dataesr/scientific-paragraphs-categorization huggingface dataset:
5 ). Only two merB genes were identified in the metagenomic dataset. These genes were identified in one Thiotrichaceae (Gammaproteobacteria) MAG (bin660) and one Rhodobacteraceae MAG (bin246), but none of these MAGs also included the merA gene (Fig. 5 ). Coverage of the MAGs was used as a proxy for the relative abundances of the corresponding populations (Table  1 ). Overall, the average coverage of the 407 recovered MAGs was 3. 5 reads per base in the contig, with a median value of 1. 62 and a maximum of 203. 12 for a cyanobacterial MAG. Most of the MAGs with hgcA, merA, or merB genes (62. 5% of the MAGs) were recovered with a coverage lower than the average coverage (Table  1 ). Nonetheless, bin805 and bin387 with hgcAB genes, bin785, bin674, bin345, bin55, and bin72 with merA gene, and bin246 with merB gene were recovered with a coverage above the average coverages of all recovered MAGs (Table  1 ).
6a ). Transcripts of merA gene were identified in both EDB and SL microbial mats (Fig. 6b ). Transcription of merA was identified for four populations: Gammaproteobacteria bin761, Rhodobacteraceae bin785, Verrucomicrobia bin674, and Desulfuromusa bin93. Only the Desulfuromusa bin93 population expressed merA at higher level than the housekeeping gene rpoB. Finally, although merA gene was detected in Balneolaceae, no merA transcript of Balneolaceae was identified in the metatranscriptomic dataset (Fig. 6b ). Transcription of merB genes was only detected in SL samples. All merB transcripts were assigned to the Thiotrichaeae bin660 and the Rhodobacteraceae bin246 populations (Fig. 6b ). When detected, the transcription level of merB gene in bin660 was higher or similar to the expression level of rpoB gene while expression of merB in bin246 was lower or similar to rpoB expression.
4  Phylogenetic tree of the hgcA genes. Binned and unbinned sequences identified in the metagenomic dataset are in bold. The tree was rooted with the fused hgcAB gene sequences. Bootstraps >0. 8 are represented by gray points. and hydrogenases was also observed. The Rhodobacteraceae (Alphaproteobacteria) bin246 population with merB gene expressed light-harvesting complex and photosynthetic reaction center genes (pufLM), nitrate reductase genes (narABG), and the Sox operon. The lower level of transcription was also detected for dehalogenase and arsenate reductase genes (Fig. 7 ).
To evaluate mercury methylation in the microbial mats, hgcA genes and transcripts were investigated. The frequency of hgcA genes identified in the metagenomic dataset (Fig. 4 ) was similar to previous reports on marine polluted sediments  23  , indicating a strong potential for methylation of mercury in microbial mats. All hgcA genes identified in genomic bins were followed by hgcB gene in the contigs, supporting the metabolic potential for mercury methylation  24. Mercury methylation genes were identified in genome of populations related to Roseimarinus (Bacteroidetes) and the uncultivated phyla of Aminicenantes, supporting previous metagenomic mining in marine environments  7. However, phylogenetic analysis of hgcA gene and transcripts as well as taxonomy of the hgcAB-bearing MAGs indicated that members of the Desulfobacterota represented most of the mercury methylator diversity in these coastal microbial mats (Figs.
Interestingly, merA gene was also identified in three Verrucomicrobiaceae populations with the genetic composition and the expression profile consistent with an anaerobic metabolism based on fermentation (Fig. 7 ). The merA gene of these Fig. 7  Gene expression profile of genomic bins potentially involved in mercury transformations. For each bin, all transcripts identified per gene over the different sampling periods were summed, then the summed number of transcripts per gene was compared to the number of rpoB gene transcripts to determine the relative expression level. List of the genes and associated dataset is provided in Supplementary Data 1. populations branched at the base of the phylogenetic tree without known references but in proximity with Desulfuromonadales sequences (Fig. 5 ), indicating these Verrucomicrobiaceae populations may represent a new group of mercury reducers under anoxic conditions.
