dataesr/scientific-paragraphs-categorization huggingface dataset:
227 of the UTC layer seems to be variable from one batch of grids to another. Therefore, applying the same intensity of glow-discharge on a grid with a frail UTC layer would break the continuous layer whereas a more robust layer would remain intact. Unfortunately, we received a batch of grids with a frail UTC layer right about the time we started our attempts to reproduce a high resolution dataset of the IC3 complex. We first suspected the complex preparation and purification procedure, before finally identifying and solving the problem. In addition to loosing the binding effects of the UTC layer, and perhaps an even more problematic phenomenon associated with the breakage of the UTC layer, is the striping of the factors from the initiation complex. Indeed, datasets collected on grids with a broken UTC layer, allowed us to obtain potential maps of good resolution for the 30S particles and the Met-tRNA i Met in the P site but density for both initiation factors were missing from the cryo-EM map even when a BS 3 cross-linking reagent was used during grid preparation.
Indeed, while the position of aIF5B as seen in the current structure is compatible with a fully assembled archaeal ribosome as seen in Sas-Chen et al. 2020 (PDB code: 6TH6) and  Armache et al. 2013 (PDB code: 4V6U)  this is not the case for aIF1A (Figure  14. 2, page 232). In its current position on the A site, aIF1A would be in clash with H69 of the LSU in the assembled ribosome. Perhaps this clash with H69 would explain how aIF1A is released form the complex, but further studies are clearly needed to fully understand the departure mechanism of aIF1A and aIF5B from the initiation complex. This thesis was part of the ANR project aimed at developing new tools for timeresolved studies of archaeal translation initiation ((ANR) TREMTI grant ANR-17-CE11-0037). In addition to solving the structures of the late stages of translation initiation, one would benefit from the study of short-lived intermediates between the IC2 and IC3 complexes.
abyssi 30S and mRNA (PDB ID code 6SWC)(Coureux et al. 2020), initiator tRNA (PDB ID codes 6SWC, 54LO, 6WOO)(Coureux et al. 2020; Monestier et al. 2017; Wang et al. 2020), aIF1A (PDB ID codes 4MNO) and the crystallographic structures of Pa-aIF5B that were determined during my thesis. In the cryo-EM maps the 30S, the mRNA, the Met-tRNA iMet and aIF1A are clearly defined. The 5'-end of the mRNA is base-paired with the 3'-end of the 16S rRNA, forming the SD:aSD duplex, and the start codon is in the P site. The Met-tRNA i Met is fully base paired with the AUG start codon in the P site and aIF1A is bound to the SSU on the A site. In class I, the density allowed an easy fit of the crystallographic structure of aIF5B with manual rigid-body adjustments of individual domains. Domain IV of aIF5B interacts with both the methionylated 3'-CCA end of the Met-tRNA i Met and aIF1A.
5 mM BS 3 (final concentration) for 12 min in a dry bath at 51 °C. Immediately after, 3. 4 µL sample from the mixture was applied to the grid at 20 °C and 90% humidity for 10 s. The sample was vitrified by plunging into liquid ethane at -182 °C, after 1. 2 s blotting (with the sensor option) using a Leica EM-GP plunger. The cryo-EM grids used were; Copper 300 mesh Quantifoil R2/1 grid covered with a 2 nm thick continuous layer of carbon. The grids were rinsed in an ethyl acetate solution and glow-discharged for 30 s at -25 mA and 0. 4 mbar in a PELCO easiGlow™ prior to grid sample deposition. The dataset was collected at the NanoImaging facility of Institut Pasteur in Paris with the help of Stéphane Tachon in September 2021. The grids were loaded into a 300 kV Titan Krios microscope (ThermoFisher) equipped with a BioQuantum-K3 imaging sys-tem (GATAN) in counting mode.
was estimated using gCTF(Zhang 2016) on the dose-weighted micrographs. Aligned micrographs with gross surface contamination and obvious gCTF output-metrics outliers were discarded. Power spectrum with nominal resolution better than 4 Å, objective-lens astigmatism better than 350 Å and within a [0. 7 -3. 8] µm defocus range were kept for further processing(Figure 11. 1, page 171). The final pool comprised of 17. 1 k micrographs, 89% of the initial dataset(Figure 11. 2, page 172).
