dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene ontology (GO) enrichment analysis Gene ontology enrichment analysis was performed using an over-representation test with clusterProfiler v. 3. 14. Promoter GC-content calculation GC-content of promoter regions in zebrafish was calculated using zebrafish developmental CAGE datasets mapped to danRer7 -egg and prim6 (ZebrafishDevelopmentalCAGE, R data package, data published in  47  ). Samples were processed using CAGEr (as described above), and normalised consensus clusters extracted. Consensus clusters were resized to 2kb width using the consensus cluster centre as a fixing point, and GC-content calculated using the GC function (seqinr R package) on the extracted sequence. Analogously, GC-content was calculated for mouse promoter regions using consensus clusters generated using all SLIC-CAGE datasets.
Expression profiling using self-organising maps (SOMs) SOM expression profiling was performed using kohonen R package for self-organising maps  48. Only tag clusters located in promoter regions were used, i. e. tag clusters within 3kb of the annotated TSSs or in 5'UTR regions, and having a minimum of 1 tpm expression value in at least one sample (annotations as described TxDb. Mmusculus. UCSC. mm10. knownGene). Prior to clustering, the matrix of tag cluster expression values was scaled across samples without centring, and final SOM clusters were visualised using ggplot2. Similarly, SOM clustering of dominant CTSSs was performed. Dominant CTSSs with at least 1 tpm expression value in at least one sample were selected prior to scaling and clustering.
5F vs mESC shifting promoters and serve as a test group in Extended Data Figure  12d. Sequences were further scanned with the nucleosome-preceding pwm using the seqPattern Bioconductor package, and the match scores visualised using the heatmaps Bioconductor package. Prior to producing metaplot, the match scores were winsorized to exclude extreme values (5 th to 95 th percentile of scores were selected). Relative scores for metaplots were then calculated by summing scores at each bp position in a stack of aligned sequences and dividing the sum with the number of sequences. Relatives scores were visualised using ggplot2.
Acknowledgments This work was supported by The  Wellcome Trust  grant ( 106954 ) awarded to B. L. and F. M. ,  MRC Core Funding  ( MC-A652-5QA10 ) and  BBSRC   Responsive Mode Grant  ( BB/R002703/1 ) awarded to H. G. L. N. C. was supported by an  EMBO   Advanced Long Term Fellowship (EMBO aALTF   626-2018 ) and a  Wellcome Trust   Seed Award  ( 217309/Z/19/Z ). B. L. is supported by the  Medical Research Council UK  ( MC UP 1102/1 ). H. G. L acknowledges support from the  National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC). Work in the Hajkova lab  is supported by  MRC  funding (MC_US_A652_5PY70) and by an  ERC  grant ( ERC-CoG-648879 -dynamic modifications ). Y. H. is supported by the  Japanese Society for the Promotion of Science (JSPS)   Postdoctoral Fellowship (PD)  No.
201700058. This study was also supported by the  European Research Council (ERC)   Advanced grant  ( ERC-2013-340551 , Birtoaction) (to LT) and grant  ANR-10-LABX-0030-INRT  and a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02  (to IGBMC). We also thank  Laurence Game  and the  MRC LMS  sequencing facility for support. Lastly, we thank  Vedran Franke ,  Peter Sarkies  and members of the  Lenhard group  for critical reading of the manuscript.
Gene regulation by promoter methylation is also found in mammals and was likely acquired independently in the sponge Amphimedon queenslandica  [16]. In all these cases TE methylation is also prominent so it is possible that the two are linked, perhaps relating to a requirement to control TE-derived promoter regions; however testing this hypothesis would melanogaster orthologues of genes that are either highly methylated (grey) or unmethylated (red) in arthropods. Shaded area is a 95% bootstrap confidence interval. Across all species in the dataset, mean methylation levels were estimated for each group of orthologous genes using a general linear mixed model. The top and bottom 20% were classified as methylated and unmethylated respectively. Only genes with orthologs in all species are shown.
Genome annotation To annotate exons in each genome we used existing annotations, excluding genes that were split across multiple contigs. To annotate regions which may contain promoters or enhancers, we took 1,000 bases upstream of each gene, excluding genes where this exceeded the contig start or end point. We annotated introns based on the position of exons, excluding genes that were split across multiple contigs (using intron_finder. py script available at https://github. com/SamuelHLewis/BStoolkit/). To annotate TEs, we used RepeatModeller v1. 0. 8 to generate a model of TEs for each genome separately, and then RepeatMasker v4. 0. 6 to annotate TEs based on the model for that genome. Within each TE, we used interproscan  [59]  to search for the following TE-associated domains: PF03184, PF02914, PF13358, PF03732, PF00665 & PF00077.
Whole genome bisulphite sequencing To measure DNA methylation on a genome-wide scale, we carried out whole-genome bisulphite sequencing. We used the DNeasy Blood and Tissue kit (QIAGEN) according to the manufacturer's protocol to extract DNA from adult somatic tissues of the following species: L. polyphemus, P. tepidariorum, S. maritima, A. vulgare, B. germanica, A. pisum, B. terrestris, N. vespilloides, H. melpomene and D. melanogaster. For I. scapularis, we used the same method to extract DNA from the IDE2, IDE8 and ISE18 cell culture. To estimate bisulphite conversion efficiency, we added a spike-in of unmethylated DNA (P-1025-1, EpiGentek) equal to 0. 01% of the sample DNA mass to each sample. We then prepared whole-genome bisulphite sequencing libraries from each DNA sample using the Pico Methyl-Seq Library Prep Kit (Zymo Research), according to the manufacturer's protocol (S1 Table for detailed sample metadata and sequence accession codes).
We sequenced these libraries on an Illumina HiSeq 2500 instrument to generate 100bp paired-end reads. We used pre-existing whole-genome bisulphite sequencing datasets for P. hawaiensis (SRR3618947,  [23] ) and A. mellifera (SRR1790690,  [62] ). To generate bisulphite sequencing data for P. citri, we extracted DNA from adult females using the DNeasy Blood and Tissue kit (QIAGEN) according to the manufacturer's protocol. To estimate bisulphite conversion efficiency, we included a spike-in of non-methylated Escherichia coli lambda DNA (isolated from a heat-inducible lysogenic E. coli W3110 strain, provided by Beijing Genomics Institute (BGI), GenBank/EMBL accession numbers J02459, M17233, M24325, V00636, X00906). Sequencing of bisulphite libraries was carried out by BGI on an Illumina HiSeq 4000 instrument to generate 150bp paired-end reads.
Bisulphite sequencing data analysis Before mapping reads to the genome, we trimmed sequencing adapters from each read, and then trimmed 10 bases from the 5' and 3' end of each read (using the script https://github. com/SamuelHLewis/BStoolkit/blob/master/BStrim. sh). We aligned bisulphite sequencing reads to each genome using Bismark v0. 19. 0  [63]  in-non_directional mode with default settings. We used MethylExtract v1. 9. 1  [64]  to estimate the level of methylation at each CpG site, calculated as the number of reads in which the cytosine is methylated divided by the total number of reads covering the cytosine, excluding sites covered by fewer than 10 reads on each strand. Due to the large number of contigs in their genome assemblies exceeding the memory limit for MethylExtract, we split the genomes of I.
RNA-Seq data analysis To investigate the link between DNA methylation and transcription, we used RNA-Seq data generated previously for arthropod somatic tissue (NCBI PRJNA386859,  [70]  and the I. scapularis IDE-8 cell line (SRR1756347, Arthropod Cell Line RNA Seq initiative, Broad Institute, broadinstitute. org). To measure the expression of each feature, we trimmed adaptors and low-quality ends using Trim Galore with default settings, and mapped RNA-Seq reads to the genome of each species using TopHat2 v2. 1. 1  [71]  with default settings for strand-specific libraries (-library-type frfirststrand mode). We counted the number of reads overlapping each feature using BEDTools coverage v2. 25. 0 in strand-specific mode, and divided the number of reads by the feature length to generate expression level estimates in fragments per per kilobase million (FPKM).
CAGE data analysis Total body RNA was extracted from L3 Drosophila melanogaster (w 1118 ) larvae using the Qiagen RNeasy kit. CAGE library preparation was performed using the nAnT-iCAGE protocol  [72]. Two biological replicates were prepared from 5 ug of total RNA each. The libraries were sequenced in single-end 50 bp-pair mode. CAGE tags (47 bp) were mapped to the reference D. melanogaster genome (assembly Release 6) using Bowtie2  [73]  with default parameters. Uniquely mapped reads were imported into R (http://www. R-project. org/) as bam files using the standard workflow within the CAGEr package  [74]. The 5' ends of reads are CAGE-supported transcription start sites (CTSSs) and the number of tags for each CTSS reflects expression levels. Raw tags were normalised using a referent power-law distribution and expressed as normalized tags per million (TPMs).
Acknowledgments We thank  L. Bell-Sakyi , the  Tick Cell Biobank ,  A. McGregor ,  R. Jenner ,  M. Akam ,  A. McLean ,  D. Collins ,  R. Kilner ,  A. Pinharanda  and  C. Jiggins  for providing arthropod samples, and  G. Filion  for providing a curated list of D. melanogaster housekeeping genes. PS is a recipient of an  EMBO Young Investigator award. Sequencing of bisulphite and CAGE libraries was carried out by the  LMS Genomics Facility.
This research was funded by the  Medical Research Council  (grant to PS) and the  Leverhulme Trust  (grant to FMJ and PS). BL was supported by  Medical Research Council UK  ( award MC_UP_1102/1 ) and  Wellcome Trust   Investigator Award   106954/Z/15/Z. LR was supported by a  NERC  fellowship ( NE/K009516/1 ) and the  Royal Society  ( RG160842 ). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
In recent years, DF has been found to provide more exhaustive descriptions of studied systems compared to when single datasets are assessed separately. For this reason, in the last decades, numerous DF methodologies have been proposed in the literature with the three tasks surveyed in the previous sections of this article in mind: data exploration, regression and classification. Nonetheless, despite the ultimate purposes behind their development, such methodologies are usually classified according to the level at which the data fusion is implemented. In the so-called low-level DF, for instance, raw datasets are simply concatenated and analyzed at the same time as a unique global ensemble. Mid-level DF extracts specific features (e. g. , principal components or latent variables) from each considered dataset which are subsequently gathered and investigated concurrently.
Finally, in high level DF, the outcomes of individual models constructed for every concerned dataset are pooled in a unique solution. Another common distinction among DF strategies is based on the nature of their underlying algorithms (sequential-if joint information is retrieved iteratively-or parallel-if joint information is retrieved collectively)  [176]. Due to the large number of existing DF approaches, the following subsections will offer only a basic and brief description of those most commonly used in the field of food authentication. In Table  3 , a detailed list of applications of DF in this domain is also reported.
Multi-Block Data Exploration One of the first exploratory multi-block methods dates back to 1987. It was proposed by Wold and Hellberg and it is called Consensus PCA (CPCA)  [177]. CPCA is the natural extension of PCA for the analysis of multiple datasets. Basically, the algorithm searches for directions of maximum variance common to all the data blocks at hand. CPCA encompasses four different steps: (i) one measured variable is selected as super-score; (ii) block loadings and scores are estimated from such a super-score; (iii) block scores are gathered in an updated super-score block which is used to calculate a set of super-weights; and (iv) super-weights are normalized and a new super-score is computed. The procedure is iterated until convergence. A few years later, in 1996, Wold himself, together with Kettaneh and Tjessem, developed a novel version of multi-block PCA, called Hierarchical PCA (HPCA), sharing similar algorithmic features with CPCA  [178].
Their main difference, though, relates to the fact that in HPCA the super-scores are normalized instead of the super-weights. It was observed that both CPCA and HPCA exhibit severe convergence issues, eventually overcome through the modifications proposed by Westerhuis and Coenegracht  [179]. Later on, in 2001, a new sequential exploratory approach (Generalized PCA-GPCA  [180] ) appeared in literature. GPCA constructs a PCA model on the matrix resulting from the concatenation of the datasets under study and extracts normalized super-scores. These super-scores are resorted to for the retrieval of block scores and loadings. The outcomes of a comprehensive comparison of different variants of CPCA, HPCA and GPCA are reported in  [181]. Almost concomitantly, in 2000, Qannari et al. implemented Common Components and Specific Weights Analysis (CCSWA)  [182, 183].
CCSWA seeks the shared information underlying all the investigated datasets. Specifically, A com orthogonal common components are extracted from a combination of the cross-product matrices corresponding to the I data blocks coped with (X i X T i ∀i ∈ [1, I]), which is weighted according to their so-called salience, i. e. , the portion of variance they explain in each one of them. Over the years, different formulations of CCSWA have been designed. CCSWA has nowadays become very well-known with the name ComDim. More recently, many alternative multi-block techniques (thoroughly reviewed in  [184, 185] ), based on the extraction of common factors, but at the same time also aimed at the recognition of the distinctive information carried by each of the individual considered datasets, have been proposed (e.
Multi-Block Regression and Classification The chemometric literature is rich in multi-block methods for regression and classification. One of the most commonly used is undoubtedly Multi-Block-PLS (MB-PLS). MB-PLS constitutes the direct extension of PLS to the data fusion field. Different variants of its algorithm have been proposed all over the years  [179] , the most recent being developed by  Qin et al. in 2001 [188]. The basic idea behind this latest formulation is that multiple predictor blocks can be concatenated and modelled simultaneously by PLS. Although extremely intuitive and yielding accurate predictions, this strategy does not provide information about the single datasets. Conversely, the solution implemented by Westerhuis and Coenegracht can be exploited for the interpretation of block scores and loadings  [179].
In this regard, it is important to stress that MB-PLS requires the datasets at hand to be scaled separately (by, e. g. , their individual sum-of-squares or their Frobenius' norm), to prevent structures with greater variance from preferentially driving the model and blurring the information carried by the others  [179]. Due to its remarkable flexibility, MB-PLS is nowadays probably the most widely applied multi-block regression approach. It is also often used in combination with LDA for addressing classification problems-MB-PLS is here known as MB-PLS-LDA or MB-PLSDA. So far, many applications of MB-PLS have been reported in the field of food analysis (see Table  3 ); it has been resorted to (i) for the investigation of sensory parameters of different nature and of their relationships with technological properties of cheese and bread samples  [189, 190] , (ii) for the prediction of meat spoilage time, wine ageing time and crude protein and moisture content in soybean flour by MIR and NIR spectroscopy  [191] [192] [193] , (iii) for the discrimination of botanical varieties of extra virgin olive oil, lemon essential oils and wines of different geographical origin by MIR, NIR and Raman spectroscopy  [194] [195] [196] , and (iv) for distinguishing added-value from low-quality products  [132].
Y is regressed onto X 1 by PLS; 2. X 2 is orthogonalized with respect to the X 1 -scores calculated in step 1, yielding the array X 2,orth. This ensures the common information shared by X 1 and X 2 is removed from the latter; 3. The PLS residuals resulting from step 1 are regressed onto X 2,orth by PLS; 4. The SO-PLS predictive model is expressed by combining the outcomes of steps 1 and 3 as: Y = X 1 B 1 + X 2 B 2 + E SO-PLS (31) where B 1 and B 2 contain the regression coefficients for X 1 and X 2 , respectively, and E SO-PLS carry the final Y-residuals. As a consequence of the deflation of the common information shared by the different datasets under study, SO-PLS provides useful insights into the unique and distinctive sources of variation within each one of them. Analogously to MB-PLS, SO-PLS has already been broadly employed in food science (see Table  3 ), also coupled to LDA for tackling classification tasks  [199] ; it has been used (i) to correlate sensory perception and chemical composition descriptors  [200] , (ii) for the quantification of particular compounds in dietary supplements  [201] , (iii) for the authentication of spirits (i.
, after subtraction of the mean spectrum calculated across the entire dataset) is partitioned as follows: X = X A + X B + X AB + X res (34) where X A , X B , and X AB account for the effect of the two factors and their interaction, respectively, while X res contains the residual variance in X not explained by the model. More specifically, X A , X B , and X AB carry identical replicates of the mean spectra corresponding to the different factor or interaction levels. Starting from the common ground defined by Equation (  33 ), the various methods developed for the analysis of multivariate designed data proceed along different directions. Among these techniques, the most popular is surely ANOVA-Simultaneous Component Analysis (ASCA  [253, 254] ), due to its relative simplicity and remarkable interpretability properties.
Validation Having built a chemometric model does not guarantee per se its quality and/or validity, as these two aspects are strictly influenced by user-dependent factors like the chosen analysis technique or the values of certain metaparameters  [2]. To assess whether it can lead to solid conclusions, the outcomes/predictions resulting from it are reliable and generalizable and its interpretation is sound and meaningful, such a model needs, therefore, to be validated  [277]. Operationally, validation consists in evaluating the model's performance on a dataset (called a test set) different from the one used in the training or calibration phase (referred to as a training or calibration set, as also specified before), but reflecting as closely as possible the distribution of future individuals and accounting for all potential sources of expected variability (e.
