dataesr/scientific-paragraphs-categorization huggingface dataset:
Code availability. The code TerraFERMA used to compute the thermal models is open-source and available here http://terraferma. github. io/. The thermodynamic code Perple_X is freely available here http://www. perplex. ethz. ch/ and information to reproduce the results are provided in the Methods section and in the Supplementary Information. Data availability. The numerical data generated for the 56 subduction transects (geotherms and water retention within the subducting slabs) will be made available at a public repository upon acceptance of publication.
Chatbot Design Vik Migraine was designed by Wefight in 2018. It aims at helping patients suffering from headaches, or their relatives, by answering questions supported by quality-checked medical information. Users can also find content related to their condition and several features such as the treatment logbook. They can use Vik Migraine through Messenger or after downloading the Vik Migraine app, available on both iOS and Android. Vik Migraine relies on both machine learning and natural language processing to understand the users' messages and render an appropriate answer. The chatbot interface is a conversation window. Patients and caregivers type their questions and the chatbot directly answers (Figure  2 ).
Study Design This is a non-interventional study. The questionnaire was sent to all patients using the Vik Migraine chatbot. They were filtered for eligibility based on the inclusion criteria (age >18 years, headaches). Participants were not paid. The data collected were anonymized and then hosted by Wefight on a server compliant with health care data storage requirements. Consent was collected online before the start of the study. In accordance with the French and European laws on information technology and civil liberties (Commission Nationale Informatique et Libertés, Règlement Général pour la Protection des Données), users had the right of use at their disposal to verify its accuracy and, if necessary, to correct, complete, and update it. They also had the right to object to their use and the right to delete these data.
Statistical Analysis Statistical analysis was performed using R, a programming language used for statistical computing. We also used FactomineR library to compute the multiple correspondence analysis (MCA). The description of the included population was carried out by the calculation of average, standard deviation, median, and quartiles for quantitative variables, numbers, and percentages for qualitative variables. A logistic regression was performed to identify the variables that are significantly linked to medication overuse. Odds ratios were based on this regression. Multiple correspondence analysis was performed to cluster the population and determine the main parameters of each subgroup: five dimensions were used for clustering. The v-test values were computed with the FactomineR library and allowed  a measure of association between variables.
DISCUSSION This article highlights and emphasizes the suitability of conversational health chatbots, as a concrete and relevant tool for conducting real-world-data-based descriptive studies with ease and great patient responsiveness. In fact, the questionnaire was live on the Vik migraine app for a period of 2 weeks and gathered engagement from 870 patients who completed the questionnaire. This allowed the studies and data teams to collect a large amount of data in a short period of time. However, some selection bias must be considered, such as the fact that a majority of the patients were young women. Both logistic regression and MCA stress the fact that opioids are often involved in medication overuse. This was shown by Sher et al. (5)  in the United States and the associated odds ratio was similar to the one calculated here.
Transposable Element Annotations and Mapping onto Blattodea Genomes To allow precise and complete detection of TEs for each species, a combination of ten tools was used: RepeatMasker (http://www. repeatmasker. org/), (accessed on 20 November 2021) RepeatModeler (http://www. repeatmasker. org/RepeatModeler/) (accessed on 20 November 2021), HelitronScanner  [41] , LTRpred  [42] , LTRharvest  [42] , mustv2  [43] , SINE-scan  [44] , Sine-Finder  [45] , MiteFinderII  [46] , and tirvish  [47]  (http: //genometools. org/tools/gttirvish. html) (accessed on 25 November 2021), implemented within the TransposonUltimate pipeline  [48]. The de novo annotated TEs from each tool were combined and filtered using BEDtools (v. 2. 27. 1)  [49]  and a custom python script, removing duplicated or overlapping TEs (more than 20% overlap), as well as TEs of lengths above 10 kb.
These filtered TEs were annotated to assign their class (RNA/DNA), subclass, and superfamily using the RFSB classifier from TransponUltimate  [48]  and as reference (training set) the TE database of insects from  Petersen et al. (2019)  [50]. The de novo TE database obtained for each species was then used to map TEs in each species' genome using RepeatMasker (http://www. repeatmasker. org/) (accessed on 20 November 2021). The TEs obtained from RepeatMasker were further filtered to remove overlapping TEs, keeping the TE with the highest RepeatMasker score, and removing low-quality TEs, with a RepeatMasker score not greater than 1. Furthermore, Kimura distances accounting for the GC level were computed with a RepeatMasker script to evaluate the dynamics of the different TE superfamilies along Blattodea evolution.
