dataesr/scientific-paragraphs-categorization huggingface dataset:
Lab validation For the first analysis where we compared the two top-down images digitized by different users, 100% of Dmatch1 for the test images were the correct reference individual among the 176 individuals forming the database. These Dmatch1 scores were higher than 0 (average ± SD: Dmatch1 top-down = 176 ± 57), and represented the user error. When we compared side-angle images to the top-down images, 100% of the test images were again matched to the correct reference individual using the program, even if Dmatch1 scores were slightly higher for the side-angle photo set comparison (Dmatch1 side-angle = 379 ± 93, V = 0, p < 0. 001, Figure  2A ). There was an average difference of 783 ± 155 between Dmatch1 and Dmatch2 when comparing the two top-down fingerprints, and 629 ± 161 when comparing the side-angle and top-down fingerprints (Figure  2B ).
Future work is needed to confirm these patterns across a wider array of localities. In the case of recaptures in the field, the best match was always the correct individual from our reference database of 84 individuals. This high performance of the method for identifying recaptures is not unique to SLF (8) but seems to exceed what has been found in other species, where correct individuals could be found in the first few best matches of reference databases of 132-358 individuals (10,  16, 17). Moreover, in our dataset, scores of the best match were much lower for recaptures than for non-recaptures, as well as for duplicated images compared to non-duplicated images, suggesting that further automatization of the image analysis is possible to reduce the need for user visual validation. Indeed, the bimodal distribution of the bestmatch score across individuals suggests an almost clear-cut transition between scores of recaptures and scores of non-recaptures, and between scores of duplicated and non-duplicated images.
The original publication for the I3S software stated that a score of less than 400 indicated a high probability of a possible match (20). Based on our analysis, we would expand this to a higher score, and any individual with a best-match score lower than 700 could be considered as a recapture, while an image with a best-match score larger than 900 is likely an unknown individual (non-recapture). Bestmatch scores in between these values were in a gray zone where recaptures and non-recaptures overlapped. Comparable thresholds were found for duplicated and non-duplicated images (700-1000). Only images with best-match scores in this gray zone would need to be manually checked, potentially drastically reducing the user input time in the analysis to a fraction of the individuals, 3% of the individuals in our field study, compared to the routine use of the program that involves visually checking at least the first match of 100% of the individuals tested.
Acknowledgments The authors thank  Siya Patel ,  Stefani Cannon ,  Sebastiano De Bona ,  Jason Gleditsch ,  Matthew Helmus ,  Anna Khan ,  Stephanie Lewkiewicz ,  Samuel Owens ,  Payton Phillips ,  Timothy Swartz  and  Jacob Woods  for their help in image digitization. We thank  Houping Liu  and two reviewers for their helpful comments on an earlier version of the manuscript.
Genome assembly from A. thaliana HiFi reads From the 66 assemblies analysed, 63 were generated for this study, and the other three have been published  7, 14, 15. q20 High Fidelity (HiFi) reads were generated with the PacBio Circular Consensus Sequencing tool ccs (version 6. 0. 0) (--all), followed by extracthifi (version 1. 0) (PacBio tools: https://github. com/PacificBiosciences/pbbioconda). When two or more libraries were sequenced on the same SMRTcell, demultiplexing was carried out with lima (version 2. 0. 0) (--same --ccs --min-score 70 --min-scoring-regions 2 --min-ref-span 0. 8 --peek-guess --split-bam-named). For 49 accessions, de novo assemblies were performed with hifiasm (version 0. 16. 1-r375) (-l0 -f0)  46  , and contigs larger than 100 kb were scaffolded into chromosomes with RagTag (version 2.
0. 1) (scaffold -q 60 -f 30000 -i 0. 5 --remove-small)  47  , using as the reference genome a version of the TAIR10 assembly in which centromeres, telomeres, organellar nuclear insertions, and 5S and 45S rDNA arrays had been masked  7. The remaining assembly gaps in the chromosome scaffolds of accessions Rabacal-1 and Tanz-1 were 'patched' with their corresponding Continuous Long Read (CLR) based assemblies using RagTag (version 2. 0. 1) (patch --join-only -f 10000 --remove-small)  47. For 14 accessions, de novo assemblies were performed with hifiasm v0. 15. 4-r343 (-l0 -f0)  46  , and contigs larger than 100 kb were scaffolded into chromosomes with RagTag v2. 0. 1 (scaffold -q 60 -f 30000 -i 0. 5 --remove-small)  47  , using as reference genome a Bionano optical map of IP-Ini-0. The assemblies of accessions Col-0 7 , Ey15-0 7 and Kew-1  14, 15  were downloaded from the European Nucleotide Archive (ENA) database, under assembly IDs GCA_946499705.
