dataesr/scientific-paragraphs-categorization huggingface dataset:
An 'explosion' never took place. 6. Similarly, depending on the datasets interrogated, the Great Ordovician Biodiversification 'Event' has been interpreted as a phase of diversity increase, and has been located on published diversity curves at different intervals. Interpretations of the GBDB dataset place the GOBE in a late Cambrian to Early Ordovician interval, whereas interpretations of the PBDB dataset indicate the GOBE to a restricted interval in the early Middle Ordovician, which is in clear contradiction with the definition and the general understanding of the term. 7. Terms such as 'radiation' or 'biodiversification' are the most suitable to designate increases of taxonomic diversity, reflecting simple, normal fluctuations, without implying a dramatic or catastrophic nature of more emotive terms, such as 'explosion,' 'revolution,' or 'event.
Acknowledgments This paper benefited largely from the reviews of  Ian Percival  ( Londonderry, Australia ) and from an anonymous referee, to whom we express our thanks. We acknowledge the input from discussions with many colleagues, including  Andrej Ernst  ( Hamburg, Germany ),  Yukio Isozaki  ( Tokyo, Japan ),  Fan Junxuan  ( Nanjing, China ),  Björn Kröger  ( Helsinki, Finland ),  Spencer Lucas  ( Albuquerque, New Mexico, USA ),  Ron Martin  ( Newark, Delaware, USA ),  Ian Percival  ( Londonderry, Australia ),  Alexandre Pohl  ( Dijon, France ),  Birger Schmitz  ( Lund, Sweden ),  Alycia Stigall  ( Knocksville, Tennessee, USA ),  Beatriz Waisfeld  ( Cordoba, Argentina ), and many others. This research was funded by the  French ANR-Ecoboost  project ( ANR-22-CE01-0003-02 ) and by  Chinese NSFC  grant  42030510.
The resulting pixel size is 0. 65 µm for the CALSPAR, CALMICR, and CALDOL samples, and 0. 325 µm for the ARAGOID sample. Volume reconstructions were performed from the radiographs corrected from flat field and background noise using an algorithm based on the Fourier transform method  (Marone and Stampanoni, 2012). After reconstruction of the 3D images, image processing was carried out with Avizo R software on data sets of 1,700 × 500 × 1,300 (CALSPAR), 1,700 × 500 × 1,300 (CALMICR), 1,450 × 1,400 × 1,100 (CALDOL) or 1,910 × 1,600 × 1,400 (ARAGOID) voxels. First, the 3D grayscale volumes were normalized, converted to 8bit integers, and denoised with a 3D median filter using a kernel size of 3 × 3 × 3 voxels. Then, the samples were registered in the same coordinate system, using either the capillary glass and chemically inert masked area (CALSPAR) or the internal structures (CALMICR, CALDOL, and ARAGOID) as landmarks.
Global dissolution rate The global dissolution rate (mol•s -1 ) of the samples is calculated after segmentation of the XMT data sets based on the changes in sample volume  (Noiriel et al. , 2020) : rdiss = dV sample ν cal • t , (3) where dV sample is the change in sample volume (m 3 ), t is the time interval between successive XMT scans, and ν cal is the molar volume of calcite or aragonite (m 3 •mol -1 ). The rate (mol•m -2 •s -1 ) can be normalized to the surface area in contact with the fluid: rdiss-norm = dV sample ν cal •S• t , ( 4 ) where S is the average surface area between two time intervals as determined by XMT. However, since the samples are partially covered by glue, normalization of the dissolution rate by the total surface area induces a bias, because a non-negligible fraction of the surface is non-reactive for samples CALMICR, CALDOL, and ARAGOID.
Discussion. Application of X-ray micro-tomography for tracking mineral and rock reactivity Like techniques measuring the surface retreat, XMT offers the possibility to determine the retreat at the fluid-mineral interface, with an estimated error of ±1 pixel (see  Noiriel et al. , 2020  for a comparison with vertical scanning interferometry measurements). Between 2. 1 and 5. 6 million data points for retreat were considered initially at the surface of the different samples. The technique has the advantage over bulk experiments on mineral powders of bypassing the process of normalization to the surface area  (Noiriel and Daval, 2017) , which depends on either the geometric surface area calculated from the crystal size or the Brunauer-Emmett-Teller (BET) surface area determined from gas adsorption FIGURE (A) Evolution of the CALSPAR geometry (displayed as a cross-section) from stochastic modeling using the set of probabilities P (j>> ) = { P c =.
