dataesr/scientific-paragraphs-categorization huggingface dataset:
IMM stands for inner mitochondrial membrane and IMS for inter membrane space. In (e), the high structural conservation of Leishmania mt-LAF8 is illustrated by structural superimposition and sequence alignment with the human mitochondrial Rsfs (PDB: 5OOL) as well as two bacterial Rsfs (PDB: 3UPS and 2O5A). (f) Schematic view of the LSU assembly intermediate of L. tarentolae presented from the intersubunit and solvent sides. Maturation factors are colored by function. Starting from the LSU assembly intermediate in (f), (g) details the dissociation of maturation factors and the association of r-proteins, considering the T. brucei LSU 3 as a late assembly intermediate. Maturation factors are colored as in (f) and r-proteins are shown in black.
ACKNOWLEDGEMENTS This work has benefitted from the facilities and expertise of the  Biophysical and Structural Chemistry platform (BPCS)  at  IECB ,  CNRS UMS3033 ,  Inserm US001, Bordeaux University. We thank  A. Bezault  for assistance with the Talos Arctica electron microscope. We thank  J. Chicher  and  P. Hamman  of the Strasbourg Esplanade proteomic analysis for the proteomic analysis. This work was funded by a  European Research Council   Starting Grant  ( TransTryp ID:759120 ) and the  IdEx junior excellence chair program  of the  Université de Bordeaux  to YH and the  French National Program "Investissement d'Avenir"  ( Labex MitoCross ), administered by the  "Agence National de la Recherche ", and referenced [ ANR-11-LABX-0057_MITOCROSS ] to MS. This project was supported by  "Institut National de la Santé et de la Recherche Médicale" (INSERM) ,  "Centre National de la Recherche Scientifique" (CNRS)  and  Université de Bordeaux.
DATA AVAILABILITY The cryo-EM maps of the mitoribosome have been deposited at the Electron Microscopy Data Bank (EMDB) with accession codes XXXX. Corresponding atomic models have been deposited in the Protein Data Bank (PDB) with PDB codes XXXX. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD018981.
Acknowledgements This work is supported by the  National Science Centre in Poland  Grant  UMO-2016/23/B/ST3/01430  ( SPINOR-BITRONICS ). WS acknowledges  National Science Centre in Poland  Grant No. 2015/17/D/ST3/00500. JK  and  WP acknowledge National Science Centre in Poland  Grant No. 2012/05/E/ST7/00240. The nanofabrication process was performed at the  Academic Centre for Materials and Nanotechnology (ACMiN)  of  AGH University of Science and Technology. The authors thank  M. Schmidt  for technical support.
RNA isolation, reverse transcription and genes expressions RNAs were isolated from 100 mg of frozen material ground in liquid N 2 using the RNAzol following the manufacturer's recommendations (Sigma-Aldrich). RNA quality was checked and DNase treatment was carried out before the synthesis by GoScript reverse transcriptase (Promega) of the cDNAs. The quantitative reverse transcription polymerase chain reaction (RT-qPCR) was done with the Go-Taq qPCR master Mix kit according to the manufacturer's instructions (Promega). RT-qPCR data analyses were carried out using RQPCRBASE, an R package working in the R computing environment for analysis of quantitative real-time PCR data  (Hilliou & Tran, 2013). The expression of the different genes was normalized against two housekeeping genes, Mtc27 (Van de  Velde et al.
Phylogeny The phylogeny data were obtained using the one-click mode of the website (http://www. phylogeny. fr; Dereeper et al. , 2008)  which includes a sequence alignment using the MUSCLE and GBLOCKS programs. Phylogenetic reconstruction was done with the PHYML program using the maximum likelihood method. Nodes with a robustness < 80% were pooled in the same phylogenetic subgroup.
Medicago truncatula phytoglobin family Research in genomic and protein libraries (JCVI, https://www. jc vi. org/medicago-truncatula-genome-database; NCBI, https:// www. ncbi. nlm. nih. gov/; UniProt, https://www. uniprot. org/) revealed that the M. truncatula genome contains 17 Phytogb genes. Phylogenetic analysis of protein sequences of M. truncatula Phytogbs, compared with Glycine max, L. japonicus and Arabidopsis thaliana Phytogbs, confirmed the presence of three Phytogb classes (Fig. S1 ). Three Phytogb1, two Phytogb3 and 12 Lbs were identified in M. truncatula, whereas G. max and L. japonicus possess two Phytogb1, and only four and six Lbs, respectively (Fig. S1 ). This large number of Lbs with distinct protein sequences (Fig. S2 ) and Affymetrix expression patterns (Fig. S3 ) highlights the still unresolved but different roles and locations of each of them within the M.
