dataesr/scientific-paragraphs-categorization huggingface dataset:
(C) Interaction network of m 6 A-related proteins. (Top panel) Interaction network of m 6 A-related proteins. Known m 6 A-reader interacting proteins collected from the STRING-db (grey nodes) were matched with the SARS-CoV-2 RNA-protein interactome obtained from Schmidt et al. , 2020 (red nodes). The m 6 A-related proteins pertaining to the core or the expanded SARS-CoV-2 interactome (as reported by Schmidt et al. , 2021) labeled in pink and green, respectively. Outer node colors indicate enriched GO biological process terms for the m 6 Arelated proteins of the network. (Bottom panel) The Venn diagram shows the number of overlapping proteins between two datasets as indicated. genes with lost m 6 A ne ga tive re gula tion of prote in s e rine /thre onine kina s e activity pe ptidyl-lys ine me thyla tion re gula tion of prote in ubiquitina tion ne ga tive re gula tion of MAP K ca s ca de his tone me thyla tion s igna l tra ns duction by p53 cla s s me dia tor re gula tion of DNA-binding tra ns cription fa ctor a ctivity his tone lys ine me thyla tion prote in a lkyla tion prote in me thyla tion mye loid ce ll diffe re ntia tion re gula tion of MAP kina s e a ctivity ne ga tive re gula tion of phos phoryla tion ne ga tive re gula tion of kina s e a ctivity pe ptidyl-lys ine modifica tion ne ga tive re gula tion of prote in kina s e a ctivity ne ga tive re gula tion of prote in phos phoryla tion re gula tion of prote in s e rine /thre onine kina s e gula tion of ne rvous s ys te m deve lopme nt De ndrite deve lopme nt re na l s ys te m deve lopme nt pos itive re gula tion of ne uroge ne s is hexos e tra ns me mbra ne tra ns port e s ta blis hme nt of prote in loca liza tion to me mbra ne glucos e tra ns me mbra ne tra ns port ca rbohydra te tra ns me mbra ne tra ns port monos a ccha ride tra ns me mbra ne tra ns port glycoprote in bios ynthe tic proce s s pos itive re gula tion of ce ll proje ction orga niza tion re gula tion of ne rvous s ys te m deve lopme nt pos itive re gula tion of ce ll deve lopme nt Ca rboxylic a cid tra ns port re gula tion of ne uron proje ction deve lopme nt Orga nic a nion tra ns port Cilium Cumulative distribution function (CDF) plots showing the cumulative density distribution of log2 fold changes in gene expression, with a comparison of lost (red) and gained (blue) m 6 A genes after infection with different SARS-CoV-2 variants.
BSA) in PBST for an hour at RT. The cells were incubated overnight at 4 °C with the following primary antibodies: anti-METTL3 rabbit antibody (1:300, ab195352; Abcam), anti-METTL14 rabbit antibody (1:300, HPA038002; Atlas Antibodies), anti-α-Tubulin mouse antibody (1:1000, T5168; Merck), anti-SARS-CoV-2 Spike glycoprotein rabbit antibody (1:300, ab272504; Abcam), anti-G3BP mouse antibody (1:500, ab56574; Abcam), anti-m 6 A rabbit antibody (1:400, 202003, Synaptic Systems), and anti-YTHDF2 rabbit antibody (1:100, ab246514; Abcam). The slides were washed three times for 5 min in PBST and subsequently incubated with secondary antibodies conjugated with Alexa Fluor 488 and Alexa Fluor 555 fluorochromes (1:800; Invitrogen), for an hour in the dark at RT. After incubation with the secondary antibodies, cells were washed three times for 5 min in PBST.
Acknowledgments We thank the core facility at  Novum, BEA, Bioinformatics  and  Expression Analysis , which is supported by the board of research at the  Karolinska Institute  and the research committee at the  Karolinska hospital  for help with sequencing, and the  Proteomic core facility at GU  for their support with LC-MS/MS. The computations and data handling were enabled by resources in project  SNIC-2022-22-85  provided by the  Swedish National Infrastructure for Computing (SNIC)  at  UPPMAX , partially funded by the  Swedish Council  through grant agreement no. 2018-05973. We acknowledge  Anders Sjölander  for assistance concerning technical and implementational aspects of the UPPMAX resources. This work was funded by grants from the  Swedish Research Council (Vetenskapsrådet)  to T.
