Motor Imagery EEG Spectral-Spatial Feature Optimization Using Dual-Tree Complex Wavelet and Neighbourhood Component Analysis

BackgroundFrequency band optimization improves the performance of common spatial pattern (CSP) in motor imagery (MI) tasks classification because MI-related electroencephalograms (EEGs) are highly frequency specific. Many variants of CSP algorithm divided the EEG into various sub bands and then applied CSP. However, the feature dimension of MI-EEG data increases with addition of frequency sub bands and requires efficient feature selection algorithms. The performance of CSP also depends on filtering techniques.MethodIn this study, we designed a dual tree complex wavelet transform based filter bank to filter the EEG into sub bands, instead of traditional filtering methods, which improved the spatial feature extraction efficiency. Further, after filtering EEG into different sub bands, we extracted spatial features from each sub band using CSP and optimized them by a proposed supervised learning framework based on neighbourhood component analysis (NCA). Subsequently, a support vector machine (SVM) is trained to perform classification.ResultsAn experimental study, conducted on two datasets (BCI Competition IV (Dataset 2b), and BCI competition III (Dataset IIIa)), validated the MI classification effectiveness of the proposed method in comparison with standard algorithms such as CSP, Filter bank CSP (CSP), and Discriminative FBCSP (DFBCSP). The average classification accuracy obtained by the proposed method for BCI Competition IV (Dataset 2b), and BCI Competition III (Dataset IIIa) are 84.02 ± 12.2 and 89.1 ± 7.50, respectively and found significant than that achieved by standard methods.ConclusionAchieved superior results suggest that the proposed algorithm can improve the performance of MI-based Brain-computer interface devices.     

The distribution of residues in a polypeptide sequence is a determinant of aggregation optimized by evolution

It has been shown that the propensity of a protein to form amyloid-like fibrils can be predicted with high accuracy from the knowledge of its amino acid sequence. It has also been suggested, however, that some regions of the sequences are more important than others in determining the aggregation process. Here, we have addressed this issue by constructing a set of “sequence scrambled” variants of the first 29 residues of horse heart apomyoglobin (apoMb_1-29), in which the sequence was modified while maintaining the same amino acid composition. The clustering of the most amyloidogenic residues in one region of the sequence was found to cause a marked increase of the elongation rate (k_agg) and a remarkable shortening of the lag phase (t_lag) of the fibril growth, as determined by far-UV circular dichroism and thioflavin T fluorescence. We also show that taking explicitly into consideration the presence of aggregation-promoting regions in the predictive methods results in a quantitative agreement between the theoretical and observed k_agg and t_lag values of the apoMb_1-29 variants. These results, together with a comparison between homologous segments from the family of globins, indicate the existence of a negative selection against the clustering of highly amyloidogenic residues in one or few regions of polypeptide sequences.     

Ecological application of wavelet analysis in the scaling of spatial distribution patterns of Ceratoides ewersmanniana

Ecological experiments are usually conducted on small scales, but the ecological and environmental issues are usually on large scales. Hence, there is a clear need of scaling. Namely, when we deal with patterns and processes on larger scales, a special connection needs to be established on the small scales that we are familiar with. Here we presented a wavelet analysis method that could build relationships between spatial distribution patterns on different scales. With this method, we also studied how spatial heterogeneity and distribution patterns changed with the scale. We investigated the distribution and the habitat of C. ewersmanniana in two plots (200 m × 200 m; the distance between these two plots is 15 km) at Mosuowan desert. The results demonstrated that spatial heterogeneity and distribution patterns were incorporated into larger scales when the wavelet scale varied from one (5 m) to four (20 m). However, if the wavelet scale was above five (25 m), the spatial distribution patterns varied placidly, the oscillation frequency of landforms stabilized at 110 m, and the dynamic quantity period of C. ewersmanniana stabilized at 115–125 m. We also identified signal mutation points with wavelet analysis and verified the heterogeneity degree of local space with position variance. We found that position variance decomposed the distribution patterns on large scales into small sampling plots, and the position with the largest variance also had the strongest heterogeneity. In a word, the wavelet analysis method could scale-up spatial distribution patterns and habitat heterogeneity. With this method and other methods derived from this one, such as wavelet scale, wavelet variance, position variance and extremely direct-viewing graphs, wavelet analysis could be widely applied in solving the scaling problem in ecological and environmental studies.     

