Synchronization and Propagation of Global Sleep Spindles

Sleep spindles occur thousands of times during normal sleep and can be easily detected by visual inspection of EEG signals. These characteristics make spindles one of the most studied EEG structures in mammalian sleep. In this work we considered global spindles, which are spindles that are observed simultaneously in all EEG channels. We propose a methodology that investigates both the signal envelope and phase/frequency of each global spindle. By analysing the global spindle phase we showed that 90% of spindles synchronize with an average latency time of 0.1 s. We also measured the frequency modulation (chirp) of global spindles and found that global spindle chirp and synchronization are not correlated. By investigating the signal envelopes and implementing a homogeneous and isotropic propagation model, we could estimate both the signal origin and velocity in global spindles. Our results indicate that this simple and non-invasive approach could determine with reasonable precision the spindle origin, and allowed us to estimate a signal speed of 0.12 m/s. Finally, we consider whether synchronization might be useful as a non-invasive diagnostic tool.     

Analysis of visual evoked potentials through Wiener filtering applied to a small number of sweeps

We introduce a method for processing visual evoked potentials, on the basis of a Wiener filter algorithm applied to a small number of consecutive responses. The transfer function of the filter is obtained by taking into account both the average of 99 sweeps (as an estimate of the true signal) and the EEG signal just before the stimulus onset (as an estimate of the noise superimposed on each individual response). The process acts as a sweep-by-sweep filter (in the sense of the mean square error) which considers the possible non-stationarities of the EEG signal during a complete clinical procedure. The average of a small number of consecutive filtered sweeps reveals variations in the morphology of the evoked responses which produce a change in the principal latencies. Applications are foreseen in neurophysiological studies of visual evoked potential responses, and in the clinic, where it is important to evaluate adaptive mechanisms, dynamic changes in single groups of visual evoked potentials and cognitive responses.     

The linear electrode array: a useful tool with many applications.

In this review we describe the basic principles of operation of linear electrode arrays for the detection of surface EMG signals, together with their most relevant current applications. A linear array of electrodes is a system which detects surface EMG signals in a number of points located along a line. A spatial filter is usually placed in each point for signal detection, so that the recording of EMG signals with linear arrays corresponds to the sampling in one spatial direction of a spatially filtered version of the potential distribution over the skin. Linear arrays provide indications on motor unit (MU) anatomical properties, such as the locations of the innervation zones and tendons, and the fiber length. Such systems allow the investigation of the properties of the volume conductor and its effect on surface detected signals. Moreover, linear arrays allow to estimate muscle fiber conduction velocity with a very low standard deviation of estimation (of the order of 0.1-0.2 m/s), thus providing reliable indications on muscle fiber membrane properties and their changes in time (for example with fatigue or during treatment). Conduction velocity can be estimated from a signal epoch (global estimate) or at the single MU level. In the latter case, MU action potentials are identified from the interference EMG signals and conduction velocity is estimated for each detected potential. In this way it is possible, in certain conditions, to investigate single MU control and conduction properties with a completely non-invasive approach. Linear arrays provide valuable information on the neuromuscular system properties and appear to be promising tools for applied studies and clinical research.     

Optimization and application of median filter corrections to relieve diverse spatial patterns in microtiter plate data

The standard (STD) 5 × 5 hybrid median filter (HMF) was previously described as a nonparametric local backestimator of spatially arrayed microtiter plate (MTP) data. As such, the HMF is a useful tool for mitigating global and sporadic systematic error in MTP data arrays. Presented here is the first known HMF correction of a primary screen suffering from systematic error best described as gradient vectors. Application of the STD 5 × 5 HMF to the primary screen raw data reduced background signal deviation, thereby improving the assay dynamic range and hit confirmation rate. While this HMF can correct gradient vectors, it does not properly correct periodic patterns that may present in other screening campaigns. To address this issue, 1 × 7 median and a row/column 5 × 5 hybrid median filter kernels (1 × 7 MF and RC 5 × 5 HMF) were designed ad hoc, to better fit periodic error patterns. The correction data show periodic error in simulated MTP data arrays is reduced by these alternative filter designs and that multiple corrective filters can be combined in serial operations for progressive reduction of complex error patterns in a MTP data array.     

