dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: The steering committee of the scientific interest group Obépine is composed of  Isabelle Bertrand  ( Laboratoire de Chimie Physique et Microbiologie pour les Matériaux et l'Environnement, LCPME, UMR 7564 CNRS-Université de Lorraine, Nancy, France ),  M. Boni  ( Institut de Recherche Biomédicale des Armées, 1 place Valérie André, F-91220 Brétigny-sur-Orge, France ),  Christophe Gantzer  ( Laboratoire de Chimie Physique et Microbiologie pour les Matériaux et l'Environnement, LCPME, UMR 7564 CNRS-Université de Lorraine, Nancy, France ),  Soizick Le Guyader  ( Ifremer Nantes, France ),  Yvon Maday  ( Institut Carnot Smiles ,  Sorbonne Université ,  Laboratoire Jacques-Louis Lions ,  UMR 7598  and  Institut Universitaire de France ),  Vincent Maréchal  ( Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, 75012, Paris, France ),  Jean-Marie Mouchel  ( Sorbonne Université, CNRS, EPHE, UMR 7619 Metis, Paris, France, e-LTER Zone Atelier Seine ),  Laurent Moulin  ( Laboratoire R&D Eau de Paris, France ),  Remy Teyssou  ( Val de Grâce, Unité de Virologie de l'IRBA.
Sampling Date GenBank Number NexClade/Pangolin Main SARS-CoV-2 Assignation for Each Sample Other SARS-CoV-2 Variants Other Variants Features: Signature Mutations Other Variants: Mutations Percentage (%) BA. 1/BA. 2 ORF1a:E91del 5. 6 E1 Nov-2021(1) ON832655 21J(Delta)/AY. 43 S:D796Y ORF7a:S44del 6. 9 5. 2 BA. 4 ORF1a:S142del 5. 1 E2 Nov-2021(2) ON832659 21J(Delta)/AY. 122 BA. 4 BA. 1/BA. 2 ORF1a:S142del S:D796Y 7. 6 5. 2 E3 Nov-2021(3) ON832662 21J(Delta)/AY. 125 BA. 1/BA. 2 S:T95I, K417N 99 95. 1 E4 Dec-2021(1) ON832661 21J(Delta)/B1. 617. 2 BA. 1/BA. 2 S:G484A 99. 4 E5 Dec-2021(2) ON832692 21J(Delta)/AY. 124 BA. 1/BA. 2 S:T95I G339D 97. 5 10 E6 Dec-2021(3) ON832691 21J(Delta)/AY. 43 BA. 4 BA. 1/BA. 2 ORF1a:S142del S:D796Y 6. 1 16. 3 E7 Dec-2021(4) ON832695 21J(Delta)/AY. 122 BA. 1/BA. 2 S:A67V 99.
Intracellular bacteria were defined as a single green (Alexa Fluor 488) fluorescent signal at least 4 pixels in size (equivalent to approximately 500 nm) on maximum intensity Z-projection, located within the limits of the actin cytoskeleton (phalloidin staining). If any signal bigger than 2 pixels was also observed in the red channel (Alexa Fluor 647), the bacteria was considered as double positive and thus extracellular. For each image, the percentage of AEC harboring intracellular bacteria as well as the number of intracellular bacteria per AEC were reported. All images were randomly and manually analyzed by 2 blinded readers using ImageJ  [54]  software. Maximum intensity Z-projection images used for analysis are available at https://omero. med. ualberta. ca/ webclient/?show=dataset-1152.
Automated analyses of confocal images (maximum intensity Z-projection) were also performed using a customized pipeline in Icy software  [55]  for counting of intracellular bacteria in eukaryotic cells (Icy analysis pipeline and script available as Supplementary methods and at https:// omero. med. ualberta. ca/webclient/?show=dataset-1152). Eukaryotic cell segmentation was performed by active contouring of the cell nuclei using the actin signal as boundary. Identification of intracellular bacteria was based on spot detection (scale 2 parameter ≅3 pixel-sized objects) analysis performed on both red (extracellular stain) and green (total stain) channels, followed by colocalization analysis of the detected spots. Spots detected in the green channel that were not colocalized with spots in the red channel were considered as intracellular bacteria, while those that colocalized in both channels were considered extracellular bacteria.