Gene Functional Annotations To link the function of Differentially Expressed Genes (DEGs) between life stages where TEs insert and the social level of blattodean species, crowdGO [51] was used with the pre-trained crowdGOFull model  [52] [53] [54] [55]  to annotate Gene Ontology terms  [56]  for each gene of each species. An enrichment of GO terms was tested with the GSEA test  [57]  with the "elim" algorithm implemented in the topGO package  [58]  in R  [59]. Multiple datasets were used to categorize GO term enrichment. First, GO terms were analysed for enrichment of biased gene expression in worker/nymph and in queen/female adult in termites/cockroaches, separately. For these analyses, the gene score used was the negative log-transformed adjusted p-value of expression bias. Genes with non-computed adjusted p-values were discarded.
Furthermore, the gene score was set to 1 for genes that displayed opposite biased expression of the life stage investigated for GO term enrichment. Secondly, GO term enrichments of genes with TEs inserted within them were performed separately for termites and cockroaches and for genes with biased expression in the different life stages, including genes with no bias for any life stage (adjusted p-value ≥ 0. 05). For the enrichment test, the gene score used was the ratio of TE insertions within a gene kilobase. A gene was considered TE-rich if its score was greater than the third quartile of the dataset. Lastly, the significant GO terms found enriched in TE-rich DEGs were filtered out. GO terms enriched in TE-rich DEGs were removed if they were also found to be significant in enrichment analyses  (1)  of DEGs in the same life stage at both social levels (eusocial and gregarious), (2) of TE-rich DEGs in the same life stage, but in a different social level, and (3) of TE-rich DEGs of different life stages (including no bias) in the same social level.
TE Insertions, Biased Gene Expression, and Social Level To investigate if TEs were inserted more frequently in genes with different expression levels among worker/nymph and queen/female adult depending on the social level of blattodean species (cockroaches: gregarious and termite: eusocial), the number of genes with a TE inserted or not was compared with regard to the gene expression bias and the social level, using phylogenetic generalized linear mixed models (PGLMMs) with a Poisson distribution and a Brownian motion covariance matrix from the brms package  [63]  (dependent variable: gene number, fixed factors: overlap with TE (yes or no) * social level (gregarious or eusocial) * gene expression bias (no bias or worker/nymph bias or queen/female adult bias), random factors: species + Brownian motion covariance matrix).
Genes with no adjusted p-value for their expression bias were discarded from the analyses. Furthermore, we investigated if TEs present within genes insert more frequently within the exon or intron in relation to the social status of the species and the expression bias of the gene, and we built a PGLMM model as above (dependent variable: gene number, fixed factors: overlap with TE (intron or exon) * social level * gene expression bias, random factors: species + Brownian motion covariance matrix). As the probability of TE insertions within genes is highly "co-"dependent on gene length, the difference of gene length (in kb) among social levels and different gene-biased expression were analysed with a GLMM with a Gamma distribution and species as a random factor implemented in the lme4 package  [64]  (dependent variable: gene length, fixed factors: social level * gene expression bias, random factors: species).
As gene length differs significantly, the proportion of genes with a TE inserted within them between social level and gene expression bias was analysed with a GLMM with a binomial distribution and species and gene length as random factors to confirm the result of the PGLMM (dependent variable: gene overlap with TE (no = 0 or yes = 1), fixed factors: social level * gene expression bias, random factors: species + gene length). In addition, to understand if TE superfamilies contributed differently to insertions within genes in cockroaches and termites, the number of TEs inserted within genes was compared in regard to the four main TE superfamilies and the social level, using phylogenetic generalized linear mixed models (PGLMMs) with a Poisson distribution and a Brownian motion covariance matrix from brms package  [63]  (dependent variable: TE number, fixed factors: overlap with gene (yes or no) * social level (gregarious or eusocial) * TE superfamily (Tc-Mariner, hAT, Gypsy, LINE), random factors: species + Brownian motion covariance matrix).
