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Reduction-of-dimensionality kinetics at reaction-limited cell surface receptors.

It has been suggested for several years that reactions between ligands and cell surface receptors can be speeded up by nonspecific adsorption of the ligand to the cell surface followed by two-dimensional surface diffusion to the receptor, a mechanism referred to as "reduction-of-dimensionality" (RD) rate enhancement. Most of the theoretical treatments of this and related problems have assumed that the receptor is an irreversibly absorbing perfect sink. Such receptors induce a depletion zone of ligand probability density around themselves. The reaction rate in this case (called "diffusion-limited") is limited only by the time required for ligands to diffuse through this depletion zone. In some cases, however, the receptor may be far from "perfect" such that a collision with a ligand only rarely leads to binding. Receptors then do not create significant local depletion zones of ligand probability density, and the reaction rate becomes strongly affected by the (small) probability of reaction success per diffusive encounter (the "reaction-limited" case). This article presents a simple theory of RD rate enhancement for reaction-limited receptors that are either reversible or irreversible binders. In contrast to the diffusion-limited theories, the reaction-limited theory presented here: (a) differs quantitatively from diffusion-limited models; (b) is simple and algebraic in closed form; (c) exhibits significant rate enhancement in some realistic cases; (d) depends strongly on the actual Brownian rather than pure diffusive nature of the ligand's motion; (e) depends (for irreversibly binding receptors only) on the kinetic rates (not just equilibria) of reversible adsorption to nontarget regions, in contrast to some previous approximate theories of reduction of dimensionality; and (f) is applicable to actual ligand/receptor systems with binding success probabilities at the opposite extreme from the perfect sink/diffusion-limited models.

Adsorption↗

Interactions between genes involved in the antioxidant defence system and breast cancer risk.

The aim of the study is to examine the association between multilocus genotypes across 10 genes encoding proteins in the antioxidant defence system and breast cancer. The 10 genes are SOD1, SOD2, GPX1, GPX4, GSR, CAT, TXN, TXN2, TXNRD1 and TXNRD2. In all, 2271 cases and 2280 controls were used to examine gene-gene interactions between 52 single nucleotide polymorphisms (SNPs) that are hypothesised to tag all common variants in the 10 genes. The statistical analysis is based on three methods: unconditional logistic regression, multifactor dimensionality reduction and hierarchical cluster analysis. We examined all two- and three-way combinations with unconditional logistic regression and multifactor dimensionality reduction, and used a global approach with all SNPs in the hierarchical cluster analysis. Single-locus studies of an association of genetic variants in the antioxidant defence genes and breast cancer have been contradictory and inconclusive. It is the first time, to our knowledge, the association between multilocus genotypes across genes coding for antioxidant defence enzymes and breast cancer is investigated. We found no evidence of an association with breast cancer with our multilocus approach. The search for two-way interactions gave experiment-wise significance levels of P=0.24 (TXN [t2715c] and TXNRD2 [g23524a]) and P=0.58 (GSR [c39396t] and TXNRD2 [a442g]), for the unconditional logistic regression and multifactor dimensionality reduction, respectively. The experiment-wise significance levels for the three-way interactions were P=0.94 (GPX4 [t2572c], TXN [t2715c] and TXNRD2 [g23524a]) and P=0.29 (GSR [c39396t], TXN [t2715c] and TXNRD2 [a442g]) for the unconditional logistic regression and multifactor dimensionality reduction, respectively. In the hierarchical cluster analysis neither the average across four rounds with replacement of missing values at random (P=0.12) nor a fifth round with more balanced proportion of missing values between cases and controls (P=0.17) was significant.

Adult↗

A joint compression-discrimination neural transformation applied to target detection.

Many image recognition algorithms based on data-learning perform dimensionality reduction before the actual learning and classification because the high dimensionality of raw imagery would require enormous training sets to achieve satisfactory performance. A potential problem with this approach is that most dimensionality reduction techniques, such as principal component analysis (PCA), seek to maximize the representation of data variation into a small number of PCA components, without considering interclass discriminability. This paper presents a neural-network-based transformation that simultaneously seeks to provide dimensionality reduction and a high degree of discriminability by combining together the learning mechanism of a neural-network-based PCA and a backpropagation learning algorithm. The joint discrimination-compression algorithm is applied to infrared imagery to detect military vehicles.

Algorithms↗

SpectralNET--an application for spectral graph analysis and visualization.

BACKGROUND: Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. RESULTS: Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). CONCLUSION: SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.

Algorithms↗

Three-dimensional mandible reduction: correction of occlusal class I in skeletal class III cases.

