dataesr/scientific-paragraphs-categorization huggingface dataset:
Cross-validated balanced accuracy was 0. 78, significantly above the randomness threshold of 0. 5. The structure-activity dataset is herewith proven to be robust and modellable by both machine-learning (SVR) and neighborhood-analysis based mapping. Activity class landscape for CHEMBL1862 was used to identify zones in chemical space in which "active" compounds tend to cluster preferentially. Note that the label "active" was assigned to compounds with the ~25% highest affinity values, according to the initial automated data curation procedure used for universal map fitting. The GTM nodes n in which active compounds were seen to preferentially reside were identified as key points if: ∑ 𝑅 𝑐𝑛 𝑐∈𝐴𝑐𝑡𝑖𝑣𝑒𝑠 ∑ 𝑅 𝑐𝑛 𝑎𝑙𝑙 𝑐 ≫ 𝑁 𝐴𝑐𝑡𝑖𝑣𝑒𝑠 𝑁 𝑎𝑙𝑙 (2) Above,   represents the responsibility of compound c with respect to node n, summed over actives (numerator) and over all training compounds (denominator) respectively, with the ratio representing the fuzzy-logic propensity to expect an active "resident" in node n.
In such a way, a given descriptor vector can be used several times leading to different SMILES. In-house CGRtools 57 software is used to verify the validity of the generated text string, directly removing any incoherent or incorrect SMILES. The following parameters were analyzed when monitoring the pertinence of the inverse QSAR approach: 1. Validity = #valid SMILES/#all generated text strings, which measures success to generate a syntactically valid SMILES string (assessed by CGRtools), starting from the input "high-affinity" 〈 ⃗ ⃗ 〉 vectors. 2. Feasibility assessing chemical feasibility and drug-likeness according to the Ertl  58  and QED  59  indices.
However, the ACoVAE decoder process, injecting randomized latent vectors (see Section 2. 1) may produce an arbitrary number of SMILES strings based on a given chemical space point. For each of the five considered chemical space points of high predicted affinity, chemically meaningful molecules were obtained (at a low success rate of 1. 34% -but this is merely an order of magnitude of the likelihood to draw a random latent vector that is "compatible" with the current chemical space position This, in part, explains why the model may be very successful in some parts of chemical space, and struggle more in other parts. Generated compounds were filtered to remove both chemically inconsistent species (by CGRtools) and duplicates, and were compared to the initial training database (ChEMBL) to compute the "novelty" rate which corresponds to the percentage of valid unique generated compounds not appearing in the training set of the model.
Structure-based pharmacophores were built based on PDB crystal structures of human Proto-oncogene tyrosine-protein kinase ABL1. The 2HZI and 2CQG crystal structures were used to generate the shared pharmacophore model which was screened against the 39 generated compounds for which pKi>9 was predicted. Ligand-based Pharmacophore. The screening of 47 inverse-SVR and inverse-Lead molecules "hidden" in a set of 328 inactive decoys allowed to understand if the two ligandbased pharmacophore models were selective enough to primarily focus on putative actives. If considered pharmacophore models were observed to be as likely to match inactive decoys, it may be inferred that "matching" the pharmacophore model is no reliable indicator of putative activity against CHEMBL1862, but merely that the ligand-based pharmacophore models are too generic (easily matched by random compounds).
The fact that these three compounds were found by both methods and predicted highly active by the SVR model indicates that these compounds may be good candidates for further testing. retrieve 92% of them. Therefore, the percentage of a library achieving LeadIT scores better (more negative) than this order of magnitude is a first rough estimate of how strongly CHEMBL-1862-focused that library is. Indeed, these percentages are significantly higher within the mixed collection of Inverse-GTM and Inverse-SVR leads (blue in Figure  10 ) than within the random subset of ZINC random decoys (orange bars). It should be noticed that only two out of three hits selected by pharmacophore models (molecules I, II and IV, Table  2 ) were validated in docking calculations as actives: the LeadIT score for molecule III was larger than the threshold of -25.
Vertical lines stretch from the minimum to the maximum pKi value within that binand it is seen that compounds with pKi>10 are placed in all the score bins except the two extremes: worst scores but also best scores. Because docking scores are clearly not a reliable indicator of compound affinity, it is difficult to imply that low LeadIT docking scores of de novo compounds are either necessary or sufficient for "validation". The only encouraging trend in Figure is that random ZINC compounds have a clearly increased propensity to be found within the worst score bin, the only one seemingly "guaranteed" free of potent actives. In this sense, the de novo compound collection can be interpreted as a CHEMBL1862-focused library. For each subset of compounds with LeadIt scores within each score bin, the distribution of the experimental pKi values of its members is detailed by the "trader style" boxes & lines, associated with the pKi scale adorning the left-hand axis.
