dataesr/scientific-paragraphs-categorization huggingface dataset:
Annotation Phase After carefully defining the annotation schema presented in the previous section and describing it into annotation guidelines, three annotators with background in computational linguistics carried out the annotation of the dataset. After a first annotation round on a data sample, the guidelines were updated to conciliate the disagreements among the annotators, in particular with respect to the boundaries of the spans to be annotated. Afterwards, 9 sections 2 from 5 different debates from different years were annotated to compute interannotator agreement, reported in Table  2  as moderate agreement. Annotations were then reconciled by an expert annotator before adding them to the released dataset. The semantic annotation platform INCEpTION  [Klie et al. , 2018]  was used to perform the annotation process.
Annotators were presented with raw data, meaning that we hide the existing annotations of argument components and relations present in the original ElecDeb60To16 dataset, to avoid annotation bias. The rest of the dataset was randomly and equally split among the three annotators, that carried out indepen-2 Debates in the ElecDeb60To16 are divided into sections, where each section starts with the moderator/panelist or an audience member asking a question on a new topic  [Haddadan et al. , 2019  dently the annotation task 3. As a result, 31 debates of the ElecDeb60To16 dataset are fully annotated with fallacies 4. We tokenized the annotated fallacious arguments to compute the average number of words in each sub-category. As expected, Slogans, Name Calling and Loaded Language cover the shortest spans, with respectively 8.
Statistics and Data Analysis Table  4  shows the distribution of fallacy categories over the debate years. Given that for some years there is more than one debate, to carry out additional analysis of diachronic changes in debating strategies, we perform data normalization. More specifically, given that debates can have a different length, different number of speech turns and even a different number of candidates debating (e. g. , in 1992), we normalize the length of the fallacious snippet with respect to the total length of the debates in one year. An example analysis (that could be of interest to scholars in political sciences) that can be made on the ElecDeb60To16-fallacy dataset is on the presence of fallacious arguments of type "Ad Hominem" in the different debate years (see Table  4 ), interesting showing that such strategy was often used by candidates in 2016.
Combining recurrence and relative positional encoding, it can model longer-term dependency than RNNs and vanilla Transformers. In the different experimental settings, the features we considered for the fallacious argument classification task are the following: political discourse speech context, fallacious argument snippet, argument component and relation labels. The argument component feature refers to the two basic argument components, i. e. , premise and claim, and the argument relation feature refers to bipolar argument relations, i. e. , attack and support, plus the equivalence relation  [Cabrio and Villata, 2018; Lawrence and Reed, 2019]. These features are extracted from the annotated argument components and relations in the ElecDeb60To16 dataset  [Haddadan et al. , 2019]. Baselines.
We also performed additional experiments with the best performing model (Longformer + loss joint loss ) to i) classify the 14 fallacious argument sub-categories, i. e. , General Ad hominem, Bias ad hominem, Tu quoque, Name-calling Labeling, Appeal to fear, Appeal to pity, Loaded Language, Flag waving, Without evidence, False authority, Popular opinion, Slippery Slope, False cause, Slogan; and ii) classify the main categories, enriching the dataset with the argument component and relation features in an ablation test setting. In all the experimental settings, 80% of the dataset has been used for training and the remaining 20% for testing. We performed the train and test split by sklearn to create the training and test sets with a random seed for the label distribution. To average the results, we performed experiments 3 times.
Concluding Remarks Fallacies remain a controversial issue in argumentation, as the argumentation schemes to identify such bad or invalid types of reasoning is somehow ineffective when applied to real world scenarios, like political debates  [Boudry et al. , 2015]. The problem is that they abstract away from the specific content and dialectical context of the fallacy. We plan to empirically investigate the connection between the argumentative content and the context of the fallacy in our dataset. Furthermore, almost every known type of fallacy is a close neighbor to sound arguments in a debate. We will study how to generate sound arguments out of the identified fallacies and their context. Finally, the investigation of how to counter the formal invalidity of these fallacious arguments through newly generated counter-arguments remains a challenging follow up of this work.
Acknowledgements This work was partly supported by the  French government , through the  3IA Côte d'Azur Investments in the Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-19-P3IA-0002. This work was partly supported also by  EU Horizon 2020  project  AI4Media , under contract no. 951911  (https://ai4media. eu/). Shohreh Haddadan hereby acknowledges that this research is supported by the  Luxembourg National Research Fund (FNR)  ( 10929115 ).