thaliana or legumes such as G. max, L. japonicus and P. sativum, but it is found in Trifolium subterraneum and Vicia faba (http://www. coolseasonf oodlegume. org/). Interestingly, the two Phytogb3 genes have only the heme binding domain (Fig. S5 ), which raises the question of whether they possess NO dioxygenase activity. Considering the confusion in the name of the M. truncatula Phytogb genes in the literature, and based on Mt4. 0 database classification, we propose to homogenize their nomenclature. Nomenclature, Affymetrix, gene (Mt4. 0 genome version from Noble database), and Symbimics accession codes  (Roux et al. , 2014)  of the 17 MtPhytogb genes are listed in Table  1.
Roux et al. (2014)ss codes and Symbimics expression of Medicago truncatula phytoglobins. Affimetrix gene codes were on https://mtgea. noble. org/v3/;Mt4. 0gene codes were on https://www. jcvi. org/medicago-truncatula-genome-database;Symbimicsdata were inRoux et al. (2014). NC, no communicated; DESEQ MEAN corresponds to the expression value of the RNA-sequencing analysis in the different zones of the nodule; FI, meristematic zone; FIID, distal infection zone; FIIP, proximal infection zone; IZ, interzone; ZIII, N 2 -fixing zone. Shades of red highlight the highest expression value for each gene.
Acknowledgements This work was supported by  INRAE , the  Centre National de la Recherche Scientifique , the  University of Nice-Sophia-Antipolis , and the  French Government  through the  LABEX SIGNALIFE program  (reference no. ANR-11-LABX-0028-01 ) and the  STAYPINK  project ( ANR-15-CE20-0005 ). We thank  Pierre Frendo  for critical reading and  William Rhamey  for proofreading the manuscript.
To ascertain possible bias induced by in vitro differentiation, we compared our dataset to the published ATAC-seq carried out on OT-I cells, naïve and in vivo-differentiated in response to bacterial infection (GSE95237 series)  (1). Of ATAC peaks from our dataset, 97% in naïve and 87% in D3 cells are also present in naïve and differentiated cells from the GSE95237 series, respectively, yielding highly similar profiles in the genome browser (SI Appendix, Fig. S1C ). This not only demonstrates the reliability of our ATAC regions but also reveals that the majority of peaks found in D3 are independent of the type of stimulus. Importantly, we further used the in-house dataset because the sequencing conditions (longer paired-end reads of 100 bp) allowed less ambiguous mapping of ATAC signals on genomic repeats and transposons, as compared to single-end sequencing of shorter reads  (26).
Higher resolution of this strategy allows mapping TEs to ATAC peak summits, as well as slopes to better define boundaries of the accessible chromatin. To distinguish putative enhancers, we split ATAC peaks into two groups: Promoters and the distal peaks (see the full pipeline in SI Appendix, Fig. S2A ). Regions situated within ±2 kb from annotated transcription start sites (TSS) were considered as holding a promoter activity. More distal peaks were considered as cis-regulatory regions. To select distal ATAC peaks with the signature of active enhancers, we performed an overlap with the hallmark histone modification H3K27ac using chromatin immunoprecipitation-sequencing (ChIP-seq) datasets for naïve and differentiated cells from the above-mentioned GSE95237 series. As these H3K27ac + distal ATAC peaks may contain a small portion of not yet unannotated promoters, we intersected them with the H3K4me3 peaks (in-house ChIP-seq), typically higher in promoters than in enhancers  (27)  (cut-off is shown in SI Appendix, Fig.
1E ). Altogether, these results show that during CD8 + T cell differentiation chromatin remodeling is initiated early on (before the first cell division) and that naïve cells embody broader enhancer accessibility and potentially higher enhancer redundancy, as compared to activated cells. Selected TE Species Are Differentially Enriched in Central and Peripheral Enhancer Domains. To colocalize TEs with active enhancers, we first defined enhancer boundaries using hallmark histone modifications: H3K27ac (GSE95237 series) and H3K4me1 (GSE95237 series), as well as H3K4me3 (in-house), present at a low level in active enhancers. We observed a comodulation of H3K4me1 and ATAC signals in D0-and D3specific developmental enhancers (SI Appendix, Fig. S4 ). Aggregation of histone marks around ATAC peak centers reveals a classic peak-valley-peak pattern  (29) , with the ATAC peak nearly perfectly coinciding with the valley (Fig.