The influences of arboraceous layer on spatial patterns and morphological characteristics of herbaceous layer in an arid plant community

The objectives of this study were to examine the effects of arboraceous layer on the spatial pattern and morphological characteristics of herbaceous layer in Elaeagnus angustifolia–Achnatherum splendens community in Ningxia Hui Autonomous Region, China. The analyses of community composition and structural characteristics as well as the investigation of soil moisture and salinity showed that different life forms of plants differ in the soil depth at which they absorb and utilize soil moisture. Wavelet analysis showed that there were differences between the spatial patterns of A. splendens in the canopy-projected regions and other regions, and the intrinsic scales were detected. The results from the buffer analysis showed that the control of arboraceous layer on the herbaceous layer on the spatial patterns and the morphological characteristics were influenced not only by canopy shading but also by other causes such as distribution patterns of roots as the morphological characteristics did not monotonically change with distance.     

A modified resonant recognition model to predict protein-protein interaction

Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement. This paper briefly introduced the original Resonant Recognition Model (RRM), and then modified it by using the wavelet transform to acquire the Modified Resonant Recognition Model (MRRM). The key characteristic of the new model is that it can predict directly the protein-protein interaction from the primary sequence, and the MRRM is more suitable than the RRM for this prediction. The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction. Translated from Journal of Shanghai University (Natural Science), 2006, 12(1): 69–73 [译自: 上海大学学报(自然科学版)]     

Distribution of innervation zone and muscle fiber conduction velocity in the biceps brachii muscle

The spatial distributions of muscle innervation zone (IZ) and muscle fiber conduction velocity (CV) were examined in nine healthy young male participants. High-density surface electromyography (EMG) was collected from the biceps brachii muscle when subjects performed isometric elbow flexions at 20% to 80% of the maximal voluntary contraction (MVC). A total of 9498 samples of IZs were identified and CVs were calculated using the Radon transform. The center and width of IZ sample distribution were compared within four different force levels and six medial to lateral electrode column positions using repeated measures ANOVA and multiple comparison tests. Significant shifts of IZ center were observed in the medial columns (Columns 5, 6, and 7) compared with the lateral columns (Columns 3 and 4) (p < 0.05). Similarly, significant differences in the IZ width were found in Column 7 and 8 compared to Column 3 (p < 0.05). In contrast, muscle CV was unaffected by column position. Instead, muscle CV was faster at 40% and 80% MVC compared to 20% MVC (p < 0.05). The findings of this study add further insights into the physiological properties of the biceps brachii muscle.     

Development and validation of a kinematically-driven discrete element model of the patellofemoral joint

Quantifying the complex loads at the patellofemoral joint (PFJ) is vital to understanding the development of PFJ pain and osteoarthritis. Discrete element analysis (DEA) is a computationally efficient method to estimate cartilage contact stresses with potential application at the PFJ to better understand PFJ mechanics. The current study validated a DEA modeling framework driven by PFJ kinematics to predict experimentally-measured PFJ contact stress distributions. Two cadaveric knee specimens underwent quadriceps muscle [215 N] and joint compression [350 N] forces at ten discrete knee positions representing PFJ positions during early gait while measured PFJ kinematics were used to drive specimen-specific DEA models. DEA-computed contact stress and area were compared to experimentally-measured data. There was good agreement between computed and measured mean and peak stress across the specimens and positions (r = 0.63–0.85). DEA-computed mean stress was within an average of 12% (range: 1–47%) of the experimentally-measured mean stress while DEA-computed peak stress was within an average of 22% (range: 1–40%). Stress magnitudes were within the ranges measured (0.17–1.26 MPa computationally vs 0.12–1.13 MPa experimentally). DEA-computed areas overestimated measured areas (average error = 60%; range: 4–117%) with magnitudes ranging from 139 to 307 mm² computationally vs 74–194 mm² experimentally. DEA estimates of the ratio of lateral to medial patellofemoral stress distribution predicted the experimental data well (mean error = 15%) with minimal measurement bias. These results indicate that kinematically-driven DEA models can provide good estimates of relative changes in PFJ contact stress.     

