dataesr/scientific-paragraphs-categorization huggingface dataset:
Strong CYP1A2 and CYP2C19 Inhibition Fluvoxamine is listed in the FDA table of strong clinical index inhibitors for both CYP2C19 and CYP1A2 and is commonly used in prospective clinical DDI studies. Concurrently with this investigation, a PBPK model for fluvoxamine that investigated the CYP1A2 inhibition pathway alone was developed using similar data to this investigation  [1]. Accordingly, the final model structure and the fitted parameters for both model alternatives were very similar, and the two model alternatives lead to similar results for DDI prediction via CYP1A2. This clearly underlines the robustness of PBPK modeling as a method for extrapolation and prediction based on given datasets and a priori information. Notably, a K i of 10 nM for the strong inhibition of CYP1A2 by fluvoxamine in the model of Britz et al.
Acknowledgments: The authors would like to thank  Chantaratsamon Dansirikul  for her contribution during the conduction of this analysis as well as  Stephan Schaller  and  Pavel Balazki  from esqLABS for their support in the extensive model qualification effort and their thorough peer-review of this work. Medical writing and editorial assistance were provided by  Andy Shepherd  and  Mary Goodsell  of Elevate Scientific Solutions.
e. several contigs can belong to the same gene. These contigs represent 198 719 complete and partial gene sequences with 1 to 47 isoforms per gene. On average 74% of reads per sample align to the de novo reference transcriptome. The alignment of the 321 413 contigs to the 26 042 predicted peptide sequences (from the genome of the Pringle Laboratory) using BLASTx, identified 132 179 contigs (41%) specific to Ap in our de novo transcriptome, which were exclusively used in the remaining of the analyses. Among these contigs, 130 037 (98%) retrieve a match from the NCBI non-redundant database (e-value < 10 -3 and % similarity > 35), 84 369 (64%) were annotated with Gene Ontology (GO) terms, 17 192 with KEGG enzyme codes, and 61 980 with InterPro IDs. GO annotations for the whole transcriptome boiled down to 9 240 unique GO terms.
In addition, the contig sequence length in base pairs (bp), the score and similarity percentage of the alignment (e-value and similarity %, respectively) represent the level of confidence in the best gene identification (i. e. gene name) derived from the BLASTx to nr (from NCBI database) alignment analysis. The DECs highlighted here were filtered for a fold change greater or equal to 5 and lower or equal to -5, as well as a percentage of similarity greater or equal to 70%. The entire list of DECs is provided in Supplemental Table  3. Among the most regulated and well supported (i. e. showing high similarity) DECs (Table  2 ), several gene families including coagulation factor, lectin, NOD-like receptor (NLR), SR, interferon regulatory factor (IRF), and TNF were highly represented. The most highly regulated immune related DECs at 1 hour post-infection are the IRF1-like sequences (FC = 795.
The TIR-domain containing MyD88 homolog in Aiptasia (Ap_MyD88), is 254 times more regulated in infected anemones at 3 hours, and remains up-regulated by a fold change of 2 after 12 hours. Ap_TRAF6, an important signal mediator likely acting downstream of Ap_MyD88 is up-regulated 254. 7 times at 6 hours and continues at a lesser degree after 12 hours. This suggests that in Ap, the recruitment of MyD88 and downstream signaling to activation of NF-κB is induced by a PAMP other than LPS and must be triggered by a PRR other than a prototypical TLR or HyLRR-2 homolog. When searching further for genes potentially interacting with MyD88 via their TIR or Death domains, a DEC up-regulated at 6 and 12 hours annotated substance-P receptor-like (SPR) containing a TIR-2 and Ig domains actually matches a TLR-2 type-1-like gene (GenBank: KXJ09394) in the Ap genome (Figure  6 ).
The Ap_TNFR1 DEC (GenBank: KXJ17455) is up-regulated 223. 9 times after 1 hour, then again at a lower level after 3, 6 and 12 hours. Several TRAFs including TRAF2, -5 and the mitogen-activated protein kinase kinase kinase 14 (MAP3K14 or NIK) among other MAPK3 homologs, are potentially acting downstream of Ap_TNFR1 and are up-regulated at 6 and 12 hours. Interestingly, more support for the TNFR1 pathway is shown with the TNF-induced protein 3 (TNFAIP3 or A20) and the TNFAIP3-interacting protein 1 (TNIP1) homologs that are both up-regulated at 3, 6, 12 hours. TNFAIP3 is an essential component of the ubiquitin-editing protein complex that ensures the transient nature of inflammatory signaling pathways. While TNIP1 inhibits the NF-κB activation and TNF-induced NF-κB-dependent gene expression by regulating the activity of TNFAIP3.