Fallacious argument categories and sub-categories with examples from the ElecDeb60To16 dataset. Fallacy Category Sub-category Sample Ad Hominem General You were totally out of control. Bias ad hominem Name-calling,Labeling Such a nasty woman! Appeal to Emotion Appeal to fear These terrorists are serious, they're deadly, and they know nothing except trying to kill. Appeal to pity I think of the man who grabbed me by the shoulder once with tears in his eyes and said his daughter was dying of cancer and he thanked me for giving him a chance to spend some time with her without losing his job because of the Family and Medical Leave Act. Loaded Language we pointed out how ridiculous this attempt was by the Environmental Protection Agency. Flag waving Communism is the enemy of all religions; and we who do believe in God must join together.
The formation of a diploid zygote is a highly complex cellular process that is entirely controlled by maternal gene products stored in the egg cytoplasm. This highly specialized transcriptional program is tightly controlled at the chromatin level in the female germline. As an extreme case in point, the massive and specific ovarian expression of the essential thioredoxin Deadhead (DHD) is critically regulated in Drosophila by the histone demethylase Lid and its partner, the histone deacetylase complex Sin3A/Rpd3, via yet unknown mechanisms. Here, we identified Snr1 and Mod(mdg4) as essential for dhd expression and investigated how these epigenomic effectors act with Lid and Sin3A to hyperactivate dhd. Using Cut&Run chromatin profiling with a dedicated data analysis procedure, we found that dhd is intriguingly embedded in an H3K27me3/H3K9me3-enriched mini-domain flanked by DNA regulatory elements, including a dhd promoter-proximal element essential for its expression.
We thus exploited datasets from embryonicderived S2 and Kc cell lines. Consistent with their occupancy by transcription factors, ATACseq peaks  [35]  -revealing hyper-accessible DNA-coincide with Polycomb regulatory elements in flies and mice  [35, 36]. Genome-wide, our small fragment peaks identified in ovaries were enriched for ATAC-seq signal, arguing that these indeed correspond to DNA regulatory elements (Fig  2D ). Enrichment at these peaks of the Polycomb protein  [37]  and the insulator protein CP190  [38]  further argues that these elements often correspond to functional PREs or insulators. Accordingly, at the borders of H3K27me3 domains, short fragment peaks were more frequently associated with CP190, confirming previous reports that this factor is associated with H3K27me3 domain boundaries  [24, 39]  (Fig  2D ).
dhd lies within an H3K27me3/H3K9me3 mini-domain flanked by DNA regulatory elements To gain insight on dhd regulation, we next sought to analyze its associated chromatin configuration. We previously showed that the active transcription modification H3K4me3 is enriched at the dhd promoter and that this mark is lost in lid KD ovaries  [5]  (S2A Fig). Using available ChIP-seq datasets from embryonic derived S2 cells, we further observed that that dhd lies within a ~5kbp mini-domain featuring two types of repressive histone modifications: H3K27me3 and H3K9me3 (S2A Fig)  [40]. H3K27me3 is the hallmark of Polycomb-based repression  [41, 42] , whereas H3K9me3 dictates Heterochromatin Protein 1 (HP1)-based repression  [43, 44]. Interestingly, this mini-domain was also found in ChIP-seq data from fly ovaries (S2A Fig)  [45].
The dhd promoter-proximal DNA regulatory element is required for dhd expression independently of its heterochromatin mini-domain We next performed sequence analysis of the dhd mini-domain border elements, screening against the flyreg. v2 transcription factor DNA binding motif database  [51, 52]. At the 5' border element, which mapped to the dhd promoter region, we identified four perfect matches for the DNA replication-related element (DRE) motif, TATCGATA (Fig  5A ). This motif is recognized by the insulator-associated factor BEAF-32  [53]  and the core-promoter factor DREF  [16]. These four DRE motifs overlap in the palindromic sequence TATCGATATCGATA, 37bp upstream of the dhd transcription start site. Consistently, BEAF-32 and DREF both occupy this element in Kc cells (S6 Fig)  [38]. Previous studies showed that BEAF-32 null females are partially fertile (~40% hatching rate)  [54] , indicating that this factor is not essential for dhd expression.
