Effect of industrial wood particle size on mechanical properties of?wood-polyvinyl chloride composites

Wood-polyvinyl chloride (PVC) composites were prepared using industrial wood particles used for manufacturing three-layer particleboards. The effect of particle size (0.25–0.5, 0.5–1, 1–2, and 2–4?mm) on the mechanical properties of the composites was investigated. The effect of cross-section size (4×10, 6×15 and 8×20?mm²) of composite pieces made by an injection moulding method was also studied. Both the particle size and specimen cross-section area significantly influenced these properties. The tensile and flexural properties as well as the impact strength in general increased with increasing particle size, and decreased with increasing cross-section size.     

Tiny Signals from the Human Brain: Acquisition and Processing of Biomagnetic Fields in Magnetoencephalography

Low-temperature superconductivity plays an important role in some specific biomedical applications, and, in particular, in non-invasive imaging methods of human brain activity. Superconducting magnets are indispensable for functional magnetic resonance imaging (fMRI) which allows functional imaging of the brain with high spatial but poor temporal resolution. Superconducting quantum interference devices (SQUIDs) are the most sensitive magnetic field detectors. Up to a few hundreds of SQUIDs are nowdays used in modern whole-head magnetoencephalography (MEG) systems. They allow tracking brain activation with a superior temporal resolution of milliseconds, which is a quintessential condition for the monitoring of brain dynamics and the understanding of information processing in the human brain. We introduce the prerequisites of MEG data acquisition and briefly review two established methods of biomagnetic signal processing: The concept of signal averaging, and the subsequent source identification as a solution of the biomagnetic inverse problem. Beside these standard techniques, we discuss advanced methods for signal processing in MEG, which take into account the frequency content of the recorded signal. We briefly refer to the prospects of Fourier analysis and wavelet transform in MEG data analysis, and suggest matching pursuit as a promising tool for signal decomposition and reconstruction with high resolution in time-frequency plane.     

Improved conformational space annealing method to treat β-structure with the UNRES force-field and to enhance scalability of parallel implementation

Successful application of physics-based protein-structure prediction methods depends on sophisticated computational approaches to global optimization of the conformational energy of a polypeptide chain. One of the most effective procedures for the global optimization of protein structures appears to be the Conformational Space Annealing (CSA) method. CSA is a hybrid method which combines genetic algorithms, essential aspects of the build-up method and a local gradient-based minimization. CSA evolves the population of conformations through genetic operators (mutations, i.e. perturbations of selected geometric parameters, and crossovers, i.e. exchange of selected subsets of geometric parameters between conformations) to a final population optimizing their conformational energy. Implementation of the CSA method with the united-residue force field (UNRES, in which each amino-acid residue is represented by two interaction sites, namely the united peptide group and the united side-chain) was enhanced by introducing new crossover operations consisting of (i) copying β-hairpins, (ii) copying remote strand pairs forming non-local β-sheets, and (iii) copying α-helical segments. A mutation operation, which shifts the position of a β-turn, was also introduced. The new operations promote β-structure, and are essential for searching the conformational space of proteins containing both α- and β-structure; without these operations, excessive preference of α-helical structures is obtained, even though these structures are high in energy. Parallelization of the CSA method has also been enhanced by removing most of the synchronization steps; the improved algorithm scales almost linearly up to 1,000 processors with over 75% average performance.     

Prediction of the structures of proteins with the UNRES force field, including dynamic formation and breaking of disulfide bonds

The presence of disulfide bonds is essential for maintaining the structure and function of many proteins. The disulfide bonds are usually formed dynamically during folding. This process is not accounted for in present algorithms for protein-structure prediction, which either deduce the possible positions of disulfide bonds only after the structure is formed or assume fixed disulfide bonds during the course of simulated folding. In this work, the conformational space annealing (CSA) method and the UNRES united-residue force field were extended to treat dynamic formation of disulfide bonds. A harmonic potential is imposed on the distance between disulfide-bonded cysteine side-chain centroids to describe the energetics of bond distortion and an energy gain of 5.5 kcal/mol is added for disulfide-bond formation. Formation, breaking and rearrangement of disulfide bonds are included in the CSA search by introducing appropriate operations; the search can also be carried out with a fixed disulfide-bond arrangement. The algorithm was applied to four proteins: 1EI0 (alpha), 1NKL (alpha), 1L1I (beta-helix) and 1ED0 (alpha + beta). For 1EI0, a low-energy structure with correct fold was obtained both in the runs without and with disulfide bonds; however, it was obtained as the lowest in energy only with the native disulfide-bond arrangement. For the other proteins studied, structures with the correct fold were obtained as the lowest (1NKL and 1L1I) or low-energy structures (1ED0) only in runs with disulfide bonds, although the final disulfide-bond arrangement was non-native. The results demonstrate that, by including the possibility of formation of disulfide bonds, the predictive power of the UNRES force field is enhanced, even though the disulfide-bond potential introduced here rarely produces disulfide bonds in native positions. To the best of our knowledge, this is the first algorithm for energy-based prediction of the structure of disulfide-bonded proteins without any assumption as to the positions of native disulfides or human intervention. Directions for improving the potentials and the search method are suggested.