Bio-assay based on single molecule fluorescence detection in microfluidic channels

A rapid bioassay is described based on the detection of colocalized fluorescent DNA probes bound to DNA targets in a pressure-driven solution flowing through a planar microfluidic channel. By employing total internal reflection excitation of the fluorescent probes and illumination of almost the entire flow channel, single fluorescent molecules can be efficiently detected leading to the rapid analysis of nearly the entire solution flowed through the device. Cross-correlation between images obtained from two spectrally distinct probes is used to determine the target concentration and efficiently reduces the number of false positives. The rapid analysis of DNA targets in the low pM range in less than a minute is demonstrated.     

Ru(TAP)3]2+-photosensitized DNA cleavage studied by atomic force microscopy and gel electrophoresis: a comparative study

Topological modifications of plasmid DNA adsorbed on a variety of surfaces were investigated by using atomic force microscopy (AFM). On mica modified with 3-aminopropyltriethoxysilane (APS) or poly-L-lysine, the interaction between the plasmid DNA and the surface "freezes" the plasmid DNA conformation deposited from solution, and the AFM images resemble the projection of the three-dimensional conformation of the plasmid DNA in solution. Modified mica with low concentrations of Mg(2+) leads to a decrease in the interaction strength between plasmid DNA and the substrate, and the AFM images reflect the relaxed or equilibrium conformation of the adsorbed plasmid DNA. Under these optimized deposition conditions, topological modifications of plasmid DNA were produced under irradiation in the presence of [Ru(TAP)(3)](2+) (TAP = 1,4,5,8-tetraazaphenanthrene), which is a non-intercalating complex, and were followed as a function of illumination time. The observed structural changes correlate well with the conversion of the supercoiled covalently closed circular form (ccc form) into the open circular form (oc form), induced by a single-strand photocleavage. The AFM results obtained after fine-tuning of the plasmid DNA-substrate interaction compare well with those observed from gel electrophoresis, indicating that under the appropriate deposition conditions, AFM is a reliable technique to investigate irradiation-induced topological changes in plasmid DNA.     

Gene selection from microarray data for cancer classification--a machine learning approach

A DNA microarray can track the expression levels of thousands of genes simultaneously. Previous research has demonstrated that this technology can be useful in the classification of cancers. Cancer microarray data normally contains a small number of samples which have a large number of gene expression levels as features. To select relevant genes involved in different types of cancer remains a challenge. In order to extract useful gene information from cancer microarray data and reduce dimensionality, feature selection algorithms were systematically investigated in this study. Using a correlation-based feature selector combined with machine learning algorithms such as decision trees, nave Bayes and support vector machines, we show that classification performance at least as good as published results can be obtained on acute leukemia and diffuse large B-cell lymphoma microarray data sets. We also demonstrate that a combined use of different classification and feature selection approaches makes it possible to select relevant genes with high confidence. This is also the first paper which discusses both computational and biological evidence for the involvement of zyxin in leukaemogenesis.     

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.     

Classification of secondary ion mass spectrometry (SIMS) micrographs to characterize chemical phases

This work demonstrates the potential of multivariate image analysis methods in the extraction of useful, problem dependent information from SIMS images. Specific algorithms have been developed to classify SIMS micrographs manually as well as automatically. A feature selection has been achieved by means of principal component analysis with a subsequent image classification.As an application example for these improved digital image processing tools chemical phases within a soldered industrial metal sample have been identified. This is of highly practical value as it was assumed that during the soldering process inhomogeneities occur along the joint site which cause a cracking of the brazed material under mechanical strain conditions.     