Our study lays the groundwork for studying the role of TEs in shifts of gene regulation during the transition to eusociality and warrants further investigations on the subject to precisely pinpoint the impact of TEs during eusocial evolution. However, our study suffered from some caveats: (1) the lack of manual curation of TEs ensuring a highly reliable TE database in Blattodea, (2) the lack of standardization in the transcriptomic methodology and the surprisingly low number of DEGs found in P. americana, rendering our subsequent analyses biased toward B. germanica results for the gregarious species, and (3) the lack of direct evidence of the TE insertions impact on gene expression among gregarious and eusocial Blattodea, as TE insertion in genes could also lead to exon disruption, exonisation, the creation of a new polyadenilation site, and alternative splicing (reviewed in  [16] ).
Species Genome Accession Transcriptome: BioProject Transcriptome: SRA Project Reference B. germanica PRJNA427252 PRJNA382128 SRP104279 [28] P. americana PRJNA414776 PRJDB1997 DRP001319 [34] Z. nevadensis https://termitegenome. elsiklab. missouri. edu/ (accessed on 20 September 2021) PRJNA203244 SRP022929 [35] C. secundus PRJNA381866 PRJNA382129 SRP104260 [28] R. speratus PRJDB2984 PRJDB3531 DRP002846 [36] M. natalensis http://gigadb. org/dataset/100057 (accessed on 30 September 2021) PRJNA382034 SRP104234 [37].
Study Design This was a longitudinal, preregistered (https://osf. io/gwfdb/) online survey using Qualtrics platform (www. qualtrics. com) following up a survey on nutritional quality and food choice motives before and during the first lockdown in France conducted in spring 2020; results have been published elsewhere  (10). In the 2020 survey, we retrospectively recorded at the same time dietary data and food choice motives for 938 individuals during the month before the first lockdown (from 17 February to 16 March 2020) and during the first month of lockdown (from 17 March to 16 April 2020). A number of 938 individuals who took part in the 2020 survey were emailed exactly 1 year later, that is on 2 May, 2021. They were asked to answer the same set of questionnaires as in 2020 regarding the 1-month period from the 1 to 30 April 2021: a food frequency questionnaire (FFQ), a food choice motives questionnaire, and a questionnaire on organic and local food consumption.
Data Collection Data were collected from 2 to 15 May to 2021. The participants were all registered in the Chemosens Platform's PanelSens database declared to the relevant authority (Commission Nationale Informatique et Libertés; CNIL; n • 1148039). Eligible participants took part in the 2020 survey and had not moved since then. All participants were contacted by email and provided consent for their participation after being informed that the purpose of the study was to investigate food choices 1 year after the first lockdown. They received a 10 e Amazon voucher in return for their participation. As in the 2020 survey, three attention check questions (e. g. , "How many times have you visited the planet Mars?") were included in various parts of the questionnaire. The study was approved by the relevant ethical evaluation committee for research (reference: Comité d'Evaluation Ethique de l'Inserm n • 20-683bis, delivered on 30 March 2021).
Individual nutrient intakes were calculated by multiplying the daily intake of each food item by its nutritional values from the SU. VI. MAX nutrient composition database  (25). Adherence to the French dietary recommendations before, during, and 1 year after the first lockdown was evaluated using the simplified PNNS-GS2 (sPNNS-GS2), an index previously designed by Chaltiel et al. (26)  to reflect adherence to the main 2017 French dietary recommendations. The sPNNS-GS2 builds on the distinction between malus components (less healthy food groups which consumption should be limited, carrying a negative score, i. e. , red meat, processed meat, sugary foods, sweet-tasting beverages, alcoholic beverages, and salt) and bonus components (healthier food groups carrying a positive score, i. e. , fruits and vegetables, nuts, legumes, whole-grain food, milk and dairy products, fish, and seafood).
