dataesr/scientific-paragraphs-categorization huggingface dataset:
Results part 2-Multi-center phantom testing Thirty-four of the magnet systems appraised were 1. 5 T and 22 were 3 T (see Supplementary Data File and Supplementary Table  S4 ) with manufacturers being 35 Siemens, 18 Philips, and 3 GE. Scan frequencies are reported in the Supplementary Data File. The planned program was 1 year, but 24 centers voluntarily extended, resulting in mean longitudinal data of 358 days, and the longest 1161 days. Quality assurance of the contributed DICOM image files identified significant protocol deviations or sequence reconstruction failures in nine submissions, the majority of which took place at the start of the study (examples in Supplementary Fig. S2 ). These were excluded with subsequent advice sent to centers permitting later inclusion in most cases, except where a tube artifact affecting > 25% of a single center's series led to exclusion of that tube from pooled analysis (see Supplementary Data File).
Fifty-two magnets non-exclusively contributed MOLLI datasets, 16 ShMOLLI, and 12 SASHA, and 2 magnets contributed SMART T 1 maps. Center-, session-, and sequence-specific T 1 mapping contributions are detailed in Supplementary Data File. During the project, there were three software upgrades at centers: two Siemens and one GE (details in Supplementary Fig. S4 ). For serial multi-center data, temperature-unadjusted CoV per tube, per phantom, per sequence, and per magnet are detailed in the Supplementary Data File. Temperature-adjusted CoV for the various native sequences together with inter-sequence and crossplatform differences, at 1. 5 T and 3 T, are summarized in Tables  1  and 2  respectively and data for post-GBCA in Supplementary Tables  S5  and S6. Results of native and post-GBCA sequence/scanner software version repeatability are provided in Tables  1  and 2  and in Supplementary Tables S5 and S6 respectively.
Based on these temperature-adjusted data, mean (x) CoV were generally higher (implying poorer repeatability) at 3 T than at 1. 5 T and for the much sparser GE data over Siemens/Philips. Over 1 year, many 1. 5 T and 3 T sequences/magnets were repeatable with x CoV < 1% and < 2% respectively. For sequences optimized for native T 1 mapping applied to T1MES native tubes, we observed excellent repeatability for several sequences, for example, Siemens MOLLI 5s(3s)3s 448B (x CoV = 0. 27%) and Philips MOLLI 3s(3s)5s (x CoV 0. 54%) at 1. 5 T (Table  1 ) and Philips MOLLI 3b(3s)5b (x CoV 0. 33%) and Siemens ShMOLLI (x CoV 0. 69%) at 3 T (Table  2 ). Among sequences optimized for post-GBCA T 1 mapping applied to T1MES post-GBCA tubes, excellent repeatability was observed for several sequences, for example, Siemens ShMOLLI 1041B (x CoV 0.
In total, 3 magnets deviated from the prescribed nominal intervals in some of their initially submitted datasets. Phantom repeatability studies represent an in vitro experiment. Some sequences and scanners that performed well in vitro may exhibit greater variability in the face of biological tissue (magnetization transfer, flow, motion), patient characteristics (fast/slow heart rates, heart rate variability, breath-hold length, implanted metal devices), and scanning process (non-isocentering, scanning in multiple planes). The T1MES phantom does not capture magnetization transfer. The biomarker performance that was measured here accounts for T 1 mapping in vitro. While this is obviously related to diagnostic performance in vivo, cardiac motion, etc. , introduce yet another degree of freedom, and different hardware/software combinations might deal with this differently.
Funding This program was funded by the following grants to GC:  European Association of Cardiovascular Imaging (EACVI)  of the  European Society of Cardiology (ESC) , the  UK National Institute of Health Research (NIHR) Biomedical Research Center (BRC)  at  University College London  ( UCL , # BRC/199/JM/101320 ), and the  Barts Charity  (# 1107/2356/MRC0140 ). GC is supported by the  National Institute for Health Research Rare Diseases Translational Research Collaboration (NIHR RD-.
