dataesr/scientific-paragraphs-categorization huggingface dataset:
And third, single-cell data acquisitions often generate multiple trait values for each individual cells. This is the case for morphological profiling as in the dataset we used here, but also for gene expression  [59]  or parameters describing the micro-environment of the cells  [60]. It would therefore be interesting to search for scPTL affecting multiple cellular traits simultaneously instead of treating cellular traits one by one. A multidimensional analysis could be performed in order to extract a set of informative meta-traits, such as principal components or representative medoids and scPTL of these meta-traits could be searched using our method. This dimension-reduction approach would benefit from the redundant information available from correlated traits (e. g. the perimeter of a cell and its area are two measurements of its size), but the biological interpretation of a probabilistic effect on a meta-trait may not be straightforward.
This was done as above for one dimension, by computing the total variance of the data, and estimating the environmental variance from the replicated experiments made on the parental strains. For 147 traits, heritability was greater than 0. 5 and scPTL were searched. Details on how these steps were implemented in R are described in S1 Methods, and the code is available in the open source ptlmapper R package (https://github. com/fchuffar/ptlmapper). The yeast genotypes we used were from Smith and Kruglyak  [66]. For the morphological traits, we pooled triplicates together in order to increase the number of cells per sample. The data then corresponded to 220 traits, measured on >600 cells per sample, with 3 BY samples, 3 RM samples, and 1 sample per segregant. We scanned the genetic map with two methods.
In this case, only 3 replicates of BY and 3 replicates of RM are then available to estimate the environmental variance. Therefore, we estimated var T as the variance of the 59 macro-trait values of the segregants and var E as (var BY + var RM ) / 2, with var BY (resp. var RM ) being the variance of the three macro-trait values of the BY (resp. RM) strain. We then scanned the genome using the scanone function from r/qtl  [67]  with a single QTL model and the multiple imputation method  [69]. Our code implementing the calls to r/qtl is available in the open source ptlmapper R package (https://github. com/fchuffar/ptlmapper).
For each one, the genome was scanned as above and the highest LOD score on the genome was retained. This generated a 1,000 x N traits matrix M perm of the hits expected by chance. At a LOD threshold L, the FDR was computed as: FDR ¼ N FalseL = N ActualL where N ActualL was the number of linkages obtained from the actual dataset at LOD > L, and N FalseL was the expected number of false positives at LOD > L, which was estimated by the fraction of elements of M perm exceeding L. The fourth method considered all coordinates of the individuals in the phenotypic space. At this step, for each morphological trait, a phenotypic space of ndim dimensions had been built as explained above by computing Kantorovich distances and applying multi-dimensional scaling. Let P1, P2 and PS be the phenotypic matrices of parent 1 (strain BY), parent 2 (strain RM) and segregants, respectively, with rows being the samples (replicates for P1 and P2, and segregants for PS) and columns being the ndim coordinates of each sample in the phenotypic space.
Acknowledgments We thank  Tamiki Komatsuzaki  for mentioning the potential usefulness of the Kantorovich metric,  Fabien Crauste ,  Olivier François  and  Emmanuel Grenier  for helpful discussions,  Satoru Nogami  for re-analysis of CalMorph images,  Arezki Boudaoud ,  Fabien Duveau ,  Olivier Gandrillon ,  Marie Sémon  and  Gérard Triqueneaux  for critical reading of the manuscript,  Sandrine Mouradian  and  SFR Biosciences Gerland-Lyon Sud  ( UMS344/US8 ) for access to flow cytometers and technical assistance, the  Pôle Scientifique de Modélisation Numérique (Lyon, France)  and the Grid'5000 testbed (www. grid5000. fr)for computer resource, developers of R, bioconductor and Ubuntu for their software, and three anonymous reviewers for their comments.
