dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We are grateful to  R. E. Lenski  for providing E. coli strains. We thank  C. Duperray  (IRMB -Montpellier) and the Montpellier RIO Imaging platform for hosting the flow cytometry measurements,  F. Débarre  and  B. Facon  for helpful comments on the manuscript, and  C. Prator  for careful English editing. We thank three  PCI Evolutionary Biology recommenders  for their constructive corrections. This study received financial support by the  French Agropolis Fondation (Labex Agro-Montpellier ,  BIOFIS  Project Number  1001-001  and  E-SPACE  project number  1504-004 ), the  European Union (ERDF) , and the  'Conseil Régional de La Réunion '.
In a landscape of fear, humans are altering key behaviors expressed by wild-living animals, including those related to foraging, reproduction and survival. When exposed to potentially lethal human actions, such as hunting or fishing, fish and wildlife is expected to behaviorally respond by becoming more timid, but proving such responses underwater in the wild has been challenging. Using a rich dataset collected in situ, we provide evidence of spearfishing-induced behavioral effects in five coastal fish species using the flight initiation distance (FID) as a proxy of predator avoidance and boldness. We document that spearfishing promotes a timidity syndrome (i. e. , an increase of the average timidity of harvested populations) and that the prey's wariness is influenced by individual size, level of protection offered through marine protected areas and the ability to recognize the risk posed by underwater human predators.
Statistical analysis We implemented a linear mixed model with random intercepts using FID as response variable, fish length as covariate, treatment (four levels: Spear-NP; Snork-NP; Spear-P; Snork-P) and taxa (five levels D. sargus, D. vulgaris, Mugilidae, S. salpa and S. tinca) as fixed effects, and the three areas (Bonifacio, Banyuls and Medes) as random effect. Model fitting was examined by checking normality of residuals and plotting theoretical quantiles versus standardized residuals. We used the "lme4" R package  (Bates et al. , 2014)  to implement the linear mixed models, and "lsmeans" R package  (Lenth, 2016)  to run multicomparisons among slopes or means. All analyses were conducted in R 3. 3. 1 (R Core Team 2016) with a 95% confidence interval.
Review of the literature A systematic search of the literature was conducted to identify key papers addressing the association between migraine and cardiovascular events in women using HCs. An initial literature search included all papers indexed on PubMed and Scopus, from inception to August 30, 2016. The following search terms were used in both databases: "migraine" AND (contraceptive OR estrogen) AND (vascular OR stroke OR "myocardial infarction" OR angina OR "coronary artery disease" OR "coronary heart disease" OR "venous thrombosis"). Both observational (cross-sectional, case-control, and cohort) and intervention studies (RCTs) were included. Two investigators from the statement Supporting Group (FP and RO) independently screened the titles and abstracts of the publications identified to verify study eligibility.
Full texts of selected studies were evaluated when appropriate. Disagreements were resolved by consensus. The reference lists and Google Scholar citations of the selected articles were also screened. To summarize the search results, a data extraction sheet was developed including the information of interest. A formal systematic review of the association between migraine and cardiovascular events and HCs as well as the risk of vascular events was not performed and is available elsewhere  [3] [4] [5] [6] [23] [24] [25] [26]. Papers retrieved from the literature search as well as summary Tables were shared among the panelists before starting the consensus procedure. The systematic literature search was repeated at the end of the consensus procedure to include all relevant papers published until  March 30, 2017.
However, migraine attack frequency for those with migraine with aura appears to be an issue for determining the risk of ischemic stroke. Findings from the World Health Organization Collaborative Study of Cardiovascular Disease and Steroid Hormone Contraception indicated that migraine with aura with migraine attacks more frequent than 12 times per year (OR 10. 4; 95% CI 2. 2-49. 4) was associated with an increased risk of ischemic stroke  [44]. Data from the Stroke Prevention in Young Women Study indicated that women with migraine with aura with a high migraine attack frequency (>12 attacks per year) had higher odds of stroke (OR 1. 7; 95% CI 1. 1-2. 8), in addition to women with recent onset of migraine with aura (OR 8. 3; 95% CI 2. 6-25. 7) [17]. According to data from the Women's Health Study, the association between migraine with aura and ischemic stroke appeared J-shaped.
