dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on these results, maintenance olaparib is approved in the USA, the EU, China, Japan and other countries worldwide for women with a BRCAm  [8] [9] [10] [11]. The phase III PAOLA-1/ENGOT-ov25 trial (NCT02477644) evaluated the addition of maintenance olaparib to bevacizumab in patients with newly diagnosed, advanced ovarian cancer who were candidates to receive bevacizumab in combination with first-line platinum-based chemotherapy and who were unselected by biomarker or surgical status  [12]. Maintenance olaparib plus bevacizumab provided a significant PFS benefit compared with placebo plus bevacizumab in the overall PAOLA-1 population (median PFS 22. 1 vs 16. 6 months; HR 0. 59; 95% CI 0. 49-0. 72; P < 0. 001). In prespecified subgroup analyses, the greatest PFS benefit occurred with olaparib plus bevacizumab versus placebo plus bevacizumab in patients who tested positive for homologous recombination deficiency (HRD; defined as a BRCAm and/or genomic instability) (median PFS 37.
Endpoints and assessments The primary efficacy endpoint in PAOLA-1 (investigator-assessed PFS by modified RECIST version 1. 1) has been reported previously  [12]. Tumor assessment scans (computed tomography or magnetic resonance imaging) were performed at baseline and then every 24 weeks (or at 12-week planned visits if there was evidence of disease progression) up to month 42 or until the date of data cutoff. This post hoc exploratory analysis evaluated investigator-assessed PFS in subgroups of patients considered higher risk and lower risk and according to biomarker profile. HRD positive was defined as a tumor BRCA mutation and/or a genomic instability score (GIS) of 42 or higher on the myChoice® HRD Plus assay (Myriad Genetic Laboratories, Inc. , Salt Lake City, UT, USA), HRD negative was defined as a GIS of less than 42 and HRD unknown was defined as an inconclusive, missing or failed test.
Statistical analysis As previously reported  [12] , PAOLA-1 was powered to detect differences in investigator-assessed PFS (modified RECIST version 1. 1) in the overall population. All efficacy data were summarized and analyzed in the intent-totreat (ITT) population (i. e. all randomized patients; full analysis set). Efficacy analyses used the electronic case report form data set, apart from the HRD analysis, for which Myriad data were used to determine HRD status. Safety data were summarized in the safety analysis set (i. e. all randomized patients who received at least one dose of study treatment). PFS was estimated using the Kaplan-Meier method. In the full analysis set, the stratified log-rank test assessed the difference between the treatment groups and the PFS HR and 95% CI were calculated using a stratified Cox proportional hazards model.
Comparisons between results in the higher-risk subgroup from PAOLA-1 and the PRIMA trial  [14]  should be made with caution given differences between the trials in study design (e. g. PAOLA-1 had an active comparator arm whereas PRIMA had a placebo comparator arm) and baseline characteristics. However, only randomized trials dedicated to evaluating the role of PARP inhibitors with or without bevacizumab can provide definitive answers. One such trial is due to start shortly (AGO-OVAR 28/ ENGOT-ov57; NCT05009082). Whereas maintenance niraparib provided a PFS benefit compared with placebo in HRD-negative patients in PRIMA  [14] , our data indicate that the addition of a PARP inhibitor to bevacizumab did not provide a benefit in lower-or higher-risk HRD-negative patients. It is worth noting that the PRIMA population is more likely to be enriched for platinum response, a known biomarker of PARP inhibitor sensitivity, than the PAOLA-1 population.
The masked alignment was used to generate a phylogenetic tree by FastTree program  50. Alpha and beta diversity matrices were generated through the q2-diversity plugin using the above-mentioned ASV feature table and rooted phylogenetic tree. De novo clustering of ASVs was performed with the cluster-features-de-novo command within vsearch plugin  47. Input features were collapsed at 97% identity, resulting in new OTU features that are clusters of the ASV features. Representative OTU sequences were then annotated with pre-trained Naive Bayes classi er trained on the Greengenes 13_8 99% OTU database using the feature-classi er plugin  51. The sequences used for training were trimmed to include only the V4 region. Taxonomic composition was summarized with the collapse method from the taxa plugin within Qiime2.
