Using operonic gene pairs to set the threshold correlation coefficient for differently expressed genes

We have developed an approach to estimating the correlations in the noise component of gene expression data. An efficient noise reduction technique has been suggested. The resulting methods have been applied to E. coli microarray data and tested on SOS response modulated genes.     

A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding

Diabetic Retinopathy (DR) harm retinal blood vessels in the eye causing visual deficiency. The appearance and structure of blood vessels in retinal images play an essential part in the diagnoses of an eye sicknesses. We proposed a less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding. Contrast Limited Adaptive Histogram Equalization (CLAHE) and morphological filters have been used for enhancement and to remove low frequency noise or geometrical objects, respectively. The hessian matrix and eigenvalues approach used has been in a modified form at two different scales to extract wide and thin vessel enhanced images separately. Otsu thresholding has been further applied in a novel way to classify vessel and non-vessel pixels from both enhanced images. Finally, postprocessing steps has been used to eliminate the unwanted region/segment, non-vessel pixels, disease abnormalities and noise, to obtain a final segmented image. The proposed technique has been analyzed on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases along with the ground truth data that has been precisely marked by the experts.     

Mining HIV dynamics using independent component analysis

MOTIVATION: We implement a data mining technique based on the method of Independent Component Analysis (ICA) to generate reliable independent data sets for different HIV therapies. We show that this technique takes advantage of the ICA power to eliminate the noise generated by artificial interaction of HIV system dynamics. Moreover, the incorporation of the actual laboratory data sets into the analysis phase offers a powerful advantage when compared with other mathematical procedures that consider the general behavior of HIV dynamics. RESULTS: The ICA algorithm has been used to generate different patterns of the HIV dynamics under different therapy conditions. The Kohonen Map has been used to eliminate redundant noise in each pattern to produce a reliable data set for the simulation phase. We show that under potent antiretroviral drugs, the value of the CD4+ cells in infected persons decreases gradually by about 11% every 100 days and the levels of the CD8+ cells increase gradually by about 2% every 100 days. AVAILABILITY: Executable code and data libraries are available by contacting the corresponding author. IMPLEMENTATION: Mathematica 4 has been used to simulate the suggested model. A Pentium III or higher platform is recommended.     

A simple and robust algorithm for microarray data clustering based on gene population-variance ratio metric

With the advent of the microarray technology, the field of life science has been greatly revolutionized, since this technique allows the simultaneous monitoring of the expression levels of thousands of genes in a particular organism. However, the statistical analysis of expression data has its own challenges, primarily because of the huge amount of data that is to be dealt with, and also because of the presence of noise, which is almost an inherent characteristic of microarray data. Clustering is one tool used to mine meaningful patterns from microarray data. In this paper, we present a novel method of clustering yeast microarray data, which is robust and yet simple to implement. It identifies the best clusters from a given dataset on the basis of the population of the clusters as well as the variance of the feature values of the members from the cluster-center. It has been found to yield satisfactory results even in the presence of noisy data.     

On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data

Signal-to-noise ratio, the ratio between signal and noise, is a quantity that has been well established for MRI data but is still subject of ongoing debate and confusion when it comes to fMRI data. fMRI data are characterised by small activation fluctuations in a background of noise. Depending on how the signal of interest and the noise are identified, signal-to-noise ratio for fMRI data is reported by using many different definitions. Since each definition comes with a different scale, interpreting and comparing signal-to-noise ratio values for fMRI data can be a very challenging job. In this paper, we provide an overview of existing definitions. Further, the relationship with activation detection power is investigated. Reference tables and conversion formulae are provided to facilitate comparability between fMRI studies.     