Statistical Analysis. Results are shown as mean ± SD unless stated otherwise. Statistical analyses between 2 or more categorical groups were performed using a 2-way ANOVA followed by Tukey's multiple comparisons test. Correlation analyses were done using the Pearson's correlation coefficient. A P value ≤0. 05 was considered statistically significant, * P < 0:05, * * P < 0:01, and * * * P < 0:001. All analyses were done using the GraphPad Prism 7. 0a (Graph-Pad Software, San Diego).
We therefore compared the effect of different "maintenance" tobramycin concentrations (100, 50, 25, and 12. 5 μg/mL) on the   For all experiments, BEAS-2B cells were infected with P. a. strain PAO1 at MOI 1, unless stated otherwise. After 4 h p. i. , extracellular bacteria were first killed with 100 μg/mL tobramycin × 15 min for all experiments. For (a) and (b), AEC cultures were then maintained in 100 μg/mL tobramycin for the remainder of the experiment, while the tobramycin maintenance concentrations ranged from 12. 5 to 100 μg/mL for (c) and (d) as indicated. The intracellular bacterial burden was measured by viable CFU count (detection limit shown as green dashed line), and cytotoxicity was measured by LDH release assay. Results shown are from data pooled from ≥4 biological replicates (≥2 independent experiments).
Acknowledgments This study was supported by research funding from the  Cystic Fibrosis Canada  ( 559985  to D. N) and the  US Cystic Fibrosis Foundation  ( NGUYEN21I0  to D. N), salary support from the  Fonds de Recherche du Quebec Santé (D. N) , and scholarships from the  Fonds de Recherche du Quebec Santé  (to K. J. M), the  Reseau en Santé Respiratoire  (to K. J. M and L. H), and the  Deutscher Akademischer Austauschdienst  (to L. H). We thank the  Immunophenotyping Platform at the Research Institute of the McGill University Health Centre  ( RI-MUHC ) for their assistance with the cell sorting. We thank  Sam Moskowitz  for providing the mCherry (pMKB1) plasmid.
Universal Illumina adapters were already included as primer overhangs. Sequencing library preparation and Paired-End sequencing (2 × 150 bp) were carried out using an Illumina Nextseq-System (Illumina, San Diego, CA, USA). Data analysis was done using R (R Core Team, 2020) with the packages DADA2  (Callahan et al. , 2016), phyloseq (McMurdie and Holmes, 2013) , Biostrings  (Pagès et al. , 2019)  and ggplot2  (Alboukadel, 2018). Phylogenetic assignments were computed using the SILVA database, release 132  (Quast et al. , 2012). Sequence reads are archived at GenBank under accession number PRJNA773502.
Structures of the bacterial communities The microbial community profiles were assessed by Illumina sequencing of 16S rRNA gene amplicons. Illumina data was generated from 66 samples taken along the flow paths. A total of 5,143,615 high-quality sequences were generated (on average 7. 9 × 10 4 sequences per sample), with Proteobacteria, Firmicutes, and Bacteroidetes being the dominant phylotypes (Fig. S3 ). Richness and alpha-diversity were similar for all but one sample based on values for Amplicon Sequence Variants (ASV = 2749,2 ± 1404,1, Table  S4 ) and Shannon index (H′ = 7,38 ± 0,70, Table  S5 ). These values were within the range previously reported for treatment wetlands receiving municipal wastewater  (Fu et al. , 2019; Guan et al. , 2015). The beta-diversity of the microbial communities along the flow paths were visualized by principal coordinate analysis (PCoA) of the Bray-Curtis dissimilarity index (Fig.
