dataesr/scientific-paragraphs-categorization huggingface dataset:
Solution chemistry Orthosilicic acid (H 4 SiO 4 ) concentrations were determined by continuous-flow colorimetric analyses (QuAAtro Axflow) at the Institut de Physique du Globe de Paris (IPGP; Paris, France) using non-acidified, 0. 2 µm filtered La Preciosa solutions. Anion concentrations were measured using ion chromatography (ICS1100 Dionex Thermo Fisher) using nonacidified, 0. 2 µm filtered La Preciosa solutions. Concentrations of major cations were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES; iCAP6200 Thermo Fisher), using acidified, 0. 2 µm filtered La Preciosa solutions. The uncertainty in the concentration measurements of orthosilicic acid, anions and cations was lower than 5 %. Activities of anions, cations and orthosilicic acid as well as saturation indices of the surface water solutions of the lakes were calculated using the Visual MINTEQ software.
Confocal laser scanning microscopy (CLSM) and correlative with scanning electron microscopy (SEM) For standard CLSM analyses, cells concentrated using cell trap units were deposited on a glass slide, covered by a coverslip and sealed with nail polish. Samples were observed by CLSM using a Zeiss LSM 710. Excitation was performed at 405 and 488 nm, and emission spectra were measured for each pixel of the images over the 405-720 nm wavelength range using a 34-channel Quasar T-PMT detector. Data were processed using the Zeiss Zen software. For correlative CLSM-SEM, cells were first washed with Milli-Q water and then deposited on a coverslip and left to dry. The coverslip was mounted onto a KorrMik Life Sciences sample holder, and the correlative Shuttle and Find software implemented in ZEN 2012 was used.
Semi-quantitative analyses of EDXS spectra were performed using the JEOL Analysis Station software. This was based on the use of k factors. After subtracting out the background noise in the EDXS spectrum, the software performed a Gaussian fit of selected elemental peaks and calculated the area under the peaks. From this, the atomic percentage of each selected element was assessed. EFTEM elemental mapping of C, Fe and Mn was performed using the three-window technique  (Hofer et al. , 1997). This technique requires three energy-filtered images: two positioned before the ionization edge (pre-edge images), which serve to calculate the background, and one positioned just after the edge (post-edge image). A calculated background image was subtracted from the post-edge image to give an elemental map, in which changes in background shape were considered.
These 16S rRNA gene sequences were used to screen a large dataset of 16S rRNA gene metabarcoding sequences of plankton from 11 Mexican lakes, including La Preciosa  (Zeyen et al. , 2021; Iniesto et al. , 2022). We detected sequences closely related (> 99 % identity) to those of the Fe-, Mn-and Si-ring-producing cyanobacteria in 9 of these 11 lakes: La Preciosa, Alchichica, Atexcac, Aljojuca, Alberca de los Espinos, Pátzcuaro, Quechulac, Tecuitlapa and Yuriria (Table  2  in the Supplement). These sequences were particu-larly abundant (representing > 1 % of all bacterial metabarcoding reads) in La Preciosa, Alchichica, Atexcac, Aljojuca and Alberca de los Espinos, sometimes reaching frequencies up to 15 %. Interestingly, these lakes were characterized by relatively high salinity and alkalinity values  (Zeyen et al.
The taxonomic affiliation of this sequence allows the investigation of the potential prevalence of this unusual biomineralization process in different lakes by the analysis of massive metabarcoding datasets. While the enrichment in Fe and Mn is moderate in Lake La Preciosa sediments, higher sedimentary enrichments can be discovered using such an approach. Although we ignore the molecular mechanisms of this biomineralization process (see Sect. 4. 2), several potential functions can be speculated for this trait. (i) High amounts of Fe and Mn are needed by cyanobacteria compared with other microorganisms  (Raven et al. , 1999; Nelson and Junge, 2015) , but, at the same time, elevated intracellular concentrations of these elements may enhance oxidative stress, therefore requiring some appropriate homeostasis  (Liu et al.
