dataesr/scientific-paragraphs-categorization huggingface dataset:
Epifluorescence microscopy analyses of RCD and automated image analysis. Nuclei from both live and dead macrophages were stained by 1 M Hoechst 33342 (ThermoFisher Scientific). Nuclei from apoptotic, pyroptotic and necroptotic macrophages were revealed by staining with by 1 M YO-PRO-1 (ThermoFisher Scientific)  (Darzynkiewicz et al. , 2011; Idziorek et al. , 1995; Rosazza et al. , 2020). Both markers were added to the culture medium and cells were incubated for 15 min at room temperature. Image acquisition was done using the EVOS M5000 microscope (ThermoFisher Scientific) for transmitted light, and florescence signals detected at red (mCherry expressing parasites), blue (Hoechst) and green (YO-PRO-1) wavelengths. Five fields were acquired at the 20x objective. Automatic Image analysis was performed using the ImageJ software package on at least 800 cells.
STAR version 2. 5. 0a  (Dobin et al. , 2013) , with default parameters, was used for alignment on the reference genome (GRCm38 from Ensembl database 94). Genes were counted using featureCounts version 1. 4. 6-p3  (Liao et al. , 2014)  from the Subreads package (parameters: -t gene -s 0). Transcriptomic data are made publicly available at the NCBI's Gene Expression Omnibus repository (Superseries GSE205860). The RCD-related gene list is provided for each pathway in Supplementary figure  1. RT-qPCR was carried out in 384-well PCR plates (Framestar 480/384, 4titude, Dominique Dutscher) using the iTaq Universal SYBR® Green Supermix (Bio-Rad) and 0. 5 μM primers with a LightCycler® 480 system (Roche Diagnostics, Meylan, France). Primer information for all targets is detailed in Supplementary table  S1.
Crossing Point values were determined using the second derivative maximum method (LightCycler® 480 Basic Software). The relative expression software tool (REST©-MCS)  (Pfaffl et al. , 2002)  was used to calculate fold change (FC) values. The list of primers for each analyzed target gene is provided in Supplementary table 2. Normalization was performed with the geometric mean of the two best reference genes ppih and mau2 as determined by the GeNorm and Normfinder programs (data not shown). For statistical analysis of gene expression levels, Cp values were first transformed into relative quantities (RQ) and normalized  (Lecoeur et al. , 2020a). Nonparametric Kruskal-Wallis tests were performed on Log transformed Normalized Relative Quantity values. Results were expressed as Z-scores calculated by relative expression compared to the day 0 unpolarized group (long-term analyses).
In silico analyses. String network analyses were performed using Cytoscape V3. 9. 1, the interaction of TFs and targets were extracted from Transcriptional regulator relationships unraveled by a sentencebased text mining database (TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions  (Han et al. , 2018). TF-targets gene networks were obtained with the ENCODE transcription Factor targets dataset.
ACKNOWLEDGEMENTS We acknowledge the financial support of the  Institut Pasteur (Paris) , the  Région Ile-de-France  (program  DIM1Health ), the  Institut Pasteur International Direction  to the  International Mixed Unit "Inflammation and Leishmania Infection ", the  ANR 21CE15004601  ELATION, and the  Institut national de la santé et de la recherche médicale (INSERM). Sheng Zhang  is part of the  Pasteur -Paris University (PPU)   International PhD program. This project has received funding from the  CNBG company, China.
Genome assembly The initial assembly was optimized using different assemblers. NextDenovo v2. 3. 1  [33] , Flye 2. 9. 1 (RRID:SCR_017016)  [34] , and Canu 2. 2 (RRID:SCR_015880)  [35]  were used for Nanopore reads. HiFiasm 0. 15. 2 (RRID:SCR_021069)  [36] , Flye 2. 9. 1, and HiCanu (using HiFi mode in Canu 2. 2)  [37]  were used for PacBio HiFi reads. NextDenovo and HiFiasm were finally used for Nanopore and HiFi reads, respectively, based on assembly continuity (see Results). Supplementary Fig. S1  shows the steps used to assemble the M. beccarii genome, and the full scripts (file named "Mbe_genome_assembly_srcipt. txt") are provided on the Figshare website  [38]. Briefly, NextDenovo was used to assemble the genome with Nanopore long reads. The assembly was then polished using Racon v1. 4.
