especially for the popularly used line-scan mode. To tackle these challenges, in this article, a novel 1.5D-SSA approach is proposed for in situ spectral-spatial feature extraction in HSI, where pixels from a small window are used as spatial information. For each sequentially acquired pixel, similar pixels are located from a window centered at the pixel to form an extended trajectory matrix for feature extraction. Classification results on two well-known benchmark HSI datasets and an actual urban scene dataset have demonstrated that the proposed 1.5D-SSA achieves the superior performance compared with several state-of-the-art spectral and spatial methods. In addition, the near real-time implementation in aligning to the HSI acquisition process can meet the requirement of online image analysis for more efficient feature extraction than the conventional offline workflow

Seroprevalence of Bartonella in Eastern China and analysis of risk factors

<p>Abstract</p> <p>Background</p> <p><it>Bartonella </it>infections are emerging in the Zhejiang Province of China. However, there has been no effort to date to explore the epidemiology of these infections in this region, nor to identify risk factors associated with exposure to <it>Bartonella</it>. The aim of this study was to investigate the seroprevalence of <it>Bartonella </it>in both patients bitten by dogs and blood donors (for control) in Eastern China, and to identify risk factors associated with exposure to <it>Bartonella</it>. As no previous data for this region have been published, this study will provide baseline data useful for <it>Bartonella </it>infection surveillance, control, and prevention.</p> <p>Methods</p> <p>Blood samples were collected from industrial rabies clinic attendees and blood donors living in eight areas of the Zhejiang Province of China, between December 2005 and November 2006. An indirect immunofluorescent antibody test was used to determine the presence of <it>Bartonella </it>in these samples. Risk factors associated with <it>Bartonella </it>exposure were explored using Chi-square tests and logistic regression analysis of epidemiological data relating to the study's participants.</p> <p>Results</p> <p><it>Bartonella </it>antibodies were detected in 19.60% (109/556) of blood samples. Seroprevalence varied among the eight areas surveys, ranging from over 32% in Hangzhou to only 2% in Jiangshan (X²= 28.22, P < 0.001). We detected a significantly higher prevalence of <it>Bartonella </it>antibodies in people who had been bitten by dogs than in blood donors (X²= 13.86, P < 0.001). Seroprevalence of <it>Bartonella </it>was similar among males (18.61%, n = 317) and females (20.92%, n = 239).</p> <p>Conclusions</p> <p><it>Bartonella </it>antibodies were encountered in people living across Zhejiang Province and the seropositivity rate among those exposed to dog bites was significantly higher than that among blood donors, indicating that dog bites may be a risk factor for <it>Bartonella </it>infection.</p

Tensor singular spectral analysis for 3D feature extraction in hyperspectral images.

Due to the cubic structure of a hyperspectral image (HSI), how to characterize its spectral and spatial properties in three dimensions is challenging. Conventional spectral-spatial methods usually extract spectral and spatial information separately, ignoring their intrinsic correlations. Recently, some 3D feature extraction methods are developed for the extraction of spectral and spatial features simultaneously, although they rely on local spatial-spectral regions and thus ignore the global spectral similarity and spatial consistency. Meanwhile, some of these methods contain huge model parameters which require a large number of training samples. In this paper, a novel Tensor Singular Spectral Analysis (TensorSSA) method is proposed to extract global and low-rank features of HSI. In TensorSSA, an adaptive embedding operation is first proposed to construct a trajectory tensor corresponding to the entire HSI, which takes full advantage of the spatial similarity and improves the adequate representation of the global low-rank properties of the HSI. Moreover, the obtained trajectory tensor, which contains the global and local spatial and spectral information of the HSI, is decomposed by the Tensor singular value decomposition (t-SVD) to explore its low-rank intrinsic features. Finally, the efficacy of the extracted features is evaluated using the accuracy of image classification with a support vector machine (SVM) classifier. Experimental results on three publicly available datasets have fully demonstrated the superiority of the proposed TensorSSA over a few state-of-the-art 2D/3D feature extraction and deep learning algorithms, even with a limited number of training samples

A novel band selection and spatial noise reduction method for hyperspectral image classification.

