dataesr/scientific-paragraphs-categorization huggingface dataset:
They also considered directness and target of hatred and the sentiment of the annotator in their annotation process. Here, we only select tweets in English that have hateful or normal sense in their annotation label. French We use the dataset introduced in  [46] , containing 13,014 tweets in English (5,647), French (4,014), and Arabic  (3, 353)  from Twitter. Here, we just select tweets that have hateful or normal sense in their annotation label that results in 1,220 samples in French. German This category consists of two different hateful datasets in German, explained in the following: -Mandl et al. [10]  created a corpus of size 17,657 in three languages English (7,005), Hindi (5,983), and German (4,669) from Twitter and Facebook, which was introduced in the first edition of HASOC track (Hate Speech and Offensive Content Identification in Indo-European Languages shared task in FIRE 2019).
The dataset was collected using a set of hashtags and keywords containing offensive content and users' timelines with potential hateful content. The dataset was annotated in a three-layer annotation schema as: 1) identification of Hate and Offensive or Non Hate-Offensive, 2) identifying the type of hate as Hate speech, Offensive, Profane, and 3) identifying whether a post is containing Targeted Insult or Untargeted. Here, we only select samples from the first and second layers of annotation labeled as hate speech or not hate speech. -Roβ et al. [25]  introduced the first hate speech corpus, consisting of 469 tweets, for the refugee crisis in German language. The aim of the study was to measure the reliability of hate speech annotations. To collect the dataset, they used a list of hashtags with potential insulting or offensive meaning towards refugees.
Two experts annotated the corpus as hate speech ornot hate speech. In addition, the offensiveness of each tweet was rated from 1 (Not offensive at all) to 6 (Very offensive). Here, we select tweets according to a complete agreement annotators, which results in 369 tweets. Indonesian We use two following datasets proposed for hate speech detection in Indonesian  [49] ,  [74]. -Alfina et al. [74]  introduced a dataset for hate speech detection in Indonesian containing 713 tweets and was collected from Twitter based on a set of hashtags related to the political events. The dataset was annotated as hate speech or not hate speech by a group of 30 college students as annotators. -Ibrohim and Budi  [49]  built an Indonesian Twitter corpus in size of 13,169 to detect hate speech and abusive language along with the target, category, and level of hate.
The dataset contains a combination of existing datasets and new dataset collected from Twitter using Twitter Search API for a duration of 7 months. The dataset was annotated by a large group of annotators using crowdsourcing mechanism and resulted in a multi-label hate speech and abusive language dataset. Here, we only select tweets that are labeled as hate speech or not hate speech. Italian Bosco et al. [13]  used two Italian corpus form Twitter and Facebook for the Hate Speech Detection (HaSpeeDe) task at EVALITA 2018. The first dataset is a collection of 4,000 Facebook posts provided by Vigna et al. [76] , and the second dataset is a collection of 4,000 tweets from Twitter built by Sanguinetti et al. [77]. To keep platform consistency across different datasets, we only use the Twitter dataset here.
The Twitter dataset was collected by considering three potential hate speech targets in the Italian context: immigrants, Muslims, and Roma and with employing a set of neutral keywords associated with these groups. Using a combination of experts and crowdsourcing annotators, the dataset was annotated as hate speech, aggressiveness, offensiveness, irony, stereotype, and intensity. Here, we only select tweets labeled as hate speech or not hate speech. Portuguese This dataset composes of 5,668 tweets in Portuguese  [48]. Tweets were collected using a set of haterelated keywords and hate-related profiles. The authors used two annotation schemas: 1) binary annotation (hate vs. nohate) relying on non-expert annotators and 2) multi-label hate speech hierarchical annotation (including 81 hate categories) relying on an expert annotator (a researcher in hate speech domain who was trained in social psychology).
Here, we only select tweets annotated with binary annotation schema as hate or no-hate. Spanish This category consists of two hateful datasets in Spanish, explained in the following: -Basile et al. [14]  introduced a multilingual hate speech dataset in English and Spanish for HatEval 2019, a shared task at SemEval 2019, which focuses on identification of multilingual hate speech against immigrants and women in Twitter. The dataset was composed of 19,600 tweets for English (13,000) and Spanish (6,600). Here, we only select the first category of annotation for Spanish (6,600), in which each tweet is labeled as hate speech or not hate speech. -Pereira-Kohatsu et al. [73]  introduced a dataset on hate speech in Spanish consisting of 6,000 tweets filtered from a corpus of two million tweets, sampled from Twitter using the Twitter Rest API.
