dataesr/scientific-paragraphs-categorization huggingface dataset:
Amplification and sequencing Amplicons for barcoded sequencing on the HiSeq 2500 platform (2 × 250 paired-end) were produced using universal primers covering the V4 to V5 region of the 16S rRNA gene (515F-Y and 926R)  [27]  that were designed for marine samples and previously used by  Peoples et al. (2019)  in the Kermadec and Mariana trenches. Archaeaspecific amplicons were generated using 517F and 958R  [28]  and sequenced with the same methodology. Unless otherwise mentioned, we report results based on the universal 16S rRNA gene dataset. For details see Brandt et al. [29]  and the Supplementary Material and Methods section.
Sequence processing and statistical analyses The sequencing data were processed for each run independently using the Dada2 pipeline, and resulting amplicon sequence variants (ASVs) tables were then merged  [30]. Negative controls were taken and treated equally to the samples along the entire laboratory process and used to determine putative contaminants. Contaminating ASVs were identified and filtered from the samples using the prevalence method of the Decontam R package  [31]. Data management, visualization, and statistical analyses were done in the R studio environment, using the packages ampvis2  [32] , phyloseq  [33] , vegan  [34] , ggplot2  [35] , and dplyr  [36]. We rarefied our data to equal sampling depths for visualizing the heatmaps, core microbiomes, and ASV frequency plots, whereas cumulative sum scaling normalization was applied using the metagenomeSeq R package  [37]  for Bray Curtis dissimilarity-based analyses, redundancy analysis, and variation partitioning.
Results and discussion We successfully sequenced 16S rRNA gene amplicons from 454 samples with universal primers and 283 samples with archaea-specific primers, respectively (see Supplementary Table  2 ), and recovered 260,266 ASVs in the universal 16S rRNA gene dataset and 28,123 ASVs in the archaea-specific dataset (Supplementary Fig. 2 ). As samples from the Atacama Trench region included sectioning at higher depth resolution (HR sectioning scheme), we will present and discuss these first.
2A ). Similar patterns were observed using different dissimilarity metrics (Bray Curtis, weighted/unweighted UniFrac), ordination techniques (NMDS/t-SNE), and sectioning schemes (HR/CR; data not shown). The downcore gradient of microbial communities was also evident within individual redox zones. Thus, samples from the same sediment horizon (e. g. , 1-2 cm) but from different sites, with geographic distances of up to 430 km, were more similar to each other than to their respective adjacent horizons, above (0-1 cm) or below (2-3 cm) (Supplementary Fig. 3A, B ). The horizontal similarity was particularly pronounced in the upper part of the oxic zone, while it decreased toward the bottom of the nitrogenous zone and increased again in the ferruginous zone (Supplementary Fig. 3A, B ). The CR sample subset contained triplicates from separate sediment cores originating from two multicorer deployments, and thus included two cores sampled within a distance of 0.
Frequency distribution of ASVs and taxonomic affiliation of cosmopolitans along the Atacama Trench Despite the large number of ASVs present in our dataset, only few were found in all samples from a given redox zone, yet these cosmopolitans tended to account for a large fraction of sequencing reads (Supplementary Fig. 7 ). This was especially pronounced in the rarefied data from the oxic zone where 365 out of 24,844 ASVs occurred across all oxic samples and comprised over 40% of all reads obtained from this zone, thereby contributing substantially to the similarity between cores and sites within the Atacama Trench (Fig. 2 ). The majority of these ubiquitous reads originated from ASVs belonging Gammaproteobacteria, Thaumarchaeota, Alphaproteobacteria, and Bacteroidetes (Supplementary Fig. 8 ).
Core microbiomes of each redox zone along the Atacama Trench To further analyze the overlaps between abundant community members across redox zones, we performed a core community analysis using the toolset of the ampvis2 R package with adjusted cutoff parameters  [32]  (see Supplementary Material and Methods). This analysis defines ASVs as part of the core microbiome, when they are above 0. 05% relative abundance and within the top 50% of all reads. This classified more than 99% of all ASVs as rare biosphere, while the remaining 441 core ASVs accounted for almost half of all reads (Supplementary Fig. 9A ). Each redox zone had a distinct core microbiome, with 196, 66, and 91 core ASVs in the oxic, nitrogenous, and ferruginous zones, respectively, comprising 10. 6%, 4. 6%, and 8. 5% of all reads.
