dataesr/scientific-paragraphs-categorization huggingface dataset:
The cotton HILIC purification was performed as described but with 150 l pipetting volume, while elution was kept at 10 l mQ water. MALDI-TOF-MS Analysis-For mass spectrometric analysis, 5 l of derivatized N-glycans were spotted onto an anchor chip MALDI target plate (Bruker Daltonics) and cocrystallized with 1 l of 1 mg/ml superDHB in ACN/mQ (1/1, v/v) containing 1 mM NaOH. Samples were allowed to dry at room temperature and were then recrystallized with 0. 5 l 5 mg/ml superDHB in ACN/mQ (1/1, v/v) containing 1 mM NaOH. MALDI-TOF-MS spectra were acquired using an Ultrafle-XtremeTM mass spectrometer in the positive-ion reflector mode, controlled by FlexControl 3. 4 software Build 119 (Bruker Daltonics). The instrument was calibrated using a Bruker peptide calibration kit. Spectra were obtained over a mass window of m/z 1000 -5000 with ion suppression below m/z 900 for a total of 10,000 shots (1000 Hz laser frequency, 200 shots per raster spot during complete random walk).
Tandem mass spectrometry (MALDI-TOF-MS/MS) was performed for structural elucidation via fragmentation in gas-off TOF/ TOF mode. Data Processing of MALDI-TOF-MS Spectra-A mean average spectrum over all the total cell line sample spectra was generated using an in-house developed script in Python 2. 7. 3 (Python Software Foundation; http://docs. python. org/py3k/reference/index. html). The average spectrum was internally recalibrated using glycan peaks of known composition (Supplemental Table  S2 ), smoothed (Savitzky Golay algorithm, peak width: m/z 0. 06, four cycles), and baselinecorrected (Tophat algorithm) using FlexAnalysis Software (Version 3. 3; Bruker Daltonics). Peaks of signal-to-noise Ͼ 2 were picked, manually revised, and analyzed in GlycoWorkbench 2. 1 stable build 146 (http://www.
eurocarbdb. org/) using the Glyco-Peakfinder tool (http://www. eurocarbdb. org/ms-tools/) for generation of a glycan compositions list. Using MassyTools version 0. 1. 5. 1, which is a novel in-house software developed for automated data processing, the resulting glycan peak list generated based on the average spectrum was used for targeted data extraction of the area under the curve for each of the CRC cell line N-glycomic profiles  (28). Within this software, background was determined dynamically and subtracted from intensities of all isotopic peaks prior to calibration of each spectrum and targeted data extraction. Several quality parameters are calculated in order to assess the quality of each individual spectrum as well as picked glycan peaks and allow for good quality data selection. Based on quality parameters, the composition list was reviewed, and only the glycans exhibiting a correct isotopic peak pattern and an average signal noise per cell line above 6 in at least 50% of the spectra of a specific cell line (including both technical and biological replicates) were used for final data extraction.
Selected glycan compositions were confirmed by MS/MS. The final peak list as well as MS/MS data are listed in Supplemental Table  S3. Further, glycan profiles had to pass at least two out of three quality criteria for inclusion: (i) total intensity Ͼ 1 ϫ 10 5 , (ii) fraction of analyte area with signal noise Ͼ 6 is more than 80%, (iii) fraction of glycan analyte intensity in total spectrum intensity Ͼ 40%. The area-under-the-curve values were rescaled to a total relative intensity of 100% for each spectrum. For the comparison of cell surface and residual pellet N-glycomes, a condensed peak list was used due to the slightly lower spectral quality which resulted in the detection of less analytes. Data and Statistical Analysis-Averages, standard deviations, and relative standard deviations were calculated for all biological replicates of the cell lines (Supplemental Table  S4 ).
