dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014). Despite the wide acceptance of MNE-Python in the scientific community, we limited our study to examine the variability of results obtained by the most commonly used Matlab-based tools. Thus, the three tested tools are developed under the same environment and using the same language. We would also highlight that the scope of the paper is limited to open-source packages although the ability of many distributed commercial software to reproduce the same preprocessing pipeline (such as BESA, Curry, and PRANA, Netstation, BrainVision Analyzer). Our choice relied on the opportunity provided by open-source tools to implement, customize and modify the script functions with no upfront financial costs. In addition, it is unclear how findings reported in this paper would generalize to other datasets or experimental paradigms.
Therefore, it would be interesting to evaluate the fluctuations of results on other datasets and tasks covering further preprocessing pipelines and steps. For instance, one may examine whether the effect of the preprocessing is more or less important in a task-free compared to task-related paradigms. In addition, one important preprocessing step that needs to be included in further preprocessing pipelines is the artifact detection method. Finally, a multiverse analysis (https://journals. sagepub. com/ doi/10. 1177/1745691616658637) of a data preprocessing pipeline examining the impact of a large set of analytic choices might be also useful to researchers to determine the effects of different decisions.
Conclusions This study sheds light on how the software tool used to preprocess EEG signals impacts the analysis results and conclusions. EEGLAB, Brainstorm and FieldTrip were used to reproduce the same preprocessing pipeline as a published EEG study performed on 500 participants. While the three software tools succeeded to infer the same conclusion of the original publication regarding the effect size estimates related to the derived ERP features and the peak latency of the obtained ERP, we observed significant differences in terms of the observed absolute voltage between EEGLAB, Brainstorm and Fieldtrip results, as well as between each of the software tools and the original results. Minor statistical differences were detected between tools in terms of ERP difference between conditions. In addition, the use of standardized pipelines documented in major software environments is more recommended to reduce variability in results, rather than developing custom pipelines.
2  and Extended Data Fig. 6 ), and we hypothesize that inversions are major drivers of centromere evolution. Inversions can displace parts of functional centromeres, resulting in centromere shifts. The most notable example of a centromere shift was found on chromosome 4A, which was highly rearranged between TA299 and TA10622 (Fig. 2b , Extended Data Fig. 6  and Supplementary Fig. 11 ). TA10622 had one distinct region with a high density of RLG_Cereba insertions. The youngest of these insertions coincided with the highest CENH3 signals. By contrast, TA299 showed two regions with high RLG_Cereba insertion densities separated by around 10 Mb. The region with the youngest insertions overlapped with the current functional centromere (as identified by high CENH3 signals), whereas the second region contained a fragment of the ancestral centromere (Fig.
Plant materials The plant material used in this study was selected from a collection of 733 accessions of wild and domesticated einkorn held at the Wheat Genetics Resource Center (WGRC) at Kansas State University 42. To generate reference assemblies, we selected one domesticated einkorn accession (T. monococcum L. subsp. monococcum; TA10622) collected in Albania and one wild accession (T. monococcum L. subsp. aegilopoides; TA299) collected in northern Iraq. These two accessions were selected on the basis of genotyping-by-sequencing data  42  as representative accessions within the respective clade (domesticated and wild race α), had low heterozygosity and available passport information. For whole-genome sequencing, a panel of 219 accessions (Supplementary Table  8 ) was selected on the basis of diversity and geographical origin  42.
Data processing was performed using the Bionano Solve v. 3. 6 software (https:// bionanogenomics. com/support/software-downloads). Omni-C library construction and sequencing. Omni-C libraries were prepared using the Dovetail Omni-C Kit for plant tissues according to the manufacturer's protocol. In brief, chromatin was fixed in place in the nucleus from 100 mg of dark-treated young leaves. An in situ digestion of the fixed chromatin was performed using an endonuclease enzyme. Chromatin was then released by lysing the cells. Chromatin ends were repaired and ligated to a biotinylated bridge adapter to capture contacts, before proximity ligation of the adapter-containing ends. After proximity ligation, cross-link-reversal and DNA purification from proteins was performed. The purified DNA was treated to remove biotin that was not internal to ligated fragments.
