dataesr/scientific-paragraphs-categorization huggingface dataset:
Mapping, samples reproducibility and gene expression levels We mapped this newly produced regeneration RNA-seq data (n = 18 samples) to the Reference transcriptome we assembled. As for the other datasets, the percentage of successful mapping is very high (above 90% on average) ensuring that we can explore the huge majority of the generated data (Additional file 5). We assessed the quality and reproducibility between our biological replicates through a Principal Component Analysis (PCA) and eliminated three samples that were identified as outliers (namely 1dpa_3, 3dpa_1 and 5dpa_3, Additional file 10). Consequently, for the 1, 3 and 5 dpa stages, our analyses only include two biological replicates, while we have 3 replicates for the NA, 0 and 2 dpa stages. In contrast to Platynereis' embryonic developmental stages, which are extremely stereotypical  [56] , the regenerative stages, while well-defined  [42] , are a little less stereotypic and show a greater degree of biological variability.
Next, we decided to apply a strategy akin to a genecandidate approach to specifically assess the importance of major gene expression regulators during regeneration: transcription factors (TF) and RNA-Binding Proteins (RBP) (Additional file 17). TFs, by controlling the rate of gene transcription, are of uttermost importance during morphogenetic processes and notably regeneration  [66]. RBPs are involved in many post-transcriptional processes leading to a fine-scale regulation of mRNA stability, localization and translation during development in animals  [67, 68]. The rationale here is to identify regeneration stages in which these types of regulators could be specifically enriched. To this end, we used a comprehensive animal TF database (AnimalTFDB 4. 0) that contains 1611 TF from mouse  [69] , and identify potentially homologous genes in our transcripts (based on BLASTX analyses).
We annotated 5248 of our transcripts as TFs, and among them, those that are specifically DEGs between sequential conditions of regeneration (88 ≤ n ≤ 236, Additional file 17). Using hypergeometric tests, we tested whether TFs are over-represented among these DEGs (in sequential comparisons). It appears that TFs are not over-represented for none of the sequential conditions (all p-values = 1). In contrast, when applying the same approach for RBPs (using the RBPDB database that contain 405 RBP from mouse  [70] ), we could highlight an over-representation of these gene expression regulators category for all conditions excepted one (5dpa vs. NA) (Additional file 17). RBPs are thus especially at play during both the initiation of regeneration, and the blastema formation and differentiation, while it is no longer the case during the last step of growth and differentiation.
planarians, cnidarians, acoels, etc. [75] [76] [77] ). We thus specifically investigated nervous system and stem cells genes markers by two different means in our dataset. First, applying the same methodology as for TFs and RBPs, we used specific nervous system and stem cells related databases  [78]  that we compared to our DEGs associated to step-wise comparisons. While nervoussystem related genes are not enriched in any conditions, stem cells genes are, for three comparisons (namely 1 vs. 2 dpa, 2 vs. 3 dpa and 5 dpa vs. NA, Additional file 17). In addition, we also explored in detail a specific selection of "nervous system" (NS) and "stem cells" (SC) genes (n = 22 and 32, respectively) that were already well known and annotated in Platynereis as larval development or regeneration genes (Additional file 18)  [41, 61, 62, 79, 80].
Regarding our selection of NS genes, we observed that many of them are up regulated at 3 dpa in comparison to 2 dpa (e. g. Sox B1, Neurogenin (ngn), Olig, Ash1, Pax 2/5/8) (Additional file 18). Among them, SoxB1, Olig, Ngn and Ash1 are part of the ancestral bilaterian toolkit of neural developmental genes  [61, 80]. Their role in patterning larval central nervous system has been well established in Platynereis. The activation of this nervous system program at 3 dpa coincides with the time at which the nervous system is being reformed during regeneration  [42] , suggesting that NS formation during both regeneration and development could rely on a similar transcriptional landscape. Within the "stem cells" genes, we mainly focused on the GMP signature previously mentioned and identified Ago, Pumilio, PufA, Smb, Tudor2, Brat, Gustavus, Ap2 and Lin28 as being up-regulated at 1 dpa in comparison to 0 dpa (Additional file 18).