Preprocessed, selected, and rescaled total cell N-glycome data was imported into SIMCA software Version 13. 0 (Umetrics AB, Umea, Sweden), and a principle component analysis (PCA) was performed to reveal outliers and batch effects and to study the effects of possible confounders. A PCA displays the variation in the data on new vectors (principle components) and makes it possible to visualize the differences in the data in a two-dimensional way. The PCA is an unsupervised model that finds "natural" variation in the data without over-fitting. The observations (ϭ cell line samples) are displayed in the score plots, while the variables (ϭ relative intensity values for each glycan) are displayed in the loading plots. The glycans located at the outskirts of the loading plot are those contributing most to the displayed principle components.
Relative intensities were first summed according to N-glycan types (pauci-mannose, high-mannose, hybrid, and complex type). Data were then rescaled to 100% excluding high-mannose-type glycans to prevent influences of possible high-mannose-type intracellular precursors. Then, additional glycan-derived traits for complex-and hybrid-type glycans were calculated (mono-fucosylation, multi-fucosylation, ␣2,6and ␣2,3-sialylation, ratio Hex versus HexNAc, and number of antennae) and compared (Supplemental Table  S5 ). Mann-Whitney and Kruskal-Wallis with Tukey's posttest with significance level ␣ ϭ 0. 05 were performed in GraphPad Prism Version 5. 04 (GraphPad Software, Inc. , La Jolla, CA) to explore differences of glycan traits with cell line characteristics such as stage of the original tumor, tumor site (primary versus metastasis), and differentiation.
Fragmentation spectra were analyzed using Data Analysis 4. 2 Build 387 (Bruker Daltonics). Analysis on AA-labeled glycans was exclusively used for structural elucidation (Supplemental Table  S3 ) and targeted search for indicative fragment ions of blood group antigens (Supplemental Table  S6 ). RNA Isolation and cDNA Synthesis-For mRNA analysis, 1 million of the cultured cells were transferred to RNase-Free Eppendorf tubes, centrifuged 5 min at 300 ϫ g, pelleted, and lysed in 500 l of lysis buffer. Lysated cells were stored at Ϫ80 °C until mRNA was specifically isolated by capture of poly(Aϩ) RNA in streptavidin-coated tubes using a mRNA Capture kit. cDNA was synthesized using a Reverse Transcription System kit following the manufacturer's guidelines. Lysates were incubated with biotin-labeled oligo(dT)20 for 5 min at 37 °C and then 50 l of the mix was transferred to streptavidincoated tubes and incubated for 5 min at 37 °C.
Glycosyltransferase mRNA Data and Statistical Analysis-To calculate the relative abundance of the genes, the formula 100 ϫ 2 (Ct glyceraldehyde-3-phosphate dehydrogenase -Ct glycosyltransferase) was used, where Ct is the cycle threshold. In this formula, the Ct value is defined as the number of PCR cycles at which the SYBR-green fluorescent signal exceeds the threshold of 0. 2 relative units  (32). Averages were calculated for nine technical replicates and the three highest and lowest values were colored in green and red, respectively (Supplemental Table  S7 ). In order to improve comparability, mRNA data was as well LOG2 transformed. To assess the quality and discriminative power, GT mRNA data were imported into SIMCA software and a PCA model was built as described above. The nine technical replicates were used as individual cross-validation groups.
The median across the included cell lines was calculated and GT mRNA data was compared with relative abundances of corresponding glycan traits based on the total N-glycome. Both derived traits based on MS data as well as GT gene expression data were imported into GraphPad Prism (GraphPad Software), and a linear regression model was calculated. CDX1-and villin expression data were based on  (33) (34) (35)  while coexpression data with Pearson correlations was retrieved from the Cancer Cell Line Encyclopedia (Supplemental Table  S8 ) via the database http://www. cbioportal. org/ (36, 37).