Novel Visible and Ultraviolet Light Photogeneration of Hydroxyl Radicals by 2-Methyl-4-nitro-quinoline-N-oxide (MNO) and 4, 4'-Dinitro-(2, 2') bipyridinyl-N, N'-dioxide (DBD)

Chemicals that upon absorption of light generate hydroxyl radicals (·OH), free of other damaging species under physiological conditions, are useful tools for the study of the biological effects of ·OH radical and for its utilization for analytical purposes. We report the novel property of 2-methyl-4-nitro-quinoline- N -oxide (MNO) and 4, 4'-dinitro-(2, 2') bipyridinyl-N, N'-dioxide (DBD) to act as photogenerators of ·OH with UV and visible light. Upon irradiation with 360–400 nm light MNO and DBD generate free radicals that convert coumarin carboxylic acid (CCA) to fluorescent 7-OH-CCA; the ·OH radical scavengers dimethylsulfoxide (DMSO) and ethanol eliminate the induction of 7-OH-CCA fluorescence. Upon 400 nm illumination in the presence of MNO, supercoiled plasmid DNA is converted to circular and strand break-age is significantly reduced in the presence of DMSO and completely absent in the absence of MNO. The conversion of CCA to 7-OH-CCA and of supercoiled plasmid to circular DNA are also observed in the absence of oxygen. Taken together, these data indicate that MNO and DBD constitute novel ·OH-generating compounds. Because currently known ·OH-photogenerating compounds require UV illumination (< 360 nm) that also damages DNA and cells directly, the property of MNO to generate ·OH upon 400 nm illumination is advantageous when studies on cells, DNA and other biomolecules are conducted.     

Cross-linking of histone proteins to DNA by UV illumination of chromatin stained with Hoechst 33342

The photochemical effects of near-UV light on chromatin labeled with the vital DNA dye Hoechst 33342 (H33342) are studied. Several types of experiments demonstrate that illumination at both 365 and 410 nm results in significant cross-linking of proteins with the DNA. Fluorescence microscopy of dye-stained Xenopus XTC-2 nuclei shows that UV illumination has effects similar to chemical fixation by formaldehyde. At 365 nm a dose of approximately 70 J/cm2 results in 50% of the DNA being cross-linked, as measured by chloroform-sodium dodecyl sulfate extraction. At 410 nm the efficiency of cross-linking was smaller by a factor of 3. Gel electrophoresis of the cross-linked proteins shows them to be predominantly core histones. The implications of these results for experiments on live cells stained with H33342, for example, fluorescence microscopy of nuclear dynamics or cell sorting, are discussed.     

关于“t,F显著性检验应用中的一个问题”之我见

在应用ｔ、Ｆ检验时，要严格地区分方差齐性与方差非齐性两种不同的情况。在两种不同的情况下，计算总方差与自由度所用的公式是不同的。     

An image analysis suite for spot detection and spot matching in two-dimensional electrophoresis gels

We propose a suite of novel algorithms for image analysis of protein expression images obtained from 2-D electrophoresis. These algorithms are a segmentation algorithm for protein spot identification, and an algorithm for matching protein spots from two corresponding images for differential expression study. The proposed segmentation algorithm employs the watershed transformation, k-means analysis, and distance transform to locate the centroids and to extract the regions of the proteins spots. The proposed spot matching algorithm is an integration of the hierarchical-based and optimization-based methods. The hierarchical method is first used to find corresponding pairs of protein spots satisfying the local cross-correlation and overlapping constraints. The matching energy function based on local structure similarity, image similarity, and spatial constraints is then formulated and optimized. Our new algorithm suite has been extensively tested on synthetic and actual 2-D gel images from various biological experiments, and in quantitative comparisons with ImageMaster2D Platinum the proposed algorithms exhibit better spot detection and spot matching.     

The classification of solvents based on solvatochromic characteristics: the choice of optimal parameters for artificial neural networks

The Taft-Kamlet-Abboud hydrogen-bond acidity, hydrogen-bond basicity and polarity-polarizability are widely used as empirical characteristics of solvent-solute interactions. These solvatochromic parameters are determined from the absorption band positions of solvatochromic probes in the standard medium and in the medium under study. The practice of solvatochromic probing is growing rapidly, and the values of solvatochromic parameters are refined from time to time. As these values are rather close for many media, the classification of media based on these values can be tedious. This increases the choice of algorithms that can be employed in order to decrease the ambiguity of classification. The classification algorithms stable to small variations of solvatochromic parameters are of special interest. The artificial neural networks (ANN) proved to be a powerful tool for the supervised classification. The paper focuses on the search of optimal parameters of probabilistic, dynamic, Elman, feed-forward, and cascade ANN for the classification of solvent on the basis of their solvatochromic characteristics. Also, the influence of data variation on the stability of classification is examined. The dynamic and probabilistic neural networks have been found to be error-free and stable; they have significantly become such a common tool for supervised classification as linear discriminant analysis.      

