dataesr/scientific-paragraphs-categorization huggingface dataset:
Strengths and Limitations This is the first study to our knowledge to apply natural language processing to understand the psychosocial impacts of Australians during the COVID-19 pandemic using linked research data. Where possible, we used validated outcome measurement and analytical tools, including the robust Stanford CoreNLP analysis tool  [26] , to strengthen findings and allow comparisons with other studies. Limitations of the study included that the sample was biased toward women and relied upon self-report measures, which can be subject to social desirability bias. However, this can also be viewed as an advantage because the vast majority of natural language processing research draws upon social media data, such as tweets (from Twitter), as this is a common application of natural language processing.
Categorization of most frequently appearing words by theoretical life domain among impact stories from Australian adults in a cross-sectional study investigating the psychosocial impacts of COVID-19 in May 2020 (N=3483). Note: the theoretical life domains of skills, optimism, reinforcement, intentions, goals, and memory, attention, and decision processes were not identified in this data set. Top 50 words categorized into each domain, n (Beliefs about capabilities JMIR Public Health Surveill 2021 | vol. 7 | iss. 7 | e29213 | p. 6 https://publichealth. jmir. org/2021/7/e29213 (page number not for citation purposes) Ryan et al JMIR PUBLIC HEALTH AND SURVEILLANCE XSL • FO.
Material and methods This was a comparative study using the data of the MEDIP (Methods of induction of labor and perinatal outcomes) prospective population-based cohort. The objective of the MEDIP study was to describe the practices of induction in France and to compare the effectiveness and safety of the different methods in current use  [19]. This assessment of women's experience was one of the planned secondary objectives of the study. The cohort included all women who had labor induced from 17 November to 21 December 2015 in all maternity units belonging to 7 perinatal health networks (n = 94). These units accounted for one-sixth of the annual deliveries in France and were representative of the entire set of French maternity units  [26]. Still birth and medically-indicated termination of pregnancy were exclusion criteria.
A follow-up of two-months was performed. Data were recorded prospectively. The midwife or obstetrician managing the woman informed the indication of IoL, the method used and the Bishop score at the onset of induction. The choice of method was based on each maternity ward's habits or was left to the practitioner's choice. Independent clinical research technicians recorded additional data from the woman's medical records. Experience of induction was assessed with a selfadministered questionnaire sent by mail or e-mail (three reminders) at 2 months postpartum. The experience of IoL was first compared between women who underwent cervical ripening as first method of IoL and those who did not, i. e. those who received oxytocin infusion as first method (exposure/outcomes associations A). Secondarily, the experience of delivery was compared according to each method of cervical ripening (exposure/outcomes associations B).
Acknowledgements The MEDIP study was conducted by  Assistance Public-Hôpitaux de Paris  ( Département de la recherche Clinique et du développement ). The authors thank all the members of the MEDIP study scientific committee:  Catherine Crenn-Hebert ,  Adrien Gaudineau ,  Frédérique Perrotte ,  Pierre Raynal ,  Elodie Clouqueur ,  Gaël Beucher ,  Catherine Deneux-Tharaux ,  Pierre-Yves Ancel , and all the local investigators for their contributions to the MEDIP study. The authors also thank the  URC-CIC Paris Descartes Necker/Cochin  ( Laurence Lecomte ,  Isabelle April  and  Marina Salomé ) for setting up and monitoring the MEDIP study, all the women who agreed to participate in it and the midwives who recruited and included them.
To measure neutralizing antibody responses against different flaviviruses, MNTs were performed in parallel against WNV (strain E101), USUV (SAAR-1776), and BAGV (Spain/RLP-Hcc1/2010) (Genbank accession nos. AF260968, AY453412 and KR108244, respectively). MNT was performed following the protocol previously described by Llorente and collaborators (2019)  [31]. Briefly, twofold serial dilutions of test sera were added in 96-well micro titer plates and mixed with an equal volume containing 100 TCID 50 of virus, resulting in final serum dilutions starting at 1:10. After 1 h incubation at 37 • C, a suspension of approximately 10 4 Vero cells was added to the wells, and the plates were incubated at 37 • C and 5% CO 2 for 4-5 days for WNV and 5-6 days for USUV and BAGV. The presence or absence of complete cytopathic effect (CPE) was determined in each well by reading the plates under an inverted light microscope.