Community composition of Archaea along the Atacama Trench Around 20% of all ASVs in the universal 16S rRNA gene dataset were classified as Archaea. However, due to known mismatches of universal 16S rRNA gene primer sets with archaeal lineages, in particular the phylum Thaumarchaeota, we thus also sequenced archaea-specific 16S rRNA gene amplicons with the same read depth. This primer set recovered approximately three times more thaumarcheotal ASVs than the universal set (4410 vs 1477) and also showed a better coverage over Euryarchaeota (3728 vs 2032), Crenarchaeota (1884 vs 707, including "Ca. Bathyarchaeia"), "Ca. Hydrothermarchaeota" (313 vs 101), and Hadesarchaea (71 vs 26). By contrast, the universal 16S rRNA gene dataset contained 17 times more "Ca. Woesearchaeota" ASVs (42,582 vs 2512), with this phylum even dominating ASV richness over Thaumarchaeota in the universal dataset.
Sequencing the HR horizons with this primer set was only successful for only a small number of the samples. As CR horizons were more successful, we focus on this dataset. Thaumarchaeota was the overwhelmingly dominant phylum in the archaeal dataset and drove most of the dissimilarity between individual redox zones (Fig. 2B ). In the oxic and nitrogenous zones, they contributed up to 99. 3% relative abundance, and other lineages, particularly Crenarchaeota, "Ca. Hydrothermarchaeota," Euryarchaeota, and "Ca. Asgardaeota," only increased in relative abundance in the ferruginous zone (Supplementary Fig. 10 ). Consequently, ordination plots of this dataset only showed a depth gradient to the bottom of the nitrogenous zone, while samples from the ferruginous zone deviated strongly from this gradient (Fig.
2B ). Estimates of absolute abundances of the individual archaeal phyla from the universal 16S rRNA gene dataset (Supplementary Fig. 11 ) showed that the relative depth-wise increase of "Ca. Asgardaeota" (0-9. 8%) and Crenarchaeota (0-32. 9%) in the archaeal dataset reflects their increase in absolute abundance from 1. 4 × 10 3 to 3. 4 × 10 5 "Ca. Asgardaeota" per ml sediment and 3. 7 × 10 2 to 4. 4 × 10 5 Crenarchaeota per ml sediment. This suggested possible growth of these lineages in hadal sediments. Globally, bacterial lineages dominate over archaeal lineages in marine water columns and surface sediments  [48, 49]. The only archaeal phylum in these habitats of comparable abundance is Thaumarchaeota. However, in coastal subsurface sediments, archaeal lineages belonging to "Ca. Lokiarchaeota" and the Miscellaneous Crenarchaeota Group (here referred to as "Ca.
9 and 6. 7 cm, respectively, and nitrate reaching ~6. 5 cm at A9. The TOC and microbial abundances showed no clear downcore pattern at these sites  [22]. At the phylum level, the hadal communities revealed by universal primers were similar in the two trenches, though the drop in Thaumarchaeota abundance was more sharply located at the oxic-nitrogenous interface in the Kermadec Trench than in the Atacama Trench (Fig. 3A  and Supplementary Fig. 12A ). The Kermadec Trench also exhibited higher relative abundances of "Ca. Woesearchaeota" (18. 2% vs 9. 2%) in deeper sediment horizons. The archaeal datasets differed more clearly between the two trenches (Supplementary Fig. 12B ). While Crenarchaeota reached almost 20% abundance in the Atacama Trench and were detected in the oxic zone, they were essentially absent in the Kermadec Trench.
In contrast, at the abyssal sites, the relative abundance of Thaumarchaeota was almost twice as high (A7: 23% K7: 24%) as in the oxic zone of hadal sediments and did not change significantly over sediment depth (Supplementary Fig. 13A ). This was particularly pronounced in the archaea-specific dataset, where Thaumarchaeota comprised more than 99% mean read abundance (Supplementary Fig. 13B ). However, in contrast to the hadal and continental slope sediments, where increased relative abundance also indicated growth, estimated absolute abundances at the abyssal plain sites generally decreased with sediment depth (Supplementary Fig. 13C ). Hence, we propose that the downcore changes in the abyssal plain sediments may reflect differential persistence and survival capabilities rather than growth, similar to conclusions from subsurface sediments  [56].
