Methods for multi-detector burst gravitational wave search

In this paper, a review of methods of data exchange and multi-detector analysis is given. Details of data exchange parameters will also be discussed. We focus on the coherent network search which turns out to be a fundamental step towards the overcoming of ambiguity in parameter estimation of a gravitational wave in the low signal-to-noise ratio regime

Laboratory investigation of the impact of air pollution on partial discharge inception voltage of insulators in a specific region

Studying the discharge characteristics of transmission line insulators in the presence of pollution, particularly when the contaminated layer is wet by rain, fog or condensation, is necessary for selecting the proper insulation. Therefore, identifying the major air pollutants as well as the most effective ones on the discharge performance of outdoor insulators is mandatory. A systematic approach has been proposed to evaluate the impact of dominant air pollutants of an area on partial discharge (P.D) inception voltage of specimen insulators. Based on the suggested method, determining the pollution constituents, defining the dominant pollutant of the area, finding the most commonly used insulators for medium and above distribution voltages within the geographical boundaries of the Central Province of Iran, as well as examining the effect of dominant air pollutant of the region on partial discharge inception voltage of designated insulators by laboratory measurements, are the necessary steps toward a comprehensive study of the subject

Enhancing the significance of gravitational wave bursts through signal classification

The quest to observe gravitational waves challenges our ability to discriminate signals from detector noise. This issue is especially relevant for transient gravitational waves searches with a robust eyes wide open approach, the so called all- sky burst searches. Here we show how signal classification methods inspired by broad astrophysical characteristics can be implemented in all-sky burst searches preserving their generality. In our case study, we apply a multivariate analyses based on artificial neural networks to classify waves emitted in compact binary coalescences. We enhance by orders of magnitude the significance of signals belonging to this broad astrophysical class against the noise background. Alternatively, at a given level of mis-classification of noise events, we can detect about 1/4 more of the total signal population. We also show that a more general strategy of signal classification can actually be performed, by testing the ability of artificial neural networks in discriminating different signal classes. The possible impact on future observations by the LIGO-Virgo network of detectors is discussed by analysing recoloured noise from previous LIGO-Virgo data with coherent WaveBurst, one of the flagship pipelines dedicated to all-sky searches for transient gravitational waves

Prospects for intermediate mass black hole binary searches with advanced gravitational-wave detectors

We estimated the sensitivity of the upcoming advanced, ground-based gravitational-wave observatories (the upgraded LIGO and Virgo and the KAGRA interferometers) to coalescing intermediate mass black hole binaries (IMBHB). We added waveforms modeling the gravitational radiation emitted by IMBHBs to detectors' simulated data and searched for the injected signals with the coherent WaveBurst algorithm. The tested binary's parameter space covers non-spinning IMBHBs with source-frame total masses between 50 and 1050 $\text{M}_{\odot}$ and mass ratios between $1/6$ and 1$\,$. We found that advanced detectors could be sensitive to these systems up to a range of a few Gpc. A theoretical model was adopted to estimate the expected observation rates, yielding up to a few tens of events per year. Thus, our results indicate that advanced detectors will have a reasonable chance to collect the first direct evidence for intermediate mass black holes and open a new, intriguing channel for probing the Universe over cosmological scales.Comment: 9 pages, 4 figures, corrected the name of one author (previously misspelled

Analysis of gut microbiota in rheumatoid arthritis patients. Disease-related dysbiosis and modifications induced by etanercept

A certain number of studies were carried out to address the question of how dysbiosis could affect the onset and development of rheumatoid arthritis (RA), but little is known about the reciprocal influence between microbiota composition and immunosuppressive drugs, and how this interaction may have an impact on the clinical outcome. The aim of this study was to characterize the intestinal microbiota in a groups of RA patients treatment-naïve, under methotrexate, and/or etanercept (ETN). Correlations between the gut microbiota composition and validated immunological and clinical parameters of disease activity were also evaluated. In the current study, a 16S analysis was employed to explore the gut microbiota of 42 patients affected by RA and 10 healthy controls. Disease activity score on 28 joints (DAS-28), erythrocyte sedimentation rate, C-reactive protein, rheumatoid factor, anti-cyclic citrullinated peptides, and dietary and smoking habits were assessed. The composition of the gut microbiota in RA patients free of therapy is characterized by several abnormalities compared to healthy controls. Gut dysbiosis in RA patients is associated with different serological and clinical parameters; in particular, the phylum of Euryarchaeota was directly correlated to DAS and emerged as an independent risk factor. Patients under treatment with ETN present a partial restoration of a beneficial microbiota. The results of our study confirm that gut dysbiosis is a hallmark of the disease, and shows, for the first time, that the anti-tumor necrosis factor alpha (TNF-α) ETN is able to modify microbial communities, at least partially restoring a beneficial microbiota

N-terminal prohormone brain natriuretic peptide (NT-proBNP) as a noninvasive marker for restrictive syndromes

Constrictive pericarditis (CP) and restrictive cardiomyopathy share many similarities in both their clinical and hemodynamic characteristics and N-terminal prohormone brain natriuretic peptide (NT-proBNP) is a sensitive marker of cardiac diastolic dysfunction. The objectives of the present study were to determine whether serum NT-proBNP was high in patients with endomyocardial fibrosis (EMF) and CP, and to investigate how this relates to diastolic dysfunction. Thirty-three patients were divided into two groups: CP (16 patients) and EMF (17 patients). The control group consisted of 30 healthy individuals. Patients were evaluated by bidimensional echocardiography, with restriction syndrome evaluated by pulsed Doppler of the mitral flow and serum NT-proBNP measured by immunoassay and detected by electrochemiluminescence. Spearman correlation coefficient was used to analyze the association between log NT-proBNP and echocardiographic parameters. Log NT-proBNP was significantly higher (P < 0.05) in CP patients (log mean: 2.67 pg/mL; 95%CI: 2.43-2.92 log pg/mL) and in EMF patients (log mean: 2.91 pg/mL; 95%CI: 2.70-3.12 log pg/mL) compared with the control group (log mean: 1.45; 95%CI: 1.32-1.60 log pg/mL). There were no statistical differences between EMF and CP patients (P = 0.689) in terms of NT-proBNP. The NT-proBNP log tended to correlate with peak velocity of the E wave (r = 0.439; P = 0.060, but not with A wave (r = -0.399; P = 0.112). Serum NT-proBNP concentration can be used as a marker to detect the presence of diastolic dysfunction in patients with restrictive syndrome; however, serum NT-proBNP levels cannot be used to differentiate restrictive cardiomyopathy from CP