T ij is the value of the flow from component i to component j, where i is the number of rows and j is the number of columns. T i. is the total outflow for component i, and T. j is the total inflow for component j: AMI intern ¼ ∑ n i¼1 ∑ n j¼1 T ij T :: log 2 T ij T :: T i: T : j ð1Þ Hr intern ¼ -∑ n i¼1 T i: T :: log 2 T i: T :: ð2Þ All flows were calculated using Excel to build a matrix. One file was created per farm. The indicators were calculated with R software using the enaR package  (Lau et al. 2017). We adapted the calculation code for the organization indicator included in the R package to account only for internal flows and thus calculated the internal flow organization indicator.
Analysis of the indicators After performing univariate and bivariate analyses, a principal component analysis (PCA) was carried out on thirteen active variables (eight structural variables and five ENA variables) (Table  2 ) using R software and the FactoMineR package  (Lê et al. 2008). Performance indicators were considered illustrative variables, and thus they were not included in the construction of principal components. We then conducted a hierarchical clustering on principal components (HCPC) analysis of the four first factors, again using the FactoMineR package. From the HCPC results, we calculated the ratio of intra inertia to total inertia, which illustrates the percentage of data variability in the HCPC analysis. We conducted a nonparametric test of multiple pairwise comparisons (Dunn) to determine whether the clusters performed identically in terms of the distributions and median values.
The significance threshold was set to a p value of 5%. Each cluster was illustrated by its paragon (individual closest to the barycenter of the cluster) using the R package igraph  (Ognyanova 2016)  to represent the flows between system components and between components and the environment. The thickness of the arrow illustrates the intensity of the flow. The number of arrows according to the number of components illustrates the density of flow. Flow organization was defined as homogeneous when all the arrows had a similar thickness. When arrows of different thicknesses were observed, the flow organization was considered heterogeneous.
Palynological analysis The Kalewa samples have a poor sporomorph preservation and the organic material is often pyritized or corroded. To construct a reliable pollen diagram, a baseline on pollen sum of 100 grains was maintained. All samples with counts < 100 were excluded resulting in 54 positive samples. Identification of fossil PPL pollen was mainly based on  Potonié (1960) ,  Germeraad, Hopping & Muller (1968) ,  Muller (1968 ), Reimann & Aye Thaung (1981)  and  Harley & Morley (1995). Palm pollen grains not included in Longapertites, Dicolpopollis, Spinizonocolpites, Proxapertites or Palmaepollenites kutchensis Venkatachala & Kar were classified as 'other palms' in the pollen diagram. The pollen diagram was constructed using the Tilia v. 2. 1. 1 software  (Grimm, 1991) , with cluster analysis program CONISS  (Grimm, 1987).
All of the slides and pollen residues are deposited at IBED. All pollen grains were counted and described under a LM LEICA DM LB2, and a Zeiss Universal microscope at IBED. In addition, a JEOL JSM•6400 SEM at the Department of Palaeontology, University of Vienna, was used to investigate the pollen sculpture, applying the single-grain analysis method by  Zetter (1989 ; see also  Halbritter et al. , 2018). LM and SEM terminologies follow  Punt et al. (2007)  and  Halbritter et al. (2018) , respectively. The morphological characters of pollen grains were measured by the software ImageJ (National Institute of Health, USA). PCA was employed in R (R Core Team, 2018) to analyse palaeoecological correlation on PPL taxa. Additionally, an analysis of variance (ANOVA) of pollen abundance among different pollen zones and a non-parametric correlation (Spearman) of taxa were performed with SPSS v.