No significant association of the enhancer-enriched subfamilies with random regions was observed, except for a few L1M species. After removing them, we obtained 84 subfamilies (among 1,200 in total) enriched in enhancer domains both by length and by copy number (see enriched subfamilies in Dataset S1 and random sampling results in Dataset S2). The most enriched are subfamilies belonging to ORR1, MTE, and MTD species of ERVs, L2s, and MIRs, as well as B1, B2, and B4 SINEs (see Fig. 2C  for the most numerically abundant subfamilies). Most L1s are depleted from enhancers, but 11 (of 300) L1M subfamilies are significantly enriched. Recently, the link between immunity and ORR1Es, MTEa and -b, and MIR3s has been highlighted by their enrichment in enhancers of dendritic cells  (37). Subfamilies demonstrate lineage-specific enrichment preferences, with ERVs enriched in cores and B1, B2, and B4 SINEs, in distal flanks (Fig.
Similar dA:dT-rich sequences are overrepresented in LINEs that span flanks. These sequences almost perfectly match enhancer dA:dT flanking lexicon. Nearly all motifs enriched in enhancer-linked TEs are present in the corresponding consensus, including Arich tails of SINEs  (51). The Fos-Jun binding motif found in B2s has been presumably evolved together with enhancers because it is not present in B2 consensus sequences. The majority of enriched motifs are absent or proportionally decreased from TE counterparts from gene deserts, suggesting a putative selective advantage for T cell cis-regulation. Since the A-rich Forkhead-like motifs are inherent to many TEs, we examined their capacity to bind Forkhead protein Foxo1, a major regulator of T cell homeostasis  (45). To this end, we exploited the public dataset of ChIP-seq for Foxo1 performed on naïve CD4 + T cells  (52) , whose transcriptome is fundamentally similar to the one of naïve CD8 + T cells  (53).
We find a remarkable overlap of Foxo1 peaks with the promoter-and active enhancer-linked ATAC peaks in naïve (D0) cells (SI Appendix, Fig. S7A ), confirming the biological relevance of the dataset. Our quantification shows that various TE species colocalize with Foxo1 peak summits in enhancers (SI Appendix, Fig. S7B ), providing altogether 20% of binding sites. This suggests substantial participation of TEs in the maintenance of naïve cell homeostasis. In sum, we show that enhancer core-linked TEs carry a rich array of ancestral motifs recognized by TFs relevant to T cell biology. These motifs are better preserved in enhancer-core TEs compared to nonaccessible TE equivalents. Flanking TEs carry sequences matching the inherent enhancer flanking lexicon, particularly featured in naïve cells, and may putatively contribute to sequence-based boundary regulation.
The Link Between TEs and Immune Functions. To identify transcriptional programs putatively rewired by TEs, we predicted enhancers' gene targets via the Genomic Regions Enrichment of Annotations Tool (GREAT)  (54). Using matching RNA-seq, we kept only developmentally comodulated enhancer/gene pairs (modulation of enhancer accessibility [by ATAC-seq] and change in gene transcription [by RNA-seq] are in concordance). This strategy allowed coupling of nearly 1,000 D0-specific genes to developmentally cobehaving enhancers (Dataset S3). Over 40% of the gene targets are shared between TE-rich and TE-poor enhancers, suggesting a high degree of putative enhancer redundancy (Fig. 6  A, Upper). A substantial proportion is uniquely assigned to TE-rich enhancers. Despite SINEs outnumbering other TE families, the majority of genes are shared between enhancers rich in SINEs and rich in other TE types (Fig.
6  A, Lower). Nearly one-third of the predicted enhancer/gene pairs in the D0-specific cluster has been previously identified by Hi-C (2) performed on naïve CD8 + T cells, likely reflecting a direct physical contact (Dataset S4). SI Appendix, Fig. S8A  illustrates the loci of Irf1 and FbxI5 genes and corresponding TE-rich enhancers confirmed by Hi-C. Gene ontology (GO) enrichment analysis performed on genes linked to TE-rich and TE-poor enhancers (split by TE lineage) showed that overrepresented functional terms mostly divide into two functional classes: Immune and metabolic (Fig. 6B ). All of the enhancer categories are enriched for the "regulation of response to stimuli" biological process. The majority of them associate with various semiredundant processes linked to leukocyte and lymphocyte biology, such as migration, adhesion, and differentiation.
To address this, we exploited the public dataset of ATAC-seq performed on hematopoietic stem cells (HSC, a common precursor of all immune lineages) (Encode ENCFF190DGJ). We observed a high proportion of CD8 + T cell enhancers and enhancers found in other immune cell types (extracted from the Enhancer Atlas 2. 0; http://www. enhanceratlas. org/index. php) premarked in progenitors (Fig. 6C ). This suggests that HCS-accessible chromatin is a major source of enhancers for immune tissues. Besides, CD8 + T cell enhancers overlap by 60% with enhancers pooled from other immune lineages (51,000 in total) (Fig. 6D ). Moreover, Heatmap showing enrichment of GO biological processes associated with TE-poor and distinct categories of TE-rich enhancers (split into lineages). GO terms are divided into two major functional classes, immune and metabolic.
