dataesr/scientific-paragraphs-categorization huggingface dataset:
Genomic DNAs were extracted by using the DNeasy Blood & Tissue Kit (QIAGEN). K. pneumoniae genomes were sequenced by using the Illumina HiSeq2500 or NextSeq500 platform, with 100-and 75-nucleotides (nt) paired-end reads respectively. Libraries of K. pneumoniae clinical isolates were constructed by using the Nextera XT kit (Illumina) and libraries of CNR146C9 clones selected during ScanLag experiments by using NEBNext Ultra II FS DNA Library prep kit, which provide a more even genome coverage  [58]. Paired-end reads were assembled into contigs with SPAdes 3. 9. 0 [59]. The complete genome sequence of CNR146C9 was determined by using the long-read PacBio technology. Accurate consensus reads obtained from raw reads with pbccs (https://github. com/nlhepler/pbccs) were assembled with Canu  [60].
The assembly was polished with Circulator  [61]  and manually corrected by mapping Illumina reads with Breseq 0. 33. 2  [62]. Variants compared to CNR146C9 complete genome sequence were identified by using Breseq  [62]. Missing regions and recombination events were identified by analysing the "Unassigned missing coverage evidence" and "Unassigned new junction evidence" sections of the Breseq outputs respectively. Polymorphisms were visually verified by using Tablet  [63]. Genome sequences were annotated with Prokka 1. 14. 5  [64]  and analysed for MLST and ARG content by using Kleborate  [65]  and Resfinder 4. 0. 1  [66]. Plasmid incompatibility groups were identified by using PlasmidFinder 2. 1  [67]. Copy number variation was estimated by counting the number of reads per gene and comparing with number of Illumina reads of the parental strain CNR146C9 by using the DESSeq2 R Package  [68].
ACKNOWLEDGEMENTS This work was supported by grants from the  French National Research Agency IBEID  ( ANR-10-LABX-62-IBEID ) and  SEQ2DIAG  ( ANR-20-PAMR-0010 ). Adriana Chiarelli  is part of the  Pasteur -Paris University (PPU) International PhD Program , with funding from the  Institut Carnot Pasteur Microbes & Santé , and the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  665807. The authors thank  Virgile Andreani  and  Gregory Bath  for discussion on HR,  Guilhem Royer  for critical reading of the manuscript,  Laurence Ma  from the  Institut Pasteur Biomics platform  ( C2RT ,  Institut Pasteur, Paris, France , supported by  France Génomique ,  ANR-10-INBS-09-09  and IBISA) for her help with Illumina sequencing.
1e ), HFD-induced obesity (Fig. 1f ) and HFr-induced obesity (Fig. 1g ). We confirmed that RBFOX2 is expressed in the liver (Fig. 1h ), and showed that hepatocytes account for most of the Rbfox2 liver expression, although Rbfox2 was also detected in endothelial cells, on the basis of the analysis of a mouse single-cell RNA-seq dataset  18  (Fig. 2a ). Other splicing factors potentially involved in AS regulation showed expression across other liver-resident cell populations (Extended Data Fig. 2 ). We generated Alb_cre -Rbfox2 LoxP/LoxP (L WT ) and Alb_cre + Rbfox2 LoxP/LoxP (L ΔRbfox2 ) mice to inactivate the Rbfox2 gene selectively in hepatocytes. Western blot analysis showed that RBFOX2 is undetectable in the liver of L ΔRbfox2 mice, confirming that hepatocytes account for most of its expression in the liver (Fig.
