dataesr/scientific-paragraphs-categorization huggingface dataset:
The main sample included 136 participants speaking between 1 to 7 languages (2. 65 languages on average), and representing a relatively wide range of linguistic diversity (34 different languages in total), see Figure  1  for a visual representation of the sample's language experience. The replication sample included 68 participants speaking to up to 5 languages, 2. 44 languages on average. All MRI anatomical images were processed with FreeSurfer's brain structural pipeline  (Fischl et al. , 2004). Multilingualism was operationalized in a continuous way, without artificially dichotomizing the sample into multi-, bi-, and monolinguals (cf. DeLuca et al. , 2019; Luk and Bialystok, 2013). Given the focus on auditory brain regions and on potential associations with language distance at the phonological level, and the fact that phonological perceptual space is known to be shaped relatively early in life (e.
Auditory cortex and language experience In a first, exploratory analysis, we investigated relationships between the composite language experience measure and the volume, surface area, and average thickness of the following auditory subregions: STG, STS, HG, HS, PT and PP, as parcellated by FreeSurfer  (Destrieux et al. , 2010). Given the large size of STG and STS, we refined their segmentations by dividing the STG into anterior and posterior part, and the STS into anterior, middle, and posterior parts (see Materials and Methods, Section 4. 6 for details). Using the lme4 R package  (Bates et al. , 2015) , we fit three linear mixed models to the extracted anatomical measures (volume, surface area, average thickness), with participants modeled as random effects, language experience, region of interest (ROI), and hemisphere as fixed effects, controlling for the covariates of age, sex and whole-brain volume, area, or mean thickness.
Interaction terms for language experience, ROI, and hemisphere were included in the models to determine whether language experience would differentially affect any of the segmented regions. Significance was calculated using the lmerTest package  (Kuznetsova et al. , 2017) , which applies Satterthwaite's method to estimate degrees of freedom and generate pvalues for mixed models. Out of all investigated cortical measures, only average thickness of the PT, bilaterally, was related to participants' language experience at p =. 01, see Table  S  1. Specifically, participants with more extensive and diverse language experience had a significantly thinner PT.
Superior temporal plane and language experience Given that full TTG duplications, triplications etc. by definition belong to the PT, and that previous work has shown relationships between TTG duplications patterns and language aptitude  (Turker et al. , 2017) , we wanted to better pinpoint the location of the above effects within PT subregions. To this end, and to gain more detailed insight into the localization of the relationship between the thickness of the PT and participants' language experience, we ran three follow-up analyses, an ROI-based one, a vertex-wise based one, as well as an analysis investigating the effect of language proficiency. First, we used an automatic toolbox (TASH; Dalboni da  Rocha et al. , 2020)  to segment gyri along the superior temporal plane (HG, or 1 st TTG, and additional TTG(s), when present), and to extract their cortical thickness, surface area and volume.
1). Out of all investigated cortical measures, only average thickness of the second TTG (bilaterally) was related to participants' language experience at p <. 01, see Table  S 2. Second, to confirm the above result, we also ran a whole-brain vertex-wise analysis in FreeSurfer. We fit a general linear model to the cortical thickness surface maps of all subjects (smoothed with a 5-mm kernel), testing for a significant effect of the language experience index across the whole brain. We found one cluster of vertices negatively related to participants' language experience at p <. 0001 (uncorrected), located in the superior aspect of the left STG, We also explored whether the proficiency attained in the spoken languages was related to the thickness of the 2 nd TTG. Analyses calculating cumulative language experience based on the participants' proficiency instead of AoA yielded similar results as above, with a significant negative relationship between the proficiency-based cumulative language experience index and the thickness of the left 2 nd TTG, and a trend towards significance for the right 2 nd TTG (see Section 7.
g. VBM or FreeSurfer). Both approaches have important advantages, the former allowing for high precision in capturing details of individual variation, the latter two being far less time-consuming and labor-intensive, and therefore more applicable to large datasets. In the current study, we capitalized on recent developments in cortical segmentation efforts and used an automatic toolbox (TASH; Dalboni da  Rocha et al. , 2020)  specifically designed for fine delineation of auditory cortex gyri, and extraction measures describing their anatomy. Coupled with visual inspection of the data, TASH provides a "best-of-both-worlds" approach, by being anatomically precise while involving little manual involvement with the data (which also tends to be error-prone). The measures we obtained align with the high degree of variability reported in previous literature, but they also seem to be more sensitive, since we identify more multiplications of the TTG than previously reported.