As an essential reprocessing method, dimensionality reduction (DR) can reduce the data redundancy and improve the performance of hyperspectral image (HSI) classification. A novel unsupervised DR framework with feature interpretability, which integrates both band selection (BS) and spatial noise reduction method, is proposed to extract low-dimensional spectral-spatial features of HSI. We proposed a new Neighboring band Grouping and Normalized Matching Filter (NGNMF) for BS, which can reduce the data dimension whilst preserve the corresponding spectral information. An enhanced 2-D singular spectrum analysis (E2DSSA) method is also proposed to extract the spatial context and structural information from each selected band, aiming to decrease the intra-class variability and reduce the effect of noise in the spatial domain. The support vector machine (SVM) classifier is used to evaluate the effectiveness of the extracted spectral-spatial low-dimensional features. Experimental results on three publicly available HSI datasets have fully demonstrated the efficacy of the proposed NGNMF-E2DSSA method, which has surpassed a number of state-of-the-art DR methods

Coastal wetland mapping with sentinel-2 MSI imagery based on gravitational optimized multilayer perceptron and morphological attribute profiles.

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery

SpaSSA: superpixelwise adaptive SSA for unsupervised spatial-spectral feature extraction in hyperspectral image.

Singular spectral analysis (SSA) has recently been successfully applied to feature extraction in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D SSA in spatial domain. However, there are some drawbacks, such as sensitivity to the window size, high computational complexity under a large window, and failing to extract joint spectral-spatial features. To tackle these issues, in this article, we propose superpixelwise adaptive SSA (SpaSSA), that is superpixelwise adaptive SSA for exploiting local spatial information of HSI. The extraction of local (instead of global) features, particularly in HSI, can be more effective for characterizing the objects within an image. In SpaSSA, conventional SSA and 2-D SSA are combined and adaptively applied to each superpixel derived from an oversegmented HSI. According to the size of the derived superpixels, either SSA or 2-D singular spectrum analysis (2D-SSA) is adaptively applied for feature extraction, where the embedding window in 2D-SSA is also adaptive to the size of the superpixel. Experimental results on the three datasets have shown that the proposed SpaSSA outperforms both SSA and 2D-SSA in terms of classification accuracy and computational complexity. By combining SpaSSA with the principal component analysis (SpaSSA-PCA), the accuracy of land-cover analysis can be further improved, outperforming several state-of-the-art approaches

Large kernel spectral and spatial attention networks for hyperspectral image classification.

Currently, long-range spectral and spatial dependencies have been widely demonstrated to be essential for hyperspectral image (HSI) classification. Due to the transformer superior ability to exploit long-range representations, the transformer-based methods have exhibited enormous potential. However, existing transformer-based approaches still face two crucial issues that hinder the further performance promotion of HSI classification: 1) treating HSI as 1D sequences neglects spatial properties of HSI, 2) the dependence between spectral and spatial information is not fully considered. To tackle the above problems, a large kernel spectral-spatial attention network (LKSSAN) is proposed to capture the long-range 3D properties of HSI, which is inspired by the visual attention network (VAN). Specifically, a spectral-spatial attention module is first proposed to effectively exploit discriminative 3D spectral-spatial features while keeping the 3D structure of HSI. This module introduces the large kernel attention (LKA) and convolution feed-forward (CFF) to flexibly emphasize, model, and exploit the long-range 3D feature dependencies with lower computational pressure. Finally, the features from the spectral-spatial attention module are fed into the classification module for the optimization of 3D spectral-spatial representation. To verify the effectiveness of the proposed classification method, experiments are executed on four widely used HSI data sets. The experiments demonstrate that LKSSAN is indeed an effective way for long-range 3D feature extraction of HSI