A novel 16-channel wireless system for electroencephalography measurements with dry spring-loaded sensors

Understanding brain function using electroencephalography (EEG) is an important issue for cerebral nervous system diseases, especially for epilepsy and Alzheimer's disease. Many EEG measurement systems are used reliably to study these diseases, but their bulky size and the use of wet sensors make them uncomfortable and inconvenient for users. To overcome the limitations of conventional EEG measurement systems, a wireless and wearable multichannel EEG measurement system is proposed in this paper. This system includes a wireless data acquisition device, dry spring-loaded sensors, and a sizeadjustable soft cap. We compared the performance of the proposed system using dry versus conventional wet sensors. A significant positive correlation between readings from wet and dry sensors was achieved, thus demonstrating the performance of the system. Moreover, four different features of EEG signals (i.e., normal, eye-blinking, closed-eyes, and teeth-clenching signals) were measured by 16 dry sensors to ensure that they could be detected in real-life cognitive neuroscience applications. Thus, we have shown that it is possible to reliably measure EEG signals using the proposed system. This paper presents novel insights into the field of cognitive neuroscience, showing the possibility of studying brain function under real-life conditions. © 2014 IEEE

Clustering exact matches of pairwise sequence alignments by weighted linear regression

<p>Abstract</p> <p>Background</p> <p>At intermediate stages of genome assembly projects, when a number of contigs have been generated and their validity needs to be verified, it is desirable to align these contigs to a reference genome when it is available. The interest is not to analyze a detailed alignment between a contig and the reference genome at the base level, but rather to have a rough estimate of where the contig aligns to the reference genome, specifically, by identifying the starting and ending positions of such a region. This information is very useful in ordering the contigs, facilitating post-assembly analysis such as gap closure and resolving repeats. There exist programs, such as BLAST and MUMmer, that can quickly align and identify high similarity segments between two sequences, which, when seen in a dot plot, tend to agglomerate along a diagonal but can also be disrupted by gaps or shifted away from the main diagonal due to mismatches between the contig and the reference. It is a tedious and practically impossible task to visually inspect the dot plot to identify the regions covered by a large number of contigs from sequence assembly projects. A forced global alignment between a contig and the reference is not only time consuming but often meaningless.</p> <p>Results</p> <p>We have developed an algorithm that uses the coordinates of all the exact matches or high similarity local alignments, clusters them with respect to the main diagonal in the dot plot using a weighted linear regression technique, and identifies the starting and ending coordinates of the region of interest.</p> <p>Conclusion</p> <p>This algorithm complements existing pairwise sequence alignment packages by replacing the time-consuming seed extension phase with a weighted linear regression for the alignment seeds. It was experimentally shown that the gain in execution time can be outstanding without compromising the accuracy. This method should be of great utility to sequence assembly and genome comparison projects.</p

Effect of laser remelting on microstructure and properties of WC reinforced Fe-based amorphous composite coatings by laser cladding

The WC reinforced Fe-based amorphous composite coatings were prepared by laser cladding with rectangular spot. The effect of laser remelting on the microstructure and properties of composite coatings was investigated. The results showed that laser remelting can reduce the cracks and porosities of the cladding coating and improve its surface quality. Large amounts of crystalline phases were precipitated at the top of the cladding and remelting coatings. However, the microstructure at the top of the remelting coating was finer compared to that at the top of the cladding coating. With increasing distance from the surface of substrate, the amorphous phase appeared within the remelting coating and large amounts of carbides rich in Fe and Mo, Fe23B6, gamma-Fe and Cr-9.1.Si-0.9 Slag phases were also precipitated in the remelting coating. As a result, the corrosion resistance of the remelting coating was higher than that of the cladding coating. The microhardness of the remelting coating was approximately 1.13 times higher than that of the cladding coating. (C) 2018 Elsevier Ltd. All rights reserved

Antimetastatic Effects of Norcantharidin on Hepatocellular Carcinoma by Transcriptional Inhibition of MMP-9 through Modulation of NF-kB Activity

The rate of morbidity and mortality of hepatocellular carcinoma (HCC) in Taiwan has not lessened because of difficulty in treating tumor metastasis. Norcantharidin (NCTD) is currently used as an anticancer drug for hepatoma, breast cancer, and colorectal adenocarcinoma. NCTD possesses various biological anticancer activities, including apoptosis. However, detailed effects and molecular mechanisms of NCTD on metastasis are unclear. Thus, HCC cells were subjected to treatment with NCTD and then analyzed to determine the effects of NCTD on cell metastasis.Modified Boyden chamber assays revealed that NCTD treatment inhibited cell migration and invasion capacities of HCC cells substantially. Results of zymography and western blotting showed that activities and protein levels of matrix metalloproteinase-9 (MMP-9) and urokinase plasminogen activator (u-PA) were inhibited by NCTD. Western blot analysis showed that NCTD inhibits phosphorylation of ERK1/2. Testing of mRNA level, quantitative real-time PCR, and promoter assays evaluated the inhibitory effects of NCTD on MMP-9 and u-PA expression in HCC cells. The chromatin immunoprecipitation (ChIP) assay for analyzing the genomic DNA sequences bound to these proteins was reactive to the transcription protein nuclear factor (NF)-kappaB, which was inhibited by NCTD. The expression of NF-kappa B was measured by western blot analysis, which revealed decreased nuclear-factor DNA-binding activity after NCTD treatment.NCTD inhibited MMP-9 and u-PA expression through the phosphorylation of ERK1/2 and NF-kappaB signaling pathway which serves as a powerful chemopreventive agent in HCC cell metastasis