A Stochastic Simulation Framework for the Prediction of Strategic Noise Mapping and Occupational Noise Exposure Using the Random Walk Approach

Strategic noise mapping provides important information for noise impact assessment and noise abatement. However, producing reliable strategic noise mapping in a dynamic, complex working environment is difficult. This study proposes the implementation of the random walk approach as a new stochastic technique to simulate noise mapping and to predict the noise exposure level in a workplace. A stochastic simulation framework and software, namely RW-eNMS, were developed to facilitate the random walk approach in noise mapping prediction. This framework considers the randomness and complexity of machinery operation and noise emission levels. Also, it assesses the impact of noise on the workers and the surrounding environment. For data validation, three case studies were conducted to check the accuracy of the prediction data and to determine the efficiency and effectiveness of this approach. The results showed high accuracy of prediction results together with a majority of absolute differences of less than 2 dBA; also, the predicted noise doses were mostly in the range of measurement. Therefore, the random walk approach was effective in dealing with environmental noises. It could predict strategic noise mapping to facilitate noise monitoring and noise control in the workplaces.     

A new analysis for membrane noise. The integral spectrum.

A new method of random data analysis has been developed with special implications for membrane noise. The integral spectrometer uses overlapping broad-band filters of simple design, whose bandwidth increases linearly with center frequency. A random two-state process, which has a Lorentzian-shaped spectral density, results in an integral spectrum whose maximum value occurs at the mean frequency of the events, and which is symmetric about that frequency on a semilog plot. 1/f noise is flat and does not distort the symmetry on the frequency axis. The integral spectrum exchanges resolution on the frequency axis for accuracy in the amplitude. The expected statistical error in amplitude has been calculated for three types of membrane noise assuming finite data. The integral spectrum compares favorably with conventional spectral densities and may be a reasonable alternative for random data analysis.     

Waveguide excitation fluorescence microscopy: a new tool for sensing and imaging the biointerface

A novel biosensing and imaging technique, the waveguide excitation fluorescence microscope, has been developed for the dynamic and quantitative investigation of bio-interfacial events in situ, ranging from ligand-receptor binding to focal adhesion formation in cell-surface interactions. The technique makes use of the evanescent field created when light travels in a mono-mode, planar optical waveguide to excite fluorescence in the near interface region. Advantages of the technique include high target sensitivity for fluorescence detection (femtomolar range), high surface specificity (ca. 100 nm perpendicular to the waveguide), large area analysis with submicron resolution, 'built-in' calibration of fluorescent light gain, and the capability to perform multi-colour imaging in situ and in real time. In this work, the sensitivity of the system has already been demonstrated through dynamic measurements of the streptavidin-biotin binding event to below 20 pM concentrations, signal to noise comparisons with conventional fluorescence microscopy have shown more than a 10-fold improvement, and surface specificity of the technique has also been illustrated in a comparison of fibroblast focal adhesion images. Thus, this new tool can be used to illuminate processes occurring at the interface between biology and synthetic surfaces in a unique manner.     

A prognostic model for the combined analysis of gene expression profiling in hepatocellular carcinoma

Microarray techniques using cDNA array and comparative genomic hybridization (CGH) have been developed for several discovery applications. They are frequently applied for the prediction and diagnosis of cancer in recent years. Many studies have shown that integrating genomic data from different sources may increase the reliability of gene expression analysis results in understanding cancer progression. Therefore, developing a good prognostic model dealing simultaneously with different types of dataset is important. The challenge with these types of data is high background noise. We describe an analytical two-stage framework with a multi-parallel data analysis method named wavelet-based generalized singular value decomposition and shaving method (WGSVD-shaving). This method is proposed for de-noising and dimension-reduction during early stage prognosis modeling. We also applied a supervised gene clustering technique with penalized logistic regression with Cox-model on an integrated data. We show the accuracy of the method using a simulated dataset with a case study on Hepatocelluar Carcinoma (HCC) cDNA and CGH data. The method shows improved results from GSVD-shaving and has application in the discovery of candidate genes associated with cancer.     

Neural noise as a real time guide for tumor site verification during stereotactic biopsy of brain tumors

Neural noise has been successfully used as a real time guide for verification of tumor site during stereotactic biopsy of brain tumors. This technique is a useful adjunct to other methods for tumor site verification and may possibly give pathophysiological information in peritumoral areas.     

PyMix - The Python mixture package - a tool for clustering of heterogeneous biological data

Background  

Cluster analysis is an important technique for the exploratory analysis of biological data. Such data is often high-dimensional, inherently noisy and contains outliers. This makes clustering challenging. Mixtures are versatile and powerful statistical models which perform robustly for clustering in the presence of noise and have been successfully applied in a wide range of applications.     