Thus, the conditions for microbial life differ quite fundamentally between abyssal plains and hadal trenches. The similar directional phylum-level changes in the continental slope and hadal sites further supported biogeochemical forcing as a main driver of microbial community composition on the level and suggested that potential effects associated with oceanic depth or hydrostatic pressure are secondary or mainly apply to finer taxonomic levels. When all sites were compared through PCoA of Bray Curtis dissimilarities, both bathyal and abyssal communities differed from hadal samples (Supplementary Fig. 14A ), and ANOSIM confirmed this distinction (p > 0. 001, R = 0. 595). The archaea-specific dataset showed similar patterns as the universal dataset except with clearer separation between the two trenches (Supplementary Fig.
3-0. 9 and 0. 7-2. 0%, respectively  [22]. We delineated the effects of redox zonation from site-site variation and TOC concentration on microbial community composition using variation partitioning on Hellinger-transformed ASV counts (Fig. 5A  and Supplementary Fig. 16A ). The unique fraction of variation statistically explained by TOC was very low (1%, p = 0. 014) yet had a large overlap of 4% with the site-to-site variability. This overlap disappeared completely when A10 was excluded, hinting that much of this trend was driven by high TOC concentrations at this single site. Thus, TOC was a poor predictor of microbial community composition in the Atacama Trench, despite the high downcore fluctuations and the broader concentration range along the trench axis than across all locations of the global dataset on abyssal and bathyal surface sediment of Bienhold et al.
Acknowledgements We would like to thank  Per Martensen  ( SDU, Denmark ),  Mathias Zabel  ( Marum, Germany ),  Morten Larsen  ( SDU, Denmark ), and  Anni Glud  ( SDU, Denmark ) for technical assistance and fruitful scientific discussions. Furthermore, our gratitude goes to the captains, crews, and scientific personnel of the RV Sonne (SO261; ship time provided by  BMBF, Germany , awarded to  Frank Wenzhoefer ,  Mathias Zabel , and  Ronnie N. Glud ) and  RV Tangaroa  ( TAN1711 ; shiptime partly funded by  Coasts & Oceans Centre of New Zealand 's  National Institute of Water & Atmospheric Research (NIWA) , awarded to  Ashley A. Rowden  and  Ronnie N. Glud ). Financial support for the study was provided by  HADES-ERC Microbial community  structure in hadal sediments: high similarity along trench axes and strong changes.
Advanced grant " Benthic diagenesis and microbiology of hadal trenches " # 669947  and the  Danish National Research Foundation  grant  DNRF145  to establish the  Danish Center for Hadal Research , both awarded to  Ronnie N. Glud. This work is part of the " Pourquoi Pas les Abysses ?" project funded by  Ifremer , and the project eDNAbyss ( AP2016 -228 ) funded by  France Génomique  ( ANR-10-INBS-09 ) and Genoscope-CEA. Lastly, we would like to acknowledge that the suggestions from the reviewers greatly improved this manuscript.
RPCs are gaseous detectors with nanosecond-level time resolution  [20, 21]. Excellent performance of the RPC detector and its associated trigger system are therefore fundamental for the ATLAS physics programme. This L1 muon barrel trigger system  [22]  selects muon candidates produced in LHC collisions; it assigns the selected candidates to the correct LHC bunch crossing and measures the transverse momentum ( T ) of the muon candidates using six predetermined programmable thresholds. This paper reports measurements of the performance of the ATLAS RPC detector and L1 muon barrel trigger system using 60. 8 fb -1 of proton-proton collision data recorded by the ATLAS experiment in 2018 at a centre-of-mass energy of 13 TeV. The paper is organised as follows. Section 2 briefly describes the RPC detector and L1 muon barrel trigger system.
Section 3 details the dataset and tools used for the measurements presented in the subsequent sections. Section 4 presents measurements of RPC detector efficiency and time resolution. This section also describes the RPC response to the passage of a muon. Section 5 presents measurements of the L1 muon barrel trigger response, including measurements of the efficiency, rates and composition of the selected muon candidates. Section 6 presents measurements of the currents in the gas volumes as a function of the operating voltage, temperature and instantaneous luminosity. Measurements of RPC counting rates are also reported in this section. The current and counting rate measurements are combined to determine an average RPC avalanche charge using zero-bias collisions. Section 7 presents studies of expected RPC detector response at the High Luminosity LHC (HL-LHC).