Quantitative Properties Verification In computer networks, metrics are essential as design factors, and the network performance is ordinarily evaluated based on particular metrics for each network layer  [43]. Starting with three temporal metrics that will be specified to analyse the proposed model as follows: -Waiting Time Metric (W T ): is the time spent by the sensor node before sending a data packet. In the proposed model, this measurement is determined by: the quantity of time that a data packet spends in place Wait before starting the transmission. As a result, the waiting time metric is obtained by subtracting the sending time from the arriving time of the data packet. The waiting time metric is plotted in Figure  10  using information saved in the defined data collector monitor. Finally, the system's average waiting time metric for all packets is calculated and appears in the simulation report (Table  2 ).
-Delay Performance Metric (DP): is the time it takes for a data packet to travel from the transmitter to the receiver and be successfully received. In the proposed model, this measurement is determined by: the duration of time spent by the data packet in place Wait before it accesses in place RC Packet in the base station. As a result, the delay performance metric is obtained by subtracting the receiving time from the sending time of the data packet. The delay performance metric is plotted in Figure  11  using information saved in the defined data collector  monitor. Finally, the system's average delay performance metric for all packets is calculated and appears in the simulation report (Table  2 ). -Waiting Time ACK Metric (W TA): after transmitting a data packet, the sensor starts waiting for a acknowledgement.
Plasmids For cell-free expression, PreS1, PreS, and full-length HBV L sequences were amplified by PCR from the HBV isolate H2815 (genotype D5, GenBank Accession Number KP322603. 1), and cloned into a pEU-E01-MCS vector (CellFree Sciences, Matsuyama, Japan). A Strep-tag II, shortly named "tag" in the following, was fused either to the N-or C-terminal end for purification  (Schmidt and Skerra, 2007) , resulting in the four constructs PreS1 tag , tag PreS, PreS tag , and tag L. The plasmids were amplified in Escherichia coli TOP10 cells (Life Technologies, Carlsbad, CA, USA). DNA was isolated using a NucleoBond Xtra Maxi kit (Macherey-Nagel). Plasmids were further purified by phenol/chloroform extraction, according to CellFree Sciences recommendations. For the bacterial expression of PreS ( E.
The precursors with unknown charge state or a charge state of 1 were excluded. The peptides selected for MS/MS acquisition were then placed on an exclusion list for 20 s using the dynamic exclusion mode to limit duplicate spectra. Peptides were identified by database searching using Sequest HT and MS Amanda with Proteome Discoverer 2. 2 software (Thermo Scientific) against the HBV sequence. Precursor mass tolerance was set at 10 ppm, and fragment mass tolerance was set at 0. 02 Da, and up to two missed cleavages were allowed. Oxidation (M), acetylation (protein N-terminus, K) and phosphorylation (S, T, Y) were set as variable modification and carbamidomethylation (C) as fixed modification. Peptides were filtered with a fixed-value PSM validator and rank 1. Phosphorylation sites were then manually validated.
Circular Dichroism Far UV circular dichroism (CD) spectra were recorded on a Chirascan spectrometer (Applied Photophysics, Leatherhead, United Kingdom) calibrated with 1S-(+)-10-camphorsulfonic acid. Measurements were carried out at room temperature in a 0. 1 cm path length quartz cuvette (Hellma). Spectra were measured in a 180-260 nm wavelength range with an increment of 0. 2 nm, band pass of 0. 5 nm, and integration time of 1 s. Spectra were processed, baseline-corrected, and smoothed with the Chirascan software. tag PreS was in 50 mM phosphate buffer pH 6. 5 at a concentration of 9. 5 µM.
ACKNOWLEDGMENTS Financial support from the  ANRS  ( ECTZ71388 ), the  CNRS  ( CNRS-Momentum 2018 ) and the IR-RMN-THC Fr3050 CNRS for conducting the research is gratefully acknowledged. The authors acknowledge the financial support from  ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Sciences and Health)  within the framework of the cancer plan for Orbitrap mass spectrometer funding.
