dataesr/scientific-paragraphs-categorization huggingface dataset:
Affy, affyPLM, and limma packages of Bioconductor, implemented in the R-statistical environment (v2. 15. 0) were used to analyse the data, including PCA analysis  55. Pathways enriched in BMAT (combined dBMAT and ruBMAT) or WAT (combined iWAT and gWAT) of rabbits, or in isolated adipocytes from femoral BM and gluteofemoral scWAT (humans), were identified using GSEA software and the Molecular Signature Database (MSigDB)  58. For rabbits, pBMAT was not included in these analyses because it contained a high proportion of RM and therefore represented a less pure BMAT sample  25. To ensure maximum compatibility with this software, rabbit gene identifiers were first converted to their corresponding human homologues using the BetterBunny algorithm  59. Volcano plots and heat maps (Pearson Distance) to visualise significantly differentially expressed transcripts (adjusted P value < 0.
Euthanasia, tissue isolation and gamma counting were done as described above for the insulin-treatment studies. PET/CT analysis was then done as described below. To assess effects of cold exposure compared to mice housed at thermoneutrality, male C57BL/6J mice aged 12 weeks were individually housed for 48 h at 28 °C, 22 °C or 4 °C. Each group was given AL access to chow diet throughout. Mice were then euthanised for tissue isolation. Mouse PET/CT analysis. PET/CT scan images were reconstructed and data was analysed using PMOD version 3. 806 (PMOD, Zurich, Switzerland). Standardised uptake values (SUV) were calculated for regions of interest, namely BAT, iWAT, gWAT; heart; bone tissue (without BM) from tibiae, femurs, and humeri; and the BM cavities within these bones. To distinguish bone tissue from BM, a calibration curve was generated using HU obtained from the acquisition of a CT tissue equivalent material (TEM) phantom (CIRS, model 091) and mouse CT scans.
The TEM phantom consists of 2-4 mm hydroxyapatite rods representing mass densities of 1. 08-1. 57 g/mL. The TEM-reconstructed CT image data was exported for analysis into PMOD and, for the extraction of TEM HU values, a VOI template was generated and placed on each rod (0. 008 mL for 2 mm and 0. 05 mL 4 mm). The calibration curve was plotted based on the calculated linear equation of the TEM HU values, in which the mouse tissue values were inserted/scaled. This ensured that, within whole bones, regions of interest were specific for bone or BM. Human PET/CT studies. Subjects with active BAT at room temperature were identified retrospectively from clinical PET/CT scans. While it is unclear why some people have active BAT at room temperature, this observation is consistent with previous reports showing that 7.
Determination of attenuation density for BMAT in humans. HU of subcutaneous fat, yellow marrow and RM were determined using Analyse 12. 0 software (AnalyzeDirect, Overland Park, KS, USA) based on data from 33 patients who had undergone paired CT and MRI scans (Table  1 ). The MRI sequence was an axial HASTE (Half Acquisition Single Shot Turbo Spin Echo) with a TE (echo time) of 50 ms, TR (repetition time) of 1000 ms, and slice thickness 8 mm. In HASTE MR techniques, BM fat corresponds to higher signal intensity compared to surrounding bone and muscle tissues. CT scanning, as described previously  70  , was performed using a 320-multidetector scanner (Aquilion ONE, Toshiba Medical Sytems, Japan). After acquisition of scout images, patients underwent non-contrast wide-volume CT using tube voltage 120 kV, tube current based on BMI and scan range from 2 cm below the carina to the base of the heart using volume sizes of 160, 140, 128, 120, 100 or 80 mm.