This work was supported by the  European Research Council  under the European Union's  Seventh Framework Programme   FP7/2007-2013 Grant Agreement n°281359  attributed to GY. DJ was supported by the  Institut Rhône-Alpin des Systèmes Complexes and program AGIR of University Grenoble Alpes. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data and method availability The yeast morphological data corresponds to the experiments described in  [22]. For the present study, raw images were re-analyzed using CalMorph 1. 0. The single-cell values and genotypes used are provided in S1 Dataset of this article. The flow cytometry data corresponding to yeast galactose response is made available from http://flowrepository. org under accession number FR-FCM-ZZPA. The simulated data of Fig 4 is available as an R package (ptldata) from https://github. com/ fchuffar/ptldata. The scPTL mapping method is made available as an open source R package (ptlmapper) which can be downloaded from https://github. com/fchuffar/ptlmapper. A tutorial of this package explains how to run the analysis on the simulated dataset.
Substituting equation (  6 ) into equation (  7 ), the dislocation strain field is obtained by solving the sextic eigenvalue problem developed by Stroh  67  with specific boundary conditions dedicated to interface dislocations: No net far-field strains. Consistency of far-field rotations with the prescribed interface misorientation. No net tractions along the interface. Interface displacement discontinuity matches the disregistry of the desired dislocation pattern. [100] [010] Elastic dipole tensor calculation. Defect P-tensors are calculated using VASP (the Vienna Ab initio Simulation Package  68  ), a plane wave-based, first principles DFT code. A face-centred cubic supercell containing 256±1 atoms ( þ 1 and À 1 for interstitial and vacancy, respectively) is used. We also performed LAMMPS (Largescale Atomic/Molecular Massively Parallel Simulator 69 ) classical potential simulations using embedded atom method potentials for Ag (ref.
Acknowledgements H. D. acknowledges support from the  DOE, Office of Nuclear Energy ,  Nuclear Energy Enabling Technologies, Reactor Materials program , under contract No. DE-NE0000533. H. D. also thanks the  DOE-NERSC  for computational support. M. C. M. acknowledges support from the  GENCI-(CINES/CCRT/IDRIS ) computer centre under Grant No. x2015-096973  and from  PRACE (Partnership for Advanced Computing in Europe)  for access to the resource  MareNostrum III at the Barcelona supercomputing centre  ( TRANSOM  project). M. J. D. acknowledges support from the  National Science Foundation  under Grant No. 1150862.
The K2K experiment noted that the outgoing kinematics of the muon were not consistent with the prediction of a neutrino interaction on a single nucleon (1p1h) in a relativistic Fermi Gas (RFG) nuclear model  [4, 5]. The nucleon axial mass was found to be 1. 014 ± 0. 014 GeV from neutrino scattering data on deuterium as well as pion electroproduction data  [6, 7]  but K2K measured M QE A to be 1. 20 ± 0. 12 GeV for interactions on a water target  [8]. MiniBooNE also reported a similar anomaly on mineral oil (CH 2 ), with a large dataset of neutrinos (M QE A of 1. 35±0. 17 GeV  [9] ) and of anti-neutrinos  [10] , and MINOS using iron as a target  [11]. Both experiments also noted a discrepancy at the lowest values of momentum transfer (Q 2 < 0. 2 GeV 2 ). SciBooNE reported similar results in  [12].
The previous T2K off-axis CCQE measurement  [13]  is also in agreement with a large M QE A. The T2K on-axis measurement  [14]  has large systematics uncertainties and is compatible with different values of M QE A , depending on whether only one muon track is reconstructed, or both the muon and proton tracks. Other datasets on nuclear targets from the NOMAD (carbon target) and MINERvA (hydrocarbon target) experiments are in agreement with an M QE A of 1 GeV. At beam energies of 3-100 GeV, the NOMAD experiment has reported an M QE A of 1. 05 ± 0. 06 GeV  [15]. The MINERνA (E ν ∼ 3. 5 GeV) experiment has also measured the CCQE cross-section with only a muon and a proton in the final state  [16]  which is consistent with the RFG model and M QE A ∼ 1 GeV. Interestingly, previous MINERνA CCQE measurements which use muon information and the calorimetric recoil energy with both neutrinos  [17]  and anti-neutrinos  [18]  prefer a transverse enhancement model, suggesting the presence of meson exchange currents  [19].