21 (RRID:SCR_017642)  [39]  and Hapo-G v1. 0  [40] ; Pseudohaploid  [41]  and Purge_Dups v1. 2. 5 (RRID:SCR_0 21173)  [42]  were used to remove duplications caused by heterozygosity in the assembly. PacBio HiFi reads were used to correct the assembly with Inspector (RRID:SCR_004923)  [43]  and RagTag v2. 0. 1  [44]. The corrected assembly was scaffolded with Hi-C reads using Scaffhic 1. 1  [45] , Juicer pipeline 1. 6 (RRID:SC R_017226)  [46] , and 3d-dna 201008 (RRID:SCR_017227)  [47]. Gapfilling was performed using TGS-GapCloser v1. 0. 1 (RRID:SCR_017 633)  [48]. BUSCO (RRID:SCR_015008) v5. 2. 2  [49]  with the database embryophyta_odb10. 2020-09-10 was used to evaluate the quality of the assembly. The completeness of the assembly was also assessed by aligning the Illumina WGS reads using BWA v0.
Gene prediction and annotation LoReAn  [58] , an automated annotation pipeline designed for eukaryotic genome annotation, was used for structural gene prediction. In addition to ab initio gene prediction, both long and short RNA-seq reads and protein sequences from 3 species, M. balbisiana, M. schizocarpa, and M. acuminata (Supplementary Table  S2 ), were used for RNA-seq and protein evidencebased gene prediction in LoReAn. The results were then input into the Funannotate pipeline v1. 8. 7  [59]  to obtain final integrated and consensus gene sets using the commands "funannotate train" and "funannotate predict" and the parameters "-max_intronlen 100000 -busco_db embryophyta -organism other. " Gene prediction completeness was evaluated by BUSCO using the database embryophyta_odb10. 2020-09-10 with the parameter "mode prot," and only the longest transcripts were used.
After gene prediction, the command "funannotate annotate" was used to functionally annotate genes. The following databases were used to annotate genes: dbCAN v9. 0 (RRID:SCR_013208)  [60] , eggNOG v5. 0. 2 (RRID:SCR_002456)  [61] , Gene Ontology (GO; RR ID:SCR_002811)  [62, 63] , KEGG (RRID:SCR_012773)  [64] , InterPro v5. 52-86 (RRID:SCR_006695)  [65] , MEROPS v12. 2 (RRID:SCR_00777 7)  [66] , Pfam v34. 0 (RRID:SCR_004726)  [67] , and UniProt v2021_03 (RRID:SCR_002380)  [68]. Because many isoforms in genes were identified via the Funannotate pipeline when short-and long-read transcripts were used for gene annotation, SUPPA v2. 3  [69]  was used to investigate alternative splicing (AS) events. AS events were classified into 7 types: skipping exon, alternative 3 splice sites, alternative 5 splice sites, mutually exclusive exons, retained intron, alternative first exons, and alternative last exons.
Whole-genome alignment and synteny analysis Syntenic blocks within the M. beccarii assembly and between the Musaceae assemblies were analyzed using MCScan (RRID:SCR_0 17650, Python version) in the jcvi package and visualized using the jcvi v1. 1. 19 [83]  and Shinycircos  [84]  packages. The default parameters for the synteny analysis in MCScan were used, with the exception that the parameter "minimum number of anchors" was set to 10. MCScanX (RRID:SCR_022067, match score 3 and match size 10)  [85]  was also used, and results were visualized using SynVisio  [86]. Dot plot alignments between Musaceae genome assemblies were generated and visualized using D-GENIES v1. 2. 0 (RRID:SCR_018967)  [87].
Nucleotide-binding site-leucine-rich repeat gene identification Nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes are the major plant resistance genes serving as an active defense against pathogens  [90]. There are generally 3 main types of NBS-LRR genes  [91] : Toll/interleukin-1 receptor NBS-LRR (TNL), N-terminal coiled-coil motif NBS-LRR (CNL), and resistance to powdery mildew 8 NBS-LRR (RNL) genes; TNL genes are absent in monocots  [91, 92]. According to the results of In-terPro/Pfam annotation in the tested Musaceae species, NBS-LRR genes were identified using the following protein domains: IPR03800, PF00931/IPR002182, PF13855/PF00560/IPR032675, and PF05659/IPR008808. NBS-LRR genes were also detected using NLR-Annotator  [93]  with default settings. Rather than using annotated proteins predicted by gene models and transcriptomic data, NLR-Annotator directly uses genomic sequences to identify possible NLR genes, which were confirmed to be most efficient in NLR gene identification.
