dataesr/scientific-paragraphs-categorization huggingface dataset:
We used the procedure and code developed by Dennis and Ponciano  21  , to look for density-dependence in the two long-term datasets (i. e. , for Anse Vata and for Baie des Citrons south). The procedure compares the goodness of fit of two state-space models fitted to time-series abundance data. The Exponential Growth state-space analysis models time-series as a density-independent process and the Ornstein-Uhlembeck state-space analysis models time-series as density-dependent. A parametric bootstrap likelihood ratio test (based on 1000 bootstrap replicates) is then performed to compare the fits of the density-dependent vs. density-independent models 4. These analyses were performed in R 4. 0. 2 using the codes provided as Supplemental Material in Dennis and Ponciano  21. We also used a simpler method that allows us to combine data for all three sites, and to compare the effects of density-dependent and stochastic factors within the same analysis.
Accounting for differences in the time-since-infection distribution between datasets, which may be achieved using samples taken from times of comparable growth rates or by including time-since-onset or time-since-infection in a regression model, will improve the reliability of statistical tests comparing Ct values between variants. However, these results assume that the variants have similar delays between exposure and symptom onset. If, for example, newer variants have systematically shorter symptom onset delays, then new variant samples obtained under symptom-based surveillance would reflect a shorter time-since-infection distribution. At the time of writing, the SARS-CoV-2 literature has a mixture of studies that either do not acknowledge the potential for biases in Ct value comparisons  [6, 26] , discuss the potential for this bias  [33, 34] , or take clear steps to account for differences in the time-since-onset or timesince-infection distribution when comparing Ct values  [5, 22].
Simulated surveillance samples and Ct values We combined the SEIR and viral kinetics models to simulate Ct values among the infected population over time. In brief, we used the SEIR model to simulate when individuals are infected with either variant over time and used the viral kinetics model to simulate their observed Ct value when sampled on a particular day post infection. Observations were simulated using two surveillance strategies described below. For the analyses underpinning Figures S1-S7, we generated 1000 simulated datasets for each sample size of 25, 50, 100, 250 or 500. We simulated datasets on day 270 of the epidemic in scenarios where the new variant had a later seed date, or day 50 when the strains were seeded on the same day. where   is the maximum time-since-infection for which individuals may still be PCR where d is the sampling delay in days; o is the incubation period in days; () is the PDF for the discretized gamma sampling delay distribution; and () is the PDF for the discretized log-normal incubation period distribution.
Estimating growth rates and viral kinetics from cross-sectional samples Using the simulated random cross-sectional samples of detectable Ct values, we jointly estimated the posterior distributions of viral kinetics parameters and the exponential growth rate that are consistent with the observed data. We repeated this process for each of the 100 simulated datasets of sample sizes of 25, 50, 100, 250 and 500 detectable Ct values per variant. Model fitting was carried out using the virosolver R package  [45]. In brief, model fitting involves: (1) defining the likelihood of observing Ct values conditional on the viral kinetics model parameters and incidence curve, assuming that incidence follows exponential growth with an unknown growth rate parameter;  (2)  defining priors for all model parameters as described in Table  S2 ; and (3) estimating the posterior distribution of all model parameters conditional on the Ct data using Markov chain Monte Carlo (MCMC).
Methods HTLV-1, STLV-1 IN and PPP2R5C constructs for prokaryotic expression. For expression of HTLV-1 IN, the previously described pET28a(+)-SUMO-H6P-STLV-1 MarB43 IN construct  16  was used to express wild-type STLV-1 IN (integrase sequence corresponding to nucleotides 4338-5231 of the STLV-1 MarB43 isolate genome, GenBank ID: AY590142. 1). Single-point mutants of STLV-1 MarB43 IN were generated by two-step PCR splicing. The following primer pairs were used for cloning SLTV-1 IN mutants: R49Q (GNM860, GNM770 and GNM861, GNM769), I147V (GNM862, GNM770 and GNM863, GNM769), A219E (GNM864, GNM770 and GNM865, GNM769), P214D (GNM866, GNM770 and GNM867, GNM769). The PCR products were then spliced together using primers GNM770 and GNM769, digested with EcoRI/XhoI and ligated into similarly digested pET28(+)-SUMO-H6P  9.