These results, suggesting an up-regulation of this multi/ pluripotency program at the wound-healing stage are in agreement with previous experimental data. Indeed, various GMP members are intensely expressed in the wound-epithelium during Platynereis posterior regeneration, including for instance Ap2  [42]. Altogether, these elements support the hypothesis that blastema formation in Platynereis may rely on dedifferentiation events, as well as the fact that amputation potentially induces extensive reprogramming of differentiated cells into progenitor cells  [42]. Overall, these extensive analyses of differentially expressed genes during various regenerations stages unveil important contributions of the nervous system and RBPs for different steps of regeneration. Many additional elements, especially members of key signaling pathways are also active during these stages and deserve a more in-depth investigation; they will be the subject of further studies.
Indeed, Maf and c-jun transcription factors, as well as various members of the Mitogen-activated protein kinase (MAPK) pathway are found in this cluster. Others elements associated with Notch, FGF, Hh, BMP, WNT, and potentially Toll and NFkB pathways could also be evidenced. Among and above them, some genes are related (See figure on next page. ) Fig. 6  Clustering of genes based on their expression dynamics during regeneration in Platynereis dumerilii A) Heatmap representation of the expression of each transcript sorted per cluster (n=12) throughout the posterior regeneration. Each line represents a transcript within its cluster, each column the regeneration stage considered. Colors indicate expression values. Expression values were taken as scaled TMM values (i. e. scaled down to have a mean of 0 and a standard deviation of 1).
With the objective to demonstrate the quality and validate the presented RNA-seq dataset, we performed whole mount in situ hybridizations (WMISH) on seven genes pertaining to three of those clusters (6, 7 and 11) (Fig. 7 ). These expression patterns were determined during the whole course of regeneration (7 stages), from 0 to 5 dpa, also encompassing the stage 4 (or 4 dpa, not sampled for the transcriptomic analyses). Of note, as WMISH are not working efficiently on non-amputated worms, we used 15 dpa worms, as a proxy of a non-amputated stage, as previously explained  [41]. Regarding the cluster 6 (expression peak at 2 dpa), we selected the gene Pdu-Collier. This gene encodes a Helix-Loop-Helix transcription factor known to be mainly involved in the ventral nerve chord formation in Platynereis larvae, being more specifically expressed in postmitotic neural cells  [79].
Samples were pooled in equimolar quantities to obtain 36 fmol of cDNA and the rapid adaptater ligation step was performed. Libraries were multiplexed by 2 on a FLO-MIN106 flowcell according to the manufacturer's protocol. For the series 1 (Blastema tissues), Illumina deep sequencing with 150 bp paired-end reads was performed, while for the series 3 (regeneration timeline), 50 bp singleend reads were obtained, using Illumina HiSeq sequencing system. For the series 2, long reads were sequenced using the Oxford Nanopore Technology (ONT) and the MinION sequencing system (Additional file 2). More specifically, sequencing was performed with the SQK-PBK004 72-hour sequencing protocol run on the MinION MkIC, using the MinKNOW software (versions 20. 10. 6). A mean of 3. 9 ± 0. 6 million passing ONT quality filter reads was obtained for each of the 2 samples.
Read processing and de novo transcriptomes assembly All Illumina short raw reads were processed with Trimmomatic  [93]  using the following command to eliminate short and low-quality reads, as well as adapter sequences: ONT long raw reads were processed with TALC  [94]  with default options. With the aim to produce a high-quality, as complete as possible reference transcriptome, we chose to combine several pre-existing and newly generated de novo transcriptomes. This strategy (Additional file 3) allows to include a diversity of biological datasets and to combine the advantages of several assembly pipelines and methods. We performed a first assembly using only the blastema short reads (series 1) using Trinity  [95]  with default options and named it the Blastema transcriptome. We then carried out a second assembly using the blastema short reads (series 1) as well as the noncorrected ONT long reads (series 2), using rnaSPAdes  [49]  with default options.
Transcriptomes quality assessment We assessed and compared the quality of the 6 transcriptomes (Blastema, Hybrid 1, Hybrid 2, Head, Embryonic, Reference) by computing a number of classic metrics. First, using Trinity  [95] , we computed total number of transcripts and genes, mean, min & max transcript length, transcript N50 (length of the shortest transcript for which longer and equal size transcripts cover at least 50% of the transcriptome sequences)  [53] , as well as Ex90-N50 (N50 for the 90% most expressed transcripts based on the blastema short reads) (Additional file 4). We further appraised the quality of the reads' mapping on each transcriptome. For that purpose, the Blastema short reads (series 1) we generated, as well as the short reads used for the assembly of the head and embryonic transcriptomes (head and developmental stages dataset, respectively, see Additional file 2) were mapped on each transcriptomes using bowtie2  [54]  with default parameters (Additional file 5).
