dataesr/scientific-paragraphs-categorization huggingface dataset:
Ascertainment of chemical mixtures Food contamination levels were obtained from the second French total diet study (TDS2) conducted by the French Agency for Food, Environmental and Occupational Health & Safety (Anses)  (ANSES, 2011a (ANSES, , 2011b)). Briefly, from 2007 Briefly, from to 2009, 20,280 , 20,280  food items were purchased in 8 French regions in order to analyse the presence of more than 400 contaminants in 1352 composite samples prepared as consumed (i. e. washing, pealing, etc. ) according to the French dietary habits  (Sirot et al. , 2009). The E3N food consumption database and the Anses food contamination database were then merged to obtain the individual daily dietary intake i to a chemical substance s (in g of substance/day), by summing over all food items, the quantity q of the food item f consumed (in g of food/day) multiplied by the contamination level c of the food item f by the substance s (in g of substance/g of food)  (Mancini et al.
Main analyses Cox proportional hazard models with age as the time-scale were applied to estimate hazard ratios (HR) and their 95 % confidence intervals (CI) for the associations between the weights of adherence to each mixture and the risk of all-cause, cancer and CVD mortality. Age at the start of follow-up was the age when answering the dietary questionnaire, and age at the end was the age at death, at the last completed questionnaire or at the end of the follow-up period which corresponded to the last update of the mortality database (in November 2014), whichever occurred first. The main exposure variables were the weights of adherence to each of the 6 chemical mixtures divided by their STD in order to estimate the HR for one STD increment. We excluded values below the 1 st (except if they were zero) or above the 99 th percentile of the main exposure variables from our models to reduce the potential impact of outliers.
, 2007; Freedman et al. , 2011). Finally, in this study, no specific information concerning organic and not organic food is available, nor for food consumption data, nor for food contamination data. This limitation can lead to a potential misclassification of the exposure, especially for pesticide intake. This study also benefits from several strengths. The analyses on more than 70,000 women followed for an average of 19 years allow the study of possible long-term health effects based on a sufficient number of cases to achieve adequate statistical power. The numerous sensitivity analyses ensure the robustness of the results. Furthermore, mortality cases were obtained from the mortality database with causes of death validated by the Inserm-CépiDc, which permits to accurately classify the participants in relation to the outcome.
A graphite cylindrical crucible of inner diameter 40 mm and height 45 mm is used; the side wall is 15 mm thick while the bottom wall is 5 mm thick. Its weight is 96. 6 g. It is set within a counter crucible, made of the same material, of inner diameter 55 mm, height 53 mm and weight 70 g. External walls are 8 mm thick. The 80. 2 g silicon load originated from a mono-crystal grown in a CZ furnace. The data acquisition chain simply consists in a wave generator emitting a burst every minute. The same transducer, mounted on the waveguide, is used to emit the bursts and listen to the echoes between two bursts. A digital scope HD06054-MS triggered by the wave generator records each burst and its echoes after travelling through the setup. We initially intended to use bursts constituted by three periods of a 4.
5 MHz sine with Material c (m. s -1 ) (longitudinal waves)   9, 10, 29, 30, 31, 32, 33, 34] ) and the resulting intensity transmission coefficients through the interfaces with normal incidence as if they were between infinite domains (see eq. (  1 )). The materials are sorted in the order of the path followed by the acoustic signal in the set-up. The range given for solid silicon celerity refers to crystal orientation (1 0 0) and (1 1 1) respectively (  [9] ) and are considered as upper and lower bounds. Consequently, acoustic impedance and transmission coefficient depending on the solid Si celerity are given as ranges. Celerity data for carbon glass have been measured experimentally and data for graphite have been extracted from times of flight. ρ (kg. m -3 ) Z = ρc (10 6 kg. m -2. s -1 ) T ∞,∞ 10 V peak-to-peak amplitude.