Two sequencing libraries were generated using Illumina-compatible adapters for each accession. Biotin-containing fragments were isolated using Streptavidin beads before PCR enrichment of the library. The two libraries were sequenced on the Illumina HiSeq X platform. Around 400 million paired-end reads (2 × 150 bp) were generated. Genome assembly. PacBio HiFi reads were assembled using hifiasm (v. 15. 1)  61  with the default parameters (https://github. com/chhylp123/ hifiasm/) to generate primary contig assemblies. We generated hybrid scaffolds by combining contig assemblies and optical maps using the hybridScaffold pipeline (Bionano Solve v. 3. 6) with the default parameters. We then integrated Omni-C read data to produce pseudomolecule assemblies using Juicer (v. 1. 6; https://github. com/aidenlab/juicer)  62  and the 3D-DNA pipeline (https://github.
5. 0. 0)  65  with the plant dataset (poales_odb10). Moreover, we generated Illumina short reads (150 bp paired-end reads, ~40-fold coverage) from leaf tissues of TA299 and TA10622 to evaluate the assemblies. Merqury (v. 1. 3) 66 was used to estimate the assembly consensus quality (QV) and completeness on the basis of the comparison of k-mers.
Marker discovery and genotyping of RILs. Both the TruSeq and Nextera libraries were sequenced on the Illumina HiSeq X10 system with 2 × 150 bp reads (Psomagen). We used custom Perl scripts for demultiplexing raw FASTQ files (https://github. com/sandeshsth/SkimSeq_ Method) obtained from Nextera sequencing  67  and TruSeq (https:// github. com/sandeshsth/Fastq). Adapters and primers were trimmed using fastp  68. Trimmed high-quality reads from the two parents were aligned to the TA299 and TA10622 assemblies separately using SAMtools  69  (v. 1. 8) and variants were called using BCFtools (v. 1. 9)  70. Variants were filtered for minimum and maximum filtered read depths of ≥6 and ≤100, respectively, and reference and alternative allele depths of ≥3. Missing and heterozygous genotypes called in either RIL parents were removed.
Genetic linkage map construction. The genetic linkage maps were constructed using JoinMap (v. 5. 0; https://www. kyazma. nl/index. php/ JoinMap/) using the bins as markers. We filtered the markers for missing data (removed >30% missing) at the population level. In JoinMap, we removed identical markers (similarity = 1) and mapped only one marker of the identical pair. We grouped the markers using minimum LOD of 6 and the markers were mapped using a regression mapping approach and the Kosambi function. The linkage maps were visualized using Mapchart (v. 2. 32; https://www. wur. nl/en/show/mapchart. htm). Linkage maps were constructed using this approach with both wild and domesticated einkorn assemblies. Comparing genetic maps to TA299 and TA10622 assemblies. We visually compared the marker order of the genetic map to the two einkorn assemblies and corrected discrepancies if all of the following three criteria were met: (1) The genetic distance between two mis-ordered loci was ≥0.
Genome annotation Transcriptome sequencing. Around 100 mg of frozen and ground tissues from roots, whole aerial parts at the seedling stage, flag leaves, fully emerged spikes, glumes and grains were used for RNA isolation using the Maxwell RSC Plant RNA Kit (AS1500) and the Maxwell RSC 48 instrument according to the kit protocol (Promega). For RNA-seq, around 10 Gb of Illumina 150 bp paired-end reads were generated for each tissue. PacBio Iso-seq SMRTbell libraries were constructed according to the standard isoform sequencing protocol (Pacific Biosciences, 101-763-800). Full-length complementary DNA was synthesized from total RNA from the six tissues separately using NEBNext Single Cell/Low Input cDNA Synthesis & Amplification Module (New England Biolabs, E6421S). The ProNex Size-Selective Purification System (Promega, NG2001) was used for size selection.
Gene model prediction. For both TA299 and TA10622, gene model prediction was performed according to a previously described method  72  with minor modifications, combining transcriptomics data, ab initio prediction and protein homology. First, TA299 and TA10622 RNA-seq data from the six tissues were mapped to their respective reference assemblies using STAR  73  (v. 2. 7. 0f; parameters: --outFilterMismatchNoverReadLmax 0. 02) and assembled into transcripts with StringTie 74 (v. 2. 1. 4; parameters : --rf -m 150 -f 0. 3 -t). Iso-seq data were mapped using minimap2  75  (v. 2. 21; parameters: -ax splice -uf -secondary=no -C5) and the redundant isoforms were further collapsed into transcript loci using cDNA_Cupcake (v. 12. 4. 0; http://github. com/Magdoll/cDNA_Cupcake; parameters: --dun-merge-5-shorter).
