dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to IRD for granting access to Mbour centre IRD and  Jean-Philippe Legrand  for his help in species identification. We also want to thank technicians of  ISRA  and vet services,  Arthur Bouyer ,  Léa Bouyer  and  Mr. Cheikh  for insect collections. This project received funding from the  US State Department  through the  Peaceful Uses Initiative , the  Joint Food and Agriculture Organization of the United Nations/International Atomic Energy Agency Division of Nuclear Techniques in Food and Agriculture , the  Department of Technical Cooperation , and the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 682387-REVOLINC ). This article reflects only the authors' views and the agency is not responsible for any use that may be made of the information it contains.
Acknowledgements We thank  Laboratoire Magmas et Volcans  for hosting  Esmail Elahpour ,  Samira Raid  from  EURAXESS  for invaluable assistance with visa requirements, and hosting problems. We also thank  Zahra Rezaee Malakooti  from Birjand cultural heritage management for her assistance. Support for the project came from a  Payamenoor University Sabbatical Grant. We thank  Chantal Bosq ,  Delphine Auclair  for their analytical support concerning  Sr-Nd  isotopes and  Claire Fonquernie  for her help in the sample preparation. This work has become an initial component of the  UNESCO Geosciences IGCP  project ' Geoheritage for Geohazard Resilience ' (No. 692 ), for a Tabas Black Lands geoheritage project.
1c  and d ). In order to decipher mechanisms underlying these clinical and histopathological findings, pathogen virulence and host factors were analyzed. A wholegenome shotgun library was prepared with Nextera XT Kit (Illumina, San Diego, CA, USA) and sequenced on the MiSeq Illumina sequencing platform (2 × 150 bp paired-end reads). All reads were screened by mapping to known alleles of virulence genes using the Short Read Sequence Typing for Bacterial Pathogens program (SRST2)  [8]. Virulence gene allele sequences were retrieved from the virulence factor database (VFDB, http://www. mgc. ac. cn/VFs/). SRST2 was also used to define the sequence type of the strain. Antibiotic-resistance predictions were performed directly from raw reads using "Mykrobe predictor S. aureus" tool  [9]. All generated sequences are available at NCBI's BioProject database under accession number PRJNA315766 (http://www.
ncbi. nlm. nih. gov/bioproject/PRJNA315766). The sequenced S. aureus isolate was a methicillin-susceptible strain harboring a staphylococcal cassette chromosome (SCC). The patient's strain genome encoded numerous pathogenesis-related genes. The virulence gene equipment of the strain notably included several cytotoxin genes encoding the gamma-hemolysin and the Panton-Valentine (LukSF-PV), LukED and LukGH leukotoxins. It also included genes involved in adhesion and biofilm formation, genes encoding various enterotoxins (A, H, K, and Q) and superantigens, exoproteases and immune response evasion proteins (Table  1 ). The patient's strain belonged to the clonal complex CC1-MSSA-SCCfus [PVL+]. Then, thrombophilic factors and immunity of the patient were examined. Initial low circulating protein antithrombin, C and S returned to normal after one month, and other factors associated with hereditary thrombophilia including activated protein C resistance, lupus anticoagulant and prothrombin G20210A mutation, were absent.
Surprisingly, comX mRNA levels remained approximately stable in all mutants. Altogether, these results imply that the ComR, ScuR and SarF paralogs might control overlapping but dedicated regulatory networks. import as an active pheromone (blue ellipses). The newly described two-Rgg system (Figure  1 -figure supplement 1) is highlighted in blue and the T arrow pinpoints the inhibitory role of SarF on ScuR. Their cognate pheromone of unknown origin is purple-colored. (B) Crystal structure of the monomeric apo form of S. thermophilus ComR (PDB ID 5JUF). The protein is shown as cartoon colored in gray. The HTH and TPR domains are labeled, as well as the linker region. Residues which are not conserved in ScuR are highlighted in red. The numbering of the a-helices is indicated to gain clarity. (C) Crystal structure of S.
thermophilus ComR in complex with sComS (LPYFAGCL) (PDB ID 5JUB). Only one ComR subunit of the dimeric ComR. sComS. DNA complex is shown in gray with ScuR substitution sites in red as in (B). The bound peptide is highlighted in blue sticks. Part of the bound DNA is shown in orange and labeled, as the HTH and TPR domains. (D) Orthogonal views of the ScuR-sBI7 model. The protein is shown as cartoon colored in beige except for residues substituted in SarF, which are highlighted in red as in Figure  8C. The bound peptide is shown in blue sticks with the conserved tryptophan (W). The numbering of a-helices is indicated to gain clarity. In the right panel, the HTH domain is hidden in the back of the figure. The model was obtained by homology modeling using the i-TASSER server and the ComR. sComS.