Unclassi ed taxonomic features, features absent in more than 90% of all samples, and features with low abundance (< 0. 01%) were all excluded from the analysis. Net average change scores and importance scores, which denote the correlation between input features and therapy stages, were exported and visualized in a volcano plot. Only features with net average change scores more than 0. 2% and importance scores within the rst tertile of distribution were retained. For time-course differential analysis, the R package maSigPro  54, 55  was used to nd taxonomic features with signi cant temporal changes and signi cant differences between experimental groups (e. g. , clinical response and CRS grade groups). Speci cally, the maSigPro algorithm de ned a generalized regressive model by dummy variables followed by two regression steps: the rst one selects features with non-at pro les by the least-squared technique and the second step creates best regression models for each feature by using stepwise regression to identify features with different pro les between experimental groups.
Acknowledgements This study was supported by the  National Natural Science Foundation of China  (grant No. 81730008 ,  81770201 ),  Key Project of Science and Technology Department of Zhejiang Province  (grant No. 2019C03016 ),  China Precision Medicine Initiative  ( 2016YFC0906300 ),  Research Center for Air Pollution and Health of Zhejiang University , and the  State Key Laboratory for Diagnosis and Treatment of Infectious Diseases , The  First A liated Hospital of Zhejiang University. We thank  Dr. David Bronson  and  Mrs. Judith Gunn Bronson  of  Stem Line Publishing Services, Inc. for their editing of this manuscript.
16, p = 0. 03; Table  3 ). This modification was not strong enough to be associated with a significant decrease in males with metabolic syndrome (data not shown). Similarly, the level of miR19b in blood plasma was negatively correlated with total-(r = -0. 19, p = 0. 01) and LDL-cholesterol (r = -0. 18, p = 0. 02). However, levels of miR34c and miR10b in blood plasma were not significantly correlated with neither anthropometric nor metabolic status. Interestingly, no correlation was observed between the blood miRNA and semen miRNA levels (data not shown). Table  1  Anthropometric. metabolic and spermatic characteristics of patients included in the study. Patients were allocated in two groups: the metabolic syndrome group (with MS) and the non-metabolic syndrome (no MS) group. P value corresponds to the difference between the metabolic and non-metabolic syndrome groups.
Acknowledgements The authors thank participants involved in Metasperme Collaboratif group *:  Nathalie Sermondade ,  Florence Eustache ,  Catherine Patrat ,  Myriam Benarroch ,  Isabelle Cedrin ,  Vanina de Larouzière ,  Emmanuelle Mathieu D' Argent ,  Angela Sutton  and  Jérôme Guechot. This study was supported by  Germethèque Biobank  ( France -Site of Tenon ), which provided samples. The authors thank the  Clinical Research Unit Pariscentre coordinator Christelle Auger.
ACKNOWLEDGMENTS We thank members of the  McLellan and Rey laboratories  for providing helpful comments on the manuscript;  F. Agou  from the  Chemogenomic and Biological Screening platform at Institut Pasteur ; the staff of the  Crystallography platform at Institut Pasteur ; the synchrotron beamlines PX2 at  SOLEIL (St. Aubin, France ), ID23-1 at the  ESRF (Grenoble, France) , and 19-ID at  Argonne  operated by  UChicago Argonne, LLC  for the  US Department of Energy (DOE) ,  Office of Biological and Environmental Research  under contract  DE-AC02-06CH11357. Funding: This work was supported by  National Institutes of Health  award  U19 AI142777  to J. S. M. , Z. A. B. , K. C. , and L. M. W. , as well as by  Institut Pasteur ,  CNRS  and grant  ANR-10-LABX-62-10 IBEID  to F. A. R. and by the  LabEx Ecofect  ( ANR-11-LABX-0048 ) of the " Université de Lyon ," within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR) , to F.
| Epidermal thickness measurement Frozen sections were stained with 1% solution of Tolonium chloride, Toluidine blue (TB) for 40 s and dehydrated with increasing concentration of ethanol before cover slipped. Slices were photographed with Nikon Eclipse Ci microscope at 4X magnification. Using ImageJ software, the mean epidermal thickness was determined by measuring the epidermal area divided by the epidermal length and was expressed in μm. This method excluded the possible bias of differences in tissue thickness between samples.
Statistical analysis All statistical analyses were carried out using GraphPad Prism V. 9. 0 (GraphPad Software, La Jolla, California, USA). P values were calculated using Student's t-tests or one-way analysis of variance. The survival periods to the humane end point were plotted using the Kaplan-Meier method with analysis for significance by the log-rank test. Significance was accepted when the p value was <0. 05. Supplementary information contains detailed method descriptions for antibody and viral vector generation, and in vitro, ex vivo, and in vivo characterization.