Finally, the transcription factor AP-1 is up-regulated at each time point and may influence the immune response via activation of Ap_TNFR1. The other cytokine-dependent pathway involves the TNFR superfamily member 16-like (TNFRSF-mb16; GenBank: KXJ29589) homologs, which contain the Death domain and are up-regulated at 3, 6 and 12 hours. This Ap_TNFRSF-mb16 homolog resembles the Fas receptor (FasR or TNFRSF6 gene) which plays a central role upon Fas ligand (FasL) binding in the physiological regulation of programmed cell death in humans via its interaction with FADD and CASP8. In the coral A. digitifera , thirteen TNFR-like sequences containing the DD were identified and proposed as mediators of apoptosis through caspase activation  (Quistad and Traylor-Knowles 2016). Interestingly, we also identified candidates potentially acting up-and down-stream of FasR.
Sequencing data processing Raw sequences in fastq files were processed to remove Illumina adapters, low quality bases and short reads using the Trimmomatic-0. 36 tool  (Bolger et al. 2014). New reference transcriptomes were assembled de novo using all of the Aptasia pallida libraries produced here and either guided by the Ap genome produced by the Pringle laboratory (version 1. 1) or genome-free using Trinity v2. 6. 5 release  (Grabherr et al. 2011). Contiguous sequences or contigs were used to retrieve gene descriptions using blatsx against the non-redundant NCBI database with the following filter: E-value < 10 -3 , and only Blast description annotation excluding the terms 'unknown', 'hypothetical', or 'uncharacterized'. In addition, coding regions were predicted using TransDecoder and the predicted peptide sequences were used to retrieve further gene annotations from several different databases using blastp against Uniprot, Pfam, Swissprot and UniRef90.
HMMER and the Pfam database were also used to identify conserved protein domains present in our predicted peptide database. Depictions of domain architecture were based on conserved Domain Architecture Retrieval Tool (NCBI) results. Transmembrane domains were identified via the TMHMM (v. 2. 0) of the DTU Bioinformatics server. Contigs belonging to the anemone were isolated from the rest of sequences using blastx against the Ap predicted protein database produced by the Pringle laboratory  (Baumgarten et al. 2015). Transcript-level quantification was estimated for gene and isoform expression by allocating multi-mapping reads among all transcripts of the IDs, hits information, folds changes and statistical results is accessible in the Supplementary Table  3.
Acknowledgements We are grateful to  Dr. Emmanuel Lemichez  for providing the Vp strain and for his participation in early discussions on the experimental design. We are grateful to  Dr. John Pringle  for sharing the Aiptasia clone CC7 with us. We also thank  Mss. Alexia Verduci  and  Mr. Rodolphe Pontier-Bres  for their help in the laboratory, and the  Professors P. Rampal  and  D. Allemand  for reading and commenting on the manuscript. We thank  Eurofins Genomics  for the sequencing. D. D. was financially supported by a  Master II fellowship  funded by the  CSM  as part of a collaboration between the  Ecosystems and Immunity team (CSM)  and the  Université Côte d'Azur.
2. 4. -Genome assembly and annotation Sequenced reads were demultiplexed automatically by the Illumina® Miseq® sequencer using the CASAVA package (Illumina, San Diego, USA) and quality-filtered with Trimmomatic version 0. 30  [59]. High-quality adapter-free reads were assembled with SPAdes version 3. 9. 0 [60]  and contigs with size <500 bp or coverage lower 10x were removed from the assembly. Assembly metrics were calculated with Quast version 4. 6. 1  [61]. Contigs were checked for contamination and completeness using CheckM 1. 0. 9  [62]. Coding gene predictions were made with Prodigal version 2. 6  [63] , rRNA and tRNA genes were detected using Barrnap version 0. 8 and CRISPR regions were detected by Minced version 0. 2. 0. Coding gene annotation was performed with Prokka version 1. 12  [64]  using the following repositories: SwissProt (The UniProt Consortium, 2017), HAMAP  [65] , TIGRFAMs  [66]  and Pfam  [67].
