dataesr/scientific-paragraphs-categorization huggingface dataset:
This work has benefited from the facilities and expertise of the high-throughput sequencing core facility of I2BC (Centre de Recherche de Gif -http:// www. i2bc. parissaclay. fr/). From 500 ng of total RNA, directional RNAseq libraries were created, using the Truseq Stranded Total RNA library prep kit (Illumina). After polyA purification, sequencing was performed on an Illumina Next-Seq500 instrument, using the NextSeq 500/550 High Output Kit (75 cycles), to obtain paired-end reads  (50-35 nucleotides [nt] ). Data analysis was performed with bcl2fastq2-2. 18. 12 for demultiplexing, Cutadapt 1. 15 for adapter trimming and FastQC v0. 11. 5 for quality control. Reads were mapped on all the genes of the reference genome of sheep (Ovis aries, Ensembl version v3. 1. 103), then counted by using FeatureCounts version 1.
Gene ontology (GO) functional analysis GO enrichment was explored with the R package ViSEAGO  [48] , with the whole O. aries genome (Ensembl version v3. 1. 103). All enriched GO terms (p ≤ 0. 01) were grouped into functional clusters using hierarchical clustering based on Wang's semantic similarity between GO terms by using the GO graph topology and Ward's criterion. The GO analysis was carried out using three conditions: control, BPA at 50 µM and BPS at 50 μM.
qPCR was performed on a CFX384 (Bio-Rad, Marnes-la-Coquette, France) with 125 nM of specific primers (Table  1 ), RT reaction (diluted 1/15) and qPCR Mastermix Plus for SYBR Green I (Bio-Rad), according to the manufacturer's instructions. The primer efficiency (E; Table  2 ) and standard curve were assessed for each gene. GeNorm software was used to confirm that among the genes assessed by qPCR, ribosomal protein L19 (RPL19) and β-actin (ACTB) were the most stable reference genes. All samples underwent qPCR runs for all genes, including RPL19 and ACTB. BioRad CFX Maestro sofware then provide the expression values for each genes (depending on Ct and on the efficiency of the primers). For each sample, the mean of expression values of RPL19 and ACTB is calculated. Then, for each candidate gene, the relative expression level of each sample is divided by the mean expression value of housekeeping genes by using the following equation, where Ct corresponds to the cycle threshold: The relative abundance of transcripts (R) are presented as the mean ± SEM of eight independent cultures, normalised to the control value at 12 h.
Statistical analysis Statistical analyses were performed with R version 4. 1. 0 using the R commander package  [88]  (except Live/Dead staining), steroidogenesis and gene expression were analysed with non-parametric analysis of variance (ANOVA) by permutation (lmperm package). The treatment effect, culture effect and treatmentby-culture interactions were assessed. Tukey's post hoc test (nparcomp package) was performed to determine differences between conditions. Logistic regression analysis was used to analyse Live/Dead staining. Significant difference was indicated when p ≤ 0. 05, and a tendency when 0. 05 < p ≤ 0. 1.
Acknowledgements The authors would like to thank  Albert Arnould  and  Thierry Delpuech  for ovine ovary collection and the  Endocrinology and Phenotyping Laboratory (Reproductive Physiology and Behaviours Unit, INRAE Nouzilly)  for the progesterone assay. This work has benefited from the facilities and expertise of the high-throughput sequencing core facility of  I2BC (Centre de Recherche de Gif  -http:// www. i2bc. paris-saclay. fr/).
First, we benchmarked and validated the TFA-based protein extraction SPEED method in a BSL-3 environment, confirming bacterial inactivation upon protein extraction, and allowing comparison of bacterial protein abundances after incubation in laboratory media or human plasma. We validated and extended previous transcriptomic data  (Chauvaux et al. 2007 ) by obtaining more sensitive and functionally relevant data. We then developed a new and affordable pipeline based on the SPEED method to study bacteria incubated in human whole blood and identified more than 5,500 proteins in the cellular fractions, composed of around 4/5 of human proteins and 1/5 of bacterial proteins, validating the possibility to use this pipeline for dual proteome studies. Cellular enrichment was performed under a hood in a BSL-3 environment before bacterial inactivation upon protein extraction, eliminating the need for expensive cell-sorting instrument or fixation step.
