dataesr/scientific-paragraphs-categorization huggingface dataset:
Experiments applied on a public dataset underline that some lessons could be learned. The interest of our proposal is illustrated through the analysis of the top extracted terms for Greece (See Section 4. 3 for more details). In the TOP10 terms at the end of February, the words "thessaloniki" and "carnival" appear. The first term refers to the first COVID-19 case in Greece who have been treated in the Thessaloniki hospital. Tweets containing this term report panic buying of antiseptic in this area two days after. Following up "carnival" highlights the Greek government decision to cancel the Xanthi Carnival. Our approach extracts terms mainly used for a certain region and period that report information about the local situation, the crisis management and opinions of inhabitants. These information would be hard to discovered using methods from the literature (See Section 4.
Description of the corpus To evaluate the H-TFIDF efficiency for extracting discriminant terms in spatial and temporal windows, the experiments are conducted on a corpus of tweets related to COVID-19. Several initiatives have been carried out since the beginning of the crisis like  (Ofli, 2020) ,  (Balech et al. , 2020)  and on the machine learning competitions platform Kaggle 1. As we want to focus on the early beginning of the outbreak, we processed the dataset collected by E. Chen  (Chen et al. , 2020)  which starts the earliest (i. e. , January 22, 2020). During this period, common words related to the COVID-19 have been used worldwide  (Alshaabi et al. , 2021). In this massive flow of similar terminologies, we want to assess the spatio-temporal anchoring of the terms extracted by H-TFIDF.
Processing pipeline The processing pipeline, available in our repository 2  has four main steps: collect, indexing & geocoding, pre-process and computation of H-TFIDF as described in fig. 2. The first step consists to retrieve tweets, also called hydrate tweets, from E. Chen corpus of tweet IDs 3. Tweets metadata and content are then both indexed into an ElasticSearch 4  database in the second step. Elastic-Search shorten search queries duration which is useful to extract subcorpora, explore data or for pre-and postprocessing  (Barbaresi and Tinoco (2018) ). To increase the number of georeferenced tweets from 2% to almost 20%, locations defined by users (i. e. user. location in tweet metadata) are geocoded  (Dredze et al. (2013) ), during the indexing, using OpenStreetMap data  5  and the open source geocoder photon hosted by Komoot 6.
However the overlap between H-TFIDF and the most frequent terms should not be 100% either because we except H-TFIDF to not extract terms also used in other countries. For the next evaluation, we highlight and compare semantic diversity of H-TFIDF selected terms compare to TF-IDF selected terms. We thus encode each term using the language model DistilBERT  (Sanh et al. , 2020)  as introduced in the related work section. To visualize this comparison, we apply a dimension reduction of the DistilBert representation of our terms with t-SNE algorithm, i. e. , we reduce the 766 dimensions to 2 and then visualize it in a scatter plot for H-TFIDF terms in fig. 6a  and for TF-IDF terms in fig. 6b. Both figures have the same scale. Each term is a dot and is defined by two coordinates in this semantic space.
These evaluations show that H-TFIDF captures terms with a strong anchoring for a region at a period. Moreover, these terms reflect local concerns since there are widely and specifically used in their spatio-temporal windows. Finally, we pointed out that these terms bring also a greater semantic richness. The workflow underlying this paper was partially reproduced using a data subset by an independent reviewer during the AGILE reproducibility review and a reproducibility report was published at https://doi. org/10. 17605/osf. io/rdnyu. The data subset and the associated configuration of the workflow are available at https://doi. org/10. 5281/zenodo. 4742151.
Acknowledgements. This study was partially funded by  EU  grant  874850 MOOD  and is catalogued as MOOD012. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission. This work was also partially supported by the  French National Research Agency  under the Investments for the Future Program, referred as  ANR-16-CONV-0004 ,  #DigitAg.
Species occurrences We used occurrences for the breeding season (May-July;  Vaughan & Vaughan-Jennings, 2005)  for the period 1992-2016 retrieving data from eBird, Global Biodiversity Information Facility (GBIF), ornitho. it, ornitho. cat and xeno-canto. org as in previous SDM studies  (Avalos & Hernández, 2015; Coxen et al. , 2017; Engelhardt, Neuschulz, & Hof, 2020; Ramellini et al. , 2020). We also included survey data from the British Trust for Ornithology (in agreement with BTO's data policy), data for Greece from the Ornithotopos database and the second European Breeding Bird Atlas (hereafter EBBA) and 2x2 km surveys in Greece (in formal agreement with the Hellenic Ornithological Society), data for the Deux-Sèvres department from the Nature79 database provided by Alexis Martineau. For Northern Italy, the dataset was integrated with nest points opportunistically collected by Dimitri Giunchi in the period 2012-2018.