This monitoring information is available in real time in the ATLAS control room during data-taking and is also recorded in a central database for subsequent offline analysis. The parameter  set is used by the DCS to control the threshold of the FE discriminators of readout channels in a panel. The absolute threshold applied at the discriminator level is determined by the FE electronics using the following expression:  thr =  reference - set , where  reference is a reference voltage which is set to 2 V. The  thr corresponds to the physical threshold applied to the RPC signal after the amplification stage  [31]. Nearly all  thr parameters are typically in the range 0. 8 V to 1. 5 V, in steps of 0. 1 V, and approximately 90% of panels use the nominal value of 1 V. This physical  thr threshold parameter is used for studies presented later.
Dataset and event selection The measurements presented here were performed using proton-proton collision data recorded by the ATLAS experiment in 2018 at a centre-of-mass energy of 13 TeV, with 25 ns spacing between LHC bunches. Only LHC fills with an integrated luminosity greater than 50 pb -1 were used for these measurements. The total integrated luminosity of the analysed dataset amounts to 60. 8 fb -1. Muon candidates are reconstructed by combining ID and MS information  [33, 34]. The precision MDT chambers are used to reconstruct muon trajectories in the (, ) plane. This plane is approximately orthogonal to the magnetic field lines, thus allowing precise measurements of the sagitta of the muon tracks. In the barrel region, the RPC detector is used to reconstruct the muon trajectory in the non-bending azimuthal (, ) plane which is transverse to the beam direction.
These distributions can be computed using muon candidates recorded in one run or in the entire dataset. The cluster size is defined as the mean value of the cluster hit multiplicity distribution for one panel. The cluster hit multiplicity distribution is averaged over all RPC panels and shown in Figure  8 (a), computed using the full 2018 dataset. The average RPC cluster is approximately 1. 5 strips wide, with the average taken separately over  and  panels and using all active RPC modules. The average  panel has a cluster multiplicity which is slightly smaller than that of the average  panel, due to the differences in the construction and readout as discussed in Section 2. 2. Figure  8 (b) shows the average cluster size distributions for all active RPC modules, which is computed as the mean of the cluster hit multiplicity distribution for each  and  panel.
Trigger rates The ATLAS data acquisition and trigger system  [43]  has an upper limit of approximately 100 kHz for the total rate of standard physics events accepted by the L1 trigger system  [18]. Muon signatures play an important role in the ATLAS physics programme and therefore a significant fraction of this L1 bandwidth is allocated to muon triggers  [16]. The L1 muon trigger was designed to be efficient in detecting muon candidates while keeping L1 muon rates within acceptable limits. In this section, rates of the L1 muon trigger as a function of the instantaneous luminosity are presented for the full L1 muon trigger and separately for the barrel and endcap triggers. Several representative runs recorded in 2018 were used for measuring L1 muon trigger rates. Small random fractions of MU10 and MU20 candidates were recorded at the constant rate of about 1 Hz each, without any additional selections applied by the HLT.
The full dataset of 60. 8 fb -1 was used for the efficiency measurements in order to maximise the number events with a  boson decaying into two muons. A smaller dataset of 3. 6 fb -1 was used for the counting rate measurements in order to simplify analysis procedures. Sufficient statistical precision was obtained using this smaller dataset. The mean RPC detector efficiency and counting rates were measured using only RPC panels with an efficiency greater than 50%. The mean RPC detector counting rate was measured in seven bins of the number of collisions per event, shown in Figure  35(a). A linear dependence of the counting rate was observed, in agreement with the measurements of the RPC counting rates as a function of the instantaneous luminosity presented in Section 6. 2. The mean RPC detector efficiency in the same seven bins were presented in Section 4.
3. Figure  35 (b) shows the mean RPC detector efficiency plotted as a function of the mean RPC detector counting rate computed separately for the seven bins of the number of collisions per event. A decrease of approximately 1% is observed within the considered range. Figure  35:  (a) Mean RPC counting rates measured in seven different intervals of the number of proton-proton collisions per event. The counting rate was averaged over all active RPC modules and is shown separately for  and  panels. (b) Correlation between the mean RPC detector efficiency and the mean RPC detector counting rate, measured in seven different intervals of the number of proton-proton collisions per event, shown separately for  and  panels. Two different datasets, corresponding to 60. 8 fb -1 and 3. 6 fb -1 , are used for the efficiency measurements and the counting rate measurements, respectively.