Using Analyse 12. 0 software, the MR and CT scans were co-registered and VOI were manually drawn around the sternum, vertebrae and subcutaneous AT. HU were extracted on a per voxel basis, and data underwent post-processing using a customised in-house software, developed in Matlab, to measure the total number of voxels across all patient HU (Fig. 6b ). ROC analysis of the CT data (MedCalc) was then conducted on per-voxel HU to determine threshold values with the greatest sensitivity and specificity to detect bone, yellow marrow and RM. Thresholds of above 300 HU were defined as bone regions, -200 to 115 HU as yellow marrow, and 115-300 as RM. Micro-CT scanning (µCT). Following euthanasia, murine tibiae were isolated, thoroughly cleaned and fixed in 10% formalin at 4 °C for 48 h. Bones were decalcified for 14 days in 14% EDTA and washed in Sorensen's phosphate buffer.
Bones were then stained for 48 h in 1% osmium tetroxide (Agar Scientific, UK), washed in Sorensen's phosphate buffer and embedded in 1% agarose, forming layers of five tibiae arranged in parallel in a 30-mL universal tube. Tubes of embedded tibiae were then mounted in a Skyscan 1172 desktop micro CT (Bruker microCT, Kontich, Belgium). Samples were scanned through 360°using a step of 0. 40°between exposures. A voxel resolution of 12. 05 μm was obtained in the scans using the following control settings: 54 kV source voltage, 185 μA source current with an exposure time of 885 ms. A 0. 5 mm aluminium filter and two-frame averaging were used to optimise the scan. After scanning, the data were reconstructed using NRecon v1. 6. 9. 4 software (Bruker, Kontich, Belgium). The reconstruction thresholding window was optimised to encapsulate the target image.
Volumetric analysis was performed using CT Analyser v1. 13. 5. 1 (Bruker microCT, Kontich, Belgium). Statistical analysis, data presentation and reproducibility. Microarray data were analysed as described above. All other data were analysed for normal distribution within each experimental group using the Shapiro-Wilk normality test. Normally distributed data were analysed by ANOVA or t tests, as appropriate. Where data were not normally distributed, non-parametric tests were used. When appropriate, P values were adjusted for multiple comparisons. Data are presented as histograms or box and whisker plots. For the latter, boxes indicate the 25th and 75th percentiles; whiskers display the range; and horizontal lines in each box represent the median. All statistical analyses were performed using Prism software (GraphPad, USA).
Acknowledgements This work was supported by grants from the  Medical Research Council  ( MR/M021394/1  to W. P. C. ;  MR/K010271/1  to R. H. S. ), the  National Institutes of Health  ( R01 DK62876  and  R24 DK092759  to O. A. M. ;  K99-DE024178  to E. L. S. ; and  P30 DK089503  to the  Michigan Nutrition Obesity Research Center) , the  Wellcome Trust-University of Edinburgh Institutional Strategic Support Fund  (to W. P. C. and K. J. S. ), and the  British Heart Foundation  (4-year BHF PhD Studentship to R. J. S. , B. J. T. , M. C. S. and B. M. N; BHF  CoRE Bioinformatics Grant  to W. P. C. ;  BHF   CoRE grant  to  A. J. D. ). W. P. C. is further supported by a  Chancellor's Fellowship  from the  University of Edinburgh. A. A. S. T was funded by the  British Heart Foundation  ( RG/16/10/32375 ).
E. J. R. v. B is supported by  SINAPSE  (the  Scottish Imaging Network). We are grateful to the  British Heart Foundation  for providing funding towards establishment of the  Edinburgh Preclinical PET/CT laboratory  ( RE/13/3/30183 ), and to  NHS Research Scotland (NRS)  for financial support of the  Edinburgh Clinical Research Facility. R. H. S and M. C. W. are supported by  The Chief Scientist Office of the Scottish Government  ( SCAF/17/02  to R. H. S. ;  PCL/17/04  to M. C. W. ). J. P. M. A is supported by  BHF   Clinical Research Training Fellowship  no. FS/17/ 51/33096. We are grateful to  John Henderson  ( BVS, University of Edinburgh ) for support with mouse husbandry;  Anish K. Amin  ( Department of Orthopaedic Surgery, Royal Infirmary Edinburgh ),  Beena Polouse  and  Frank Morrow  ( Edinburgh Clinical Research Facility ,) for help with human studies;  Tashfeen Walton  and  Christophe Lucatelli  ( Edinburgh Imaging, University of Edinburgh ) for radiotracer production;  Robert K.