Same color is given to the hierarchically related processes, which are ranked by descending order starting with the parental term. (C) Bar plot showing proportion of enhancers in distinct immune lineages overlapping with ATAC-seq peaks in HSC (ENCFF190DGJ). (D) Bar plot showing overlap between enhancers and genes (fragments per kilobase of transcript per million mapped reads ≥ 0. 5) active in CD8 + T cells (in-house datasets) and enhancers (Enhancer Atlas 2. 0) and genes [Lara-Astiaso et al. (55) ] active in other immune cells. (E) Barplot showing proportion of TE-rich among enhancers unique to immune tissue and unique to nonimmune tissues. Results of the Fisher's exact test is shown to illustrate significantly higher proportion of TE-rich enhancers in the "immune" population, as compared to the "nonimmune" population.
S9A ) from the Enhancer Atlas database 2. 0. Cross-tissue comparison revealed 25,000 enhancers unique to immune tissues (immune enhancers) and 70,544 unique to nonimmune tissues (nonimmune enhancers). The majority of genes collectively expressed by immune cells (60%) colocalize with immune enhancers (within 100 kb around the TSS). To address putative TE cooption, we applied TE subfamily enrichment tests. Because it was impossible to precisely map centers and define domains, we trimmed public enhancer coordinates down to ±750 bp from the midpoint, obtaining uniform 1,500-bp regions. Significance of TE enrichment was inferred on TE copies located within loosely defined enhancer cores (400 bp around midpoint) and the full enhancer. This test revealed a significantly higher number of immune enhancers as TE-rich, as compared to the nonimmune enhancers (70% and 58%, respectively) (Fig.
ACKNOWLEDGMENTS. We thank  George Kassiotis  and  Jean-Christophe Andrau  for critical reading of the manuscript and fruitful discussions;  Morgane Thomas-Chollier  and  Leandro Quadrena  for fruitful discussions; and  Fiorella Kotsias  for critical reading of the manuscript. This work is supported by  Institut Curie ,  Inserm Paris Sciences et Lettres University , and Grants  ANR-11-LABX-0043  and  ANR-10-IDEX-0001-02 PSL ,.
(C) Bar plot showing proportion of enhancers in distinct immune lineages overlapping with ATAC-seq peaks in HSC (ENCFF190DGJ). (D) Bar plot showing overlap between enhancers and genes (fragments per kilobase of transcript per million mapped reads ≥ 0. 5) active in CD8 + T cells (in-house datasets) and enhancers (Enhancer Atlas 2. 0) and genes [Lara-Astiaso et al. (55) ] active in other immune cells. (E) Barplot showing proportion of TE-rich among enhancers unique to immune tissue and unique to nonimmune tissues. Results of the Fisher's exact test is shown to illustrate significantly higher proportion of TE-rich enhancers in the "immune" population, as compared to the "nonimmune" population. (F) Violin plot showing number of TE instances around genes (±100 kb from the TSS) expressed or not in CD8 + T cells and all other immune cells, as well as genes enriched in GO "immune system process" and its higher hierarchy, "response to stimulus.
The complete series of reports is available at http://poc. sedoo. fr/source/indexGarde. php?current= 20170602&nav=Reports (last access: 22 September 2020). Concerning the oceanic conditions, several remote sensing datasets were exploited using the SPASSO (Software Package for an Adaptive Satellite-based Sampling for Ocean campaigns https://spasso. mio. osupytheas. fr/; last access: 22 September 2020) in order to guide the cruise through a Lagrangian adaptive sampling strategy with the aim of avoiding regions of complex circulation and dynamics (fronts, small-scale eddies). The idea behind this approach was to aim at a situation where the air-sea exchanges dominate and lateral advection and diffusion can be neglected. Such an approach was already successfully adopted during several previous cruises such as LATEX  (Nencioli et al.