6 Å translation). The view shows that the conformation of aIF5B:Met-tRNA i Met in IC3 is almost equivalent to that of eIF5B:Met-tRNA i Met in the S. cerevisiae 80S:eIF5B. Note that a positioning of H69 as observed in 6WOO would not be compatible with aIF1A. The IC3 model has been resized to allow superimposition onto 6WOO in order to take into account differences due to pixel size determination. IC3 and the following step are schematized above the structures. (C) Binding state of the Met-tRNA i Met. Comparison of the Met-tRNA i Met as observed in 6WOO (cyan) and IC3 (yellow) with the P/P tRNA as observed in a ribosome:EF-Tu:tRNA complex (PDB ID 5AFI, magenta, (98)).
C G G K N Y. N D V T G D V I V F E. 70S:IF2 complex IC3, P. abyssi 30S:aIF5B Binding of the CCA-end of the initiator tRNA The electrostatic potential surfaces of e/aIF5B-IF2-DIV bound to initiator tRNA are shown for P. abyssi (A), S. cerevisiae (PDB ID 6WOO (15)) (B) and E. coli (PDB ID 3JCJ (16)) (C) Main residues involved in interaction with the initiator tRNA are shown. In panel C, R847, F848, E860 are involved in the recognition of the formyl group. Residues C815 and C861 that stabilize the binding pocket through a disulfide bond are also shown. These residues are widely conserved in bacteria. (D) Multiple sequence alignment of domains IV of a/eIF5B and E. coli IF2.
Then a sprayer would deposit the reaction mixture on a cryo-EM grid plunging into liquid ethane. Immersion in the liquid ethane solution would instantly stop the reaction and trap the short lived intermediates in their conformation. Micrographs acquisition of this cryo-EM grids would thus provide information about all available intermediates in the frozen sample. Data processing using heterogeneity classification procedures would then sort out the different intermediates and the different conformational states in the dataset, and allow their structure determination.
Acknowledgements To my PhD supervisors; Emmanuelle,  Pierre-Damien and Yves September 2020 | Volume 11 | Article 584152 Zavialov , A. V. , Hauryliuk, V. V. , and Ehrenberg, M. (2005). Splitting of the posttermination ribosome into subunits by the concerted action of RRF and EF-G. Mol. Cell 18,  675-686. doi: 10. 1016/j. molcel. 2005. 05. 016Zheng, A. , Yu, J. , Yamamoto, R. , Ose, T. , Tanaka, I. , and Yao, M. (2014). X-ray structures of eIF5B and the eIF5B-eIF1A complex: the conformational flexibility of eIF5B is restricted on the ribosome by interaction with eIF1A. Acta Crystallogr. D Biol. Crystallogr. 70,  3090-3098. doi:  10. 1107/ S1399004714021476  Zhou, J. , Lancaster, L. , Donohue, J. P. , and Noller, H. F. (2013). Crystal structures of EF-G-ribosome complexes trapped in intermediate states of translocation.
ACKNOWLEDGEMENTS This work was supported by grants from the  Centre National de la Recherche Scienti ue  and ole polyte hni ue to nit i te de Recherche n°7654 and by a grant from the  Agence Nationale de la Recherche  ( ANR-17-CE11-0037 ). Cryo-EM data benefited from access to the following facilities: eBIC,  Diamond Light Source, UK ;  ESRF, Grenoble ;  Pasteur Institute, Paris  and the  Interdisciplinary Center for Electron Microscopy of Ecole Polytechnique (CIMEX). We also thank  Célia Plisson-Chastang  ( CBI, Toulouse ) and  Magali Blaud  ( CiTCom, Paris ) for their kind gifts of cryo-EM grids at critical stages of this work.