Apart from showing that individual anatomy appears to constrain the architecture of phonological representations, our findings also support the idea that experience with typologically similar languages might be different from experience with typologically distant languages  (Antoniou, 2018; Berthele, 2020; Li et al. , 2014). Indeed, across two independent datasets, we show, for the first time, that cortical thickness of early auditory brain regions is related to the degree of one's language experience coupled with typological distance between the languages one knows. These findings indicate that early auditory regions seem to represent (or be shaped by) phoneme-level cross-linguistic information, contrary to the most established models of language processing in the brain, which suggest that phonological processing happens in more lateral posterior STG and STS.
Typological distance measures Across the two samples, the participants spoke a total of N = 36 different languages (n = 34 in the main sample, and n = 20 in the replication sample): Arabic, Bengali, Cantonese, Creole (Antiguan), Czech, Danish, Dutch, English, Farsi, Finnish, French, German, Greek, Gujarati, Hakka, Hebrew, Hindi, Hokkien, Hungarian, Irish, Italian, Japanese, Korean, Luxembourgish, Malay, Mandarin, Portuguese, Punjabi, Russian, Sindhi, Spanish, Swahili, Swedish, Swiss German, Urdu and Vietnamese. The PHOIBLE database  (Moran et al. , 2019)  and open-source software  (Dediu and Moisik, 2016)  were used to construct three measures of typological distance between the languages: distances between the distinctive phonological features describing the phonemes of each phonological inventory, distances between the sets of phonemes of the phonological inventories, and similarity in counts of phonological classes that share certain features, as detailed below.
Acoustic and articulatory features For each documented phonological inventory, PHOIBLE includes their phonemes (see Section 4. 3. 2 below) and the lower-level distinctive acoustic and articulatory features describing the phonemes. These features offer a qualitative description of differences between discrete sound categories, and are based on articulatory phonology  (Hayes, 2011). Every phoneme in every language is described by the following 37 binary features: tone, stress, syllabic, short, long, consonantal, sonorant, continuant, delayed release, approximant, tap, trill, nasal, lateral, labial, round, labiodental, coronal, anterior, distributed, strident, dorsal, high, low, front, back, tense, retracted tongue root, advanced tongue root, periodic glottal source, epilaryngeal source, spread glottis, constricted glottis, fortis, raised larynx ejective, lowered larynx implosive, click.
Phonemes Phonemes are individual speech sounds such as vowels and consonants, representing the smallest units of sound that can change the meaning of a word when substituted. Lists of phonemes belonging to each of the languages represented in the data-base were exported from PHOIBLE  (Moran et al. , 2019)  and represented as strings. For a given pair of languages A and B, a pairwise string distance between phonemes of both languages was computed with the Jaccard distance method: 1 -𝐽(𝐴, 𝐵) = |𝐴 ∩ 𝐵| |𝐴 ∪ 𝐵| representing the inverse of the ratio between the size of the intersection and the size of the union of the two sets of phonemes, and varying between 0 (both languages having exactly the same phonemes) and 1 (no phonemes in common). In addition, using the language-specific phoneme-level information, we constructed a cross-linguistic description of individual languages and used it to compute a measure of cumulative phoneme inventory for each participant.
