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PubMed2001

Dynamics of inherent structure in supercooled liquids near kinetic glass transition.

We present a description of the dynamics of a supercooled binary Lennard-Jones liquid in term of the potential landscape of the system. The slowing down of the dynamics in supercooled liquids near the kinetic glass transition is related to the existence of basins in the potential landscape. The inherent structures that are the local potential minima in the configuration space obtained by a quench process are employed to represent the configurations of the basins. We present time correlation functions of the inherent structure, both the self and the coherent part, as a function of wave vector. We also calculated the mean-square displacement, and the non-Gaussian parameter of the van Hove self-correlation function. Compared with the dynamics of original configurations, the short-time relaxation has almost been eliminated by the quench process. However, the long-time alpha relaxations remain essentially the same. We conclude that the long-time alpha relaxation is the result of cross-basin transition in the potential landscape in the configuration space.

Journal Article↗

PubMed2006

[Nonlinear Kalman filter simultaneous determination of mixture of phenol and 2-chlorophenol].

An analysis method of nonlinear absorbance for binary composition mixture was proposed. The nonlinear absorbance was composed of the contributions from both themselves and the change in their contributions due to mixing, as well as the contribution from their interaction. The mixture of phenol and 2-chlorophenol was simultaneously determined using nonlinear Kalman filter UV spectrophotometry. UV absorption spectra were obtained with 32 standard solutions (range 1-15 mg x L(-1)) and 71 wavelengths(250-290 nm). Standard work matrix of extended Kalman filter was performed from partial least-squares method. The vectors function Jacobi matrix was obtained by linearization of Taylor series for nonlinear absorbance formula. The recovery experiment showed that the extended Kalman filter simultaneous determination of mixture of phenol and 2-chlorophenol that is not only exact, but also stable.

English Abstract↗

PubMed1999

Multiobjective genetic optimization of diagnostic classifiers with implications for generating receiver operating characteristic curves.

It is well understood that binary classifiers have two implicit objective functions (sensitivity and specificity) describing their performance. Traditional methods of classifier training attempt to combine these two objective functions (or two analogous class performance measures) into one so that conventional scalar optimization techniques can be utilized. This involves incorporating a priori information into the aggregation method so that the resulting performance of the classifier is satisfactory for the task at hand. We have investigated the use of a niched Pareto multiobjective genetic algorithm (GA) for classifier optimization. With niched Pareto GA's, an objective vector is optimized instead of a scalar function, eliminating the need to aggregate classification objective functions. The niched Pareto GA returns a set of optimal solutions that are equivalent in the absence of any information regarding the preferences of the objectives. The a priori knowledge that was used for aggregating the objective functions in conventional classifier training can instead be applied post-optimization to select from one of the series of solutions returned from the multiobjective genetic optimization. We have applied this technique to train a linear classifier and an artificial neural network (ANN), using simulated datasets. The performances of the solutions returned from the multiobjective genetic optimization represent a series of optimal (sensitivity, specificity) pairs, which can be thought of as operating points on a receiver operating characteristic (ROC) curve. All possible ROC curves for a given dataset and classifier are less than or equal to the ROC curve generated by the niched Pareto genetic optimization.

Algorithms↗

PubMed1979

Babesia microti: morphology, distribution and host relationship in Germany.

A short review has been given about recent studies on Babesia microti in natural animal host's near Munich (Southern Germany). An infected area has been studied in order to elucidate the outdoor relationship between the local strains and their preferred hosts, the European field vole Microtus agrestis. The seasonal variation of the parasites prevalence in voles shows a rise in the early summertime (71% of the catches infected). Roundish forms predominate in the erythrocytes. Multiplication never takes place by binary fission, but in "Maltese cross" form or budding-like. Normally the parasite is enveloped with a simple elementary membrane only, as shown by electron microscopy. The arthropod host is still not identified, infections of human beings not observed. This is the first finding and full description of B. microti in Germany.

Animals↗

PubMed2006

Phase separation in binary polymer/liquid crystal mixtures: network breaking and domain growth by coalescence-induced coalescence.