Synthetic data The envelope of the signal is plotted as a full red line. It has been extracted from the time-series, using the envelope built-in function of Matlab ™ and low-pass filtering the result with a 4 MHz cut off frequency. While the waveguide is moved vertically, the echo I 1 (with a time of flight denoted t f ) is tracked by applying a correlation algorithm between time series for each given position and a reference signal. The latter is taken when the waveguide tip was into contact with the crucible for which the time of flight for I 1 is denoted t f,0. The delay τ = t f -t f,0 corresponding to the maximum of the cross-correlation function was considered as the time of flight in the liquid between the waveguide tip and the crucible bottom. The celerity of ultrasounds in liquid silicon was taken as c l = 3920 m.
Acknowledgements This work was carried out as part of the  ASTRES  and  BRASSOA  projects supported by the institut  Carnot Ingénierie@Lyon  and  AWINSI  project by  Ingénierie@Lyon and Énergies du Futur. The authors thanks  Benoît Viniere  for his preliminary work on data analysis,  Beatrice Drevet  for fruitful discussion,  Damien Ponthenier  for the induction furnace setup and  Benoît Marie  for conducting the EBSD analysis. The authors want to thank the anonymous referees for their suggestions improving the manuscript. For the purpose of Open Access, a CC-BY public copyright licence has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission.
The neurospheres (SPHERES II) were allowed to differentiate for one week. The presence of neurons (TUJ1, blue), astrocytes (GFAP, red) and oligodendrocytes (Olig2, green) was assessed by immunostaining. Nuclei (DNA) were stained with DAPI (white). D. Diameters of the secondary neurospheres (SPHERES II) derived from primary neurospheres that had been exposed to control or IL-22 condition. Data represent means ± s. e. m of 3 independent experiments (n= 426 neurospheres in control condition, n = 492 neurospheres in IL-22 condition). ns: not significant. E. Western blot and quantification of TUJ1, GFAP and Olig2 expression in SPHERES II derived from primary neurospheres that had been exposed to control or IL-22 condition. Data represent means ± s. e. m of 4 independent experiments, ns, no statistically significant difference.
Second, there is no official production data to correlate with reported coal consumption. Third, expert studies  [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]  lack methodological detail. Estimates are typically based on rough bottom-up calculations of activity and distribution of kiln technologies at the national level without considering regional differences in operating practices. Finally, despite consultation among brick manufacturers and independent groups of industry experts, an extreme paucity of datasets has impeded robust energy benchmarking for this industry. Therefore, this study aims to resolve the disparities in energy consumption in brick production through a transparent framework that includes sufficient information on raw materials, process variables, practices and kiln counts, with attention to regional diversity.
Energy performance of brick kilns The energy performance of kilns is expressed as the energy consumed (megajoules, MJ) per unit mass of total fired bricks (kilograms, kg), referred to as specific energy consumption (SEC, MJ kg -1 brick). This study uses reported information on production numbers, weight of bricks and fuel quantities collected during field surveys to estimate the range of SEC (see Methods). Compared to existing reports (Supplementary Table  24 ), this study includes a larger number of datasets and greater regional coverage. Energy performance is primarily a function of kiln technology. In India (as in other Southeast Asian countries), traditional technologies such as BTKs and clamps are dominant  8, 19, 20  , with conservative shares of advanced versions such as zigzag and vertical-shaft brick kilns (VSBKs).
While methodological improvements involving further disaggregation of SECs by additional behavioural and geographical factors can be attempted in future studies to improve sustainability assessments, uncertainty can be reduced by improving the collection of official data on brick production and fuel consumption. This calls for a sectoral reform that ensures the registration of all brick kilns in operation through formalization under unified administrative oversight. This will also enable monitoring and tracking of the performance of this industry in the future. Third, this work provides the largest field survey and remote sensing datasets on the Indian brick sector available so far. The survey dataset can be used to develop and disseminate standard operating protocols for sectorwide best practices.
