118 research outputs found
Anti-Inflammatory Effects of Metformin Irrespective of Diabetes Status
Rationale: The diabetes drug metformin is under investigation in cardiovascular disease but the molecular mechanisms underlying possible benefits are poorly understood.
Objective: Here we have studied anti-inflammatory effects of the drug and their relationship to anti-hyperglycaemic properties.
Methods and Results: In primary hepatocytes from healthy animals, metformin and the IKKβ inhibitor BI605906 both inhibited TNFα-dependent IκB degradation and expression of pro-inflammatory mediators IL-6, IL-1b, and CXCL1/2. Metformin suppressed IKKα/β activation, an effect which could be separated from some metabolic actions, in that BI605906 did not mimic effects of metformin on lipogenic gene expression, glucose production and AMPK activation. Equally AMPK was not required either for mitochondrial suppression of IκB degradation. Consistent with discrete anti-inflammatory actions, in macrophages metformin specifically blunted secretion of pro-inflammatory cytokines, without inhibiting M1/M2 differentiation or activation. In a large treatment naïve diabetes population cohort, we observed differences in the systemic inflammation marker, Neutrophil to Lymphocyte Ratio (NLR), following incident treatment with either metformin or sulfonylurea monotherapy. Compared to sulfonylurea exposure, metformin reduced the mean log-transformed NLR after 8-16 months by 0.09 units (95% CI=0.02-0.17, p=0.013), and increased the likelihood that NLR would be lower than baseline after 8-16 months (OR 1.83, 95% CI=1.22-2.75, p=0.00364). Following up these findings in a double blind placebo controlled trial in nondiabetic heart failure (trial registration: NCT00473876), metformin suppressed plasma cytokines including the ageing-associated cytokine CCL11.
Conclusions: We conclude that anti-inflammatory properties of metformin are exerted irrespective of diabetes status. This may accelerate investigation of drug utility in non-diabetic cardiovascular disease groups
Development and Validation of Risk Prediction Models for Cardiovascular Events in Black Adults: The Jackson Heart Study Cohort
Cardiovascular risk assessment is a fundamental component of prevention of cardiovascular disease (CVD). However, commonly used prediction models have been formulated in primarily or exclusively white populations. Whether risk assessment in black adults is dissimilar to that in white adults is uncertain
Characterization of inpaint residuals in interferometric measurements of the epoch of reionization
To mitigate the effects of Radio Frequency Interference (RFI) on the data analysis pipelines of 21 cm interferometric instruments, numerous inpaint techniques have been developed. In this paper, we examine the qualitative and quantitative errors introduced into the visibilities and power spectrum due to inpainting. We perform our analysis on simulated data as well as real data from the Hydrogen Epoch of Reionization Array (HERA) Phase 1 upper limits. We also introduce a convolutional neural network that is capable of inpainting RFI corrupted data. We train our network on simulated data and show that our network is capable of inpainting real data without requiring to be retrained. We find that techniques that incorporate high wavenumbers in delay space in their modelling are best suited for inpainting over narrowband RFI. We show that with our fiducial parameters discrete prolate spheroidal sequences (DPSS) and CLEAN provide the best performance for intermittent RFI while Gaussian progress regression (GPR) and least squares spectral analysis (LSSA) provide the best performance for larger RFI gaps. However, we caution that these qualitative conclusions are sensitive to the chosen hyperparameters of each inpainting technique. We show that all inpainting techniques reliably reproduce foreground dominated modes in the power spectrum. Since the inpainting techniques should not be capable of reproducing noise realizations, we find that the largest errors occur in the noise dominated delay modes. We show that as the noise level of the data comes down, CLEAN and DPSS are most capable of reproducing the fine frequency structure in the visibilities
Metabotropic glutamate receptor 5 as a potential target for smoking cessation
Rationale Most habitual smokers find it difficult to quit smoking because they are dependent upon the nicotine present in tobacco smoke. Tobacco dependence is commonly treated pharmacologically using nicotine replacement therapy or drugs, such as varenicline, that target the nicotinic receptor. Relapse rates, however, remain high and there remains a need to develop novel non-nicotinic pharmacotherapies for the dependence that are more effective than existing treatments. Objective The purpose of this paper is to review the evidence from preclinical and clinical studies that drugs that antagonise the metabotropic glutamate receptor 5 (mGluR5) in the brain are likely to be efficacious as treatments for tobacco dependence. Results Imaging studies reveal that chronic exposure to tobacco smoke reduces the density of mGluR5s in human brain. Preclinical results demonstrate that negative allosteric modulators (NAMs) at mGluR5 attenuate both nicotine self-administration and the reinstatement of responding evoked by exposure to conditioned cues paired with nicotine delivery. They also attenuate the effects of nicotine on brain dopamine pathways implicated in addiction. Conclusions Although mGluR5 NAMs attenuate most of the key facets of nicotine dependence they potentiate the symptoms of nicotine withdrawal. This may limit their value as smoking cessation aids. The NAMs that have been employed most widely in preclinical studies of nicotine dependence have too many \u201coff target\u201d effects to be used clinically. However newer mGluR5 NAMs have been developed for clinical use in other indications. Future studies will determine if these agents can also be used effectively and safely to treat tobacco dependence
HI 21cm Cosmology and the Bi-spectrum: Closure Diagnostics in Massively Redundant Interferometric Arrays
New massively redundant low frequency arrays allow for a novel investigation
of closure relations in interferometry. We employ commissioning data from the
Hydrogen Epoch of Reionization Array to investigate closure quantities in this
