dataesr/scientific-paragraphs-categorization huggingface dataset:
Motif Analysis To identify possible motifs associated with editing sites, we repeated a methodology adapted from previous studies  (Liew et al. 2017). For this, flanking regions surrounding each editing event were extracted (up to 200 nt) and grouped by conversion. Each sequence set was randomly divided into a test and training set. De novo identification of motifs in the training set used MEME  (Bailey and Elkan 1994)  with a maximum of 10 motifs, each of maximum width 60 nucleotides. These motifs were then searched for in the test set using MAST  (Bailey and Gribskov 1998). As a control, each motif was also used to search a data set of 10,000 sequences comprising the same distribution of lengths and base distributions as the training set. Analyses were carried out using the MEME suite v4.
Statistical Analysis Unless stated otherwise, all statistical analyses were performed using R v3. 3. 2, all scripts are available from https:// github. com/DacksLab/RNAediting, last accessed March 21, 2018 and further details are provided along with the statistical results. Normal distribution of variables was tested using the Shapiro-Wilk test and a visual inspection of the distribution was performed before applying any further tests. In case of normality two-tailed Student's t-test or ANOVA were used, if not, Wilcoxon tests were performed. When the data were not independent, post hoc Tukey's test was used on mixed linear models. Statistical correlations were tested with Spearman's rank tests (for molecular weight vs. hydrophobicity analysis, as to remain consistent with  Mungpakdee et al.
Acquisition of New Sequence Data for the Dinoflagellates Pyrocystis lunula and Karenia mikimotoi As previous studies based on single taxon sampling points yielded inconclusive results regarding editing function, we first obtained genomic and transcriptomic data for two additional dinoflagellates, the peridinin dinoflagellate P. lunula and the fucoxanthin dinoflagellate Kare. mikimotoi (Materials and Methods). We identified 11 full-length P. lunula plastome-derived coding regions (atpA, atpB, petB, petD, psaA, psaB, psbA, psbB, psbC, psbD, and psbE) by comparison to a dinoflagellate-specific database, following removal of bacterial and nuclear contaminants (Materials and Methods; supplementary fig. S1  and table S1, Supplementary Material online). In the transcriptome assemblies, all 11 genes appeared monocistronic.
Comparison of genomic and transcriptomic sequences revealed that four genes had conventional AUG start codons while three had AUU, two UUG, one UCG, and one CUU (supplementary table S1, Supplementary Material online). Bioinformatic searches for tRNA genes (Materials and Methods) yielded no putative hits. The plastomes of peridinin dinoflagellates typically occur as minicircles (fig. 1 ), and three P. lunula minicircle sequences are archived in GenBank for psbA (AF490365), psbC (AF490366), and rpl28-rpl33 (AF490367). Our assemblies matched the psbA and psbC entries with the exception of SNPs that may be strain-specific and discrepancies in length. Our psbC assembly is 1,701 nucleotides compared with 4,811 nucleotides for AF490366, and differs from AF490366 in upstream noncoding content, suggesting these two sequences may represent different copies of the same gene, potentially with different subcellular localizations  (Laatsch et al.
2014; Dorrell et al. 2016). The sequences obtained through these reactions not only serve as a secondary control for the Kare. mikimotoi RNAseq data but may also provide specific insights into transcripts that occur at low abundance in total RNA pools, but were specifically amplified through the RT-PCR experiments performed, for example, transcripts that extend past the 3 0 poly(U) site of the corresponding gene, which typically are at much lower abundance, and have diminished editotypes compared with polyuridylylated transcripts  (Dang and Green 2009; Dorrell and Howe 2012; Dorrell et al. 2016 ). We screened 26,699 nt aligned transcript sequences, corresponding to 4,361-bp genomic sequence from Kare. mikimotoi, and 2,217 nt aligned transcript sequences, corresponding to 684bp genomic sequence from Karl.