Ancestral genome reconstruction AnChro  [94]  was used to reconstruct ancestral genomes of Musaceae members with ginger (Zingiber officinale, GenBank accession number of GCA_018446,385. 1) as an out-group. SynChro  [95]  was used to identify conserved syntenic blocks between different pairs of genomes; the blocks in the two genomes (with the shortest path connecting them in the phylogenetic tree) were then used to infer ancestral gene order by comparing them with the reference genomes. During syntenic block inferences, the stringency parameter, which determines the number of reciprocal best hits within a syntenic block, was set to 3.
The Nexdenovo and Hifiasm assemblers displayed superior contig numbers, average and minimum lengths, and N50 values for Nanopore and HiFi reads, respectively; these 2 assemblies were used in subsequent steps of the genome assembly process. Details of the Nexdenovo and Hifiasm assembly results are shown in Table  1. After Hi-C read scaffolding, the assembly size was 569,617,942 bp with 449 scaffolds, the N50 value was 67,088,101 bp, and 551,683,906 bp (96. 85%) of the sequences were assembled into 9 chromosomes (Table  1 , Fig. 2A ). The largest   1 ). BUSCO assessment of the final genome sequence assembly yielded a completeness score of 98. 4% for the Embryophyta (1,614 core genes) datasets, including 1,510 (93. 6%) complete and singlecopy genes and 78 (4. 8%) complete and duplicated genes.
The consensus monomer sequence was 10,402 bp long with a GC content of 60%. The consensus sequence included 17 copies of a tandem repeat (MuTR; GenBank AM905874 to AM905898), although the number of MuTR repeats varied between rDNA monomers in the Nanopore long-molecule reads. Excluding MuTR repeats, present at multiple genome sequence sites, the 45S rDNA repeat was present in 5. 0% of the examined short sequence reads. The 5S rDNA monomer was 432 bp long with a GC content of 55. 8%, and it was present in 0. 11% of the reads; the major sites were located on chr8 at bp 16,100,000 and chr7 at bp 37,424,000. Peaks associated with the higher GC content of rDNA sequences (assembly average 38. 7% GC) are shown in the GC content plot (Fig. 2B ).
Gene prediction and annotation A total of 39,112 genes encoding 45,461 proteins were predicted in M. beccarii. Of these genes, 38,756 (85. 25%) were functionally annotated (Supplementary Table  S8 ) with a completeness score of 94. 8% in the embryophyta_odb10 database, according to the BUSCO assessment. Alternative splicing events were detected in 4,602 genes. Retained introns were the most frequent events (2,847), followed by alternative 3 splice sites events (830). Other detected events include skipping exon events (313), alternative 5 splice sites (424), mutually exclusive exons  (8) , alternative first exons  (109) , and alternative last exons (103). A total of 3,168 genes encoding TF in M. beccarii were identified, and this was similar to the number of TF genes identified in the genomes of M.
Gene family evolution Gene family expansion due to duplications in Musa, including M. beccarii, was mainly caused by ancient WGD events. TFs are abundant in Musaceae species and higher than all the other monocots used in our dataset, with the exception of Zingiber officinale (Fig. 2C , Supplementary Table  S9 ), which is a tetraploid species. The enrichment analysis of M. beccarii revealed that genes encoding TFs are some of the main genes retained following WGD events (Supplementary Table  S23 ; Supplementary Figs. S8  and S9A ). The fact that Musaceae species have experienced the same WGD events indicates that the retained genes encoding TFs play key roles in mediating adaptation to stress by TFs in Musaceae, which is consistent with results of previous studies of the genomes of M. acuminata and M.
No gene families in these pathways have undergone contraction. Because typical terpenoid synthase genes are characterized by 2 conserved domains with Pfam ID PF01397 and PF03936  [112] , the comparison of the genes with these domains in Musaceae revealed that the number of primary terpenoid synthase genes in M. beccarii is low (Supplementary Table  S9 , Fig. 2C ). Gene families that have undergone significant expansion in M. beccarii, which are mostly involved in transcription, carbohydrate metabolism, and membrane transport (Supplementary Tables S16, S17, Supplementary Fig. S6 ), mediate various processes related to plant growth, development, and defenses  [113] [114] [115] [116]. Therefore, genes in expanded families might help balance the growth, development, and defense in M. beccarii.
The cell wall as a defense (lipid metabolism and adenosine triphosphate-binding cassette transporters) Although they do not play a direct role in defense, genes encoding proteins that mediate the synthesis of substances such as cutin, suberin, and wax play a critical role in "physical defense" in plants  [117] [118] [119]. They are lipids  [117, 120]  formed by fatty acids and glycerol. They comprise the extracellular hydrophobic layer of cell walls in plants, provide mechanical support, and protect plants from desiccation, extreme temperatures, UV light, and attack by pathogens and pests  [117, 119]. According to the KEGG PATHWAY database  [121] , lipid metabolism comprises 16 pathways, and genes involved in 14 of these pathways were identified in our study species (Supplementary Table  S9 ).