The intuition was that the vocative particle (''yA'') is mainly used in directing the speech to a specific person or group and this vocative is widely observed in all Arabic dialects containing offensive language. Then 10k out of the initial corpus was selected and annotated as offensive or clean. If a tweet is offensive, then annotator searched for any potential vulgar or hate speech content. Therefore, each tweet is given one or more labels: offensive, vulgar, hate speech, or clean. In this study, we consider tweets annotated as offensive or clean. Danish This dataset contains 3,600 comments collected from different three popular social media platforms among Danish speakers: Twitter, Facebook, and Reddit. An initial platform-specific lexicon containing abusive terms in Danish collected through a crowd-sourcing mechanism in Reddit was used in data collection process  [15].
The annotation process followed the three-layer annotation scheme proposed in  [12] , for English, to identify the type and the target of offense. Here, we just used the first level of annotation where each comment is annotated as offensive or non-offensive. English The Offensive Language Identification Dataset (OLID) containing over 14,000 English tweets, is introduced at SemEval-2019 for identification of offensive language, the type of offensive content, and the target of offensive in English  [12]. The OLID targeted different kinds of offensive content and was annotated using a fine-grained three-layer annotation scheme to identify the type and the target of offense as well. In the first level of annotation, tweets are annotated as offensive or non-offensive. In the second level, offensive tweets are annotated as targeted insult or untargeted, and in the third level, targeted offensive tweets are annotated as individual, group, or other.
Here, we just used the first level of annotation where each tweet is annotated as offensive or non-offensive. Greek The first version of this dataset, named Offensive Greek Tweet Dataset (OGTD), contains 4,779 posts from Twitter collected between May and June 2019  [45]. Different sampling strategies were used in collecting data including: 1) using popular and trending hashtags in Greek attributed to the television programs, reality and entertainment shows and political tweets, querying for tweets containing keywords usually found in offensive content such as curse words, expletives and their plural forms, and searching for tweets containing (eisai, ''you are'') as a keyword. Following the same annotation guidelines proposed in  [12] , the dataset was annotated as offensive, not offensive and spam, by a group of three volunteers annotators through the LightTag 10  platform.
The spam tweets were filtered out from the dataset. To enrich the corpus for OffensEval 2020, the second version of the dataset, in size 5,508, was collected and annotated in November 2019 with the same approach used in the first version. The combination of two versions results in 10,287 tweet samples that we use in this study. Turkish This dataset contains over 35,000 tweets extracted from Twitter using Twitter streaming API, from March 2018 to September 2019  [16]. Although a list of frequent words in Turkish tweets was used to filter Twitter streams, all the tweets were sampled uniformly without any strategy such as offensive keywords for extracting offensive content specifically. To annotate the corpus by volunteers, the annotation guidelines proposed in  [12]  with a small divergence was used; where at the top level, tweets were labeled as offensive or non-offensive and then offensive content were labeled as targeted or profanity.
Similar to  [12] , the targeted offensive content were divided to individual, group, or other. Here, we just used the first level of annotation where each tweet is annotated as offensive or non-offensive. Persian This dataset has been created by the authors of this paper in another research work (which is under review in a journal now) to investigate the problem of offensive language detection in Persian as a low-resource language. This dataset contains 6,000 tweets crawled from Twitter using Twitter streaming API in a two-month interval from June to August 2020. We used two main strategies for data collection: 1) random sampling and 2) lexicon-based sampling in which we used the HurtLex 11  as a multilingual computational lexicon of offensive, aggressive, and hateful words organized in 17 categories in over 50 languages including Persian.
Results and discussion Sequencing and assembly of the H. schachtii genome. We measured (Figure  S1 ), sequenced (BioProject PRJNA722882), and assembled the genome of H. schachtii (population Bonn) using a combination of flow cytometry, Pacific Biosciences sequencing, and Illumina sequencing. H. schachtii has the largest genome (160-170 Mb) of any cyst nematode measured/sequenced to date (Table  S1 ). It was sequenced to 192-fold coverage using Pacific Biosciences sequencing (fragment n50 of 16 kb), and 144-fold coverage using Illumina sequencing (150 bp Paired end reads). The final, polished, contamination-free (Figure  S2 ), assembly (v1. 2) included approximately 179 Mbp contained within 395 scaffolds: 90% of the sequence is contained on scaffolds longer than 281,463 bp (n=154). The assembly is a largely complete haploid representation of the diploid genome, as evidenced by core eukaryotic genes being largely present, complete and single copy (CEGMA 93.
