Classification and Spectral Evolution of Outbursts of Aql X-1

We present a broad classification of all outbursts detected with the All-Sky Monitor (ASM) on the Rossi X-Ray Timing Explorer (RXTE) and the Monitor of All Sky X-Ray Image (MAXI) of Aql X-1. We identify three types of outbursts; long-high, medium-low, and short-low, based on the duration and maximum flux. We analyse the trends in the "phase-space" of flux-derivative versus flux to demonstrate the differences in the three identified outburst types. We present a spectral analysis of the observations of Aql X-1 performed by the Proportional Counter Array (PCA) onboard RXTE during the 2000 and 2011 outbursts of the long-high class and the 2010 outburst of the medium-low class. We model the source spectrum with a hybrid thermal/non-thermal hot plasma emission model (EQPAIR in XSPEC, Coppi 2000) together with a Gaussian component to model the Fe K_alpha emission line. We construct time histories of the source flux, the optical depth of the corona (tau), the seed photon temperature (kT_bb) and the hard state compactness (l_h) for these three outbursts. We show that the physical parameters of either classes reach the same values throughout the outbursts, the only difference being the maximum flux. We discuss our results in the terms of modes of interaction of the star with the disc and size of the disc kept hot by irradiation. We conclude that irradiation is the dominant physical process leading to the different classes of outbursts.Comment: MNRAS accepted. 12 pages, 9 figures, 3 table

Partial accretion in the propeller stage of low mass X-ray binary Aql X--1

Aql X--1 is one of the most prolific low mass X-ray binary transients (LMXBTs) showing outbursts almost annually. We present the results of our spectral analyses of RXTE/PCA observations of the 2000 and the 2011 outbursts. We investigate the spectral changes related to the changing disk-magnetosphere interaction modes of Aql X--1. The X-ray light curves of the outbursts of LMXBTs typically show phases of fast rise and exponential decay. The decay phase shows a "knee" where the flux goes from the slow decay to the rapid decay stage. We assume that the rapid decay corresponds to a weak propeller stage at which a fraction of the inflowing matter in the disk accretes onto the star. We introduce a novel method for inferring, from the light curve, the fraction of the inflowing matter in the disk that accretes onto the NS depending on the fastness parameter. We determine the fastness parameter range within which the transition from the accretion to the partial propeller stage is realized. This fastness parameter range is a measure of the scale-height of the disk in units of the inner disk radius. We applied the method to a sample of outbursts of Aql X--1 with different maximum flux and duration times. We show that different outbursts with different maximum luminosity and duration follow a similar path in the parameter space of accreted/inflowing mass flux fraction versus fastness parameter.Comment: 16 pages, 5 figures, 5 tables, accepted for publication in Ap

Detection and tracking of repeated sequences in videos

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2007.Thesis (Master's) -- Bilkent University, 2007.Includes bibliographical references leaves 87-92.In this thesis, we propose a new method to search different instances of a video sequence inside a long video. The proposed method is robust to view point and illumination changes which may occur since the sequences are captured in different times with different cameras, and to the differences in the order and the number of frames in the sequences which may occur due to editing. The algorithm does not require any query to be given for searching, and finds all repeating video sequences inside a long video in a fully automatic way. First, the frames in a video are ranked according to their similarity on the distribution of salient points and colour values. Then, a tree based approach is used to seek for the repetitions of a video sequence if there is any. These repeating sequences are pruned for more accurate results in the last step. Results are provided on two full length feature movies, Run Lola Run and Groundhog Day, on commercials of TRECVID 2004 news video corpus and on dataset created for CIVR Copy Detection Showcase 2007. In these experiments, we obtain %93 precision values for CIVR2007 Copy Detection Showcase dataset and exceed %80 precision values for other sets.Can, TolgaM.S

Accurate and Scalable Techniques for the Complex/Pathway Membership Problem in Protein Networks

A protein network shows physical interactions as well as functional associations. An important usage of such networks is to discover unknown members of partially known complexes and pathways. A number of methods exist for such analyses, and they can be divided into two main categories based on their treatment of highly connected proteins. In this paper, we show that methods that are not affected by the degree (number of linkages) of a protein give more accurate predictions for certain complexes and pathways. We propose a network flow-based technique to compute the association probability of a pair of proteins. We extend the proposed technique using hierarchical clustering in order to scale well with the size of proteome. We also show that top-k queries are not suitable for a large number of cases, and threshold queries are more meaningful in these cases. Network flow technique with clustering is able to optimize meaningful threshold queries and answer them with high efficiency compared to a similar method that uses Monte Carlo simulation