Mapping the global distribution of NLRs Occurrence data (Supporting Information, Table  S1  were downloaded from the Global Biodiversity Information Facility (GBIF, https://www. gbif. org) on 5 August 2019. 'Observation', 'Human observation' and 'Preserved specimen' including coordinates were included to reduce false records. The coordinates of the downloaded occurrences were then imported on the software GeoMapApp v. 3. 6. 10 (http://www. geomapapp. org) to generate modern distribution maps of the NLRs. 'Human observation' and 'Preserved specimen' data on Calaminae (including Calamus, Daemonorops and Ceratolobus) and Nypa swere examined by R package 'CoordinateCleaner'  (Zizka et al. , 2019)  to exclude problematic points (e. g. zero coordinates, GBIF headquarters and biodiversity institutions) (Supporting Information, Figs S1-S4), as other records fall well within distribution ranges of previous studies.
Mapping the global Eocene distribution of PPL taxa with emphasis on the Palaeogene of India and South-East Asia We compiled published global Eocene records of Dicolpopollis, Longapertites, Spinizonocolpites and Palmaepollenites kutchensis (Supporting Information, Table  S2 ) without Proxapertites as it has NLRs belonging to different plant families (e. g. Annonaceae, Araceae and Nymphaeaceae). We extended this compilation with data on the distribution of Dicolpopollis, Longapertites, Spinizonocolpites and Proxapertites in the Palaeogene of the Indian subcontinent and South-East Asia for the comparison of species diversity (Supporting Information, Tables  S3, S4 ). This compilation is based on the Palynodata database  (Palynodata Inc. & White, 2008). However, we cross-checked these records where possible, and augmented the record with additional references.
ACKNOWLEDGEMENTS We are indebted to all the members of the  Myanmar Paleoclimate and Geodynamics Research Group (MyaPGR)  for sample collection. We thank  Henry Hooghiemstra  for pollen identification and orientation at the start of the PhD project of  H. Huang ;  Annemarie Philip  and  Jan van Arkel  for processing pollen samples and microphotography, respectively ( University of Amsterdam );  Eko Budi Lelono  ( LEMIGAS, Indonesia ) for permission to use data from his unpublished PhD thesis;  Malcolm Jones  ( PLS Ltd. , UK ) for processing pollen samples;  Christopher Scotese  ( Northwestern University, US ) and  Thijs de Boer  ( University of Amsterdam ) for guidance on GPlates software and mapping, respectively. We are grateful to  Carlos Jaramillo  and an anonymous reviewer for the constructive comments.
Androgen deprivation promotes cholesterol accumulation in the prostate As LXRs nuclear receptors are important regulators of cholesterol homeostasis within the cell, particularly in macrophages, we then evaluated the cholesterol content of CW and LXR DKO prostates. Castration induced accumulation of cholesteryl esters (CE) in CW prostates  (Fig 3A and S5 Data). Consistent with a role of LXR in regulation of cholesterol homeostasis in prostate  [19] , this phenomenon was dramatically increased in LXR DKO prostates (almost 8-fold compared to CW), which also accumulated free cholesterol following androgen deprivation ( Altogether, these data show that castration induces cholesterol accumulation in the prostate. This phenomenon is dramatically amplified by ablation of LXR and may be involved in immune infiltration.
Bars represent mean of DKO mice (Fig  7G ). Altogether, these data showed that chronic inflammation resulting from aberrant accumulation of phagocytosis-deficient macrophages in LXR DKO prostates was associated with STAT3 signaling deregulation and could result in epithelial neoplasia. triplicates ± SEM. Statistical analyses were performed via Kruskal-Wallistest. � p < 0. 05, �� p < 0. 01, ��� p < 0. 001 and ns. For numerical raw data, please see S1 Data. For raw immunoblots, please see S1 Blots. For supporting dataset, please see S2, S7 and S8 Datas. Cx, castrated; GO, gene ontology; hrOPN, human recombinant osteopontin; LXR, liver X receptor; LXR DKO, LXR alpha and beta double knock-out; ns, nonsignificant; OPN, osteopontin; Sh, sham-operated; RT-qPCR, quantitative reverse transcription PCR; SPP1, secreted phosphoprotein 1; TCGA, The Cancer Genome Atlas.
