dataesr/scientific-paragraphs-categorization huggingface dataset:
To do this, we solved a Fokker Planck equation using the Salammbô code  (Beutier and Boscher, 1995; Bourdarie et al. , 1996; Varotsou et al. , 2005 Varotsou et al. , , 2008)). Salammbô is a physical model of the Earth's radiation belts, which was developed at ONERA in the 1990s. Salammbô is a three dimensional code (energy, pitch angle, L * ) and gives the electron distribution function anywhere in the radiation belts. In this study, we exclude radial diffusion and focus solely on the interactions of the energetic electrons with the waves and the atmosphere. Mixed pitch angle-energy diffusion coefficients are also neglected. In order to estimate the time evolution of the electron fluxes for the extreme values of the electron density, the fluxes have been calculated at given L * values for particles bouncing near the loss cone.
Acknowledgements. We acknowledge  Roger Anderson  for provision of the CRRES plasma wave data. The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  262468  (SPACECAST) and the  UK Natural Environment Research Council. Finally, we especially thank the  French Space Agency (CNES)  for their support for several years on the development of WAPI code. Topical Editor  G. Balasis  thanks two anonymous referees for their help in evaluating this paper.
Kernel Density Estimator The coordinates of the segmented structures were processed for density estimation programmed in the ks library  11  in the R programming language  12  : the probability density function f for each data sample of n coordinates X 1 , X 2 , …, X n was estimated. A nonparametric, unbinned kernel density estimator was used. At each of the data points, a kernel function K was centered. The kernel functions were then summed to form the kernel density estimator f ˆ : H H ( ) ( ) x x X = - = ∑ 1 1 n K i n i in which K H is the Gaussian kernel with mean zero and variance matrix H. To estimate H (also known as the bandwidth), the plug-in selector in the ks library was used, which has been shown to be reliable for two-dimensional and three-dimensional spatial distributions. 13 For visualizing kernel density estimates, probability contours were f ˆ employed.
7 For less than 10 cell, the box plots of estimated MISEs showed a large variability, with significant improvement in estimated MISE when adding data for each subsequent cell. The estimated MISE only marginally diminished between 20 and 30 cells and did virtually not change thereafter. Density maps from data acquired at 20× were more heterogeneous than those from 100× acquisition (pink) for small numbers of cells; however, all density maps became stable for more than 30 cells. This demonstrated that using density estimation with micropatterned cells at a magnification of 20× is robust from only several tens of cells for both sizes of micropattern. In addition, the impact of cell shape on density mapping was analyzed. The MISE was computed for the density maps of lysosomes in cells adhering to the five different micropattern shapes present on the CYTOO plate starter (Fig.
Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: K. S. received funding from the  Fondation pour la Recherche Médicale en France  and  Association pour la Recherche sur le Cancer. J. -P. G. is supported by the  Association pour la Recherche sur le Cancer. This project was further supported by grants from  Agence Nationale de la Recherche  (# 2010 BLAN 122902 ), the  Centre National de la Recherche Scientifique and Institut Curie , and the  OSEO ISI program  to CYTOO.
The aCGH platform and analysis steps were identical for all patients. A total of 56 patients were included in this study. For all cases MYCN status was confirmed by Fluorescent in situ hybridization (FISH)  [21]  and no discordance between aCGH and FISH has been observed. Clinical data (sex, date of birth, date at diagnosis, INSS tumour stage, localisation, MIBG uptake, sequence of treatment, relapse or progression, date and status of last follow up), biological data (urinary catecholamine at diagnosis) and histology (tumours were classified according to INPC, with focus on the differentiation of the tumour cells) were retrospectively collected. Patients were treated in French centres from the SocieteFrancaise des Cancers de l'Enfant (SFCE) according to national or international treatment protocols.