Furthermore, mining of the available genomes in IMG database for merA and merB genes highlighted that over the 4530 genomes with merB gene, only 34% (1545) also include merA, supporting the occurrence of merB alone in our MAGs and recent observations  20. Both Rhodobacteraceae and Thiotrichaceae populations were found to express merB (Fig. 6 ), indicating an active methylmercury detoxification process in SL microbial mat. Together our results suggest that methylmercury cleavage and mercury reduction were dissociated in the microbial mats, with each reaction being carried out by taxonomically different lineages. The accumulation of mercury is detrimental for the cells, therefore additional merAindependent mechanism for mercury export or detoxification should occur in these populations. Various biotic and abiotic merAindependent detoxification process have been proposed  20, 37  , with notably the reduction of HgII to Hg0 in photomixotrophic microorganisms, providing a plausible mechanism of mercury detoxification for the Rhodobacteraceae bin246 population that strongly expressed phototrophic and mixotrophic genes.
Metagenomic library preparation, sequencing, and analysis Metagenomes were constructed for all 30 samples using the BioScientific PCR-free kit following the manufacturer's protocol while metatranscriptomes of each sample were prepared using the TruSeq Stranded mRNA sample prep kit (Illumina, San Diego, CA, USA) following the manufacturer's instructions. Metagenomes and metatranscriptomes were sequenced using Illumina HiSeq (2 × 100 bp) platform at the Genotoul sequencing facility leading to a total 670 Gb of sequences (370 GB of metagenomes and 300 GB of rRNA-depleted metatranscriptomes). Datasets were quality filtered using the Trimmomatic v. 0. 39 tool  38  , keeping both R1 and R2 reads when reads overlapped. The 16S rRNA reads were isolated from the metagenomic reads using REAGO 1. 1 (ref.
Binning and functional characterization For MAGs reconstruction, all quality filtered sequences were pooled and co-assembled using MEGAHIT  42  , then coassembly was uploaded to IMG/ MER platform for gene annotation  43. Read coverage of the contigs was carried out using bwa-mem (http://bio-bwa. sourceforge. net), followed by the binning of the contigs longer than 2000 bp by MetaBAT-2 (ref. 44 ). The completeness and contamination level of the MAGs were then evaluated using CheckM  45. Only MAGs with a contamination level under 5% and completeness above 50% were analyzed. Open reading frames (ORFs) were identified using Prodigal  46  and compared against COG, Pfam, TIGRfam, and KEGG databases on IMG/MER platform, leading to 5. 7 × 10 6 genes with product name (58. 96% of the genes). Since hgcA gene is poorly characterized on large public databases, we identified hgcA genes in the dataset using the publicly available hgcA hidden markov model profile and metabolisHMM  47.
In addition, all contigs with were manually inspected to detect the presence of the hgcB gene downstream of hgcA. For the merA and merB genes identification, erroneous annotations were identified in the KEGG database. Therefore, merA and merB were also identified separately with metabolisHMM using KOFAM K00520 for merA and a homemade hidden markov model of merB genes, filtered with an evalue threshold of e -120. To validate gene identifications, recovered amino acid sequences of hgcA, merA, and merB genes were aligned with reference sequences downloaded from NCBI using Clustal Omega  48  and amino acid phylogenetic trees were generated using Fasttree 2 (ref. 49 ). Phylogenetic trees were visualized using iTol v. 4 (ref. 50 ). When the gene sequence was complete, recovered MerA sequences were manually examined for the presence of cysteine pairs that are potentially essential for MerA activity  20.
Expression profile analysis To determine gene expression levels of each gene identified in MAGs, all metatranscriptomic reads passing quality filtration were mapped against all open reading frames identified in MAGs using bwa-mem. Due to rRNA depletion step malfunction, the September 2012 metatranscriptomes of EDB microbial mats were removed from the analysis. For each MAGs, the coverage value of genes of interest was normalized to the coverage of the RNA polymerase subunit B (rpoB) gene to discriminate between low and high expression level compared to housekeeping genes. Results were represented in heatmaps using the software environment R (v. 4. 0. 3) and R Studio (v. 1. 3. 1093).
DATA AVAILABILITY Assembled metagenome data are in IMG/MR (https://img. jgi. doe. gov/mer/) under the following accession numbers: 3300040774-3300040799. Coassembly is also available on IMG/MR under accession number 3300040801. Bins with mercury cycling genes were deposited in Figshare (https://doi. org/10. 6084/m9. figshare. 15015303v2). Environmental metadata were previously published  15. Nucleotide sequences are available in the NCBI Genbank database under Bioproject accession number SRP063590.