6% 09 women) Kurth, 2016 Women aged 25-Prospective, 115,541 women 1989-Stroke Migraine with Questionnaire; No Not specified B [2] 42 years at baseline cohort 2011 and without ICHD aura vs no migraine Albieri, 2016 Subjects aged 25-Retrospective, 49,711 ischemic 2003-Stroke Triptan Administrative No Not specified C [35] 80 years (subanalysis in cohort strokes 2011 prescription code (triptan women aged 25-prescription); No 50 years) ICHD Champaloux, Women aged 15-Population-25,887 ischemic 2006-Ischemic Migraine with or Administrative Not specified C 2017 [10] 49 years based, case-strokes 2012 stroke without aura vs code (ICD-9-CM); control no migraine Nno ICHD NA indicates not applicable; GPRD indicates General Practice Research Database; HC indicates hormonal contraceptives; ICD-9-CM indicates International Classification of Diseases, Ninth Revision, Clinical Modification; ICHD indicates International Classification of Headache Disorders.
Illumina sequencing and assembly of paired-end reads Illumina paired-end libraries were constructed from purified DNA or from RCA products using the TruSeq PE cluster kit, to obtain a 350 bp mean insert size. The Illumina HiSeq 2500 sequencing run was carried out at the University of Arizona Genetics Core facility (Tucson, AZ). The reads were de-multiplexed, and quality was assessed using FASTQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). The adapters were removed, and reads were trimmed using TRIMMOMATIC v0. 32  [35]  and assembled de novo using SeqMan NGen software v12 (DNASTAR), with and without plant host sequence filtering. For filtering, the T. cacao nuclear genome (host plant) contigs were downloaded from GenBank Accessions CM001879. 1, CM001888. 1, FR7222157. 1, and KE132922.
Annotation of badnavirus-like genomes The assembled apparently full-length genome sequences were first annotated using BLAST2GO  [38]  to confirm their identity as badna-like viruses, and then subjected to BLASTn  [39]  analysis to determine the top hits (similarity scores) based on all sequences available in the Genbank database. Based on established convention for the genus Badnavirus, the first nt of the predicted tRNA met priming site was assigned as the first nt coordinate  [40, 41]  for each full-length CSSD-badnaviral genome sequence. The predicted ORFs were identified for CSSD genome sequences using the NCBI ORF Finder tool, and the conserved protein domains (CPD) were predicted using the NCBI Conserved Domain Database (CDD)  [42]. The predicted CPDs were aligned for comparisons using MUSCLE  [43] , implemented in CLC Sequence Viewer 7.
Pairwise and phylogenetic analyses of coding regions and complete genome sequences Genome sequences for 122 badnavirus isolates representing 37 species were downloaded from GenBank and the genome was considered intact or based on the RT-RNase H region used for taxonomic assignment to species. A haplotype search implemented in FaBox v1. 41 was used to identify and remove sequences sharing 100% nt identity  [44] , leaving 87 full-length genome and RT-RNase H region sequences for the analyses, respectively. Pairwise distances were determined using Standard Demarcation Tool (SDTv1. 2) software  [43] , for 87 fulllength genomes or RT-RNase H regions (1230 bp). The sequence alignments were carried out in MUSCLE  [43] , implemented in CLC Sequence Viewer 7. 5. Phylogenetic analysis of genomic and RT-RNase H sequences was carried out with the Maximum Likelihood (ML) algorithm available in MEGA6  [45].
Illumina and sanger sequences The Illumina reads were assembled into 14 full-length badnaviral genomes ranging from 6920 to 7172 bp in size. Also, BLASTn analysis against the GenBank database identified a large number of partial contigs with significant matches to CSSD-badnaviral sequences. However, in this report, only complete badnaviral genome sequences were considered, and they are derived from 410,662-31,417,994 reads with a depth of coverage of 36-6600. PCR amplification was carried out using sequencespecific primers (Additional file 1: Table  S1 ) and Sanger sequencing of six full-length genome sequences, representing two CSSV and three CRVV full-length genomes, and one partial genome assembled from CRVV isolate CI286. Illumina and Sanger sequence comparisons indicated that the cloned viral amplicons were nearly identical in length and sequence, at 99.