Up-regulatedby SARS-CoV-22MOI in A549 from  GSE147507 SARS-CoV perturbation UpGenes Vero   E6  from GSE30589:GPL570:5 SARS-CoV perturbationUpGenes Vero E6fromGSE30589:GPL570:7 Genes whose expression is altered by MHV infection Up-regulated by MHV-A59 in murine spleen 3d from  GSE146074   SARS-CoV perturbation UpGenes Vero E6  from GSE30589:GPL570:4 Up-regulated by SARS-CoV-2 in cardiomyocytes from GSE150392 Genes whose expression is altered by SARS-COV-2 infection Up-regulatedby SARS-CoV-1 in Calu-3from GSE148729 Up-regulatedby SARS-CoV-22MOI in Calu-3 from GSE147507 Up-regulated by SARS-COV-2 infection of Calu3 cells Up-regulatedby SARS-CoV-2 infection in Vero  E6  from  GSE153940  Up-regulatedby SARS-CoV-20. 2MOI in A549 transduced with ACE2 vector from  GSE147507 SARS-CoV perturbation UpGenes Vero   E6  from GSE30589:GPL570:2 Up-regulated by MERS infection of Calu3 cells Up-regulatedby MERS-CoV in Calu-3 24hr from GSE139516 Up-regulatedby SARS-CoV-22MOI in A549 transduced with ACE2 vector from GSE147507 SARS-CoV perturbationUpGenes Vero E6 from GSE30589:GPL570:1 Up-regulated by MERS-CoV in Calu-3 fromGSE122876 B.
Data access All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) under accession number GSE188477. The source code used for the analysis of data in this study is available at GitHub (https://github. com/AkramMendez/ m6a_sarscov2) and as Supplemental Code.
Analysis of m 6 A-readers from publicly available SARS-CoV-2 interactome We collected publicly available data from the SARS-CoV-2 interactome in  Schmidt et al. , 2021 to  analyze the reported interactions of known m 6 A-readers. For this, we selected proteins with a significant log2 enrichment over the background. As reported by the original authors, the SARS-CoV-2 RNAprotein interactome can be further divided into a core (adjusted p-value < 0. 05) and expanded interactome (adjusted p-value < 0. 2), containing 57 and 119 RNA interacting proteins, respectively. After matching the significantly enriched SARS-CoV-2 RNA-protein interactome data with the publicly available m 6 A-reader protein interactors collected from STRINGdb, we found 16 overlapping proteins. From these, 8 proteins belong to the core SARS-CoV-2 RNA-protein interactome (YTHDF2, YBX1, SYNCRIP, PABPC1, MOV10, DDX3X, HNRNPA1, and IGF2BP2) and 8 proteins (IGF2BP1, HNRNPA3, HNRNPA0, HNRNPAB, HNRNPL, G3BP1, PCBP2, and HNRNPA2B1) form part of the expanded SARS-CoV-2 interactome.
Analysis of RNA-seq data for differential gene expression Aligned reads were quantified using featureCounts from the Rsubread package  (Liao et al. 2019)  following differential expression analysis with DESeq2 using two replicates per condition  (Love et al. 2014). Genes were considered differentially expressed if they had an absolute log2 fold change value > 1 and an adjusted p-value cutoff of < 0. 01 for Vero data and log2 fold change > 0. 5 and adjusted p-value < 0. 1 for HBE data. Heatmap visualization of differentially expressed genes was generated using the pheatmap package in R. Principal component analysis of gene expression patterns after infection with different SARS-CoV-2 variants was calculated on the regularized normalized counts obtained from DESeq2 using the factoextra package.