Experimental and theoretical investigation of ligand effects on the synthesis of ZnO nanoparticles

ZnO nanoparticles with highly controllable particle sizes(less than 10 nm) were synthesized using organic capping ligands in Zn(Ac)2 ethanolic solution. The molecular structure of the ligands was found to have significant influence on the particle size. The multi-functional molecule tris(hydroxymethyl)-aminomethane (THMA) favoured smaller particle distributions compared with ligands possessing long hydrocarbon chains that are more frequently employed. The adsorption of capping ligands on ZnnOn crystal nuclei (where n = 4 or 18 molecular clusters of(0001) ZnO surfaces) was modelled by ab initio methods at the density functional theory (DFT) level. For the molecules examined, chemisorption proceeded via the formation of Zn...O, Zn...N, or Zn...S chemical bonds between the ligands and active Zn2+ sites on ZnO surfaces. The DFT results indicated that THMA binds more strongly to the ZnO surface than other ligands, suggesting that this molecule is very effective at stabilizing ZnO nanoparticle surfaces. This study, therefore, provides new insight into the correlation between the molecular structure of capping ligands and the morphology of metal oxide nanostructures formed in their presence

Word correlation matrices for protein sequence analysis and remote homology detection

<p>Abstract</p> <p>Background</p> <p>Classification of protein sequences is a central problem in computational biology. Currently, among computational methods discriminative kernel-based approaches provide the most accurate results. However, kernel-based methods often lack an interpretable model for analysis of discriminative sequence features, and predictions on new sequences usually are computationally expensive.</p> <p>Results</p> <p>In this work we present a novel kernel for protein sequences based on average word similarity between two sequences. We show that this kernel gives rise to a feature space that allows analysis of discriminative features and fast classification of new sequences. We demonstrate the performance of our approach on a widely-used benchmark setup for protein remote homology detection.</p> <p>Conclusion</p> <p>Our word correlation approach provides highly competitive performance as compared with state-of-the-art methods for protein remote homology detection. The learned model is interpretable in terms of biologically meaningful features. In particular, analysis of discriminative words allows the identification of characteristic regions in biological sequences. Because of its high computational efficiency, our method can be applied to ranking of potential homologs in large databases.</p

Physicochemical property distributions for accurate and rapid pairwise protein homology detection

<p>Abstract</p> <p>Background</p> <p>The challenge of remote homology detection is that many evolutionarily related sequences have very little similarity at the amino acid level. Kernel-based discriminative methods, such as support vector machines (SVMs), that use vector representations of sequences derived from sequence properties have been shown to have superior accuracy when compared to traditional approaches for the task of remote homology detection.</p> <p>Results</p> <p>We introduce a new method for feature vector representation based on the physicochemical properties of the primary protein sequence. A distribution of physicochemical property scores are assembled from 4-mers of the sequence and normalized based on the null distribution of the property over all possible 4-mers. With this approach there is little computational cost associated with the transformation of the protein into feature space, and overall performance in terms of remote homology detection is comparable with current state-of-the-art methods. We demonstrate that the features can be used for the task of pairwise remote homology detection with improved accuracy versus sequence-based methods such as BLAST and other feature-based methods of similar computational cost.</p> <p>Conclusions</p> <p>A protein feature method based on physicochemical properties is a viable approach for extracting features in a computationally inexpensive manner while retaining the sensitivity of SVM protein homology detection. Furthermore, identifying features that can be used for generic pairwise homology detection in lieu of family-based homology detection is important for applications such as large database searches and comparative genomics.</p

Fifteen-Year Population Attributable Fractions and Causal Pies of Risk Factors for Newly Developed Hepatocellular Carcinomas in 11,801 Men in Taiwan

Development of hepatocellular carcinoma (HCC) is a multi-factorial process. Chronic infections with hepatitis B virus (HBV) and hepatitis C virus (HCV) are important risk factors of HCC. Host factors, such as alcohol drinking, may also play a role. This study aims to provide a synthesis view on the development of HCC by examining multiple risk factors jointly and collectively. Causal-pie modeling technique was applied to analyze a cohort of 11,801 male residents (followed up for 15 years) in Taiwan, during which a total of 298 incident HCC cases were ascertained. The rate ratios adjusted by age were further modeled by an additive Poisson regression. Population attributable fractions (PAFs) and causal-pie weights (CPWs) were calculated. A PAF indicates the magnitude of case-load reduction under a particular intervention scenario, whereas a CPW for a particular class of causal pies represents the proportion of HCC cases attributable to that class. Using PAF we observed a chance to reduce around 60% HCC risk moving from no HBV-related intervention to the total elimination of the virus. An additional ∼15% (or ∼5%) reduction can be expected, if the HBV-related intervention is coupled with an HCV-related intervention (or an anti-drinking campaign). Eight classes of causal pies were found to be significant, including four dose-response classes of HBV (total CPW=52.7%), one independent-effect class of HCV (CPW=14.4%), one HBV-alcohol interaction class (CPW=4.2%), one HBV-HCV interaction class (CPW=1.7%), and one all-unknown class (CPW=27.0%). Causal-pie modeling for HCC helps clarify the relative importance of each viral and host factor, as well as their interactions