We used data with a maximum positional uncertainty of 5. 5 km to fit the resolution of predictors  (Guisan, Thuiller, & Zimmermann, 2017) , retaining the first occurrence in chronological order (i. e. the one reported in the first year available for the cell) when more than one point occurred in the same cell. We cut observations on the species' range to exclude vagrant individuals in non-breeding areas, developing a range shapefile (see Fig. 1 ) based on the BirdLife species distribution dataset (http://datazone. birdlife. org/species/requestdis), the EBBA1  (Hagemeijer & Blair, 1997)  and the EBBA2  (Keller et al. , 2020). We then applied a spatial filtering of the data to reduce spatial autocorrelation  (Kramer-Schadt et al. , 2013) , using the R package 'spThin' (v. 0. 2. 0; Aiello-Lammens et al.
Pseudo-absence selection We generated a set of pseudo-absence points (i. e. surrogates of true absences;  Barbet-Massin et al. , 2012) , also using them as background points for Maxent. Pseudo-absences are often drawn at random in the study area, whereas occurrences typically show a spatial bias. Therefore, models could discriminate between the environmental features of sampled and unsampled areas, rather than between those of suitable and unsuitable areas  (Phillips et al. , 2009). To avoid this problem,  Phillips et al. (2009)  proposed the 'target-group' method, where pseudo-absences consist of other species' data collected with the same methods used for the focal species. We used an approach similar to the observer-oriented one  (Milanesi, Mori, & Menchetti, 2020). We considered as our pseudo-absences GBIF data of other bird species recorded by the same observers of the presence data, assuming they were collected with the same sampling bias.
However, this approach could only be implemented on the GBIF data, and some biases might still arise. Therefore, we developed an alternative approach and refer to it as the 'Kernel density approach'. We generated a large number of pseudo-absences, weighting their spatial distribution according to a fixed kernel density estimated on all the presence points, by means of the 'adehabitatHR' (v. 0. 4. 18) R package  (Calenge, 2006). We used the ad hoc method for the estimation of the smoothing parameter  (Worton, 1989). The procedure was independently repeated on the occurrences separated by year for the period 1992-2016. For both pseudo-absence selection methods, if a cell contained more than one record, the chronologically oldest was retained in the dataset. Finally, we removed pseudo-absences outside the study area and randomly sampled them to obtain a 0.
Model evaluation We evaluated models based on the block cross-validation procedure implemented in the 'ENMeval' (v. 0. 3. 1) R package  (Muscarella et al. , 2014). In this procedure, data are partitioned into four geographically independent bins; four models are then produced, with three bins used for training and the remaining one used for testing. We used as performance statistics the area under the curve (AUC;  Fielding & Bell, 1997)  of the receiver operating characteristic plot, and the Continuous Boyce Index (CBI;  Boyce et al. , 2002). The AUC metric was computed with the R package 'biomod2  ' (v. 3. 3-7, Thuiller et al. , 2016) ; the CBI with the 'ecospat' (v. 3. 1) R package  (Di Cola et al. , 2017). To detect overfitting in our models, we computed the difference between AUCtest and AUCtrain (AUCdiff;  Radosavljevic & Anderson, 2014).
Projections The models developed during the model-selection phase were either projected on the static variables (static SDMs) or on variables representing the year 2016 (dynamic SDMs). The year 2016 was selected to describe current conditions as it was the closest year to the present time represented in our climatic dataset. The selected model was then projected to future conditions. We initially used a static LULC (i. e. the 2016 LULC variables) in future predictions to constrain climatic outputs  (Pang, De Alban, & Webb, 2021). We then performed an additional analysis projecting models calibrated with the same historical LULC database on scenarios from the future dynamic LULC database  (Chen et al. , 2020). However, the difference between LULC databases did not allow quantitative interpretations of mean breeding suitability change.