Clinical characteristics All patients from the 3 groups presented non familial and non syndromic NB. Detailed clinical characteristics and histology data for patients of groups 1 and 2 are summarised in Tables  1  and 2  respectively. For group 1, patients were of all INSS stages, the primary site occurred in different localisations, clinical features and histology were heterogeneous (Table  1 ). Interestingly, three patients (NB0791, NB0830, NB0039) in this group had normal urinary catecholamines and no uptake of MIBG at scintigraphy at diagnosis. For NB0039, lung metastasis was observed at diagnosis. Of note, NB0037 had an atypical presentation with a lumbar primary site with metastatic relapse located at the spermatic cord. Patients in groups 2 (Table  2 ) and 3 (data not shown) shared clinical features with NB of advanced stage.
In NB, in previous studies, regional amplifications other than MYCN have been occasionally described with low recurrence and most often concomitantly with MNA  [5, 6, 14, [17] [18] [19]. The objective of this study was now to report in detail the clinical features of patients with NB harbouring amplification(s) other than MYCN. In this more extensive series, we confirm the rarity of regional amplifications occurring either without MNA or together with MNA (1% and 3% of all cases, respectively) as detected by aCGH. Examples of different genomic profiles with genomic amplifications obtained in neuroblastoma by array comparative genomic hybridization for each group at diagnosis. For each panel, the genome wide aCGH profile is shown with zoom on the amplified regions. Genomic profiles were obtained using the NimbleGen R platform, and images were generated using the SignalMap R software.
Figure1. Examples of different genomic profiles with genomic amplifications obtained in neuroblastoma by array comparative genomic hybridization for each group at diagnosis. For each panel, the genome wide aCGH profile is shown with zoom on the amplified regions. Genomic profiles were obtained using the NimbleGen R platform, and images were generated using the SignalMap R software. Log2 ratio = 0 corresponds to a balanced tumour/normal DNA ratio. Amplification is indicated by plots with a log2 ratio $1, 5 (corresponding to a tumour/ normal DNA ratio $3). (A) Example of group 1 profile (NB0760): NB without MYCN amplification but harbouring amplifications at loci 12q13-14 and 12q24. (B) Example of group 2 profile (NB0863): NB with MYCN amplification (2p24) and harbouring amplification at locus 12q14.
Acknowledgments The authors wish to thank the following associations:  APAESIC (Association des Parents et des Amis des Enfants Soignes al'Institut Curie), Association Hubert Gouin, ''Les Bagouz aManon'' and ''Enfants et Sante'', and ''Les amis de Claire ''. The authors thank  Virginie Bernard  for her help in the creation of a. BED file.
TIDE software TIDE code was written in R, version 3. 1. 1. TIDE requires as input a control sequence data file (e. g. obtained from cells transfected without RGEN Cas9), a sample sequence data file (e. g. DNA from a pool of cell treated with RGEN Cas9) and a character string representing the sgRNA sequence (20 nt). The sequencing data files (. abif or. scf format) are imported into TIDE using the R Bioconductor  (15)  package sangerseqR (version 1. 0. 0). Additional parameters have default settings but can be adjusted if necessary. The web interface was constructed using the shiny R package, with some code adapted from the Poly Peak Parser web tool (http: //spark. rstudio. com/yostlab/PolyPeakParser/). The latter is a genotyping tool that can identify heterozygous short indels in sequence traces, but it cannot resolve sequences with complex indel mixtures  (16).
TIDE first aligns the sgRNA sequence to the control sequence to determine the position of the expected Cas9 break site. Next, the control sequence region upstream of the break site is aligned to the experimental sample sequence in order to determine any offset between the two sequence reads. Alignments are done using standard Smith-Waterman local alignment implemented in the BioStrings package in Bioconductor. From here on, the software uses the peak heights for each base, as determined by the sequence analysis software provided by the manufacturer of the capillary sequencing equipment (we used 3730 Series Data Collection Software V4 and Sequencing Analysis Software V6). TIDE uses these peak heights to determine the relative abundance of aberrant nucleotides over the length of the whole sequence trace.
