dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments AA received financial support from the  Department of Science and Technology, Government of India, (New Delhi, India)  through the  INSPIRE Faculty program. MSBS received  Australian Government Research and Training Program  funding for post-graduates to study at the  Australian National University (Canberra, ACT, Australia ). FC acknowledges support from the  European Union  ( FEDER funds   POCI/01/0145/FEDER/007728  and  POCI/01/0145/FEDER/007265 ) and  National Funds (FCT/MEC ,  Fundação para a Ciência e a Tecnologia and Ministério da Educação e Ciência ) under the Partnership Agreements  PT2020 UID/MULTI/04378/2013  and  PT2020 UID/QUI/50006/2013. EC is supported by an  Australian Research Council   Future fellowship  ( FT3 140100085 ). AK was supported by the Miguel Servet contract financed by the  CP13/00150  and  PI15/00862  projects, integrated into the National R + D + I and funded by the  ISCIII (General Branch Evaluation and Promotion of Health Research)  and the  European Regional Development fund (ISCIII-FEDER).
Control 4T1 cells were sensitive to 102 drugs at 10µM concentration, while the two Mki67 -/-clones were sensitive to 99 and 98 respectively, with 82 hits common to the three cell lines (Extended Data Fig. 9c ; Supplementary Data). This suggests that Ki-67 loss does not qualitatively alter the drug-sensitivity profiles. We next determined the IC50 of 10 common hits used in cancer therapy. Importantly, Mki67 -/-cells were markedly more sensitive to all the molecules tested (Fig. 4h ; Extended Data Fig. 9d ). Third, genes directing mouse major histocompatibility complex (MHC) class I-mediated antigen presentation Tap2, Psmb8 and Psmb9 were downregulated in Mki67 -/-4T1 cells (Extended Data Fig. 8c ; Supplementary Table  2 ). Flow cytometry confirmed lower expression of the mouse MHC class I molecules H2D and H2K (Fig.
DNA replication assay, EdU labelling Cells were treated with 10 µM 5-ethynyl-2'-deoxyuridine (EdU; LifeTechnologies) for the indicated time, harvested, washed once with cold PBS, resuspended in 300µL cold PBS and fixed with 700µL ice-cold 100% ethanol. Click reaction was performed according to the manufacturer instructions (Click-iT™ Plus EdU Alexa Fluor™ 647 Flow Cytometry Assay Kit; Invitrogen) and cells were analysed by flow-cytometry (BD FACSCanto II). FlowJo® software was used for analysis.
Immunophenotyping and flow cytometry analyses Cells isolated from peripheral lymph nodes tumor (draining and non-draining for tumorbearing mice) as well as spleen and bone marrow, were stained with Sytox blue or Live/dead fixable viability dye (Life Technologies and eBioscience, respectively) together with the appropriate conjugated anti-CD3, CD45, CD62L, CD4, CD8, CD44, CD25, F4/80, CD19, B220, NK1. 1, Gr1, CD11b, CD11c, Ter119, CD71 and Foxp3 antibodies (eBioscience/ThermoFisher or Becton Dickinson, San Diego, CA). Intracellular staining for Foxp3 was performed using the eBioscience Fixation/Permeabilization kit. Cells were analysed on a FACS Fortessa (BD Biosciences) flow cytometer. Data analyses were performed using FlowJo Mac v. 10 (Tree Star) or DIVA (Becton Dickinson) software. Prior to intracellular cytokine stainings, cells were activated with PMA (Sigma-Aldrich; 100 ng/ml)/ Ionomycin (Sigma-Aldrich; 1ug/ml) in the presence of brefeldin A (Sigma-Aldrich; 10ug/ml) for 3.
