dataesr/scientific-paragraphs-categorization huggingface dataset:
Abstract. -For a finite extension F of Qp, Drinfeld defined a tower of coverings of P 1 KP 1 (F ) (the Drinfeld half-plane). For F = Qp, we describe a decomposition of the p-adic geometric étale cohomology of this tower analogous to Emerton's decomposition of completed cohomology of the tower of modular curves. A crucial ingredient is a finiteness theorem for the arithmetic étale cohomology modulo p whose proof uses Scholze's functor, global ingredients, and a computation of nearby cycles which makes it possible to prove that this cohomology has finite presentation. This last result holds for all F ; for F = Qp, it implies that the representations of GL 2 (F ) obtained from the cohomology of the Drinfeld tower are not admissible contrary to the case F = Qp. Les trois auteurs sont membres du projet ANR-19-CE40-0015-02 COLOSS ; ils remercient le rapporteur pour sa lecture attentive et ses suggestions.
Demultiplexing was done with CASAVA (Illumina). Numbers of reads ranged from 28,488 to 32,398 between samples. Raw paired-end reads were quality-filtered  (Schmieder and Edwards 2011) , read merging was done with FLASH  (Magoč and Salzberg 2011) , and forward and reverse primer sequences were removed using CutAdapt, with no mismatches allowed in primer sequences. After pretreatment, chimeras were removed with a proprietary protocol based on Usearch 6. 1. Sequences were grouped at 97% identity into operational units (OTUs) with Uclust v1. 2. 22q  (Edgar 2010). Taxonomic assignments of OTUs were obtained with the naive Bayesian classifier (v2. 2) method  (Wang et al. 2007 ) and the Greengenes database (version 13_8; www. green genes. gov)  (Wang et al. 2007).
Sequencing, metagenome assembly, and annotations Library preparations from DNA of the DCM-degrading consortium and sequencing using Illumina and PacBio technologies were performed by Genoscreen (Lille, France). The Illumina library was prepared with the Nextera XT sample prep kit (Illumina) and sequenced in a paired-end 2x100 bp HiSeq 2500 rapid run. Illumina sequence reads for genome assembly were trimmed of adapters using Adapter-Removal and default settings  (Schubert et al. 2016). Reads were further trimmed for quality using a custom python3 script (https:// github. com/ kwasm und/ Trim-Illum ina) and the command line Illumina_trim. py-f Dhb2. pair1. truncated. fastq-r Dhb2. pair2. truncated. fastq-o reads. filtered-q 15-m 50-b 8. For PacBio, DNA fragmentation and ligation of single-molecule real-time (SMRT) adaptors was followed by BluePippin size selection (Sage Science) set at 4 kb, and PacBio sequencing was performed on one SMRT cell using PacBio RSII technology with P6 chemistry.
Quality control was done using FastQC v0. 11. 5  (Andrews 2010 ). An initial hybrid assembly was obtained using Unicycler (v 0. 4. 8) (Wick et al. 2017 ) with default settings using the quality-controlled Illumina paired-end sequences and PacBio sequences. The resulting contigs were binned using Meta-BAT 2 (v 2. 15)  (Kang et al. 2019)  with default settings for automatic binning. The Dehalobacterium genome bin was identified by BLASTX of contigs against the NCBI-nr database. Quality-controlled PacBio sequences from the original unbinned data were then mapped to the draft Dehalobacterium bin using minimap2 (v 2. 17)  (Li 2021)  with default settings, and mapped sequences were collected and converted to fastq files using SAMtools (v 1. 12)  (Li et al. 2009 ). Collected PacBio sequences were then reassembled with all quality-controlled Illumina sequences using Unicycler and automatically binned with MetaBAT 2 as described above.
Additional mapping of Illumina sequences to the initial draft Dehalobacterium bin, or by further iterations of PacBIO sequence mapping to the draft Dehalobacterium bins and/or reassembly, did not result in further assembly improvement. The resulting Dehalobacterium genome bin was automatically annotated using the RAST server  (Aziz et al. 2008)  in "classic" mode. Manual BLASTP analysis of selected protein sequences derived from the 19 obtained contigs against the NCBI-nr database gave best hits to D. formicoaceticum strain DMC for all contigs, suggesting the absence of contaminating contigs. Genome bin quality was assessed using CheckM (v1. 1. 1) (Parks et al. 2015). Additional automatic gene and protein annotations were performed on the MAGE MicroScope platform  (Vallenet et al. 2020). Average Nucleotide Identity (ANI) analyses were conducted with the JSpeciesWS server in BLASTN mode ("ANIb")  (Richter et al.
