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PubMed2005

SLLE for predicting membrane protein types.

Introduction of the concept of pseudo amino acid composition (PROTEINS: Structure, Function, and Genetics 43 (2001) 246; Erratum: ibid. 44 (2001) 60) has made it possible to incorporate a considerable amount of sequence-order effects by representing a protein sample in terms of a set of discrete numbers, and hence can significantly enhance the prediction quality of membrane protein type. As a continuous effort along such a line, the Supervised Locally Linear Embedding (SLLE) technique for nonlinear dimensionality reduction is introduced (Science 22 (2000) 2323). The advantage of using SLLE is that it can reduce the operational space by extracting the essential features from the high-dimensional pseudo amino acid composition space, and that the cluster-tolerant capacity can be increased accordingly. As a consequence by combining these two approaches, high success rates have been observed during the tests of self-consistency, jackknife and independent data set, respectively, by using the simplest nearest neighbour classifier. The current approach represents a new strategy to deal with the problems of protein attribute prediction, and hence may become a useful vehicle in the area of bioinformatics and proteomics.

Algorithms↗
PubMed2004

A new 3-d approach to determine functional morphology of cercopithecoid molars.

Functional relationships between diet and tooth morphology form an integral part of primatological and paleontological research. Previously, mostly two-dimensional parameters have been used to compare and interpret the complex crown morphology of cercopithecine and colobine molars. However, as teeth are three-dimensional objects, any dimensional reduction in describing their morphology must result in loss of information. In the current study we use a high resolution optical topometric system to record crown morphology in different wear stages in order to extract three-dimensional (3-d) parameters from virtual 3-d models. Structural parameters such as relief index, occlusal surface area, enamel area and strike and dip of cusp slopes as well as wear facets can be calculated, reflecting the changing occlusal topography of molars due to attrition and abrasion. By comparing mostly fruit-eating cercopithecines and leaf-eating colobines, functional implications of tooth wear, occlusal jaw movement and resulting relief can be inferred. Our analyses show that the main differences in occlusal relief between these genera do not change with wear. Colobines maintain higher occlusal relief, whereas in cercopithecines all cusps wear flat quite rapidly. Detailed models of wear for cercopithecoid molars will be used to reconstruct diet and will enhance our knowledge of the paleoecology of Cercopithecoidea.

Animals↗
PubMed2005

GA-fisher: A new LDA-based face recognition algorithm with selection of principal components.

This paper addresses the dimension reduction problem in Fisherface for face recognition. When the number of training samples is less than the image dimension (total number of pixels), the within-class scatter matrix (Sw) in Linear Discriminant Analysis (LDA) is singular, and Principal Component Analysis (PCA) is suggested to employ in Fisherface for dimension reduction of Sw so that it becomes nonsingular. The popular method is to select the largest nonzero eigenvalues and the corresponding eigenvectors for LDA. To attenuate the illumination effect, some researchers suggested removing the three eigenvectors with the largest eigenvalues and the performance is improved. However, as far as we know, there is no systematic way to determine which eigenvalues should be used. Along this line, this paper proposes a theorem to interpret why PCA can be used in LDA and an automatic and systematic method to select the eigenvectors to be used in LDA using a Genetic Algorithm (GA). A GA-PCA is then developed. It is found that some small eigenvectors should also be used as part of the basis for dimension reduction. Using the GA-PCA to reduce the dimension, a GA-Fisher method is designed and developed. Comparing with the traditional Fisherface method, the proposed GA-Fisher offers two additional advantages. First, optimal bases for dimensionality reduction are derived from GA-PCA. Second, the computational efficiency of LDA is improved by adding a whitening procedure after dimension reduction. The Face Recognition Technology (FERET) and Carnegie Mellon University Pose, Illumination, and Expression (CMU PIE) databases are used for evaluation. Experimental results show that almost 5 % improvement compared with Fisherface can be obtained, and the results are encouraging.

Algorithms↗
PubMed2004

Brain-computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch.