Liver expression of RBFOX2 is controlled by FOXA1/2 To characterize the upstream regulators of RBFOX2 in human liver, we used FANTOM5 CAGE datasets of transcription initiation sites, as the RBFOX2 gene has a complex architecture with multiple promoters  19  (Fig. 6a ). Expression of RBFOX2 in the human hippocampus or aortic smooth muscle is driven by one proximal and one distal promoter, whose activity levels are similar. In hepatocytes, the distal promoter has predominant control over RBFOX2 expression (threefold change compared to the proximal promoter). CAGE data detected a third promoter in human after RBFOX2 over-expression in hepatocytes (n = 6). f, LC-MS lipidomic analysis showing total levels of free cholesterol, cholesteryl ester, sphingomyelin and ceramide normalized to liver tissue mass and PUFA/non-PUFA TG ratio in male mice fed a HFr diet after transduction with pAd-RBFOX2 or pAd-GFP control (n = 8-9).
Article https://doi. org/10. 1038/s42255-022-00681-y hepatocytes that does not overlap with previously annotated RBFOX2 promoters (Fig. 6a ). This and the common distal promoter account for most of the RBFOX2 transcription in human hepatocytes. Both show enrichment of H3K4me3 and H3K27ac by chromatin immunoprecipitation with sequencing (ChIP-seq) in adult human liver, consistent with their role as active hepatic promoters. Analysis of liver ChIP-seq datasets showed that the RBFOX2 promoters are bound by FOXA1/2 in humans (Fig. 6a , bottom insets) and mouse (Extended Data Fig. 6h ). FOXA1/2 are winged helix transcription factors implicated in bile acid metabolism and protection from liver cholestasis  35. To validate the role of FOXA1/2 in Rbfox2 regulation, we knocked down Foxa1 and Foxa2 in mouse hepatoma Hepa1-6 cells, which led to a significant decrease in Rbfox2 expression (Fig.
Scarb1 is an RBFOX2 target with therapeutic potential To explain the molecular mechanisms underlying the role of RBFOX2 in lipid homeostasis, we designed SSOs to regulate AS of RBFOX2 downstream targets. The efficacy of the SSOs was tested by PCR followed by capillary electrophoresis. SSOs showing potent activity for each splicing event were further used in lipidomic analyses in primary hepatocytes (Supplementary Table  6 ). LC-MS metabolomics revealed that RBFOX2-deficient hepatocytes have increased lipid accumulation (Extended Data Fig. 6i ), confirming the role for RBFOX2 in lipid metabolism in hepatocytes. While SSO7. 9, promoting changes in Numb   or shRNA against Foxa1/2 (shF1/2) (n = 5-6). c, Microarray analysis of HepG2 cells with adenoviral FOXA1 over-expression (n = 8) (GSE30447)  36.
Article https://doi. org/10. 1038/s42255-022-00681-y that expression of the SR-BI isoform is associated with increased lipid uptake from 1,1′-dioctadecyl-3,3,3′,3′-tetramethylindocarbocyanine perchlorate (Dil)-HDL lipoproteins (Fig. 7j ). To investigate how SR-BI/II isoforms contribute to the role of RBFOX2 in vivo, we treated L WT and L ΔRbfox2 mice with Scr control or SSO8. 3 to antagonize the SR-BI isoform in L ΔRbfox2 mice fed a HFr diet. SSO8. 3 efficiently reverted the SR-BI to SR-BII ratio in L ΔRbfox2 mice liver (Extended Data Fig. 10h ). Blood lipoproteins were isolated and lipid composition was quantified. Cholesterol and phospholipid content in HDL was decreased in L ΔRbfox2 mice and these differences were abolished by reverting the SR-BI to SR-BII ratio, confirming the contribution of this isoform switch to RBFOX2 role in the liver (Fig.
Finally, samples were resuspended in 3% ACN, 1% FA and run in an Orbitrap Fusion running in MS3 mode  55  as described previously  53. RAW data were converted to mzXML format using a modified version of RawFileReader and searched using the search engine Comet 56 against a mouse target-decoy protein database (Uniprot, 11 June 2019) that included the most common contaminants. Precursor ion tolerance was set at 20 ppm and product ion tolerance at 1 Da. Cysteine carbamidomethylation (+57. 0215 Da) and TMT tag (+229. 1629 Da) on lysine residues and peptide N termini were set up as static modifications. Up to two variable methionine oxidations (+15. 9949 Da) and two miss cleavages were allowed in the searches. Peptide-spectrum matches were adjusted to a 1% FDR with a linear discriminant analysis  57  and proteins were further collapsed to a final protein-level FDR of 1%.
