dataesr/scientific-paragraphs-categorization huggingface dataset:
The samples were analyzed on 7900HT Fast Real-Time PCR System (Applied Biosystems) using universal cycling conditions as written above. Cas9 expression was quantified using a relative standard curve method by normalization to the endogenous control cytochrome oxidase (COX), while CathB and Arf10 expressions were normalized to the endogenous controls COX and elongation factor 1 (EF-1) as described previously  [35, 46]. As a control, we determined baseline CathB and Arf10 expressions in nontransgenic (NT) plants, which we presented as an average of expression in ten plants (Data S7). Relative CathB and Arf10 abundance in transgenic lines was normalised to the averaged expression in NT.
Bioinformatics analysis of miRNA precursors Potato mature miRNA and precursor sequences for MIR160a and MIR160b were obtained from miRBase (https://www. mirbase. org/; Accession No. MI0025955, MI0025956) and for MIR390a from the study of Križnik et al. [27]. The secondary structures and minimal folding free energies (MFEs) values of wild-type and mutated pre-miRNAs were predicted using RNAfold (Vienna RNA package v2. 4. 18) with default settings  [31]. MFEI values were calculated as described by  [61]. Based on RNAfold results the secondary structures were redrawn and miRNA/miRNA * duplex regions were highlighted using RNA Folding/Annotation tool of The Small RNA Workbench v4. 5  [50].
Acknowledgements We thank  Prof. Holger Puchta  ( Botanical Institute II, Karlsruhe Institute of Technology, Karlsruhe, Germany ) for providing the pDeCas9 backbone,  Prof. Blanca San Segundo  ( CRAG, Barcelona ) for advice regarding sgRNA design and  Mathilde Merrer ,  Marie-Paule Kermarrec ,  Lidija Matičič ,  Nastja Marondini ,  Brina Dragar ,  Nina Kobe  and  Barbara Jaklič  for technical assistance. We acknowledge the  BrACySol BRC (INRA Ploudaniel, France)  that provided us with the cv. Désirée that was used for protoplasting. Potato plants for stable transformation were obtained from the  Institute of Plant Breeding  and  Acclimatisation-National Research Institute, Młochów, Poland  (cv. Rywal) and from the  Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle (Saale) ,  Germany  (cv.
Désirée). This research was financially supported by the  Slovenian Research Agency  (research core funding no. P4-0165  and projects  J4-2544 ), the  Investissement d'Avenir program  of the  French National Agency of Research  for the project  GENIUS  ( ANR-11-BTBR-0001_GENIUS ), the  Institut Carnot Plant2Pro program  for the project  POTATOCRISP  and  COST Action PlantEd  ( CA18111 ).
Data Availability Sequences and secondary structures of mutated pre-miRNAs were deposited to Zenodo (https://doi. org/ 10. 5281/zenodo. 5727015; [36] ). Destination plasmids with inserts and their maps were deposited to Addgene (Addgene ID): pDe_Cas9_KANA_R160a (173756), pDe_ Cas9_KANA_R160b (173757), pDe_Cas9_KANA_R390a (173758). The sRNA sequencing data from three cr-MIR160a lines (i. e. line 3, line 6, line 11) and three cr-MIR390 lines (i. e. line 2, line 6, line 7) were deposited to the Sequence Read Archive (SRA) at the NCBI database (accession number PRJNA783147).
Introduction Metastatic castration-resistant prostate cancer (mCRPC) is a molecularly heterogeneous disease, with 20% to 30% of patients harboring deleterious alterations in DNA damage repair genes, including those with direct or indirect roles in homologous recombination repair (HRR; refs. [1] [2] [3] [4]. The PROfound study (NCT02987543) is the first phase III, randomized, multicenter trial to show that a PARP inhibitor, olaparib, improves radiographic progression-free survival and overall survival compared with control [physician's choice of next-generation hormonal agent (NHA) enzalutamide or abiraterone] in patients with mCRPC with alterations in genes involved directly or indirectly in HRR and disease progression on prior NHA  (1, 5). It is the largest PARP inhibitor study to date  (6)  to conduct central, prospective tissue next-generation sequencing (NGS) to screen patients to determine eligibility for enrollment.
Study design The PROfound study (NCT02987543) is a phase III, prospective, randomized, open-label, multicenter, global trial. The study comprises two cohorts: cohort A includes patients with alterations in BRCA1, BRCA2, or ATM (assigned regardless of any co-occurring alteration in other genes), and cohort B includes patients with alterations in 12 other HRR genes. Additional details of the study design can be found in the primary publication  (1). This trial was performed in accordance with the principles of the Declaration of Helsinki, Good Clinical Practice guidelines, and the AstraZeneca policies on bioethics and was approved by an Institutional Review Board or ethics committee in the investigational sites. All patients provided written informed consent.
