dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis All analyses were carried out in R 3. 5. 1  [53]. Because total abundance contained non-integers even before rescaling, it was log(x + 1) transformed before modelling with Gaussian errors. The studies in the dataset vary widely in many aspects of sampling. We therefore fitted mixedeffects models (as implemented in lme4 version 1. 1. 18. 1  [54]  with bobyqa numerical optimisation) to reduce heterogeneity caused by among-study differences in sampling methodology and macroecological gradients such as latitude. Our previously listed hypotheses were tested by comparing a maximally complex model with three simpler models (see Additional file 1 for model structures) that lacked the hypothesised effects. The full model included six main fixed effects-land-use type and intensity (LUI), above-ground or soil assemblage (habitat layer), and the four soil properties (rescaled to the range 0-1 to aid fitting)-plus each soil property's interaction with land-use type and habitat layer, the interaction between land-use type and habitat layer, and the three-way interactions of each soil property with land-use type and habitat layer.
Acknowledgements This is a contribution from the  Imperial College Grand Challenges in Ecosystems  and the  Environment Initiative. We thank the following who either contributed or collated data:  Dr Andrew D. Barnes ,  Dr David Bogyó ,  Professor Céline Boutin ,  Joanna Zawadzka ,  Ilona Leighton-Goodall ,  Keiron Brown  and the  Earthworm Society of Britain. This study is part of the  PREDICTS  project, which is endorsed by the  GEO BON.
Dimensionality reduction of the two expression datasets was then performed individually using tSNE (23,937 mappable genes in the Affymetrix data and 24,917 mappable and encodable RNAseq gene transcripts). Next, we identified 1793 genes differentially expressed among the resultant expression clusters that were common to both datasets using analysis of variance (P < 0. 05). Unsupervised hierarchical clustering of these 1793 genes using the RNAseq data yielded seven tumor clusters (Fig. 1c ), similar to tSNE (Fig. 1e ). Tumors with MN1 rearrangement, including five containing the MN1-BEND2 gene fusion (Table  1 ), formed a discrete RNA cluster, as did all BRAF V600E -mutant tumors combined with three tumors without BRAF  V600E. The latter showed mutations of other MAPK, and frequently PI3K/AKT/ mTOR pathway genes (Fig.
4d [d-f] ). MN1 and BEND2 immunoreactivity were also confirmed by western blot in a MN1-BEND2 case with available frozen tumor (C29) (Supplementary Fig. 4 ). MN1-BEND2 tumors also demonstrated strong cytoplasmic IGF2 immunoreactivity (Fig. 4e [a-c ]), which was absent or weak in non-MN1-BEND2 AB-like tumors (Fig. 4e [d-f] ). MN1-BEND2 and MAPK-ABC tumor gene expression overlaps that of NEC and vRG, and tRG and oRG, respectively. We next pcw when cellular proliferation in the developing brain peaks. Indeed, H19 message is enriched in human vRG as determined by single cell RNAseq  47. The period prior to 25 pcw corresponded to the highest initial expression of NEC genes within the database, e. g. , SOX1, DACH1, PROM1, NOTCH1, NOTCH2, ITGA6 and NRARP; vRG genes, e. g. , H19, FOXJ1 and HES1; the tRG gene PALLD, and a few oRG genes, e.
g. , SHOX and ZFX. Deleterious SNPs and other non-fusion mutations of XIE genes occurred slightly more frequently in MAPK-ABC tumors (5. 0/ case) and the BRAF V600E -mutant subgroup (5. 3/case) compared to MN1-BEND2 cases (4. 0/case), and in females (5. 9/case) compared to males (3. 0/case) (Supplementary Fig. 11B , Supplementary Data 2). However, fusions involving XIE genes occurred 3. 6-fold more frequent in MN1-BEND2 tumors (4. 0/case) than MAPK-ABC tumors (1. 2/case) and 4. 6-times as frequent in females compared to males (2. 3 vs. 0. 5/case, respectively). Expression of AB-like tumor genes correlate with gene promoter methylation. In order to confirm that differential expression of AB-like tumor genes can be, in some cases, attributable to differential promoter methylation, we integrated RNAseq expression and DNA methylation data using the mCSEA R package  59  , which correlates differentially methylated regions (DMRs) with expression of nearby downstream genes.
