Photometric defocus observations of transiting extrasolar planets

We have carried out photometric follow-up observations of bright transiting extrasolar planets using the CbNUOJ 0.6m telescope. We have tested the possibility of obtaining high photometric precision by applying the telescope defocus technique allowing the use of several hundred seconds in exposure time for a single measurement. We demonstrate that this technique is capable of obtaining a root-mean-square scatter of order sub-millimagnitude over several hours for a V $\sim$ 10 host star typical for transiting planets detected from ground-based survey facilities. We compare our results with transit observations with the telescope operated in in-focus mode. High photometric precision is obtained due to the collection of a larger amount of photons resulting in a higher signal compared to other random and systematic noise sources. Accurate telescope tracking is likely to further contribute to lowering systematic noise by probing the same pixels on the CCD. Furthermore, a longer exposure time helps reducing the effect of scintillation noise which otherwise has a significant effect for small-aperture telescopes operated in in-focus mode. Finally we present the results of modelling four light-curves for which a root-mean-square scatter of 0.70 to 2.3 milli-magnitudes have been achieved.Comment: 12 pages, 11 figures, 5 tables. Submitted to Journal of Astronomy and Space Sciences (JASS

The Analysis of Mark-recapture Data with Individual Heterogeneity via the H-likelihood

Mark-recapture methods play a key role in ecological studies monitoring wild animal populations. One consideration in analyzing mark-recapture data is individual variation in the detection rate. Classical methods for modelling heterogeneity require numerical integration and may be computationally intensive. This thesis presents a novel approach based on the h-likelihood to remedy such difficulties by avoiding numerical integration. First, I present the h-likelihood approach for fitting the fundamental model describing individual heterogeneity in mark-recapture studies. The conditional likelihood approach allows the model to be regarded as a generalized linear mixed model (GLMM). I construct the h-likelihood for the model in the context of this GLMM. The population size is estimated via the Horvitz-Thompson estimator. Second, I extend my approach to fit advanced models accounting for individual heterogeneity along with variation over time and individuals’ trap responses. The conditional likelihood approach enables these models to be treated as vector GLMMs. The approach from the first project is adapted to fit these models with multi-dimensional response variables. The Horvitz-Thompson estimator is again employed to estimate the population size. Finally, I develop the h-likelihood approach to fit more flexible models describing individual heterogeneity. As standard models assume a linear relationship, I apply the structure of generalized additive models through B-spline, which can be considered as a GLMM with the conditional likelihood penalized for roughness. Again, I apply the h-likelihood to fit this model and to estimate the population size using the Horvitz-Thompson estimator

Combined DNA Methylation and Gastric Microbiome Marker Predicts Helicobacter pylori-Negative Gastric Cancer.

BACKGROUND/AIMS: While DNA methylation and gastric microbiome are each associated with gastric cancer (GC), their combined role in predicting GC remains unclear. This study investigated the potential of a combined DNA methylation and gastric microbiome signature to predict Helicobacter pylori-negative GC. METHODS: In this case-control study, we conducted quantitative methylation-specific polymerase chain reaction to measure the methylation levels of DKK3, SFRP1, EMX1, NKX6-1, MIR124-3, and TWIST1 in the gastric mucosa from 75 H. pylori-negative patients, including chronic gastritis (CG), intestinal metaplasia (IM), and GC. A combined analysis of DNA methylation and gastric microbiome, using 16S rRNA gene sequencing, was performed in 30 of 75 patients. RESULTS: The methylation levels of DKK3, SFRP1, EMX1, MIR124-3, and TWIST1 were significantly higher in patients with GC than in controls (all q<0.05). MIR124-3 and TWIST1 methylation levels were higher in patients with IM than those with CG and also in those with GC than in those with IM (all q<0.05). A higher methylation level of TWIST1 was an independent predictor for H. pylori-negative GC after adjusting for age, sex, and atrophy (odds ratio [OR], 15.15; 95% confidence interval [CI], 1.58 to 145.46; p=0.018). The combination of TWIST1 methylation and GC microbiome index (a microbiome marker) was significantly associated with H. pylori-negative GC after adjusting for age, sex, and atrophy (OR, 50.00; 95% CI, 1.69 to 1,476; p=0.024). CONCLUSIONS: The combination of TWIST1 methylation and GC microbiome index may offer potential as a biomarker for predicting H. pylori-negative GC

On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models

We consider estimating the parametric components of semi-parametric multiple index models in a high-dimensional and non-Gaussian setting. Such models form a rich class of non-linear models with applications to signal processing, machine learning and statistics. Our estimators leverage the score function based first and second-order Stein's identities and do not require the covariates to satisfy Gaussian or elliptical symmetry assumptions common in the literature. Moreover, to handle score functions and responses that are heavy-tailed, our estimators are constructed via carefully thresholding their empirical counterparts. We show that our estimator achieves near-optimal statistical rate of convergence in several settings. We supplement our theoretical results via simulation experiments that confirm the theory

dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble

Traditional regression models, including generalized linear mixed models, focus on understanding the deterministic factors that affect the mean of a response variable. Many biological studies seek to understand non-deterministic patterns in the variance or dispersion of a phenotypic or ecological response variable. We describe a new R package, dalmatian, that provides methods for fitting double hierarchical generalized linear models incorporating fixed and random predictors of both the mean and variance. Models are fit via Markov chain Monte Carlo sampling implemented in either JAGS or nimble and the package provides simple functions for monitoring the sampler and summarizing the results. We illustrate these functions through an application to data on food delivery by breeding pied flycatchers (Ficedula hypoleuca). Our intent is that this package makes it easier for practitioners to implement these models without having to learn the intricacies of Markov chain Monte Carlo methods

Two distinct red giant branch populations in the globular cluster NGC 2419 as tracers of a merger event in the Milky Way

Recent spectroscopic observations of the outer halo globular cluster (GC) NGC 2419 show that it is unique among GCs, in terms of chemical abundance patterns, and some suggest that it was originated in the nucleus of a dwarf galaxy. Here we show, from the Subaru narrow-band photometry employing a calcium filter, that the red giant-branch (RGB) of this GC is split into two distinct subpopulations. Comparison with spectroscopy has confirmed that the redder RGB stars in the $hk$[=(Ca$-b)-(b-y)$] index are enhanced in [Ca/H] by $\sim$0.2 dex compared to the bluer RGB stars. Our population model further indicates that the calcium-rich second generation stars are also enhanced in helium abundance by a large amount ($\Delta$Y = 0.19). Our photometry, together with the results for other massive GCs (e.g., $\omega$ Cen, M22, and NGC 1851), suggests that the discrete distribution of RGB stars in the $hk$ index might be a universal characteristic of this growing group of peculiar GCs. The planned narrow-band calcium photometry for the Local Group dwarf galaxies would help to establish an empirical connection between these GCs and the primordial building blocks in the hierarchical merging paradigm of galaxy formation.Comment: 4 pages, 4 figures, 1 table, accepted for the publication in ApJ