Evidence of the ability of animals to track their niche is contrasting  (Devictor et al. , 2008; Chen et al. , 2011)  and many terrestrial organisms have been shown to shift their distribution at a sufficient pace to track recent temperature changes  (Chen et al. , 2011). Species affected by global change may also persist under unfavourable conditions being phenotypically plastic and becoming locally adapted  (Valladares et al. , 2014). Moreover, biological interactions and movement/dispersal constraints might prevent them from colonizing newly suitable areas  (Bateman et al. , 2013; Holloway, Miller, & Gillings, 2016). Our results point toward a critical use of the target-group pseudo-absence method, and we recommend its explicit testing especially when using multi-source citizen science datasets.
Panel on Climate Change (IPCC) General Circulation Models (GCMs) that gained the minimum score in interdependence (Sanderson, Knutti, & Caldwell, 2015) : ACCESS1-3, CESM1-BGC, CMCC-CM and MIROC5. We considered two emission scenarios corresponding to the IPCC's Representative Concentration Pathways 4. 5 and 8. 5 (RCP 4. 5 and RCP 8. 5) for the years 2030, 2050, 2070 and 2090. Bioclimatic variables for every GCMxRCP combination and time interval were produced from the climatic variables available in the CHELSA CMIP-5 timeseries (Karger et al. , 2017). Future LULC scenarios were retrieved from Chen et al. , 2020. To create a LULC dataset consistent with bioclimatic scenarios, we selected LULC projections for the MIROC general circulation model (the remaining GCMs used for bioclimatic variables were not available), under RCP 4.
Evaluation metrics of four species distribution modelling strategies for the Eurasian stone-curlew: single static, ensemble static, single dynamic and ensemble dynamic. The following single algorithms have been used: Artificial Neural Networks (ANN), Classification Tree Analysis (CTA), Generalized Additive Models (GAM), Generalized Boosting Models (GBM), Generalized Linear Models (GLM), Maximum entropy (Maxent), Random Forest (RF). The following metrics are reported: Area Under the ROC (Receiver Operating Characteristic) Curve (AUC) of the training and test datasets and their difference; Continuous Boyce Index (CBI). Quality of Spatial Prediction (QSP) and Plausibility of Response Curve (PRC) are subjective scores (value of zero, one or two) describing the ecological validity of a model. The model used for future projections is highlighted in bold.
DATA ACCESSIBILITY STATEMENT Presence data used to develop species distribution models for the Eurasian stone-curlew are available at: https://doi. org/10. 6084/m9. figshare. 16727296. v1 Longitude, latitude and year are reported for each observation. Data from the British Trust for Ornithology are omitted in agreement with the data access policy. Data from ornitho. it are accompanied by a citation, in compliance with the site's rules.
Study overview Data from the Survey of Health, Ageing and Retirement in Europe (SHARE) were collected every two years between 2004 and 2017 (7 waves of data collection) on adults 50 years of age or older living in 27 European countries (n = 139,556). From June to September 2020, SHARE participants (n = 52,310) responded to additional questions in the SHARE COVID-19 questionnaire. 20 Questions included whether they had been tested positive for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and whether this infection had resulted in hospitalization. To be included in the study, participants should be aged 50 years or older, have completed at least one regular (not COVID-19) SHARE questionnaire between 2004 and 2017. They should also have indicated whether they were infected by the SARS-CoV-2 and/or whether they were hospitalized due to COVID-19 in the SHARE COVID-19 questionnaire (Figure  1 ).
Data analyses Main analyses Three logistic regression models were fitted. Model 0 tested the association between muscle strength and COVID-19 hospitalization, adjusting for height. Model 1 tested the association between the established risk factors (i. e. age, sex, height, body-mass index, cardiovascular disease, respiratory disease, diabetes, cancer, chronic kidney disease, and rheumatoid arthritis) and COVID-19 hospitalization. Model 2 tested the association between grip strength and COVID-19 hospitalization, while adjusting for the established risk factors. Age was centred on mean age (i. e. 68. 8 years) and divided by 10, so that the coefficient reflected the effects of increased odds of COVID-19 hospitalization over a 10-year period. 24 Grip strength was standardized so that the coefficient reflected the effects associated with an increase of 1 standard deviation (SD).
Robustness analyses Two robustness analyses were conducted. In the first robustness analysis, the dataset was analysed using a rare-events logistic regression,  25  which corrects for the bias associated with rare events. To account for the estimated fraction of patients hospitalized due to COVID-19 in the European population from June to September 2020, we used a tau parameter of 84/100,000 based on COVID-19 hospitalization data that were available from May 2020 (Supporting information, Table  S1 ). These data likely underrepresent the true number of COVID-19 hospitalizations, as hospitalizations that occurred earlier than May 2020 were not included, thereby making the analysis more conservative. In addition, we corrected for our case-control sampling design using the weighting method (R Zelig package).
