dataesr/scientific-paragraphs-categorization huggingface dataset:
Differential expression, Gene Ontology (GO) and Reactome Functional Interaction (FI) network analysis Differential expression analysis of protein-coding genes was done by aligning RNAseq reads with STAR 2. 5. 1b (https://github. com/alexdobin/STAR) and running Cufflinks 2. 2. 1 (http://cole-trapnelllab. github. io/cufflinks/) using Ensembl gene annotations (release 87). Transcripts showing 2-fold difference and p-value < 0. 05 were considered as differentially expressed. Gene ontology analysis (GO) and functional enrichment in the molecular function, biological process and cellular component categories were performed using DAVID, Panther (http://www. pantherdb. org/) and GO (http://geneontology. org/) online databases. Highly enriched "Chromatin-remodeling", "Cellular response to DNA damage" and "Cell division" GO categories were further analyzed using the Reactome (http://www.
reactome. org) database and Reactome FIViz Cytoscape plugin  55  , to identify network modules of highly connected genes and their significantly enriched (FDR<0. 05) biological processes. the "Chromatin-remodeling", "Cellular response to DNA damage" and "Cell division" GO categories upregulated in TC32 EV-treated MRC5 cells. Note that the network consists of 46 (of total 54) mRNAs and 11 externally added linkers labeled red (see full list in Extended Data Table  6c ). Signal specificity was defined by the respective FMO controls and isotope control antibodies. FIGURE LEGENDS.
Representation of repeat RNAs in plasma EVs of EwS patients, L1 potential and genomic locations. a, Representation of RepeatMasker-annotated RNAs in plasma EVs from EwS patients with localized (n=5) or metastatic disease (n=7). Data are mean FPM ± SEM; *p<0. 05, one-tailed unpaired t-test. b, Outline of L1 structural organization (top) and scatter plots of RNAseq read coverage (bottom) of L1 ORFs (annotated as "pol"; gEVE database http://geve. med. u-tokai. ac. jp). The Y-axis, number of uniquely mapped ORFs; the X-axis, breadth of coverage (length covered/total length of the entire element). Histograms, number of unique L1 ORFs with different breadths of coverage. c, Box plots, number of unique L1 ORFs at different breadth of coverage from (b). Comparisons made between the number of ORFs in metastatic vs localized; p-values were calculated by Wilcoxon rank sum test.
Read abundance in each bin is reflected by color coding, with dark blue indicating higher coverage. No bar plots are shown for cases with no reads. EVs from EwS cell lines are enriched in repeat RNAs and depleted of mRNAs. a, Distribution of normalized non-ribosomal RNAseq reads identified in three indicated EwS cell lines treated with vehicle control (-) or sodium arsenate (SA). Exonic non-coding (NC), exonic coding, intronic and intergenic transcripts are shown. b, Representation of repeat RNA families in combined cell line (Cell) and EV datasets from (a). Data are mean ± SEM (n=3); *p<0. 05, Wilcoxon rank sum test. Data are linked to Extended Data Table 4. c, Representation of RepeatMasker-annotated RNAs in EVs from EwS cell lines treated with PBS (control) or SA from (b); *p<0. 05, one-tailed unpaired t-test.
RNAseq read coverage of L1 and ERV ORFs. a, c, Scatter plots of RNAseq read coverage of L1 (a) and indicated ERV (c) ORFs detected in A4573, TC32 and TC71 cells and their EVs; combined datasets for cells and EVs are shown. The Y-axis, number of uniquely mapped ORFs; the X-axis, breadth of coverage (length covered/total length of the locus). Histograms, number of unique ORFs at different breadths of coverage. b, d, Box plots, number of unique ORFs with different breadth of coverage from (a, c). Comparisons made with mock-treated cells; p-values calculated by Wilcoxon rank sum test. Data are linked to Extended Data Table 5b. Extended Data Figure 6 I TC32 EVs are enriched with chr. 2, 4, 10 and 21 pericentromeric transcripts. a, Genome-wide mapping of satellite RNAseq reads derived from EwS cell lines treated with SA or vehicle control (ctrl) or their respective EVs, as indicated.
