dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and resources The European Strong Motion flatfile is available at https ://esm. mi. ingv. it//flatfi le-2018/ with persistent identifier PID: 11099/ESM_flatfile_2018. The analyses in this study have been performed in R software  (Team 2013). In particular, we used the libraries rlmm  (Koller 2016) , dplyr  (Wickham et al. 2019b) , ggplot2  (Wickham et al. 2019a) , ggmap  (Kahle et al. 2019) , viridis  (Garnier 2019) , etc. The electronic supplement provides all the data used, derived, and presented in this study. We further encourage readers to contact the authors for more details, GMM implementation in OpenQuake™  (Pagani et al. 2014) , regression scripts, etc. Acknowledgements Open Access funding provided by Projekt DEAL. We are grateful to the swift and insightful reviews by Prof. Sinan Akkar and an anonymous reviewer, and Prof.
John Douglas in his capacity as the handling editor of this manuscript at Bulletin of Earthquake Engineering. The contributions of the Sreeram Reddy Kotha (corresponding author) in this research are funded by the SIGMA2 consortium (EDF, CEA, PG&E, SwissNuclear, Orano, CEZ, CRIEPI) under Grant-2017-2021. The model development has benefitted immensely from feedbacks provided by Dr. Paolo Traversa, SIGMA2 scientific committee, and the collaborators in Horizon 2020 (Grant No. 730900) "Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe (SERA)" project. 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.
Follow-up No specific follow-up is organised for patients ending, prematurely or not, their participation in the study. Pregnancy course and outcome of all participants will be recorded on electronic case report forms (eCRFs) and added to the database. If the responses to the questionnaires suggest a mental disorder, the woman will quickly benefit (within a week) of an appointment with a psychiatrist in order to confirm, or not, the presence of a pathology and thus suggest the most appropriate care. Woman can also, in accordance with current healthcare practices, request to benefit from psychological support regardless of the results of the questionnaires.
Data collection and management The collection of women characteristics and immediate postpartum questionnaires will be performed at the time of inclusion in paper version. The postpartum evaluation at 2 months will be performed 8 weeks post partum (±1 week) with an online questionnaire. Data for this study will be recorded on an eCRF and are presented in table  2. The data from all women will be centrally stored in Nancy. Data management will be carried out by Nancy CIC-EC (INSERM CIC 1433). The conditions for data transfer of all or part of the study database are decided by the study sponsor and are the subject of a written contract. This study and the data collected fall within the scope of Reference Methodology MR001.
The main analysis will consist of a multivariable linear regression with the LAS score as the dependent variable, the presence of confinement as the explanatory variable and with the variables already known in the literature as co-variables (mode of delivery (spontaneous/intervention), mode of entry into labour (spontaneous/triggered), marital status (married/partner/couple/single) as well as the assumed confounding factors: Analyses for secondary objectives will be conducted on the same principle. The change between the two measurement times is calculated at the individual level by the difference in score between the two times. All women participating in the study will be included in the analyses, in case of missing data for multivariable analyses, if data are missing at random the analysis will be done with a listwise deletion, and a sensitivity analysis will be conducted using a multiple imputation method.
1121-3 and R. 5121-13 of the French Public Health Code) the persons having direct access to source data will take every precaution required to ensure the confidentiality of information relating to investigational medicinal products, studies, participants, notably concerning the identity of these, as well as the results obtained. These persons, as the investigators themselves, are subject to professional confidentiality. During the study, or at its conclusion, data collected regarding participants that are sent to the sponsor by the investigators (or all other specialists involved) will be coded by the inclusion number of the patient in the study. At no point should the names of participants or their addresses appear unencrypted. The presentation of the data processing results cannot in any case allow the direct or indirect identification of persons lending themselves to research.
However, the published results were preliminary because the evaluation was made at 2 days post partum only, so it might have reflected baby blues more than real postnatal depression. Moreover, the diagnostic was not confirmed by a psychological specialist, what we will perform in our study. Two other studies, registered in ClinicalTrials. gov (NCT04368208 and NCT04366817) and evaluating postnatal depression during COVID-19 epidemic, will be started soon but without control group, which is a major difference with the presented protocol. This proposed study will investigate the potential negative psychological impact of confinement during COVID-19 epidemic. ] [13] [14].