2C ), which might underlie its ability to adapt to environmental conditions. Furthermore, GT family 61 (GT61) genes encode proteins involved in xylan biosynthesis for the cell wall  [163, 164]. Xylans are hemicelluloses that can affect cell wall recalcitrance and aid defense against herbivores and pathogens  [165]. The number of GT61 genes was highest in M. beccarii among Musaceae species (Supplementary Table  S37 , Fig. 2C ). DupGen_finder revealed that 32 (66. 7%) of these genes were derived from ancient WGDs; this finding is consistent with the results of a previous study showing that the major duplication of GT families stemmed from WGD events  [164, 166]. Experimental studies are needed to fully characterize BGCs.
Data Availability We deposited the sequenced reads to NCBI Sequence Read Archive under the accession number SRR16526886 for the Nanopore reads, SRR16526885 for PacBio HiFi reads, SRR16526887 for the Illumina WGS reads, SRR16588090 and SRR16588091 for the Illumina Hi-C reads, SRR16351760 for the Illumina RNA-seq reads, and SRR16351759 for the PacBio Iso-seq reads. The high-quality genome sequence was submitted to GenBank under the accession number JAIWVJ000000000. Genome Assembly, gene annotation data, and transcriptomic data are also available on the Banana Genome Hub  [177]  for download or exploration via a dedicated genome browser (Jbrowse) and syntenic browser (SynVisio). All supporting data are available in Figshare  [38]  and the Giga-Science GigaDB database  [178].
Introduction Knowledge on past earthquakes still needs to be improved. The reliability of historical earthquake 326 Alessandro  Stocchi et al. [see  Lambert et al. , 2015, for more details]. Due to the nature of the historical sources used for the construction of the database, the uncertainties related to the evaluation of the intensity levels and their locations can be very large. In SisFrance database, epicentral intensities and locations are associated to quality factors that reflect the confidence in the numerical values deduced from the historical sources and testimonies. In some cases, the observations simply allow to deduce that the event was felt at a site but information is not sufficient to assign an intensity. The models used to describe the relation between the macroseismic intensity, magnitude, and distance from the epicenter or hypocenter introduce further uncertainty [see  Baumont et al.
However, the studies focus on qualitative comparisons; updating of initial magnitudes or intensities is not performed. This paper is organized as follows. We first give an overview of the approach by introducing the relevant equations for updating the magnitudes of historical earthquakes. We then provide a description of the building database and show how the detailed and generic fragility curves that are required for the analysis of large building stocks are computed. For the purpose of illustration, the methodology is finally applied to the historical earthquake of La-Tour-du-Pin in France that has occurred on February 18th 1889.
Methodology Updating of the initial probability distribution of magnitude of a historical earthquake is carried out in a Bayesian framework. This requires the knowledge of the damage state D of buildings. The initial or prior distribution of the magnitude is defined by the best estimate and the associated uncertainty of the historical magnitude given in the earthquake catalogue. Here, the data from FCAT catalogue [see  Manchuel et al. , 2018]  is used. The posterior magnitude f (m|D) is then computed by updating the prior magnitude distribution f M (m) according to the Bayes theorem: f (m|D) ∝ P (D|m) • f M (m), (1) where P (D|m) is the probability of observing the damage state D for a given magnitude m: P (D|m) = P (D|IM, m, r, s) f IM (IM|m, r, s) f R (r ) × f S (s) dr ds d IM. (2) P (D|IM, m, r, s) is the probability of damage D for a given value of intensity measure IM of the earthquake.
Building damage database Existing structures and buildings that have experienced the earthquake under consideration provide useful testimonies about the past event. Data regarding the impact of the earthquake on the structural state needs to be collected and analyzed. This type of information is generally collected by archeoseismologists from in situ and archive investigations [e. g. Poursoulis, 2017 , Benjelloun et al. , 2021]. This new information is used to perform the Bayesian updating of the magnitude assigned to a historical earthquake. Indeed, particular cracks or traces of repair work can be recorded of a past earthquake  [Colombi et al. , 2008]. In order to select structures that may have encountered a relatively large seismic load, a criterion of selection is considered. This criterion evaluates if the approximated fundamental period of the structure falls into the period range of maximum pseudoacceleration obtained from regulatory spectral acceleration  [CEN, 2005]  for the area of interest.