Genome sequencing and assembly Cells were harvested in the late exponential phase of growth. Genomic DNA was extracted using a standard PCI DNA extraction protocol, coupled to dithionite addition in order to dissolve iron precipitates  [24]. In order to identify and check the purity of the strain, the 16S rRNA genes were amplified from genomic DNA by PCR using Bac8F and 1492Runi 16S rRNA gene universal bacterial primers (1492Runi: 5′-CGGTTACCTTGTTACGACTT-3′; Bac8F: 5′-AGAGTTTGATCATGGCTCAG-3′). Sanger sequencing was then performed on the amplified sequences, and sequences were compared to the NCBI nucleotide collection. In addition, the 16S rRNA gene sequence was also extracted from the total genome assembly using the barrnap module of Prokka (v1. 14. 6 -https://github. com/tseemann/prokka), for a better accuracy and to get full length 16S rRNA gene sequence  [25].
Short read DNA genome sequencing was performed by Fasteris SA (Plan-les-Ouates, Switzerland) using the Illumina MiSeq technology (2×150 bp paired-reads, Nano V2 chemistry). Post-quality controls were performed by sequencing facilities and checked also with FastQC (v0. 11. 8 -https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Short reads were filtered with fastp (v0. 20. 1 -https://github. com/OpenGene/fastp) [26]. A long read additional DNA sequencing was performed with a MinIon (Oxford Nanopore) with the rapid sequencing kit (SQK-RAD004) and a R9. 4. 1 Flow Cell. Genome was assembled into contigs by using the Unicycler pipeline for de novo assembly (version: 0. 4. 9 -https://github. com/rrwick/Unicycler), and its dependencies ( spades. py v3. 13. 0 ; makeblastdb v2. 12. 0+ ; tblastn v2.
12. 0+ ; bowtie2-build v2. 4. 4 ; bowtie2 v2. 4. 4 ; samtools v1. 13 ; java v15. 0. 1 ; pilon v1. 23) with 'bold' parameters  [27]. Genome assembly statistics were obtained with Quast (v5. 0. 2; https:// github. com/ablab/quast) and used to compare different assemblies. Genome assembly visualization was plotted with Bandage (v0. 8. 1 -http://rrwick. github. io/Bandage/) in order to detect potential plasmids from obtained contigs and then checked with viralVerify python script which predicts plasmid and virus sequences (https://github. com/ablab/viralVerify) based on HMMs  [28, 29]. Genome completeness and potential contamination were controlled with CheckM (v1. 1. 2 -https://ecogenomics. github. io/CheckM/), and whole genome average coverage was calculated using BBMap (v38. 87 -BBMap -Bushnell B.
Genomic and taxonomic features Identification and classification of the CRISPR-Cas systems were performed by using the CRISPRCas Finder webserver, with default parameters (https://crisprcas. i2bc. paris-saclay. fr/) [32]. The prediction of laterally transferred gene clusters (genomic islands) was performed with the IslandViewer4 webserver (http://www. pathogenomics. sfu. ca/islandviewer/) based on a GenBank file generated by Prokka against the reference genome of Thermosulfurimonas marina, and IslandPath-DIMOB and SIGI-HMM predictions were considered  [33]. To study the taxonomic position of the strain, 16S rRNA gene sequence was first compared to the NCBI nucleotide collection. Pairwise 16S rRNA gene sequence similarity was then determined using the EzTaxon-e server (https://www. ezbiocloud.
php) [39]. For a more accurate classification, average nucleotide identity (ANI) and average amino acid identity (AAI) scores were also calculated, using software default parameters. OrthoANIu scores were calculated against the genomes of Thermosulfurimonas dismutans (ASM165258v1) and Thermosulfurimonas marina (ASM1231758v1), using the ANI calculator tool provided by the EzBioCloud web server (https://www. ezbiocloud. net/tools/ani) [40, 41]. Average amino acid identity was calculated using the AAI calculator of the Enveomics collection (http://enve-omics. ce. gatech. edu/) with Prokka output  [42]. We considered the following thresholds for classification: for 16S rRNA sequences, identity <98. 7 % for a new species and <94. 5 % for a new genus  [43, 44]. For digital DDH, score <70 % for a new species  [45].
For ANI, score <94-96 % for a new species  [46] , and <70. 85-76. 56 % for a new genus with alignment fraction  [47]. For AAI, scores comprised 95 and 100 % for a same species, and between 65 and 95 % for a same genus  [48]. A standard protein blast (blastp) search using catalytic subunit of nitrate reductase (NapA) amino acid sequence of strain Thermosulfurimonas sp. F29 (WP_221171702) as query against the non-redundant protein sequences (nr) database of the NCBI was performed to obtain the closest bacterial sequences annotated as NapA, the periplasmic nitrate reductases, or molybdopterincontaining oxidoreductases genes. Twenty of these close sequences belonging to well-described species were selected for phylogenetic analysis. The alignment was performed using the CLUSTALW programme implemented in mega (v7.