Correlation analysis of the gene expression changes was performed between cells infected with different SARS-CoV-2 variants and also at the level of differentially expressed genes in different infection conditions. Publicly available data were used as well  (Riva et al. 2020). Functional enrichment analysis was carried out using clusterProfiler  (Yu et al. 2012)  or Enrichr  (Chen et al. 2013 ) against the Gene Ontology biological process or Enrichr COVID-19 related gene sets databases, respectively, with the selection of significantly enriched terms (p-value < 0. 05). GO term clustering and visualization analysis was carried out using ClueGO  (Bindea et al. 2013 ) from Cytoscape v3. 8. 2. Briefly, common enriched terms across any pair of variants previously identified with clusterProfiler and associated with m 6 A related genes were selected and their relationships were analyzed using ClueGO, enabling the visualization of shared genes between common terms.
( BSA) in PBST for an hour at RT. The cells were incubated overnight at 4 °C with the following primary antibodies: anti-METTL3 rabbit antibody (1:300, ab195352; Abcam), anti-METTL14 rabbit antibody (1:300, HPA038002; Atlas Antibodies), anti-α-Tubulin mouse antibody (1:1000, T5168; Merck), anti-SARS-CoV-2 Spike glycoprotein rabbit antibody (1:300, ab272504; Abcam), anti-G3BP mouse antibody (1:500, ab56574; Abcam), anti-m 6 A rabbit antibody (1:400, 202003, Synaptic Systems), and anti-YTHDF2 rabbit antibody (1:100, ab246514; Abcam). The slides were washed three times for 5 min in PBST and subsequently incubated with secondary antibodies conjugated with Alexa Fluor 488 and Alexa Fluor 555 fluorochromes (1:800; Invitrogen), for an hour in the dark at RT. After incubation with the secondary antibodies, cells were washed three times for 5 min in PBST.
Down-regulatedby SARS-CoV-1 in Calu-34hr fromGSE148729 Down-regulated by MHV-A59in murine liver 5d fromGSE146074 Down-regulatedby SARS-CoV-2 in liver organoids fromGSE151803 Down-regulated by MHV-A59in murine liver 3d fromGSE146074 Down-regulatedby SARS-CoV-2 2 MOI in Calu-3 fromGSE147507 Down-regulatedby SARS-COV-2 infection ofCalu3 cells SARS-CoV perturbationDownGenes Vero E6fromGSE30589:GPL570:3 Down-regulatedby SARS-CoV-2 in Calu-3 24hrfromGSE148729 Down-regulatedby SARS-CoV-2 in Calu-34hr fromGSE148729 Down-regulatedby MERS infectionofCalu3 cells Down-regulatedby MERS-CoV in Calu-36hr fromGSE139516 SARS-CoV perturbationDownGenes Vero E6fromGSE30589:GPL570:1 Down-regulatedby SARS-CoV-2 0. 2 MOI in A549 transduced withACE2 vector fromGSE147507 SARS-CoV perturbationDownGenes Vero E6fromGSE30589:GPL570:4 Down-regulatedby SARS-CoV-1 in Calu-3fromGSE148729 SARS-CoV perturbationDownGenes Vero E6fromGSE30589:GPL570:2 Down-regulatedby MERS-CoV in Calu-3fromGSE122876 Down-regulatedby MERS-CoV in Calu-324hrfromGSE139516 Down-regulatedby SARS-CoV-2 2 MOI in A549 transduced withACE2 vector fromGSE147507 Down-regulatedby SARS-CoV-2 infectionin Vero E6 fromGSE153940.
NMR experiments NMR spectra were recorded on Bruker Avance III HD 600-MHz (resonance assignment experiments) and 850-MHz (resonance assignments and time-course experiments) spectrometers running TopSpin 3. 2 software and equipped with N 2 -cooled and He-cooled inverse HCN triple-resonance cryogenic probes, respectively. Spectra were processed with TopSpin 3. 2 (time-course experiments) or NMRPipe  [27]  (all other experiments) and analysed in CcpNmr Analysis v2. 4  [28].
