dataesr/scientific-paragraphs-categorization huggingface dataset:
Amongst various possible methods of multilateration, we used an optimization algorithm that minimizes the following cost function C f for the i-th measurement: C f i (x, y) = N a ∑ j=1 r i,j -(x j -x) 2 + (y j -y) 2 (3) ( xi , ŷi ) = argmin C f i (x, y) (x,y)∈R 2 where (x, y) are the test point coordinates, N a is the number of antennas, r i,j is the i-th radial distance from antenna j, and ( xi , ŷi ) is the most probable tag position. The minimization of this cost function was performed using the Trust-region optimization algorithm  [49]  implemented in the Scipy-optimize Python module.
Aknowledgements This work was partially funded by the  ANR   LABCOM GEO3ILAB  and by the  Region Auvergne Rhone Alpes RISQID  project. This work is part of the "Habitability" LABEX coordinated by OSUG. We acknowledge experimental help from  B. Vial ,  M. Langlais ,  G. Scheiblin ,  G. Bièvre  from  ISTerre.
2] and CCR7 [clone G043H7]), followed by fixation and permeabilization (Intracellular Fixation & Permeabilization Buffer Set, Thermo Fisher) and stained for intracellular cytokines TNFa [clone Mab11] and IFNg [Clone B27]). Cells were acquired with a Celesta flow cytometer (BD Immunocytometry Systems) and the data analyzed using FlowJo Version 10. 8. 0 (TreeStar). The percentage of cells expressing TNFa/IFNg and CD107a after stimulation were compared using non-parametric tests.
Clinical data Clinical data required for the analysis were extracted from the DIVAT (for "Donn ees Informatis ees et Valid ees en Transplantation") database which was carried out prospectively, exhaustively, and independently by clinical research associates on key dates during postoperative follow-up of clinical and biological data of all incident transplanted patients at our institute. The data are subject to an annual audit to warrant quality and completeness. Recipient characteristics include age, gender, transplantation rank (first transplantation or retransplantation), year of transplantation, the initial kidney disease (possibly recurrent or not), and the cytomegalovirus (CMV) serology, history of diabetes, history of arterial hypertension, history of cardiovascular disease, CMV serology, number of HLA-A-B-DR incompatibilities, ABO mismatch, donor-specific antibodies (DSA), anti-HLA class I and II immunization.
In case of violation, variables were transformed. We used R version 3. 6. 3 and the package 'base', 'dplyr', 'survival', 'etm', 'plotrix', 'prodlim', 'data. table', 'splines', 'officer', 'flextable', 'lattice', 'forestplot', 'gtools', 'lme4' and 'lmerTest' packages for all data analyses (see https:// www. r-project. org/).
Fourthly, although we have included many covariates in the multivariable statistical models, we cannot exclude unobserved confounders, for example, ethnicity and socioeconomic status, which were not collected in our database. Finally, in multivariable analyses, we did not consider individuals with missing data on the covariates, which might reduce the statistical power leading to non-significant results. However, our effort to limit missing data was considerable. As shown in Table  1 , 20/30 variables studied contained no missing data. Of the 10 variables with missing data, 4 variables contained only 1 case with missing data. Overall, about 5% of total cases were excluded from the multivariable analysis because of at least 1 missing data. We have looked at variables with missing data and found no overt reason to suspect the presence of « missing not at random » (MNAR), where the cause of missing data is directly linked with the value of those data.
Acknowledgements This study has been registered with  French Research Ministry :  RC13_0251 , last agreement  N°13334 , CNIL N°f or the cohort: 891735. This work was performed in the context of the  DHU Oncogreffe , the  LabEx IGO  ( ANR-11-LABX-0016-01 ), the  ANR  project  BIKET  ( ANR-17-CE17-0008 ) and the  ANR  project  KTD-innov  ( ANR-17-RHUS-0010 ) thanks to French government financial support managed by the  National Research Agency. We thank the biological resource centre for biobanking ( Nantes Universit e, CHU Nantes, Centre de Ressources Biologiques (CRB), F-44000 Nantes, France ) (BRIF:  BB-0033-00040 ). We also thank  S. Le Floch , clinical research assistant and the CIMNA platform for blood lymphocyte phenotyping. *DIVAT Cohort Collaborators (Medical Doctors, Surgeons, HLA Biologists) in Nantes:  Gilles Blancho ,  Julien Branchereau ,  Diego Cantarovich ,  Agn es Chapelet ,  Jacques Dantal ,  Cl ement Deltombe ,  Lucile Figueres ,  Claire Garandeau ,  Magali Giral ,  Caroline Gourraud-Vercel ,  Maryvonne Hourmant ,  Georges Karam ,  Clarisse Kerleau ,  Christophe Masset ,  Delphine Kervela ,  Sabine Lebot ,  Aur elie Meurette ,  Simon Ville ,  Christine Kandell ,  Anne Moreau ,  Karine Renaudin ,  Anne Cesbron ,  Florent Delbos ,  Alexandre Walencik , and  Anne Devis.
