dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT, Argentina ;  YerPhI, Armenia ;  ARC, Australia ;  BMWFW  and  FWF, Austria ;  ANAS ,  Azerbaijan ;  CNPq  and  FAPESP ,  Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ;  CERN ;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias, Colombia ;  MEYS CR, Czech Republic ;  DNRF  and  DNSRC ,  Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU, France ;  SRNSFG, Georgia ;  BMBF ,  HGF  and  MPG, Germany ;  GSRI, Greece ;  RGC  and  Hong Kong SAR, China ;  ISF  and  Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ;  CNRST, Morocco ;  NWO, Netherlands ;  RCN, Norway ;  MEiN, Poland ;  FCT, Portugal ;  MNE/IFA, Romania ;  MESTD, Serbia ;  MSSR ,  Slovakia ;  ARRS  and  MIZŠ, Slovenia ;  DSI/NRF, South Africa ;  MICINN, Spain ;  SRC  and  Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Canton of Bern and Geneva, Switzerland ;  MOST, Taiwan ;  TENMAK ,  Türkiye ;  STFC ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
In addition, individual groups and members have received support from  BCKDF ,  CANARIE ,  Compute Canada  and CRC, Canada;  PRIMUS 21/SCI/017  and  UNCE SCI /013 ,  Czech Republic ;  COST ,  ERC ,  ERDF ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France ;  DFG  and  AvH Foundation, Germany ;  Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF, Greece ;  BSF-NSF  and  MINERVA, Israel ;  Norwegian Financial Mechanism 2014-2021 ,  Norway ;  NCN  and  NAWA ,  Poland ;  La Caixa Banking Foundation ,  CERCA Programme Generalitat de Catalunya  and  PROMETEO  and  GenT Programmes Generalitat Valenciana ,  Spain ;  Göran Gustafssons Stiftelse ,  Sweden ;  The Royal Society  and  Leverhulme Trust, United Kingdom.
Background: Many studies have evaluated the use of hydroxychloroquine in COVID-19. Most retrospective observational studies demonstrate a benefit of using HCQ on mortality, but not most randomized clinical trials. Methods: We analyzed raw data collected from a cohort of 30,423 patients with COVID-19 cared for at IHU Méditerranée Infection in Marseille France and extracted from the DRYAD open data platform. We performed univariate and multivariable logistic regressions with all-cause mortality within six weeks. Multivariable logistic regressions were adjusted for sex, age group (<50, 50-69, 70-89 and > 89 years), periods (or variants), and type of patient management. Results: Among 30,202 patients for whom information on treatment was available, 191/23,172 (0. 82%) patients treated with HCQ-AZ died, compared to 344/7030 (4.
89%) who did not receive treatment with HCQ-AZ. HCQ-AZ therapy was associated with a lower mortality than treatment without HCQ-AZ (odds ratio (OR) 0. 16; 95% confidence interval (CI), 0. 14-0. 19). After adjustment for sex, age, period, and patient management, HCQ-AZ was associated with a significantly lower mortality rate (adjusted OR (aOR) 0. 55, 95% CI 0. 45-0. 68). On a subsample of 21,664 patients with available variant information, results remained robust after adjustment on sex, age, patient management and variant (aOR 0. 55; 95% CI 0. 44-0. 69). On a subsample of 16,063 patients, HCQ-AZ was still associated with a significantly lower mortality rate (aOR 0. 47, 95%CI 0. 29-0. 75) after adjustment for sex, age, period, patient management, vaccination status and comorbidities. Conclusion: Analysis of this large online database showed that HCQ-AZ was consistently associated with the lowest mortality.
Because RCTs and other prospective trials are no longer possible due to the disappearance of the epidemic, it is essential to collect  retrospective data and make them available to the scientific and medical community. In this study, we aim to analyze the factors associated with death at six weeks according to variables contained in a database which is freely available under a Creative Commons Zero (CC O) license, including data on a cohort of 30,423 patients  [13, 14]. While being aware of the disadvantages of observational studies, we believe that this study on more than 30,000 patients, the largest monocentric cohort worldwide, could provide important insights for policy makers on the treatment of COVID with the hydroxychloroquine-azithromycin combination.