ACKNOWLEDGEMENTS Cryo-EM data benefited from access to the following facilities: eBIC,  Diamond Light Source, UK ;  ESRF, Grenoble ;  Pasteur Institute, Paris  and the  Interdisciplinary Center for Electron Microscopy of Ecole Polytechnique (CIMEX). X-ray data were collected at Soleil synchrotron Proxima-1 and Proxima-2 beamlines. We thank the staff at these facilities,  Alistair Siebert ,  Daniel Clare ,  Yuriy Chaban ,  David Owen  ( Diamond Light Source ),  Daouda Traoré ,  Michael Hons ,  Alessandro Grinzato  ( ESRF, Grenoble ),  Ileana Florea  (CIMEX),  William Shepard ,  Martin Savko  ( PX2 ) and  Pierre Legrand  ( PX1 ) for their help with cryo-EM and X-ray data collections. We are particularly indebted to  Stéphane Tachon  ( Institut Pasteur ) for his help during the final cryo-EM data collection at the  NanoImaging Core platform  ( EQUIPEXCACSICE ,  ANR-11-EQPX-0008 ).
DATA AVAILABILITY Cryo-EM maps have been deposited in the Electron Microscopy Data Bank under the accession numbers EMD-14731 (Map A, 30S:mRNA:ASL-Met-. β1 α1 β2 α2 η1 β3 α3 β4 α4 β5 α5 η2 α6 β6 η3 β7 α7 β8 β9 β10 β11 β12 β13 η4 β14 β15 β16 η5 β17 α8 β18 β19 α9 β20 α10 β21 α11 β22 α12 β23 η6 β24 β25 β26 β27 β28 β29 β30 β31 α13 η7 α14 α15.
Data processing in RELION In the following part, we will lay out a typical data processing procedure and give details about each step. The ultimate goal of a data processing is to obtain a 3D potential map from 2D micrographs. There are various open source software packages that can be used for cryo-EM processing, we used RELION (pronounced rely-on) as our main data processing software. RELION (for REgularised LIkelihood OptimisatioN) is a software package that employs an empirical Bayesian approach for cryo-EM structure determination. It is developed in the group of Sjors Scheres at the MRC Laboratory of Molecular Biology Scheres 2012. It is the most popular data processing software package of these last years as referenced by the EM Data Bank. Its graphical user interface allows easy processing of cryo-EM datasets.
The structures (PDB IDs 5FG3  (11) , 1G7S  (12) , 3WBI  (13) , 4NCL (  14 )) were superimposed on domain I of aIF5B. The color code is the same as in Figure  2. Rmsd were calculated with Pymol. Movements of DII and DIII (rotation, translation) were calculated with Pymol using the script https://raw. githubusercontent. com/speleo3/pymol-psico/master/psico/orientation. py. Domains III of the two structures were superimposed. aIF5B:GDP is shown at the foreground and aIF5B in IC3 is shown using transparent cartoons as a reference. The view shows the bending of the h12 helix originating in the middle of it, at a sequence very rich in hydrophilic residues. Stretches of lysine and glutamate residues are found in eukaryotes and archaea. DIV can rotate freely with respect to helix 12, a motion originating from the region of K464 (see also supplementary Figure  5 ).
We tried to circumvent this problem by keeping the IC3 complex at 4 °C before grid preparation. Instead, we trapped the factor in an unusual conformation where domain IV was bound to the Met-tRNA i Met and the core domains were thought to contact the head of the ribosome near the uS13-uS19 region. Unfortunately, the density was very blurry, poorly resolved and did not allow any interpretation as to how might aIF5B be bound to the SSU. We were finally able to capture the IC3 complex in a more stable conformation and solve its structure, using bis(sulfosuccinimidyl)suberate (BS 3 ) as cross-linking reagent. The second issue related to grid preparation procedure and grid quality. We lost a significant amount of time, trying to reproduce the high resolution dataset of the IC3 complex due to poorly reproducible grid preparation procedures (chapter 9).