1) (NucPlot. py --minobed 2)  49  , while a modified version of the script 'hetDetection. R'  50  was used to identify and quantify heterozygosity stretches within the satellite arrays, based on the bed file emitted by NucFreq. Briefly, a heterozygosity stretch was defined as a region where the second most common base is present in at least 10% of reads, in at least 5 positions within a 500 bp region (https://github. com/mrvollger/NucFreq). Representative plots of primary and secondary allele coverage are displayed in Extended Data Fig. 1  and Extended Data Fig. 8d. We identified six accessions (11C1, AUZE-A-5, Ey15-2, GAIL-B-11, IP-Fel-2 and IP-Ini-0) with more than 1. 5 Mb of collapsed centromeric regions. Collapsed regions were defined as those with coverage that was three standard deviations above the genome-wide mean  49.
Single nucleotide polymorphism analysis and PCA Genome assemblies of each A. thaliana accession were aligned to the HiFi assembly of Col-0 using minimap2 (version 2. 24)  51  , using the parameters 'minimap2 -a -x asm5 --cs -r2k -t 16'. SNPs and short indels were called from the alignment using bcftools (version 1. 15. 1)  52. Repetitive regions of the Col-0 HiFi assembly were masked using RepeatMasker for AthCEN178, AthCEN159, transposable elements, organelle sequences and rDNA. Transposable element sequences were downloaded from the TAIR10 website. SNPs were further filtered to exclude repeat-masked regions of the Col-0 HiFi genome, requiring no missing sites and being strictly biallelic, which resulted in 2,011,142 SNPs. PCA was performed using the R package ggbiplot.
Identification of centromere-spanning haplotypes cenhaps HiFi reads from each accession were aligned to the HiFi-based assembly of Col-0, including the mitochondrial and chloroplast genomes from TAIR10, with pbmm2 (version 1. 9. 0) (align --sort --preset CCS) (https://github. com/PacificBiosciences/pbmm2), which is a wrapper of minimap2  51. DeepVariant (version 1. 3. 0) (--model_type=PACBIO)  53  was used to create individual genome call sets, and GLnexus (version 1. 4. 1)  54 to merge calls into a single cohort. We filtered out SNP and indel variants that overlapped centromeres, telomeres, organellar nuclear insertions, and 5S and 45S rDNA arrays, as previously defined  7  , with GATK (version 4. 1. 3. 0) (VariantFiltration --exclude-intervals <repeats. bed>)  55. In addition, we performed variant hard-filtering at the population level with bcftools (version 1.
0)  56  , and we assigned the minor allele as 1 and the major allele as 0. To examine the haplotype structure, we clustered accessions based on the Hamming distance of the polymorphisms flanking the centromeres in Col-0, using the R package cultevo. We generated heatmaps to represent the haplotype structure using the R package pheatmap. Samples were coloured based on their AthCEN178 similarity group (Supplementary Table  2 ).
Tandem repeat annotation and analysis The Tandem Repeat Annotation and Structural Hierarchy (TRASH) pipeline was used to identify Arabidopsis thaliana AthCEN178 and AthCEN159, and Arabidopsis lyrata AlyCEN169 and AlyCEN179 tandem repeats (https://github. com/vlothec/TRASH). Genomic sequences were divided into 1 kb windows that were analysed for their repetitive content using k-mer counts (proportion of non-unique k-mers). Local concatenation of the windows with high k-mer scores produced regions with repetitive sequence content. The periodicity of tandem repeats within these regions was calculated based on distances between identical k-mers. This was in turn used for iterative sampling of the region, where each sampled sub-sequence was locally mapped, using matchPattern from R package Biostrings, and scored for the fraction of sequence covered by the mappings.
