dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion We use a community ecology approach, drawn from the study of macroorganisms, was used to investigate bacterial community spatial distributions at the micro-scale across four different soils. The micro-sampling (few hundred milligrams: from 100 to 500mg) was carried out on undisturbed soil so as to take the spatial distribution of taxa in the micro-samples into account (within and between micro-samples). The analysis of bacterial diversity data combined with soil micro sampling suggested that bacteria can be classified in each soil as core or satellite taxa, depending on their distribution at the small scale.
Funding This work was cofunded by the  EC2CO MicrobiEn AO2012-779949  ' Les communautés bactériennes dans les sols extrêmes: les paramètres de leur structure et leur composition ' and the  Labex DRIIHM ,  French program  " Investissements d'Avenir"  ( ANR-11-LABX-0010 ) which is managed by the  ANR. We thank  La Vanoise National Parc  for soil sampling authorization.
4b. The AERONET database  (AERONET, 2021)  provides AOD values at 440, 675, 870 and 1020 nm together with further products such as the lidar ratio and linear depolarization ratio. The lidar ratio and particle linear depolarization ratio are retrieved by means of the AERONET v3 shown together with the mean of the four-wavelength retrieval (orange squares, standard retrieval) and the six-and seven-wavelength retrieval (pink and purple squares, GRASP method). The GRASP retrieval at 1640 nm (not shown) leads to lidar ratios of 94 ± 8 (using six wavelengths) and 101 ± 4 sr (using seven wavelengths). inversion algorithm  (Dubovik and King, 2000; Giles et al. , 2019; Sinyuk et al. , 2020) , which assumes a spheroidal dust particle shape  (Dubovik et al. , 2006). Additionally to the standard four wavelengths, the inversion was performed using the GRASP algorithm at six wavelengths (plus 500 and 1640 nm) and seven wavelengths (plus 380 nm,  Dubovik et al.
As a final remark, it would be helpful -and a good addition to field observations -if laboratory measurements of the depolarization and lidar ratios at all three wavelengths (in the 180 • backscatter direction) could be realized for well-defined size fractions of real dust particles with real irregular-shape characteristics. An effort was already started by  Miffre et al. (2016). Data availability. The BERTHA lidar data can be obtained upon request from Moritz Haarig (haarig@tropos. de). The Polly XT data are available in the PollyNET database (https://polly. tropos. de/) (PollyNET, 2021). The AERONET data are available via the AERONET web page (https://aeronet. gsfc. nasa. gov/) under the site "Leipzig" (AERONET, 2021). Author contributions. MH performed the lidar measurements, analyzed the data and wrote the manuscript.
Acknowledgements. The authors thank  Mark Vaughan ,  Oleg Dubovik  and the anonymous reviewer for their helpful comments and suggestions improving the quality of the paper. Financial support. The publication of this article was funded by the  Open Access Fund of the Leibniz Association. Review statement. This paper was edited by  Farahnaz Khosrawi  and reviewed by  Mark Vaughan ,  Oleg Dubovik , and one anonymous referee.
This procedure provides insights in the convergence properties of the training stage which is a major algorithmic issue  [8]  for Neural Networks. For exemple, in  [6] , various tests for x → x 2 implemented within a dense Neural Network of width 3 and arbitrary depth k completely failed to recover the accuracy O(4 -k ). In the tests below which are based on the structure explained in this document, the asymptotic accuracy is much better captured. Elementary tests were implemented 2 in Julia (see https://julialang. org) using an automatic differentiation library  [16]  and an optimization library  [14]. The integral in (3. 9) being evaluated by quadrature with uniform discretization, it is equivalent to say that the dataset for training is D = {(x i , H(x i )) , i = 1, 2,. , N } where 0 ≤ x i = i-1 N -1 ≤ 1.
Since we take N = 1000 in our tests, the dataset is oversampled. We used various standard optimizers, such as the Newton-Raphson method, the LBFGS quasi-Newton method or a simple Gradient Descent method with line research. Tests with the Gradient Descent showed that the optimum is unchanged but the rate of convergence is much slower. We expect that with Stochastic Gradient Descent  [8] , the convergence will be even slower. So to display results with good accuracy obtained in a reasonnable time, we present the results obtained with the Newton or quasi-Newton method. We record the accuracy in function of the depth k. For increasing values of k, we report the numerical L 2 (I) error E(k) = J(W k ) where the optimal value W k has been obtained at the end of the training and K(k) which is the value of the constant K evaluated in function of W k.