Q Q P W G V D E N T Y S L S R R S D T K D K A F R D Q A R W L L L K K L K V VF G I E. C G G K N Y. N D V T G D V I V F E. 70S:IF2 complex IC3, P. abyssi 30S:aIF5B Binding of the CCA-end of the initiator tRNA The electrostatic potential surfaces of e/aIF5B-IF2-DIV bound to initiator tRNA are shown for P. abyssi (A), S. cerevisiae (PDB ID 6WOO (15)) (B) and E. coli (PDB ID 3JCJ (16)) (C) Main residues involved in interaction with the initiator tRNA are shown. In panel C, R847, F848, E860 are involved in the recognition of the formyl group. Residues C815 and C861 that stabilize the binding pocket through a disulfide bond are also shown. These residues are widely conserved in bacteria. (D) Multiple sequence alignment of domains IV of a/eIF5B and E. coli IF2.
Un essai clinique randomisé évaluant l'utilisation d'anakinra chez des patients présentant une forme modérée de COVID-19 n'a pas démontré d'effet sur l'aggravation clinique ou la mortalité des patients  [38]. Le consortium CORIMUNO à l'origine de cet essai clinique évalue actuellement l'utilisation de l'anakinra chez des patients hospitalisés en réanimation (NCT04341584). L'activation de l'inflammasome NLRP3 est largement associée à la pyroptose, une mort cellulaire hautement inflammatoire faisant intervenir la gasdermine D (GSDMD). Le disulfirame, un inhibiteur de l'ancrage de la GSDMD à la membrane et donc de la formation des pores qui en résultent, fait l'objet de deux études cliniques randomisées. Ces dernières visent à évaluer l'efficacité du disulfirame à prévenir la complication des formes modérées de la maladie (NCT04485130 et NCT04594343).
Le diméthylfumarate (DMF), utilisé dans le traitement de la sclérose en plaques récurrenterémittente, inhibe directement l'activation de la GSDMD par succinylation de ses cystéines  [39]. Le consortium RECOVERY, piloté par l'université d'Oxford (Royaume-Uni), évalue actuellement l'utilisation de DMF dans la prise en charge des patients hospitalisés atteints de COVID-19 (NCT04381936). L'inflammasome NLRP3 et sa cascade de signalisation : une cible thérapeutique dans la COVID-19 ? En amont, cibler l'activation de l'inflammasome NLRP3 Il est possible d'inhiber l'activation de l'inflammasome NLRP3 soit en ciblant la protéine elle-même, soit en interférant avec des évènements nécessaires à son activation, comme la polymérisation des microtubules, ou avec des conséquences de son activation, comme le clivage de la caspase 1.
Deux inhibiteurs de l'inflammasome NLRP3, le DFV890 et le dapansutrile, qui sont en cours de développement respectivement pour le traitement de l'urticaire familial au froid et la crise de goutte, ont été repositionnés dans la prise en charge de la COVID-19. Le DFV890 fait l'objet d'une étude clinique de phase II afin d'évaluer son efficacité chez des patients atteints de défaillance respiratoire liée à la pneumopathie associée à l'infection par le SARS-CoV-2 (NCT04382053). Le dapansutrile (ou OLT1177) qui est un inhibiteur de l'inflammasome NLRP3, a été développé pour réduire l'activation de NF-κB  [30]. Il est actuellement évalué dans un essai clinique de phase II dans la prise en charge de patients souffrant d'une forme modérée de COVID-19 mais présentant des facteurs de risque de développer des formes sévères (NCT04540120).
Une façon d'inhiber l'action des inflammasomes est de cibler les caspases responsables de la maturation des cytokines inflammatoires IL-1β et IL-18. L'emricasan est un inhibiteur pan-caspases (il inhibe toutes les caspases). Il a été initialement évalué pour la prise en charge de patients atteints de stéatose hépatique non alcoolique. Une étude clinique de phase I est en cours afin de déterminer la tolérance de cet inhibiteur dans la prise en charge des patients atteints de COVID-19 légère (NCT04803227) ; les laboratoires Histogen et Ame-SYNTHÈSE REVUES de forme grave de la maladie. Cette étude conclut à une diminution de la sévérité, de la mortalité et de la durée d'hospitalisation des patients  [40].