Counts of phonological classes Both the lists of phonemes and phonological features describing them can be used to derive a more abstract, higherlevel description of individual languages in terms of which and how many phonological classes their phonological inventories contain. Using the PHOIBLE inventories and feature system,  Dediu and Moisik (2016)  proposed a comprehensive list of 167 phonological classes, along with a method for selecting a language's phonemes belonging to given classes and computing their counts. The system describes how many "segments", "consonants", "vowels", "diphthongs" etc. a given language has, and counts further classes that share certain features (e. g. , "bilabial consonants", "front vowels", "clicks", see Supplementary Information, Section 7. 8 for the full list).
e. , where the two languages have exactly the same phonological systems) to 1 (i. e. , the two languages have completely different phonological systems). The SYNCSA R package  (Debastiani and Pillar, 2012 ) (https://rdrr. io/cran/SYNCSA/) was used to perform calculations. For a similar methodology, but applied to lexical distance measures, see  Kepinska et al. (2023).
Neuroimaging data acquisition and processing For all participants, structural MRIs with 176 sagittal slices were acquired using a T1-weighted three-dimensional (3D) Modified Driven Equilibrium Fourier Transform (MDEFT) sequence. The resulting images had a matrix size of 256 × 224, yielding a final resolution (voxel size) of 1 × 1 × 1 mm. Repetition time (TR)/echo time (TE)/inversion time (TI) was: [12. 24/3. 56/530 ms] for the main sample (from a 1. 5T Siemens Sonata scanner), and 7. 92/ 2. 48/910 ms for the replication sample (from a 3T Siemens Trio scanner). The T1 images were denoised using the spatially adaptive nonlocal means filter (SANLM;  Manjón et al. , 2010)  in MATLAB within the CAT12 toolbox. The images were then processed with FreeSurfer's (version 7. 2) brain structural pipeline  (Fischl et al.
Parcellation of the auditory regions The reconstructed surfaces were parcellated into regions using an atlas-based procedure  (Destrieux et al. , 2010). The auditory regions were delineated using the following labels from the Destrieux atlas: planum polare of the superior temporal gyrus (G_temp_sup-Plan_polar; PP), anterior transverse temporal gyrus (of Heschl) (G_temp_sup-G_T_transv; referring to HG, or to the anterior-most TTG), transverse temporal sulcus (S_temporal_transverse; HS), planum temporale, or temporal plane of the superior temporal gyrus (G_temp_sup-Plan_tempo; PT), superior temporal sulcus (parallel sulcus) (S_temporal_sup; STS), and lateral aspect of the superior temporal gyrus (G_temp_sup-Lateral; STG). The STG and the STS labels were further divided into smaller regions using Free-Surfer's freeview software and FreeSurfer command line tools.
Cumulative phoneme inventory and the second TTG The two models including the 'cumulative phoneme inventory' measure were statistically significant: (1) Left hemisphere: F(5,124) = 8. 308, p <. 001, and (2) Right hemisphere: F(5,90) = 3. 42, p = 0. 007, see further Table  S 3     ) for the average cortical thickness of the second TTG (left and right), as predicted by the "cumulative phoneme inventory" index. For comparison, models with the cumulative language experience measure not accounting for typology, and cumulative language experience weighted by overlaps between languages at the level of phonemes are also reported. Last two rows present model comparison results (additional variance explained and BF10 and BF01 values are also reported). 7. 5 Language experience in participants with a single TTG Kolmogorov-Smirnov normality tests were run on all models' residuals, revealing that parametric linear models were appropriate for the present data (all ps >.
Acknowledgments This work was supported by the  Wellcome Trust  (grant numbers  203147/Z/16/Z  and  205103/Z/16/Z ) to CJP. The authors gratefully acknowledge support by the  NCCR Evolving Language, Swiss National Science Foundation (SNSF)  Agreement # 51NF40_180888 , and by the  SNSF  grant # 100014_18238. OK was funded by the  Marie Jahoda Stipendium from University of Vienna.
2008)  to the assembly of the 21 chromosome sequences of cultivar CHINESE SPRING  (IWGSC 2018). Since the first release in 2018, the IWGSC integrated additional information coming from optical mapping and long reads in order to improve the quality of the assembly by correcting misordered scaffolds and filling gaps. This led to release RefSeq v2. 0 and v2. 1 in 2021  (Zhu et al. 2021). Besides the methodological challenge of assembling this genome, the work performed to deliver an annotation is not well known and often poorly considered. Annotation consists of the identification of sequence features providing biological information, and it represents one of the most difficult tasks in genome sequencing projects. It is far from being obvious. However, annotation is the data mostly accessed by users, contrary to the genome sequence.
