Association Between Fetal Exposure to Famine and Anthropometric Measures in Adulthood: A Regression Discontinuity Approach.

OBJECTIVE: The aim of this study was to investigate the differences in six anthropometric measurements of people born during and immediately after the 1959 to 1961 Great Chinese Famine using a regression discontinuity approach. METHODS: Data were drawn from the baseline of the China Kadoorie Biobank study, and a subset of data from 76,912 participants was analyzed. We performed regression discontinuity among participants who were born during the famine (October 1959 to October 1962) and immediately after the famine period (November 1962 to October 1964) by using local linear and parametric regressions. All analyses were conducted by sex and study area. RESULTS: Significantly, there were increases of 0.30 kg/m2 (P = 0.007) in BMI, 0.81 kg (P = 0.028) in weight, 8.57 mm (P = 0.004) in waist circumference, and 5.07 mm (P = 0.004) in hip circumference for rural women who were exposed to famine during their fetal period compared with those who were not exposed to famine in utero. However, such statistically significant increases in anthropometric values were not observed in local linear regression and most parametric models among rural men or in the urban population. CONCLUSIONS: Rural Chinese women who were exposed to famine during the fetal period were observed to have higher levels of BMI, weight, waist circumference, and hip circumference in adulthood.

Color disease leaf image segmentation using NAMS superpixel algorithm.

BACKGROUND: Disease leaf segmentation in color image is used to explore the disease shape and lesion regions. It is of great significance for pathological diagnosis and pathological research. OBJECTIVE: This paper proposes a superpixel algorithm using Non-symmetry and Anti-packing Model with Squares (NAMS) for color image segmentation of leaf disease. METHODS: First of all, the NAMS model is presented for color leaf disease image representation. The model can segment images asymmetrically and preserve the characteristics of image context. Second, NAMS based superpixel (NAMS superpixel) algorithm is proposed for clustering pixels, which can represent large homogeneous areas by super squares. By this way, the impact of complex background and the data redundancy in image segmentation can be reduced. RESULTS: Experimental results indicate that compared with segmenting the original image directly and manipulating by Simple Linear Iterative Clustering (SLIC) superpixel, the proposed NAMS superpixel performs more excellently in not only saving storage but also adhering to the lesion region edge. CONCLUSIONS: The outcome of NAMS superpixel can be regarded as a preprocess procedure for leaf disease region detection since the method can segment the image into superpixel blocks and preserve the lesion area.

Concept Factorization With Adaptive Neighbors for Document Clustering.

In this paper, a novel concept factorization (CF) method, called CF with adaptive neighbors (CFANs), is proposed. The idea of CFAN is to integrate an ANs regularization constraint into the CF decomposition. The goal of CFAN is to extract the representation space that maintains geometrical neighborhood structure of the data. Similar to the existing graph-regularized CF, CFAN builds a neighbor graph weights matrix. The key difference is that the CFAN performs dimensionality reduction and finds the neighbor graph weights matrix simultaneously. An efficient algorithm is also derived to solve the proposed problem. We apply the proposed method to the problem of document clustering on the 20 Newsgroups, Reuters-21578, and TDT2 document data sets. Our experiments demonstrate the effectiveness of the method.

Brain MR image segmentation using NAMS in pseudo-color.

Image segmentation plays a crucial role in various biomedical applications. In general, the segmentation of brain Magnetic Resonance (MR) images is mainly used to represent the image with several homogeneous regions instead of pixels for surgical analyzing and planning. This paper proposes a new approach for segmenting MR brain images by using pseudo-color based segmentation with Non-symmetry and Anti-packing Model with Squares (NAMS). First of all, the NAMS model is presented. The model can represent the image with sub-patterns to keep the image content and largely reduce the data redundancy. Second, the key idea is proposed that convert the original gray-scale brain MR image into a pseudo-colored image and then segment the pseudo-colored image with NAMS model. The pseudo-colored image can enhance the color contrast in different tissues in brain MR images, which can improve the precision of segmentation as well as directly visual perceptional distinction. Experimental results indicate that compared with other brain MR image segmentation methods, the proposed NAMS based pseudo-color segmentation method performs more excellent in not only segmenting precisely but also saving storage.

