dataesr/scientific-paragraphs-categorization huggingface dataset:
Rhipicephalus sanguineus (s. l. ) haplotyping Products of PCR amplification of the mitochondrial 12S rRNA genes of all R. sanguineus (s. l. ) ticks used in the study were sequenced by BIOFIDAL-DTAMB (FR BioEnvironment and Health, Lyon, France). Sequences obtained were manually corrected by visual analysis of the electropherogram, aligned and assembled by haplotypes using Bioedit v7. 0. 5. 3  [32]. The consensus sequence of each R. sanguineus (s. l. ) haplotype was then used as query sequences using BLAST against the NCBI nucleotide database. The 400 bp sequences of R. sanguineus (s. l. ) ticks were then aligned with sequences of R. sanguineus (s. l. ) from different parts of the world and a phylogenetic tree was built using the Maximum Likelihood method. The nucleotide evolution model of Hasegawa, Kishino and Yano was selected based on the Akaike Information Criterion corrected following the previously described method  [33].
The taxonomy assignments of OTUs were performed with Naive Bayes Classifier using Silva database v. 119. Contaminants were removed from the analysis using a homemade script that suppresses OTUs if their relative abundance (proportion of reads) in a given sample was not at least 10× higher than in the negative control. For all the analyses, an even number of sequences were used. Richness (sobs, chao1), α-diversity (1/λ, H′) and βdiversity (Bray-Curtis) indices were calculated (Additional file 1: Table  S2 ). Non-parametric analysis of microbiota homogeneity (Homogeneity of Variance, HOMOVA) and differentiation (Analysis of Molecular Variance, AMOVA) among samples were performed with MOTHUR pipeline, following the method previously described  [37]. The HOMOVA analysis compares the homogeneity of the communities within the groups and the AMOVA compares the differences of the microbial communities among the groups.
The images were digitally captured to investigate embryo quality, an important issue in the worldwide chain of in vitro production in the commercial market. Applications of this type of database can be used as an atlas in a tutorial for embryologists inexperienced with bovine blastocyst classification or in specialized in vitro fertilization clinics to search for novel, alternative ways to assess the embryo. Computational algorithms can be elaborated, aiming at a better classification of bovine blastocysts, seeking to eliminate human subjectivity, and using a non-invasive technique such as a digital image. Recently, this aim was attained by an automated bovine embryo evaluation based on image processing and artificial intelligence  4.
Embryo image capture and embryologists' classifications A database containing 482 images captured of bovine blastocysts was used. The images were obtained using an inverted microscope (Olympus IX71) at 32x magnification and the software Lucam Capture v6. 30; images were stored in JPG format in 8-bit colour (RGB) at a resolution of 1,280 × 1,024 pixels, as described in ref. 4. Each image contained only one embryo, which was approximately centred in the visual field. To capture the image, the position of the blastocyst was standardized such that the plane of focus was in the largest diameter of the embryo and the inner cell mass (ICM) was perpendicular to the focal plane (Fig. 1 ). Three experienced embryologists from the company, who were responsible for routine laboratory work, classified each image into three quality grades according to IETS standards  1.
Image standardization Each laboratory applies a different standard to capturing images; therefore, standardizing these methods has become indispensable. For the segmentation and extraction of information to find similar conditions independently of the images' original features, all factors must be considered. All of the algorithms were developed using the MATLAB platform  8, 9  and enabled the automated analysis of the images without the need for user intervention. For standardization, the software consecutively followed the steps of image import, conversion to greyscale, resolution and proportion adjustment, and intensity adjustment, as shown below. This standardization is better explained in our related work  4. 1. Image import: The image was imported into the MATLAB platform, regardless of image format (BMP or JPG); 2.
Conversion to greyscale: Colour information was not used in the interpretation of images and thus all images were converted to 8-bit greyscale. This process also rendered all subsequent steps faster since a decrease of the spectral image size occurs (from the three RGB bands to only one); 3. Resolution and proportion adjustment: In addition to different resolutions, the images had different proportions. An image of 640 × 480 pixels has a proportion of 0. 75, while for an image of 1,280 × 1,024 pixels, the proportion is 0. 8. Therefore, to only resize the images for one of the resolutions would distort the proportions of the other. We choose 640 × 480 pixels as the default resolution because it is the lowest standard and provides sufficient information for interpretation. Using the lowest resolution also increases the efficiency of the subsequent steps of the algorithm; besides, it does not create minimum resolution limitations for the final software.