Des virus ubiquitaires dans les milieux naturels Pour mieux définir la composition de la communauté virale d'un échantillon, les génomes (ou fragments de génomes) qui ont été obtenus seront comparés aux génomes de virus connus et référencés dans les bases de données comme la base RefSeq du National Center for Biotechnology Information (NCBI). Certains groupes de virus sont souvent dominants quel que soit le type d'écosystème examiné (Figure  2 )  [8] [9] [10] [11] [12] [13] [14] [15]. En effet, la majorité des séquences générées sont similaires à des génomes de virus qui infectent des bactéries comme les Caudoviricetes (virus à queue à ADNdb) et les Microviridae (petits virus icosaédriques à ADNsb). Viennent ensuite des virus qui infectent les cellules eucaryotes, comme les Cressdnaviricota (petits virus icosaédriques à ADNsb contenant un gène conservé initiant leur réplication, comme les Circo-, Nano-, ou Geminiviridae), et les Phycodnaviridae (grands virus nucléocytoplasmiques).
Ce dernier point est d'ailleurs révélé par les résultats très différents obtenus en fonction de l'écosystème étudié. En effet, les génomes identifiés spacers CRISPR similaires, trace d'une infection anté-rieure dans le génome bactérien, ou en cherchant un génome microbien ayant une signature génomique similaire, témoin d'une adaptation du génome viral à son hôte [30]. En ce qui concerne les 598 Microviridae auxquels nous avons fait référence, aucun hôte n'a pu être pré-dans des écosystèmes peu étudiés, et pour lesquels dit, sans doute du fait que les bases de données de SYNTHÈSE peu de virus ont pu être cultivés (tels que le sol, les génomes microbiens indiquent actuellement que ces tourbières, etc. ), contiennent de nombreux gènes qui virus infectent des souches de microbes non cultivables.
Elles miques reste crucial. Cela peut être réalisé par une devraient également permettre de mieux prédire les analyse phylogénétique reposant sur des gènes mar-hôtes que ces virus infectent à partir de leur génome. Les génomes viraux sont souvent décrits comme le plus grand réser- queurs, c'est-à-dire des gènes que tous les virus d'un Relier les virus identifiés par métagénomique, et leur(s) voir de matériel génétique inexploré sur la Terre. En effet, pour groupe possèdent. Par exemple, tous les Caudoviricetes hôte(s), sur la seule base de leur séquence génomique, les 125 000 génomes viraux issus d'écosystèmes variés, 75 % des bactériens et archéens contiennent les gènes qui codent reste, à l'heure actuelle, encore limité par le manque 2,8 millions de protéines qu'ils codent n'ont aucune similarité avec la grande sous-unité de la terminase (TerL), la protéine de techniques fiables et les biais de représentation des protéines de virus cultivés [22] et sont diverses, puisque regrou- majeure de capside de type HK97, et la protéine portail pour les virus et les microbes dans les bases de données pées en plus de 1,2 million de groupes d'orthologues.
From this systematic review, Glaves et al. noted that adipose tissue total amount, waist circumference, and visceral fat were associated with metabolic flexibility, mainly when assessed by using the classic euglycemic clamp method. Which functional aspects of adipose tissue can directly influence and explain such association remains elusive. One may speculate that adipose tissue amount, distribution, or both could influence circulating adipokines and metabolites, affecting oxidative tissue capacity and tissue fuel availability (i. e. metabolic flexibility). Kalafati et al. conducted additional work about adipose tissue by taking advantage of publicly available datasets. They assessed the contribution of relative macrophage frequencies on overall subcutaneous adipose tissue gene expression.
Remerciements : Les auteurs remercient Pfizer, pour son soutien financier à la réalisation de l'étude, A + A pour la réalisation opérationnelle. Les services de rédaction médicale ont été fournis par Céline Rouger de l'agence Medical Education Corpus. Le laboratoire Pfizer n'a eu aucun rôle dans l'interprétation et la discussion des données, ou dans la préparation de ce manuscrit. Déclaration de liens d'intérêts : les auteurs T. B. , M. S. , J. M. T. , J. M. D. , I. K. déclarent ne pas avoir de liens d'intérêts. F. J. : Boards, présentation, honoraires : Pfizer, Novartis, Janssen. L. G. A. : Invitation congrès : Mundipharma, Pfizer, Roche, Vifor.