New insights on ChlD1 function in Photosystem II from site-directed mutants of D1/T179 in Thermosynechococcus elongatus

The monomeric chlorophyll, Chl_D1, which is located between the P_D1P_D2 chlorophyll pair and the pheophytin, Pheo_D1, is the longest wavelength chlorophyll in the heart of Photosystem II and is thought to be the primary electron donor. Its central Mg²⁺ is liganded to a water molecule that is H-bonded to D1/T179. Here, two site-directed mutants, D1/T179H and D1/T179V, were made in the thermophilic cyanobacterium, Thermosynechococcus elongatus, and characterized by a range of biophysical techniques. The Mn₄CaO₅ cluster in the water-splitting site is fully active in both mutants. Changes in thermoluminescence indicate that i) radiative recombination occurs via the repopulation of *Chl_D1 itself; ii) non-radiative charge recombination reactions appeared to be faster in the T179H-PSII; and iii) the properties of P_D1P_D2 were unaffected by this mutation, and consequently iv) the immediate precursor state of the radiative excited state is the Chl_D1⁺Pheo_D1^? radical pair. Chlorophyll bleaching due to high intensity illumination correlated with the amount of ¹O₂ generated. Comparison of the bleaching spectra with the electrochromic shifts attributed to Chl_D1 upon Q_A^? formation, indicates that in the T179H-PSII and in the WT*3-PSII, the Chl_D1 itself is the chlorophyll that is first damaged by ¹O₂, whereas in the T179V-PSII a more red chlorophyll is damaged, the identity of which is discussed. Thus, Chl_D1 appears to be one of the primary damage site in recombination-mediated photoinhibition. Finally, changes in the absorption of Chl_D1 very likely contribute to the well-known electrochromic shifts observed at ~430?nm during the S-state cycle.     

Molecular interaction of manganese based carbon monoxide releasing molecule (MnCORM) with human serum albumin (HSA)

In the present study, the interaction between the HSA and MnCORM in vitro under physiological conditions, was investigated through ultraviolet-visible (UV-vis) absorption, fluorescence, time-resolved fluorescence, circular dichroism (CD), Fourier transform infrared (FT-IR) spectroscopic techniques and in silico molecular docking methods. Binding parameters such as the binding constant, number of binding sites and binding force were obtained from the fluorescence data. Thermodynamic interaction revealed that the reaction was spontaneous (ΔG < 0) and hydrogen bond and van der Waals interaction were primarily involved in the binding. The changes induced in the secondary structure conformation due to the MnCORM interaction were monitored using CD and FT-IR spectroscopic techniques. The results showed reduction in α-helix conformation and corresponding increase in β-sheet and unordered structures due to slight unfolding. The time-resolved fluorescence decay confirmed the static quenching mechanism of the MnCORM. The molecular docking studies revealed that the MnCORM interacted at Sudlow’s site II of domain IIIA through hydrogen bond and van der Waals interactions. In order to understand the drug distribution and elimination, studies on the drug molecule interaction with HSA are vital. Therefore, it is evident that MnCORM interacts with HSA through ground state complex formation and thus suitable for in vivo delivery.     

Exploring protein-protein interactions using the site-identification by ligand competitive saturation methodology

Protein docking methods are powerful computational tools to study protein-protein interactions (PPI). While a significant number of docking algorithms have been developed, they are usually based on rigid protein models or with limited considerations of protein flexibility and the desolvation effect is rarely considered in docking energy functions, which may lower the accuracy of the predictions. To address these issues, we introduce a PPI energy function based on the site-identification by ligand competitive saturation (SILCS) framework and utilize the fast Fourier transform (FFT) correlation approach. The free energy content of the SILCS FragMaps represent an alternative to traditional energy grids and they can be efficiently utilized to guide FFT-based protein docking. Application of the approach to eight diverse test cases, including seven from Protein Docking Benchmark 5.0, showed the PPI prediction using SILCS approach (SILCS-PPI) to be competitive with several commonly used protein docking methods indicating that the method has the ability to both qualitatively and quantitatively inform the prediction of PPI. Results show the utility of the SILCS-PPI docking approach for determination of probability distributions of PPI interactions over the surface of both partner proteins, allowing for identification of alternate binding poses. Such binding poses are confirmed by experimental crystal contacts in our test cases. While more computationally demanding than available PPI docking technologies, we anticipate that the SILCS-PPI docking approach will offer an alternative methodology for improved evaluation of PPIs that could be used in a variety of fields from systems biology to excipient design for biologics-based drugs.