dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis was conducted as followed: The individual populations were tested for normal distribution (Shapiro Wilk test) and homogeneity of variances (Folded F test). If the populations were normal distributed and the variances were homogeneous, a t-test for independent samples was conducted (See Fig. S3  B -Boxplot LDL height), otherwise the non-parametric Mann-Whitney-U test was used (See Fig. S3  A -Boxplot LDL width). Before tracking the LDL protrusions, the videos were drift-corrected using the "Image Stabilizer" plugin of Fiji  [97]  from Kang Li and Steven Kang (https://www. cs. cmu. edu/~kangli/code/Image_Stabilizer. html). To track the protrusion's mobility, the "Manual Tracking" plugin of Fiji was utilized (https://github. com/fiji/Manual_Tracking). The area of the protrusion's motion was calculated using the Fiji freehand selection and analysis tool.
Acknowledgements We thank  Johann Krebs ,  Ivan Vidakovic ,  Robert Reimer  and  Gerhard Ledinski  for their contribution in sample preparation, and the IT services of ILL for installation of the workstation ( Holger Gebhard ,  Sylvain Dufour ). We also thank  Maria Bacia-Verloop  for the tests on cryoelectron tomography and cryo-EM grid screening at the Glacios microscope of the IBS EM platform. The IBS EM platform is part of the  Grenoble Instruct-ERIC center  (ISBG; UMS 3518  CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology (PSB ), supported by  FRISBI  ( ANR-10-INBS-05-02 ), and  GRAL , financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche)   CBH-EUR-GS  ( ANR-17-EURE-0003 ). The electron microscope facility is supported by the  Auvergne-Rhône-Alpes Region , the  Fondation Recherche Médicale (FRM) , the  fonds FEDER , and the  GIS-Infrastructures en Biologie Santé et Agronomie (IBISA).
Funding This work was supported by the  Foundation CFM  through a  JP Aguilar scholarship  for the PhD thesis of A. C. ; the  Austrian Science Fund (FWF) : project no. I 6211-B  to K. K. and the  Agence nationale de la recherche (ANR) : project no. ANR-22-CE91-0003-01  to J. P. ; the  Amadeus  project from  Campus France  [no. 42183RD ] and accordingly the WTZ-grant by the  OeAD  [project no:  FR 09/2019  and  FR 05/2023 ]; S. S. holds a Ph. D. scholarship of the  Austrian Science Fund (FWF)   PhD program   W1250  NanoCell; we acknowledge the  Institut Universitaire de.
This set of data was subjected to regression analyses in which 3 categories of variables were successively introduced: (1) general cognitive abilities (i. e. , working memory, executive functions, visual attention and language) (2) basic numerical skills and (3) previously learnt arithmetic operations. Our main goal was to assess the specific weight of this last variable on children's performance for each arithmetic operation after the role of the other variables had been taken into account. As just stated, four general cognitive abilities were entered in the models. We entered a measure of visual attention because performing calculations first requires the encoding of the problem operands and arithmetic signs  (Thevenot et al. , 2011; Thevenot & Barrouillet, 2006 , 2010). Children who are able to deliberately focus their attention and to resist distraction are more efficient during this phase  (Ortega et al.
METHOD Participants Our research included 435 children attending school in classrooms from Grades 2 to 5 (105 to 111 pupils per school level) in a range of public and private schools all over the Paris region in France. Written informed consent to participate was obtained from all the parents or legal tutors of the children involved. All procedures performed in this study have been conducted in compliance with the recommendations of the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Because only behavioural data were collected in a nonvulnerable population of children, the official approval of a committee of ethics was not required. Our research protocol was however accepted by inspectors of the French National Education. Table  1  shows the characteristics of the sample: gender, parents' socio-professional category (SPC, following the recommendation of French national statistics office, the higher socio-professional category between the two parents was retained to classify children in one of three SPC categories).
Material and procedure The protocol was established on the basis of the BMT-i (Modulable Battery of Computerized Tests), which assesses cognitive skills  (Billard et al. , 2021). Eight speech therapists and three neuropsychologists were in charge of the testing after having been trained to the test administration. The tests were administered in one or two 45-minute sessions, at least 15 days apart, during school hours. Children were first tested on language skills, then on numerical skills, then on working memory and finally on executive functions and visual attention. The tests were administered using a secure website using a Surface Pro3 tablet operating under Windows 8. The items needed to be read out loud were recorded in advance and read out by the software. Responses were either recorded automatically or noted by the examiner.
