An Active Contour Model for the Segmentation of Images with Intensity Inhomogeneities and Bias Field Estimation

Intensity inhomogeneity causes many difficulties in image segmentation and the understanding of magnetic resonance (MR) images. Bias correction is an important method for addressing the intensity inhomogeneity of MR images before quantitative analysis. In this paper, a modified model is developed for segmenting images with intensity inhomogeneity and estimating the bias field simultaneously. In the modified model, a clustering criterion energy function is defined by considering the difference between the measured image and estimated image in local region. By using this difference in local region, the modified method can obtain accurate segmentation results and an accurate estimation of the bias field. The energy function is incorporated into a level set formulation with a level set regularization term, and the energy minimization is conducted by a level set evolution process. The proposed model first appeared as a two-phase model and then extended to a multi-phase one. The experimental results demonstrate the advantages of our model in terms of accuracy and insensitivity to the location of the initial contours. In particular, our method has been applied to various synthetic and real images with desirable results.     

Pré-traitement d’un volume IRM par filtrage anisotrope robuste pour la segmentation de l’épaule

A novel pre-treatment process for image segmentation, based on anisotropic diffusion and robust statistics, is presented in this paper. Image smoothing with edge preservation is shown to help upper limb segmentation (shoulder segmentation in particular) in MRI datasets. The anisotropic diffusion process is mainly controlled by an automated stopping function that depends on the values of voxel gradient. Voxel gradients are divided into two classes: one for high values, corresponding to edge voxels or noisy voxels, one for low values. The anisotropic diffusion process is also controlled by a threshold on voxel gradients that separates both classes. A global estimation of this threshold parameter is classically used. In this paper, we propose a new method based on a local robust estimation. It allows a better removing of noise while preserving edges in the images. An entropy criterion is used to quantify the ability of the algorithm to remove noise with different signal to noise ratios in synthetic images. Another quantitative evaluation criterion based on the Pratt Figure of Merit (FOM) is proposed to evaluate the edge preservation and their location accuracy with respect to a manual segmentation. The results on synthetic and MRI data of shoulder show the assets of the local model in terms of areas homogeneity and edges locations.     

Tabu search model selection for SVM

A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. The aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together. The selection of the simplification level by vector quantization, of a feature subset and of support vector machines hyperparameters are performed by tabu search method to optimize the defined decision function quality criterion in order to find a good sub-optimal model on tractable times.     

Supervised principal component analysis for gene set enrichment of microarray data with continuous or survival outcomes

MOTIVATION: Gene set analysis allows formal testing of subtle but coordinated changes in a group of genes, such as those defined by Gene Ontology (GO) or KEGG Pathway databases. We propose a new method for gene set analysis that is based on principal component analysis (PCA) of genes expression values in the gene set. PCA is an effective method for reducing high dimensionality and capture variations in gene expression values. However, one limitation with PCA is that the latent variable identified by the first PC may be unrelated to outcome. RESULTS: In the proposed supervised PCA (SPCA) model for gene set analysis, the PCs are estimated from a selected subset of genes that are associated with outcome. As outcome information is used in the gene selection step, this method is supervised, thus called the Supervised PCA model. Because of the gene selection step, test statistic in SPCA model can no longer be approximated well using t-distribution. We propose a two-component mixture distribution based on Gumbel exteme value distributions to account for the gene selection step. We show the proposed method compares favorably to currently available gene set analysis methods using simulated and real microarray data. SOFTWARE: The R code for the analysis used in this article are available upon request, we are currently working on implementing the proposed method in an R package.     