0. Proteins were degraded by incubation with 30 μg of proteinase K (Roche) at 37 °C for 1 h. DNA was precipitated at -20 °C overnight in the presence of ethanol supplemented with 20 μg of glycogen and then resuspended in 1. 5× agarose loading dye. The DNA products were separated by electrophoresis through 1. 5% agarose gel and stained with ethidium bromide and imaged using the BioRad GelDox XR + imager. The data were analysed and the IC 50 calculated as previously described  9. In brief, the DNA band that corresponds to the concerted integration product was quantified by densitometry in ImageLab 4. 1 (Bio-Rad). All experiments were done in triplicate. Quantified data points were fitted to dose-response curves in Prism 9. The cumulative standard deviation for each drug was calculated as an average of the upper limit and lower limit values: Lower limit ¼ IC 50 10 log SEðIC 50 Þ Upper limit ¼ IC 50 10 log SEðIC 50 Þ Where SE denotes the standard error computed by Prism 9.
Four μL of STLV-1 intasome:INSTI complexes were spotted on the graphene side of the coated grid. The grids were incubated for 1 min at 22 °C and 95% humidity, blotted for 2. 5 s prior to plunge-freezing in liquid ethane using a Vitrobot Mark IV instrument (Thermo Fisher Scientific). Cryo-EM data were collected on a 300-keV Titan Krios G3i cryo-electron microscope (Thermo Fisher Scientific) equipped with a Gatan GIF BioQuantum energy filter and a K3 Summit direct electron detector (Gatan). Micrographs were acquired in dose-fractionation mode, at a calibrated magnification corresponding to 1. 1 Å per physical pixel (0. 55 Å per super-resolution pixel) at the detector. A total electron exposure of 50 e/Å 2 was fractionated across 41 movie frames. A 20-eV energy slit and a defocus range of -0.
7 to -3. 6 μm were used for all data collections. The number of micrograph movie stacks acquired for RAL, BIC and XZ450 complexes were 17744, 8502 and 9188, respectively. Image processing and 3D reconstruction. The micrograph movie frames were aligned, binned to the physical pixel size and summed, applying dose weighting as implemented in MotionCor2  43. Contrast transfer function (CTF) parameters were estimated using Gctf-v1. 06  44. At this stage, images with crystalline ice contamination were discarded; 14,271 (intasome-RAL data set), 6129 (BIC) and 7719 (XZ450) micrographs were retained for further processing. Particles were picked with Gautomatch-v0. 53 (http://www. mrc-lmb. cam. ac. uk/kzhang/) using STLV intasome 2D class averages  16  , low-pass filtered to 18 Å resolution, as templates.
The IN A219E mutation was introduced in the model and the apparent conformational changes of some of the residues and vDNA bases were modelled for in real-space in Coot 0. 9. 4  51. RAL and BIC structures were obtained from existing structures with accession codes 3OYA and 6RWM. The ligands were rigid-body fitted and locally refined. Density for the magnesium atoms was apparent and allowed their unambiguous positioning and water molecules were added to complete the Mg 2+ coordination sphere. The model was duplicated and rigid-body docked in Chimera 1. 12. 0 52 to form the missing symmetrical part. The model was manually adjusted, and the final real-space refinement was conducted using Phenix version dev-4142 and the density modified map. Secondary structure restraints and basepair/base stacking definitions based on the model, metal bond, ligand restraints and NCS constraints for the two halves of the symmetric nucleoprotein assembly were used.
The integrated provirus copy number was calculated as follows: (tax copy numbers)/(2×gapdh copy number)  53  , and normalised to the DMSO-treated control which was set to 100%. The PVL data were fitted to a dose-response curve in Prism 7 and the EC 50 was calculated  9. Alu-PCR was performed as previously described in ref. 55. In brief, the first PCR reaction was done with Phusion polymerase starting from 80 ng of gDNA using the ALU-specific primers (Alu-F/Gag-R). The nested PCR was run diluting the amplificated product 1:50. The reaction was done as a qPCR using Gag-specific primers (Gag-R/Gag-F) and the Luna SYBR dye reaction mix (New England Biolabs). The integrated provirus copy numbers were normalised to GAPDH and Jurkat cells treated with DMSO and infected with HTLV-1 were set to 100%.