An AthCEN178 consensus sequence for each accession was calculated based on MAFFT alignment of all repeats (--retree 2), and the most common nucleotides per position, with a frequency of at least 50%, were extracted. To generate a global consensus sequence, frequency tables from each accession were aligned, averaged and a consensus sequence extracted, as before. Levenshtein edit distance between each repeat and the accession-specific consensus sequence was calculated using the stringdist function from the stringdist R package  57. To calculate similarity between AthCEN178 sequences of a pair of chromosomes, the number of identical AthCEN178 instances found within the other chromosome, without excluding duplicated repeats, was calculated reciprocally. These values were normalised by the number of AthCEN178 repeats in both sets, so that values are in range of 0-100%, where 0 means no shared repeats and 100% means the chromosomes share the same repeat library.
Heat maps were constructed by ordering the chromosome pairs using hierarchical cluster analysis, using the R function hcluster from the package amap. Centromere similarity groups were visually inferred from the heat maps. Satellite higher-order repeats (HORs), understood as repeat block duplications, were identified using the TRASH HOR module. AthCEN178 repeats from a single chromosome were globally aligned using MAFFT (--kimura 1 --retree 1), and each pair of repeats was scored for the number of disagreements. Each uninterrupted series of repeat pairs, with a lower than threshold number of disagreements (n=5), was reported as a HOR instance. HORs consisting of fewer than three monomers per block were discarded. Following HOR counting, the count value for each repeat is divided by the number of AthCEN178 on that chromosome, and expressed as a percentage, which we term the HOR score.
Saturation analysis of the unique AthCEN178 sequences was calculated using the R package vegan, using the function specaccum, with 1,000 permutations of the binary matrix of AthCEN178 presence/absence. To generate sequence identity heat maps, the StainedGlass package was used  59. Genome regions of interest were analysed using a 5 or 10 kb window and the mm_f option set as 10 kb.
ATHILA and transposon annotation and analysis To achieve large-scale, yet sensitive and highly accurate, discovery and analysis of ATHILA LTR retrotransposons, we designed a new tool, ATHILAfinder (https://github. com/eliasprim/ATHILAfinder). The ATHILAfinder pipeline uses sequence motifs that are specific to the junctions between the internal domain and the LTRs of ATHILA elements as primary seeds. These motifs were identified using the manually curated and deeply annotated ATHILA dataset from the Col-CEN genome assembly  6  , and comparison with ATHILA and non-ATHILA transposable elements (TE) in the TAIR10 TE database  60   CATCAAGCTTTTTGGCGCCGT. The main difference between the three seeds is the length and composition of the intervening sequence between the terminal pentamer of the 5′ LTR (underlined left) and the PBS (underlined right).
Intact elements were defined when both 20-mers were found. This approach identified 8,709 ATHILA elements in the 66 A. thaliana accessions and 885 in the two A. lyrata accessions; however, additional high-quality intact ATHILA will be missed by ATHILAfinder, when small deletions occur that overlap the seeds, or in the case of short indels within the seeds. To retrieve these elements, we included a homology BLASTn-based recovery step that uses as query the intact ATHILA dataset and a minimum coverage threshold of 90%. The candidate loci then undergo a similar process as above based on the 20-mers to refine and identify with high precision the external boundaries and the junctions of the internal domain with the LTRs. This approach further identified 541 intact ATHILA in A. thaliana and 109 in A.
When required, the number of input sequences for multiple sequence alignments were reduced using CD-HIT  62. We then run MAFFT with the G-INS-i or FFT-NS-i parameters depending on the number of input sequences, and FastTree (-nt) to generate the maximum likelihood tree  63. Phylogenetic trees were visualised and annotated using FigTree (http://tree. bio. ed. ac. uk/software/figtree/), and iTOL 64. To    6 ). Matched ATHILA were then aligned with MAFFT (--globalpair --maxiterate 1000) to calculate substitutions, indels and alignment length. We followed two different approaches for the retrieval of the complete sequence of the integrase gene, for every family (Extended Data Fig. 9d ), which was based on the number of mutations in the coding domain of ATHILA elements. When most or all elements of a family contained a large number of mutations, identification of the correct sequence is very challenging, because the gag-pol ORF was broken into numerous smaller fragments in different frames.