Joint Sparse Representation and Embedding Propagation Learning: A Framework for Graph-Based Semisupervised Learning.

In this paper, we propose a novel graph-based semisupervised learning framework, called joint sparse representation and embedding propagation learning (JSREPL). The idea of JSREPL is to join EPL with sparse representation to perform label propagation. Like most of graph-based semisupervised propagation learning algorithms, JSREPL also constructs weights graph matrix from given data. Different from classical approaches which build weights graph matrix and estimate the labels of unlabeled data in sequence, JSREPL simultaneously builds weights graph matrix and estimates the labels of unlabeled data. We also propose an efficient algorithm to solve the proposed problem. The proposed method is applied to the problem of semisupervised image clustering using the ORL, Yale, PIE, and YaleB data sets. Our experiments demonstrate the effectiveness of our proposed algorithm.

HPIP promotes thyroid cancer cell growth, migration and EMT through activating PI3K/AKT signaling pathway.

Hematopoietic pre-B cell leukemia transcription factor (PBX)-interacting protein (HPIP), a co-repressor for the transcription factor PBX, is a nucleo-cytoplasmic shuttling protein. Increasing evidence suggests that HPIP is an oncogene which is frequently overexpressed in many human carcinomas. However, the role of HPIP in thyroid carcinoma is still unclear. Therefore, in this study, we investigated the role of HPIP in thyroid carcinoma, and explored the underling mechanism. We found that the expression of HPIP is upregulated in thyroid carcinoma cell lines. Knockdown of HPIP inhibits thyroid carcinoma cell proliferation, migration/invasion and epithelial-mesenchymal transition (EMT). HPIP knockdown also reduces thyroid tumor growth in nude mice. Furthermore, knockdown of HPIP significantly inhibits the expression of phosphorylated PI3K and AKT in thyroid carcinoma cells. Taken together, these results suggest that knockdown of HPIP inhibits the proliferation, migration and EMT by suppressing the PI3K/AKT pathway, and HPIP may be a potential therapeutic target for the treatment of thyroid carcinoma.

[Effects of Aptamer-siRNA Nucleic Acid Compound on Growth and Apoptosis in Myeloid Leukemia Cell Line K562].

OBJECTIVE: To explore the effects of aptamer-siRNA nucleic acid compound on growth and apoptosis in myeloid leukemia cell line K562. METHODS: the changes of cellular morphology and structure were observed by using fluorescence microscope, laser confocal microscope, JEM-4000EX transmission electron microscopy; MTT assay were performed to evaluate the sensibility of K562 cells to aptamer-siRNA compound, the apoptosis was detected by DNA gel electro-phoresis. RESULTS: The remarkably changes of morphology and structure of K562 cells treated with 200 µmol/L aptamer-siRNA were observed under fluorescence microscopy and electromicroscopy. As compared with control, the aptamer-siRNA compound showed more inhibitory effect on K562 cells and there was significant difference (P<0.05). The MTT assay showed that the IC50 value of aptamer-siRNA compound for K562 cells was 150 µmol/L. According to agarose gel electrophoresis observation, when the aptamer-siRNA compound showed effect on K562 cells, the typical DNA lader could be observed. CONCLUSION: The aptamer-siRNA compound can significantly induce K562 cell apoptosis, and provide reference for gene therapy of patients with chronic myelocytic lenkemia.

Manifold Adaptive Label Propagation for Face Clustering.

In this paper, a novel label propagation (LP) method is presented, called the manifold adaptive label propagation (MALP) method, which is to extend original LP by integrating sparse representation constraint into regularization framework of LP method. Similar to most LP, first of all, MALP also finds graph edges from given data and gives weights to the graph edges. Our goal is to find graph weights matrix adaptively. The key advantage of our approach is that MALP simultaneously finds graph weights matrix and predicts the label of unlabeled data. This paper also derives efficient algorithm to solve the proposed problem. Extensions of our MALP in kernel space and robust version are presented. The proposed method has been applied to the problem of semi-supervised face clustering using the well-known ORL, Yale, extended YaleB, and PIE datasets. Our experimental evaluations show the effectiveness of our method.