Normalization of miRNA qPCR high-throughput data: a comparison of methods

Low-density quantitative real-time PCR (qPCR) arrays are often used to profile expression patterns of microRNAs in various biological milieus. To achieve accurate analysis of expression of miRNAs, non-biological sources of variation in data should be removed through precise normalization of data. We have systematically compared the performance of 19 normalization methods on different subsets of a real miRNA qPCR dataset that covers 40 human tissues. After robustly modeling the mean squared error (MSE) in normalized data, we demonstrate lower variability between replicates is achieved using various methods not applied to high-throughput miRNA qPCR data yet. Normalization methods that use splines or wavelets smoothing to estimate and remove Cq dependent non-linearity between pairs of samples best reduced the MSE of differences in Cq values of replicate samples. These methods also retained between-group variability in different subsets of the dataset.     

Breakpoint identification and smoothing of array comparative genomic hybridization data

SUMMARY: We describe a tool, called aCGH-Smooth, for the automated identification of breakpoints and smoothing of microarray comparative genomic hybridization (array CGH) data. aCGH-Smooth is written in visual C++, has a user-friendly interface including a visualization of the results and user-defined parameters adapting the performance of data smoothing and breakpoint recognition. aCGH-Smooth can handle array-CGH data generated by all array-CGH platforms: BAC, PAC, cosmid, cDNA and oligo CGH arrays. The tool has been successfully applied to real-life data. AVAILABILITY: aCGH-Smooth is free for researchers at academic and non-profit institutions at http://www.few.vu.nl/~vumarray/.     

The role of visual information in numerosity estimation

Mainstream theory suggests that the approximate number system supports our non-symbolic number abilities (e.g. estimating or comparing different sets of items). It is argued that this system can extract number independently of the visual cues present in the stimulus (diameter, aggregate surface, etc.). However, in a recent report we argue that this might not be the case. We showed that participants combined information from different visual cues to derive their answers. While numerosity comparison requires a rough comparison of two sets of items (smaller versus larger), numerosity estimation requires a more precise mechanism. It could therefore be that numerosity estimation, in contrast to numerosity comparison, might rely on the approximate number system. To test this hypothesis, we conducted a numerosity estimation experiment. We controlled for the visual cues according to current standards: each single visual property was not informative about numerosity. Nevertheless, the results reveal that participants were influenced by the visual properties of the dot arrays. They gave a larger estimate when the dot arrays consisted of dots with, on average, a smaller diameter, aggregate surface or density but a larger convex hull. The reliance on visual cues to estimate numerosity suggests that the existence of an approximate number system that can extract numerosity independently of the visual cues is unlikely. Instead, we propose that humans estimate numerosity by weighing the different visual cues present in the stimuli.     

Detection and quantification of tau aggregation using a membrane filter assay

Aggregation of the microtubule-associated protein tau contributes to the formation of neurofibrillary lesions in Alzheimer's disease and is a useful marker of disease progression. Although filter trap assays have been employed to assess the extent of tau aggregation in cells and tissues as well as in vitro, their performance relative to other assay modalities has not been reported. To clarify this issue, the ability of the filter trap approach to quantify aggregation of purified recombinant full-length tau protein in vitro was examined as a function of membrane chemistry in a 96-well format. Results showed that nitrocellulose yielded the greatest assay sensitivity relative to polyvinylidene fluoride or cellulose acetate at equal membrane porosity. However, all combinations of filter chemistries, porosities, and monoclonal detection antibodies yielded nonlinear correlations between signal intensity and analyte concentration. When corrected for nonlinearity, the filter trap assay determined a value for the critical monomer concentration for tau aggregation that was statistically identical to determinations made by electron microscopy assay. The data suggest conditions under which filter trap assays can be used to estimate tau aggregation kinetics.     