c Heatmaps for expression of subsets of XIE genes from the Affymetrix data. IGF2 is absent in the NCBI annotation for the HTA 2. 0 array as of April 2021. IGF2-AS (arrow) may serve as a surrogate marker for IGF2 and was upregulated in MN1-rearranged tumors and normal choroid plexus (choroid plxs). The latter develops as an out-pouching of the ependyma and is known to express IGF2 128. The XIST gene message is indicated by the arrowhead. MN1-rearranged tumors are highlighted in red, MAPK-AB in blue, and MAPK-C in purple. Complete source data for heatmaps are available as Supplementary Fig. 8-   involved the medulla or spinal cord and some clustered near HGNET-MN1 by tSNE analysis of DNA methylation  70. All but one in these anatomic regions occurred in males. Two frontal lobe cases were in females.
Informed consent for the use of archival biopsy or autopsy tissue was not obtained, nor required by the institutional review board. No patients or patient families were compensated for these studies. Institutional review board approval from the University of Louisville was obtained for use of control and case material (Approval number 17. 0984). MN1-rearrangement was determined by FISH (cases C3, C7, C10, C13, C16, C21, and C29 5 ) and confirmed by CNV analysis using the Illumina EPIC BeadChip 850K microarray data from these and two additional samples (C43 and C44). DNA methylation. Genomic DNA methylation analysis was performed on an additional fourteen tumors (C8, C15-18, C21, C27, C31, C35-36, C41-44) using the Illumina EPIC BeadChip 850K microarray similar to as previously described  6, 111, 112.
PCR product (700 ng) was hybridized overnight to SureSelect XT Human All Exon V5 + lncRNA baits. The hybridized fragments were captured on magnetic beads and PCR-amplified using uniquely barcoded index primers. Barcoded libraries were pooled and underwent paired-end 150 bp sequencing on an Illumina HiSeq4000 instrument (Illumina, San Diego, CA). For gene expression, read alignment was performed with hisat2-2. 0. 5  113  to human genome GRCh38 and aligned files were converted to bam and sorted with samtools v1. 3. 1  114. Alignment QC was generated with RSeQC v2. 6. 4  115. Counts from the subread package v1. 5. 1  116  were used to count the number of reads aligning to Ensembl IDs according to GRCh38. 86 annotation. Fusion detection was performed using JAFFA  117  after merging across replicate lanes.
After overnight hybridization, the hybridization cocktail mix was extracted from the array. Each array was washed and stained using Affymetrix GeneChip Fluidics Station 450 according to the user manual (P/N 08-0295) and the arrays were scanned using GeneChip Scanner 7G. Each array image was collected using the GeneChip Command Console, QC, saved as. cel files and saved for further analysis. RNA expression analysis, dimensionality reduction, and clustering. For the RNAseq data, transcripts with 0 counts > 50% were removed. Data were normalized with Limma-voom R package with quantile normalization  123. The Ensembl IDs were mapped to unique gene symbols using the Bioconductor org. Hs. eg. db package (ver: 3. 13. 0) using median summarization. For the microarray data, probe-level data were generated by analyzing the raw signal intensities from the raw Affymetrix CEL data files for each probe set using the rma algorithm for background intensity correction and normalization in the oligo package (ver: 1.