Improvements for daily practice The following improvements were mentioned by preclinical/clinical users as their need for daily practice: unified licensing and registration of available radionuclides and kits in Europe (80% respondents), information about transport and logistics network in Europe (55% respondents), database of available radionuclides and the geographic location of the supply site (50% respondents), and some specified equipment/ technologies (e. g. , collimators etc. ) (45% respondents). On-site training with the visit of international experts is also a wish (40% respondents) as is outsourced crucial training for technical personnel (32,5% respondents) and medical doctors (30% respondents). Some of the respondents' countries send patients to other countries for specified nuclear medicine examinations and/or treatment procedures.
2021). In side-by-side comparisons published in the literature, the inclusivity of HTS tests was identical to or better than the other compared tests (molecular, immunological, or bioassays)  (Al Rwahnih et al. 2015; Hanafi et al. 2020; Velasco and Padilla 2021). On the other hand, the exclusivity of HTS tests is intrinsically high but depends on the number of reads generated for the detected virus (and the genome coverage of the virus), the quality of these reads, as well as the bioinformatic procedure (e. g. , the software, the parameters, and the database used) at each step of the bioinformatics analysis. The repeatability and reproducibility of HTS tests have been evaluated in the literature and were generally very high, with up to 100% repeatability and reproducibility for virus detection  (Bester et al.
The concentration of virus reads in the sequence dataset from such a sample was observed previously as very high in the growing conditions of the greenhouse  (Tamisier et al. , unpublished data). Importantly, Musa species are not a host for BYDV, so the presence of reads from BYDV in any Musa sample reveals a cross-sample contamination. In total, the sequenced samples corresponded to 78 different plants on which 702 (78 × 9) individual predictions could be made with the nine virus species mentioned above.
Bioinformatics analyses The obtained sequence reads (FastQ file format) were quality controlled on both ends using a minimal nucleotide Phred quality score of 25 and a minimal length of 35 bp with BBDuk  (Kechin et al. 2017) (v38. 37). Then, the trimmed reads were merged, and duplicated merged reads were removed using Dedupe  (Bushnell et al. 2017) (v38. 37)  with kmer seed 31. All the cleaned reads (merged and unmerged) were mapped to the custom-built database with 820 whole genomes of BanMMV, BBrMV, BBTV, BSV, CMV, and BYDV, which were available from NCBI (accessed 12/12/2020; Supplementary Table  S4 ). Except for BanMMV, all the other tested viruses had more than one reference genome available, and all the cleaned reads were mapped to these available references at the same time. The mapping tool from GeneiousPrime (2020.
Calculation of diagnostic sensitivity The diagnostic sensitivity was calculated according to recent recommendations for the statistical analysis of validation datasets  (Massart et al. 2022 ). The known formula was used (true positive/(true positive + false negative)) but without considering diluted samples or alien controls. However, for analytical sensitivity calculation, diluted samples were used. The occurrence of false positives was monitored and investigated. Alien controls were used to evaluate the impact of threshold determination on the balance between diagnostic sensitivity and false positive occurrence.
Generated sequencing data In total, 136 libraries were sequenced by HTS from individual or pooled total RNA in five separate HTS runs, each comprising 19 to 38 libraries (Supplementary Table  S1 ). These libraries originated from 78 different Musa accessions, nine positive controls corresponding to the same mix of plants (same for all batches except for batch 1), seven negative controls (virusfree Musa accessions), 11 alien controls, and 18 pooled samples (containing Musa infected with BanMMV, BSOLV, BBrMV, BBTV, and CMV at various dilution levels). The data (about 2 billion reads in total) generated by each independent sequencing batch is summarized in Table  1  and publicly available (https:// www. ncbi. nlm. nih. gov/bioproject/PRJNA777477). Whatever the batch, the minimal and maximal number of reads per sequencing library were 4.