Software Nowadays, multiple software tools for chemometric modelling, both commercial and open-source, are available and easily accessible. A detailed list can be found in  [1]. In particular, a collection of freely downloadable Matlab (The Mathworks Inc. , Natick, MA, USA) functions for the implementation of most of the methods described in this review can be found at https://www. chem. uniroma1. it/ romechemometrics/research/algorithms/.
Image analysis. Microscopy images processing was performed using the open source ImageJ/Fiji (download at https://fiji. sc/) and quantitative image analysis was performed using the free MicrobeJ plugin (download at http://microbej. com) (Ducret et al. , 2016). Cells outlines were detected automatically based on the segmentation of phase contrast image. When required, cell outlines were corrected using the Manualediting interface of MicrobeJ plugin. Cell size was automatically extracted and plotted on GraphPad Prism software. Nucleoids were detected automatically using the Feature detection interface of MicrobeJ plugin (tolerance = 500) and the nucleoids number and area were extracted and plotted over the time on GraphPad Prism software. ZapA-GFP and ParB-GFP (ori and ter) localisation was performed by automatic detection using the Feature detection interface of MicrobeJ plugin (tolerance = 60 for ParB P1 -GFP and 500 for ZapA-GFP).
Localization of ZapA-GFP was automatically extracted and density plots and heat-map plots were generated with MicrobeJ plugin. Distance between ZapA-GFP rings and the cell pole was extracted automatically and plotted on GraphPad Prism software. Demographs and Kymographs of mCherry fluorescence distribution were generated automatically using MicrobeJ. For analysis of P sulA -mCherry intracellular intensity after UV was extracted from MicrobeJ and the fold-increase of fluorescence signal was plotted over time on GraphPad Prism software. Analysis of the cell lineages and identification of the type of filament divisions (midcell and tip-division) were performed manually by following individual cells on time-lapse microscope images.
Acknowledgments The authors thank the  National BioResource Project, Coli Genetic Stock Center ,  F. Cornet ,  Y. Yamaichi ,  E. Gueguen  and  E. Cascales  for providing strains;  Sarah Bigot  for critical reading of the manuscript, and  A. Ducret  for valuable help with MicrobeJ and for writing custom analysis codes. Funding: C. L. acknowledges the  ATIP-Avenir program , the  Schlumberger Foundation for Education and Research  ( FSER 2019 ); FINOVI ( AO-2014 ) for funding to J. C. and microscopy equipment; La ligue contre le cancer for flow cytometer founding. Author contributions: C. L. and J. C. conceived the project, designed the study, analysed the data and wrote the paper; C. L. , J. C. and A. D. performed the experiments and made the bacterial strains and plasmids. Declaration of interests: The authors declared no competing interests.
There is a natural way of using this temporal closeness estimation to empirically find the exact top-k nodes according to our temporal closeness metric. This approach simply consists in running our estimate temporal closeness computation algorithm, in finding the top-K nodes for the estimated temporal closeness, with K > k, and in computing the exact temporal closeness of these nodes. Our third contribution is an extensive experimental validation of this approach with a dataset of 45 medium/large temporal graphs. Indeed, we show empirically that using this method we can retrieve the actual top-100 nodes of all large graphs we have considered by choosing K = 1024, that is (with a little abuse of notation), in time O(2048 × m), which is between 10 and 100 times faster than computing the temporal closeness of all nodes.
Experimental Results In our experiments, we used 45 medium/large real-world temporal graphs taken from different application domains, that is, collaboration, communication, and transportation domains. For sake of brevity, we describe our results by referring to a sample of our dataset (see Table  1 ): the entire dataset and the entire set of experimental results are shown in the Appendix A (Table  A1 ). Here, we are going to use the following temporal graphs. Every node corresponds to an actor and two actors are connected by their collaboration in a movie, where the appearing time of an edge is the year of the movie. We use the whole temporal collaboration graph and the ones induced by the comedy and the fantasy genres  [45]. • MELB. Nodes are transport stops and temporal edges are connections traversed by a public vehicle: the edge appearing time is the arrival time (see  [30, 31] ).