Data The construction, quality control and regulatory aspects of the database used in this study were recently described in detail elsewhere  [15]. Briefly, data from 30,423 patients with COVID-19 cared for at IHU Méditerranée Infection in Marseille France were provided from the electronic patient record (EPR) which centralizes all medical information in the hospital. Inclusion criteria were all patients over the age of 18, with PCR-proven COVID-19 who received treatment in the hospital, either as an inpatient or as an outpatient, between March 2, 2020 and December 31, 2021. Treatment data were extracted from medical records and from pharmacy files. The rational for the off-label prescription of AZ and/or HCQ has been reported elsewhere  [16]. Deaths were recorded on the EPRs but also in the French National Death Registry (INSEE) database.
All data were anonymized. The final dataset available in the online database contained the following variables: age (range), gender, pandemic period, outpatient, inpatient, HCQ (hydroxychloroquine), AZ (azithromycin), IVM (ivermectin), virus genomic variant, ICU treatment, time of death, vaccination status, obesity, diabetes, blood pressure, asthma, cancer, immunodeficiency, chronic cardiac disease, chronic obstructive pulmonary disease (COPD), and autoimmune disease. A description of the file structure is reported in detail in the "read me" file in the database folder. For this analysis, raw data were downloaded from DRYAD, https://doi. org/10. 5061/dryad. ksn02v78v.
Participants The database we analyzed contain data from 30,423 patients, and were collected between March 2, 2020, and December 31, 2021. Due to the anonymization process of the database, the mean and median age values were not available. The distribution of patients by age range and Tukey's correction was used to calculate P values and odds ratios for the treatment group variables (model B). a A total of 221 patients were excluded due to missing treatment data (see Table  1 ). b Crude odds ratio with 95% confidence interval. c Adjusted odds ratio with 95% confidence interval. d HCQ: Hydroxychloroquine, AZ: Azithromycin, IVM: Ivermectin. the demographic characteristics of the 30,423 included patients are detailed in Table  1. Some 47. 7% of patients were male. Of the 30,423 patients, treatment information was available for 30,202 of them (99.
2% vs 5. 8%, P =. 057). The monocentric nature of the study might limit the generalizability of the findings, but this design might also reinforce internal validity  [11, 19]. Besides the limitation of patient selection, there is also the limitation of some physicians being outliers in the institution itself, perhaps thus leading to other subtle additional variations in patient care. Non-prescription of HCQ was previously reported in details, the first reason being not proposed by the physician, then, patient with cardiac contra-indication, patient refusing the treatment, patient with potential risk for interaction and others  [20]. Moreover, the monocentric nature of this study attenuates the selection bias in relation with a common standard of care  [21]. The role of severity of the disease in the outcome is also clearly a limitation of this analysis and, unfortunately, cannot be analyzed in this database, because the NEWS score is not available.
Funding This work was performed by academic physicians/researchers working in the  IHU Méditerranée Infection. IHU Méditerranée Infection  is funded by the  French government  and received a grant from the  Agence Nationale de la Recherche :  ANR-15-CE36-0004-01  and the  ANR "Investissements d'avenir" (Investments in the Future) ,  Méditerranée infection   10-IAHU-03 , and was also supported by the  Région Provence-Alpes-Côte d'Azur.
2. 3. 1 In-Situ µ-XRF Spectroscopy and Microdensitometry Coupled, spatially resolved measurements of multi-elemental distribution and radiographic density were performed using an Itrax Multiscanner (Cox Analytical Systems, Mölndal, Sweden) equipped with a µ-XRF detector and an X-ray photodetector. The X-ray source was a Cu target tube operated at a voltage of 50 kV and a current of 30 mA using a scanning step of 500 µm. The X-ray beams used for microdensitometry and µ-XRF spectroscopy were 10 µm and 100 µm in size, respectively. The peaks of Al, P, Ni, Fe, S, K, Cr, Ca, Mn, Ti, and Cl were identified in the µ-XRF spectra by semi-automatic detection with the Qspec software. Compared to elements detected by bulk elemental analysis with ICP-MS (Section 2. 3. 2), the list was short of Sr, Rb, and Nd, all present below the µ-XRF detection limit, and of Mg and Cu, whose peaks interfered with those of the Cu target tube.