Acknowledgements We thank  Drs Mark Cragg  and  Stephen Beers  ( University of Southampton ) for their valuable advice and critical review of the manuscript. We thank  Huguette Schultz ,  Chantal Hoffmann ,  Eline Winter ,  Fadi Azar ,  Julie Hortelano ,  Dominique Villeval ,  Murielle Gantzer ,  Virginie Nourtier ,  Juliette Kempf ,  Mathilda Kovacek , and  Ann-Helen Fischer  for their technical contributions.
Data availability statement The datasets generated during and/or analyzed during this current study have been deposited or are available from the corresponding author on reasonable request. The assigned accession number for RNAseq data reported in this paper is GEO: GSE176052. Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
We first verified that anesthesia had no significant effect on the homeostasis of oyster commensal microbiota. To this end, we compared the microbiota of eight non-treated control oysters (NTC, i. e. , not anesthetized, not injected with ASW) and eight anesthetized control oysters (AC, i. e. , oysters kept dry for 12 h and anesthetized for 2 h). This comparison was performed at time 0 before oysters were injected with Cg-BigDef1 or sterile artificial seawater, used as a control. We then examined the effect of Cg-BigDef1 on oyster commensal microbiota by comparing the microbiota of eight oysters injected with Cg-BigDef1 or ASW (control) at three time points after injection (0, 6, and 24 h) (Figure  1A ). To compare microbiota composition over time and conditions, we generated a global dataset from a total of 7,320,778 raw reads obtained by Illumina MiSeq sequencing of the 64 oysters analyzed.
Establishment of a Collection of Culturable Bacteria from C. gigas Microbiota To further investigate the role of Cg-BigDefs in controlling the oyster microbiota, we built a collection of culturable bacterial strains representing 21 genera associated with healthy and diseased C. gigas  [30, 31]. Among them, we included genera repeatedly associated with oyster diseases, such as Arcobacter, Aeromonas, Marinomonas, Marinobacterium, Pseudoalteromonas, Psychrobacter, Sulfitobacter, Tenacibaculum, and Vibrio  [21, 30, 32, 33]. We obtained 16S rDNA sequences (V3-V4 loop) for 46 bacteria isolated from oysters with known health status (healthy or diseased). In addition, we purchased three type-strains of the genera of interest that were needed as a reference for the MALDI database (one Pseudoalteromonas and two Alteromonas).
All 16S sequences exhibited ≥95% identity with a known type-strain sequence included in the analysis (Figure  2 ). For 41 strains of the 50 strains in the collection, we acquired molecular mass fingerprints by MALDI Biotyping. Strains with taxonomic assignation by 16S phylogeny but no match in MALDI databases were used to enrich the MALDI databases of marine bacteria https://doi. org/10. 12770/261d7864-a44c-43ab-b0c6-57fdaf7360ac (accessed on 14 October 2022).
Gene Cloning and Chemical Synthesis of Cg-BigDef1 and Cg-BigDef5 To explore the impact of Cg-BigDefs sequence diversification on the control of oyster microbiota, we focused on Cg-BigDef1 and Cg-BigDef5, which belong to two phylogenetically distinct groups within this peptide family, as shown previously in  [24]. We first cloned the gene encoding Cg-BigDef5 (Cg-bigdef5 gene; GenBank: OP191676). Two distinct exons were found to encode the two putative domains of the molecule (Figure  3A ), as previously found in Cg-bigdef1  [27]. The first exon of Cg-bigdef5 encodes the predicted signal peptide (or predomain, 23 residues), the prodomain (13 residues), and the N-terminal domain of the mature Cg-BigDef5 (42 residues). The second exon encodes a short linker (3 residues) and the C-terminal β-defensin-like domain (42 residues), with the canonical spacing of cysteines for big defensins [Cys-Xaa(4-14)-Cys-Xaa(3)-Cys-Xaa(13-14)-Cys-Xaa(4-7)-Cys-Cys] [27] (Figure  3B ).
Oysters Oysters with limited genetic diversity were obtained as follows. Genitor oysters were collected in 2015 from the Le Dellec area in Brest bay, which is devoid of shellfish farming. The first generation of full siblings was produced, named F14, as described in  [21]. From this family, two oysters were used to generate a second generation of full siblings, referred to as F14V (Decicomp project ANR-19-CE20-004). Offspring were kept at the Ifremer hatchery in Argenton (France) up to day 40. Then, they were grown at the Ifremer station in Bouin (France) until they were 10 months old.