Improvement of SIMS image classification by means of wavelet de-noising

Classification is a powerful tool for the extraction of chemical information from analytical images. However, classification may sometimes not be applicable, when the clusters corresponding to single phases overlap due to high noise levels, such that they are not significantly distinguishable. We investigate the effect of the wavelet shrinkage de-noising algorithm on the subsequent classification of analytical images. By application of wavelet de-noising the distinction of phases in classification can be significantly improved. Received: 16 April 1996 / Accepted: 14 August 1996     

In silico screening of ssDNA aptamer against Escherichia coli O157:H7: A machine learning and the Pseudo K-tuple nucleotide composition based approach

This study was planned to in silico screening of ssDNA aptamer against Escherichia coli O157:H7 by combination of machine learning and the PseKNC approach. For this, firstly a total numbers of 47 validated ssDNA aptamers as well as 498 random DNA sequences were considered as positive and negative training data respectively. The sequences then converted to numerical vectors using PseKNC method through Pse-in-one 2.0 web server. After that, the numerical vectors were subjected to classification by the SVM, ANN and RF algorithms available in Orange 3.2.0 software. The performances of the tested models were evaluated using cross-validation, random sampling and ROC curve analyzes. The primary results demonstrated that the ANN and RF algorithms have appropriate performances for the data classification. To improve the performances of mentioned classifiers the positive training data was triplicated and re-training process was also performed. The results confirmed that data size improvement had significant effect on the accuracy of data classification especially about RF model. Subsequently, the RF algorithm with accuracy of 98% was selected for aptamer screening. The thermodynamics details of folding process as well as secondary structures of the screened aptamers were also considered as final evaluations. The results confirmed that the selected aptamers by the proposed method had appropriate structure properties and there is no thermodynamics limit for the aptamers folding.     

A liquid core waveguide fluorescence detector for multicapillary electrophoresis applied to DNA sequencing in a 91-capillary array

A new laser-induced fluorescence (LIF) detector for multicapillary electrophoresis is presented. The detection principle is based on waveguiding of the emitted fluorescence from the point of illumination to the capillary ends by total internal reflection (TIR) and imaging of the capillary ends. The capillaries themselves thus act as liquid core waveguides (LCWs). At the illumination point, the capillaries are arranged in a planar array, which allows clean and efficient illumination with a line-focused laser beam. The capillary ends are rearranged into a small, densely packed two-dimensional array, which is imaged end-on with high light collection efficiency and excellent image quality. Wavelength dispersion is obtained with a single prism. Intercapillary optical crosstalk is less than 0.5%, and rejection of stray light is very efficient. The detector is applied to four-color DNA sequencing by gel electrophoresis in a 91-capillary array, with simple fluorescein and rhodamine dyes as fluorophores. Since the imaged two-dimensional array is so compact, the detector has a high potential for very large-scale multiplexing.     

Enzyme classification using multiclass support vector machine and feature subset selection

Proteins are the macromolecules responsible for almost all biological processes in a cell. With the availability of large number of protein sequences from different sequencing projects, the challenge with the scientist is to characterize their functions. As the wet lab methods are time consuming and expensive, many computational methods such as FASTA, PSI-BLAST, DNA microarray clustering, and Nearest Neighborhood classification on protein–protein interaction network have been proposed. Support vector machine is one such method that has been used successfully for several problems such as protein fold recognition, protein structure prediction etc. Cai et al. in 2003 have used SVM for classifying proteins into different functional classes and to predict their function. They used the physico-chemical properties of proteins to represent the protein sequences. In this paper a model comprising of feature subset selection followed by multiclass Support Vector Machine is proposed to determine the functional class of a newly generated protein sequence. To train and test the model for its performance, 32 physico-chemical properties of enzymes from 6 enzyme classes are considered. To determine the features that contribute significantly for functional classification, Sequential Forward Floating Selection (SFFS), Orthogonal Forward Selection (OFS), and SVM Recursive Feature Elimination (SVM-RFE) algorithms are used and it is observed that out of 32 properties considered initially, only 20 features are sufficient to classify the proteins into its functional classes with an accuracy ranging from 91% to 94%. On comparison it is seen that, OFS followed by SVM performs better than other methods. Our model generalizes the existing model to include multiclass classification and to identify most significant features affecting the protein function.     

