dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion We have proposed and evaluated decOM as a tool predict the metadata class of a given metagenomic sample by using a Microbial Source Tracking framework, in order to help paleogeneticists better assess the source content of their ancient samples. Because it was built using a Microbial Source Tracking framework, it can also help determine the composition of any other microbial community (not necessarily ancient or of oral origin), which is a common question in microbiome studies. Let us clarify that our goal is not to define an ancient oral microbial community per se, but to give the user an indication on the quality of their sample in terms of ancient genetic material. We leave for immediate future work the extensions of decOM to other MST tasks, which could be readily done by creating a k-mer matrix of metagenomic samples of interest with their associated labels and estimating the source proportions using decOM.
The utility of decOM was established on a collection of aOral metagenomic samples and their possible contamination sources, in a leave-one-out set up experiment where every sample was compared against all others. To control for an overly optimistic performance, we performed a stratified 5-fold cross-validation experiment making sure all the samples from the same BioProject belonged to the same fold. Finally, decOM was tested on an external validation data set of 254 aOral samples that were not part of initial collection of metagenomic aOral samples and metagenomes of other contaminants and in a simulated ancient calculus metagenome. We acknowledge that our method would classify the synthetic sample tested on this paper as an mOral sample instead of aOral despite having predicted the largest proportion of aOral source contribution when compared to mSourceTracker or FEAST.
However, considering decOM has already proven to be useful on real data, we leave further tuning of the method on synthetic data to be part of the upcoming work. In almost every setting, decOM outperformed two of the most widely used techniques in the field of MST in the multi-class classification task of predicting the label of a metagenomic samples as the source environment with the highest proportion. Ideally, we would test decOM on a collection of ancient oral samples with known proportions for each source environment; unfortunately, to our knowledge, such a data set does not exist. The task of creating a synthetic data set with such characteristics poses additional challenges regarding how to avoid overlapping species (originating genomes) between each source environment and would ultimately not be a good representation of a real sample.
For this reason, we focused on the evaluation of each method by using the metadata class prediction of a hard label rather than by confirming the proportion predictions were the most accurate. It could be argued that the lower performance of mSourceTracker and FEAST compared to decOM in the multi-class classification task described in this study was due to limitations of the input taxonomybased clustering table given to the methods. Better results might be achieved by using a larger database or a tool other than Kaiju to estimate taxonomic abundances. To evaluate this, we conducted an additional experiment in which we constructed another taxonomy-based clustering table with KrakenUniq  [35]  (see Supplementary File, Section 2). Results in this paper are shown only for the taxonomic abundance profile based on Kaiju, which can also be replicated using public data sets and which, in any case, yielded the best results for the competing methods.
Funding R. C was supported by  ANR Transipedia, Inception  and  PRAIRIE grants  ( PIA/ ANR16-CONV-0005 ,  ANR-18-CE45-0020 ,  ANR-19-P3IA-0001 ). This method is part of a project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  956229.
4a ). Thymidine chase strongly reduced the PLA signal of BRCA2-EdU in both unperturbed replication and replication stress conditions suggesting that at a large fraction of the BRCA2 pool is associated specifically with nascent DNA (Fig. 2b ). Similar to BRCA2 WT, BRCA2-R3052W was associated with nascent DNA in both conditions although the overall PLA signal was   32  P-labeled substrates. The data represent the mean from three independent experiments. Error bars, SD. See also Supplementary Fig. 1. c Quantification of the relative cell viability monitored by MTT assay upon treatment with increasing doses of the PARP inhibitor Olaparib, as indicated. The data represent the mean ± SD of four independent experiments. (ns, not significant). d Frequency of mCherry positive cells in cells transfected with the promoter-less donor plasmid (AAVS1-2A-mCherry) without (-TALEN) (open circles) or with (+TALEN) nucleases (filled circles).
