dataesr/scientific-paragraphs-categorization huggingface dataset:
Available spectroscopic data First of all, we compiled a list of 4951 high quality and publicly available spectroscopic redshifts (of which 4829 are of galaxies and 122 of stars) from a number of surveys and references (the full list is included in the catalogue README file). When two or more measurements were available for the same object, the one with the highest quality flag assigned was kept. We then used the spec-z's to optimise the calibration of photometric redshifts as described in Sections 5. 3. 1 and 5. 3. 2. We point out that the list used in this procedure includes the VANDELS release DR3 Fig. 5 : Comparison of photo-z's and spec-z's for the runs of (left to right, top to bottom) the three codes LePHARE, EAzY, zphot, plus (bottom right panel) the CANDELS official estimates from  Dahlen et al.
H-detected galaxies To obtain photometric redshifts for the H-detected sources in G13 that do not have a spectroscopic observation, we exploited independent estimates from three SED-fitting software tools fed with the new photometric catalogue, namely LePhare  (Arnouts et al. 1999; Ilbert et al. 2006) , EAzY  (Brammer et al. 2008) , and zphot  (Fontana et al. 2000). We run EazY and zphot on our local machines, while LePhare was run remotely via the GAZPAR web portal 3. For LePhare we used the same setting and parameters used in  Ilbert et al. (2009)  for their COSMOS catalogue, which include templates by  Polletta et al. (2007)  plus additional starburst templates generated using BC03. For the EAzY runs we used the built-in set of templates described in  Brammer et al. (2008) , Sect. 2. 2; we did not apply the bayesian prioring option, because testing the possible configurations with a preliminar photo-z vs.
5) we obtain NMAD=0. 011 and η=0. 22%, which is comparable to the values obtained by  Ilbert et al. (2013)  for their UltraVISTA DR1 30 bands catalogue on COSMOS (with the "zCOSMOS bright" sample of ∼9400 spectroscopic redshifts at i + < 22. 5, they find NMAD=0. 008 and η=0. 6%). The first three panels of Fig. 5 , and Fig. 6 , show the comparison of the photometric and spectroscopic redshifts for each of the three independent runs and for the final median average, also reporting the NMAD and η statistics. The three runs yield comparable accuracy, with EAzY giving slightly better results on the full sample, and LePhare on Fig. 9 : Six examples of SED-fitting of photometric sources exploiting the full 43 bands dataset. Red squares are the CANDELS photometry from G13; black squares are the new photometric measurements, and the solid line is the best fit model for the redshift estimate.
2, and to AGNs, using the catalogue by  Cappelluti et al. (2016)  together with the VANDELS DR4 data (we release the photo-z and physical parameters estimates anyway for AGNs). The flags are described in Table  3  (the fractions are from the delayed-τ fit, but they are almost identical for the τ run); with the chosen criteria, ∼ 6% of the objects are assigned a bad flag, indicating an unreliable fit. To conclude our analysis, we compared our best photometric redshift estimates with the official CANDELS provided in the catalogues data release  (Dahlen et al. 2013). The results in terms of photo-z/spec-z comparison for the CANDELS catalogue are shown in the fourth panel of Fig. 6. The new ASTRODEEP-GS43 redshift estimates reach a higher accuracy both in terms of the NMAD and of the outliers fraction.
0 with adaptive prioring, background subtraction, individual kernels and astrometric registration), and the combination of different software tools for photometric redshifts estimates (LePhare, EAzY and z-phot), comparisons with the CANDELS official data show an overall good agreement, with a noticeable improvement in the quality of the photometric redshift estimates, which reach NMAD=0. 015nd η = 3%; considering only the bright objects (I814<22. 5) we obtain NMAD=0. 011 and η=0. 22%. The catalogue is available for download from the ASTRODEEP websiste, at the url http://www. astrodeep. eu/astrodeep-gs43-catalogue/.
Acknowledgements. Part of this study was funded using the  EU   FP7SPACE  project  ASTRODEEP (Ref. No:  312725 ), supported by the  European Commission. This work is partly based on tools and data products produced by GAZPAR operated by  CeSAM-LAM  and  IAP. This research has made use of the  SVO Filter Profile Service  (http://svo2. cab. inta-csic. es/theory/fps/)supported from the  Spanish MINECO  through grant  AYA2017-84089. The Cosmic Dawn Center is funded by the  Danish National Research Foundation  under grant no. 140. BMJ is supported in part by  Independent Research Fund Denmark  grant  DFF -7014-00017. The authors thank  Tao Wang  and  Mengyuan Xiao  for their contributions to the work.