Acknowledgements The authors would like to thank  Katy Bernhard  for her support in operation and sampling of the treatment systems in Langenreichenbach,  Arif Ahmed ,  Angie Buchhold ,  Hanna Dambeck ,  Amelie Weber ,  Julia Nikolaus  and  Grit Weichert  for experimental support, and  Heinz Köser  for helpful AMR JPIs. Further funding was by The  Helmholtz Association of German Research Centers through its research program  " POF IV ", by a doctoral stipend of the  Henrich-Böll-Foundation  to CAK, and a  Georg Forster (HERMES)  Postdoctoral Fellowship of the  Alexander von Humboldt-Foundation  to OOA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DNA Extraction and Sequencing Methodology DNA was extracted from fresh samples (with no prior freezing/thawing step) using the Ultra-Deep Microbiome Prep kit (Molzym, Bremen, Germany) according to the manufacturer's instructions for tissues, then frozen to -80 • C. DNA was transferred to GenoScreen (Lille, France) for sequencing. DNA was prepared using the Kit Nextera XT (Illumina, San Diego, CA) with an input of 1 ng, and sequencing was performed on an Illumina HiSeq4000 with 150-base paired-end reads. Samples with a concentration > 0. 05 ng/µL were be prepared according to the standard library preparation protocol (with 12 cycles of amplification); those with concentrations < 0. 05 ng/µL were prepared according to the library preparation protocol with 20 cycles of amplification. The reads have been deposited as the Meta-prOSpEcT bioproject (PRJNA803614).
Bioinformatic Analyses For Illumina reads, the Trimmomatic v. 0. 36 package  (Bolger et al. , 2014)  was used to remove Illumina adapter sequences and to trim low-quality ends of reads at the beginning of any 10-base wide sliding window with an average Phred quality score < 30. Reads whose length was < 90 bases after trimming were discarded. Putative artifactual replicate reads were filtered out using a homemade script that retains the longest sequence among those with identical first hundred nucleotides (or all bases for fragments of 90-99 nt), in either forward or reverse reads. Any forward or reverse reads without their corresponding paired read were discarded. To control for human DNA contamination, we removed all reads assigned to Homo sapiens based on the CLARK v. 1. 2. 5  (Ounit et al.
, 2015)  classification (with parameter -m 0) against NCBI/RefSeq (  Kitts et al. , 2016)  reference human genome sequence assembly GRCh38. p7. The remaining reads were classified at the species levels using MetaPhlAn2  (Truong et al. , 2015)  with default settings and CLARK  (Ounit et al. , 2015)  (with parameters -m 0 -c 0. 8) against a collection of representative and reference prokaryotic genomes from the (NCBI/RefSeq database downloaded on 14 June 2018). In the final analysis, we considered bacteria at the species level with relative abundance higher than 1%. Reads corresponding to bacterial and archaeal 16S rRNA genes were first selected using USEARCH  (Edgar, 2010)  8. 1. 1861 (-usearch_global -id 0. 9 -query_cov 1 -top_hit_only -strand both) against the EzBioCloud 16S database  (Yoon et al.
, 2017)  (downloaded on 4 January 2018). The selected reads were then classified by mapping to the EzBioCloud 16S database with USEARCH software (-usearch_global -id 0. 95 -e-value 0. 00001strand both -top_hits_only -maxaccept 20). If a read had multiple best hits (identical scores), we retained the match to the organism with higher counts in the sample analyzed. Acquired antimicrobial resistance genes were searched for with ResFinder  (Zankari et al. , 2012)  v. 3. 2 (-mp blastn -t 0. 9 -l 0. 6) and the ResFinder database (as of 1 October 2019). Individual reads were mapped to the ResFinder database using USEARCH (-usearch_global -id 0. 90e-value 0. 001 mincols -100 -strand both -top_hits_only -maxaccept 20). Then, reads from samples of the same patient were pooled to increase the in silico sequencing depth and assembled using metaSPAdes  (Nurk et al.