1%) in 5 min, 7 to 23% buffer B in 70 min, 23 to 45% buffer B in 30 min and 45 to 95% buffer B in 5 min at a flow rate of 250 nL/min for up to 132 min. Column temperature was set to 60°C. Mass spectra were acquired using Xcalibur software using a data-dependent Top 10 method with a survey scans (300-1700 m/z) at a resolution of 60,000 and MS/MS scans (fixed first mass 100 m/z) at a resolution of 15,000. The AGC target and maximum injection time for the survey scans and the MS/MS scans were set to 3. 0x10 6 , 100 ms and 1. 0x10 5 , 45 ms respectively. The isolation window was set to 1. 6 m/z and normalized collision energy fixed to 28 for HCD fragmentation. We used a minimum AGC target of 2. 0x10 3 for an intensity threshold of 4. 4x10 4. Unassigned precursor ion charge states as well as 1, 7, 8 and >8 charged states were rejected and peptide match was disable.
Exclude isotopes was enabled and selected ions were dynamically excluded for 45 seconds. LC-DIA-MS for DIA For the Y. pestis incubation in human blood, two libraries for DIA were constructed from i) CD14 and CD66b cell enrichments + Nanotrap ® -enriched Y. pestis and ii) pure culture of Y. pestis in LB at 37°C. Mass spectra were acquired in DIA mode with the XCalibur software using the same nanochromatographic system coupled on-line to a Q Exactive TM HF Mass Spectrometer. For each sample, 1 µg of peptides was injected into a reverse phase column (home-made column, 45 cm x 75 µm ID, 1. 9 µm particles, 100 Å pore size, ReproSil-Pur Basic C18 -Dr. Maisch GmbH, Ammerbuch-Entringen, Germany) Article: Page 18 of 44 after an equilibration step in 100% solvent A (H 2 O, 0. 1% FA). Peptides were eluted using the same multi-step gradient and temperature.
MS data was acquired using the Xcalibur software with a scan range from 295 to 1170 m/z. The DIA method consisted in a succession of one MS scan at a resolution of 60,000 and 36 MS/MS scans of 1 Da overlapping windows (isolation window = 25 m/z) at 30,000 resolution. The AGC (Automatic Gain Control) target and maximum injection time for MS and MS/MS scans were set to 3. 0x10 6 , 60 ms and 5. 0x10 5 , auto respectively. The normalized collision energy was set to 28 for HCD fragmentation. Bioinformatic analyses MaxQuant analyses: DDA raw files were processes using Max-Quant software version 1. 6. 10. 43  (Cox and Mann 2008)  with Andromeda search engine  (Cox, Neuhauser, et al. 2011). The MS/MS spectra were searched against a UniProt Y. pestis database (3,909 entries the 25/04/2019). Variable modifications (methionine oxidation and N-terminal acetylation) and fixed modification (cysteine carbamidomethylation) were set for the search and trypsin with a maximum of two missed cleavages was chosen for searching.
pestis CO92 database containing 3,991 entries, including 3,915 unique proteins from the initial CO92 Sanger sequencing  (Parkhill et al. 2001) , reannotated by RefSeq (Accession GCF 000009065. 1 ASM906, accessed in 2020) and 76 small open frame (sORF) encoded peptides (SEP) recently identified  (Cao et al. 2021 ) and the UniProt Homo sapiens database (20,361 entries the 03/03/2023). We applied the default BGS Factory Settings for both Pulsar Search and Library Generation. Briefly, full tryptic digestion allowing two missed cleavages, carbamidomethylation as a fixed modification on all cysteines, oxidation of methionines, and protein N-terminal acetylation as dynamic modifications were set. We used the search archive properties to create specific Y. pestis and human libraries as well as a combined library with a control of the FDR.
Spectronaut analyses: Spectronaut v. 16. 0. 220606 (Biognosys AG)  (Bruderer et al. 2015)  was used for DIA-MS data analyses. Data extraction was performed using the default BGS Factory Settings. Briefly, for identification, both precursor and protein FDR were controlled at 1%. For quantification, peptides were grouped based on stripped sequences and Qvalue was used for precursor filtering. MaxLFQ was used with no imputation and no cross run normalization strategies.
