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Abstract Background This paper compares the most common digital signal processing methods of exon prediction in eukaryotes, and also proposes a technique for noise suppression in exon prediction. The specimen used here which has relevance in medical research, has been taken from the public genomic database - GenBank. Methods Here exon prediction has been done using the digital signal processing methods viz. binary method, EIIP (electron-ion interaction psuedopotential) method and filter methods. Under filter method two filter designs, and two approaches using these two designs have been tried. The discrete wavelet transform has been used for de-noising of the exon plots. Results Results of exon prediction based on the methods mentioned above, which give values closest to the ones found in the NCBI database are given here. The exon plot de-noised using discrete wavelet transform is also given. Conclusion Alterations to the proven methods as done by the authors, improves performance of exon prediction algorithms. Also it has been proven that the discrete wavelet transform is an effective tool for de-noising which can be used with exon prediction algorithms.

Document forgeries that involve modification of the materials used, such as ink and paper, provide evidence of any malpractices being performed. Forensic specialists use different techniques to identify and classify these samples; however, the most preferred method is to use nondestructive techniques to avoid any potential damage to the original specimen under investigation. Hyperspectral imaging has already been explored in several application domains and used as a powerful method in forensic investigations to extract information about the materials under examination. To precisely classify the material information and utilize the hyperspectral imaging technique's potential, we probed the potential of some hybrid spectral similarity measures to classify different commonly used paper samples. A comparison of these methods is quantitatively presented in this article. Hybrid spectral similarity algorithms are tested on forensic analysis of paper data. We compared the classification capabilities of various hybrid spectral similarity algorithms on hyperspectral data of 40 different paper samples. The overall accuracy (OA), kappa K̂, Z‐score of kappa (ZK̂), and the 95% confidence interval of kappa (CI(K̂)) are used for comparison. The SID‐SAM and SID‐SCA produced an overall accuracy of 88% and 87%, respectively, which is highest among the hybrid spectral similarity measures tested. [ABSTRACT FROM AUTHOR]

Long noncoding RNAs (lncRNAs) which were initially dismissed as "transcriptional noise" have become a vital area of study after their roles in biological regulation were discovered. Long noncoding RNAs have been implicated in various developmental processes and diseases. Here, we perform exon mapping of human lncRNA sequences (taken from National Center for Biotechnology Information GenBank) using digital filters. Antinotch digital filters are used to map out the exons of the lncRNA sequences analyzed. The period 3 property which is an established indicator for locating exons in genes is used here. Discrete wavelet transform filter bank is used to fine-tune the exon plots by selectively removing the spectral noise. The exon locations conform to the ranges specified in GenBank. In addition to exon prediction, G-C concentrations of lncRNA sequences are found, and the sequences are searched for START and STOP codons as these are indicators of coding potential. [ABSTRACT FROM AUTHOR]

Genomic studies have become noncoding RNA (ncRNA) centric after the study of different genomes provided enormous information on ncRNA over the past decades. The function of ncRNA is decided by its secondary structure, and across organisms, the secondary structure is more conserved than the sequence itself. In this study, the optimal secondary structure or the minimum free energy (MFE) structure of ncRNA was found based on the thermodynamic nearest neighbor model. MFE of over 2600 ncRNA sequences was analyzed in view of its signal properties. Mathematical models linking MFE to the signal properties were found for each of the four classes of ncRNA analyzed. MFE values computed with the proposed models were in concordance with those obtained with the standard web servers. A total of 95% of the sequences analyzed had deviation of MFE values within ±15% relative to those obtained from standard web servers. [ABSTRACT FROM AUTHOR]

The optic disc may be distinguished in eye fundus images as a rounded shape having high intensity values. An efficient localization and segmentation of optic disc in colour retinal images is a significant task in an automated retinal image analysis process. It is a challenging task to detect optic disc in all types of retinal images, that is, normal, healthy images. This paper presents an automated method to locate and segment the optic disc in all types of retinal color fundus images using histogram based particle swarm optimization techniques. The technique is tested on 235 images obtained from publicly available database DRION, and certain images obtained from an ophthalmologist. The performance analysis was done using the ground truth of DRION database. From the scatter plot, it is shown that the ground truth and detected optic disc centers have a high positive correlation. The results of the new method seem promising and useful to clinical work. [ABSTRACT FROM AUTHOR]