Sequencing of cDNA libraries and data processing FastQC was used to perform quality control of the sequencing. Using the tool STAR 2. 6. 0a(2), all the reads that passed the quality control were aligned to the mouse reference genome (GRCm38. p6) and the counts per gene were quantified. The release 93 of the Ensembl mouse genome annotations were used for establishing the coordinates of each gene and their transcripts. Differential expression analysis was performed in R using the DESeq2(2) library embedded in an in-house script. After normalization of the read counts per gene, a negative binomial generalised linear model was fitted considering single factor design for assessing the differential expression between Mki67 knockout and wild-type samples. Gene set enrichment analysis of the 4T1 Mki67 -/-cell line vs WT cell line was performed using the javaGSEA(3) desktop application with a log-fold-change pre-ranked gene list.
Acknowledgements We thank  D. Grimanelli  for help with transcriptome analysis, and  P. Jay ,  D. Santamaria ,  M. E. Hochberg  and  M. Serrano  for comments on the manuscript. K. M and M. S. were funded by the  Ligue Nationale Contre le Cancer (LNCC) ; P. S. and K. M. received funding from  Worldwide Cancer Research (WWCR). D. F. and L. K. are Inserm employees. This work was initiated and finalised with support from the  LNCC  and continued with support from  Worldwide Cancer Research (WWCR)  and the  French National Cancer Institute (INCa). The chemical library screen relied on support provided by the  Programme Opérationnel FEDER-FSE 2014-2020 Languedoc Roussillon.
µ-Raman Microscopy Raman microscopy was carried out at a scientific laboratory in DCR Lisbon using a Labram 300 Jobin Yvon spectrometer, equipped with a solid state 50-mW laser operating at 532 nm. Spectra were recorded as an extended scan. The system was calibrated using a silicon standard. The laser beam was focused either with a 50× or a 100× Olympus objective lens. The laser power at the surface of the samples was controlled with neutral density filters (optical densities 0. 3 and 0. 6). Raman data analysis was performed using LabSpec 5 software. All spectra are presented as acquired without any baseline correction or other treatment.
Acknowledgments: The authors acknowledge the  Portuguese Foundation for Science and Technology (FCT-MCTES ) for PhD grant  PD/BD/135053/2017 , the  Research Units VICARTE  ( UID/EAT/00729/2019 ) for supporting this project. L. Saguì  and  Soprintendenza Archeologica di Roma  gave us access to the glass samples studied. Daneo ( Stazione Sperimentale del Vetro, Murano ) for the colorimetric measurements;  C2TN (Center for Nuclear Science and Technology ) for PIXE analyses,  Eva Mariasole Angelin  for the  FORS analyses at scientific laboratory in DCR Lisbon  and  Prof. M. Bacci  for his personal suggestions on the interpretation of FORS spectra.
Isolation and growth conditions In 2017 we isolated from a human sputum sample an unidentified bacterial strain. The study was validated by the ethics committee of IHU Méditerranée Infection under number 2016-011. A screening was made by MALDI-TOF MS on a Microflex LT spectrometer (Bruker Daltonics, Bremen, Germany) as previously described  [7]. The obtained spectra (Fig. 1 ) were imported into MALDI Biotyper 3. 0 software (Bruker Daltonics) and analysed against the main spectra of the bacteria included in the database (Bruker database constantly updated with Microbes Evolution Phylogeny and Infections (MEPHI) database; http://www. mediterraneeinfection. com/article. php?larub=280&titre=urmsdatabase). The initial growth was obtained after 48 hours' culture on Columbia agar with 5% sheep's blood in anaerobic conditions at 37°C at pH 7.
Strain identification The 16S rRNA gene was sequenced in order to classify this bacterium. Amplification was done by using the primer pair fD1 and rP2 (Eurogentec, Angers, France), and sequencing by the Big Dye Terminator v1. 1 Cycle Sequencing Kit and the ABI Prism 3130xl Genetic Analyzer capillary sequencer (Thermo-Fisher, Saint-Aubin, France), as previously described  [8]. The 16S rRNA nucleotide sequences were assembled and corrected using CodonCode Aligner software (http://www. codoncode. com). Strain Selenomonas felix exhibited a 96% sequence identity with Selenomonas dianae strain ATCC 43527 (GenBank accession no. NR_041805. 1), the phylogenetically closest species with standing in nomenclature (Fig. 2 ). We consequently classified this strain as a member of a new species within the genus Selenomonas, family Selenomonadaceae, phylum Firmicutes.