Geneset characteristics All genesets used in this study are presented in Extended data Workbook 1  21. The Chr 21 geneset was obtained from NCBI Gene. A total of 250 unique gene IDs were obtained at the time of manuscript preparation (September 1st, 2020). The AD-DS geneset, consisting of 292 genes, was obtained from GeneWeaver using "Alzheimer's Down Syndrome" as the search term. The geneset was originally generated via gene2mesh v. 1. 1. 1 (updated: 2019-01-07) from Medical Subject Headings (MESH Terms) GS236695 • [MeSH] Amyloid beta-Peptides: D016229. The AD risk factor geneset is comprised of 279 genes, many of which were identified and/or confirmed through a large scale GWAS of 71,880 clinically diagnosed AD and AD-by-proxy cases and 383,378 controls  22. The DEX genesets for the DFC (842) and CBC (570) were obtained from The Down Syndrome Developmental Brain Transcriptome database.
Functional analyses Gene ontology characterization of the genesets was performed in both DAVID and the Gene Ontology database for Biological Process (BP). The Benjamini corrected P-value was used to determine enrichment significance. Functional information based on GO annotations for the genes associated with a keyword search term related to AD and DS were identified and noted. Gene Ontology pathway enrichment was used to further characterize the AD-DS and Chr21 genesets in order to obtain a broader overview of collective gene function. The Benjaminicorrected P-value was used to determine significance.
APP protein interaction network The APP protein-protein interaction network was built in STRING (version 11. 0), based on experimentally validated interactions. The combined scores for the interactions are computed by combining the probabilities from the different evidence channels and corrected for the probability of randomly observing an interaction. First and 2nd shell interactions are included in the network. The network was exported from STRING and analyzed in Cytoscape (version 3. 7). Network bottlenecks and clusters were identified with Cytoscape plugins CytoHubba (version 0. 1) and MCODE (version 1. 6. 1), respectively.
APP protein-protein interaction network An APP protein-protein interaction network was created to identify genes from the genesets evaluated in this study that are connected to APP through 1st and 2nd shell interactions. A total of 362 proteins make up the network (Extended data Workbook 6  57  ). The APP protein interaction network overlaps by 48 genes with the AD-DS geneset, 41 with the AD risk factor geneset, 21 with the DEX DFC, 12 with the DEX CBC geneset and four with the Chr 21 geneset. The shared genes are highlighted in the network to visualize and forecast additional genes that are potentially involved in APP signaling and that are relevant to both AD and DS (Figure  4A ). The top proteins that bridge (bottlenecks) the different sections of the network and that may signify information flow are: APP, ENSG00000259680 (a novel protein coding gene with similarity to immunoglobulin heavy chain variable region.
Given the central role of APP related processes in the pathology of AD and DS, all of the proteins in the APP interaction network are either potential risk factors for AD or may contribute to disease progression in both AD and DS. Taken together, our findings indicate that oxidative stress, cell death/apoptosis, and inflammation/immune system processes likely underlie the pathogenesis of both AD and DS. Figshare: Extended Data Workbook 6. APP Network File: APP protein-protein interaction network, https://doi. org/10. 6084/ m9. figshare. 13106777. v1  57.
The substrates were glued onto a magnetic disk and an electric contact was made with (conductive) silver paint (Aldrich Chemicals). The STM tips were mechanically cut from a Pt/Ir wire (80%/20%, diameter 0. 25 mm). The images were obtained at room temperature. The raw STM data were processed through the application of background flattening, and the drift of the piezo was corrected using the underlying WSe2 lattice as a reference. Photoluminescence spectroscopy characterization: The PL spectroscopy of monolayer WSe2 were acquired with a Renishaw InVia spectrometer equipped with a 532 nm laser. Monolayer WSe2 flakes were mechanically exfoliated from WSe2 crystals onto SiO2/Si substrate. The excitation power was kept below 1 mW to avoid the local heating and damaging effect. The Si Raman peak at 520.