We also assessed gene completeness for each transcriptome using BUSCO (Metazoan gene set) (Additional file 6)  [97]. Given the redundancy of transcriptome assemblies compared to genome assemblies, single-copy and duplicated genes were merged together. We finally evaluated how transcriptomes could recapitulate known coding and protein sequences. We computed the percentage of P. dumerilii coding sequences on the NCBI Nucleotide database  [98]  covered by at least one transcript and for that purpose; we blasted each transcriptome to the NCBI database using a e-value cutoff of 10 -5 and reported the number of coding sequence with a match. Similarly, we computed the percentage of transcripts covered by at least one Swiss-Prot protein sequence (using the non-redundant database), using blast with e-value cutoff of 10 -5 and the qcov output format to compute the query coverage (Additional file  6).
We finally performed additional BLAST analyses to assess the quality of our assembly and evaluate our previous BLAST results. We first used the transcript sequences of a well-reconstructed and annotated annelid genome, Owenia fusiformis  [55] , to perform BLASTX searches with the coding sequences of the 6 species mentioned above. Second, we performed a similar BLAST search, using our transcript sequences as indicated in  [55] , namely on a non-redundant gene database from well-annotated annelid genomes (Capitela teleta, Dimorphilus gyrociliatus, Eisenia andrei, Lamellibrachia luymesi, Paraescarpia echinospica, Riftia pachyptila and Streblospio benedicti). For both analyses, a e-value cutoff of 10 -3 was used to consider a hit as significant.
Gene ontology and enrichment analysis for differentially expressed genes and clusters We further analyzed the content of DEG lists as well as clusters. We performed Gene Ontology (GO) -term enrichment analyses using clusterProfiler  [104]. For the DEGs, a full list of enriched GO terms is provided in Additional file 14 and the top 10 for the category "Biological processes" are presented thanks to a dotplots. We specifically focused on the lists of genes generated from step-wise comparisons (i. e. 0dpa vs. 1dpa, 1dpa vs. 2dpa, 2dpa vs. 3dpa, 3dpa vs. 5dpa, 5dpa vs. non-amputated). For clusters, GO terms enrichments were represented using the treeplot function from clusterProfiler, using "average" for the hclust method (Additional files 19 and 20). In addition, we computed over-representation of genes in specific "functional" categories, specifically transcription factors (TFs), RNA-binding proteins (RBP), as well as nervous system development (NS), and stemcells markers (SC) thanks to available databases from the literature.
Acknowledgements This study is dedicated to the memory of our friend and mentor, the late  Professor Michel Vervoort. We are indebted to all members of the  Gazave & Vervoort lab  for their support and suggestions on this study, especially  Zoé Velasquillo  for her help with biological material production, as well as  Luis Carreira  for his participation to initial ISH experiments. We are grateful to  Dr. Jerome H. L. Hui  and  Wenyan Nong  for their critical input in early stages of this study. We also thank the  D. Arendt's laboratory  for sharing the Otp and Brachyury clones. We thank  Corinne Blugeon  for her input on the method section. We wish to thank the staff of the animal facility for their help with the Platynereis culture.
The LOD for this study was found at 0. 1%. We used QuantaSoft software (Bio-Rad, Hercules, CA, USA) which allows the calculation of the variant allele frequency (VAF) which correspond to the frequency at which the allele of a variant is found in a population, expressed in percentage. The results have been manually reviewed to allow an accurate interpretation. A sample was considered mutated if at least two ddPCR analyses found a VAF above the mutation threshold. In case of discordance of results between the duplicates, a triplicate was performed.
Results in the context of published literature The characteristics of our cohort are overall consistent with published data  [9] : a majority of the patients were over 60 years old, overweight and with initially localized tumors. Of note, most of our patients presented a low grade cancer (68. 2%), which contrasts with other study (33. 3% of low grade carcinoma among 60 patients in Casas et al. study  [10] ), but this difference may be due to the selection of a population of patients treated by endocrine therapy. Regarding the treatments received, the patients included were treated between 2008 and 2020, while immunotherapy was not yet available in France (reimbursement of the pembrolizumablenvatinib combination in 2022). The very low rate of circulating ESR1 mutation at baseline in our study is in line with the histological identification of ESR1 mutation in only 1.