DNA complex (PDB ID 5JUB) as template. DOI: https://doi. org/10. 7554/eLife. 47139. 002 The following figure supplement is available for figure  1 : ScuR is an alternative self-sufficient sensor that controls salivaricin production but keeps competence off In order to validate our transcription profile analyses, we performed promoter-probe assays, as previously described for ComR  (Mignolet et al. , 2018). We first expanded our collection of luciferase reporter strains (composed of comS, comX and bacteriocin gene promoters) to include and monitor the sptA promoter (P sptA ), and next transformed all of them with a scuR overexpression cassette. Finally, we measured promoter activity in presence or absence of sComS (synthetic octapeptide that corresponds to the shortest active form of XIP: LPYFAGCL) during cell growth (Figure  2A ).
Selective recognition of targeted promoters Next, we carried out in vitro mobility shift assays to assess the direct interaction between proteins and promoter probes in absence or presence of decreasing concentration of synthetic peptides. We included a P comX probe as a negative control for ScuR/SarF. We were again able to corroborate our promoter activity data at the magnitude and protein-peptide/DNA specificity levels (Figure  6A  and Figure  6 -figure supplement 1A, B and C). While insensitive to sComS (Figure  6 -figure supplement 1A), the ScuR regulator displayed the weakest affinity for P comS , and the strongest one toward P sptA , starting from 0. 08 mM of sBI7 peptide and complexing the total amount of probe at maximal  concentrations (presence of a doublet presumably due to a second state of oligomerization or a conformational change) (Figure  6A ).
Alternatively, we suspect that promoters are not responsive to ComR and ScuR pheromone within the same timeframe. In this case, ScuR might promote ComS production in a time interval during which the comX promoter is silenced or unreactive. As for SarF, it would exclusively control SptA/B production. According to our overexpression phenotype in a scuR deletion mutant (Figure  2D ) and the peptide-promoted occupancy of P sptA Arrows symbolize the species-specific control of quorum sensing regulators on developmental processes. (C) Close view of the ScuR peptide-binding pocket. The model of the peptide-bound form of ScuR was obtained by homology modeling using the i-TASSER server and the ComR. sComS. DNA complex (PDB ID 5JUB) as template. It is shown as surface colored in beige except for residues substituted in SarF, wich are highlighted in red.
The bound sBI7 peptide is shown in blue sticks and its residues LPFWLIL are labeled. The C-terminal glycine, deeply buried in the pocket, is not visible in this view. The figure was prepared using the graphic software PyMol. DOI: https://doi. org/10. 7554/eLife. 47139. 018 (Figure  6A ), the regulation turns out to be primarily direct. However, SarF might indirectly alter P sptA responsiveness through an inhibitory effect on ScuR activity, given that sarF loss provokes a faint over-activation of ScuR (Figure  4D ). Considering their degree of similarity, ComR, ScuR and SarF feature a pronounced selectivity in their pheromone nature, their DNA occupancy and their transcriptional activation rate. Rational alterations as small as single point mutations or one-nucleotide insertion (Figure  6C, D  and E ) reshuffle or partially switch the permissiveness of the tripartite complex (pheromone-regulator-promoter).