A small-angle light scattering (SALS) technique together with optical microscopy observation are used to investigate phase separation kinetics in films of low molecular weight thermotropic liquid crystal (4-cyano-4'-n-octyl-biphenyl, 8CB) with flexible polymer (polystyrene, PS). The growth of domains is studied as a function of time, film thickness, and film composition. The light scattering results are correlated with the images obtained by optical microscopy observation. In this paper, we study the breaking of a bicontinuous network of polymer in liquid crystal into droplets and their further growth via the coalescence-induced coalescence mechanism. The appearance of droplets in the system leads to a strong scattering at small wave vectors, while the bicontinuous network gives a peak at a nonzero wave vector. Superposition of these scattering intensities leads to the appearance of a second peak in the full scattering intensity signal, when the bicontinuous network starts to break up into disjointed elongated domains. Finally, both peaks merge into a single peak, which moves quickly toward zero wave vectors, indicating a complete transformation of elongated domains into spherical droplets of variable size. We found that the separation process does not depend on the size of the system. Irrespective of the sample thickness, the network breaks into fragments always at the same time after temperature quench. On the basis of morphological analysis, we found that the average size of the droplets which formed from the network grows with time, t, as t(alpha), alpha = 0.9 +/- 0.1, in the isotropic phase and in the nematic phase.

Journal Article↗

PubMed2006

Recursive gene selection based on maximum margin criterion: a comparison with SVM-RFE.

BACKGROUND: In class prediction problems using microarray data, gene selection is essential to improve the prediction accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVM-RFE) has become one of the leading methods and is being widely used. The SVM-based approach performs gene selection using the weight vector of the hyperplane constructed by the samples on the margin. However, the performance can be easily affected by noise and outliers, when it is applied to noisy, small sample size microarray data. RESULTS: In this paper, we propose a recursive gene selection method using the discriminant vector of the maximum margin criterion (MMC), which is a variant of classical linear discriminant analysis (LDA). To overcome the computational drawback of classical LDA and the problem of high dimensionality, we present efficient and stable algorithms for MMC-based RFE (MMC-RFE). The MMC-RFE algorithms naturally extend to multi-class cases. The performance of MMC-RFE was extensively compared with that of SVM-RFE using nine cancer microarray datasets, including four multi-class datasets. CONCLUSION: Our extensive comparison has demonstrated that for binary-class datasets MMC-RFE tends to show intermediate performance between hard-margin SVM-RFE and SVM-RFE with a properly chosen soft-margin parameter. Notably, MMC-RFE achieves significantly better performance with a smaller number of genes than SVM-RFE for multi-class datasets. The results suggest that MMC-RFE is less sensitive to noise and outliers due to the use of average margin, and thus may be useful for biomarker discovery from noisy data.

Algorithms↗

PubMed2004

Parallel screening of selective and high-affinity displacers for proteins in ion-exchange systems.

This paper employs a parallel batch screening technique for the identification of both selective and high-affinity displacers for a model binary mixture of proteins in a cation-exchange system. A variety of molecules were screened as possible displacers for the proteins ribonuclease A (RNAseA) and alpha-chymotrypsinogen A (alpha-chyA) on high performance Sepharose SP. The batch screening data for each protein was used to select leads for selective and high-affinity displacers and column experiments were carried out to evaluate the performance of the selected leads. The data from the batch displacements was also employed to generate quantitative structure-efficacy relationship (QSER) models based on a support vector machine regression approach. The resulting models had high correlation coefficients and were able to predict the behaviour of molecules not included in the training set. The descriptors selected in the QSER models for both proteins were examined to provide insights into factors influencing displacer selectivity in ion-exchange systems. The results presented in this paper demonstrate that this parallel batch screening-QSER approach can be employed for the identification of selective and high-affinity displacers for protein mixtures.

Cation Exchange Resins↗

PubMed2006

Support vector machine implementations for classification & clustering.

