Soft, adhesive and conductive composite for electroencephalogram signal quality improvement.

Since electroencephalogram (EEG) is a very small electrical signal from the brain, it is very vulnerable to external noise or motion artifact, making it difficult to measure. Therefore, despite the excellent convenience of dry electrodes, wet electrodes have been used. To solve this problem, self-adhesive and conductive composites using carbon nanotubes (CNTs) in adhesive polydimethylsiloxane (aPDMS), which can have the advantages of both dry and wet electrodes, have been developed by mixing them uniformly with methyl group-terminated PDMS. The CNT/aPDMS composite has a low Young's modulus, penetrates the skin well, has a high contact area, and excellent adhesion and conductivity, so the signal quality is enhanced. As a result of the EEG measurement test, although it was a dry electrode, results comparable to those of a wet electrode were obtained in terms of impedance and motion noise. It also shows excellent biocompatibility in a human fibroblast cell test and a week-long skin reaction test, so it can measure EEG with high signal quality for a long period of time.

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data.

BACKGROUND: Due to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity. METHODS: We aim to detect type 2 diabetes (T2D) causal SNP combinations from a GWAS dataset with optimal filtration and to discover the biological meaning of the detected SNP combinations. Optimal filtration can enhance the statistical power of SNP combinations by comparing the error rates of SNP combinations from various Bonferroni thresholds and p-value range-based thresholds combined with linkage disequilibrium (LD) pruning. T2D causal SNP combinations are selected using random forests with variable selection from an optimal SNP dataset. T2D causal SNP combinations and genome-wide SNPs are mapped into functional modules using expanded gene set enrichment analysis (GSEA) considering pathway, transcription factor (TF)-target, miRNA-target, gene ontology, and protein complex functional modules. The prediction error rates are measured for SNP sets from functional module-based filtration that selects SNPs within functional modules from genome-wide SNPs based expanded GSEA. RESULTS: A T2D causal SNP combination containing 101 SNPs from the Wellcome Trust Case Control Consortium (WTCCC) GWAS dataset are selected using optimal filtration criteria, with an error rate of 10.25%. Matching 101 SNPs with known T2D genes and functional modules reveals the relationships between T2D and SNP combinations. The prediction error rates of SNP sets from functional module-based filtration record no significance compared to the prediction error rates of randomly selected SNP sets and T2D causal SNP combinations from optimal filtration. CONCLUSIONS: We propose a detection method for complex disease causal SNP combinations from an optimal SNP dataset by using random forests with variable selection. Mapping the biological meanings of detected SNP combinations can help uncover complex disease mechanisms.