Editing Is Not Distributed Based on Phylogenetic Affiliation We considered whether specific editing events were conserved across orthologous plastid genes from multiple dinoflagellate species. For this, we compared global editing events across a 17 species (1,121 nt) alignment of psbA sequences, including both peridinin and fucoxanthin dinoflagellates, for which we could access both a gDNA and mRNA sequence from both GenBank and other published transcriptome resources (supplementary fig. S3  and table S5, Supplementary Material online)  (Keeling et al. 2014; Dorrell et al. 2017). We identified 336 suspected editing events across the entire alignment. Of these, only 40 specific events (i. e. , the same interconversion at the same position) were found to occur in more than one species (supplementary fig.
Effect of Editing on Protein Size and Hydrophobicity It was previously reported that editing in S. minutum resulted in decreased molecular weight but increased hydrophobicity of proteins  (Mungpakdee et al. 2014) , as measured by GRAVY score  (Kyte and Doolittle 1982). We calculated molecular weight and hydrophobicity across our data set (supplementary table S7, Supplementary Material online) and found negligible correlation between them (fucoxanthin  -Effect of editing on GC content. This figure shows the inherent GC content bias among codon positions in dinoflagellates, with higher GC content in positions one and two, and the effect of editing to increase GC content significantly in positions one and two, but not three. Bars are color-coded by codon position. Net GC increase defined as the difference between GC-enriching (i.
GBE Genome Biol. Evol. 10(4):1019-1038 doi:10. 1093/gbe/evy057 Advance Access publication March 14, 2018 plastid affiliation (fig. 5D ). Repeating these analyses considering only nonsynonymous editing events, which actually change the resulting amino acid sequence, or considering nucleotide sequences, gave similar results (supplementary table S8, Supplementary Material online). Comparison of individual taxa revealed a weaker correlation in both Kare. mikimotoi and S. minutum (fig. 5E ), and these values were significantly (P value < 0. 05) different when compared with both Karl. veneficum and P. lunula (S. minutum) or P. lunula only (K. mikimotoi). -Effect of editing on protein size and hydrophobicity. This figure shows scatter plots between molecular weight difference and GRAVY score before and after editing for fucoxanthin and peridinin dinoflagellate plastid sequences (A), and peridinin dinoflagellate data sets, Pyrocystis lunula and Symbiodinium minutum (B).
Acknowledgments The authors wish to thank  Manoj Khadka  for culturing the Pyrocystis lanula used in this study,  David Lea-Smith  and  Shilo Dickens  (DNA sequencing facility,  Department of Biochemistry, University of Cambridge ) for assistance in generating expanded gDNA sequence libraries for Karenia mikimotoi. R. G. D. was supported by an  EMBO   Long-Term Fellowship  ( ALTF 1124-2014 ). C. B. additionally thanks the  French Government "Investissements d'Avenir" programmes MEMO LIFE  ( ANR-10-LABX-54 ),  PSL* Research University  ( ANR-11-IDEX-0001-02 ), and  Oceanomics  ( ANR-11-BTBR-0008 ). Work in the Dacks Lab is supported by a  Discovery Grant  from the  Natural Sciences and Engineering Research Council of Canada  ( RES0021028 ). C. M. K. is funded by an  Alberta Innovates Health Solutions Fulltime Studentship  and a  Canada Vanier Graduate Scholarship.
This one-vertex reconstruction efficiency is evaluated for different numbers of displaced light parton or b quark jets. The largest disagreement between data and simulation gives an 11. 5% relative uncertainty in the one-vertex efficiency, implying a 23% relative uncertainty in the two-vertex efficiency. Varying the scale parameter of the exponential distribution or the amount that the direction is smeared within reasonable values has negligible effect on the difference between data and simulation. The difference in vertex reconstruction efficiency between data and simulation could also depend on the magnitude of the artificial displacement. This dependence is found to be small, and the resulting difference in the signal d VV templates has a negligible effect on the signal yield extracted from the fit.