Methods Since 2018 we have been collecting information on the prevalence of nicotine vaping as part of the Global State of Tobacco Harm Reduction project (GSTHR). This project maps the global, regional and national availability and use of SNP, the regulatory responses to these products, and the public health potential of tobacco harm reduction. Information is collected from the literature and a network of national and regional correspondents on smoking prevalence and mortality, SNP and NRT use and regulations and controls across 201 countries (262 including territories and signi cant within-country regions). The database has been designed to compile country-based information over time and product categories Country level data are available on the GSTHR website (https://gsthr. org) and updated in real time as new information becomes available.
Data on vaping are reported where available from nationally representative surveys of the general adult population. Inclusion criteria for the studies used in our analysis were that they were recent (published between 2012 and 2020) general population surveys and included information on daily or current nicotine vaping (which was often de ned as use of e-cigarettes). We found surveys on nicotine vaping from 49 countries (see Results). The question arises as to how to derive a global estimate of vaping. The problem of missing data is quite common in the social sciences and epidemiology. Data loss begins at the survey implementation stage. The selected sample is never fully complete. Certain groups of people are always unavailable. Researchers try to avoid statistical error caused by missing data and the most commonly used of these is post-strati cation weighting  [14] [15] [16] [17].
To achieve better control over the quality and reliability of the process, a synthetic goodness-of-t characteristic can be calculated. It can be based on the standard deviation and the number of cases in the aggregated groups. This is analogous to a simpli ed con dence interval. A further issue regards the availability and relevance of data on country characteristics from which similarity between countries may be inferred. We used the WHO region, the World Bank income classi cation and the legal status of NVP. Our database (https://gsthr. org) lists countries which ban the sale of NVP. The second methodological problem is that surveys are conducted in different years and are often not repeated. Most of the available data we found allow for the calculation of an estimate for 2018 (32 studies are from 2017-2018, 14 are earlier and 3 are later).
Estimation for countries without nicotine vaping survey data Based on the available information from the surveys, we calculated the average prevalence of vaping for each WHO region, World Bank income classi cation group and the legal status of the sale of e-cigarettes. Unfortunately, as we expected, some groups are very poorly represented (see These three factors gave us four income groups, six regions and three sales statuses, which allowed us to separate 72 subgroups. For each of the groups the average prevalence of vaping was calculated. These 72 values were used as substitutes for the prevalence gures in the countries belonging to the group. Of course, not all subgroups were represented. For the rst (1 -in Fig. 3 ) -the most detailed, three-factorsubdivision we had information for only 13 subgroups, which allowed us to calculate estimates for 83 countries.
All remaining gaps were lled with the fth step. We started with 49 known countries. The rst step increased this number to 83, the next to 161, next to 190, the fourth one gave only three more countries, increasing the number of countries to 193, and the fth to 198. There were still three countries left. We attributed the average value obtained from all known countries to these countries. Figure  3  shows detailed information of this process. The number of vapers was calculated by taking as the denominator the total adult population (over 15 years old) obtained from the UN database for 2018  [2].
There is also wide variation in survey de nitions of vaping. It is obvious from the data collected and assessed in our database that the available information is often of poor quality -survey samples are sometimes insu ciently large to provide su cient precision to measure such small sections of the population. The sampling method of many studies is not probabilistic, which makes it impossible to generalise the measured values correctly per population. It is well known that monitoring risk behaviours in relatively small or hidden populations is not straightforward. Moreover, despite the fact there is considerable global and international debate about vaping nicotine products, most countries are taking no steps to monitor use at a national level. Evidence based policymaking is very di cult in the absence of evidence.
Foraminifera ultrastructural observation and image processing Four specimens per experimental time point (4-20 h) plus one un-fed (control) specimen were examined. From each specimen, a minimum of 50 TEM images was taken, including images detailing the degradation vacuoles (5-27 images of degradation vacuoles per specimen). The ultrastructure was examined at different parts of the images focusing (a) in the cell interior to document vitality, (b) on degradation vacuoles to determine their content, and (c) at the exterior to survey for microbes entrained in remnant "reticulopodial trunk" material, which can be extended outside foraminiferal tests during feeding and locomotion  (Anderson and Lee, 1991). Images are deposited at PANGAEA with DOI number XXX. To obtain an overview of the entire specimen and localize putative methanotrophs at the test (shell) aperture, images were compiled automatically using the stitching-feature in Adobe Photoshop CS2.