The error bars represent the mean ± SEM of three independent experiments. See also Supplementary Fig. 2b. e Quantification of the surviving fraction of BRCA2 +/+ and BRCA2 -/-or BRCA2 -/-stable clones expressing BRCA2 WT or the variants C315S, S73L, R3052W, assessed by clonogenic survival upon exposure to MMC at the indicated concentrations. Data represent the mean ± SD of three independent experiments. f Quantification of the surviving fraction of BRCA2 +/+ and BRCA2 -/-or stable clones expressing BRCA2 WT or the variants C315S, S273L, R3052W, assessed by clonogenic survival upon exposure to HU at the indicated concentrations. Data represent the mean ± SD of three independent experiments. See also Supplementary Fig. reduced (Fig. 2a ). Next, we generated cell lines expressing a mutant of BRCA2 located at the extreme C-terminus of BRCA2, S3291A (Supplementary Fig.
Cells left untreated (UNT) or treated with HU (30′ or 1 h 0. 2 mM) are shown. An individual signal is observed (focus) if the two probed proteins (RAD51 and EdU-Biotin) are in close proximity (<40 nm). For all the experiments we carried out two single-antibody control (anti-RAD51 and anti-biotin) to assess the specificity of the PLA signal. The scale bar indicates 10 μm. (Bottom) Quantification of RAD51 recruitment was measured as the number of PLA foci observed per nucleus. The data represent the mean + SEM with 200-300 cells analyzed in each experimental data set at each time point. The number of independent experiments performed was as follows: BRCA2 WT: (UNT n = 9, 5′ n = 4, 30′ n = 8,1 h n = 8); C315S: (UNT n = 7, 5′ n = 2, 30′ n = 4, 1 h n = 5); S273L: (UNT n = 3, 30′ n = 2, 1 h n = 2); S3291A: (UNT n = 4, 5′ n = 3, 30′ n = 2, 1 h n = 2); R3052W: (UNT n = 5, 5′ n = 3; 30′ n = 3; 1 h n = 3); BRCA2 -/-: (UNT n = 3, 5′ n = 3, 30′ n = 3, 1 h n = 3 ).
Statistical difference was determined by the Kruskal-Wallis test followed by Dunn's multiple comparison tests; the p-values show significant differences compared to the BRCA2 WT clone. ns not significant, **p < 0. 01 (p = 0. 0058 at NT BRCA2 WT vs. S3291A), ****p < 0. 0001. b (Left) Representative images of in situ PLA on nascent DNA between biotinylated EdU detected with anti-biotin antibody and RAD51specific antibody in DLD1 BRCA2 WT cells after 4 h Thymidine chase in cells left untreated or treated with HU (30′ 0. 2 mM). (Right) Quantification of RAD51 recruitment measured as the number of PLA foci observed per nucleus after 4 h Thymidine chase in BRCA2 WT cells. The data represent the mean + SEM of two independent experiments with 200-300 cells analyzed in each experimental data set at each time point.
Cells were lysed and DNA fibers were stretched onto glass slides and then dried and fixed in methanol/acetic acid (3:1) for 10 min. The DNA fibers were denatured with 2. 5 M HCl for 1 h, washed with PBS, and blocked with 2% BSA in PBS-Tween for 60 min. IdU replication tracts were revealed with a mouse anti-BrdU/IdU antibody from BD Biosciences (347580; 1:100) and CldU tracts with a rat anti-BrdU/CldU antibody from Eurobio (ABC117-7513; 1:100). The following secondary antibodies were used: Alexa fluor 488 antimouse antibody (Life A21241; 1:100) and Cy3 anti-rat antibody (Jackson ImmunoResearch 712-166-153; 1:100). Fibers were visualized and imaged by Carl Zeiss Axio Imager Apotome using ×40 Plan Apo 1. 4 NA oil immersion objective and acquired using Zeiss Zen 3. 1 software. Replication tract lengths were analyzed using ImageJ software.