Gene expression analysis by RT-QPCR Total RNA was extracted from ovaries of 3-day-old females using the NucleoSpin RNA isolation kit (Macherey-Nagel), following the instructions of the manufacturer. 1μg of total RNA was reverse transcribed using the SuperScript II Reverse Trancriptase kit (Invitrogen) with oligo (dT) primers. RT-qPCR reactions were performed in duplicates as described previously  [5]. Primer sets used are provided in S1 Table. Statistical tests were performed using GraphPad Prism version 9. 2. 0 for Mac OS (GraphPad Software).
Sequencing data processing Paired-end reads were mapped to the release 6 of the D. melanogaster genome using Bowtie2 (v. 2. 4. 2). To compare samples with identical readcount for genome coverage quantifications, we employed Downsample SAM/BAM (Galaxy Version 2. 18. 2. 1). To obtain short fragment datasets for DNA regulatory element identification, peak calling and visualization, we selected fragments shorter than 120 bp from SAM files. These were typically a small minority of all fragments, as our Cut&Run datasets were largely dominated by nucleosome-sized fragments (150-250bp). We therefore could separately analyze modification-bearing nucleosome coverage (for which the complete Cut&Run dataset, "All fragments", was a good approximation) or putative regulatory element coverage (<120bp fragments).
For genomic track visualization, we used bamCoverage from deepTools 2. 0 (Galaxy Version 3. 3. 2. 0. 0) to calculate read coverage per 25bp bin for all fragments or 10bp bin for short fragments, with paired-end extension. Peak calling was done on sorted short fragments (<120 bp) with MACS2 (v. 2. 1. 1. 20160309) with the following parameters:-nomodel,-p-value = 0. 0001,-keep-dup = all and the rest by default. To establish a high-confidence short fragment peak list we retained peaks that were present in biological replicates from the control genotype. Genome browser views screenshots were produced with the IGV software, for Cut&Run we used a 25bp bin for all fragments and a 10bp bin for short fragments (<120bp). For the midpoint-plot of fragment sizes around short fragment peaks, the length of each fragment was plotted as a function of the distance from the fragment midpoint to the summit of the peak identified by MACS2.
Acknowledgments We thank  Dr. Kami Ahmad  for sharing reagents and advice on Cut&Run. We are grateful to the TRiP projects and  Bloomington Drosophila Stock Center  for fly stocks. We acknowledge the contribution of  SFR Biosciences  ( UAR3444/CNRS ,  US8/Inserm ,  ENS de Lyon ,  UCBL ) facilities PLATIM and Arthro-tools. Sequencing was performed by the GenomEast platform, a member of the ' France Genomique' consortium. We would like to thank  Raphae ¨lle Dubruille  and  Francesca Palladino  for critical reading of the manuscript. We also warmly thank them, as well as  Lucas Waltzer  and all members of the  Loppin lab  for their input throughout this project.
Methods: The case for this study was a group discussion with members of a multidisciplinary consortium that works on developing a personalized eHealth intervention for atherosclerotic cardiovascular disease. Using stakeholder mapping, health and medicine experts, big data scientists, software developers, and an innovation manager (N=8) were invited to participate. The discussion was based on a user scenario and structured according to the Six Thinking Hats of de Bono, representing 6 different types of thinking. The discussion was recorded, transcribed verbatim, and analyzed thematically with the use of ATLAS. ti software. Results: First, informative and intuitive thinking served the preparatory purpose of familiarization with the project details and other participants. Second, positive and critical thinking constituted the body of the discussion and resulted in an in-depth conversation.
Setting The CARRIER consortium is a Dutch initiative in the South Limburg region that aims to reduce the burden of atherosclerotic cardiovascular disease (ASCVD) with the help of a personalized eHealth intervention. The consortium consists of experts in health and medicine, big data science, software development, and, lastly, ethical and legal experts in the medical domain. The objective of the project is to develop a big data-driven intervention to detect high-risk individuals, prevent cardiac events through health behavior changes, and ultimately reduce morbidity and mortality from ASCVD  [21]. The content and delivery mode of the personalized eHealth intervention are to XSL • FO RenderX be developed by the consortium through cocreative design with end users and other stakeholders.