densely packed grid array of 14m antennas operating at 100 MHz to 200 MHz. We
investigate techniques that utilize closure phase spectra for redundant triads
to estimate departures from redundancy for redundant baseline visibilities. We
find a median absolute deviation from redundancy in closure phase across the
observed frequency range of about 4.5deg. This value translates into a
non-redundancy per visibility phase of about 2.6deg, using prototype
electronics. The median absolute deviations from redundancy decrease with
longer baselines. We show that closure phase spectra can be used to identify
ill-behaved antennas in the array, independent of calibration. We investigate
the temporal behavior of closure spectra. The Allan variance increases after a
one minute stride time, due to passage of the sky through the primary beam of
the transit telescope. However, the closure spectra repeat to well within the
noise per measurement at corresponding local sidereal times (LST) from day to
day. In future papers in this series we will develop the technique of using
closure phase spectra in the search for the HI 21cm signal from cosmic
reionization.Comment: 32 pages. 11 figures. Accepted to Radio Scienc
Optimizing Sparse RFI Prediction using Deep Learning
Radio Frequency Interference (RFI) is an ever-present limiting factor among
radio telescopes even in the most remote observing locations. When looking to
retain the maximum amount of sensitivity and reduce contamination for Epoch of
Reionization studies, the identification and removal of RFI is especially
important. In addition to improved RFI identification, we must also take into
account computational efficiency of the RFI-Identification algorithm as radio
interferometer arrays such as the Hydrogen Epoch of Reionization Array grow
larger in number of receivers. To address this, we present a Deep Fully
Convolutional Neural Network (DFCN) that is comprehensive in its use of
interferometric data, where both amplitude and phase information are used
jointly for identifying RFI. We train the network using simulated HERA
visibilities containing mock RFI, yielding a known "ground truth" dataset for
evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model
is performed on observations from the 67 dish build-out, HERA-67, and achieves
a data throughput of 1.6 HERA time-ordered 1024 channeled
visibilities per hour per GPU. We determine that relative to an amplitude only
network including visibility phase adds important adjacent time-frequency
context which increases discrimination between RFI and Non-RFI. The inclusion
of phase when predicting achieves a Recall of 0.81, Precision of 0.58, and
score of 0.75 as applied to our HERA-67 observations.Comment: 11 pages, 7 figure
Detection of Cosmic Structures using the Bispectrum Phase. II. First Results from Application to Cosmic Reionization Using the Hydrogen Epoch of Reionization Array
Characterizing the epoch of reionization (EoR) at via the
redshifted 21 cm line of neutral Hydrogen (HI) is critical to modern
astrophysics and cosmology, and thus a key science goal of many current and
planned low-frequency radio telescopes. The primary challenge to detecting this
signal is the overwhelmingly bright foreground emission at these frequencies,
placing stringent requirements on the knowledge of the instruments and
inaccuracies in analyses. Results from these experiments have largely been
limited not by thermal sensitivity but by systematics, particularly caused by
the inability to calibrate the instrument to high accuracy. The interferometric
bispectrum phase is immune to antenna-based calibration and errors therein, and
presents an independent alternative to detect the EoR HI fluctuations while
largely avoiding calibration systematics. Here, we provide a demonstration of
this technique on a subset of data from the Hydrogen Epoch of Reionization
Array (HERA) to place approximate constraints on the brightness temperature of
the intergalactic medium (IGM). From this limited data, at we infer
"" upper limits on the IGM brightness temperature to be
"pseudo" mK at "pseudo" Mpc (data-limited)
and "pseudo" mK at "pseudo" Mpc
(noise-limited). The "pseudo" units denote only an approximate and not an exact
correspondence to the actual distance scales and brightness temperatures. By
propagating models in parallel to the data analysis, we confirm that the
dynamic range required to separate the cosmic HI signal from the foregrounds is
similar to that in standard approaches, and the power spectrum of the
bispectrum phase is still data-limited (at dynamic range)
indicating scope for further improvement in sensitivity as the array build-out
continues.Comment: 22 pages, 12 figures (including sub-figures). Published in PhRvD.
Abstract may be slightly abridged compared to the actual manuscript due to
length limitations on arXi
Mitigating Internal Instrument Coupling for 21 cm Cosmology. II. A Method Demonstration with the Hydrogen Epoch of Reionization Array
We present a study of internal reflection and cross-coupling systematics in Phase I of the Hydrogen Epoch of Reionization Array (HERA). In a companion paper, we outlined the mathematical formalism for such systematics and presented algorithms for modeling and removing them from the data. In this work, we apply these techniques to data from HERA's first observing season as a method demonstration. The data show evidence for systematics that, without removal, would hinder a detection of the 21 cm power spectrum for the targeted Epoch of Reionization (EoR) line-of-sight modes in the range 0.2 h −1 Mpc−1 < < 0.5 h −1 Mpc−1. In particular, we find evidence for nonnegligible amounts of spectral structure in the raw autocorrelations that overlaps with the EoR window and is suggestive of complex instrumental effects. Through systematic modeling on a single night of data, we find we can recover these modes in the power spectrum down to the integrated noise floor, achieving a dynamic range in the EoR window of 106 in power (mK2 units) with respect to the bright galactic foreground signal. Future work with deeper integrations will help determine whether these systematics can continue to be mitigated down to EoR levels. For future observing seasons, HERA will have upgraded analog and digital hardware to better control these systematics in the field
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