Description of more robust data related to these cancers is thus warranted to improve our knowledge and the clinical management. The TMRG (Tumeurs Malignes Rares Gynecologiques) network is a national French National Cancer Institute-accredited network dedicated to rare gynecological malignant tumors management including systematic second lecture for pathology and dedicated regional and national tumor boards. One of the main goal is also to develop multicenter research projects focused on rare gynecological cancers  [9] [10] [11]. As first objective, we aimed to describe clinical presentation at diagnosis and treatment-related data of all patients with uterine clear cell cancer recorded in the TMRG network. Secondary objectives were to describe survival data (Progression-Free Survival and Overall Survival), as well as protein, transcriptomic, and genomic profiles and to evaluate their prognostic value.
Patients and methods This work was a retrospective, multi-center, observational study. We retrieved all cases recorded in the TMRG database with sufficient pathological and clinical data available with a data cut-off on November 2017. Patients enrolled in this study should have been diagnosed with endometrium or cervix carcinoma with a clear cell component representing at least 10% of the tumor. For biological analyzes, they should have personally signed and dated informed consent (whether TRMG network or institutional consents). Patients with non-uterine CCC or with uterine carcinoma without CC component were excluded. All tumor samples were reviewed by expert pathologists as systematically performed within the TMRG network. Our major endpoint was to describe patients (age, body mass index, hypertension, diabetes, in utero exposition to diethylstilbestrol, and personal and family history of cancer) and disease (FIGO stage, lymph node involvement, lymphovascular invasion, and pure clear cell or mixed histology) features.
Transcriptomic analyzes For cases with sufficient tumor area, RNA was isolated and RNA templates were analyzed using the NanoString nCounter ® Dx Analysis System  [18]. A dedicated custom gene panel was developed including main genes implicated in gynecological cancers. This custom panel included 29 genes plus all genes from the Pan-Cancer pathway panel (770 genes from 13 canonical pathways). 200 to 500 ng of total RNA were used as input and sample hybridization was performed according to the manufacturer's instructions. Sample detection and analysis were completed on an nCounter ® Digital Analyzer where genes were counted by scanning 555 Fields-of-view (FOV). The 410 endometrial endometrioid tumors from TCGA (out of 560 samples) and the 237 ovarian clear cell cancer (OvCC) from  [19, 20]  were used as controls, as well as eleven normal ovarian samples from the same external data set  [19, 21].
Array-comparative genomic hybridization (aCGH) Array-CGH was performed in order to study DNA copy number profile. Tumor DNA was extracted from FFPE blocks or hematoxylin-eosin-safran (HES) slides by automated methods using the EZ-1 tissue kit, QIA-GEN ™ , according to the manufacturer's instructions. The cases with DNA of sufficient quality (determined on Agilent Bioanalyzer (Agilent Technologies, Massy, France)) were analysed as previously described  [22]  using high-resolution 4 × 180 K CGH microarrays (SurePrint G3 Human CGH Microarray Kit, Agilent Technologies, Massy, France). Genomic data from the 393 endometrial endometrioid tumors from TCGA were used as control  [21].
Array-CGH All probes for aCGH were mapped according to the hg19/NCBI human genome mapping database. Log2ratio were segmented with Circular Binary segmentation (CBS) algorithm. We used two different threshold values (log2 ratio >|0. 15| and |0. 9|) to distinguish low (gain/loss) from high (amplification/deletion) level copy-numberalterations (CNA), respectively. Percentage of genome altered was calculated as the sum of altered probes divided by the total number of probes. To identify recurrent copy number alterations, we used the Genomic Identification of Significant Targets in Cancer (GISTIC) 2. 0 algorithm calculated by multiple random iterations, with an amplification/deletion threshold > 0. 9, confidence level 0. 90, and a corrected threshold probability q < 0. 25. A HRD (Homologous Recombination Deficiency) score, based on losses of heterozygosity, was calculated for each tumor sample from all tested aCGH genes  [25].
Gene expression profiling (nCounter ® platform, Nanostring ™ , Seattle, WA, USA) Raw data processing, quality control, and normalization were performed using the nSolver ™ 4. 0 analysis software. Briefly, data processing of raw counts was done with background subtraction defined by the geometric mean of the eight negative control probes. Next, quality control of samples was checked according manufacturer requirement in the nSolver ™ 4. 0. Finally, normalization was done with the geometric mean algorithm using the 40 housekeeping and the six positive control probes. Processed data were then log2-transformed prior analysis. Messenger RNA analyzes were conducted with the Cancer Pathway panel plus 29 custom genes (Additional file 2: Table  S2 ). Unsupervised analysis was done using hierarchical clustering using the Cluster program with data median-centered on genes  [26] , Pearson correlation as similarity metrics and centroid linkage clustering as parameters.