Translational Relevance Precision medicine with PARP inhibitors is now available for men with metastatic castration-resistant prostate cancer harboring homologous recombination repair gene alterations. Genomic sequencing to identify actionable mutations face different challenges in different cancers. The PROfound phase III trial (NCT02987543) molecularly prescreened patients with metastatic castration-resistant prostate cancer for eligibility, and to date, constitutes the largest dataset of prostate cancer samples sequenced in real time for the purposes of determining eligibility for participating in a prospective clinical trial. In PROfound, 69% of patients had a next-generation sequencing result. Evaluation of sample characteristics highlighted that the availability of a high-quality tumor tissue sample is key to obtaining an next-generation sequencing result.
This included advice on collecting multiple core needle biopsies (CNBs), creating blocks for cytology specimens using enrichment methods to maximize yield, minimizing tissue depletion of the blocks for initial diagnosis, guidance on using an 18-gauge needle and CT for lymph node and soft tissue, guidance on using an 11-to 14-gauge bone-cutting needle (with or without a coaxial trocar) for bone samples, optimal sample preservation methods, and preventing cross-contamination between samples. To evaluate whether this focus on education led to an improvement in generation of NGS results, data were analyzed for samples received for testing at FMI prior to and after October 1, 2017, at both sample and patient level.
Statistical analysis and modeling Statistical analyses were performed using SAS version 9. 4. For data on rates of NGS result generation, 95% confidence intervals (CIs) were obtained using the Clopper-Pearson method. For individual factors for which P values are reported, x 2 and Wilcoxon and Kruskal-Wallis rank-sum tests were applied as appropriate. To evaluate the predictive power of the sample characteristics on obtaining an NGS result, a multivariate logistic regression model was generated. For the modeling analysis, the characteristics considered were sample type, organ site, collection method, sample age, total tissue volume, tumor content (nuclei), and DNA yield. The model was built, using data from the PROfound study, through selection of any significant two-way interactions of sample characteristics, as well as purposeful selection of single characteristics using available information from the tissue samples.
Assessment of other clinical factors Although DNA yield and percentage tumor nuclei were higher for samples that had an NGS result, when evaluated at organ level, it was noted that some metastatic samples (e. g. , liver and lung) had less overlap in the range of percentage tumor nuclei and DNA yields for samples yielding an NGS result over samples where an NGS result was not obtained (data not shown), suggesting that other clinical factors (e. g. , tumor grade and Gleason score) may potentially influence generation of a test result. To investigate further, another model evaluated available data including primary tumor grade information and Gleason score as additional factors (Supplementary Fig. S6 ). These data were primarily available for patients randomized in the PROfound study; therefore, the dataset was biased to a population of patients with samples yielding NGS results (with a select few who did not have an initial result) and should thus be interpreted with caution.
Acknowledgments This study was funded by  AstraZeneca  and is part of an alliance between  AstraZeneca  and  Merck Sharp & Dohme Corp , a subsidiary of  Merck & Co. , Inc. , Kenilworth , NJ. Medical writing assistance was provided by  Juliet Fawcett ,  PhD  for Mudskipper Business Ltd. , funded by  AstraZeneca  and  Merck Sharp & Dohme Corp. , a subsidiary of  Merck & Co. , Inc. , Kenilworth, NJ. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U. S. C. Section 1734 solely to indicate this fact. Received November 4, 2021; revised December 21, 2021; accepted January 26, 2022; published first January 28, 2022.
For in vitro aerosol-cell exposures, using the VITROCELL Cloud 12 system, the aerosol cloud was allowed to settle for 20min before removing cells. When necessary, nebulized Ab solutions were filtered on a 0. 45µm syringe filter (Millipore, France) prior to further analysis. Nebulizers were washed extensively between each nebulization session using 0. 22µm autoclaved water and PBS. Subsequently, nebulized PBS was analyzed by flow cell microscopy and the nebulizer was considered cleaned and operating for Ab nebulization when the nebulized PBS samples contained less than 300 particles/mL. To rule out that cell activation may be attributable to contamination released during nebulization, the activation potency of nebulized PBS (vehicle solution) was also investigated in vitro. Our analysis revealed that nebulized PBS had no impact on MoDC activation (data not shown).