Four families, however, ATHILA5, ATHILA1, ATHILA2 and ATHILA6b, contained several elements with a complete gag-pol ORF (Extended Data Fig. 9a-9c ). Using this subset of elements, we were able to build the consensus sequence of integrase for these families. We then constructed a Hidden Markov Model profile based on these four consensus sequences, and used FraHMMER (https://github. com/TravisWheelerLab/FraHMMER), to identify homologies in the other families, even in the presence of frameshift mutations. We detected complete integrase hits for ATHILA7, ATHILA9, ATHILA4c and ATHILA0, but not for the remaining 6 families (ATHILA6a, ATHILA3, ATHILA4, ATHILA4a, ATHILA8a, and ATHILA7a). This was expected based on our previous scans of full-length elements using a collection of HMMs for transposon genes, which failed to retrieve the.
Code availability statement The TRASH algorithm is available at https://github. com/vlothec/TRASH, the ATHILAfinder algorithm is available at https://github. com/eliasprim/ATHILAfinder, and additional custom code associated with the manuscript are available at https://github. com/vlothec/pancentromere. Data availability statement: Arabidopsis thaliana and Arabidopsis lyrata accessions used for sequencing are held in the authors' laboratories and seeds are freely available on request. The genome assemblies analysed in this study are available under the following accession numbers: (i) 48 A. thaliana HiFi assemblies have been submitted to the European Nucleotide Archive (ENA) database under project number PRJEB55353 (ERP140242) (this study), (ii) 15 A. thaliana HiFi assemblies have been submitted to the ENA database under project number PRJEB55632 (ERA17524869) (as part of a separate study by G.
S. and D. W. ), (iii) two A. thaliana HiFi assemblies (Col-0 and Ey15-2) are available at the ENA database under project number PRJEB50694 (ERP135313)  7  , (iv) one A. thaliana HiFi assembly (Kew-1) from the Darwin Tree of Life is available under project accession PRJEB51511  14, 15  , and can also be accessed at: https://portal. darwintreeoflife. org/data/root/details/Arabidopsis%20thaliana, (v) ONT reads from the Ler-0, Cvi-0 and Tanz-0 accessions have been submitted as ArrayExpress accession E-MTAB-12009 (this study), while those for the accession Col-0 were previously available as ArrayExpress accession E-MTAB-10272 6 , (vi) CENH3 Ilumina ChIP-seq reads from Col-0, Ler-0, Cvi-0 and Tanz-0 have been submitted as ArrayExpress accession E-MTAB-11974 (this study), and (vii) two A. lyrata HiFi assemblies are available at the ENA database under project number PRJEB50329 (ERP134897)  26.
616,546 intact ATHILA and 8,776 soloLTRs contained a TSD in A. thaliana, and 806 and 333 in A. lyrata. We identified open reading frames (minimum 300 bp) in the internal domain of the intact elements using getorf in EMBOSS61, and the core domains of GAG and POL by running HMMER (version 3. 3. 2) (-E 0. 001 --domE 0. 001) (http://hmmer. org/), using a collection of Hidden Markov Models (HMMs) downloaded from Pfam (http://pfam. xfam. org/) that describe the genes of Ty3/Gypsy LTR retrotransposons: PF03732 for GAG; PF13650, PF08284, PF13975, PF00077 and PF09668 for protease; PF00078 for reverse transcriptase; PF17917, PF17919 and PF13456 for RNase-H; PF00665, PF13683, PF17921, PF02022, PF09337 and PF00552 for integrase and PF03078 for the ATHILA-specific domain. Given that all.
Materials and methods Study design. We used the transcriptomics data from Lemonnier et al. obtained in MeDALL (Mechanisms of the Development of Allergy)  10. The analysis comprised a cross-sectional study carried out in participants from three MeDALL cohorts using whole blood. It compared participants with single allergic disease (asthma, dermatitis or rhinitis) or with multimorbidity (A + D, A + R, D + R, or A + D + R) to those without asthma, dermatitis or rhinitis and non-allergic participants. We characterised the molecular pathways associated to R alone and R + A using an interactomics approach. Datasets. Differentially expressed genes (DEGs) for R alone and for R + A were obtained from the MeDALL gene expression study  [10] [11] [12] [13]. The full dataset is supplied in Supplementary Table  S1.