Foraminiferal genetics Six of 13 specimens analyzed for genetics were positively amplified and sequenced (Fig. S3 ). The sequences are deposited in GenBank under the accession numbers MN514777 to MN514782. When comparing them via BLAST, they were between 98. 6% and 99. 6% identical to published sequences belonging to foraminifera identified as the morphospecies N. labradorica, from the Skagerrak, Svalbard and the White Sea  (Holzmann and Pawlowski, 2017; Jauffrais et al. , 2018). Sequences were also included in an alignment comprising other nonionids implemented in Seaview (not shown) and corrected manually to check the BLAST search. This step confirmed the BLAST identification.
Acknowledgments We thank the captains, crew members and scientists onboard  R/V Kronprins Haakon  and  ROV AEgir Team  for their assistance;  Anne-Grethe Hestnes  for growing the methanotroph culture. Florence Manero ,  Romain Mallet  and  Rodolphe Perrot  at the  SCIAM microscopy facility University of Angers  are to thank for their expertise with the TEM and SEM. We thank  Sunil Vadakkepuliyambatta  for helping to prepare the map presented in Figure 1;  Sophie Quinchard  ( LPG-BIAF ) for supporting the molecular analysis. Funding was received through the  Research Council of Norway ,  CAGE (Center for Excellence in Arctic Gas Hydrate Environment and Climate , project number  223259 ) and  NORCRUST  (project number  255150 ) to GP, EG, and  CS. CS position was funded through the  MOPGA (Make Our Planet Great Again)  fellowship by  CAMPUS France , the  NORCRUST  project and the  University of Angers.
Acknowledgements M. F. C. , A. V. , M. K. -F. , M. M. , G. L. , and L. H. contributed equally to this work. The authors gratefully thank  Dr. Gerhard Frank  and  Alexander Matthies  for their support in the use of the SEM and the preparation of RNA samples. A. L. -E. acknowledges the financial support provided by the  German Research Foundation  ( LE 3418/2-1 ). R. P acknowledges the support provided by the  German Research Foundation (DFG) , project number  248122450. M. F. C. thanks  CONICYT  for his postdoctoral fellowship. A. R. B. and A. K. B. acknowledge the financial support provided by the  German Research Foundation  ( TRR225 ,  A01 ,  C03 ). P. C. and B. P. were supported by the  Interdisciplinary Center for Clinical Research (IZKF)  of the  University of Erlangen-Nuremberg. All authors thank  Dr.
Procedure Adult participants (� 18 years) were recruited from the general population using a snowballing technique over a 7-year period from March 2011 to January 2018. Informed consent was obtained by clicking on an "I agree" checkbox. This study was carried out in accordance with the recommendations of the Australian Code for the Responsible Conduct of Research. The protocol was approved by the Human Research Ethics Committee at the University of Southern Queensland (approval number: H11REA023, H13REA169, H16REA015). All participants gave written informed consent in accordance with the Declaration of Helsinki. Analyses were conducted using IBM SPSS Statistics for Windows, Version 25  [42].
Data screening All cases were screened for implausible responses (e. g. , scoring 0 or 16 on each subscale) and deleted where identified. The In The Mood website requires responses to all items prior to submission, and hence no missing values were detected. As found in previous studies of mood  [15, 17] , significant deviation from univariate normality was evident and expected for some subscales (e. g. , depression), being typical for distributions of negative mood scores  [5]. It was judged that levels of skewness and kurtosis were unlikely to affect findings of multivariate analysis methods, particularly given the very large sample size. The full range of scores from 0-16 was recorded within the study sample for each of the BRUMS subscales.
Discussion Theoretically, especially given the large sample, mean scores for all six mood dimensions should have approximated a T-score of 50. However, it was shown that all subscale means deviated significantly from normative means, although effect sizes were uniformly small (Table  2 ). This finding provides a rationale for revisiting existing tables of normative values for the BRUMS  [25]  with the aim of refining them to better reflect the current dataset, which is the largest and most representative available. Between-group comparisons identified similar results to those reported previously. In common with the current findings, previous research on mood profile clusters has consistently identified a higher prevalence of the iceberg profile among males and a higher prevalence of the negative mood profiles among females  [15, 17, 18].