5, 1 mM DTT, 1 mM MgCl 2 , 2 mM CaCl 2 ). The protein-DNA complexes were resolved on 6% native polyacrylamide gels in 1xTAE buffer (40 mM Tris acetate, 0. 5 mM EDTA) at 70 V for 75 min. The gels were dried and analyzed with a Typhoon PhosphorImager (Amersham Biosciences) using Image Quant software (GE Healthcare). In all EMSAs, the ratio of DNA-protein complexes were calculated as the percentage of bound DNA compared with the free DNA.
0, 1 mM DTT, 2 mM ATP, 1 mM MgCl 2 , 2 mM CaCl 2 , 0. 1 mg/ml BSA (NEB). The reaction was started by adding 4 nM molecules of the donor template dsDNA (oAC1076 and oAC1077 1:1) and the mix was further incubated for 30 min at 37 °C. The reaction was stopped by incubation with 0. 25% SDS and 0. 5 mg/ml Proteinase K for 10 min. The samples were loaded on a 6% polyacrylamide gel and migrated at 70 V for 75 min. The gels were dried and analyzed with a Typhoon PhosphorImager (Amersham Biosciences) using Image Quant software (GE Healthcare). The percentage of DNA strand exchange product was calculated as labeled product divided by the total labeled input DNA in each lane.
EdU cell cycle analysis To label replicated DNA, cells were incubated with 10 μM EdU for 2 h. Samples were collected by trypsinization and incorporated EdU was detected using the Click-iT EdU Alexa Fluor 647 Flow Cytometry Assay Kit (Molecular Probes-Thermo Fisher Scientific) according to manufacturer's instructions. Cells were re-suspended in PBS containing 20 μg ml -1 propidium iodide (Sigma) and 10 μg ml -1 RNase A (Sigma) before samples were processed using flow cytometry (BD FACSCalibur, BD Biosciences). A number of 10,000 events were analyzed per condition using FlowJo software.
Acknowledgements We thank all members of  Carreira lab  for their input on this manuscript. We thank  Sarah Lambert  for helpful discussions on this project. We would like to acknowledge  Xavier Veaute (CEA-DRF-IRCM-CIGEx)  for the purification of RPA. We thank  Laetitia Besse  from the  Cell and Tissue Imaging Facility of the Institut Curie (PICT) , a member of the  France BioImaging National Infrastructure  ( ANR-10-INBS-04 ), and  Charlene Lasgi  from the  Flow Cytometry platform of Institut Curie, Orsay. This research was funded by grants from the  Agence National de Recherche  ( ANR-17-CE12-0016 ),  Institut National du Cancer (INCa- DGOS_8706 ), and  Agencia Española de Investigacion (MCIN/AEI)  ( PID2020-115977RB-I00 ) to A. C. and  Jeunes Chercheuses Jeunes Chercheurs  from the  Agence National de la Recherche  ( REPLIBLOCK ANR-17-CE12-0034-01 ) to C.
R. A predoctoral Fellowship from  Fondation ARC pour la recherche sur le cancer and French Ministry of Education  supported D. V. A predoctoral Fellowship from  Fondation pour la Recherche Medicale (FRM)  supported I. D. , A. M. , and C. v. N. L. A-A. was funded by an  FPI  Fellowship from  Agencia Española de Investigacion (MCIN/AEI)  ( PID2020-115977RB-I00 ). R. C. was funded by a  PSL (Paris Science et Lettres ) predoctoral Fellowship and R. L. was funded by  Fondation ARC  pour la recherche sur le cancer.
Furthermore, we propose a "reconciledTB" list making a link with results directly obtained from whole genome sequencing (WGS) data and results obtained from classical genotyping analysis (data inferred from SpoTyping and MIRUReader). Data visualization graphics and trees generated provide additional elements to better understand and confer associations among information overlap analyses. Additionally, comparison between data entered in an international genotyping database (SITVITEXTEND) and ensuing data obtained from the pipeline not only provide meaningful information, but further suggest that simpiTB could also be suitable for new data integration in specific TB genotyping databases.