Scope of the paper A concept that seemed particularly handy to describe synthetically the relationship between two variables is elasticity. Taken from economics  (Marshall, 1890) , it has been applied in hydrology to describe the sensitivity of the changes in streamflow to changes in a climate input variable without requiring a rainfall-runoff model  (Schaake and Liu, 1989; Andréassian et al. , 2016). To our knowledge, it has never been applied with explicit consideration of catchment memory. The goal of our paper is threefold: 1. To present a method, based on the concept of elasticity, that not only can provide an index relevant to catchment memory, but can also characterize its dynamic in a manner analogous to a forgetting curve  (Ebbinghaus, 1885) ; In France, discharge series (Q) were extracted at a daily time step from the French Hydro database  (Leleu et al.
, 2014). Only catchments that are not regulated according to this database were selected. Corresponding catchment areas vary between 5 km 2 and 26,900 km 2. Precipitation (P ) and temperature (T ) data were extracted from the SAFRAN atmospheric reanalysis produced by Météo-France (8x8 km and averaged over the catchment upstream areas;  Vidal et al. , 2010). In Sweden, discharge series were extracted at a daily time step from the SMHI database. Only stations having less than 5% of their area regulated are used (information extracted from www. smhi. se). Catchment areas vary between 1 km 2 and 14,400 km 2. Precipitation and temperature data were extracted at a daily time step from the PTHBV database (4x4 km;  Johansson, 2002). For all stations, potential evaporation (E 0 ) was estimated following  Oudin et al.
(2005). Daily hydroclimatic data were aggregated at the annual scale for the purpose of this study, with the hydrological year starting on October 1 st. We accepted a maximum of 10% of missing data per year. All 685 stations also respect a period of at least 10 years of hydroclimatic data without any gaps in order to be able to capture long-term memory. In the end, between 23 and 59 gauged years were available for our catchments. These time series are also used to build five hydroclimatic descriptors for each catchment with the average of the annual values of Q and P , E 0 , Q/P and P/E 0. In addition, the percentage of the watershed area covered by lakes is used as a supplementary descriptor. This information is provided by SMHI for Swedish catchments (extracted from www. smhi. se). For France, the Lake Water Bodies according to the Water Framework Directive is used (extracted from geo.
g. , Figure  1 ): We found that a simple exponential parameterization would not be enough as it does not allow lags. Gamma distribution (Equation  3 ) requires the calibration of two parameters: a shape parameter α and a scale parameter β: ω(i) = i α-1 β α Γ(α) e -i β (3) where Γ(α) is the Gamma function evaluated at α. For the sake of simplicity, before fitting a Gamma distribution, we first fit a simple annual elasticity model (a zero-memory model), and use a statistical significance test (Student's t-Test with pvalue < 0. 01), to decide whether equation 2 improves significantly on equation 1. If the improvement is not significant, we conclude on the absence of pluriannual memory for that catchment, and keep the simplest representation (that of equation 1). Parameters of both equations were calibrated using particle swarm optimization (PSO) through the hydroPSO R package (Zambrano-Bigiarini and Rojas, 2013).
Acknowledgements. This work was funded by the project  AQUACLEW , which is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate , and funded by  FORMAS (SE) ,  DLR (DE) ,  BMWFW  (AT), IFD (DK),  MINECO (ES) ,  ANR  (FR) with co-funding by the  European Commission  [Grant  690462 ]. The authors gratefully acknowledge the reviews of  Gaëlle Tallec ,  Charles Perrin , and  Antoine Pelletier.
For protein identification, raw data were processed with the MS Data Converter software (AB Sciex) for generating. mgf data files, and protein identification was performed using the MASCOT search engine (Matrix science, London, UK) against the ORS3257 database with carbamidomethylation of cysteine residues set as a fixed modification and oxidation of methionine residues as a variable modification. Peptide and fragment tolerance were respectively set at 20 ppm and 0. 05 Da. Proteins were validated when identified with at least two unique peptides and only peptides with Mascot ion scores above identity threshold at less than 1% FDR (false discovery rate) were considered  [22]. This analysis was performed in triplicate. Predictions of the cellular localization of proteins were carried out using the tools available on the MicroScope platform (https://mage.
