dataesr/scientific-paragraphs-categorization huggingface dataset:
Co-evolution analysis approaches have already been reported to be of great interest to predict inter-residue contacts with good accuracy  [19]  and they have been integrated, amongst other features, in AF2. Therefore, we performed a co-evolution analysis using the I-COMS server. For that, we used a sequence dataset that was specifically prepared for each protein according to their corresponding protein family. Indeed, only sequences from related organisms (gamma-and beta-proteobacteria) showing a similar composition in ubiquinone biosynthetic proteins to the one observed in E. coli and thus potentially having the same protein organisation for the Ubi metabolon (in this case three hydroxylases and two methyltransferases) were selected for the dataset construction. Altogether, the final dataset contains 1336 sequences of UbiJ and UbiK that have been aligned in a concatenated multiple sequence alignment (MSA).
Co-evolved residues were predicted using the plmDCA algorithm, which is the one used by CCMpred  [30]  and considered so far as being the best statistical method for contact prediction  [20]. It is worth noting that this type of analysis is more efficient when using a non-redundant dataset. For this reason, a sequence identity cut-off of 62% was used to cluster the concatenated UbiJ and UbiK sequences, which resulted in 282 clusters. Only the 100 best scores were plotted and analysed to find the optimal number of best residue pairs (Figure  S2 ). When inspecting the plmDCA score distribution, two cut-off points can be highlighted. The first one is viewed between the third and the fourth best predictions, with a score of 0. 198 and 0. 151, respectively (red lines in Figure  S2 ). The second one is observed after the fifth best prediction with a score of 0.
Ubiquinone Release In this section, we aim at exploring further the hypothetical role of UbiJ SCP2, which is the binding of UQ 8 biosynthetic precursors and the release of UQ 8 into the membrane. Our results have shown that the interface region between SCP2 and the membrane is consistent when considering only the single SCP2 or the full heterotrimer. From this point, it was decided to study in detail the binding and release of UQ 8 from the single UbiJ SCP2 domain. To predict the UQ 8 binding into SCP2, docking experiment was first carried out using Autodock4 and the X-ray structure (PDB code: 6H6O) as protein reference. Unfortunately, the docking results were not satisfying, due to the closed conformation of the crystallographic structure. To tackle this issue, a manual docking of the UQ 8 was guided through structural comparison of SCP2 X-ray structures in complex with lipid (PDB code: 4jgx, 1pz4) and pocket search.
Co-Evolution Sequence Dataset We downloaded one genome per species of beta-and gamma-proteobacteria from the NCBI database (last accessed October 2020) and obtained respectively 419 beta-and 1225 gamma-proteobacteria genomes. We then annotated these genomes for proteins of the ubiquinone biosynthetic pathway. To this end we used HMM (hidden Markov model) protein profiles that were built on a set of phylogenetically curated sequences  [39]. A similarity search with the HMM protein profiles was run against the selected genomes using the hmmscan program from the HMMER suite (v3. 3. 2)  [40]. Proteins with HMMER hits with an e-evalue < 0. 001 and with a protein profile coverage >50% were selected. Finally, only the genomes showing a similar composition in ubiquinone biosynthetic proteins to the one seen in Escherichia coli (three hydroxylases and two methyltransferases), had their UbiJ and UbiK proteins extracted for analyses.
Molecular Modelling Setup Local version of Alphafold2 from GitHub (https://github. com/deepmind/alphafold, accessed on 7 April 2022) was used with default parameters to model monomeric (Al-phafold2 V2. 1) and multimeric (Alphafold2-multimer v2. 2) structures of UbiJ and UbiK. Regarding monomer modelling, several MSA were built as follows: (i) JackHHMER (HMMER 3. 3. 2 [44] ) and HHBlits (HHBlits 3. 3. 0 [45] ) were used to search for sequences from Uniref90 (v2020_01  [46] ), MGnify (v2018_12  [47] ), Uniclust (v2018_12  [48] ) and Big Fantastic Database (BFD). (ii) hmmsearch (HMMER 3. 3. 2) was used to search for sequences having structural information from the PDB70 database  [49]. For each targeted sequence (UbiJ and UbiK), five models were generated. Regarding multimer modelling, the only difference is the addition of UniProt database for sequence search and the generation of 25 models at the end.