Acknowledgements. We thank  Lucie Franco  and  Michael Bourges  for helping with FACS experiments;  Nina Zeyen ,  Mélanie Poinsot ,  Jena Johnson  and  Daniel Nothaft  for help in the field;  Franck Bourdelle  for help with STXM experiments; and  Mickael Trichet  for help with lyophilization. The authors acknowledge  SOLEIL  for the provision of beam time. Last, we would also like to thank several instrumentation facilities at IMPMC that enabled streamline measurement and analysis of datasets presented in this work. We extend our gratitude to  Maxime Guillaumet  and  Keevin Béneut  for the spectroscopy platform;  Imène Esteve ,  Stéphanie Delbrel  and  Béatrice Doisneau  for SEM support;  Ludovic Delbes  and  Benoît Baptiste  for X-ray diffraction facility; and  Jean Michel Guignier  for the TEM platform.
Financial support. This research has been supported by the  European Research Council FP7 Ideas: European Research Council  (grant nos. 307110  and  787904 ), the  Centre National de la Recherche Scientifique  (" SAMBA" project, Interrvie program ) and the  Agence Nationale de la Recherche  (grant no. ANR-18-CE02-0013-02 ). Review statement. This paper was edited by  Jack Middelburg  and reviewed by two anonymous referees.
Experimental Section Theoretical Simulation: The wavelength-dependent transmission, T(), absorption, A(), and the profile of the electric field, E(x, ), in the ST-OSCs were analyzed using TMM simulation over the wavelength range from 300 to 1000 nm. Refractive indices of ITO, Au and Ag layers are taken from refractiveindex. info database. The optical constants of the functional layers of the PM6:BTP-eC9:L8-BO (0. 6:0. 4:0. 8) blend layer, PEDOT:PSS-Nb 2 C, Na 3 AlF 6 and ZnS were measured using variable angle spectroscopic ellipsometry (VASE). In the simulation, considering incoming light from the glass side and normal to the cell, and the functional layers are regarded as the homogenous media with the optical properties determined by the wavelength dependent optical constants. Assuming no non-geminate recombination losses, the theoretical predication of the maximum J SC in the ST-OSCs was calculated using the following expressions: EQE(𝜆) = ∫ 2𝜋𝜀 0 cn (𝜆) k (𝜆) 𝜆 |E (x, 𝜆)| 2 dx ( 4 ) J SC = ∫ EQE(𝜆)F (𝜆) 𝜆e hc d𝜆 ( 5 ) where  0 is vacuum permittivity, c is the speed of light, n() and k() are the wavelength dependent refractive index and extinction coefficient of the active layer, x is position of the electric field in the active layer, e is the elementary charge and h is Planck constant.
Acknowledgements This work was financially supported by  National Natural Science Foundation of China  ( 12174244 ). S. W. acknowledges the funding support from the  Program for Professor of Special Appointment (Eastern Scholar ) at the Shanghai Institutions of  Higher Learning  and the  Shanghai Rising-Star Program  ( 19QA1403800 ). F. Z. is thankful for the financial support by the  Research Grants Council, Hong Kong Special Administrative Region, China  ( 12302419 ,  C5037-18GF , and  N_HKBU201/19 ),  SZ-HK-Macau Science and Technology Plan Project  ( SGDX20201103095400005 ), and  Guangdong Basic  and  Applied Basic Research Fund GDSTC  ( 2022A1515010020 ).
The experimental signature would have a final state of 3µ plus large missing transverse energy. This final state is not included in this analysis. Both the ATLAS and CMS Collaborations have measured the cross-sections of the SM Z → 4µ process  [14] [15] [16]. The measurement by ATLAS was used by theorists to set limits in the parameter space of the L µ -L τ model  [11]. The CMS Collaboration has directly searched for the Z boson in the mass region between 5 to 70 GeV with the 4µ final state using 77. 3 fb -1 of data  [17] , and set upper limits on the Z to muon coupling strength, g, of 0. 004 -0. 3 at 95% confidence level, depending on the Z mass. The organization of this paper is as follows. The ATLAS detector is described in the next section. The dataset and Monte Carlo samples used in this analysis are detailed in section 3.
JHEP07(2023)090 3 Dataset and Monte Carlo simulations The data used for this analysis were recorded using single-muon and multi-muon triggers, corresponding to an integrated luminosity of 139 fb -1 after the application of data quality requirements  [22]. The transverse momentum (p T ) thresholds for the single-muon trigger vary from 20 to 26 GeV, for the di-muon trigger from 10 to 14 GeV, and for the tri-muon trigger from 4 to 6 GeV, depending on the data-taking periods  [23]. The overall trigger efficiency in the phase space defined by the event selections used in this analysis is higher than 98%.