Genome sequencing Genomic DNA was extracted using the EZ1 biorobot (Qiagen, Courtaboeuf, France) with the EZ1 DNA tissue kit, then sequenced by MiSeq technology (Illumina, San Diego, CA, USA) with the Nextera XT Paired end (Illumina), as previously described  [9]. The assembly was performed with a pipeline incorporating different software (Velvet  [10] , Spades  [11]  and Soap Denovo  [12] ) on trimmed (Trimmomatic  [13] ) or raw data. GapCloser was used to reduce assembly gaps. Scaffolds <800 bp and scaffolds with a depth value lower than 25% of the mean depth were removed. The best assembly was selected by using different criteria (17 scaffolds, 19 contigs). The genome of strain Marseille-P3560 T is 2 402 833 bp long with a 56. 8 mol% G+C content and contains 2393 predicted genes. The degree of genomic similarity of strain Marseille-P3560 T with closely related species was estimated by OrthoANI software  [14].
Acknowledgements This work was funded by the  IHU Méditerranée Infection (Marseille, France)  and by the  French government  under the 'Investissements d'avenir' (Investments for the Future) programme managed by the  Agence Nationale de la Recherche (National Agency  for Research; Méditerranée Infection  10-IAHU-03 ). This work was also supported by ' Région Provence Alpes Côte d'Azur' and European funding via 'Fondo Europeo de Desarrollo Regional' (FEDER). The authors thank  Hitachi Corporation  for providing the TM4000Plus tabletop microscope. They also thank  M. Lardière  from  IHU-Méditerranée Infection, Marseille  for English-language review and  A. Caputo  from  IHU-Méditerranée Infection, Marseille  for submitting the genomic sequences to GenBank.
DAPI (1/10000, Molecular Probes) was used to stain DNA in fixed cells (5 minutes at 4˚C). EdU staining was also performed after surface staining, fixation, and permeabilization with the Click-it EdU flow cytometry assay kit Fixed CMs were resuspended in BD Cytofix/Cytoperm Fixation/Permeabilization Kit (BD Biosciences), permeabilized in BD Perm/Wash buffer for 15 minutes, incubated with primary antibodies for 2 hours at 4˚C, and incubated with Alexa Fluor-conjugated secondary antibodies for 30 minutes at 4˚C. Prior to acquisition on ImageStream, nuclei were stained with 20 μM DRAQ5 (Biostatus, UK) and filtered with 100-μm cell strainer (Fisher Scientific). Data acquisition was performed using an Amnis ImageStreamX cytometer (Luminex). Files were collected with a cell classifier applied to the bright-field (BF) channel to capture events larger than 20 μm and included BF, FITC, PECy7, and DRAQ5 images.
At least 30,000 cells were analyzed for each sample, and all images were captured with the 40× objective. Data analysis was performed with IDEAS software (version 6. 0, Luminex). For each sample, only intact CMs, selected based on Actinin and DRAQ5 signal intensity, were considered for subsequent analysis. For the morphometric analysis, we applied a morphology mask to the BF channel, and for assessment of the number of nuclei per cell, we used the DRAQ5 images. Culture and live-cell imaging E 15. 5, P2, P4, and adult cardiac cells were isolated as above, and HSA + cells were sorted following the gating strategy in S1A Fig. For the neonatal and adult cells, 20 mM BDM (Sigma-Aldrich) was added throughout the isolation procedure  [48]. HSA + cells were plated in 0. 1% collagen (Life Technologies) for E 15.
Wholeheart acquisitions were obtained using the high-content imaging system (IN Cell Analyzer 2000, GE Healthcare). Isolated fixed CMs were resuspended in 10% FBS-PBS and spun onto superfrost slides in a cytocentrifuge (ThermoFisher). Cytospins were incubated with primary antibodies overnight at 4˚C, followed by 1-hour incubation with Alexa Fluor-conjugated secondary antibodies. Acquired images were edited and quantified using the Image J version 1. 51d software (NIH).