Methods The simulation of the response of the heterostructure detector was done using the Geant4 framework  [23]. Each heterostructure layout was designed in a CAD software (Autodesk Inventor 2019). The overall volume of the pixel was kept constant throughout the study at 3 × 3 × 15 mm 3 varying the geometry of the matrix and/or the filler. The matrix and filler substructures were used as input of the simulation (STL files) and linked to a specific material (i. e. BGO for the matrix). For each simulation, 50000 511 keV gamma rays were generated along the long axis of the pixel. To account for the spatial nonuniformity of the heterostructure, the incident 511 keV gamma rays were randomly emitted across the front face of the detector (small pixel surface). Each incoming gamma ray and the secondary particles created were tracked throughout the heterostructure to quantify the energy deposited in the matrix and filler components.
These data were then used to calculate the equivalent stopping power and the energy sharing capability of the specific heterostructure. The simulation results were validated against several criteria. The attenuation length was checked against monolithic reference samples (LSO and BGO) and against the standard analytic estimation of the probability of interaction in the crystal. The required number of simulated events to reach a statistically representative result was also checked with a study of the attenuation length and energy sharing capability as a function of the number of initial gamma ray events. The performance assessment and optimization of the heterostructure design is a multi-variable problem. The parameter space to survey is large with variables such as pitch, diameter/thickness, gap, structure type and filler and matrix materials choice to survey.
Background: The emergence of multidrug-resistant bacteria remains poorly understood in the wild ecosystem and at the interface of habitats. Here, we explored the spread of Escherichia coli containing IncI1-ST3 plasmid encoding resistance gene cefotaximase-Munich-1 (bla CTX-M-1 ) in humaninfluenced habitats and wild fauna using a genomic approach. Methods. Multilocus sequence typing (MLST), single-nucleotide polymorphism comparison, synteny-based analysis and data mining approaches were used to analyse a dataset of genomes and circularised plasmids. Results. CTX-M-1 E. coli sequence types (STs) were preferentially associated with ecosystems. Few STs were shared by distinct habitats. IncI1-ST3-bla CTX-M-1 plasmids are disseminated among all E. coli phylogroups. The main divergences in plasmids were located in a shuffling zone including bla CTX-M-1 inserted in a conserved site.
Materials and Methods Genomic dataset. For this study, we collected 122 E. coli whole genome sequences (WGSs) containing IncI1-ST3 plasmids and bla CTX-M-1 (Supplementary Tables  S1  and S2 ). The dataset includes WGSs sequenced during this study (n = 43) and recovered from GenBank (n = 79) after filtering for quality (ATCG assembly size >4. 5 Mb, contigs number <200 and N50 > 60,000) and the availability of metadata. The sources of strains were humans (n = 57), domestic animals (n = 11), food or food-producing animals (n = 37), wild animals (n = 13)  [20, [31] [32] [33] [34] [35] [36] [37]  and municipal wastewater (n = 1). Three strains were from unknown origins. Of this collection, 30 human strains and 13 strains isolated from wild animals were sequenced for this study (Supplementary Table  S1 ).
The other data were collected from the NCBI Short Read Archive (SRA) or the European Nucleotide Archive (ENA) by screening of the E. coli genomes of the GenBank database (Supplementary Table  S2 ). The screening for encoding CTX-M-1-and IncI1-ST3-specific alleles was performed with DIAMOND and blastn software, respectively, using 100% identity threshold and 100% coverage threshold. Likewise, 20,668 non-redundant complete plasmids collected from GenBank were screened for the IncI1-ST3 feature and the presence of bla CTX-M-1. It resulted in a collection of 39 IncI1-ST3-bla CTX-M-1 circularised plasmids (Supplementary Table  S3 ). Whole genome sequencing (WGS). This was performed using the next-generation sequencing platform of the teaching hospital of Clermont-Ferrand, France. DNA was extracted with a DNeasy UltraClean Microbial kit (Qiagen, Hilden, Germany).