0-99. 9% nt identity, between the respective sequences. BLASTn analysis for the 14 apparently full-length CSSD-badnaviral genome sequences indicated the top hits in the GenBank database were consistent to previously reported CSSD-badnaviral sequences based on similarity score, sequence coverage, and e-value of zero. A comparison of the resultant Next-gen Illumina and Sanger DNA sequences using the above criteria, as well as: location of the first nt coordinate, number and location of predicted ORFs ≥10 kDa, and presence of the hallmark-badnaviral non-coding sequence region, indicated that both approaches yielded the same genome sequence. The Illumina sequences have been deposited in GenBank as the Accession numbers, KX592571-KX592584.
Pairwise nucleotide identity for species demarcation The SDT analysis of the RT-RNase H region identified four species based on ≥80% badnavirus species cutoff established by the International Committee on the    2 ). The best-represented species is CSSV, at 10 isolates, including those reported here and GenBank, from Ghana (AJ608931, AJ609019, and AJ609020) and Togo (AJ534983 and L14546), making it the most widely distributed species thus far. CSSCDV and CSSTAV are reported so far from Cote d'Ivoire (JN606110) and Togo (AJ781003), respectively. And, the herein proposed species, CRVV, represented by isolates from Cote d'Ivoire (CI275, CI286) and Ghana (GH64 and GH67), had the greatest extent of intraspecific divergence among the known CSSD-badnaviruses, at 28-30% (70-72% identity) (Table  3 ).
By pairwise distance analysis of the RT-RNase H region, the 14 newly determined and seven GenBank CSSD-associated genome sequences shared 72-100% nt identity (Table  2 ). In contrast, the corresponding RT-RNase H region for the 21 isolates and 80 other badnaviral RT-RNase H sequences (GenBank) was 58-70% identical. Notably, CaMMV and CYVBV are as divergent from each other (40%), at 60% shared nt identity, as they are from West African CSSD-badnaviruses. Although SDT distinguished four species (≥80%) irrespective of RT-RNase H or complete viral genome sequences, species group composition was inconsistent, in particular, for the CSSD isolates. For example, SDT analysis for complete CRVV genomes (≥80% threshold) delimited CI286, and the group containing CI275, GH64, and GH67, as two groups. In contrast, the RT-RNase H analysis identified the same four isolates as one species (Tables 2 and 3).
Phylogenetic analyses The phylogenetic relationships were determined, based on the RT-RNase H region of 87 GenBank accessions and the 14 CSSD-badnaviral sequences. Despite the accepted use of this region for badnavirus species demarcation, the ML tree was unresolved, e. g. as a polytomy, at ≥70% bootstrap support. Within the badnavirus polytomy, the West African CSSD isolates grouped separately from the other badnaviruses, with 99% bootstrap support, indicating a close phylogenetic relationship among cacaoinfecting viruses in West Africa (Fig. 3a ). In contrast, ML analysis of the complete genome sequences for the same isolates (CSSD and 87 badnaviral genome sequences from GenBank), resolved three clades, each well supported by ≥70% bootstrap values. For discussion purposes here, the clades have been arbitrarily labeled as I, II and III (Fig.
Acknowledgements We gratefully acknowledge funding for this project from the  USDA-ARS  through the  Specific Cooperative Agreement # 6038-21000-023-07  titled:  Development and Optimization of Molecular Diagnostics Method for Qualitative and Quantitative Detection of Cacao Swollen Shoot Virus  with  MARS Inc. through Trust Agreement # 58-6631-6-123 , titled:  Genetic Improvement of Theobroma cacao , and from the  Borlaug Fellowship Program , funded by the  USDA-Foreign Agricultural Services  with support from the  World Cocoa Foundation.
The Genbank Accession number for each cacao-infecting badnavirus reference sequence is indicated. Predicted open reading frames (ORFs) were located using the ORF Finder tool (NCBI). The country abbreviations used are: CI-Cote d'Ivoire, GH-Ghana, and TG-Togo, for West African field isolates studied here, and available in GenBank. The conserved protein domains are indicated as: DUF domain of unknown function, Zn zinc knuckle finger, Pepsin pepsin-like aspartate protease, RT reverse transcriptase, RNase H ribonuclease H, PHD plant homeodomain finger, Trim trimeric-dUTPase, H, ND no domain predicted. The asterisk *Clade = phylogenetic clade designation based on full-length genome sequence (Fig. 3b).
which contained the test trial. We measured listeners' eye movements and observed their referential choice as they listened to speech from the live speaker. All of the objects used in the displays were unusual and lacked conventional names. Figure  1  depicts a round and figure  2  is an example of a test trial. The test trial provided the data to test our hypotheses. As can be seen in figure  2  (which corresponds to trial i in figure  1 ), a test trial included three alternative referents that varied with respect to their status in the discourse. The negated alternative (e. g. the jellyfish) was a named object that would be referred to through a negation. The unmentioned alternative is an object that had never been named by anyone in the discourse. Finally, there is the critical alternative, whose labelling varies across the three conditions.