Noda (2016)  reviews a number of other compressive accretionary margins which all seem to develop an active backthrust at the edge of the forearc basin like in our models. However, most of these seismic studies place the splay fault and its backthrust at the limit of the continental crust which posits, in a way, a stable position through time, at least relative to the upper plate. In our simulations, the splay fault roots at the location of the 450 • C isotherm along the décollement (Fig. 10a ). This isotherm at 15 km depth corresponds to greenschist facies metamorphic conditions. Using typical sediments composition, perplex software yields typical continental crust seismic attributes with 5. 5 > V p > 5 km s -1 and V s ∼ 2. 5 km s -1. With volcanoclastic sediments, larger velocities are expected for similar metamorphic conditions.
Code availability. The version of pTatin2d and the input files used in this contribution are archived following the FAIR principle at https://doi. org/10. 5281/zenodo. 4911354 (May and Le Pourhiet, 2021). Input files are located in published_inputs/Pajang2021_SE. Data availability. Data are available at https://zenodo. org/record/ 4911354/preview/ptatin2d. zip#tree_item354  (May and Le Pourhiet, 2021). Video supplement. All simulations are available as movies archived at https://doi. org/10. 5281/zenodo. 5599365 (Pajang et al. , 2021b). Author contributions. SP designed the simulation, ran the simulation prepared the figure and wrote the paper, LLP implemented the heat flow boundary condition, LLP and NC participated in the interpretation of the simulation, application to natural cases and writing of the paper.
Surface Roughness Measurements Surface roughness measurements were carried out in alignment with the ISO 11 562  [39]  standard to permit the comparison of the surface RS between the different specimens with similar surface roughness. The roughness was measured with the contact profilometer Hommel T8000 using the TKL300 probe and assessed with the Turbo Wave V7. 59 software (Jenoptik Industrial Metrology GmbH, Villingen-Schwenningen, Germany). The measurements were performed along four lines of 10 mm length with a spacing of 133 μm as shown in Figure  2. The roughness measurements were carried out on each lateral surface of the specimen at mid height, corresponding to the measurement location of the RS in plane 1-1 0 (Figure  1b ).
Acknowledgements This work was funded by the  BAM Focus Area   Materials  project  AGIL "Microstructure Development in Additively Manufactured Metallic Components: from Powder to Mechanical Failure "  and ProMoAM "Process monitoring of Additive Manufacturing ". The authors are thankful for the financial support and the fruitful cooperation with all partners. This work is based upon experiment 1-02-276 performed at the SALSA instrument operated by the  Institut-Laue-Langevin, Grenoble, France. Open access funding enabled and organized by Projekt DEAL.
Signal and background events were modelled using different Monte Carlo (MC) generators as listed in Table  1. The response of the ATLAS detector was simulated  [27]  using the Geant44 toolkit  [28]  and simulated events were reconstructed with the same algorithms as those applied to data  [21]. The type-III seesaw signal model was implemented in the MadGraph5_aMC@NLO  [29]  generator at leading order (LO) using FeynRules  [30]  and the NNPDF3. 0lo [31]  parton distribution function (PDF) set. All decays of L ± and N 0 into the different leptonic flavours and subsequent decays of the W , Z and H are considered. Matrix element (ME) events were interfaced to Pythia 8. 230  [32]  for parton showering with the A14 set of tuned parameters  [33]  and the NNPDF2. 3lo PDF set  [34]. The signal crosssection and its uncertainty at next-to-leading-order (NLO) plus next-to-leading-logarithm (NLL) accuracy were calculated from SU(2) triplet production in an electroweak chargino-neutralino model  [35, 36].
