dataesr/scientific-paragraphs-categorization huggingface dataset:
Yet, most of the ARGs characterized using functional metagenomics were not deposited in ARG databases until the creation of ResFinderFG v1. 0 in 2016  61. Since then, the database has not been updated but another database called FARME DB was made including data coming from 30 publications  62. Nevertheless, it contains all the inserts sequences selected with functional metagenomics and therefore it also contains genes that are not ARGs  63. Therefore, we updated ResFinderFG v1. 0 by including more publications using functional metagenomics to characterize ARGs and we made a curation effort to ensure that the sequences described are the unique ARGs responsible for the resistance phenotype in the initial insert sequence. ResFinderFG v2. 0 includes more ARGs coming from human-associated samples  [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22].
For example, characterization of the gut resistome with functional metagenomics showed that its ARGs were not well described in ARG databases  14. Inclusion of these ARGs is therefore important for future metagenomic characterization of resistomes. Regarding the ARG family concerned, most of the new ARGs included compared to ResFinderFG v1. 0 are glycopeptides/cycloserine or beta-lactams resistance genes. Glycopeptides/cycloserine resistance genes were selected using cycloserine, an antibiotic used in the therapy of tuberculosis caused by multi resistant mycobacteria  67. Beta-lactams resistant genes are of high concern because beta-lactams antibiotics are widely used against priority pathogens  68. Using ResFinderFG v2. 0, sulfonamides/trimethoprim, glycopeptides/cycloserine, beta-lactams, phenicols, cyclines, quinolones, macrolides/lincosamides/streptogramins and aminoglycosides resistance genes were evidenced studying three GMGC gene subcatalogs (human gut, soil and aquatic).
As expected, regarding their representation in the database, the most frequent unigene hits were glycopeptide, cycloserine, sulfonamides/trimethoprim resistance genes. Analogous analyses performed with other databases showed that ResFinderFG v2. 0 detected a comparable or higher number of ARGs depending on the other database used. Beta-lactamase encoding genes were the most represented ARGs in unigene hits shared by all databases. Yet, ResFinderFG v2. 0 allowed the detection of a significant proportion of beta-lactamases encoding genes which were not detected with other databases. It was expected since many publications using functional metagenomics reported beta-lactamase encoding genes distant from the ones described in ARG databases  11, 14, 31, 34, 46, 54, 59  and a distant one has been evidenced recently from soil samples  39.
Other antibiotic families were even more specifically associated with ResFinderFG v2. 0, such as sulfonamides/trimethoprim, phenicols, glycopeptides/cycloserine resistance genes. Our study has limitations, however. To ensure that genes included are true ARGs, we selected only insert which had part of their sequence annotated as an ARG by PROKKA. Thus, ARGs not identified by PROKKA may have been missed. Moreover, we did not recheck whether described sequences were actually conferring resistance in vitro. Only the sequence corresponding to the ARG annotation was included and we were not able to determine if the surrounding insert DNA sequence was required to produce the resistant phenotype. Yet, since the original accession numbers are available in each ResFinderFG v2. 0 ARG sequence header, researchers can easily obtain the complete insert DNA sequence to investigate.
Conclusion ResFinderFG v2. 0 is the new version of the ResFinderFG v1. 0 database and includes 1'631 additional ARGs. This makes possible the detection of ARGs which would not be identified using other currently used databases. Nevertheless, other databases also contain ARGs that are absent in ResFinderFG v2. 0. Therefore, to make an exhaustive description of the resistome of a sample, ResFinderFG v2. 0 should be used alongside other databases.
Funding This work was funded by the  Joint Program Initiative for Antimicrobial Resistance (JPIAMR) EMBARK (Establishing a Monitoring Baseline for Antimicrobial Resistance in Key environments)  project ( International Development Research Centre ,  IDRC , grant  109304-001  to LPC,  Agence Nationale de la Recherche ,  ANR , grant  ANR-19-JAMR-0004  to ER).