RNA sequencing analysis RNA sequencing libraries were prepared using the Directional mRNA-Seq Sample Prep with polyA selection (Illumina, San Diego, California) following the manufacturer's protocol. Singleend sequencing Illumina HiSeq 4000 was performed by the GenomEast platform (ANR-10-INBS-0009). The quality of reads was checked using FASTQC (v0. 11. 8). Filtered out reads correspond to reads with length lower than 40 bases after adapter and low bases quality removal. Reads were mapped onto the mm10 version of the Mus musculus genome using STAR version 2. 5. 3a. Normalized coverage was obtained by geneBodyCoverage tool from RSeQC version 2. 6. 4. Quantification of gene expression was performed using HTSeq version 0. 6. 1p1 with annotations coming from Ensembl version 90. Differential gene expression analysis was performed with DESeq2 package (v1.
24. 0). Comparisons of interest were performed using the statistical method proposed by Anders and Huber  [60]. p-Values were computed using the Wald test and adjusted for multiple testing using the Benjamini and Hochberg method  [61]. A gene identified as differentially expressed has an adjusted p-value lower than 0. 05 and an absolute log2 Fold Change (logFC) value greater than 1. 00. All RNA sequencing analyses were performed by R studio (v1. 0. 136). Heatmaps were generated using R « gplots » v3. 0. 1. and "pheatmap 1. 0. 12" libraries. Dataset is available using GEO accession number GSE134137.
Cytokines quantification by multiplex assay Prostates were lysed in TER buffer (pH 7. 4) (Tris 50 mM, EDTA 5 mM, and NaCl 250 mM) supplemented with NaF 1 mM, Na 3 VO 4 2 mM, phenylmethylsulfonyl fluoride 1 mM, and complete protease inhibitor cocktail (Roche). Cytokines quantification from prostate lysates was then performed using LEGENDplex Mouse Inflammation Panel (13-plex) bead-based immunoassay (BioLegend Ref. 140150) according to manufacturer's instructions. Data were acquired on an LSRII flow cytometer (BD Biosciences, San Jose, California) and analyzed using LEGENDplex data analysis software v8. 0.
Finally, cells were washed and kept at 4˚C until processing. Data were acquired on an LSRII flow cytometer (BD Biosciences) and analyzed using the FACSDiva Software (v6. 1. 3, BD Biosciences). Cells were first selected based on their size (FSC) and granularity (SSC) characteristics. Doublets were excluded from the analysis, thanks to the FSC/SSC criteria. Finally, live cells were gated based on their negative staining for the viability marker before they were selected for the various markers tested. The percentages presented for each cell population are reported as positivity minus the corresponding isotype control. All FCS files are available on flowrepository platform (https://flowrepository. org/) using ID: FR-FCM-Z33J.
A single-cell suspension of peritoneal flush was stained with APC-coupled anti-F4/80 (Biolegend Ref. 123116) to identify macrophages. Cells were analyzed on an Attune NxT Flow Cytometer with Attune NxT software v2. 6 (Invitrogen). Phagocytic index was determined as double positive CFSE F4/80 + expressed in % CFSE-labeled cells.
Groups are composed of at least 4 animals. Bars represent mean ± SEM. Statistical analyses were performed via Mann-Whitney test. � p < 0. 05, �� p < 0. 01, ��� p < 0. 001 and ns. Scale bars, 100 μm; insets, 20 μm. For numerical raw data, please see S1 Data. For supporting dataset, please see S2 Data. AP, anterior; ARG, androgen responsive genes; Cx, castrated; CW, control wild-type; DP, dorsal; LP, lateral; LXR, liver X receptor; LXR DKO, LXR alpha and beta double knock-out; ns, nonsignificant; RT-qPCR, quantitative reverse transcription PCR; Sh, sham-operated; VP, ventral. https://doi. org/10. 1371/journal. pbio. 3000948. g001.