Circular Hough Transform: In this work, we used the circle Hough transform (CHT), which has become widespread in circle detection processes in digital imaging  10. Once the binary image was obtained, the Hough transform detects the embryo circumference by mapping the image and thus provides an isolated embryo background image. The algorithm performed the detection of circles in two stages: in the first stage, it searches by circles of radius between 100 and 150 pixels; then, it searches radii between 150 and 250 pixels for greater accuracy. These values were obtained by algorithm verification using the entire database. Therefore, both initial blastocysts (smaller) and expanded blastocysts (larger) can be correctly detected. At the end of both searches, in each image, the detected circle's metrics are compared and the largest radius is used after the best circle is detected; 4.
Blastocyst isolation: After circumference detection identified the blastocyst, we used this next step to isolate it. Three versions of the blastocyst image were generated. In the first, the radius of the circumference is increased by 5 pixels to make sure the zona pellucida is included (called ER); in the second, the radius was reduced by 40 pixels in order to discard the trophectoderm, selecting the inner cell mass (ICM) and blastocoel for analysis only (called RR); and in the third version, we obtained the difference between ER and RR. Therefore, only the trophectoderm region was isolated in the image (called TE; Fig. 2 ). The pixel values, which determined the expansion (ER) and the contraction (RR) of the blastocysts images, were obtained by assessment of the image database.
Technical Validation Three highly experienced embryologists conducted the experiments and embryo production, and performed the blastocyst evaluation according to the IETS grading system. The Kappa index among the embryologists was 0. 572 (482 images, P o0. 001), which shows that there was a moderate agreement among the evaluations. The inverted microscope (Olympus IX71 at 32x magnification) used to take the blastocyst images that compose the data records from this paper were maintained under technical and professional conditions and calibration. The images were saved by well-established software (Lucam Capture v6. 30) with proper image resolution to guarantee quality and high standard representations of the blastocysts. The image processing techniques used in this work are approved worldwide and used by the scientific community, as well as analysed by peer-reviewed journals and image processing books  2, 18, 19.
Acknowledgements The authors' research is supported by grants  2012/50533-2, 2013/05083-1, 2006/06491-2 ,  2011/06179-7, 2012/20110-2  and  2016/19004-4  from  São Paulo Research Foundation (FAPESP). We also thank  Agência UNESP de Inovação (AUIN)  for processing the national and international patents of the invention.
Code availability The homemade software used in image processing was developed in the MATLAB platform including functions from Image Processing and Image Acquisition toolboxes (MATLAB 2017a, The MathWorks Inc. , Natick, MA). The image processing code developed on MATLAB is available at Figshare as files with. m extensions together with the present dataset (Data Citation 1). The version used for programming was R2017a, but the code is also compatible with earlier released versions (e. g. , R2012).
Software versions The authors suggest the use of recent MATLAB versions for image processing due to toolbox function updates in the newest versions. The releases after version 2014b offer a new graphic system and other functions that improved image-processing techniques (https://www. mathworks. com/help/matlab/ graphics-changes-in-r2014b. html). Other software commonly used in image processing is ImageJ (ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA), an open source program that has similar image processing functionalities that were used to analyse the embryo images. The software Lucam Capture v6. 30 was used to capture the image from an inverted microscope (Olympus IX71) at 32x magnification.
Data Records The data record associated with this paper is available at the Figshare repository (Data Citation 1). All the 482 blastocyst images described in the methodology step 'Embryo image capture and embryologists' classifications' are identified by a title beginning with Bovine blastocyst image-blq, followed by a number n, which ranges from 1 to 482 in *. jpeg. All images are also provided as a zip archive ('Blastocyst images. zip'). The variables extracted from the images, which were determined by 'Description of the chosen variables', are described in the file named 'Quantitative variables and classification of bovine blastocysts' in *. xls format (available at the Figshare repository). Each row describes a bovine blastocyst image (identified by the ID previously described), and columns D to AM describe the quantitative variables.
tuberculosis H37Rv (GenBank accession no. NC_000962. 3) using BioEdit software (Ibis Biosciences, Abbott company, Carlsbad, CA), or CLC Sequence Viewer (CLCbio, Qiagen, Redwood City, CA), including at least 41 nucleotides from the promoter region. DNA from retrospective isolates was amplified in in-country reference laboratories, and sequencing was outsourced to Macrogen Europe (Amsterdam, the Netherlands). After organizing several trainings on generation and analysis of sequences, the pncA sequences were analyzed at the incountry reference laboratories using MEGA6  [23]. The full procedures are available in S2 File. Samples received in Milan were also analyzed by whole genome sequencing (WGS). For WGS, genomic DNA was extracted from cultured isolates using the QIAamp DNA Mini kit (Qiagen, Hilden Germany) according to manufacturer's instructions.