Genome annotation Genome was analysed and annotated with the fast annotation software Prokka, the MicroScope-Microbial Genome Annotation Analysis Platform-(MaGe) (https://mage. genoscope. cns. fr/microscope/home/index. php), using KEGG and BioCyc databases, the NCBI integrated PGAP pipeline, and RAST server (v2. 0 -https://rast. nmpdr. org/), with default parameters and databases for all of the four software/pipelines  [25, [52] [53] [54]. Functional annotation of predicted CDSs was further blasted with UniProtKB +Swiss Prot database (release 2021_09). Hydrogenase classification was checked using the HydDB webtool (https://services. birc. au. dk/hyddb/)  [55]. All dsr genes were extracted and analysed with the DiSCo perl script (v. 1. 0. 0, https://github. com/Genome-Evolution-and-Ecology-Group-GEEG/DiSCo) from Prokka protein output sequences  [56].
Two of the CDSs were subjected to subcellular localization prediction with the BUSCA server (http://busca. biocomp. unibo. it/) [58]. CDSs were then compared to the UniProtKB database, and all CDSs coding for hypothetical proteins were analysed with the InterProScan webserver (https://www. ebi. ac. uk/interpro/) to perform functional predictions and domain identification (e. g. MOSC). Molybdopterin subunits and chaperone sequences microsynteny was then analysed and plotted with SimpleSynteny (https://www. dveltri. com/simplesynteny/) [59]. The genomes of strain F29, T. dismutans, T. marina, T. ammonigenes, D. thermophilus, and D. thermomarina were compared against the three predicted protein sequences of strain F29. Protein sequences homologies between strains were calculated with Clustal Omega (https://www.
General genomic properties, genomic plasticity and taxonomy Although hybrid sequencing was performed, only a draft genome could be reconstructed. Main genomic features are reported in Table  1. The assembled genome of strain F29 consisted of nine contigs, including four contigs of less than 5 000 bp, with an overall size of 2 345 565 bp and a G+C content of 58. 09 %. The longest contig was 889 860 bp, the N50 was 737 680 bp and the L50 was 2. CheckM estimated the genome to be 99. 18 % complete based on the presence of default single-copy marker genes (two markers were missing), with 1. 85 % contamination (five markers were duplicated) and a coding density of about 93. 3 %. Hybrid sequencing allowed the detection of two or more putative plasmid contigs, including one circular, observed with Bandage and confirmed by applying the viralVerify script to all contigs.
The first putative plasmid (p1) was 145 269 bp (contig 4) in size; the second one was a non-putative circularized plasmid (p2) of 69 636 bp in size (contig 5). In addition, contigs 6 (1354 bp), 7 (1341 bp), 8 (1177 bp) and 9 (788 bp) were smaller and then uncertain to be chromosomal or plasmidic. Contig 4, named p1, was relatively long and could not be circularized and it is therefore not possible to state with certainty that it is a plasmid. The median size of known prokaryotic plasmids is 53 212 bp according to the PLSDB database (https://ccb-microbe. cs. uni-saarland. de/plsdb/) but unknown within the class Thermodesulfobacteria, because no plasmids have been reported so far in bacteria of this class. p2, and possibly p1, are thus the first reported plasmids in the genus Thermosulfurimonas and possibly in the Thermodesulfobacteria class.
By comparing the six genomes of sulphur-disproportionating strains of marine hydrothermal vent origin, eight shared CDSs were found (https://doi. org/10. 6084/m9. figshare. 19669152). These eight CDSs code for: a cation efflux system protein, a MOSC domain protein (putative molybdenum cofactor sulphurase), a metallo-beta-lactamase family protein, an uncharacterized DUF1566 domain-containing protein, a molybdopterin oxidoreductase iron-sulphur subunit B, a molybdopterin oxidoreductase catalytic subunit A, a formate dehydrogenase subunit alpha and a putative phosphate-selective porin. The uncharacterized DUF1566 domain-containing protein, the metallo-beta-lactamase family protein and the putative phosphateselective porin were not associated with any clear and known function by InterProScan searching.