The districts were then selected randomly and no district was preferred on the basis of any specific kiln parameter. Our aim was to cover as many states as possible to capture a nationally representative sample. The Google Earth Pro software was used for mapping because it is open source and has a user-friendly interface to map and organize the marked locations. In the exercise, the district area was scanned grid by grid and each kiln was manually tagged on the basis of certain visual cues. The characteristic shape of BTK is oval and that of zigzag is rectangular. Consultation with industrial experts and brick owners from surveys suggests that this is around 95% valid as a zigzag kiln requires zigzag-line firing, which can be achieved better with a rectangle shape. Both kiln types have a chimney at the centre.
Since the survey responses relied mostly on verbal communication, it was prone to erroneous data entries. Thus, a meticulous quality check was carried out for each survey entry. Only responses that were found to have unrealistic values were discarded. This still yielded 210 BTK, 58 clamp and 22 zigzag samples. These are not only larger than all previous studies combined but also include greater regional coverage. For geotagging, several independent scans were repeated at random districts by multiple research scholars to validate for human error. Kiln operating conditions were also checked; common signs of a neglected kiln were dense vegetation cover over a kiln area, lack of soil preparatory activity around a kiln and absence of any major clusters around a kiln. Further, to prevent confusion with other industries, effort was made to scan the surrounding areas for signs of eroded soils, stacks of green bricks, muddy patches and lack of vegetation (also used in recent image-processing studies  18  ).
3 ). Finally, a regression was calculated for a series of different cases that excluded all districts from Bihar, Uttar Pradesh, Haryana, Assam or Punjab, or included only geotagged data, public portal data or RTI data (Supplementary Table  13 ). Moreover, the effectiveness of the model was validated by comparing the model output to a historical estimate (Supplementary Table  14 ). The technology-distributed districtwise production from the geotagging was used to estimate the mean technology share for state and region. For each of the 19 states, there were at least 3 districts geotagged. Thus, for each state, a mean production was estimated under each technology type by averaging data for the districts. Using this, a mean fractional share per technology type was estimated as the ratio of mean production for a technology type to the total statewise mean production.
Estimating energy use In our approach for calculating energy and fuel consumption, we began by estimating the state-level brick production 'B s ' (millions per yr) using the regression model. For each state, the production, B s was apportioned into various kiln technologies 'k' using the kiln share 't k,s ' (%) based on geotagging datasets. Further, for each kiln technology, the production was apportioned into specific characteristics 'c k , s ' (%) that influence their energy performance. For BTK and zigzag, production was split into three categories of fuel mix and for clamps, three categories of production capacity. The amount of brick produced was converted to mass of the bricks produced using the representative weight 'w s ' (kg) of one fired-clay brick in that state. Subsequently for each kiln technology-factor combination, the corresponding specific energy consumption 'SEC c , k ' (MJ kg -1 ) was used to estimate total energy.
Acknowledgements This work was supported by the  Indian Ministry of Environment Forest and Climate Change  under the  NCAP-COALESCE  project (Grant No. 14/10/2014-CC(Vol. II) ). We thank the internal review committee of the  NCAP-COALESCE  project for their comments and suggestions on this paper. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Ministry. The Ministry does not endorse any products or commercial services mentioned in this publication. 1  Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Mumbai, India. 2 Pune, Maharashtra, India. 3  Greentech Knowledge Solutions Pvt. Ltd. , New Delhi, India. 4De Boer Damle (India) Pvt. Ltd. , Pune, Maharashtra, India. 5Coal and Energy Division,  CSIR-North East Institute of Science and Technology, Jorhat, India.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy All input data used in this study are presented in the supplementary data. Any additional data can be made available upon request to the corresponding author.