0 and the updates since the first version. Besides the description of the work performed, this chapter is also a current opinion to consider the degree of approximation, the limits Annotation relies on the combination of approaches: (i) the homology-based method using alignment/mapping algorithms searching for sequence similarity either with proteins, showing that a sequence is conserved across evolution, and/or transcriptomic data, showing that a sequence is expressed; (ii) the ab initio methods, i. e. , predictions using statistical models such as hidden Markov models (HMMs); (iii) structural feature-based method through the identification of intrinsic information like motifs at the borders of transposons. It thus relies on a combination of software, algorithms, and adapted reference libraries.
It was launched individually on each scaffold (or chunks for large ones) of RefSeq v1. 0 in parallel while positions of features were subsequently calculated on pseudomolecules. The different steps and tools launched by the pipeline are described below: • Step 1: TE annotation and sequence masking. TEs were identified by similarity-search using CLARITE and ClariTeRep  (Daron et al. 2014). CLARITE used RepeatMasker with cross_match as search engine for optimized accuracy  (Smit et al. 1996 (Smit et al. -2004)). Nucleotides assigned to TEs were then masked so that the following steps, i. e. , ab initio predictions and similarity-searches, were all performed on the masked genome sequence. • Step 2: Gene modeling. Ab initio gene models were predicted using two gene finders previously trained with a wheat gene dataset: FGeneSH (http://linux1.
softberry. com/ berry. phtml) and AUGUSTUS  (Stanke et al. 2006). Evidence-driven gene predictions were also computed following three different strategies giving different weights to protein and transcript similarities. The first approach was based on homology with proteomes of related species. Similarity-search was performed using BLAST  (Zhang et al. 2000)  and significant hits, filtered with finetuned thresholds, were then used for splicedalignment using EXONERATE  (Slater and Birney 2005)  ). This approach is well suited to precisely determine the obvious structure of a large fraction of the protein-coding genes by taking advantage of their evolutionary conserved nature. However, the main limit here was the lack of similarity at the protein extremities which may lead to incomplete alignment that prevents from finding the start and/or stop codons.
Thus, TriAnnot utilized an iterative extension in order to identify in-frame start and stop codons for gene modeling. Models with partial structure were flagged pseudogenes. Gene models not supported by homology with Poaceae proteins or by transcription evidence were simply discarded (i. e. , ab initio only). Models sharing similarity with known proteins and for which splicing sites were supported by transcript evidence were classified as high confidence. Low-confidence genes also share similarity with known proteins and transcripts but lack support for some splicing sites and/or position of start/stop codons. Finally, genes sharing similarity with known proteins but over less than 70% of the length of its best BLAST hit were classified as pseudogenes. Thus, TriAnnot predicted 107,226 gene models: 65,884 HC and 41,342 LC genes, plus an additional 73,044 pseudogenes on the IWGSC RefSeq v1.
PGSB Gene Prediction Pipeline The procedure implemented in the PGSB annotation pipeline differs in many aspects from that of TriAnnot. It is based on mapping all available evidence on unmasked genome sequence and filtering out TE-related predictions afterward. It was all evidence-driven, not using any ab initio gene finder. Then, TransDecoder (https://github. com/ TransDecoder/TransDecoder/) was used to predict the coding frame for each transcript while considering the most upstream start codon by default. These predictions were then aligned against a set of reference proteins from angiosperms in UniProt, and protein domains were also searched for. These data were given to TransDecoder for selecting the most probable CDS for each model. Since TEs were not masked prior to mapping evidence, PGSB predictions were filtered out afterward based on similarity-search with TE-related proteins from the PTREP library (https://botserv2.
g. , a penalty was applied to introns larger than 10 kb. After selecting the representative model, Mikado was used to identify additional high-quality alternatively spliced transcripts, only those that met a series of stringent requirements. The most important were: a CDS overlapping at least 60% of the representative CDS, without any retained intron, and with only verified exon/intron junctions. Eventually, to enrich the annotation, coordinates of UTRs were added based on comparing models and aligned transcripts with PASA  (Haas et al. 2008).
