dataesr/scientific-paragraphs-categorization huggingface dataset:
" Complementary and independent French Survey. The data of another online survey were used to evaluate the robustness of the temporal evolution of smell and taste changes. This survey was conducted in the French population between April 8 and May 8, 2020 and aimed at characterizing chemosensory disorders in people with and without COVID-19, as well as their consequences on quality of life. The data of 950 respondents were eligible for comparison with data from the GCCR survey, i. e. , responses where both the date of completion and the date of smell loss onset were provided. Only responses that were complete and from people who were responding to the questionnaire for the first time and were over age 18 were included. This survey was approved by the CNRS ethics committee. Data collection was strictly anonymous.
Allergies incidence in previous years were calculated from the ratio of consultations for Allergy to general consultations at the emergency room (ER) in hospitals. Data dealing with the health status across countries (number of COVID-19 cases and deaths for each day) were downloaded on May 22, 2020 from the European Centre for Disease Prevention and Control databank (ECDC, https:// www. ecdc. europa. eu/en). Data regarding healthcare system stress in France (hospitalizations, CCRU entries and deaths) were also downloaded on May 22 from the French Public Health website (Géodes, Santé Publique France, https:// geodes. santepubliquefrance. fr/#c=home). Here, we use the term CCRU (Critical Care Resuscitation Unit) to translate the French hospital service of "Réanimation. " Raw data were normalized across regions with regard to their number of inhabitants as estimated by INSEE.
The temporal evolution of the stringency of government response was retrieved from the Oxford COVID-19 (https://www. bsg. ox. ac. uk/research/research-projects/coronavirus-government-response-tracker). Here, the stringency level of a country is computed according to which measures of a list of items (e. g. , school closures, cancellation of public events, international travel controls, etc. ) are undertaken. For the post-lockdown situation, the color assigned by the French government to each department was downloaded on May 12 from the government website. Only data before May 11 (the initial lift of the lockdown) were included in the analyses. Statistical analyses. Statistical analyses were pre-registered at the Open Science Framework (OSF). Data were analyzed using R software (4. 0) and its standard packages (maps, ggplot, etc.
Acknowledgements This work was supported financially by  EXTREM-O (CNRS MITI) ,  CONFINEZ2  (CNES), the  CORODORAT grant  ( IDEX-Lyon-Université de Lyon ), by the  French government , through the  UCAJEDI  " Investments in the Future " project managed by the  ANR  grant No. ANR-15-IDEX-01. The authors thank  Sébastien Buthion ,  Conceição Silva ,  Brigitte Perucca ,  Clément Blondel ,  Stéphanie Younès ,  François Maginot , and their teams from the  CNRS communication units , as well as the  Global Consortium for Chemosensory Research (GCCR). Deployment of the GCCR survey in multiple languages by the  Pennsylvania State University  ( Penn State ) was partially supported by funds from  James  and  Helen Zallie  and the  Penn State Sensory Evaluation Center.
Nearest neighbour context groups The stacking interaction involves two nearest neighbour (NN) base pairs, by considering also the anking base pairs to each side of the NN, this results in 8768 possible context combinations present in this dataset. Using the BP context groups, this number can be reduced by introducing NN context groups formed by the intersection of BP groups sharing the same sequence steps, which we describe as follows. For two consecutive mismatch base pairs B 1 P 1 and B 2 P 2 , each in its own trimer context, we need effectively to consider tetramer contexts such as N 1 B 1 B 2 N 3 /N 2 P 1 P 2 N 4 In a similar way as for BP context groups, we will combine all possible NN contexts into a group h N 1 B 1 B 2 N 3. N 2 P 1 P 2 N 4 i which is formed by the intersection of groups fN 1 B 1 B 2 =N 2 P 1 P 2 g and fB 1 B 2 N 3 =P 1 P 2 N 4 g.
Acknowledgements  L. O. Oliveira  and  G. Weber  acknowledge support by  Fundação de Amparo a Pesquisa do Estado de Minas Gerais (Fapemig, Brazil) ;  Conselho Nacional de Desenvolvimento Cientíco e Tecnológico (CNPq, Brazil)  and  Coordenação de Aperfeiçoamento de Nível Superior (Capes, Brazil). Adam S. Long  acknowledges funding and support by The  Forensic Science Service® ,  Solihull Parkway ,  Birmingham Business Park, Birmingham ,  B37 7YN, UK.