Diagnostic diagrams of emission-line ratios have been used e xtensiv ely to categorize extragalactic emission regions; ho we ver, these diagnostics are occasionally at odds with each other due to differing definitions. In this work, we study the applicability of supervised machine-learning techniques to systematically classify emission-line regions from the ratios of certain emission lines. Using the Million Mexican Model database, which contains information from grids of photoionization models using cloudy , and from shock models, we develop training and test sets of emission line fluxes for three key diagnostic ratios. The sets are created for three classifications: classic H II regions, planetary nebulae, and supernova remnants. We train a neural network to classify a region as one of the three classes defined abo v e giv en three ke y line ratios that are present both in the SITELLE and MUSE instruments' band-passes: [O III ] λ5007/H β, [N II ] λ6583/H α, ([S II ] λ6717 + [S II ] λ6731)/H α.
Ho we ver, discrepancies between the two diagnostic lines, along with issues se gre gating shocked gas emission from AGN emission, have led to the creation of new classification schemes  (Constantin & Vogeley 2006 ; K e wley et al. 2006 ; de Souza et al. 2017 ; D'Agostino et al. 2019 ). Moreo v er, the data used to create the diagnostic lines were determined from integrated spectra of galaxies rather than resolved, parsec-scale objects. New integral field units (IFUs) are revolutionizing the way in which extragalactic emission-line regions are studied by providing both the spectral and spatial information at the same time  (e. g. H énault et al. 2003 ; Kreckel et al. 2017 ; Della Bruna et al. 2020 ; McLeod et al. 2021 ). Using these instruments, se veral observ atories are deploying large le gac y surv e ys; SIGNALS is the Star formation, Ionized Gas, and Nebular Abundances Le gac y Surv e y currently being conducted at the Canada-France-Hawaii Telescope (CFHT;  Rousseau-Nepton et al.
2019 ). The program uses o v er 350 h of observing time on the CFHT's new imaging Fourier Transform Spectrometer, SITELLE (e. g. Baril et al. 2016 ; Martin & Drissen 2017 ; Drissen et al. 2019 ). SITELLE produces spectral cubes that contain more than 4 million spaxels with a varying spectral resolution ( R ∼ 1-10 000). This enables a detailed study of extragalactic emission regions in which we can resolve structures spatially and obtain tight constraints on their emission-line ratios. In this paper, we explore the use of artificial neural networks (ANNs) to categorize extragalactic emission-line objects into H II regions, SNRs, and PNe. In Section 2. 1 , we describe the synthetic data set and the neural network architecture used in our analysis. In Section 3 , we share the results of our network and compare them with traditional classification techniques.
Synthetic data In order to train and test our classification methodology, a set of emission-line ratios labelled by the region's type (i. e. H II , PNe, or SNR) is required. We use the Million Mexican Model Database (3MdB;  Morisset et al. 2015 ; Alarie & Morisset 2019  ) and its ancillary databases to construct the training, validation, and test sets). 3MdB contains several grids of simulations that use the photoionization code Cloudy ( v. 17 ) to emulate the expected emission from different ionizing sources and their surrounding ISM  (Ferland et al. 2017 ). We take the line intensity values of lines of interest (discussed in detail below), which are quoted in ergs/s in the 3MdB. We use the 3MdB project entitled BOND  (Asari et al. 2016  ) to obtain lines ratios from classic H II regions; similarly, we use the PNe (associated with  Delgado-Inglada et al.
2014  ) project to obtain line ratios for planetary nebulae: the data can be found under PNe 2021 in the 3MdB. Since these models were run expressly for this project, we include a brief description here. The primary difference between the previous version, PNe 2020 , and the updated version is that the O/H grid now covers a wider range of values (from -5. 46 to -2. 96) with finer sampling, and the N/O ratio was left free during the simulations. Together with other minor changes, the new models are more representative of physical planetary nebulae expected to be seen by the SIGNALS collaboration. We further constrain the planetary nebulae sample following the methodology outlined in  Delgado-Inglada et al. ( 2014 ). Regarding the H II regions, we only consider a subset of the entire BOND simulation set in 3MdB since the original set contains models that are not likely to represent H II regions that can be found in nature.
A method to select a subset of the model database, along with a detailed discussion on the necessity to subsample the database, is presented for the case of giant H II regions in  Amayo et al. ( 2021 ). In this study, we created a subset of the model that remains broader, constraining only the physical parameters within the range expected in local galaxies; we retain only the completely filled geometry (i. e. the gas fills the entire volume), which allows us to focus on a younger population of H II regions (the ages are between 1 and 6 Myr;  Cedr és et al. 2013 ; Stasinska et al. 2015 ; Asari et al. 2016 ). Moreo v er, we constrain the ionization parameter, log(U), between -3. 5 and -2. 5, the metallicity proxy, 12 + log(O/H), between 7. 4 and 9. 0, and the nitrogen-to-oxygen ratio, log(N/O), between -2 and 0  (Kashino & Inoue 2019 ; P érez-Montero et al.