Acknowledgements Yuda Zhao  and  Sai Manoj Gali  contributed equally to this work. Y. Z. and P. S. conceived the experiment and designed the study. Y. Z. performed the PL, AFM, XPS characterization and carried out the device fabrication and characterization. C. W. carried out the STM study. S. M. G. , A. P. , and A. S. performed the quantum-chemical calculations. D. B. and S. M. G. supervised the theoretical work. Y. Z. wrote the paper. All authors discussed the results and contributed to the interpretation of data as well as to editing the manuscript. Device fabrication was carried out in part at the nanotechnology facility eFab (IPCMS, Strasbourg). We acknowledge funding from  European Commission  through the  ERC  project  SUPRA2DMAT  ( GA-833707 ), the  Marie-Curie IEF   STELLAR  ( GA-795615 ), the  Graphene Flagship Core 3 project  ( GA-881603 ), the  Agence Nationale de la Recherche  through the  Labex.
projects  CSC  ( ANR-10-LABX-0026 CSC ) and  NIE  ( ANR-11-LABX-0058 NIE ) within the  Investissement d'Avenir program  ( ANR-10-120 IDEX-0002-02 ), the  International Center for Frontier Research in Chemistry (icFRC). The work in Mons is supported by the  Belgian National Fund for Scientific Research (FRS-FNRS ), within  FNRS-PDR-TOREADOR project  ( T. 0051. 18 ). Computational resources were provided by the  Consortium des Équipements de Calcul Intensif (CÉCI)  funded by  F. R. S. -FNRS  under Grant  F. R. S. -FNRS  under the conventions  Nº 2. 4. 617. 07. F and Nº 2. 5020. 11. DB is FNRS Research Director. Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)).
Acknowledgements. We would like to warmly thank  Darryl D. Holm ,  Dan Crisan ,  Colin Cotter ,  Igor Shevchenko ,  Bertrand Chapron , and  Etienne Mémin  for helpful discussions and for having enabled the collaborations which have lead to this paper. Financial support. This research has been supported by the  National Science Foundation Division of Ocean Sciences  (grant no. 1350795 ), the  Office of Naval Research Office of Naval Research Global  (grant no. N00014-17-1-2963 ), and the  Engineering and Physical Sciences Research Council  (grant no. EP/N023781/1 ). Review statement. This paper was edited by  Wansuo Duan  and reviewed by two anonymous referees.
Studies on sex differences in academic skills have often reported diverging results depending on the type of evaluation used, with girls typically obtaining better school grades and results at national examinations, and boys scoring higher at standardized tests. In this paper, we provide a framework for better understanding and interpreting these differences, integrating previously established factors that affect variations in the gender gap across evaluation types: writing skills, stakes, self-discipline and grading bias. We apply this framework to a dataset containing the results of 23,451 French students in three evaluations characterized by different combinations of these factors: teacher evaluations, national examinations, and standardized tests. We find that, overall, girls show lower performance than boys in mathematics and higher in French.
In addition, we estimated equation (1) controlling for socio-demographic variables. We subsequently included individual cognitive variables as covariates in order to see whether they would explain the observed differences. Missing data in covariates (see Table  2 ) were dealt with using multiple imputation  (Rubin, 1987)  (PROC MI and PROC MIANALYZE with MCMC method and 10 imputed datasets). measures to what extent the discrepancy between teacher evaluation scores and national examinations (DNB) scores differs between male and female students. Similarly, reflects to what extent the discrepancy between standardized test scores and national examinations (DNB) scores differs between male and female students. We can indeed derive from the above equation that: 4 We could not cluster at the school or class level because such information was not available.