Colour measurement Measurement was performed on Whaam! prior to and after cleaning with a Konica Minolta CM-2600d spectrophotometer, using a D65 light source, a 10° observer, collected in both specular components included (SCI) and excluded (SCE) modes. Measurement area was ∅ 8 mm and data were collected in the same locations as the gloss measurements (below), in triplicate. Values were averaged, and the standard deviation was calculated. The colour difference was determined using the CIE 2000 formula.
Energy-dispersive x-ray analysis (SEM/EDX) SEM/EDX elemental analysis was carried out on paint fragments and cross-sections taken from Whaam! and dried aqueous extracts from the painting surface using a LEO 1455VP scanning electron microscope (SEM) with INCA software, using back-scattered electron imaging (BSE), 20 kV, 15-mm working distance, and 100 Pa air pressure.
FTIR spectroscopy Transmission FTIR spectroscopy was carried out using a Thermo Scientific Nicolet iN10 MX microscope, using a single diamond cell. 64 scans were collected at a resolution of 4 cm -1 across a 4000 to 600 cm -1 range, and data were processed using Omnic 9 software. Discreet water-droplet extracts were also performed on Whaam! to characterise the deposited soiling layer, via pipetting 50 µL deionised water onto the paint surface (white alkyd, Blue Ben-Day dots, yellow, red and black Magna passages) for 5, 20 and 60 s, as determined by individual paint responses. After extraction, the water droplets were pipetted onto glass slides and the dried extracts analysed in transmission mode. ATR -FTIR (attenuated total reflectance) spectroscopy was carried out to explore cleaning system residues and changes to mock-up sample paint surfaces using a Thermo Scientific Nicolet iZ10 system and germanium ATR crystal.
64 scans were collected at a resolution of 4 cm -1 across a 4000 to 400 cm -1 range and data was processed using Omnic 9 software. Measurements were obtained in triplicate, and spectra were explored for characteristic absorption bands of the cleaning materials evaluated. Micro reflectance FTIR-2D imaging was carried out on the mock-up samples to explore the presence of cleaning system residues using a Cary 620-670 FTIR microscope, equipped with a Focal Plane Array 128 × 128 detector (Agilent Technologies) in reflectance mode, with an open aperture and a spectral resolution of resolution of 8 cm -1 for 128 scans, which was selected to improve the signal-to-noise ratio of the reflectance spectra. The background was acquired using a gold plate. Control unsoiled and soiled areas, as well as areas treated with different cleaning systems, were analysed in triplicate.
Acknowledgements The authors would like to acknowledge and thank the following people and institutions: all NANORESTART partners; Tate Conservation and Research Departments-in particular  Joyce Townsend ,  Luigi Galimberti ,  Pip Laurenson ,  Deborah Potter ,  Jane McCree  and  Annette King ;  Tate Curatorial Department , in particular  Matthew Gale ,  Gregor Muir  and  Frances Morris ;  Tate Photography Department ;  Tom Learner ,  Getty Conservation Institute US ;  Roy Lichtenstein Foundation , US;  Mark Golden ,  Golden Artist Colors US ;  Chris Stavroudis , private practice US;  Sandra Amann ,  Amman  and  Estabrook Conservation Associates, US ; the three peer reviewers and  Richard Brereton.
CONRAD resonance analysis and results The experimental datasets available in the EXFOR database of the prompt neutron multiplicity for 239 Pu, are those measured by Fréhaut  [8]  and Gwin  [9, 10]  with a given normalization. An accurate evaluation must include the (n,γf) channel for J π = 1 + resonances  [1] , while the current one only treats these resonances as decaying through immediate fission. For this purpose a new calculation has been performed using the R-Matrix code CONRAD. Total and partial cross sections, and neutron multiplicities have been analyzed together. The partial fission widths for immediate fission have been extracted from the JEFF-3. 3 evaluation, and the partial fission widths for the two-step fission reactions have been taken from the work of Trochon et al. : Γ γ f (J π = 0 + ) = 7.
Conclusions and perspectives A new evaluation of the prompt neutron multiplicities for 239 Pu(n,f) taking into account both, the "immediate" and the "two-step" contributions to the total fission cross section, is summarized in this paper. The CONRAD code has been used to perform a multiple analysis of the total and partial cross sections, as well as the total and prompt neutron multiplicities. According to the prompt neutron multiplicity, two sets of experimental data, measured by Fréhaut and Gwin, are provided at low energies in the EX-FOR database. A normalization factor must be assigned to each of the two data sets. The ν p experimental data have been fitted in order to calculate ν nγ f , ν 0 , ν 1 , and the normalization parameter. In order to perform the calculations, the resonance parameters for the immediate fission have been extracted from the JEFF-3.