Comparing three methods for variance estimation with duplicated high density oligonucleotide arrays

Microarray experiments are being increasingly used in molecular biology. A common task is to detect genes with differential expression across two experimental conditions, such as two different tissues or the same tissue at two time points of biological development. To take proper account of statistical variability, some statistical approaches based on the t-statistic have been proposed. In constructing the t-statistic, one needs to estimate the variance of gene expression levels. With a small number of replicated array experiments, the variance estimation can be challenging. For instance, although the sample variance is unbiased, it may have large variability, leading to a large mean squared error. For duplicated array experiments, a new approach based on simple averaging has recently been proposed in the literature. Here we consider two more general approaches based on nonparametric smoothing. Our goal is to assess the performance of each method empirically. The three methods are applied to a colon cancer data set containing 2,000 genes. Using two arrays, we compare the variance estimates obtained from the three methods. We also consider their impact on the t-statistics. Our results indicate that the three methods give variance estimates close to each other. Due to its simplicity and generality, we recommend the use of the smoothed sample variance for data with a small number of replicates. Electronic Publication     

肌肉动态收缩期间表面肌电信号的时频分析

介绍了用于肌肉动态收缩期间非平稳表面肌电信号的时频分析方法。用短时傅里叶变换、Wigner-Ville分布及Choi-Williams分布计算了表面肌电信号的时频分布,用于信号频率内容随时间演化的可视化观察。通过计算瞬时频谱参数,对肌肉疲劳的电表现进行量化描述。分析了反复性的膝关节弯曲和伸展运动期间从股外侧肌所记录的表面肌电信号。发现和在静态收缩过程中观察到的平均频率线性下降不同,在动态收缩期间瞬时平均频率的变化过程是非线性的并且更为复杂,且与运动的生物力学条件有关。研究表明将时频分析技术应用于动态收缩期间的表面肌电信号可以增加用传统的频谱分析技术不能得到的信息。     

A new approach to intensity-dependent normalization of two-channel microarrays

A two-channel microarray measures the relative expression levels of thousands of genes from a pair of biological samples. In order to reliably compare gene expression levels between and within arrays, it is necessary to remove systematic errors that distort the biological signal of interest. The standard for accomplishing this is smoothing "MA-plots" to remove intensity-dependent dye bias and array-specific effects. However, MA methods require strong assumptions, which limit their general applicability. We review these assumptions and derive several practical scenarios in which they fail. The "dye-swap" normalization method has been much less frequently used because it requires two arrays per pair of samples. We show that a dye-swap is accurate under general assumptions, even under intensity-dependent dye bias, and that a dye-swap removes dye bias from a single pair of samples in general. Based on a flexible model of the relationship between mRNA amount and single-channel fluorescence intensity, we demonstrate the general applicability of a dye-swap approach. We then propose a common array dye-swap (CADS) method for the normalization of two-channel microarrays. We show that CADS removes both dye bias and array-specific effects, and preserves the true differential expression signal for every gene under the assumptions of the model.     

Investigation of a two-point maximum entropy regularization method for signal enhancement applied to magnetoencephalography data

A two-point maximum entropy method (TPMEM) was investigated for post-acquisition signal recovery in magnetoencephalography (MEG) data, as a potential replacement of a low-pass (LP) filtering technique currently in use. We first applied TPMEM and the LP filter for signal recovery of synthetically noise corrupted MEG “phantom” data sets in which the true underlying signal was known. Results were quantified with the use of visual plots, percent error histograms, and the statistical parameters root mean squared error and Pearson’s correlation coefficient. Synthetically noise corrupted data from a simulated magnetic dipole was used to quantify the improvements gained in using TPMEM over LP filters in reconstructing known dipole parameters such as position, orientation, and magnitude. Finally, we applied TPMEM and LP filters to a sample MEG patient data set. Our results show that TPMEM has improved noise-reduction and signal recovery capabilities than those of the LP filter, and furthermore data processed with TPMEM shows less error in the reconstructed dipole parameters. We propose that TPMEM can be used for MEG signal processing, resulting in improved MEG source characterization.     

