dataesr/scientific-paragraphs-categorization huggingface dataset:
After centrifugation, cell pellets were resuspended and incubated with RNAse and propidium iodide (Thermo Fisher Scientific) and were analyzed using a Gallios flow cytometer (Beckman Coulter). Computerized gating was applied on the side and forward scatter to exclude small debris and on a pulse width and integral peak of red fluorescence to eliminate aggregates. The data were analyzed by Kaluza ® acquisition software.
Phylogenomic analysis of all Bradyrhizobium genomes used in this study A custom set of 155 genomes was gathered covering 28 different Bradyrhizobium species, as well as selected genomes from the genera Nitrobacter and Rhodopseudomonas as outgroups. The genomes listed in Table  S1  (available in the online version of this article) were downloaded from the National Center for Biotechnology Information (NCBI) RefSeq or GenBank databases and used as input for the bioinformatic pipeline Pantagruel  [26]  to build a phylogenomic database. In short, coding sequences (CDSs) and the corresponding protein sequences were extracted from the RefSeq or GenBank annotation and then clustered into homologous gene families using MMSeqs2  [27]. Homologues were aligned with clustal Omega  [28]  and reverse-translated into CDS alignment with PAL2NAL  [29].
Prediction of nod and T3SS clusters All genome sequences used in this study (Table  S1 ) were downloaded using the MicroScope platform (http://www. genoscope. cns. fr/ agc/ microscope). Genome annotations were performed de novo using the MicroScope pipeline  [32]. A screen of nod and T3SS clusters in each genome was performed by searching the key genes nodA and rhcN. Reference CDSs were extracted from the Bradyrhizobium diazoefficiens USDA110 T genome (blr2025 and blr1816, respectively) and searched against the PkGDB genome database using the blastn algorithm as implemented on the MicroScope platform with the following parameters: query coverage ≥70 %, identity ≥40 %. The syntenic organizations of regions surrounding nodA and rhcN homologues found in bradyrhizobial genomes were then compared with each other using the MaGe genomic visualization system available on the MicroScope platform.
Phylogenetic analysis The concatenated NodABC and RhcC2NV amino acid sequences were aligned with clustalw using mega X software  [33]. Phylogenetic trees were constructed from the resulting alignments using the ML method as implemented in mega X software. Branch support was also estimated using mega X by performing bootstrap analyses of 500 replicates. Final editing was carried out using Figtree (http:// tree. bio. ed. ac. uk/ software/ figtree/) and further editing was performed with graphics software. The same procedure was used to construct the SctN phylogenetic tree using the SctN homologues from other T3SS-containing bacteria (Rhizobiales, Enterobacteriales, Aeromonadales, Pseudomonadales, Rhodobacterales, Burkholderiales, Oceanospirillales, Xanthomonadales and Chlamydiales) that are available in the NCBI database.
Prediction of RhcI-T3SS clusters gain and loss events Evolutionary analyses of the presence/absence of RhcIa-and RhcIb-T3SS clusters were performed using the program Gloome  [34]. We used the Mixture model, which allows one to study differences in both gain and loss probabilities. The expectations and probabilities for both gain and loss events were estimated using stochastic mapping and a predicted event was considered as valid if the probability was at least 0. 5.
Estimation of individual gene family evolution scenarios and co-evolution We used gene tree/species tree reconciliation to obtain a finer estimation of the scenarios of events that marked the evolutionary history of individual genes within the RhcI-T3SS clusters and nod operons. This approach integrates the information from gene phylogenies to infer events of gene duplication, horizontal transfer and loss (DTL) that occurred over the diversification of the gene family  [35]. In short, we used the software GeneRax to estimate ML scenarios as well as to make a Bayesian sample of close-to-optimal scenarios  [36]. We applied this procedure to the gene families nodA, nodB, nodC, rhcC2, rhcD, rhcN, rhcV, nopC, nopM, nopL and nopT, as well as to a sample of control gene families from the bradyrhizobial pangenome; we used the core-genome-based tree of 155 bradyrhizobia and outgroups described above as a reference species tree.