Study design and subjects This was a prospective single-center observational cohort study. All patients who were admitted between April to June 2020 to any of the intensive care units (ICU) of the University Hospital of Besançon (France) for a COVID-19 infection were contacted upon hospital discharge and invited to participate in the trial. The study consisted of a follow-up at 3, 6 and 12 months after symptom onset (NCT04519320, 19/08/2020). Cardiopulmonary exercise capacities were only assessed at the 12 months followup. Patients were eligible if they were > 18 and < 80 years old and had initially confirmed SARS-CoV-2 infection by quantitative RT-PCR on nasal swabs or bronchoalveolar lavage. Patients were excluded if they were known to have prior chronic respiratory insufficiency, if they had a significant psychiatric pathology, or if they had a life expectancy estimated at less than one year.
CT image acquisition and analysis Chest CT scans were acquired in the supine position at full inspiration without contrast medium (Revolution CT; GE Healthcare, Milwaukee, WI, USA). CT images were assessed by two readers blinded to clinical data that evaluated the presence and extent of ground-glass opacities (GGOs), reticulations, bronchiectasis, emphysema and honeycombing as defined by the glossary of terms of the Fleischner Society  [10].
Statistical analyses Statistical analysis was performed using GraphPad Prism version 9. 0 (GraphPad Software Inc. , San Diego, CA, USA). Normal distribution of quantitative variables was tested by the Kolmogorov-Smirnov test. Descriptive statistics are presented as mean ± standard deviation (SD), median (25th to 75th percentile), or number (%), as appropriate. Student's T or Mann-Whitney U-test tests were computed to assess statistical differences between groups for normal or non-normal quantitative variables, respectively. Categorical variables were analyzed by Fisher's exact test when appropriate. Correlations were examined by Spearman rank test or Pearson test. Multiple linear regression was applied for peak V′O 2 (ml/kg/ min) as dependent variable, using a stepwise approach of potential determinants that showed significant associations in previous univariate analysis.
A B S T R A C T Ambient noise tomography (ANT) has proved to be efficient in resolving crustal structures for different purposes. Despite the numerous studies carried out in Gran Canaria in the past for purposes of geothermal exploration, ANT had never been used before on this island. We applied this technique to understand Gran Canaria island's shallow structure, determining the 3-D S-wave velocity model of the island and focusing on its implications for geothermal exploration. We used data from 30 seismic stations installed between October 2019 and February 2020. Our findings revealed five relevant velocity anomalies helpful in understanding the island's geology and its geothermal potential. In particular, we identified two high-velocity anomalies in the central part of the island aligned with the primary northwest-southeast structural trends of Gran Canaria.
6. A). More recently, magnetotellurics data were used by  Ledo et al. (2021)  to obtain the first electrical resistivity model of Gran Canaria island. The dataset used in this study consists of 100 magnetotellurics soundings distributed around the island from July to September 2017. The 3-D resistivity model shows a very heterogeneous island, characterized by high-resistivity contrasts associated with porous materials, fractures with or without water, and clays. Further, they observed a few near-vertical conductive structures in the south and eastern parts of the island (< 10 Ωm). The authors related these vertical structures with hydrothermal alteration and smectite clays generated by small isolated convective cells.
, 2021). In addition, two permanent stations operated by INVOLCAN for volcano monitoring (Fig. 1 ) were operational during the three months of the campaign. Data were acquired at a sampling rate of 100 Hz. First, the recorded data were pre-processed. A network-based method was used to automatically remove time windows containing earthquakes. The method is based on the analysis of the network covariance matrix (equivalent in the Fourier domain of the cross-correlations matrix), of which the width of the eigenvalues distribution (called spectral width) is a proxy of the number of seismic sources composing the wavefield  (Seydoux et al. , 2016; Soubestre et al. , 2018). Applying this technique to each five-minute-long time window of our data automatically indicates whether a coherent signal (e.