TMT quantitative values we obtained from MS3 scans. Only those with a signal-to-noise >100 and an isolation specificity >0. 7 were used for quantification. Each TMT was normalized to the total signal in each column. To allow the comparison of both TMT, those proteins quantified in both TMT, data were normalized using the bridge channels present in each TMT. Quantifications included in Supplementary Table  1  are represented as relative abundances. Newly generated proteomic datasets are publicly available as described below.
RNA-seq sequencing and analysis RNA was quality controlled with a 2100 BioAnalyser (Agilent). Poly(A) enrichment of samples with RNA integrity number > 8, was performed and libraries were prepared using the NEBNext Ultra II RNA Library Prep Kit for Illumina and multiplexed using NEBNext Multiplex Oligos for Illumina (NEB, E7760S and E7335S). Sequencing was carried out with 100 basepair paired end reads with HiSeq 2500 (Illumina). Newly generated RNA-seq datasets are publicly available as described below. Previously published datasets of mice fed a HFr diet were obtained from GSE123896 (ref. 16 ). Data were processed using RTA v. 1. 18. 54, with default filter and quality settings. The reads were demultiplexed with CASAVA v. 2. 17. Reads were aligned to Ensembl mouse genome (GRCm38) using Tophat2 (v.
2. 0. 11)  61  with the argument '-library-type fr-firststrand'. Reference sequence assembly and transcript annotation were obtained from Illumina iGenomes (https:// support. illumina. com/sequencing/sequencing_software/igenome. html). Gene-based read counts were obtained using featureCounts function from Rsubread Bioconductor package  62. Normalization and differential expression analysis were performed using DEseq2 (ref. 63 ) or edgeR-voom-limma  [64] [65] [66] [67]  bioconductor packages. AS was primarily analysed with rMATs  68. Differentially spliced sites were kept for data visualization if they passed the following threshold: P < 0. 05; FDR < 0. 1 and absolute(IncLevelDifference) >0. 1 or <-0. 1. Gene ontology analysis was carried out by using GOseq Bioconductor package  69. A list of differentially expressed genes with adjusted P value of less than or equal to 0.
The gradient elution program was performed for both ion modes according to the Supplementary Table  4 , yielding a total run time of 10 min per sample. The injection volume for the positive and negative ion mode was 5 and 10 µl, respectively. The ionization was performed using a heated electrospray ionization source and the parameters for the positive or negative mode are as follows: capillary voltage 3. 5 or -2. 5 kV, heater temperature 438 °C, capillary temperature 320 °C, S-lens radio frequency level 50, sheath, auxiliary and sweep gas flow rate are 53, 14 and 1 unit, respectively. The mass accuracy was calibrated before sample analysis for both ion modes. High-resolution mass spectrometric (70,000 at m/z 200) data were acquired in profile mode using the full scan setting (m/z 200-2,000).
Automatic gain control was set to 1 × 10 6 and maximum MS1 injection time at 200 ms. Lipidomics data acquisition was performed with Xcalibur software (v. 4. 1). Peak picking was performed using XCMS  72  , and features normalized to isotopically labelled internal standard and dry tissue weight. For in vitro anlysis, intensities were normalized to internal standard and total ion count unless otherwise indicated. Lipid identification was performed by accurate mass using an in-house database. Bile acid analysis was carried out using LC-MS/MS in an Acquity I-Class binary UPLC system (Waters) coupled to a triple quadrupole Xevo TQ-XS mass spectrometer as previously described (https://www. waters. com/webassets/cms/library/docs/720006261en. pdf) (Waters). Chromatographic separation was performed on a CORTECS T3 column (Waters, 30 × 2.
CAGE-seq and ChIP-seq data processing Mapped CAGE-supported transcriptional start sites from the FAN-TOM5 project  [73] [74] [75]  were imported to R (http://www. R-project. org/) as CAGE-supported transcriptional start site tables. Replicate samples were merged and normalized using the standard workflow within the CAGEr package  76. Processed bigwig files corresponding to human adult liver ChIP-seq signal P value were obtained from the ENCODE portal  77  : FOXA1 (ENCFF058DKS)  78  , FOXA2 (ENCFF902TMK)  78  , K3K4me3 (ENCFF610REU)  79  and H3K27ac (ENCFF012XAP)  79. Mouse liver FOXA1 ChIP-seq data (GSE106379) was retrieved from ref. 80.
PCR with reverse transcription (RT-PCR) analysis RNA was reverse transcribed using High-Capacity complementary DNA Reverse Transcription Kit (Thermo Fisher Scientific). Taqman Gene Expression Assays (Thermo Fisher Scientific) with probes from the Roche Universal Probe Library or Fast SYBR-Green Mix (Thermo Fisher Scientific) were used for quantification on QuantStudio 7 Flex Real-Time PCR system (Thermo Fisher Scientific). All data were analysed using a relative standard curve or the delta CT method. Ribosomal 18S RNA was used to normalize samples in all cases. AS analysis was carried out using primers designed for detecting more than one mRNA isoform. Targets were amplified by PCR and were analysed by capillary electrophoresis by using the QIAxcel Advanced System (Qiagen). AS was calculated as percentage spliced in (PSI) of a specific splicing event across samples (PSI = (long isoform)/(long isoform + short isoform) × 100).
2. 4. 2a) (https://github. com/alexdobin/STAR) with the following parameters: 'STAR -runThreadN 8 -runMode alignReads -genomeDir GRCh38 Gencode v25 -genomeLoad LoadAndKeep -readFilesIn read1, read2, -readFilesCommand zcat -outSAMunmapped Within -outFilter-MultimapNmax 1 -outFilterMultimapScoreRange 1 -outSAMattributes All -outSAMtype BAM Unsorted -outFilterType BySJout -outFilter-ScoreMin 10 -alignEndsType EndToEnd -outFileNamePrefix outfile'. For the overamplification correction of eCLIP samples we used a barcode collapse python script 'barcode_collapse_pe. py' available on GitHub (https://github. com/YeoLab/gscripts/releases/tag/1. 0). For the eiCLIP samples we used a custom python script to swap random barcodes from the first 7 nucleotides of the FASTQ sequence line to the FASTQ header line.
RNA-maps around RBFOX2-regulated exons Alternatively spliced and control exons were selected from L WT and L ΔRbfox2 liver RNA-seq samples analysed by 'junctionSeq' Bioconductor package (https://bioc. ism. ac. jp/packages/3. 4/bioc/html/Junction-Seq. html) from the Splicing Events table MATS. SE with the following parameters: • Up-regulated exons: P < 0. 05, FDR < 0. 1, IncLevelDifference >0. 1A ; Ext. Fig. 1C ) have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD036976. In addition, the full list of proteins identified across the experiments has been included in Supplementary Table  1.
Acknowledgements We thank  I. Miguel-Aliaga  and  D. Withers  for the critical reading of this manuscript and  T. Vidal-Puig  and  M. Vacca  for helpful discussions. We thank  C. Amourda  and the other members of the  Vernia laboratory (M. G. R. )  for help and useful discussions. We thank  G. Riddihough  ( Life Science Editors ) for help with editing the manuscript. Some of the figures were created with BioRender. com. We also thank  K. Morris  for modelling PLA2G6 structure,  H. Kramer  for advice in metabolomic experiments and  S. Khadayate  for ingenuity pathway analysis. We thank the members of the  LMS-MRC  genomics, transgenics, whole animal physiology imaging facility, proteomics, bioinformatics and the members of the  LMS-MRC  administrative team for their excellent assistance. We thank the  LMS/NIHR Imperial Biomedical Research Centre Flow Cytometry Facility  for the support.