BACKGROUND: The human face is a three-dimensional structure. However, many studies on mandible reduction procedures are based on a two-dimensional concept, with a particular focus on one dimension: the width of the lower face. Many Korean individuals have class I occlusion with a skeletal class III pattern. When a conventional mandible reduction procedure is used on such individuals to reduce the width of the lower face, an unsatisfactory facial outcome may result because the changes in the other two dimensions of the lower face, vertical height and anteroposterior distance, are disregarded. This is true because the skeleton of the lower face is well developed in all three dimensions. METHODS: Bimaxillary surgery that reduces all three dimensions of the lower face was planned and conducted by the authors. This mandible reduction procedure reduced the protrusion of both the maxilla and the mandible and, at the same time, shortened the lower face height. The procedure consisted of a Le Fort I osteotomy for superior and/or posterior repositioning of the maxilla, followed by mandibular counterclockwise rotation and setback via a bilateral sagittal split osteotomy. Genioplasty was performed as necessary. RESULTS: With this procedure, the entire lower face volume is reduced, and the overall profile is improved. CONCLUSIONS: Three-dimensional mandible reduction greatly enhances the aesthetic outcome by improving the skeletal characteristics of the face common in the Korean population so that the facial measurements more closely approach normal values.

Adult↗

Electrostatics and reduction of dimensionality produce apparent cooperativity when basic peptides bind to acidic lipids in membranes.

The binding of pentalysine to phospholipid vesicles depends in a sigmoidal manner on the mole fraction of acidic lipid in the vesicles. A simple analysis demonstrates that this apparent cooperativity is probably due to both the reduction of dimensionality that occurs when the first basic residue binds to an acidic lipid in the membrane and the Boltzmann accumulation of the peptide in the electrostatic diffuse double layer produced by the charged lipids.

Electrochemistry↗

Mining the structural knowledge of high-dimensional medical data using isomap.

The paper describes an application of a new, non-linear dimensionality reduction method, named Isomap, for mining the structural knowledge from high-dimensional medical data. The algorithm was evaluated on two publicly available medical datasets: the pathological dataset of breast cancer (241 malignant samples) and the gene expression dataset from the lung (186 tumours). It was found by Isomap that the approximate intrinsic dimensionalities of these two datasets were as low as three. The spatial structures of both datasets were presented in low-dimensional space. Isomap, as a general tool for dimensionality reduction analysis, is helpful in revealing the nonlinear structural knowledge of high-dimensional medical data.

Algorithms↗

Multivariate strategies in functional magnetic resonance imaging.

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a 'mind reading' predictive multivariate fMRI model.

Brain↗

Local Context Finder (LCF) reveals multidimensional relationships among mRNA expression profiles of Arabidopsis responding to pathogen infection.

A major task in computational analysis of mRNA expression profiles is definition of relationships among profiles on the basis of similarities among them. This is generally achieved by pattern recognition in the distribution of data points representing each profile in a high-dimensional space. Some drawbacks of commonly used pattern recognition algorithms stem from their use of a globally linear space and/or limited degrees of freedom. A pattern recognition method called Local Context Finder (LCF) is described here. LCF uses nonlinear dimensionality reduction for pattern recognition. Then it builds a network of profiles based on the nonlinear dimensionality reduction results. LCF was used to analyze mRNA expression profiles of the plant host Arabidopsis interacting with the bacterial pathogen Pseudomonas syringae. In one case, LCF revealed two dimensions essential to explain the effects of the NahG transgene and the ndr1 mutation on resistant and susceptible responses. In another case, plant mutants deficient in responses to pathogen infection were classified on the basis of LCF analysis of their profiles. The classification by LCF was consistent with the results of biological characterization of the mutants. Thus, LCF is a powerful method for extracting information from expression profile data.

Arabidopsis↗

Gene expression profile of empirically delineated classes of unexplained chronic fatigue.

OBJECTIVES: To identify the underlying gene expression profiles of unexplained chronic fatigue subjects classified into five or six class solutions by principal component (PCA) and latent class analyses (LCA). METHODS: Microarray expression data were available for 15,315 genes and 111 female subjects enrolled from a population-based study on chronic fatigue syndrome. Algorithms were developed to assign gene scores and threshold values that signified the contribution of each gene to discriminate the multiclasses in each LCA solution. Unsupervised dimensionality reduction was first used to remove noise or otherwise uninformative gene combinations, followed by supervised dimensionality reduction to isolate gene combinations that best separate the classes. RESULTS: The authors' gene score and threshold algorithms identified 32 and 26 genes capable of discriminating the five and six multiclass solutions, respectively. Pair-wise comparisons suggested that some genes (zinc finger protein 350 [ZNF350], solute carrier family 1, member 6 [SLC1A6], F-box protein 7 [FBX07] and vacuole 14 protein homolog [VAC14]) distinguished most classes of fatigued subjects from healthy subjects, whereas others (patched homolog 2 [PTCH2] and T-cell leukemia/lymphoma [TCL1A]) differentiated specific fatigue classes. CONCLUSION: A computational approach was developed for general use to identify discriminatory genes in any multiclass problem. Using this approach, differences in gene expression were found to discriminate some classes of unexplained chronic fatigue, particularly one termed interoception.