CODE AVAILABILITY The merB gene HMM profile is available in Figshare (https://doi. org/10. 6084/m9. figshare. 15015303. v2). Software version: REAGO 1. 1, Mothur 1. 44. 3, Trimmomatic v0. 39, Megahit 1. 2. 9, bwa 0. 7. 17, CheckM v. 1. 1. 2, R 4. 0. 3 and R Studio 1. 3. 1093.
Dynamic light scattering (DLS) and ζ -potential Batch DLS measurements were performed using a Malvern Zetasizer Ultra instrument (Worcestershire, UK) equipped with a 633 nm He-Ne laser. Particles were analysed as synthesized without prior filtration to avoid material losses, e. g. , aggregates and large populations (previous filtration with a 1. 2 μm filter did not affect the size distribution results). The particle concentration was fixed to 0. 5 mg/mL to obtain an attenuator factor of 6 and 7. Experimental measurements were performed with a fixed 173 °scattering angle at 25 °C after an equilibration step of 2 min. Each sample was recorded in triplicate using at least 20 data sets acquired for 10 s each. The correlation function was analysed by the general-purpose method (using a nonnegative least square algorithm) for derivation of the intensity-weighted particle size distribution, intensity-weighted mean diameter (D h-DLS ) and polydispersity index (PDI).
The carrier liquid was composed of Milli-Q water containing 0. 02% NaN 3 prefiltered at 0. 1 μm. The particle concentration was set to 0. 5 mg/mL and the injection volume was 10 μL. Analyses were performed at room temperature. The collected data were analyzed using the Astra 6. 1. 7. software package. The weight-based size distribution was calculated by using the hydrodynamic diameter acquired by DLS and sample concentration measured by the RI detector.
5 B ). The relative proportion of both main peak and tail was assessed from the cumulative weight-based distribution. Seventy percent of dextran-NPs were found to have a diameter equal to or below 400 nm, whereas particles with a diameter between 400 nm and 1 μm represented only 30% of the sample. The intensity correlation functions of particle fractions obtained at different elution times are depicted in Fig. 5  C. In all cases, cumulant analysis provided a good fit of the single exponential decay, attesting to the quality of the data acquired from the DLS detector.
Acknowledgments The authors express their highest gratitude to  Christine Choqueux  ( LVTS, Paris, France ) and  UTC (Compiègne, France)  for the SEM images and to  Samira Benadda  and  CRI U1149  imaging facilities for confocal imaging training. We also thank  Rémi Le Borgne  and the  ImagoSeine facility , member of the  France Bioimaging Infrastructure Supported by  Grant  ANR-10-INBS-04  from the  French National Research Agency , for their valuable help with TEM images. This work was supported by  INSERM ,  Université de Paris ,  Université Paris 13 ,  UMS34 FRIM  and  Université de Rouen Normandie , and received the financial support from the  ANR-16-CE18-0031  NANOPACT. The authors thank the Normandy region and the  European Union  for their financial support in the purchase of equipment (project  Finns ).
In this paper, the performance of the reconstruction and identification of electrons and photons with the CMS detector in Run 2 is presented. The Run 1 results are reported in Refs. [4, 5]. The new results are based on pp collision data collected during 2016-2018, and correspond to a total integrated luminosity of 136 fb -1  [6] [7] [8]. The pp collisions were delivered with a 25 ns bunch spacing, and an average number of interactions per beam crossing (pileup or PU) increasing through the years from 22 to 32. In addition, the reconstruction of electrons and photons in lead-lead (PbPb) ion collisions is presented, which requires specific updates because of the significantly higher particle multiplicity compared with pp collisions. The PbPb collisions were recorded in 2018 at a nucleon-nucleon (NN) center-of-mass energy of √ s NN = 5.
