Special issue on microscopic image processing

Cataloged from PDF version of article

Implicit motif distribution based hybrid computational kernel for sequence classification

Motivation: We designed a general computational kernel for classification problems that require specific motif extraction and search from sequences. Instead of searching for explicit motifs, our approach finds the distribution of implicit motifs and uses as a feature for classification. Implicit motif distribution approach may be used as modus operandi for bioinformatics problems that require specific motif extraction and search, which is otherwise computationally prohibitive. Results: A system named P2SL that infer protein subcellular targeting was developed through this computational kernel. Targeting-signal was modeled by the distribution of subsequence occurrences (implicit motifs) using self-organizing maps. The boundaries among the classes were then determined with a set of support vector machines. P2SL hybrid computational system achieved ∼81% of prediction accuracy rate over ER targeted, cytosolic, mitochondrial and nuclear protein localization classes. P2SL additionally offers the distribution potential of proteins among localization classes, which is particularly important for proteins, shuttle between nucleus and cytosol. © The Author 2004. Published by Oxford University Press. All rights reserved

Identification of Novel Reference Genes Based on MeSH Categories

Cataloged from PDF version of article.Transcriptome experiments are performed to assess protein abundance through mRNA expression analysis. Expression levels of genes vary depending on the experimental conditions and the cell response. Transcriptome data must be diverse and yet comparable in reference to stably expressed genes, even if they are generated from different experiments on the same biological context from various laboratories. In this study, expression patterns of 9090 microarray samples grouped into 381 NCBI-GEO datasets were investigated to identify novel candidate reference genes using randomizations and Receiver Operating Characteristic (ROC) curves. The analysis demonstrated that cell type specific reference gene sets display less variability than a united set for all tissues. Therefore, constitutively and stably expressed, origin specific novel reference gene sets were identified based on their coefficient of variation and percentage of occurrence in all GEO datasets, which were classified using Medical Subject Headings (MeSH). A large number of MeSH grouped reference gene lists are presented as novel tissue specific reference gene lists. The most commonly observed 17 genes in these sets were compared for their expression in 8 hepatocellular, 5 breast and 3 colon carcinoma cells by RT-qPCR to verify tissue specificity. Indeed, commonly used housekeeping genes GAPDH, Actin and EEF2 had tissue specific variations, whereas several ribosomal genes were among the most stably expressed genes in vitro. Our results confirm that two or more reference genes should be used in combination for differential expression analysis of large-scale data obtained from microarray or next generation sequencing studies. Therefore context dependent reference gene sets, as presented in this study, are required for normalization of expression data from diverse technological backgrounds. © 2014 Ersahin et al

Special issue on microscopic image processing

[No abstract available

Cancer Cell Cytotoxicities of 1-(4-Substitutedbenzoyl)-4-(4-chlorobenzhydryl) piperazine Derivatives

Cataloged from PDF version of article.A series of novel 1-(4-substitutedbenzoyl)-4-(4-chlorobenzhydryl)piperazine derivatives 5a-g was designed by a nucleophilic substitution reaction of 1-(4-chlorobenzhydryl) piperazine with various benzoyl chlorides and characterized by elemental analyses, IR and H-1 nuclear magnetic resonance spectra. Cytotoxicity of the compounds was demonstrated on cancer cell lines from liver (HUH7, FOCUS, MAHLAVU, HEPG2, HEP3B), breast (MCF7, BT20, T47D, CAMA-1), colon (HCT-116), gastric (KATO-3) and endometrial (MFE-296) cancer cell lines. Time-dependent cytotoxicity analysis of compound 5a indicated the long-term in situ stability of this compound. All compounds showed significant cell growth inhibitory activity on the selected cancer cell lines

p53 mutations as fingerprints of environmental carcinogens

Mutations of the p53 tumor suppressor gene occur in a great majority of human cancers. The protein product of p53 gene is involved in DNA damage response. Consequently, p53 gene may be a preferred target for environmental carcinogens, which also act as DNA-damaging agents. This is probably why p53 mutations are frequent in cancers linked to environmental carcinogens. Moreover, these carcinogens leave molecular fingerprints on the p53 gene. Thus, the study of p53 mutation spectra has been a useful approach to implicate suspected carcinogens to different human cancers. This review provides further insight into the significance of p53 mutation spectra in ten common human malignancies (skin, liver, lung, bladder, breast, head and neck, esophagus, stomach and colorectal cancers, and hematological malignancies), in relation with environmental carcinogens

Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy Images

Cataloged from PDF version of article.More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms

Microscopic image classification via WT-based covariance descriptors using Kullback-Leibler distance

In this paper, we present a novel method for classification of cancer cell line images using complex wavelet-based region covariance matrix descriptors. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a new region descriptor utilizing the dual-tree complex wavelet transform coefficients as pixel features is computed. WT as a feature extraction tool is preferred primarily because of its ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines, and approximate shift invariance property. We propose new dissimilarity measures between covariance matrices based on Kullback-Leibler (KL) divergence and L 2-norm, which turn out to be as successful as the classical KL divergence, but with much less computational complexity. Experimental results demonstrate the effectiveness of the proposed image classification framework. The proposed algorithm outperforms the recently published eigenvalue-based Bayesian classification method. © 2012 IEEE

Microscopic image classification using sparsity in a transform domain and Bayesian learning

Some biomedical images show a large quantity of different junctions and sharp corners. It is possible to classify several types of biomedical images in a region covariance approach. Cancer cell line images are divided into small blocks and covariance matrices of image blocks are computed. Eigen-values of the covariance matrices are used as classification parameters in a Bayesian framework using the sparsity of the parameters in a transform domain. The efficiency of the proposed method over classification using standard Support Vector Machines (SVM) is demonstrated on biomedical image data. © 2011 EURASIP

Synthesis of Novel 6-(4-Substituted piperazine-1-yl)-9(b-D-ribofuranosyl) purine Derivatives, Which Lead to Senescence-Induced Cell Death in liver Cancer Cells

Cataloged from PDF version of article.Novel purine ribonucleoside analogues (9-13) containing a 4-substituted piperazine in the substituent at N-6 were synthesized and evaluated for their cytotoxicity on Huh7, HepG2, FOCUS, Mahlavu liver, MCF7 breast, and HCT116 colon carcinoma cell lines. The purine nucleoside analogues were analyzed initially by an anticancer drug-screening method based on a sulforhodamine B assay. Two nucleoside derivatives with promising cytotoxic activities (11 and 12) were further analyzed on the hepatoma cells. The N-6-(4-Trifluoromethylphenyl)piperazine analogue 11 displayed the best antitumor activity, with IC50 values between 5.2 and 9.2 mu M. Similar to previously described nucleoside analogues, compound 11 also interferes with cellular ATP reserves, possibly through influencing cellular kinase activities. Furthermore, the novel nucleoside analogue 11 was shown to induce senescence-associated cell death, as demonstrated by the SA beta-gal assay. The senescence-dependent cytotoxic effect of 11 was also confirmed through phosphorylation of the Rb protein by p15(INK4b) overexpression in the presence of this compound