Settings and participants. The interactome. The first-degree interactomes of the DEGs for R alone and for R + A were independently generated using the IntAct database, which contains a curated collection of > 10 6 experimentally determined protein-protein interactions in human cells  14. Data was downloaded via the IntAct web-based tool at the European Bioinformatics Institute (https:// www. ebi. ac. uk/ intact/). Ensembl Gene IDs were used instead of HGNC names to avoid ambiguities. Self-interactions and expanded interactions were discarded. Interactomes are supplied in Supplementary Table  S2. Network density was calculated as the number of edges with respect to the maximal possible number of edges. Random distributions were used to test the degree of interconnectedness of the interactomes.
Functional annotation. The interactomes were functionally annotated using the DAVID web-based tool  15  , with the Reactome database as the source of functional information  16  and the default gene background. Functional pathways were considered significant with FDR < 0. 05. In order to simplify the functional annotation and remove redundancy, pathways associated to diseased or defective cellular processes were removed and we only considered pathways in the intermediate levels (levels 3 and 4) of the Reactome hierarchy. Furthermore, we assigned each pathway to a generic functional family ("Signal Transduction", "Cell Cycle", etc. ) in order to help interpreting the results. We did this by (1) clustering all the pathways according to their Szymkiewicz-Simpson overlap 17 ; (2) identifying the best partition of using the Pearson's Gamma method  18  implemented in the fpc R package; and (3) asigning each cluster in the best partition to a generic pathway (i.
Nonetheless, the modeling of forgetting remains one of the longstanding unresolved issues in the field of experimental psychology and is not the subject of consensus  Klammer and Gueldenberg (2019). In  Averell and Heathcote (2011) , the authors propose a general memory model based on a study of a large dataset and Bayesian model selection to account for the student's capacity to forget, where a power function seemed to be favored  Anderson and Schunn (2013). Another popular model of forgetting is the exponential forgetting curve of  Ebbinghaus Ebbinghaus (2013) , yet this model was initially conducted by Ebbinghaus himself in an incomplete study. Nonetheless,  Murre and Dros (2015)  attempted to replicate Ebbinghaus' experimentation and findings; eventually they showed that the experimental results were similar to Ebbinghaus' curve, therefore supporting the relevance of his model.
It is in this context that we produced our datasets for the following two experiments. Having at our disposal the syllabuses of courses taught during the two years of a master's degree at IMT Nord Europe, a French engineering school, we identified the number of courses available, their temporal availability, the number of knowledge taught in each course, the prerequisites for each one of them, the overall distributions of knowledge and how many courses at average a student should attend. Nonetheless, some information was missing from these syllabuses and we had to presuppose them. This was the case for the knowledge mastery that each course brings to a student. To approximate this value, we use our prior knowledge of the courses we knew and contact some of the referees of the other courses.
Thus, we have extrapolated the gathered data to define a data generation model that is representative of a five-year curriculum to produce simulated datasets. The size of the course catalog C was set in between [300; 500] courses, and the size of the catalog of knowledge K was set in between  [200; 600]. The maximal amount of prerequisites a course can have P was set in between [4; 5] and the maximal amount of knowledge taught by a course T was set up to 5. The selection of knowledge taught and used as prerequisites followed a uniform distribution and the mastery M was set in between [0. 25; 0. 75]. Finally, we set the number of courses S a student has to choose at each academic term in between [10; 20]. A dataset can therefore be expressed as the combination of C, K, T , P, S, M , plus the random seed used during computation.
We produce, for the same configuration, 30 different datasets by changing the problem seed. To reduce the combinatoric, we select C in increments of 10, K in increments of 50 and S in increments of 1. By doing so, we have produced 124740 different instances of the problem (which is roughly equal to 15 GB of experimental data). Even if our studies are based on simulated data, we have made them as close as possible to real-life scenarios. Nonetheless, we had no prior knowledge that, given a student profile and an objective, a solution whether exists or not -since this relates to directly solving FIC/DK-P. Please note that there are also some biases using simulated data (e. g. the effect of the distribution used), yet this was an essential first step in order to study FIC/DK-P. We are currently working to elaborate on a real problem instance that could be shared with the community.
We also had to randomly generate the student objective. Essentially, it is defined according to the available dataset: the prerequisites and objective requisites are taken from K, the set of all knowledge that can be taught in the institution. The amount of prerequisites and requisites was set in between [2; 4] and the expected mastery of each of these was picked in between [0. 5; 0. 95]. We attempted to convey that students should be fairly proficient in the knowledge essential for their future employment.