GHGE were derived from the French food environmental impact database Agribalyse 3. 0 drawn up by the French Agency for Ecological Transition that includes GHGE values for 2,480 common food items based on Life Cycle Analyses of food products  (28). The items of the FFQ were associated with all the corresponding food items from Agribalyse 3. 0. GHGE of each item of the FFQ were calculated as the average GHGE of individual foods from Agribalyse 3. 0 associated with each item. GHGE of participants' daily diets were calculated by multiplying the daily intake of each food item by its associated GHGE per kg. We also calculated short-term GHGE and mid-term GHGE, as the difference in GHGE of diets between during and before the first lockdown and between 1 year later and before the first lockdown, respectively.
Statistical Analyses We followed an analytic plan that was preregistered prior to 2021 data collection (https://osf. io/gwfdb/). Any deviations from the preregistered analytic plan are described in Supplementary Table  S1. Only participants who completed the 2020 and 2021 surveys were included in the analyses. Participants who failed at least one attention check were excluded. We analyzed data from participants who reported plausible energy intake before, during, and 1 year after the first lockdown, that is, ≥500 and ≤3,500 kcal/ day for women, and ≥800 and ≤4,000 kcal/day for men  (32, 33). We compared participants that were included and excluded from the present analyses based on sociodemographic measures and dietary outcomes (2020 data). Cronbach's α were calculated for the organic and local food consumption questionnaire (before, during, and after 1 year, respectively): organic score (0.
Four multiple linear regressions testing the effect of motives on nutritional quality and GHGE at both short-and mid-term were run. They were also replicated controlling for age, gender, educational level, and declared initial BMI. All statistical analyses were performed using SAS version 9. 4 (SAS Institute, Inc. 2013 SAS R 9. 4. Cary, NC). The level of significance was set at p < 0. 05 for preregistered primary, secondary, and exploratory analyses.
90, p-value = 3. 58 × 10 -4 ). In the Moroccan environment, all lines performed better compared to the CVI environment, (β pop-CVI = 2. 63, p-value = 0. 0151; β pop-Mor = 5. 86, p-value < 2 × 10 -16 ). There was no significant difference in fitness for the Moroccan and CVI lines in the Moroccan-simulated environment (b = 0. 337, p-value = 0. 679). (Fig. 5d , Supplementary Data 4). Taken together these results highlight the challenging climatic conditions plants would have faced upon colonization of CVI, consistent with the results from the climate niche analysis (Fig. 4 ). Evidence for ongoing multivariate adaptation in Santo Antão. Next, we examined the nature of adaptation in Cape Verde by capitalizing on over twenty years of studies on Cvi-0. We identified QTL, candidate genes and specific functional variants from a meta-analysis of 129 QTL mapping studies and associated finemapping studies conducted in a recombinant population produced from a cross between Cvi-0 and Ler-0  46  (Fig.
Methods Plant material. We collected plants over a series of field expeditions between 2012 and 2019 on Santo Antão and Fogo, the two islands where A. thaliana had been documented in herbarium records. In total, we present data for 335 lines from CVI (Supplementary Data 1, Fig. 1a , Supplementary 1), including 189 lines from 26 stands across four regions in Santo Antão (Cova de Paúl, Lombo de Figueira, Pico da Cruz and Espongeiro), and 146 lines from 18 stands across three regions in Fogo (Lava, Monte Velha and Inferno). The 62 Moroccan lines used in the study were first presented in  95  and were sequenced in  25. Climate data. Climate data used in our analyses were retrieved from the Worldclim Project  96  and CGIAR Consortium (CGIAR-CSI)  97  (Supplementary Method 2). Sequencing. We sequenced the 335 Cape Verde Islands lines and Cvi-0 using Illumina Hi-Seq and HiSeq3000 machines (Supplementary Method 3).
Genomic DNA was extracted using the DNeasy Plant Mini kits (Qiagen), fragmented using sonication (Covaris S2), and libraries were prepared with Illumina TruSeq DNA sample prep kits (Illumina), NEBNext Ultra II FS DNA Library Prep Kit (New England Biolabs) and NEBNext Ultra II DNA Library Prep Kit (New England Biolabs). Libraries were immobilized and processed onto a flow cell with cBot (Illumina) and subsequently sequenced with 2x 100-150 bp paired end reads. We assessed DNA quality and quantity via capillary electrophoresis (TapeStation, Agilent Technologies) and fluorometry (Qubit and Nanodrop, Thermo Fisher Scientific). Due to changes in product availability over time, there were some slight differences among sequencing runs. SNP identification and genotyping. We aligned the raw Illumina sequence data for the CVI samples together with previously sequenced Eurasian  98  and Moroccan samples  25  to the Arabidopsis TAIR10 reference genome and we identified and genotyped variants (Supplementary Method 4, https://github.