Missing values were imputed using the impute. mle function of the R package imp4p  (Giai Gianetto et al. 2020). Statistical testing was conducted using a limma t-test thanks to the R package limma  (Ritchie et al. 2015 ). An adaptive Benjamini-Hochberg procedure was applied on the resulting p-values thanks to the function adjust. p of the cp4p R package  (Giai Gianetto et al. 2016 ) using the robust method described in  Pounds et al. 2006  to estimate the proportion of true null hypotheses among the set of statistical tests. The proteins associated to an adjusted p-value inferior to a FDR (false discovery rate) level of 1% and an absolute log2(fold-change) superior to 1 have been considered as significantly differentially abundant proteins. Finally, the proteins of interest were therefore those which emerged from this statistical analysis supplemented by those which were considered to be present from one condition and absent in another.
Next, depending on the normalization strategy, intensity values only for bacterial or human proteins were normalized either by median centering within conditions or by median centering on all conditions (Giai Gianetto 2023). This method consists in estimating the median of intensities in a sample j (med j ), to substract this median to all the intensities x in the sample j and to add the average of median intensities (y = x -med j + average(med j )). Missing values were imputed using the impute. slsa function of the R package imp4p  (Giai Gianetto et al. 2020). Statistical testing was conducted using a limma t-test thanks to the R package limma  (Ritchie et al. 2015 ). An adaptive Benjamini-Hochberg procedure was applied on the resulting p-values thanks to the function adjust. p of the cp4p R package  (Giai Gianetto et al.
2016 ) using the robust method described in  Pounds et al. 2006  to estimate the proportion of true null hypotheses among the set of statistical tests. The proteins associated to an adjusted p-value inferior to a FDR (false discovery rate) level of 1% and an absolute log2(fold-change) superior to 1 have been considered as significantly differentially abundant proteins. Finally, the proteins of interest were therefore those which emerged from this statistical analysis supplemented by those which were considered to be present from one condition and absent in another. We used the R package clusterProfiler v4. 6. 2 for enrichment and gene set enrichment analysis  (Wu et al. 2021). For the bacterial proteome in plasma, the GSEA function was used based on UniProt accession number and mapped to an in-house annotation file extracted from the CO92 Gene Ontology terms downloaded from the QuickGo website accessed in April 2023  (Binns et al.
2009 ). The enrichKEGG function was used with the online KEGG dataset for the ype organism accessed in April 2023, based on the CO92 loci. Analysis were performed with the detected proteins as statistical background. For the human proteome analysis, the gseGo was used with the org. Hs. eg. db v3. 16. 0 as annotation database and statistical background, and the gseKEGG was used with the online annotation for the hsa organism accessed in April 2023 as annotation database and statistical background.
Acknowledgments We are grateful to all members of the Yersinia research unit and the  Yersinia National Reference Laboratory, WHO Collaborating Research and Reference Centre  for Plague FRA-140 for insightful discussions. The project received funding from  Institut Pasteur, Universit é Paris Cit é, CNRS ,  LabEX Integrative Biology of Emerging Infectious Diseases  ( ANR-10-LBX-62-IBEID ),  Agence de l'Innovation de D éfense (AID -DGA) ,  Fondation pour la Recherche M édicale  ( FDT20220401-5222 ) and the  Inception program (Investissement d'Avenir  grant  ANR-16-CONV-0005 ). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Most of the figures were created with BioRender. We declare no conflict of interest.
Context. Gaia has been in operations since 2014, and two full data releases (DR) have been delivered so far: DR1 in 2016 and DR2 in 2018. The third Gaia data release expands from the early data release (EDR3) in 2020, which contained the five-parameter astrometric solution and mean photometry for 1. 8 billion sources by providing 34 months of multi-epoch observations that allowed us to systematically probe, characterise, and classify variable celestial phenomena. Aims. We present a summary of the variability processing and analysis of the photometric and spectroscopic time series of 1. 8 billion sources carried out for Gaia DR3. Methods. We used statistical and machine learning methods to characterise and classify the variable sources. Training sets were built from a global revision of major published variable star catalogues.