Genome scanning for native peptide identification To seek for the cognate peptide of ScuR/SarF in S. salivarius HSISS4 genome, we extract all open reading frames (ORF; start codon = AUG, GUG, TUG or CUG) of more than eight codons from HSISS4 chromosome with the CLC Main Workbench 7. 0 software (https://www. qiagenbioinformatics. com/products/clc-main-workbench/). Next, we use the motif scanner (FIMO tool) of the MEME suite (http://meme-suite. org/) to blast a peptide sequence matrix from the last seven residues of the twenty-two non redundant synthetic peptides against 63. 306 ORFs. We analyzed the first 206 top hit (p-value threshold of 10 À4 ) and looked for stretch positioning in the global polypeptide/protein (close to N-or C-terminus), and genetic regulatory elements (e. g. promoter, RBS and terminators) in the vicinity of the corresponding ORF.
Fluorescence polarization (FP) We incubated the peptide-protein complex in black well 96-well plates (Greiner, Alphen a/d Rijn, The Netherlands) and we performed anisotropic measurement with a multi-wells plate reader (Hidex Sense, Hidex, Turku, Finland) in polarization mode. Filter settings were 485/10 nm and 535/20 nm for excitation and emission, respectively (25 flashes, Lamp Power of 50, focus 5. 5 mm). The octamer synthetic peptides were conjugated to FITC, an aminohexanoic acid spacer (Ahx) and an isoleucine residue linker at the N-terminus (Peptide 2. 0, Chantilly, VA, USA). They were added at a final concentration of 10 nM. The purified Strep-tagged proteins were added in 3:3 serial dilutions from 1 mM in a final volume of 100 ml of binding buffer (20 mM Tris pH 7. 5, 150 mM NaCl, 1 mM EDTA, 10% glycerol) and incubated 10 min at 30˚C before data acquisition.
12. 9 (http://bowtie-bio. sourceforge. net/bowtie2) and samtools V0. 1. 18 (http://samtools. sourceforge. net/) algorithms to yield BAM files containing the read coordinates. We imported these files into SeqMonk V0. 23. 0 (www. bioinformatics. babraham. ac. uk/projects/) to assess the total number of reads for each coding sequence (CDS). The dataset was exported into an excel file for further analyses. First, the dataset was standardized to CDS-mapped reads per million overall reads. Then, we estimated a ratio of CDS-mapped reads in mutants vs WT. All RNAseq data were deposited in the GEO database under accession number GSE120640.
In silico modeling Homology modeling of ScuR was performed by the i-TASSER server for protein 3D structure prediction  (Zhang, 2008 ) by using the crystal structure of the ComR. sComS. DNA complex from S. thermophilus (PDB ID 5JUB)  (Talagas et al. , 2016)  as template. The resulting model displayed a confidence score of 1. 45 and a TM-score of 0. 92 ± 0. 06, reflecting a high confidence and a topology highly similar to the template  (Roy et al. , 2010; Yang et al. , 2015; Zhang, 2008). The bound peptide was manually modified to accommodate the sequence of sBI7 and the resulting complex was optimized using the Gromacs energy minimization server NOMAD-REF  (Lindahl et al. , 2006). The figures of 3D structures were prepared using PyMol (The PyMOL Molecular Graphics System, Version 2. 0 Schro ¨dinger, LLC).
(C) Close view of the ScuR peptide-binding pocket. The model of the peptide-bound form of ScuR was obtained by homology modeling using the i-TASSER server and the ComR. sComS. DNA complex (PDB ID 5JUB) as template. It is shown as surface colored in beige except for residues substituted in SarF, wich are highlighted in red. The bound sBI7 peptide is shown in blue sticks and its residues LPFWLIL are labeled. The C-terminal glycine, deeply buried in the pocket, is not visible in this view. The figure was prepared using the graphic software PyMol. DOI: https://doi. org/10. 7554/eLife. 47139. 018.
Study population Fig. 1  details the procedure of this study. The study was conducted among French Hajj pilgrims traveling together to Mecca, from August 27th to September 20th, 2016, with one specialized travel agency in Marseille. Pilgrims older than 18 years were included on a voluntary basis, and participants were asked to sign a written consent form. Upon inclusion, the participants were questioned using a standardized pretravel questionnaire that included demographic and chronic disease data and vaccination status. Health issues were recorded by a medical doctor who travelled with the group of pilgrims. We considered that participants suffered RTIs if they presented with cough and/or rhinitis and/or sore throat. Influenza like illness (ILI) was defined as the association of cough, sore throat and fever  [11].