The low-frequency asynchronous switch design (LF-ASD) was introduced as a direct brain-computer interface (BCI) technology for asynchronous control applications. The LF-ASD operates as an asynchronous brain switch (ABS) which is activated only when a user intends control and maintains an inactive state output when the user is not meaning to control the device (i.e., they may be idle, thinking about a problem, or performing some other action). Results from LF-ASD evaluations have shown promise, although the reported error rates are too high for most practical applications. This paper presents the evaluation of four new LF-ASD designs with data collected from individuals with high-level spinal cord injuries and able-bodied subjects. These new designs incorporated electroencephalographic energy normalization and feature space dimensionality reduction. The error characteristics of the new ABS designs were significantly better than the LF-ASD design with true positive rate increases of approximately 33% for false positive rates in the range of 1%-2%. The results demonstrate that the dimensionality of the LF-ASD feature space can be reduced without performance degradation. The results also confirm previous findings that spinal cord-injured subjects can operate ABS designs to the same ability as able-bodied subjects.

Adult↗
PubMed2002

Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study.

BACKGROUND: A method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expression. Because of the complexity and the high dimensionality of microarray gene expression profiles, the dimensional reduction of raw expression data and the feature selections necessary for, for example, classification of disease samples remains a challenge. To solve the problem we propose a two-level analysis. First self-organizing map (SOM) is used. SOM is a vector quantization method that simplifies and reduces the dimensionality of original measurements and visualizes individual tumor sample in a SOM component plane. Next, hierarchical clustering and K-means clustering is used to identify patterns of gene expression useful for classification of samples. RESULTS: We tested the two-level analysis on public data from diffuse large B-cell lymphomas. The analysis easily distinguished major gene expression patterns without the need for supervision: a germinal center-related, a proliferation, an inflammatory and a plasma cell differentiation-related gene expression pattern. The first three patterns matched the patterns described in the original publication using supervised clustering analysis, whereas the fourth one was novel. CONCLUSIONS: Our study shows that by using SOM as an intermediate step to analyze genome-wide gene expression data, the gene expression patterns can more easily be revealed. The "expression display" by the SOM component plane summarises the complicated data in a way that allows the clinician to evaluate the classification options rather than giving a fixed diagnosis.

Cluster Analysis↗
PubMed2006

Unified picture of ferromagnetism, quasi-long-range order, and criticality in random-field models.

By applying the recently developed nonperturbative functional renormalization group (FRG) approach, we study the interplay between ferromagnetism, quasi-long-range order (QLRO), and criticality in the d-dimensional random-field O(N) model in the whole (N, d) diagram. Even though the "dimensional reduction" property breaks down below some critical line, the topology of the phase diagram is found similar to that of the pure O(N) model, with, however, no equivalent of the Kosterlitz-Thouless transition. In addition, we obtain that QLRO, namely, a topologically ordered "Bragg glass" phase, is absent in the 3-dimensional random-field XY model. The nonperturbative results are supplemented by a perturbative FRG analysis to two loops around d = 4.

Journal Article↗
PubMed1997

The zygomatic-sphenoid fracture line in malar reduction. A cadaver study.

OBJECTIVE: To demonstrate the persistent malar displacement and distraction of the zygomatic-sphenoid fracture line that is possible after alignment of the frontozygomatic and infraorbital rim fractures of a displaced malar fracture. DESIGN: Nonblinded cadaver study. SUBJECTS: Three fresh cadaver heads and 1 representative clinical example. INTERVENTION: The cadaver heads were subjected to blunt trauma to the malar eminence. Reduction of the malar unit was performed either with attention to the frontozygomatic and infraorbital rim fractures alone or with concomitant inspection of the zygomatic-sphenoid fracture line. The representative case was repaired with a trans-conjunctival approach for inspection of the zygomatic-sphenoid fracture line. RESULTS: Persistent malar asymmetry is possible after the reduction of displaced malar fractures when only the frontozygomatic and infraorbital rim buttresses are used for reference. In each case in our study, the zygomatic-sphenoid fracture line remained distracted. Alignment of the zygomatic-sphenoid fracture restored premorbid malar position. CONCLUSION: Inspection of the zygomatic-sphenoid fracture line can contribute significantly to the precise 3-dimensional reduction of displaced malar fractures.