The Gaia variability processing has many ramifications that are disseminated in several other articles. The variety of results makes the situation somewhat intricate. Here, we provide a summary of the results (see Table  1 ) as well as the associated queries in Appendix C. In total, 14 198 022 sources (variable sources, galaxies, sources in the Gaia Andromeda Photometric Survey, GAPS) have DR3 outputs processed by the Gaia Data Processing Centre of Geneva (DPCG). In the context of a 'real-time' variability analysis of the Gaia photometric data, it is important to mention that the Gaia Science Alert system  (Hodgkin et al. 2021)  published 10 765 alerts between June 2016 and December 2019. A subset of 2 612 sources detected between 25 July 2014 and 28 May 2017 are published along with Gaia DR3.
In addition to the time series for variable objects, DR3 includes time series and statistical information for all 1 257 319 sources observed within a cone of 5. 5 • opening directed towards the Andromeda galaxy (GAPS,  Evans et al. 2023) , independent of their variability status. In the GAPS data set, 12 618 sources are flagged as variables. Furthermore, the signal of the processed sources is known to be affected by scan angle orientation effects inducing spurious variability  (Holl et al. 2023). Such effects allowed us to identify 2 451 364 galaxies using supervised machine learning classification, which are included in DR3 in a dedicated catalogue. The structure of the article is as follows: In Sect. 2, we present the properties of the data from which DR3 results are derived. In Sect. 3, we describe the methods used and the obtained results.
Photometric time series Photometric data in the G, G BP , and G RP photometric bands were used from July 25, 2014, to May 28, 2017, that is 34 months. The sampling cadence depends on the spacecraft's 'scanning law', that is, the mode by which Gaia spins and precesses around its rotation axis. During the 34 months of observations processed here, two different scanning laws were used: the Ecliptic Pole Scanning Law (EPSL) during the first 28 days of operations and then the Nominal Scanning Law (NSL); see Sect. 1. 3. 2 of the Gaia DR3 documentation  (van Leeuwen et al. 2022). The implementation of these two scanning laws resulted in higher cadences and numbers of observations for objects near the ecliptic poles and the ecliptic latitude β = ±45 • , compared to elsewhere on the sky. We analysed 1 840 947 142 sources, corresponding to the number of input sources with five or more 'valid' (i.
e. non-NaN) measurements in the G band. The Gold, Silver, and Bronze photometric sets (see  Riello et al. 2021)  were used. The variability analysis was carried out for the G field-of-view (FoV), G BP , and G RP. The G astrometric field (AF) CCD data, which are of higher cadence and are used to determine the G FoV photometry, were only used for the short-timescale analysis, and were not  made public in Gaia DR3. The total number of photometric measurements in G BP , G (FoV and Per-CCD), and G RP photometric bands amounts to 367 billion. Eventually, the total number of sources published in DR3 is 1 811 709 771, with 11 754 237 sources having epoch photometry time series and 1898 having RV time series. We repeat here that the variable source data set comprises 10 509 536 entries. Figure  1  shows the number of FoV measurements per band (G, G BP , G RP ) for the published variable sources.
Data cleaning Despite the high quality of the Gaia products, the raw time series (photometric or radial velocities) require some cleaning in order to be able to carry out a proper variability analysis. A set of operators applying different cleaning steps are applied. For a detailed description, we refer to  Holl et al. (2018)  and the DR3 documentation  (Rimoldini et al. 2022). In DR3, the chain of operators for photometric analysis was modified compared to DR2 (see DR3 documentation, Sect. 10. 2. 3): a new operator called MultiBandOutlierRemoval-Operator (MORO) was introduced. MORO takes advantage of the quasi-simultaneous observations in the G, G BP , and G RP , and thus the three-band transits have the same 'transitid' qualifier. MORO was inserted after GaiaFluxToMagnitudeOperator, which converted raw fluxes into magnitudes using the band zero points  (Riello et al.
Variability detection During the successive data releases, we used several approaches to the variability detection. The variability pipeline reflects the different approaches through general variability detection (GVD) and special variability detection (SVD) paths (see Fig. 3 ). For the GVD path, the most classical approach relies on hypothesis testing using the uncertainty of individual FoV measurements. As explained in  Eyer et al. (2017) , this was difficult to pursue, because a re-scaling of the uncertainty and the presence of outliers have to be taken into account. For Gaia DR1, we used a modification of this approach to establish empirical p-value determinations from different statistics of constant stars. As the variable star analysis was focused around the South Ecliptic pole, constant stars were selected only from the OGLE surveys.