The inserts on each H&E image show the slide layout and position of the image captured. T-tumor; M-muscle cells; S-stroma; N-necrosis. Scale bars, 20 μm. Extended Data Figure 9 I HERV-K and HSAT RNAs is strongly associated with increased expression of IFN-stimulated viral response transcripts. a, Heatmap of the IFNstimulated gene expression and CD274/PD-L1, PDCD1/PD-1 and IDO1 (shown in blue) in 25 EwS tumors from the dbGaP RNAseq dataset generated by Crompton et al. , 2014. Increasing levels of HERV-K or HSAT (represented by combined HSAT2, (CATTC)n and (GAATG)n reads) in each sample are shown on the top as gray scale gradient. Samples are split into "low" (n=19) or "high" (n=6) groups based on their HERV-K/HSAT coverage relative to the mean value across all samples. Patients with metastasis are marked red.
Acknowledgements We are thankful to  Victoria Luchenko , a wonderful person and devoted researcher at the  National Institutes of Health (NIH)  who recently lost her fight against cancer, for inspiration and fruitful discussions. We also would like to thank all patients and their families, and the personnel of the Genetic Somatic Unit and the  Institute of Pathology  for their help in collecting blood samples. We thank  B. D. Crompton  and  K. Stegmaier  for sharing their RNAseq EwS datasets,  C. Lachance  and  S. Wells  for assistance with  Bio-Plex  and ddPCR experiments,  M. Shure ,  B. Samelson ,  A. Patel ,  E. Ortenberg  and  M. Bourbonnière  for ViewRNA ISH and PrimeFlow RNA assay support, and  A. M. El-Naggar  for xenografted EwS sections. The authors would like to acknowledge the  Spatio-Temporal Targeting and Amplification of Radiation Response (STTARR) program  and its affiliated funding agencies.
Data availability RNAseq data generated from 12 EwS patients' plasma EVs and from A4573, TC32 and TC71 EwS cell lines, mock-or SA-treated, and their respective EVs were deposited at the Sequence Read Archive (SRA) under the accession number PRJNA548159 (pending). Source data for the figures and reagents are provided with the paper and are also available from the corresponding authors upon request.
The major bands in cellular RNA preparations correspond to 18S and 28S ribosomal RNAs and tRNAs. Representation of repeat RNAs in plasma EVs of EwS patients, L1 potential and genomic locations. a, Representation of RepeatMasker-annotated RNAs in plasma EVs from EwS patients with localized (n=5) or metastatic disease (n=7). Data are mean FPM ± SEM; *p<0. 05, one-tailed unpaired t-test. b, Outline of L1 structural organization (top) and scatter plots of RNAseq read coverage (bottom) of L1 ORFs (annotated as "pol"; gEVE database http://geve. med. u-tokai. ac. jp). The Y-axis, number of uniquely mapped ORFs; the X-axis, breadth of coverage (length covered/total length of the entire element). Histograms, number of unique L1 ORFs with different breadths of coverage. c, Box plots, number of unique L1 ORFs at different breadth of coverage from (b).
Values close to 0. 5 indicate equal proportion of sense and antisense transcripts, while ratios close to zero or 1 represent transcripts in sense or antisense orientation, respectively. Read abundance in each bin is reflected by color coding, with dark blue indicating higher coverage. No bar plots are shown for cases with no reads. EVs from EwS cell lines are enriched in repeat RNAs and depleted of mRNAs. a, Distribution of normalized non-ribosomal RNAseq reads identified in three indicated EwS cell lines treated with vehicle control (-) or sodium arsenate (SA). Exonic non-coding (NC), exonic coding, intronic and intergenic transcripts are shown. b, Representation of repeat RNA families in combined cell line (Cell) and EV datasets from (a). Data are mean ± SEM (n=3); *p<0. 05, Wilcoxon rank sum test.
Data are linked to Extended Data Table 4. c, Representation of RepeatMasker-annotated RNAs in EVs from EwS cell lines treated with PBS (control) or SA from (b); *p<0. 05, one-tailed unpaired t-test. RNAseq read coverage of L1 and ERV ORFs. a, c, Scatter plots of RNAseq read coverage of L1 (a) and indicated ERV (c) ORFs detected in A4573, TC32 and TC71 cells and their EVs; combined datasets for cells and EVs are shown. The Y-axis, number of uniquely mapped ORFs; the X-axis, breadth of coverage (length covered/total length of the locus). Histograms, number of unique ORFs at different breadths of coverage. b, d, Box plots, number of unique ORFs with different breadth of coverage from (a, c). Comparisons made with mock-treated cells; p-values calculated by Wilcoxon rank sum test. Data are linked to Extended Data Table 5b.