56). Batch effect related to the sample preparation kit was reduced using the combat function of the sva package (ver: 3. 4). The probes were mapped to unique gene symbols using the hta20transcriptcluster. db package (ver: 8. 8) with median summarization. Differential gene expression between groups of samples in RNAseq and Affymetrix datasets was performed using analysis of variance thresholding with a two-sided p-value to 0. 05. Expression values of the genes differentially expressed in both datasets were scaled and denoted on a heatmap with the respective samples. Heatmaps in Figs. 1, 5, 8, S1-S3, S7, S8, and S11, S12 were generated by unsupervised hierarchical clustering. Dimensionality reduction of the expression data with t-Distributed Stochastic Neighbor Embedding (tSNE) was performed to facilitate visualization using the following parameters (dimensions = 2, perplexity = 4, iterations = 5000) using the Rtsne package (ver: 0.
15). All analyses were performed using R software (ver: 4. 1). Gene set enrichment analysis (GSEA). GSEA was performed using GSEA software from the Broad Institute, Cambridge, MA (ver: 4. 1) 124,125 using the following settings (permutations: 1000; permutation type: gene_set; enrichment statistic: weighted; metric for ranking genes: t Test). Differential gene promoter methylation and expression correlation. Raw methylation files were read and normalized using the sesame package (ver: 1. 10. 4) with the default settings. Differentially methylated gene promoters were identified using the mCSEA package (ver: 1. 12). Correlation between methylation of gene promoters and expression levels was quantified using the mCSEAIntegrate function. This correlates differentially methylated regions (DMRs) with the expression of nearby downstream genes  59.
Raw data output was imported into nSolver for normalization (http://www. nanostring. com/products/nSolver). For analysis, data are the geometric mean normalized and then log10. For heatmap generation, data were scaled and truncated as follows. For scaling, the data are mean centered and divided by the SD of the miR. Truncation was done at a value of 4. 0 to adjust for rare very large outlier values in the matrix. Immunohistochemistry. Immunohistochemistry was done using 4 µ thick sections of FPPE tissue. Immunohistochemical stains for GFAP clone EP672Y (Cell Marque 258R-16, lot 32653, 1:25), MN1 (Proteintech 24697-1-AP, lot 00021048, 1:40) and IGF2 (Abcam; ab9574, lots GR31975-61, and GR31975-64, 1:500) were performed on the Leica Bond III using Leica Epitope Retrieval 1 (Leica; AR9961) for GFAP, and Epitope Retrieval 2 (Leica; AR9640) for MN1 and IGF2.
Uncropped and unprocessed scans of the most important blots are supplied in the Source Data file. Animals. Animal studies were performed in accordance with the guidelines of the European Community and French Ministry of Agriculture and were approved by the "Direction deṕartementale de la protection des populations -Paris (authorization 9343201702211706561). Mice used in this study are Centrin2-GFP (CB6-Tg(CAG-EGFP/CETN2)3-4Jgg/J; The Jackson Laboratory). Animals were grouphoused with free access to food and water in controlled temperature conditions (room temperature controlled at 21-22 °C, humidity between 40 and 50%), and exposed to a conventional 12-h light/dark cycle. Experiments were performed on embryos at embryonic (E) days E16, and neonate pups at postnatal day 0-10 (P0-10) of both sexes.
Acknowledgements We thank:  Elizabeth Rushing ,  University Hospital of Zurich, Switzerland; Mohanpal Dulai, Trillium Health Partners, Mississauga, Ontario ;  Michael Nowacki ,  Norton Hospital ,  Louisville KY ;  Roger McLendon ,  Duke University, Durham, NC; Matthew Schniederjan, Emory University, Atlanta, GA ;  Marta Couce ,  Case Western Reserve University, Cleveland , OH;  Maria-Magdalena Georgescu ,  Louisiana State University Health Shreveport, Shreveport, LA ;  Cheryl Palmer ,  University of Utah, Salt Lake City, UT ;  Mousa Al Abbadi ,  University of Jordan, Amman, Jordan ; and  Werner Paulus ,  University of Munster, Münster, Germany  for previously contributing cases,  Eric Lipp ,.