n} (n is by default set to 10) are constructed from the control sample trace by shifting all peaks by the appropriate number of positions to the left or right, respectively. This is done for each of the four bases, after which the vectors of the four bases are concatenated so that the decomposition is done for all bases combined. Next, the sequence trace from the mutated DNA sample is assumed to be a linear combination of the wild-type and the modeled indel traces. This combination is then resolved by standard non-negative linear modeling, for which we used the R package nnls. R 2 is calculated to assess the goodness of fit. The P-value associated with the estimated abundance of each indel is calculated by a twotailed t-test of the variance-covariance matrix of the standard errors. In order to account for systematic differences between the sequence trace intensities of the control and mutated DNA, the fitting parameters are then multiplied by a constant factor such that their sum equals R 2.
The TIDE method In the first step of TIDE, a stretch of about 500-1500 bp around the editing site is PCR amplified from genomic DNA isolated from the cell pool that was treated with the targeted nuclease. A parallel PCR amplifies the same stretch of DNA from a control cell pool lacking the nuclease or sgRNA. Both PCR products are then directly subjected to conventional capillary ('Sanger') sequencing, a basic technology that is available in most laboratories. In the DNA sample from the cells expressing the targeted nuclease, the sequence trace after the break site consists of a mixture of signals derived from unmodified DNA and sequences that are each shifted by a different number of nucleotides due to insertions and deletions (Figure  1a ). Based on the quantitative sequence trace data, the TIDE software first visualizes the proportion of aberrant base sig-nals along the sequence traces in an intuitive graph (Figure  1b , step 1).
This enables the user to visually inspect the sequence deviation caused by the targeted nuclease, and verify that the break site is located as expected. Subsequently, the TIDE software decomposes the composite sequence trace into its individual components by means of multivariate non-negative linear modeling, with the control sequence trace serving as a template to model the individual indel components. This decomposition results in an estimate of the relative abundance of every possible indel within a chosen size range (Figure  1b , step 2). The software provides the R 2 value as a goodness-of-fit measure, and calculates the statistical significance for each indel. In the TIDE webtool, the sequence segment used for decomposition can be interactively adjusted, which is helpful in case the sequence traces are locally of poor quality.
In vitro proof of principle In order to test our approach, we first constructed a series of artificial samples consisting of wild-type DNA mixed with DNA carrying various indels in a broad range of relative concentrations. We then performed standard capillary sequencing and fed the resulting data into the TIDE algorithm. The constituents of the mixes could be identified and quantified with great accuracy. In a mixture of wildtype and +1 insertion DNA our algorithm was able to detect the insertion quantitatively with a sensitivity down to ∼2. 5% (Figure  2a ; Supplementary Figure  S1 ), and generally predicted the correct base (Figure  2a , inset; Supplementary Figure  S1 ). Even a -15 deletion could be reliably detected when mixed 1:10 with wild-type DNA (Figure  2b ; Supplementary Figure  S2a ).
Advantages of TIDE Genome editing techniques like CRISPR, TALENs and ZFPs are now widely used to alter specific sequences in genomes of cultured cells. However, the efficacy and the spectrum of mutations vary greatly depending on the RGEN target site and cells used (  (17, 18)  and this report). Hence, a fast and cost-effective approach to determine the efficacy of RGENs is essential to optimize the genome editing strategies. TIDE takes advantage of the fact that nonhomologous end-joining repair of DNA DSBs leaves an indel at the break site. By decomposition of the quantitative sequence trace data, the TIDE software identifies and quantifies these indels. This allows researchers to quickly determine the efficiency of the RGENs and rationally estimate the number of cell clones that must be picked and screened in order to obtain a clonal line with a particular indel of interest.
Comparison to other methods Several other methods have been used to assess genome editing efficacies. Cloning and sequencing of 50-100 individual DNA molecules provides an accurate characterization of the indel spectrum, but this is obviously more laborintensive and 25-50 times more expensive than TIDE. Nextgeneration sequencing (NGS) of bulk PCR products, followed by analysis using software, such as CRISPR-GA (  9 ), provides a highly detailed estimate of the indel spectrum, but this method is only cost-effective if a large number of samples are multiplexed; moreover, in most research institutes NGS takes several weeks. For the creation of gene knockouts, homology-based integration of a selectable marker gene at the induced break site can facilitate the isolation of the desired clonal line (19).