Molecular Dynamics Simulation Setup All MD simulations were performed using Gromacs 2021. 3  [50]  with CHARMM36 [51] and Martini 3  [52]  force-fields for atomistic and coarse-grained (CG) models, respectively. For all systems with the membrane, the composition of lipids was the following: palmitoyloleoyl phosphatidylethanolamine (POPE, 70%), palmitoyloleoyl phosphatidylglycerol (POPG, 25%) and cardiolipin (LNDCL2, 5%). This ratio is in agreement with the literature  [36]. Membrane bilayer was built using CHARMM-GUI  [53]  or insane script  [54]  for all-atom and CG systems, respectively.
Umbrella Sampling Umbrella sampling (US) simulations were performed using Gromacs 2021. 3. Conformations used for US simulations were generated through the pulling code of Gromacs software. The pulling was performed between the centre of mass of SCP2 and a carbon atom (C33) close to the end of UQ 8 tail. The pulling vector was directed according to z-axis towards the membrane (Figure  S9A ), with a pulling rate of 0. 01 nm/ps, a force constant of 1000 kJ/mol/nm and a number of steps of 600. From the pulling trajectory, 20 windows uniformly spaced of 2 Å along the z coordinate of the mass centre of the quinone head were defined to perform US simulations. Unlike pulling, the reaction coordinate for US was based on the orthogonal vector defined by the middle plan of the membrane bilayer and the z-axis of the mass centre of the quinone head (Figure  12A ).
Here, we used the conceptual framework of Shannon entropy as a proxy of cell-to-cell variation, to analyze, for each gene, cell-to-cell gene expression variability during HSC differentiation in normal human bone marrow single-cell RNA sequencing (scRNA-seq) datasets. We observed a peak of entropy along all the differentiation pathways (erythroid, granulocytic, dendritic, and B lymphoid). Notably, genes with the highest entropy variation, in a given differentiation pathway, corresponded to genes known as pathway-specific whereas genes with the highest expression variation were common to all pathways. Finally, we analyzed the bone marrow from patients with myelodysplastic syndromes which are characterized by ineffective differentiation; by using the same approach as above, we observed, in the affected patients, a higher level of entropy in the most immature states of differentiation.
S1C ). We also showed, for each sub-population, the expression of classical markers  [23] [24] [25] [26]  to validate that these data are representative of normal hematopoiesis (Additional file 1: Fig. S1B ). Using the Slingshot package  [27] , we first sorted the cells along a pseudotime from the most immature (CD34+ HSC) to the most mature states available in all the differentiation pathways with sufficient cell numbers (Additional file 1: Table  S1 ). We then computed intercellular entropy as a measure of cell-to-cell gene expression variability over a sliding window of 50 cells moving along the pseudotime, with a step of 10 cells (Additional file 1: Fig. S2 ). For erythropoiesis, 444 cells were ordered as expected: HSCs (CD34+ HSC), megakaryocyte-erythroid progenitors (CD34+ MEP), early erythroid progenitors (CD34+ ERP-early), erythroid progenitors (CD34+ ERP), immature erythroblasts (early-erythroblast), and mature erythroblasts.
Intercellular entropy finally decreased to a minimum in the pre-B cells (Fig. 1D ). Importantly, we confirmed the existence of peaks of intercellular entropy on another available scRNA-seq dataset established on 24,088 cells obtained from another healthy bone marrow (HBM2)  [25, 28]  (Additional file 1: Fig. S3 , Table  S1 ). We also confirmed that the presence of the intercellular entropy peak does not depend on the size of the sliding window (Additional file 1: Fig. S4 ). Altogether, these analyses indicate that during normal human hematopoiesis, a transient increase of cell-to-cell gene expression variability is a common feature of all the major hematopoietic differentiation pathways.