We retained only the models for which flux of the three strong lines used in classification is higher than 1 per cent that of the H α emission. This ensures that all lines are visible in the original emission spectra and can be detected in the SIGNALS program (e. g. Rousseau-Nepton et al. 2019 ). There are 31 911 H II regions, 266 238 PNe, and 355 683 SNR. We randomly sampled 30 000 models from each grid to reduce training time and have consistently sized samples. To avoid a bias in the network towards data with more training set data, we have set the number of PNe and SNR samples to be the same order of magnitude as the H II sample; our testing revealed that 30 000 samples is sufficient to well-classify both the PNe and SNR regions without artificially injecting bias. The reduction in sample size does not affect results since the grid sampling remains mostly uniform o v er the target parameter space.
g. Breiman 2001  ). The neural network was built using tensorflow (v2. 4. 0)  (Abadi et al. 2016 )  implemented in python (v3. 6. 0). A standard grid-search algorithm to determine the number of layers and nodes within each layer, implemented in sklearn (v0. 23) , revealed three layers with 126, 256, and 128 nodes, respectively, to be optimal. In each layer, the nodes are subject to the standard relu acti v ation function; we use the categorical cross-entropy loss function. We implement two dropout layers of 25 per cent in between the first and second layers and the second and third layers. Since the softmax acti v ation function is used on the last layer (i. e. the output  layer), each node of the output layer has a final logit value associated with the probability of the classification being an H II region (0), a planetary nebula (  1 ), or a supernova remnant (2).
We also employ the Adam optimizer algorithm described in  Kingma & Ba ( 2017 ). Further network details can be seen in the demo code at https: //github. com/sitelle-signals/Pamplemousse. )/H α) as inputs to the network. We stress that the data used for these line ratios (and thus the confusion matrix results) are valid only for data well-sampled by the test set. If, in reality, the test set is not representative of observation, then the results are expected to be worse than what is reported here. Each element of the confusion matrix can be interpreted as the efficiency (percentage of accurate classifications) at which the neural network categorizes the emission region y to their true type x. For example, the first row indicates that the network correctly categorizes H II regions 93 per cent of the time but that it misclassifies H II as either PNe or SNR 5 per cent and 2 per cent of the time, respectively.
Application to diffuse ionized gas In addition to classical H II re gions, superno va remnants, and planetary nebula, diffuse ionized gas (DIG) is an important feature of the ISM in many galaxies (e. g. Reynolds 1984 ; Walterbos & Braun 1994 ; Haffner et al. 2009 ). Therefore, we constructed a fourth classification set based on DIG simulations from 3MdB using the same methodology discussed in Section 2. 2. We use the DIG HR database and filter out H II regions by only retaining regions for which phi OB < 4. 5. 2  We retrain the artificial neural network developed in Section 2. 3 with DIG regions as a fourth classification. We provide the confusion matrix in Fig. 5  ; the figure reveals that the network retains its accuracy of determining H II regions, planetary nebulae, and supernova remnants while achieving an accuracy of over 70 per cent in classifying DIGs.
The data We extracted the PNe and SNR sources from the  Ciardullo ( 2004 )  and  Long et al. ( 2018 )  catalogues, respectively. In order to also RASTAI 2, 345-359 (2023) include a significant fraction of the H II regions visible in the field, we used a combination of the information provided from an emission map produced by adding all emission line maps together and the known position of the PNe and SNR. Using the ds9 tools, we handpicked a large fraction of the H II regions while keeping only the good candidates, excluding regions with not well-defined contours or obvious contamination. Fig. 7  shows all regions selected in the final test set. Some H II regions harbour a small o v erlap with a previously identified SNR. We tried to minimize the impact of these o v erlaps and a v oid selecting H II regions that were particularly well blended with the SNRs.