Results were displayed using TreeView program  [26]. In association with hierarchical classification, we used quality Threshold (qT) clustering to select clusters of gene by specifying minimum correlation and size values. Robustness of clusters was assessed using the R-package pvclust with uncentered Pearson correlation distance, the average agglomerative method and 100 bootstrap replications as parameters to assess the robustness of clusters  [27]. The AU (Approximately Unbiased) p-values provided by multiscale bootstrap resampling indicate the robustness of tumor clusters, larger the p-values, more robust the clusters.
Gene expression profiling of public data Our Nanostring UCCC data were completed by three public transcriptomic data sets including the Uterine Corpus Endometrial Carcinoma data set from TCGA, following Illumina RNA-seq processing, normalization, and publication through the UCSC Xena database  [28] , and the Winterhoff (GSE73614) and Bolton data sets  [19, 20]. We merged these datasets by using COMBAT (empirical Bayes) as batch effects removal method  [29] , included in the inSilicoMerging R/Bioconductor package  [30]. When multiple probes mapped to the same GeneID, we retained the one with the highest variance. Accurate normalization of the merged data sets including 786 commons genes was assessed by t-distributed Stochastic Neighbor Embedding (t-SNE). The 410 endometrial endometrioid tumors from TCGA (out of 560 samples) and the 237 OvCC from  [19, 20]  were selected for further analysis with our 47 UCCC.
Eleven normal ovarian samples from TCGA ovarian cancer data set and Winterhoff 's set were also used as controls. Supervised analysis was done using a moderated t-test with empirical Bayes statistic  [31]  included in the limma R package (version 3. 5. 2; http:// www. cran. r-proje ct. org/). False discovery rate was applied to correct the multiple testing-hypothesis: the significant genes were defined by p < 0. 05%, q < 1%, and fold change (FC) superior to |1. 5×|  [32]. Using the Gene Expression Signature (GES) obtained, each sample was classified using the nearest centroid algorithm. Robustness of classifier was done by tenfold cross validation using 1000 iterations using same parameters as supervised analysis and prediction accuracy was assessed using exact binomial test with the greater one-sided hypothesis.
Ontology analysis of the differential gene list was based on the Reactome terms of the Database for Annotation, Visualization and Integrated Discovery (DAVID; david. abcc. ncifcrf. gov/). In order to assess the potential vulnerability or actionability of tumor samples to certain anti-cancer drugs used or in development in endometrial cancer  [33] , we applied to each dataset separately the following multigene signatures: two signatures predictive for response to immune checkpoint inhibitors (ICI), the T cell-inflamed signature (TIS) and immunologic constant of rejection signature (ICR)  [34, 35] , and the Rbsig signature predictive for resistance to CDK4/6 inhibitors  [36]. All genomic data supporting our results can be found in Additional file 11: Materials S1, Additionnal file 12: Materials S2.
Statistical analyzes The Pearson's Chi2 test (categorical variables) and Wilcoxon test (continuous variables) were used to compare descriptive items. PFS (progression-free survival) was defined as the time from diagnosis to disease relapse, progression, or death from any cause. OS (overall survival) was defined as the time from diagnosis to death from any cause. Cause of death was collected to discriminate cancer-related events to death from other causes. Data concerning patients without disease progression or death at last follow-up were censored. Survival curves were estimated using the Kaplan-Meier method. Followup was estimated by the reverse Kaplan-Meier method. The prognostic impact of clinicopathological features was assessed by the Cox regression method in univariate and multivariate analyses and p-values estimated with the Wald test.
Clinical outcome Median follow-up was 24. 7 months (95 CI  [16. 0-33. 3] ). Among the 68 patients included in this study, 39 (57%) were still disease-free at time of last news. Seventeen (25%) patients experienced disease progression with distant metastases. Ten had a loco-regional relapse prior to or concomitantly with distant metastases. Four other patients displayed pelvic relapse without metastasis. Metastases occurred in lymph nodes (N = 7), lungs or liver (N = 5 each), peritoneum (N = 3), pleura (N = 2), bones (N = 1), and brain (N = 1). Metastatic site details were not available in one patient. At the date of the database locked, 17 patients were deceased, including 16 for whom death was related to cancer. Out of 51 alive patients, 39 were disease-free, one with disease in partial response to treatments, four with disease progression, and data was missing in seven cases.