Using whole genome presence/absence data to untangle function in 12 Drosophila genomes

The Drosophila 12 genome data set was used to construct whole genome, gene family presence/absence matrices using a broad range of E value cutoffs as criteria for gene family inclusion. The various matrices generated behave differently in phylogenetic analyses as a function of the e-value employed. Based on an optimality criterion that maximizes internal corroboration of information, we show that values of e-105 to e-125 extract the most internally consistent phylogenetic signal. Functional class of most genes and gene families can be accurately determined based on the D. melanogaster genome annotation. We used the gene ontology (GO) system to create partitions based on gene function. Several measures of phylogenetic congruence (diagnosis, consistency, partitioned support , hidden support) for different higher and lower level GO categories, were used to mine the data set for genes and gene families that show strong agreement or disagreement with the overall combined phylogenetic hypothesis. We propose that measures of phylogenetic congruence can be used as criteria to identify loci with related GO terms that have a significant impact on cladogenesis.     

Structural prediction of peptides bound to MHC class I

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.     

Cardiac function-related gene expression profiles in human atrial myocytes

To obtain insights into the molecular pathogenesis of heart failure in humans, we have analyzed the expression profiles of>12,000 genes in a total of 17 human specimens of right atrial myocytes. From this large data set, we here tried to identify gene clusters, expression level of which is correlated precisely with clinical parameter values of cardiac function. We could reveal that cardiac myocytes with normal sinus rhythm were clearly differentiated, in the point of view of gene expression, from those with atrial fibrillation. Further, an expression profile-based prediction of arrhythmia by a newly developed "weighted-distance method" could efficiently diagnose our samples. We could even construct calculation formulae for the values of left ventricular ejection fraction based on the expression level of selected genes. To our best knowledge, this is the first report to indicate that pumping ability of heart can be predicted by any measures of atrium.     

Optimality of Enzymatic Physiological Systems

An optimality criterion is proposed for physiological systems based on limited proteolysis. The proposed criterion is applied to the prourokinase-activated fibrinolytic system, for which the existence of optimal (according to this criterion) substrate concentrations is demonstrated. It is also shown that the optimal substrate concentrations depend on the values of kinetic constants.     

A least squares algorithm to determine the mechanical time constant distribution of the lung during forced expiration

A method to determine the mechanical time-constant distribution of the lung during a forced expiration manoeuvre is proposed. The method is based on a least squares algorithm constrained to give reasonably smooth non-negative solutions. The smoothing constraint was imposed by minimizing the second derivative of the distribution function in accordance with the physiological meaning of the time-constant distribution. Nevertheless, the obtained solution depends greatly on the relative weights of the two terms in the objective function to be minimized i.e., the error on the fit of the volume signal and the smoothness of the distribution function. To select the optimum smoothing weight, a criterion based on the stability of the reconstructed distribution shape was defined. The performance of the algorithm and that of the defined criterion were evaluated by using simulated signals of forced expired volume. The error of reconstructed distributions was quantified by means of the area enclosed between this distribution and the original one used to generate the simulated volume signal. The results obtained showed that for all the analyzed signals: (1) There is a value of the weight of the smoothing constraint which gives rise to a solution that is optimum in a least squares sense. (2) The proposed stabilization criterion enables us to approach this optimum solution from experimental signals.     

Rotations of the 2B sub-domain of E. coli UvrD helicase/translocase coupled to nucleotide and DNA binding

Escherichia coli UvrD is a superfamily 1 DNA helicase and single-stranded DNA (ssDNA) translocase that functions in DNA repair and plasmid replication and as an anti-recombinase by removing RecA protein from ssDNA. UvrD couples ATP binding and hydrolysis to unwind double-stranded DNA and translocate along ssDNA with 3′-to-5′ directionality. Although a UvrD monomer is able to translocate along ssDNA rapidly and processively, DNA helicase activity in vitro requires a minimum of a UvrD dimer. Previous crystal structures of UvrD bound to a ssDNA/duplex DNA junction show that its 2B sub-domain exists in a “closed” state and interacts with the duplex DNA. Here, we report a crystal structure of an apo form of UvrD in which the 2B sub-domain is in an “open” state that differs by an ∼ 160° rotation of the 2B sub-domain. To study the rotational conformational states of the 2B sub-domain in various ligation states, we constructed a series of double-cysteine UvrD mutants and labeled them with fluorophores such that rotation of the 2B sub-domain results in changes in fluorescence resonance energy transfer. These studies show that the open and closed forms can interconvert in solution, with low salt favoring the closed conformation and high salt favoring the open conformation in the absence of DNA. Binding of UvrD to DNA and ATP binding and hydrolysis also affect the rotational conformational state of the 2B sub-domain, suggesting that 2B sub-domain rotation is coupled to the function of this nucleic acid motor enzyme.     