The use DNA barcodes would greatly facilitate identification of aquatic oligochaete species and establishment of ecological diagnostics of sediments  5. The mitochondrial cytochrome c oxidase (COI) barcode was suggested for identification of aquatic and terrestrial oligochaetes  [6] [7] [8]. A 10% threshold of COI divergence has been considered appropriate for distinguishing aquatic oligochaete species  5, [9] [10] [11]. However, this threshold needs adjustment for some species  12. A reference database of COI sequences of aquatic oligochaetes based on the analysis of specimens collected in Switzerland is currently being developed  5. High-throughput sequencing allows the molecular analysis of a large number of samples at the same time and has been proposed as a cost-effective way to assess biodiversity in routine biomonitoring  [13] [14] [15].
21 proposed this methodology as replacement of Sanger sequencing for purposes of molecular barcoding (establishment of databases of molecular barcodes) or biodiversity monitoring, but it could be also suitable for assessing the biological quality of an ecosystem. This approach is more time consuming than the (e)DNA metabarcoding methods, but it optimizes the quality of ecological diagnoses by providing correct and precise data on species abundance. Such a high-throughput DNA barcoding method would only be practicable for routine analyses if the number of specimens sequenced per site could be limited to less than 100. Identification of 100 oligochaete specimens is required for the morphological method 2 ; however, only a part of the specimens can be identified morphologically to the species level.
The posterior region of each specimen was cut off transversally and transferred to a 1 ml tube containing 0. 03 ml absolute ethanol (1 tube per specimen). The anterior part of some specimens (12 to 24 per site) was preserved in absolute ethanol for further morphological identification, in case some of the preserved specimens corresponded to new lineages (=species), i. e. were unassigned using our Swiss COI reference database  5  or GenBank public database (NCBI). Specimen samples for genetic analysis were stored at -20 °C until DNA extraction. Before DNA extraction, samples were dried for 24 hours at room temperature. The workflow from fixation of specimens to calculation of genetic indices is shown in Fig. 1. DNA extraction, PCR amplification, library preparation and Illumina sequencing. Total genomic DNA was extracted from tissue samples using the guanidine thiocyanate method described by Tkach and Pawlowski (1999)  24.
The raw sequences are accessible in the Short Read Archive under the BioProject number PRJNA563268. The COI sequences of 313 bp corresponding to new lineages for our local reference COI database (one sequence per lineage) obtained as part of this study are provided in Supplementary File S1. In the near future, we will Sanger sequence a large segment of COI (658 bp) using universal primers  27  of the specimens corresponding to these lineages. We will deposit these sequences (658 bp) in the European Nucleotide Archive as part of a future publication on the update of our COI reference database. Analysis of sequences. Bioinformatic analyses were performed using an in-house pipeline (SLIM  28  ). Raw fastq reads were quality-filtered by removing any sequence with a mean quality score of 30 and removing all sequences with ambiguous bases or any mismatch in the tagged primer.
Paired-end reads were then assembled using simple bayesian algorithm implemented in PANDAseq  29. Chimera removing and the OTUs clustering at 97% were performed using VSEARCH  30. To assign the sequence corresponding to each tagged specimen to a specific lineage (or species), we considered that the sequences diverging by less than 10% (in COI) belonged to the same species, except for some species within the genera Nais and Uncinais (8%)  5. The obtained sequences were first taxonomically assigned based on our local COI reference database  5  using VSEARCH algorithms  30. Then, each unassigned sequence using our local reference database was taxonomically assigned based on GenBank database using BLAST (http://www. ncbi. nlm. nih. gov/BLAST/Blast. cgi). Sequences that could not be assigned using these databases were identified either through morphological analysis of corresponding anterior part or by building barcode trees.