Data availability All relevant data are available from the authors upon reasonable request. The source data underlying Figs. 1b-e, g, 2a, b, 3a, c, e, f, 4a, b, 5a, c, e-g, 6b, e, 7b-f, S2C, D, S3B-E, S4A-D, F, S5A-D and S6B-C are provided as a Source Data file. Microarray data for analysis of rabbit cohorts 1 and 2 has been made publicly available on the NCBI GEO platform  72  under the series ID GSE138690. The Molecular Signature Database (MSigDB, v6. 2), used for Gene Set Enrichment Analysis, can be accessed at https://www. gsea-msigdb. org/gsea/msigdb/collections. jsp Source data are provided with this paper.
The study in human cohort 5 was approved by the Caldicott Guardian for NHS Lothian. Studies in cohort 2, for collection and analysis of human tissues (Fig. 1f, g , Supplementary Fig. 2C, D ), were assigned ethics number 10/S1102/39. Subjects in cohort 3, who underwent paired MRI and CT scans (Fig. 6a-c ), were part of the SALTIRE 2 trial (NCT02132026) or PRE18FFIR trial (NCT02278211), in which participants were consented for anonymised data to be used in ethically approved studies. Only baseline measurements not related to the trial outcomes, and not any of the pre-specified endpoints from the PRE18FFIR and SALTIRE 2 trials, are reported herein. These paired MRI-CT studies were assigned REC number 16/SS/0166 and were further approved by the United Kingdom (UK) Administration of Radiation Substances Advisory Committee.
Radiolabeling and experiments with 161 Tb and 177 Lu The chelators L2, L3 and L4 were radiolabeled with 161 Tb, as well as the clinically-employed 177 Lu. The stability of the resultant radiometal complexes was compared to those of their  161  Tb-and 177 Lu-(DOTA) -counterparts. The stability of the metal complexes was investigated after diluting them using various solvents, with and without the addition of scavengers to prevent radiolysis. In addition, stability with regard to transmetallation and transchelation was determined and the labeling kinetics at 37 °C evaluated. ). For some of the data points, the standard deviation were small and, hence, not visible.
Single crystal X-ray diffraction measurements. Crystallographic data were collected at 193(2) K on a Bruker-AXS Kappa APEX II Quazar diffractometer equipped with a 30W air-cooled microfocus source (LuL3 and TbL3), using MoKα radiation (λ=0. 71073 Å) or on a Bruker-AXS D8-Venture equipped with a CMOS Area detector and a 30W aircooled microfocus source (LuL4 and TbL4), using CuKα radiation (λ=1. 54178 Å). Phi-and omega-scans were used. Space groups were determined on the basis of systematic absences and intensity statistics. Semi-empirical absorption correction was employed. 33 The structures were solved using an intrinsic phasing method (SHELXT),  34  and refined using the least-squares method on F 2. 35 All non-H atoms were refined with anisotropic displacement parameters. Hydrogen atoms were refined isotropically at calculated positions using a riding model with their isotropic displacement parameters constrained to be equal to 1.
ccdc. cam. ac. uk/data_request/cif. DFT calculations. Density functional theory (DFT) calculations on the LuL3 and LuL4 complexes were carried out with the g09 program package within the hybrid-meta GGA approximation with the TPSSh functional. 37, 38 Geometry optimizations were followed by analytical frequency calculations to establish the nature of the optimized structures as true energy minima. Bulk solvent effects (water) were considered with the integral equation formalism variant of the polarized continuum model (IEFPCM) using the default radii internally stored in g09 to describe the solute cavities. 39 Relativistic effects were considered with the aid of the small-core quasi-relativistic effective core potential of Dolg et al. , which includes 28 electrons in the core for the lanthanides, and the related ECP28MWB_GUESS basis set.