Data availability The raw and processed RNAseq, Affymetrix, MethylationEpic BeadChip, and Nanostring microRNA data generated in this study are deposited at the public repository Gene Expression Omnibus (GEO) under the accession numbers GSE165351, GSE165813, GSE166569, and GSE196697, respectively. The remaining data are available within the Article, Source Data File, Supplementary Data, and Supplementary Information. Source data are provided with this paper.
Abbreviations: AFL, adult frontal lobe; AAM, adult amygdala; ACP, adult choroid plexus; ATH, adult thalamus; FCE, fetal cerebrum; FCM, fetal cerebellum; FCG, fetal germinal matrix; FHP, fetal hippocampus; PCM, pediatric cerebellum, PHP, pediatric hippocampus; PTH, pediatric thalamus. d tSNE analysis of RNAseq expression data. e Hierarchical clustering and f tSNE analysis of Affymetrix HTA2 RNA expression data. Classical PXA tumors cluster separately from MAPK-ABC AB-like tumors demonstrating that they are separate biologic entities. Source data: Supplementary Fig. 1, Gene Expression Omnibus (GEO) accession numbers GSE165351, GSE165813. Case No. Age Gender Location Ki-67% Mitoses (per 10 hpf) Necrosis Survival (months) MN1-rearranged MN1-BEND2 C3 C10 C13 C21 C29 C44 C7 b C43 b C16 16 12 9 4 18 9 33 10 8 F F F F F F F F M L Parieto-occipital R Frontoparietal R Frontoparietal L Parieto-occipital L Parietal R Parietal L Temporal R Parietal L Temporal 20 4 25 15 5 NA 10 10 0 3 0 14 3 1 NA 3 6 0 + -+ + -+ + + - 138 a 147 a 68 a 131 a 227 a 48 a 120 a 120 a 144 a MAPK Supercluster MAPK-A BRAF-V600E C6 33 F R Frontal 5 2 - 59 BRAF-V600E C9 BRAF-V600E C11 38 25 F F R Frontal R Occipital 10 20 2 4 -+ 123 141 a BRAF-V600E C12 BRAF-V600E C23 BRAF-V600E C24 12 20 20 F F F R Frontal R Temporal L Parietal 17 22 10 3 3 2 + + - 2 15 a 79 a C14 71 F L Occipital 4 5 + 18 MAPK-B Group C C20 C39 C8 C17 C31 C18 C19 55 20 40 20 28 9 36 M F F F F M M L Parietal L Parietal R Temporal L Occipital R Frontal L Parasagittal R Frontal 25 35 NA 10 6 3 45 2 11 7 3 0 0 10 + + -+ --+ 40 132 a 168 a 279 a 25 a 222 a 252 a C22 9 M R Parietal 15 2 - 47 Others STE ATAB C27 C1 C30 b C38 C40 C4 42 10 19 14 25 44 M F M M F F NA L Parietal L Frontal R Temporal R Frontal L Temporo-occipital 25 NA 1 60 20 4 0 0 10 3 0 75 --+ + -- NA 208 a 12 a 154 a 67 a NA c C26 58 F R Parietal NA 5 + 184 C35 76 M L Frontotemporal 10 5 + 7 NTD C15 40 M R Temporal 10 5 - 2 C41 C42 57 19 M M Temporal L Temporal 14 2 3 0 + - 28 14 a.
5MN1-BEND2 and MAPK-ABC tumor-associated genes demonstrate fetal age-specific expression and cluster with vRG and oRG marker genes, respectively. The developmental expression time course of NSC markers and select genes overexpressed or mutated in MN1-BEND2 and MAPK-ABC ABlike tumors was obtained from the Allen Human Developmental Transcriptome database and subjected to unsupervised hierarchical clustering. MN1-BEND2 tumor-associated genes (relatively overexpressed or mutated) are depicted in red and MAPK-ABC tumor associated genes are in blue. Genes associated with NEC, vRG, tRG and oRG are indicated. The x-axis age bar progressive color scheme is arbitrary. Transcript expression is normalized by reads per kilobase of transcript per million mapped reads (RPKM) to compensate for RNAseq generation of more sequencing reads from longer RNA molecules.