Control samples for detector systematics The detector systematics are described in detail in  [2]. The systematics on track efficiency in the FGD and TPC, particle identification, charge identification and momentum scale and resolution were evaluated by dedicated data and simulation comparisons using independent control samples (specially-selected event samples that are designed to be sensitive to specific sources of uncertainty). In addition to reconstruction-related detector uncertainties, we also estimated the uncertainties on the number of simultaneous events (pileup) and OOFV events. Pions and nucleons from initial neutrino interactions in FGD1 can re-interact and be absorbed further in the detector. While the intranuclear final state interactions are simulated by the NEUT generator, the secondary interactions (SI) which follow these from pions and nucleons are treated by Geant4.
In summary, all the systematic uncertainties are included in the fit as nuisance parameters, except for signal modeling and flux uncertainties. Finally the effect from the statistical uncertainty on the Monte Carlo samples is included in the bin-by-bin efficiency in Eqn. 5. To evaluate all the systematic and statistical uncertainties, we produce a large number of toy experiments. To asses the statistical uncertainty, the number of reconstructed events in each bin is fluctuated according to the Poisson distribution in each of the toy experiments. To evaluate the systematics, the values of the parameters governing the various systematic uncertainties are varied in each toy dataset according to a Gaussian distribution, following the prior covariance matrices. A summary of statistical and all the systematic errors is shown in Fig.
On the other hand, the explicit reconstruction and identification of protons in Analysis I can be affected by cross-section modeling and proton FSI uncertainties. The selection of Analysis II is not subject to uncertainties related to the proton kinematics, as it does not attempt to measure the protons in the event. In particular, events which have two protons, where both are energetic enough to result in a reconstructible track in the detector, are included in Analysis II while they are excluded in Analysis I. These events can be due to nucleon-nucleon correlations or to meson exchange current processes or can be due to proton production through FSIs. Studies with generators which include these effects suggest that in this dataset we would expect to see around 70 events with two visible protons (to be compared with a number of signal selected events of about 10000).
We find that predictions from these new models agree with the data; in particular, the data suggest the presence of 2p2h with respect to pure CCQE predictions with the Random Phase Approximation (RPA). On the other hand, the data also agree, inside current uncertainties, with the NEUT and GENIE simulations. In NEUT, the value of the M QE A is higher than it is in GENIE to achieve agreement with recent datasets. Explicit implementation of nuclear effects (RPA and 2p2h) is included in more recent NEUT versions that will be used in future analyses. Quantifying the agreement between the data and the various models is not straightforward. The correlations between uncertainties in different bins must be considered, but the experimental measurements are affected by theoretical systematics, which are especially large in the backward region.
ACKNOWLEDGMENTS We thank the  J-PARC  staff for the superb accelerator performance and the  CERN NA61 collaboration  for providing valuable particle production data. We acknowledge the support of  MEXT, Japan ;  NSERC  (grant  SAPPJ-2014-00031 ),  NRC  and  CFI ,  Canada ;  CEA  and  CNRS/IN2P3 , France;  DFG, Germany ;  INFN, Italy ;  National Science Centre (NCN) , Poland; RSF,  RFBR  and  MES , Russia; MINECO and ERDF funds, Spain; SNSF and SERI, Switzerland; STFC, UK; and  DOE, USA. We also thank  CERN  for the UA1/NOMAD magnet,  DESY  for the HERA-B magnet mover system,  NII  for  SINET4 , the  WestGrid and SciNet consortia in Compute Canada , and  GridPP, UK. In addition, participation of individual researchers and institutions has been further supported by funds from:  ERC  ( FP7 ),  H2020 RISE-GA644294-JENNIFER ,  EU ;  JSPS, Japan ;  Royal Society, UK ;  DOE   Early Career program ,  USA.
Simulation setting We evaluated the proposed method with simulated datasets with focus on single cytosine level in the case and control group. Our simulation is intended for the detection of differential methylation sites when there is cancer heterogeneity. Here, the cancer heterogeneity is described by the multiple modes of distributions. We conduct a statistical test for H0 : Fi = Gi ↔ H1 : Fi = Gi under significance levels 5%, and we compare the results to those of the other methods, i. e. , DiffVar, MMD, KS, MWW and EB. We used several packages; missMethyl  (Phipson, et al. , 2014) , limma  (Ritchie, et al. , 2015)  written in R and mmd  (Gretton et al. , 2006)  (http://www. kyb. tuebingen. mpg. de/bs/people/ar thur/code/mmd. zip) written in MATLAB. The setting of missMethyl is default and mmd with options alpha = 0.