Adult↗
PubMed2006

Spectral embedding finds meaningful (relevant) structure in image and microarray data.

BACKGROUND: Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing measurements across thousands of variables. Principal components analysis (PCA) is a linear dimensionality reduction (DR) method that is unsupervised in that it relies only on the data; projections are calculated in Euclidean or a similar linear space and do not use tuning parameters for optimizing the fit to the data. However, relationships within sets of nonlinear data types, such as biological networks or images, are frequently mis-rendered into a low dimensional space by linear methods. Nonlinear methods, in contrast, attempt to model important aspects of the underlying data structure, often requiring parameter(s) fitting to the data type of interest. In many cases, the optimal parameter values vary when different classification algorithms are applied on the same rendered subspace, making the results of such methods highly dependent upon the type of classifier implemented. RESULTS: We present the results of applying the spectral method of Lafon, a nonlinear DR method based on the weighted graph Laplacian, that minimizes the requirements for such parameter optimization for two biological data types. We demonstrate that it is successful in determining implicit ordering of brain slice image data and in classifying separate species in microarray data, as compared to two conventional linear methods and three nonlinear methods (one of which is an alternative spectral method). This spectral implementation is shown to provide more meaningful information, by preserving important relationships, than the methods of DR presented for comparison. Tuning parameter fitting is simple and is a general, rather than data type or experiment specific approach, for the two datasets analyzed here. Tuning parameter optimization is minimized in the DR step to each subsequent classification method, enabling the possibility of valid cross-experiment comparisons. CONCLUSION: Results from the spectral method presented here exhibit the desirable properties of preserving meaningful nonlinear relationships in lower dimensional space and requiring minimal parameter fitting, providing a useful algorithm for purposes of visualization and classification across diverse datasets, a common challenge in systems biology.

Algorithms↗
PubMed2005

Partial least squares dimension reduction for microarray gene expression data with a censored response.

An important application of DNA microarray technologies involves monitoring the global state of transcriptional program in tumor cells. One goal in cancer microarray studies is to compare the clinical outcome, such as relapse-free or overall survival, for subgroups of patients defined by global gene expression patterns. A method of comparing patient survival, as a function of gene expression, was recently proposed in [Bioinformatics 18 (2002) 1625] by Nguyen and Rocke. Due to the (a) high-dimensionality of microarray gene expression data and (b) censored survival times, a two-stage procedure was proposed to relate survival times to gene expression profiles. The first stage involves dimensionality reduction of the gene expression data by partial least squares (PLS) and the second stage involves prediction of survival probability using proportional hazard regression. In this paper, we provide a systematic assessment of the performance of this two-stage procedure. PLS dimension reduction involves complex non-linear functions of both the predictors and the response data, rendering exact analytical study intractable. Thus, we assess the methodology under a simulation model for gene expression data with a censored response variable. In particular, we compare the performance of PLS dimension reduction relative to dimension reduction via principal components analysis (PCA) and to a modified PLS (MPLS) approach. PLS performed substantially better relative to dimension reduction via PCA when the total predictor variance explained is low to moderate (e.g. 40%-60%). It performed similar to MPLS and slightly better in some cases. Additionally, we examine the effect of censoring on dimension reduction stage. The performance of all methods deteriorates for a high censoring rate, although PLS-PH performed relatively best overall.

Algorithms↗
PubMed2005

A testing framework for identifying susceptibility genes in the presence of epistasis.