Acknowledgements We thank the captains and crews of the R/V Pourquoi pas? and the Nautile submersible team for their efficiency, as well as the chief scientists and scientific parties of the MoMARSAT 2019 [19]. We acknowledge especially  Françoise Lesongeur  for the sampling and conditioning of samples, and  Erwan Roussel  and  Xavier Phillippon  for gas and ionic chromatography analyses. The LABGeM (CEA/Genoscope  CNRS UMR8030 ), the  France Génomique and French Bioinformatics Institute  national infrastructures (funded as part of  Investissement d'Avenir program  managed by  Agence Nationale pour la Recherche , contracts  ANR-10-INBS-09  and  ANR-11-INBS-0013 ) are acknowledged for support within the MicroScope annotation platform.
Funding information This work was supported by the  French-Russian collaborative Project (CNRS/RFBR) Neptune  ( PRC Russie 2017 n°281295 ) to M. J. , by the  Sino-French IRP   1211 MicrobSea  to K. A. , by the  ISblue  project , Interdisciplinary graduate school for the blue planet  ( ANR-17-EURE-0015 ) and co-funded by a grant from the  French government  under the program " Investissements d'Avenir" (Theme 2 , project  DISMUT ) to K. A. The study was supported by grants from the  French Ministry of Higher Education and Research , and from the  Region Bretagne , to M. A and S. Y.
Environmental data. The study region includes Southern New England and The Gulf of Maine. We selected environmental covariates known to influence marine fish distributions and abundances. Depth, temperature (bottom and surface) and salinity (bottom and surface) were measured in situ during trawl surveys. Missing values were augmented with the data-assimilative HYbrid Coordinate Ocean Model (HYCOM) daily and then monthly data. HYCOM is an oceanographic model that produces 32 vertical layers including ocean temperature, salinity, sea surface height, and wind stress as well as other 3-and 4-dimensional variables. The system uses the Navy Coupled Ocean Data Assimilation (NCODA) system  19  for data assimilation. NCODA uses the model forecast as a first guess in a multivariate optimal interpolation (MVOI) scheme and assimilates available satellite altimeter observations (along track obtained via the Naval Oceanographic Office Altimeter Data Fusion Center satellite) and in situ sea surface temperature as well as available in situ vertical temperature and salinity profiles from expendable bathythermographs, Argo floats, and moored buoys  20.
Seven HYCOM models (HYCOM + NCODA Global 1/12° Reanalysis GLBu0. 08 Expts 19. 0, 19. 1, 90. 9, 91. 0, 91. 1, 91. 2) were temporally concatenated to create a continuous dataset of BT and salinity, ranging from 1992 to 2017. These model runs differed slightly in their configurations (time steps, advection scheme, mixing, vertical structure, slight change in NCODA, and MVOI transition to 3-dimensional analysis in 2013), but the differences are not expected to influence the applicability of the output  21. The numbers of filled in missing values were 787 (7. 0%) surface salinity (SSAL), 735 (6. 5%) surface temperature (SST), 809 (7. 2%) bottom temperature (BT), and 850 (7. 6%) bottom salinity (BSAL). Chl a was obtained from the MODIS satellite (monthly rasters from 2003 to 2019) on a monthly time step  22  , with missing values filled using the SeaWIFS satellite  23 (1998 to 2009).
Temperature, salinity and Chl a data that were not collected in situ were downloaded using Google Earth Engine (HYCOM and MODIS)  24. Benthic substrate (grain size in mm, referred to as SEDSIZE), subregion (Gulf of Maine or Southern New England), benthic land position (high, low, mid), and seabed form data (depression, high flat, high slope, low slope, GJAM. To study the influence of the environmental covariates on the joint distribution of marine fish and invertebrate species we use the generalized joint attribute model (GJAM)  12  and the corresponding R package (Version 2. 5)  26. Briefly, this multivariate Bayesian model allows us to jointly model the marine fish community and accounts for responses to the environment that can include combinations of continuous and discrete responses (e.
In the final model, sensitivity is highest for depth. Subregion remains as a two-level factor and there is strong inverse prediction for that variable as well (Fig. 1d ). In the variable-selection stage, each model was run on the entire fall dataset for 5000 iterations and a burn-in of 800. Inverse prediction results from the spring model indicated similar patterns; thus, the same variables were used for the spring and fall. We compare the model selected above using inverse prediction to a model selected using the more traditional method of out-of-sample prediction. For out-of-sample prediction, we fitted all combinations of 11 environmental variables (BT, BSAL, SST, SSAL, Chla, depth, sediment size, subregion, position, seabed form) plus interaction terms between depth and SEDSIZE, BT, BSAL, SST, SSAL and chlorophyll.