The libraries were prepared with a Nextera XT Kit (Illumina, San Diego, CA, USA), and they were sequenced by the Illumina MiSeq system, generating 2 × 301-base pair (bp) paired-end reads. Fastp software v0. 19. 10 [38]  was used for quality filtering of Illumina reads, and SPAdes was used for short reads assembly  [39]. The mean sequencing depth was ≥163×; the number of assembled contigs ranged between 51 and 175, the mean contig number was 99. 77, the N50 ranged between 63,075 and 383,707, and the mean contig number was 186,502. The genome sizes ranged between 4,667,864 and 5,338,201 nucleotides. The raw reads have been deposited in the European Nucleotide Archive (ENA, https://www. ebi. ac. uk/ena) under project accession number PRJEB36175. Molecular typing. E. coli phylogroups and multilocus sequence typing (MLST) were determined in silico according to the Clermont Typing method  [40]  and Achtman's MLST scheme  [41].
The molecular typing of isolates was performed by core genome SNP-based typing (cgSNP). BactSNP was used to perform cgSNP using the E. coli core genome downloaded from the Enterobase website (https://enterobase. warwick. ac. uk) as a reference, as previously described  [42, 43]. After the filtration of recombination zones detected by Gubbins  [44] , a phylogenetic tree was inferred from the resulting alignment by maximum likelihood using RAxML  [45]. Antimicrobial gene detection. The antimicrobial-resistant genes were identified by alignment against a database including the online databases CARD  [46] , Resfinder  [47] , and the NCBI National Database of Antibiotic Resistant Organisms (https://www. ncbi. nlm. nih. gov/pathogens/antimicrobial-resistance/ (accessed on 1 April 2021)) using a 95% minimum threshold for the breadth of coverage and identity percentage, as previously described  [48].
Whole Genome Typing of E. coli Harbouring Plasmids IncI1-ST3 and bla CTX-M-1 To best understand the large diffusion of the CTX-M-1 ESBL, we collected a dataset comprising 122 E. coli WGSs containing IncI1-ST3 replicon and bla CTX-M-1 isolated from humans, human-influenced habitats and wild fauna. The corresponding genomes were classified into eight major phylogenetic branches by SNP-based core genome typing. These major branches corresponded to the E. coli phylogroups (Figure  1 ). Most genomes belonged to phylogroups B1 (35. 8%, n = 43), A (20. 8%, n = 25), B2 (12. 5%, n = 15), C (12. 5%, n = 15) and G (10%, n = 12). The distribution of genomes among the phylogroups significantly differed depending on the originating source (Fisher test, pvalue < 0. 001). Since the genomes of phylogroups A and B1 frequently originated from food or food-producing animals (50% and 67%, respectively), the B2 genomes preferentially originated from humans (93.
Discussion The increase in antimicrobial resistance worldwide is a result of inappropriate use of antimicrobials during the last decades, including those used for human medication and animal husbandry. This broad use increases the selective pressure on both commensal and pathogenic bacteria, which can spread between different ecosystems  [1, 54]. Livestock animals may act as reservoirs of AMR and multidrug resistant bacteria. This can lead to dissemination of AMR into humans directly by contact and the food chain or indirectly from the environment  [1, 54]. In this study, we analysed genomic data belonging to E. coli isolates collected from humans, animals (food-producing animals, companion animals, wildlife), and food samples to understand the interaction between these ecosystems in the diffusion of IncI1-ST3 plasmids encoding bla CTX-M-1.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/10. 3390/microorganisms9071471/s1, Figure  S1 : Frequency (a) and correlation index (b) of antimicrobial resistance genes in non-redundant E. coli harbouring bla CTX-M-1 and IncI1-ST3 plasmids, Figure  S2 : Multiple corresponding analysis performed from the synteny of shuffling region of bla CTX-M-1 IncI1-ST3 plasmids, Figure  S3 : Heatmap showing the presence (red) and the absence (blue) of synteny blocks in the shuffling region of plasmids IncI1-ST3-bla CTX-M-1 , Figure  S4 : Hierarchical classification of plasmids IncI1-ST3-bla CTX-M-1 based on the synteny of the shufflon region, Table  S1 : Metadata associated with the Escherichia coli isolates sequenced during this study, Table  S2 : Dataset of Escherichia coli whole genome sequences (WGSs) containing replicon IncI1-ST3 encoding bla CTX-M-1 , Table  S3 : Circularised bla CTX-M-1 -encoding plasmids IncI1-ST3 included in this study.