The term pH is defined as -log[H + ]. E°′ and Q were determined from acid region of the calibration curves. The liquid-junction potential, Ej, was found to be negligible under the experimental conditions used. The value of Kw = [H + ][OH -] was found to be equal to 10 -13. 78 by titrating a solution of known hydrogen-ion concentration at the same ionic strength in the alkaline pH region, considering E°′ and Q valid for the entire pH range. b) Calculations. The potentiometric data were refined with the HYPERQUAD software,  42  and speciation diagrams were plotted using the HySS software. 43 The overall equilibrium constants βi H and βMmHhLl are defined by βMmHhLl = [MmHhLl]/[M] m [H] h [L] l (βi H = [HhLl]/[H] h [ L] l and βMH-1L= βML(OH) × Kw). Differences, in log units, between the values of protonated (or hydrolyzed) and non-protonated constants provide the stepwise (log K) reaction constants (being KMmHhLl = [MmHhLl]/[MmHh-1Ll][H]).
5) was used. Elution proceeded over 55 mm from the baseline to the front line. For this purpose, radiometal complexes diluted at 0. 15 MBq/μL were pipetted (2 μL) onto the TLC plate. The 161 Tb-and 177 Lulabeled DOTA complex migrated with the liquid phase (Rf = 0. 4) and non-chelated 161 Tb and 177 Lu remained at the start line (Rf = 0). Retention of the 161 Tb-labeled L2, L3 and L4 or 177 Lu-labeled L2, L3 and L4 was determined at Rf = 0. 2. The TLC plate was exposed to a super resolution phosphor screen (Type MS, PerkinElmer, Waltham MA, U. S. A. ) for 15 seconds, which was then read using a Cyclone Plus Storage Phosphor System (PerkinElmer, Waltham MA, U. S. A. ) and analyzed with Optiquant software (version 5. 0). Quantifications of the TLC signals with the indicated software enabled the determination of intact radiometal complex over total activity in the different conditions of the experiments.
ACKNOWLEDGEMENTS R. T. and M. B. acknowledge the  Ministère de l′Enseignement Supérieur et de la Recherche  and the  Centre National de la Recherche Scientifique. They also thank the "Service Commun" of NMR  facilities of the  University of Brest. R. T. , M. B. and O. R. also thank the  Guerbet group  and the  Association Nationale de la Recherche et de la Technologie  for the CIFRE fellowship of G. N. C. P. -I. thanks  Centro de Supercomputación de Galicia  for providing access to computing facilities. G. N. acknowledges the  Paul Scherrer Institute  and the  Guerbet group  for the 2 months of short mission in Villigen in 2019. C. M. and N. vdM. acknowledge  Fan Sozzi-Guo  for assistance of the experiments involving 161 Tb.
Finally, we observed a small population of inflammatory (csf1r, alox5), vascular and endothelial (fli1, cav1) cells that may be spinal cord resident cells or a contamination from the dissection. However, no mesodermal or skin contamination was identified (Figures  3B  and 3C ). We then analysed the changes in the transcriptome during regeneration (Figure  3D ). A comparison of the clusters between 0dpa (yellow) and 3dpa (green) on a t-SNE representation shows that whilst some cell types cluster together (Schwann cells, DRGs), neural progenitors display a big shift in their transcriptome. Interestingly, foxm1 is expressed in a specific cluster of neural progenitors only at 3dpa (Figure  3E ). To characterise the foxm1+ cells, we identified DE genes between the foxm1+ cluster and the rest of the dataset (Figures  S3B  and S3C ).