These methods thus fall in the forward modeling category. An example of this is the TinyTim software  (Krist 1995)  for the Hubble Space Telescope. The second, or nonparametric, approach is based on data only, using unresolved stars in the field as direct measurements of the PSF and estimating the PSF at galaxy positions from these measure-ments. The PSFEx software  (Bertin 2011)  is a typical example of such an approach, and has been successfully applied to real data in the context of weak lensing shape measurement (for instance in the DES survey:  Jarvis et al. 2016; Zuntz et al. 2018). The PSF modeling approach used in CFHTLenS  (Miller et al. 2013)  and KiDS  (Kuijken et al. 2015)  similarly falls in this category, though unlike PSFEx, it allows for discontinuities in the PSF variations at the boundaries between charge-coupled devices (CCD), and can thus be fit on the whole field at once rather than on each detector individually.
In this paper, we expand the RCA method by capturing spatial variations of the PSF through a set of PSF graphs. We can thus estimate the PSF at any arbitrary position in the field, while preserving all the properties of the RCA-recovered PSF field. This leads to a new approach that can deal, similarly to PSFEx, with both super-resolution and spatial variations, simultaneously. The python library is freely available 1. Neither of these models prove sufficient, in their current state, to achieve Euclid requirements. This is likely mostly driven by the number of available stars. For a purely nonparametric PSF modeling approach to reach the accuracy required for Euclid weak lensing, more stars than those available within a single CCD of a science exposure will thus need to be used. This could either rely only the instrument's temporal stability, or a modeling of the PSF's variations with time.
Spatial interpolation of the PSF Several standard methods exist to perform spatial interpolation, that is, to estimate the (unknown) value of some function f at a new position u = (x, y) given its measurements at several other positions: ( f (u k )) k. See  Gentile et al. (2013)  for a review of such methods in the particular framework of PSF spatial interpolation. The most natural (and the one used by PSFEx) is probably the use of a polynomial function of positions: A&A proofs: manuscript no. source f (u) = i, j≥0 i+ j≤d x i y j Q i j , (14) where the maximum polynomial degree d is a user-selected parameter, and the (Q i j ) i, j are chosen so that f (u k ) ≈ f (u k ) at every position where f was observed (in our case, u k ∈ U stars ). We note that the particular set-up of PSFEx shown in Eq.
PSF modeling We first assumed the star images described in Sect. 5. 1 had already been extracted, and we performed both super-resolution and spatial interpolation using RCA, as described in Sects. 3 and 4. This yielded a set of 297 RCA-estimated 42 × 42 PSFs, ĤRCA , at galaxy positions per SNR level. PSFEx was designed to run on catalogs extracted using companion software Source Extractor, and thus requires a little more setup. For each SNR level, we first created a fake full image of 12 000 × 12 000 pixels, into which the 300 stars are placed at their respective positions. We then ran Source Extractor on the resulting images, with parameters selected so that all stars were detected and extracted correctly, and no spurious detections occurred. When run, PSFEx performs a further selection across all objects extracted by Source Extractor, which is usually desirable for fitting the PSF model to appropriate stars.
Galaxies and observations We performed galaxy-image simulations using the freely available GalSim software 2   (Rowe et al. 2015). The galaxy parameters are identical to those used in several branches of the GREAT3 challenge  (Mandelbaum et al. 2014) , themselves based on fitting the COSMOS population. This gives us a population of 2 040 000 galaxies that are either drawn from a single Sersic profile, or composed of both a bulge (following a de Vaucouleurs profile) and a disk (with an exponential profile). We applied 204 different random shear values, each of them to a set of 10 000 different galaxies. These sets include the 90-degree rotated counterpart to each galaxy, so as to ensure intrinsic ellipticity truly averages at zero. The main difference between our image simulations and those used in GREAT3 is, naturally, the PSF used.
Because of the considerable difference between those two approaches, especially with regard to how the PSF is taken into account, we perform our experiments with one method of each type. The most well-known moments-based approach is the KSB method, first introduced by  Kaiser et al. (1995). Various improvements and implementations of the KSB method have since then been proposed. In the present work, we use its implementation within the HSM  (Hirata & Seljak 2003; Mandelbaum et al. 2005 ) library of GalSim, where the size of the (circular) Gaussian window function is matched to that of the observed galaxy. For model fitting, we use the freely available im3shape package 3  described in  Zuntz et al. (2013). In the results shown in Sect. 6. 3, im3shape was run with most parameters left to default, except for those related to the images stamp size, noise level, ranges for the estimation of the object's centroid and PSF handling (see Appendix B for a complete list).