Statistical analysis and results The HistFitter  [74]  statistical package is used to fit the predictions to the data in the CRs and SRs. The fit considers the m T,3 and H T + E miss T distributions for the threeand four-lepton channels, respectively, calculating lower limits on the heavy-lepton mass with a test statistic based on the binned profile likelihood in the asymptotic approximation  [75] , whose validity is tested using a pseudo-experiment approach. The lower limits on the mass are calculated at 95% confidence level (CL) and the binning is chosen to optimise the sensitivity to signal. The various components of the background predictions are validated in the corresponding VRs. Background and signal contributions are modelled by a product of independent Poisson probability density functions representing the likelihood of the fit.
In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; 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 GIF, 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, UK. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers.
For example, an extended encapsulation of the analysis is often provided for measurements in the framework of RIVET (http://rivet. hepforge. org/). This information is taken from the ATLAS Data Access Policy, which is a public document that can be downloaded from http://opendata. cern. ch/record/413 [opendata. cern. ch]. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
41 kJ/mol). It was also predicted in third position by CopraRNA (217. 74 kJ/mol) (Table  1 ). The putative interaction region of 22 nt between Srn024 and the sap mRNA predicted by IntaRNA is located between the AUG start codon region of sap mRNA and the first 2 to 24 nt of Srn024 (Fig. 3 ; also see Fig. S3 )  (18). Thus, in silico predictions suggest that Srn024 affects sap expression by RNA-RNA pairing. Srn024 does not affect the transcription of sap. To evaluate sap transcription, a strain in which the srn024 gene was inactivated (Dsrn024) was constructed along with a complemented strain containing a recombinant vector carrying srn024 (Dsrn024-pTCV-P Tet ::srn024). The amount of sap transcripts was determined by reverse transcription-quantitative PCR a Gene predicted by the three tools. T1-2 (RT-qPCR) in NEM316-pTCVP Tet (empty vector), Dsrn024-pTCV-P Tet , and Dsrn024-pTCV-P Tet :: srn024 strains.
ACKNOWLEDGMENTS N. J. was the recipient of a Ph. D. fellowship from  Région Centre-Val de Loire. We are grateful to  Philippe Bouloc  for critical reading of the manuscript and helpful discussions. We thank  Clément Vigouroux ,  Pierre-Hugues Bibak ,  Daniel Niquet ,  Lucie Noel , and  Vanessa Rong  for technical assistance. N. J. and M. -F. L. conceived and designed the study, N. J. and E. M. collected the data, N. J. , E. M. , E. C. , and M. -F. L. analyzed data, N. J. and M. -F. L. wrote the first draft of the manuscript, and all authors carefully reviewed the manuscript and proposed significant modifications.
Powdered samples were introduced into a Lindemann capillary (0. 5 mm in diameter), and the temperature was varied using a 700 series Oxford Cryostream Cooler. For 1Aol, x-ray patterns were acquired isothermally between 150 K and 475 K in order to determine the lattice parameters as a function of temperature and the volume variation at the FOPT. The data were analyzed with Materials Studio software. 34 First, a Pawley fit was performed to find cell parameters, profiles, the zero offset, and asymmetry. Then, a Rietveld refinement was performed, assuming a rigid body molecule. For the tetragonal and plastic phases of 1Aol, the intramolecular distances and angles reported from single crystal diffraction by Amoureux et al. 26  were kept. For the plastic phases of 1Aol and 2Aol, the energy minimization Materials Studio Forcite module was used to obtain the intramolecular distances and angles.
| Bird species database We compiled a list of known breeding bird species for each island, including extinct, extirpated and established introduced species, following the taxonomy used by Birdlife International (Handbook of the Birds of the World & BirdLife International, 2018). Given our focus on the temporal changes in species composition within islands, we also included island-level extirpations. For simplicity, we use the terms 'extinction' and 'extinct' for both global and local extinctions. We excluded marine birds, non-breeding migrants, occasional breeders, vagrant and accidental species, and focus on regularly breeding terrestrial and freshwater species, since these are the most dependent on island resources and also have particularly high rates of extinction and introduction on oceanic islands  (del Hoyo et al.