ViewRNA images were taken from areas denoted by squares. The inserts on each H&E image show the slide layout and position of the image captured. T-tumor; M-muscle cells; S-stroma; N-necrosis. Scale bars, 20 μm. Extended Data Figure 9 I HERV-K and HSAT RNAs is strongly associated with increased expression of IFN-stimulated viral response transcripts. a, Heatmap of the IFNstimulated gene expression and CD274/PD-L1, PDCD1/PD-1 and IDO1 (shown in blue) in 25 EwS tumors from the dbGaP RNAseq dataset generated by Crompton et al. , 2014. Increasing levels of HERV-K or HSAT (represented by combined HSAT2, (CATTC)n and (GAATG)n reads) in each sample are shown on the top as gray scale gradient. Samples are split into "low" (n=19) or "high" (n=6) groups based on their HERV-K/HSAT coverage relative to the mean value across all samples.
Phase II increased the neutron capture efficiency using the higher capture cross section of 35 Cl by adding NaCl to the D 2 O. In addition to the increased cross section, the neutron capture on 35 Cl resulted in a cascade of γ-rays summing to a Outer H 2 O Inner H 2 O Acrlyic Vessel PSUP D 2 O FIG. 1. The SNO detector  [19]. higher energy of 8. 6 MeV, better separating this signal from radioactive backgrounds. Phase III added a Neutral Current Detector (NCD) array inside the active volume for an independent measure of neutron production inside the detector. These NCDs were high purity nickel tubes containing 3 He gas, and they were instrumented to utilize the 3 He as a proportional counter for thermal neutrons  [25]. For Phase III only there are two sources of detector data: the PMT array data as in Phase I and Phase II and the NCD array data.
As these datasets are treated differently in analyses, the PMT data from Phase III will be referred to simply as Phase III with the NCD data being Phase IIIb. A combined analysis of Phase I and II data led to the first low energy measurement of the electron neutrino survival probability  [26]. That analysis was later extended to incorporate Phase III data  [19] , and the analysis described in this paper was based on the analysis described in  [19]. SNO developed a highly detailed microphysical simulation of the detector called SNOMAN  [27]. This software could be configured to exactly reflect the experimental conditions at any particular time (for example, the values of the trigger settings during a particular run), allowing accurate Monte Carlo reproduction of the data. Monte Carlo simulations of the various signal and background events generated with statistics equivalent to many years of livetime were used extensively in this analysis.
A. Data selection Data selection proceeds in a number of steps. The data are organized in time periods called runs, and the first step is to select runs with nominal detector conditions. This analysis uses the same run list developed for the full analysis of all three phases of the SNO data  [19]. There is also an event-level selection within each run. These cuts remove instrumental backgrounds, muons, and muon followers from the dataset. Again, for this analysis we use the same reconstruction corrections, data cleaning, and high-level cuts used in  [19]  for identifying physics events. We define a region of interest for the analysis in terms of effective recoil electron kinetic energy T eff and radial position r, requiring r < 5. 5 m, and 3. 5 MeV < T eff < 20 MeV. Phase III data is included with a higher range of energies, 6.
G. Bias and pull testing Significant testing was done on Monte Carlo datasets to ensure the statistical robustness of the fit. For all tests in this section, the solar signal was generated with an assumed k 2 value of 10 -4 s/eV to test the sensitivity near existing limits, and all other parameters were chosen by randomly sampling the prior distributions for each fake dataset. The fit was run on each dataset including only the signals and backgrounds for that stage. The fitted values of all floated parameters were recorded and used to produce bias and pull distributions. No significant bias was found, and pull widths were found to be consistent with expectations.
VI. CONCLUSION Neutrinos are known to have mass, allowing for potential decays to lighter states. However, analyses of solar neutrino data assuming the MSW solution to the solar neutrino problem are consistent with a non-decaying scenario. By analyzing the entire SNO dataset, using a model that predicts the survival probability of electrontype solar neutrinos allowing for the decay of mass state ν 2 , we were able to set a limit on the lifetime of neutrino mass state ν 2 : k 2 > 8. 08 × 10 -5 s/eV at 90% confidence. Combining this with measurements from other solar experiments results in a new best limit of k 2 > 1. 04 × 10 -3 s/eV at 99% confidence. The improvement from the previous limit, k 2 > 7. 2 × 10 -4 s/eV at 99% confidence  [14] , was driven by the inclusion of additional solar flux measurements and updated analyses of previously considered experiments.