Data management and analysis Data from the Union study were merged with retrospective data into a single Excel sheet database. All samples with poor quality pncA sequences (non-analyzable) were excluded from analysis. Descriptive statistics and calculation of different proportions were done for aggregated data in different categories and sub-categories. The Pearson's chi-square and Fisher's exact tests were used to test for associations of PZA resistance with potential risk factors. Data analysis was performed using STATA version 14. 2 software (College Station, TX: STATA Corp. ), and a p-value <0. 05 was considered significant.
Study population and sampling coverage Among 623 isolates considered for this study, 50 (8%) were excluded from analysis due to non-analyzable pncA sequences, due to poor quality of the sequence. Of the 573 (92%) included for analysis, 408 (71%) originated from the Union study of whom 342 (84%) were isolated from previously treated TB patients. For the remaining 165 isolates collected from routine surveillance of drug resistance, the treatment history of the patient was unknown for 56 (34%), and 72 of 109 (66%) isolates with documented treatment history were collected from new TB patients, with the majority (42 of 72, 58%) of new TB patients originating from Rwanda. Most samples were tested for INH, with majority of them being resistant (proven MDR-TB) (Fig  1 ). However, as some were INH-sensitive (proven RR-TB), and some samples had no data for INH, we considered our sampling as RR-TB rather than MDR-TB.
Staff from several NRL and research laboratories were indeed trained on sample preparation for (outsourced) sequencing and data analysis in 2013-2015, by the Institute of Tropical Medicine, Antwerp Belgium, in collaboration with the ITM TB network, WANETAM Plus and OFID Pasteur network. Even if sequencing facilities are expensive and scarcely available, we proved that a sequencing approach is possible for NRLs in Africa when outsourcing is used for the sequencing itself, and the staff may focus on the preparation of samples by PCR, and on the informatics' analysis. As a resource for laboratory managers who would like to implement this approach in their own setting, our detailed procedures are added as S2 File. Furthermore, preliminary results at ITM indicate that the PCR can start directly from DNA extracted from smear-positive sputum samples.
Acknowledgments We thank all staff members who have helped in collecting and/or analyzing samples in the different countries. We are particularly grateful to  Nneka Onyepu  from  NIMR in Lagos, Nigeria , for her help in discussing the study design despite the inability to contribute suitable data. We are also thankful to  Mr. Etienne Mpabuka  from  Rwanda Biomedical Center  who greatly supported the pncA training. The membership of the "Union short  MDR-TB regimen study group " is:  Albert Ignaleamoko ,  Alberto Piubello ,  Alimata Bakayoko ,  Armand Van Deun ,  Arnaud Trebucq ,  Bassirou Souleymane ,  Christopher Kuaban ,  Daniela Cirillo ,  Dissou Affolabi ,  Eliane Kamanzi ,  Elisa Tagliani ,  Georges Kabuya ,  Hans Rieder ,  Ju ¨rgen Noeske ,  Kobto Ghislain Koura ,  Manuella Onambele ,  Martial Ouedraogo ,  Martin Gninafon ,  Melissa Sander ,  Michel Gasana ,  Michel Kaswa ,  Mourad Gumusboga ,  Nyandwi Stany ,  Raymond Nguessan ,  Sara Eyangoh ,  Souba Diande, Souleymane Hassane, Thadee Ndikumana, Timothee Ouassa, Valentin Fikouma, Valerie Schwoebel, Zacharie Kashongwe, Zelika Hamidou.