Automated graph regularized projective nonnegative matrix factorization for document clustering.

In this paper, a novel projective nonnegative matrix factorization (PNMF) method for enhancing the clustering performance is presented, called automated graph regularized projective nonnegative matrix factorization (AGPNMF). The idea of AGPNMF is to extend the original PNMF by incorporating the automated graph regularized constraint into the PNMF decomposition. The key advantage of this approach is that AGPNMF simultaneously finds graph weights matrix and dimensionality reduction of data. AGPNMF seeks to extract the data representation space that preserves the local geometry structure. This character makes AGPNMF more intuitive and more powerful than the original method for clustering tasks. The kernel trick is used to extend AGPNMF model related to the input space by some nonlinear map. The proposed method has been applied to the problem of document clustering using the well-known Reuters-21578, TDT2, and SECTOR data sets. Our experimental evaluations show that the proposed method enhances the performance of PNMF for document clustering.

A case-control study on the relationship between salt intake and salty taste and risk of gastric cancer.

AIM: To investigate the relationship between salt intake and salty taste and risk of gastric cancer. METHODS: A 1:2 matched hospital based case-control study including 300 patients with gastric cancer and 600 cancer-free subjects as controls. Subjects were interviewed with a structured questionnaire containing 80 items, which elicited information on dietary, lifestyle habits, smoking and drinking histories. Subjects were tested for salt taste sensitivity threshold (STST) using concentrated saline solutions (0.22-58.4 g/L). Conditional logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (95% CI). RESULTS: Alcohol and tobacco consumption increased the risk of gastric cancer [OR (95% CI) was 2.27 (1.27-4.04) for alcohol and 2.41 (1.51-3.87) for tobacco]. A protective effect was observed in frequent consumption of fresh vegetable and fruit [OR (95% CI) was 0.92 (0.58-0.98) for fresh vegetable and 0.87 (0.67-0.93) for fruit]. Strong association was found between STST ≥ 5 and gastric cancer [OR = 5.71 (3.18-6.72)]. Increased STST score was significantly associated with salted food intake and salty taste preference (P < 0.05). CONCLUSION: A high STST score is strongly associated with gastric cancer risk. STST can be used to evaluate an inherited characteristic of salt preference, and it is a simple index to verify the salt intake in clinic.

A hybrid neural network system for prediction and recognition of promoter regions in human genome.

This paper proposes a high specificity and sensitivity algorithm called PromPredictor for recognizing promoter regions in the human genome. PromPredictor extracts compositional features and CpG islands information from genomic sequence, feeding these features as input for a hybrid neural network system (HNN) and then applies the HNN for prediction. It combines a novel promoter recognition model, coding theory, feature selection and dimensionality reduction with machine learning algorithm. Evaluation on Human chromosome 22 was approximately 66% in sensitivity and approximately 48% in specificity. Comparison with two other systems revealed that our method had superior sensitivity and specificity in predicting promoter regions. PromPredictor is written in MATLAB and requires Matlab to run. PromPredictor is freely available at http://www.whtelecom.com/Prompredictor.htm.

A strict solution for the optimal superimposition of protein structures.

Existing methods for the optimal superimposition of one vector set on another in the comparison of parts or the whole of related protein molecules are based on the precondition that the centroids of the two sets are coincident. As a result, the translation components of the transformation are artificially removed from the superimposition process. This is obviously not strict in the mathematical sense. The theorem presented in this paper is a strict solution for the optimal superimposition of two vector sets, which is in fact the problem of the weighted optimal rigid superimposition of two vector sets. Examples show its advantages compared with the method of simply coinciding the centroids of the two vector sets for the translation transformation.