7. 3  [25]  at leading-order (LO) accuracy in QCD by using the Universal FeynRules Output (UFO) format  [26, 27]. Based on theoretical calculations  [28, 29]  for related processes, the appropriate NNLO/LO correction factor K of 1. 3 is used to correct the MC LO signal cross-sections. In the signal production simulations, the NNPDF2. 3nlo set  [30]  is used for parton distribution function (PDF) for the pp collisions. Photos++ 3. 61  [31, 32]  is used to simulate the effect of QED final-state radiation. The MC simulated events are generated for a range of masses and coupling parameters of the L µ -L τ model. The Z mass ranges from 5 to 81 GeV, and the value of coupling constant g ranges from 0. 008 to 0. 316, as summarized in table  1. The value of the g at each Z mass was chosen to be close to the expected experimental sensitivity to allow a sensitive search for a very small Z signal in the full Run 2 dataset.
GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Türkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; 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 MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; 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.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias , Colombia; MEYS CR, Czech Republic; DNRF and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG, Germany ;.
The family of Malacoherpesviridae is currently represented by only two viruses infecting molluscs, Ostreid herpesvirus 1 (OsHV-1) and Haliotid herpesvirus 1 (HaHV-1), both causing detrimental infections in aquaculture species. Malacoherpesvirus-like sequences were also detected through genome sequencing projects in amphioxus (Branchiostoma species) and annelid worm (Capitella teleta), suggesting the existence of a hidden diversity of malacoherpesviruses in aquatic animals. Here, to extend the knowledge on m a l a c o h e r p e s v i r u s d i v e r s it y , w e s e a r c h e d f o r t h e p r e s e n c e of malacoherpesvirus relatives in genomic, transcriptomic and metagenomic datasets, including from the Tara Oceans expedition, and report 4 novel malacoherpesvirus-like genomes (MalacoHV1-4).
Genomic analysis suggested gastropods and bivalves as the most probable hosts for these new malacoherpesviruses. Phylogenetic analysis based on the family B DNA polymerase placed the novel MalacoHV1 and MalacoHV3 as sister lineages of OsHV-1 and HaHV-1, respectively, whereas MalacoHV2 and MalacoHV4 showed higher divergence. The viral genome found associated with amphioxus together with MalacoHV4 formed a sister clade to the mollusc and annelid malacoherpesviruses, suggesting an early divergence of the two virus assemblages. In conclusion, although relatively rare in the available sequence databases, the previously undescribed malacoherpesviruses, MalacoHV1-4, circulate in aquatic ecosystems and should be considered as possible emerging viruses under changing environmental conditions.
Search for Malacoherpesviridae hits in the NCBI SRA database Taking advantage of the taxonomic classification of the reads deposited in the NCBI SRA database performed with the Taxonomy Analysis Tool (STAT)  (Katz et al. , 2021) , we used Google BigQuery to mine the taxonomic results using the following query syntax: "SELECT *; FROM `nih-sra-datastore. sra. metadata`; WHERE organism = 'Malacoherpesviridae'", resulting in a list of SRA accession IDs with the corresponding number of identified reads (SRA accessed the 1 st of July 2022). This output table was populated with the related experimental metadata retrieved from the SRA database using the ffq tool  (Gaĺvez-Merchań et al. , 2023)  (Supplementary Table  1 ). Manual inspection of the metadata table allowed the detection of datasets associated with projects focusing on malacoherpesviruses (OsHV-1 or HaHV-1) and these were excluded according to the purpose of our analysis.
The re maining SRA samples including more than 1,000 Malacoherpesviridae reads were downloaded and further analyzed. In particular, the reads in fastq format were trimmed for quality using fastp v0. 23. 2  (Chen et al. , 2018)  with the following parameters: -cut_front -cut_tail -cut_window_size 4 -cut_me an _q uality 25qualifi ed_quality_phred 25 -unqualified_percent_limit 30 -n_base_limit 5 -length_required 70 -detect_adapter_for_pe -trim_poly_x -trim_poly_g -low_complexity_filter. Trimmed reads were either directly denovo assembled per sample or, in the case of SRA dataset produced for whole genome sequencing of a given species, the reads were firstly mapped on the reference genome (as declared in the metadata, Supplementary Table  1 ) using the CLC mapper with similarity and read length set to 0.