Bioinformatic analysis Flow cytometry data analysis was performed in FCS files of live CD45 -Ter119 -CD31 -cell fraction using R package flowCore from R version 3. 2. 4 revised (2016-03-16 r70336) and the interface R Studio version 0. 99. 467 (R foundation)  [50]. Subsequently, gating, as described in Fig  1  and in S1 Fig, was used to define each population. Map clustering of the flow cytometry data was performed using custom R scripts from R package t-SNE to dimensionality reductiont-SNE (R foundation)  [51]  and Bioconductor. org package flowSOM to visualize Self-Organizing and Minimal Spanning Trees (Spanning Trees, R foundation)  [52, 53]. Gene expression raw data (BioMark Fluidigm, Applied Biosystems) of sorted cells at the population level was normalized with HPRT, and data are presented in 2 -ΔCt.
Single-cell gene expression analysis was performed in cells that expressed at least 1 of 3 housekeeping genes (Hprt, Gapdh, or Actb), and Ct values were used to the following analysis. A Ct value of 21 was the maximum value considered as expressed gene, and the background (i. e. , nondetected) Ct value was 38. qRT-PCR data were processed with the QLUCORE (Qlucore AB 2008-2015, Sweden) software, and pheatmap package (version 1. 0. 10) R (R version 3. 5. 0, R foundation) was displayed in uncentered Pearson's correlation unsupervised hierarchical clustering and PCA either for surface phenotype or transcripts.
Statistical analysis All results are shown as mean ± SD. Statistical significance was determined using the Student t test, except when comparing the frequency of HSA + CMs from E 13. 5 to P7 (one-way ANOVA followed by Tukey test). The statistical analysis of the data was performed using Sig-maPlot software (p < 0. 05 was considered statistically significant, R foundation) or QLUCORE (Qlucore AB 2008-2015, Sweden) software for the multidimensional analysis of multiplex qRT-PCR (two-way ANOVA, p = 0. 007, q = 0. 01).
Acknowledgments We thank the members of P. P. O. and A. C. laboratories for fruitful discussions. We are grateful to the  i3S Animal Facility, Advanced Flow Cytometry, Unit b. IMAGE, Advanced Light Microscopy Unit , to the  Pasteur Institute Flow Cytometry Core facility  for Sony SP6800 implementation ( K. Futamura  and  C. Ait-Mansour );  D. Montarra 's and  S. Meilhac 's lab,  P. Vieira ,  A. Bandeira ,  H. Maiato , and especially to  P. Pereira  for critical reading of the manuscript. We thank  M. Rujano ,  Z. Garcia ,  D. Sassoon , and  S. Tajbakhsh ;  P. Bousso  for the ubiquitin-GFP reporter mice; and  S. Chea  and  V. Rouilly  for help in the analysis of single cell transcriptional data.
Study design Between January 2014 and September 2016, the data of 440 consecutive men with LUTS or complications related to BPO who underwent a PVP or GreenLEP at our institution were prospectively collected. Surgical indications were based on guidelines of the European Association of Urology (EAU)  [4]. Patients with prostate volume < 80 g on preoperative trans-rectal ultrasonography (TRUS) were excluded as well as patients with a post-operative follow-up < 12months, preoperative IIEF5 score < 5 (not sexually active), a history of neurological disorder or pelvic radiotherapy, untreated urinary tract infection, history of previous urethral surgery, prostate cancer (PSA > 4ng/ml and positive prostate biopsy or suspect digital-rectal examination), and procedures performed by low experienced surgeons (<100 procedures).