2016). "Methyltransferase" proteins were subject to eggNOG-mapper (v2. 0. 0)  (Cantalapiedra et al. 2021 ) using default settings (minimum hit e-value 0. 001, minimum hit bit-score 60, minimum % of identity 40, minimum % of query coverage 20) with "-m diamond" option. They were also analyzed for conserved domains using the Conserved Domain search tool (Marchler-Bauer and Bryant 2004) against the Conserved Domain Database (CDD)  (Lu et al. 2020 ) using default settings and a default e-value of 0. 01. A phylogenomic tree was made on the IQ-TREE web-server using automatic substitution model selection (best-fit model LG+F+I+G4) and ultrafast bootstrapping (1000×)  (Nguyen et al. 2015). The underlying alignment consisted of concatenated protein sequences derived from single copy marker genes retrieved from CheckM.
Peptide identification was conducted by Proteome Discoverer (v2. 2, Thermo Fisher Scientific) using Seques-tHT as a search engine and a fasta file of proteins from the draft genome of the Dehalobacterium strain EZ94 as the database. A false discovery rate threshold of 1%, defined versus a decoy database (Target Decoy PSM evaluator implemented in Proteome Discoverer) was set for peptide identification. Label-free quantification of detected proteins was performed with Minora node implemented in Proteome Discoverer on the basis of area counts of corresponding peptide peaks in precursor scans.
Sequencing of a stable DCM-degrading consortium and analysis of the dominant Dehalobacterium strain In a previous study, we showed that our DCM-degrading anaerobic mixed culture was composed mainly of bacteria belonging to the genera Dehalobacterium,  Desulfovibrio, Acetobacterium, and Wolinella (Trueba-Santiso et al. 2020). The 16S rRNA gene amplicon data confirmed that the DCM-degrading consortium was dominated by a Dehalobacterium population (87. 1%±1. 9 relative abundance), with lower relative abundances of the genera Acetobacterium (7. 6%±1. 2), Desulfovibrio (4. 7%±1. 0), and Wolinella (0. 6%±0. 1) (Table  S1 ). The very low variability observed between biological replicates shows the stability of the consortium under the selective conditions used. Metagenomic sequencing on DNA of the consortium culture performed using Illumina and PacBio techniques, followed by genome binning of the obtained contigs, allowed recovery of a draft genome of the dominant Dehalobacterium population.
It consisted of 3,650,560 bp in 19 contigs with a G+C content of 43. 2%. This is closely similar to the reported genome of D. formicoaceticum strain DMC of 3,766,545 bp and has identical G+C content  (Chen et al. 2017 , NCBI accession number GCA_002224645. 1). The draft genome of our Dehalobacterium strain displayed an ANIb value of 98. 6% to the D. formicoaceticum strain DMC genome, with 93% alignment. The genome was estimated to be 95. 7% complete from CheckM analysis, with 2% contamination, as reported for the complete circular genome of D. formicoaceticum strain DMC. This suggests that CheckM missed and/or misassigned marker genes from these organisms and that the genome of the Dehalobacterium strain from our DCM-degrading consortium is near-complete. Close similarity of our strain to strain DMC was also supported by phylogenomic analysis of 34 single copy marker proteins (Fig.
formicoaceticum strain DMC (Table  1 ). Notably, no reductive dehalogenase genes were detected in the genome. In this context, we also searched the automatically annotated genome for proteins with "methyltransferase" annotations and classified them further according to conserved domains from CDD searches and by mapping to the eggNOG database (eggNOG-mapper). Apart from Mec proteins, these identified 124 genes associated with C1 transfer, including 25 diversely annotated as mono-, di-, or trimethylamine:corrinoid methyltransferases (Table  S2 ). The genome of strain EZ94 also encodes a complete set of genes for the Wood-Ljungdahl pathway (Fig. 3 ). This is in accordance with previous reports of anaerobic DCM-degrading bacteria and indicates strain EZ94 has the capacity to metabolize DCM through this pathway.
The third most abundant protein was MecC, annotated as methylcobalamin methyltransferase (160005) (Table  S4 ). These two protein products of the mec gene cluster display protein identities of 88-95% and 92-96% with MecB and MecC from DCM-degrading D. formicoaceticum DMC, "Ca. D. elyunquensis" and "Ca. F. warabiya", respectively (Table  1 ). Worthy of note, other related proteins in databases show only low levels of identity (<54. 9% for MecB and <40. 4% for MecC), suggesting these methyltransferases are specific to DCM-degrading bacteria of the Peptococcaceae. The second most abundantly produced protein (  50017 ) is a 131 amino acid long protein unlike any well-characterized protein in databases, automatically annotated as pyridoxamine-5′-phosphate oxidase family protein with a potential flavin-mononucleotide binding domain.