8 (CLC Genomics, Qiagen, US) and only the unmapped reads were de-novo assembled. Reads were assembled using metaspades v. 3. 15. 5  (Nurk et al. , 2017)  using the automatically determined kmer sizes based on read lengths and sample sizes. The obtained contigs were screened for the presence of Malacoherpesviridae genes by tblastn using all the OsHV-1 and HaHV-1 predicted proteins as queries and the positive contigs were used as reference for mapping the raw reads from the B C A FIGURE 1 Overview of the bioinformatic approaches used to identify novel malacoherpes-like viruses. (A) The taxonomic classification of the reads from the NCBI SRA database has been investigated to identify samples containing malacoherpesvirus reads. Analysis of selected SRA samples did not reveal novel malacoherpes-like viruses, only the previously characterized ones.
(B) The NCBI nucleotide database was scanned for the presence of malacoherpesviruses using tblastn with OsHV-1 and HaHV-1 proteomes used as queries. The major capsid protein (MCP) of known malacoherpesviruses have been used to build an HMM, which was used to scan metagenomic databases. (C) The four novel identified malacoherpesvirus (MalacoHV1-4) genomes were annotated and comprehensively analyzed. DNA polymerase family B proteins were used as markers for phylogenetic analysis. corresponding samples. For specific cases, the relevant contigs were refined using the iterative assembler PRICE v1. 2  (Ruby et al. , 2013) , run with the following parameters: -rfq 90. 95 -icf 1 1 5 -nc 30 -dbmax70 -mol 30 -tol 10 -mpi 80 -target 90 2 1 1. Raw reads were back-mapped on the malacoherpesvirus contigs using the CLC mapper tool, to evaluate their relative coverages.
To search for additional malacoherpesvirus contigs in the NCBI databases, we used tblastx searches using the malacoherpesvirus proteomes as queries against the NCBI nucleotide database excluding the taxonomic term "Viruses". The relevant contigs identified were subjected to Open Reading Frame (ORF) prediction using prodigal v2. 6. 3  (Hyatt et al. , 2010)  and the predicted proteins were compared with malacoherpesvirus proteomes obtained from OsHV-1 and HaHV-1 by blastp.
Genome-resolved meta genomics of Malacoherpesviridae A Hidden Markov Model (HMM) of the putative Malacoherpesviridae major capsid protein (MCP) was build using hmmbuild based on four aligned proteins: YP_024643. 1 (OsHV-1) YP_006908720. 1 (HaHV-1) ELT95197. 1 (C. teleta) and XP_019628941. 1 (B. belcheri). The latter two hits represented viral genes associated with these metazoan genome assemblies because of putative integrations of co-sequencing. The HMM model was further updated with the novel hits (Data Sheet 1). HMMer v3. 3. 2  (Eddy, 2011)  was used in combination with the MCP model to scan metagenomic datasets deposited in the MGnify database (https://www. ebi. ac. uk/metagenomics/). Moreover, the MCP profile was used for guidance to perform a targeted genome-resolved metagenomic survey of Tara Oceans.
7. 15  (Li and Durbin, 2009)  (minimum identity of 95%) and stored the recruited reads as BAM files using samtools  (Li et al. , 2009; Eren et al. , 2021)  profiled each BAM file to estimate the coverage and detection statistics of each contig, and combined mapping profiles into a merged profile database for each metagenomic set. We then clustered contigs with the automatic binning algorithm  CONCOCT (Alneberg et al. , 2014)  by constraining the number of clusters per metagenomic set to a number ranging from 50 to 400 depending on the set (total of 2,550 metabins from ~12 million contigs)  (Delmont et al. , 2022a; Delmont et al. , 2022b). Here, we identified four metabins with a hit for the Malacoherpesviridae MCP HMM, and performed targeted genome-resolved metagenomics to characterize and manually curate metagenome assembled genomes (MAGs) corresponding to Malacoherpesviridae.
Annotation of viral proteomes and phylogenetic inference of orthologs The proteomes of the identified viruses were annotated using blastp searches against the NCBI nr database and using InterProScan v. 90  (Jones et al. , 2014). Orthofinder v2. 5. 4  (Emms and Kelly, 2019)  was used to trace phylogenetic inference of orthologs based on the proteomes of the novel viruses and selected metazoans, namely using the available genomic-predicted datasets of molluscs, crustaceans, annelids and cephalochordates (NCBI Genome database, accessed the 21 st of September 2022, Supplementary Table  2 ).