Dynamic Light Scattering (DLS) analysis of antibodies Native and nebulized Ab were analyzed by dynamic light scattering with a Dynapro Nanostar® (Wyatt Technology, USA) after appropriate dilution to 100 μg/ml in PBS1X1X in UVette (Eppendorf, France). For each sample, the acquisition was performed 10 times for 7 seconds each, at a temperature of 25°C with a detection angle of 90°. Particle diameter -(nm) and polydispersion index (pdi) were analyzed using DynamicsTM software (Wyatt Technology, USA). Samples with less than 70% of successful analyses were con-sidered multimodal and non-analyzable as recommended by the manufacturer (depicted as n/a).
Flow cell microscopy (FCM) of antibodies Native and nebulized Ab were analyzed by flow cell microscopy with an Occhio® FC200S+ (Occhio, Belgium) after appropriate dilution to 100 μg/ml in medium in the flowcell. Briefly, 250 µL of each antibody solution passed continuously in the flow cell, where particles were automatically detected, sized and counted by a camera. Particle counting, size assessment and distribution were analyzed using CallistoTM software (Occhio, France).
Analysis of MoDC activation by flow cytometry After 18 hours of stimulation, MoDC were harvested and saturated in PBS1X supplemented with 2% of FCS, 2 mM EDTA and 1X of human Fc-Block (Becton Dickinson, France) for 15 min at 4°C. Cells were washed and stained in FACS buffer (PBS1X supplemented with 2% FCS and 2 mM EDTA) with antibodies described in Supplementary table 2, for 20min at 4°C in the dark. Negative FMO controls were generated for each parameter analyzed. All antibodies were from Biolegend (UK). Data acquisition was made on the 8-colors MACSQuant flow cytometer (Miltenyi Biotec, Germany) on a minimum of 10,000 living cells. MoDC analysis was performed using VenturiOne software (Applied Cytometry, USA). Analysis was made on singlet (FSC-A / FSC-H gating) and CD45+ live cells (negative for LiveDead staining).
Cells were isolated through a 100µm cell strainer and purified with a 20% Percoll (GE Healthcare, France) density gradient. Cell preparation was centrifuged at 400 g for 10 min at 4°C, and the pellet was resuspended in FACS buffer, and counted in a hemocytometer chamber. Immune cell phenotyping by flow cytometry BALF, lung and spleen cells isolated as described above were saturated in PBS1X supplemented with 2% of FCS, 2 mM EDTA and 1X of human Fc-Block (BectonDickinson, France) for 15 min at 4°C. Cells were washed and stained with antibodies described in Supplementary table 2 for 20min at 4°C in the dark. Data acquisition was made on the 8-colors MACSQuant flow cytometer (Miltenyi Biotec, Germany) on a minimum of 100,000 living cells. Analysis was performed using VenturiOne software (Applied Cytometry, USA).
Analysis was made on singlet (FSC-A / FSC-H gating) and CD45+ live cells (negative for LiveDead staining). Immune cell phenotypes were determined as described in the Supplementary table 2. Data were normalized based on the mean concentration of respective native Ab conditions or expressed as a number of cell calculated as follow: % of the total CD45+ immune cell population x total number of cell. For Annexin-V/PI staining, data were normalized as follows: % of the positive population for nebulized Ab / % of the positive population for native Ab.
Statistical analysis Differences between experimental groups were determined using Kruskal-Wallis or t-test comparing two groups, using one or two-way analysis of variance (ANOVA) followed by Newman-Keuls or Bonferroni post-test (for comparison between more than two groups), after confirmation that the data were normally distributed. All statistical tests were performed with GraphPad Prism software (version 4. 03 for Windows, GraphPad Software Inc. , USA). All data are presented as mean ± standard error of the mean (SEM). The threshold for statistical significance was set to p<0. 05.
We observed that administration of nebulized mIgG2b-1 provoked a significant increase of both late apoptotic and necrotic spleen cells while lung and airway cells were suffering from necrosis. as compared to mice treated with native Ab (Figures  5A-5F ) or with nebulized mIgG2b-2 (Supplementary Figure  5 ). The type of cell death could also be determined by the analysis of mediators released in the environment. When comparing BALs from animals treated with native and nebulized mIgG2b-1, we did not observe any significant difference in the production of TNF, IL-6, IL-1b or CXCL1 (KC) (Figures  5G-J ). These data suggested that the cell contraction occurring after single or multiple airway administration of nebulized and aggregated IgG was associated with an inflammatory silent cell death process.