The performance of image processing algorithms can be significantly improved by the application of multi‐resolution image representation with directional features. Directionlet transform (DT) is one such representation which has gained popularity over the past few years as an anisotropic, perfect reconstruction and critically sampled basis function with directional vanishing moments along any two directions. In this study, the authors propose a spatially adaptive image denoising scheme for Gaussian noise based on DT by considering the dependences of the directionlet coefficients across different scales. The image is first decomposed using DT and the coefficients so obtained are modelled using a bivariate heavy tailed 'pdf' with a local variance parameter to account for inter‐ and intra‐scale dependencies of the coefficients. The DT is made adaptive to the local dominant directions in the image by identifying the dominant directions in the spatially segmented image through the computation of a parameter called 'directional variance'. Bayesian 'maximum a posteriori' estimator is then used to compute the noise free coefficients from the bivariate models of the signal and noise. The denoised image is obtained from the transform coefficients, which were modified using the bivariate shrinkage function, using directional information and inverse DT. Experimental results show that the bivariate shrinkage in directionlet domain achieves better performance than that in wavelet domain, in terms of numerical and perceptual quality. [ABSTRACT FROM AUTHOR]

Background: This paper compares the most common digital signal processing methods of exon prediction in eukaryotes, and also proposes a technique for noise suppression in exon prediction. The specimen used here which has relevance in medical research, has been taken from the public genomic database - GenBank. Methods: Here exon prediction has been done using the digital signal processing methods viz. binary method, EIIP (electron-ion interaction psuedopotential) method and filter methods. Under filter method two filter designs, and two approaches using these two designs have been tried. The discrete wavelet transform has been used for de-noising of the exon plots. Results: Results of exon prediction based on the methods mentioned above, which give values closest to the ones found in the NCBI database are given here. The exon plot de-noised using discrete wavelet transform is also given. Conclusion: Alterations to the proven methods as done by the authors, improves performance of exon prediction algorithms. Also it has been proven that the discrete wavelet transform is an effective tool for de-noising which can be used with exon prediction algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for automatic determination of position and orientation of spine in digitized spine X-rays using mathematical morphology. As the X-ray images are usually highly smeared, vertebrae segmentation is a complex process. The image is first coarsely segmented to obtain the location and orientation information of the spine. The state-of-the-art technique is based on the deformation model of a template, and as the vertebrae shape usually shows variation from case to case, accurate representation using a template is a difficult process. The proposed method makes use of the vertebrae morphometry and gray-scale profile of the spine. The top-hat transformation-based method is proposed to enhance the ridge points in the posterior boundary of the spine. For cases containing external objects such as ornaments, H-Maxima transform is used for segmentation and removal of these objects. The Radon transform is then used to estimate the location and orientation of the line joining the ridge point clusters appearing on the boundary of the vertebra body. The method was validated for 100 cervical spine X-ray images, and in all cases, the error in orientation was within the accepted tolerable limit of 15°. The average error was found to be 4.6°. A point on the posterior boundary was located with an accuracy of ±5.2 mm. The accurate information about location and orientation of thespine is necessary for fine-grained segmentation of the vertebrae using techniques such as active shape modeling. Accurate vertebrae segmentation is needed in successful feature extraction for applications such as content-based image retrieval of biomedical images. [ABSTRACT FROM AUTHOR]

Peptide identification algorithms rely on the comparison between the experimental tandem mass spectrometry spectrum and the theoretical spectrum to identify a peptide from the tandem mass spectra. Hence, it is important to understand the fragmentation process and predict the tandem mass spectra for high-throughput proteomics research. In this study, a novel method was developed to predict the theoretical ion trap collision-induced dissociation (CID) tandem mass spectra of the singly, doubly, and triply charged tryptic peptides. The fragmentation statistics of the ion trap CID spectra were used to predict the theoretical tandem mass spectra of the peptide sequence. The study estimated the relative cleavage frequency for each pair of adjacent amino acids along the peptide length. The study showed that the cleavage frequency can be directly used to predict the tandem mass spectra. The predicted spectra show a high correlation with the experimental spectra used in this study; 99.73% of the high-quality reference spectra have correlation scores greater than 0.8. The new method predicts the theoretical spectrum and correlates significantly better with the experimental spectrum as compared to the existing spectrum prediction tools OpenMS_Simulator, MS2PIP, and MS2PBPI, where only 80, 85.76, and 85.80% of the spectral count, respectively, has a correlation score greater than 0.8., Competing Interests: The authors declare no competing financial interest., (Copyright © 2020 American Chemical Society.)

A wide range of methods with or without sequence alignment have been used to study molecular phylogeny for information on the evolution of species. Two approaches to construct the phylogenetic tree using (a) direct correlation of protein sequences and (b) difference between the Discrete Fourier Transform coefficients are described. The proposed methods use a transformation where each amino acid is represented by its Electron-Ion Interaction Potential (EIIP) value. Phylogenetic tree of two mammalian orders, primates and cetacea, is generated based on Fitch-Margoliash, Neighbour-Joining and UPGMA methods and compared. The phylogenetic tree of evolutionary relationships thus obtained can be used for comparison of species and gene sequences. The information thus gathered provide meaningful insights into the pattern and process of evolution which will help researchers in developing new breeds of animals and plants.