Summary A search for long-lived particles decaying into multijet final states has been performed using proton-proton collision events collected with the CMS detector at a center-of-mass energy of 13 TeV in 2015 and 2016. The data sample corresponds to an integrated luminosity of 38. 5 fb -1. No excess yield above the prediction from standard model processes is observed. At 95% confidence level, upper limits are placed for models of R-parity violating supersymmetry in which the long-lived particles are neutralinos or gluinos decaying solely into multijet final states or top squarks decaying solely into dijet final states. The data exclude cross sections above approximately 0. 3 fb for particles with masses between 800 and 2600 GeV and mean proper decay lengths between 1 and 40 mm. For mean proper decay lengths between 0.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC IUT , and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Ger-many) ;  GSRT (Greece) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI  and  FEDER (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Individuals have received support from the  Marie-Curie program  and the  European Research Council and Horizon 2020  Grant, contract No. 675440  ( European Union ); the  Leventis Foundation ; the A. P. Sloan Foundation; the  Alexander von Humboldt Foundation ; the  Belgian Federal Science Policy Office ; the  Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; the  Agentschap voor Innovatie door Wetenschap en Technologie  ( IWT-Belgium ); the  F. R. S. -FNRS  and  FWO (Belgium)  under the  "Excellence of Science -EOS" -be. h project n. 30820817; the  Ministry of Education, Youth and Sports (MEYS)  of the Czech Republic; the  Lend ület ("Momentum") Programme  and the  János Bolyai Research Scholarship of the Hungarian Academy of Sciences , the  New National Excellence Program ÚNKP , the  NKFIA  research grants  123842 ,  123959 ,  124845 ,  124850  and  125105  (Hungary); the  Council of Science and Industrial Research, India ; the  HOMING PLUS program  of the  Foundation for Polish Science , cofinanced from  European Union, Regional Development Fund , the  Mobility Plus program of the Ministry of Science and Higher Education , the  National Science Center (Poland) , contracts Harmonia  2014/14/M/ST2/00428 ,  Opus 2014/13/B/ST2/02543 ,  2014/15/B/ST2/03998 , and  2015/19/B/ST2/02861 , Sonata-bis  2012/07/E/ST2/01406 ; the  National Priorities Research Program  by  Qatar National Research Fund ; the  Programa Estatal de Fomento de la Investigaci ón Científica y Técnica de Excelencia María de Maeztu , grant  MDM-2015-0509  and the  Programa Severo Ochoa del Principado de Asturias ; the Thalis and Aristeia programs cofinanced by  EU-ESF  and the  Greek NSRF ; the  Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University  and the  Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; the  Welch Foundation , contract  C-1845 ; and the  Weston Havens Foundation (USA).
A search is presented for the decays of heavy exotic long-lived particles (LLPs) that are produced in proton-proton collisions at a center-of-mass energy of 13 TeV at the CERN LHC and come to rest in the CMS detector. Their decays would be visible during periods of time well separated from proton-proton collisions. Two decay scenarios of stopped LLPs are explored: a hadronic decay detected in the calorimeter and a decay into muons detected in the muon system. The calorimeter (muon) search covers a period of sensitivity totaling 721 (744) hours in 38. 6 (39. 0) fb -1 of data collected by the CMS detector in 2015 and 2016. The results are interpreted in several scenarios that predict LLPs. Production cross section limits are set as a function of the mean proper lifetime and the mass of the LLPs, for lifetimes between 100 ns and 10 days.
If the stopped LLPs decay in the calorimeters, relatively large energy deposits occurring in the intervals between collisions could be observed. Furthermore, if the stopped LLPs decay into muons, displaced muon tracks out of time with the collisions could be detected. In this paper we present two searches for stopped LLPs that decay out of time with respect to the presence of proton bunches in the detector. One search targets hadronic decays detected in the calorimeters, and the other looks for decays to muon pairs in the muon system. These two search channels are analyzed independently using data collected by the CMS experiment in 2015 and 2016 with separate dedicated triggers. The calorimeter (muon) search uses √ s = 13 TeV data corresponding to an integrated luminosity of 38. 6 (39. 0) fb -1 collected with LHC pp collisions separated by 25 ns during a search interval totaling 721 (744) hours.
