dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION We identified five ancestral groups involved in the origin of the analyzed M. acuminata accessions based on sequence data from 14 M. acuminata wild and nine 'AAcv' cultivated banana diploid accessions, one triploid 'AAA' cultivar and one diploid M. balbisiana accession as outgroup. Four of these ancestral groups corresponded to previously characterized M. acuminata subspecies, i. e. ssp. banksii/ssp. microcarpa, ssp. malaccensis, ssp. zebrina and ssp. burmannica/ssp. siamea. The fifth one, found only in cultivars, corresponded to one or two new unknown origins. Our results revealed diverse and always more complex than anticipated inter(sub)specific chromosome mosaic patterns, involving four to five ancestral groups for some cultivars, and implying multiple hybridization steps.
We showed that this 'Pisang Madu' ancestral group was present in several of the 'AAcv' analyzed here. It was also present in the 'Grande Naine' accession, a representative of the Cavendish dessert banana subgroup that accounts for 50% of the bananas produced worldwide. This ancestral group is thus an important contributor to modern-day cultivars and should be characterized to facilitate its use in banana breeding programmes. The 'Pisang Madu' accession is one of several apparently similar accessions that were surveyed in Sarawak State on the island of Borneo (Malaysia) and in Perak State in continental Malaysia in 1960/1961  (Rosales et al. , 1999). Based on available passport data (https://www. crop-dive rsity. org/mgis/accession/01GLP005109, (Ruas et al. , 2017) ,  (Rosales et al. , 1999) ), the accession studied here corresponded to one of the Sarawak State accessions.
EXPERIMENTAL PROCEDURES Transcriptome dataset A set of 23 diploid M. acuminata accessions comprising 14 seedy and nine parthenocarpic accessions (Table  1 ) was selected to represent the wild M. acuminata contributors to cultivated bananas and different groups of diploid cultivars ('AAcv'). Plant materials were collected from field grown plants maintained in the CIRAD collection hosted by CRB Plantes Tropicales Antilles CIRAD-INRA in Guadeloupe (France), except for five accessions that were from the Collection Musac ees of CARBAP in Cameroon. Leaf, flower and fruit tissues from each accession were harvested and separately stored in RNAlater â solution (QIAGEN, https://www. qiagen. com). Total RNA was extracted as described in  Jourda et al. (2014)  with lithium chloride 2 M precipitation for fruit samples.
For each accession, one pool of 65% flower RNA, 20% fruit RNA and 15% leaf RNA was used for library construction, except for the 'Banksii ITC0620' accession (for which only flowers and fruit were used) and 'Khae (Phrae)' and 'Pa Rayong' accessions [for which flowers (80%) and leaves (20%) were used]. RNA sequencing was performed using the Illumina Tru-SEQ RNA Kit (Illumina Inc, https://www. illumina. c om/) and the Illumina mRNA-seq paired-ends protocol on a HiSeq2000 sequencer for 2 9 100 cycles. Sequence data for the 'Mala' accession were pooled from two sequencing experiments. Data from 10 of the wild accessions were part of the study of  Clement et al. (2017).
Genomic dataset Raw reads from the draft B genome sequence of M. balbisiana accession 'Pisang Klutuk Wulung'  (Davey et al. , 2013)  were retrieved from the NCBI Sequence Read Archive (ID: SRR956987). Leaf material of the triploid 'Grande Naine' accession (Cavendish subgroup, 'AAA' genome) was collected from field grown plants hosted in the CRB Plantes Tropicales Antilles CIRAD-INRA collection in Guadeloupe. Total DNA was isolated using a modified MATAB method  (Risterucci et al. , 2000)  and was used to construct a 5-kb insert mate-pair library that was sequenced with the Illumina HiSeq platform at GENOSCOPE (http://www. genoscope. cns. fr) to obtain 2 9 101 bp paired-end reads.
Read filtering RNA-seq read filtering. For each library, adaptor sequences were removed from raw paired-end reads using the Cutadapt program  (Martin, 2011). Cutadapt was also used to remove the first seven bases of each RNA-seq read in which nucleotide-specific mismatch errors may occur  (van Gurp et al. , 2013). Reads were filtered based on a minimum length of 35 and a mean base quality of 30 using arcad_hts_2_Filter_Fastq_On_Mean_Quality. pl and ar-cad_hts_3_compare_fastq_paired_v5. pl programs available on the South Green Bioinformatics platform (https://github. com/South GreenPlatform/arcad-hts). © 2020 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd, The Plant Journal, (2020), 102, 1008-1025 DNA-seq read filtering. For each library, reads were trimmed from both ends until a base quality ≥20 was reached.
