dataesr/scientific-paragraphs-categorization huggingface dataset:
To ease the clustering of temporal video frame sequences based on their correspondence to a particular whitecap, each point of Q(t) with Q(t) > Q threshold was extended in time within ±1-s intervals using the morphological dilation procedure  [49]. This dilation helps to regularize the clustering procedure and select record intervals that correspond to individual whitecaps. The second processing step is performed for each breaking event. During the lifetime of a particular whitecap, a set of binarized video frames associated with this event was again analyzed using the Matlab Image Processing Toolbox to measure the geometric characteristics of the detected breakers (binary regions). The temporal evolution of whitecap geometric properties-area, S, major axis length, L, centroid coordinates, X-was retrieved.
Funding: The data analysis and wave breaking model development performed in this work were funded by the  Russian Science Foundation  under grant No. 21-47-00038. The field experiments were funded by the  MHI RAS State Order (Goszadanie)  No. 0555-2021-0004. Sea surface electromagnetic modeling was funded by the  RSHU State Order (Goszadaine)  No. 0763-2020-0005. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
METHODS We built our empirical corpus by compiling three kinds of documents: scientific publications, institutional reports and conference reports. First, in order to collect relevant scientific publications, we searched the Web of Science database with specific key-words in the field "topic": "regulation," "governance," "politics" in combination with "gen* editing" (a search in "all fields" yields too many unrelated results, for terms like "regulation" can be present in the affiliation of an author). Our search yielded 358 results (we used version 5. 35 of the Web of Science, before its update on the seventh of July 2021). For our analysis, we relied on the categorizations of the Web of Science, such as "research axes. " Research axes have been created to unify the systems of classification within the Web of Science databases and are automatically attributed to journals.
In order to do our lexical analysis, we used IRaMuTeQ. IRaMuTeQ is a program for the multidimensional and statistical analysis of a corpus of text  (Ratinaud, 2021). It is based on the Max Reinert classification method  (Reinert, 1983; Reinert, 1986; Reinert, 1990) , an analysis which is based on a hierarchical descending classification. While IRaMuTeQ offers three classification methods, we have used simple classification in this article (see Supplementary File SA). This statistic classification takes place on a segment of text, and makes it possible to obtain a near exhaustiveness of the sentences of the study corpus in the final classification since the terms are compared with each other within the entire text. We build here on our previous study of online discussions about do-ityourself biology via IRaMuTeQ  (Meyer and Vergnaud 2020).
We hold that bringing together multiple methods allows us to discuss the politics of gene editing in a richer and more multifaceted way, and that this opens up fertile dialogues between geography, sociology and political science. Our findings are of course limited by our research design. We have analysed, via one open source program, the publications present in the Web of Science and selected seven countries within our corpus. Further research could thus examine the topic by searching other databases (i. e. , Scopus, PubMed), selecting other countries (i. e. , countries from the Global South) and use other programs (i. e. , Hyperbase, TXM, VOSviewer) to do other kinds of analyses. Further research could also expand our research questions by analysing in more detail who speaks (what kinds of scientists, institutions, groups or committees are visible and/or authoritative in the debate -and how are they linked?) and how expertise is made, distributed and demarcated.
The cross sections of ICEC in the proton-water setup were computed with the R-matrix method as implemented in the UKRmol+ package. A review of the Rmatrix methods can be found in  [27]  and details of the UKRmol+ package are presented in  [28]. In the following, we summarize the method and the implementation used in this work. In the R-matrix approach, the configuration space is partitioned into an inner and an outer region defined by a sphere of radius a around the center of mass of the full system. The inner region contains the multielectron description of the full system composed of N + 1 electrons (i. e. 11 electrons in this work). In the outer-region only a single free electron is treated and its interaction with the N remaining electrons (i. e. 10 electrons) is described in terms of a multipole expansion.
They correspond to the 11 lowest states of H + -H 2 O and 7 lowest states of H(1s) -H 2 O +. The latter are the final states of the ICEC processes. The computed energies of the target states are given in Tab. I for a distance R between the proton and the oxygen atom of 8 Å. The cc-pVDZ basis set was used and the CASSCF orbitals were optimized using the MOLPRO package  [29, 30]. We employed "continuum" orbitals with angular momemtum up to =6, which are described with 25 Bsplines type orbitals of order 6 in each. The R-matrix radius a was fixed at 25 a. u. and the maximum values in the Legendre expansion of the mixed nuclear attraction and 2-electron integrals (see  [28]  for further details on the implementation and parameters) were fixed at 35 and 45, respectively. Convergence with respect to the numerical parameters was checked by performing additional calculations with i) the R-matrix radius fixed at 30 and 35 a.