Acknowledgements We thank  Drs A. Purkiss  and  P. Walker  for computer and software support. We thank professor  C. Bangham  for generously sharing with us clone 11. 50. We are grateful to  Pavel Afonine  and  Oleg Sobolev  for their support in using Phenix. real_space_refine. This work is supported by the  Wellcome Trust  ( Investigator Award  to G. N. M. ,  107005/Z/ 15Z ). Work in P. C. laboratory is supported by  US National Institutes of Health  grant  P50 AI150481  and the  Francis Crick Institute  ( FC001061 ), which receives its core funding from  Cancer Research UK  ( FC001061 ), the  UK Medical Research Council  ( FC001061 ) and the  Wellcome Trust  ( FC001061 ). Data were collected at the  London Consortium Electron Microscopy Facility  at The  Francis Crick Institute  funded by  Wellcome Trust  ( 206175/Z/17/Z  to Professor Xiaodong Zhang).
X. Z. , S. J. S. , T. R. B. and S. H. H. are supported by the  NIH   Intramural Program ,  Center for Cancer Research, National Cancer Institute  and by grants from the  NIH   AIDS Intramural Targeted Program   (IATAP). This article is independent research funded by the  National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the  National Institute for Health Research  or the  Department of Health.
An RF > 1 indicates more overlap than expected of two independent groups. Note that the ''GADD45B-DMR-regulon'' comprising 447 genes is summarized in the Figure  S1E. (I) GO-analysis of the ''GADD45B-DMC-regulon'' reveals neuron-related gene categories enriched after Gadd45b overexpression 14d p. i. Gene ontology analysis (PANTHER version 15. 0) and the PANTHER overrepresentation test with the GO-Slim annotation dataset 'biological process' identified significantly overrepresented GO categories. The first 15 significantly overrepresented GO categories with the highest fold enrichment are displayed with the fold enrichment on the left y-axis (black points) and the FDR value on the right y-axis (gray points). Note that the RRBS analysis of differentially methylated regions (DMRs) in the SNpc region after injection of AAV8-mGadd45b or AAV8-control is shown in Figures  S1A-S1F.
While this indicates that the specific regions with methylation changes induced by Gadd45b overexpression are not stable over time, the general location in open sea regions and in introns of genes of DMRs and DMCs is maintained as a specific and stable footprint of Gadd45b overexpression, which we termed ''GADD45B-regulon''. Furthermore, there was an overlap of more than half of the genes containing intronic hypomethylated or hypermethylated DMCs at 14d and at 90d (Figure  1H ). In order to quantify the significance of the overlaps between genes containing hypo-or hypermethylated DMCs at 14d and 90d, we used the calculation of the representation factor, which is the number of overlapping genes divided by the expected number of overlapping genes drawn from two independent groups. We used the MGI international database resource for the laboratory mouse estimate of 24,241 coding genes as the total number of mouse genes.
We used Gene ontology analysis (PANTHER version 15. 0; http://pantherdb. org) and the PANTHER overrepresentation test with the GO-Slim annotation dataset 'biological process'. 145 GO categories were significantly overrepresented with a fold change >2 and an FDR <0. 05. The first 15 significantly overrepresented GO categories are displayed in Figure  1I. Of those, 10 GO categories are explicitly related to neuronal functions with two prevailing categories, namely synaptic function and organization, and neurodevelopment and neurogenesis. This is very similar to what we found for DMRs (Figure  S1F ) and suggests that Gadd45b is involved in the specific regulation of gene body methylation of neuronrelated genes.
LINE-1 methylation is affected by Gadd45b overexpression Heterochromatin alterations can unsilence normally repressed TEs, including LINE-1 elements  (Van Meter et al. , 2014). LINE-1 are a potential source of DNA damage  (Belgnaoui et al. , 2006; Blaudin de Theet al. , 2018; Gasior et al. , 2006; Simon et al. , 2019). Repeat elements are detected by RRBS and the Bismark software used for mapping of the RRBS reads only considers uniquely mapped reads to avoid any bias during the methylation calling. Thus, reads that are mapping with the same mapping score to multiple locations on the reference genome, which will be the case for most reads derived from repetitive elements, will not be considered. We exploited these facts to explore the methylation status of LINE-1 elements in our ) in intergenic regions.