Acknowledgements. The Euclid PSFs used in this work were provided by  Koryo Okumura ,  Samuel Ronayette  and  Jérôme Amiaux. The authors are grateful to  Lance Miller ,  Henk Hoekstra  and  Christopher Duncan  for their comments on an earlier draft of this paper, to the anonymous, external referee for their comments and suggestions, to  Arnau Pujol  and  Florent Sureau  for numerous helpful discussions and advice on shape measurement, and to  Imane El Hamzaoui  for her help with some of the figures in this paper. This work was supported by the  Centre National d'Etudes Spatiales  and the  European Community  through the grant DEDALE (contract no. 665044 ) within the  H2020 Framework Program. JB acknowledges support by  Fundação para a Ciência e a Tecnologia  through national funds ( UID/FIS/04434/2013 ) and Investigador FCT contract  IF/01654/2014/CP1215/CT0003.
As SNPs close to the sex locus might be genetically linked to the locus in a particular cross only, we used public whole-genome resequencing data to validate the sex-linked SNPs detected in this cross (Fig. 1a , b, Additional file 1: Tables  S7-8 ). We searched for SNPs that were sex-linked in our cross and that were always heterozygous in males and homozygous in females in the validation dataset. We found that the X haplotype of the sex locus spanned 111 kb on the V. sylvestris chromosome 2 (4,810,929 to 4,921,949 bp, Fig. 1b, c , Additional file 1: Fig. S2-5 ), and we obtained a final dataset of 1865 XY SNPs (Additional file 3).
S2 ) as well as the mean mapping coverage in our re-sequencing dataset along the X haplotype, searching for two-fold reductions in males. This revealed eight regions spanning 500 to 5500 bp and affecting one predicted gene (Additional file 1: Fig. S3 ). We detected more insertions of transposable elements in the Y haplotype than the X one (Table  2 , Additional file 1: Fig. S2 , Additional file 4, Additional file 5). The comparison between the X, Y, and the Yh haplotypes showed strong similarities between Y and Yh haplotypes and confirmed that the Yh haplotype derives from the Y haplotype (Fig. 2 , Additional file 1: Fig. S3  and S4 ). We however found that transposable elements' insertions are more numerous on the Yh haplotype compared to its Y counterpart (Table  2 ). Most of these insertions are located close to the APRT3 gene (Additional file 1: Fig.
S4 ). Our assembly of the Yh haplotype was compared with published long-read-based sequences of Yh from Zinfandel [X-Yh]  [19] , Cabernet Sauvignon [X-Yh]  [20] , and Chardonnay [Yh-Yh′]  [21]. Sequence identity was respectively 99. 93%, 99. 97%, and 99. 62%. Dot plots showed a full synteny between the Yh Genotype of nine V. sylvestris individuals (top) and thirteen grapevine cultivars (bottom) in a validation dataset of public whole-genome re-sequencing, at locations of candidates XY SNPs. Red, purple, blue, and white marks represent XX, XY, YY, and missing genotypes, respectively. In the cultivar panel, 11 cultivars are heterozygous (XY) for the sex-linked SNPs, with a Yh haplotype closely derived from Y (see text), while two cultivars (Chardonnay and Riesling) are homozygous YhYh. YhYh genotypes were previously found only in hermaphrodite cultivars  [6].
The black lines highlight the limits of shared XY SNPs between the cross re-sequenced in the present study and the validation dataset. (c) Gene content and annotation and in the sex locus (approximate position). Genes highlighted in red are absent (Δ) or possess a frameshift deletion in the X haplotype (*). Genes highlighted in blue are induced in males. The yellow gene is absent in the Y haplotype. (d) Synonymous divergence between X and Y allele in the sex locus (+/-standard error), reflecting the age of recombination suppression. dS was only computed for genes present in both haplotypes haplotypes. To explore the potential differences among V. vinifera cultivars, we investigated the SNP genotypes at the sex locus in publicly available DNA-seq data of 13 hermaphrodite grapevine cultivars  [8]  (Additional file 1: Table  S9 ).
Finding candidate male-sterility genes The classical genetic models for sex determination in grapevine suggested that the sex locus contains two genes (with a dominant So = suppressing ovule allele for the first gene, and a recessive sp = suppressing pollen for the second one, see  [3] ). According to these models, females are so sp/so sp and males So Sp/so sp; the X haplotype should thus bear the sp recessive allele and the Y haplotype the So dominant one. To identify candidates, we compared the gene content of X, Y, and Yh haplotypes (Fig. 3a ) and searched for presence/absence patterns and loss-of-function mutations (Fig. 3b ). We also mapped a public RNA-seq dataset of males, females, and hermaphrodites flower buds of V. sylvestris and V. vinifera at four developmental stages  [27]  (Additional file 1: Table  S11 ), measuring the total (Fig.