Datasets We apply our method to methylation data of glioblastoma multiforme (GBM) and lower grade glioma (LGG) from The Cancer Genome Atlas (TCGA). GBM is the primary brain tumor that progresses with malignant invasion destroying normal brain tissues (TCGA, 2008), arising through two pathologically distinct routes, de novo and as secondary tumors from LGG  (Wiencke et al. , 2006). In this analysis, we compare the methylation levels in the LGG and GBM groups, and then specify the differential methylation sites. The detection of differential methylation levels is a clue for revealing epigenetic mechanisms of development from LGG to GBM. We focus on mean, variance, and shape differences using EB, DiffVar, KS and D 3 M and compare the results. Here we briefly describe the datasets and preprocessing as follows.
All the samples are hybridized to Illuminas Infinium HumanMethylation450K arrays, including 485,577 CpG sites, which is downloadable from TCGA portal sites. Each CpG site contains 145 samples and 530 samples in GBM and LGG, respectively. We remove CpG sites on the X and Y chromosomes and SNP control probes(rs1-rs65). We also use HumanMethylation450 v1. 2 SNP Update Table to remove SNP related probes by minor allele frequency ≤ 1%. As a result, we get 351,932 probes. Missing values in both groups are inferred using R package pcaMethods  (Stacklies, 2007)  with pca functions since DiffVar doesn't accept missing values and the number of the probes including missing values cannot be ignored. We remove the batch effect using the ComBat function in SVA package  (Leek, et al. , 2012)  with default settings.
It is important for the further analysis, such as the pathway analysis since we often use the filtered gene set, e. g. , using top 1,000 significant sites. D 3 M Among the detected sites, we investigated sites with the smallest 1,000 q-value. Heat map and Q-Q plots of the top 1,000 sites are shown in Figures  3 (A ) and (B). Comparing heat maps and Q-Q plots, the methylation levels are easy to interpret in the latter. From the Q-Q plot, we could see that the top 1,000 sites tend to be hypermethylated in LGG (with the reverse in GBM). Among the top significant 1,000 pairs of distributions of GBM and LGG, we can observe that there are mainly two patterns in terms of the Q-Q plot. Then, we cluster the 1000 curves in Q-Q plot into two classes; we consider input data as Zi = (Qi,1, Qi,2,. , Qi,K , Q i,1 , Q i,2 ,.
CONCLUSION In this study, we proposed a novel method, D 3 M, for detecting differential methylation sites based on distribution-valued data. We showed that distribution shape includes interesting information other than that found using mean-and variance-based methods. A simulation study indicated that D 3 M is capable to detect various situations. In the application to the GBM and LGG dataset in the TCGA cohort, we identified 1,000 sites with the smallest q-values. Most of the sites detected by D 3 M show strong heterogeneity and tend to be hyper-and hypo-methylated in LGG and GBM, respectively, as found in previous studies. Since the GBM and LGG dataset contains a large number of significantly different sites, including  279,008, 191,050, 297,493 and 255,317  sites for D 3 M, EB, KS, and DiffVar, respectively; at the FDR ≤ 1%, it is difficult to understand the methylation levels at these sites.
FUNDING  European Research Council (ERC)  [2011 StG 281659, MeioSight];  Fondation Simone et Cino del Duca  and the  Fondation Schlumberger pour l'Enseignement et la Recherche ;  Labex Saclay Plant Sciences (SPS)  [ ANR-10-LABX-0040-SPS  to I. J. P. B. ]. Funding for open access charge:  ERC  [2011 StG 281659, MeioSight].
Additionally, access to a diverse volume of raw data can be used to develop improved software to address limitations of existing programs. Finally, access to a collection of varied experimental data will undoubtedly benefit the training and education of practitioners. The Worldwide Protein Data Bank  13, 14  (wwPDB) has illustrated how these achievements can be realized with the collection of reduced experimental data, in the form of structure factor amplitudes. Complementing this resource by preserving raw experimental data and making it available to a broad community promises a profound scientific impact in structural biology and other biomedical disciplines that face the challenges of preserving large data sets. While the primary role of the SBGrid Consortium (www. sbgrid. org) has been to curate and support a collection of data processing software applications and to organize communitywide computing support  15  , SBGrid has also been active in the management of raw, experimental data sets.