Data samples The LHC accelerates two proton beams in opposite directions such that the protons collide at several points along the LHC ring, including one at the CMS detector. Each LHC beam consists of a number of proton bunches arranged into an irregular pattern of "trains"  [36]. Within a train, the proton bunches are nominally spaced 25 ns apart, with a larger spacing between trains to account for the needs of the injection process. In an LHC orbit there are 3564 bunch slots (BXs), which are 25 ns long. Each BX could be filled with proton bunches, which usually occupy the first 2. 5 ns of the BX, or could be empty. The trains may be spaced such that there could be multiple empty BXs between filled BXs. To search for LLP decays during these empty BXs, dedicated triggers select events at least two BXs away from any proton bunches.
9 (36. 2) fb -1 was recorded, spanning a trigger livetime of 586 (589) hours. We do not consider the possibility of LLPs that were produced in 2015 but decayed in 2016. In both the 2015 and 2016 searches, we use cosmic run data collected by dedicated triggers as a control sample. These dedicated cosmic run data were recorded during LHC machine technical stops, several days after collision runs. A negligible amount of long-lived signal produced during collisions could have decayed during these cosmic runs for the lifetimes considered in this analysis. The instrumental noise background estimate is extrapolated from the instrumental noise measured in these control samples. Most of the other sources of background are estimated from sideband regions of the main data sample, except for the cosmic ray muon background in the calorimeter search, which is estimated from MC simulation.
However, because the R-hadrons interact an average of ten times in the calorimeter, their flavor is effectively randomized. Some fraction of these R-hadrons are sufficiently slow moving to come to a stop in the detector material. Because they are doubly charged, MCHAMPs ionize heavily and thus a significant number also stop in the detector. In Stage 2, the parent LLP or R-hadron is constrained to decay at the stopping position defined in Stage 1. The LLP decay is simulated by a second GEANT4 step, and the decay products are propagated through the detector. Finally, in Stage 3, a pseudo-experiment MC simulation is conducted to estimate the probability for stopped particle decays to occur in the time window between collisions when data is being collected. The Stage 3 MC simulation determines an effective integrated luminosity by using the good data-taking periods and the LHC filling scheme to calculate the fraction of stopped particle decays that occur when the trigger is live.
The uncertainty in the cosmic ray muon background is due to the uncertainty in the estimate of muons that escape detection by passing through uninstrumented regions of the CMS detector, which is necessarily estimated from simulation. Since data in the uninstrumented regions are ipso facto not available to compare to simulation, we define equivalent fiducial volumes of instrumented regions of the muon system. Using these as a proxy for the uninstrumented regions, we assess the reliability of the simulation by comparing data and simulation. We find the average discrepancy between cosmic ray muon data and simulation in the number of detected muons traveling through various fiducial regions in the detector to be about 32%, and we assign this to be the systematic uncertainty in the cosmic ray muon background estimate.
The systematic uncertainty is then defined as the difference between the results from the two binning schemes. We find a halo background estimate of 1. 1 ± 0. 1 (2. 6 ± 0. 2) events in 2015 (2016) data. Finally, the background estimation of instrumental noise is performed using control data in dedicated cosmic runs with no beams in the LHC, which include only cosmic ray muon and noise events. We select cosmic runs taken several days after pp collision runs so that there would be little chance for the signal to appear. After applying all selection criteria on the control data, we observe 2 events in each of the 2015 and 2016 control data. We then subtract the expected cosmic ray muon background from the total event yield, obtaining a noise background estimate of 0. 3 +2. 4 -0. 3 (0. 0 +2. 2 -0.
Muon search In the muon search, a small number of cosmic ray muon background events remains after applying the full event selection to the data. The cosmic ray muon background is estimated by extrapolating the data from a background-dominated region into the signal region. We ap-ply the full event selection to the data except the ∆t DT criterion and invert the ∆t RPC criterion. We then fit the ∆t DT distribution with the sum of two Gaussian distributions and a Crystal Ball function  [60] , since ∆t DT is relatively Gaussian with a long asymmetrical tail. Next, we compute the integral of the fit function, for ∆t DT > -20 ns. Then, we compute the same integral after having tightened the selection criteria on ∆t RPC to -50 < ∆t RPC < -7. 5 ns, then -45 < ∆t RPC < -7. 5 ns, etc. in steps of 5 ns up to -10 < ∆t RPC < -7.
