Identification of azimuthal light scattering signatures to selectively track changes in subnuclear refractive index profile of epithelial cell models

Arifler, Dizem
Guillaud, Martial
We construct stochastically inhomogeneous epithelial cell models via simulation of Gaussian random fields; the extent and correlation length of subnuclear refractive index fluctuations are based on values quantified from high-resolution images of cervical tissue. We then employ the finite-difference time-domain method to simulate azimuth-resolved light scattering patterns of the constructed models. We process these two-dimensional patterns and calculate a series of Haralick features with the ultimate goal of identifying signatures that directly point to changes in subnuclear refractive index profile. Our results show that azimuthal contrast calculated over specific angular ranges is highly sensitive to the extent and correlation length of refractive index fluctuations. This metric is insensitive to changes in the overall size and mean refractive index of the constructed models, thereby allowing for selective tracking of changes in subnuclear refractive index variations.


Using artificially generated spectral data to improve protein secondary structure prediction from Fourier transform infrared spectra of proteins
Severcan, M; Haris, PI; Severcan, Feride (Elsevier BV, 2004-09-15)
Secondary structures of proteins have been predicted using neural networks from their Fourier transform infrared spectra. To improve the generalization ability of the neural networks, the training data set has been artificially increased by linear interpolation. The leave-one-out approach has been used to demonstrate the applicability of the method. Bayesian regularization has been used to train the neural networks and the predictions have been further improved by the maximum-likelihood estimation method. T...
Characterization and prediction of protein interfaces to infer protein-protein interaction networks
Keskin, Ozlem; Tunçbağ, Nurcan; GÜRSOY, Attila (2008-04-01)
Complex protein-protein interaction networks govern biological processes in cells. Protein interfaces are the sites where proteins physically interact. Identification and characterization of protein interfaces will lead to understanding how proteins interact with each other and how they are involved in protein-protein interaction networks. What makes a given interface bind to different proteins; how similar/different the interactions in proteins are some key questions to be answered. Enormous amount of prot...
Employing decomposable partially observable Markov decision processes to control gene regulatory networks
Erdogdu, Utku; Polat, Faruk; Alhajj, Reda (2017-11-01)
Objective: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).
A neuro-fuzzy MAR algorithm for temporal rule-based systems
Sisman, NA; Alpaslan, Ferda Nur; Akman, V (1999-08-04)
This paper introduces a new neuro-fuzzy model for constructing a knowledge base of temporal fuzzy rules obtained by the Multivariate Autoregressive (MAR) algorithm. The model described contains two main parts, one for fuzzy-rule extraction and one for the storage of extracted rules. The fuzzy rules are obtained from time series data using the MAR algorithm. Time-series analysis basically deals with tabular data. It interprets the data obtained for making inferences about future behavior of the variables. Fu...
Estimation of thermally stimulated current in as grown TlGaSeS layered single crystals by multilayered perceptron neural network
Kucuk, Ilker; Yildirim, Tacettin; Hasanlı, Nızamı; Ozkan, Husnu (Elsevier BV, 2011-6)
This paper presents an artificial neural network approach to compute thermally stimulated current (TSC) in as-grown T1GaSeS layered single crystals. The experimental data have been obtained from TSC measurements. The network has been trained by a genetic algorithm (GA). The results confirmed that the proposed model could provide an accurate computation of the TSC.
Citation Formats
D. Arifler and M. Guillaud, “Identification of azimuthal light scattering signatures to selectively track changes in subnuclear refractive index profile of epithelial cell models,” 2019, vol. 11073, p. 0, Accessed: 00, 2020. [Online]. Available: