dataesr/scientific-paragraphs-categorization huggingface dataset:
Ground Truth Generation Matlab ® software (MathWorks, version R2019b, Natick, MA, USA) was used to train and test image preparation, ground-truth generation and the neural network. Generation of ground truth for DLASA training and testing purpose was carried out on the 239 patients mentioned above. For each of them, L3 vertebrae CT slices were manually extracted from the baseline PET-CT scans. Ground truths were generated by a trained operator (author MJ, a medical doctor, after training for manual segmentation of muscle and fat), who used an algorithm requiring manual contouring of regions of interests (ROIs), which corresponded to 10 different muscle groups (as reported by  Burns et al. )  and to visceral and subcutaneous adipose tissues. Automatic pixel selection, using Hounsfield density thresholds of (-29 to +150 HU) for muscle and (-190 to -30 HU) for fat, was then performed.
DLASA Trainings and Validation For each of the 12 ROIs, a distinct U-Net neural network (6-layer depth) was created and trained using Matlab DeepLearning Toolbox extension. The training was performed on a NVIDIA GTX 970 (4 GB DDR) CPU. The initial learning rate was set at 10 -3 , using the Adam optimizer, with a maximum of 300 epochs. The output of all 10 CNN trained for automatic segmentation of muscle groups, was pooled to constitute the muscle ROI. The DLASA was then confirmed on the 49 slices of the testing set. The automatic segmentation performance was evaluated using Dice's formula as previously published  [14, 28] : Dice = 2(A ∩ M) A + M where A corresponds to the automatic segmentation matrix, M the manual segmentation matrix, and ∩ the intersection. Cancers 2021, 13, 4503 4 of 12.
Data Availability Statement: Request for access to the study data can be asked by email to the corresponding authors. Conflicts of Interest: HG reports grants, personal fees or non-financial support from Gilead Sciences, Janssen, Celgene, Roche, Takeda; LO, Advisory board: Roche, Takeda; honoraria: Celgene, Janssen, Roche; FM has received honoraria from Bristol-Myers Squibb and Janssen and served as a consultant or advisor to Celgene, Bayer, Abbvie, Verasteem, Gilead, Servier, Roche/Genentech, and Epizyme; HT, Consulting and advisory board: Roche, Janssen-Cilag, Karyopharm, Astra-Zeneca, Lectures: Roche, Bristol-Myers-Squibb, Servier; VR Infinity Pharmaceuticals, Bristol-Myers Squibb, PharmaMar, Gilead Sciences, AZD, Epizyme, Incyte, MSD, Servier, Roche, arGEN-X BVBA; CT Honoraria: Amgen, Celgene, Jazz Pharma, Kyte/Gilead, Novartis, Servier, Roche, Janssen; Research funding: Roche, Celgene, Aspira; CH Honoraria: Roche, Janssen-Cilag, Gilead, Takeda, Miltenyi and Servier; Travel grants: Amgen and Celgene; GD Board: Roche, takeda; Travel: Roche AbbVie Pfizer, Grants: takeda, roche; LF Honoraria: Roche, AstraZeneca, Servier, Takeda, Abbvie, Janssen; Advisory boards: Takeda, Roche, Gilead, AbbVie, Janssen-Cilag; Travel grants: La Roche, Gilead, Abbvie and Janssen-Cilag; PF Roche Genentech, celgene, Abbvie, Janssen and gilead; OH, Celgene research grant, Alexion research grant, Ab science co-founder research grant consulting, Inatherys co-founder research grants; GC has received honoraria from Janssen, Sanofi, Abbvie, Gilead, Roche, Celgene, Novartis, Takeda and served as a consultant or Celgene, Roche/Genentech; CB is board member for Roche; MA: Advisory Board: Takeda, Bristol-Myers-Squibb, Karyopharm, Gilead, Incyte, Research Grants: Roche, Johnson & Johnson, Takeda, Travel Grants: Roche, Bristol-Myers-Squib, Celgene, Gilead, Abbvie; ROC reports grants, personal fees and non-financial support from Roche Genentech, during the conduct of the study; reports personal fees from MSD, BMS, Abbvie, Amgen, Celgene, reports grants and personal fees from Takeda, GILEAD/kite, outside the submitted work; SLG reports grants, personal fees or non-financial support from Roche Genentech, during the conduct of the study; reports personal fees from Celgene, reports grants and personal fees from Janssen-Cilag; GILEAD/kite, Servier outside the submitted work.
1994  and modified by Kaartinen et al. 2010) were tried. The PCR conditions were as follows: 95°C for 5min, followed by 40 cycles of (i) 95∘C for 30s, (ii) 48°C for 90sec, and 72°C for 1min with a final extension at 60°C for 30min. PCR products were shipped to Genewiz (Radolfzell, Germany) for their Sanger sequencing. The obtained sequences were checked and compared to either reliable sequences in GenBank (Eupelmus species -see Al  Khatib et al. 2014 Khatib et al. , 2016)) , either to unpublished ones (see Appendix). Molecular analyses were carried out with MEGA-X software.
Acknowledgements We thank the  University of Torino  for fruitful collaborations and the  Interreg Alcotra project 'Sauvegarde de l'ecosystème châtaigne'  for initial funding. We also thank the  'Syndicat National des Producteurs de châtaignes ' as well as several extension services (CTIFL,  INVENIO, FREDON, Chambres d'Agriculture)  for their help in releasing T. sinensis and participating to post-release surveys. Finally, we would like to thank  Jean-Claude Malausa  who initiated this project, and the  ISA-team 'Research and Development in Biological Control ' for their contribution to intensive field sampling. The biological control program against the chestnut gall wasp in France was granted by the  National Program ECOPHYTO ('CYNIPS ' ,  2011-2014; 'CYNIPS2', 2016 'CYNIPS2', -2017 ) ) and by the  Plant Health and Environment Division of INRAE  (2015).
Matrix-filling Monte Carlo methods can be successfully used to estimate higher-order k-and aeigenmodes and eigenvalues of the Boltzmann equation. A novel matrix-filling Monte Carlo approach has been recently developed in TRIPOLI-4 Ò in order to solve the a-eigenvalue problem, as a complement to the standard fission matrix method. The behavior of the fundamental and higher-order eigenpairs in the presence of decoupling factors is illustrated through a few relevant homogeneous and heterogeneous numerical benchmarks, with special emphasis on eigenvalue separation. We then revisit a selected experiment carried out during the EPILOGUE program in the EOLE critical facility of CEA Cadarache. Using spectral analysis by Monte Carlo as a 'numerical experiment' might help, e. g. , in conceiving a future campaign in a zero-power research reactor.
By fixing the origin of the spa-tial reference system in the center of the reactor, a uniform Cartesian grid is applied on the radial plane, dividing the two coordinates in N x ¼ N y ¼ 48 spatial bins from À52:8 cm to 52:8 cm. The axial coordinate is integrated over the whole height of the system (N z ¼ 1). The angle discretization is kept at M x ¼ M y ¼ 2 and M z ¼ 1. The energy domain is divided into G ¼ 3 groups: a fast region in the interval [20 MeV, 94. 66 keV], an epithermal region [94. 66 keV, 0. 625 eV] and a thermal region [0. 625 eV, 10 À5 eV]. Nuclear data are taken from the JEFF 3. 1. 1 library  (Santamarina et al. , 2009) , with the same J ¼ 8 precursors families for all fissile nuclei. The total number of elements of the matrix for the a-eigenvalue problem is about 2 Â 10 9. As for the k-eigenvalue problem, we used the fission matrix capability of TRIPOLI-4 Ò.
The data is scaled by a constant factor, for instance using the function "bamCompare" provided by the "deepTools" package  [3]. Unfortunately, this normalization approach does not account for differences in the efficiency of immunoprecipitation and DNA amplification biases. Other methods such as Normalization of ChIP-Seq (NCIS) by Liang and Keleş  [4] , which extended CisGenome  [5] , use for the signal normalization the control sample. Here, the genome is divided into non-overlapping bins; a normalization factor is calculated as r = i∈B ChIP_signal i∈B Control_signal , where B corresponds to the set of bins that belong to the background. In CisGenome  [5] , the authors determine background regions as regions with total counts t ≤ 1 and bin-width w = 100 , and the normalization factor r is computed given these parameters.
In sum, existing methods usually evaluate the normalization parameters based on common ChIP-seq peaks, or by using only the control, and do not accept additional information, e. g. , gene expression, to establish a baseline. In this paper, we fill this gap by creating a user-friendly R package for ChIP-seq data inter-sample normalization. This method is based on the biological assumption that genes with constant expression across samples have, on average, similar protein binding intensities. Indeed, the correlation between gene expression and the deposition of chromatin marks or transcription factor binding has been documented by a number of studies  [12] [13] [14] [15]  (see also Additional file 1: Figure  S1 ).
Implementation We propose a normalization method called ChIPIN for ChIP-seq Inter-sample Normalization of fragment density signals across multiple conditions. Our method is based on the following assumption: on average, no difference in true ChIP-seq signal should be observed in the regulatory regions of genes whose expression is constant across samples/ conditions. Our algorithm comprises three main steps (Fig. 1A ). An additional, 4th step allows the user to verify the specificity of the antibody used. The tool is implemented as a user-friendly R package available online on GitHub (https:// github. com/ Boeva Lab/ CHIPIN). The first three steps correspond to the first function of the package, called CHIPIN_normalize, described in "Determining genes with constant expression between conditions/samples", "Building matrix containing ChIP-seq intensity values" and "Performing the normalization" sections.
Determining genes with constant expression between conditions/samples The CHIPIN method is based on the assumption that, on average, no difference in true ChIP-seq signal should be observed in the regulatory regions of genes whose expression stays constant across samples/conditions. These genes are determined by the package using gene expression data (RNA-seq or micro-array). For this purpose, we compute the mean and the standard deviation of the Count Per Million (CPM) values for each Fig. 1  Outline of the CHIPIN package. A Four step approach implemented in CHIPIN. B Definition of "constant genes" (red) used as a baseline for the ChIP-seq signal normalization: "constant genes" are 10% (default) of genes with the lowest standard deviation of read counts selected from different ranges of gene expression (100 bins).
We define the least variable, "constant" genes as genes that show the smallest standard deviation values across samples/conditions (default: 10%) in each expression group (Fig. 1B , "constant genes" are depicted in red). The output of this step is a standard. bed file with gene coordinates. In addition to the CPM format, the user can provide expression data in the form of Fragments Per Kilobase per Million mapped reads (FPKM) or Transcripts Per Kilobase Million reads (TPM), where read counts are normalized for the gene length; in this case, CHIPIN will transform the FPKM or TPM counts into values proportional to CPM using available transcript annotations (exon lengths).
Building matrix containing ChIP-seq intensity values Using. bigWig density files provided by the user and the. bed file containing coordinates of "constant genes", CHIPIN computes for each sample/condition the density of signal across "constant genes" and their flanking regions (± n Kb, n = 4 by default) (Fig. 1C ). For this step, we use the function "computeMatrix" provided by the "deepTools" package  [3]. The computeMatrix function offers the possibility to rescale all genes to the same length (default: 40 kb). The rescaled gene body regions and their additional flanking regions upstream and downstream of the gene body are then segmented into bins (default bin size: 10 bp). Finally, the average signal per bin for each gene is calculated. The output of this step is one matrix per sample/condition with element corresponding to the cumulated density signal per bin and per gene (Fig.
1C ). Of note, CHIPIN does not explicitly take into account Input or IgG control tracks. Instead, we recommend the users to work with densities tracks already normalized for the background signal using one of the available tools. In this study, we used the. bigWig output of the HMCan software  [11]. Indeed, peak calling with HMCan allows for the correction of the ChIP-seq signal for copy number and GC-content biases; it reduces the background noise by subtracting the control signal with a constant reflecting the noise ratio of the ChIP sample.
Quantile normalization To perform quantile normalization, ChIP-seq signal intensity matrix calculated in "Building matrix containing ChIP-seq intensity values" section is first averaged for genes with similar binding intensity (Additional file 2: Figure  S2 , default number of gene groups k = 20). The quantile normalization is then performed on averaged intensities of these k gene groups (Fig. 1D ) and the discovered normalization function is applied to the original ChIP-seq signal intensity values from. bigWig file. The mathematical transformation to obtain the normalized values from the non-normalized values for each group is learned using functions "smooth. spline" and "predict" from the R package "stats".
Profiling ChIP-seq intensity around TSS as a function of gene expression level The CHIPIN package offers the possibility to profile the average ChIP-seq signal around gene TSSs according to gene expression values. Gene expression data, FPKM values or microarray values of all genes, are separated into three groups using k-means clustering: high-, medium-and low-expressed genes. Then the ChIP-seq signal around TSSs of these three groups of genes is visualized using a density profile (see "Qualification of the specificity of the antibody used" section for an example of application).
this normalization strategy provides stable and trustable results. The two other methods, although not covering the whole spectrum of normalization approaches, are representative of the two hypotheses behind normalization strategies: the one based on the strongest and the other -on overall signal. To perform the comparison, we used ChIP-seq densities (. bigWig files) obtained with the HMCan software  [11]. In our comparison of the methods, we addressed two questions: (i) Can the CHIPIN normalization strategy efficiently match signal densities across conditions and across replicates? and (ii) Does the CHIPIN normalization preserve biological differences between protein binding density signals?.
g. , H3K27me3 and H3K9me3) in the vicinity of their TSS  [19]. Here, we provide an example how the plot_expression function of the CHIPIN package allowed detection on unspecific binding of an antibody (Fig. 5 ). We applied CHIPIN on two ChIP-seq experiments: H3K27ac in adrenocortical carcinoma samples (antibody ab4729, Abcam) and H3K27me3 in the shSpi1-A2B cells (antibody ACM39155, Active Motif ). The histone modification ChIP-seq data were coupled with RNA-seq experiments measuring gene expression in the corresponding samples. CHIPIN generated profiles of average density distributions around TSSs for three groups of genes: low, medium and highly expressed genes (Fig. 5 ). As expected, for the adrenocortical carcinoma samples, the density around the gene TSS of the profiled H3K27ac mark positively correlated with gene expression confirming the specificity and high quality of the antibody used (Fig.
This suggests a potential non-specific binding of this antibody targeting H3K27 methylation to acetylated lysines, also documented by Rothbart et al. [20]  and quantile normalization. Both methods are more efficient than other published normalization approaches we tested (Figs. 2, 3, 4). CHIPIN uses as input. bigWig files that are generated by the majority of peak callers. However, in our validation study we gave preference to the HMCan software  [11]. Indeed, HMCan is a peak-caller that corrects for the GC-content and copy number biases and removes the background signal if an Input experiment is provided. The use of HMCan is strongly recommended for the comparison of cancer samples with differences in the copy number aberration profiles. Our method was initially developed for ChIP-seq data and it can be used without any limitation to other density profiles such as those constructed for ATAC-seq or DNase hypersensitivity data.
g. , HMCan-diff  [16] , to determine differentially enriched regions. Here, we used two data sets to compare CHIPIN with other available normalization methods. For the second data set, profiling H3K27ac in the shSpi1-A2B cell line in two conditions, two replicates were available for each condition. For the validation of our method, we chose replicate 1 for the first condition and replicate 2 for the second condition to have a stronger batch effect. In this comparison, CHIPIN outperformed the other methods (Figs. 3, 4 ; Tables  1, 2 ). However, we also assessed the performance of CHIPIN in the setting where we were normalizing ChIP-seq density profiles obtained within the same replicate/batch (Fig. 3A, 4A , Additional file 4: Figure  S4  and Additional file 5: Table  S1 ). Importantly, in this case as well, CHIPIN outperformed the other normalization methods.
Conclusions Here, we present a novel method for the inter-condition normalization of ChIP-seq data when spike-in data are not available. The method is based on the assumption that, on average, protein binding in regulatory regions of genes stably expressed or silent across the conditions is also constant across the conditions. We provide a choice of normalization parameters and options. In particular, the user can choose between a linear normalization with a non-zero intercept and quantile normalization. The method is implemented in a user-friendly R package available on GitHub: https:// github. com/ Boeva Lab/ CHIPIN. Several examples are presented in the GitHub vignette and test data are provided.
Funding This work was funded through institutional support from  Centre National de la Recherche Scientifique ,  Institut National de la Santé et de la Recherche Médicale, ATIP-Avenir , the  ARC Foundation  [ ARC-RAC16002KSA-R15093KS ], and the " Who Am I?" Laboratory of Excellence   ANR-11-LABX-0071 , funded by the  French Government  through its  Investissement d′Avenir program , operated by the  French National Research Agency  [ ANR-11-IDEX-0005-02 ] to V. B. (salary, data generation, infrastructure); the  SIRIC CARPEM  [No. INCA-DGOS-INSERM_12561 ] to G. K. (salary); l' Institut National du Cancer  [ 20141PLBIO06-1 ] to C. G. , M. E. (data generation);  Institut Thématique Multi-Organisme-cancer  [ C13104L ] to C. G (data generation);  Ministère de l'Enseignement Supérieur  to S. G;  Région Ile-de-France [ARDoC ,  N° 17012698 ] to L.
Introduction GSpace is a program that can simulate neutral genomic data with recombination under a wide range of demographic models. It is based on a backward in time generation-by-generation (gen-by-gen) approach, coupled with an efficient recombination algorithm and flexible models for dispersal and subpopulation sizes. It simulates the ancestry of a sample of haploid or diploid individuals (in both cases following a standard haplo-diploid sexual life cycle), carrying one or more chromosomes. Individual dispersal is generally restricted in space in natural populations (isolation by distance:  Endler, 1979; Rousset, 1997; Wright, 1943). To represent this fact, GSpace considers a lattice of subpopulations of any size (down to single individuals) connected by limited dispersal, according to different possible dispersal distributions, including in particular fat-tailed distributions, such as the Zeta (discretized Pareto,  Patil and Joshi, 1968 ) and the Sichel  (Chesson and Lee, 2005).
In all these respects, GSpace retains and extends for genomic data some of the previous features of the IBDsim software  (Leblois et al. , 2009). The current version considers only timehomogeneous models but time-heterogeneous models will be implemented in future versions similarly to IBDsim. As gen-by-gen algorithms are expected to be slower than those involving n-coalescent approximations, we performed simulations to check the feasibility of simulating genomic data by such algorithms, and compared computation times with those of alternative software based on n-coalescent approximations, such as msprime  (Kelleher et al. , 2016) , FastSimcoal2  (Excoffier et al. , 2013) , exact coalescence algorithms implemented as DTWF in msprime python package [back-in-time  Wright-Fisher simulator, Nelson et al.
Implementation GSpace combines some parts of the modified Hudson's algorithm  (Hudson, 1983)   At each generation t, the coordinates of the parent of each individual carrying ancestral lineages are randomly drawn in a 2D backward dispersal distribution of the position of a parent given the position of the lineage. The backward distributions are deduced by assuming that dispersal occurs independently in each dimension forward in time, and can automatically handle spatial heterogeneity (i. e. different forward migration rates and size of sub-populations on the lattice) as well as various edge effects. The program can consider (i) uniform, geometric and discretized Gaussian, Zeta and Sichel forward dispersal distributions, including the stepping stone and island models as special cases, as well as (ii) a custom forward migration rate matrix.
Compilation, automated checks, inputs and outputs The program is written in modern Cþþ (  17 ) and can be compiled on any operating system with a modern compiler (gþþ ! 7. 5, clang ! 6. 0) with simple command line arguments, or using the CMake build system (both the command line arguments and the CMake commands are provided in the manual). The CMake build includes unit tests for each part of the program and functional tests comparing simulation outcomes in terms of probability of identity of pairs of genes at one and two loci to theoretical results (see  Rousset, 2004 and Vitalis and Couvet, 2001). GSpace's runs can be controlled both by a settings file and by command-line arguments, which together allow the easy specification of many parameters, and quick changes of selected parameters between simulations.
Funding This work used the following HPC platforms: INRA MIGALE (http://migale. jouy. inra. fr)and  GENOTOUL (Toulouse Midi-Pyrenees), Montpellier Bioinformatics Biodiversity  supported by the  LabEx CeMEB  ( ANR-10-LABX-04-01 ), and CBGP host platform. All authors were supported by the  Agence Nationale de la Recherche  (RL & TV: projects GENOSPACE  ANR-16-CE02-0008  and  Labex Cemeb ProLag ;  FR & RL:  project  INTROSPEC   ANR-19-CE02-0011 ).
Concretely, as illustrated in Figure  5 , the KDD stages encompass the following: (2) understanding the scope of the application field; (3) creation of the target dataset; (4) data cleaning and preprocessing; (5) data reduction and projection: reducing the number of variables to be analyzed by reducing the dimensionality of the data, extracting invariant representations, or searching for relevant characteristics; (6) matching the goals of the KDD process with the right method(s) in data mining;  (7)  exploratory analysis and selection model and hypothesis: selection of the data mining algorithm and method that will be used for the pattern search; (8) data mining: searching for interesting patterns in a particular form of representation, which includes rule and tree classification, regression, and clustering; (9) data postprocessing and visualization: interpretation of the patterns found with possible return to any step from 1 to 7 for a new cycle; and (10) action on discovered knowledge.