Band Detection

In this paper we discuss the application of spatial-domain filters for solving the problem of automatic lane detection on gel electrophoresis computer images. The problem can be posed as the determination of the number, location and orientation of lanes on the image, based on the analysis of their gray-level intensities. A novel iterative filtering algorithm is proposed based on the periodicity of image projection pattern. The algorithm is compared with clustering and frequency-domain filtering methods in terms of their effectiveness and efficiency from which a trade-off solution is presented.

MeCP2 is a methyl CpG binding protein whose key role is the recognition of epigenetic information encoded in DNA methylation patterns. Mutation or mis-regulation of MeCP2 function leads to Rett syndrome as well as a variety of other Autism Spectrum Disorders. Here, we have analyzed in detail the properties of six individually expressed human MeCP2 domains spanning the entire protein with emphasis on their interactions with each other, with DNA, and with nucleosomal arrays. Each domain contributes uniquely to the structure and function of the full-length protein. MeCP2 is ~60% unstructured, with nine interspersed α-Molecular Recognition Features (α-MoRFs), which are polypeptide segments predicted to acquire secondary structure upon forming complexes with binding partners. Large increases in secondary structure content are induced in some of the isolated MeCP2 domains and in the full-length protein by binding to DNA. Interactions between some MeCP2 domains in cis and trans seen in our assays, likely contribute to the structure and function of the intact protein. We also show that MeCP2 has two functional halves. The N-terminal portion contains the methylated DNA binding domain (MBD) and two highly disordered flanking domains which modulate MBD-mediated DNA binding. One of these flanking domains is also capable of autonomous DNA binding. In contrast, the C-terminal portion of the protein which harbors at least two independent DNA binding domains and a chromatin specific binding domain is largely responsible for mediating nucleosomal array compaction and oligomerization. These findings lead to new mechanistic and biochemical insights regarding the conformational modulations of this intrinsically disordered protein, and its context-dependent in vivo roles. † Supported by NIH GM070897 and International Rett Syndrome Foundation (CLW); NIH GM066834 (JCH); NIH GM027616 and OD945 (LMG); NIH LM007688, GM071714, the program of the Russian Academy of Sciences for "Molecular and Cellular Biology", and the IUPUI Signatures Center Initiative (VNU).

DNA has been shown to exist in a variety of different structural forms. X-ray crystallography has revealed the existence of three distinct tertiary structures, namely A-, B-and Z-DNA. BDNA is well established as being the predominant form of DNA in the living cell. The biological existence and functional relevance of both Aand Z-DNA is less clear. In addition to these defined structures, DNA can also be induced to bend or kink by a myriad of factors, both intrinsic and extrinsic to the DNA sequence. By default, biological cues must exist to prompt transitions between these forms should they occur in vivo, the nature of which, whether physiological, cell-cycle or developmentally based is unknown. It is known that CpG methylation can promote transitions between the A-, Band ZDNA isoforms. DNA methylation has also been shown to have effects on both secondary and tertiary DNA structure. These alterations can affect the positioning of the primary DNA packaging proteins in vitro and poten...

This paper presents a methodology for correcting band distortions in Thin-Layer Chromatography (TLC) images. After the segmentation of image lanes, the intensity profile of each lane column is spatially aligned with a reference profile using a modified version of the Correlation Optimized Warping (COW) algorithm. The proposed band correction methodology was assessed using 105 profiles of TLC lanes. A set of features for band characterization was extracted from each lane profile, before and after band distortion correction, and was used as input for three distinct one-class classifiers aiming at band identification. In all cases, the best results of band classification were obtained for the set lanes after band distortion correction.

A novel methodology of DNA gel image analysis has been proposed that is based on 2D image processing methods instead of previously used 1D Gaussian deconvolution. The algorithms specifically tailored to band boundary detection and image intensity homogeneity characterization have been developed. The proposed methodology involves a modified type of user interaction.

Biocontrols are one of the factors to be considered in the search for sustainable agricultural activities. The amount of samples to be analyzed while searching for such agents draws mandatory the use of technological tools that accelerate the processes involved and improve the accuracy of the experimental results. The molecular characterization of organisms is one of the steps involved while searching for such biocontrols. Several techniques used for the molecular characterization of organisms make use of gel electrophoresis images, which permit to work at coarser but faster level of information analyisis than genetic sequencing. During the creation of gel electrophoresis images, two types of distortions occur. The first type originates on the gel creation process due to variations of the electric field and temperature gradients. The second type is induced in the image capture process and involves optical distortions, such as the barrel distortion. This project is focused on lane de...