Phylogenetic analysis of family B DNA polymerase A large dataset of family B DNA polymerase sequences  (Gaïa et al. , 2023)  was used to identify the position of Malacoherpesviridae and Alloherpesviridae and their closest branching clades. The dataset consists of eukaryotic sequences from Kazlauskas et al. (Kazlauskas et al. , 2020) , and sequences identified from metagenome-assembled genomes of environmental viruses  (Gaïa et al. , 2023). Duplodnaviria and Naldaviricetes sequences, obtained by HMM searches on their genomes collected from the NCBI viral genomic database (accessed April 2022) and from Weinheimer & Aylward  (Weinheimer and Aylward, 2022 ) (for the Jumbo phages), are also included. Redundant sequences (>95% identity) were removed using CD-HIT v4. 7  (Fu et al. , 2012) , and long branches were iteratively removed.
The most distant clades to Malacoherpesviridae and Alloherpesviridae were removed, and their remaining related clades were reduced to randomly selected representatives in order to increase the resolution of the resulting phylogenetic tree. The sequences from this restricted dataset are available in fasta format as Data Sheet 2. The alignments were performed using the FFT-NS-2 algorithm of MAFFT v7. 490  (Katoh and Standley, 2013)  for the large dataset and using the L-INS-i algorithm for the restricted dataset, and sites with more than 70% gap were trimmed using Goalign v0. 3. 1  (Lemoine and Gascuel, 2021). Tree reconstructions were done with IQ-TREE v1. 6. 12  (Nguyen et al. , 2015)  with the VT+F+R6 substitution model, as determined to be most suitable for the analyzed data using ModelFinder  (Kalyaanamoorthy et al.
Malacoherpesvirus reads in the SRA databases are largely associated with known hosts To search for new relatives of malacoherpesviruses, we first explored the NCBI Sequence Read Archive (SRA). The occurrence of the term 'Malacoherpesviridae' in the taxonomic annotations of the reads of the whole SRA database revealed 237 samples with more than 1,000 matching reads (Supplementary Table  1 ). A large fraction of these samples (N=168) corresponds to RNA-seq datasets produced in the effort to characterize the transcriptional dynamics during the OsHV-1 and HaHV-1 infections. Fifty-three samples represented DNA sequencing datasets of viral strains linked to the same RNAseq studies or from samples collected from diseased hosts in different locations. The 16 remaining malacoherpesvirus-positive samples contained reads associated with non-invertebrate hosts and we explored these datasets more closely.
One of these samples originated from DNA sequencing of Oncorhynchus nerka (sockeye salmon) collected in the Pitt Lake (British Columbia, Canada) and contained more than 54k malacoherpesvirus spots in addition to the spots corresponding to the salmon DNA. De-novo assembly of the nonsalmon reads allowed the detection of 24 contigs highly similar to the OsHV-1 µVar genome (99. 3% of average nucleotide identity [ANI] over 46% of covered genome length). Further six DNA sequencing datasets from the turtle Platysternon megacephalum genome project included ~12k malacoherpesvirus spots. Again, assembly of the nonturtle reads revealed fragments covering up to 78. 2% of the OsHV-1 genome (99. 6% of ANI). Finally, a number of malacoherpesvirus spots was found in 2 DNA sequencing samples of Bos taurus (N=31,625) and in 6 RNA-seq samples of the plant Cleistogenes songorica (N=34,845); the contigs assembled from these datasets also corresponded to OsHV-1 strains and covered >65% of the viral genome length (Data Sheet 3).
Malacoherpesvirus-like sequences are rare among global metagenomic datasets We next explored an alternative approach for detection of new malacoherpesviruses. In particular, we searched for a hallmark malacoherpesvirus protein, namely, the major capsid protein (MCP), in various public sequence databases. HMMER search queried with the OsHV-1 MCP sequence against the MGnify database  (Mitchell et al. , 2020)  resulted in a significant match to a protein originating from a shotgun metagenome from a study exploring antibiotic resistance genes in an aquaculture facility in Yantai City, China  (Wang et al. , 2018). Out of the 12 different DNA-seq samples included in this study, only one originating from the wastewater of the aquaculture facility was positive for the malacoherpesvirus MCP. Metagenomic assembly of the reads yielded more than 3 million contigs, with 8 likely representing a new virus, which we tentatively named MalacoHV1/China/2018 (we used a naming convention where the virus name epithet is followed by the geographical region and year of isolation/ sequencing; Table  1  and Figure   2 ).