Preparation and Characterization of Ti(2)O(3) Films Deposited on Sapphire Substrate by Activated Reactive Evaporation Method

(001)-oriented Ti(2)O(3) films were epitaxially grown on a(001)-face of sapphire single-crystalline substrate by an activated reactive evaporation method. The formation ranges of stoichiometric and nonstoichiometric Ti(2)O(3) films were determined as a function of the substrate temperature (Ts), the oxygen pressure (Po(2)) and the deposition rate. Stoichiometric Ti(2)O(3) films were grown at Ts≧673K under Po(2)≧1.0×10(-4)Torr, which showed the metal-insulator transition with a sharp change in electrical resistivity from 3.5×10(-2) to 2.6×10(-3)Ωcm at 361K. Nonstoichiometric films prepared under less oxidized conditions did not exhibit the transition. The nonstoichiometry of the Ti(2)O(3)films was discussed in terms of excess Ti ions

Intercellular exchange of Wnt ligands reduces cell population heterogeneity during embryogenesis

Wnt signaling is required to maintain bipotent progenitors for neural and paraxial mesoderm cells, the neuromesodermal progenitor (NMP) cells that reside in the epiblast and tailbud. Since epiblast/tailbud cells receive Wnt ligands produced by one another, this exchange may average out the heterogeneity of Wnt signaling levels among these cells. Here, we examined this possibility by replacing endogenous Wnt3a with a receptor-fused form that activates signaling in producing cells, but not in neighboring cells. Mutant mouse embryos show a unique phenotype in which maintenance of many NMP cells is impaired, although some cells persist for long periods. The epiblast cell population of these embryos increases heterogeneity in Wnt signaling levels as embryogenesis progresses and are sensitive to retinoic acid, an endogenous antagonist of NMP maintenance. Thus, mutual intercellular exchange of Wnt ligands in the epiblast cell population reduces heterogeneity and achieves robustness to environmental stress

Generalized equilibria for color-gradient lattice Boltzmann model based on higher-order Hermite polynomials: A simplified implementation with central moments

We propose generalized equilibria of a three-dimensional color-gradient lattice Boltzmann model for two-component two-phase flows using higher-order Hermite polynomials. Although the resulting equilibrium distribution function, which includes a sixth-order term on the velocity, is computationally cumbersome, its equilibrium central moments (CMs) are velocity-independent and have a simplified form. Numerical experiments show that our approach, as in Wen et al. [{Phys. Rev. E \textbf{100}, 023301 (2019)}] who consider terms up to third order, improves the Galilean invariance compared to that of the conventional approach. Dynamic problems can be solved with high accuracy at a density ratio of 10; however, the accuracy is still limited to a density ratio of 1000. For lower density ratios, the generalized equilibria benefit from the CM-based multiple-relaxation-time model, especially at very high Reynolds numbers, significantly improving the numerical stability.Comment: 22 pages, 8 figure

A generalized linear model for decomposing cis-regulatory, parent-of-origin, and maternal effects on allele-specific gene expression

Joint quantification of genetic and epigenetic effects on gene expression is important for understanding the establishment of complex gene regulation systems in living organisms. In particular, genomic imprinting and maternal effects play important roles in the developmental process of mammals and flowering plants. However, the influence of these effects on gene expression are difficult to quantify because they act simultaneously with cis-regulatory mutations. Here we propose a simple method to decompose cis-regulatory (i.e., allelic genotype, AG), genomic imprinting (i.e., parent-of-origin, PO), and maternal (i.e., maternal genotype, MG) effects on allele-specific gene expression using RNA-seq data obtained from reciprocal crosses. We evaluated the efficiency of method using a simulated dataset and applied the method to whole-body Drosophila and mouse trophoblast stem cell (TSC) and liver RNA-seq data. Consistent with previous studies, we found little evidence of PO and MG effects in adult Drosophila samples. In contrast, we identified dozens and hundreds of mouse genes with significant PO and MG effects, respectively. Interestingly, a similar number of genes with significant PO effect were detect in mouse TSCs and livers, whereas more genes with significant MG effect were observed in livers. Further application of this method will clarify how these three effects influence gene expression levels in different tissues and developmental stages, and provide novel insight into the evolution of gene expression regulation.Comment: 27 pages, 3 figures, 2 tabl

Dark Quest. I. Fast and Accurate Emulation of Halo Clustering Statistics and Its Application to Galaxy Clustering