ACKNOWLEDGMENTS This research was supported by:  Canada: Natural Sciences and Engineering Research Council, Industry Canada, National Research Council, Northern Ontario Heritage Fund ,  Atomic Energy of Canada, Ltd. , Ontario Power Generation, High Performance Computing Virtual Laboratory ,  Canada Foundation for Innovation ,  Canada  Research Chairs program; US:  Department of Energy Office of Nuclear Physics, National Energy Research Scientific Computing Center, Alfred P. Sloan Foundation ,  National Science Foundation , the  Queens Breakthrough Fund, Department of Energy National Nuclear Security Administration  through the  Nuclear Science and Security Consortium; UK: Science and Technology Facilities Council (formerly Particle Physics and Astronomy Research Council ); Portugal: Fundação para a Ciência e a Tecnologia.
)-notation, giving only exponential parts of a running time, ignoring polynomial factors (for details about parameterized complexity, we refer to the recent monographs  [1, 2] ). In a nutshell, parameterized complexity exploits the fact that the really hard instances are pathological and real-world processes generate data which, although enormous in size, are very structured and governed by simple processes. It seems straightforward that biological datasets abide by this "law of low real-world complexity". While excellent surveys of fixed-parameter tractable (FPT) problems in bioinformatics exist  [3] [4] [5] [6] , this work tries to present an update, summarizing the state of the FPT-art as well as reporting on some additional "hot topics". We further aim at: 1. iterating that parameterized algorithms are a viable and widespread tool to attack NP-hard problems in the context of biological data; and 2.
Genome Assembly and Sequence Analysis Producing the genetic code (sequence of bases A, C, G, and T) present in a sample lies at the core of almost all research in molecular biology. This, however, requires solving an intricate puzzle with millions of pieces ("reads"), involving many computational problems on sequences, such as the SCAFFOLDING problem  [40]. Moreover, to draw conclusions from sequences, they have to be analyzed and compared. Such comparisons are made difficult, at the most basic level, by the amount of genetic variation and errors that can appear all along the sequences. A central task is to detect character insertions, deletions, and substitutions within a set of strings representing "similar" DNA or peptide sequences, thereby identifying a common structure  [41].
Acknowledgments: The authors want to thank  Fran Rosamond  for suggesting the topic as well as everyone who helped collect interesting results for the manuscript, in particular  Jesper Jansson ,  Steven Kelk ,  Fabio Pardi ,  Yann Ponty ,  Eric Rivals ,  Celine Scornavacca ,  Krister Swenson , and  Norbert Zeh. Finally , we thank the anonymous reviewers for their helpful and productive comments.
Aging is regarded as a major risk factor for neurodegenerative diseases. Thus, a better understanding of the similarities between the aging process and neurodegenerative diseases at the cellular and molecular level may reveal better understanding of this detrimental relationship. In the present study, we mined publicly available gene expression datasets from healthy individuals and patients affected by neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, and Huntington's disease) across a broad age spectrum and compared those with mouse aging and mouse celltype specific gene expression profiles. We performed weighted gene co-expression network analysis (WGCNA) and found a gene network strongly related with both aging and neurodegenerative diseases. This network was significantly enriched with a microglial signature as imputed from cell type-specific sequencing data.
, 2013; Carmona-Gutierrez et al. , 2016; Plaza-Zabala et al. , 2017). In the recent years, time-effective and low-cost highthroughput sequencing made it possible to analyze whole transcriptomes of humans and rodents even on a single-cell level. Here, we use a systems biology approach to compare publicly available gene expression datasets from healthy humans and patients affected by complex neurodegenerative disorders [Alzheimer's disease (AD), Huntington's disease (HD), and Parkinson's disease (PD)] across a broad spectrum of ages to address the question how 'healthy aging' turns into 'poor aging. ' We obtain relevant transcriptional modules based on their co-expression relationship and further demonstrate that some modules are enriched in specific cell types such as microglia. Since studying the human brain transcriptome is limited to post-mortem tissue analyses, most knowledge about brain aging comes from mouse or lower vertebrate models  (Rosenthal and Brown, 2007).
Criteria for Selection and Data Sources Gene expression profile (microarray and RNA-seq) datasets available on Gene Expression Omnibus (GEO) were selected. For analysis of aging and disease, datasets from studies with multiple time points across age for control and neurodegenerative disease brains were selected. Human AD, HD, and PD disease studies that met these selection criteria, GSE33000 [microarray, prefrontal cortex, brain  (Narayanan et al. , 2014) ] and GSE43490 [microarray, substantia nigra, brain  (Corradini et al. , 2014) ] were analyzed. For cross species comparison, mouse hippocampus RNA-seq datasets from studies with multiple time points across age were selected: GSE61915  (Stilling et al. , 2014) , GSE73503  (Aaronson and Rosinski, 2015)  and GSE83931  (Bundy et al. , 2017).
