All-sky search for short gravitational-wave bursts in the second Advanced LIGO and Advanced Virgo run

We present the results of a search for short-duration gravitational-wave transients in the data from the second observing run of Advanced LIGO and Advanced Virgo. We search for gravitational-wave transients with a duration of milliseconds to approximately one second in the 32-4096 Hz frequency band with minimal assumptions about the signal properties, thus targeting a wide variety of sources. We also perform a matched-filter search for gravitational-wave transients from cosmic string cusps for which the waveform is well modeled. The unmodeled search detected gravitational waves from several binary black hole mergers which have been identified by previous analyses. No other significant events have been found by either the unmodeled search or the cosmic string search. We thus present the search sensitivities for a variety of signal waveforms and report upper limits on the source rate density as a function of the characteristic frequency of the signal. These upper limits are a factor of 3 lower than the first observing run, with a 50% detection probability for gravitational-wave emissions with energies of ∼10-9 Mc2 at 153 Hz. For the search dedicated to cosmic string cusps we consider several loop distribution models, and present updated constraints from the same search done in the first observing run

Search for Eccentric Binary Black Hole Mergers with Advanced LIGO and Advanced Virgo during Their First and Second Observing Runs

When formed through dynamical interactions, stellar-mass binary black holes (BBHs) may retain eccentric orbits (e > 0.1 at 10 Hz) detectable by ground-based gravitational-wave detectors. Eccentricity can therefore be used to differentiate dynamically formed binaries from isolated BBH mergers. Current template-based gravitational-wave searches do not use waveform models associated with eccentric orbits, rendering the search less efficient for eccentric binary systems. Here we present the results of a search for BBH mergers that inspiral in eccentric orbits using data from the first and second observing runs (O1 and O2) of Advanced LIGO and Advanced Virgo. We carried out the search with the coherent WaveBurst algorithm, which uses minimal assumptions on the signal morphology and does not rely on binary waveform templates. We show that it is sensitive to binary mergers with a detection range that is weakly dependent on eccentricity for all bound systems. Our search did not identify any new binary merger candidates. We interpret these results in light of eccentric binary formation models. We rule out formation channels with rates ⪆100 Gpc-3 yr-1 for e > 0.1, assuming a black hole mass spectrum with a power-law index ≲2

Search for gravitational waves from Scorpius X-1 in the second Advanced LIGO observing run with an improved hidden Markov model

We present results from a semicoherent search for continuous gravitational waves from the low-mass X-ray binary Scorpius X-1, using a hidden Markov model (HMM) to track spin wandering. This search improves on previous HMM-based searches of LIGO data by using an improved frequency domain matched filter, the $\mathcal{J}$-statistic, and by analysing data from Advanced LIGO's second observing run. In the frequency range searched, from $60$ to $650\,\mathrm{Hz}$, we find no evidence of gravitational radiation. At $194.6\,\mathrm{Hz}$, the most sensitive search frequency, we report an upper limit on gravitational wave strain (at 95\% confidence) of $h_0^{95\%} = 3.47 \times 10^{-25}$ when marginalising over source inclination angle. This is the most sensitive search for Scorpius X-1, to date, that is specifically designed to be robust in the presence of spin wandering

Search for intermediate mass black hole binaries in the first and second observing runs of the Advanced LIGO and Virgo network

Gravitational-wave astronomy has been firmly established with the detection of gravitational waves from the merger of ten stellar-mass binary black holes and a neutron star binary. This paper reports on the all-sky search for gravitational waves from intermediate mass black hole binaries in the first and second observing runs of the Advanced LIGO and Virgo network. The search uses three independent algorithms: two based on matched filtering of the data with waveform templates of gravitational-wave signals from compact binaries, and a third, model-independent algorithm that employs no signal model for the incoming signal. No intermediate mass black hole binary event is detected in this search. Consequently, we place upper limits on the merger rate density for a family of intermediate mass black hole binaries. In particular, we choose sources with total masses M=m1+m2ϵ[120,800] M and mass ratios q=m2/m1ϵ[0.1,1.0]. For the first time, this calculation is done using numerical relativity waveforms (which include higher modes) as models of the real emitted signal. We place a most stringent upper limit of 0.20 Gpc-3 yr-1 (in comoving units at the 90% confidence level) for equal-mass binaries with individual masses m1,2=100 M and dimensionless spins χ1,2=0.8 aligned with the orbital angular momentum of the binary. This improves by a factor of ∼5 that reported after Advanced LIGO's first observing run