, 2017) v. 3. 12. 0 (-k 21,33,55,77,99,127). To calculate genome coverage of a given species (species of interest), we used the QUAST  (Gurevich et al. , 2013)  5. 0. 2 "Genome fraction" result obtained by mapping contigs generated by metaSPAdes against the NCBI/RefSeq reference/representative genomic sequence of the species in question. For Sanger sequences, reads (forward and reverse) were assembled with Sequencher (v 5. 4) and the taxonomic assignment was achieved using BLASTn  (Altschul et al. , 1990)  on the nr/nt database with a percentage of homology greater than 99%.
Antibiotic Resistance Determinants A total of 99 antibiotic resistance genes were detected in 56 samples (from 24 patients). Details are available in Supplementary Table  2  and Supplementary Figure  3. The two most frequent ARGs were an aminoglycoside nucleotidyltransferase (aadA24, 16/99, 16. 2%) and an aminoglycoside phosphotransferase (aph3 -IIa, 14/99, 14. 1%). We aimed to assess whether there was a coverage threshold for a correct inference of antimicrobial susceptibility pattern from metagenomic data (Supplementary Table  2 ). For that, we selected 29 bacteria for which an antibiogram had been performed to connect with the antibiotic resistance determinants found in the metagenome. We were able to link 28 antibiotic resistance determinants to phenotypic traits (e. g. , the penicillin resistance gene blaZ to penicillin resistance in S.
Acknowledgements M. L. P. and A. G. contributed equally to this work. This work was supported by  Marie Sklodowska-Curie Research and Innovation Staff Exchanges (RISE)  under the grant agreement No  823989  " IONBIKE. "The financial support received from  CONICET  and  ANPCyT (Argentina)  is also gratefully acknowledged. Thanks to the  Flexible Electronic Department (FEL) of Ecole des Mines de Saint-Etienne (EMSE)  for the combined mechanical/electrical characterization.
Purpose: Older cancer patients are underrepresented in clinical trials. We aimed to understand barriers to clinical trial recruitment in women aged 70 years old (yo) or over with metastatic breast cancer (MBC). Methods: We used the national Epidemio-Strategy and Medical Economics MBC Data Platform, a French multi-center real life database. We selected MBC women over 70yo, without central nervous system metastases, with at least one line of systemic treatment, between January 1 st , 2008 and December 31 st , 2016, and had no other cancer in the 5 years before MBC. The primary objective was to evaluate the proportion of patients enrolled in clinical trials according to their age. Secondary objective was to identify variables associated with enrollment. Results: 5552 women were aged ≥ 70 (median 74yo; IQR72-77).
14611 were less than 70. Of the older ones, 239 (4%) were enrolled in a clinical trial during rst line of treatment, compared with 1529 (10. 5%) for younger ones. Multivariable analysis of variables predicting for enrollment during rst line of treatment in older patients were younger age (OR 0. 50 [95%CI; 0. 33-0. 76] for the 80-85yo class; OR 0. 17 [95%CI; 0. 06-0. 39] for the 85yo and more class), good ECOG Performance Status (PS 0-1) (OR 0. 15 [95%CI; 0. 08-0. 27] for the PS 2-4 class), HER2+ disease (OR 1. 78 [95%CI;), type of treatment (chemotherapy/targeted therapy/immunotherapy OR 5. 01 [95%CI; 3. 13-8. 18]), and period (OR 1. 65   [95%CI; for 2012-2016, compared to 2008-2011). Conclusions: In this large database, few older MBC patients were enrolled in a trial compared with younger ones.