3A ). Nevertheless, transcriptomic profiles of uterine CCC (N = 47) and endometrioid endometrial cancers (N = 410) were not fully similar and supervised analysis identified 154 genes differentially expressed between them (Fig. 3B ). To remove the genes that could be associated to TCGA subtype obtained by ProMisE molecular classification  [38] , a multivariate logistic regression analysis with the glm function in R's stastistical package was done on the 154 genes to compare uterine CCC to endometrioid endometrial tumors stratified with TCGA subtype. Off the 154 genes, nine were classified as dependent to the ProMisE classification and excluded from further analysis. Ontology analysis of the remaining 145-gene list showed that genes involved in immune response and cellular matrix regulation were more expressed in uterine CCC than in endometrioid tumors (Fig.
B Volcano plot of differential mRNA expression between uterine CCC and the TCGA endometrioid endometrial carcinoma data set with 154 genes differentially expressed (moderated t-test: p < 5%, q < 10% and |FC|> 1. 5x). C Ontology analysis based on the Reactome database. The best 20 pathways are represented Univariate analysis identified P16 as the only IHC marker significantly associated with PFS (Table  5 ). Cases without expression of P16 had a longer median PFS than cases with expression (64. 8 months vs 17. 3 months respectively; p = 0. 003, log-rank test; Fig. 5A ). Multivariate analysis for PFS including p16, FIGO stage (I-II vs. III-IV), and lymphovascular invasion showed that P16 prognostic value was independent from these clinicopathological features: HR = 6. 67, 95 CI  [1. 37-33. "immune response subgroup" of transcriptomic analyzes experienced a very good prognosis, with a 3-y overall survival of 100% versus 33 to 68% in the two other clusters (Fig.
2C ). Moreover, to reinforce the robustness of this classification in the absence of external validation set, we used the Pvclust algorithm and confirmed the low uncertainty of the hierarchical clustering with AU p-values of 95% to 98%  [27]. Supervised analysis of aCGH data using GISTIC2. 0 algorithm did not identify copy number alterations correlated to disease progression at the False Discovery Rate threshold of 0. 25 (Additional file 10: Fig. S5 ). Alterations of the PI3K/AKT, MAPK, DNA repair, or SWI/SNF pathways, explored by TMA, NGS, and aCGH, were not predictive of poorer PFS: HR = 2. 38.
The BOUQUET study (NCT04931342), a basket trial dedicated to rare ovarian tumors is ongoing. A similar design may be developed for advanced endometrial cancer with specific molecular alterations. One of the potential limitation was to include both pure and mixed CCC, partially guided by real-world practice where CCC uterine tumors often display other components (mainly endometrioid or serous). For instance a recent cohort reported 60% of mixed uterine CCC  [60]. Third, our protein analyzes were based on TMA results. Tumor heterogeneity may thus have been missed. Concerning transcriptomic analyzes, no external validation could be done as no independent public data is available in uterine CCC. Finally, the low amount of DNA of sufficient quality (old FFPE samples) did not allow us to explore mutation profile in all cases.
Acknowledgements Authors would like to thank  Pr Emmanuelle Charafe-Jauffret  and the  ICEP (IPC/CRCM  experimental pathology) platform for their help in TMA analyses. We also want to thank all pathologists involved in the diagnostic process of the patients included in this work:  Dr Marie-Aude Le Frere-Berda  (APHP),  Dr Isabelle Treilleux  ( Centre Leon Berard ),  Dr Laurent Arnould  ( Centre Georges-Francois Leclerc ),  Dr Eliane Mery  ( IUCT Oncopole ),  Dr Sebastien Henno  ( Centre Eugene Marquis ),  Pr Emmanuelle Charafe-Jauffret (Institut Paoli-Calmettes ). We also would like to give special thanks to the clinical research associates, medical oncologists and medical interns who actively contributed to data collection/management or other organizational aspects of the TMRG network. We equally wish to thank all clinicians and pathologists of the TMRG network for their work and implication which is essential as it contributes to promote research studies such as this one, in the field of rare gynecological cancers.