Parameter identification of the calvin photosynthesis cycle

Summary This article is concerned with the determination of kinetic parameters of the Calvin photosynthesis cycle which is described by seventeen nonlinear ordinary differential equations. It is shown that the task requires dynamic data for several sets of initial conditions. The numerical technique is based upon an algorithm for non-linear optimization and Gear's numerical integration scheme for stiff systems of differential equations. The sensitivity of the parameters to noise in the data is tested with a method adapted from Rosenbrook and Storey. A preliminary set of parameters has been obtained from a preliminary set of experimental data. The numerical methods are then tested with synthetic data derived from these parameters. The mathematical model and the results obtained in the simulation are used as an aid in designing new experiments.     

An empirical comparison of information-theoretic criteria in estimating the number of independent components of fMRI data

Background

Independent Component Analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components of fMRI data is critical to reduce over/under fitting. Although various methods based on Information Theoretic Criteria (ITC) have been used to estimate the intrinsic dimension of fMRI data, the relative performance of different ITC in the context of the ICA model hasn''t been fully investigated, especially considering the properties of fMRI data. The present study explores and evaluates the performance of various ITC for the fMRI data with varied white noise levels, colored noise levels, temporal data sizes and spatial smoothness degrees.

Methodology

Both simulated data and real fMRI data with varied Gaussian white noise levels, first-order auto-regressive (AR(1)) noise levels, temporal data sizes and spatial smoothness degrees were carried out to deeply explore and evaluate the performance of different traditional ITC.

Principal Findings

Results indicate that the performance of ITCs depends on the noise level, temporal data size and spatial smoothness of fMRI data. 1) High white noise levels may lead to underestimation of all criteria and MDL/BIC has the severest underestimation at the higher Gaussian white noise level. 2) Colored noise may result in overestimation that can be intensified by the increase of AR(1) coefficient rather than the SD of AR(1) noise and MDL/BIC shows the least overestimation. 3) Larger temporal data size will be better for estimation for the model of white noise but tends to cause severer overestimation for the model of AR(1) noise. 4) Spatial smoothing will result in overestimation in both noise models.

Conclusions

1) None of ITC is perfect for all fMRI data due to its complicated noise structure. 2) If there is only white noise in data, AIC is preferred when the noise level is high and otherwise, Laplace approximation is a better choice. 3) When colored noise exists in data, MDL/BIC outperforms the other criteria.     

A BASIC program for the removal of noise from reaction traces using Fourier filtering

Software for the removal of noise from reaction curves usingthe principle of Fourier filtering has been written in BASICto execute on a PC. The program inputs reaction traces whichare subjected to a rotation -inversion process, to produce functionssuitable for Fourier analysis. Fourier transformation into thefrequency domain is followed by multiplication of the transformby a rectangular filter function, to remove the noise frequencies.Inverse transformation then yields a noise-reduced reactiontrace suitable for further analysis. The program is interactiveat each stage and could easily be modified to remove noise froma range of input data types. Received on October 20, 1988; accepted on January 10, 1989     

A direct substitution, equation error technique for solving the thermographic tomography problem

A new technique for solving the combined state and parameter estimation problem in thermographic tomography is presented. The technique involves the direct substitution of known skin temperatures into the finite difference form of the bio-heat transfer equation as formulated for solving an initial value problem with a convection boundary condition at the skin surface. These equations are then used to solve the inverse bio-heat transfer problem for the unknown subcutaneous tissue temperatures and physiological parameters. For a small number of nodal points, closed form algebraic solutions are obtained. For larger sets of equations, a hybrid technique is used in which the problem is initially posed as an unconstrained optimization problem in which the model equation error is minimized using the conjugate gradient descent technique to get close to a solution. Then a generalized Newton-Raphson technique was used to solve the equations. A numerical simulation of a one-dimensional problem is investigated both with and without noise superimposed on the input (transient) skin temperature data. The results show that the technique gives very accurate results if the skin temperature data contains little noise. It is also shown that if the physical properties of the tissue and the metabolism are known, that a given set of proper transient skin temperature inputs yields a unique solution for the unknown internal temperatures and blood perfusion rates. However, the similar problem with known blood perfusion rates and unknown metabolisms does not yield a unique solution for the internal temperatures and metabolisms.     