Predictive power of different obesity measures for the presence of diastolic dysfunction

Objective: Body mass index (BMI) and waist circumference (WC) as measures of obesity have some limitations. The aim of this study was to evaluate whether one measure could predict the presence of diastolic dysfunction (DD) more accurately than the other measures. Methods: A total of 91 obese patients without any other risk factors for DD were prospectively enrolled. Echocardiographic examination was performed. DD was defined and categorized according to recent guidelines. The study participants were divided into 2 groups according to the presence of DD. Weight, height, and WC were measured; BMI and waist-to-hip ratio (WHR) were calculated; and a body shape index (ABSI) was calculated as WC/(BMI2/3height1/2). The associations between ABSI, BMI, WHR, and WC and the presence of DD were examined using logistic regression analyses. Analysis of covariance was used to examine the differences. Results: WC and BMI were significantly greater in subjects with DD (p=0.049 and 0.051, respectively). A greater BMI, WC, and WHR increased the risk of the presence of DD (BMI-DD: odds ratio [OR]=1.096, p=0.024; WC-DD: OR=1.059, p=0.007; WHR-DD: OR=2.363, p=0.007). After adjustment for age and sex, only BMI continued to be significantly associated with DD (p=0.031). ABSI was not associated with DD. Conclusion: After adjustment for age and sex, BMI was the only predictor of DD in obesity. Despite its limitations, BMI may still be a potentially more accurate measure of DD compared with other obesity measures. © 2018 Turkish Society of Cardiology

Reconstruction of the temporal signaling network in Salmonella-infected human cells

Salmonella enterica is a bacterial pathogen that usually infects its host through food sources. Translocation of the pathogen proteins into the host cells leads to changes in the signaling mechanism either by activating or inhibiting the host proteins. Using high-throughput ‘omic’ technologies, changes in the signaling components can be quantified at different levels; however, experimental hits are usually incomplete to represent the whole signaling system as some driver proteins stay hidden within the experimental data. Given that the bacterial infection modifies the response network of the host, more coherent view of the underlying biological processes and the signaling networks can be obtained by using a network modeling approach based on the reverse engineering principles in which a confident region from the protein interactome is found by inferring hits from the omic experiments. In this work, we have used a published temporal phosphoproteomic dataset of Salmonella-infected human cells and reconstructed the temporal signaling network of the human host by integrating the interactome and the phosphoproteomic datasets. We have combined two well-established network modeling frameworks, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach. The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data. We believe that integrated approaches have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections

Farklı nitelikteki biyolojik ağların entegrasyonu ve yerel topolojik özellik vektörleri tabanlı karşılaştırılması

TÜBİTAK EEEAG Proje01.12.2016In this project, we developed a framework for the analysis of integrated genome-scale networks using using directed graphlet signatures. In addition, we developed a novel graph layout algorithm specific for visualizing aligned networks. Analysis of integrated genome-scale networks is a challenging problem due to heterogeneity of high-throughput data. There are several topological measures, such as graphlet counts, for characterization of biological networks. In this project, we present methods for counting small sub-graph patterns in integrated genome-scale networks which are modeled as labeled multidigraphs. We have obtained physical, regulatory, and metabolic interactions between H. sapiens proteins from the Pathway Commons database. The integrated network is filtered for tissue/disease specific proteins by using a large-scale human transcriptional profiling study, resulting in several tissue and disease specific sub-networks. We have applied and extended the idea of graphlet counting in undirected protein-protein interaction (PPI) networks to directed multi-labeled networks and represented each network as a vector of graphlet counts. Graphlet counts are assessed for statistical significance by comparison against a set of randomized networks. We present our results on analysis of differential graphlets between different conditions and on the utility of graphlet count vectors for clustering multiple condition specific networks. Our results show that there are numerous statistically significant graphlets in integrated biological networks and the graphlet signature vector can be used as an effective representation of a multi-labeled network for clustering and systems level analysis of tissue/disease specific networks. In addition, the proposed graph layout algorithm can be used to visualize the similarities and differences between aligned regions of these network