ACKNOWLEDGMENTS This project has received funding from the  LabEx MiChem part of French  state funds managed by the  ANR  within the  "Investissements d'Avenir" program  under reference  ANR-11-IDEX-0004-02. L. S. C. is thankful for support by the  European 5448 Research Council (Advanced Investigator  Grant No. 692657 ). J. D. G. acknowledges support from the  UK-AMOR consortium  ( EP/R029342/1 ).
*These authors contributed equally to this work. Supported by the  UK Regenerative Medicine Platform  ( MR/K026739/1 ), the  Medical Research Council (MRC) as part of the Centre for Drug Safety Science  ( MR/L006758/1 ) and the  Centre for Preclinical Imaging  ( MR/L012707/1 ),  North West Cancer Research  ( CR1136 ), the  North West Cancer Research Centre Doctoral Training Partnership , and an award from the  University of Liverpool Technology Directorate Voucher Scheme. B. K. Y. C. is supported by the  North West England MRC Fellowship Scheme in Clinical Pharmacology and Therapeutics , which is funded by the  MRC, Eli Lilly and Co. ,  Novartis ,  Roche Pharma ,  UCB Pharma , the  University of Liverpool , and the  University of Manchester  ( MR/ N025989/1 ).
We also excluded data collected in 2012, given that field procedures were not yet standardized at that time. The effect of boar presence on snake abundance was analyzed using generalized linear modeling (GLMs; MASS package). In this model, the dependent variable was the total snake count per session. The independent variables were the islands and the timing of the invasion event (i. e. , before or after wild boar outbreak, named time for conciseness). We used a negative binomial distribution instead of a Poisson distribution to account for large over-dispersion in the counts (c-hat ≈ 1. 5-2. 0 for all datasets). As monitoring effort (number of searching days) varied per session, we used the number of searching days (log-transformed) as an offset in the model. To fulfill BACI analysis requirements  [41] , we fitted the model in which the rough number of snakes observed depends on the interaction between time (before 2016 versus after 2016; BA) and islands (CI).
1a ). Our sites were selected to capture the gradient of temperature and precipitation conditions under which forests occur globally. Insects and other animals (hereafter collectively termed insects for brevity) had unrestricted access to wood placed on the forest floor in the uncaged treatment in our experiment, while they were excluded from wood in the closed cage treatment using mesh cages (Extended Data Fig. 1 ). Our estimate of the effect of insects on wood decomposition was quantified as the difference between decomposition rates in the uncaged and closed cage treatments. This measure can be considered the "net effect of insects", consisting of direct consumption of wood by insects and indirect effects via interactions with microbes. The latter include, for example, competition for resources, grazing on fungal mycelia, creation of entry ports or vectoring, and can thus either increase  25  or decrease wood decomposition  26, 27.
Uncertainties related to the underlying data, the statistical models, and other assumptions necessary for upscaling our experimental results were assessed in a global sensitivity analysis. This analysis bounded the uncertainty of global annual carbon release from deadwood and the net effect of insects at approximately ±25% around the mean. Of the various sources of uncertainty that were considered, the underlying data on deadwood carbon stocks contributed most strongly to overall uncertainty (Fig. 3 ; Extended Data Table  2 ; Supplementary Information section 2). Our results suggest that global deadwood carbon cycle assessments could be improved by more accurately quantifying deadwood stocks in tropical forests. While the effects of wildfire were included in our deadwood carbon map via the underlying inventory data, we did not explicitly consider deadwood carbon release from fire.
Statistical analyses of the decomposition experiment All statistical analyses were performed in R version 4. 0. 4  52. For each site, we derived information on average climate conditions from WorldClim (v2)  53  , specifically BIOMOD variables 1 (mean annual temperature) and 12 (mean annual precipitation sum). We modelled relative wood mass loss of local tree species over time using a Gaussian generalized linear mixed model (function glmer in package lme4 54 , version 1. 1. 26) with log link. Dry mass of each individual log at time t served as the response variable and log-transformed initial dry mass (t = 0) was used as an offset term. For each increase of one time unit (one year), the relative reduction is given by exp(β). Note that the model contained no intercept due to the constraint exp(β) 0 = 1.