BACKGROUND: We describe Support Vector Machine (SVM) applications to classification and clustering of channel current data. SVMs are variational-calculus based methods that are constrained to have structural risk minimization (SRM), i.e., they provide noise tolerant solutions for pattern recognition. The SVM approach encapsulates a significant amount of model-fitting information in the choice of its kernel. In work thus far, novel, information-theoretic, kernels have been successfully employed for notably better performance over standard kernels. Currently there are two approaches for implementing multiclass SVMs. One is called external multi-class that arranges several binary classifiers as a decision tree such that they perform a single-class decision making function, with each leaf corresponding to a unique class. The second approach, namely internal-multiclass, involves solving a single optimization problem corresponding to the entire data set (with multiple hyperplanes). RESULTS: Each SVM approach encapsulates a significant amount of model-fitting information in its choice of kernel. In work thus far, novel, information-theoretic, kernels were successfully employed for notably better performance over standard kernels. Two SVM approaches to multiclass discrimination are described: (1) internal multiclass (with a single optimization), and (2) external multiclass (using an optimized decision tree). We describe benefits of the internal-SVM approach, along with further refinements to the internal-multiclass SVM algorithms that offer significant improvement in training time without sacrificing accuracy. In situations where the data isn't clearly separable, making for poor discrimination, signal clustering is used to provide robust and useful information--to this end, novel, SVM-based clustering methods are also described. As with the classification, there are Internal and External SVM Clustering algorithms, both of which are briefly described.

Algorithms↗

PubMed1993

Odor-intensity interaction in binary mixtures.

Four principles underlying odor summation in mixtures were examined on the bases of 10 Ss' magnitude estimates of 41 mixtures and their components (pyridine and DMDS). Thus, the overall intensity summation for mixtures are compared to self-addition reflected by the power function for single substances: Hypoaddition is a characteristic of both. Compromise is found for 25% of the mixtures, but it is not inherent in the power function. No level dependency is found for mixtures, although it is in accordance with the power function. Asymmetrical summation exists for mixtures and is inconsistent with the power function. Therefore, the idea of a congruence in odor-intensity summation processes is rejected. The results also constitute a successful cross-validation of the angle alpha (99 degrees vs. 102 degrees) of the perceptual vector model reported by B. Berglund et al. (1973).

Adolescent↗

PubMed2002

Concentration fluctuations in the binary mixture hexane-nitrobenzene with static and dynamic x-ray scattering.

We report on a small-angle x-ray scattering study of the hexane-nitrobenzene binary fluid mixture near its critical point. The use of an ultrabright x-ray undulator synchrotron source enabled us to measure the temperature dependence of the static structure factor with unprecedented contrast, and the large coherent flux of this source provided a probe for the fluctuation dynamics via the x-ray photon correlation spectroscopy technique. We find that the intensity and correlation lengths diverge with the expected three-dimensional Ising critical exponents, and the dynamical correlation function decays exponentially with correlation times as small as 250 micros. In the range of wave vector studied here (1.2-2.6 x 10(-3) A(-1)) the concentration fluctuations relaxed diffusively with a diffusion constant consistent with that determined from visible light scattering measurements.

Journal Article↗

PubMed1984

Stabilized binary hierarchic classifier in cytopathologic diagnosis.

A binary tree classifier (BTC) algorithm for computer-assisted cell image analysis has been developed that overcomes the problem of overtraining due to inadequate sample size/dimensionality ratio at the higher-order nodes of a hierarchic decision structure. Provisions have been introduced that ensure that decision rules created at each node are based on samples representative of the subpopulation routed to the node. These provisions eliminate problems caused by truncation effects resulting from the application of decision rules at preceding decision nodes. The BTC performs better than do single-stage classifiers in situations where the categories' mean vectors are not well separated and no equality of covariance matrices exists. In applications in which noticeable deterioration of classifier performance on test-set data is common, the classification success rate of the BTC algorithm is not statistically significantly different between the training-set and test-set data.

Cells↗

PubMed2004

Hierarchy revealed in the specification of three skeletal fates by Sox9 and Runx2.