Computational intelligence techniques in bioinformatics

Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included.     

Boosted feature selectors: a case study on prediction P-gp inhibitors and substrates

Feature selection is commonly used as a preprocessing step to machine learning for improving learning performance, lowering computational complexity and facilitating model interpretation. This paper proposes the application of boosting feature selection to improve the classification performance of standard feature selection algorithms evaluated for the prediction of P-gp inhibitors and substrates. Two well-known classification algorithms, decision trees and support vector machines, were used to classify the chemical compounds. The experimental results showed better performance for boosting feature selection with respect to the standard feature selection algorithms while maintaining the capability for feature reduction.     

Robustified least squares support vector classification

Support vector machine (SVM) algorithms are a popular class of techniques to perform classification. However, outliers in the data can result in bad global misclassification percentages. In this paper, we propose a method to identify such outliers in the SVM framework. A specific robust classification algorithm is proposed adjusting the least squares SVM (LS‐SVM). This yields better classification performance for heavily tailed data and data containing outliers. Copyright © 2009 John Wiley & Sons, Ltd.     

Photoreversible DNA condensation using light-responsive surfactants

A means to control DNA compaction with light illumination has been developed using the interaction of DNA with a photoresponsive cationic surfactant. The surfactant undergoes a reversible photoisomerization upon exposure to visible (trans isomer, more hydrophobic) or UV (cis isomer, more hydrophilic) light. As a result, surfactant binding to DNA and the resulting DNA condensation can be tuned with light. Dynamic light scattering (DLS) measurements were used to follow lambda-DNA compaction from the elongated-coil to the compact globular form as a function of surfactant addition and light illumination. The results reveal that compaction occurs at a surfactant-to-DNA base pair ratio of approximately 7 under visible light, while no compaction is observed up to a ratio of 31 under UV light. Upon compaction, the measured diffusion coefficient increases from a value of 0.6 x 10(-8) cm2/s (elongated coil with an end-to-end distance of 1.27 microm) to a value of 1.7 x 10(-8) cm2/s (compact globule with a hydrodynamic radius of 120 nm). Moreover, the light-scattering results demonstrate that the compaction process is completely photoreversible. Fluorescence microscopy with T4-DNA was used to further confirm the light-scattering results, allowing single-molecule detection of the light-controlled coil-to-globule transition. These structural studies were combined with absorbance and fluorescence spectroscopy of crystal violet in order to elucidate the binding mechanism of the photosurfactant to DNA. The results indicate that both electrostatic and hydrophobic forces are important in the compaction process. Finally, a DNA-photosurfactant-water phase diagram was constructed to examine the effects of both DNA and surfactant concentration on DNA compaction. The results reveal that precipitation, which occurs during the latter stages of condensation, can also be reversibly controlled with light illumination. The combined results clearly show the ability to control the interaction between DNA and the complexing agent and, therefore, DNA condensation with light.     

Surface plasmon near-field imaging of very thin microstructured polymer layers

We report on the near-field imaging of microstructured polymer layers deposited on an homogeneous metal thin film on which a surface plasmon mode is excited. The microstructures in the polymer layers are designed by electron beam lithography, and the near-field imaging is performed with a photon scanning tunneling microscope (PSTM). We show that, despite their very small height, the microstructures can be conveniently imaged with a PSTM thanks to the field enhancement at the surface of the metal thin film supporting the surface plasmon. The influence of the illumination conditions on the contrast of the PSTM images is discussed. In particular, we show that both the field enhancement and the near-field intensity distribution around the microstructures depend dramatically upon the illumination conditions, leading to the conclusion that the PSTM is well suited for spatially resolved near-field surface plasmon sensing purposes.