Indeed, we observed a significant correlation (p < 2. 10 -16 ) with a moderate intensity (r < 0. 34) between delta-expression and delta-entropy for each differentiation pathway (Fig. 2A ). What is worthy of note is that this correlation was markedly reduced for some genes, which prompted us to further analyze the set of genes with the highest delta-entropy or delta-expression. For each differentiation pathway, we selected the 20 genes with the highest delta-entropy (20-entropy) and the 20 genes with the highest deltaexpression (20-expression). While most of the genes in the 20-entropy lists were specific to each differentiation pathway, the 20-expression lists were highly similar, with 14 genes common to all lineages (common 20-expression genes) (Fig. 2B , Additional file 1: Fig. S6 ). Importantly, the same observations were made on the previously studied dataset, highlighting the same specific lists of genes (Additional file 1: Fig.
Different functions for delta-entropic and delta-expressed genes We used the STRING database  [29]  to analyze the interaction network and functional GO enrichment of the 20-entropy gene lists and common 20-expression genes. For each gene list, we showed strong functional interactions between genes (Fig. 3A ). Moreover, the functional GO enrichment (biological process and molecular function) in each gene list highlighted specific functions and processes of the corresponding differentiation pathway (e. g. , oxygen transport for erythropoiesis) (Fig. 3B ). More precisely, the 20-entropy list for erythropoiesis included 9 genes involved in hemoglobin synthesis (HBA1, HBA2, HBB, HBD, HBM, BLVRB, ALAS2, SLC25A37, AHSP) and 4 genes involved in other important erythropoietic processes (CA1, CA2, GYPA, GYPB).
The long noncoding RNA (MALAT1)  [30]  was also on the common 20-expression list. Again, very similar results were obtained on the second dataset retrieved from the healthy bone marrow (HBM2). Altogether, these analyses show that the highest deltaentropic genes are not only specific to each hematopoietic differentiation pathway but are also known to play a major role in specific functions and processes of these pathways. On the contrary, most of the highest deltaexpressed genes are common to all differentiation pathways, encoding proteins essential for cell survival and proliferation. Among all the genes, those encoding transcription factors are especially important given their ability to influence the expression of a large number of genes. We conducted an analysis of the variation of the intercellular entropy of the genes identified as bona fide transcription factors.
Genomics studies Bone marrow mononuclear cells were purified on Ficoll gradients, and pellets were processed for nucleic acid extraction using a DNA/RNA Kit (Qiagen). Genomic DNA was studied by high-throughput sequencing of 45 genes recurrently mutated in myeloid malignancies using a panel designed on Human genome hg19, and sequencing was performed on Ion PGM ™ (Life Technologies) on a dedicated 318 V2 chip  [60]. Libraries were prepared using Ion AmpliSeq library kit2 384 (Life Technologies) according to the manufacturer's instructions. The average coverage per gene was ≥ 500×. Reads were aligned against human genome build 19 (hg19) and analyzed for single nucleotide variant (SNV) calling with the NextGENe software (Soft-Genetics, Chicago, IL) and with an in-house pipeline (Polydiag, Institut Imagine, Université de Paris).
We reported all clinically relevant variants with a variant allele frequency (VAF) cutoff at 2%. All the samples were also screened for ASXL1 (including c. 1934dupG; p. G646WfsX12) and SRSF2 mutations by Sanger sequencing. Moreover, aligned reads from. bam files were visualized using the Integrative Genomics Viewer v2. 3 from the Broad Institute (Cambridge, MA, USA). Assessment of variant implication was performed based on population databases (dbSNP and GnomAD), mutation databases (COSMIC), and prediction software (Alamut, mutation taster, OncoKB, and Cancer Genome Interpreter).
Gene-cell expression matrices Healthy donor total bone marrow scRNA-seq datasets used in this study were published by Granja et al. (HBM1)  [19, 20]  and Stuart et al. (HBM2)  [25, 28]. For HBM1, gene-cell expression matrix files were downloaded from https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSE13 9369 (GSM4138872_scRNA_ BMMC_D1T1. rds. gz, GSM4138873_scRNA_BMMC_ D1T2. rds. gz). Since the authors removed the ribosomal and mitochondrial genes from the gene-cell matrix, the original. bam files were downloaded from https:// trace. ncbi. nlm. nih. gov/ Traces/ sra/? run= SRR10 343065 (SRR10343065/ scRNA_BMMC_D1T1. bam. 1) and https:// trace. ncbi. nlm. nih. gov/ Traces/ sra/? run= SRR10 343066 (SRR10343066/ scRNA_BMMC_D1T2. bam. 1) and therefore processed using the DropEst  [61]  software.
