Molecular mechanisms of epigenetic variation in plants

Natural variation is defined as the phenotypic variation caused by spontaneous mutations. In general, mutations are associated with changes of nucleotide sequence, and many mutations in genes that can cause changes in plant development have been identified. Epigenetic change, which does not involve alteration to the nucleotide sequence, can also cause changes in gene activity by changing the structure of chromatin through DNA methylation or histone modifications. Now there is evidence based on induced or spontaneous mutants that epigenetic changes can cause altering plant phenotypes. Epigenetic changes have occurred frequently in plants, and some are heritable or metastable causing variation in epigenetic status within or between species. Therefore, heritable epigenetic variation as well as genetic variation has the potential to drive natural variation.     

LES analysis of flow and turbulent mixing in an unsteady jet

The flow and mixing process of unsteady jets are fundamentally analyzed by large eddy simulations. The effects of nozzle velocity and turbulence intensity on the turbulent eddy structure and mixing process between the nozzle fluid and ambient fluid were investigated. The results show that a toroidal‐shaped vortex, which emerges around the jet tip, primarily accelerates the entraining flow. Also, increasing the turbulence intensity in the nozzle encourages mixing in the jet without changing the jet‐contour. Furthermore, when the rise‐up time of the initial nozzle velocity is elongated, turbulent mixing is suppressed. © 2007 Wiley Periodicals, Inc. Heat Trans Asian Res, 36(5): 303–313, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/htj.20158     

Toward unsupervised adaptation of LDA for brain-computer interfaces

There is a step of significant difficulty experienced by brain-computer interface (BCI) users when going from the calibration recording to the feedback application. This effect has been previously studied and a supervised adaptation solution has been proposed. In this paper, we suggest a simple unsupervised adaptation method of the linear discriminant analysis (LDA) classifier that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks. For this, we first introduce three types of adaptation procedures and investigate them in an offline study with 19 datasets. Then, we select one of the proposed methods and analyze it further. The chosen classifier is offline tested in data from 80 healthy users and four high spinal cord injury patients. Finally, for the first time in BCI literature, we apply this unsupervised classifier in online experiments. Additionally, we show that its performance is significantly better than the state-of-the-art supervised approach.     

Trading variance reduction with unbiasedness: the regularized subspace information criterion for robust model selection in kernel regression

A well-known result by Stein (1956) shows that in particular situations, biased estimators can yield better parameter estimates than their generally preferred unbiased counterparts. This letter follows the same spirit, as we will stabilize the unbiased generalization error estimates by regularization and finally obtain more robust model selection criteria for learning. We trade a small bias against a larger variance reduction, which has the beneficial effect of being more precise on a single training set. We focus on the subspace information criterion (SIC), which is an unbiased estimator of the expected generalization error measured by the reproducing kernel Hilbert space norm. SIC can be applied to the kernel regression, and it was shown in earlier experiments that a small regularization of SIC has a stabilization effect. However, it remained open how to appropriately determine the degree of regularization in SIC. In this article, we derive an unbiased estimator of the expected squared error, between SIC and the expected generalization error and propose determining the degree of regularization of SIC such that the estimator of the expected squared error is minimized. Computer simulations with artificial and real data sets illustrate that the proposed method works effectively for improving the precision of SIC, especially in the high-noise-level cases. We furthermore compare the proposed method to the original SIC, the cross-validation, and an empirical Bayesian method in ridge parameter selection, with good results.     

Effect of polymerization reaction inhibitor on mechanical properties and surface reactivity of bioactive bone cement