Applying an unsupervised principle component analysis (PCA) revealed only little variation between replicates (technical as well as biological) of each cell line as seen by close clustering of scores (Supplemental Figs. S2A  and 2B ). The dataset was further analyzed for possible batch effects. PCA scores were colored according to batches (Supplemental Fig. S2C ), and technical replicates distributed on different plates as well as different sample preparations were compared. Based on this comparison, batch effects were found to be minor and no correction was applied. Relative intensities for all glycans in the biological replicates of the cell lines with standard deviation and relative standard deviation are given in Supplemental Table  S4. Relative standard deviations were below 20% for peaks with relative intensities above 5%, with exception for cell line C10, showing slightly higher variation.
The relative abundance of high-mannose-type N-glycans on the cell surface with respect to the residual pellet is given in Fig. 2B , showing that high-mannose-type N-glycans are expressed on plasma membrane proteins but to a lower extent as compared with their presence as intracellular precursor. The cell line C10 is an exception and shows similar levels of high-mannose-type glycans on the cell surface and the residual pellet. An exemplary MS spectrum comparing the cell surface N-glycome of CaCo2 cells with the residual pellet containing intracellular precursors is shown in Supplemental Fig. S4. For further data analysis, a PCA model based on biological replicates (averaged technical replicates) was generated using SIMCA software. This resulted in a model explaining 84. 4% (R 2 Xcum) of the data with 13 principal components (PCs) featuring a good prediction power of 65.
Neuroimaging study We used VBM to investigate the neuroanatomical correlates of anterograde memory performances in the MCI patients. To map the regions of atrophy related to the memory deficit, we tested correlation with the GM volume at a voxel level with the scores on both memory tests in the patients. Each participant underwent a high-resolution anatomical MRI scan at inclusion. T1-weighted threedimensional anatomical images were obtained using VBM analyses included image pre-processing and statistical analyses. These steps were carried out using the SPM12b software package (Wellcome Department of Imaging Neuroscience, London; http://www. fil. ion. ucl. ac. uk/ spm) running on Matlab R2010a (MathWorks, Natick, MA, USA). Anatomical MRI images were spatially preprocessed using standard procedures  [61].
For each detected cluster, partial correlation analyses were conducted between the mean GM volume of the cluster and FCSRT or DMS-48 scores while taking into account the same set of nuisance covariates (results shown in Additional file 1: Figure  S1 ). When no correlations were found using FWE, a less stringent statistical threshold of P < 0. 001, uncorrected, was considered. A cluster spatial extent of 50 voxels was used in this case to avoid irrelevant and isolated detections, unless no correlation over 50 voxels was found at all. The software Xjview (http://www. alivelearn. net/xjview8/) allowed us to identify the brain regions and to determine the number of voxels within each region included in each cluster. In the present work, we refer to the hippocampus according to the AAL atlas, namely as the hippocampus proper plus the dentate gyrus and uncus  [63].
Acknowledgements The authors are grateful to the patients and their families. They would also like to thank  Barbara Jung ,  Clélie Phillipps ,  Nadine Longato ,  Catherine Kleitz ,  Mathias Bilger ,  Laetitia Monjoin ,  Emmanuelle Ehrhardt ,  Jenny Kemp ,  Laure Di Bitonto ,  Natacha Vogt ,  Laetitia Berly ,  Timothée Albasser ,  Anne Botzung ,  Mélanie Stackfleth ,  Xavier De Petigny ,  Christine Delmaire  and  Nassim Jeremie  for their participation in this study.
05. results are presented for a sample size of 3,000 subjects and with the default assumption of lack of between-assay error, unless otherwise specified. Simulations and analyses were performed using Stata/Se, version 13 (Statacorp, college Station, tX). Our code for the continuous outcome is provided in the Supplemental Material (eappendix 2, the code for the binary outcome is available upon request; http://links. lww. com/ eDe/B27).
Impact of Exposure Misclassification on Type 1 Error When the real effect of exposure was assumed to be null and one biospecimen was used to assess exposure, the P value of the parameter associated with exposure was below 0. 05 for    5% of the simulated datasets, whatever the icc considered (etables 5, 6; http://links. lww. com/eDe/B27), suggesting that under the hypotheses of our simulations, using one biospecimen to assess exposure did not increase the risk of type 1 error. For a given number of biospecimens, the risk of type i error was slightly higher with SiMeX and regression calibration (range: 5%-8%) compared with the pooling approach (range: 4%-6%, etables 5, 6; http://links. lww. com/eDe/B27).