Algorithms↗

Principal components analysis competitive learning.

We present a new neural model that extends the classical competitive learning by performing a principal components analysis (PCA) at each neuron. This model represents an improvement with respect to known local PCA methods, because it is not needed to present the entire data set to the network on each computing step. This allows a fast execution while retaining the dimensionality-reduction properties of the PCA. Furthermore, every neuron is able to modify its behavior to adapt to the local dimensionality of the input distribution. Hence, our model has a dimensionality estimation capability. The experimental results we present show the dimensionality-reduction capabilities of the model with multisensor images.

Algorithms↗

Nonlinear dynamics of heart rate variability during experimental hemorrhage in ketamine-anesthetized rats.

Indexes of heart rate variability (HRV) based on linear stochastic models are independent risk factors for arrhythmic death (AD). An index based on a nonlinear deterministic model, a reduction in the point correlation dimension (PD2i), has been shown in both animal and human studies to have a higher sensitivity and specificity for predicting AD. Dimensional reduction subsequent to transient ischemia was examined previously in a simple model system, the intrinsic nervous system of the isolated rabbit heart. The present study presents a new model system in which the higher cerebral centers are blocked chemically (ketamine inhibition of N-methyl-D-aspartate receptors) and the system is perturbed over a longer 15-min interval by continuous hemorrhage. The hypothesis tested was that dimensional reduction would again be evoked, but in association with a more complex relationship between the system variables. The hypothesis was supported, and we interpret the greater response complexity to result from the larger autonomic superstructure attached to the heart. The complexities observed in the nonlinear heartbeat dynamics constitute a new genre of autonomic response, one clearly distinct from a hardwired reflex or a cerebrally determined defensive reaction.

Anesthesia↗

A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand.

This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay.

Action Potentials↗

Manifold learning for robot navigation.

In this paper we introduce methods to build a SOM that can be used as an isometric map for mobile robots. That is, given a dataset of sensor readings collected at points uniformly distributed with respect to the ground, we wish to build a SOM whose neurons (prototype vectors in sensor space) correspond to points uniformly distributed on the ground. Manifold learning techniques have already been used for dimensionality reduction of sensor space in navigation systems. Our focus is on the isometric property of the SOM. For reliable path-planning and information sharing between several robots, it is desirable that the robots build an internal representation of the sensor manifold, a map, that is isometric with the environment. We show experimentally that standard Non-Linear Dimensionality Reduction (NLDR) algorithms do not provide isometric maps for range data and bearing data. However, the auxiliary low dimensional manifolds created can be used to improve the distribution of the neurons of a SOM (that is, make the neurons more evenly distributed with respect to the ground). We also describe a method to create an isometric map from a sensor readings collected along a polygonal line random walk.

Artificial Intelligence↗

Actor-critic models of the basal ganglia: new anatomical and computational perspectives.

A large number of computational models of information processing in the basal ganglia have been developed in recent years. Prominent in these are actor-critic models of basal ganglia functioning, which build on the strong resemblance between dopamine neuron activity and the temporal difference prediction error signal in the critic, and between dopamine-dependent long-term synaptic plasticity in the striatum and learning guided by a prediction error signal in the actor. We selectively review several actor-critic models of the basal ganglia with an emphasis on two important aspects: the way in which models of the critic reproduce the temporal dynamics of dopamine firing, and the extent to which models of the actor take into account known basal ganglia anatomy and physiology. To complement the efforts to relate basal ganglia mechanisms to reinforcement learning (RL), we introduce an alternative approach to modeling a critic network, which uses Evolutionary Computation techniques to 'evolve' an optimal RL mechanism, and relate the evolved mechanism to the basic model of the critic. We conclude our discussion of models of the critic by a critical discussion of the anatomical plausibility of implementations of a critic in basal ganglia circuitry, and conclude that such implementations build on assumptions that are inconsistent with the known anatomy of the basal ganglia. We return to the actor component of the actor-critic model, which is usually modeled at the striatal level with very little detail. We describe an alternative model of the basal ganglia which takes into account several important, and previously neglected, anatomical and physiological characteristics of basal ganglia-thalamocortical connectivity and suggests that the basal ganglia performs reinforcement-biased dimensionality reduction of cortical inputs. We further suggest that since such selective encoding may bias the representation at the level of the frontal cortex towards the selection of rewarded plans and actions, the reinforcement-driven dimensionality reduction framework may serve as a basis for basal ganglia actor models. We conclude with a short discussion of the dual role of the dopamine signal in RL and in behavioral switching.

