Semimartingale decomposition of convex functions of continuous semimartingales by Brownian perturbation

In this note we prove that the local martingale part of a convex function f of a d-dimensional semimartingale X = M + A can be written in terms of an It^o stochastic integral \int H(X)dM, where H(x) is some particular measurable choice of subgradient of f at x, and M is the martingale part of X. This result was first proved by Bouleau in [2]. Here we present a new treatment of the problem. We first prove the result for X' = X + eB, e > 0, where B is a standard Brownian motion, and then pass to the limit as e tends to 0, using results in [1] and [4].Comment: 16 pages. Re-submitted to ESAIMPS December, 201

Multi-tissue transcriptome-wide association studies.

A transcriptome-wide association study (TWAS) attempts to identify disease associated genes by imputing gene expression into a genome-wide association study (GWAS) using an expression quantitative trait loci (eQTL) data set and then testing for associations with a trait of interest. Regulatory processes may be shared across related tissues and one natural extension of TWAS is harnessing cross-tissue correlation in gene expression to improve prediction accuracy. Here, we studied multi-tissue extensions of lasso regression and random forests (RF), joint lasso and RF-MTL (multi-task learning RF), respectively. We found that, on our chosen eQTL data set, multi-tissue methods were generally more accurate than their single-tissue counterparts, with RF-MTL performing the best. Simulations showed that these benefits generally translated into more associated genes identified, although highlighted that joint lasso had a tendency to erroneously identify genes in one tissue if there existed an eQTL signal for that gene in another. Applying the four methods to a type 1 diabetes GWAS, we found that multi-tissue methods found more unique associated genes for most of the tissues considered. We conclude that multi-tissue methods are competitive and, for some cell types, superior to single-tissue approaches and hold much promise for TWAS studies

X-ray Reflection Spectroscopy of the Black Hole GX 339-4: Exploring the Hard State with Unprecedented Sensitivity

We analyze {\it simultaneously} six composite {\it RXTE} spectra of GX 339--4 in the hard state comprising 77 million counts collected over 196 ks. The source spectra are ordered by luminosity and spanthe range 1.6\% to 17\% of the Eddington luminosity. Crucially, using our new tool {\tt pcacorr}, we re-calibrate the data to a precision of 0.1\%, an order of magnitude improvement over all earlier work. Using our advanced reflection model {\tt relxill}, we target the strong features in the component of emission reflected from the disk, namely, the relativistically-broadened Fe K emission line, the Fe K edge and the Compton hump. We report results for two joint fits to the six spectra: For the first fit, we fix the spin parameter to its maximal value ($a_*=0.998$) and allow the inner disk radius $R_{\rm in}$ to vary. Results include (i) precise measurements of $R_{\rm in}$, with evidence that the disk becomes slightly truncated at a few percent of Eddington; and (ii) an order-of-magnitude swing with luminosity in the high energy cutoff, which reaches $>890$ keV at our lowest luminosity. For the second fit, we make the standard assumption in estimating spin that the inner edge of the accretion disk is located at the innermost stable circular orbit ($R_\mathrm{in} = R_\mathrm{ISCO}$) and find $a_* = 0.95^{+0.03}_{-0.05}$ (90\% confidence, statistical). For both fits, and at the same level of statistical confidence, we estimate that the disk inclination is $i = 48\pm 1$ deg and that the Fe abundance is super-solar, $A_\mathrm{Fe} = 5\pm1$.Comment: Accepted for publication in ApJ, 20 pages, 13 figure

Variability in high-mass X-ray binaries

Strongly magnetized, accreting neutron stars show periodic and aperiodic variability over a wide range of time scales. By obtaining spectral and timing information on these different time scales, we can have a closer look into the physics of accretion close to the neutron star and the properties of the accreted material. One of the most prominent time scales is the strong pulsation, i.e., the rotation period of the neutron star itself. Over one rotation, our view of the accretion column and the X-ray producing region changes significantly. This allows us to sample different physical conditions within the column but at the same time requires that we have viewing-angle-resolved models to properly describe them. In wind-fed high-mass X-ray binaries, the main source of aperiodic variability is the clumpy stellar wind, which leads to changes in the accretion rate (i.e., luminosity) as well as absorption column. This variability allows us to study the behavior of the accretion column as a function of luminosity, as well as to investigate the structure and physical properties of the wind, which we can compare to winds in isolated stars.Comment: 6 pages, 4 figures, accepted for publication in Astronomische Nachrichten (proceedings of the XMM-Newton Workshop 2019

Simultaneous multiwavelength observations of V404 Cygni during its 2015 June outburst decay strengthen the case for an extremely energetic jet-base