The Sr and Nd concentrations in the loading solutions were approximately 100 ppb and 5 ppb, respectively. Because of the low concentration of Nd in some of the wood samples (a few ppb of dry mass), it was sometimes difficult to obtain sufficient amounts of Nd for isotope analyses. Therefore, the corresponding data were discarded because of deteriorated precision and accuracy at these signal levels, as indicated by the measurement of standard solutions of known isotope compositions at similar levels. To reduce the formation of oxides, N 2 was used as an additional gas during Nd measurement sessions. Ions were detected using Faraday cups, with masses of 86 and 146 in the centre cup for Sr and Nd, respectively. Blank signals were corrected using on-peak zeroes (leading as well to the subtraction of Kr-86 for Sr measurements), and mass-dependent isotope fractionation was corrected by using the "natural" ratios 88Sr/86Sr (8.
2. 4 Data Analysis 2. 4. 1 Mass, Volume, and Density Measurements The specimens were weighed systematically before (m before ) and after (m after ) carbonization, and the relative mass loss (RML) was computed as the difference in the weight of the identical specimen before and after carbonization. (2) The specimen size (transverse surface and longitudinal thickness) and density of the specimens were assessed after carbonization, and their relative loss (RL) was computed by comparing the values between the control and carbonized specimens. rl x x x x carbonized control control ( ) 100 (3) Wood density was assessed in two independent ways. First, the bulk specimen density was determined as the ratio of mass to volume of the total specimen, where the volume was estimated from the dimensions measured after carbonization.
For the purpose of this study, we focused on the bulk chemical composition of wood rather than on the chronology of tree development and the µ-XRF scans were treated as average concentrations over wood compartments and specimens. Before data processing, individual measurements were checked for outliers, which were identified as values differing by a factor of 10 or more from the average concentration value. Outliers were removed for two wood specimens measured with ICP-MS measurements (two measurements from the tree's D heartwood at 600°C and one measurement from the tree's D ray 2 sapwood carbonized at 800°C) and one measurement from μ-XRF scan (from the tree's D ray 1 non-carbonized). Elemental and isotope composition data were further processed using R software (version 4. 1. 2). Descriptive analysis was carried out with principal component analysis (PCA) on a dataset of ICP-MS concentrations (n = 58) with carbonization temperature as a supplementary quantitative variable and wood compartment and site as supplementary qualitative variables, with the help of the "fviz_pca" function from the package "factoextra"  (Kassambara & Mundt 2020).
Linear models were selected with the help of the AIC score computed by the function AIC (Table  A1  in the Appendix) and processed with ANOVA type 2. Models were checked for the normality of their residual distributions by plotting histograms and quantile-quantile plots. The significance of random effects was evaluated with the help of a Chi-Square test using the function "ranova" from the package "ImerTest"  (Kuznetsova et al. 2017). First, a model for non-carbonized wood was constructed using the average relative elemental concentrations from µ-XRF spectroscopy (n = 10) as a response variable and wood compartment (heartwood/sapwood), tree, and ray as explanatory variables. The wood compartment was considered a fixed effect because of its fixed relationship with chemical composition, while concentrations in trees and rays, which vary depending on individual observations, were treated as random effects.
Two sub-models were constructed: one with relative concentrations in the form of arbitrary units and the second with relative concentrations normalized to wood density to reduce the matrix effects. In XRF, density is linked to signal intensity, and µ-XRF fluorescence is more strongly absorbed by high-density samples. To study intra-tree variation, a simplified model including only the wood compartment as a fixed effect and the ray as a random effect was applied to a subset of non-carbonized specimens of the three rays of tree D. The adjusted variation associated with the ray effect was computed with the help of the "icc" function from the package "performance"  (Lüdecke et al. 2021). We noted that the dataset was unbalanced regarding the site factor, with only one tree from the Bogny-sur-Meuse location and three trees from the Cagouillères location.
5% of the variation, which indicated its dominant impact on the overall dataset variability (Fig. 4c ). The second axis singled out elements S, Ca, and Mg associated with sapwood and calcareous sites, and elements Al, Fe, and Nd associated with heartwood and acidic sites. Sapwood and heartwood were clearly distinguished on the second axis and partially on the first axis (Fig. 4d ). The sites overlapped on the first axis but were discriminated on the second axis (Fig. 4b ). Nevertheless, the localization of several samples into non-corresponding site groups (Fig. 4b ) indicated a slight interference between heartwood/sapwood and site factors. The interaction between the site and the wood compartment was not tested because of the small number of samples per site (Section 2. 4. 2). PCA indicated that elemental concentrations in carbonized wood were influenced by multiple factors, and it would be impossible to firmly associate a single concentration value with a specific substrate without further information on other factors, especially the carbonization temperature.