A non-exhaustive list of software tools for TB analysis was recently reviewed  (Couvin et al. , 2021). Other databases like Mycobrowser (https://m ycobrowser. epfl. ch/) provide manually curated annotations and appropriate tools to facilitate genomic and proteomic study of pathogenic mycobacteria  (Kapopoulou et al. , 2011). Table  1  shows a nonexhaustive list of databases that could be used to analyze MTBC strains. Development of pipelines for whole genome sequencing (WGS) analysis of MTBC isolates is helpful for TB strains characterization and in-depth epidemiological surveillance. Numerous pipelines already exist, but poor or no association with classical genotyping methods and databases were used within these pipelines or tools. The aim of this study was therefore to propose an integrated software tool allowing easy comparison between classical genotyping and WGS (both FASTQ and/or FASTA formatted) TB data.
We hereby describe the simpiTB pipeline which permits to extract meaningful information from WGS data and allows to combine genomic, phylogenetic and clustering analyses in existing TB databases (SITVIT) or those under development (SITVITEXTEND). simpiTB is freely available at: https://github. com/dcouvin/simpiTB. The tool is also available through the Galaxy KaruBioNet instance (http://calamar. univ-ag. fr/c3i/galaxy_karubionet. html).
Pipeline Several software tools were employed to build the simpiTB pipeline mainly written using the Perl programming language (https://github. com/dcouvin/simpiTB). To facilitate the use of the tool, no extra Perl modules were used in the development. simpiTB requires following software tools: SPAdes (version 3. 11. 1) for de novo genome assembling  (Bankevich et al. , 2012) , SpoTyping (version 2. 1) for spoligotyping data prediction, MIRUReader for MIRU-VNTRs prediction, SpolLineages (version 1. 0. 1) for TB family prediction  (Couvin et al. , 2020) , TBProfiler (version 4. 4. 2) for drug resistance and lineage prediction, Mykrobe (version 0. 10. 0) for drug resistance and lineage prediction, ResFinder (version 4. 0) for drug resistance prediction  (Bortolaia et al. , 2020) , Prodigal (version 2.
Development and launch simpiTB can be launched from FASTQ reads or FASTA genome assemblies belonging to the MTBC. The simpiTB Perl program depends on other programs mentioned in the aforementioned workflow. These tools can be installed independently by users or by using the "installer_-Ubuntu. sh" file (tested on Linux Ubuntu 18. 04). Besides, a Singularity image  (Kurtzer et al. , 2017)  has also been generated to facilitate the launch of the program (https://github. com/dcouvin/simpiTB). When users provide FASTQ files as input, they can be assembled using SPAdes for better prediction of MIRU-VNTRs data with MIRUReader. However, MIRUReader and SpoTyping can be run with FASTQ or FASTA files.
Output files simpiTB software tool produces a wide variety of output files. Among them: the reconciledTB_list. xls which is the main summary file, grapetree. tab (GrapeTree input file), grapetreeNJ. nwk (Newick NJ phylogenetic tree inferred by GrapeTree), spollineages. csv (SpolLineages input file), outputSL. csv (SpolLineages result), phyloviz_profiles. tab (PHYLOViZ online's input tabulated profiles)  (Nascimento et al. , 2017). Help is available when using the provided script deposited on GitHub. Furthermore, some test output files were made available on the GitHub page of the tool. Supplementary Table  S1  shows a brief summary of 45 genomes obtained from the NCBI RefSeq repository.