Limitations In light of the following limitations, conclusions must be interpreted with caution. First, our inferences regarding the extent to which each characteristic affects sex differences are purely based on assumptions from the literature, since we did not have the data to measure these characteristics. Further research with more comprehensive datasets are needed to address this issue, as it is also possible that we omitted other evaluation characteristics that may affect the gap across evaluations. For example, in mathematics, the presence of problems with spatially based solution strategies and multiple solution paths may influence the sex difference in favor of boys  (Gallagher et al. , 2002). Second, our model is somewhat simplistic: it assumes that there are no interactions between the different evaluation characteristics, and that students' ability and evaluation characteristics affect results in the different evaluations in the same way across sexes.
Acknowledgements We would like to thank (i)  E. Lamaud ,  J. -M. Bonnefond  and  D. Garrigou  for their contribution to the field data collection, and (ii)  C. Chipeaux  and  D. Loustau  for their help with our experiment set-up at the Salles ICOS station. Financial support from the  Department 'Environnement et Agronomie' of INRA  is gratefully acknowledged. Finally, we thank the three anonymous reviewers for their helpful comments.
ACKNOWLEDGEMENTS We thank the anonymous referee for his/her insightful comments that substantially improved the conclusions of the paper. ER thanks  Jean Teyssandier ,  Giuseppe Lodato ,  Andrew King ,  Sergei Nayakshin ,  Diego Muñoz  and  Nienke van der Marel  for fruitful discussion. ER and RA acknowledge financial support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  681601 ).
Sample A representative sample of 170 census tracts was selected (figure  1 ), covering all neighbourhoods, with an average of two census tracts per neighbourhood. In each census tract, we collected data on various elements related to alcohol availability: on-premises (bars and restaurants) and off-premises (retail stores), and advertising and promotion elements in the public space. To obtain representative data for the city, we conducted stratified random sampling of census tracts in each neighbourhood (except for the two neighbourhoods composed of a single census tract, which were both included). In general, we sampled two census tracts for each neighbourhood, but if these covered less than 5% of the surface, we randomly selected more until 5% was reached (figure  1 ).
Measures Field data collection was carried out using the validated OHCITIES Instrument. 25  OHCITIES is a questionnaire that uses systematic social observation to characterise different variables related to alcohol availability and promotion in the urban environment. This instrument had been previously developed and validated showing high inter-rater and test-retest reliability values (higher than 0. 80) for most of its variables. 25 The OHCITIES Instrument works with a mobile application and we collected the data using a smartphone. It also allowed us to geolocate all the recorded information and to take pictures. Four trained observers completed the observations in the 170 selected tracts, randomly distributed among observers and observation days. As a previous study observed only minor differences between mornings and evenings,  15  data were collected in working days, 16:00-20:00.
Although the reliability of OHCITIES has been validated previously, in order to detect whether there were significant differences in data collection between observers and seasons, we performed repeated observations in 37 census tracts (13 in different seasons, 7 with different observers, and 17 with both different observers Open access and seasons) and found no significant differences (data not shown). The main dependent variables for this study were the densities of the urban elements that may foster alcohol consumption: availability of alcohol; and alcohol advertising and promotion visible from the public space. Availability of alcohol included on-premises (bars, restaurants, pubs) and off-premises (supermarkets, grocery stores, specialised alcohol outlets), regardless of whether they were open or closed at the time of the observation.
As elements promoting alcohol, we included both the number of premises that had any alcohol advertising or promotion visible from the street (posters, awnings, signs, items with logos in the premises' terrace), and the number of advertising or promotion items located in outdoor public places beyond the outlets (billboards, posters on bus stop shelters, vinyls on urban buses). We obtained the densities of each element by calculating the number of elements recorded in a census tract per 1000 residents in that census tract. Independent variables were derived from the city statistical reports, which provide most data at the neighbourhood level. Thus, for these variables we ascribed to each census tract its neighbourhood value. The main independent variable was tourism pressure,  26  which we estimated using a previously proposed indicator.