In this paper a method for the automatic DNA spots classification and extraction of profiles associated in DNA polyacrilamide gel electrophoresis is presented and it integrates the use of image processing techniques and chemometrics tools. A software which implements this method was developed; for feature extraction a combination of a PCA analysis and a C4.5 decision tree were used. To obtain good results in the profile extraction only DNA spots are useful; therefore, it was necessary to solve a two-class classification problem among DNA spots and no-DNA spots. In order to perform the classification process with high velocity, effectiveness and robustness, comparative classification studies among support vector machine (SVM), K-NN and PLS-DA classifiers were made. The best results obtained with the SVM classifier demonstrated the advantages attributed to it in the literature as a two-class classifier. A Sequential Cluster Leader Algorithm and another one developed for the restoration of pattern missing spots were needed to conclude the profiles extraction step. The experimental results show that this method has a very effective computational behavior and effectiveness, and provide a very useful tool to decrease the time and increase the quality of the specialist responses.

In this paper a method for the automatic DNA spots classification and extraction of profiles associated in DNA polyacrilamide gel electrophoresis is presented and it integrates the use of image processing techniques and chemometrics tools. A software which implements this method was developed; for feature extraction a combination of a PCA analysis and a C4.5 decision tree were used. To obtain good results in the profile extraction only DNA spots are useful; therefore, it was necessary to solve a two-class classification problem among DNA spots and no-DNA spots. In order to perform the classification process with high velocity, effectiveness and robustness, comparative classification studies among support vector machine (SVM), K-NN and PLS-DA classifiers were made. The best results obtained with the SVM classifier demonstrated the advantages attributed to it in the literature as a two-class classifier. A Sequential Cluster Leader Algorithm and another one developed for the restoration of pattern missing spots were needed to conclude the profiles extraction step. The experimental results show that this method has a very effective computational behavior and effectiveness, and provide a very useful tool to decrease the time and increase the quality of the specialist responses.

In this paper a method for the automatic DNA spots classification and extraction of profiles associated in DNA polyacrilamide gel electrophoresis is presented and it integrates the use of image processing techniques and chemometrics tools. A software which implements this method was developed; for feature extraction a combination of a PCA analysis and a C4.5 decision tree were used. To obtain good results in the profile extraction only DNA spots are useful; therefore, it was necessary to solve a two-class classification problem among DNA spots and no-DNA spots. In order to perform the classification process with high velocity, effectiveness and robustness, comparative classification studies among support vector machine (SVM), K-NN and PLS-DA classifiers were made. The best results obtained with the SVM classifier demonstrated the advantages attributed to it in the literature as a two-class classifier. A Sequential Cluster Leader Algorithm and another one developed for the restoration of pattern missing spots were needed to conclude the profiles extraction step. The experimental results show that this method has a very effective computational behavior and effectiveness, and provide a very useful tool to decrease the time and increase the quality of the specialist responses.

Abstract—Biocontrols are one of the factors to be considered in the search for sustainable agricultural activities. The amount of samples to be analyzed while searching for such agents draws mandatory the use of technological tools that accelerate the processes involved and improve the accuracy of the experimental results. The molecular characterization of organisms is one of the steps involved while searching for such biocontrols. Several techniques used for the molecular characterization of organisms ...

In this paper a method for the automatic DNA spots classification and extraction of profiles associated in DNA polyacrilamide gel electrophoresis is presented and it integrates the use of image processing techniques and chemometrics tools. A software which implements this method was developed; for feature extraction a combination of a PCA analysis and a C4.5 decision tree were used. To obtain good results in the profile extraction only DNA spots are useful; therefore, it was necessary to solve a two-class classification problem among DNA spots and no-DNA spots. In order to perform the classification process with high velocity, effectiveness and robustness, comparative classification studies among support vector machine (SVM), K-NN and PLS-DA classifiers were made. The best results obtained with the SVM classifier demonstrated the advantages attributed to it in the literature as a two-class classifier. A Sequential Cluster Leader Algorithm and another one developed for the restoration of pattern missing spots were needed to conclude the profiles extraction step. The experimental results show that this method has a very effective computational behavior and effectiveness, and provide a very useful tool to decrease the time and increase the quality of the specialist responses.