In this last mapping step, for each accession, paired-end reads and singleend reads were aligned independently. Resulting 'sam' files were merged using PICARD Tools v2. 7. 0 (http://broadinstitute. github. io/ picard). For each accession, redundant reads were removed using MARKDUPLICATE from PICARD Tools v2. 7. 0 and reads were split into exon segments using the SPLITNCIGARREADS tool of GATK  v3. 3 (McKenna et al. , 2010). Split reads were locally realigned around indels using GATK INDELREALIGNER. DNA-seq. Filtered DNA-seq reads were aligned to Musa reference genomes comprising the M. acuminata nuclear reference sequence version 2  (Martin et al. , 2016) , 12 mitochondrial scaffolds and the chloroplast genome sequence  (Martin et al. , 2013)  using BWA v0. 7. 15 with the mem algorithm  (Li, 2013).
Reads aligning at several positions were removed using SAMTOOLS v1. 3  (Li et al. , 2009). For each accession, redundant reads were removed using MarkDuplicate from PICARD Tools v2. 7. 0. Reads were locally realigned around indels using the GATK v3. 3 INDELREALIGNER. Variant calling. For each accession, at each covered position, all mapped bases with a mapping quality ≥ 10 were counted with the bam-readcount program (https://github. com/genome/ bam-readcount). Variant sites were filtered according to the following criteria: (i) only data points covered by at least 10 reads were considered; (ii) only variant alleles supported by at least three reads and having a frequency ≥0. 05 in at least one accession were kept; and (iii) sites showing at least one variant were kept for variant calling. For each accession and at each variant site, a genotype was called based on the maximum likelihood of all possible genotypes, calculated based on a binomial distribution assuming a sequencing error rate of 0.
Heterozygous sites and accession-specific alleles The percentage of heterozygous sites for each accession (i. e. percentage of heterozygous sites/total number of sites in the vcf file) and the percentage of accession-specific alleles (i. e. percentage of sites having at least one allele present only in the accession) were calculated on the final vcf file using the vcf2struct. 1. 0. py custom script available at https://github. com/SouthGreenPlatform/vcf Hunter.
Identification of ancestry informative alleles On the basis of the ADMIXTURE analysis, all accessions predicted as homogeneous for a cluster for the best K value were selected to attribute alleles to ancestral groups. Polymorphic SNPs for these accessions were re-coded as follows: for each accession and each allele at a variant site, two lines were generated, a first one in which the allele presence state was coded as 0 or 1 and a second line in which the allele absence state was coded as 0 or 1 (Figure  S4 ). Alleles present or absent in all accessions were discarded. A COA was then performed on the transposed matrix with R software v3. 2. 2 using the ADE4 package  (Dray and Dufour, 2007). Allele coordinates on the synthetic axes were clustered using the mean shift algorithm implemented in SCIKIT-LEARN  (Pedregosa et al.
, 2011)  with a bandwidth of 1. 16. Allele grouping along synthetic axes was plotted using MATPLOTLIB  (Hunter, 2007). After identification of allele groups that were informative on ancestry, the relative contributions of each group to each of the 25 accessions was checked by calculating grouped allele proportions (i. e. number of grouped alleles from a group/total number of grouped alleles in one accession). The complete process was performed using custom python scripts vcf2struct. 1. 0. py available at https://github. com/SouthGree nPlatform/vcfHunter.
These probabilities were calculated for each accession and along each chromosome on sliding windows of n clustered SNPs sites (Figure  S6 ). The overlap between two windows was n À 1. For our analysis, the window was 301 clustered SNPs sites. The probabilities calculated for each window were attached to the central SNP position of the window. Ancestries were assigned to the position based on a major probability rule. Only probabilities >0. 1 were considered. For each accession, as the SNPs were not phased, a minimum number of recombination was assumed and therefore contiguous regions of the same ancestry were attributed to one of the homologous chromosomes forming pseudo-haplotypes. The complete process was performed using custom python scripts vcf2linear. 1. 1 available at https://github.