A practical,high-resolution,microcomputer-based method for the analysis of relaxation data exhibiting multicomponent exponential decays

We have developed and tested a practical, rapid, high-resolution, microcomputer-based method for the analysis of multicomponent exponential decays. The analysis utilizes the Fourier deconvolution technique and includes methods to reduce noise both in the input data and in the results. The developed method is particularly well suited for analysing decays consisting of a wide range of decay times. The method resolves two exponential decays differing by a factor of two when the input data are mathematically generated and without noise, and resolves two decays differing by a factor of three when 2% Gaussian noise is present in the same data. The method lends intself to routine analysis of any relaxation process consisting of exponential decays, including biomedical applications such as enzyme kinetics, circulatory transport functions, pharmacokinetics, plasma exchange therapy, and analysis of compartmental models for any process.     

An Optimized Cascaded Stochastic Resonance for the Enhancement of Brain MRI

Background

The contrast enhancement of Magnetic Resonance Imaging (MRI) data is quite challenging as the noise present in this data also get amplified in this process. Dynamic Stochastic Resonance (DSR) is the technique that utilizes the noise to enhance the contrast of MRI data.

SoFAR: software for fully automatic evaluation of real-time PCR data

Quantitative real-time PCR has proven to be an extremely useful technique in life sciences for many applications. Although a lot of attention has been paid to the optimization of the assay conditions, the analysis of the data acquired is often done with software tools that do not make optimum use of the information provided by the data. Particularly, this is the case for high-throughput analysis, which requires a careful characterization and interpretation of the complete data by suitable software. Here we present a software solution for the robust, reliable, accurate, and fast evaluation of real-time PCR data, called SoFAR. The software automatically evaluates the data acquired with the LightCycler system. It applies new algorithms for an adaptive background correction of signal trends, the calculation of the effective signal noise, the automated identification of the exponential phases, the adaptive smoothing of the raw data, and the correction of melting curve data. Finally, it provides information regarding the validity of the results obtained. The SoFAR software minimizes the time required for evaluation and increases the accuracy and reliability of the results. The software is available upon request.     

Selection to minimise noise in living systems and its implications for the evolution of gene expression

Gene expression, like many biological processes, is subject to noise. This noise has been measured on a global scale, but its general importance to the fitness of an organism is unclear. Here, I show that noise in gene expression in yeast has evolved to prevent harmful stochastic variation in the levels of genes that reduce fitness when their expression levels change. Therefore, there has probably been widespread selection to minimise noise in gene expression. Selection to minimise noise, because it results in gene expression that is stable to stochastic variation in cellular components, may also constrain the ability of gene expression to respond to non‐stochastic variation. I present evidence that this has indeed been the case in yeast. I therefore conclude that gene expression noise is an important biological trait, and one that probably limits the evolvability of complex living systems.     

A non-invasive acoustic and vibration analysis technique for evaluation of hip joint conditions

The performance evaluation of THA outcome is difficult and surgeons often use invasive methods to investigate effectiveness. A non-invasive acoustic and vibration analysis technique has recently been developed for more-in-depth evaluation of in vivo hip conditions.Gait kinematics, corresponding vibration and sound measurement of five THA subjects were analyzed post-operatively using video-fluoroscopy, sound and accelerometer measurements while walking on a treadmill. The sound sensor and a pair of tri-axial accelerometers, externally attached to the pelvic and femoral bone prominences, detected frequencies that are propagated through the femoral head and acetabular cup interactions. A data acquisition system was used to amplify the signal and filter out noise generated by undesired frequencies. In vivo kinematics and femoral head sliding quantified using video fluoroscopy were correlated to the sound and acceleration measurements.Distinct variations between the different subjects were identified. A correlation of sound and acceleration impulses with separation has been achieved. Although, in vivo sounds are quite variable in nature and all correlated well with the visual images.This is the first study to document and correlate visual and audible effects of THA under in-vivo conditions. This study has shown that the development of the acoustic and vibration technique provides a practical method and generates new possibilities for a better understanding of THA performance.