Attenuated Total Reflectance Fourier Transformed Infrared Spectroscopy Infrared spectra were recorded with a Nicolet iS5 spectrometer, with a diamond ATR accessory, and the following settings: 16 scans, spectral resolution of 4 cm -1 , wavenumber range of 4000-400 cm -1 , area of analysis of 62 µm. Two samples per set per treatment were analyzed and three faces investigated (Tv, Tg, Rd). For each face, three spectra were acquired along the diagonal with v9. 2. 86 OMNIC software. The spectra were processed with the v1. 2. 16 Spectragryph software. After baseline correction, the band at 1050 cm -1 was set at 1 to normalize spectra and the intensity of bands at 1158 and 1506 cm -1 listed. These bands are assigned to holocellulose and lignin content, respectively  [41] [42] [43]. The degree of degradation was evaluated by calculating the ratio R1 = 1158/1506 and compared before and after extraction  [44, 45].
Raman Spectroscopy Raman spectra were acquired with a Renishaw LabRam Aramis Horiba, with the following parameters: 632. 8 nm laser, 100-2000 cm -1 range, 100× objective, 1800 lines/mm grating, 1000 µm confocal pinhole, 100 µm spectrometer entrance slit, 10% of laser power (0. 99 mW at the surface of the sample). Two samples per set per treatment were analyzed and three faces investigated (Tv, Tg, Rd). Short acquisitions (5 accumulations of 10 s) were performed at the center of faces Tg and Rd, while long acquisitions (10 accumulations of 25 s) were carried out at the center of face Tv. The spectra were processed with v1. 2. 16 Spectragryph software for baseline, SVN correction, and normalization.
X-ray Fluorescence X-ray fluorescence (XRF) spectra were recorded with a portable Tracer 5 g XRF spectrometer (Bruker), with the following parameters: 20 kV, 60 µA, no filter, Spectral Mode, 60 s acquisition and window aperture of 3 mm. Two samples per set per treatment were analyzed and three faces investigated (Tv, Tg, Rd). For each face, one spectrum was acquired at the center of the faces. The spectra were processed with v1. 2. 16 Spectragryph software. After baseline correction, the band at 2. 69 keV (Rh) was set at 1 for normalization and the intensity of bands of sulfur S (2. 31 keV), calcium Ca (3. 70 keV), and iron Fe (6. 40 keV) listed. Their intensities were compared before and after treatment.
Statistical Validation In order to better assess the obtained results, experimental data were statistically analyzed using v 3. 6. 3. Rstudio software. Measured values were tested by Shapiro-Wil normality test. Then Principal Component Analysis (PCA) and two-way analysis of variance (ANOVA) were performed to determine statistical differences between the extraction protocols, as well as which variables affect the most the extraction results (Table  2 ). Significance was established at a p-value level of 0. 05. Tukey's HSD significance was applied to find which significance variables affected most the study.
S3 ). Initial analysis suggested that the scRNA-Seq identified 4 overarching populationsearly embryonic tissue (e. g. , endodermal Afp hi , mesodermal Prrx1 hi , or neural Sox2 hi ), mixed non-myocyte tissue (Col1a1 hi , Fbln2 hi , Emcn hi , Upk3b hi , Lmod1 hi ), and ventricular (Myl2 hi , Myl7 lo , Myh7 hi , Kcne1 hi ) and atrial (Myl7 hi , Myh6 hi , Nppa hi , Sln hi ) cardiomyocyte populations across 26 cell types (Fig. S3a , Fig. S4 , Fig. S5a-d , Fig. S6a , Data S1). Thus, our scRNA-Seq could sample the expected cellular composition of the developing heart at a gross level. We could discriminate major populations and subpopulations of working and non-working cardiomyocytes across our scRNA-Seq dataset of the developing murine heart. To better understand cardiomyocyte populations in the developing heart, we undertook more extensive analysis of the cardiomyocyte lineage.
First, we sought to confirm Stereo-seq could faithfully recapitulate known biology in the specific context of the developing murine heart. We were able to resolve the expected changes in gene expression and spatial distribution across both the developing and adult mouse heart (Fig. S13a-c ). Confident in examining global changes in gene expression across our Stereo-seq dataset, we sought to ensure this matched with the appropriate cell type coexpression of genes. We identified a range of working and non-working cardiomyocyte populations in our analysis of stages E12. 5, E14. 5, and P3, alongside expected non-cardiomyocyte populations (Fig. 2b, c , Fig. S14 ). We omitted P56 from cell type classification to ensure comparability with our scRNA-seq data, which does not extend beyond P3. To minimize the potential of bias in our cell type classification; we also used additional supervised learning workflows to classify cells using our scRNA-Seq dataset as a reference for cell-type specific gene expression (Table  S2 )  26, 27.