ACKNOWLEDGEMENTS This study was supported by funding from the  Agropolis Fondation  ' ARCAD ' project No. 0900-001  and the  Agropolis Fondation  ' GenomeHarvest ' project (ID  1504-006 ) through the  French 'Investissements d'avenir' programme (Labex Agro :  ANR-10-LABX-0001-01 ) and by the  CGIAR Research Programme on Roots, Tubers, and Bananas (RTB). The authors thank  Laure Saun  e,  Sylvain Santoni ,  Jacques David  and  Sylvain Gl  emin for RNA-seq libraries and sequencing within the framework of the  ARCAD  project and  Karine Labadie (Genoscope)  for DNA sequencing. We also thank  CRB Plantes Tropicales Antilles CIRAD-INRA Guadeloupe France  and the  Collection Musac ees CARBAP Cameroon  for providing plant materials. This work was also supported by the  CIRAD -UMR AGAP HPC Data Center of the South Green Bioinformatics platform  (http://www.
DATA AVAILABILITY STATEMENT The RNA-seq reads are available under the NCBI Bioproject PRJNA326055. DNA-seq reads for accession 'Grande Naine' are available under accession number PRJEB32153. The vcf file and raw local ancestry assignment statistics plots are available at the Banana Genome Hub  (Droc et al. , 2013)  under https://banana-genome-hub. southgreen. fr/ download.
, 2011). For algal-symbont identification, five colonies of each species were randomly selected to extract DNA from a small subsample preserved in 100% EtOH using a DNeasy PowerSoil Kit (Qiagen, Germany) following the manufacturer's protocol. The purity of DNA was checked with a NanoDrop 1000 (Thermo Thermo Scientific, United States). For the Symbiodiniaceae identification, the internal transcribed spacer ITS2 was amplified using zITSf (5 -CCGGTGAATTATTCGGACTGACGCAGT-3 ) and ITS4 (5 -TCCTCCGCTTATTGATATGC-3 ) as described in  Noda et al. (2017). A PCR amplification of 35 cycles was performed (95 • C for 30 s, 51 • C for 45 s, and 72 • C for 2 min). PCR products with around 724 bp were submitted to sanger sequencing, then individually checked for similarity with the Basic Local Alignment Search Tool (BLAST) of the National Center for Biotechnology Information (NCBI) database  (Wheeler et al.
Trait Analysis A total of 13 quantitative traits were used to document intraand inter-specific variations (data available the Dryad Digital Repository: https://doi. org/10. 5061/dryad. qz612jmd7): (1) OM; (2) chl a; Trait variations were depicted using violin plots and data were compared between species using Welch t-tests, assuming normality but without assumption of the equality of variances among samples. In addition, Levene's tests was used to assess the equality of variances in traits between species. A Pearson's r correlation matrix was produced between each pair of trait variables while retaining only individuals with completed observations (see Supplementary Figure  1 ). Substantial correlation was detected using a cut-off at | r | > 0. 8. A principal component analysis (PCA) was generated on scaled trait variables, and variations among individuals visualized on a biplot.
Intraspecific variations were assessed by multivariate homogeneity of species dispersions and compared between the two species using a permutation test. A permutational multivariate analysis of variance (PERMANOVA) computed on Euclidean distances was used to test for the difference between the two species. R (v 3. 6. 1,R Core Team, 2019) and the packages "corrplot, " "factoextra, " "ggplot 2, " "ggpubr, " "tidyr, " and "vegan" were used to analyze the data and produce the figures. The source code for data analysis is available at https://github. com/ vianneydenis/stranger-things. git.
ACKNOWLEDGMENTS Authors would like to express their gratitude to  Yehuda Benayahu  and  Catherine S. McFadden 's help with the identification of the octocorals and their comments on this manuscript. In addition, the authors thank  Sen-Lin Tang  for his support for molecular experiments and  Jing-Yi Tseng  for the stable isotope analysis. Authors are further grateful to  Vicky Yuting Lin  for contributing to the collection of the wrong species.
B. 2. Numerical methods for the stability analysis A linear stability analysis of the fluid-structure problem requires us to first determine nonlinear steady solutions of (2. 1) so as to then solve the eigenvalue problem (2. 5). The problems are discretized on a symmetric mesh (with respect with the axis y = 0). A Newton method is used to compute iteratively nonlinear steady solutions. After assembling the sparse matrices with the FREEFEM  (Hecht 2012 ) software, the MUMPS library  (Amestoy et al. 2013 ) is used to perform the matrix inversions. When it comes to the steady flow, a uniform velocity of amplitude unity is prescribed at the inlet boundary x = -15, slip conditions are taken at the top and bottom far-field boundaries y = ±25 and a stress-free outflow condition is taken for the outflow at x = 50.