For each species, the expression profiles of the gene/s annotated with the EC codes at each step are shown (green for host, purple for parasite), if present. B) Phylogenetic tree inferred from the protein alignment of the top 50 most similar sequences identified in the NCBI non-redundant database to each of 14 animal putative PANC genes. Most PANC homologues from plant-parasitic nematodes (purple), including HsPANC, group in a single monophyletic sub-clade with sequences from actinobacteria. C) The two predicted H. schachtii PANC orthologues are adjacent, in the same orientation, and located towards the middle of a 1. 3 Mb scaffold. RNAseq read mapping (graph) and intron:exon structure of both genes (5:6, blocks above) are similar, and confirm that they do not arise from contamination. These data support the hypothesis that the plant-encoded enzymes of the first part of the vitamin B5 biosynthesis pathway support nematode infection, and may thereby function as nematode susceptibility genes.
Twelve-days-old plants were inoculated with 60 -70 surface-sterilized juveniles. Small root segments containing nematodes were marked under a stereo microscope. The infected area including nematodes was then hand-dissected and transferred to liquid nitrogen. Several hundred segments were collected for each biological replicate. Uninfected root segments adjacent to the infected area were used as a control. Frozen tissue was lysed to a fine powder with a tissue lyser at 30 Hz (2 x 2 min). RNA was extracted from frozen powder using the RNeasy Plant Mini Kit (Qiagen) following the manufacturer's instructions and using both the optional shredder columns and on column DNA digestion. RNA quality was assessed using Bioanalyzer. Library construction (150 bp paired end stranded RNAseq) and sequencing was carried out by Novogene (Bioproject: PRJNA722882).
Sequencing was provided by the Norwegian Sequencing Centre (www. sequencing. uio. no), a national technology platform hosted by the University of Oslo and supported by the "Functional Genomics" and "Infrastructure" programs of the Research Council of Norway and the Southeastern Regional Health Authorities. In addition, one 150 bp paired end (350 bp insert size) Illumina library was prepared from the same DNA (NEBNext Ultra II DNA Library Prep Kit for Illumina) and sequenced to approximately 140 fold coverage (24. 6 Gb) using the service provided by NovoGene (GenBank Accession PRJNA722882). All PacBio subreads (Bioproject: PRJNA722882) were error corrected using Canu snapshot v1. 7 +137 changes (r8829 73d5caa1b1087b65f7853ecbebc1bb1dcbd1bc14) (Koran et al. 2017), with the following additional parameters: corOutCoverage=300 corMhapSensitivity=normal.
Briefly, reads were mapped to the assembly using minialign version 0. 5. 2 (https://github. com/ocxtal/minialign) to determine the coverage of the assembled contigs. The contigs were compared to the GenBank nt database using BLASTn (reporting taxonomic information). Contigs were then classified based on the weight of the BLAST hits. Contigs identified as fungal, bacterial, plant or viral were removed. Thus, yielding a contamination free unpolished contig level assembly. Haplotigs and either extremely high or extremely low coverage contigs were removed (using purge haplotigs (a-70))  (Roach, Schmidt, and Borneman 2018). Remaining contigs were upgraded using FinisherSC  (Lam et al. 2015)  and scaffolded using iterative rounds of SSPACE_longreads  (Boetzer and Pirovano 2014)  and gapFinisher  (Kammonen et al.
All instances were inspected in a genome browser and corrected, giving rise to the final manually finished assembly version 1. 2 (GenBank Accession JAHGVF000000000). Scripts, commands, and config files are available at: https://github. com/peterthorpe5/Heterodera_schachtii_genome_assembly and https://github. com/sebastianevda/H. schachtii_genome/assembly_and_manual_finishing_scripts).