, 2011; Doglioli et al. , 2013; Petrenko et al. , 2017) , KEOPS2 (d  'Ovidio et al. , 2015) , OUTPACE  (Moutin et al. , 2017; de Verneil et al. , 2018)  and OSCAHR  (Rousselet et al. , 2019). During PEACETIME, we used the following datasets: (1) near-real-time altimetry data from the AVISO Mediterranean regional product (now distributed by CMEMS, https://resources. marine. copernicus. eu/?option= com_csw&view=details&product_id=SEALEVEL_MED_ PHY_L4_NRT_OBSERVATIONS_008_050, last access: 11 November 2020), where the altimetry-derived currents were then processed by SPASSO to derive Eulerian and Lagrangian diagnostics of ocean circulation: Okubo-Weiss parameter, particle retention time and advection, finite-size Lyapunov exponent (e. g Fig. 2 ); (2) the sea surface temperature (level 3 with resolutions of 4 and 1 km), and (3) the chlorophyll concentration (level 3 with a resolution of 1 km, MODIS Aqua and NPP VIIRS sensors combined after 27 May 2017 into a unique product) provided by CMEMS -Copernicus Marine Environment Monitoring Service (http://marine.
Conclusion The PEACETIME oceanographic expedition conducted in spring 2017 cruised over a 20 • longitudinal gradient across the western and central Mediterranean Sea during the season characterized by strong stratification, low productivity and high probability of wet dust deposition. Those conditions were required in order to fulfill the objectives of the project aiming at quantifying the biogeochemical processes at play after atmospheric deposition and its impact on ecosystem functioning. Different atmospheric situations were encountered during the cruise. In particular, luckily, three contrasted C. Guieu et al. :  Objectives and strategy of the PEACETIME oceanographic campaign (May-June 2017) situations in terms of atmospheric deposition characterized the long-duration stations, allowing the acquisition of a large dataset under different dynamical and biogeochemical in situ conditions to explore the chemical and ecosystem response to deposition.
It will contribute to significantly improve the tracer-based mapping of lithogenic dust deposition over the ocean. Data availability. Underlying research data are being used by participants of the "PEACETIME" campaign to prepare other manuscripts, and therefore data are not publicly accessible at the time of publication. Data will be accessible once the special issue is completed (January 2021): at the French INSU/CNRS LEFE CYBER database: http://www. obs-vlfr. fr/proof/php/PEACETIME/ peacetime. php, last access: 29 October 2020. Scientific coordinator: Hervé Claustre; data manager, webmaster: Catherine Schmechtig. The policy of the database is detailed here: http://www. obs-vlfr. fr/ proof/dataconvention. php (last access: 29 October 2020). Supplement. The supplement related to this article is available online at: https://doi.
Acknowledgements. This study is a contribution to the  PEACE-TIME  project (http://peacetime-project. org), a joint initiative of the MERMEX and ChArMEx components supported by  CNRS-INSU ,  IFREMER ,  CEA  and  Météo-France  as part of the program MISTRALS coordinated by  INSU. PEACETIME was endorsed as a process study by GEOTRACES, PEACETIME cruise (https://doi. org/10. 17600/17000300 ). We thank the captain and the crew of the  R/V Pourquoi Pas ? for their professionalism and their work at sea. We warmly thank  Louise Rousselet ,  Alain de Verneil  and  Alice Della Penna  for their precious help with SPASSO and the daily bulletins;  Hélène Ferré  from  OMP/SEDOO  for her management of the Peacetime Operation Center; and  Louis Prieur  for fruitful discussion on the characterization of the ocean processes observed at the FAST station.
It is therefore essential to investigate the existence and properties of scPTLs in humans. In this study, we searched for human scPTL that affect cell-tocell variability of gene expression traits. Because differences in mean expression levels are usually accompanied by differences in variability  5, 13, 14  , we took special care to distinguish genetic loci that modulate variability per se from those that affect mean expression, and thereby variability. In other words, we were interested in expression 'dispersion' (variability after accounting for the mean). In model organisms, the distinction between dispersion and variability can be made by applying dedicated genetic crosses  8, 15. In humans, however, covariation of mean and variability cannot be decoupled experimentally. Even if single-cell data can provide fine-scale descriptions of the genetic control of gene expression  16  , loci affecting variability are sometimes identified simply because of their effect on mean expression  17.
CD55 is the Decay-Accelerating Factor, which protects cells from the complement system  22. It has additional roles by transducing signals in T cells  23. CD86 is a co-stimulator of CD28-mediated T cell activation  24. CD63 is a tetraspanin protein with numerous regulatory roles  25  , including exosomebased co-stimulation of T cells. We cultured, immunostained and analyzed a total of 50 Yoruba cell lines, in 6 biological replicates for each antigen and cell line. Note that, to avoid any biases of signal amplifications or crosstalk between fluorescent channels, each sample was stained with only one, directly-labeled antibody. We processed the data as above to derive distributions of expression values of G1-gated cells. We first plotted, for all samples, the CV (variability) of these distributions as a function of the mean (Fig.