These complexes degrade plant cell wall polysaccharides into soluble sugars which are subsequently imported and metabolized by the bacterium. The genome of R. cellulolyticum encodes sixty-two cellulosomal enzymes typically carrying a dockerin module  [5]. This module allows them to be incorporated into the cellulosomal complexes through high affinity interaction with the cohesin modules of the cellulosomal scaffolding protein (CipC)  [6]. The catalytic modules of the plant cell wall degrading enzymes belong mostly to the glycoside hydrolase (GH) and to a lesser extent, to the pectate lyase, and the carbohydrate esterase families, as classified by the CAZY database (http://www. cazy. org/) [5, 7]. In addition to the genes encoding cellulosomal enzymes, twelve other additional genes are predicted to encode non-cellulosomal secreted glycoside hydrolases.
According to the CAZY database, the GH5 catalytic domain of Cel5I belongs to the subfamily 2, in which most of the characterized members exhibit an endoβ-1,4-glucanase activity  [20]. The genome of R. cellulolyticum encodes another six GH5 enzymes. Unlike Cel5I, they all contain a dockerin domain allowing them to integrate in cellulosomes. Among them two endoglucanases, Cel5A and Cel5D, and a mannanase, Man5K, were previously characterized  [21, 22, 23]. A former study of cel5I expression in R. cellulolyticum revealed that the gene is transcribed as a monocistronic transcription unit and regulated through a carbon catabolite repression mechanism  [13]. The expression level of cel5I was reported to be ten times higher when R. cellulolyticum is grown on cellulose compared to cellobiose as substrate.
Cells were afterwards washed, and incubated with the secondary antibody (1/400, anti-Rabbit IgG, Hilyte Fluor Tm 488-labeled, Eurogentec) for 1 h in the dark. Observation was carried out with an inverted epifluorescence microscope (Nikon TiE-PFS) coupled to a CCD camera (Hamamatsu OrcaR2). A 100x NA1. 3 oil PhC objective and adequate filter set (Semrock HQ GFP) were used to record contrast phase and green fluorescence images. Analysis was performed using Image J software to combine contrast phase and GFP pictures for every field. GFP distribution of single cells cross section was measured with the plugin RGBProfilesTool. Data represent the mean of ten individual cells.
Bioinformatics analysis Predictions of domains from Cel5I amino acids sequence were performed using on line tools: Interpro (http://www. ebi. ac. uk/interpro/), Signal P (http://www. cbs. dtu. dk/services/SignalP/), Superfamily (http://supfam. cs. bris. ac. uk/SUPERFAMILY/index. html), CATH (http://www. cathdb. info/) and ScanProsite (http://prosite. expasy. org/scanprosite/) [48] [49] [50] [51] [52]. Amino acid sequences were compared with those in the NCBI database using the BLAST program  [53]  (http://blast. ncbi. nlm. nih. gov. gate1. inist. fr/Blast. cgi). Multiple sequence alignments were performed with the ClustalW2 program (http://www. ebi. ac. uk/Tools/msa/clustalw2/)  [54].
Whole-genome sequencing and processing PLGGs and normal controls from CBTTC/CHOP and DFHCC/PLGA Consortium were sequenced at BGI@CHOP, and The Broad Institute of MIT and Harvard. DNA was randomly fragmented, and libraries prepared for paired-end sequencing on an Illumina HiSeq 2000. Sequencing files from recently published PLGG datasets were accessed  1 , 2. Read pairs were aligned to reference genome hg19 (Build 37) using the Burrows-Wheeler Aligner (bwa) with options -q 5 -l 32 -k 2 -o 1  3. Reads were sorted by coordinates, normalized, cleaned and duplicates were marked using SAMtools and Picard. Base quality score assignments were recalibrated to control for biases due to flow cell, lane, dinucleotide context and machine cycle using the Genome Analysis Toolkit (GATK)  4. Copy-number alterations were evaluated using SegSeq  5.