It is also important to note that the SNR radii were selected using the visible morphology of the SNR optical component (expanding shell). Nevertheless, we did not exclude known SNRs that are embedded in H II regions emission. We will discuss later in this section the impact of this inherent contamination. Our literature re vie w re vealed 84 H II regions, 6 planetary nebulae, and 24 supernova remnants (see Fig. 7  ). Ho we ver, upon further investigation of the spectra captured by SITELLE of these regions, we rejected eight H II regions, four planetary nebulae, and two supernova remnants as having insufficient signal-to-noise 3 to properly fit the emission lines. In order to extract the line ratios for these regions, we used the spectral analysis software LUCI. Using SN1 ( R ∼ 1800), SN2 ( R ∼ 1800), and SN3 ( R ∼ 5000) observations of the M33 field (PI: Laurie Rousseau-Nepton), we fit the following strong emission-lines using a sincgauss  F 0 ,λ = F obs ,λ e τ λ = F obs ,λ e τ V q λ , ( where F obs, λ is the observed flux, τ λ is the optical depth at a given wavelength, τ V is the optical depth in the V -band, and the shape of the dust attenuation curve is parametrized by q λ ≡ τ λ / τ V.
A P P E N D I X A : C R I T E R I O N F O R F I LT E R I N G DATA In this section, we describe the filters applied to the 3MdB data set for reproducibility's sake. The python application of these filters can be found in the notebook entitled generating data. ipynb. To filter the H II regions, we keep lU mean values equal to -2. 5, -3. 0, and -3. 5. We only retain fr == 3. 0. We only keep ab 0 values between -5. 4 and -3. 0. The only additional filtering we apply on the PNe data set is com6 == 1. For the shock models, we only take emis VI. model type = 'shock' and abundances. name = 'Allen2008 Solar'.
RASTAI 2 ,  345-359  (2023) doctoral excellence scholarship. JH-L acknowledges support from  NSERC  via the  Disco v ery grant program , as well as the  Canada Research Chair program. NVA acknowledges the support of the  Royal Society  and the  Newton Fund  via the award of a  Royal Society-Ne  wton  Adv anced Fello wship  (grant  NAF \ R1 \ 180403 ), and of  Funda c˜ ao de Amparo à Pesquisa e Inova c˜ ao de Santa Catarina (FAPESC)  and  Conselho Nacional de Desenvolvimento Cient ífico e Tecnol ógico (CNPq). CR is grateful to the  Fonds de recherche du Qu ébec -Nature et Technologies (FRQNT) , for SIGNALS team financial support, and to the  Natural Sciences and Engineering Research Council of Canada (NSERC)  KG is supported by the  Australian Research Council  through the  Disco v ery Early Career Researcher Award  ( DECRA ) Fellowship  DE220100766  funded by the  Australian Go  v ernment.
K. G. is supported by the  Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions  ( ASTRO 3D ), through project number  CE170100013. The research of L. Chemin is funded by the F ondec yt Re gular project  1210992  from  Agencia Nacional de Investigacion y Desarrollo de Chile. Software: The following softwares were utilized in this work: PYTHON (Van Rossum & Drake 2009 ), NUMPY (van der Walt et al. 2011 ), SCIPY (Virtanen et al. 2020 ), MATPLOTLIB (Hunter 2007 ), PANDAS (McKinney 2010 ), SEABORN (Waskom et al. 2017 ), (Robitaille et al. 2013 ), TENSORFLOW (Abadi et al. 2016 ), KERAS (Chollet 2015 ), LUCI (Rhea et al. 2021 ).
To avoid distortions in perspective the optimal distance is about halfway the height of the subject  [1] , in our case the palm tree. The procedure followed is the construction of a linear regression between the age of the palm tree, considering those of well-known age, and the values of their stipe/crown ratio (Table  1 ). Several methods have been used here to construct the linear regression: a, least squares, b, Zellner's Bayesian simple univariate normal linear regression and c, the Bayesian method provided by Stan (see below). We worked using R Studio, within the R framework. R is a free software environment for statistical computing and graphics  [31]. The RStudio  [34]  integrated development environment (IDE) is a set of tools created to improve productivity in the R and Python languages.
