A FLEXIBLE PROCEDURE FOR POSITIVE–UNLABELED LEARNING & PERIODS ESTIMATION FOR MIRAS USING MULTI-BAND LIGHT CURVES AND INVERSE PERIOD-LUMINOSITY RELATIONS

This dissertation contains two independent projects: the first project develops a general methodology for solving the Positive–Unlabeled (PU) learning problem, and the second project creates a hierarchical Bayesian model that solves a specific astronomical problem – periods estimation for Miras. In the first project, we deal with the PU learning which considers two samples, a positive set P with observations from only one class and an unlabeled set U with observations from two classes. The goal is to classify observations in U. Class mixture proportion estimation (MPE) in U is a key step in PU learning. Blanchard et al. (2010) show that MPE in PU learning is a generalization of the problem of estimating the proportion of true null hypotheses in multiple testing problems. Motivated by this idea, we propose a flexible framework: fistly reduce the problem to one dimension via construction of a probabilistic classifier trained on the P and U data sets, and then apply a one–dimensional mixture proportion method to the observation class probabilities. The flexibility of this framework lies in the freedom to choose the classifier and the one–dimensional MPE method. Using this framework, we propose two mixture proportion estimators: one adapts ROC technique (Storey, 2002; Scott, 2015), and another adapts isotonic regression (Patra and Sen, 2015). Theoretically we prove the consistency of these two estimators. Empirically we demonstrate that our proposed estimators have competitive performance on simulated waveform data and a protein signaling problem. And the implementations of our estimators are tuning parameter free. The second project of this dissertation is to present an inverse Period-Luminosity relation (PLR) enhanced multi-band semi-parametric model (SP3) to efficiently recover periods for quasiperiodic variable stars such as Miras. Mira variables are promising distance indicators because the oxygen-rich type Miras follow a tight PLR in the near-infrared. However, the Mira light curves are quasi-periodic, making their period estimation significantly challenging. In recent few years, several methods have been developed to estimate period for Miras. He et al. (2016) develop a single-band semi-parametric model based on the Gaussian processes tool. Yuan et al. (2018) extend the above model to a multi-band case. These two models are designed for fitting observations for single Mira (single-band or multi-band) and do not use the PLR. To borrow the strength across light curves, our proposed SP3 model uses inverse Period-Luminosity relation (iPLR) to adaptively feed a frequency prior to each light curve. This model outperforms existing methods in various simulated data sets

Mid-infrared variability of changing-look AGN

It is known that some active galactic nuclei (AGNs) transited from type 1 to type 2 or vice versa. There are two explanations for the so-called changing look AGNs: one is the dramatic change of the obscuration along the line-of-sight, the other is the variation of accretion rate. In this paper, we report the detection of large amplitude variations in the mid-infrared luminosity during the transitions in 10 changing look AGNs using WISE and newly released NEOWISE-R data. The mid-infrared light curves of 10 objects echoes the variability in the optical band with a time lag expected for dust reprocessing. The large variability amplitude is inconsistent with the scenario of varying obscuration, rather supports the scheme of dramatic change in the accretion rate.Comment: Published by ApjL, 7 pages, 3 figures, 2 table

Long-Term Decline of the Mid-Infrared Emission of Normal Galaxies: Dust Echo of Tidal Disruption Flare?

We report the discovery of a sample of 19 low redshift (z<0.22) spectroscopically non-Seyfert galaxies that show slow declining mid-infrared (MIR) light-curves (LCs), similar to those of tidal disruption event (TDE) candidates with extreme coronal lines. Two sources also showed a relatively fast rising MIR LCs. They consist of 61% sample of the WISE MIR variable non-Seyfert galaxies with SDSS spectra. In a comparison sample of optically selected Seyfert galaxies, the fraction of sources with such a LC is only 15%. After rejecting 5 plausible obscured Seyfert galaxies with red MIR colours, remaining 14 objects are studied in detail in this paper. We fit the declining part of LC with an exponential law, and the decay time is typically one year. The observed peak MIR luminosities ($\nu L_\nu$) after subtracting host galaxies are in the range of a few 10^42 to 10^44 erg~s^-1 with a median of 5x10^43 erg~s^-1 in the W2 band. The black hole masses distribute in a wide range with more than half in between 10^7 to 10^8 ~M_sun, but significantly different from that of optical/UV selected TDEs. Furthermore, MIR luminosities are correlated with black hole masses, the stellar mass or luminosity of their host bulges. Most galaxies in the sample are red and luminous with an absolute magnitude of r between -20 to -23. We estimate the rate of event about 10^-4 gal^-1~yr^-1 among luminous red galaxies. We discuss several possibilities for the variable infrared sources, and conclude that most likely, they are caused by short sporadic fueling to the supermassive black holes via either the instability of accretion flows or tidal disruption of stars.Comment: 32 pages, 12 figures. Accepted to MNRA