The expression of the mitochondrial and ribosomal genes obtained was then reincorporated into the previously filtered gene-cell matrix. For HBM2, gene-cell expression matrix files were downloaded from https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSM36 81518 (GSM3681518_MNC_ RNA_counts. tsv. gz) and https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSM36 81520 (GSM3681520_ MNC_HTO_counts. tsv. gz). For Ctrl1, Ctrl3, MDS2, and MDS4 samples, the Cell Ranger software (https:// suppo rt. 10xge nomics. com/ single-cellg ene-expre ssion/ softw are/ pipel ines/ latest/ what-is-cell-ranger) was used to process the raw data from Illumina sequencing and to generate the genecell expression matrices. The reads were aligned on the GRCh38 reference genome by STAR with the ENSEMBL annotation.
Dimensionality reduction The gene-cell expression matrices of each sample were normalized with SCtransform  [62]. After normalization, the gene-cell matrices were subjected to dimensionality reduction techniques such as principal component analysis (PCA) and UMAP  [63]  using Seurat  [25]. We also used the Scanpy  [64]  python package to calculate the ForceAtlas2 (FA). For HBM1 and HBM2, we chose not to use ribosomal and mitochondrial genes in order to improve the results.
Shannon entropy estimation To estimate the Shannon entropy, the best upper bound estimator (BUB) was computed as developed in Paninski et al. [67]. The N/m ratio is critical for the choice of the proper entropy estimator, with N being the number of cells used to compute the entropy, and m being the number of bins, here the largest number of RNA molecules found in each cell. For small N/m ratios (less than 100), the BUB was shown to outperform the maximum likelihood estimator, the Miller-Madow, or the Jackknifed estimator, by minimizing the maximum error. Since we are using sliding windows of 50 cells, the choice of the BUB estimator prevailed. The R script for this estimation is available on this OSF project: https:// osf. io/ 9mcwg/.
Introduction The characterisation of strong shaking and its likelihood of occurrence across a region forms the primary point of connection between the seismic hazard model (relating to the location, nature and probability of occurrence of strong shaking) and the risk model (addressing distribution of exposed building and the probability of observing given levels of damage and loss when subject to strong shaking). Building upon recent databases of ground motion in Europe  (Lanzano et al. 2019 ) and current developments in the field of ground motion modelling, the 2020 European Seismic Hazard Model (ESHM20) has developed a comprehensive ground motion model (GMM) that characterises the expected level of shaking, its aleatory uncertainty and its regional variability  (Kotha et al. 2020 ) and adopts a scaled backbone GMM logic tree to represent the epistemic uncertainty  (Weatherill et al.
2020 and references therein) , and discuss whether these address the needs of the ESRM20. The subsequent chapters then focus on the development of the site characterisation model for Europe, beginning with the compilation and harmonisation 1 3 of relevant data sets before moving onto the construction of pan-European maps of inferred V S30. The construction of the input data sets is only part of the complete process of site characterisation, however. The manuscript therefore proceeds to explain how the database of strong motion data are combined with the pan-European maps of site properties in order to calibrate the site amplification terms of the European ground motion models adopted within ESHM20. We outline a mixed data driven approach that uses the relations between observed station-to-station ground motion residuals ( S2S S ) and regional scale mappable site proxies to calibrate the model parameters, ensuring that the variability in the resulting surface ground motion accounts for additional uncertainty associated with the use of regionally mapped site properties in the amplification models rather than detailed sitespecific studies.
(2019)  for New Zealand,  Vilanova et al. (2018)  for Portugal, Di  Capua et al. (2016)  and  Forte et al. (2019)  in Italy and  Panzera et al. (2021)  for Switzerland. Finally, some hybrid methods combine different information, such as terrain classes, lithostratigraphic criteria and DEM information (e. g. ,  Stewart et al. 2014; Seyhan et al. 2014; or Ahdi et al. 2017). The reliability of such methodologies is strongly dependent on the correlations between classified units and site conditions proxies databases. In spite of the difficulties and limitations in predicting a local-scale effect at regional or continental scale (e. g. ,  Lemoine et al. 2012; Seyhan et al. 2014) , those methodologies are widely used to address 1 3 territorial planning and the generation of real-time ShakeMaps  (Thompson et al.