Specimens taxonomically assigned using such trees were identified to the family or subfamily level. To construct these trees, the neighbour-joining method as implemented in Seaview v. 4. 4. 0 was applied  31  , with 1,000 bootstrap replicates. The genetic distances between our sequences and GenBank's sequences and between the sequences taxonomically assigned by building barcode trees were calculated using the K2P model in MEGA 5. 1  32. oligochaete indices. To analyse the genetic data, we applied the same index calculations (for streams and lake) as for the morphological analysis: For assessing the biological quality of fine/sandy sediments of streams we applied the IOBS index 2,3 calculated according to the following formula: = - IOBS 10ST 1 where S is the total number of taxa identified among 100 oligochaete specimens examined per sample, and T is the percentage of tubificids with or without hair setae that is dominant in the sediment sample (mature and immature worms combined).
Statistical analyses. Linear regressions between the IOBS index values, percentages of sensitive taxa (lake) and percentages of the families/subfamilies that were frequent in our samples (Tubificinae with hair setae, Tubificinae without hair setae, Naidinae, Pristininae, Lumbriculidae and Enchytraeidae) obtained using the morphological and genetic data were performed. For each relationship, we determined the coefficient of determination R 2 , the slope (a) of the linear regression line genetic (y)/morphology (x) (y = ax + b) and applied the Pearson test. These analyses were performed using the Free Statistics and Forecasting Software  34. Prior to statistical analysis, a log-linearization was applied to the IOBS data.
Results oligochaete diversity. In streams, about 1170 specimens were identified morphologically versus 693 genetically. In Lake Geneva, 700 specimens were identified morphologically versus 330 genetically. Almost all specimens (>99%) were successfully sequenced and assigned to taxa. In stream samples, 38 taxa were identified morphologically (16 Tubificinae, 6 Naidinae, 1 Pristininae, 4 Lumbriculidae, 9 Enchytraeidae and 2 Lumbricidae), while 63 lineages were identified genetically (30 Tubificinae, 7 Naidinae, 1 Prisininae, 3 Lumbriculidae, 20 Enchytraeidae and 2 Rhyacodrilinae) (Supplementary Tables  S2  and S3 ). Of these 63 lineages, 51 were identified using existing databases (45 with the Swiss database, 6 with GenBank), 4 by morphological analysis and 8 by constructing a barcode tree. In the lake, 24 taxa were identified morphologically (17 Tubificinae, 4 Naidinae and 3 Lumbriculidae), while 30 lineages were identified genetically (23 Tubificinae, 4 Naidinae, 2 Lumbriculidae and 1 Haplotaxidae) (Supplementary Tables  S4  and S5 ).
Of these 30 lineages, 24 were identified using the Swiss database, 5 by morphological analysis and 1 by constructing a barcode tree. Eighteen new lineages (i. e. unassigned in the Swiss database and GenBank) were found in stream and lake sediments. Most of these (11) were identified to the family/subfamily or genus level. While only about half the number of specimens were analysed genetically, more taxa were identified overall by the high-throughput DNA barcoding approach than by morphological analysis. This can be explained by the genetic detection of species which are very difficult or impossible to identify morphologically. For example, we were able to identify several cryptic species in Tubifex tubifex and Limnodrilus hoffmeisteri and some species in the family Enchytraeidae (Fridericia perrieri, Buchholzia appendiculata, etc.
A compromise should therefore be found between the applicability of this approach in routine (reasonable time and costs) and the imperative to ensure correct quality of the ecological diagnoses. In view of our study, the analysis of 33 specimens per site provides reliable assessments of sediment quality while being not too time consuming. It is important to continuously enrich the COI reference database in parallel to the development of the genetic indices to optimize the quality of ecological diagnoses. The local database and oligochaete inventories can be efficiently complemented by preserving the anterior parts of the analysed specimens and by morphologically identifying them once the specimens have been sequenced. In this study, out of a total of 18 new lineages detected, 9 were identified morphologically, among them 7 to the species level.
It is essential that the COI database contains sequences identified to the species level, if possible. Indeed, the ecology of lineages assigned to the family or subfamily level is often uncertain. For example, even if most taxa in Enchytraeidae and Lumbriculidae are classified as sensitive to pollution, these families include some resistant and polyphyletic taxa, for example Enchytraeus buchholzi (Enchytraeidae) and Lumbriculus variegatus (Lumbriculidae)  5, 35. In addition, new lineages of tubificids cannot be easily assigned to the groups with or without hair setae as these two groups are not monophyletic  5. In our dataset, most of the new lineages were identified to the subfamily or family level, but this had no impact on the results of environmental diagnoses as these new lineages were mainly found in streams and within other   1.