The results suggest that there are neurophysiological changes in the motor cortices of children with migraine that can be measured with neuromagnetic imaging techniques. The findings expand the ability to study the cerebral mechanisms of migraine using MEG and may facilitate the development of new therapeutic strategies in migraine treatment via alterations in cortical excitability.

No abstract

CTFs were used as effective metal-free catalysts for the oxidative cleavage of lignin model compounds and organosolv lignin.

No abstract

No abstract

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis.

High-resolution knee joint vibroarthrographic (VAG) signals can help physicians accurately evaluate the pathological condition of a degenerative knee joint, in order to prevent unnecessary exploratory surgery. Artifact cancellation is vital to preserve the quality of VAG signals prior to further computer-aided analysis. This paper describes a novel method that effectively utilizes ensemble empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA) algorithms for the removal of baseline wander and white noise in VAG signal processing. The EEMD method first successively decomposes the raw VAG signal into a set of intrinsic mode functions (IMFs) with fast and low oscillations, until the monotonic baseline wander remains in the last residue. Then, the DFA algorithm is applied to compute the fractal scaling index parameter for each IMF, in order to identify the anti-correlation and the long-range correlation components. Next, the DFA algorithm can be used to identify the anti-correlated and the long-range correlated IMFs, which assists in reconstructing the artifact-reduced VAG signals. Our experimental results showed that the combination of EEMD and DFA algorithms was able to provide averaged signal-to-noise ratio (SNR) values of 20.52 dB (standard deviation: 1.14 dB) and 20.87 dB (standard deviation: 1.89 dB) for 45 normal signals in healthy subjects and 20 pathological signals in symptomatic patients, respectively. The combination of EEMD and DFA algorithms can ameliorate the quality of VAG signals with great SNR improvements over the raw signal, and the results were also superior to those achieved by wavelet matching pursuit decomposition and time-delay neural filter.

The design of highly efficient catalysts for the cleavage of various lignin linkages is the key step in the depolymerization of lignin. In this paper, strong-base-modified covalent triazine frameworks (CTFs) were reported to be effective in the cleavage of the β-O-4 linkage. With the modification, the cleavage rate of the β-O-4 linkage was enhanced remarkably. Insight into the catalytic mechanism illustrated that the strong basic sites assisted the activation of Cβ–H bonds, which accounted for the promotion effect. The catalyst was further applied to the depolymerization of organosolv lignin, which showed 87% conversion of the β-O-4 linkage. This strategy should be useful in the catalyst design for lignin depolymerization.