Horses Of 1856 serum samples collected from horses and tested by panflavivirus cELISA (IDvet) between 2010 and 2019 in Catalonia within the WNV Surveillance Program, 182 tested positive, and of them, 164 had enough serum to be tested by MNT at the ANSES laboratory (Maisons-Alfort, France). Horses' samples originated also from different sources, which comprised the Equine Veterinary Hospital where both healthy horses and horses suspected to be infected by WNV were sampled. Other samples were obtained through surveys carried out in horses' slaughterhouses, a cross-sectional survey in horses' holdings throughout Catalonia, or surveys in horses' holdings in the proximity of confirmed WNV outbreaks. All those samples were investigated through MNT for the detection of specific neutralizing antibodies against WNV, USUV, and TBEV using WNV (strain IS-98-ST1, Genbank accession no.
AF481864. 1, provided by P. Desprès, IPP), USUV (strain France 2018, Genbank IF MT863562. 1), and TBEV (strain Hypr, Genbank accession no. U39292. 1), following the protocol described by Beck and collaborators (2015)  [32]. Samples were classified as positive for WNV, USUV, or TBEV infection if positive for that virus by MNT and negative for the remaining viruses, or when the titer for a virus (WNV, USUV or TBEV) was at least 4 times higher than the titers for the remaining viruses. Otherwise, the sample was classified as infected by an "undetermined flavivirus". The distribution of samples in birds and horses collected within the WNV Surveillance Program in Catalonia between 2010 and 2019 is shown in the Supplementary Figure  S1.
Only data from horses and chickens were considered to reduce the risk of the animals having been infected in a location different from where they were sampled. To avoid the analysis being conditioned by having several infected animals from the same farm (because of within-farm analyses after detection), we chose farms as units and used unique locations for a given flavivirus. As a result, 58 locations (44 WNV and 14 USUV) were included in the analysis, nine of which corresponded to chickens and 49 to horses.
Acknowledgments: We are very grateful for the technical contribution of  Núria Pujol  and  Raquel Rivas  from  IRTA-CReSA in the laboratory work , and all the people that participate in the WNV Surveillance Program in Catalonia, in particular the personnel of the  Departament d'Acció Climàtica, Alimentació i Agenda Rural and of the Departament de la Vicepresidència i de Polítiques Digitals i Territori , as well as the clinical equine veterinarians and the personnel of the  Equine Veterinary Hospital.
Funding:  IRTA-CReSA  and  CISA-INIA (CSIC)  are funded by  PID2020-116768RR-C21/C22  projects. The work was funded by the  Departament d'Acció Climàtica, Alimentació i Agenda Rural. IRTA is supported by the  CERCA Programme/Generalitat de Catalunya. The  National Institute for Agricultural and Food Research and Technology (CISA, INIA-CSIC)  was also funded by  E-RTA2015-00002-CO2-01  project. Informed Consent Statement: Not applicable.
, 2015). The basic difference between MF and UPG is that relationships can exist among MF but not among UPG. MF under single-trait models provided the best results among other methods to model missing pedigrees in simulated datasets  (Bradford et al. , 2019). The performance of genetic evaluation models is usually evaluated by cross-validation  (Gianola and Schön, 2016). A statistic widely used for this purpose is the predictive ability, which is defined as the correlation between EBV and the phenotypes of the individual adjusted for the fixed effects in the model  (Legarra et al. , 2008). When divided by the square root of the heritability, the predictive ability gives an estimator of the accuracy of the model. Legarra and Reverter (2018)  derived a method called the LR method to obtain estimates of bias, dispersion, accuracy, and other parameters related to the quality of EBV.
Data The dataset used in this study was provided by Cobb-Vantress Inc. (Siloam Springs, AR). The traits used in the evaluation were weight (WGT), carcass yield component 1 (CYC1), feed efficiency 1 (FE1), carcass yield component 2 (CYC2), and feed efficiency 2 (FE2). The number of records and genotyped birds per trait and the number of birds used to validate the models (i. e. , focal individuals) are presented in Table  1. Genotypes from the 60k single nucleotide polymorphism (SNP) panel were available for 107,216 birds and pedigree information was available for 242,413 birds. About 1% of the birds in the whole pedigree had missing parents. Quality control removed SNP with call rates lower than 0. 9, minor allele frequencies lower than 0. 05, and heterozygosity deviation greater than 0. 15 from the Hardy-Weinberg equilibrium expectation  (Wiggans et al.