Bayesian scenario samples were used to compute co-evolution scores between gene families, using an approach adapted from Lassalle et al. [26]  and detailed further in File S1. ML and Bayesian samples of scenarios, summaries of the inferred transfer events and of the co-evolution analysis are available as part of online Supporting Data at the Figshare data repository (http:// dx. doi. org/ 10. 6084/ m9. figshare. 12191103). Species tree inference and reconciliation computations were performed on the MRC Cloud Infrastructure for Microbial Bioinformatics (MRC CLIMB) cloud-based computing servers  [37].
General features of Bradyrhizobium genomes and phylogenomic analysis of the genus In this study we considered 146 bradyrhizobial genomes and 9 genomes from related genera that were available in the public databases. A large proportion (106 out of 146) of the brayrhizobia strains sequenced were isolated from the nodules of various legumes species. The plants from which the most isolates were sequenced are: (i) G. max (36 strains), (ii) Vigna unguiculata (12 strains), (iii) Aeschynomene spp. (9 strains), (iv) Phaseolus spp. (9 strains) and (v) Lupinus spp. (7 strains). The other strains originated from various environments (different types of soils, water, biofilm, plant or animal tissue) (Table  S1 ). The majority of bradyrhizobial genomes have sizes ranging from 7 to 10 Mb, with an average of 8.
41. The scale bar indicates the number of amino acid changes per site. The symbol ¥ denotes the strains that were not isolated from root nodules. According to this phylogenetic analysis, the taxonomic classification of several strains needs to be revised. Firstly, the four small-genome strains branched with Rhodopseudomonas, which indicates that they do not belong to the genus Bradyrhizobium but rather to another genus within the family Bradyrhizobiaceae. With this in mind, the genome size of these four strain genomes appears to fit well within the range of sizes observed for Rhodospeudomonas (3. 7-5. 4 Mbp) and Nitrobacter (3. 6-5. 0 Mbp) genomes (NCBI genome database, last accessed September 2019), contradicting the idea that they had undergone some recent genome reduction event. Within the genus Bradyrhizobium, several strains do not belong to the species to which they were assigned.
The environment for isolation of the strains is indicated in the field next to the strain names on the left-hand tree Acacia dealbata (Ac. de), Aeschynomene afraspera (Ae. af), Aeschynomene americana (Ae. am), Amphicarpaea bracteata (Am. br), Andira inermis (An. in to summarize that information in a consensus scenario for the whole gene cluster (see high-probability co-events in the online Supporting Data; http:// dx. doi. org/ 10. 6084/ m9. figshare. 12191103). For greater ease of interpretation, we thus chose to complement our analysis with a simpler method of inference of the history of events of gain and loss of Rhc clusters. We used the program Gloome, which relies on the profile of presence and absence of the whole RhcI clusters in genomes and performs a stochastic mapping of events over the reference tree, to estimate a single scenario of evolution for each of the RhcI cluster types along the Bradyrhizobium tree  [34].
Possible new cases of Bradyrhizobium strains nodulating through their T3SS in the absence of NF Considering the environment of isolation of the strains, it appears that 38 out of the 40 strains isolated from soils or other environments, but not nodules, lack both the nod and RhcI-T3SS genes. The only two exceptions are B. sacchari BR10280 T and B. japonicum FN1, isolated respectively from sugarcane roots and from Manitoba soils. These 38 strains are distributed in 5 out the 6 phylogroups and notably include all the 6 representative strains of phylogroup 6 represented in our dataset (Fig. 1 ). This suggests that the presence of the symbiotic genes could have a negative impact on bacterial fitness in the absence of an interaction with a plant, and be counter-selected outside of nodules. Furthermore, the strains from the divergent phylogroup six may not have a genomic background allowing them to nodulate legume plants upon acquisition of a symbiotic island.
The diversity and the effectors number are most probably underestimated T3Es are often modular proteins constituted of multiple conserved domains  [73]. This particular architecture complicates the search for homologues because of the low level of similarity between effector proteins outside of these conserved domains. Thus, the search for specific functional domains has been proposed as a more sensitive approach to identify new candidate effector proteins  [74]. Among the rhizobial effectors, five functional domains have been identified: two eukaryotic domains, NEL (for novel E3 ligase/IPR029487) identified in NopM  [75, 76]  and the SUMO protease domain (peptidase family C48/IPR003653) identified in NopD and BEL2-5  [77, 78] ; the YopT-type domain (peptidase family C58/IPR006473) identified in NopT  [79, 80] , the YopJ-type domain (peptidase family C55/IPR005083) identified in NopJ  [72]  and the recent identified MIIA domain (metal ion-inducible autocleavage/IPR011086) identified in NopE  [81].