Virus carriage according to respiratory symptoms Among 108 asymptomatic pilgrims sampled in France before travel, 5/108 (4. 6%) were positive for rhinovirus/enterovirus. The only pilgrim suffering respiratory symptoms before travel was positive for rhinovirus/enterovirus. Among the 21 pilgrims sampled at symptom onset in Saudi Arabia, 20 tested positive for at least one virus with high proportion of rhinovirus/enterovirus (81. 0%), coronavirus 229E (33. 0%) and influenza A (23. 8%) (Table  2 ). Seven of the 21 symptomatic pilgrims had ILI and their carriage rate did not significantly differ from that of other ill pilgrims (data not shown). Of the 21 pilgrims sampled at the time of symptom onset, 19 were resampled before leaving Saudi Arabia, allowing the proportion of individuals who allowed their viral transport back to the country to be calculated.
g. , symbolic and cultural valorisation), we used housing prices as a practical thermometer to gauge a fairly comparable social value, granted by households and urban actors in different city areas. Explaining the spatial distribution of street value using metrics of the urban environment is a challenging task for which no unified theoretical approach exists. Natural movement theory  [11] , urban morphology approaches  [8] , human ecology and its subsequent reinterpretations by urban geography  [12]  propose different and concurrent theoretical frameworks. Together with the aforementioned methodological issues, this justifies more exploratory approaches in high-dimensional databases of metrics of the urban environment, which can today be investigated through ML. In this paper, we thus propose an ensemble method based on ML-and thus able to handle non-linearity and interactions among regressors-to analyse the relationship between a comprehensive set of features of the urban environment and street value.
The approach is thus both theory-driven (even if only weak theoretical assumptions are used when identifying suitable metrics of the urban environment) and data-driven (through the SFS and subsequent GB protocol). Since the modelling part of the method hereby proposed is based on GB (details on its functioning are provided in the next section), we suggest using the same kind of regressor for performance evaluation at this stage. Furthermore, to obtain a statistically robust selection, it is crucial to utilise training and testing subsets. Firstly, the algorithm should be trained on a random subset of the whole dataset. Secondly, its performance should be evaluated on the portion of dataset that has not been used. To further increase the robustness of the result, k-fold cross validation should also be implemented.
A standard method to measure SA is the Moran's test  [45]. This technique quantifies SA based on both the locations and the values of the observations simultaneously. Given a set of georeferenced values, the Moran's test assesses whether their pattern is clustered, dispersed, or random. The outputs of this test are an index (i. e. , Moran's I) that varies between -1 and 1 and a p-value. The former provides information on the magnitude of SA and on the type of spatial relationship, clustered for positive values and dispersed for negative ones. The p-value evaluates the statistical significance of the index. The matrix of spatial relationships that informs the computation of the Moran's I can be based, for example, on inverse (Euclidean) distance or contiguity. Since our dataset consists of street segments, we propose computing an inverse distance matrix based on topological steps rather than Euclidean distances, where each step corresponds to a street segment.
e. , local accuracy, missingness, and consistency) that guarantee the quality of local approximations. The algorithm by Lundberg and Lee is able not only to provide local predictions with relative feature importance, but also-and most importantly for the scope of our work-to compute such values for the entire dataset and then summarise such information in dependence plots and a summary plot  [47]. The former shows the effects that a single feature has on model outcomes and its level of interaction with another feature, which is automatically chosen by the algorithm. The latter provides an overview of the importance of all the features used in the model and also the range of their effects (positive and negative). Both dependence plots and a summary plot are crucial to understand and interpret the effects that each feature (i.
Application to the French Riviera This section presents the results of the application of the proposed methodology to the street values, obtained from the aggregation of the housing transactions exchanged in the period 2008-2017, in the French Riviera. This section is structured as follows: firstly, we briefly introduce the study area; secondly, we illustrate the datasets from which we extracted our metrics; thirdly, we present the street values (our target variable) in the French Riviera and the metrics of the urban environment considered (our estimators); and, finally, we illustrate and interpret the outcomes of the ensemble method proposed.
