We have developed a new Magnetic Resonance Electrical Impedance Tomography (MREIT) algorithm, RSM-MREIT algorithm, for noninvasive imaging of electrical conductivity distribution using only one component of magnetic flux density. The proposed RSM-MREIT algorithm uses Response Surface Methodology (RSM) algorithm for optimizing the conductivity distribution through minimizing the errors between the measured and calculated magnetic flux density. A series of computer simulations have been conducted to assess the performance of the proposed RSM-MREIT algorithm to estimate electrical conductivity values of the scalp, the skull, and the brain tissue, in a three-shell piece-wise homogeneous head model. Computer simulation studies were conducted in both a spherical and realistic geometry head model with a single variable (the brain-toskull conductivity ratio) and three-variables (the conductivity of the brain, the skull, and the scalp). The relative error between the target and estimated head conductivity values were less than 12% for both the single-variable and three-variable simulations. These promising simulation results demonstrate the feasibility of the proposed RSM-MREIT algorithm in estimating electrical conductivity values in a piece-wise homogeneous head model of the human head, and suggest that the RSM-MREIT algorithm merits further investigation.

A new approach has been developed by combining the K-mean clustering (KMC) method and a modified convolution kernel compensation (CKC) method for multi-channel surface electromyogram (EMG) decomposition. The KMC method was first utilized to cluster vectors of observations at different time instants and then estimate the initial innervation pulse train (IPT). The CKC method, modified with a novel multi-step iterative process, was conducted to update the estimated IPT. The performance of the proposed K-means clustering - Modified CKC (KmCKC) approach was evaluated by reconstructing IPTs from both simulated and experimental surface EMG signals. The KmCKC approach successfully reconstructed all 10 IPTs from the simulated surface EMG signals with true positive rates (TPR) of over 90% with a low signal-to-noise ratio (SNR) of −10dB. Over 10 motor units were also successfully extracted from the 64-channel experimental surface EMG signals of the first dorsal interosseous (FDI) muscles when a contraction force was held at 8 N by using the KmCKC approach. A ‘two-source’ test was further conducted with 64-channel surface EMG signals. The high percentage of common MUs and common pulses (over 92% at all force levels) between the IPTs reconstructed from the two independent groups of surface EMG signals demonstrates the reliability and capability of the proposed KmCKC approach in multi-channel surface EMG decomposition. Results from both simulated and experimental data are consistent and confirm that the proposed KmCKC approach can successfully reconstruct IPTs with high accuracy at different levels of contraction.

We have developed a new algorithm for magnetic resonance electrical impedance tomography (MREIT), which uses only one component of the magnetic flux density to reconstruct the electrical conductivity distribution within the body. The radial basis function (RBF) network and simplex method are used in the present approach to estimate the conductivity distribution by minimizing the errors between the 'measured' and model-predicted magnetic flux densities. Computer simulations were conducted in a realistic-geometry head model to test the feasibility of the proposed approach. Single-variable and three-variable simulations were performed to estimate the brain-skull conductivity ratio and the conductivity values of the brain, skull and scalp layers. When SNR = 15 for magnetic flux density measurements with the target skull-to-brain conductivity ratio being 1/15, the relative error (RE) between the target and estimated conductivity was 0.0737 +/- 0.0746 in the single-variable simulations. In the three-variable simulations, the RE was 0.1676 +/- 0.0317. Effects of electrode position uncertainty were also assessed by computer simulations. The present promising results suggest the feasibility of estimating important conductivity values within the head from noninvasive magnetic flux density measurements.

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Lycoris longituba is one of the species belonging to the Amaryllidaceae family. Despite its limited distribution, endemic to central eastern China, this species displays an exceptionally wide diversity of flower colors from purple, red, orange, to yellow, in nature. We study the natural variation of floral color in L. longituba by testing the components of water-soluble vacuolar pigments – anthocyanins – in its petals using high-performance liquid chromatography coupled with photodiode array detection and electrospray ionization mass spectrometry. Four anthocyanins were identified, cyanidin-3-sophoroside (Cy3So), cyanidin-3-xylosylglucoside (Cy3XyGlc), cyanidin-3-sambubioside (Cy3Sa), and pelargonidin-3-xylosylglucoside (Pg3XyGlc), which occur at various amounts in L. longituba petals of different colors. A multivariate analysis was used to explore the relationship between pigments and flower color. Anthocyanins have been thought to play a major role in acting as a UV screen that protects the plant's DNA from sunlight damage and attracting insects for the purpose of pollination. Thus, knowledge about the content and type of anthocyanins determining the petal coloration of Lycoris longituba will help to study the adaptive evolution of flowers and provide useful information for the ornamental breeding of this species.