In 2012, SBGrid prototyped a system based on Globus technology  [16] [17] [18] [19]  to move diffraction data between Harvard, The Advanced Photon Source, and the Stanford Synchrotron Radiation Light source  19. To support the outstanding needs of the global structural community, we have established a publication system for experimental diffraction data sets that supports published structural coordinates: the Structural Biology Data Grid (SBDG). The SBDG project was initiated with a collection of X-ray diffraction image data sets as well as a few additional data set types contributed by many SBGrid Consortium laboratories. The collection supports a diverse subset of over 68 peer-reviewed publications and represents a sampling of numerous structure determination approaches. To evaluate the utility of such a data grid, we reprocessed all published diffraction data sets in this initial collection with modern software and compared the derived statistics against those reported in the original publications.
The SBDG limits its collection to data sets that support journal publications, referred to as 'primary data'. For X-ray diffraction data, this primary data consists of experimental diffraction images supporting a derived structural model and journal publication. Release of this primary data by the SBDG coincides with publication of the resulting manuscript and for the structural biology data sets of related PDB files. As of 1 September 2015, the SBDG stores a diverse collection of 117 data sets, including 111 X-ray diffraction data sets and a handful of other data types including computational decoys and data sets from MicroED, lattice light-sheet microscopy and molecular dynamics (Supplementary Table  1 ). These published data sets, contributed by 50 laboratories with diffraction data sets collected at 11 synchrotron facilities (Fig.
1 ) and several home sources, originated 94 structures and 68 journal publications. The X-ray diffraction data sets range in size from 126 MB (ref. 20) to 20 GB (ref. 21) with a mean of 4. 9 GB and a total of 573 GB of storage. Extrapolating from this initial collection, which is quite diverse and registers at just over 0. 5 TB, our current 100 TB file system could immediately support roughly twenty thousand X-ray diffraction data sets (Fig. 2 ). The SBDG's collection of data sets can be accessed from the data. sbgrid. org website. On the home page, deposited data sets are organized into laboratory and institutional collections (Fig. 3a ). Hyperlinked collection pages provide a list of selected data sets along with the data set's corresponding data Digital Object Identifier (DOI), a link to the journal publication, the PDB ID, a link to the PDB entry, and a link to the depositors' laboratory website.
Disclosure Software tools developed under this program will be incorporated into open source software and released to the community. Manuscripts and white papers describing various phases of the project will be released on a regular basis. Adoption All biomedical image data will be converted to the master formats, such as OME-TIFF or HDF5. Community tools to create, analyse, and manipulate diffraction images will be extended to include support for these formats. All biomedical data are assigned Digital Object Identifiers through the CDL EZID system, and follow modified DataCite and Dataverse metadata schemas. Associated metadata are registered with the International DOI Foundation, making it virtually permanent and independent of SBGrid and Harvard computing infrastructure. All data sets published through the SBDG will be citable using Force 11 recommendations.
External dependencies The ability to reprocess some older data sets and verify master format conversions could depend on access to a specific version of data processing software. As data sets enter our repository, they will be reprocessed with our Data Reprocessing Pipeline (one of several we will develop as part of our Data Mining Pipelines). Data Reprocessing Pipelines will be archived within our system, issued DOIs, and interlinked with the data sets. It is worth noting that, since 2002, SBGrid has been archiving structural biology applications and, therefore, has access to previous software versions that might be required to reprocess older data sets.
Technical protection mechanism The security of the deposited data will be maintained by the DAA. The DAA will join with the Library of Congress sponsored NDSA and the data architect working on the project will ensure that NDSA recommendations are being followed. additional information for each data set including download instructions and the fully formatted data set citation for inclusion in manuscripts, following best practices set by the Joint Declaration of Data Citation Principles  24. A Data set Page can also be located by searching the SBDG for a PDB code, although often several related data sets are used to determine a single set of macromolecular coordinates. As the Data Grid is developed, the Data set Pages will include additional functionality, with more information on how to reprocess data sets, extended data statistics, and discussion forums allowing users to annotate data sets after publication.