1 H NMR spectra were recorded at 300, 400, or 500 MHz either in a Varian Direct Drive (Varian Inc. , Palo Alto, CA, USA) or Bruker Avance NEO (Bruker BioSpin GmbH, Rheinstetten, Germany). Chemical shifts are reported in ppm using the residual solvent peak as a reference (CHCl 3 : δ = 7. 26 ppm, CH 3 OH: δ = 3. 31 ppm, and (CH 3 ) 2 SO: δ = 2. 50 ppm). Data are reported as follows: chemical shift, multiplicity (s: singlet, d: doublet, t: triplet, q: quartet, quint: quintuplet, m: multiplet, dd: doublet of doublets, dt: doublet of triplets, td: triplet of doublets, and bs: broad singlet), coupling constant (J in Hz) and integration. 13 C NMR spectra were recorded at 75, 101, 126 or 151 MHz using broadband proton decoupling, and chemical shifts are reported in ppm using the residual solvent peaks as a reference (CHCl 3 : δ = 77.
16 ppm, CH 3 OH: δ = 49. 00 ppm, and (CH 3 ) 2 SO: δ = 39. 52 ppm). Carbon multiplicities were assigned by distortionless enhancement by polarization transfer (DEPT) techniques. High-resolution mass spectra (HRMS) were recorded by EI at 70 eV on a Micromass AutoSpec mass spectrometer (Waters Co. , Milford, MA, USA) or by ESI on a Waters VG AutoSpec mass spectrometer (Waters Co. ). For the synthesis of the compounds described herein, several precursors were required to be prepared, including 2-[3-(4-bromo-3-methyl-phenoxy)propoxymethyl]thiophene (precursor VII, Scheme S4 in the SM) and 2,7-di-[tert-butyldimethylsilyloxy]-xanthone (precursor VIII, Schemes S4 and S5 in the SM). Compounds 1 (Schemes S1-S3) and 4 (Scheme S6) were prepared as previously described, isolated as pure samples and showed NMR spectra identical to reported data  [40, 51].
Funding: This research: including APC charges, was funded by the  Spanish Agencia Estatal de Investigación (Ministry of Science and Innovation)  and the  European Regional Development Fund  [grant numbers  CTQ2014-56370-R ,  CTQ2014-53598 , and  CTQ2017-85658-R] ;  Fundación Ramón Areces ; and the  initiative Solidaridad Entre Montañas. J. H. acknowledges financial support from the  Flemish government  through long-term structural funding Methusalem ( CASAS2 ,  Meth/15/04 ).
While donor 17 represents a single case, the observation supports the idea that BNT162b2induced expansion of non-canonical memory ICOS+CD38+ CD4 and CD8 T cells may be critical to drive plasmablast expansion, antibody production, and protective immunity to SARS-CoV-2. We next examined the antibody sequence relationships of B cells elicited by vaccination from the longitudinally collected donor. Interestingly, a cluster of antibody heavy chain sequences, referred to as cluster 720, was found to exhibit high similarity with public antibody sequences from the CoV AbDab database  (35)  of individuals with natural COVID-19 infection (Figure  7B ). Consistent with previous studies, this is indicative of a convergent B cell response from natural infection and vaccination  (36). Interestingly, however, despite the high similarity in heavy chain sequences, the members of cluster 720 utilized a diversity of light chain V-genes and CDRL3 sequences.
Acknowledgements We thank members of the  Vanderbilt Center for Immunobiology  for input and  Summer Brown  for administrative assistance. This work was supported by  Human Immunology Discovery Initiative of the Vanderbilt Center for Immunobiology  (JCR, EMW,  RHB, JMI) ,  CTSA  award No. UL1TR000445  (EMW, JCR), the  National Institutes of Health   KL2TR002245  (EMW),  K12HD043483  (RHB),  R01CA226833  (JMI, SMB, CER),  U01AI125056  (JMI, SMB),  U54CA217450  (JMI),  P30CA68485  ( VUMC Flow Cytometry Shared Research ),  R01AI131722-S1  (ISG),  R01AI157155  (JEC),  R01DK105550  (JCR),  R01CA217987  (JCR),  R01AI153167  (JCR),  R01AI127521  (JSM);  HHSN  contracts  75N93019C00074  (JEC),  DARPA HR0011-18-2-0001  (JEC),  Hays Foundation COVID-19 Research Fund (ISG) ,  Fast Grants Mercatus Center of George Mason University (ISG, JEC) , the  Dolly Parton COVID-19 Research Fund at Vanderbilt (JEC) ,  Welch Foundation   F-0003-19620604  (JSM), and  William Paul Distinguished Innovator Award  of the  Lupus Research Alliance  (JCR).