Acknowledgements This paper uses data from SHARE Waves 1, 2, 3 (SHARELIFE), 4, 5, 6, and 7, and 8 (DOIs:  10. 6103/SHARE. w1. 600, 10. 6103/ SHARE. w2. 600, 10. 6103/SHARE. w3. 600, 10. 6103/SHARE. w4. 600, 10. 6103/SHARE. w5. 600, 10. 6103/SHARE. w6. 600, 10. 6103/SHARE. w7. 711,  10. 6103/SHARE. w8cabeta. 001 ). The SHARE data collection was primarily funded by the  European Commission  through  FP5  ( QLK6-CT-2001-00360 ),  FP6  ( SHARE-I3 :  RII-CT-2006-062193 , COMPARE:  CIT5-CT-2005-028857 , SHARELIFE:  CIT4-CT-2006-028812 ), and  FP7  (SHARE-PREP: no. 211909 , SHARE-LEAP: no. 227822 , SHARE M4: no. 261982 ). Additional funding from the  German Ministry of Education and Research , the  Max Planck Society for the Advancement of Science , the  US National Institute on Aging  ( U01_AG09740-13S2 ,  P01_AG005842 ,  P01_AG08291 ,  P30_AG12815 ,  R21_AG025169 ,  Y1-AG-4553-01 ,  IAG_BSR06-11 ,  OGHA_04-064 ,  HHSN271201300071C ) and from various national funding sources is gratefully acknowledged (www.
48 × 10 -8 ). rs7856850 was also associated with OCD in a meta-analysis of OCD case/control genome-wide datasets (p = 0. 0069). The direction of effect was the same as in the community sample. Polygenic risk scores from OC traits were significantly associated with OCD in case/control datasets and vice versa (p's < 0. 01). OC traits were highly, but not significantly, genetically correlated with OCD (r g = 0. 71, p = 0. 062). We report the first validated genome-wide significant variant for OC traits in PTPRD, downstream of the most significant locus in a previous OCD GWAS. OC traits measured in the community sample shared genetic risk with OCD case/control status. Our results demonstrate the feasibility and power of using trait-based approaches in community samples for genetic discovery.
Genetic data DNA was extracted manually from saliva using standard methods (see the supplement for additional details). We excluded any samples with concentrations <60 ng/µl and insufficient quality based on agarose gels. We genotyped 5645 samples on the Illumina HumanCoreExome-12v1. 0_B (HumanCore) and 192 samples on the Illumina HumanOmni1-Quad V1. 0_B (Omni) bead chip arrays (Illumina, San Diego, CA, USA) at The Centre for Applied Genomics (Hospital for Sick Children, Toronto, CA). There were 538,448 markers on the HumanCore and 1,140,419 markers on the Omni array. Quality control (QC) was conducted separately for each array using standard methods with PLINK v1. 90  14. Sample exclusion and selection criteria are described in the supplemental methods and Supplemental Figure  S1. Imputation was performed separately for all platforms and sample sets, using Beagle v4.
1 using the data from phase 3, version 5 of the 1000 Genomes project for reference (http://bochet. gcc. biostat. washington. edu/beagle/1000_Genomes_phase3_v5a/). We excluded individuals who were non-Caucasian based on principal component (PC) analysis and included only one participant from each family (inferred sibs or half-sibs, see supplement and Supplemental Figure  S2 ). Genetic data will be available through the SickKids Healthy Kids Biobank.
In secondary analyses, we evaluated the association between SNPs and the collapsed TOCS score, and between SNPs and CBCL-OCS, using zero-inflated negative binomial likelihood ratio tests, using the function zeroinfl from the R package pscl (v1. 5. 2). This model was chosen because of the high proportion of zero scores that created a j-shaped distribution. The test is a mixture of two models: a negative binomial model, which contributes to zero and positive scores, and a logit model, which contributes to possible inflation of zero scores (point mass at 0) compared to what a negative binomial model predicts. These analyses used non-standardized scores for the collapsed TOCS and CBCL-OCS so the model adjusted for the covariates and the association of SNP allele dosage with the OC trait scores is tested against the null of having no effects on both the logit part and the negative binomial part using likelihood ratio tests.