47 and 0. 49  16  (Extended Data Fig. 6b ). The delineation of forest biomes was taken from FAO  57. We calculated deadwood carbon stocks at a spatial grain of 5' by relating deadwood carbon stocks to total live carbon stocks (i. e. , deadwood carbon fraction). To quantify regional deadwood carbon fractions, we used data compiled by Pan et al. non-living woody biomass not contained in the litter, either standing, lying on the ground, or in the soil" with a diameter >10 cm. We extended our deadwood carbon pool estimate to include all deadwood >2 cm diameter by applying an expansion factor based on empirical allometric relationships  59. Our global map of deadwood (Fig. 1a ) thus represents the total amount of carbon stored in standing and downed deadwood with a diameter of >2 cm for the reference year 2010.
Acknowledgments We thank the administration of the  Bavarian Forest National Park  for financing the setup of the experiment and all members of the local teams for their contribution in the field and laboratory. We especially thank  David Blair  who operated the site in Victoria, Australia, until his unexpected death in 2019. We thank  Bodo von Rentzel ,  Jörg Ganzhorn ,  Axel Gruppe ,  Mark Harmon ,  Sandra Muller  and  Sandra Irwin ,  Makiling Center for Mountain Ecosystems, University of the Philippines Los Banos , the  Ministerio del Ambiente de Ecuador , the  Instituto Nacional de Biodiversidad de Ecuador and the foundation "Nature and Culture International"  for their support. S. S. was supported by the  German Academic Exchange Service (DAAD)  with funds from the  German Federal Ministry of Education and Research and the People Programme of the European Union  ( Marie Curie Actions ; grant number  605728 ).
Characterization SEM is performed using a field-emission Philips XL-30 microscope equipped with a through lens detector (TLD). The measurements are carried out in a vacuum of 10 -6 mbar, at an acceleration C-AFM is carried out using a Nanotec Electronica S. L. AFM tool and WSxM software  [15]. We use cantilevers from Nanosensors (PtSi-NCH, PtSi coating, nominal resonance frequency of 330 kHz and spring constant of 42 Nm -1 ) and from Budget Sensors (ElectriMulti75-G, CrPt coating, nominal resonance frequency of 75 kHz and spring constant of 2. 8 Nm -1 ). Topography and current images are taken simultaneously in contact mode at room conditions. The tip is used as a moving electrode with constant voltage applied to it while the specimen contacted by the bottom Pt/Ti electrode is grounded.
ACKNOWLEDGMENTS This work has been supported by the  Spanish MICINN  (grant nr. FIS2017-82415-R , grant nr. MAT2017-83722-R , " María de Maeztu "  Programme for Units of Excellence in R&D  ( CEX2018-000805-M) ), within the framework of  UE M-ERA. NET 2018 program  under  StressLIC  Project (grant nr. PCI2019-103604  and  PCI2019-103594 ) and by the  Comunidad Autónoma de Madrid  (grant nr. SI1/PJI/2019-00055 , contract nr. PEJD-2019-PRE/IND-15769  and  PEJ-2018-AI/IND-10072 ). The work at  Sandia National Laboratories  was supported by the  Laboratory-Directed Research and Development (LDRD) Programs , including the  Harry Truman Fellowship Program  ( Y. Li ). Sandia National Laboratories  is a multimission laboratory managed and operated by  National Technology and Engineering Solutions of Sandia, LLC , a wholly owned subsidiary of  Honeywell International Inc.
Rare variant association analysis We compared the burden of rare KDR variants in patients of European descent with (1) unselected TOF (primary analysis) or (2) patients with any type of CHD other than TOF (secondary analysis; these comprised a broad spectrum of CHDs, varying from mild to severe defects, described in detail elsewhere  9, 13  ) and controls. Cases were drawn from four different CHD cohorts for which exome or genome sequence data was previously generated. 8, 9, 13, 14 The non-Finnish European (NFE) subset of gnomAD v2 with exome data (gnomAD-NFE; n = 56,885)  15  was used as control data set. We assessed rare variants in the canonical KDR transcript (ENST00000263923) in two different categories: protein-truncating variants (PTVs, i. e. , nonsense, frameshift, and splice site variants) and missense variants.