We perform an ensemble of $N$-body simulations with $2048^3$ particles for 101 flat $w$CDM cosmological models sampled based on a maximin-distance Sliced Latin Hypercube Design. By using the halo catalogs extracted at multiple redshifts in the range of $z=[0,1.48]$, we develop Dark Emulator, which enables fast and accurate computations of the halo mass function, halo-matter cross-correlation, and halo auto-correlation as a function of halo masses, redshift, separations and cosmological models, based on the Principal Component Analysis and the Gaussian Process Regression for the large-dimensional input and output data vector. We assess the performance of the emulator using a validation set of $N$-body simulations that are not used in training the emulator. We show that, for typical halos hosting CMASS galaxies in the Sloan Digital Sky Survey, the emulator predicts the halo-matter cross correlation, relevant for galaxy-galaxy weak lensing, with an accuracy better than $2\%$ and the halo auto-correlation, relevant for galaxy clustering correlation, with an accuracy better than $4\%$. We give several demonstrations of the emulator. It can be used to study properties of halo mass density profiles such as the mass-concentration relation and splashback radius for different cosmologies. The emulator outputs can be combined with an analytical prescription of halo-galaxy connection such as the halo occupation distribution at the equation level, instead of using the mock catalogs, to make accurate predictions of galaxy clustering statistics such as the galaxy-galaxy weak lensing and the projected correlation function for any model within the $w$CDM cosmologies, in a few CPU seconds.Comment: 46 pages, 47 figures; version accepted for publication in Ap

Exploring faint white dwarfs and the luminosity function with Subaru HSC and SDSS in Stripe 82

We present 4,987 white dwarf (WD) candidates selected from matched stars between the multi-band imaging datasets of Subaru Hyper Suprime-Cam (HSC) Survey and SDSS in the Stripe82 region covering about 165 deg$^2$. We first select WD candidates from the "reduced proper motion" diagram that is obtained by combining the apparent magnitude in the range $i=19$ -- 24 and the proper motion measured by comparing the astrometric positions of each object between the two datasets over about 14 yr time baseline. We refine the WD candidates by fitting blackbody and template WD atmosphere models to HSC photometries for each candidate, enabling the estimation of photometric distance and tangential velocity ($v_{\rm t}$) with respect to the Sun. The deep HSC data allows us to identify low-temperature ($<4000$ K) and faint WD candidates down to absolute magnitude, $M_{\rm bol}\simeq 17$. We evaluate the selection function of our WD candidates using the mock catalogue for spatial and kinematic distributions of WDs in the (thin and thick) disc and halo regions based on the standard Milky Way model. We construct the samples of disc and halo WD candidates by selecting WDs with the cuts of tangential velocity, $40<v_{\rm t}/[{\rm km}~{\rm s}^{-1}]<80$ and $200<v_{\rm t}/[{\rm km}~{\rm s}^{-1}]<500$, respectively. The total number densities of the disc and halo WDs are $(9.45 \pm 0.94) \times 10^{-3}$ pc$^{-3}$ and $(4.20 \pm 1.74) \times 10^{-4}$ pc$^{-3}$, respectively. Our LFs extend down to fainter absolute magnitudes compared with previous work. The faint WDs could represent the oldest generation of building blocks in the tens of billions of years of our Milky Way's assembly history.Comment: 18 pages, 18 figures, submitted to MNRA

Simulations of Wide-Field Weak Lensing Surveys I: Basic Statistics and Non-Gaussian Effects

We study the lensing convergence power spectrum and its covariance for a standard LCDM cosmology. We run 400 cosmological N-body simulations and use the outputs to perform a total of 1000 independent ray-tracing simulations. We compare the simulation results with analytic model predictions. The semi-analytic model based on Smith et al.(2003) fitting formula underestimates the convergence power by ~30% at arc-minute angular scales. For the convergence power spectrum covariance, the halo model reproduces the simulation results remarkably well over a wide range of angular scales and source redshifts. The dominant contribution at small angular scales comes from the sample variance due to the number fluctuations of halos in a finite survey volume. The signal-to-noise ratio for the convergence power spectrum is degraded by the non-Gaussian covariances by up to a factor 5 for a weak lensing survey to z_s ~1. The probability distribution of the convergence power spectrum estimators, among the realizations, is well approximated by a chi-square distribution with broadened variance given by the non-Gaussian covariance, but has a larger positive tail. The skewness and kurtosis have non-negligible values especially for a shallow survey. We argue that a prior knowledge on the full distribution may be needed to obtain an unbiased estimate on the ensemble averaged band power at each angular scale from a finite volume survey.Comment: 11 pages, 11 figures. Accepted for publication in the Astrophysical Journal. Corrected typo in the equation of survey window function below Equation (18). The results unchange