Monitoring cross-contamination burden with the alien control As Musa species are not a host for BYDV, the cross-sample contamination between the alien control and any other samples or control can be monitored by identifying and counting BYDV reads in each sequencing dataset. The results are summarized in Table  2. Table  2  underlines several trends in the cross-contamination level according to the analysis of the detection of BYDV reads in Musa samples. First, the cross-contamination level is highly variable depending on the sequencing batch. Indeed, the ratio between reads generated in the alien control(s) and the crosscontaminating (BYDV) reads ranged between 190× and 7,269× in the different batches. In addition, the number of Musa samples with cross-contaminating (BYDV) reads is also variable, from less than 10% (n = 2 in batch 1) to more than 90% (n = 15 in batch 2).
These results confirmed the overall cross-contamination burden observed with the alien control. Despite recent recommendations for the statistical analysis of validation datasets for plant pests  (Massart et al. 2022) , the serially diluted samples (up to 10,000× dilution) were kept in this preliminary analysis (explaining the higher false negative rate for batch 4). As a result, even with dilutions included, the alien threshold presented a low false positive rate (2%), showing that even with a low virus concentration or number of reads in a sample, no additional misidentification was observed. In fact, the two false positives for this batch corresponded to two BBrMVs, which are discussed later. The application of the "10 reads" threshold improved the accuracy considerably (ranging between 84 and 93% for the viruses other than BSV species) with up to 10-fold reduction of the false positive rate while still ranging between 3 and 16%.
This means that reads from integrated BSV could be potentially detected in the absence of viral particles. To analyze the false negative rate in more depth, the detected BSV reads were summarized depending on the Musa genome and the presence of viral particles (Table  4 ). For the plants without BSV particles, a marked difference was observed between accessions containing a B genome (whatever their ploidy and genome combination) and accessions with only an A genome. Indeed, BSV reads were detected in more than 90% of the samples (31 of 34 datasets) with a B genome, with an average number of 224 reads. For 12 samples, the number of BSV reads was above the alien threshold of the corresponding batch. In contrast, 100 times fewer BSV reads were detected in the samples without a B genome, with an average number of three reads.
Higher inclusivity at the isolate and species level for the HTS test The HTS test detected a BSIMV infection in accession ITC1843 and a BSMYV infection in accession ITC1599, al- though these samples were indexed as negative for the respective viruses. To investigate these divergent results, the primers used during virus PCR detection were aligned with the whole genome sequence generated. Six and two mismatches were found for the BSIMV and BSMYV sequences, respectively. Based on the alignment of these new genome sequences and on the sequences available on the NCBI database for each species (accessed on 24/10/2019, with two and seven whole genome sequences available for BSIMV and BSMYV, respectively), new primers were designed (Supplementary Table  S3 ). These primers were tested on both accessions and compared with the former primers following the IC-PCR protocol to validate the presence of viral particles as BSMYV can be integrated in the Musa B genome.
b BSV = banana streak virus. ampeloviruses. Pairwise comparison of these contigs with reference sequences of other species belonging to family Closteroviridae confirmed that these contigs belonged to the genus Ampelovirus but were divergent from known species. The highest similarity at the amino acid level was 85% for one of the contigs with sugarcane mild mosaic virus (SCMMV, GenBank accession No. MN116751). These results will be detailed and discussed in a future publication. In addition, two contigs presented homologies with RNA1 and RNA2 of crinivirus species. They were detected in accession ITC1905 and corresponded to nearly complete genomes, with the highest genome identity of 63% and 66% with RNA1 and RNA2 of lettuce chlorosis virus  (GenBank FJ380118 and FJ380119). An in-depth analysis of these contigs and their presence in Musa germplasm will be presented in another publication  (Rong et al.
Diagnostic sensitivity of the HTS test The alien control datasets and the datasets from diluted samples were not included in the in-depth analysis of diagnostic sensitivity (DSE), as recommended in the recent guidelines for statistical analysis of validation datasets  (Massart et al. 2022). Therefore, a total of 111 datasets were included in the analysis, corresponding to a total number of 999 detection events. Among the 111 datasets, 58 were generated from plants without viruses, whereas 53 came from plants infected by at least one virus species (and up to five). According to PCR detection results, a total of 120 virus detections should occur within these 53 datasets. Using the alien threshold, 115 true positives were detected, representing a DSE of 96%. Considering only the nonintegrated viruses (CMV, BBTV, BanMMV ,and BBrMV), 81 virus detections should be observed (among 444 events).