Imaging Sixteen of the 33 patients underwent chest computed tomography (CT) scans at baseline and at 12 months of ART (Figure  S1 ). CT scans were performed without contrast in a Siemens Somatom emotion apparatus (16 sections). Thymic volume was considered as the mean of measurements, blindly performed by two independent operators, in cm 3. Thymic index, assessed by one of the operators, was determined by scoring the presence of thymic tissue as opposed to adipose tissue: (0) thymus entirely replaced by fat; (1) minimal, barely recognizable, soft tissue; (2) minimal, but more obvious, soft tissue; (3) moderate soft tissue; (4) moderate soft tissue of greater extent, almost mass like; (5) mass-like appearance that raises concern for a thymoma  (23). Both operators were blinded to any demographic or clinical data besides the HIV serostatus.
5 (UCHL1), anti-HLA-DR-PeCy7 (L243), anti-CD8-APC (RPA-T8), anti-CD4-APC/Cy7 (RPA-T4), and anti-CD3-Pacific Blue (OKT3) was used for Panel 1; a mixture of anti-CD45RA-FITC (HI100), anti-CD3-PE (OKT3), anti-CD45RO-PerCP/Cy5. 5 (UCHL1); anti-CD31-PeCy7 (WM59), anti-CCR7-Alexa Fluor 647 (G043H7), anti-CD4-APC/Cy7 (RPA-T4), and anti-CD8-Brilliant Violet 421 (RPA-T8) was used for Panel 2; and a mixture of anti-Ki67-FITC (MOPC-21), anti-FoxP3-PE (PCH101), anti-CD127-PerCP/Cy5. 5 (AO19D5), anti-CD31-PeCy7 (WM59), anti-CD25-APC (BC96), anti-CD4-APC/Cy7 (RPA-T4), anti-CD45RA-Pacific Blue (HI100), and anti-CD3-V500 (UCHT1) was used for Panel 3. All antibodies were from BioLegend, except for anti-CD3-V500 (BD Horizon), anti-FoxP3-PE (eBiosciences) and anti-Ki67 (BD Pharmigen). Samples were acquired using a BD LSRII flow cytometer using FACS DIVA software (Becton and Dickinson, NJ, USA); data were analyzed using FlowJo Software (Tree Star, OR, USA).
Statistical Analysis Statistical analysis was performed in the computing environment R (v3. 4. 2) using RStudio (v1. 1. 383). Overall, a p-value <0. 05 (α) was considered statistically significant; for multiple comparisons, Bonferroni corrections were applied and differences or effects that remained significant (< α/number of comparisons) are indicated in the Figures with a * The R package traj (v1. 2) was used to identify two clusters of patients based on their CD4 + T cell counts trajectories from baseline to 36 months of ART. The package implements the 3-step procedure proposed by Leffondré et al. (27)  to identify clusters of longitudinal trajectories: first, 24 summary measures (Table  S1 ) that describe features of the trajectories were calculated  (28) ; second, a principal component analysis was performed on these 24 measures to select measures that best describe the main features of the trajectories, based on eigenvalues (>1); finally, trajectories were classified into clusters based on the previously selected factors.
We used the nlme package (v3. 1. 137) in R to fit nonlinear mixed effects models to the CD4 + T cell count and the CD4/CD8 ratio data, based on the exponential model described by Li et al. (9) : X(t) = A + B [1 -exp(-t / τ )] where X(t) represents CD4 + T cell count or CD4/CD8 ratio at one given time point t; A represents the theoretical CD4 + T cell count or CD4/CD8 ratio at baseline; B represents the theoretical maximum increase that could be reached; t refers to time (in months) since ART initiation; and τ is a constant representing the timescale over which X(t) reaches its maximum value. The arguments used in the CD4 + T cell count models were: fixed = list (A ∼ 1, B ∼ group, τ ∼ group) and random = A + B ∼ 1 | Patient_ID, where Patient_ID correspond to the identification of each patient.
The arguments used in the CD4/CD8 ratio models were: fixed = list (A ∼ 1, B ∼ group, τ ∼1) and random = A + B + τ ∼ 1 | Patient_ID. Normality of data distributions was assessed by the Shapiro-Wilk test. Correlations were assessed using the Pearson or Spearman rank tests, depending on the underlying distribution. For quantitative data, depending on the underlying distributions, comparisons between two independent groups were performed using an independent t-test or Wilcoxon/Mann-Whitney Utest, unless otherwise stated. Cohen's d or r, respectively, was calculated as a measure of effect size. Within-group comparisons between two time points were performed with the paired-samples t-test (for approximately normally-distributed data); in this case, Cohen's d was calculated based on the standard deviation of the differences.