71377 for tree D (calcareous site) and 0. 71709-0. 71779 for tree O (acidic site). According to the linear model including temperature, wood compartment, and trees (Table  5 ), both wood compartment (F-value = 12. 79, p-value = 0. 003) and temperature (F-value = 7. 17, p-value = 0. 018) had significant impacts on the Sr isotope ratio. Heartwood had higher 87Sr/86Sr ratios than sapwood, and 87Sr/86Sr ratios tended to slightly decrease with temperature, with a visible step between 200 and 400°C. Nevertheless, it was the variation induced by trees, including Štulc et al. differences in their geographical origin that was responsible for 96% of the Sr isotope variation over the whole dataset, and both wood compartment and temperature effects were thus regarded as very weak.
3. 5 Nd Isotope Signatures εNd of non-carbonized wood from trees at the calcareous site varied between -9. 3 (tree A sapwood) and -11. 6 (tree B heartwood), with a global mean ± SD value of -10. 4 ± 1. 0 (n = 6), and -11. 3 ± 0. 5 (n = 3) for heartwood and -9. 6 ± 0. 4 (n = 3) for sapwood (Fig. 7 ). The εNd dataset did not comply with the requirement of a minimal number of measurements to perform the model previously used for elemental and Sr isotope data (Section 2. 5), such that only partial ANOVA tests were performed. The ANOVA test (p-value < 0. 05, not shown here) indicated the absence of any significant difference between trees at the same site (calculated for non-carbonized wood from trees A, B, and D from the calcareous site, n = 6). The relationship between εNd values in heartwood and sapwood was unclear: for non-carbonized wood, the εNd values of heartwood and sapwood of tree D overlapped with uncertainty, although heartwood was characterized by a somewhat lower εNd value, whereas at 800°C, sapwood displayed lower εNd values than heartwood (Fig.
4 ), Ca was identified as a characteristic of the calcareous site, while other elements were associated with the acidic site. In particular, the concentrations of Ca normalized to Sr and Nd were approximately 10 times higher for the calcareous site than for the acidic site. Amongst the thermostable elemental ratios, the Ca/Mn and Mg/Mn ratios showed the highest discrimination potential for carbonized wood, given the high inter-site and low wood compartment differences in our dataset. Temperature has a significant effect on the Sr isotope ratio. The reason for such variation in Sr isotope ratios with carbonization temperature is unclear, given the fact that such "radiogenic" isotope ratios are in theory insensitive to isotope fractionation but might include intra-specimen variability (see Section 4.
In our study, wood Ca/Sr was more elevated at the calcareous site than at the acidic site, which is unexpected considering only the aforementioned physiological processes. To our knowledge, the Ca/Sr ratio in wood has not yet been explored for its potential in distinguishing sites of tree growth, although the proxy is familiar to archeologists working on paleodiets to infer meat vs. vegetal composition  (Runia 1987; Sponheimer et al. 2005). As expected, wood from the Cagouillères site, situated on limestone, had lower 87Sr/86Sr ratios (0. 7138 ± 0. 0010) than wood from the Bogny-sur-Meuse site (0. 7174), which was developed on more radiogenic schists. The values measured in this study were compared with the Sr isotope ratios of plant samples indicated in the IRHUM database for metropolitan France  (Willmes et al.
2014; 2018) , available online at http://80. 69. 77. 150/. A tree from a sampling point in the IRHUM database situated 18 km from the Bogny-sur-Meuse site had 87Sr/86Sr = 0. 7123, while grass from sampling points situated respectively 11. 5 and 23 km from the Cagouillères site had 87Sr/86Sr equal to 0. 7108 and 0. 7118. Therefore, values from the IRHUM database matched with our values acquired at the calcareous site (0. 71381 ± 0. 00103 for heartwood at Cagouillères; for this comparison, we considered only heartwood as the tree rooting depth during heartwood formation is more comparable with that of grass). However, there was a considerable discrepancy between the values obtained for the acidic site (0. 71736 ± 0. 00026 at Bogny-sur-Meuse) and the IRHUM database. This discrepancy might be due to small-scale variability in the underlying rocks or to differences in rooting depth between trees from the two sites (Section 4.