The tools are available at Cran R Project  [12]. The R Stats Package  [32]  furnished lm that is used to fit linear models, including multivariate ones. It can be used to carry out regression, single stratum analysis of variance and analysis of covariance. We use lm to build a classical least squares regression model in which normality of residuals is assumed. Then we plot the data and the regression line. After introducing one S/C (Stipe/Crown coefficients, S/C), value we create a data frame of age values and their corresponding probability densities, and a ggplot® with the predicted value and probability density function. In parallel, based on the approach of Zellner  [48] , we specifically designed the script in R for the Bayesian approach to linear regression. In the simple univariate normal linear regression model, we have one random variable (hence the term "univariate"), the "dependent" variable (Stipe/Crown coefficients, S/C), whose variation is to be explained, at least in part, by the variation of another variable, the "independent" variable (age of the palm tree, abbreviated as age).
Acknowledgements This research received support from the  INIA  projects  RF2007-00010-C03  and  RF2010-00006-C02  ( European Regional Development Fund   2007-2013 ),  Prospección y recogida de recursos fitogenéticos autóctonos de  palmera datilera y especies silvestres emparentadas and from the  Ministerio de Ciencia e Innovación of Spain  project  CGL 2009-10215  and from the  Canary Islands Research Agency, Innovation and Information Society Agency (Agencia Canaria de Investigación, Innovación y Sociedad de la Información) , through  FEDER CANARIAS Program  ( 2014-2020 ); reference  PROID2020010029. Project  PALMHYBRID  to  Dr. Pedro A. Sosa. We would like to thank  Dr. Rosa Ros  and  Dr. Olaf Werner , and our students  Minerva Vidal ,  Lidia Ortega ,  Darío Muñoz ,  Ana Lozano ,  Lúa Martínez ,  Warda Maizi ,  Angela García ,  Clara Fernández-Marcote ,  Ana Cano ,  Alba Tesón ,  Diego Cárceles  and  Salvador Alcázar  for their invaluable help in the experimental part.
SURFBAT performs a TDT test based on the methods that incorporate genotype uncertainty (as we are using imputed genotypes) given by Taub et al,  [45]. SURFBAT locates and weights the contribution of surrogates using the Li-Stephens model for genotype imputation that is used by leading software IMPUTE5  [46]. A fuller description of the calculations of SURFBAT are given in the methods. We demonstrate the properties of SURFBAT through simulation using the 1000 Genomes project  [47]  (1000G) and data from 856 individuals with Whole Genome Sequencing (WGS) data from the FranceGenRef project  [48, 49]  (FGR). This is followed by a demonstration using true data from 346 case individuals diagnosed with Brugada syndrome  [50].
Results For both the simulation study and real-data example, we will first define a control group. In both analyses our panel of control individuals involves 2504+856 individuals or 5008+1716 haplotypes attained by merging the 2504 individuals from the 1000G project with 856 individuals with WGS data from FGR. The steps used for construction of this panel are given in the Methods. The final dataset involves 10,252,495 bi-allelic variants across the 22 autosomal chromosomes.
Simulation Study We compared SURFBAT against a traditional GWAS adjusted on six principal components (Figure  1 ) using a simulation set-up devised to demonstrate the properties of our method. To this end, we constructed a group of 450 case individuals as mosaics of the control group. Mosaic construction was performed with R-package Mozza (https://github. com/genostats/Mozza) and is described in the Methods. For the purposes of the simulation, we only simulated a short genome (chromosomes 10-22). The case individuals have an admixed ancestry profile with 50% of their chromosome chunks coming from the AFR populations of 1000G (African continent) and the other 50% from the French samples of FGR. We added a signal of purely local ancestry on Chromosome 11 by simulating an excess of FGR haplotypes, and a more specific signal on Chromosome 20, with an excess of FGR haplotypes carrying the alternative allele for the variant rs197819 (chosen randomly).
4. Red lines indicate genome wide significance (5e-8). SURFBAT takes advantage of the highly efficient software IMPUTE5, hence this could allow for huge imputation reference panels (such as the HRC or even TOPMED) to be used as control panels for GWAS. As IMPUTE5 will search out the most appropriate haplotypes in the reference panel for each target haplotype; the presence of haplotypes in the reference panel that are not highly relevant to the imputation of a given target haplotype is not problematic. In this aspect, SURFBAT therefore resembles the UNICORN method proposed by Bodea et al,  [55]  where case individuals are compared to a large ensemble of reference individuals. In both the simulation study and real data example presented here, performing a traditional GWAS adjusted for principal components was in fact more powerful than SURFBAT.