STORM Climate Change synthetic tropical cyclone tracks

UPDATE 22/06/2023: Tom Russell (Oxford University) and colleagues have created global .tiff maps for the return period datasets. You can find them here: https://zenodo.org/record/7438145Datasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al (2020)). The dataset is generated by extracting the climate change signal from each of the four general circulation models listed below, and adding this signal to the historical data from IBTrACS. This new dataset is then used as input for STORM, and resembles future-climate (2015-2050; RCP8.5/SSP5) conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones.Climate change information from the following models is used in this study (each model has its own 10.000 years of STORM data):1) CMCC-CM2-VHR42) CNRM-CM6-1-HR3) EC-Earth3P-HR4) HAdGEM3-GC31-HMSee Roberts et al (2020) for more information on these models.</p

STORM Climate Change synthetic tropical cyclone tracks

UPDATE 22/06/2023: Tom Russell (Oxford University) and colleagues have created global .tiff maps for the return period datasets. You can find them here: https://zenodo.org/record/7438145Datasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al (2020)). The dataset is generated by extracting the climate change signal from each of the four general circulation models listed below, and adding this signal to the historical data from IBTrACS. This new dataset is then used as input for STORM, and resembles future-climate (2015-2050; RCP8.5/SSP5) conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones.Climate change information from the following models is used in this study (each model has its own 10.000 years of STORM data):1) CMCC-CM2-VHR42) CNRM-CM6-1-HR3) EC-Earth3P-HR4) HAdGEM3-GC31-HMSee Roberts et al (2020) for more information on these models.</p

Association between convalescent plasma treatment and mortality in COVID-19: a collaborative systematic review and meta-analysis of randomized clinical trials

Abstract Background Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, https://doi.org/10.17605/OSF.IO/GEHFX ). Methods In this collaborative systematic review and meta-analysis, clinical trial registries (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform), the Cochrane COVID-19 register, the LOVE database, and PubMed were searched until April 8, 2021. Investigators of trials registered by March 1, 2021, without published results were contacted via email. Eligible were ongoing, discontinued and completed randomized clinical trials that compared convalescent plasma with placebo or no treatment in COVID-19 patients, regardless of setting or treatment schedule. Aggregated mortality data were extracted from publications or provided by investigators of unpublished trials and combined using the Hartung–Knapp–Sidik–Jonkman random effects model. We investigated the contribution of unpublished trials to the overall evidence. Results A total of 16,477 patients were included in 33 trials (20 unpublished with 3190 patients, 13 published with 13,287 patients). 32 trials enrolled only hospitalized patients (including 3 with only intensive care unit patients). Risk of bias was low for 29/33 trials. Of 8495 patients who received convalescent plasma, 1997 died (23%), and of 7982 control patients, 1952 died (24%). The combined risk ratio for all-cause mortality was 0.97 (95% confidence interval: 0.92; 1.02) with between-study heterogeneity not beyond chance (I2 = 0%). The RECOVERY trial had 69.8% and the unpublished evidence 25.3% of the weight in the meta-analysis. Conclusions Convalescent plasma treatment of patients with COVID-19 did not reduce all-cause mortality. These results provide strong evidence that convalescent plasma treatment for patients with COVID-19 should not be used outside of randomized trials. Evidence synthesis from collaborations among trial investigators can inform both evidence generation and evidence application in patient care

Association between convalescent plasma treatment and mortality in COVID-19: a collaborative systematic review and meta-analysis of randomized clinical trials

Abstract Background Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, https://doi.org/10.17605/OSF.IO/GEHFX ). Methods In this collaborative systematic review and meta-analysis, clinical trial registries (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform), the Cochrane COVID-19 register, the LOVE database, and PubMed were searched until April 8, 2021. Investigators of trials registered by March 1, 2021, without published results were contacted via email. Eligible were ongoing, discontinued and completed randomized clinical trials that compared convalescent plasma with placebo or no treatment in COVID-19 patients, regardless of setting or treatment schedule. Aggregated mortality data were extracted from publications or provided by investigators of unpublished trials and combined using the Hartung–Knapp–Sidik–Jonkman random effects model. We investigated the contribution of unpublished trials to the overall evidence. Results A total of 16,477 patients were included in 33 trials (20 unpublished with 3190 patients, 13 published with 13,287 patients). 32 trials enrolled only hospitalized patients (including 3 with only intensive care unit patients). Risk of bias was low for 29/33 trials. Of 8495 patients who received convalescent plasma, 1997 died (23%), and of 7982 control patients, 1952 died (24%). The combined risk ratio for all-cause mortality was 0.97 (95% confidence interval: 0.92; 1.02) with between-study heterogeneity not beyond chance (I2 = 0%). The RECOVERY trial had 69.8% and the unpublished evidence 25.3% of the weight in the meta-analysis. Conclusions Convalescent plasma treatment of patients with COVID-19 did not reduce all-cause mortality. These results provide strong evidence that convalescent plasma treatment for patients with COVID-19 should not be used outside of randomized trials. Evidence synthesis from collaborations among trial investigators can inform both evidence generation and evidence application in patient care

Measurement of the Ωc0\Omega_c^0 lifetime at Belle II

We report on a measurement of the $\Omega_c^0$ lifetime using $\Omega_c^0 \to \Omega^-\pi^+$ decays reconstructed in $e^+e^-\to c\bar{c}$ data collected by the Belle II experiment and corresponding to $207~{\rm fb^{-1}}$ of integrated luminosity. The result, $\rm\tau(\Omega_c^0)=243\pm48( stat)\pm11(syst)~fs$, agrees with recent measurements indicating that the $\Omega_c^0$ is not the shortest-lived weakly decaying charmed baryon