, 2014). To obtain a complete list of bird species for each island, we identified extant breeding species, including introduced species, from Avibase  (Lepage, 2018) , HBW Alive  (del Hoyo et al. , 2014) , IUCN Red List of Threatened Species (IUCN, 2020; Fig. S3. 1) and regional field guides (Table  S2. 2). In particular, for introductions, we also used the Global Avian Invasions Atlas  (Dyer et al. , 2017a)  and specific literature (Table  S3. 3). We considered as extinct the native species classified as Extinct and Extinct in the Wild in the IUCN Red List of Threatened Species (IUCN, 2020), and as Extinct or Extirpated in Avibase  (Lepage, 2018). To improve the list of extinct species and capture extinctions prior to 1500 AD, we analysed specific literature  (Hume, 2017; Paleobiology Database, 2018; Fig.
S3. 2), and thoroughly reviewed extinction records for each target island (Table  S3. 4). Species classified as Probably Extinct in the literature (especially in  Hume, 2017)  and Critically Endangered -Probably Extinct in the IUCN Red List of Threatened Species were carefully analysed and considered extinct only when the IUCN Red List of Threatened Species supported this claim. We only included extinct taxa if these had been identified to species level, which is often not possible from fossil or historical records. The final database comprised 759 species, including 214 extinct, 172 introduced and 445 extant natives (Table  S1. 1).
| Bird species traits For each species, we gathered information on body mass, foraging time, diet, foraging strata, volancy and habitat (Table  S3. 5). These traits are commonly used in studies evaluating bird functional diversity and summarising the effects of species on ecological processes and on responses of communities to environmental change  (Boyer, 2008; Luck et al. , 2012; Sobral et al. , 2016; Sayol et al. , 2021). For extant species, our main source of information regarding average body mass, foraging time ('diurnal' or 'nocturnal'), diet and foraging strata was the EltonTraits database  (Wilman et al. , 2014). For the 40 (out of 617) species missing from this database, we inferred traits from the closest species in the genus (Table  S3. 6). We treated average body mass both as a continuous variable, and as an ordinal trait, based on the 5-quantiles categories: 'very small', 'small', 'medium', 'large', and 'very large'.
b We considered nonspecialized birds, species that forage in most strata between ground and aerial, and thus attributed them the average value of 3. 5. We computed the pairwise functional distances between each pair of species using the Gower dissimilarity index, giving the same weight to each trait (range: 0 -0. 887), and then calculated the contribution of each trait to the resulting distance matrix, using respectively dist. ktab and kdist. cor in 'ade4' package (Table  S3. 7;  Dray & Dufour, 2007). We analysed the distance matrix through a principal coordinate analysis (PCoA) with the Cailliez correction for negative eigenvalues to extract orthogonal axes for the hypervolume delineations, using the pcoa function in the 'ape' package  (Paradis et al. , 2004). To construct the trait space, we retained the first eight PCoA axes, which cumulatively explained 81.
| RESULTS Our database included 759 species in 2709 island populations, distributed across 74 oceanic islands (10 single islands and 64 belonging to 11 archipelagos; Table  S1. 1 and Fig. S3. 6). Of these, 214 species and 280 populations were extinct, 172 species and 801 populations were introduced, and the remaining (445 species and 1628 populations) were extant natives. Some species were introduced to an island but native to another, or extinct from one island while extant on another.
ACKNOWLEDGEMENTS This work was funded by the  Portuguese Government "Fundação para a Ciência e para a Tecnologia" (FCT/Ministério da Ciência, Tecnologia e Ensino Superior ), through  FCS' PhD grant  ( PD/BD/140832/2018 ) and the  Centre for Ecology, Evolution and Environmental Changes 's (cE3c's) Unit funding ( UIDB/00329/2020 ). We are grateful to  Dr Ruben Heleno  for his help to validate the Galápagos bird list. We sincerely thank  João Gameiro  for his comments and suggestions on the final version of the manuscript.