com/HancockLab). To eliminate false variant calls due to duplications not represented in the reference genome, we filtered out genomic regions with coverage higher than twice the genomic average. Further, for trait mapping, we used a pipeline based on GATK  9  for the additional analyses of short indels using a modified version of the best practices workflows for germline short variant discovery (https://github. com/ HancockLab/SNP_and_Indel_calling_Arabidopsis_GATK4). Average coverage across samples was 19. 4x (range from 9. 3x to 51. 7x) after alignment to the TAIR10 reference genome. Plant growth and phenotyping. For all experiments, seeds were stratified in the dark in Petri dishes on water-soaked filter paper for one week at 4 °C prior to sowing. After stratification, seeds were sown in 7 × 7cm pots containing a standard potting compost mix.
Four seeds were sown per pot and plants were thinned to one plant per pot, after germination. Further details can be found in Supplementary Method 5. We simulated the CVI growing season in a custom Bronson growth chamber based on hourly environmental data at a collection site (Supplementary Fig. 9 ), where we measured air and soil temperature, air humidity and precipitation using data loggers. The experiment began with September 1, 2016 conditions, when we observed plants germinating at the field site. Photoperiod was set to track daylength (number of sunlight hours) in CVI. We simulated dawn and dusk by increasing light intensity by 50 µM every 15 minutes until 200 µM (full light) and decreasing it by 50 µM every 15 min until dark, respectively. At the same time points, far-red light decreased from 50 to 0 µM at dawn and increased from 0 to 50 µM at dusk.
The only exceptions were the Moroccan Rif, North Middle Atlas and High Atlas populations where fewer samples are available (respectively, 8, 13, and 16). We pruned the data set for short-range linkage disequilibrium <--indeppairwise 50 10 0. 1>, and for missing data <--geno 0> using PLINK v. 1. 90 and removed multi-allelic variants. We produced neighbour-joining trees using the R package ape v. 3. 5  102  (https://github. com/HancockLab/CVI). We used custom scripts to estimate nucleotide diversity (θ) in CVI, Morocco and Eurasia by computing Tajima's (θ π ) and Watterson's estimators (θ w ), as well as for deriving the site frequency spectra (SFS) (https://github. com/HancockLab/ CVI). The joint site frequency spectrum (JSFS) between islands was computed on a subsampled set of 40 individuals per island.
We excluded sites with more than 5% missing data, CpG sites, due to their hypermutable nature, pericentromeric regions, which are rich in satellite repeats, and other repeat regions identified with Heng Li's SNPable approach (http://bit. ly/snpable). The JSFS between CVI versus Morocco was computed using both CVI islands together and was polarized to the outgroup species Arabidopsis lyrata. We aligned short-read data for 27 A. lyrata genomes to the A. thaliana reference genome (TAIR10) and retained for analyses only SNPs that were not polymorphic in A. lyrata and for which there were no missing data. To polarize the JSFS between islands, we reconstructed the most likely ancestral state at every SNP based on variation in Morocco, the best modern representative of the original colonizing lineage.
0. 0. 4  105. We used a representative subset of 148 CVI genomes from the two islands. As donors, we used the 13 mainland clusters previously identified (nine in Eurasia  26  , four in North Africa  25  ). Each donor population was randomly subsampled to 20 samples 100 times, and for each subsampling we ran Chromopainter ten times for a total of 1000 replicated analyses of each Cape Verdean genome (https://github. com/ HancockLab/CVI). We inferred colonization time by obtaining an upper bound based on the minimum coalescence time between CVI and Morocco, and a lower bound based on the maximum coalescence time within the CVI clade (Supplementary Methods 7 and 8). We inferred split times between the two Cape Verde Islands, among subpopulations within islands and between CVI and Morocco using the crosscoalescence rate (CCR) statistic in the MSMC2 framework  17, 18  as well as with dadi v.