Acknowledgements. Kim H. Stadelmaier  and  Joaquim G. Pinto  thank the  AXA Research Fund  for support. Patrick Ludwig  is supported by the  Helmholtz Climate Initiative REK-LIM  (regional climate change; https://www. reklim. de/en,last access: 6 December 2021). Kim H. Stadelmaier , Patrick Lud-wig,  Xiaoxu Shi , and  Gerrit Lohmann  thank the  German Climate Computing Centre (DKRZ, Hamburg)  for providing computing resources. This study is a contribution to the  PALEOLINK  project (http://pastglobalchanges. org/science/wg/ 2k-network/projects/paleolink/intro, last access: 6 December 2021) within the  PAGES 2k Network ; it is also a contribution to the  PalMod  and PACMEDY projects funded by the  BMBF.
Code and data availability. The source code of AWI-ESM is available from the AWI-based SVN repository (https://fesom. de/models/awi-cm/, FESOM, 2021). The data are available in Kageyama et al. (2021). Data used in the MPI-ESM-P PI and LGM simulations are available in Jungclaus et al. (2012a) (DOI: https: //doi. org/10. 1594/WDCC/CMIP5. MXEPlg) and Jungclaus et al. (2012b) (DOI: https://doi. org/10. 1594/WDCC/CMIP5. MXEPpc) respectively. The WRF data will be archived at DKRZ (German Climate Computing Centre) and are available from the corresponding author upon request. PMIP3 boundary conditions can be obtained at https://wiki. lsce. ipsl. fr/pmip3/doku. php/pmip3:design: 21k:final (last access: 6 December 2021). Vegetation cover and land use data from CLIMAP (1984) can be obtained at https://iridl.
Symbol Unit Description ). We use the measured ice-fabric data from both ice cores published by  Weikusat et al. (2017)  and  Durand et al. (2009) , respectively, to validate our polarimetric-radar data inferences. At Dome C, data were additionally collected at 20 stations along with a 36 km long profile across the dome, enabling us to track ice-fabric variability in the dome-flank transition zone. Surface topography at Dome C  (Helm et al. , 2014; Howat et al. , 2019)  exhibits an ice dome elongated in the SW-NE direction (Fig. 1a ). Surface velocities are too slow (<0. 02 ma -1 ) for reliable detection with satellite imagery. GPS measurements show that ice-flow direction follows the surface maximum gradient direction, increases with distance from the dome, and is near-parallel to the transect described above  (Vittuari et al.
, 2006)  as shown in Fig. 2b. We refer to these measurements as quad-polarimetric measurement. Radar data at Dome C were collected at 20 sites in January 2014. One of the sites is located within walking distance of the ice-core site EDC. The remaining 19 sites (termed E(ast)0-E18, and W(est)0. 5-W18, with the numbers relating to the distance in km away from the dome) are aligned in a profile which is approximately perpendicular to the long axis of the dome and parallel to the flowline (Fig. 1a ). At EDML, data were collected in January 2017, approximately 2. 7 km north-east of the ice-core site EDML (Fig. 1b ). More information related to the individual ApRES sites are shown in Appendix A.
We follow  Jordan et al. (2019)  and apply a 1D convolutional derivative, which also avoids phase unwrapping. In Appendix E, we show that the quad-polarimetric measurement (Fig. 2c ) can be used to synthesize the full radar return from any antenna orientation using a matrix transformation S N (θ ± γ) = R(θ ± γ)S N (θ)R (θ ± γ), ( 18 ) where γ is the angular offset from θ. Equation (  18 ) is the mathematical equivalent to rotating the antennas in the field for each polarimetric configuration. As demonstrated in Fig. E1 , we find no significant differences between the synthesized and the full azimuthal rotation dataset with 22. 5°increments. Hence, if v3 is vertical a quad-polarimetric measurement is sufficient as it contains the full angular information.
5 , (C9) arg (s HV ) = tan -1 sin(2zk x ) + r sin(2zk y ) cos(2zk x ) + r cos(2zk y ). (C10) Appendix D: Polarity of the coherence phase gradient This section details the relationship between the polarity of the phase gradient and the corresponding directions of the Eigenvectors. Care has to be taken here, as the de-ramping during ApRES data acquisition is equivalent to a complex conjugation of the received signal. If this is not accounted for, the inferred Eigenvector v1 and v2 will be swapped. More specifically, for a received signal at θ = 0°: s HH = A (Γ x cos(2zk x ) + jΓ x sin(2zk x )) , (D1) s V V = A (Γ y cos(2zk y ) + jΓ y sin(2zk y )) , (D2) so that the coherence phase results in: C HHV V = (cos(2z(k x -k y )) + j sin(2z(k x -k y ))) , (D3) φ HHV V (θ = 0) = 2z(k x -k y ), (D4) and conversely for θ = 90°: φ HHV V (θ = 90°) = 2z(k y -k x ).