(F and G) Immunohistological staining (F) and flow cytometry analysis (G) of F4/80 + MOs representation in 1-month Cx or Sh CW and LXR DKO prostates. Groups are composed of at least 4 animals. Bars represent mean ± SEM. Statistical analyses were performed via Mann-Whitney test. � p < 0. 05, �� p < 0. 01, ��� p < 0. 001 and ns. Scale bars, 100 μm; insets, 20 μm. For numerical raw data, please see S1 Data. For supporting dataset, please see S2-S4 Datas. For flow cytometry raw data, please see S1 FlowCytometry RawDataFCS. Cx, castrated; CW, control wild-type; LXR, liver X receptor; LXR DKO, LXR alpha and beta double knock-out; MOs, macrophages; MPs, mononuclear phagocytes; neutro, neutrophils; ns, nonsignificant; Sh, sham-operated. https://doi. org/10. 1371/journal. pbio. 3000948. g002.
01, ��� p < 0. 001 and ns. For numerical raw data, please see S1 Data. For supporting dataset, please see S2-S6 Datas. CW, control wild-type; Cx, castrated; LXR, liver X receptor; GSEA, Gene Set Enrichment Analyses; LXR DKO, LXR alpha and beta double knock-out; NES, Normalized Enrichment Score; ns, nonsignificant; RT-qPCR, quantitative reverse transcription PCR; Sh, sham-operated. https://doi. org/10. 1371/journal. pbio. 3000948. g005.
Groups are composed of at least 4 animals. Bars represent mean ± SEM. Statistical analyses were performed via Mann-Whitney test. � p < 0. 05, �� p < 0. 01, ��� p < 0. 001 and ns. Scale bars, 100 μm; insets, 10 μm. For numerical raw data, please see S1 Data. For supporting dataset, please see S2 Data. Cx, castrated; CW, control wild-type; JAK/STAT, Janus kinase/signal transducers and activators of transcription; LXR, liver X receptor; LXR, liver X receptor; LXR DKO, LXR alpha and beta double knock-out; ns, nonsignificant; PIN, prostatic intraepithelial neoplasia; RT-qPCR, quantitative reverse transcription PCR; Sh, sham-operated. https://doi. org/10. 1371/journal. pbio. 3000948. g007.
Acknowledgments RNA sequencing was performed by the  IGBMC Microarray and Sequencing platform , a member of the " France Genomique" consortium  ( ANR-10-INBS-0009 ). The authors thank  Sandrine Plantade ,  Philippe Mazuel  and  Khirredine Ouchen  for mouse care,  Christelle Blavignac  ( Centre Imagerie Cellulaire Sante, CICS, UniversiteClermont Auvergne, Clermont-Ferrand ) for technical support for flow cytometry analyses, Anipath technical staff ( GReD, Clermont-Ferrand ) for histological analyses,  Jean-Paul Saru  for western blot analyses,  Jean-Christophe Pointud  for mouse castration surgery, and  Bioinformatic platform (GReD, Clermont-Ferrand ) and GenomEast platform ( IGBMC, Strasbourg ) for NGS analysis. We thank  Pierre Val  for his helpful scientific discussion of this manuscript.
This work was supported by institutional support from  Centre National de la Recherche Scientifique  (https://www. cnrs. fr/), Institut National de la Santeet de la Recherche Medicale  (https:// EDC-CaP Plan Cancer 2014-2019 (https://www. ecancer. fr/Plan-cancer/) INCa  ( Institut National  du cancer, https://www. e-cancer. fr/)and  CAP/ TransCAP project INCa, CLARA (Canceropole Lyon Auvergne Rhone Alpes , https://www. canceropoleclara. com/),AURA region. LB was funded by  ERFD and AURA region  ( CPER/FEDER DEFI EPICURE AV0010836 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
05, �� p < 0. 01, ��� p < 0. 001 and ns. For numerical raw data, please see S1 Data. For supporting dataset, please see S2-S6 Datas. CW, control wild-type; Cx, castrated; LXR, liver X receptor; GSEA, Gene Set Enrichment Analyses; LXR DKO, LXR alpha and beta double knock-out; NES, Normalized Enrichment Score; ns, nonsignificant; RT-qPCR, quantitative reverse transcription PCR; Sh, sham-operated. https://doi. org/10. 1371/journal. pbio. 3000948. g005.