The idea was to provide a single filtered dataset of HC genes to people only interested in large-scale whole-genome analyses while keeping information of LC genes to people interested in the characterization of a particular region. First classification parameter was the completeness of the model, i. e. , the presence of both a start and a stop codon. HC genes were complete with significant homology with plant (Magnoliophyta) proteins retrieved from Swiss-Prot and TrEMBL. LC genes were, either complete but without significant homology with plant proteins or, incomplete with or without significant homology. The 269,428 gene models were split into 107,891 HC (40%) and 161,537 LC (60%) protein-coding genes. The number of HC genes was much closer to the expected value for plants (~ 35,000 genes per haploid genome), and this became the reference dataset used by the community.
Finding Homeologous Groups Based on HC Genes Only Can Lead to False Conclusions and Highlights the Requirement of Considering LC Genes Considering the conclusion of the latter paragraph, it implies that comparing the A-B-D gene repertoires was strongly impacted by the input gene dataset. Homeologous groups were inferred from gene trees. Initially, trees were built with the complete set of HC and LC genes which revealed that considering HC genes only led to considerably overestimate the level of variability between A-B-D subgenomes, because many LC genes were, in fact, orthologous to HC genes (i. e. , homeologous in the hexaploid) even though functional annotation revealed that some LC genes represented mis-predicted TE-genes (e. g. , transposase-like genes). The solution adopted was to work on a filtered gene dataset: 181,036 genes (103,757 HC and 77,279 LC genes; instead of 269 k initially) that do not correspond to either TE-related functions or to pseudogenes.
No Evidence of Any Biased Gene Fractionation and Importance of Gene Duplications Regarding gene presence/absence, no evidence for biased partitioning was observed  (IWGSC 2018). In contrast, comparisons support gradual loss/duplications that have occurred after A-B-D divergence in the diploid, tetraploid ancestors, and after hexaploidization event in modern bread wheat. Before gene loss, a gene may lose function because of silencing or change in expression, so that the first evidence of diploidization might be observed at the expression level. Hence, RNASeq data analyses showed that there was an equal contribution of the three homeologous genomes to the overall gene expression, demonstrating the absence of global subgenome dominance (IWGSC 2014).
TE Modeling Given the amount of TEs shaping the wheat genome, predicting the presence of TE copies along assembled sequences has always been a prerequisite to avoid false predictions of coding genes that are in fact coding parts of TEs. Efforts to manually annotate TEs with their precise borders were made since the beginning of wheat BAC sequencing and a high-quality reference databank of wheat TE sequences was initiated in 2002 with TREP  (Wicker et al. 2002)  and completed in 2014 with the ClariTeRep library  (Daron et al. 2014 ) (which includes TREP). ClariTeRep originated from manual curation of ~ 3200 TEs along the first large (Mb-sized) contiguous sequences produced on chromosome 3B  (Choulet et al. 2010). This implies that the wheat TE library used for similarity-search might be biased toward elements from the B-subgenome, and depleted for A and D subgenomes.
RefSeq V1. 0 Functional Annotation Gene ontology terms, PFAM, and InterPro domains were assigned to gene models. A function was assigned to 82% (90,919) of HC genes in RefSeq Annotation v1. 0. RNASeq-based transcription evidence was found for 85% and 49% of HC and LC genes, respectively. In addition, naming of gene function for each gene was performed by using the AHRD tool (Automated Assignment of Human Readable Descriptions, https://github. com/groupschoof/AHRD, version 3. 3. 3). This program generates informative functional annotations from BLAST outputs while avoiding retrieving too many "unknown" or "uncharacterized" functions. BLAST outputs against the following databases were parsed by AHRD: Swiss-Prot, Arabidopsis Araprot 11, and a subset of TrEMBL for Viridiplantae. A filter was then applied in order to discard genes with functions related to TEs.