Materials and apparatus For each of the 18 experimental items, there were pictures of six different unconventional objects: one for the negated alternative, one for the unmentioned alternative, one for the critical alternative, plus three different filler objects. These were downloaded from the internet and were presented in greyscale so that they could not be identified using colour. We tracked listeners' eye movements using an EyeLink 1000 eyetracker (SR Research). The system used a remote tabletop camera, allowing relatively free head movement. Gaze data were recorded at a sampling rate of 500 Hz.
Data analysis All analyses were performed and all the graphics created through R  [31]. Our analysis considered both selection data (i. e. what the listener chose to click on) and eye-tracking data. For the selection data, we analysed the likelihood of choosing the unnamed alternative (a binary variable) using linear mixedmodels with crossed random effects for subjects and items  [32]. Because these data were categorical, we used a generalized linear mixed model with a binomial variance function and logit link. We tested all effects using model comparison, comparing models with identical random effects but with the fixed effect(s) of interest removed from one of the models. All models included the maximal random effects structure justified by the design  [33] : by-subject and by-item random intercepts, as well as by-subject and by-item random slopes for each of the two variables used to code the three levels of the condition variable.
These analyses were performed using the R add-on package lme4, v. 0. 999999-2  [31]. Visual-world eye data are more difficult to analyse, because one needs to conduct a time-series analysis on a large dataset that accounts for temporal dependencies due to repeated sampling as well as for dependencies introduced by the experimental design. To overcome these challenges, we performed all analyses of the eye data using resampling techniques: specifically, bootstrapping to obtain confidence limits and an adapted version of the cluster randomization approach that is commonly used to analyse complex datasets in neuroimaging  [34, 35]. In adapting the approach for visual-world data, we followed Barr et al. [36]. Here, we provide an overview of the approach. Technical details are available in appendix A.
Eye data We now turn to the eye-tracking data in order to determine the extent to which we can isolate linguistic decoding processes in this task (i. e. independently of common ground considerations). We measure eye gaze to the three test-display objects at the offset of the negated expression (e. g. the end of the word 'jellyfish' in 'not the jellyfish'). We used the offset because we assumed that listeners would begin by gazing at the negated object. If common ground is processed in parallel with the linguistic information in this task, gazing at the unnamed alternative in the two speakers-two precedents condition should resemble gazing at the unnamed alternative in the one speaker-one precedent condition because, in both cases, negated utterances such as 'not the jellyfish' leave open two possible reference assignments, including the unnamed alternative.
Introduction Dust will be a critical issue for future fusion devices such as ITER. Generated through various processes related to plasma/wall interactions, dust grains are an important source of impurities having well known consequences in terms of radiative losses and plasma instabilities generation  [1]. Dust can be observed using CCD cameras as they interact with the plasma, through recycling photon emission and thermal emissivity. Cameras provide with videos on which image processing routines can be applied in order to detect dust events and measure dust trajectories. In Section 2 , the DUMPRO (DUst Movie PROcessing) routines developed at CEA to extract dust trajectories is presented. In the case of intrinsic dust, the experimental data obtained by image processing is delicate to analyze, since a dust trajectory depends on the dust material, temperature, size and electric charge, among others.
Dust injection was performed in several tokamaks and allows constriction of some of these parameters. Several codes dedicated to the modeling of dust transport in plasmas already exist, namely MIGRAINe  [2]  , DUSTT  [3]  and DTOKS  [4]  , among others. In Section 3 , the newly developed DUMBO (DUst Migration in a plasma BOundary) code will be briefly presented. The aim of this work is to prepare for the installation of a dust gun-type injector on the WEST tokamak, as well as the image processing and simulation tools developed for data analysis. Since the injector design is similar to that of the KSTAR dust injector  [5]  , the 2015 KSTAR dust injection experiment will be analyzed using these tools, as an example, in Section 4.