1c ). The unaffected sister did not carry either KDR variant. KDR-p. (Gly345Trp) and KDR-p. (Gly537Arg) were not found in an internal data set of 390 controls of Moroccan descent or in any of the publicly available reference databases, including 3,065 individuals from North Africa and the Middle East in five population genetic data sets (Supplemental table  5 ). They were predicted to be deleterious by the in silico prediction tools SIFT and PolyPhen and had high CADD scores (35 and 34). The variants were located in the extracellular immunoglobulin(Ig)-like domains 4 and 5 of VEGFR2, and the affected residues were evolutionarily conserved (Fig. 1d, e ). The only other gene harboring multiple variants was COL5A2. Although these variants were shared by the affected patients, they were proven to be in cis, inherited from the unaffected father (Supplementary table 4).
Limitations Although we showed that the KDR variants in the index family are absent from publicly available reference data sets and 390 internal Moroccan controls, larger region-specific population genetic data sets will be required to fully confirm the rarity of the variants detected in Moroccan patients. In this study we only focused on rare variants in KDR and did not explore a multigenic inheritance. We did not functionally test all variants identified in patients and are therefore not able to conclude on their pathogenicity. We used individuals from the gnomAD collection as a control data set in the burden analysis. Although there are some inevitable limitations to this approach, it has been shown to be successful in other inherited cardiac disorders. 34.
ACKNOWLEDGEMENTS We gratefully acknowledge the patients and their families for participation. The authors thank  Sylvia Mantels  and  Lia C. J. M. Engelfriet-Rijk  for their valuable work recruiting patients and managing the CONCOR registry. We acknowledge the  Labatt Family Heart Centre Biobank, Hospital for Sick Children , funded by the  Ted Rogers Centre for Heart Research , for access to sequencing data. A subset of data used in this study was generated by the  Pediatric Cardiac Genomics Consortium (PCGC) , under the auspices of the  National Heart, Lung, and Blood Institute (NHLBI) 's  Bench to Bassinet Program  (dbGaP study accession: phs001194. v2. p2). The PCGC program is funded by the  NHLBI, National Institutes of Health, US Department of Health and Human Services  through grants  UM1HL128711 ,  UM1HL098162 ,  UM1HL098147 ,  UM1HL098123 ,  UM1HL128761 ,  U01HL131003.
FUNDING This work has been funded by research grants from the  DHF  ( CVON  project  2014-18  CONCOR-genes, to C. R. B. and V. M. C. ), the  CHF  (to C. R. B. ) and the  PROCEED  project , ERA PerMed Joint Translational Call Initiative  (to S. M. , C. R. B. , and M. -P. H. ). C. R. B. is funded by research grants from  NWO  ( VICI fellowship ,  016. 150. 610 ) and the  Leducq Foundation  ( 17CVD02 ). V. M. C. is funded by the  Leducq Foundation  ( 14CVD01 ). The work of E. M. L. is partly financed by the  NWO  ( VIDI-91718361 ) and the  CVON RESCUED  project. S. M. holds the  Heart and Stroke Foundation of Canada/Robert M Freedom Chair in Cardiovascular Science. The work of P. K. and M. M. was supported in part by the  Division of Intramural Research  of the NHGR. The work of I. T. and T. P.
was supported by  GA4K at Children's Mercy hospital. This work was supported by the CNCHD with funding from the  Federal Ministry of Education and Research  ( 01GI0601 until 2014 ) and the  DZHK (German Centre for Cardiovascular Research ; as of 2015). The work of F. V. Y. T. is financed by a research grant from the  DHF  ( E. Dekker  grant  2014T053 ). Research reported in this publication was supported by the  NCATS  of the  NIH  ( TL1TR001880 ) and by the  ITMT of the Perelman School of Medicine, University of Pennsylvania (S. E. S). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH. B. D. K. is supported by a BHF personal chair. S. G. W. is supported by the  BHF. R. W. is supported by an  ACS  fellowship. J. B. is funded by a senior clinical investigator fellowship of the  FWO-Flanders.
5 and 9 meV were combined to obtain a map of the spectrum over multiple Brillouin zones and over an energy range from 2. 5 to 5. 5 meV. For both measurements, the rod-shaped single crystal was aligned with (h 0 l) in the scattering plane [notation from the crystallographic unit cell, Fig. 1(c) ]  [55]. The CAMEA data shown in Figs. 1(c )-1(f) were extracted from the raw measured intensities using the software MJOLNIR  [62] , which corrects these intensities using measurements on a standard vanadium sample. The two datasets shown in Fig. 2 (a) differed by sample masses and counting times, which for the Ba 0. 9 Sr 0. 1 CuSi 2 O 6 sample were measured for approximately 7. 5 minutes per point, while the BaCuSi 2 O 6 data were measured for approximately 15 minutes per point. The comparison was made by scaling the BaCuSi 2 O 6 data so that the integrated intensity of all three modes matched the integrated intensity of the single mode observed in Ba 0.
