The electro-encephalogram (EEG) is useful for clinical diagnosis and in biomedical research. EEG signals, however, especially those recorded from frontal channels, often contain strong electro-oculogram (EOG) artifacts produced by eye movements. Existing regression-based methods for removing EOG artifacts require various procedures for preprocessing and calibration that are inconvenient and time-consuming. The paper describes a method for removing ocular artifacts based on adaptive filtering. The method uses separately recorded vertical EOG and horizontal EOG signals as two reference inputs. Each reference input is first processed by a finite impulse response filter of length M (M = 3 in this application) and then subtracted from the original EEG. The method is implemented by a recursive least-squares algorithm that includes a forgetting factor (lambda = 0.9999 in this application) to track the non-stationary portion of the EOG signals. Results from experimental data demonstrate that the method is easy to implement and stable, converges fast and is suitable for on-line removal of EOG artifacts. The first three coefficients (up to M = 3) were significantly larger than any remaining coefficients.

A Pilot Studyonoelastography is now routinely used in medical sonography since Ophir et al 1 first described the principles of strain imaging in 1991. The technique can assess the elastic properties of tissues, and static elastography has found clinical application in diagnosing lesions of the breast, 2 thyroid, 3 lymph nodes, 4 and prostate gland. 5 The basic principle of sonoelastography is to produce displacement within the tissue by physical compression. By comparing the images taken before and after the application of compression, sonoelastography can show different degrees of displacement. 1 However, the stiffness of the tissue can only be obtained by comparing it with surrounding normal tissues. The degree of the compression applied by the operator may alter the results of sonoelastography. -Mei Chen, MD, PhD, Li-Gang Cui, MD, PhD, Ping He, MD, PhD, Wei-Wei Shen, MD, MS, Ya-Jun Qian, MD, MS, Jin-Rui Wang, MD, BS Received June 4, 2012, Xiang ORIGINAL RESEARCHObjectives-The purpose of this study was to investigate the feasibility of using quantitative shear wave elastography for assessing the functional integrity of the Achilles tendon and to summarize the changes in elasticity of ruptured Achilles tendons in comparison with normal controls.Methods-Thirty-six normal and 14 ruptured Achilles tendons were examined with shear wave elastography coupled with a linear array transducer (4-15 MHz). The elasticity value of each Achilles tendon in a longitudinal view was measured.Results-The mean elasticity value ± SD for the normal Achilles tendons was 291.91 ± 4.38 kPa (note that there are saturated measurement phenomena for the normal Achilles tendon, so the actual value will be >300 kPa), whereas the ruptured Achilles tendons had an elasticity value of 56.48 ± 68.59 kPa. A statistically significant difference was found in relation to the findings in healthy volunteers (P = .006).Conclusions-Our results suggest that shear wave elastography is a valuable tool that can provide complementary biomechanical information for evaluating the function of the Achilles tendon.

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Multiple familial trichoepithelioma (MFT) is an autosomal dominant skin disease characterized by the presence of many small benign tumors with pilar differentiation predominantly on the face. The first locus has been previously mapped to chromosome 9p21, but no gene for MFT has been identified to date. To identify the disease gene in a large Chinese family, we initially performed linkage analysis with microsatellite markers from 9p21, but failed to confirm the linkage to this region. Previous publications showed MFT and familial cylindromatosis (FC) can occur within one family and in a single person. Therefore, we speculated that the cylindromatosis gene (CYLDI gene) responsible for FC may be related to the pathogenesis of MFT. In view of that, we genotyped all available individuals using 11 microsatellite markers spanning the CYLDI gene region at 16q12-q13. We identified the linkage of MFT to this region. Mutation analysis in the CYLDI gene detected a frameshift mutation, designated as c.2355-2358delCAGA. The study firstly identified the cylindromatosis gene responsible for MFT and showed that different mutations of the CYLDI gene can give rise to distinct clinical and histological expression such as FC and MFT.

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify “causal” relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time.

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