Animals↗

The role of global and feature based information in gender classification of faces: a comparison of human performance and computational models.

Most computational models for gender classification use global information (the full face image) giving equal weight to the whole face area irrespective of the importance of the internal features. Here, we use a global and feature based representation of face images that includes both global and featural information. We use dimensionality reduction techniques and a support vector machine classifier and show that this method performs better than either global or feature based representations alone. We also present results of human subjects performance on gender classification task and evaluate how the different dimensionality reduction techniques compare with human subjects performance. The results support the psychological plausibility of the global and feature based representation.

Algorithms↗

Comparison of geometric morphometric outline methods in the discrimination of age-related differences in feather shape.

BACKGROUND: Geometric morphometric methods of capturing information about curves or outlines of organismal structures may be used in conjunction with canonical variates analysis (CVA) to assign specimens to groups or populations based on their shapes. This methodological paper examines approaches to optimizing the classification of specimens based on their outlines. This study examines the performance of four approaches to the mathematical representation of outlines and two different approaches to curve measurement as applied to a collection of feather outlines. A new approach to the dimension reduction necessary to carry out a CVA on this type of outline data with modest sample sizes is also presented, and its performance is compared to two other approaches to dimension reduction. RESULTS: Two semi-landmark-based methods, bending energy alignment and perpendicular projection, are shown to produce roughly equal rates of classification, as do elliptical Fourier methods and the extended eigenshape method of outline measurement. Rates of classification were not highly dependent on the number of points used to represent a curve or the manner in which those points were acquired. The new approach to dimensionality reduction, which utilizes a variable number of principal component (PC) axes, produced higher cross-validation assignment rates than either the standard approach of using a fixed number of PC axes or a partial least squares method. CONCLUSION: Classification of specimens based on feather shape was not highly dependent of the details of the method used to capture shape information. The choice of dimensionality reduction approach was more of a factor, and the cross validation rate of assignment may be optimized using the variable number of PC axes method presented herein.

Journal Article↗

Most ovine mitral annular three-dimensional size reduction occurs before ventricular systole and is abolished with ventricular pacing.

BACKGROUND: Conventional surgical thinking indicates that mitral annular (MA) size reduction plays a key role in mitral valve closure, and most MA size and shape changes are thought to occur during left ventricular (LV) systole. The influences of left atrial (LA) and LV systole on MA size and shape, however, remain debated. METHODS AND RESULTS: Eight radiopaque markers were placed equidistantly around the MA and imaged using high-speed simultaneous biplane videofluoroscopy in seven closed-chest, sedated sheep before and during asynchronous LV pacing. Marker images were used to compute the three-dimensional coordinates of each marker every 16.7 ms throughout the cardiac cycle, allowing calculation of three-dimensional MA area, septal-lateral (SL) dimension, and commissure-commissure (CC) dimension under control and LV pacing conditions. Maximum MA area occurred in early diastole, and minimum MA area near end-diastole; maximum area reduction was 12+/-1% (P< or =.001). Interestingly, 89+/-3% of area reduction occurred before LV systole. During this "presystolic" period, SL decreased by 8+/-1% and CC by 2+/-1%; the SL/CC ratio fell from 0.73+/-0.02 to 0.69+/-0.01 (P< or =.005), indicating a less circular shape at end-diastole. With LV pacing, total MA area reduction was similar (13+/-2 versus 12+/-1%, P=NS versus control); however, all MA area reduction occurred during LV systole with minimum MA area occurring at end-systole. Presystolic shortening in both SL and CC dimensions was lost, and presystolic ellipticalization disappeared. CONCLUSIONS: Changes in MA size and shape coincident with LA systole included area reduction and shape change prior to the onset of LV contraction. These presystolic changes vanished when LA systole was absent (LV pacing). Thus, LA systole plays a pivotal role in MA size reduction and shape alteration. The unexpected timing of these MA dynamics should be taken into account during mitral valve reparative procedures.

Animals↗