The number of reads per transcript was determined using HTseq and the idxstats files were used to identify differentially expressed (DE) genes using the general linear model glmQLFit in DESeq2 with R. DE genes with a |log2(FC)|>1 and FDR<0. 01 were considered significant. For the clustering, the k-mean was determined at 5 using the Elbow method and enriched gene ontology (GO) terms were identified using Fidea  [31]. The full RNAseq dataset was then uploaded onto the Ingenuity Pathway Analysis (IPA, Qiagen) software. Upstream regulatory analysis was performed for DE genes with a |Log2FC|>1 and FDR<0. 001 (992 genes for 0dpa/1dpa and 2720 genes for 0dpa/3dpa). This analysis predicts upstream molecules such as transcription factors that may be causing the observed change in gene expression  [32].
RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-8785. For single cell RNAseq (scRNAseq), ten spinal cords at 0 and 3dpa were isolated and transferred in Modified Ringer's (MR, 100mM NaCl, 1. 8mM KCl, 2mM CaCl2, 1mM MgCl2, 5mM Hepes pH7) with 20µM Actinomycin D for 15 min to prevent de novo synthesis of mRNA  [33]. The spinal cords were transferred in CMF-MR (100mM NaCl, 1. 8mM KCl, 1mM EDTA and 5mM Hepes pH7), cut in small pieces using a fine scapel and incubated in 200µL of 0. 5% Trypsin-EDTA without dye (GIBCO) for 30 min at 28ºC with shaking at 600rpm. After a 5 min spin at 300rcf at 4ºC, the spinal cords were incubated for 15 min at room temperature (RT) in 180µL of MR with 0. 3U. µL -  of DNaseI (D5025, Sigma).
Single cell RNAseq analysis -Building custom genome for mapping: The X. tropicalis genome v. 9. 1 was downloaded from Xenbase. org. A number of genes had duplicated entries where the same gene ID was assigned to different gene names. The majority of these genes had an overlapping transcript, we therefore used the GFFReads merging option to merge these transcripts. -Data pre-processing: The sequence files from the sequencer were processed using 10x Genomics custom pipeline Cell Ranger v2. 2. 0. The fastq files were aligned to the custom genome using the default parameters of Cell Ranger. The pipeline identified the barcodes associated with cells and counted UMIs mapped to each cell. Cell Ranger uses STAR aligner to align reads to the genome discarding all the counts mapping to multiple loci.
The uniquely mapped UMI counts are reported in a gene by cell count matrix represented as a sparse matrix format. The Cell Ranger's aggr command was used to aggregate the samples from 0 and 3dpa while keeping the default down-sampling parameter enabled. -Filtering: Low-quality cells were removed from the dataset to ensure that the technical noise did not affect the downstream analysis. Three commonly used parameters were used for cell quality evaluation: the number of UMIs per cell barcode (library size), the number of genes per cell barcode and the proportion of UMIs that are mapped to mitochondrial genes. Cells that have lower UMI counts than three Median Absolute Deviation (MAD) for the first two matrices and cells having higher proportion of reads mapped to mitochondrial genes with a cutoff of four MADs were filtered out.
These genes are called HVG. HVG genes were then used to reduce the dimensions of the dataset using PCA. The dimensions of dataset were further reduced to 2D using t -SNE, where 1 to 14 components of the PCA were given as input. The cells were grouped into their putative clusters using the dynamic tree cut method. Dynamic tree cut method identified the branch cutting point of a dendgoram dynamically and combined the advantage of both hierarchical and K-medoid clustering approach. This method identified 15 clusters in the population. -Identification of marker genes: To identify the marker gene for a cluster, we compared a given cluster with all other clusters. We selected the marker genes for the cluster using manual curation of DE genes with an FDR<0. 01. For PANTHER analysis, all DE genes between the foxm1+ cluster were uploaded and tested against the pseudo-bulk of all the genes expressed in the scRNAseq experiment.