The RNA-seq and Iso-seq transcripts were merged using StringTie  74  (v. 2. 1. 4; parameters: --merge -m 150) for each accession into a pool of candidate transcripts and Transdecoder (v. 5. 5. 0; https://github. com/ TransDecoder/TransDecoder) was used to find potential open reading frames and to predict protein sequences within the candidate transcript set. For the ab initio gene predictions, we used BRAKER2 (v. 2. 1. 2)  76  and FgeneSH (v. 8. 0. 0; http://www. softberry. com). In brief, BRAKER2 gene prediction was trained supported by RNA-seq and Iso-seq data (parameters: --softmasking --gff3 --cores=48 --nocleanup --bam='list of BAM files'). For the FgeneSH prediction, the TA299 and TA10622 pseudomolecules were repeat masked using a de novo repeat library constructed using the EDTA pipeline  77  and the TREP database  78 (v.
19). FgeneSH annotation was performed using the monocot matrix for the gene prediction. For the protein homology evidences, we used the translated proteins from gene annotations of T. urartu  79  , Aegilops tauschii  80  , wild emmer (Zavitan)  81  , hexaploid wheat (Kariega 72 and ArinaLrFor  23  ), barley (Morex v. 3)  82  , the related grass species Brachypodium distachyon  36  and rice  83  , and the Triticeae and Poaceae protein sequences downloaded from the UniProt database (2021_03). All protein sequences were mapped against the TA299 and TA10622 assemblies using Genom-eThreader  84  (v. 1. 7. 1; parameters: -startcodon -finalstopcodon -species rice -gcmincoverage 70 -prseedlength 7 -prhdist 4 -gff3out). We used EVidenceModeler  85  (v. 1. 1. 1) to join all of the gene evidences from transcriptomics, ab initio predictions and protein alignments with weights adjusted according to the input source (FgeneSH = 2; BRAKER2 = 1; protein homology = 6; transcriptomics = 12).
Finally, we performed two rounds of isoform and UTR prediction using the PASA pipeline (v. 2. 5. 1)  86  with the default parameters. Gene models were classified into high and low confidence according to the classification criteria used by the International Wheat Genome Sequencing Consortium  24  and a previous study  82. In brief, protein-encoding gene models were considered to be complete when start and stop codons were present. A comparison against PTREP  78  , UniPoa (Poaceae database of annotated proteins from UniProt_2021_03) and UniViri (Viridiplantae database) was performed using DIAMOND  87  (v. 2. 0. 9) and a BUSCO (v. 5. 2. 2) analysis against the poales database (v. 10; parameters: -m prot -c 20 -l poales). Gene candidates were further classified using the following criteria: a high-confidence (HC) gene model is complete with a hit in the UniViri database and/or in UniPoa and/or BUSCO poales database and not PTREP.
A low-confidence (LC) gene model is incomplete and has a hit in the UniViri or UniPoa or BUSCO poales database but not in PTREP, or the protein sequence is complete with no hit in UniViri, UniPoa, BUSCO poales and PTREP. Putative functional annotations were assigned to HC and LC transcripts using a protein comparison against the UniProt database (2021_03) and PFAM domain signatures and Gene Ontology were assigned using InterproScan 88 (v. 5. 55-88. 0).
To determine the TE content of einkorn centromeres, centromeric DNA was annotated using the automated TE annotation software EDTA  77  with TREP as curated input library  78. For the analysis of genes in centromeres, all annotated genes that showed homology to TEs were removed, as it is very probable that they are TEs that were annotated as genes by mistake. RLG_Cereba dating. Full-length copies of RLG_Cereba and RLG_Quinta retrotransposons were identified using our previously described pipeline  105. This was done in addition to the automated TE annotation described above to extract high-quality datasets of full-length TEs. The insertion ages of full-length LTR retrotransposons were determined on the basis of the divergence of the two LTRs, which are identical at the time of insertion, and which accumulate mutations over time  106.