Gene prediction and annotation. Hard and soft genome masking was performed as described in (Eves-van den  Akker et al. 2016; Thorpe et al. 2018). Briefly, Repeatmodeler (version DEV) was used to identify repetitive regions. The resulting identified repetitive elements were masked using RepeatMasker along with RepBaseRepeatMaskerEdition-20170127 models. To additionally identify transposons, TransposonPSI version 08222010  (Haas 2007)  and LTRharvest version 1. 5. 9  (Ellinghaus, Kurtz, and Willhoeft 2008)  from Genometools  (Gremme, Steinbiss, and Kurtz 2013)  was used. All RNAseq reads were remapped to the soft masked genome, separated by strand, and used to provide stranded hints for Braker2 gene prediction (-f 2000, allowing Augustus to train itself for both exon and utr models). After considerable optimisation of extrinsic config files for exon and UTR prediction (available at https://github.
com/sebastianevda/H. schachtii_genome/Gene_predictions), an extremely high quality set of annotations resulted (termed annotation version 1). The version 1 predicted proteins were annotated with DIAMOND-BLASTp (version v0. 9. 24. 125)  (Buchfink, Xie, and Huson 2015)  searched against GenBank NR database. The resulting. xml file was imported into BLAST2GO version 5  (Conesa et al. 2005). BLAST2GO was used to annotate the gene models using GO version May 9 th 2019 and Interproscan  (Quevillon et al. 2005). Gene duplication analyses were performed using the similarity searches from DIAMOND-BlastP (1e-10) with MCSanX toolkit  (Wang et al. 2012). PFAM domains in protein coding sequences were predicted using hmmsearch version 3. 2. 1  (Eddy 2011 ) and the Pfam-A. hmm definitions release 30 (http://ftp.
ebi. ac. uk/pub/databases/Pfam/). Signal peptide and transmembrane prediction was performed using signalP 4. 0  (Petersen et al. 2011)  and Phobius version 1. 01  (Käll, Krogh, and Sonnhammer 2004). Effector identification. Genes related to previously reported effectors in G. rostochiensis supported by gland cell expression were identified in the H. schachtii genome as previously (Eves-van den  Akker et al. 2016). In brief, an inclusive list of effectors was generated by sequence similarity alone (either BLAST (e-value < 1e-5) or using HMM searches (for glutathione synthetase-like effectors  (Lilley et al. 2018 ) (PF03199. 10 and PF03917. 12) and SPRYdomain (PF00622. 23) containing effectors  (Rehman et al. 2009 ) (HMMsearch with gathering threshold cut off)). Putative effector families were aligned and manually curated based on expert knowledge of effector characteristics (e.
Horizontal gene transfer and phylogenetics. We used BLAST+ to perform a similarity search of H. schachtii proteins against the nr database. Alien index (AI) was calculated with the Alienness web server  (Rancurel, Legrand, and Danchin 2017). We then used the AvP software (https://github. com/GDKO/AvP) to perform the HGT analyses. Proteins with AI above 10 were selected and grouped based on the percentage of shared BLAST hits (70%) using single linkage clustering. For each group we extracted the FASTA sequences of the BLAST hits from the NR database. Sequences for each group were aligned using MAFFT  (Katoh and Standley 2013)  and FastTree  (Price, Dehal, and Arkin 2010)  was used for phylogenetic inference. In total 540 proteins (containing alternative transcripts) were analysed, with 349 being confirmed as HGT candidates (222 of them had only hits from Non Metazoan taxa), 97 proteins had a non-conclusive topology, and in 94 proteins the hypothesis of being an HGT candidate was rejected.
Each remaining non-Metazoan PanC was used to identify the top 50 most similar sequences in NCBI using BLAST. A non-redundant list of subjects was aligned to all queries using MUSCLE, and refined  (Edgar 2004 ). The alignment was trimmed using Trimal  (-gappyout, (Capella-Gutiérrez, Silla-Martínez, and Gabaldón 2009) ). Model selection (LG+F+I+G4) and phylogenetic inference (1000 bootstraps) was carried out using IQ-TREE 2  (Minh et al. 2020) ). The phylogenetic tree was midpoint rerooted, and formatted using Figtree1. 4. RNAseq read mapping, normalisation, differential gene expression analysis and clustering. RNAseq reads are available under Bioproject: PRJNA722882. All reads were analysed with FastQC v0. 11. 8 (Andrews and Others 2010) and trimmed using BBduk v38. 34 (https://sourceforge.