Microarray Data Preparation and Annotation Two human aging and neurodegenerative disease microarray datasets were downloaded from GEO using GEOquery  (Davis and Meltzer, 2007) : GEO33000 (dual-color) and GEO43490 (single-color). Gene expression matrices were obtained from GEO, which consisted of normalized log 10 (Cy5/Cy3) test/reference for GSE33000 (raw 'CEL' files not available) and normalized log 2 signal intensity for GSE43490. Gene expression matrices were converted to linear space, quantile normalized with Lumi 2. 32. 0  (Du et al. , 2008) , and then log 2 +1-scaled. ProbeIDs were annotated using the appropriate GPL file downloaded from GEO. The output metadata and gene expression files were merged in the same order. This resulted in a combined sample size of 637 human aging and human neurodegenerative disease data points.
RNA-Seq Data Preparation and Annotation Raw fastq RNA-seq reads were obtained for mouse hippocampus aging and mouse cerebral cortex cell types. GSE83931 and GSE73503 were total RNA samples prepared for singleend (NEBnext Ultra mRNA library preparation kit) and paired-end sequencing (standard Illumina library preparation protocols), respectively. GSE61915 and GSE52564 were polyA samples prepared for single-end sequencing using the TruSeq library preparation kit. Fastq files were processed on Cyverse's Atmosphere cloud-computing platform led by Arizona State University, Tempe, AZ, United States  (Goff et al. , 2011; Merchant et al. , 2016). Briefly, raw reads were obtained with sratoolkit. 2. 9. 1  (Leinonen et al. , 2011)  and quality was evaluated with fastqc_v0. 11. 7  (Brown et al. , 2017).
We employed the tophat 2. 1. 1 aligner  (Trapnell et al. , 2009)  to map reads to the mouse reference genome mm10 (GRCm38. 92) from Ensembl  (Zerbino et al. , 2018)  with the annotation file from GENCODE (GRCm38. 92), using recommended/default tophat parameters for paired-end and single-end reads (parameters: -b2 -veryfast -no-coverage-search -no-novel-juncs), which generated bam files from samples aligned at ∼90% or more. The mapped bam files were sorted with samtools-0. 1. 19 (Li et al. , 2009). Sorted bam files were processed with HTSeq 0. 10. 0  (Anders et al. , 2015)  to obtain counts of reads aligned to genes (parameters: -r pos -t gene_name). The output metadata and gene expression htseq-count files were merged, normalized to the log 2 +1 scale using edgeR 3. 22. 5  (Robinson et al.
, 2009)  and filtered for a cpm library size of >1 count in ≥3 samples, which resulted in a combined mouse aging sample size of 71. Additionally, for GSE52564, the sorted bam files were processed with cuffdiff2 from cufflinks_2. 1. 1-4 (Trapnell et al. , 2012)  to obtain a list of differentially expressed genes or cell type signature genes as defined by a fold change >20 in comparison of the given cell type to all other cell types. Our list was comparable to the previously published cell type signature gene list obtained from this same GSE52564 dataset  (Zhang et al. , 2014).
Adjusting for Surrogate Variables Including Batch Effects We used the Surrogate Variable analysis (SVA 3. 28. 0) R package  (Leek and Storey, 2007; Leek et al. , 2012)  for removal of confounding effects from the combined human microarray expression data and combined mouse RNA-seq expression data (SVA model: ∼study or batch + gender + disease + age; null model: ∼study or batch + gender). Because tissue and study/batch were highly correlated, we only used one of these confounding factors (study/batch) for SVA modeling to avoid multicollinearity. For the estimation of variable effects by SVA the "be" method was used. SVA itself does not give the adjusted or normalized expression values and is therefore used in combination with the linear regression model (LM) fitting function from the Limma 3.
Data Used for WGCNA Matching of unique microarray gene symbols between the human SVA+LM normalized datasets (GSE33000 and GSE43490) left 7301 common genes that were run through the WGCNA meta-analysis pipeline. For the mouse data, gene symbols were first mapped to human gene symbols using biomaRt  [version 2. 36. 1, (Kasprzyk, 2011) ], then arranged by expression variability in descending order, and finally the same number of genes as in human (7301) were used as input for WGCNA.