5E ) (Additional File 7: Table  4 ). Based on the GO term enrichment analysis of the biological processes using FunRich software, we observed that these proteins, specifically expressed in each condition, were linked to different biological processes (Fig. 5F ). An effect on the cellular metabolic and energy pathways was found in Frozen and FrozenCell tumoroid conditions compared to Fresh tumoroids (with: AMY1A, SDR9C7, CDA, ARG1, FKBP, NDUFB10, DDO, ADH5, and SIAE). In addition, in the Frozen and FrozenCell condition, we have identified proteins involved in apoptosis like the apoptosis facilitator BCL2L13, ATG5, and TXNRD2. In the Fresh tumoroids, a higher number of proteins linked to cell communication and to signal transduction were identified. Interestingly, some of the specific proteins identified in fresh tumoroids were involved in the immune response, such as GZMB (Granzyme B) expressed by cytotoxic T and NK cells, the cell adhesion molecule Siglec1 (Sialoadhesin) expressed by macrophages, as well as CD163 (Cluster Differentiation 163), a marker of antiinflammatory macrophages, AMBP (alpha-1 microglobulin/bikunin) precursor of a glycoprotein synthesized by lymphocytes and CD177 (CD177 molecule), a marker of neutrophil activation.
In order to characterize the impact of these proteins, the GO terms analysis of each cluster was carried out with Cytoscape and ClueGO software, allowing to generate the networks connecting the proteins overexpressed (in red) and underexpressed (in green) to their biological process. The results showed that in the fresh condition (clusters 3-4) (Fig. 6A, B ), the cellular respiration and the amino acid metabolism were underexpressed compare to frozen tumoroids and FrozenCell tumoroids, which could be explained by the cryopreservation. In another hand, the assembly of the cell-substrate junction and the RNA translation by RNA polymerase appeared to be overexpressed in the Fresh condition compared to the other conditions, and the biological process of cell adhesion which can be explained by a conservation or a faster reformation of the extracellular matrix and the cell compaction.
The HeatMap shows two clusters of proteins over-or underexpressed in tumor compared to the different tumoroids. Functional annotation and characterization of these two clusters of proteins were performed using the FunRich software. The results showed that the biological processes underexpressed in the tumor compared to tumoroids are different processes involved in cellular metabolic and energy process such as cellular respiration (with NDUFV1, GPD2, CS, NDUFS1, SDHA) and carbohydrate derivative metabolic process (GPD2, ENO2, CAD, UGGT1, HK1, PFKP, FASN, AMPD2, MOGS), transport with two principal pathway detected: the protein import into nucleus (with Fig. 7  Proteomics analysis comparing the primary tumor to their derived tumoroids. A Venn diagram representing specific proteins in tumor of origin, Fresh, Frozen, and FrozenCell tumoroids (n = 3 for each condition).
HE, immunohistochemistry, and immunofluorescence staining The tumoroids were fixed in 2% paraformaldehyde with 0. 1% glutaraldehyde for 24 h followed by dehydration in 20% sucrose for 24 h, embedding in gelatin and freezing at -80 °C. Standard H&E staining was carried out on 5 μm thick tumor and tumoroid sections to appreciate the cellular and tissue structure details, using Tissue-Tek Prisma ® Automated Slide Stainer. Images were acquired on a Nikon Eclipse NI-U with the Nikon Elements BR 4. 50. 00 software. The immunohistochemical staining was carried out on 5 μm thick tumor and tumoroid sections using an automated protocol developed for the Discovery XT automated slide staining system (Ventana Medical Systems, Inc. ). Tumor and tumoroid sections were incubated for 40 min with the appropriate antibody before incubation with Discovery UltraMap anti-Rabbit (760-4315, Roche) or anti-mouse horseradish peroxidase (HRP) (760-4313, Roche) secondary antibodies and the Discovery ChromoMap DAB kit reagents (760-159, Roche).
