The relationship between radio power at 22 and 43 GHz and black hole properties of AGN in elliptical galaxies

We investigate the relationship between radio power and properties related to active galactic nuclei (AGNs). Radio power at 1.4 or 5 GHz, which has been used in many studies, can be affected by synchrotron self-absorption and free-free absorption in a dense region. On the other hand, these absorption effects get smaller at higher frequencies. Thus, we performed simultaneous observations at 22 and 43 GHz using the Korean VLBI Network (KVN) radio telescope based on a sample of 305 AGN candidates residing in elliptical galaxies from the overlap between the Sloan Digital Sky Survey (SDSS) Data Release 7 and Faint Images of the Radio Sky at Twenty-Centimeters (FIRST). About 37% and 22% of the galaxies are detected at 22 and 43 GHz, respectively. Assuming no flux variability between the FIRST and KVN observation, spectral indices were derived from FIRST and KVN data and we found that over 70% of the detected galaxies have flat or inverted spectra, implying the presence of optically thick compact regions near the centres of the galaxies. Core radio power does not show a clear dependence on black hole mass at either low (1.4 GHz) or high (22 and 43 GHz) frequencies. However, we found that the luminosity of the [OIII] $\lambda$5007 emission line and the Eddington ratio correlate with radio power more closely at high frequencies than at low frequencies. This suggests that radio observation at high frequencies can be an appropriate tool for unveiling the innermost region. In addition, the luminosity of the [OIII] $\lambda$5007 emission line and the Eddington ratio can be used as a tracer of AGN activity. Our study suggests a causal connection between high frequency radio power and optical properties of AGNs.Comment: 14 pages, 13 figures, 5 tables, Accepted for publication in A&

Monte Carlo Bayesian Reinforcement Learning

Bayesian reinforcement learning (BRL) encodes prior knowledge of the world in a model and represents uncertainty in model parameters by maintaining a probability distribution over them. This paper presents Monte Carlo BRL (MC-BRL), a simple and general approach to BRL. MC-BRL samples a priori a finite set of hypotheses for the model parameter values and forms a discrete partially observable Markov decision process (POMDP) whose state space is a cross product of the state space for the reinforcement learning task and the sampled model parameter space. The POMDP does not require conjugate distributions for belief representation, as earlier works do, and can be solved relatively easily with point-based approximation algorithms. MC-BRL naturally handles both fully and partially observable worlds. Theoretical and experimental results show that the discrete POMDP approximates the underlying BRL task well with guaranteed performance.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

rMAPS: RNA map analysis and plotting server for alternative exon regulation.

RNA-binding proteins (RBPs) play a critical role in the regulation of alternative splicing (AS), a prevalent mechanism for generating transcriptomic and proteomic diversity in eukaryotic cells. Studies have shown that AS can be regulated by RBPs in a binding-site-position dependent manner. Depending on where RBPs bind, splicing of an alternative exon can be enhanced or suppressed. Therefore, spatial analyses of RBP motifs and binding sites around alternative exons will help elucidate splicing regulation by RBPs. The development of high-throughput sequencing technologies has allowed transcriptome-wide analyses of AS and RBP-RNA interactions. Given a set of differentially regulated alternative exons obtained from RNA sequencing (RNA-seq) experiments, the rMAPS web server (http://rmaps.cecsresearch.org) performs motif analyses of RBPs in the vicinity of alternatively spliced exons and creates RNA maps that depict the spatial patterns of RBP motifs. Similarly, rMAPS can also perform spatial analyses of RBP-RNA binding sites identified by cross-linking immunoprecipitation sequencing (CLIP-seq) experiments. We anticipate rMAPS will be a useful tool for elucidating RBP regulation of alternative exon splicing using high-throughput sequencing data

Discover hidden splicing variations by mapping personal transcriptomes to personal genomes.

RNA-seq has become a popular technology for studying genetic variation of pre-mRNA alternative splicing. Commonly used RNA-seq aligners rely on the consensus splice site dinucleotide motifs to map reads across splice junctions. Consequently, genomic variants that create novel splice site dinucleotides may produce splice junction RNA-seq reads that cannot be mapped to the reference genome. We developed and evaluated an approach to identify 'hidden' splicing variations in personal transcriptomes, by mapping personal RNA-seq data to personal genomes. Computational analysis and experimental validation indicate that this approach identifies personal specific splice junctions at a low false positive rate. Applying this approach to an RNA-seq data set of 75 individuals, we identified 506 personal specific splice junctions, among which 437 were novel splice junctions not documented in current human transcript annotations. 94 splice junctions had splice site SNPs associated with GWAS signals of human traits and diseases. These involve genes whose splicing variations have been implicated in diseases (such as OAS1), as well as novel associations between alternative splicing and diseases (such as ICA1). Collectively, our work demonstrates that the personal genome approach to RNA-seq read alignment enables the discovery of a large but previously unknown catalog of splicing variations in human populations