According to the typology considered for old masonry structures, different formula extracted from the literature are considered. For the fundamental period estimation of simple regular masonry buildings, the formula from EC8  [CEN, 2005]  is used: T = C t • H 3 4 , ( 5 ) where H is the height of the building and C t is a regression coefficient taken equal to 0. 05 for masonry structures. As regards churches in particular, the estimation of the fundamental period for bell towers is extracted from database using ambient vibration measurements  [Limoge-Schraen et al. , 2016 , Michel et al. , 2008 ]. An additional estimation of the main fundamental period along the transversal direction of a church can be obtained according to the work of  Lopez et al. [2019] : T = 1 16. 41 • H 0. 4 W 0. 3 E -0.
2 , ( 6 ) where H and W are the height and length along the transversal direction of the church, respectively. E is the average Young's modulus. For slender masonry structures like the bell tower, the main fundamental period can be estimated according to the work of  Shakya et al. [2016] : T = 1 3. 58 • H B 0. 57. ( 7 ) In this equation, H denotes the height of the bell tower and B its width. Figure  1  illustrates the correspondence between the period range of the fundamental periods of the structures in the database and the plateau of the spectral acceleration for soil classes B (red curve) and C (yellow curve)  [CEN, 2005]. In order to analyze its damage state, the vulnerability of each structure is defined by its probability of damage P (D|IM). For a first approximation of the vulnerability of the structures, global fragility curves for small masonry buildings like the ones proposed in  Pitilakis et al.
La-Tour-du-Pin case study In order to illustrate the methodology, the historical earthquake of La-Tour-du-Pin in France that occurred on 18th February 1889 is analyzed. The specificity of this study is linked to the fact that this earthquake has a moderate estimated magnitude M w = 4. 4 with a large uncertainty err M w = 0. 42  [Manchuel et al. , 2018]. This large uncertainty is due to the fact that the intensity data points assigned to this earthquake in the 2014 SisFrance database are based on only very few testimonies. The macroseismic intensities in the database are evaluated with the Medvedev-Sponheuer-Karnik intensity scale  [Medvedev and Sponheuer, 1969]. The macroseismic intensity provides an empirical appraisal of the effects of an earthquake based on observed damage. The Intensity Data Points (IDP) represent earthquake effects in terms of damages and population at different locations.
Construction of the building database The building database is established with the following criteria: maximum epicentral distance, age of the structure (i. e. , pre-earthquake construction date), typology, and associated potential vulnerability. The first two criteria allow to restrict the study to buildings that may have been affected by the earthquake of La-Tour-du-Pin. The last criterion is used to limit the data to buildings that could have suffered damage according to the magnitude assigned in  Manchuel et al. [2018]. Indeed, buildings with a large median capacity do not carry much information for updating small magnitudes. In order to cover the epicentral region where structures may have been damaged by the historical earthquake, a set of seventy-three cities was considered for a database of building damages  [Poursoulis, 2017].
According to the SisFrance database, the region has experienced several historical earthquakes. This means that one particular building or structure may have experienced more than one event. The historical earthquakes that have induced observable testimonies and potential effect on structures (Intensity MSK ≥ V) according to SisFrance are listed in Table 2. The occurrence of several earthquakes affecting the same structure may affect the damage state; in other words, the testimonies of damage observed today may be caused by another event than the one considered here. In order to be sure that observed damage is due to the La-Tour-du-Pin earthquake, we analyze the area of influence of each historical earthquakes summarized in Table  2. For this purpose, based on historical data and the attenuation model of intensity with distance from  Arroucau et al.
[2006] , a region of interest for La-Tour-du-Pin earthquake is determined. Figure  3  shows the isoseismal curves obtained for the events of Table  2. From these analyses it can be concluded that multiple impacts of the La-Tour-du-Pin earthquake and the other historical events of Table 2 is not expected in an area delimited by a radius of fifteen kilometers around the potential epicenter of La-Tour-du-Pin earthquake. This choice is confirmed by the data extracted from SisFrance database. Figure  4  shows the IDP associated to the historical earthquakes listed in Table  2. The IDP associated to other earthquakes than the one of La-Tourdu-Pin are small (i. e. , with intensity MSK lower than the ones associated to damage) in the region of interest defined. Corrençon-en-Vercors (Dauphiné) VII-VIII V I 0 stands for the epicentral intensity and I for the maximum intensity among the Intensity Data Points provided in the SisFrance database for the region of interest.