The matrix elements are computed at NLO for the three processes pp → Z + N jets , where N jets = 0, 1, 2, and merged with the parton showers using the FxFx  [25]  scheme with a merging scale of 30 GeV. The NNPDF 3. 0 (2016) and 3. 1 (2017-2018) with leading order (LO), in 2016, and next-to-nextleading order (NNLO), in 2017-2018, parton distribution functions (PDFs)  [26]  are used. Simulated event samples for γ + jet final states from direct photon production are generated at LO with PYTHIA. The NNPDF2. 3 LO PDFs  [27]  are used for these samples. A detailed detector simulation based on the GEANT4 (v9. 4. 3)  [28]  package is applied to all generated events. The presence of multiple pp interactions in the same and nearby bunch crossings is incorporated by simulating additional interactions (including also those out-of-time coming from neighbouring bunch crossings) with a multiplicity that matches that observed in data.
Other objects, such as hadronic jets, may also produce electron-like signals, leading to such objects being misidentified as electron candidates. The better the reconstruction algorithm, the lower the misidentification rate per event. The larger the number of multiple interactions in an event, the larger the misidentification rate. Figure  5  shows the number of misidentified electron candidates per event in different p T ranges (for DY + jets MC events simulated with the different detector conditions corresponding to the three years of the Run 2 data taking period), as a function of the number of pileup vertices. The significant suppression of the misidentification rate in 2017 and 2018 is due to the new pixel detector. The slightly better results in 2017 with respect to 2018 are due to the better conditions and calibrations used in the Legacy data set.
This change produced a significant rate reductions with minimal efficiency losses. To operate with the new pixel detector, DC-DC converters were installed. After a few months of smooth operation, some converters started to fail once the luminosity of the accelerator was increased, at the beginning of October 2017, leading to a decreasing efficiency toward the end of the year. For these reasons related to the pixel detector, 2017 trigger performance is slightly worse than for the other years, in particular for the double-electron trigger, where the retuning of the tight working point does not have any effect. In Fig. 10 , the 2017 efficiencies of the single-and double-electron HLT paths are reported as a function of the number of reconstructed primary vertices. In 2017 the majority of the high-pileup data was recorded at the end of the year, the same time the pixel DC-DC convertors exhibited efficiency losses.
In 2017 the majority of the high-pileup data was recorded at the end of the year, the same time the pixel DC-DC convertors exhibited efficiency losses. Thus the efficiency loss versus number of vertices in the event is not solely due to the pileup. However the efficiency loss is significant only for 2. 0 < |η| < 2. 5. between the true energy of an e/γ and its reconstructed energy, thus the regression prediction for the target is the correction factor to be applied to the measured energy to obtain the best estimate of the true energy. The regression input variables, represented by the vector x, includes the object and event parameters most strongly correlated with the target. The regression is implemented as a gradient-boosted decision tree, and a log-likelihood function is employed  [39] : 1.
Additional electron identification variables Additional tracker-related variables are used for the identification of electrons. One such discriminating variable is |1/E -1/p|, where E is the SC energy and p is the track momentum at the point of closest approach to the vertex. Another important variable for the electron identification is |∆η seed in | defined as |η seedη track |, where η seed is the position of the seed cluster in η, and η track is the track η extrapolated from the innermost track position. Similarly, |∆φ in | = |φ SCφ track | is another discriminating variable that uses the SC energy-weighted position in φ instead of the seed cluster φ. An example of the ∆φ in distribution in data and simulation is shown in Fig. 23  for electrons in the barrel and endcap regions. An important source of background to prompt electrons arises from secondary electrons produced in conversions of photons in the tracker material.
Selection efficiency and scale factors The electron and photon identification efficiencies, as well as the electron reconstruction efficiency, are measured in data using a tag-and-probe technique that utilizes Z → ee events  [4]  described in detail in Sec. 4. 7. The identification efficiency is measured for transverse energies above 10 GeV for electrons and above 20 GeV for photons. For photon identification efficiency no requirement is applied on the track and charge of the probe, pretending in this way to identify an electron from Z boson decay as a photon. The performance achieved for the two reference electron selections is shown in Fig. 26. The electron identification efficiency in data (upper panels) and data-to-simulation efficiency ratios (lower panels) for the cut-based identification and the veto working point (left), and the BDTbased identification loosest working point (right) are shown as functions of the electron E T.