Electro/magnetoencephalography beamforming with spatial restrictions based on sparsity

We present a source localization method for electroencephalographic (EEG) and magnetoencephalographic (MEG) data which is based on an estimate of the sparsity obtained through the eigencanceler (EIG), which is a spatial filter whose weights are constrained to lie in the noise subspace. The EIG provides rejection of directional interferences while minimizing noise contributions and maintaining specified beam pattern constraints. In our case, the EIG is used to estimate the sparsity of the signal as a function of the position, then we use this information to spatially restrict the neural sources to locations out of the sparsity maxima. As proof of the concept, we incorporate this restriction in the “classical” linearly constrained minimum variance (LCMV) source localization approach in order to enhance its performance. We present numerical examples to evaluate the proposed method using realistically simulated EEG/MEG data for different signal-to-noise (SNR) conditions and various levels of correlation between sources, as well as real EEG/MEG measurements of median nerve stimulation. Our results show that the proposed method has the potential of reducing the bias on the search of neural sources in the classical approach, as well as making it more effective in localizing correlated sources.     

Toward pyrosequencing on surface-attached genetic material by use of DNA-binding luciferase fusion proteins

Mutation detection and single-nucleotide polymorphism genotyping require screening of large samples of materials and therefore the importance of high-throughput DNA analysis techniques is significant. Pyrosequencing is a four-enzyme bioluminometric DNA sequencing technology based on the sequencing-by-synthesis principle. Currently, the technique is limited to simultaneous analysis of 96 or 384 samples. Earlier, attempts to increase the sample capacity were made using micromachined filter chamber arrays where parallel analyses of nanoliter samples could be monitored in real time. We have developed a strategy for specific immobilization of the light-producing enzyme luciferase to the DNA template within a reaction chamber. By this approach, luciferase is genetically fused to a DNA-binding protein (Klenow polymerase or Escherichia coli single-stranded DNA-binding (SSB) protein) and to a purification handle (Z(basic)). The proteins are produced in E. coli and purified using cation and anion exchange chromatography with removal of Z(basic). The produced proteins have been analyzed using an assay for complete primer extension of DNA templates immobilized on magnetic beads detected by pyrosequencing chemistry. Results from these experiments show that the proteins bind selectively to the immobilized DNA and that their enzymatic domains were active. Z(basic)-SSB-luciferase produced the highest signal in this assay and was further exploited as enzymatic reagent for DNA sequencing.     

A novel array-based assay of in situ tissue transglutaminase activity in human umbilical vein endothelial cells

Transglutaminases (TGs), a family of calcium-dependent transamidating enzymes, are involved in functions such as apoptosis and inflammation and play a role in autoimmune diseases and neurodegenerative disorders. In this study, we describe a novel array-based approach to rapidly determine in situ TG activity in human umbilical vein endothelial cells and J82 human bladder carcinoma cells. Amine arrays were fabricated by immobilizing 3-aminopropyltrimethoxysilane on glass slides. The assay was specific and highly reproducible. The average coefficient of variation betweens spots was 2.6% (n = 3 arrays), and the average correlation coefficients between arrays and between arrays/reactions were 0.998 and 0.976, respectively (n = 3 arrays). The assay was successfully applied to detect changes in TG activity induced by maitotoxin and to analyze inhibition of the TG activation with cystamine and monodansyl cadaverine. In addition, the assay demonstrated that intracellular reactive oxygen species regulate the maitotoxin-induced activation of TG. Thus, the array-based in situ TG activity assay constitutes a rapid and high-throughput approach to investigating the roles of TGs in cell signaling.     