This result supports the hypothesis that the observed decrease in the RPC detector efficiency at higher instantaneous luminosity is due to the increase in RPC counting rates in individual modules. Figure  36 : (a) Correlation between the mean RPC module efficiency and the mean RPC counting rate, measured in seven different intervals of the number of proton-proton collisions per event, for one RPC module in both the  and  views. (b) Distribution of the slope parameters extracted from a linear fit of the efficiency versus counting rate dependence of each RPC panel. Two different datasets, corresponding to 60. 8 fb -1 and 3. 6 fb -1 , are used for the efficiency measurements and the counting rate measurements, respectively. Only RPC panels with an average efficiency larger than 50% were used for this plot.
Conclusions Measurements of the performance of the ATLAS RPC detector and L1 muon barrel trigger were obtained using data from 60. 8 fb -1 of √  = 13 TeV proton-proton collisions at the LHC recorded in 2018. The response of individual RPC modules was measured using muons produced in decays of  bosons, including measurements of efficiency, cluster hit multiplicity and time resolution. The RPC response was also measured as a function of time and as a function of the mean number of proton-proton collisions per bunch crossing. Performance of the L1 muon barrel trigger was also measured using muons produced in decays of  bosons, including measurements of trigger efficiency as a function of several variables. Acceptance rates were measured for the L1 muon trigger thresholds and the composition of accepted events was studied using reconstructed muon candidates.
In addition, individual groups and members have received support from  BCKDF ,  CANARIE, Compute Canada ,  CRC  and  IVADO ,  Canada ;  Beĳing Municipal Science & Technology Commission, China ;  COST ,  ERC ,  ERDF ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex  and  ANR, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF, Greece; BSF-NSF  and GIF, Israel;  La Caixa Banking Foundation ,  CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden ;  The Royal Society and Leverhulme Trust ,  United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from  CERN , the ATLAS Tier-1 facilities at  TRIUMF (Canada) ,  NDGF (Denmark, Norway, Sweden) ,  CC-IN2P3  (France),  KIT/GridKA (Germany) ,  INFN-CNAF (Italy) ,  NL-T1  (Netherlands),  PIC (Spain) ,  ASGC.
Whole genome sequencing To extract genomic DNA, isolates were grown overnight in YPD at 30°C and DNA isolated from ~10 9 cells using a Qiagen Genomic Buffer Set and a Qiagen Genomic-tip 100/G according to manufacturer's instructions. Libraries were prepared using the Nextera XT DNA Library preparation kit protocol (Illumina) with an input of 2 ng/μL in 10 μL. Each isolate was sequenced using Illumina HiSeq 2000 generating 101 bp paired reads. The nuclear genome sequences and General Feature Files (GFF) for C. albicans SC5314 reference genome (version A22) were downloaded from the Candida Genome Database (http://www. candidagenome. org/). Alignment, coverage, ploidy, heterozygosity and variant calling were performed as previously described  (77). Average coverage levels for SC5314, 529L and CHN1 were 141X, 466X and 245X, respectively.
The pooled amplicon library was sequenced at the Rhode Island Genomics and Sequencing Center at the University of Rhode Island (Kingston, RI) on the Illumina MiSeq platform with paired-end sequencing (2 x 250 bp), using the 600-cycle kit. Raw 16S rRNA reads were subjected to quality filtering, trimming, de-noising, and merging using the Qiime2 pipeline (version 2018. 11) (82). Taxonomic classification was done using the pre-trained Naive Bayes classifier and the q2-feature-classifier plugin trained on the SILVA 132 99% database. Beta diversity was calculated using the phyloseq package (version 1. 30. 0) (83)  in R (version 3. 6. 2) and visualized using PCoA with a Bray-Curtis test. Raw sequence data were uploaded and made available on the NCBI Sequence Read Archive under BioProject number PRJNA735873.
The ASV tables, taxonomic assignment, and sample metadata were assembled using the phyloseq package construct  (83). Construction of the sequence table and phyloseq object, and all subsequent end-analyses were performed using R (version 3. 4). Raw sequence data were uploaded on the NCBI Sequence Read Archive under BioProject number PRJNA734639 (see Supplemental Table  2  for associated metadata). Beta diversity was visualized using PCoA with a Bray-Curtis test. Between-group differences were tested using a permanova (Adonis function via the Vegan package in RStudio 1. 4) (87). Isolates were co-inoculated in a 1:1 ratio and their proportions were determined using nourseothricin selection upon recovery from fecal pellets. Panels show means ± SEM from 4 single housed mice. Figure Legends.