We present results of multiband optical photometry of the black hole X-ray binary system V404 Cygni obtained using Wheaton College Observatory's 0.3m telescope, along with strictly simultaneous INTEGRAL and Swift observations during 2015 June 25.15--26.33 UT, and 2015 June 27.10--27.34 UT. These observations were made during the 2015 June outburst of the source when it was going through an epoch of violent activity in all wavelengths ranging from radio to $\gamma$-rays. The multiwavelength variability timescale favors a compact emission region, most likely originating in a jet outflow, for both observing epochs presented in this work. The simultaneous INTEGRAL/Imager on Board the Integral Satellite (IBIS) 20--40 keV light curve obtained during the June 27 observing run correlates very strongly with the optical light curve, with no detectable delay between the optical bands as well as between the optical and hard X-rays. The average slope of the dereddened spectral energy distribution was roughly flat between the $I_C$- and $V$-bands during the June 27 run, even though the optical and X-ray flux varied by $>$25$\times$ during the run, ruling out an irradiation origin for the optical and suggesting that the optically thick to optically thin jet synchrotron break during the observations was at a frequency larger than that of $V$-band, which is quite extreme for X-ray binaries. These observations suggest that the optical emission originated very close to the base of the jet. A strong H$\alpha$ emission line, probably originating in a quasi-spherical nebula around the source, also contributes significantly in the $R_C$-band. Our data, in conjunction with contemporaneous data at other wavelengths presented by other groups, strongly suggest that the jet-base was extremely compact and energetic during this phase of the outburst.Comment: 15 pages, 2 tables, 5 figures. Accepted for publication in Ap

Functional effects of variation in transcription factor binding highlight long-range gene regulation by epromoters.

Identifying DNA cis-regulatory modules (CRMs) that control the expression of specific genes is crucial for deciphering the logic of transcriptional control. Natural genetic variation can point to the possible gene regulatory function of specific sequences through their allelic associations with gene expression. However, comprehensive identification of causal regulatory sequences in brute-force association testing without incorporating prior knowledge is challenging due to limited statistical power and effects of linkage disequilibrium. Sequence variants affecting transcription factor (TF) binding at CRMs have a strong potential to influence gene regulatory function, which provides a motivation for prioritizing such variants in association testing. Here, we generate an atlas of CRMs showing predicted allelic variation in TF binding affinity in human lymphoblastoid cell lines and test their association with the expression of their putative target genes inferred from Promoter Capture Hi-C and immediate linear proximity. We reveal >1300 CRM TF-binding variants associated with target gene expression, the majority of them undetected with standard association testing. A large proportion of CRMs showing associations with the expression of genes they contact in 3D localize to the promoter regions of other genes, supporting the notion of 'epromoters': dual-action CRMs with promoter and distal enhancer activity

An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat

Abstract: In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called genome-wide association studies, are of the highest societal importance, as they are of central importance to medicine, crop-breeding, etc. We investigated three phenotype prediction problems: one simple and clean (yeast), and the other two complex and real-world (rice and wheat). We compared standard machine learning methods; elastic net, ridge regression, lasso regression, random forest, gradient boosting machines (GBM), and support vector machines (SVM), with two state-of-the-art classical statistical genetics methods; genomic BLUP and a two-step sequential method based on linear regression. Additionally, using the clean yeast data, we investigated how performance varied with the complexity of the biological mechanism, the amount of observational noise, the number of examples, the amount of missing data, and the use of different data representations. We found that for almost all the phenotypes considered, standard machine learning methods outperformed the methods from classical statistical genetics. On the yeast problem, the most successful method was GBM, followed by lasso regression, and the two statistical genetics methods; with greater mechanistic complexity GBM was best, while in simpler cases lasso was superior. In the wheat and rice studies the best two methods were SVM and BLUP. The most robust method in the presence of noise, missing data, etc. was random forests. The classical statistical genetics method of genomic BLUP was found to perform well on problems where there was population structure. This suggests that standard machine learning methods need to be refined to include population structure information when this is present. We conclude that the application of machine learning methods to phenotype prediction problems holds great promise, but that determining which methods is likely to perform well on any given problem is elusive and non-trivial

Vela X-1 as a laboratory for accretion in High-Mass X-ray Binaries

Vela X-1 is an eclipsing high mass X-ray binary (HMXB) consisting of a 283s accreting X-ray pulsar in a close orbit of 8.964 days around the B0.5Ib supergiant HD77581 at a distance of just 2.4 kpc. The system is considered a prototype of wind-accreting HMXB and it has been used as a baseline in different theoretical or modelling studies. We discuss the observational properties of the system and the use of the observational data as laboratory to test recent developments in modelling the accretion process in High-Mass X-ray Binaries (e.g., Sander et al. 2018; El Mellah et al. 2018), which range from detailed descriptions of the wind acceleration to modelling of the structure of the flow of matter close to the neutron star and its variations.Comment: 4 pages, 2 figures, proceedings of the 12th INTEGRAL conference "INTEGRAL looks AHEAD to Multimessenger astronomy" in Geneva (Switzerland) on 11-15 February 201