An efficient testing strategy called the "focused interaction testing framework" (FITF) was developed to identify susceptibility genes involved in epistatic interactions for case-control studies of candidate genes. In the FITF approach, likelihood-ratio tests are performed in stages that increase in the order of interaction considered. Joint tests of main effects and interactions are performed conditional on significant lower-order effects. A reduction in the number of tests performed is achieved by prescreening gene combinations with a goodness-of-fit chi2 statistic that depends on association among candidate genes in the pooled case-control group. Multiple testing is accounted for by controlling false-discovery rates. Simulation analysis demonstrated that the FITF approach is more powerful than marginal tests of candidate genes. FITF also outperformed multifactor dimensionality reduction when interactions involved additive, dominant, or recessive genes. In an application to asthma case-control data from the Children's Health Study, FITF identified a significant multilocus effect between the nicotinamide adenine dinucleotide (phosphate) reduced:quinone oxidoreductase gene (NQO1), myeloperoxidase gene (MPO), and catalase gene (CAT) (unadjusted P = .00026), three genes that are involved in the oxidative stress pathway. In an independent data set consisting primarily of African American and Asian American children, these three genes also showed a significant association with asthma status (P = .0008).

Black or African American↗
PubMed2002

Maxillary tooth size variation in dentitions with palatal canine displacement.

The association between the presence of palatally displaced maxillary canines (PDC) and other anomalies is well known, particularly with regard to the size and form of the lateral incisor. The present study investigated the connection between the maxillary canine anomaly and the existence of a reduction in the size of the other teeth in the maxilla in 58 consecutively treated patients (37 females, 21 males) aged 11-15 years, and compared these with a control group of 40 consecutively treated subjects (20 males, 20 females) with normally erupted maxillary canines. The findings indicate that the teeth of PDC males are reduced in size and similar to those of PDC females, in sharp contrast to the general population, where males have larger teeth. This is the result of a dimensional reduction in the teeth of PDC males, which was statistically significant for all teeth (P < 0.01) in the bucco-lingual (B-L) dimension, and for the first premolars and first molars in the mesio-distal (M-D) dimension. The sole teeth affected for both sexes were the lateral incisors in their B-L dimension. Bilaterally and unilaterally affected subjects presented differing M-D and B-L tooth dimensions.

Adolescent↗
PubMed2006

Smoking may affect the alveolar process dimensions and radiographic bone density in maxillary extraction sites: a prospective study in humans.

PURPOSE: Loss of ridge width and height typically occur after tooth extraction. This study aimed to investigate whether smoking would effect alveolar ridge remodeling after tooth extraction. MATERIALS AND METHODS: Twenty-one individuals (11 nonsmokers, 10 smokers) requiring a nonmolar extraction in the upper jaw were selected. Radiographs were taken 7 and 180 days after surgery, and the following parameters obtained: alveolar process height (AH), alveolar process width (AW), radiographic bone density in the postextraction socket (BDS), and in the pre-existing bone apically (BDPB). RESULTS: Six months after surgery, intragroup analysis showed that both groups presented a significant reduction in AH, while only smokers had a significant reduction in AW, BDS, and BDPB (P < .05). Furthermore, intergroup analysis showed that smokers presented lower BDS (91.45 pixels +/- 26.62 and 59.53 pixels +/- 19.99, for nonsmokers and smokers, respectively; P = .006) and continued to present lower BDPB (129.34 pixels +/- 42.10 and 89.29 pixels +/- 29.96, for nonsmokers and smokers, respectively; P = .023). Additionally, smokers presented a tendency for lower AH and AW than nonsmokers, but this was not statistically significant. CONCLUSION: Within the limits of the present study, smoking may lead to a more significant dimensional reduction of the residual alveolar ridge and postpone postextraction socket healing.

Absorptiometry, Photon↗
PubMed1998

Feature extraction for on-line EEG classification using principal components and linear discriminants.