Reconstituting the dynamics of non-genetic characteristics of cancer cells has become possible with the advent of single-cell approaches (Figure  1 ). In contrast to genetic information-data of qualitative nature-for which heterogeneity has been accessible with bulk sequencing datasets, transcriptomic and epigenomic information-of quantitative naturecan hardly be deconvoluted from average signals. The seminal invention of high-throughput single-cell transcriptomics in 2015  [6, 7]  has opened an unlimited field of applications to simultaneously characterize thousands of individual cells from a complex biological sample at the genomic, transcriptomic and more recently epigenomic scale. In addition to cellular mapping of cancers, single cell transcriptomics are now starting to bring out in the open adaptive strategies of cancer cells in particular during response to therapy  [8].
Materials and methods We selected 3,269 published ancient human genomes from the Palaeolithic to the Middle Ages in Eurasia, including 126 ancient genomes from southern Central Asia  (Damgaard et al. , 2018; Narasimhan et al. , 2019)  for which DNA sequencing data were generated by whole-genome capture or hybridization techniques, from the v42. 4 dataset available at https://reich. hms. harvard. edu/allen-ancient-dna-resourceaadr-downloadable-genotypes-present-day-and-ancientdna-data. From this selection we retained unrelated individuals with more than 10,000 Single Nucleotide Polymorphisms (SNPs) covered on the 1,240 k chip. Ancient individuals from three recent publications on the Middle East  (Skourtanioti et al. , 2020)  and the Mongolian steppe  (Jeong et al. , 2020; Gnecchi-Ruscone et al.
, 2021)  were added. The data thus gathered do not document periods and regions uniformly. The Neolithic and Bronze Age are by far the best documented periods in the dataset for southern Central Asia, Iran, Anatolia and the Levant. The Eurasian Steppe (Western, Central and Eastern Steppe) and Europe are the regions for which the most genomes have been published, with a more homogeneous coverage of the different periods. The vast majority of published genomes from Central Asia are concentrated in the steppe area. The strong interest in this particular region is explained by the discovery that it is the source of major migrations that changed the European genetic landscape. Furthermore, these results came very early in the history of ancient genomics, with the first large-scale studies publishing hundreds of genome-wide data  (Allentoft et al.
However, this paper specifically targeted the period of the Oxus civilization (Bronze Age, with the sites of Bustan, Gonur-Tepe, Dzharkutan, and Sappali-Tepe) and only provided three genomes from the Late Iron Age associated with the Kushan Empire, at the Ksirov site in Tajikistan  (Narasimhan et al. , 2019). To establish a framework for comparison, the ancient dataset was combined with 1,388 Eurasian, 109 Yoruba and 3 Mbuti individuals from two modern public datasets, from the SGDP project  (Mallick et al. , 2016)  and the 1000 Genomes project (The 1000 Genomes Project  Consortium et al. , 2015). From the resulting dataset, two different datasets were assembled with the modern data from Central Asia generated in our laboratory: -The first, called the 300k-dataset, was assembled with the genome-wide capture data obtained in our laboratory including 527 individuals belonging to 18 Central Asia populations  (Marchi et al.
Genetic overview To obtain an initial idea of the genetic diversity and proximity between the present-day and ancient populations of South Central Asia, a principal component analysis (PCA) was performed with smartpca  (Patterson et al. , 2006)  on 1,388 present-day Eurasian individuals and 527 presentday Central Asian individuals from the 300k-dataset. The 3,269 ancient samples were projected onto the PCA thus calculated. An ADMIXTURE analysis  (Alexander et al. , 2009)  was also performed for the 300k-dataset with a subset of 236,665 SNPs pruned for linkage disequilibrium (PLINK --indep-pairwise 200 25 0. 4). The ADMIXTURE analyses were performed for a number of clusters (K) between 2 and 15, with 20 replicates for each K. The K with the best cross-validation score were selected.