Bacterial Strains and Culture Conditions Strains and media are listed in the Ifremer Sextant catalog at https://doi. org/10. 12770/0d529567-92fd-4dcd-9d9c-70e98ab6f772 (accessed on 14 October 2022). Isolation of bacteria from oyster flesh and antibacterial assays were performed in Zobell medium at 20 °C. Zobell medium is composed of artificial seawater (ASW)  [44]  supplemented with 0. 4% bactopeptone and 10% yeast extract, pH 7. 8. Bacteria were isolated from the flesh of live oysters affected by the Pacific Oyster Mortality Syndrome (susceptible families F11, F14, and F15 from the Decipher project ANR-14-CE19-0023)  [21]. Additional bacteria isolated from diseased oysters were provided by the French National Reference Laboratory (Ifremer, La Tremblade, France). Finally, bacteria isolated from healthy commercial or wild oysters were included.
Molecular Phylogeny Based on 16S RNA Taxonomic assignment down to the genus was performed for each strain by molecular phylogeny based on the sequence of the V3-V4 region of 16S rRNA obtained by Sanger sequencing. In order to consolidate the phylogenetic tree, at least one Genbank reference sequence corresponding to a Type (T) strain per genus of interest was added from the NCBI database (Table  S4 ). The 95 sequences were trimmed at 405 bp (V3-V4 loop) using BioEdit and aligned using ClustalW. The phylogenetic tree was constructed by the Maximum Likelihood method with the Kimura 2-parameter model  [45]  using MEGA X software  [46]  and annotated using ITOL software  [47]. The branches are supported by the bootstrap method with 500 iterations.
One microlitre of each extract was deposited three times (technical replicates) on a MALDI target (Bruker Daltonics, Bremen, Germany), air-dried, and coated with 1 µL of fresh alpha-cyano-4-hydroxycinnamic acid matrix in a saturating amount in a solution of 50% ACN and 2. 5% TFA (Bruker Daltonics, Bremen, Germany). The MALDI MS spectra of these spots were acquired with an Autoflex III Smartbeam MALDI-TOF MS, recording masses ranging from 2,000 to 20,000 Da using standard parameters (flexControl 3. 4, Bruker Daltonic, Bremen, Germany), and interrogated against the existing databases. For bacteria species not present in the existing MALDI Biotyper ® reference mass spectra libraries, a reference spectrum was created and entered in our local database, as follows: the bacterial extract was spotted 8 times, each spot analyzed three times, for a total of twenty-four recorded spectra per bacterial strain.
After manual checking, the twenty better spectra were transformed into an average spectrum by the MBT Compass Explorer software. The BTS (bacterial test standard) serves as a calibrator and contains Escherichia coli extract. The reference libraries used for the analysis are the official Bruker MALDI Biotyper ® spectral library (MBT reference library https://www. bruker. com/en/products-and-solutions/microbiology-and-diagnostics/microbial-identification/maldi-biotyper-library-ruo. html; accessed on 10 October 2022) and the freely available EnviBase exclusively dedicated to the identification of potentially pathogenic Vibrio in marine mollusks (seanoe. org) [48].
Molecular Cloning and Sequence Data Analysis The Cg-BigDef5 gene was PCR-amplified using specific primers (Fw: 5′-AATCAAGTCAACATGAACAG-3′; Rv: 5′-TTATCCTAGATTTCTAGGTC-3′) based on a transcript sequence previously found in publicly available databases  [24] , cloned into a pGEM-T Easy vector (Promega) and then sequenced using the Sanger dideoxy methodology (Applied Biosystems 3500 Series Genetic Analyzer). Exon-intron boundaries were defined by the alignment of cDNA and genomic sequences. Nucleotide sequences were manually inspected and translated using the ExPASy Translate Tool http://web. expasy. org/translate/ (accessed on 1 September 2022). Prediction of signal peptides and other posttranslational processing was carried out using the ProP 1. 0 server https://services. healthtech. dtu. dk/service.
Peptide Synthesis and Net Charge Calculation Cg-BigDef1 was synthesized as already described, using a combination of solidphase chemical synthesis and native chemical ligation (NCL) followed by a thermodynamically controlled oxidative folding step  [26, 27, 49]. Cg-BigDef5 was obtained following a similar synthetic scheme (see Supplemental Figure  S5  and  [34]  for optimization and details, as well as for 3D structure determination by NMR). Peptide net charges at pH = 7. 4 (oyster physiological pH) were predicted using the IPCprotein pKa dataset  [50]. Cg-BigDef1 net charge = +6. 05 (6. 00 negative charges and 12. 05 positive charges, calculated taking into account 12 Arg, 4 Asp, 2 Glu, 3 His, and 8 Tyr). Cg-BigDef5 net charge = +6. 95 (4. 00 negative charges and 10. 95 positive charges, calculated taking into account 7 Arg, 3 Asp, 1 Glu, 2 His, 3 Lys, 8 Tyr, and the N-terminal amine group).