7 s and T = 3. 2 s (limits defined in Section 4) was selected and inversed at depth to obtain the reconstructed model. Figure  S5. A shows the probability distribution for Vs at each depth, together with the input and reconstructed models. The difference between the input and reconstructed model is minimal, which indicates that the model has been correctly reconstructed at all depths. Second, we calculated Rayleigh wave group velocity sensitivity kernels for the mean S-wave 3-D velocity model at different periods (Fig. S5. B ). We used the software senskernel-1. 0 (Colorado University) to calculate the partial derivatives of the Rayleigh wave fundamental mode relatively to the S-wave at periods of T=1 s, T = 2 s and T=3 s. The result shows that the maximum sensitivity at all periods is at 2 km depth.
Conclusions We obtained the first 3-D S-wave velocity model of Gran Canaria island by using ANT. We used data from 28 temporary and two permanent broadband seismic stations. We applied a conventional methodology to extract dispersion curves for all the pairs of stations. Subsequently, we applied a non-linear multiscale inversion to obtain 2-D group velocity maps for different periods according to  Cabrera-Pérez et al. (2021). Finally, we applied a Bayesian transdimensional approach to perform the depth inversion. The 3-D S-wave velocity model shows the presence of very well differentiated velocity anomalies: two with high-velocity (H1 and H2) and three with low-velocity (L1, L2 and L3). The high-velocity anomalies could be related to intrusive volcanic bodies, related to the early phases of the island formation and aligned along the main structural trend of the island.
Acknowledgments This work has been possible thanks to the projects Impulso a  la energía geotérmica de alta entalpía en Canarias  ( 187G0132 ), financed by  Gobierno de Canarias  and  TERMOVOLCAN  ( RTC-2017-6627-3 ), co-financed by the  Spanish National R&D  plan. We acknowledge anonymous reviewers for their constructive commentaries and valuable suggestions, which improved the quality of the manuscript.
Background: Statistical monitoring involves the review of prospective study data collected in participating sites to detect intra/inter patients and sites inconsistencies. We report methods and results of statistical monitoring in a phase IV clinical trial. Method: PRO-MSACTIVE is a study evaluating ocrelizumab in active relapsing multiple sclerosis (RMS) patients in France. Specific statistical methods (volcano plots, mahalanobis distance, funnel plot …) have been applied to a SDTM database to detect potential issues. R-Shiny application was developed to generate an interactive web application in order to ease site and/or patients identification during statistical data review meetings. Results: The PRO-MSACTIVE study enrolled 422 patients in 46 centers between July 2018 and August 2019. Three data review meetings were held between April and October 2019 and 14 standard and planned tests were run on study data, with a total of 15 (32.
Study design (Fig. 1) PRO-MSACTIVE is a national, multicenter, open-label, single-arm phase IV French study, conducted in patients with active RMS (relapsing-remitting multiple sclerosis [RRMS], or secondary progressive multiple sclerosis  [SPMS] ). The primary endpoint, which was assessed at week (W) 48, was defined as the proportion of patients free of disease activity defined by no relapses and no T1 gadolinium-enhancing lesions nor new or enlarging T2 lesions using brain magnetic resonance imaging (MRI). Disease activity, disability and PROs using 6 questionnaires for disease severity (MS symptoms' severity scale  [SymptoMScreen]   Data domains studied during SM were: demographics, baseline and disease characteristics, dates of scheduled visits, laboratory data, vital signs, Brain MRIs, AEs and PROs.
The graphical representation was box-plots displaying Mahalanobis distances overall and by site. Thresholds and reference lines as shown in Fig. 4  were only used to easily identify outliers and were based on the 5th percentile. Additional information can be found in the Statistical Monitoring Plan (Appendix 1). Table  1  describes all SM tests and associated graphical representation that were applied. These SM tests were applied to the CDISC SDTM (Study Data Tabulation Model)  [3]  study database extracted at specific time points using SAS version 9. 4 and R software. The study team was responsible for providing the following information and documents: protocol, annotated eCRF, data review plan, list of critical variables, list of active sites at each trigger point, snapshot of the database every time a trigger point was reached, reviewing the report provided by the statistical monitor to identify areas of potential interest and investigation of findings, with a follow-up through resolution for any actions identified.