Grouped Multivariate Variational Mode Decomposition With Application to EEG Analysis.

OBJECTIVE: In this paper, a novel extended form of multivariate variational mode decomposition (MVMD) method to multigroup data named as grouped MVMD (GMVMD) is proposed. GMVMD is distinct from MVMD as it extracts common frequencies with strong correlations among regional channels. METHODS: Firstly, GMVMD utilizes a new clustering algorithm named as frequencies grouping algorithm to classify the nearest common frequencies among all channels to specified groups. Secondly, a generic variational optimization model which is extended from MVMD is formulated. Thirdly, alternating direction method of multipliers (ADMM) is utilized to obtain optimal solution of GMVMD model. RESULTS: The proposed method introduces an extra parameter to decide the number of clusterings which need to be specified by the user. The effectiveness and superiority of the algorithm are demonstrated on a series of experiments. The utility of GMVMD is verified by grouping real-world electroencephalogram (EEG) data having similar center frequencies successfully. CONCLUSION: GMVMD outperforms MVMD in mode-alignment, signal reduction error and et al. Significance: GMVMD can obtain more accurate center frequencies and less signal reduction error than MVMD.

Novel Polyetherimide Dielectrics: Molecular Design, Energy Storage Property, and Self-Healing Performance.

The development of high-temperature resistant dielectrics with excellent dielectric properties and self-healing behavior is crucial for the application of metallized film capacitors. In this work, a series of polyetherimide (PEI) dielectric films are designed and fabricated. The introduction of polar groups is in favor to the increase of permittivity, and the flexible connection such as the ether group will facilitate the reduction of dielectric loss. Moreover, the oxygen elements are beneficial to the "self-healing" of metallized film capacitors. Consequently, the permittivity of 3.53-4.00, dissipation factor of 0.281-0.517%, and Weibull breakdown strength of 347-674 MV m-1 are obtained for the PEI dielectrics. In addition, PEI-4 (BPADA-BAPP) and PEI-8 (BPADA-MDA) are selected to further investigate dielectric breakdown (150 °C), electrical displacement-electric filed (D-E) loop (at room temperature and 150 °C) as well as self-healing performance, which will evaluate their potential in practical applications. The results show that PEI-8 has stable breakdown field strength and high charge-discharge efficiency at elevated temperatures. Metallized film capacitor based on PEI-8 exhibits excellent self-healing performance, with pleasing self-clear morphology, high breakdown voltage, and reduced self-healing energy. Therefore, PEI-8 is considered as a potential candidate for metallized film capacitors applied under harsh conditions.

An improved BECT spike detection method with functional brain network features based on PLV.

Background: Children with benign childhood epilepsy with centro-temporal spikes (BECT) have spikes, sharps, and composite waves on their electroencephalogram (EEG). It is necessary to detect spikes to diagnose BECT clinically. The template matching method can identify spikes effectively. However, due to the individual specificity, finding representative templates to detect spikes in actual applications is often challenging. Purpose: This paper proposes a spike detection method using functional brain networks based on phase locking value (FBN-PLV) and deep learning. Methods: To obtain high detection effect, this method uses a specific template matching method and the 'peak-to-peak' phenomenon of montages to obtain a set of candidate spikes. With the set of candidate spikes, functional brain networks (FBN) are constructed based on phase locking value (PLV) to extract the features of the network structure during spike discharge with phase synchronization. Finally, the time domain features of the candidate spikes and the structural features of the FBN-PLV are input into the artificial neural network (ANN) to identify the spikes. Results: Based on FBN-PLV and ANN, the EEG data sets of four BECT cases from the Children's Hospital, Zhejiang University School of Medicine are tested with the AC of 97.6%, SE of 98.3%, and SP 96.8%.

Circular RNA circMET contributes to tamoxifen resistance of breast cancer cells by targeting miR-204/AHR signaling.