Acknowledgements The LABGeM ( CEA/Genoscope  and  CNRS UMR8030 ), the  France Génomique and French Bioinformatics Institute  national infrastructures (funded as part of the  Investissement d'Avenir programme  managed by  Agence Nationale pour la Recherche , contracts  ANR-10-INBS-09  and  ANR-11-INBS-0013 ) are acknowledged for support with the MicroScope annotation platform. Computational calculations were performed on the Imperial College high-performance computing (HPC) cluster and on the  MRC Cloud Infrastructure for Microbial Bioinformatics (MRC CLIMB ) cloud-based computing servers.
BAHD1 is a heterochomatinization factor recently described as a component of a multiprotein complex associated with histone deacetylases HDAC1/2. The physiological and patho-physiological functions of BAHD1 are not yet well characterized. Here, we examined the consequences of BAHD1 deficiency in the brains of male mice. While Bahd1 knockout mice had no detectable defects in brain anatomy, RNA sequencing profiling revealed about 2500 deregulated genes in Bahd1 -/-brains compared to Bahd1 +/+ brains. A majority of these genes were involved in nervous system development and function, behavior, metabolism and immunity. Exploration of the Allen Brain Atlas and Dropviz databases, assessing gene expression in the brain, revealed that expression of the Bahd1 gene was limited to a few territories and cell subtypes, particularly in the hippocampal formation, the isocortex and the olfactory regions.
RNA-seq data and gene functional analysis Total RNA was extracted from one hemisphere of adult mice brains (n = 3 per genotype) or from the whole brain of E16. 5 embryos (n = 3 per genotype) using RNeasy Kit (Qiagen), according to the manufacturer's instructions. The RNA Integrity Number median was 9. 3 (range from 8. 1 to 10; Bioanalyser Agilent). We purify polyadenylated mRNAs and build an RNA library, using TruSeq Stranded mRNA Sample Prep Kit (Illumina, #RS-122-9004DOC) as recommended by the manufacturer. Directional library were checked for concentration and quality on DNA chips first with the Bioanalyser Agilent, then with sensitive fluorescent-based quantitation assays ("Quant-It" assays kit and QuBit fluorometer, Invitrogen). The 12 cDNA libraries were sequenced using a HiSeq 2500 sequencer (Illumina) in 65 bases V4 single-end mode in three technical replicates.
The procedures for Read mapping on the reference genome (from Ensembl-92 GRCm38 assembly), gene counting, normalization and statistical analyzes are available in the Supplementary information. Functional gene analysis was performed on protein-encoding genes with Ingenuity Pathway Analysis (Ingenuity1 Systems, www. ingenuity. com, version 2018) software and DAVID (http://david. abcc. ncifcrf. gov, 2018 version 6. 8  [19] ). The range of p-values were calculated using the right-tailed Fisher's Exact Test, which compares the number of user-specified genes to the total number of occurrences of these genes in the respective functional/pathway annotations stored in the Ingenuity Pathways Knowledge or DAVID databases. Gene clusters with less than 10 genes were not considered in the analysis.
Quantification of transcript levels by real-time quantitative PCR (RT-QPCR) We used RNA extracted from half brains of Bahd1-KO and -WT mice (n = 4 per genotype) or from the whole brain of 7 Bahd1-Het1 mice and WT littermates (n = 7 per genotype). Genomic DNA was removed by treatment with TURBO DNA-free TM kit (Ambion). cDNAs were generated from 1 μg total RNA using the AffinityScript QPCR cDNA Synthesis Kit (Agilent). Quantitative Real-Time PCR was performed on StepOne Plus Real-Time PCR Systems (Applied Biosystems) using KAPA SYBR1 FAST qPCR Master Mix (2X) Kit (Kapa Biosystems), as specified by the supplier. Each reaction was performed in triplicate. Data were analyzed by the ΔΔCt method. Target gene expression data were normalized to the relative expression of mouse Ywhaz gene. Hrpt gene was used as a control gene.