The constrained topography and the pressures coming from the tourism industry make the real estate market of this region particularly tense, but, at the same time, interesting to study. Areas with high concentration of second homes and tourism-related amenities coexist at close distance and sometimes are even intermingled with less valued districts and streets. The recent establishment of universities and R&D activities (e. g. , the techno-park of Sophia Antipolis) further complexifies the picture. The Principality of Monaco, due to its fiscal and regulatory specificities, is a separate (and extremely expensive) real estate market and lies outside our study area. Next, we present the datasets concerning housing transactions and aspects of the urban environment, from which the metrics of the urban environment were then extracted.
Datasets To compute the metrics of street value and the urban environment for the selected study area, we accessed five different data sources: PERVAL, the French registry of housing transactions; BD TOPO, the French GIS dataset of geographic features, in vector format; census data and SIRENE, for the housing stock and urban functions; an openly accessible web platform and OpenStreetMap (OSM), for public transport data; and an official DEM, for information on the physical conformation of the area under exam. Next, we present each data source in more detail.
Perval PERVAL  [48]  is the official registry of housing transactions in France. It is owned and kept updated by the Notaires de France (the French association of notaries). The transactions in such registry are subdivided according to the type of property exchanged (e. g. , flats, houses, garages, offices, and plots of land). For the purpose of this study, we only focused on the transactions of flats and houses. In addition to the price taxes included and the georeferentiation (i. e. , latitude and longitude), the transactions belonging to these two typologies present further information, such as number of rooms, habitable surface, energetic label, and presence of a garden. However, the coverage of such information is not even across the dataset. For example, while the number of rooms is provided for almost all transactions (i.
e. , 99%), the build period is given only for 76% of them. Coverage statistics for each feature are provided with PERVAL data and were computed on the information collected in 2016. To carry out the analysis, we acquired PERVAL data for the study area, for the period 2008-2017, a time span that we considered wide enough to obtain a sufficiently detailed characterisation of the housing market. This consisted of 150,116 housing transactions, 124,236 of which belonged to the typology flat, and 25,880 to the typology house. The median inflation-adjusted price for a flat, in the time span considered, was e 193,650. While the median inflation-adjusted price for a single house, in the same time period, was e 475,589. In Table  1 , we provide more details on number of transactions and median prices, for each year.
[49]  is an official GIS database, freely accessible for research purposes, that provides vector data on various geographic entities, such us, roads, buildings, and natural features. It is issued and kept updated by IGN (the French National Institute of Geography and Forestry). BD TOPO is constituted by several datasets, one for each type of geographic entity. For the purpose of this analysis, we focused on the one containing the street network of the area under exam. This specific dataset not only had georeferenced representations of street segments, but also information on further aspects, such as street width, category (e. g. , highway, regional road, local street), and travel directions. BD TOPO data are also necessary to identify and characterise urban fabrics through the MFA protocol previously mentioned  [33].
Census Data and Sirene In France, the French National Institute of Statistics and Economic Studies (INSEE) produces both census data and SIRENE, a dataset of commercial establishments and services. SIRENE  [50]  is monthly updated by INSEE and is freely accessible on the open portal of the French government. It covers the entirety of France and provides information on many aspects of commercial establishments, for example, name and surname of the owner, geographic coordinates, opening date, and category. The latter is based on a detailed taxonomy created by INSEE, which consists of several nested categories. For the purpose of this analysis, we focused on the meso-categories Commerce de détail (retail) and Services (services). The former contained, for example, the micro-categories shoes shop, clothes shop, and coffee shop.
The latter contained, for example, the micro-categories bank, estate agency, school and post office. The SIRENE data used in this work date back to March 2018 and consist of 26,041 establishments, 7993 of which were retail and 18,048 were services. Census data are available for local areas, which are relatively coarse spatial units, with at least 2000 inhabitants. There are 426 census areas in our study area and each of them typically includes 50-100 built-up street segments. We decided not to use census data concerning socioeconomic aspects (e. g. , social status, revenues, and education level) as these could be considered proxies of street values. We thus limited the use of this data source to obtain only a description of the housing stock in each spatial unit under exam. To be more specific, this concerned presence of second homes, vacant homes, and social housing.