We introduced an inhibitor to the polymerization reaction of bioactive bone cement (AWC) consisting of MgO-CaO-SiO2-P2O5-CaF2 apatite and wollastonite containing glass-ceramic powder and bisphenol-alpha-glycidyl methacrylate based resin, together with an increased amount of accelerator but without any prolongation of its setting time in order to improve the degree of polymerization and decrease the amount of incompletely polymerized monomers on the cement surface. A comparison was made between the AWC containing the inhibitor [AWC(I+)] and the AWC without it [AWC(I-)] with regard to setting parameters, mechanical properties, and surface reactivity in vitro and in vivo. The proportion of glass-ceramic powder added to the AWC was 70% (w/w). The total amount of heat generation and the peak temperature of the AWC(I+) during polymerization were slightly greater than those of the AWC(I-). The mechanical strength of AWC(I+) was higher than that of the AWC(I-) under wet conditions. In simulated body fluid, the width of the Ca-P rich layer on the surface of the AWC(I+) was less than that on the AWC(I-) after 28 days of immersion, although the rate of apatite formation on the top surface of the AWC(I+) was almost identical to that on the AWC(I-) surface. Histological examination using rat tibiae up to 26 weeks revealed that the bioactivity of the AWC(I+) was equivalent to that of the AWC(I-). Scanning electron microscopy and energy-dispersive X-ray microanalysis demonstrated that the Ca-P rich layer in the AWC(I+) was significantly narrower than that in the AWC(I-) at the same time points. These results indicate that introduction of the inhibitor improved the mechanical properties of the AWC and made the Ca-P rich layer narrower, but it had no adverse effect on bioactivity.     

CFD analysis of a high‐speed unsteady jet

The flow of a high‐speed unsteady jet is analyzed using computational fluid dynamics for an incompressible flow with the k–ε turbulence model. The pseudo‐nozzle concept is applied to the inlet condition with a large pressure gradient. The results show that the time history of the jet development agrees with the experimental data for methane and hydrogen fuels. In addition, the effect of the injection condition on the development of the jet tip is well described with this model. Furthermore, the effects of inlet conditions of the turbulence intensity and scale on the flow are investigated. © 2006 Wiley Periodicals, Inc. Heat Trans Asian Res, 36(1): 1– 12, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/htj.20141     

Particle deposition and removal in wet cleaning processes for ULSImanufacturing

Particle deposition on wafer surfaces in solutions can be described by the well-documented principles of colloid science. Particle concentration, solution pH, and ionic strength in solutions are all important factors which determine the number of particles which deposit on wafer surfaces immersed in liquids. maximum particle deposition is observed in high ionic strength acidic solutions and is reduced as solution pH increase. Particle removal efficiencies in various solutions were also investigated; NH₄OH-H₂O₂-H ₂O solutions were optimized in NH₄OH content around the ratio of 0.05:1.15 (0.05 part NH₄OH, 1 part H₂O₂, 5 parts H₂O). Wafer damage as measured by surface micro-roughness was not increased during NH₄ OH-H₂O₂-H₂O treatment using this ratio     

Mechanical and biological properties of two types of bioactive bone cements containing MgO-CaO-SiO2-P2O5-CaF2 glass and glass-ceramic powder

The electrostatic steering of charged ligands toward the active site of Torpedo californica acetylcholinesterase is investigated by Brownian dynamics simulations of wild type enzyme and several mutated forms, in which some normally charged residues are neutralized. The simulations reveal that the total ligand influx through a surface of 42 A radius centered in the enzyme monomer and separated from the protein surface by 1-14 A is not significantly influenced by electrostatic interactions. Electrostatic effects are visible for encounters with a surface of 32 A radius, which is partially hidden inside the protein, but mostly within the solvent. A clear accumulation of encounter events for that sphere is observed in the area directly above the entrance to the active site gorge. In this area, the encounter events are increased by 40% compared to the case of a neutral ligand. However, the differences among the encounter rates for the various mutants considered here are not pronounced, all rate constants being within +/- 10% of the average value. The enzyme charge distribution becomes more important as the charged ligand moves toward the bottom of the gorge, where the active site is located. We show that neither the enzyme's total charge, nor its dipole moment, fully account for the electrostatic steering of ligand to the active site. Higher moments of the enzyme's charge distribution are also important. However, for a series of mutations for which the direction of the enzyme dipole moment is constant within a few degrees, one observes a gradual decrease in the diffusional encounter rate constant with the number of neutralized residues. On the other hand, for other mutants that change the direction of the dipole moment from that of the wild type, the calculated encounter rate constants can be very close to that of the wild type. The present work yields two new insights to the kinetics of acetylcholinesterase. First, evolution appears to have built a redundant electrostatic steering capability into this important enzyme through the overall distribution of its thousands of partially charged atoms. And second, roughly half of the rate enhancement due to electrostatics arises from steering of the substrate outside the enzyme; the other half of the rate enhancement arises from improved trapping of the substrate after it has entered the gorge. The computational results reproduce qualitatively, and help to rationalize, many surprising experimental results obtained recently for human acetylcholinesterase.