Across vertebrates, there are three principal skeletal tissues: bone, persistent cartilage, and replacement cartilage. Although each tissue has a different evolutionary history and functional morphology, they also share many features. For example, they function as structural supports, they are comprised of cells embedded in collagen-rich extracellular matrix, and they derive from a common embryonic stem cell, the osteochondroprogenitor. Occasionally, homologous skeletal elements can change tissue type through phylogeny. Together, these observations raise the possibility that skeletal tissue identity is determined by a shared set of genes. Here, we show that misexpression of either Sox9 or Runx2 can substitute bone with replacement cartilage or can convert persistent cartilage into replacement cartilage and vice versa. Our data also suggest that these transcription factors function in a molecular hierarchy in which chondrogenic factors dominate. We propose a binary molecular code that determines whether skeletal tissues form as bone, persistent cartilage, or replacement cartilage. Finally, these data provide insights into the roles that master regulatory genes play during evolutionary change of the vertebrate skeleton.

Animals↗

PubMed2005

Self-consistent generalized Langevin equation for colloidal mixtures.

A self-consistent theory of collective and tracer diffusion in colloidal mixtures is presented. This theory is based on exact results for the partial intermediate scattering functions derived within the framework of the generalized Langevin equation formalism, plus a number of conceptually simple and sensible approximations. The first of these consists of a Vineyard-like approximation between collective and tracer diffusion, which writes the collective dynamics in terms of the memory function related to tracer diffusion. The second consists of interpolating this only unknown memory function between its two exact limits at small and large wave vectors; for this, a phenomenologically determined, but not arbitrary, interpolating function is introduced: a Lorentzian with its inflection point located at the first minimum of the partial static structure factor. The small wave-vector exact limit involves a time-dependent friction function, for which we take a general approximate result, previously derived within the generalized Langevin equation formalism. This general result expresses the time-dependent friction function in terms of the partial intermediate scattering functions, thus closing the system of equations into a fully self-consistent scheme. This extends to mixtures a recently proposed self-consistent theory developed for monodisperse suspensions [Yeomans-Reyna and Medina-Noyola, Phys. Rev. E 64, 066114 (2001)]. As an illustration of its quantitative accuracy, its application to a simple model of a binary dispersion in the absence of hydrodynamic interactions is reported.

Journal Article↗

PubMed2004

Brain tumor classification based on long echo proton MRS signals.

There has been a growing research interest in brain tumor classification based on proton magnetic resonance spectroscopy (1H MRS) signals. Four research centers within the EU funded INTERPRET project have acquired a significant number of long echo 1H MRS signals for brain tumor classification. In this paper, we present an objective comparison of several classification techniques applied to the discrimination of four types of brain tumors: meningiomas, glioblastomas, astrocytomas grade II and metastases. Linear and non-linear classifiers are compared: linear discriminant analysis (LDA), support vector machines (SVM) and least squares SVM (LS-SVM) with a linear kernel as linear techniques and LS-SVM with a radial basis function (RBF) kernel as a non-linear technique. Kernel-based methods can perform well in processing high dimensional data. This motivates the inclusion of SVM and LS-SVM in this study. The analysis includes optimal input variable selection, (hyper-) parameter estimation, followed by performance evaluation. The classification performance is evaluated over 200 stratified random samplings of the dataset into training and test sets. Receiver operating characteristic (ROC) curve analysis measures the performance of binary classification, while for multiclass classification, we consider the accuracy as performance measure. Based on the complete magnitude spectra, automated binary classifiers are able to reach an area under the ROC curve (AUC) of more than 0.9 except for the hard case glioblastomas versus metastases. Although, based on the available long echo 1H MRS data, we did not find any statistically significant difference between the performances of LDA and the kernel-based methods, the latter have the strength that no dimensionality reduction is required to obtain such a high performance.

Artificial Intelligence↗

PubMed2006

Leishmania and sand flies: proximity to woodland as a risk factor for infection in a rural focus of visceral leishmaniasis in west central Venezuela.