This produced information on insertion age and precise chromosomal location for each full-length RLG_Cereba retrotransposon. Chromosome collinearity and similarity analysis. Collinearity of chromosomal segments was visualized using 1 kb sequence segments of one chromosome in BLASTN searches against the other chromosome. The positions of the top BLASTN hits were used for the dot plot alignments. A sliding step of 10 kb was used. The chromosome comparison was done using the original Perl script blast_compare_chromosome that is available at GitHub (https://github. com/Wicker-Lab/Monococcum_ genome_scripts). To complement our homology and annotation-based repeat analysis, we used the ChIP-Seq mapper tool, which is part of the RepeatExplorer2 software collection. First, repeats were identified by clustering short sequencing reads with RepeatExplorer2, which does not depend on the reference genome and would therefore also identify repetitive elements missing from the reference.
For this, around 20× coverage Illumina short reads were downsampled to the recommended coverage equivalent of 0. 5× using seqkit (https:// github. com/shenwei356/seqkit). CENH3 ChIP and control reads were then mapped onto the identified repeat clusters using the ChIP-Seq mapper tool. Two repeat clusters passed the threshold of greater than fivefold enrichment. The unique sequences contained in these two clusters were then queried using BLASTN searches against the nrTREP20 repeat database. In one of the clusters, all 38 sequences showed very high homology to either the RLG_Cereba or RLG_Quinta consensus sequence, whereas, in the other cluster, 37 out of 42 sequences were RLG_Cereba or RLG_Quinta sequences. Thus, ChIP-Seq mapper did not identify any additional repeat clusters enriched in CENH3 that were not found in the homology and annotation-based repeat analysis.
Acknowledgements We thank the members of the  KAUST Bioscience Core Laboratory  for sequencing support;  E. Cavalet-Giorsa  for providing information on einkorn domestication and migration;  L. Aouini  for assistance with RNA extraction;  L. Zou  for greenhouse support;  B. Gill  for providing seeds of the tin3 mutant; and  T. Quilichini  for SEM images. We acknowledge support by the plant growth facility and the  GENTYANE  platform of the  Clermont-Ferrand INRAE Centre  for assisting in NGS sequencing; the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  (Bioinfo Genotoul, http://bioinfo. genotoul. fr) and the  KAUST supercomputing  facilities (https://www. hpc. kaust. edu. sa) for providing computing resources; GrainGenes resources for hosting the online database; and the  University of Maryland supercomputing  resources (http://hpcc.
umd. edu) for developing the einkorn database and tin3 analysis. This publication is based on work supported by the  King Abdullah University of Science and Technology , the  UK Biotechnology and Biological Sciences Research Council (BBSRC ;  BB/P016855/1 ), the  Mexican Consejo Nacional de Ciencia y Tecnología (CONACYT ;  2018-000009-01EXTF-00306 ), the  Global Institute for Food Security  (to R. D. ), the  European Research Council  ( ERC-2019-COG-866328 ) and the  United States Department of Agriculture National Institute of Food and Agriculture (USDA-NIFA ; award  2020-67013-31460 ). D. -H. K. was supported by  WGRC/IUCRC  and  NSF  (grant  1822162 ).
Data availability The raw sequencing data used for de novo genome assemblies, the RNA-seq and Iso-seq data for the annotation, the ChIP-seq reads and the whole-genome sequencing reads of 218 einkorn accessions are available at the EBI-ENA under study number PRJEB61155. The two reference assemblies, annotations, VCF files and CpG methylation frequencies are available at DRYAD (https://doi. org/10. 5061/dryad. v41ns1rxj). Raw fastq files and demultiplexed fastq files of the RIL population have been deposited at the National Center for Biotechnology Information (NCBI) SRA database under BioProject accession PRJNA879879. The barcode indices key file with required information for demultiplexing can be obtained at DRYAD (https://doi. org/10. 5061/dryad. v41ns1rxj). CENH3 BED files (CENH3 peaks) and mapped files (.
bam) of CENH3 and H3K4me3 for all replicates are available through the Dryad database (https://doi. org/10. 5061/dryad. 0p2ngf24b). Whole-genome sequencing data of the tin3-mutant bulk have been deposited at GenBank under BioProject PRJNA938447. IBSpy variations tables of the 218 einkorn accessions, and MUMer alignments of the two einkorn assemblies against the ten bread wheat assemblies are available online (https:// opendata. earlham. ac. uk/wheat/under_license/toronto/Uauy_2022-09-24_IBSpy_Triticum_monococcum_introgressions/). An interactive webpage has been developed for this study to visualize various genome characteristics of TA299 and TA10622. The webpage features a JBrowse.
