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Environmental seismic disturbances limit the sensitivity of LIGO gravitational wave detectors. Trains near the LIGO Livingston detector produce low frequency (0.5–$10\mathrm{H}\mathrm{z}$) ground noise that couples into the gravitational wave sensitive frequency band (10–$100\mathrm{H}\mathrm{z}$) through light reflected in mirrors and other surfaces. We investigate the effect of trains during the Advanced LIGO third observing run, and propose a method to search for narrow band seismic frequencies responsible for contributing to increases in scattered light. Through the use of the linear regression tool Lasso (least absolute shrinkage and selection operator) and glitch correlations, we identify the most common seismic frequencies that correlate with increases in detector noise as 0.6–$0.8\mathrm{H}\mathrm{z}$, 1.7–$1.9\mathrm{H}\mathrm{z}$, 1.8–$2.0\mathrm{H}\mathrm{z}$, and 2.3–$2.5\mathrm{H}\mathrm{z}$in the LIGO Livingston corner station.

 

Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory.

 

Previous work demonstrated an accelerated in vitro and in vivo wound healing response when insulinoma cells were used to deliver insulin to scratches in keratinocyte mono- layers and chronic diabetic excise wounds in mice, respectively. When combined with mesenchymal stem cells (MSCs), the response was amplified (healing in 14 vs. 35+ days). To isolate the role of insulin in the response and exclude any potential contribution of an un known cancer moiety released by the insulinoma cells, the effect on wound healing of multiple lines derived from the same insulinoma was examined. 

Previous work demonstrated an accelerated in vitro and in vivo wound healing response when insulinoma cells were used to deliver insulin to scratches in keratinocyte mono- layers and chronic diabetic excise wounds in mice, respectively. When combined with mesenchymal stem cells (MSCs), the response was amplified (healing in 14 vs. 35+ days). To isolate the role of insulin in the response and exclude any potential contribution of an unknown cancer moiety released by the insulinoma cells, the effect on wound healing of multiple lines derived from the same insulinoma was examined. 

Abstract Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic. 

We search for gravitational-wave (GW) transients associated with fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst Project, during the first part of the third observing run of Advanced LIGO and Advanced Virgo (2019 April 1 15:00 UTC–2019 October 1 15:00 UTC). Triggers from 22 FRBs were analyzed with a search that targets both binary neutron star (BNS) and neutron star–black hole (NSBH) mergers. A targeted search for generic GW transients was conducted on 40 FRBs. We find no significant evidence for a GW association in either search. Given the large uncertainties in the distances of our FRB sample, we are unable to exclude the possibility of a GW association. Assessing the volumetric event rates of both FRB and binary mergers, an association is limited to 15% of the FRB population for BNS mergers or 1% for NSBH mergers. We report 90% confidence lower bounds on the distance to each FRB for a range of GW progenitor models and set upper limits on the energy emitted through GWs for a range of emission scenarios. We find values of order 10⁵¹–10⁵⁷erg for models with central GW frequencies in the range 70–3560 Hz. At the sensitivity of this search, we find these limits to be above the predicted GW emissions for the models considered. We also find no significant coincident detection of GWs with the repeater, FRB 20200120E, which is the closest known extragalactic FRB.