We considered as census tracts enduring high tourism pressure those located in neighbourhoods with the highest quartile of tourist beds relative to the resident population. We fitted Poisson regression models with robust error variance obtaining crude rate ratios for each category with their respective 95% CIs. We used the lowest level of tourism pressure as the reference category. Finally, to control for confounding by socioeconomic level, we fitted another Poisson regression model with robust error variance adjusting for the neighbourhood AFII. All statistical analyses were carried out using the Stata V. 14 software package while the spatial analyses were done with QGIS 3.
Contact angle measurements Water contact angles on different substrate surfaces were measured using a contact angle measurement device OCA20 (DataPhysics Instruments, Filderstadt, Germany). A 2-μl water droplet was deposited on a substrate, then an image of the droplet was taken after 5 s, and contact angles were determined from the fit of the droplet's shape with a sphere using SCA 202 software (DataPhysics Instruments, Filderstadt, Germany). All the contact angle measurements were done before the pull-off force assays except for PLL-PEG-coated glass.
The force transducer was then moved upwards with a velocity of 200 μm s -1 until detachment of the pupa. The signal from the force transducer was amplified using Transbridge TBM4M and digitized using Lab-Trax-4 data acquisition hardware (World Precision Instruments). Three phases could be distinguished on force curves recorded using LabScribe v3 (iWorks, Dover, NH, USA) (Fig. 2A ). During the initial phase, the pupa was not attached to the tape and force with no pupa weight was measured. The pulling phase started when the pupa came into contact with the tape. During this phase, the pupa was stretched until it detached from the substrate (maximal force). Finally, during the resting phase, the force went down to a basal value including pupal weight. Overall, the adhesion strength we measured corresponds to the weakest interface: adhesion of the  The substrate on which the pupa was naturally sticking was fixed on a lift table using a clamp and brought into contact with a piece of double-sided sticky tape attached to a metal platelet.
Acknowledgements We acknowledge  T. Roeder  and  C. Sandberg  ( Kiel University, Germany ) for help with fly culture,  S. De Beco  ( Universitéde Paris, Paris ) for help with PLL-PEG coating and  L. Corté ( Mines-ParisTech, ESPCI, Paris ) for discussions. We also thank the  ImagoSeine facility , member of the  France BioImaging infrastructure  supported by the  French National Research Agency  ( ANR-10-INSB-04 ,  «Investments  for the future») and  N. Moisan  for technical assistance with confocal microscopy. One stock obtained from the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ) was used in this study.
Funding The research leading to this paper has received funding from the  European Research Council  under the European Community's  Seventh Framework Program  (FP7/2007-2013 Grant Agreement no. 337579 ) to V. C. -O. F. B. was supported by a PhD fellowship from  École normale supeŕieure Paris-Saclay  and by a travel grant from  COST ENBA (European Cooperation in Science and Technology, European Network of Bioadhesion Expertise ,  CA-15216 ). S. G. was supported by the  German Science Foundation (Deutsche Forschungsgemeinschaft , project  GO 995/38-1 ).
A reminder email message was sent three weeks after the first email message. e second part of the study was an online French survey sent in 2018 January to the 50 simulation centres registered in the SoFraSimS database. e questionnaire was created by experts in CAs and simulation (AB, DB, and DBa) and included 17 questions about centre characteristics, whether CAs are being used in these centres and if so, how they are being implemented. is questionnaire was also officially approved by the SFAR (Committee on Risk Management) and SoFraSimS (French Society of Simulation in Healthcare).
More precisely, we expected that ectomycorrhizal fungi, and pathotrophic fungi, often indicators of mature forests, would be also more sensitive to forest history and therefore to the quantity of ancient forests than bacteria and saprotrophic fungi. We sampled 40 soil cores per 0. 5 ha, pooled in 8 composite samples per plot in 27 landscapes and sequenced ITS1 and 16S markers by Illumina-Mi seq. To identify functional groups of fungi, we relied on their taxonomy and the use of public databases. Our results partly follow the HAH, as fungal richness was positively related with the quantity of ancient forests in the landscape and not by the focal patch size. Ectomycorrhizal and pathotrophic fungi were positively affected by the ancient forest cover, and so were saprotrophic ones, but not bacteria.