A nonparametric approach to the analysis of longitudinal data via a set of level crossing problems with application to the analysis of microarray time course experiments

Here we develop a completely nonparametric method for comparing two groups on a set of longitudinal measurements. No assumptions are made about the form of the mean response function, the covariance structure or the distributional form of disturbances around the mean response function. The solution proposed here is based on the realization that every longitudinal data set can also be thought of as a collection of survival data sets where the events of interest are level crossings. The method for testing for differences in the longitudinal measurements then is as follows: for an arbitrarily large set of levels, for each subject determine the first time the subject has an upcrossing and a downcrossing for each level. For each level one then computes the log rank statistic and uses the maximum in absolute value of all these statistics as the test statistic. By permuting group labels we obtain a permutation test of the hypothesis that the joint distribution of the measurements over time does not depend on group membership. Simulations are performed to investigate the power and it is applied to the area that motivated the method-the analysis of microarrays. In this area small sample sizes, few time points and far too many genes to consider genuine gene level longitudinal modeling have created a need for a simple, model free test to screen for interesting features in the data.     

Dynamic assessment of environmental damage based on the optimal clustering criterion – Taking oil spill damage to marine ecological environment as an example

Aimng at the evaluation of environmental damages, we proposed a dynamic evaluation approach based on the optimal cluster criterion. Firstly, a method for sample data standardization was introduced. After determining the measurement of damage grade, we applied the system cluster analysis approach to classify the grades of corresponding environmental pollution events. Then, the optimal cluster level was evaluated based on the optimal clustering criterion. By using the marine environmental damages caused by 17 marine oil spill events as a sample, we tested the dynamic evaluation method proposed in this article with its practicability. Further, by comparing it with some traditional damage evaluation methods, we found that their evaluation results were consistent. All these have shown that the dynamic evaluation approach proposed in this paper can meet the requirement of environmental damage evaluation. Finally, directions of future researches were pointed out.     

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.     

Design of cellular porous biomaterials for wall shear stress criterion

The microfluidic environment provided by implanted prostheses has a decisive influence on the viability, proliferation and differentiation of cells. In bone tissue engineering, for instance, experiments have confirmed that a certain level of wall shear stress (WSS) is more advantageous to osteoblastic differentiation. This paper proposes a level‐set‐based topology optimization method to regulate fluidic WSS distribution for design of cellular biomaterials. The topological boundary of fluid phase is represented by a level‐set model embedded in a higher‐dimensional scalar function. WSS is determined by the computational fluid dynamics analysis in the scale of cellular base cells. To achieve a uniform WSS distribution at the solid–fluid interface, the difference between local and target WSS is taken as the design criterion, which determines the speed of the boundary evolution in the level‐set model. The examples demonstrate the effectiveness of the presented method and exhibit a considerable potential in the design optimization and fabrication of new prosthetic cellular materials for bioengineering applications. Biotechnol. Bioeng. 2010;107:737–746. © 2010 Wiley Periodicals, Inc.     