This could imply that a genetic selection should be directed towards the varieties with large organ sizes, large nectar reservoirs, or other flower features that correlate with the nectar quality or quantity-related traits  [5, 13]. Quantitative data on such traits in relation to the pollination efficiency, on the other hand, is scarce. The kinetics of nectar secretion are also not consistent throughout the day, making quantification more difficult. In Cucurbita pepo, nectar secretion is dependent on the daytime and the sex of the flower. Some plants also have the ability to reabsorb most or all of the unconsumed nectar  [14, 15]. Nevertheless, when discussing the floral traits which are suitable for breeding, it was suggested that any character that enhances plant-pollinator interactions and concurrently bears a high genetic variance is a potential target  [16, 17].
Reconstruction of 3D Images All the resulting projection images were reconstructed using the NRecon software (v. 1. 7. 3. 0, Bruker-micro-CT, Kontich, Belgium) with post-alignment, beam hardening corrections (41%), and a ring artifact reduction  (8). Three-dimensional images were cropped, rotated, and registered using the Data Viewer program (v. 1. 5. 6. 2 64 bit, Bruker-micro-CT, Kontich, Belgium) in order to optimize and facilitate the measurements on the different data sets.
Computation of Nectary Surface and Volume Image processing tools, programmed in Python, were used on each micro-CT dataset in order to virtually extract the 3D structure of the nectary. The first step is the creation of a volume of interest starting from the first slide to the last slide where the 3D delimitation of the nectary area is observed. A threshold is then used in order to separate the voxels from the cellular tissues of the flower and those from the air. A Z-projection of the height of each element is generated, with the grey level corroborating with the height of the elements. Watershed segmentation is then used to obtain the contours of the compartments. The external contours of the nectary are selected using the threshold. The lower contours are computed by interpolation from the external contours, since they could not be obtained by the threshold.
Statistical Analysis All statistical analyses were performed with the RStudio software, except for the linear regression and correlation analyses, which were done in Excel. The RStudio packages used are: tidyverse, ggpubr, outliers, and rstatix. For the comparison of male and female samples, the means of four different parameters were analyzed: the flower width, nectary cross-sectional area, nectary surface area, and volume values. Each population was tested with a Shapiro test to check if it follows a normal statistical distribution. A second step consisted of carrying out a Levene's test, which determined if the population variances were equal or unequal. All Shapiro tests demonstrated that the samples are parametric. The Levene's test result revealed equal variances between the samples for the parametric values for two flower parameters: the width and cross-sectional area, for which a Student's t-test was applied.
Nevertheless, adjusting the sample inside a polystyrene block was sufficient to avoid flower vibrations, generating a clear image. With this setting, the micro-CT imager allowed for the mounting and acquisition of the scans in about 30 min. We performed the scans at two resolutions. Scanning of whole male (Figure  3a, c ) and female flowers (Figure  3b, d ) was acquired in three adjacent CT slices at a resolution of 7. 5 µm/px. Then, the scans were automatically merged with the NRecon Bruker software. The initial dataset of a single female flower contained more than 6000 files, representing about 124 Gigabytes (GB). However, after reconstruction and cropping, the dataset size was reduced to about 41 GB. The micro-CT 3D reconstruction of whole male and female flowers showed the entire external flower structure with the two common parts (corolla and calyx) and the hairy distinct one aborted ovary versus ovary.
Cross-sectional views provided inner details on the sexual reproduction components and nectaries. Scans focused on the nectaries were acquired as a single CT slice, at a resolution of 5 µm/px (Figure  4c-j ). The initial dataset of the focused scan contained 1029 files, representing about 20. 7 GB. Reconstruction and cropping reduced the dataset size to about 4 GB for the male flowers and 7 GB for the female flowers. The image datasets, coming either from the micro-CT software or the Bruker 3D suite, were saved as images in a 16-bits tiff file format, and as such can be accessed using almost any image-processing software.