Breast cancer is a prevalent female malignancy and tamoxifen remains the first-line treatment for breast cancer, but tamoxifen resistance is a frequent clinical problem. Circular RNAs (circRNAs) are a bunch of noncoding RNAs with circular structures and play crucial roles in cancer development. Here, we aimed to explore the unreported function of circMET in the modulation of tamoxifen resistance of breast cancer cells. The expression of circMET was upregulated in tamoxifen-resistant breast cancer cells. The depletion of circMET significantly reduced the cell viability and proliferation in tamoxifen-resistant breast cancer cells and the co-treatment of tamoxifen promoted the effect. Mechanically, the luciferase activity of circMET and was repressed by miR-204-5p and AHR 3'UTR in the cells. The expression of miR-204-5p was elevated by circMET knockdown. The expression of AHR was downregulated by miR-204-5p or circMET depletion, while the miR-204-5p inhibitor reversed the effect of circMET depletion in cells. The overexpression of circMET enhanced the cell viability and proliferation of MCF7-Re and T47D-Re cells but miR-204-5p or AHR depletion blocked the phenotype. Clinically, the expression of circMET and AHR has enhanced in tamoxifen-resistant samples compared with tamoxifen sensitive samples, but miR-204-5p presented a revered expression in the samples. Consequently, we concluded that circular RNA circMET contributed to tamoxifen resistance of breast cancer cells by targeting miR-204-5p/AHR signaling.

Epileptic State Classification based on Intrinsic Mode Function and Wavelet Packet Decomposition.

The scalp electroencephalogram (EEG) signal based epileptic seizure analysis has been comprehensively studied in the past. But existing researches are generally concerned with the seizure/non-seizure detection. Few attention has been paid to the epileptic preictal state classification, which is found to be evidently more important to the injury prevention. In this paper, we study the epileptic preictal state classification for seizure prediction. The one hour preictal EEG signal is divided into non-overlapped equilong segments. Statistical features of the first intrinsic mode function (FIMF) of the empirical mode decomposition (EMD) of the EEG signal as well as the 4-level wavelet packet decomposition (WPD) of the FIMF are extracted for the EEG signal representation. A five-state classification problem is formulated, including one interictal, three preictal, and one seizure states. Experiments on the benchmark epilepsy EEG database collected by the Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) using several popular classifiers are provided for the effectiveness demonstration.

Trichostatin A and Tamoxifen inhibit breast cancer cell growth by miR-204 and ERα reducing AKT/mTOR pathway.

MicroRNAs (miRs) are small non-coding RNAs aberrantly expressed in human tumors. Increasing evidence suggests that miRNAs are functionally important in cancers. We demonstrated miR-204 exerts its function by targeting gene involved in tumor growth and chemotherapy drugs reactivity. Here, we show that Trichostatin A (TSA) could increase ERα expression in MCF-7 and MDA-MB-231 cells by reducing miR204. Analysis of tumors growth inhibition shows that TSA promotes ERα expression, which could be reversed by miR-204 mimic transfection. When miR-204 is down regulated, the inhibition of TAM on breast cancer cells is enhanced. Caspase 3 activity is also increased. TSA and TAM combination inhibits Mcl-1 expression by decreasing phosphorylation of AKT induced by ERα increase in vivo and in vitro.

Removal and Destruction of Organic Contaminants in Water Using Adsorption, Steam Regeneration, and Photocatalytic Oxidation: A Pilot-Scale Study.

The overall objective of this pilot-scale study is to investigate the technical feasibility of the removal and destruction of organic contaminants in water using adsorption and photocatalytic oxidation. The process consists of two consecutive operational steps: (1) removal of organic contaminants using fixed-bed adsorption; and (2) regeneration of spent adsorbent using photocatalysis or steam, followed by decontamination of steam condensate using photocatalysis. The pilot-scale study was conducted to evaluate these options at a water treatment plant in Wausau (Wisconsin) for treatment of groundwater contaminated with tetrachloroethene (PCE), trichloroethene (TCE), cis-dichloroethene (cis-DCE), toluene, ethylbenzene (EB), and xylenes. The adsorbents used were F-400 GAC and Ambersorb 563. In the first treatment strategy, the adsorbents were impregnated with photocatalyst and used for the removal of aqueous organics. The spent adsorbents were then exposed to ultraviolet light to achieve photocatalytic regeneration. Regeneration of adsorbents using photocatalysis was observed to be not effective, probably because the impregnated photocatalyst was fouled by background organic matter present in the groundwater matrix. In the second treatment strategy, the spent adsorbents were regenerated using steam, followed by cleanup of steam condensate using photocatalysis. Four cycles of adsorption and three cycles of steam regeneration were performed. Ambersorb 563 adsorbent was successfully regenerated using saturated steam at 160 °C within 20 hours. The steam condensate was treated using fixed-bed photo-catalysis using 1% Pt-TiO2 photocatalyst supported on silica gel. After 35 minutes of empty bed contact time, more than 95% removal of TCE, cis-DCE, toluene, EB, and xylenes was achieved, and more than 75% removal of PCE was observed. In the case of activated carbon adsorbent, steam regeneration was not effective, and a significant loss in adsorbent capacity was observed.