Analysis details The two data sets analyzed were recorded during the p-Pb (in 2013) and the Pb-Pb (in 2010) runs at a center of mass energy of √ s NN = 5. 02 TeV and √ s NN = 2. 76 TeV, respectively. The Pb-Pb run had equal beam energies giving a nucleon-nucleon center of mass system with rapidity y NN = 0. However, the p-Pb run had different beam energies per nucleon for the p and Pb beam, and resulted in a center of mass system moving in the laboratory frame with y NN = 0. 465. All kinematic variables are reported in the laboratory frame. Charged particles are detected using the time projection chamber (TPC), the primary tracking detector of ALICE. The TPC has an angular acceptance of 0 < ϕ < 2π, |η| < 0. 9 for tracks with full radial track length (ϕ is the azimuthal angle and η is the pseudo-rapidity), and |η| < 1.
The left panel of Fig. 1  shows that the star symbols (charge independent measurements) in p-Pb collisions exhibit a decrease with increasing multiplicity, qualitatively consistent with the expectation of correlations dominated by non flow effects. When fitting these data points with the function a/M b at large multiplicity, we find b = 0. 3. The value b = 1 is expected if high-multiplicity events are a linear superposition of low multiplicity events  [25]. This deviation from 1 might indicate the existence of another mechanism that increases c 2 {2}, or that the relative fraction of few particle correlations is increasing with multiplicity. In the same plot, we present measurements of like-sign correlations, calculated by measuring c 2 {2} for positive and negative tracks separately, and forming the average.
This model, similarly to the DPMJET model, contains only non flow, and as expected, c 2 {2} attenuates more rapidly than the data. Finally, the right panel of Fig. 2  presents the two-particle results in Pb-Pb collisions after applying a ∆η gap to reduce the contribution from non flow. It is seen that at multiplicities N ch 1000, the measurements with various ∆η gaps converge, indicating the dominance of anisotropic flow. The measurements at lower multiplicities depend on ∆η gap significantly, indicating non flow plays a prominent role. In Fig. 3 , we compare c 2 {2} for p-Pb and Pb-Pb with |∆η| > 1. 4 to minimize the contribution from non flow. Both systems have similar values of c 2 {2} at low multiplicity, however the Pb-Pb data points rise more rapidly for higher multiplicities. This maybe explained by higher eccentricities (therefore higher anisotropies) in Pb-Pb collisions found from a CGC inspired cluster model for the initial conditions at similar multiplicities  [22]  (not shown).
The diversity of populations in domestic species offers great opportunities to study genome response to selection. The recently published Sheep HapMap dataset is a great example of characterization of the world wide genetic diversity in sheep. In this study, we re-analyzed the Sheep HapMap dataset to identify selection signatures in worldwide sheep populations. Compared to previous analyses, we made use of statistical methods that (i) take account of the hierarchical structure of sheep populations, (ii) make use of linkage disequilibrium information and (iii) focus specifically on either recent or older selection signatures. We show that this allows pinpointing several new selection signatures in the sheep genome and distinguishing those related to modern breeding objectives and to earlier post-domestication constraints.
Introduction Domestication of animals and plants has played a major role in human history. With the advance of high-throughput genotyping and sequencing technologies, the analysis of large datasets in domesticated species offers great opportunities to study genome evolution in response to phenotypic selection  [1]. The sheep was one of the first grazing animals to be domesticated  [2]  in part due to its manageable size and an ability to adapt to different climates and diets with poor nutrition. A large variety of breeds with distinct morphology, coat color or specialized production (meat, milk or wool) were subsequently shaped by artificial selection. Since the release of the 50K SNP array  [3] , it is now possible to scan genetic diversity in sheep in order to detect loci that have been involved in these various adaptive selection events.