Nevertheless, D. formicoaceticum strain DMC and Ca. F. warabiya cannot grow with formate either. Therefore, the metabolic function of this formate dehydrogenase remains unknown. Dehalobacterium formicoaceticum strains are indeed unique among anaerobic DCM-dechlorinating bacteria in that DCM fermentation is their only growth-supporting metabolism known so far. Other bacteria with the mec cassette associated with DCM utilisation appear to be metabolically more versatile. For instance, the mixed culture containing "Ca. D. elyunquensis" also ferments dichloroacetate to acetate, carbon dioxide, and hydrogen  (Chen et al. 2021). In this case, dichloroacetate dehalogenation involves an (S)-2-haloacid dehalogenase not found in D. formicoaceticum. Interestingly, re-examination of published proteomic data  (Chen et al.
F. Somatic and axonal CC recordings of the depolarizations evoked by the photolysis of glutamate at the different dendritic varicosities (and soma) shown in E. Brown dots indicate the timing of the laser pulse. Laser pulse duration and power are 100 µs and 2 mW, respectively. G. CR between the soma and axon for each tested dendritic varicosity as a function of distance of the stimulated site to the soma. Dotted line is the average. H. Somatically recorded eEPSP amplitude as a function of distance. I. EPSPs amplitude ratios as a function of distance between somatic and axonal recording sites. The continuous red line shows the fit with an exponential function. Data include experiments with sEPSPs and eEPSPs. Individual points correspond to the average ratio calculated from the average of all the detected axonal and somatic EPSPs in each cell.
Experimental Methods: Device fabrication: First the bottom gate electrode was patterned using electron beam lithography. The lithography (Zeiss Supra 40 with Raith Elphy Plus software) was done on spin-coated PMMA (poly(methyl methacrylate)) resist and then developed in 1:3 MIBK-IPA (methyl isobutyl ketoneisopropyl). Then 5 nm titanium + 45 nm gold was deposited by e-gun evaporation on a silicon/silicon dioxide (300 nm) substrate, followed by lift-off in acetone and rinsing in isopropanol. ReS2, CIPS, and h-BN flakes were exfoliated on PDMS polymer and then subsequently transferred and carefully positioned on top of the previously patterned back gate electrode forming the desired stack. 60 The positioning and transfer were done using a lab-built micro positioner transfer station. Another electron beam lithography step was then done for the top contacts followed by the evaporation of 10 nm titanium + 140 nm gold and lift-off.
Acknowledgements We acknowledge  Eucor -The European Campus  for financial support through the QUSTEC funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  Agreement No. 847471. We also acknowledge the support of the  Institut Universitaire de France (IUF). This work was also partly supported by  ANR MixDFerro  ( ANR-21-CE09-0029 ) and  VANDAMME  ( ANR-21-CE09-0022 ), and by  IdEx Unistra  ( ANR 10 IDEX 0002 ),  SFRI STRAT ' US  project ( ANR 20 SFRI 0012 ) and  EUR (QMat- ANR-18-EUR-0016 ) under the framework of the  French Investments for the Future Program. We thank  Arnaud Boulard  and  René Baehr  from the IPCMS workshop for the realization of the transfer station. We acknowledge  Ankita Ram  for helping with the preparation of the 2D materials.
Eggs Weight Loss, Hatching Events, and Hatchability Eggs weights (EW) were recorded at E0 and E18 and were used to determine the relative egg weight loss (EWL) at E18 of incubation using the Formula (1): EWL % ð Þ ¼ EW0 g ð Þ À EW18 g ð Þ EW0 g ð Þ Â 100 ð1Þ where EW0 is the eggs weight at E0 and EW18 is the eggs weight at E18. From the 450 th hour to the end of incubation (504 th hour of incubation), the time of the occurrence of external pipping (EP) and chick hatching for individual eggs was recorded every 3 h. The hatched chicks were recorded. These data were used to determine the EP time (time between setting and EP), EP duration (duration between EP and hatching), the total incubation duration (time between setting and hatching) and the spread of hatch according to treatments. Hatchability of fertile eggs was also determined using the Formula (2): Hatchability % ð Þ ¼ Number of hatched chicks at the end of incubation Number of fertiles eggs transferred to hatching basket Â 100 ð2Þ Chick Quality and Day-Old Chick Body Weights at Hatching At the end of incubation, chick quality was determined on fifty chicks per treatment group using Tona scoring method  (Tona et al.