4(c) , where the crosses represent the fields at which spectra were collected, and these lines yield a set of estimated critical points, H c,j , with uncertainties δH c,j. The corresponding temperature values, with their uncertainties, were determined relative to a field-calibrated RuO 2 thermometer (Sec. IIC). The data pairs (H c,j , δH c,j ) and (T c,j , δT c,j ) together constitute the NMR phase boundary near the quantum critical point, which we show as the blue and open circles in Fig. 4(d ). The blue line and shading represent the results of our statistical analysis of the quantum critical properties of the NMR phase boundary, which we discuss below. To assist with the interpretation of the measured phase boundary, we performed large-scale QMC calculations of a model with only one type of bilayer, following the established procedure  [54]  summarized in Sec.
To obtain individual parameter estimates and error bars, the posterior distribution is compressed in various ways. We use the mean of the marginal distribution of each parameter, which provides the prediction with the smallest error given the input data  [81]. To quantify these errors, we construct the highest posteriordensity credible intervals (CIs), which are the shortest intervals containing a fixed fraction of the posterior density  [82] , and show the commonly chosen 68% and 95% CIs. Figure  4(d)  shows the mean of the posterior distributions of the model function of Eq. (  1 ) as a blue line for the NMR dataset, D NMR , and an orange line for our QMC data, D QMC , while the shaded regions indicate the 68% and 95% CIs. Referring back to the issue of the width of the quantum critical regime, the two posterior distributions were constructed on the basis of the 6 NMR phase-boundary data points falling below the temperature T NMR max = 412 mK (blue circles) and the 17 QMC points below T QMC max = 426 mK (orange squares).
4 K, and hence that this is an appropriate width to adopt for the scaling regime. This criterion terminates our NMR dataset at T NMR max = 412 mK, corresponding to the 6 phase-boundary points used for the analysis shown in Figs. 4(d ) and 5(a)-5(d). However, we note that the criterion is far from exact, and in the present case an almost identical estimate with lower uncertainties, φ = 0. 665 +0. 040 -0. 054 , is obtained by including the 7th data point. The evolution of the estimated φ on inclusion of the 9th and 10th data points indicates both the onset of complex and manifestly nonmonotonic crossover behavior beyond the critical regime and the accuracy of the analysis beyond features visible in Figs. 4(d)  or Figs. 5(e). The discrepancy between the forms of behavior exhibited by our NMR and QMC data could lie in interaction terms neglected in our minimal magnetic Hamiltonian, or in possible disorder effects arising due to Sr substitution, which we discuss further in Sec.
ACKNOWLEDGMENTS We thank  W. J. Coniglio  for technical support. We are grateful to  I. Fisher ,  T. Kimura ,  S. Sebastian , and the late  C. Berthier  and  C. Slichter  for their foundational contributions to this study. This work is based in part on experiments performed at the  Swiss Spallation Neutron Source, SINQ , at the  Paul Scherrer Institute. A portion of the work was performed at the  National High Magnetic Field Laboratory , which is supported by the  National Science Foundation  Cooperative Agreement No. DMR-1644779 , the  State of Florida , and the  United States Department of Energy. We thank the  Swiss National Science Foundation  and the  ERC grant Hyper Quantum Criticality (HyperQC)  for financial support. Work in Tallinn and travel to large facilities to perform  NMR  and torque measurements were funded by the  European Regional Development Fund  ( TK134 ) and the  Estonian Research Council  ( PRG4 ,  IUT23-7 ).
Work in Toulouse was supported by the  French National Research Agency (ANR)  under projects  THER-MOLOC ANR-16-CE30-0023-02  and  GLADYS ANR-19-CE30-0013 , and by the use of HPC resources from  CALMIP  (Grant No. 2020-P0677 ) and  GENCI  (Grant No. x2020050225 ). We acknowledge the support of the  LNCMI-CNRS , a member of the  European Magnetic Field Laboratory (EMFL) , of the Swiss Data Science.