Participants Eight stakeholders, consisting of 4 health and medicine experts, of whom 2 were cardiologists and 2 were health service researchers, 2 software developers, 1 data scientist, and 1 innovation manager, were invited to participate in the Six Thinking Hats of de Bono discussion. The participants' characteristics are presented in Table  1. The outcome of the discussion is described below by viewpoint, and a summary of the themes per viewpoint is presented in Table  2. The first 2 viewpoints (informative and intuitive thinking) served a preparatory purpose, enabling individuals to familiarize themselves with the details of the topic and the other participants. Then, the body of the discussion consisted of the middle 2 viewpoints (positive and critical thinking). These viewpoints resulted in an in-depth discussion and were therefore the most time-consuming viewpoints.
Limitations and Strengths Many publications have described the eHealth development process, that is, reporting on their iterations toward a final product or service; yet, only a few have provided in-depth reflections on the development process itself, such as the experienced barriers or facilitators  [15]. Nevertheless, there is a need for such information to improve multidisciplinary working in eHealth and other fields  [10, 11, 14]. This paper provided a detailed explanation of such a "tool," its application, and its outcomes based on a real-world case from a complex multidisciplinary eHealth consortium. Therefore, the application and process of cocreation and the subsequent practical lessons can be considered a strength. A limitation arising from this descriptive approach is that no qualitative or quantitative data were collected on the participants' self-reported experiences.
Results Our experimental strategy (Fig  1A ) was as follows. First, we evaluated the progression of the human CD34+ cord blood cells toward defined fates after cytokine stimulation using scRNAseq. This approach allowed us to assess quantitatively the phenotypic heterogeneity and identify subpopulations at each time point within the time window defined by our previous study  [26]. Then, we investigated the genome-scale changes of the chromatin structure using wholecell population-level ATAC-seq (referred to as bulk ATAC-seq). scATAC-seq was used at a critical time point to confirm the conclusions. Finally, we analyzed the data to determine how global chromatin changes are related to global transcription changes. As the hematopoietic system is a very well-studied experimental model and the key individual elements are well known, we focused our analysis on the less known global tendencies rather that individual genes and chromatin elements.
Note the rapid increase in the number of genes expressed per cell between 5 h and 24 h and the slow decrease after a plateau between 24 h and 72 h. (C) WGCNA reveals groups of genes with similar dynamic patterns in the average mRNA expression in donor1 and donor2. Details about WGNCA are given in Materials and methods. Note that category 1 reproduces the best overall dynamic pattern observed for genes showing detectable expression in single cells in (B). Category 1 = 5,194 genes (donor1) and 5,518 genes (donor2), category 2 = 3,977 genes (donor1) and 2,602 (donor2), category 3 = 1,089 genes (donor1) and 609 genes (donor2). (Numerical values available in scRNA-seq repository GSE156734: "GSE156734_Spread_MARSseq_data_all_filters_20200728. csv") scATAC-seq, single-cell ATAC sequencing; scRNA-seq, single-cell RNA sequencing; WGCNA, weighted correlation network analysis.
The 3 largest categories together sum up to more than 10,200 genes for donor1 and 8,700 genes for donor2. Although with slightly different dynamics, all gene categories display an initial increase followed by a decrease, pointing to a genome-wide phenomenon. Thus, the CD34+ cells of both donors responded to cytokine stimulation in a similar way, with a strong, but transient, gene up-regulation both in terms of gene and transcripts numbers. To investigate the structure of the cell population, we first visualized the data using the usual dimension reduction method, Uniform Manifold Approximation and Projection (UMAP) (Fig 2A  , 2B, 2G  and 2H ). For both donors, the cells collected at different time points clustered separately, suggesting a clear time progression in gene expression. In order to identify cell clusters with similar gene expression patterns, characterize their lineage progression and the possible trajectories of the cells, we analyzed our data using Clustering And Lineage Inference in Single-cell Transcriptional Analysis (CALISTA).