16S rRNA Metabarcoding Bacterial metabarcoding was performed using 16S rRNA gene amplicon sequencing. Libraries were generated using the Illumina two-step PCR protocol targeting the V3-V4 region (341F: 5′-CCTACGGGNGGCWGCAG-3′; 805R: 5′-GACTACHVGGG-TATCTAATCC-3′)  [52]. Sequencing was performed at the Bioenvironment Platform (University of Perpignan). A total of 64 libraries were paired-end sequenced with a 2 × 250 bp read length on a MiSeq system (Illumina) according to the manufacturer's protocol. Sequencing data were processed using the SAMBA pipeline v3. 0. 1. The SAMBA workflow, developed by the SeBiMER (Ifremer's Bioinformatics Core Facility), is an opensource modular workflow to process eDNA metabarcoding data. SAMBA is developed using the NextFlow workflow manager  [53]. All bioinformatics processes are mainly based on the use of the next-generation microbiome bioinformatics platform QIIME 2  [54]  (version 2020.
2) and the approach of grouping sequences in ASV (Amplicon Sequence Variants) using DADA2 v1. 14,  [55] ). Taxonomic assignment of ASVs was performed using a Bayesian classifier trained with the Silva database v. 138 using the QIIME feature classifier  [56]. Statistical analyses were also performed with R (R Core Team, 2020) using the R packages Phyloseq v1. 38. 0  [57]  and Vegan v2. 6-2  [53]. For α-diversity, we used the full data set to analyze differences in regularity (calculated as H/ln (S), where H is the Shannon-Wiener index, and S is species richness) and species richness (total number of species) using SAMBA pipeline and ANOVA. For β-diversity, the ASV matrix of all 64 libraries was preliminarily normalized. Briefly, after verification of the rarefaction curves produced with the ggrare function  [57] ), libraries were sub-sampled to 45,361 reads using the rarefy_even_depth function.
We used the STAMPS software  [59]  to represent an extended error bar. Statistical differences were assessed by Welch's t-test with the Benjamini-Hochberg procedure, which controls the false discovery rate (FDR). Finally, we used DESeq2 v1. 36. 0  [60]  to identify ASVs whose abundance significantly varies in oysters injected with Cg-BigDef1 or ASW (control) for the last kinetic point (i. e. , T24). Differential abundance was analyzed using a negative binomial method implemented in the DESeq2 package as recommended by  [57]. For this latter analysis, we only considered ASVs with an adjusted p value < 0. 01. Note that ASVs lacking genera annotation and qualified as "unknown" were not considered for result interpretation.
A melting temperature curve of the amplicon was then performed to verify the specificity of the amplification. Relative quantification of 16S bacterial DNA copies was calculated by the 2 -ΔΔCq method  [61]  using the mean of the measured cycle threshold values of a reference gene (Cg-EF1α (elongation factor 1α), GenBank: AB122066), as a calibrator. Data Availability Statement: MALDI spectra, 16S sequences and amplicon sequences for microbiota analysis have been made available on the Ifremer Sextant catalog https://doi. org/10. 12770/261d7864-a44c-43ab-b0c6-57fdaf7360ac (accessed on 14 October 2022) ; https://doi. org/10. 12770/0d529567-92fd-4dcd-9d9c-70e98ab6f772 (accessed on 14 October 2022. Raw sequence data of 16S sequencing for Metabarcoding analysis are available on on the Ifremer Sextant catalog https://doi.
Acknowledgments: We thank  Céline Garcia  from  Ifremer ,  EU Reference Laboratory for mollusk diseases (La Tremblade, France ),  Céline Cosseau  (IHPE) as well as  Yannick Gueguen  ( MARBEC, Sète ) and  Julien de Lorgeril  ( Ifremer, LEAD and ENTROPIE, New Caledonia ) for providing oyster bacterial isolates. We thank  Jean-François Allienne  ( plateforme Bioenvironnement, University of Perpignan Via Domitia ) for his help in building the 16S metabarcoding library and next-generation sequencing. We also warmly thank  Cyril Noël  from the  SEBIMER bioinformatics platform (Ifremer)  for his precious help in processing and analyzing metabarcoding data. We thank  Eve Toulza ,  Léo Duperret  (IHPE) and  Carolane Giraud  ( IHPE, LEAD-ENTROPIE ) for the fruitful discussions throughout this project, as well as  Jamal Saad (IHPE)  for critical reading of the manuscript.