Performance of recalibrated data sets During the LHC long shutdown 2 that began in 2019, the CMS experiment initiated a program of recalibrating the full Run 2 data set. It involved the realignment and recalibration of all the subdetector components and the related physics objects. The simulation was also improved with a more accurate description of the data in terms of dynamic inefficiencies, radiation damage, and description of the detector noise. Electron and photon reconstruction and identification performance strongly depend upon improvements on measurements with the ECAL subdetector. An updated method to monitor and correct the ECAL crystal transparency loss due to radiation damage has been introduced. In parallel a more granular calibration of the crystals has been performed allowing to calibrate precisely also the highest pseudorapidity region of the calorimeter.
Their resulting invariant mass has to be consistent with the Z boson mass (60 < m ee < 120 GeV). The energy of each of the two hits must fall within the range 10 < E < 120 GeV. The lower threshold is motivated by the minimal energy constraint applied to reconstruct good quality electrons, whereas the upper threshold is applied to include only ECAL signals below the lowest gain switch threshold described in Section 6. 2. The resulting resolution for the full Run 2 inclusive data set is shown in Fig. 35  as a function of the effective energy of the dielectron system, which depends on the individual energies of the two electrons measured in the two seed crystals as E eff = E 1 E 2 / √ E 2 1 + E 2 2. For 2017 data, the Legacy calibration is used. The resolution is extracted as the σ parameter of a Gaussian fit to the core of the distribution of the time difference between the two electrons.
1), and should not be in the barrel-endcap transition region or in the problematic HCAL region for 2018 PbPb data, because in 2018, a 40 • section of one end of the hadronic endcap calorimeter lost power during the data-taking period. All PbPb data is affected by this power loss. Tag-and-probe electrons are defined as described in Section 4. 7. The tag-and-probe pairs are required to be oppositely charged and to have an invariant mass in the range 60-120 GeV. For the loose identification working point the data-to-simulation correction factor is smaller than 3%, both in the barrel and the endcaps. Several sources of systematic uncertainty are considered. The main uncertainty is related to the model used in the mass fit, and is estimated by comparing alternative distributions for signal and background.
The events are further subdivided into three centrality regions: 0-10, 10-30, and 30-100%. The electrons are required to have a minimum E T of 20 GeV, pass the loose identification selection, and to be located outside the ECAL transition region or the problematic HCAL region. The invariant mass distributions are fitted with a DSCB distribution, from which the mean values are extracted. This is performed separately for data and simulation, and the ratio of the extracted mean values to the worldaverage Z boson mass  [36]  is used as a correction factor applied to the mean energy scale. The energy resolutions are extracted after first applying the scale factors derived to shift the invariant mass distributions back to the nominal Z boson mass. The energy scale and resolution spreading correction factors are applied to the ECAL energy component of the reconstructed electrons, with the final electron momentum obtained by redoing the ECAL-tracker recombination.
The first two sources of systematic uncertainty are evaluated by constructing the invariant mass distributions after varying the electron selection criteria. The variations considered are to tighten the selection criteria from the loose to the medium working point, and increase the electron E T threshold from 20 to 25 GeV. The difference between the mean values of the nominal and the varied distributions is used as an estimate of the systematic uncertainty. The residual discrepancy between the corrected and the nominal Z boson mass is also assumed as a systematic uncertainty, and is smaller than 1 (3)% in the barrel (endcap) region. A comparison of the Z → ee invariant mass peak between data and simulated Drell-Yan events generated with MadGraph5 at NLO  [21]  is shown in Fig. 37. The electron energy in simulation has been corrected using scale corrections and resolution spreading, and electron reconstruction and identification efficiency corrections have also been applied.
The reconstruction efficiency in data is better than 95% in the E T range from 10 to 500 GeV. The data-to-simulation efficiency ratios, both for electron reconstruction and for the various electron and photon selections, are compatible with unity within 2% over the full E T range, down to an E T as low as 10 (20) GeV for electrons (photons) when using the 2017 data reconstructed with the dedicated Legacy calibration. Identification efficiencies target three working points with selection efficiencies of 70, 80, and 90%, respectively. The energy resolution and energy scale measurements, together with the relevant identification efficiencies, remain stable throughout the full Run 2 data-taking period (2016-2018). For the 2017 data-taking period, the dedicated Legacy calibration brings an improvement of up to 50% in terms of relative energy resolution in the ECAL, as well as an improved agreement between data and simulation, leading to smaller reconstruction and identification efficiency correction over the entire E T and η ranges.