Design and pattern conversion of NHAs We designed a periodic nanostructure with triangular, octagonal, and square holes using computer-aided design (CAD) software. The patterns were exported to graphic database system II (GDSII) stream format data and then converted into the format accepted by our EB machine (BEF20 format for the F7000S-VD02 EB writer) using conversion software (GenISys Beamer  (10)  ). The sizes of the triangular (a tri ), octagonal (a o ), and square (a sq ) holes were varied from 50 to 270 nm with a pitch of 10 nm. The period of each array (p trihex , p ohex , p osq , p sqhex ) was twice the hole size (2a tri , 2a o , 2a o , 2a sq ) (see Fig. 3 ). The shapes had a one-to-one relationship with the corresponding CP stencil pattern; the 200 nm square was composed of a 2 × 2 array of 100 nm square CP patterns.
The same patterns (triangular, octagonal, and square holes) were also composed of rectangles with a height of 10 nm in the VSB method. During the conversion, PEC  (11, 12)  was also considered. When fabricating a mixture of microand nanopatterns, EB scattering in both the forward and backward scattering processes must be taken into account. The dose of each nanopattern depends on the size of the structure and its periodicity; therefore, PEC is indispensable when various periodic structures are exposed simultaneously. PEC was performed using the same conversion software (Beamer). PEC resulted in a dose ratio of 1. 3 between the edge and the center of the exposure area for patterns with the 200-nm-size, 400-nm-pitch square holes, octagonal hole hexagonal array, and octagon hole square array.
Acknowledgments This work was supported by the  MEXT Nanotechnology Platform. SPM was performed using the apparatus of the  University of Tokyo Institute of Innovation, School of Engineering's Nanotechnology Platform Advanced Characterization Platform. All the other processes were performed in the  University of Tokyo VDEC Nanotechnology Platform Nanofabrication Center at Takeda Sentanchi Supercleanroom. The invaluable support of the  VDEC, University of Tokyo , and Faculty of Engineering cleanroom support team is especially acknowledged. JSPS Kakenhi  16H04345  and  French   ANR-15-CE33-0022  grants are acknowledged.
The backgrounds are evaluated using data wherever possible. The general strategy is to define control regions, each dominated by a different background process and with negligible contamination from signal events, through modification of the nominal selection requirements. These control regions are used to measure the m jj and m T shapes and probabilities for background events to satisfy the VBF selection requirements. If the background contribution from a particular process is expected to be small or if the above approach is not feasible, the m jj and m T shapes are taken from simulation. In these cases, scale factors, defined as the ratio of efficiencies measured in data and simulation, are used to normalize the predicted rates to the data. The paper is organized as follows. The CMS detector is described in Section 2.
The first level trigger (L1), composed of custom hardware processors, selects events at a rate of around 100 kHz. The second level trigger, based on an array of microprocessors running a version of the full event reconstruction software optimized for fast processing, reduces the event rate to around 1 kHz before data storage. A detailed description of the CMS detector, along with a definition of the coordinate system and relevant kinematic variables, can be found in Ref. [24].
Double counting of the partons generated with MADGRAPH5 aMC@NLO and POWHEG interfaced with PYTHIA is removed using the MLM  [46]  matching scheme. The LO cross sections are used to normalize simulated signal events, while NLO cross sections are used for simulated backgrounds  [36, 42, 47, 48]. For both signal and background simulated events, additional pileup interactions are generated with PYTHIA and superimposed on the primary collision process. The simulated events are reweighted to match the pileup distribution observed in data. The background samples are processed with a detailed simulation of the CMS apparatus using the GEANT4 package  [49] , while the CMS fast simulation package  [50]  is used to simulate the CMS detector for the signal samples.