This paper introduces an Automatic Target Recognition (ATR) method based on X Band Radar image processing. A software which implements this method was developed following four principal stages: digital image formation, image preprocessing, feature selection through a combination of C4.5 Decision Tree and PCA and classification using SVM. The automatic process was validated using two images sets, one of them containing real images with natural noise levels and the other with different degrees of impulsive noise contamination. The method achieves a very nice computation behavior and effectiveness, high accuracy and robustness in noise environments with a low storage memory and high decision speed.

In this paper a method for the automatic DNA spots classification and extraction of profiles associated in DNA polyacrilamide gel electrophoresis is presented and it integrates the use of image processing techniques and chemometrics tools. A software which implements this method was developed; for feature extraction a combination of a PCA analysis and a C4.5 decision tree were used. To obtain good results in the profile extraction only DNA spots are useful; therefore, it was necessary to solve a two-class classification problem among DNA spots and no-DNA spots. In order to perform the classification process with high velocity, effectiveness and robustness, comparative classification studies among support vector machine (SVM), K-NN and PLS-DA classifiers were made. The best results obtained with the SVM classifier demonstrated the advantages attributed to it in the literature as a two-class classifier. A Sequential Cluster Leader Algorithm and another one developed for the restoration of pattern missing spots were needed to conclude the profiles extraction step. The experimental results show that this method has a very effective computational behavior and effectiveness, and provide a very useful tool to decrease the time and increase the quality of the specialist responses.

Introduction: The analysis of polyacrylamide gels is currently carried out manually or automatically. In the automatic method, there are limitations related to the acceptable degree of distortion of lane and band continuity. The available software cannot deal satisfactorily with this type of situations. Therefore, the paper presents an original image analysis method devoid of the aforementioned drawbacks.

Gel electrophoresis has significantly influenced the progress achieved in genetic studies over the last decade. Image processing techniques that are commonly used to analyze gel electrophoresis images require mainly three steps: band detection, band matching, and quantification and comparison. Although several techniques have been proposed to fully automate all steps, errors in band detection and, hence, in quantification are still important issues to address. In order to detect bands, many techniques were used, including image segmentation. In this paper, we present two novel, fullyautomated techniques based on the standard deviation and its derivative to perform segmentation and to detect protein bands. Results show that even for poor quality images with faint bands, segmentation and detection are highly accurate.

Gel electrophoresis has significantly influenced the progress achieved in genetic studies over the last decade. Image processing techniques that are commonly used to analyze gel electrophoresis images require mainly three steps: band detection, band matching, and quantification and comparison. Although several techniques have been proposed to fully automate all steps, errors in band detection and, hence, in quantification are still important issues to address. In order to detect bands, many techniques were used, including image segmentation. In this paper, we present two novel, fullyautomated techniques based on the standard deviation and its derivative to perform segmentation and to detect protein bands. Results show that even for poor quality images with faint bands, segmentation and detection are highly accurate.

Gel electrophoresis has significantly influenced the progress achieved in genetic studies over the last decade. Image processing techniques that are commonly used to analyze gel electrophoresis images require mainly three steps: band detection, band matching, and quantification and comparison. Although several techniques have been proposed to fully automate all steps, errors in band detection and, hence, in quantification are still important issues to address. In order to detect bands, many techniques were used, including image segmentation. In this paper, we present two novel, fullyautomated techniques based on the standard deviation and its derivative to perform segmentation and to detect protein bands. Results show that even for poor quality images with faint bands, segmentation and detection are highly accurate.

Gel electrophoresis is routinely used to separate and analyse macromolecules in biological systems. Although many of these electrophoretic techniques necessitate the denaturing of the analytes prior to their analysis, blue native polyacrylamide gel electrophoresis (BN-PAGE) permits the investigation of proteins/enzymes and their supramolecular structures such as the metabolon in native form. This attribute renders this analytical tool conducive to deciphering the metabolic perturbations invoked by metal toxicity. In this review, we elaborate on how BN-PAGE has led to the discovery of the dysfunctional metabolic pathways associated with disorders such as Alzheimer's disease, Parkinson's disease, and obesity that have been observed as a consequence of exposure to various metal toxicants.

Abstract Gel electrophoresis is routinely used to separate and
analyse macromolecules in biological systems. Although
many of these electrophoretic techniques necessitate the denaturing
of the analytes prior to their analysis, blue native polyacrylamide
gel electrophoresis (BN-PAGE) permits the
investigation of proteins/enzymes and their supramolecular
structures such as the metabolon in native form. This attribute
renders this analytical tool conducive to deciphering the metabolic
perturbations invoked by metal toxicity. In this review,
we elaborate on how BN-PAGE has led to the discovery of the
dysfunctional metabolic pathways associated with disorders
such as Alzheimer’s disease, Parkinson’s disease, and obesity
that have been observed as a consequence of exposure to
various metal toxicants.