Acknowledgements Microdensitometry and µ-XRF spectroscopy measurements were supported by  SILVATECH (Silvatech, INRAE , 2018. Structural and functional analysis of tree and wood facility, doi: 10. 15454/1. 5572400113627854E12 )from UMR 1434 SILVA, 1136 IAM, 1138  BEF , and 4370  EA LERMAB Research Centre INRAE Nancy-Lorraine , for participation in. The SILVATECH facility was supported by the  French National Research Agency through the Laboratory of Excellence ARBRE  ( ANR-11-LABX-0002-01 ). Sr and Nd isotope analyses were supported by the  IPGP multidisciplinary program  PARI and by  Paris-IdF region SESAME  Grant No. 12015903. The study received financial support from  CNRS  ( MITI ISOTOP AAP 2020 -CARBONARA ,  80PRIME2020 ), from  CNRS and Ministry of Culture (MITI Chantier scientifique de Notre-Dame de Paris) , and from  ANR  ( ANR CASIMODO -ANR-20-CE03-0008 ).
2006; Walser et al. 2020). There is thus historical evidence that Icelandic eruptions can produce very substantial SO 2 and halogen emissions that have devastating local impacts. Understanding gas emissions and their variation in such systems is therefore an important aspect of hazard assessment and scenario construction. In this paper we compare and contrast multidisciplinary datasets from the eruptions at Fimmvörðuháls and Holuhraun. We look in particular at the emissions of halogen species in both cases, and at the hazards presented in the near-field.
2010; Bureau et al. 2000 Bureau et al. , 2010;; Cadoux et al. 2018; Donovan et al. 2014). Cadoux et al (2018)  further develop an S-Cl-Br degassing model that suggests that the gas phase will initially be high in S, but as S becomes degassed, will become higher in Cl and Br  (Cadoux et al. 2018). We use our data to analyse the different degassing pathways at Eyjafjallajökull and Holuhraun. We supplement the limited data available for volcanic gas emissions from Icelandic volcanoes with new observations, and evaluate their implications for monitoring and management of volcanic hazards from eruptions. In particular, we report (i) observations of BrO/SO 2 ratios for the emissions from Fimmvörðuháls, Eyjafjallajökull and Holuhraun; (ii) observations of SO 2 fluxes associated with the eruption of Eyjafjallajökull; and (iii) examine periodicity in these and other gas datasets.
UV spectroscopy All UV spectra were collected using an Ocean Optics USB2000 + spectrometer mounted on a tripod, with data acquisition controlled by DOASIS software  (Kraus 2006). Stationary observations, with fixed fields of view, were made for measurement of SO 2 and BrO abundances, and traverses beneath the plume carried out for flux measurements. At Fimmvörðuháls, only stationary observations were made. Traverses were not possible due to the small plume and the location of the eruption. Traverse measurements were made for both Eyjafjallajökull summit and Holuhraun eruptions. A single stationary instrument was used at two locations over the four-day period at the Fimmvörðuháls fissure (Fig. 2a ). A range of elevations over the vent were measured to determine changes in plume composition.
An exposure time of 140 ms was used with 25 co-adds, and ~ 470 plume data were recorded per traverse. Wind direction (and/or plume height) was highly variable during this period and there appeared to be a second branch of the plume travelling ENE, which we could not access due to icy roads. Between 17:46 and 18:58 UT, the plume direction shifted and we were able to perform a further three traverses (TR8 to TR10) 10-35 km west of the crater. For these traverses exposure times of 160 to 200 ms were optimal, with 22 to 18 co-adds. An average of 350 plume measurements were recorded per traverse. A map of the traverses is included as Figure  A5. At Holuhraun, traverses were performed using the methodology outlined in  Pfeffer et al. (2018) , where traverse data are presented. The methods used the same scripts as detailed above, with exposure time and co-adds selected prior to the traverse according to the light conditions.
Gas retrievals from UV spectra All data were analysed using DOASIS software  (Kraus 2006) , with JScript's available at http:// www. geog. cam. ac. uk/ resea rch/ proje cts/ doasr etrie val/. Each recorded spectrum was first corrected for electronic offset and dark current, and then shifted to give the best coincidence with a high-resolution solar emission spectrum  (Kurucz 1995) , convolved with a 0. 45 nm FWHM Gaussian slit-function. Column amounts of gases were retrieved from each spectrum following standard differential optical absorption spectroscopy procedures  (Platt 1994; Platt and Stutz 2008). During all fits a small shift (± 0. 1 nm) and squeeze (0. 99-1. 01) of the absorption cross sections were allowed to account for optical misalignments and shift in the spectral calibration of the spectrometer  (Stutz and Platt 1996).