RNA-sequencing and analysis pipeline Following RNA extraction (RNeasy, Qiagen), library construction was performed using a non-strand specific Illumunia TruSeq protocol. Flowcell cluster amplification and sequencing were performed according to the manufacturer's protocols using HiSeq 2000/2500, with a 76 bp paired-end run including an eight-base index barcode read. RNAsequencing files were downloaded from published datasets 1 , 2. RNA-seq bam files were transformed to fastq files using the Picard SamToFastq algorithm. Raw paired-end reads were aligned to the reference genome hg19 and preprocessed using PRADA (Pipeline for RNA-sequencing Data Analysis)  14. We used PRADA within Firehose to determine geneexpression levels, exon expression levels, quality metrics, and for detection of fusion transcripts.
Whole exome sequencing WES was performed from FFPE samples (without matched control). These samples were used to confirm driver alterations identified by the WGS. DNA was extracted using the QIAGEN DNA Blood and Tissue kit. Libraries with a 250 bp average insert size were prepared by Covaris sonication, followed by double-size selection (Agencourt AMPure XP beads) and ligation to specific barcoded adaptors (Illumina TruSeq) for multiplexed analysis. Exome hybrid capture was performed with the Agilent Human All Exon v2 (44 Mb) bait set. Sequence data were aligned to the hg19 reference genome with the Burrows-Wheeler Aligner with parameters [-q 5 -l 32 -k 2 -t 4 -o 1]. Aligned data were sorted, duplicatemarked, and indexed with Picard tools. Base-quality score recalibration and local realignment around insertions and deletions was achieved with the Genome Analysis Toolkit.
Gene-expression analysis of neural stem cells expressing MYB-QKI RNA was extracted from three independently generated pools of mNSC expressing one of eGFP, MYBtr, MYB-QKI5, MYB-QKI6 or QKItr. Gene expression profiles were assayed using Affymetrix Mouse Gene 2. 0 ST microarrays (Affymetrix, Santa Clara, CA). CEL files were RMA normalized  24. Comparative marker selection analysis  25  was performed in GenePattern using default settings. Genes with p-value <0. 05 and q-value <0. 35 were considered significant. GSEA was performed using the C2 (CP) gene sets (MSigDB). Genesets with nominal p-values <0. 05 were considered significant. The MYB-QKI signature was defined using the ClassNeighbours module of GenePattern (default settings).
Acknowledgments We would like to thank and acknowledge the  DFHCC/Pediatric Low-grade Astrocytoma Consortium and the Children's Brain Tumor Tissue Consortium  including additional participating sites: Ann and  Robert Lurie  We acknowledge the following funding sources:  A Kids' Brain Tumor Cure Foundation Pediatric Low-Grade Astrocytoma Foundation  (PB, KLL, RB, MWK, LG,  CS, ACR) ,  R01NS085336  ( ACR, AJW, PBS, MS ),  Voices Against Brain Cancer (ACR) ,  Children's Brain Tumor Foundation  (ACR, AJW),  Stop and Shop Pediatric Brain Tumor Program (PB, MWK ), Path to Cure Foundation (KLL),  NIH   PO1CA142536  (CS, KLL, RB),  St Baldrick's Foundation (PB) ,  American Brain Tumor Association (LR) ,  Team Jack Foundation (PB, MWK, RB, LG) ,  Andrysiak Fund for LGG (MWK) ,  Broad Institute Scientific Projects to Accelerate Research and Collaboration (SPARC)  grant (AG),  Jared Branfman Sunflowers For Life Fund For Pediatric Brain And Spinal Cancer Research (PB, RB, SS) ,  Sontag Foundation  (KLL, RB),  Nuovo-Soldati Foundation (GB) ,  Philippe Foundation(GB) ,  Fondation Etoile de Martin (JG) ,  Damon   Runyon-Sohn Pediatric Fellowship Award (AJW), Hyundai Scholar Grant  (AJW),  Bear Necessities Pediatric Cancer Foundation (AJW, ACR) Rally Foundation for Childhood Cancer Research (AJW) ,  K08NS087118  (SHR),  Pediatric Brain Tumor Foundation  (RB, PB),  Thea's Star of Hope (ACR, AJW).