Seventy-six true positives were detected (DSE = 94%). When analyzing the DSE per viral species, the DSE dropped to 75% for BBTV. This high false negative rate is likely due to low abundance of BBTV reads in the different datasets from infected samples, with an average of 380 reads and a maximum of 2,397 reads per dataset. Of note, the positive samples with BBTV corresponded to a 5× dilution of the concentration due to the sample pooling. The alien threshold was fixed based on the abundance of reads in the alien controls that was between 10× and 72× higher than the maximum number of BBTV reads. Therefore, even if the number of BBTV reads of several positive datasets is below the alien threshold, it is unlikely that they come from a cross-contamination. They could therefore be interpreted as positive results during expert review of the data.
Investigating unexpected results The DSE reached 100% for the other viruses, except for Ban-MMV. Indeed, one dataset (ITC1536) did not generate BanMMV reads, although it was indexed as positive. For further verification, the preserved lyophilized leaf and the extracted total RNA used for the HTS was tested for BanMMV using IC-RT-PCR and RT-PCR, respectively. BanMMV was not detected from extracted total RNA, confirming the absence of BanMMV RNA in the extract, but a positive result was obtained using IC-RT-PCR. The most probable origin of this result is the heterogeneous distribution of BanMMV inside the plant tissues and its absence in the sampled tissue. An unexpected detection of CMV in samples ITC1498 and 1565 (batch 3) was observed as the accessions tested negative during PCR, but 845 and 686 reads were detected, respectively.
Mapping of CMV reads on a reference genome presented a genome coverage (all 3 RNA fragments) of 99. 4% with 17. 6 mean depth and 97. 8% with 14. 7 mean depth for samples ITC1498 and ITS1565, respectively. When analyzing the sequence of the primers used during PCR and the reads generated, a single mismatch was observed. The CMV contigs obtained for this dataset and the ones generated from other datasets with CMV infection for this batch of samples (two positive controls and the sample BBrMV2 No. 208) were compared, but they were different, with many SNPs between them, suggesting that this detection did not come from cross-contamination between these samples. As the RNA extract was not available, additional sequencing from a new sample prepared from the same plant (sampled from lyophilized tissue) showed no trace of CMV (0 reads observed on approximately 9 M reads per sample).
We later investigated the case by analyzing the viral reads presence in samples coming from different projects (RNA extracted in another laboratory) but with the library prepared and sequenced in the same batch by the sequencing provider. Importantly, two samples from an independent project presented a high quantity of CMV reads: On a total of 12 M (10 M) reads, there were 8 M (5 M) reads, corresponding to 708,736 (1 M) unique reads mapped on the CMV genome. The SNP profile of the alignment was compared with the SNP profile of the aligned reads from ITC1498 and ITC1565. Nearly all (>99%) the SNPs detected in the ITC1565 (1498) were also found in external sample 1 (2) suggesting two specific crosscontamination events. The SNP list observed after mapping the four datasets on the RNA3 reference sequence is detailed in Supplementary Table  S5.
The rate of contamination from the original sample was low as it corresponded to 0. 01 and 0. 02% for ITC1565 and 1498, respectively. For batch 4, 199 and 121 reads of BBrMV were observed in datasets from ITC1854 and ITC1858, respectively, even though both accessions were PCR-negative for BBrMV. After mapping, the genome coverage observed was 72% with 3 mean depth and 61% with 1. 7 mean depth for ITC1854 and ITC1858 respectively. The primer sequences were not covered by the sequencing reads, and the presence of mismatches could not be analyzed. As the genome coverage and depth were low, an SNP profiling analysis could not be done; instead, a deduplication analysis was performed by comparing these 199 and 121 reads with the 38,666 reads generated from the most abundant BBrMV sample. First, the 320 reads were deduplicated, leading to 249 unique reads (79%).