Methods We used the national Epidemio-Strategy and Medical Economics (ESME) Metastatic Breast Cancer (MBC) Data Platform [18], a multi-center real life database using a retrospective data collection process in 18 French Cancer Centers. This database compiles data from Patient's Electronic medical records. Cases selected were adult patients with MBC whose rst metastasis was treated between January 1 st , 2008 and December 31 st , 2016. In compliance with the authorization delivered by the French Data Protection agency to R&D Unicancer (Registration ID 1704113 and authorization N°DE-2013. -117, NCT03275311), only aggregated statistical reports were provided. We selected women who were 70 years old or over at the time of MBC diagnosis, who received at least one line of systemic treatment and who had no other (non-breast) cancer in the 5 years before MBC, and lack of 'Central Nervous System' (CNS) metastases.
Statistical analysis Descriptive statistics were used to summarize patients' initial characteristics at diagnosis of metastatic disease. They were compared between groups using Chi-2's or Fisher's exact test for categorical data and Student T-test or non-parametric Wilcoxon's test for continuous data; a p value <0. 05 was considered statistically signi cant. The reverse Kaplan-Meier method was used to estimate the median follow-up duration, beginning at the date of diagnosis of metastatic disease. For the secondary endpoint (variables associated with inclusion in a clinical trial in older patients), in order to estimate the relative contribution of each variable, we used a multivariable logistic model. Variables included in the analysis were age at MBC diagnosis (continuous variable or in classes according to the age distribution in patients aged 70 and over), time from primary cancer to MBC diagnosis (de novo/recurrent), metastatic sites at diagnosis, phenotype (HER2+, HR+HER2-or TN), ECOG Performance status (0-1/2-4/not available), type of treatment (CT/TT/immunotherapy alone or with ET versus ET alone), years of MBC diagnosis  (2008-2011 versus 2012-2016)  and number in ESME database per center per year, also called "Center Cize" (<500; 500-800; >800 new patients per year).
Similarly, the relative survival of patients included and patients not included in a clinical trial was estimated using the mortality tables by year, age, sex and year of birth in France (loaded at the web site of The Human Mortality Database). A p-value of less than 0. 05 was considered statistically signi cant. All analyses were performed using the software R. (R Core Team (2017); the relative survival analysis was computed using Relsurv package of R.
This can be interpreted in two ways: one is that patients included in clinical trials have a better natural prognosis and are selected for trials because of their better general condition. The other could be that having access to innovative treatments may increase life expectancy among these patients. Our data do not allow to identify the respective weight of these two potential complementary factors linked to a longer observed survival, because of the retrospective setting, and lack of certain data (unavailable ECOG PS values in almost 50% and unavailable comorbidities, geriatric assessments and causes of death). Of note, a better overall survival when included in a clinical trial was also observed in the Korean MBC Database in a general MBC population  [21]. Unfortunately, as mentioned, no geriatric data was available to better characterize older patients, such as cognitive status, depression, or functional autonomy, which could highly change the probability of enrollment in a clinical trial.
The number of referrals, patient's eligibility and nally numbers of patients enrolled were not available in this database preventing from any conclusions regarding patient's selection. Finally, due to its retrospective nature, data regarding study type (chemotherapy, supportive care or radiotherapy for example) or study phase, was lacking in up to 40% in chemotherapy studies. The QUALISAGE study (NCT03230305), closed to enrollment, will probably help to answer these questions. Finally, in our study, more patients were included during the 2012-2016 period compared to the 2007-2011 period. This improvement was also observed in younger patients in the rst line setting. However, the inclusion rate of older patients increased over time when all treatment lines were taken into account, unlike younger patients.
Conclusion In this large real-life database, fewer older MBC patients were enrolled in a trial compared to younger ones. In older patients, variables associated with such participation to clinical research were younger age (< 80 yo), good ECOG Performance Status (0-1), HER2+ disease, and investigational treatment consisting of chemotherapy/targeted therapy/immunotherapy. Older patients were more enrolled in clinical trials between 2012 and 2016 compared to the 2008-2011 period (5. 6% versus 2. 7%). Most of these factors raise questions on drug availability and perceived potential bene ts by investigators and medical teams. Accrual of older patients with cancer in MBC and other disease types should be more encouraged.