A number of different models have been proposed as descriptions of the speciesabundance distribution (SAD). Most evaluations of these models use only one or two models, focus on only a single ecosystem or taxonomic group, or fail to use appropriate statistical methods. We use likelihood and AIC to compare the fit of four of the most widely used models to data on over 16,000 communities from a diverse array of taxonomic groups and ecosystems. Across all datasets combined the logseries, Poisson lognormal, and negative binomial all yield similar overall fits to the data. Therefore, when correcting for differences in the number of parameters the logseries generally provides the best fit to data. Within individual datasets some other distributions performed nearly as well as the log-series even after correcting for the number of parameters.
Data We compiled data from citizen science projects, government surveys, and literature mining to produce a dataset with 16,209 communities, from nine taxonomic groups, representing nearly 50 million individual terrestrial, aquatic, and marine organisms. Data for trees, birds, butterflies and mammals was compiled by  White, Thibault & Xiao (2012)  from six data sources: the US Forest Service Forest Inventory and Analysis (FIA; USDA Forest Service, 2010), the North American Butterfly Association's North American Butterfly Count (NABC; North American Butterfly  Assoc, 2009) , the Mammal Community Database (MCDB;  Thibault et al. , 2011), Alwyn Gentry's Forest Transect Data Set (Gentry; Phillips & Miller, 2002) , the Audubon Society Christmas Bird Count (CBC; National Audubon  Society, 2002) , and the US Geological Survey's North American Breeding Bird Survey (BBS;  Pardieck, Ziolkowski Jr & Hudson, 2014)  (see Table  1  for details).
The publicly available datasets (FIA, MCDB, Gentry, and BBS) were acquired using the EcoData Retriever (http://data-retriever. org;  Morris & White, 2013). Details of the treatment of these datasets can be found in Appendix A of  White, Thibault & Xiao (2012) , but in general data were analyzed at the level of the site defined in the dataset and a single year of data was selected for each site. We modified the data slightly by removing sites 102 and 179 from the Gentry data due to issues with decimal abundances appearing in raw data due to either data entry or data structure errors. Data on Actinopterygii, Reptilia, Coleoptera, Arachnida, and Amphibia, were mined from literature by Baldridge and are publicly available  (Baldridge, 2013)  (see Table  1  for details). These data were collected at the level of the site defined in the publication if raw data were available at that scale, and at the scale of the entire study otherwise.
We did this to assess the fit of the model while ignoring corrections for the number of parameters and the influence of similarities to other models in the set of candidate models. This also allows us to make more direct comparisons to previous analyses that have not corrected for the number of parameters (i. e. ,  Ulrich, Ollik & Ugland, 2010; Alroy, 2015)  Model fitting, log-likelihood, and AICc calculations were performed using Python (Van Rossum & Drake, 2011) and R (R Core  Team, 2016). Python packages used for analysis include numpy  (Oliphant, 2007; Van der Walt, Colbert & Varoquaux, 2011) , matplotlib  (Hunter, 2007) , sqlalchemy  (Bayer, 2014 ), pandas (McKinney, 2010) , macroecotools  (Xiao et al. , 2016) , and retriever  (Morris & White, 2013). R packages used for analysis include ggplot2  (Wickham, 2009) , magrittr  (Bache & Wickham, 2014) , tidyr  (Wickham, 2016) , and dplyr  (Wickham & Francois, 2016).