Some of the A. thaliana INP1 knock-out mutants may be fertile  [38] , while those mutants in maize are infertile  [40]. ETOL1 shows homology to NPG1, which is essential to pollen germination in A. thaliana  [41] , and the ethylene pathway has been shown to be determinant in floral morph determination in Cucumis melo  [42]. NAP1 encodes a nucleosome assembly protein in A. thaliana. Two homologs of UAP56A, a DEAD-box ATP-dependent RNA helicase, have been shown to regulate programmed cell death during tapetum development in Oryza sativa  [43] , and disrupting them led to male sterility. None of these three genes were significantly expressed in the early developmental stages of the RNA-seq dataset that were produced to study ovule development (Additional file 1: Fig. S7 ), likely because male sterility occurs later than female sterility in V.
Whole-genome sequencing (PacBio-long reads) of Sylvestris C1-2 The accession of Sylvestris C1-2 with plant code 8500. Col. C1-2, origin of the geographic location Sainte-Croix-en-Plaine, Haut-Rhin (68), France, was used for extraction of highmolecular-weight genomic DNA. The extraction was performed by the CNRGV (Centre National de Ressources Génomiques Végétales) -INRAE Toulouse. They started with 1G of frozen material (-80 °C), crushed it with liquid nitrogen, and used the Genomic-tip 100/G kit (Qiagen) for the extraction. The SMRT library preparation and sequencing on PacBio RSII platform (P6-C4 chemistry) was done by the IGM Genomics Center at the University of California, San Diego, following the standard PacBio protocols. A total of 129X coverage was obtained in order to perform de novo genome assembly.
Nanodrop analysis indicated that concentrations were always above 110 ng μl -1 , and that more than 5 μg DNA was available for all samples. Quality (fragment size) was checked using capillary electrophoresis (Fragment Analyzer) and was satisfactory. Based on quality check, we adapted sonication (using Covaris E220) in order to get mostly fragments of 250 bp. Twelve Illumina libraries were constructed and were pooled for two lanes of sequencing on an Illumina Hiseq 4000 machine in 2 × 100 bp paired-end mode. This yielded between 85 and 149 million reads per individual (Additional file 1: Table  S2 ), which roughly corresponded to 17X to 30X coverage of re-seq data per individual.
Genome assembly and phasing The FALCON-integrate 1. 8. 4 tool used is available on github: https://github. com/Pacific-Biosciences/FALCON-integrate/tree/1. 8. 4. The FALCON parameters used for the Vitis sylvestris genome assembly are taken from the genome assembly of V. vinifera cv Cabernet Sauvignon paper  [10]  (pa_HPCdaligner_option = -v -dal128 -e0. 75 -M60 -l2500 -k18 -h1250 -s100 -w8; ovlp_HPCdaligner_option = -v -dal128 -M60 -e. 96 -l1500 -s100 -k24 -h1250; pa_DBsplit_option = -a -x500 -s200; ovlp_DBsplit_option = -s200; falcon_sense_ option = --output_multi --min_idt 0. 70 --min_cov 4 --max_n_read 400 --n_core 8; fal-con_sense_skip_contained = False; overlap_filtering_setting = --max_diff 120 --max_cov 120 --min_cov 4 --bestn 10 --n_core 8). The phasing and haplotypes creation was performed with Falcon-unzip, with default parameters.
Assembly polishing and finishing After the FALCON run, we performed additional polishing with Illumina reads. First, Illumina reads were aligned with bwa  [61]  mem and -M option on FAL-CON's genome assembly. Then, alignments were filtered with samtools to keep only primary alignments and concordant pairs. Finally, alignments were filtered with bamtools to keep alignments with an edit distance ≤ 5. These filtered aligned reads are used to polish the assembly with the pacbio-util (version 0. 2) from pacbio-utilities tool (https://github. com/douglasgscofield/PacBio-utilities). Then, the same alignment was performed on the genome polished with pacbio-utils, and this genome was polished with Illumina reads and PILON (v1. 22 -https://github. com/ broadinstitute/pilon). A few haplotigs may have remained in the primary contigs file.