OrthoMCl analyses. Orthogroup analyses were precomputed and already available for plants and nematodes. For plants, we focused on a subset of eight plant species (Amborella trichopoda (outgroup), the monocots Hordeum vulgare and Zea mays, the solanaceous Solanum lycopersicum and S. tuberosum, the fabaceous Glycine max, and the brassicaceous Brassica rapa (ssp. rapa) and A. thaliana -GreenPhyl v5 -MCL matrix for level 4 clusters  (Guignon et al. 2021) ). Counts per species per orthogroup were generated using a custom python script (https://github. com/sebastianevda/H. schachtii_genome/tree/main/MCL_parsing). Orthogroups, or subsets of orthogroups that contain genes of interest (e. g. genes in a particular expression cluster), were visualised using UpSetR R package. For nematodes, we focused on a subset of eight nematodes species (the free living nematode Caenorhabditis elegans (outgroup), the pine wilt nematode Bursaphelenchus xylophilus, the rootknot nematodes Meloidogyne graminicola and M.
KEGG mapping. The KEGG Automatic Annotation Server Ver. 2. 1 (https://www. genome. jp/kaas-bin/kaas_main) was used to annotate metabolic processes in the host and the parasite using the bi-directional best hit strategy (using the default dataset and parameters with the following exception: minimum bit score increased to 100, Table  S8 ). The KEGG reconstruct server was used to populate existing pathways with metabolic process information.
Acknowledgments The work at  University of Bonn  was supported by the  Federal Ministry of Education and Research, Germany (BMBF)  (Grant  031A326B  to FMWG) and by the  German Research Foundation (DFG)  (Grant  SI1739/3-1  and  SI1739/5-1  to SS). MSH was supported by a fellowship from  German Academic Exchange Service (DAAD). The work at  University of California Davis  was supported by the  National Science Foundation (NSF)  (Grant  IOS-1954929 ) and  National Institute of Food and Agriculture (NIFA). Work on plant-parasitic nematodes at the  University of Cambridge  is supported by  DEFRA  licence  125034/359149/3 , and funded by  BBSRC  grants  BB/R011311/1 ,  BB/N021908/1 , and  BB/S006397/1. CP received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  882941.
PT was supported by the  University of St Andrews Bioinformatics Unit , funded by  Wellcome Trust ISSF  awards  105621/Z/14/Z  and  204821/Z/16/Z. Work at  Iowa State University  was supported by  Hatch and State of Iowa funds  and a grant by the  North Central Soybean Research Program. J. L. L-T was supported by a  NWO domain Applied and Engineering Sciences VENI  grant ( 14250 ) and a  Wageningen University & Research  Experimental Plant Sciences strategic funds grant. MGS was supported by  NWO domain Applied and Engineering Sciences VENI  grant ( 17282 ).
Data availability This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession JAHGVF000000000. The version described in this paper is version JAHGVF010000000. Code and scripts used in the manuscript are available here: https://github. com/sebastianevda/H. schachtii_genome/Gene_predictions and https://github. com/peterthorpe5/Heterodera_schachtii_genome_assembly. Genomic and RNAseq reads can be found under Bioproject: PRJNA722882.
Participants A total of 17 bvFTD patients were recruited to the clinical observational study (ECOCAPTURE, Clinicaltrials. gov: NCT03272230) from an AP-HP (Paris Public Hospitals) expert clinical site (the "National reference centre on FTD", "Institut de la Mémoire et de la Maladie d'Alzheimer"-IM2A) at the Pitie-Salpeˆtriere Hospital. Inclusions were carried out between September 2017 and January 2021. Patients were diagnosed according to the International Consensus Diagnostic Criteria  (Rascovsky et al. , 2011). Eighteen HC were recruited by public announcement. HC subjects were matched to bvFTD patients for age, gender and education level. All participants underwent the same cognitive and behavioural assessments, as well as the same MRI protocol for the study. The demographic characteristics, cognitive tests and behavioural disinhibition scores of bvFTD patients and HC are described in Table  1.
g. repetitive movements, perseveration) and social disinhibition (e. g. unwarranted or excessive familiar behaviour towards the investigator, lack of manners). The detailed ECOCAPTURE ethogram with some examples for each behaviour is shown in Table  2. See the complete ECOCAPTURE ethogram at Mendeley Data  (Batrancourt et al. , 2022). Thus, for each subject, behavioural data was obtained by behavioural coding from 45-minute footage, using a manual video annotation tool (The Observer XT®, Noldus) (DT). Behavioural coding data were collected through the continuous sampling method (all occurrences of behaviours were recorded) and conducted based on the ECOCAPTURE ethogram (Table  2 ). The number of times a behaviour of interest occurs per video during the 45-minute sample session was counted in each individual.