Subjects The present study was approved by the Local Ethics Committee (Comitato Etico Università Federico II, ref. no. 253/13). The screened population was composed of volunteers who freely agreed to participate to the study. All the participants were adults (≥18 years of age) who gave their written informed consent, in keeping with Italian Bioethics Law and the Declaration of Helsinki. Five hundred and eight volunteers were recruited in the outdoor hospital of the "Campus Salute Onlus" held in several locations from October 2014 through February 2019 (www. campussalute. it). All the included subjects were part of a project to investigate the role of lifestyle in preventing chronic diseases. The study is therefore a part of a large database started in 2010 to investigate the health status of the general population of Campania Region inhabitants.
Statistical Analysis Statistical analyses were performed using MedCalc 14. 0 (MedCalc, Ostend, Belgium) software and the Statistical Package for the Social Sciences Software Version 25 (SPSS v. 25, IBM, Armonk, NY, USA). Results were expressed as means ± standard deviation (SD) for continuous variables and as frequencies for categorical variables. The Kolmogorov-Smirnov goodness of fit test was used to assess the hypothesis of a normal distribution of data. Most variables did not have a Gaussian distribution; thus, non-parametric tests were used for group comparisons (Kruskal-Wallis test). Correlations were calculated with the Spearman's correlation coefficient. Fisher's exact test was used to compare categorical data. The level of significance was set at α = 0. 05, with a two-sided level.
15 ± 12. 77 years. BMI distribution was the same between smokers and non-smokers. Sex and smoking habit were not different between the enrolled subjects, when population was divided into two groups according to the median age. The characteristics of the studied population are shown in Table  1. Since only a subgroup of subjects performed both the flavor test and gustometry test, the data in the table are shown as separate columns. Subjective chemosensory function was investigated for the whole population. Self-evaluated flavor ranged from 3 to 10 with a mean of 7. 53 ± 1. 51, while self-evaluated taste ranged from 3 to 10 with a mean of 7. 98 ± 1. 56. In the overall population, the flavor score (FS) ranged from 5 to 21 with a mean of 14. 47 ± 3. 06, and the mean gustometry score (GS) of the 136 subjects performing gustometry was 14.
Results We applied our simulation tool to the SARS-CoV-2 epidemic in France. We used the partitions of région and département in the country for the regions and sub-regions of our model. Most of the parameters we used were gathered from the literature and statistical data made available by the French government. A few others were obtained empirically, mainly the number of contagious people in each région at the beginning of the simulations, and the constant of propagation of the SARS-CoV-2. The per-age values of the percentage of lethality  [42] , illness duration and percentage of local and remote travellers are shown on Table  A2 , the various rates of contamination on Table  A3 , and the initial number of contagious people in each département on Table  A1  in Appendix A. In order to test our population-centred algorithm, we first ran simulations without countermeasures and without any travel possibility, either local or remote.
Discussion We developed a hybrid model and simulation programme derived from standard models and simulation techniques widely used in the fields of epidemic propagation and biochemistry. Our approach used an original variant of the Gillespie SSA with tau-leaping, where the inner algorithm can be easily switched from stochastic discrete to deterministic continuous. This allowed us to compare these two methods of simulation. To test our approach we applied it to the SARS-CoV-2 epidemic in France, for which relevant data were available. We also tested the consequences and the efficiency of the lockdown countermeasure applied in France for 55 days. In order to gain spatial localisation but with an efficient population-centred algorithm where the population was supposedly being homogeneous, we partitioned the territory into relatively small units for which an instance of the population-centred simulation was run.