The beat goes on: Rhythmic modulation of cortical potentials by imagined tapping.

A frequency analysis was used to tag cortical activity from imagined rhythmic movements. Participants synchronized overt and imagined taps with brief visual stimuli presented at a constant rate, alternating between left and right index fingers. Brain potentials were recorded from across the scalp and topographic maps made of their power at the alternation frequency between left and right taps. Two prominent power foci occurred in each hemisphere for both overt and imagined taps, one over sensorimotor cortex and the other over posterior parietal cortex, with homologous foci in opposite hemispheres arising from oscillations 180° out of phase. These findings demonstrate temporal isomorphism at a neural level between overt and imagined movements and illustrate a new approach to studying covert actions. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Renaming Detection

Finding changed identifiers is important for understanding the difference between two versions of a program and for detecting and resolving conflicts while merging variants of a program together. Standard practice for differencing and merging relies on line based techniques that do not recognize renamed identifiers. The design and implementation of a tool to automatically detect renamed identifiers between two versions of a program is presented. The system uses an abstract representation of language constructs to enable language awareness without introducing language dependence. Modules for Java and Scheme have been written. The detector works with multiple file pairs, taking into account renamings that span several files. A case study is presented that demonstrates proof of concept. The detector is part of a suite of intelligent differencing and merging programs that exploit the static semantics of programming languages.     

Direct Segmentation of Algebraic Models for Reverse Engineering

In Reverse Engineering a physical object is digitally reconstructed from a set of boundary points. In the segmentation phase these points are grouped into subsets to facilitate consecutive steps as surface fitting. In this paper we present a segmentation method with subsequent classification of simple algebraic surfaces. Our method is direct in the sense that it operates directly on the point set in contrast to other approaches that are based on a triangulation of the data set. The segmentation process involves a fast algorithm for k-nearest neighbors search and an estimation of first and second order surface properties. The first order segmentation, that is based on normal vectors, provides an initial subdivision of the surface and detects sharp edges as well as flat or highly curved areas. One of the main features of our method is to proceed by alternating the steps of segmentation and normal vector estimation. The second order segmentation subdivides the surface according to principal curvatures and provides a sufficient foundation for the classification of simple algebraic surfaces. If the boundary of the original object contains such surfaces the segmentation is optimized based on the result of a surface fitting procedure.     

Benchmarking Least Squares Support Vector Machine Classifiers

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.     

Analysis of Test Specimens for Cohesive Near-Bond Failure of Fiber-Reinforced Polymer-Plated Concrete

The failure mechanisms of reinforced concrete (RC) members change due to the application of externally bonded fiber-reinforced polymer reinforcement. Although an extensive literature is available describing the failure mechanisms of poststrengthened flexural systems, brittle failure modes caused by bond failure, such as midspan debonding and end peeling, need to be further investigated in order to identify and quantify the fracture processes that result in bond failure. Simplified experimental tests have been designed to idealize the bond between the laminate and the RC member. However, it is unclear how the simplified test results can be related to the actual flexural debonding failures. This paper investigates and compares two bond failure tests: a simplified test (or simple shear test) and a recently proposed shear/normal test. After discussing the characteristics of both tests and how they relate to the midspan debonding and end peeling failures, the shear/normal test is studied in more detail using a nonlinear finite-element fracture mechanics program. The program accounts for cohesive localized and distributed concrete crack damage and is capable of describing the geometrical discontinuities that induce different brittle failure mechanisms. The numerical results compare well with available experimental data and help explain the crack formation and propagation pattern up to specimen failure. Parametric studies are presented to elucidate the influence of different material parameters on the failure mechanisms.     

Design of an active-passive device for human ankle movement during functional magnetic resonance imaging analysis

Functional magnetic resonance imaging analysis has made major strides in recent years, both because of the development of new scanners and owing to magnetic resonance compatible systems that make it possible to stimulate parts of the human body during analysis. The significant gains in our knowledge of the brain that can thus be achieved justify efforts to construct machines with control circuits suitable for this purpose. This paper presents a magnetic resonance compatible mechatronic device with electropneumatic control that can be used to move one or both feet during functional magnetic resonance imaging analysis of the cerebral motor zones. The system is innovative and original. The results obtained at the end of the investigation were good, and demonstrated that the design is feasible.     

Regulation and Release of Vasoactive Endoglin by Brain Endothelium in Response to Hypoxia/Reoxygenation in Stroke

In large vessel occlusion stroke, recanalization to restore cerebral perfusion is essential but not necessarily sufficient for a favorable outcome. Paradoxically, in some patients, reperfusion carries the risk of increased tissue damage and cerebral hemorrhage. Experimental and clinical data suggest that endothelial cells, representing the interface for detrimental platelet and leukocyte responses, likely play a crucial role in the phenomenon referred to as ischemia/reperfusion (I/R)-injury, but the mechanisms are unknown. We aimed to determine the role of endoglin in cerebral I/R-injury; endoglin is a membrane-bound protein abundantly expressed by endothelial cells that has previously been shown to be involved in the maintenance of vascular homeostasis. We investigated the expression of membranous endoglin (using Western blotting and RT-PCR) and the generation of soluble endoglin (using an enzyme-linked immunosorbent assay of cell culture supernatants) after hypoxia and subsequent reoxygenation in human non-immortalized brain endothelial cells. To validate these in vitro data, we additionally examined endoglin expression in an intraluminal monofilament model of permanent and transient middle cerebral artery occlusion in mice. Subsequently, the effects of recombinant human soluble endoglin were assessed by label-free impedance-based measurement of endothelial monolayer integrity (using the xCELLigence DP system) and immunocytochemistry. Endoglin expression is highly inducible by hypoxia in human brain endothelial monolayers in vitro, and subsequent reoxygenation induced its shedding. These findings were corroborated in mice during MCAO; an upregulation of endoglin was displayed in the infarcted hemispheres under occlusion, whereas endoglin expression was significantly diminished after transient MCAO, which is indicative of shedding. Of note is the finding that soluble endoglin induced an inflammatory phenotype in endothelial monolayers. The treatment of HBMEC with endoglin resulted in a decrease in transendothelial resistance and the downregulation of VE-cadherin. Our data establish a novel mechanism in which hypoxia triggers the initial endothelial upregulation of endoglin and subsequent reoxygenation triggers its release as a vasoactive mediator that, when rinsed into adjacent vascular beds after recanalization, can contribute to cerebral reperfusion injury.     

Inference and uncertainty quantification of stochastic gene expression via synthetic models

Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model, to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of non-trivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.     

An operating system independent WORM archival system

We describe the organization of a general purpose data archival system for Write-Once, Read-Many (WORM) optical disks. The system has been designed for large-scale and long-term data storage and retrieval. The archival system is independent of the operating system, flat, self-consistent, does not use any write cache on magnetic disk, and allows the exploitation of auxiliary information on magnetic disk, which can be rebuilt immediately in case of a crash, to speed up file retrieval. A library in C language, called pODLIB, has been implemented as a portable interface to the archival system.