Phylogenetic analyses The phylogenetic tree produced by GrapeTree (i. e. "grapetreeNJ. nwk" output) is based on the ~500 RefSeq genome assemblies. It makes it possible to more or less clearly delimit the predicted spoligotyping TB families (Supplementary Fig. S1 ). For example, one may notice the presence of genome assemblies belonging to the Beijing family at the top right of the circular tree. One may also notice the relatively high proportion of assemblies belonging to Euro-American lineage (L4). A subset of data containing 45 empirically selected genome assemblies from the RefSeq repository was used to draw phylogenetic trees based on different methods: Parsnp, Roary+Fasttree, GrapeTree and Mashtree (https://github. com/lskatz/mashtree) (Katz et al. , 2019 ), and show some example outputs we could get with simpiTB.
Fig. 2  shows core genome phylogenetic trees constructed with Parsnp and Roar-y+Fasttree. All spoligotyping-based predicted TB families were quite well separated in the two trees, however, in the core genome alignment tree built using Roary, one genome assembly belonging to LAM family (GCF_014899605. 1) appeared near assemblies belonging to T and X families (Fig. 2B ). As expected, trees based on core genome analyses produced better results than trees constructed using GrapeTree (on spoligotypes and 24-MIRU-VNTRs) or Mashtree. Mashtree creates trees from FASTA genome sequences or FASTQ reads using Mash distances  (Ondov et al. , 2016). Fig. 3  shows output trees from GrapeTree and Mashtree (using the same dataset of 45 genomes). One may notice that some inconsistencies may exist on these trees.
Discussion WGS data becoming increasingly accessible, we need tools to analyze them in depth, as well as make it easier to compare results between similar tools. simpiTB pipeline might be helpful to tentatively identify specific TB genomes and compare data with SITVIT databases and tools  (Couvin et al. , 2019; Couvin et al. , 2020). Reconstruction of Fig. 3. Trees built with GrapeTree (A) and Mashtree (B). phylogenetic trees using GrapeTree from spoligotyping and MIRU-VNTRs data could allow users to compare different kinds of phylogenetic analyses. Snippy software tool (https://github. com/tseemann/s nippy) could be used to build core SNP phylogenetic trees  (Perdigão et al. , 2020). Trees obtained from Snippy could then be compared to those generated using GrapeTree or other tree building methods.
Unlike other tools, simpiTB allows analyzing sequencing data directly from FASTQ reads or FASTA assemblies. In addition, the simpiTB tool is designed to help end-users perform more in-depth analyses and/or compare results data from different sources. Due to the limited storage and computing capacity of our computer servers, the ability to analyze Mycobacterium tuberculosis WGS data from this tool is limited. Another limitation of this software tool is the fact that it is mainly available through the command line for the moment. However, efforts are being made to make this tool available via our recently released Galaxy KaruBioNet (http://calamar. univ-ag. fr/c3i /galaxy_karubionet. html) platform  (Couvin et al. , 2022). Presently, simpiTB is better suited to analyze FASTQ short reads (e. g.
Illumina) data or FASTA genome assemblies; hence a future improvement would be to allow it to analyze data obtained from long reads devices. Some software tools already allow treatment of such data (e. g. TBProfiler), nonetheless, simpiTB is an easily adaptable tool that would be ready to receive future improvements as and when needed in a stepwise manner. Further enhancements will be made to simpiTB in the future to extend its analysis and data representation/visualization capabilities.
Conclusion In conclusion, simpiTB allows analyzing MTBC WGS data in combination with classical genotyping data (spoligotyping and MIRU-VNTRs). This software tool allows users to get a detailed summary file ("recon-ciledTB_list"), as well as outputs allowing them to perform phylogenetic  or clustering analyses. simpiTB will be particularly helpful for the construction of the new generation of SITVIT databases linking the genotyping (SITVITEXTEND) vs WGS data (SITVITgeno). The simpiTB program is freely available on GitHub. Supplementary data to this article can be found online at https://doi. org/10. 1016/j. meegid. 2023. 105466.