2001; Bell 2003). This underlines the great potential of radio observations in unveiling a mixture of AGN and star-forming galaxies. Nevertheless, radio observations need to be supplemented by multiwavelength data to fully characterise the nature of the radio sources. Outstanding progress has been made through the analysis of large samples of radio-selected AGN in the local Universe  (Smolčić 2009; Best & Heckman 2012). For instance,  Smolčić (2009)  identified a twofold population of radio-emitting AGN, namely high-excitation and low-excitation radio galaxies (HERGs and LERGs, respectively), on the basis of the presence of high-or low-excitation lines in their optical spectra taken from the Sloan Digital Sky Survey (SDSS;  York et al. 2000). Interestingly, the author found that HERGs preferentially live in galaxies within the green valley (in terms of optical  [NUV-r]  colours and stellar mass, M ), while LERGs usually populate the red sequence of massive and passive systems.
Smolčić, Smolcic et al. 2017b) , reaching a 1σ sensitivity of 2. 3 µJy beam -1. The rich multiwavelength (X-ray to radio) data set of photometry and redshifts available in the COSMOS field allows us to investigate the physical properties of these sources from a panchromatic perspective. The main goals of the present work are twofold: first, to provide a value-added catalogue that includes classification and physical properties for each 3 GHz VLA-selected source in the COSMOS field, and, second, to explore the multiwavelength properties of AGN hosts for different classes of radio-selected AGN out to z 6. The paper is structured as follows. In Sect. 2 we describe our sample selection and the cross-match with ancillary photometry. In Sect. 3 we decompose the multiwavelength spectral energy distribution (SEDs), while the classification of our sample is discussed in Sect.
Optical to (sub)millimetre photometry The COSMOS field benefits from an exquisite photometric data set, covered from the X-rays to the submillimetre domain 1. Cross-matching our 3 GHz selected sample to existing ancillary data is essential to derive physical properties of galaxies. The multiwavelength photometry is taken from the COSMOS2015 catalogue  (Laigle et al. 2016) , which combines optical photometry 2  , the most recent UltraVISTA (DR2 3  ) data over the central 1. 5 deg 2 in the near-infrared (NIR) bands Y, J, H, and K s 4  , and MIR photometry obtained from the Infrared Array Camera (IRAC), which is recently complemented by deeper IRAC 3. 6 and 4. 5 µm observations with the Spitzer Large Area Survey with Hyper-Suprime-Cam (SPLASH;  Steinhardt et al. 2014; P. Capak et al. in prep.
). In addition, this data set has been crossmatched with 24 µm photometry  (Le Floc'h et al. 2009 ) from the Multi-Band Imaging Photometer for Spitzer (MIPS). Laigle et al. (2016)  provides further details. The cross-match to associate a possible optical-NIR counterpart with each radio source is fully described in  Smolcic et al. (2017a)  (see their Sect. 3). First, they excluded stars and masked regions in the COSMOS2015 catalogue because of the less accurate optical photometry, which reduces the effective area of the COSMOS field to 1. 77 deg 2 and our 3 GHz selected sample to 8 696 radio sources. Secondly, they performed a nearestneighbour matching, by selecting for each radio source only candidate counterparts within 0. 8 searching radius and, at the same time, requiring a false match probability (i.
in prep. )  and PdBI  (Smolčić et al. 2012; Miettinen et al. 2015). The (sub)mm positions were cross-matched to the COSMOS2015 positions via a nearest neighbour matching, using 1. searching radius (the smallest beam width of the (sub)mm data we collected). A thorough visual inspection of the counterpart associations has been performed for the 1. 3 mm detected ALMA sources (68% of the (sub)mm photometry we collected), which is detailed in  Brisbin et al. (2017)  and  Miettinen et al. (2017). We also collected X-ray data from the Chandra-COSMOS  (Elvis et al. 2009; Civano et al. 2012 ) and COSMOS-Legacy catalogues  (Civano et al. 2016). The optical-NIR counterparts of Xray sources were matched via a maximum likelihood algorithm and are presented in  Marchesi et al. (2016). We matched their catalogue to our 3 GHz selected sample of 7 729 optical-NIR counterparts via COSMOS2015 IDs.