The red spotted line indicates the mean tendency. Note the general tendency to pass through a maximum at 5 h. (A minority of peaks displayed different evolution; they are shown on S5 Fig). (Numerical values available in bulk ATAC-seq repository GSE156733: "GSE156733_readCount_00h_Xvivo. txt. gz"; "GSE156733_readCount_05h_Xvivo. txt. gz"; "GSE156733_readCount_24h_Xvivo. txt. gz"; "GSE156733_readCount_48h_Xvivo. txt. gz". "GSE156733_peaks_intersection_00h_Xvivo_ann. csv. gz"; "GSE156733_peaks_intersection_05h_MP_ann. csv. gz"; "GSE156733_peaks_intersection_24h_MP_ann. csv. gz"; "GSE156733_peaks_intersection_48h_MP_ann. csv. gz". Numerical values available in bulk ATAC-seq repository GSE156733: "GSE156733_DEseq2_results_05h_MP_vs_00h_Xvivo. txt. gz"; "GSE156733_DEseq2_results_24h_MP_vs_05h_MP.
txt. gz"; "GSE156733_DEseq2_results_48h_MP_vs_24h_MP. txt. gz"; "GSE156733_readCount_00h_Xvivo. txt. gz"; "GSE156733_readCount_05h_Xvivo. txt. gz"; "GSE156733_readCount_24h_Xvivo. txt. gz"; "GSE156733_readCount_48h_Xvivo. txt. gz"; "GSE156733_peaks_intersection_00h_Xvivo_ann. csv. gz"; "GSE156733_peaks_intersection_05h_MP_ann. csv. gz"; "GSE156733_peaks_intersection_24h_MP_ann. csv. gz"; "GSE156733_peaks_intersection_48h_MP_ann. csv. gz"). https://doi. org/10. 1371/journal. pbio. 3001849. g004 number of genes expressed per cell and the level of mRNAs were close to maximum at this time point. We used the same number of cells as in the bulk version (5,000 cells). The results are shown on Fig 5. Overall, more than 250,000 accessible sites (peaks) were detected after aggregation of the single-cell data.
This number is comparable with the number of peaks detected in each individual donor in the bulk ATAC-seq experiment. Most of these peaks in scATAC-seq were shared by a low number of cells (the median value is only 120 cells sharing a peak), signifying that half of the peaks were present in less than 2. 4% of the cells (Fig  5A ). If we consider only the promoter-spanning insertions, the median number of sharing cells was 382 (Fig  5B ), whereas the median number of the cells sharing the same intergenic regions peaks was 111 (Fig  5C). When the results of the bulk and single-cell approaches were compared, we found that 99% of the peaks obtained by "bulk" ATAC-seq were also detected by the single-cell approach. In addition, the commonly detected peaks between bulk and single-cell ATAC-seq are also the most shared peaks between cells in the single-cell dataset.
To do this, we expanded the circle of potential players to TFs other than those analyzed above. We explored the expression of every TF-coding gene in our dataset. We sought to determine if there was an association between the changes in the expression of TF-coding genes in general and changes in the expression of their target genes. We assumed that the mRNA levels of TF-coding genes were acceptable surrogates of the TF protein abundance in a cell. If the expression of the TF-coding genes precedes that of their target genes, it is possible that the transcription burst of the target genes is at least partially induced by the TFs. In order to test this hypothetic association, we categorized the genes according to the variation of their mRNA levels. This classification is based on the number of UMIs detected in a cell (see Materials and methods section for details).
5 × 10 -7 ). (Numerical values available in scRNA-seq repository GSE156734 used for differential expression "GSE156734_Spread_MARSseq_data_all_filters_20200728. csv". Numerical values available in bulk ATAC-seq repository GSE156733 for open/closed promoter information: "GSE156733_peaks_intersection_00h_Xvivo_ann. csv. gz"; "GSE156733_peaks_intersection_05h_MP_ann. csv. gz"; "GSE156733_peaks_intersection_24h_MP_ann. csv. gz"; "GSE156733_peaks_intersection_48h_MP_ann. csv. gz") DE, differentially expressed; scRNAseq, single-cell RNA sequencing; TF, transcription factor. https://doi. org/10. 1371/journal. pbio. 3001849. g006 and further supported by the increased number of the available binding sites seen in our study (S5 Fig). Coherent transcription profiles emerge from this heterogeneous transitory state concomitantly with the gradual chromatin compaction.