Acknowledgements We are highly grateful to various contributors who kindly provided genotyping data to the SpolDB/SITVIT databases as well as all the users of these tools. We thank  Paúl L. Durán ,  Carlos A. Vazquez-Chacon  and  Gilberto Vaughan  for their help regarding ResFinder. We would also like to thank  Alexis Dereeper  for his help in integrating the tool into our Galaxy platform.
miru-vntr plus. org/ ReSeqTB collaborative effort for a centralized worldwide TB relational sequencing data platform https://www. reseqtb. org/ the sixth international multimarker http://www. pasteur-gu SITVIT2 database for studying MTBC genetic adeloupe. fr:8081/ diversity and molecular epidemiology SITVIT2/ SITVITBovis database and mapping tool to get an improved overview of animal and human cases caused by Mycobacterium bovis http://www. pasteur-gua deloupe. fr:8081/SITV IT_Bovis/ TBDB repository containing the scripts and data to generate all files required to run TBProfiler https://github. com/jodyphelan/tbdb tbvar Mycobacterium tuberculosis variome resource http://genome. igib. res. in/tbvar/ TB Portals an open-access, web-based platform for global drug-resistant-tuberculosis data sharing and analysis https://tbportals.
We present Atacama Large Millimeter/submillimeter Array (ALMA) Band 7 observations of a remarkably bright galaxy candidate at = -+ z 16. 7 phot 0. 3 1. 9 (M UV = -21. 6), S5-z17-1, identified in James Webb Space Telescope (JWST) Early Release Observation data of Stephen's Quintet. We do not detect the dust continuum at 866 μm, ruling out.
Before concluding that CEERS-93316 and S5-z17-1 are remarkably bright z ∼ 17 galaxies, it is essential to rule out or confirm the lower-z solution via further observations. In this paper, we present ALMA Band 7 DDT follow-up for S5-z17-1, which is one of these remarkably UV-bright z ∼ 17 candidates discovered in JWST ERO data of Stephan's Quintet. This is the first far-IR (FIR) characterization of either of these z ∼ 17 candidates with ALMA, 40 setting the benchmark to understand and interpret similarly high-z candidates identified 40 CEERS-93316 is too far north to be accessible by ALMA and has been observed in NOEMA DDT (#D22AC, PI: S. Fujimoto; see Arrabal  Haro et al. 2023a). in the future JWST observations. The structure of this paper is as follows. In Section 2, we describe the observations and the data reduction of both JWST and ALMA.
JWST Stephan's Quintet, a group of five local galaxies, was observed with NIRCam and MIRI in the JWST ERO program  (Pontoppidan et al. 2022 ). S5-z17-1 falls in the coverage of NIRCam filters, but none of MIRI. The NIRCam images were taken in six bands: F090W, F150W, F200W, F277W, F356W, and F444W, covering 42 arcmin 2. The exposure time in each filter is ∼1200 s. We use reduced and calibrated NIRCam imaging products that are publicly available, 41  and here we briefly explain the reduction and calibration procedure. The JWST pipeline calibrated level-2 NIRCam imaging products were retrieved and processed with the grizli pipeline  (Brammer & Matharu 2021; Brammer et al. 2022)  in the same manner as in  Bradley et al. (2023). The NIRCam photometric zero-point correction was applied with CRDS context jwst_0942.
pmap, including detector variations. 42  The derived photometric zero-points are consistent with those derived by other teams with a JWST ERS program  (Boyer et al. 2022; Nardiello et al. 2022). While the consistent calibration results from a more recent calibration file of jwst_0989. pmap have been confirmed within 3%  (Bradley et al. 2023 ), we add a potential systematic uncertainty to the flux measurement by 10% of the total flux in the following analyses to obtain secure results. The fully calibrated images in each filter were aligned with the Gaia Data Release 3 catalog (Gaia Collaboration et al. 2021), coadded, and drizzled at a 20 mas and 40 mas pixel scale for the short-wavelength (SW: F090W, F150W, F200W) and long-wavelength (LW: F277W, F356W, F444W) NIRCam bands, respectively.