Protein loop structure prediction with flexible stem geometries

The structure prediction of loops with flexible stem residues is addressed in this article. While the secondary structure of the stem residues is assumed to be known, the geometry of the protein into which the loop must fit is considered to be unknown in our methodology. As a consequence, the compatibility of the loop with the remainder of the protein is not used as a criterion to reject loop decoys. The loop structure prediction with flexible stems is more difficult than fitting loops into a known protein structure in that a larger conformational space has to be covered. The main focus of the study is to assess the precision of loop structure prediction if no information on the protein geometry is available. The proposed approach is based on (1) dihedral angle sampling, (2) structure optimization by energy minimization with a physically based energy function, (3) clustering, and (4) a comparison of strategies for the selection of loops identified in (3). Steps (1) and (2) have similarities to previous approaches to loop structure prediction with fixed stems. Step (3) is based on a new iterative approach to clustering that is tailored for the loop structure prediction problem with flexible stems. In this new approach, clustering is not only used to identify conformers that are likely to be close to the native structure, but clustering is also employed to identify far-from-native decoys. By discarding these decoys iteratively, the overall quality of the ensemble and the loop structure prediction is improved. Step (4) provides a comparative study of criteria for loop selection based on energy, colony energy, cluster density, and a hybrid criterion introduced here. The proposed method is tested on a large set of 3215 loops from proteins in the Pdb-Select25 set and to 179 loops from proteins from the Casp6 experiment.     

Extraction of melodies in behavioural sequences

A method is proposed that extracts a set of phrases, or “melodies”, from a behavioural sequence, using a technique for extracting and compressing chains based on Information Theory. These melodies are validated by reference to a statistical criterion. An application of this method to the analysis of the behavioural sequences of two groups of mice, the first observed during the day, the second during the night, is described. The advantages and the limitations of the method are discussed.     

基于熵准则的鲁棒的RBF谷胱甘肽发酵建模

在谷胱甘肽的发酵过程建模中, 当试验数据含有噪音时, 往往会导致模型预测精度和泛化能力的下降。针对该问题, 提出了一种新的基于熵准则的RBF神经网络建模方法。与传统的基于MSE准则函数的建模方法相比, 新方法能从训练样本的整体分布结构来进行模型参数学习, 有效地避免了传统的基于MSE准则的RBF网络的过学习和泛化能力差的缺陷。将该模型应用到实际的谷胱甘肽发酵过程建模中, 实验结果表明: 该方法具有较高的预测精度、泛化能力和良好的鲁棒性, 从而对谷胱甘肽的发酵建模有潜在的应用价值。     

Bayesian Hierarchical Modeling and Selection of Differentially Expressed Genes for the EST Data

Summary Expressed sequence tag (EST) sequencing is a one‐pass sequencing reading of cloned cDNAs derived from a certain tissue. The frequency of unique tags among different unbiased cDNA libraries is used to infer the relative expression level of each tag. In this article, we propose a hierarchical multinomial model with a nonlinear Dirichlet prior for the EST data with multiple libraries and multiple types of tissues. A novel hierarchical prior is developed and the properties of the proposed prior are examined. An efficient Markov chain Monte Carlo algorithm is developed for carrying out the posterior computation. We also propose a new selection criterion for detecting which genes are differentially expressed between two tissue types. Our new method with the new gene selection criterion is demonstrated via several simulations to have low false negative and false positive rates. A real EST data set is used to motivate and illustrate the proposed method.     

基于熵准则的鲁棒的RBF谷胱甘肽发酵建模

在谷胱甘肽的发酵过程建模中, 当试验数据含有噪音时, 往往会导致模型预测精度和泛化能力的下降。针对该问题, 提出了一种新的基于熵准则的RBF神经网络建模方法。与传统的基于MSE准则函数的建模方法相比, 新方法能从训练样本的整体分布结构来进行模型参数学习, 有效地避免了传统的基于MSE准则的RBF网络的过学习和泛化能力差的缺陷。将该模型应用到实际的谷胱甘肽发酵过程建模中, 实验结果表明: 该方法具有较高的预测精度、泛化能力和良好的鲁棒性, 从而对谷胱甘肽的发酵建模有潜在的应用价值。