2014) , as well as previous regional and global scale seismic risk analyses  (Silva et al. 2020). Though the use of databases of inferred V S30 is commonplace, whether derived from topography, geology or some hybrid of the two, some perspective is needed as to what such models can deliver and what their implications are for characterising uncertainty in seismic risk analysis. When measured directly at a site from surface wave dispersion, seismic reflection or borehole logs, V S30 is merely a property of the upper layers of the soil. In reality, the local amplification of seismic waves at a site are influenced not only by the material properties of the upper 30 m, but rather from the entire soil column, and in some cases the surrounding subsurface topography and geology. For the purposes of empirical ground motion modelling, linear site amplification at a given spectral period of oscillation is related to V S30 primarily though correlation between the residual of the observed ground motion at a site, once corrected for the source and path effects, and the V S30 values of the sites in question.
2012). Why is this then a problem for seismic risk analysis? The limitations of the use of inferred V S30 arise not necessarily from the increased uncertainty in the resulting site amplification, but rather in the manner this uncertainty is treated in the seismic risk calculations. When developing the ground motion models, the databases of observed strong motions will likely contain a substantial proportion of records from stations for which the V S30 is measured directly, in some cases along with other parameters such as depth to bedrock, horizonal to vertical spectral ratio etc. The remainder of the V S30 values of the sites may come from inferred properties such as the topographic slope or seismic design code site class. The resulting amplification model, f SITE T, V S30 , and its corresponding site-to- site variability, S2S (T) , will most likely reflect a predominantly measured V S30 site condi- tion.
Development of a harmonised surface geology map for Europe A geological map informs on the nature (lithology) and age (stratigraphy) of outcropping formations, without specific information on their thickness. At continental scale, superficial and/or weathered formations are generally not reported, except for alluvial deposits. As there does not exist a harmonized geological map at European scale describing superficial formations for the full extent of the region required for the ESRM20 risk model, we created a composite map derived from three existing maps: The harmonisation of geological data from the aforementioned sources followed five steps: 1. Synthetic lithological coding of ProMine geological map: all the lithologies described in ProMine dataset were reprocessed to produce a simplified lithological coding based on the nature of the geological formations.
4. Output of stratigraphic information for each lithological polygon. We decided to keep the information on geological systems for Quaternary and Tertiary formations, which are less consolidated materials and therefore prone to produce site effects, and to keep only eras information for more ancient geological formations. The final stratigraphic information corresponds to 9 codes detailed in "Appendix A" Table  4. 5. Production of the final litho-stratigraphic information through the combination between the lithological codes and stratigraphic codes described above. The resulting harmonised geological database is shown in Fig. 1 , both in terms of lithological and stratigraphic classifications.
Database of V S30 observations In order to build a European site amplification model, we collected as many publicly available data as possible on the site characterization of seismological stations (with measured V S30 values). The different contributions came from (see Fig. 2 ): • The Engineering Strong Motion (ESM) database describing site characterization for European Strong Motion stations  (Luzi et al. 2020  and references cited therein) with 469 measured V S30 values • Database of V S30 measurements compiled during the SHARE project  (Yenier et al. 2010; Lemoine et al. 2012 ) including specific site characterisation data from Switzerland [ETHZ]  (Fäh and Huggenberger 2006; Fäh et al. 2007; Havenith et al. 2007; Poggi 2011; ETHZ, 2015)  and France   2021 ) is also included (not shown in Fig.
2 ) The final database contains 1626 measured V S30 values across Europe (Fig. 2 ) and its corresponding distribution with respect to Eurocode 8 site class is shown Fig. 3. The V S profile measurement methods used for estimating the V S30 value depend mainly on the operator responsible for the acquisition. The distribution of V S30 values in terms of acquisition methods shows that most of the data come from active seismic acquisition (e. g. , Multi-Channel Analysis of Surface Waves [MASW], Spectral Analysis of Surface Waves [SASW], seismic refraction, etc. ), which represents 44% of the dataset, and from borehole methods, which represent 33% of the dataset. The use of different acquisition methods for estimating V S profiles can give rise to uncertainties, but the recent papers of  Garofalo et al.