Databases and gene expression analysis tools The databases used in our study were the NCBI database (Genbank, UniGene), version 18 of human protein atlas (https://www. proteinatlas. org/) [20]  and versions 2019 of mouse encode database  [21] , Allen Brain Atlas (ABA) for mouse brain (https://mouse. brain-map. org/) [22]  and human brain (http://www. brain-map. org/) [23] , DropViz (http://dropviz. org/) [24]  and Harmonizome (https://amp. pharm. mssm. edu/Harmonizome/) [25]  databases. In regards to ABA ISH dataset  [22] , anatomically comprehensive expression patterns of over 16,000 genes were obtained at cellular resolution in the 56-day-old male C57BL/6J mouse brain. ISH photomicrographs of the Bahd1 probe (RP_050421_03_A07) were manually analyzed for the expression of Bahd1 in brain areas, and inspected at multiple magnification levels and evaluated for gene expression consistency.
Upregulated genes grouped into 75 highly significant categories (selected according to p<0. 0005 and at least 10 genes per category, S2 Table ), of which 22 (29%) were involved in the development, growth or death of brain cells, functions of the nervous system (e. g. , behavior, cognition, or mood disorders) and/or the amount of neurotransmitters (Table  1A ). In addition, three gene clusters were associated with metabolism: the concentration of lipids, obesity and the oxidation of carbohydrates. Downregulated genes grouped into 16 significant categories (S3 Table ), of which 10 (62%) encompassed genes involved in bacterial infections, immunity and/or inflammation (Table  1B ). IPA results combined with a functional annotation with the DAVID software  [19]  and a bibliography search showed that half of the downregulated genes were involved in infection, immunity and/or host defense (S4 Table ).
Of note, 26 of these genes were identified as interferon-response genes in the interferome database  [26]  (Table  2 ). Real-time quantitative PCR (RT-qPCR) analysis validated the decreased expression of representative genes encoding important mediators of innate immunity, including lysozyme 1 (Lyz1), B and T lymphocyte attenuator (Btla), surfactant-associated protein A1 (Sftpa1), chemokine (C-C motif) ligand 9 (Ccl9), interferon-activable protein 204 (Ifi204), and myeloid nuclear differentiation antigen-like (Mndal). The results were consistent with the results of the RNA-seq analysis (Fig  2 ). To conclude, at the level of global brain-wide analyses, the dominant feature of the transcriptional variation between the brains of Bahd1-KO and Bahd1-WT mice was the overexpression of genes involved in neurological functions, behavior, cognition and learning, along with the under-expression of genes involved in innate immunity and bacterial diseases.
Expression profiling of the BAHD1 encoding gene in the mouse and human brain The study of the entire transcriptome of the brain only detected global transcriptional changes resulting from direct and indirect effects of the Bahd1-null mutation, within a very complex organ composed of billions of diverse cells with highly specialized functions. Therefore, a prerequisite for targeted local analyses of BAHD1 function in the brain was to obtain a better knowledge of the expression of the gene coding for BAHD1. To this end, we surveyed publicly available transcriptomic datasets and in situ hybridization (ISH) datasets. Transcriptome databases derived from human  [20]  and mouse  [21]  tissue samples indicated that the BAHD1-encoding gene is expressed at low levels in a wide range of tissues in both human and mouse and does not belong to a class of genes that are specifically enriched in the brain, such as Grin1 (encoding the NMDA-type subunit 1 of the ionotropic glutamate receptor) (S3 and S4 Figs).
An overview of the expression of Bahd1 across 12 non-overlapping brain structures covering the entire brain confirmed the specific enrichment in these three regions (Fig  3B ). We then interrogated the mouse brain cell atlas accessible through the interactive online software DropViz  [24]  to profile the relative Bahd1 mRNA expression in 262 cell types (690,000 individual cells) sampled from nine regions of the adult mouse brain. The RNA expression patterns enabled the classification of cells into transcriptionally distinct clusters and subclusters  [24]. Forty-six subclusters belonging to seventeen clusters showed the highest expression of Bahd1 (>1. ). Interestingly, consistent with the fact that HDAC1 is mainly expressed in glia, whereas HDAC2 is expressed in neuronal cells  [28] , Bahd1 was co-expressed with Hdac1 in the Polydendrocyte [#4-7] subcluster, in which Hdac2, Rcor1 and Grin1 were not expressed.
