Classification of human carcinoma cells using multispectral imagery

In this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. The experimental results depict satisfactory performance, and the proposed method is versatile for various microscopy magnification options. © 2016 SPIE

Data and model driven hybrid approach to activity scoring of cyclic pathways

Analysis of large scale -omics data based on a single tool remains inefficient to reveal molecular basis of cellular events. Therefore, data integration from multiple heterogeneous sources is highly desirable and required. In this study, we developed a data- and model-driven hybrid approach to evaluate biological activity of cellular processes. Biological pathway models were taken as graphs and gene scores were transferred through neighbouring nodes of these graphs. An activity score describes the behaviour of a specific biological process was computed by owing of converged gene scores until reaching a target process. Biological pathway model based approach that we describe in this study is a novel approach in which converged scores are calculated for the cellular processes of a cyclic pathway. The convergence of the activity scores for cyclic graphs were demonstrated on the KEGG pathways. © 2011 Springer Science+Business Media B.V

Carcinoma cell line discrimination in microscopic images using unbalanced wavelets

Cancer cell lines are widely used for research purposes in laboratories all over the world. In this paper, we present a novel method for cancer cell line image classification, which is very costly by conventional methods. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a correlation descriptor utilizing the fractional unbalanced wavelet transform coefficients and several morphological attributes as pixel features is computed. Directionally selective textural features are preferred primarily because of their ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines. A Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel is employed for final classification. Over a dataset of 280 images, we achieved an accuracy of 88.2%, which outperforms the classical correlation based methods. © 2012 IEEE

Identification of relative protein bands in polyacrylamide gel electrophoresis (PAGE) using a multi-resolution snake algorithm

In polyacrylamide gel electrophoresis (PAGE) image analysis, it is important to determine the percentage of the protein of interest of a protein mixture. This study presents reliable computer software to determine this percentage. The region of interest containing the protein band is detected using the snake algorithm. The iterative snake algorithm is implemented in a multi-resolutional framework. The snake is initialized on a low-resolution image. Then, the final position of the snake at the low resolution is used as the initial position in the higher-resolution image. Finally, the area of the protein is estimated as the area enclosed by the final position of the snake

Phase and TV Based Convex Sets for Blind Deconvolution of Microscopic Images

In this paper, two closed and convex sets for blind deconvolution problem are proposed. Most blurring functions in microscopy are symmetric with respect to the origin. Therefore, they do not modify the phase of the Fourier transform (FT) of the original image. As a result blurred image and the original image have the same FT phase. Therefore, the set of images with a prescribed FT phase can be used as a constraint set in blind deconvolution problems. Another convex set that can be used during the image reconstruction process is the Epigraph Set of Total Variation (ESTV) function. This set does not need a prescribed upper bound on the Total Variation (TV) of the image. The upper bound is automatically adjusted according to the current image of the restoration process. Both the TV of the image and the blurring filter are regularized using the ESTV set. Both the phase information set and the ESTV are closed and convex sets. Therefore they can be used as a part of any blind deconvolution algorithm. Simulation examples are presented. © 2015 IEEE

Anthropology is the discipline but the goal is ethnography

In this debate piece, I argue that there is something more important than the discipline of anthropology, and that is the ability of anthropologists to study the world through ethnography and transmit that understanding back to global populations as education. An inwardly directed concern only with our discipline can sometimes constrain both of these tasks

CXXC5 as an unmethylated CpG dinucleotide binding protein contributes to estrogen-mediated cellular proliferation.

Evidence suggests that the CXXC type zinc finger (ZF-CXXC) protein 5 (CXXC5) is a critical regulator/integrator of various signaling pathways that include the estrogen (E2)-estrogen receptor α (ERα). Due to its ZF-CXXC domain, CXXC5 is considered to be a member of the ZF-CXXC family, which binds to unmethylated CpG dinucleotides of DNA and through enzymatic activities for DNA methylation and/or chromatin modifications generates a chromatin state critical for gene expressions. Structural/functional features of CXXC5 remain largely unknown. CXXC5, suggested as transcription and/or epigenetic factor, participates in cellular proliferation, differentiation, and death. To explore the role of CXXC5 in E2-ERα mediated cellular events, we verified by generating a recombinant protein that CXXC5 is indeed an unmethylated CpG binder. We uncovered that CXXC5, although lacks a transcription activation/repression function, participates in E2-driven cellular proliferation by modulating the expression of distinct and mutual genes also regulated by E2. Furthermore, we found that the overexpression of CXXC5, which correlates with mRNA and protein levels of ERα, associates with poor prognosis in ER-positive breast cancer patients. Thus, CXXC5 as an unmethylated CpG binder contributes to E2-mediated gene expressions that result in the regulation of cellular proliferation and may contribute to ER-positive breast cancer progression

Anti-cancer and anti-hepatitis C virus NS5B polymerase activity of etodolac 1,2,4-triazoles

Arachidonic acid is an unsaturated fatty acid liberated from phospholipids of cell membranes. NSAIDs are known as targets of cyclooxygenase enzyme (COX-1, COX-2 and COX-3) in arachidonic acid metabolism. This mechanism of COX-2 in carcinogenesis causes cancer. In addition, COX-2 plays a role in the early stages of hepatocarcinogenesis. Hepatitis C virus (HCV) infection is cause of liver cirrhosis and hepatocellular carcinoma (HCC). The aim of our study was to improve effective agents against HCV. A novel series of new etodolac 1,2,4-triazoles derivatives (4a-h) have been synthesized and investigated for their activity against HCV NS5B polymerase. Compound 4a was found to be the most active with IC50 value of 14.8 M. In accordance with these results, compound 4a was screened for anti-cancer activity on liver cancer cell lines (Huh7, Mahlavu, HepG2, FOCUS). Compound 4a showed anti-cancer activity against Huh7 human hepatoma cell line with IC50 value of 4.29 M. Therefore, compound 4a could be considered as a new anti-cancer and anti-HCV lead compound. © 2015 Informa UK Ltd

Multiplication-free Neural Networks [Çarpmasiz Yapay Sinir Aʇi]

In this article, a multiplication-free artificial Neural Network (ANN) structure is proposed. Inner products between the input vectors and the ANN weights are implemented using a multiplication-free vector operator. Training of the new artificial neural network structure is carried out using the sign-LMS algorithm. Proposed ANN system can be used in applications requiring low-power usage or running on microprocessors that have limited processing power. © 2015 IEEE

Unsupervised segmentation of live cell images using gaussian modeling [Gauss tabanli modelleme kullanarak canli hücre görüntü leri̇ni̇n ögreti̇ci̇si̇z bölütlenmesi̇]

The first step of targeted cancer drug development is to screen and determine drug candidates by in vitro measuring the effectiveness of the drugs. The tests developed for this purpose can be time consuming due to their procedures and cannot be conducted in every laboratory due to the required hardwares. On the other hand, an image-based screening test has a potential to be less time consuming since it can directly be carried out on the live cell images and to be more extensively used because of the availability of its required equipments and their relatively less expensive cost. With such an image-based test, it is possible to quantify the cell death by finding cellular regions and comparing it against the control group. In this work, we propose a new method that automatically locates the cellular regions by the unsupervised segmentation of live cell images. This method relies on approximately locating cellular regions and the background with gradient-based thresholding and morphological operators and then finding the final boundaries by modeling the gradient of these regions with Gaussians. Working on the images of different cell lines captured with different magnifications, our experiments show that the proposed method leads to promising results. © 2011 IEEE