Triple Negative (HR-HER2-). ET: Endocrine Therapy. CT/TT/IT: chemotherapy/Targeted Therapy/Immunotherapy. "Center size" represent the number of enrollment in ESME database per center per year Multivariate odds ratios associated with being included in a trial in rst-line of treatment in patients aged 70yo or more with Metastatic Breast Cancer (MBC). Odds Ratio. TN: Triple Negative (HR-HER2-). ET: Endocrine Therapy. CT/TT/IT: chemotherapy/Targeted Therapy/Immunotherapy. "Center size" represent the number of enrollment in ESME database per center per year.
Availability of data and material: The datasets analyzed during the current study are available in the Epidemio-Strategy and Medical Economics (ESME) Metastatic Breast Cancer (MBC) Data Platform. The database of the ESME program or the database of the MBC cohorts are currently not accessible. For any speci c demand, please contact the corresponding author. Each demand will be examined on a case-by-case basis by the scienti c committee Code availability: All analyses were performed using the software R. (R Core Team (2017). Authors' contributions: MB conceived the study. MB, MC, AV and CB contributed to the design of the study. MC contributed to the acquisition and analysis of the data. MB, MC, AV and CB drafted the manuscript. All authors contributed to the interpretation of data, critically revised the manuscript, and gave nal approval.
Study population A total of 328 MS patients were prospectively enrolled at four European MS centres from July 2016 to December 2017 (68 subjects from Hospital Clinic of Barcelona, Spain; 95 subjects from Oslo University Hospital, Norway; 73 subjects from Charité-Universitaetsmedizin Berlin, Germany; 92 subjects from Ospedale Policlinico San Martino, Genoa, Italy). All MS patients were invited for a two-year follow-up between January 2017 and March 2020, resulting in 280 subjects (85 %) completing the longitudinal study. For our study, 312 MS patients (95 %) met all criteria with available clinical, MRI, and serum NfL data at baseline, while 242 MS patients (86 %) fulfilled the requirements at follow-up (Table  1 ). All patients provided demographic information, personal MS history, blood samples, and assessment of Expanded Disability StatusScale (EDSS).
MRI pre-and post-processing at Berlin reading centre Pre-processing included alignment to Montreal Neurological Institute (MNI) -152 standard space (using fslreorient2std), white and grey matter brain masking (using Computational Anatomy Toolbox 12 Toolbox for MATLAB)  (Ashburner and Friston, 2005) , N4-bias field correction (Advanced Normalization Tools, https://stnava. github. io/ANTs/) and linear, rigid body registration of T2-weighted (FLAIR) images to T1-weighted (MPRAGE, BRAVO, and FSPGR) images (FSL FLIRT)  (Jenkinson et al. , 2002; Jenkinson and Smith, 2001). First, MPRAGE scans were brain extracted and a transformation matrix was created using rigid body registration (6 degrees of freedom) of the individual brain extracted MPRAGE images to MNI-152 space with FSL FLIRT  (Jenkinson et al.
Statistical analysis Analyses were performed using R 4. 0. 3 (R Core Team, 2017). Continuous variables were normalized by subtracting the mean of each variable and dividing by the standard deviation using the generic scalefunction in R. Linear regression analysis were also conducted on baseline measures of GD and T2LV at inclusion using the lm function in R (R Core Team, 2017). To investigate longitudinal associations between serum NfL and GD and T2LV from inclusion to two-year follow-up, two separate rLMM were conducted examining GD or T2LV as dependent variables. We used rlmer from the robustlmm R package  (Koller, 2016) , to model our longitudinal data, to better include extreme values than conventional LMMs. A conventional LMM was also performed using lmer from the lme4-package  (Bates et al.