2009 (Salvato et al. , 2011)). An exhaustive list of spectroscopic redshifts was compiled  (April 2015, Salvato et al. in prep. )  and made internally accessible to the COSMOS team. Most of the spectroscopic redshifts used in this paper were taken from the zCOSMOS survey  (Lilly et al. 2007 (Lilly et al. , 2009)) , either the public zCOSMOS-bright or the proprietary zCOSMOS-deep database, the DEep Imaging Multi-Object Spectrograph (DEIMOS,  Capak et al. in prep. ) , and the FOcal Reducer and low dispersion Spectrograph (FORS2,  Comparat et al. 2015). M. Salvato et al. (in prep. ) provide for a full reference list. For each radio source with a multiwavelength counterpart in this spectroscopic compilation, we replaced the photometric redshifts with new spectroscopic values only in case of secure or very secure measurements 5.
SED-fitting decomposition of 3 GHz sources In this section, we fit the multiwavelength SEDs of our radio sources to disentangle the AGN emission from that related to the host-galaxy. It is well known that radio-selected samples contain distinct galaxy populations (e. g. Condon 1984; Windhorst et al. 1985; Gruppioni et al. 1999 ) in terms of star formation and AGN properties. Therefore, fitting the multiwavelength SEDs may provide meaningful results only if AGN and galaxy templates are both taken into account. We used both the SED-fitting code magphys 6  (da Cunha et al. 2008), and the three-component SED-fitting code sed3fit by  Berta et al. (2013) , which accounts for an additional AGN component  7. The aforementioned references provide for a full description of these SED-fitting codes. Here we briefly outline the main prescriptions that are relevant for our analysis.
The magphys code is designed to reproduce a variety of galaxy SEDs, from weakly star-forming to starbursting galaxies, over a wide redshift range 8. This code relies on the energy balance between the dust-absorbed stellar continuum and the reprocessed dust emission at infrared wavelengths. This recipe ensures that optical and infrared emission originating from star formation are linked in a self-consistent manner, but does not account for a possible AGN emission component. The threecomponent SED-fitting code presented by  Berta et al. (2013)  combines the emission from stars, dust heated by star formation, and a possible AGN-torus component from the library of  Feltre et al. (2012 , see also Fritz et al. 2006). This approach results in an effectively simultaneous three-component fit. For each best-fit parameter, the code provides a corresponding probability distribution function (PDF), which enables the user to obtain reliable confidence ranges for parameter estimates (see e.
Classification of 3 GHz radio sources We combine SED-fitting decomposition (Sect. 3) with other multiwavelength AGN diagnostics to reach a more complete census of AGN in our sample. These additional indicators are taken from X-ray, MIR and radio data, which allow us to identify two main populations of radio-selected AGN in our sample: moderate-to-high radiative luminosity AGN (HLAGN) and lowto-moderate radiative luminosity AGN (MLAGN). Hereafter, we refer to these populations as HLAGN (X-ray, MIR, and SEDselected AGN) and MLAGN (radio-excess sources that are not HLAGN), as explained in the next sections. This naming convention comes from the idea that the selection criteria based on SED-fitting, X-ray, and MIR data preferentially select higher luminosity AGN, where the term "luminosity" here refers to the AGN radiative luminosity (L rad,AGN ), which is a proxy of the SMBH accretion rate (BHAR; e.
Catalogue description The value-added catalogue presented in this section includes classification and selected physical properties used in this work for our 3 GHz radio sample with optical-NIR counterparts (7 729 sources in total). We also list the individual criteria used in this work to classify our radio sources (columns 14 to 17). This way, any user can easily retrieve our classification or adjust it to a different set of selection criteria. The catalogue will be made available through the COSMOS IPAC/IRSA database 12. Here we describe its structure, following the same format of   (14, 15, 16) =(0,0,0) ∧ (17)=1.
Distribution of AGN properties Fig. 11  shows the distribution of both AGN radiative and mechanical power for our classes of AGN. We calculated the AGN radiative power (L rad,AGN ) of HLAGN from the best-fit AGN template obtained with the threecomponent SED-fitting code sed3fit for sources both with and without radio excess. The typical uncertainties on L rad,AGN are around 0. 4 dex for sources with (≥99%) significant AGN component (i. e. SED-AGN, see Sect. 4), while we took the L rad,AGN from the 95 th percentile of the corresponding PDF obtained from the sed3fit code for HLAGN not identified as such from SED decomposition (30% of HLAGN); this is equivalent to an upper limit at 90% confidence level on the AGN radiative luminosity. Fig. 11  shows the normalised distributions of L rad,AGN , separately for HLAGN (blue) and for the subsample with radio excess (blue thicker).