Data Availability Statement Data sharing is not applicable to this article as no data sets were generated or analyzed during the current study. The Quasi-DYNamic earthquake simulator (QDYN)  (Luo et al. , 2017)  used to compute our numerical models of earthquake cycles is available online (github. com/ydluo/qdyn). QDYN is freely available for academic research purposes and licensed by GNU General Public License, Version 3.
4 Another perspective is a comparative study across Akkadian and West Semitic languages (for example, the new volume on hate by  Riley). 5 In this article, by conducting statistical analysis with language technological methods, we aim to advance lexical semantic work on Akkadian fear words, especially for lexemes that occur frequently. The article is structured as follows. Section 2 discusses previous lexical research on "fear" in Akkadian, based on Akkadian dictionaries. Section 3 describes our dataset. The Akkadian text data we are using comes from the Open Richly Annotated Cuneiform Corpus (Oracc). We are relying on Oracc annotations to a large degree, but we have used some preprocessing to enrich the data set. Section 4 describes our methodology very briefly, with references to our previous work, where more details can be found.
3. 1 Data from Oracc Our data13 comes from the Open Richly Annotated Cuneiform Corpus (Oracc),14 one of the largest, and regularly updated, digital corpora of Sumerian and Akkadian texts. Our data is a snapshot of the corpus from February 2019 and consists of 16,487 texts and over 1. 8 million words. 15 The text material used for this article contains all texts tagged in Oracc as being written in the Akkadian language. 16 Some of the documents in Oracc are multilingual, but we did not include such texts (e. g. , those written in Sumerian in addition to Akkadian). We did, however, take into account texts where 13 The research data used for and generated during our research is available online at Zenodo: http://doi. org/10. 5281/zenodo. 3634325 (February 4, 2020). 14 http://oracc. museum. upenn.
edu (February 5, 2020). 15 Because Oracc subprojects designated with the words "Scores" and "Sources" only contain duplicate texts, which are available elsewhere in the parent projects, we decided to leave them outside our data. 16 Some of the texts in the SAAo project have been tagged as being written in either Neo-Assyrian or Neo-Babylonian. These texts were included in the dataset. the language of the whole document had not been indicated, but where all the individual words were tagged as Akkadian. Thus, we rely solely on the metadata provided for each document in Oracc. The texts tagged as being "lexical" in Oracc were omitted, since they refer to a great number of rare words, many of which are only attested in the lexical lists. We also searched for duplicate texts in the corpus and have kept only one copy of each for the analysis.
17 The dataset used for analysis consists of 7,346 texts, most of which are from the Neo-Assyrian period (5,638) and come from Nineveh (4,641). Many of our texts are various kinds of letters (2,247), but royal inscriptions (1,487) and legal transactions (1,404) are also well represented, as well as astrological and astronomical texts (613). 18 The texts contain over one million words. 19 The data for this article is part of a larger dataset that concentrates on research on "love" and "anger" in addition to "fear. " Therefore, the words of interest in the dataset include-in addition to five verbs of fear-the verb râmu ("to love") and eight verbs related to anger: agāgu, ezēzu, kamālu, labābu, raʾābu, šabāsu, šamāru and zenû. The inclusion of these verbs in our dataset does not have a direct effect on our analysis of the five fear-related verbs.
3. 2 Preprocessing of the Data Since Akkadian is an inflected language, we decided to use only the lemmasthe dictionary or citation forms-of the words. 20 We wanted to explore the effects of preprocessing of linguistic data on the results. In order to study these, we made two different versions of our dataset to be analyzed with two language technological methods: Pointwise Mutual Information (PMI) and fastText (see Section 4). Level 1. Basic. In the first level, we use lemmatized texts downloaded from Oracc with some minor changes. As annotation of a text always involves interpretation by a scholar and because Oracc is composed of a number of subprojects, the way a word is annotated in different texts is not always consistent. Therefore, we have standardized divine names and some place names,.