GFP resonance assignments Backbone resonance assignments of folding-reporter GFP-ssrA were obtained from 2D 15 N-HSQC, 3D HNCO, 3D HN(CO)CACB and 3D HNCACB spectra measured at 42 °C on U-[ 15 N, 13 C] labelled protein at 600 lM concentration in storage buffer (20 mM Tris-HCl pH 7. 5, 100 mM NaCl, 10 mM MgCl 2 , 10 % D2O). A set of side-chain assignment experiments consisting of 2D constant-time 13 C-HSQC  [29, 30] , 3D HCCH-TOCSY  [31]  and 3D H(CCCO)NH  [32]  spectra were recorded on the same sample for assignment of methyl groups. Met-e assignments were obtained from a 3D 13 C-HMQC-NOESY- 13 C-HMQC spectrum, comparing the experimental inter-methyl NOE peaks with those predicted from the GFP crystal structure (PDB entry 2B3Q). Assignments were transferred to 55 °C and 60 °C with the aid of a series of constant-time 13 C-HSQC spectra acquired at different temperatures.
Theory and calculations Pseudo-3D spectra from the time-series were analysed in CcpNmr Analysis. Peaks were automatically picked in all pseudo-3D planes. Peak intensities were obtained as peak-heights for which the spectral noise was taken as the experimental error. Intensity changes for all peaks from either three or two replicates (for the data at either 60 or 55 °C, respectively) were fitted to three different decay models using non-linear curve-fitting in OriginPro 9. 1 (Origin Lab): I ¼ A Á e Àt T decay þ I 0 ð1Þ IðtÞ ¼ A Á e Àt T decay þ B Á t þ I 0 ð2Þ IðtÞ ¼ A 1 Á e Àt T decay;1 þ A 2 Á e Àt T decay;2 þ I 0 ð3Þ For each sampled peak, the triplicate measurements were used to assess statistical significance. The F-test was used to quantify the improvement of the fit with the increasing complexity of the fit-functions for each peak through all repetitions using the formula: where SS null,1/2/3 are the fit-errors (sum-of-squares) of the model with a lower number of parameters (corresponding to the nullhypothesis, i.
Errors were estimated from the spectral noise. The fitted curve is shown in red; the shortest exponential time-constant T decay is given as a mean of triplicate measurements with uncertainty corresponding to the standard error of the mean. a. u. , arbitrary units. D) Shortest exponential time-constants T decay for all analysed peaks. Each peak is represented by a square, which is color-coded according to the function used to fit its decay. The average T decay of all peaks is 314 ± 30 s (range indicated by the pink lines). The peaks with T decay values smaller or larger than the average T decay by more than one standard deviation are indicated by blue and pink arrows, respectively. E) Representation of ranges of T decay values for all analysed methyl groups on the GFP-crystal structure (PDB entry 2B3Q).
3F ). The fact that this time-constant is of the same order-of-magnitude as the time-constant of the decay of GFP peaks (T decay = 227 ± 42 s), suggests a tight coupling of the unfolding and degradation processes in the proteolysis reaction. Finally, datasets recorded at 55 °C gave similar results, albeit with slower decay times (average T decay with PAN alone, 426 ± 67 s; average T decay with PAN-20S CP, 419 ± 78 s) (Fig. 4 ). Interestingly, at this lower temperature, the 20S CP had lost its capacity to stimulate PAN activity, as the time-constants measured with and without the 20S CP were virtually identical.
The average T decay of all peaks is 227 ± 42 s (range indicated by the pink lines). Blue arrows indicate peaks whose T decay values are smaller than the average by more than one SD. E) Representation of ranges of T decay values for all analysed methyl groups on the GFP-crystal structure (PDB entry 2B3Q). Methyl groups with T decay values close to or smaller than the average are shown in green and blue, respectively. F) Growth curves of peaks attributed to unstructured proteolysis products with their exponential fits and shortest time-constants T products. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article. ) area of future research will focus on the identification and characterization of these dynamics and on understanding how these processes are optimized to work at high temperature in thermophilic organisms.