This prompted to examine a database annotating full-length LINE-1 elements (L1Basev2  (Penzkofer et al. , 2017) ) to see if intronic and intergenic DMRs and DMCs could coincide with possibly active LINE-1. Ten DMRs and 62 DMCs overlapped with a full-length LINE-1. More than half of them (7 out of 10 DMRs and 38 out of 62 DMCs, Figures 5H andS3C), were hypomethylated. This data indicates a widespread change in the methylation status of LINE-1 elements upon Gadd45b overexpression (14d p. i. ). The most differentially methylated LINE-1 elements are hypomethylated and belong to young L1 families (L1Md-F2, L1Md-T, L1Md-A) suggesting a possible expression of these individual LINE-1 loci. Interestingly, L1-iDMCs are frequently located in introns of genes related to neuronal functions (Figure  5G ).
Gadd45a was expressed at very low levels and did not show a change in expression upon Gadd45b overexpression (Figure  S4A ). Of note, 4 out of these 5 genes belong to the GADD45B-DMC-regulon (Figure  1H ). Using RNA-seq data of laser-microdissected SNpc from wild-type mice (GEO GSE72321  (Rekaik et al. , 2015) ; and unpublished), we compared the expression levels of the selected candidate genes to those of dopaminergic neuron-specific markers such as Th, dopamine transporter Slc6a3 or the homeogene, Engrailed 1 (En1) (Blaudin de Theet al. , 2016). We found high levels of expression of Satb1 compared to the other selected genes, indicating that Satb1 is strongly expressed in the SNpc (Figure  S4B ). Exonic and intronic DMCs within the gene region of downregulated genes comprised hypo-as well as hypermethylated CpGs.
ac. uk/projects/trim_galore/) version 0. 4. 1. Reads were then aligned to the murine reference genome mm10/GRCm38 using bismark v0. 20. 0  (Krueger and Andrews, 2011). Bismark is a specialized tool for mapping bisulfite-treated reads such as the ones generated in RRBS-seq experiments. Bismark requires that the referenced genome first undergoes an in-silico bisulfite conversion while transforming the genome into forward (C / T) and reverse strand (G / A) versions. The reads producing a unique best hit to one of the bisulfite genomes were then compared to the unconverted genome to identify cytosine contexts (CpG, CHG or CHH -where H is A, C or T). The cytosine2coverage and bis-mark_methylation_extractor modules of Bismark were used to infer the methylation state of all cytosines (for every single uniquely mappable read) and their context, and to compute the percentage methylation.
The reported cytosines were filtered to get only the CpGs covered in each sample. The spike-in control sequences were used at this step to check the bisulfite conversion rates and to validate the efficiency of the bisulfite treatment. Methylkit v1. 7. 0  (Akalin et al. , 2012) , an R/Bioconductor package, was used to perform the differential methylation analysis between the two groups of samples. The CpG dataset was filtered for low coverage (CpGs with coverage less than 10X in all samples per comparative group were discarded) and for extremely high coverage to discard reads with possible PCR bias (CpGs with coverage higher than the 99. ninth percentile were discarded). The data was then normalized for read coverage distribution between samples. A pairwise comparison was performed for first versus second group of samples to identify differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs), the latter with a window and step size of 1000bp.
Methylkit uses logistic regression to compare the methylation percentages between groups at a given CpG/region. All DMCs and DMRs were annotated with the R/Bioconductor package annotation  (Cavalcante and Sartor, 2017) , with the refGene and CpG island annotations from UCSC. The annotation comprised two categories: (i) distance to a CpG island and (ii) regional annotation. The distance related annotation classified DMCs and DMRs whether they overlapped a known CpG island, 2000 bp of the flanking regions of the CpG islands (shores), 2000 bp of the flanking regions of the shores (shelves) or outside these regions (open sea). The regional analysis classified DMCs and DMRs in four groups, namely, exons, introns, promoters and intergenic regions. Details on the number of total reads obtained, number of uniquely aligned reads, mapping efficiency, number of CpGs detected are available in Table  S1.
Images were analyzed by the same experimenter using ImageJ software. For foci quantifications the foci counting Fiji Plug-in was used. In addition, an image analysis plug-in was developed for the ImageJ/Fiji software, using Bio-Format (openmicroscopy. org), mcib3D and GDSC (Alex Herbert from Sussex University) libraries. First, nuclei that belonged to TH + neurons were manually marked with the plug-in Cell Counter,an xml file for each image containing 3D nuclei coordinates was saved. Then, nucleus channel was filtered with a median filter (radius = 4) and a Difference of Gaussian (DOG) (sigma1 = 30, sigma2 = 15), a binary mask was done with an Otsu threshold. Only nuclei that are associated to the nuclei defined in the xml file were kept. MeCP2 and H3K9me3 foci detections were performed using a median filter (radius = 2), DOG (sigma1 = 10, sigma2 = 2), binary mask was done with a MaxEntropy threshold, then 3D objects (foci) that had a volume comprised between 1.