19 A list of texts used in our analysis is available at Zenodo in the "textInformation" (textInformation/documents_used) folder, whereas the analyzed texts themselves can be found in the "textsAnalysed" folder. 20 We only used verbs, nouns and adjectives for the analysis; the rest of the words were replaced with an underscore as a placeholder. and combined different personal and royal names, as well as some other word classes, into one lemma each (e. g. , "PERSON" or "ROYALTY"). 21 We analyzed the above-mentioned verbs of fear, love and anger and their derivatives when they appeared more than nine times in our dataset. This threshold was chosen so that each verb of emotion or at least one of its derivatives is included in our analysis. Accordingly, some rare verbs of anger like kamālu are not included in the analysis but represented by their more common derivatives (e.
For example, the different translations of the verb rabû ("to be / become big") were harmonized as "big," which led to the adjective rabû being considered the same word as the verb. 22 Some synonymous translations (for example, rabû "big" and "great") are difficult to detect automatically. Thus, we manually improved the results of the automatic separation of homonyms. We compiled a list of all words that had two or more different translations after the automated separation. Based on this list we manually reduced 2,350 words with synonymous translations to 1,114 words. 23 In the texts to be ana-21 For further details on how we processed the data, see  Svärd et al. 2018, 227-228; Alstola et al. 2019a, 162-164. The lists of standardized divine and place names and information on the standardization process are available in the Zenodo files lists/gods_combined_variants and lists/places_combined_variants.
22 A list of all words after we had automatically separated homonyms (and combined the derivatives to their verbal roots) are in the lists/words_automaticHomonymSeparation file at  Zenodo. 23  The list of Akkadian words with the same meaning but different English translations (e. g. , palāhu "to fear" and "to be afraid") is available in lists/notHomonyms at Zenodo. We thank Aino Haavisto for her help in compiling this list. lyzed, homonyms are distinguished by a running number starting with "_1. " Even if a word has no homonyms, it is marked with "_1" placed after the word. Secondly, in order to have more data per word for our methods to process, we combined all the derivatives of the emotion verbs with their verbal roots. 24 In level 1, all homonymous words (such as the word adru with the meanings "be afraid," "dark" and "threshing floor") were in the analysis considered as just one word.
As the homonym separation was done first, only the instances of adru that in Oracc have the translation "be afraid" were combined with adāru_2 "be afraid" in level 2. 25 After the process we had, for example, three tokens for the lemma rabû: rabû_1 "big" (4,299 attestations), rabû_2 "set" (184) and rabû_3 "fourth" (1). We did not standardize the spelling of lemmas, and thus rabû_3 "fourth" and rebû_1 "fourth" are kept separate. The tokens of the verbs of fear are adāru_2 (159 attestations), galātu_1 (87), palāḫu_1 (637), parādu_1 (169) and šaḫātu_2 (51). The tokens of the verbs of love and anger are râmu_1 (488), agāgu_1 (69), ezēzu_1 (205), kamālu_1 (29), labābu_1 (22), raʾābu_1 (55), šabāsu_1 (42), šamāru_1 (65) and zenû_1 (72). Only râmu, adāru, šaḫātu and šamāru had homonyms. The rich dataset described in this section is fully available online (http://doi.
org/10. 5281/zenodo. 3634325), with readme files and explanations. The dataset includes seven folders: 1) "lists" includes, for example, lists of standardized divine names and place names as well as frequency lists of the words analyzed, 2) "network-data" includes Gephi datasets and the raw data used to create the graphs, 3) "oracc_data" contains all the words extracted from Oracc in February 2019, 4) "scripts" includes the scripts created to analyze our data with PMI, 5) "textInformation" contains metadata on the whole text corpus we analyzed, 6) "textsAnalysed" includes the actual text files that were analyzed with PMI and fastText, and 7) "visualizations" contains visualizations of the networks as PDF files.
4. 1 Pointwise Mutual Information Pointwise Mutual Information (PMI) is one of the most popular statistical association measures used in automatic collocation extraction. The core idea of 24 We thank Jason Silverman for suggesting this method to us. A similar approach is used by Andrew J. Riley in his recent study on hate in the ancient Near East  (Riley 2017, 11). 25 The list of the words after all the preprocessing was done is in the lists/wordsWithTrans-lations_level2 file at Zenodo. PMI is to describe word association as a logarithmic ratio of the observed joint probability p(a,b) of the words to the expected chance of their co-occurrence under the assumption of probabilistic independence p(a)p(b). 26 In other words, PMI measures the ratio of the observed word co-occurrence probabilities to their co-occurrence probabilities in a hypothetical scenario where the word order of the corpus was randomized and all syntactic and semantic information was lost.