Context. Gaia DR3 contains 1. 8 billion sources with G-band photometry, 1. 5 billion of which with G BP and G RP photometry, complemented by positions on the sky, parallax, and proper motion. The median number of field-of-view transits in the three photometric bands is between 40 and 44 measurements per source and covers 34 months of data collection. Aims. We pursue a classification of Galactic and extra-galactic objects that are detected as variable by Gaia across the whole sky. Methods. Supervised machine learning (eXtreme Gradient Boosting and Random Forest) was employed to generate multi-class, binary, and metaclassifiers that classified variable objects with photometric time series in the G, G BP , and G RP bands. Results. Classification results comprise 12. 4 million sources (selected from a much larger set of potential variable objects) and include about 9 million variable stars classified into 22 variability types in the Milky Way and nearby galaxies such as the Magellanic Clouds and Andromeda, plus thousands of supernova explosions in distant galaxies, 1 million active galactic nuclei, and almost 2.
3. 1. 1). For brevity, we refer to Table  1  for selected publications related to these classes, with representatives identified in various surveys. Machine learning is a practical tool to automate classification tasks that involve multiple known classes and a possibly high number of attributes to identify such classes and distinguish them from others (e. g. see  Debosscher et al. 2007; Sarro et al. 2009; Blomme et al. 2010; Richards et al. 2011; Dubath et al. 2011). Herein, we present how a supervised classification was applied to Gaia DR3 data to classify variable sources into two dozen classes (plus galaxies). In particular, we describe the details concerning the construction of the training set and of the classifiers, the verification of the results, and the generation of an overall classification score.
(  4 ) MICROLENSING, S, and SOLAR_LIKE classifications were not used as input for the respective SOS modules. (  5 ) SOLAR_LIKE classifications included more types (flaring stars) than the rotational modulation SOS module. (  6 ) GALAXY classifications were made available exclusively in the galaxy_candidates table. (*) Less than 12 sources classified as a given class were matched with the literature  (Gavras et al. 2023)  after the removal of training set objects (aimed at reducing biases in the estimates of completeness and contamination rates). For these classes, training sources were included in the assessment of completeness and contamination (for upper or lower limits, respectively). also results from other coordination units 4  of the Gaia Data Processing and Analysis Consortium (DPAC), as described in Gaia Collaboration (2023a).
Perhaps, their classification will improve with per-CCD photometry in the next Gaia data release. This group of variability types includes also some SPB stars, which are not mentioned in this class group denomination because of the attempt to split them into their own class  (Sect. 4. 21). This class was considered by the upper main-sequence oscillator SOS module, but no source satisfied its requirements (see Sect. 10. 14 of the Gaia DR3 documentation;  Rimoldini et al. 2022 ). These candidates were selected from three multi-class and binary meta-classifiers (Sect. 3. 2) with some minimum probability level. The following additional conditions were required (employing field names in the vari_summary table). 1. The values of std_dev_mag_g_fov were set above the third quartile of the standard deviations in G, in 0.
E. 32a). The weakness of the signal and the typically small number of observations in transit implied the necessity of a dedicated period search algorithm such as that used in the planetary transit SOS module  (Panahi et al. 2022) , rather than the generic computationally efficient generalised Lomb-Scargle method used for all the classes  (Heck et al. 1985; Zechmeister & Kürster 2009). Consequently, the EP classifications were limited to the selection of the corresponding SOS module and their scores were set to one. A binary XGBoost classifier (Sect. 3. 2) was used to extract a sample of 18 383 potential candidates, without the application of a minimum probability threshold. All of these sources were processed by the planetary transit SOS module, which returned 214 objects, including known stars with planetary transits as well as new candidates.
Almost half of the genuine variable sources (4. 7 million) and several classes are available uniquely as classification results (in the vari_classifier_result table), while the other variable sources are (also) included among the SOS module results. Galaxies were detected by an artificial signal of Gaia, which might have led to a biased yet, nevertheless, numerous addition to the extra-galactic content of this data release. In Gaia DR4, the number of photometric epochs will double and additional input data types, such as the G BP and G RP spectra (as time series, as well as averaged in time) and radial velocities, will be available. Together with ongoing developments in our attribute extraction and classification techniques, the discernibility of variability types is expected to further improve and allow for a significant increase in the number of classified sources and related classes.