The study focuses on the problems of dimensionality reduction by means of principal component analysis (PCA) in the context of single-trial EEG data classification (i.e. discriminating between imagined left- and right-hand movement). The principal components with the highest variance, however, do not necessarily carry the greatest information to enable a discrimination between classes. An EEG data set is presented where principal components with high variance cannot be used for discrimination. In addition, a method based on linear discriminant analysis (LDA), is introduced that detects principal components which can be used for discrimination, leading to data sets of reduced dimensionality but similar classification accuracy.

Brain Damage, Chronic↗
PubMed2005

Incremental online learning in high dimensions.

Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function approximation in high-dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs nonparametric regression with locally linear models. In order to stay computationally efficient and numerically robust, each local model performs the regression analysis with a small number of univariate regressions in selected directions in input space in the spirit of partial least squares regression. We discuss when and how local learning techniques can successfully work in high-dimensional spaces and review the various techniques for local dimensionality reduction before finally deriving the LWPR algorithm. The properties of LWPR are that it (1) learns rapidly with second-order learning methods based on incremental training, (2) uses statistically sound stochastic leave-one-out cross validation for learning without the need to memorize training data, (3) adjusts its weighting kernels based on only local information in order to minimize the danger of negative interference of incremental learning, (4) has a computational complexity that is linear in the number of inputs, and (5) can deal with a large number of-possibly redundant-inputs, as shown in various empirical evaluations with up to 90 dimensional data sets. For a probabilistic interpretation, predictive variance and confidence intervals are derived. To our knowledge, LWPR is the first truly incremental spatially localized learning method that can successfully and efficiently operate in very high-dimensional spaces.

Algorithms↗
PubMed2006

Osteogenesis induced by autologous bone marrow cells transplant in the pediatric skull.

BACKGROUND AND PURPOSE: The ability of cranial bone to repair defects of continuity is limited and it is mostly dependent on the age of the patient. In infancy and in early pediatric age, the scarce thickness of the calvarial bones and the need for a harmonic development of the child's skull limit the application of most of the surgical procedures usually utilized in older patients. We tested the ability of mononucleated cells, derived from the patient's bone marrow and transplanted on the site of the cranial bone defect, to increase the rate of mineralization of the autologous osteogenesis to obtain the complete restoration of the skull continuity. METHOD: Four children, aged 26, 28, 37, and 79 months, respectively, affected by a stabilized and persistent cranial bone defect of posttraumatic or postsurgical origin, were treated. A sandwich-shaped shell, made of extrused absorbable polylactic copolymers material, was used to hold in place a freeze-dried mineralized collagen matrix associated with a nonceramic hydroxyapatite scaffold, where autologous bone marrow mononucleated cells were inseminated. RESULTS: In all patients, a rapid autologous bone osteogenesis was observed with a clear dimensional reduction of the bone defect few months after the autologous bone marrow cells seeding. CONCLUSIONS: The preliminary results of this research suggest the use of autologous bone marrow cells to increase the autologous osteogenesis in early pediatric age in cases in which correction of skull bone defects is best realized with autologous bone.

Age Factors↗
PubMed2004

Clustered components analysis for functional MRI.

A common method of increasing hemodynamic response (SNR) in functional magnetic resonance imaging (fMRI) is to average signal timecourses across voxels. This technique is potentially problematic because the hemodynamic response may vary across the brain. Such averaging may destroy significant features in the temporal evolution of the fMRI response that stem from either differences in vascular coupling to neural tissue or actual differences in the neural response between two averaged voxels. Two novel techniques are presented in this paper in order to aid in an improved SNR estimate of the hemodynamic response while preserving statistically significant voxel-wise differences. The first technique is signal subspace estimation for periodic stimulus paradigms that involves a simple thresholding method. This increases SNR via dimensionality reduction. The second technique that we call clustered components analysis is a novel amplitude-independent clustering method based upon an explicit statistical data model. It includes an unsupervised method for estimating the number of clusters. Our methods are applied to simulated data for verification and comparison to other techniques. A human experiment was also designed to stimulate different functional cortices. Our methods separated hemodynamic response signals into clusters that tended to be classified according to tissue characteristics.