The plasma background was determined using several diagnostics on discharge #13101: EFIT data for the magnetic equilibrium and poloidal magnetic field, charge exchange spectroscopy for the ion temperature ( T i ) profile, line integrated density for the electron density ( n e ). Profiles were extended in the SOL using exponential decays respecting a C 1 match with the core profiles. The n e profile was determined from the integrated density using a square root profile in the core, and we assumed T e = T i. Finally, quantities were assumed to remain constant over flux surfaces. Profiles for n e and T i are provided in Fig. 4. The toroidal magnetic field was ∼3 T, and the plasma ions flow velocity map can be seen in the background of Fig. 5 (a). The ion flow is predominantly parallel, even though E × B and ∇B × B drift velocities are taken into account.
Processing of sequencing data Short-read data was trimmed using Cutadapt  (Martin, 2011)  and aligned using TopHat2  (Kim et al. , 2013). Differential gene expression analysis was performed for all pairwise comparisons between conditions and time points using Cufflinks  (Trapnell et al. , 2012). Our threshold for significance was q<0. 01. We considered a gene to be expressed if it had a normalized count of fpkm (fragments per kilobase of transcript per million mapped reads)>5. Raw sequencing data and fpkm normalized expression data files have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE92933.
Hormone-repressed genes had the opposite pattern: abnormally high when cultured without hormone but significantly lower in culture with hormone. Additional information is provided in the supplementary Materials and Methods. The Venn diagrams presented in Fig. 2  were generated using the VennDiagram package of R  (Chen and Boutros, 2011)  after identifying the genes that were unique or common in each pairwise comparison. We provide the gene lists that were used to create these graphs in Tables  S1-S5. These lists also include information about whether the expression of each gene in culture without hormone is abnormally low or high, relative to its levels in uncultured discs. We identified enriched Gene Ontology (GO) terms (Biological Process and Cellular Component) within each hormone-responsive dataset using the R-package ClusterProfiler (v 1.
9)  (Yu et al. , 2012). To reduce redundancy, we used the simplify function of this package; similarity cutoff=0. 95 and BH-corrected P-value cutoff=0. 01. We also required enriched GO terms to have more than five genes included in the data. In Figs  3, 4  and Fig. S2 , we only present the subcategories that have more than one enriched GO term. For selected genes, we plotted the change in expression levels across all conditions (Fig. 5 ; Figs S2, S3, S6) using the pheatmap package in R. We took the log 2 of the normalized counts (fpkm) in each condition divided by that in the uncultured wing discs (at 96 h AEL, time 0 h). For the 589 genes regulated by 20E (either exclusively or also by insulin), we manually classified functional groupings after reviewing related literature, GO term assignments and FlyBase annotations.
Discs were dissected in PBS and then fixed and stained according to standard methods (detailed in the supplementary Materials and Methods). Imaging of fixed samples was performed using an Olympus FV1000 laser-scanning confocal microscope fitted with an Olympus BX61 inverted stand and motorized xyz stage, driven by FV10-ASW 1. 7 software. Wing discs were imaged using either an Olympus UApochromat 40×1. 35NA oil immersion or an Olympus UPlanSApochromat 60×1. 35NA oil immersion objective. All discs were imaged and analyzed, except in very rare cases when the discs were damaged or misshapen during mounting.
Imaging was performed using a Zeiss spinning-disc microscope consisting of an AxioObserver inverted stand, motorized xyz stage with temperature control set to 25°C, Yokogawa CSU-X1 scanhead, and a Zeiss AxioCam MRm camera (2×2 binning), all controlled by Axiovision software. Discs were imaged through a Zeiss C-Apochromat 63×1. 2NA water immersion objective heated to 25°C with an objective heater. To capture the whole pouch, we acquired a 2×2 tiled region (10% overlap). Each region consisted of a z-stack of 65-85 frames, spaced 0. 5 µm apart. Tiled z-stacks were captured every 5 min. We kept light exposure as low as possible to achieve a segmentable image. We used a power meter (PT9610, Gigahertz-optik, Munich, Germany) to measure the power of the laser through a 10×/0. 45 NA objective within a week of the experiment.