A nonlinear regularization approach to early vision

We propose a new class of approaches to smooth visual data while preserving significant transitions of these data as clues for segmentation. Formally, the given visual data are represented as a noisy (image) function , and we present a class of continuously formulated global minimization problems to smooth . The resulting function can be characterized as the minimizer of a specific nonquadratic functional or, equivalently, as the result of an associated nonlinear diffusion process. Our approach generalizes the well-known quadratic regularization principle while retaining its attractive properties: For any given , the solution to the proposed minimization problem is unique and depends continuously on the data . Furthermore, convergence of approximate solutions obtained by finite element discretization holds true. We show that the nodal variables of any chosen finite element subspace can be interpreted as computational units whose activation dynamics due to the nonlinear smoothing process evolve like a globally asymptotically stable network. A corresponding analogue implementation is thus feasible and would provide a real time processing stage for the transition preserving smoothing of visual data. Using artificial as well as real data we illustrate our approach by numerical examples. We demonstrate that solutions to our approach improve those obtained by quadratic minimization and show the influence of global parameters which allow for a continuous, scale-dependent, and selective control of the smoothing process. Received: 23 February 1994 / Accepted in revised form: 28 July 1994     

Revealing Time-Unlocked Brain Activity from MEG Measurements by Common Waveform Estimation

Brain activities related to cognitive functions, such as attention, occur with unknown and variable delays after stimulus onsets. Recently, we proposed a method (Common Waveform Estimation, CWE) that could extract such brain activities from magnetoencephalography (MEG) or electroencephalography (EEG) measurements. CWE estimates spatiotemporal MEG/EEG patterns occurring with unknown and variable delays, referred to here as unlocked waveforms, without hypotheses about their shapes. The purpose of this study is to demonstrate the usefulness of CWE for cognitive neuroscience. For this purpose, we show procedures to estimate unlocked waveforms using CWE and to examine their role. We applied CWE to the MEG epochs during Go trials of a visual Go/NoGo task. This revealed unlocked waveforms with interesting properties, specifically large alpha oscillations around the temporal areas. To examine the role of the unlocked waveform, we attempted to estimate the strength of the brain activity of the unlocked waveform in various conditions. We made a spatial filter to extract the component reflecting the brain activity of the unlocked waveform, applied this spatial filter to MEG data under different conditions (a passive viewing, a simple reaction time, and Go/NoGo tasks), and calculated the powers of the extracted components. Comparing the powers across these conditions suggests that the unlocked waveforms may reflect the inhibition of the task-irrelevant activities in the temporal regions while the subject attends to the visual stimulus. Our results demonstrate that CWE is a potential tool for revealing new findings of cognitive brain functions without any hypothesis in advance.     

Quantitative and rapid analysis of transglutaminase activity using protein arrays in mammalian cells

We developed a novel on-chip activity assay using protein arrays for quantitative and rapid analysis of transglutami-nase activity in mammalian cells. Transglutaminases are a family of Ca²⁺-dependent enzymes involved in cell regulation as well as human diseases such as neurodegenerative disorders, inflammatory diseases and tumor progression. We fabricated the protein arrays by immobilizing N,N′-dimethylcasein (a substrate) on the amine surface of the arrays. We initiated transamidating reaction on the protein arrays and determined the transglutaminase activity by analyzing the fluorescence intensity of biotinylated casein. The on-chip transglutaminase activity assay was proved to be much more sensitive than the [³H]putrescine-incorporation assay. We successfully applied the on-chip assay to a rapid and quantitative analysis of the transgluta-minase activity in all-trans retinoic acid-treated NIH 3T3 and SH-SY5Y cells. In addition, the on-chip transglutaminase activity assay was sufficiently sensitive to determine the transglutaminase activity in eleven mammalian cell lines. Thus, this novel on-chip transglutaminase activity assay was confirmed to be a sensitive and high-throughput approach to investigating the roles of transglutaminase in cellular signaling, and, moreover, it is likely to have a strong potential for monitoring human diseases. These authors contributed equally to this work.