Life history traits related to asexual reproduction All analyses were performed using RStudio software (version 3. 6. 1 (2019-07-05)). Comparisons of the distributions of the age at the first reproduction between tumorous and control groups were performed by a two-sample Kolmogorov-Smirnov test. A Wilcoxon test was also performed to compare the medians of these two groups. To compare the traits related to asexual reproduction -(1) Budding rate, (2) the gap between two reproductive episodes, and (3) the number of buds produced per reproductive episode according to hydras status (control or tumorous) and the pathological status, generalized linear mixed-effects models were used (taking into account the non-normality of the variables). Random effects of the individual and bud detachment's date were added to correct the part of variability explained by the measurement of the same individual over time.
The fixed effects were the status of the hydras (control or tumorous) and their age. Model selection was based on the weight of the Akaike information criterion (AICc) and following the procedure described by  (Zuur et al. , 2009). The formulas of all different models constructed as well as the value of AICc's weight were obtained with the ''MuMIn'' package (Barto n, 2022) are summarized in Table  S1  and see Data S1 and Tables  S5  and S6  in for the details. All model residuals' distribution was checked with the ''DHARMa'' package  (Hartig, 2021)  which applies an over-dispersion, outlier, and Kolmogorov-Smirnov test. A Poisson distribution with a logarithmic link function was used in the models explaining the budding rate and the gap between two reproductive episodes because they correspond to count data.
ACKNOWLEDGMENTS We are grateful to Alexander Klimovich for providing us with hydras and his valuable advice that helped us during the follow-up of this study. We want to thank the two anonymous referees for their constructive comments that contributed to improving this article. This work was funded by the MAVA Foundation and by the following grants: ANR TRANSCAN (ANR-18-CE35-0009), ARC Linkage (LP170101105), and a CNRS International Associated Laboratory Grant. The APC was funded by the ANR TRANSCAN (ANR-18-CE35-0009). This work was supported by the international research project CANECEV.
Multilocus Sequence Analysis (MLSA) This method is based on sequence comparison of housekeeping genes in the bacterial genome and generated a robust and highly discriminatory super tree that has been used to infer phylogeny in the genus Bifidobacterium  [14]. MLSA was performed by concatenating fragments from seven conserved genes, i. e. , clpC, dnaB, dnaG, dnaJ1, purF, rpoC, and xfp. The homologous sequences of the target genes were obtained from 12 bifidobacterial strains' genomes using an in-house python script, concatenated and aligned with Clustal W  [15]. Phylogenetic relationships were inferred using Maximum Likelihood-based on the Kimura 2-parameters model  [16]. Bootstrap values of the tree were computed by resampling 100 times. The phylogenetic analysis was conducted in MEGA7  [17].
Genome Sequencing and Annotation The samples were sequenced at the Integrated Microbiome Resource (IMR, Dalhousie University, Halifax, NS, Canada)  [18]. The assembly was also conducted by IMR using SMRT analysis software. After assembling the whole genome, the location of protein-coding sequences, tRNA genes, rRNA genes, tmRNA genes, and CRISPR were predicted using Prokka v1. 12-beta  [19]. Prokka conducts analysis based on ISfinder, NCBI Background Reference Gene DB (with tbl2asn v25. 8 and blastp v2. 2 tool), UniProtKB DB (with blastp v2. 2 tool), and HMM DB (with hmmscan v3. 1 tool). The following tools were used to predict each functional element: Prodigal v2. 6  [20]  for CDS prediction, RNAmmer v1. 2  [21]  for rRNA prediction, Aragorn v1. 2  [22]  for tRNA and transfer-messenger RNA (tmRNA) prediction, MinCED (https://github.
com/ctSkennerton/minced; accessed on 13 June 2022) and CRISPRFinder  [23]  (last modified 9 May 2017) for Clustered regularly interspaced short palindromic repeats (CRISPRs) prediction and cmscan v1. 1  [24]  for sRNA prediction. The protein-coding genes were functionally annotated using an hmm search (HMMER v3. 3. 2, November 2020)  [25]  with HMMs profiles (for a total of 4983 profiles) of Bifidobacteriales (last modified on 23 March 2020) extracted from the eggNOG v5. 0. 0 database  [26]. We opted for HMMs profiles to obtain homologous sequences because of their sensitivity to detecting homology. The data were processed in a Python environment v3. 6. 7 through the platform Jupyter Notebook v6. 0. 3 using Pandas v1. 0. 4 and Numpy v1. 18. 5 packages. The Classes of Clusters of orthologous groups (COG) and Functional Categories assignment were performed to an E-value cutoff of 1E-5.