MeCP2 is a methyl CpG binding protein whose key role is the recognition of epigenetic information encoded in DNA methylation patterns. Mutation or mis-regulation of MeCP2 function leads to Rett syndrome as well as a variety of other Autism Spectrum Disorders. Here, we have analyzed in detail the properties of six individually expressed human MeCP2 domains spanning the entire protein with emphasis on their interactions with each other, with DNA, and with nucleosomal arrays. Each domain contributes uniquely to the structure and function of the full-length protein. MeCP2 is ~60% unstructured, with nine interspersed α-Molecular Recognition Features (α-MoRFs), which are polypeptide segments predicted to acquire secondary structure upon forming complexes with binding partners. Large increases in secondary structure content are induced in some of the isolated MeCP2 domains and in the full-length protein by binding to DNA. Interactions between some MeCP2 domains in cis and trans seen in our assays, likely contribute to the structure and function of the intact protein. We also show that MeCP2 has two functional halves. The N-terminal portion contains the methylated DNA binding domain (MBD) and two highly disordered flanking domains which modulate MBD-mediated DNA binding. One of these flanking domains is also capable of autonomous DNA binding. In contrast, the C-terminal portion of the protein which harbors at least two independent DNA binding domains and a chromatin specific binding domain is largely responsible for mediating nucleosomal array compaction and oligomerization. These findings lead to new mechanistic and biochemical insights regarding the conformational modulations of this intrinsically disordered protein, and its context-dependent in vivo roles.

In this paper a method for the automatic DNA spots classification and extraction of profiles associated in DNA polyacrilamide gel electrophoresis is presented and it integrates the use of image processing techniques and chemometrics tools. A software which implements this method was developed; for feature extraction a combination of a PCA analysis and a C4.5 decision tree were used. To obtain good results in the profile extraction only DNA spots are useful; therefore, it was necessary to solve a two-class classification problem among DNA spots and no-DNA spots. In order to perform the classification process with high velocity, effectiveness and robustness, comparative classification studies among support vector machine (SVM), K-NN and PLS-DA classifiers were made. The best results obtained with the SVM classifier demonstrated the advantages attributed to it in the literature as a two-class classifier. A Sequential Cluster Leader Algorithm and another one developed for the restoration of pattern missing spots were needed to conclude the profiles extraction step. The experimental results show that this method has a very effective computational behavior and effectiveness, and provide a very useful tool to decrease the time and increase the quality of the specialist responses.

This paper introduces an Automatic Target Recognition (ATR) method based on X Band Radar image processing. A software which implements this method was developed following four principal stages: digital image formation, image preprocessing, feature selection through a combination of C4.5 Decision Tree and PCA and classification using SVM. The automatic process was validated using two images sets, one of them containing real images with natural noise levels and the other with different degrees of impulsive noise contamination. The method achieves a very nice computation behavior and effectiveness, high accuracy and robustness in noise environments with a low storage memory and high decision speed.

This third part contains these chapters:

17 Anatomy: Fluorescence Microscopy
18 Aerodynamics: Video Analysis of Pigeon Flight
19 Mathematics: Visual Solutions to a Logic Problem
20 Applied Social Studies: Masks in Social Work
21 Pathology: Diagnosis of a Kidney Disease
22 Epigraphy: Three-Dimensional Laser Scanning of Inscribed Stones
23 Geochemistry: Deformation of Grains in Sandstone
24 Food Science: Electrophoresis Gels of Cheddar Cheese 25 Zoology: Automated Recognition of Individual Cetaceans
26 Art History: Political Meanings of John Heartfield’s Photographs
27 Microbiology: Visualizing Viruses
28 Oceanography: Imaging the Sea Bed Using Side-Scanning Sonar
29  Philosophy: Arabic and Russian Visual Tropes
30  Legal pedagogy: Teaching Visual Rhetoric to Law Students

This file also contains the Afterword, which is an essay on the politics of publishing in different countries. In the Afterword, I justify my choice of a German publisher, and write about which presses are considered appropriate for young scholars in the U.S., U.K., and German-speaking countries. The politics is a sensitive issue, and as far as I know it hasn't been addressed elsewhere.