GIBA: a clustering tool for detecting protein complexes

Background: During the last years, high throughput experimental methods have been developed which generate large datasets of protein - protein interactions (PPIs). However, due to the experimental methodologies these datasets contain errors mainly in terms of false positive data sets and reducing therefore the quality of any derived information. Typically these datasets can be modeled as graphs, where vertices represent proteins and edges the pairwise PPIs, making it easy to apply automated clustering methods to detect protein complexes or other biological significant functional groupings. Methods: In this paper, a clustering tool, called GIBA (named by the first characters of its developers' nicknames), is presented. GIBA implements a two step procedure to a given dataset of protein-protein interaction data. First, a clustering algorithm is applied to the interaction data, which is then followed by a filtering step to generate the final candidate list of predicted complexes. Results: The efficiency of GIBA is demonstrated through the analysis of 6 different yeast protein interaction datasets in comparison to four other available algorithms. We compared the results of the different methods by applying five different performance measurement metrices. Moreover, the parameters of the methods that constitute the filter have been checked on how they affect the final results. Conclusion: GIBA is an effective and easy to use tool for the detection of protein complexes out of experimentally measured protein - protein interaction networks. The results show that GIBA has superior prediction accuracy than previously published methods

Which clustering algorithm is better for predicting protein complexes?

<p>Abstract</p> <p>Background</p> <p>Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.</p> <p>Results</p> <p>In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.</p> <p>Conclusions</p> <p>While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: <url>http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm</url></p

MicroRNA Expression Profiling of the Porcine Developing Brain

BACKGROUND: MicroRNAs are small, non-coding RNA molecules that regulate gene expression at the post-transcriptional level and play an important role in the control of developmental and physiological processes. In particular, the developing brain contains an impressive diversity of microRNAs. Most microRNA expression profiling studies have been performed in human or rodents and relatively limited knowledge exists in other mammalian species. The domestic pig is considered to be an excellent, alternate, large mammal model for human-related neurological studies, due to its similarity in both brain development and the growth curve when compared to humans. Considering these similarities, studies examining microRNA expression during porcine brain development could potentially be used to predict the expression profile and role of microRNAs in the human brain. METHODOLOGY/PRINCIPAL FINDINGS: MicroRNA expression profiling by use of microRNA microarrays and qPCR was performed on the porcine developing brain. Our results show that microRNA expression is regulated in a developmentally stage-specific, as well as a tissue-specific manner. Numerous developmental stage or tissue-specific microRNAs including, miR-17, miR-18a, miR-29c, miR-106a, miR-135a and b, miR-221 and miR-222 were found by microarray analysis. Expression profiles of selected candidates were confirmed by qPCR. CONCLUSIONS/SIGNIFICANCE: The differential expression of specific microRNAs in fetal versus postnatal samples suggests that they likely play an important role in the regulation of developmental and physiological processes during brain development. The data presented here supports the notion that microRNAs act as post-transcriptional switches which may regulate gene expression when required

Differential gene expression between wild-type and Gulo-deficient mice supplied with vitamin C

The aim of this study was to test the hypothesis that hepatic vitamin C (VC) levels in VC deficient mice rescued with high doses of VC supplements still do not reach the optimal levels present in wild-type mice. For this, we used a mouse scurvy model (sfx) in which the L-gulonolactone oxidase gene (Gulo) is deleted. Six age- (6 weeks old) and gender- (female) matched wild-type (WT) and sfx mice (rescued by administering 500 mg of VC/L) were used as the control (WT) and treatment (MT) groups (n = 3 for each group), respectively. Total hepatic RNA was used in triplicate microarray assays for each group. EDGE software was used to identify differentially expressed genes and transcriptomic analysis was used to assess the potential genetic regulation of Gulo gene expression. Hepatic VC concentrations in MT mice were significantly lower than in WT mice, even though there were no morphological differences between the two groups. In MT mice, 269 differentially expressed transcripts were detected (≥ twice the difference between MT and WT mice), including 107 up-regulated and 162 down-regulated genes. These differentially expressed genes included stress-related and exclusively/predominantly hepatocyte genes. Transcriptomic analysis identified a major locus on chromosome 18 that regulates Gulo expression. Since three relevant oxidative genes are located within the critical region of this locus we suspect that they are involved in the down-regulation of oxidative activity in sfx mice