19 research outputs found
Preseason risk factors associated with hamstring injuries in club rugby players
No full textIncludes bibliographical references
Explosions and structural fragments as industrial hazard: domino effect and risks
No full textInternational audienceThis study deals with industrial accidents and domino effects that may occur in an industrial plant, the initial accident being supposed to take place in any of the tanks either under or at atmospheric pressure. This initial sequence might generate sets of structural fragments, fire balls, blast waves as well as critical losses of containment (liquid and gas) that threaten the surrounding facilities and may cause serious damages. The structural fragments, the blast wave and the fire ball can be described following database and feedback collected from past accidents. The vulnerability of the potential targeted tanks is investigated in order to assess the risk of propagation of the first sequence of industrial hazard. Cascading sequences of accidents, explosions and fires can take place, giving rise to the domino effect. This risk of domino effect occurrence is investigated herein. The interaction and the behavior of the targets affected or impacted by the first explosion effects are described by adequate simplified mechanical models: perforation and penetration of metal fragments when they impact surrounding tanks, as well as global failure such as overturning, buckling, excessive bending or shear effects, etc. Sensitivity analysis is performed thanks to Monte Carlo simulations: the probability of impact and risk of failure of target tanks are reported. A comparison between risks due to blast wave and fragments impacts is performed
Domino effects and industrial risks: Integrated probabilistic framework – Case of tsunamis effects
No full textInternational audienc
P1725How to prevent persistent phrenic nerve palsy in the setting of second generation cryoballoon ablation?
No full textP1100Starting atrial fibrillation ablation program: a comparison between second generation cryoballoon and contact force sensing radiofrequency catheters
No full textThe young supernova experiment data release 1 (YSE DR1): light curves and photometric classification of 1975 supernovae
No full textWe present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multicolor PanSTARRS1 griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host-galaxy associations, redshifts, spectroscopic and/or photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z approximate to 0.5. We present relative SN rates from YSE's magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multisurvey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (similar to 71%) SNe Ia, 339 (similar to 23%) SNe II, and 96 (similar to 6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time.</p
Differentiation-dependent glycosylation of cells in squamous cell epithelia detected by a mammalian lectin
No full textThe squamous stratified epithelia contain a proliferative (harboring mitotic activity) and a differentiating compartment. Due to the potential of protein-carbohyd rate interactions to regulate cellular activities we introduced a mammalian lectin to cyto- and histochemical analysis. We answer the questions of whether and to what extent this new probe can pinpoint differentiation-dependent glycosylation changes in sections and in culture of keratinocytes. Material and Methods: Purification and labeling enabled monitoring of galectin-3 reactivity in frozen sections of human and pig epidermis and basal cell carcinomas as well as in culture of keratinocytes. The staining pattern of the lectin was correlated with the staining profile of other cell markers including desmosomal proteins, beta(1) integrin, and the proliferation marker Ki-67. The Dolichos biflorus agglutinin (DBA) sharing binding reactivity of galectin-3 to the A type histoblood group epitope was used for comparison. Results: Both lectins exhibit suprabasal binding. However, their profiles were not identical, substantiated by lack of coinhibition. Strong DBA reactivity was also observed in a limited number of basal layer cells, namely in cells without the expression of the proliferation marker Ki-67. Cultured mitotic epidermal cells have no reactivity for DBA. Presence of ligands for this plant lectin was connected with decreased positivity of nuclei for Ki-67 and the occurrence of ring-shaped nucleoli, micronucleoli or absence of nucleoli. Considering colocalization the pattern of galectin-3-binding sites coincided with the presence of desmosomal proteins such as desmoplakin-1 and desmoglein but not beta(1) integrin, a potential ligand. Interestingly, studied basal cell carcinomas expressed no binding sites for galectin-3, while a limited number of cells were DBA-reactive. Conclusion: The expression of galectin-3-binding sites and also DBA-reactive glycoligands correlates with an increased level of differentiation and/or cessation of proliferation in the examined squamous stratified epithelia. Further application of tissue lectins for characterizing ligand expression and its modulation is an important step to reveal functional relevance
The Young Supernova Experiment Data Release 1 (YSE DR1) Light Curves
No full textThis is the official Zenodo data release of the Young Supernova Experiment Public Data Release 1 (YSE DR1) light curves associated with the paper, "The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae". YSE DR1 is comprised of processed multi-color Pan-STARRS1 (PS1)-griz and Zwicky Transient Facility (ZTF)-gr photometry lightcurve files in the SNANA data format of 1975 transients with host galaxy associations, redshifts, spectroscopic/photometric classifications, and additional data products from November 24th, 2019 to December 20, 2021. See Aleo et al. (2022) for details. "yse_dr1_zenodo.tar.gz" -- All lightcurve data with no cut on signal to noise (S/N). "yse_dr1_zenodo_snr_geq_4.tar.gz" -- All lightcurve data with S/N &gt;= 4. This can be used to recreate the analysis in Aleo et al. (2022). "parsnip_results_for_ysedr1_table_A1_full_for_online" -- The full version of Table~C2 in Aleo et al. (2022). The full ParSNIP (tertiary classification) results for YSE DR1. NOTE: An example tutorial on how to download the YSE DR1 data (full sample, spec sample, phot sample), grab metadata, and recreate a plot from the paper can be found on Github.</span
Improving wastewater-based epidemiology to estimate cannabis use: focus on the initial aspects of the analytical procedure
No full textWastewater-based epidemiology is a promising and complementary tool for estimating drug use by the general population, based on the quantitative analysis of specific human metabolites of illicit drugs in urban wastewater. Cannabis is the most commonly used illicit drug and of high interest for epidemiologists. However, the inclusion of its main human urinary metabolite 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THC-COOH) in wastewater-based epidemiology has presented several challenges and concentrations seem to depend heavily on environmental factors, sample preparation and analyses, commonly resulting in an underestimation. The aim of the present study is to investigate, identify and diminish the source of bias when analysing THC-COOH in wastewater. Several experiments were performed to individually assess different aspects of THC-COOH determination in wastewater, such as the number of freeze-thaw cycles, filtration, sorption to different container materials and in-sample stability, and the most suitable order of preparatory steps. Results highlighted the filtration step and adjustment of the sample pH as the most critical parameters to take into account when analysing THC-COOH in wastewater. Furthermore, the order of these initial steps of the analytical procedure is crucial. Findings were translated into a recommended best-practice protocol and an inter-laboratory study was organized with eight laboratories that tested the performance of the proposed procedure. Results were found satisfactory with z-scores ≤ 2
The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae
No full textWe present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multicolor PanSTARRS1 griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host-galaxy associations, redshifts, spectroscopic and/or photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z approximate to 0.5. We present relative SN rates from YSE's magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multisurvey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (similar to 71%) SNe Ia, 339 (similar to 23%) SNe II, and 96 (similar to 6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time.</p