The use of the alien threshold follows the same objective without the need for subsampling. Indeed, sub-sampling will have no effect because it also reduces proportionally the alien threshold for detection by rarefying the number of cross-contaminating alien virus reads in the samples. In conclusion, our report demonstrates the usefulness of an alien control to monitor cross-contamination burden, although specific cross-contamination events remain difficult to trace back. For routine virus detection of Musa germplasm, the use of the HTS test and the alien threshold, completed by an expert analysis of the results, fit the purpose of viral detection. In addition, the HTS test must be complemented by a targeted IC-PCR for BSV if BSV reads are detected in datasets generated from germplasm containing the B genome to confirm the presence of viral particles.
ACKNOWLEDGMENTS We thank  Angelo Locicero  for technical support and  Gladys Rufflard  for administrative support. Delphine Masse  ( ANSES, La Réunion, France ),  Kathy Crew  and  John Thomas  ( Queensland Alliance for Agriculture and Food Innovation, Brisbane, Australia ), and  Mathieu Chabannes  and  Marilyne Caruana  ( CIRAD, Montpellier, France ) are also acknowledged for kindly providing reference samples. We also thank  John Thomas  for his suggestions after carefully reading the manuscript.
Source data are provided as a Source Data file. especially endemic species/lineages, are all derived from ancestors inhabiting open habitats in southern high mountains and adjacent lowlands to the south of the Arctic (Supplementary Figs. 42-46). This finding suggests that there was not substantial habitat shift, i. e. , the immigrants of the Arctic were pre-adapted to open and cold habitats  14, 29. Our biogeographic analyses with 13 predefined regions show that Arctic species are derived from multiple sources, in agreement with previous results  28, 29. Most biotic dispersals of the Arctic flora were from western North America (~54%, 14/26 of all dispersal events), whereas there were far fewer dispersal events from other regions (Supplementary Table  4 ). The Arctic interchanges with western North America existed during the entire assembly process of the Arctic flora (Fig.
Data collection We selected 32 angiosperm clades that contain Arctic and non-Arctic species and have sufficient molecular data available to infer a timecalibrated phylogeny covering the major taxonomic and geographical diversity of each clade (see details in Supplementary Note). We included 3626 species, of which 548 are distributed in the Arctic and 40 are endemic (restricted to the Arctic). These selected taxa belong to 10 orders and 16 families (following APG IV  47  ) across the angiosperm tree of life (Supplementary Fig. 1 ; Supplementary Table  1 ). The DNA datasets for the phylogenetic analysis were assembled using sequences that were generated in this study or downloaded from GenBank (Supplementary Data 3). Total genomic DNA was extracted from silica-gel-dried leaves or herbarium specimens using DNeasy Mini Plant Kits (Tiangen Biotech, Beijing, China).
Phylogenetic analysis and divergence time estimation For each clade, we performed sequence alignments for each locus in MAFFT v. 7. 037  48 and manually adjusted with Geneious v. 9. 1. 4  49. Referring to Li et al. 50 , we first conducted maximum likelihood analysis in IQ-TREE v. 2. 1. 2  51 and removed the strongly conflicting taxa (>70% bootstrap values for different placements) between the plastid and nuclear trees  52. After removing sixty-eight species, all of which are non-Arctic (Supplementary Data 3), a total of 3588 species was used for subsequent analyses. For the final concatenated datasets, we used BEAST v. 1. 8. 4  53  to infer time-calibrated phylogenies using an uncorrelated relaxed molecular clock model, a Yule prior, and the GTR + I + Γ model for each locus separately. All analyses were run for 50 million generations, sampling every 5000 generations.
The data were smoothed by calculating mean values using a sliding window approach with a time frame of 0. 5 Ma. In addition, we extracted dispersal times from different regions into the Arctic and calculated their respective MDisEs using the same approach. To assess the 95% confidence intervals of the MDE origination and peaks, we re-sampled 1000 bootstrap pseudoreplicates from the raw credibility intervals (used for inferring the MDisE and MDivE rates above) using the R package sample function, and then repeated the calculations to find the MDE origination and peaks. We identified the change points in the MDE curves using the segmented regression method  61  via the R package segmented function  62. We modeled the trend with 1, 2, 3 and 4 change points and used the Bayesian Information Criterion (BIC) to determine the optimal model.