Summary A search has been presented for long-lived particles that stopped in the CMS detector after being produced in proton-proton collisions at a center-of-mass energy of 13 TeV at the CERN LHC. The subsequent decays of these particles to produce calorimeter deposits or muon pairs were looked for during gaps between proton bunches in the LHC beams. In the calorimeter (muon) search, with collision data corresponding to an integrated luminosity of 2. 7 (2. 8) fb -1 in a period of sensitivity corresponding to 135 (155) hours of trigger livetime in 2015 and to an integrated luminosity of 35. 9 (36. 2) fb -1 in a period of sensitivity of 586 (589) hours of trigger ) [pb] 0 χ ∼ q q → g ( Β ) g g ~ → (pp σ 2 -.
, 2013). These consortia included data of somatic mutations, gene expression, methylation and copy number alterations, along with clinical data. They share the ambitious goal of detecting crucial genes and molecular mechanisms underlying tumorigenesis in any major cancer type  (Cancer Genome Atlas Research Network, 2008). Several methods implemented so far used mutation data to detect candidates cancer drivers  (Davoli et al. , 2013)  ,  (Schroeder et al. , 2014)   (Schroeder et al. , 2014; de Souza et al. , 2014) )  but although all these methods were demonstrated as effective, it remains critical to clarify the consequences of each mutation and to link these patterns with possible underlying biological interpretation. For example, some mutations bring premature stop codon and carry out reduced dosage of mRNA transcripts.
Data preparation and analysis The legacy level-3 data of the PanCancer studies (18 cancer types) from the The Cancer Genome Atlas (TCGA) cohort deposited in the Genomic Data Commons (GDC) Data Portal were used in this study and downloaded in May 2017. RNA-seq raw counts of 7364 cases (6652 TP and 712NT samples) as legacy archive, and using the reference of the hg19 genome were downloaded, normalized and filtered using the R/Bioconductor package TCGAbiolinks version 2. 5. 9  (Colaprico et al. , 2016)  using GDCquery(), GDCdownload() and GDCprepare() functions for tumor types (level 3, and platform "IlluminaHiSeq_RNASeqV2"), as well as using data. type as "Gene expression quantification" and file. type as "results". This allowed to pull out the raw expression signal for expression of a gene for each case following the TCGA pipeline used to create Level 3 expression data from RNA Sequence data that uses MapSplice  (Wang et al.
, 2010)  to do the alignment and RSEM to perform the quantification  (Li et al. , 2010). Integrative analysis using mutation, clinical and gene expression were performed following our recent TCGA's workflow  (Silva et al. , 2016). For the case study n. 2 and n. 3 we used Breast invasive carcinoma (BRCA) from TCGA as deposited in the GDC Data Portal. In particular, RNA-seq raw counts of 1211 BRCA cases as legacy archive, and using the reference of hg19 were downloaded, normalized and filtered using the R/Bioconductor package TCGAbiolinks following the above pipeline. Among BRCA samples 1097 were Primary solid Tumor (TP) and 114 solid Tissue Normal (NT). The aggregation of the two matrices (tumor and normal) for both tumor types was then normalized using within-lane normalization to adjust for GC-content effect on read counts and upper-quantile between-lane normalization for distributional differences between lanes applying the TCGAanalyze_Normalization() function adopting the EDASeq protocol  (Risso et al.
, 2011)  and  (Bullard et al. , 2010). Molecular subtypes, mutation data and clinical data were pulled using TCGAbiolinks and the following functions: TCGAquery_subtype(), GDCquery_maf() retrieving somatic variants that were called by the MuTect2 pipeline, and GDCquery_clinic() respectively. For BRCA tumors with PAM50 classification  (Ciriello et al. , 2015)  were stratified in 5 molecular subtypes: Basal-like (98), HER2-enriched (58), Luminal A (231), Luminal B (127) and Normal-like (8). Normal-like samples were not considered in this analysis due for the limited number of samples availability. Tumor stages information was retrieved from the clinical data grouping to main stages (I,II,III,IV) each subgroups Ia, IIb, IIIc etc.