In contrast, there were much higher levels of Hdac2 than Hdac1 in neuron populations with the highest expression of Bahd1 (Fig  3D ). Finally, we explored the Allen Human Brain Atlas  [23]  (http://human. brain-map. org) to evaluate BAHD1 expression in the human brain. This atlas includes microarray data for six adult human brain tissue samples spanning ~300 brain structures. The microarray data for BAHD1 expression obtained with two independent probes showed the consistent enrichment of Bahd1 expression in the hippocampal formation in all six brains (S6 Table ). We also browsed the Harmonizome database  [25] , which provides an integrative analysis of these microarray data to highlight regions with the highest expression of a gene relative to that in other regions, regardless of the individual.
5; p value<0. 05) (S5 Table), of which the highest number (i. e. , 18 subclusters) was in the hippocampus neurons (Fig3C). Therefore, both the ISH and DropViz datasets showed consistent Bahd1 expression in the hippocampus. The other subclusters encompassed neurons in the globus pallidus, posterior and frontal cortex, substantia nigra and cerebellum. Only one subcluster did not belong to a neuron type but to neuroglia in the Polydendrocyte. Tnr. Cyth3 [#4-7], which is a subcluster in the globus pallidus (S5 Table). Of note, the highest expression level of Bahd1 was found in this subcluster (Fig3D). We determined the expression levels of Hdac1 and Hdac2 in the same subclusters with Bahd1 expression, as well as that of Rcor1 (as an example of a gene encoding a subunit of another HDAC1/2 complex) and the brain-specific gene Grin1 (Fig3D and S5 Table.
(d) Heat map showing the highest expression values for Bahd1 in the cell subclusters in the DropViz database in comparison with that of Hdac1, Hdac2 and Rcor1. Grin1 was used as a control for a brain-specific gene. GP: Globus Pallidus; HP: Hippocampus; SN: Substantia Nigra; PC: Posterior Cortex; FC: Frontal Cortex. https://doi. org/10. 1371/journal. pone. 0232789. g003.
This paper outlines the methods used to produce the website (https://epiforecasts. io/covid/), and data resource  9  , we have developed that presents real-time estimates and forecasts of reported cases by date of infection and the respective timevarying reproduction numbers globally, regionally, nationally and subnationally for Covid-19. This website relies on methods implemented in the EpiNow2 R package and data aggregated in the covidregionaldata R package, both developed by the authors  10, 11. Our estimates overcome some of the limitations of naive implementations that derive estimates for the reproduction number directly from numbers of reported cases without adjusting (or with only partial adjustments) for the delay from infection to symptom onset or from onset to notification. Our approach also incorporates multiple sources of uncertainty that if excluded can bias estimates.
Data We use daily counts of confirmed cases and deaths reported by the European Centre for Disease Control from the last 12 weeks for all analyses conducted at the national level  11, 12. To estimate the delay from symptom onset to reporting (once confirmed with a positive laboratory test), we use all cases from a publicly available linelist for which onset and notification dates are available  11, 13. This linelist combines all known linelist data from over 100 countries at the time of writing. Countries are only included in the reported estimates if within the last 12 weeks they have fewer than 14 days with non-zero case counts. This restriction reduces the likelihood of spurious estimates for countries with limited transmission or case ascertainment. For sub-national analyses, the data is aggregated using the covidregionaldata R package developed by the authors.
Amendments from Version 1 In this update we include details of our new open-source time-varying reproduction method that is based on inferring latent infections rather than attempting to reconstruct them via backsampling as discussed in the previous version of this article. This approach reduces bias in estimates and increases the potential for rapid changes over time. We also discuss, and provide links to, our flexible scheduling framework, which is developed in partnership with the Met office, and our dataverse, where version controlled reproduction number estimates can be found. Any further responses from the reviewers can be found at the end of the article so they were considered to be static over the 12 weeks of data considered each day. This results in an onset to case report delay distribution with a mean of 6.
5 days and a standard deviation of 17 days and an onset to death report delay distribution with a mean of 13. 1 days and a standard deviation of 11. 7 days. For computational reasons the maximum allowed delay is set to be 30 days. Dataset specific estimates are detailed on the respective country pages. Estimated delays are routinely updated as new data becomes available. As data may also be right truncated due to unrecorded delays (i. e the delay between a case report and its appearance in an aggregated data set) we truncate all time-series to exclude the last 3 days of data, based on qualitative inspection of the stability of case counts in the datasets used.