Data analyses All the MS data were processed with MaxQuant (version 1. 5. 6. 5) software  [43]  using the Andromeda search engine  [44]. Proteins were identified by searching MS and MS/MS data against a database of Canis lupus familiaris obtain from Uniprot database and containing 45401 sequences. For identification, the FDR at the peptide spectrum matches (PSMs) and protein level was set to 1%. Label-free quantification of proteins was performed using the MaxLFQ algorithm with the default parameters. Analysis of the proteins identified were performed using Perseus (version 1. 5. 6. 0) software  [45, 46]. Multiple-sample tests were performed using ANOVA test with a p-value of 5% and preserving grouping in randomization. Visual heatmap representations of significant proteins variation were obtained using hierarchical clustering analysis.
Multi-Layer Process Columns with a first layer of NDs were dynamically grafted with streptavidin by flowing a solution of streptavidin at a concentration ranging between 5 and 15 µM in phosphate buffer, 67 mM, pH 7. 4). The streptavidin capture was monitored by in situ UV detection at 280 nm (mid-layer). The process was then repeated with the same sample of biotinylated NDs as that used for the first-layer grafting. This layering process was repeated for up to 3 layers of NDs. Frontal weak affinity chromatography experiments were performed on an Agilent 7100 capillary electrophoresis system (Agilent Technologies, Waldbronn, Germany) equipped with a diode array detector and an external nitrogen pressurization device to achieve pressures up to 12 bar. System control and data acquisition were performed using 3D-CE Chemstation software B.
Patients and methods This is a prospective multicentre observational cohort study. Patients admitted between October 1, 2020 (week 40/20) and May 30, 2021 (week 21/21) in one of the 11 participating ICUs from Greater Paris area hospitals included in the ANTICOV study (NCT04733105) were eligible for study inclusion. Inclusion criteria were as follows: age ≥ 18 years, SARS-CoV-2 infection confirmed by a positive reverse transcriptase-polymerase chain reaction (RT-PCR), patient admitted in the ICU who required mechanical ventilation more than 48 h for acute respiratory failure. Patients with a low viral load (PCR cycle threshold (Ct) > 32) in nasopharyngeal samples were not included in the study. The primary endpoint was the difference in prevalence of the first VAP according to SARS-CoV-2 variants.
Statistical analysis. We used a competing risk model (cumulative incidence function of the Gray model)  5, 6  to properly estimate the effect of COVID-19 on VAP development, while considering death and ventilator weaning as competing events using cmprsk package developed by Gray in R software ([http:// biowww. dfci. harva rd. edu/ ~gray/ cmprsk_ 2. 1-4. tar. gz]). Two-sided p values < 0. 05 were considered significant.
Isolation and Analysis of RNA These procedures followed MIQE standard recommendations and were conducted as previously described  [33]. The oligonucleotide sequences, designed using Primer Express software, are shown in Table  S1. Quantitative PCRs (qPCRs) were performed using SYBR qPCR premix Ex TaqII from Takara (Ozyme, Saint Cyr L'Ecole, France) and assays were run on a StepOne Plus ABI real-time PCR machine (Perkin Elmer Life and Analytical Sciences, Boston, MA, USA). The expression of selected genes was normalized to that of 36B4 housekeeping gene and then quantified using the comparative-∆Ct method.
Funding: This work was supported by  CNRS ,  Inserm  and by the  joint "Nutribrite" grant  from the  French Agence Nationale de la Recherche   ANR-15-CE14-0033  and  Deutsche Forschungsgemeinschaft ID :  KL973/13-1 , by "HepAdialogue" from the  French Agence Nationale de la Recherche   ANR-17-CE14-0015  and by  Nutricia Research Foundation  ( 2022-29 ). C. C. is supported by grants from  ANR  and from  Université Côte d'Azur (ATER). Institutional Review Board Statement: Not applicable.
Many research areas, such as transportation behavior modeling  [3, 12, 13, 23] , need to analyze and understand the complex phenomenon of interactions between different entities. Connected and autonomous vehicles, as well as connected human beings and Internet of Things objects, will be considered as agents. The proposed ABM follows the latest advancements in the fields of agent-oriented software engineering and agent-based simulation. The Janus platform  [11, 14, 15]  and its associated SARL meta-model and agent programming language 1  [6, 8, 10, 29]  are used for building the simulator. The source code of the simulator is published as open source code on Bitbucket. 2  This paper is structured as follows: Sect. 2 presents several related works. The different agent behaviors for vehicles and connected objects are detailed in Sects.