Simultaneous inference of periods and period-luminosity relations for Mira variable stars

The Period--Luminosity relation (PLR) of Mira variable stars is an important tool to determine astronomical distances. The common approach of estimating the PLR is a two-step procedure that first estimates the Mira periods and then runs a linear regression of magnitude on log period. When the light curves are sparse and noisy, the accuracy of period estimation decreases and can suffer from aliasing effects. Some methods improve accuracy by incorporating complex model structures at the expense of significant computational costs. Another drawback of existing methods is that they only provide point estimation without proper estimation of uncertainty. To overcome these challenges, we develop a hierarchical Bayesian model that simultaneously models the quasi-periodic variations for a collection of Mira light curves while estimating their common PLR. By borrowing strengths through the PLR, our method automatically reduces the aliasing effect, improves the accuracy of period estimation, and is capable of characterizing the estimation uncertainty. We develop a scalable stochastic variational inference algorithm for computation that can effectively deal with the multimodal posterior of period. The effectiveness of the proposed method is demonstrated through simulations, and an application to observations of Miras in the Local Group galaxy M33. Without using ad-hoc period correction tricks, our method achieves a distance estimate of M33 that is consistent with published work. Our method also shows superior robustness to downsampling of the light curves

Vascular niche IL-6 induces alternative macrophage activation in glioblastoma through HIF-2α.

Spatiotemporal regulation of tumor immunity remains largely unexplored. Here we identify a vascular niche that controls alternative macrophage activation in glioblastoma (GBM). We show that tumor-promoting macrophages are spatially proximate to GBM-associated endothelial cells (ECs), permissive for angiocrine-induced macrophage polarization. We identify ECs as one of the major sources for interleukin-6 (IL-6) expression in GBM microenvironment. Furthermore, we reveal that colony-stimulating factor-1 and angiocrine IL-6 induce robust arginase-1 expression and macrophage alternative activation, mediated through peroxisome proliferator-activated receptor-γ-dependent transcriptional activation of hypoxia-inducible factor-2α. Finally, utilizing a genetic murine GBM model, we show that EC-specific knockout of IL-6 inhibits macrophage alternative activation and improves survival in the GBM-bearing mice. These findings illustrate a vascular niche-dependent mechanism for alternative macrophage activation and cancer progression, and suggest that targeting endothelial IL-6 may offer a selective and efficient therapeutic strategy for GBM, and possibly other solid malignant tumors

Distance-dependent plasmon-enhanced fluorescence of upconversion nanoparticles using polyelectrolyte multilayers as tunable spacers

Lanthanide-doped upconversion nanoparticles (UCNPs) have attracted widespread interests in bioapplications due to their unique optical properties by converting near infrared excitation to visible emission. However, relatively low quantum yield prompts a need for developing methods for fluorescence enhancement. Plasmon nanostructures are known to efficiently enhance fluorescence of the surrounding fluorophores by acting as nanoantennae to focus electric field into nano-volume. Here, we reported a novel plasmon-enhanced fluorescence system in which the distance between UCNPs and nanoantennae (gold nanorods, AuNRs) was precisely tuned by using layer-by-layer assembled polyelectrolyte multilayers as spacers. By modulating the aspect ratio of AuNRs, localized surface plasmon resonance (LSPR) wavelength at 980 nm was obtained, matching the native excitation of UCNPs resulting in maximum enhancement of 22.6-fold with 8 nm spacer thickness. These findings provide a unique platform for exploring hybrid nanostructures composed of UCNPs and plasmonic nanostructures in bioimaging applications