Classification Supervised classification was performed on the variables detected in the GVD path after a period search and modelling of the time series. This package provided ranking scores for the GVD variables for a total of 24 class groups (in addition to galaxies); see Table  3. For a detailed description of classification and its output, we refer the reader to  Rimoldini et al. (2023). Here, we provide a summary of the high-level variability types in a pie chart shown in Fig. 4. Data sets from the literature were assembled in order to train the classifiers;  Gavras et al. (2023)  provides a detailed explanation of the cross-match with Gaia of the different catalogues from the literature. Rimoldini et al. (2023)  cross-matched the sources found in a selection of 152 catalogues with Gaia data.
Cepheids and RR Lyrae stars The Cepheids and RR Lyrae stars were present already in the first two Gaia Data Releases. Upon each release, updates to the pipeline and its output were published for RR Lyrae stars (vari_rr_lyrae, see  Clementini et al. 2023) , and Cepheids (vari_cepheid, see  Ripepi et al. 2023). From the Fourier decomposition, astrophysical parameters of importance such as the metallicity could be derived. The RR Lyrae star catalogue contains nearly double the number of objects listed in Gaia DR2. It is the largest catalogue over the whole sky with 270 905 entries, reaching the very faint limit of Gaia with the mean G mag of 21. 14. The Cepheid sample reaches 15 006 3  with 5221, 4663, 4616, 321, and 185 in the Large Magellanic Cloud (LMC), the Small Magellanic Cloud (SMC), M31, M33, and field stars or small Milky Way satellites.
From the literature, several reclassifications were carried out that added to the Cepheid class, amounting to 327 objects, and 474 Cepheid candidates were not reported in the literature. In addition, RV time series for 1898 sources are included. To demonstrate the beautiful Gaia data set with G, G BP , G RP , and RV measurements, an Image of the Week was published presenting the Hertzsprung progression 4.
Exoplanetary transits The photometric precision of Gaia is high and stable enough that the data can be used to detect exoplanetary transits. The first confirmed transiting exoplanet of Gaia, Gaia-1b, was announced in March 2021 as a Gaia Image of the Week 5. This object was detected using the combined G, G BP , G RP photometric measurements of Gaia; see  Panahi et al. (2022a). Spectroscopic observations were conducted by the Large Binocular Telescope in Arizona in order to confirm its nature. In the article presenting the method to select candidates  (Panahi et al. 2022a ), a second confirmed case is also announced, Gaia-2b. The DR3  (of which two are now confirmed, Gaia-1b and Gaia-2b). Very probably, the 39 remaining candidates contain some other planetary systems awaiting RV confirmation.
Microlensing The microlensing events are being detected in real time by the Gaia Science Alert system (cf. Hodgkin et al. 2021). This led, for example, to discoveries such as the Gaia16aye event, which was fully resolved thanks to its complexity (binary lens) and to intense ground-based follow-ups  (Wyrzykowski et al. 2020 ). However, a detailed a posteriori analysis of all the Gaia data searching for microlensing effects gives a different perspective and can lead to new discoveries. Microlensing candidates were selected from a preliminary 'extractor' method and confirmed in the SOS package, which fitted the Paczynski model with and without blending; see  Wyrzykowski et al. (2023). In the archive table vari_ms_microlensing, there are 363 reported events, among which 90 were not previously reported and were not discovered by other surveys.
Verification, validation, and overlaps The variability analysis underwent several verification and validation steps towards the final publication in DR3. As part of operational runs, so-called 'violation rules' were implemented to omit the values of output parameters beyond the expected ranges. For example, negative errors or periods/frequencies are not allowed. In some cases, complex rules were implemented, which depended on other parameters. Such rules allowed the early detection of software bugs and their correction for the final operational run. Before exporting our data for inclusion in the pre-DR3 archive, different teams validated their samples of candidate variable objects based on different criteria described in detail in the relevant papers. Either automatic, reproducible criteria were used, or visual inspections were carried out.
g. SVD and microlensing extractor). SOS packages in the GVD path take a range of classification probabilities as input sources; that is, the best class only was not necessarily considered. One can therefore find, for example, a source in the vari_short_timescale table whose best class is 'SOLAR_LIKE'. Eventually, checks were performed when exporting our data to DPAC and inclusion in the pre-releases of the DR3 archive. We note that further sources were removed at the DPAC level before eventual publication in DR3.