Enzymatic classification in B. pseudocatenulatum JCLA3 was predicted from B. pseudocatenulatum JCM 1200 deposited on the KEGG database (GenBank Assembly GCA_001025215. 1, T_number: T03932 and Org_code: bpsc). The assignment was BLAST All-versus-All based using Blastp v2. 8. 1 and an in-house python script. The data were processed in a Python environment v3. 6. 7 through the platform Jupyter Notebook v6. 0. 3 using Pandas v1. 0. 4 and Numpy v1. 18. 5 packages. The best hits were selected to an E-value cutoff of 1E-5 and an Identity percentage greater than 40%. In addition, the architecture between both sets of proteins was compared using the Pfam functional annotation. The Pfam domains were predicted using pfam_scan. pl script v1. 6. A circular plot was used to visualize CDS sequences and Enzyme classes.
The circular plot was performed using an in-house python script through the platform Jupyter Notebook v6. 0. 3 using functions of the Matplotlib v3. 0. 3 package. The putative operons for all CDSs were predicted using the Operon-Mapper tool  [27] , where default settings were used. GFF and FNA (complete genome in FASTA format) files were used. The data were processed through Jupyter Notebook v6. 0. 3 using Pandas v1. 0. 4 and Numpy v1. 18. 5 packages. A circular plot was used to visualize CDS sequences and Enzyme classes. The circular plot was performed as mentioned above. For the visualization of the genome, the following information was used: (i) the sequence of the complete genome in FASTA format (*. fna), and (ii) the genomic features in GFF (General Feature Format) format v3. 0. The Genome circular plot was performed as mentioned above.
Bifidobacteria are believed to play an important role in carbohydrate fermentation in the colon. They can indeed ferment various complex carbon sources, such as gastric mucin, xylo-oligosaccharides, (trans)-galactooligosaccharides, soybean oligosaccharides, malto-oligosaccharides, fructooligosaccharides, pectin, and other plant derived-oligosaccharides, although the ability to metabolize carbohydrates is species-and strain-dependent and via bifid-shunt such have been reported in Bifidobacterium NCC2705  [34]. B. pseudocatenulatum JCLA3 contains a variety of genes that code for different enzyme activities (Table  S2 ), such as glycosyl hydrolases, with the formation of acetate, lactate, ethanol, and even small amounts of succinate, as end products  [35] , which could reflect its adaptation to the human's gastrointestinal environment  [36].
Supplementary Materials: The following supporting information can be downloaded at: https://www. mdpi. com/article/10. 3390/microorganisms10112100/s1, Table  S1 : Bifidobacterium pseudocatenulatum encoded enzymes. Table  S2 : Bifidobacterium pseudocatenulatum's genes associated with carbohydrate metabolism, their respective enzymes, and the possible pathway in which they play a role according to the KEGG database.
The outcomes and best treatment strategies for germline BRCA1/2 mutation (gBRCAm) carriers with metastatic breast cancer (MBC) remain uncertain. We compared the overall survival and the first line progression free survival (PFS1) of patients with a gBRCAm identified at initiation of first-line treatment with those of BRCA wildtype (WT) and not-tested (NT) individuals in the ESME real-world database of MBC patients between 2008 and 2016 (NCT03275311). Among the 20 624 eligible patients, 325 had a gBRCAm, 1138 were WT and 19 161 NT. Compared with WT, gBRCAm carriers were younger, and had more aggressive diseases. At a median follow-up of 50. 5 months, median OS was 30. 6 (95%CI: 21. 9-34. 3), 35. 8 (95%CI: 32. 2-37. 8) and 39. 3 months (95% CI: 38. 3-40. 3) in the gBRCAm, WT and NT subgroups, respectively.
Baseline performance status was not included in the multivariable analysis since 50% of data were missing. For each variable, the hazard ratio (HR) was presented with 95% confidence intervals (CI). To avoid guarantee time bias related to oncogenetic testing, as well as to provide sensitivity analyses, two other approaches have been considered: (i) A Cox model with a time-varying variable (oncogenetic groups change over time) and (ii) analyses at different landmark time points (3-and 6-months after treatment initiation). For the latter, BRCA status was defined according to oncogenetic testing performed before the landmark time, and patients who progressed or were censored before the landmark time were excluded. All analyses were performed using Stata software, version 16 (StataCorp LLC, College Station, TX).