18 µL). The recorded nectar volume (μl) was then used to project the nectar volume level inside each flower type, allowing a precise 3D overview of it (Figure  6c-f ). To manipulate and process each 3D dataset, we used the Python language libraries. We defined a grey level threshold to select only the air-filled regions (the darker pixels) inside the flower. Using the linear image resolution of 5 μm/px, we calculated the volume represented by each voxel (3D pixel) according to the following formula: 1 voxel (Vox) = 1. 25×10 -7 μl (5 × 5 × 5 = 125 μm 3 ) Next, starting at the flower base and scanning upward, each voxel of the selected air-filled area was kept until reaching the recorded volume of the sampled nectar. This volume of interest is extracted and saved as a new 3D dataset, which can be displayed as an overlay on the initial greyscale images.
To reconstruct a honey bee visitation event, we first acquired 3D images of the honey bee morphology (Figure  7a ) and interior organs (Figure  7b ). In the morphology scan, the topological and geometric properties of the abdomen, thorax, and the head are easily identified. In the anatomy scan, the heart, the honey sac (crop), the flight muscles, the brain, and the proboscis can be easily distinguished (Figure  7b ). To ease the manipulation of the 3D dataset, we made a subsample of the 3D rendering by selecting only the honey bee's head. To assess the flower accessibility, we performed a modeling of the honey bee visitation by superimposing three 3D datasets: the honey bee, melon  The recorded nectar volume (µL) was then used to project the nectar volume level inside each flower type, allowing a precise 3D overview of it (Figure  6c-f ).
To manipulate and process each 3D dataset, we used the Python language libraries. We defined a grey level threshold to select only the air-filled regions (the darker pixels) inside the flower. Using the linear image resolution of 5 µm/px, we calculated the volume represented by each voxel (3D pixel) according to the following formula: 1 voxel (Vox) = 1. 25 × 10 -7 µL (5 × 5 × 5 = 125 µm 3 ) Next, starting at the flower base and scanning upward, each voxel of the selected airfilled area was kept until reaching the recorded volume of the sampled nectar. This volume of interest is extracted and saved as a new 3D dataset, which can be displayed as an overlay on the initial greyscale images. To reconstruct a honey bee visitation event, we first acquired 3D images of the honey bee morphology (Figure  7a ) and interior organs (Figure  7b ).
In the morphology scan, the topological and geometric properties of the abdomen, thorax, and the head are easily identified. In the anatomy scan, the heart, the honey sac (crop), the flight muscles, the brain, and the proboscis can be easily distinguished (Figure  7b ). To ease the manipulation of the 3D dataset, we made a subsample of the 3D rendering by selecting only the honey bee's head. To assess the flower accessibility, we performed a modeling of the honey bee visitation by superimposing three 3D datasets: the honey bee, melon flower, and the reconstructed nectar volume. The honey bee's head was placed deep into the flower (Figure  7c, d ), depending on the available space between the petals and the surrounding floral organs. This resulted in a plausible position in which the honey bee's proboscis easily reaches the bottom of the flower.
The main challenge we encountered during the analysis of flower datasets is the residual nectar left inside the flowers prior to the scanning. If a significant amount remains inside the flower, it hinders the identification of the boundaries between the individual micro-CT slices, altering the organ morphology assessment during the 3D image processing. The investigation of the two flower types by micro-CT allowed for the visualization of the surface and internal floral features (Figures  2  and 3 ). Surface organs such as trichomes on the calyx can be easily revealed and quantified  [60] [61] [62]. Flower trichomes have been associated with the attraction of pollinators, the repulsion of non-winged insect pests, or the regulation of the temperature inside the flower  [63]. As floral shape variation often reflects the differences in plant pollination systems, particularly for plant species with specialized plant-pollinator interactions  [64] , studying floral geometric measures using micro-CT can be instrumental not only for flower development, but also for plantpollinator co-evolution.