Quality control of the assembly Assembly statistics such as number of contigs, N50 and L50, were calculated with a home-made script. Genome assembly completeness was assessed with BUSCO  [62]  with the genome mode, the embryophyta_odb9 lineage, and the Arabidopsis species options (version 2. 0 -https://gitlab. com/ezlab/busco). Nucmer tool (from MUMmer tool: https://sourceforge. net/projects/mummer/ -nucmer (version 3. 1) parameters: -maxmatch -l 100 -c 500) and the grapevine reference genome  [5]  (PN40024, version 12X. 2 -https://urgi. versailles. inra. fr/Species/Vitis/Data-Sequences/Genome-sequences) were used to align the Sylvestris assembly to the PN40024 reference assembly in order to see completeness of this Sylvestris assembly. Nucmer and grapevine reference genome were also used to anchor and order Sylvestris contigs into chromosomes (with additional parameters -r -q), as it was done in the Chardonnay genome assembly paper  [63].
BAC sequencing and assembly of X, Y, and Yh haplotypes The sequences of the 7 sex-region BACs of V. sylvestris and the 6 BACs of Cabernet Sauvignon were obtained via a PacBio RSII sequencer (P6C4 chemistry). Sequencing was done in a pool of 20 individually tagged BAC clones. We used 2 μg of each individual BAC clone DNA to prepare the PacBio SMRT® 10 kb library. GeT-PlaGe Genomic Platform (INRAE-Toulouse, France) handled the sample loading on the RSII device and the data retrieval. We performed the detection and removal of residual E. coli sequences on raw reads. After a second cleaning step consisting in detecting and removing the vector sequences, individually tagged BAC sequences were assembled with the HGAP workflow (https://github. com/PacificBiosciences/Bioinformatics-Training/wiki/HGAP).
The protein databases used for the genes annotation were the TAIR10, swissprot, and uniprot plants databases. The transcriptomes used for the genes annotation were our Sylvestris transcriptome, 812 manual annotated genes, and transcript sequences of V. vinifera from NCBI -2017-11-08. The configuration file with all the parameters used is available as supplementary data (egnep. Vvi. for_Sylvestris. cfg). Primary contigs and haplotigs were annotated separately, with the same parameters. Finally, we obtained two gene annotations, one for primary contigs and one for haplotigs, with different files: gene annotation in a gff3 file, gene/ mrna/cds/ncrna/protein sequences in separate fasta files, and some statistics per gene.
BAC annotation The BAC sequences' annotation (V. sylvestris Y haplotype P2, V. sylvestris Y haplotype P1, V. sylvestris X haplotype, V. vinifera Cabernet Sauvignon Yh and X haplotype sequences and the annotation with EuGene  [16]  (Eukaryote Pipeline -v1. 4 -http://eugene. toulouse. inra. fr/)) was started with the same parameters and reference files than for the whole-genome annotation. The protein databases used for the genes annotation were the TAIR10, swissprot, and uniprot plants databases. The transcriptomes used for the genes annotation were our Sylvestris transcriptome, 812 manual annotated genes, and transcript sequences of V. vinifera from NCBI -2017-11-08. The configuration file with all the parameters used is available as supplementary data (egnep. Vvi. for_Sylvestris. cfg). Then, the annotations specific to BACs were extracted and used as final BACs' annotation.
Development of SEX-DETector++ We developed a new version of the SEX-DETector software that implements a probabilistic method to study SNPs segregation in a family, taking into account genotyping errors. SEX-DETector++ is coded in C++ and uses additional algorithmic optimization to reduce the running time and memory usage by about two orders of magnitude compared to the original code. SEX-DETector++ thus allows convenient usage on large, genome-wide genotyping datasets for which the original code would require prohibitively large running times and memory. The underlying model is the same as in the original code, but new functionalities were added to deal with genomic data as an input of the method (in the vcf format). The code is publicly available at https://gitlab. in2p3. fr/sex-det-family/sex-detector-plusplus, where technical and installation details can also be found.
25 and 0. 75 for heterozygote genotypes in individual samples, maximum coverage of twice mode of the Gaussian distribution for each sample, only bi-allelic SNPs, minor frequency variant higher than 0. 05, and maximum rate of missing data 0. 2. A SNP database was built with gsnap utilities to perform a SNP-tolerant mapping at the next iteration. We carried out two iterations of SNP-tolerant mapping until the rate of discovery of new XY SNPs (see below) became low. Finally, a 4th step of SNP-tolerant mapping with more stringent parameters (-m 0. 05, primary alignments only) was performed in order to reduce the rate of false-positive SNPs.