Acknowledgements. This work presents results from the  European Space Agency (ESA)  space mission Gaia. Gaia data are being processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating A14, page 21 of 105 Rimoldini, L. , et al. : A&A 674, A14 (2023) in the Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www. cosmos. esa. int/gaia. The Gaia archive website is https: //archives. esac. esa. int/gaia. The Gaia mission and data processing have financially been supported by, in alphabetical order by country: the  Algerian Centre de Recherche en Astronomie, Astrophysique et Géophysique of Bouzareah Observatory ; the  Austrian Fonds zur Förderung der wissenschaftlichen Forschung (FWF)   Hertha Firnberg Programme  through grants  T359 ,  P20046 , and  P23737 ; the  BELgian federal Science Policy Office (BELSPO)  through various  PROgramme de Développement d'Expériences scientifiques (PRODEX)  grants, the  Research Foundation Flanders (Fonds Wetenschappelijk Onderzoek)  through grant  VS.
Anders), the  Severo Ochoa Centre of Excellence Programme  ( SEV2015-0493 ), and  MICIN/AEI/10. 13039/501100011033  (and the  European Union  through  European Regional Development Fund  ' A way of making Europe' ) through grant  RTI2018-095076-B-C21 , the  Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia 'María de Maeztu')  through grant  CEX2019-000918-M , the  University of Barcelona 's  official doctoral programme  for the development of an  R+D+i  project through an  Ajuts de Personal Investigador en Formació (APIF ) grant, the  Spanish Virtual Observatory  through project  AyA2017-84089 , the  Galician Regional Government, Xunta de Galicia , through grants  ED431B-2021/36 ,  ED481A-2019/155 , and  ED481A-2021/296 , the  Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC) , funded by the  Xunta de Galicia  and the  European Union  ( European Regional Development Fund   -Galicia 2014-2020 Programme ), through grant  ED431G-2019/01 , the  Red Española de Supercomputación (RES)  computer resources at MareNostrum, the  Barcelona Supercomputing Centre -Centro Nacional de Supercomputación ( BSC-CNS ) through activities  AECT-2017-2-0002 ,  AECT-2017-3-0006 ,  AECT-2018-1-0017 ,  AECT-2018-2-0013 ,  AECT-2018-3-0011 ,  AECT-2019-1-0010 ,  AECT-2019-2-0014 ,  AECT-2019-3-0003 ,  AECT-2020-1-0004 , and  DATA-2020-1-0010 , the  Departament d'Innovació, Universitats i Empresa de la Generalitat de Catalunya  through grant  2014-SGR-1051  for project ' Models de Programació i Entorns d'Execució Parallels' (MPEXPAR ), and  Ramon y Cajal  Fellowship  RYC2018-025968- I funded by  MICIN/AEI/10.
Liverpool Telescope, which is operated on the island of La Palma by Liverpool John Moores University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias with financial support from the United Kingdom Science and Technology Facilities Council, and (iii) telescopes of the Las Cumbres Observatory Global Telescope Network. This work made use of software from H2O (H2O. ai 2020), Postgres-XL (https://www. postgres-xl. org), Java (https://www. oracle. com/ java/), R (R Core Team 2018), TBase database management system (https: //github. com/Tencent/TBase), and TOPCAT/STILTS (Taylor 2005). Appleton Laboratory (RAL): PP/D006511/1, PP/D006546/1, PP/D006570/1, ST/I000852/1, ST/J005045/1, ST/K00056X/1, ST/K000209/1, ST/K000756/1, ST/L006561/1, ST/N000595/1, ST/N000641/1, ST/N000978/1, ST/N001117/1, ST/S000089/1, ST/S000976/1, ST/S000984/1, ST/S001123/1, ST/S001948/1, ST/S001980/1, ST/S002103/1, ST/V000969/1, ST/W002469/1, ST/W002493/1, ST/W002671/1, ST/W002809/1, and EP/V520342/1.