Statistics When 2 or more conditions were compared, the normality of the distribution of the data was tested using a Shapiro-Wilk test. For two conditions, a two-tail unpaired t-test was used. If three or more conditions were compared, a one-way ANOVA followed by a Tukey Post-hoc test was used. For the rose plots and the cumulative distribution(Figure  S4 ), a Kolmogorov-Smirnov test was used. All the statistical tests were done using Prism 8. and day 3 (SC_d0d3). The whole tail dataset was obtained from  [9]. (E) Tadpoles at NF50 were amputated, fixed at the indicated time and then processed for WISH using a probe specific for foxm1. (F) Total RNA was isolated from regenerating tails at indicated time points post amputation, reversed transcribed into cDNA and analysed for foxm1 expression by qPCR, using ef1a as a reference gene (n=3).
Acknowledgments DP and LP are supported by a  BBSRC-DTP  PhD studentships. This work was supported by a  Wellcome Trust   Seed Award  (ref  205894/Z/17/Z ), the  Manchester Regenerative Medicine Network (MaRMN) ,  The University of Manchester Strategic Fund (ISSF)  to K. D and MRC single-cell centre award [MR/M008908/1] to S. M. B. We thank the  EMBL core facility  for performing the bulk RNAseq experiments, the  Genomics Core Facility  ( University of Manchester ) for the scRNAseq and the  National Xenopus Resource  ( NXR, Marine Biology Laboratories, Woods Hole, USA ) for their bioinformatics workshop. We thank  Nancy Papalopulu  ( University of Manchester ) for the anti-Sox3 and anti-Myt1 antibodies and  Enrique Amaya ,  Raman Das  and  Shane Herbert  for comments on the manuscript.
, 2002; Catani & De Schotten, 2008; Chenot et al. , 2019; Hau et al. , 2016). In this work, the person performing the task of segmentation (i. e. , drawing the ROIs, following the protocol) will be referred to as rater. Manual segmentation can be performed in software such as, but not limited to, DTI studio  (Jiang, Van Zijl, Kim, Pearlson, & Mori, 2006) , Trackvis  (Wang, Benner, Sorensen, & Wedeen, 2007) , exploreDTI  (Leemans, Jeurissen, Sijbers, & Jones, 2009) , MITK Diffusion  (Neher et al. , 2012) , FiberNavigator  (Chamberland, Whittingstall, Fortin, Mathieu, & Descoteaux, 2014) , or MI-Brain  (Rheault et al. , 2016)  (Figure  1 ). Once a bundle of interest is segmented from a tractogram, the analysis varies according to the research question. It is common to report asymmetry or group difference in bundle volume  (Catani et al.
| Quantifying reproducibility in tractography When performing segmentation, it is crucial that raters perform the tasks as closely as possible to the dissection plan. Even if a single individual performs all segmentations, the possibility of mistakes or erroneous decisions about landmarks exists  (Boccardi et al. , 2011; Entis et al. , 2012; Frisoni et al. , 2015). High reproducibility is often an assumption, if this assumption is false, the consequence could lead to inconsistent outcomes and erroneous conclusions. To assess the level of reproducibility of raters, identical datasets need to be segmented blindly more than once  (Frisoni et al. , 2015; Gisev, Bell, & Chen, 2013; Gwet, 2012). The literature on the subject, specifically for tractography, is quite sparse. Reported values for intra-rater and inter-rater variability are sometimes hidden in Section 2.
, 2007; Yendiki et al. , 2011). The first, and probably most complete, publication on the subject of protocol reproducibility assessment was from  Wakana et al. (2007). The acquisition and algorithmic choices for tractography could be considered suboptimal (low spatial/angular resolution, diffusion tensor) and the framework inadequate for the now more common large-scale collaborations. The publication, despite providing a robust design, had a limited number of raters and duplicated data. The importance of reproducibility assessment, sparse literature on the subject, and limited availability of protocols support the need for the work presented in this study. To come back to tractography, the main message of our work is simple: Any study involving a manual segmentation protocol must provide a quantification of its measurement error, if it was never F I G U R E 1 Illustration of the dissection plan of the PyT using the MI-Brain software  (Rheault, Houde, Goyette, Morency, & Descoteaux, 2016).