Code availability Custom scripts for variant calling are available at GitHub (https://github. com/IBEXCluster/Wheat-SNPCaller). The necessary codes and steps to genotype the RIL population were published previously  67  and are available in a separate file on the DRYAD database (https://datadryad. org/ stash/share/v20dkVsSTj3toGn-CHG92eUSgre17uMT5AH_6LE2GDM). Codes and custom scripts for analysing einkorn introgressions into bread wheat are available at GitHub ( ht tp s: // gi th ub. c om /U au y-La b/ mo no co cc um _introgressions).
explorer enabling visualization of the whole genome, gene models, transposable elements, variants positions and synteny. For the identification of homologous sequences in other wheat varieties, a BLAST server has been set up. This BLAST server enables searches against individual wheat subgenomes and chromosomes independently. Synteny between TA10622 and other wheat genomes can be visualized in the synteny tab located on the webpage. The database can be accessed online (https://wheat. pw. usda. gov/GG3/pangenome). Source data are provided with this paper.
As alien invasive species are a key driver of biodiversity loss, understanding patterns of rapidly changing global species compositions depends upon knowledge of invasive species population dynamics and trends at large scales. Within this context, the Ponto-Caspian region is among the most notable donor regions for aquatic invasive species in Europe. Using macroinvertebrate time series collected over 52 years  at 265 sites across 11 central and western European countries, we examined the occurrences, invasion rates, and abundances of freshwater Ponto-Caspian fauna. We examined whether: (i) successive Ponto-Caspian invasions follow a consistent pattern of composition pioneered by the same species, and (ii) Ponto-Caspian invasion accelerates subsequent invasion rates. In our dataset, Ponto-Caspian macroinvertebrates increased from two species in 1972 to 29 species in 2012.
Our study investigates the presence of Ponto-Caspian species in central and western Europe over time. For this, we use a comprehensive dataset of macroinvertebrate time series and investigate trends in the distribution of alien species, their population growth and rates of spread, as well as possible underlying mechanisms modulating their establishment success. To our knowledge, this endeavor represents the largest study to investigate the spatio-temporal accumulation of Ponto-Caspian species and their relative abundance, as well as their potential impact. We hypothesized that: (i) the number and dominance (expressed as relative abundances) of Ponto-Caspian macroinvertebrate species across available time series in primarily central and western Europe has increased over time; (ii) there is an inverse relationship between the numbers of these Ponto-Caspian species and the distance to their native ranges; and (iii) Ponto-Caspian macroinvertebrate invasions result in reduced time intervals between successive invasions.
Data collection To investigate the presence of Ponto-Caspian species in central and western European freshwater streams and rivers, we used a recently collated database containing 1816 macroinvertebrate time series from 22 European countries  (Haase et al. in review; Haubrock et al. 2022b; Soto et al. 2023a). We define the Ponto-Caspian region as the Black, Azov, and Caspian Seas, as well as the adjacent lagoons and lower river stretches (Fig. 1 ). Each time series contained samples collected in a minimum of eight, not necessarily consecutive, sampling years. Individual time series were sampled from the same geographic location (hereafter, site) in all years. Survey methods (e. g. kick sampling) varied among time series, but were kept constant within each time series (Table  S1 ). Each time series was harmonized to have a comparable sampling effort over time, with sampling occurring once annually within three consecutive months and consistent species-level taxonomic resolution.
2019). This approach was applied to all Ponto-Caspian macroinvertebrates combined, and for the key Ponto-Caspian taxa i. e. amphipods, bivalves, and gastropods, individually. These analyses were performed using the rma. mv function of the metafor R package (version 3. 8-1; Viechtbauer 2010). The same approach was repeated using the richness of all Ponto-Caspian species and within each key taxa in place of raw abundances. Due to an insufficient number of Ponto-Caspian bivalves (n = 2) and gastropods (n = 1), the meta-regressions were not performed on these taxa. Additionally, for each time series, we extracted the first recorded occurrence of any alien invasive species, and the first occurrence of each Ponto-Caspian species. We then plotted the cumulative richness as an estimate of invasion rates, using the stat_ecdf function in the ggplot2 R package  (Wickham et al.