The hypothesis It was recently suggested that smoking may promote cellular uptake mechanisms of SARS-CoV-2 through CHRNA7 signalling  [20] , and subsequently, CHRNA7 transcripts were correlated with hACE2 levels  [21] , feeding the possible connections between hACE2 and nAChR localizations in airway epithelial cells. In addition, in silico analysis of nAChR ligands suggested an interaction between SARS-CoV-2 and nAChRs  [22] [23] [24] , while nicotine exposure may facilitate SARS-CoV-2 infection in vitro  [25]. Nicotinic receptors are ligand-gated ion channels consisting of 5 membrane-spanning subunits selected from 16 proteins (CHRNA1-A7, A9-A10, B1-B4, D, E, G) that all harbour at least one extracellular domain of approximately 200 residues. Going one step further, we suggest that nAChRs may bind to SARS-CoV-2-RBD (Receptor Binding Domain) similar to hACE2 and potentially act as co-Abbreviations: ACE-2, Angiotensin converting enzyme-2; BR, Binding region; COVID-19, Coronavirus disease 2019; ETD, Extracellular topological domain; nAChRs, Nicotinic acetylcholine receptors; PDB, Protein data bank; RBD, Receptor binding domain; SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2; vdw, Van der Waals.
The residue highlighted in yellow corresponds to the 24th residue involved in van der Waals contact distance but not in the immediate proximity of other residues involved in the binding, therefore it was not included in the analysis. (B) The crystal structure (Protein Data Bank: 6M0J) shows the 4 motifs highlighted in (A) with similar colour codes. The crystal structure of SARS-CoV-2 spike receptor-binding domain bound with hACE2 (PDB ID: 6M0J  [52] ) was highlighted and exported from the viewer of the Research Collaboratory for Structural Bioinformatics (RCSB; www. rcsb. org). are important transmembrane neurotransmitter receptors for acetylcholine in the mammalian brain  [40]. They modulate key neuronal functions such as excitability, synaptic transmission, and plasticity. They can change neuronal network states and consequently whole-brain responses to internal and external inputs and have been linked to disease  [41].
A possibility is also the vagus nerve. It is well established that it contains most nAChR subunits  [48]. According to each individual clinical context including but not limited to respiratory diseases and smoking history  [49, 50] , nAChR binding to SARS-CoV-2-RBD may facilitate virus attachment to cell surface before virus entry, or serve as a decoy either harmful as it would Fig. 2. Clustal alignment of nAChRs with hACE2 binding motifs. Data from the UniProt Knowledgebase (UniProtKB,  [53] ) were used to perform a sequence alignment study of matching residues between hACE2/SARS-CoV-2 binding sites and nAChR protein sequences. The UniProtKB/Swiss-Prot accession numbers of the sequences used for analyzing the similarities are the following: ACE2 (Q9BYF1), CHRNA1 (P02708), CHRNA2 (Q15822), CHRNA3 (P32297), CHRNA4 (P43681), CHRNA5 (P30532), CHRNA6 (Q15825), CHRNA7 (P36544), CHRNA9 (Q9UGM1), CHRNA10 (Q9GZZ6), CHRNB1 (P11230), CHRNB2 (P17787), CHRNB3 (Q05901), CHRNB4 (P30926), CHRNG (P07510), CHRNE (Q04844), CHRND (Q07001).
Materials and Methods We conducted an audit using an online survey containing 12 questions (one question per screen) for data collection. The primary method of accessing and completing the survey was via an online well-known and established online survey platform (SurveyMonkey ® (Momentive Inc. , San Mateo, CA, USA), www. surveymonkey. com, accessed on 28 June 2021) to facilitate widespread distribution of the survey and ease of data collection. The survey company hosted and collected the survey data and only participants who were sent the email could connect to the link and respond to the questionnaire. The survey company guarantees data encryption and is certified by EU-U. S. Privacy Shield. The questionnaire was drafted after a thorough review of the current guideline and evaluated for clarity of questions, appropriateness of responses, and ease of participation.
Discussion This audit, using an online survey, represents the largest collection of data for OTCD, providing an overview of 495 patients, reporting key patient characteristics including the diagnosis and management from three large countries. While there have been large international datasets involving all types of UCDs, there are no reports focusing on OTCD, which provides a similar audit and compares data from different countries  [27]. Turkey has a larger population size compared to France and the UK, which have almost the same number of inhabitants. However, the number of patients reported from Turkey is lower than the two other countries. This may be related to the geographical distribution of the metabolic centres in Turkey. While almost two thirds of the metabolic centres are located in the three largest cities (Ankara, Istanbul and Izmir) in Turkey, only 9 of the 78 remaining cities have metabolic centres.