We subsequently used FUMA to conduct a gene-based GWAS of the TOCS standardized total score with MAGMA using a Bonferonni correction for the number of protein-coding genes included  15  (fuma. ctglab. nl). We tested each genome-wide significant variant for colocalization with brain eQTLs using LocusFocus  16  (https://locusfocus. research. sickkids. ca/). We examined the 14 GTEx sets from brain tissue types and examined SNPs within ±1 Mbp of each SNP. We estimated SNP heritability using both GCTA 17 v1. 91. 2-beta (http://cnsgenomics. com/software/gcta/) with further exclusion of cousins and SNPs with AR2 > 0. 9 and LDSC  18  (v1. 0. 0, https://github. com/bulik/ldsc) calculated from SNPs in HapMap3. We used LDSC  19  to examine the genetic correlation of TOCS total scores with the 850 phenotypes available on LD Hub (http://ldsc.
Acknowledgements We thank the families and children that participated in this study and the  Ontario Science Centre  for their collaboration and support. This study was funded by the  Canadian Institutes of Health Research  (P. D. A. : MOP-106573; R. J. S. :  MOP-93696 ). P. D. A. is supported by the  Alberta Innovates Translational Health Chair in Child and Youth Mental Health. The  OCD Collaborative Genetics Association Study (OCGAS)  was funded by the following  NIMH  Grant Numbers:  MH071507  (G. N. ), MH079489 (D. A. G. ),  MH079487  (J. M. ),  MH079488  (A. F. ), and  MH079494  (J. K. ). Y. Y. S. and W. G. were also supported by the  Intramural Research Program  of the  NIMH  ( MH002930-06 ). The IOCDF-GC was supported by a grant from the  David Judah Foundation  (a private, nonindustry-related foundation established by a family affected by OCD), MH079489 (D.
We gratefully acknowledge all the studies and databases that made GWAS summary data available:  ADIPOGen (Adiponectin genetics consortium), C4D (Coronary Artery Disease Genetics Consortium), CARDIoGRAM (Coronary ARtery DIsease Genome wide Replication and Meta-analysis), CKDGen (Chronic Kidney Disease Genetics consortium) ,  dbGAP (database of Genotypes and Phenotypes) ,  DIAGRAM (DIAbetes Genetics Replication And Meta-analysis), ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis ),  EAGLE (EArly Genetics & Lifecourse Epidemiology Eczema Consortium , excluding 23andMe),  EGG (Early Growth Genetics Consortium) ,  GABRIEL (A Multidisciplinary Study to Identify the Genetic and Environmental Causes of Asthma in the European Community ), GCAN ( Genetic Consortium for Anorexia Nervosa ),  GEFOS (GEnetic Factors for OSteoporosis Consortium) ,  GIANT (Genetic Investigation of ANthropometric Traits ),  GIS (Genetics of Iron Status consortium), GLGC (Global Lipids Genetics.
1 µg/L is in the range of the highest permethrin concentrations found in surface water as it tends to bind to sediments where concentrations up to 20 µg/kg can be found  (NORMAN EMPODAT Database, 2020). We have further demonstrated that a 28 day exposure to both 1 and 10 µg/L did not induce any observable defect on growth or survival  (Blanc et al. , 2020). Chemical analysis was performed on water samples at day 0, 4, 14, 21 and 28. The results, presented in  (Blanc et al. , 2020) , excluded cross-contamination of the solvent controls and confirmed exposure, although measured concentrations were approx. 10 times lower than the nominal ones  (Blanc et al. , 2020). At 28 days post-fertilization, juveniles were transferred into clean water and bred according to standard protocols  (Westerfield, 2007).
, 2007). Water was exchanged between each run to avoid inter-individual interferences. All behavioral data were automatically analyzed using Ethovision XT 13. 0 (Noldus). The tank was divided into 3 virtual zones: top, middle and bottom  (Sackerman et al. , 2010). A list of measured exploratory variables is presented in Table  S1. Statistical analyses were performed using Principal Component Analysis (PCA) (package factoextra, R v3. 6. 1) on sex-specific datasets (each including 10 metrics, 3 generations, 3 treatments) as preliminary analyses revealed that the sex was a significantly contributing factor (Text S2). One-way Analysis of Variance followed by Bonferroni´s Multiple Comparison test was applied on major principal components (PC) as well as on a selection of individual variables related to exploratory behavior (time spent in top third zone; distance travelled in top third zone; average entry duration) and defensive innate behavior (freezing as the frequency of events at a speed < 1% of the running average; angular velocity as a mean to represent erratic movements via sharp changes in direction).