Posthatch Growth Performance At the end of the hatching, 125 chicks per treatment were transferred to the research farm and were weighed to determine their initial body weight. During the experimental period, all the birds were weekly weighed per group. The daily weight gain (DWG) was computed as the difference in body weight between 2 consecutive measurements divided by the number of days between weights. The amount of feed given and the remaining feed were measured daily. These data were used to calculate the daily feed intake (DFI) using this equation: DFI g ð Þ ¼ Feed given g ð Þ À remaining feed g ð Þ Number of birds Â number of days ð5Þ The FCR was computed by dividing DFI by DWG. The mortalities were recorded daily. These data allow the calculation of the mortality rate (MR) using this formula: MR % ð Þ ¼ Number of dead birds during a period Number of birds at the beginning of the period Â 100 ð6Þ Blood Sample Collection and Hormonal Analysis (T3, T4) At the end of the rearing period, blood samples (n = 15 per treatment) were collected from brachial veins in nonheparinized tubes with a 2 mL syringe and centrifuged at 3,000 rpm for 15 min.
Context. The Gaia third Data Release (DR3) presents the first catalogue of full-sky variable young stellar object (YSO) candidates observed by the Gaia space telescope during the initial 34 months of science operations. Aims. Numerous types of variable stars were classified using photometric data collected by Gaia. One of the new classes presented in the Gaia DR3 is the class of YSOs showing brightness variability. We analysed 79 375 sources classified as YSO candidates in order to validate their young nature and investigate the completeness and purity of the sample. Methods. We cross-matched the Gaia DR3 YSO sample with numerous catalogues from the literature, including YSO catalogues based on optical and infrared data, as well as catalogues of extragalactic sources and Galactic variable stars.
Introduction Gaia (Gaia Collaboration 2016) is a cornerstone mission of the European Space Agency. The mission is not only the most ambitious stellar (and extragalactic) astrometric project ever, but also one of the best transient discovery machines today. While other surveys, such as YSOVAR  (Morales-Calderón et al. 2011) , ASAS-SN  (Shappee et al. 2014; Kochanek et al. 2017) , and ZTF  (Masci et al. 2019) , provide photometric data and light curves for millions of sources, including young stellar objects The KYSO table is only available at the CDS via anonymous ftp to cdsarc. cds. unistra. fr  (130. 79. 128. 5)  or via https:// cdsarc. cds. unistra. fr/viz-bin/cat/J/A+A/674/A21 (YSOs), Gaia has the advantage of observing the whole sky. It collects photometric observations of some 1. 8 billion stars down to a faint limit of 20.
7 mag in the G band and obtains low-resolution spectra down to ∼19 mag for an average of ∼80 epochs during the nominal five-year mission (which was completed and then extended until the end of 2022; hopefully it will be extended to the end of 2025 but this remains to be confirmed), although the cadence is highly dependent on the scanning law  (Gaia Collaboration 2016). Gaia started monitoring the whole sky on 25 July 2014 and collects multi-epoch multi-band spectrophotometry and astrometric data for sources crossing its two fields of view  (FoVs). A description of the Gaia mission (spacecraft, instruments, survey, and measurement principles) as well as the structure and activities of the Gaia Data Processing and Analysis Consortium (DPAC) can be found in Gaia  Collaboration (2016). The Gaia Data Release 3 (DR3) became public on 13 June 2022, and among many different products that have the potential to increase our fundamental understanding of the Galaxy it provides photometry in three passbands (Gaia G, G BP , and G RP ), five-parameter astrometry, and radial velocities (RVs) collected over the initial 34 months of observations; these are the most relevant quantities for our case.
A summary of the Gaia DR3 contents and survey properties is provided in Gaia Collaboration  (2023). One important research area, where collecting large amounts of data brings fundamental new results, is star formation. How the Sun -and stars in general -was born is identified as one of the most important questions of modern astronomy. Throughout their early evolution, stars show different features in their spectral energy distribution (SED). Initially, protostars are deeply embedded in their parental clouds, surrounded by dense dust and gas envelopes, which allow their detection only at sub-millimetre (mm) and far-infrared (FIR) wavelengths. At later stages when the envelope dissolves, young stars become apparent at optical wavelengths as well, which allows us to detect them with the Gaia space telescope.