UPG were defined as in  Misztal et al. (2013) , where the Quaas-Pollack (QP) transformation  (Quaas and Pollak, 1981)  is applied to the pedigree relationship matrix (A), the genomic relationship matrix (G), and the pedigree relationship matrix among genotyped animals (A 22 ). Inbreeding coefficients estimated as in  Aguilar and Misztal (2008)  were used to compute both inverses of A and A 22 ; therefore, inbreeding coefficients were updated when inbreeding for UPG was considered. Inbreeding for UPG was calculated as the average inbreeding of known parents of the same period (t), that is, F upg = Fpt  (VanRaden, 1992). For MF, their covariance matrix (Γ) was computed by generalized least squares for multiple populations  (Legarra et al. , 2015; Garcia-Baccino et al. , 2017 )  using Gammaf90 from the BLUPF90 software suite (not publicly released).
If only UPG are considered, the nongenetic differences are not going to be modeled. The consideration of α as a fixed effect instead of random was to avoid the reestimation of variance components and to keep model simplicity. Although the reestimation of variance components may not be a limitation for smaller datasets, it can be time-consuming when the number of genotyped animals and traits is large. This can lead to a limitation in the interval between successive genetic evaluations and, therefore, become a problem for companies. In this study, two estimators for the accuracy were used. Predictive ability is widely used but depends on model specification and will tend to be higher with over-parametrized models. That is, when increasing the number of fixed effects, the predictive ability will tend to overestimate the accuracy of GEBV.
Conclusions The inclusion of animals from an external group into local genetic evaluations should be correctly modeled to obtain accurate, unbiased, and not under-/over-dispersed GEBV. This can be done by including an extra fixed effect in the model, by using UPG or MF. For most of the traits in this study, the best scenario includes an extra fixed effect and with an extra adjustment as UPG with estimated inbreeding or MF. When the groups to define the MF greatly differ in the number of animals, predictions can be biased. In such a situation, it may be better to use UPG. Further research on this topic is needed. In this study, UPG without estimated inbreeding did not converge. Thus, taking into account estimated inbreeding for UPG is recommended. The use of MF reduced the condition number of the system, resulting in faster convergence for both models; however, this result cannot be generalized to other models and datasets.
In addition, application of genome-wide data used for phylogenetic analyses in several genera can prevent the problems that arise in phylogenies based on a limited number of loci that lack sufficient phylogenetic signal  (Arbizu et al. 2016; Escudero et al. 2014; Hou et al. 2015; Stetter and Schmid 2017). The recognition of hybrids plays an important role in phylogenetics, as well as in pest risk assessment, but identification of hybrids is currently difficult. Therefore, the main objective of this study was to develop a new strategy to reliably identify and characterize known and novel Phytophthora hybrids. A broad collection of 614 isolates was gathered, representing 132 Phytophthora and four outgroup species. This collection was comprised of reference isolates and potential hybrids that were selected based on ITS sequencing and morphological characterization.
The entire collection was subjected to GBS and subsequent reference-free data analysis. This generated several critical genome fingerprint features: the absence/presence of GBS loci, the total number of loci per isolate, and the number of allelic variants per locus (Fig. 1b ). The second objective was to combine allele count analysis with genome size measurements for the identification of polyploid hybrids (Fig. 1b ). The third objective was to improve the Phytophthora phylogeny by constructing a genus-wide phylogenetic tree of exclusively non-hybrid species by applying both a concatenation-and a coalescent-based phylogenomics method to the GBS data, followed by anchoring the hybrids to their progenitors.
Their genetic relationship was determined based either on the GBS results and phylogenomic analyses in this study, or sequencing during pre-screening, or both. The rDNA ITS sequence of the isolates was used for preliminary identification and selection (data not shown). Isolates with heterozygous sites Table  1  Abbreviations of species names used in tables and figures in the ITS sequence received special attention as potential hybrids. Cultures were either immediately used for flow cytometry (FCM) or DNA extraction after hyphaltipping or were stored at 12 °C in sterile water until further processing. Additional isolates or DNA were obtained from laboratories participating in the EU Resipath project (ILVO, PRC, JKI, SLU, DGAL, UMR IAM, CRAW, ABI; see author affiliations). We also included DNA from isolates from the Westerdijk Institute (Utrecht, The Netherlands; CBS) and Wageningen Plant Research (Wageningen, The Netherlands; PRI).