OBJECTIVE: To relate entomological, epidemiological and geographical data to understand the transmission dynamics of visceral leishmaniasis (VL) in a closed focus in western Venezuela. METHODS: Spatial and temporal patterns of Lutzomyia pseudolongipalpis, the most prevalent phlebotomine sand fly species (99.7%), were studied in El Brasilar, Curarigua, Lara State, Venezuela, a small rural community of 20 dwellings and 118 inhabitants. The sand fly population was monitored using Centers for Disease Control light traps monthly throughout 1 year in the domestic and sylvatic habitats and for 3 months in all inhabited houses. RESULTS: Temporal variation followed the yearly bimodal pattern of precipitation with the highest population densities in April and December. Infection with flagellates suggestive of Leishmania spp. was detected in 0.01% of 10,026 dissected females of L. pseudolongipalpis, which proved to be highly endophilic. Prevalence of Leishmania infection in people, as measured by the leishmanin skin test, was correlated with distance of the houses from the woodland and with sand fly abundance. A logistic regression model showed that for people who live in the village, the proximity to the woodland (linear) should be considered a risk factor for Leishmania infection (binary) (z = -2.02, P = 0.04, OR = 0.98, 95% CI = 0.97-0.99). This was consistent with the association between the proportion of VL infection and the log of sand fly abundance, which was negatively correlated with distance from the woodland. CONCLUSION: We discuss strategies that might be useful in controlling VL transmission in this endemic focus.

Animals↗

PubMed2005

Cell-free protein synthesis in an autoinduction system for NMR studies of protein-protein interactions.

Cell-free protein synthesis systems provide facile access to proteins in a nascent state that enables formation of soluble, native protein-protein complexes even if one of the protein components is prone to self-aggregation and precipitation. Combined with selective isotope-labeling, this allows the rapid analysis of protein-protein interactions with few 15N-HSQC spectra. The concept is demonstrated with binary and ternary complexes between the chi, psi and gamma subunits of Escherichia coli DNA polymerase III: nascent, selectively 15N-labeled psi produced in the presence of chi resulted in a soluble, correctly folded chi-psi complex, whereas psi alone precipitated irrespective of whether gamma was present or not. The 15N-HSQC spectra showed that the N-terminal segment of psi is mobile in the chi-psi complex, yet important for its binding to gamma. The sample preparation was greatly enhanced by an autoinduction strategy, where the T7 RNA polymerase needed for transcription of a gene in a T7-promoter vector was produced in situ.

Cell-Free System↗

PubMed2000

Construction of a binary bacterial artificial chromosome library of Petunia inflata and the isolation of large genomic fragments linked to the self-incompatibility (S-) locus.

The Solanaceae family of flowering plants possesses a type of self-incompatibility mechanism that enables the pistil to reject self pollen but accept non-self pollen for fertilization. The pistil function in this system has been shown to be controlled by a polymorphic gene at the S-locus, termed the S-RNase gene. The pollen function is believed to be controlled by another as yet unidentified polymorphic gene at the S-locus, termed the pollen S-gene. As a first step in using a functional genomic approach to identify the pollen S-gene, a genomic BAC (bacterial artificial chromosome) library of the S2S2 genotype of Petunia inflata, a self-incompatible solanaceous species, was constructed using a Ti-plasmid based BAC vector, BIBAC2. The average insert size was 136.4 kb and the entire library represented a 7.5-fold genome coverage. Screening of the library using cDNAs for the S2-RNase gene and 13 pollen-expressed genes that are linked to the S-locus yielded 51 positive clones, with at least one positive clone for each gene. Collectively, at least 2 Mb of the chromosomal region was spanned by these clones. Together, three clones that contained the S2-RNase gene spanned approximately 263 kb. How this BAC library and the clones identified could be used to identify the pollen S-gene and to study other aspects of self-incompatibility is discussed.

Chromosomes, Artificial, Bacterial↗

PubMedDate not supplied

Virtual screening of molecular databases using a support vector machine.

The Support Vector Machine (SVM) is an algorithm that derives a model used for the classification of data into two categories and which has good generalization properties. This study applies the SVM algorithm to the problem of virtual screening for molecules with a desired activity. In contrast to typical applications of the SVM, we emphasize not classification but enrichment of actives by using a modified version of the standard SVM function to rank molecules. The method employs a simple and novel criterion for picking molecular descriptors and uses cross-validation to select SVM parameters. The resulting method is more effective at enriching for active compounds with novel chemistries than binary fingerprint-based methods such as binary kernel discrimination.

Algorithms↗