Gaia data Gaia DR3 represents a significant improvement over Gaia DR2. The parallax precisions increased by 30 percent and proper motion precision improved by a factor of 2. Also, the systematic errors on the astrometry are lower by 30%-40% for the parallaxes, while those on the proper motions are lower by a factor ∼2. 5. The longer temporal baseline of the observations also resulted in increased precision of the photometry and much better homogeneity across colour, magnitude, and celestial position. While Gaia measures the position and brightness of the objects in its FoV, many parameters are provided that describe the quality of the data. Through the variability processing pipeline  (Eyer et al. 2023) , many other parameters are derived, which are helpful during the identification of variable sources and their verification.
Other catalogues We used several existing YSO catalogues from the literature based on optical and infrared surveys and observations, including data from Gaia DR2, infrared data from 2MASS, and the Spitzer and WISE surveys. These catalogues are the  Marton et al. (2019)  probabilistic YSO catalogue, based on Gaia DR2 optical data, 2MASS near-infrared (NIR) photometry, and WISE mid-infrared (MIR) observations from the AllWISE catalogue, the SPICY (Spitzer/IRAC Candidate YSO) catalogue of  Kuhn et al. (2021)   VISTA NIR survey. While the Gaia DR3 YSO candidate sample is based on optical variability, the listed catalogues from the literature used the IR excess in the SED as a signature of the young nature. We also cross-matched the Gaia DR3 YSO candidates with the SIMBAD database  (Wenger et al.
(2019). In cases where we used 2MASS and AllWISE data for the validation, we relied on these catalogues, matched the Gaia DR3 YSO sources to the DR2 positions, and inferred the data from the DR2xAllWISE (including 2MASS) table. In other cases, when the cross-match with such accuracy was not already available, we used the TOPCAT software  (Taylor 2005 ) with a search radius of 1.
To see the IR excess distribution of the KYSOs and the Gaia DR3 YSOs, we cross-matched them with the 2MASS catalogue using a 1 matching radius. Of the 79 375 Gaia DR3 YSOs, 76 879 (97%) had a NIR counterpart, while 10 613 (91%) of the 11 665 KYSOs had a match in the 2MASS database. Figure  5  shows the distributions of the Gaia DR3 YSO candidates and the KYSO sources on the 2MASS J-H versus H-K s colour-colour diagram. Both samples occupy the same region on the diagram, and are mostly located between 0 < J -H < 1. 5 and 0 < H -K s < 1. Further details of the above-mentioned distributions are listed in Table  1 , where we list the median and mean values, the standard deviation, and the threshold values below and above which 5% of the samples are located. Figures showing the distributions are found in Appendix B.
Cross-match with the SIMBAD database The Object Type in SIMBAD is defined as a hierarchical classification, which emphasises the physical nature of the object rather than a peculiar emission in some region of the electromagnetic spectrum or the location in a peculiar cluster or external galaxy. Therefore, objects are only classified as peculiar emitters (in radio, IR, red, blue, UV, X-ray, or gamma ray) if nothing more about the nature of the object is known; that is, if it cannot be decided whether the object is a star, a multiple system, a nebula, or a galaxy  (Wenger et al. 2000). The total number of objects we find in the SIMBAD database using a 1 search radius is 20 531. The first column in Table  6  is the SIMBAD main_type, the second column shows how many objects with the given SIMBAD main_type were found in the Gaia DR3 YSO sample, while the last column is the percentage of the given main_type according to the total number of 20 531 associated SIMBAD objects.
Acknowledgements. We are thankful for our anonymous referee for all the comments and suggestion that helped us to improve the paper. This work has made use of data from the  European Space Agency  (ESA) mission Gaia (https: //www. cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/ dpac/consortium). Funding for the DPAC has been provided by national institutions, some of which participate in the Gaia Multilateral Agreement, which include, for Switzerland, the  Swiss State Secretariat for Education, Research and Innovation  through the  ESA Prodex program , the ' Mesures d'accompagnement ', the ' Activités Nationales Complémentaires ', the  Swiss National Science Foundation , and the  Early Postdoc. Mobility fellowship; for Hungary, the  ESA PRODEX  contract nr.
4000129910. G. M. acknowledges support from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101004141. This project has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  716155  (SACCRED). This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France (DOI: 10. 26093/cds/vizier). This work made use of software R (R Core Team 2018) and  TOPCAT/STILTS  ( Taylor 2005 ).