Data assembly The sequence reads were submitted to a custom pre-processing pipeline which consisted of: (1) demultiplexing and sorting of the reads based on the barcodes using the GBSX software v1. 1. 5 (Herten et al. 2015) ; (2) trimming of the adapters, removal of the remainder of the restriction sites at the 5′-and 3′-ends and discarding reads smaller than 10 bp using cutadapt v1. 16  (Martin 2011)  and FastX toolkit v0. 0. 14 (https:// hannonlab. cshl. edu/fastx_toolkit/index. html); (3) merging of the forward and reverse reads that had a minimum overlap of 10 bp using PEAR v0. 9. 8  (Zhang et al. 2014) ; and (4) several filtering steps using FastX toolkit, prinseq-lite  (Schmieder and Edwards 2011), OBITOOLS v1. 2. 5 (Boyer et al. 2016 ) and pairfq 0. 14 (https://github. com/sestaton/Pairfq).
In this last step reads with a mean phred quality score of less than 25, reads that were shorter than 30 bp or those that contained any Ns were discarded. Additionally, singletons, non-merged paired reads and merged reads that still contained a PstI or HpaII restriction site were discarded using the "obigrep" function of OBITOOLS. The trimming and quality filtering script were parallelized using "GNU parallel"  (Tange 2018). Loci identification We performed GBS on hundreds of Phytophthora isolates but the reads were not mapped to a particular reference genome sequence, nor were reference genome sequences used for SNP discovery. There were two reasons for this: (1) reference genomes were available for only 21 Phytophthora species at the time of the analysis; and (2) analysis of the species themselves was not in the scope of this study.
Instead, we used a reference-free approach as implemented in GibPSs  (Hapke and Thiele 2016)  to construct a tag collection covering all possible genome complements in the dataset including genome sequences not covered by the 21 sequenced genomes. All merged reads were consecutively analysed with indloc, poploc, and indpoploc to identify loci and additionally to correct errors by indloc. This was done using the programs' default settings, except for the frequency threshold method in indloc where we used a character frequency threshold of 0. 1 (instead of 0. 2). These settings were chosen after testing different parameter combinations and checking reproducibility of biological and technical replicates (see below for detailed analyses). Loci with a length of 32 to 250 bp were selected with data selector.
Indelchecker was used to identify loci that contained indels and these loci were removed from the dataset. Depth analyzer was used to assess the read depth of the loci and loci that were sequenced extremely deeply were subsequently discarded, as these could belong to repeats, transposons, mitochondria, etc. This was done by removing loci with a median depth percentage of less than 1% and a median scaled depth of more than 0. 1. Finally, split loci were removed from the dataset. Isolates with a deviating curve in the depth analysis graph were potentially contaminated with non-Phytophthora loci and were thus omitted from the analysis. Additionally, all remaining loci in the dataset were used in a local BLAST search against a custom database composed of a representative set of prokaryotes ("ref_prok_rep_genomes" from ftp://ftp.
ncbi. nlm. nih. gov/blast/db), refseq genomes of fungi ("fungi_XX_genomic" from ftp://ftp. ncbi. nlm. nih. gov/refseq/release/ fungi), the human genome (build 38) and all available whole-genome shotgun sequence data of the genus Phytophthora in the NCBI WGS database. Isolates for which more than 250 GBS tags had significant BLASTn hits (E < 1e-4) with non-Phytophthora sequences were removed from the dataset and the GibPSs pipeline was restarted, as loci derived from contaminations could lead to inflated numbers of GBS loci for that isolate.
Determination of shared loci and allele similarity The GibPSs output table containing the identified genotypes (genotypes. txt) was exported and used as input for a custom-made Python script that performs a pairwise comparison of all isolates and determines: (1) how many loci an isolate contains; (2) how many loci are shared for each pair; and (3) the allele similarity of these shared loci based on the haplotype data, i. e. the percentage of the loci that have at least one identical allele in common (see Fig. 1c ). For each pair of isolates the Dice similarity coefficient  (Dice 1945)  was calculated using the number of loci of each isolate and the number of loci they share. This coefficient was subsequently multiplied with the allele similarity, resulting in a combined similarity index (CSI), expressed as a value between 0% (no alleles shared) and 100% (all loci shared and allele similarity of 100%).