Information sources A comprehensive literature search was conducted on October 22, 2014. Search strategies were developed in consultation with search experts using a combination of subheadings and text words (Supplement A; see online supplementary material at ftp. liebertpub. com). The databases EMBASE, MEDLINE (via Ovid SP), Cochrane Central, PubMed as supplied by publisher, Web of Science, PsycINFO, SCOPUS, and CINAHL were searched. In addition, grey literature was examined via Google Scholar, opengrey. eu, and dissertation databases (openthesis. org, dissertation. com). Reference lists and citation indices of the included articles and relevant reviews were inspected to identify additional relevant citations. All selected studies were downloaded to the reference management database Endnote X5  15  and duplicates were removed.
Data collection and assessment of methodological quality Two reviewers (MC and ACS) independently extracted data and assessed the risk of bias of included studies. Any discrepancies were resolved by discussion or consulting a third author (SP). A data extraction form was developed based on the Effective Practice and Organisation of Care Cochrane Review Group (EPOC) data collection checklist,  17  and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. 18 In addition, topic-relevant criteria about guidelines, adherence, and influencing factors were extracted. Guideline recommendations were classified as ''strong'' or ''weak/moderate'' recommendations. Strong recommendations were defined as being based on good quality randomized controlled trials (RCTs).
Attrition and detection bias were not reported for Objective 1 because these were considered irrelevant for the percentage adherence obtained. We accounted for risk of bias by narratively describing studies with a low (none of the criteria was rated as high risk of bias) and moderate (<50% of the criteria was rated as high risk of bias) risk of bias separately for the three objectives. To enhance interrater reliability, data extraction and risk of bias forms were pilot-tested on three studies that were likely to be included in the review. Interrater reliability was assessed by calculating concordance rates between the two independent reviewers in data screening, data extraction and risk of bias assessment.
Data synthesis Because of heterogeneity in settings, guidelines, populations, statistical methods, and outcomes, meta-analytic techniques were not used. Instead, we conducted a narrative synthesis of results stratified by objective. For every guideline recommendation that was examined in at least two studies, mean guideline adherence was calculated by adding up the total number of patients treated according to the guideline recommendation and subsequently dividing them by the total number of patients eligible for the guideline. In addition, the percentage adherence was presented separately for strong and moderate/weak recommendations. We also compared the differences in percentage adherence for relatively more invasive (e. g. , intracranial pressure monitoring and intracranial operation) and less invasive (e.
Treatment of studies with multiple publications Multiple publications refer to the situation where more than one article has been written based on the same dataset. 23 Multiple publications assessing the same guideline in an overlapping time period and setting were dealt with by extracting information from the study that could be used for the most study objectives. If the number of objectives was similar across studies with multiple publications, the article that included the largest number of patients was chosen. Articles from the same dataset that assessed different guidelines or that were conducted during a different study period or in a different setting were analyzed separately.
Study characteristics We included 22 studies, reported in 25 publications (Table  1 ). Three articles were removed from the analyses because of multiple publications. 10, 24, 25 Two more studies were based on the same dataset,  26, 27  but the study describing the least number of objectives  26  was still included for extracting the amount of adherence to another guideline recommendation. 3] [44] [45] [46] [47]  Twelve studies described multicenter studies  [26] [27] [28] [29] [30] [31] 34, 36, 40, 41, 44, 46  with a median of eight (range 2-155) hospitals included. All studies were  onducted in North America (n = 9) or Europe (n = 13) and were published between 2002 and 2014. Six of the included studies  33, 40, 41, 43, 44, 46  examined adherence to more than one guideline recommendation (mean number of guideline recommendations in studies describing more than one guideline recommendation: 3.
Studies about ICP monitoring were performed in Europe and North America and performed during ICU admission. Most studies used a multicenter design. The studies with the lowest and highest percentage adherence to ICP monitoring guidelines were comparable multicenter studies performed in North America. The study with the highest percentage adherence was based on the TBI-Trac database, which is a database from the BTF aiming to track and improve adherence, while the study with the lowest percentage was based on general trauma databases. A visual display of adherence per guideline is provided in Figure  2. After removing studies with a high risk of bias (n = 3), adherence to the NICE guidelines was 75%. Adherence to other guidelines did not differ substantially. To assess whether strength of recommendation was related to guideline adherence, we divided guidelines into strong, and moderate/weak recommendations.