The Sheep HapMap dataset, which includes 50K genotypes for 3000 animals from 74 breeds with diverse world-wide origins, provides a considerable resource for deciphering the genetic bases of phenotype diversification in sheep. In the first analysis of this dataset  [4] , the authors looked for selection by computing a global F ST among the 74 breeds at all SNP in the genome. They identified 31 genome regions with extreme differentiation between breeds, which included candidate genes related to coat pigmentation, skeletal morphology, body size, growth, and reproduction. Further studies took advantage of the Sheep HapMap resource to detect genetic variants associated with pigmentation  [5] , fat deposition  [6] , or microphtalmia disease  [7]. An other study  [8]  performed a genome scan for selection focused on American synthetic breeds, using an F ST approach similar to that in  [4].
The 74 breeds of the Sheep HapMap dataset have a strong hierarchical structure, with at least 3 distinct differentiation levels: an inter-continental level (e. g. European breeds vs Asian breeds), an intra-continental level (e. g. Texel vs Suffolk European breeds), and an intra-breed level (e. g. German Texel vs Scottish Texel flocks). Recent studies  [9] [10] [11] [12]  showed that, when applied to hierarchically structured data sets, F ST based genome scans for selection may lead to a large proportion of false positives (neutral loci wrongly detected as under selection) and false negatives (undetected loci under selection). Besides, the heterogeneity of effective population size among breeds implies that some breeds are more prone to contribute large locus-specific F ST values than others  [10].
We showed that using this approach greatly increases the detection power of selective sweeps from SNP chip data, and also enables to detect soft or incomplete sweeps. These latter selection scenarios are particularly relevant in breeding populations, where selection objectives have likely varied along time and where the traits under selection are often polygenic. In this study we provide a new genome scan for selection based on the Sheep HapMap dataset, where we distinguish selective sweeps within and between 7 broad geographical groups. The within group analysis aims at detecting recent selection events related to the diversification of modern breeds. It is based on the single marker FLK test  [10]  and on its haplotypic extension hapFLK  [13]. The FLK test is an extension of the Lewontin and Krakauer (LK) test  [14]  that accounts for population size heterogeneity and for the hierarchical structure between populations.
In the paper presenting the Sheep HapMap dataset  [4] , 31 selection signatures were found, corresponding to the 0. 1% highest single SNP F ST. Using FLK and hapFLK, we confirmed signatures of selection for 10 of these regions. Considering the two analyses were performed on the same dataset, this overlap can be considered as rather small. Two reasons can explain this. First, the previous analysis was based on the F ST statistic. Although this statistic is commonly used for selection scans, it is prone to produce false positives when the population tree harbors unequal branch lengths (i. e. unequal effective population sizes)  [10]. In particular, strongly bottlenecked breeds will contribute high F ST values preferentially even under neutral evolution, because their smaller effective population size implies a larger variance of allele frequencies.
Similarly, the two closest SNP to ASIP (s66432 and s12884) present suggestive FLK p-values of respectively 7:510 {4 and 6:810 {5 in the ASI group, and one (s12884) is significantly differentiated between the ancestral groups. All these genes have previously been reported as being likely selection targets and/or associated to color patterns in different mammalian species. Finally, we found a signal for selection centered on the BNC2 gene, that has recently been associated with skin pigmentation in humans  [30]. All population groups present at least one selection signature which is very likely related to one of the above genes, reflecting the widespread importance of color patterns to define sheep breeds. Inferring a precise history of underlying causal mutations for color patterns in this dataset is hard for several reasons: the precise phenotypic characterizations of coat color patterns in the Sheep HapMap breeds are not available; the 50K SNP array used does not offer sufficient density to associate a given selection signature to a specific set of polymorphisms; Finally, from the literature it appears that a large number of genes and mutations can be considered a priori as potentially causal for a given pigmentation pattern.