Algorithms↗
PubMed2000

Indirect posterior reduction and fusion of the traumatic herniated disc by using a cervical pedicle screw system.

OBJECT: In this study the authors retrospectively review 16 patients with traumatic disc herniation secondary to middle and lower cervical spine injuries who underwent a single posterior reduction and fusion procedure in which a cervical pedicle screw system was used. The study was undertaken to evaluate whether the procedure effectively reduced the disc herniation and whether it can be safely conducted without performing anterior decompressive surgery. METHODS: A total of 73 patients with middle and lower cervical spine injuries were identified. In 50 patients, pre- and postoperative magnetic resonance (MR) images were obtained, and disc herniation was defined as the presence of an extruded disc that deformed the thecal sac or nerve roots. Traumatic disc herniation was revealed in 16 patients (32%) who underwent a single posterior reduction/fusion procedure in which a cervical pedicle screw system was used. The average follow-up period was 4.25 years (2-6.25 years). In all patients the average kyphotic deformity was 18 degrees, which was corrected to 0.7 degrees lordosis postoperatively. Anterior translation was reduced from 8 to 0.7 mm. The preoperative disc height ratio of 63% (normal 100%) was improved to 104%. Preoperative MR images revealed traumatic disc herniation in all 16 patients; postsurgery, reduction or reversal of disc herniation was observed in all patients. Thecal sac and/or spinal cord compression had disappeared after indirect decompression was achieved using a posterior procedure. No additional decompressive procedures were required to remove residual herniated disc material. Preoperatively, four patients presented with cervical radiculopathy, 10 with myelopathy (eight incomplete and two complete), and two without neurological symptoms. At final follow up, complete recovery was observed in all four patients with radiculopathy and improvement of at least one Frankel grade was shown in six patients (60%) with myelopathy. There were no cases of neurological deterioration immediately after surgery or during the course of the follow-up period. In all patients solid bone union was demonstrated, and there were no implant-related complications. CONCLUSIONS: Traumatic disc herniation may occur frequently in association with injury of the cervical spine. The incidence of traumatic disc herniation in our series was 32%. The cervical pedicle screw system allowed three-dimensional reduction of the injured cervical segment and reduction or reversal of a disc herniation. After surgery, compression of the thecal sac and/or spinal cord had disappeared. The cervical pedicle screw system provides effective and safe fixation of the cervical spine injury-related traumatic disc herniation, and the surgery can be performed safely in a single posterior-approach procedure without need of additional anterior decompressive interventions.

Adolescent↗
PubMed2005

Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging.

For bioluminescence imaging studies in small animals, it is important to be able to accurately localize the three-dimensional (3D) distribution of the underlying bioluminescent source. The spectrum of light produced by the source that escapes the subject varies with the depth of the emission source because of the wavelength-dependence of the optical properties of tissue. Consequently, multispectral or hyperspectral data acquisition should help in the 3D localization of deep sources. In this paper, we describe a framework for fully 3D bioluminescence tomographic image acquisition and reconstruction that exploits spectral information. We describe regularized tomographic reconstruction techniques that use semi-infinite slab or FEM-based diffusion approximations of photon transport through turbid media. Singular value decomposition analysis was used for data dimensionality reduction and to illustrate the advantage of using hyperspectral rather than achromatic data. Simulation studies in an atlas-mouse geometry indicated that sub-millimeter resolution may be attainable given accurate knowledge of the optical properties of the animal. A fixed arrangement of mirrors and a single CCD camera were used for simultaneous acquisition of multispectral imaging data over most of the surface of the animal. Phantom studies conducted using this system demonstrated our ability to accurately localize deep point-like sources and show that a resolution of 1.5 to 2.2 mm for depths up to 6 mm can be achieved. We also include an in vivo study of a mouse with a brain tumour expressing firefly luciferase. Co-registration of the reconstructed 3D bioluminescent image with magnetic resonance images indicated good anatomical localization of the tumour.

Algorithms↗