This index was used for species delimitation of non-hybrid isolates. Saturation curves were constructed showing the dependency of "estimated genetic similarity scores" (shared loci and % allele similarity) versus "total number of reads per sample" (Figure  S2 ). The curves were calculated based on a subset of the reads (computationally subsampled as a random set of all reads per sample at 0. 25, 0. 5, 0. 75, 1, 1. 5, 2, 3, and 3. 5 M) compared to all available data per sample as reference for a selected number of isolates. This was done either within a single sample (Figure  S2a-b;  i. e. comparing subsampled datasets to all available reads of that sample), or between samples (Figure  S2c -f; i. e. comparing subsampled datasets of the first sample against all available reads from the second sample.
Allele count analysis The GibPSs output table containing the identified genotypes (genotypes. txt) was read into Rstudio v1. 0. 153 running R v3. 4. 3 and stored as a data table, a high performance version of a data frame, using the data. table package v1. 11. 2 (Dowle  and Srinivasan   2018). For each isolate the number of loci with one, two, three and four alleles was calculated. These data were visualized as a stacked bar chart using ggplot2 v2. 2. 1  (Wickham 2016).
Hierarchical clustering and Phylogenomic analyses GBS resulted in two types of data. Absence/presence of GBS loci was used in hierarchical clustering while SNP data was used for phylogenomic analyses. Hybrids and suspected hybrids were removed from the GBS dataset and only the data of representative genotypes (max. three per species) from the non-hybrid species (and species that are suspected to be non-hybrids) were retained. For the hierarchical clustering, the locus data from a selection of non-hybrid isolates (144 isolates; not more than three genotypes per species) was read into Rstudio and converted to binary data (presence = 1, absence = 0). Subsequently a dendrogram was inferred using the supraHex package v1. 26. 0  (Fang and Gough 2014)  with the Euclidean distance, Minimum Evolution algorithm with ordinary least-squares fitting ("fastme.
ols") and 500 bootstrap replications. For the phylogenomic analysis we applied two phylogenetic methods on the GBS data: (1) a method based on the concatenated SNPs as identified by GibPSs; and (2) a coalescent-based method (ASTRAL-III). For both methods, a dataset was made that consisted of one genotype per non-hybrid species (97 isolates). This reduced dataset was used to select the loci that were present in at least 80% or 30% of these isolates (referred to below as loci80 and loci30, respectively) using a custom R script. This corresponded to 29 and 1610 loci, respectively. Phylogenetic analyses were subsequently applied on the selection of non-hybrid isolates (144 samples, see hierarchical clustering). For the concatenation-based method, all loci were concatenated and aligned into a single fasta file using a custom Perl script.
Next, the fasta file was converted to a phylip file using jModeltest2 v2. 1. 10 (  Darriba et al. 2012 ) and all invariant SNPs were removed from the alignment using the "ascbias. py" script from https://github. com/btmartin721/raxml_ascbias. This resulted in 1062 and 61,111 variant SNPs for loci80 and loci30, respectively. Phylogeny was assessed using maximum likelihood with the program RAxML v8. 2. 10 (Stamatakis 2014), using the GTRCAT model without rate heterogeneity with a correction for ascertainment bias. No invariant SNPs appeared in the dataset, thus the Lewis correction was used. Statistical support was calculated by applying 500 bootstrap runs. For the coalescent-based method, the sequence of each locus was aligned using a custom Perl script resulting in per locus alignment files.
2006). The average locus length was 126. 6 bp and the median 120. 0 bp (range 32-250 bp). Taken together, this means that about 1 in 43 nucleotides carry a polymorphism across the complete dataset spanning 661 isolates belonging to > 100 different species. There were no loci common to all isolates, underlining the high genome diversity within the genus and the need for reference-free data analysis. Saturation curves in a selection of representative isolates were used to investigate the relationship between total read depth and accuracy of our genetic similarity scores (the number of shared loci and the allele similarity). We analysed the data within samples (Figure  S2a-b ) and between samples of diploid (Figure  S2c-d ) and polyploid (Figure  S2e -f) hybrids. For diploid non-hybrid species (e.