Gene ontology analysis Gene ontology analysis (PANTHER version 15. 0) was done using the PANTHER overrepresentation test with the GO-Slim annotation datasets 'biological process', 'molecular function' and cellular component' and the 'mus musculus' gene set as the reference list. Fisher's exact test was used to compute statistical significance of overrepresentation with the false discovery rate (FDR) set at p < 0. 05. The first 15 categories, ordered by fold enrichment, are displayed along with the corresponding FDR value.
QUANTIFICATION AND STATISTICAL ANALYSIS Unless otherwise stated, the graphs represent the mean of replicates and the error bar the standard error of the mean (SEM). Error bars, values of n and mean G SEM are as stated in the figure legends. Results were considered as statistically significant for p value <0. 05; in some cases, the exact p value is given (*p < 0. 5, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001 in all experiments). Normality test were performed prior to the statistical test and unless stated otherwise, the non-parametric two-tailed Mann-Whitney test was applied. All statistical analysis was done with the software Prism. For the bioinformatic analysis of the RRBS, we formulated the null hypothesis that there are no differences in methylation between the two groups. After the p values have been computed, Methylkit, an R package for DNA methylation analysis and annotation, uses the sliding window model (SLIM) to correct p values to q-values for multiple comparison tests.
Statistically significant DMCs and DMRs were identified with a qvalue cutoff <0. 01 and a methylation difference higher than 25%. In order to quantify the significance of the overlaps between genes containing hypo-or hypermethylated CpGs at 14d and 90d, we used the calculation of the representation factor using the tool provided on http://nemates. org/MA/progs/overlap_stats. html. We used the MGI international database resource for the laboratory mouse estimate of 24,241 coding genes (http://www. informatics. jax. org/mgihome/ homepages/stats/all_stats. shtml) to estimate the total number of mouse genes. The representation factor is the number of overlapping genes divided by the expected number of overlapping genes drawn from two independent groups. A representation factor (RF) > 1 indicates more overlap than expected of two independent groups, a representation factor <1 indicates less overlap than expected, and a representation factor of 1 indicates that the two groups share the number of genes expected for independent groups of genes.
To test for the statistical significance of these overlaps, we calculated the representation factor (RF) and the corresponding p value. An RF > 1 indicates more overlap than expected of two independent groups. Note that the ''GADD45B-DMR-regulon'' comprising 447 genes is summarized in the FigureS1E. (I) GO-analysis of the ''GADD45B-DMC-regulon'' reveals neuron-related gene categories enriched after Gadd45b overexpression 14d p. i. Gene ontology analysis (PANTHER version 15. 0) and the PANTHER overrepresentation test with the GO-Slim annotation dataset 'biological process' identified significantly overrepresented GO categories. The first 15 significantly overrepresented GO categories with the highest fold enrichment are displayed with the fold enrichment on the left y-axis (black points) and the FDR value on the right y-axis (gray points).
Data and code availability The RRBS data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE176544 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE176544). The original western blot image and microscopy data reported in this paper will be shared by the lead contact upon request. This paper does not report original code. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
ACKNOWLEDGMENTS This work was supported by grants from the ''Fondation de France'' (00086320 to J. F. ), the''Fondation du College de France'' (to J. F. ) and the French Agence Nationale de la Recherche (ANR) under reference ANR-20-CE16-0022 NEURAGE (to J. F. ). We are grateful to all primary donors for their financial contributions to this work. We thank the "Fondation BettencourtSchueller". We thank the animal facility members for their essential contributions. We gratefully acknowledge Julien Dumont and the College de France Orion imaging facility (IMACHEM-IBiSA), member of the French National Research Infrastructure France-BioImaging (ANR-10-INBS-04), which received support from the program «Investissements d'Avenir»ANR-10-LABX-54 MEMOLIFE. We thank Yves Dupraz for the manufacturing of a customized mouse brain slicer.