SBF005\1050 ), which is supported by the  British Heart Foundation ,  Diabetes UK , the  Global Challenges Research Fund , the  Government Department for Business, Energy and Industrial Strategy  and the  Wellcome Trust. C. R. S. was supported by a  Sir Henry Dale  fellowship jointly funded by the  Wellcome Trust  and the  Royal Society  (grant no. 215454/Z/19/Z ). S. V. was supported by the  Medical Research Council (MRC).
See Figure  1  for an image montage showing the main components of the nebula. [7] ). Black contours show the emission of 13 CO J = 2-1 but just for the plane containing the symmetry axis of the nebula (obtained from data by Alcolea, Neri, Bujarrabal  [10]  after a 3D-image reconstruction process similar to that described in Section 3). Here and in the rest of the figures, the spatial units are east and north offsets in arc-sec. The molecular gas component of the nebula has been studied through interferometric observations of low-J transitions of both 12 CO and 13 CO  [2, [10] [11] [12]. These observations, with spatial resolutions between 0. 5 and 1. 0, showed that the molecular gas is located in the equatorial structure dividing the two lobes, in the walls of the two lobes, and at the two ends of the nebula (the polar tips).
The New NOEMA Observations The new data presented in this paper were acquired using the IRAM NOrthern Extended Millimetre Array, also known as NOEMA mm-wave interferometer, located at the Plateau de Bure (French Alps). The observations were carried out in Spring 2018 using eight to nine antennas in A and C configurations. The data include baselines ranging from 18 to 760 m, which at the observed frequencies, results in spatial resolutions of 0. 6 to 0. 8. We used the new PolyFix correlator, which provides simultaneous observations of an 8+8 GHz baseband in dual-polarisation with a spectral resolution of 2 MHz (2. 5 to 3. 0 km s -1 at the observed frequencies). We observed two frequency setups, for a total baseband coverage of 32 GHz divided into four continuous chunks within the 2 mm and 1.
Once we retrieve the position along the line of sight for all pixels in the data cube, we can perform axial averaging around the symmetry axis and mirror averaging with respect to the equatorial plane by simply assuming that these two symmetries are well preserved in the nebula, as the real observational data suggest. In fact, the value of the inclination α can be determined by maximising the cylindrical symmetry in the resulting 3D models for the nebula. In our case, the 13 CO data yield an α value of 40 • ± 5 •. The result of applying this procedure to the three observed J = 2-1 CO lines can be seen in Figure  2. The plots display the expected brightness distribution of the emission of these lines for a plane containing the symmetry axis of the nebula. Axial and equatorial mirror averaging has also been applied.
As for the case of the CO isotopologues, to increase the S/N of the maps, we have first applied the 3D-image reconstruction and averaging process to the SO data cubes (using the same clean beam for all the transitions). Then, we fit straight lines to Equation (  4 ) for each pixel in the resulting images. The upper limits for the non-detected lines have been taken into account in this fitting by using a Markov-Chain Monte-Carlo method  [23]. The results for T ex and N can be seen in Figure  3. We derive excitation temperatures in between 10 and 50 K, in line with what was previously found for CO (but beware of possible underexcitation in the case of SO). We also derive SO densities of 0. 05-0. 2 molecules per cm -3. Assuming the previously derived values of 1. 5 × 10 5 cm -3  [2]  for the total molecular densities in these equatorial regions, our findings result in fractional abundances with respect to H 2 , i.
Furthermore, phenology is highly related to the seasonal dynamics of a growth environment, therefore, in the context of global warming, the phenology of many plants, especially crops, may have changed  [6]. Crop phenology is usually monitored with optical satellite images using vegetation indices. For example, Pan et al. in 2015  [26]  analyzed the phenology of winter wheat and summer corn in the Guanzhong Plain in the Shanxi Province, China by using Normalized Difference Vegetation index (NDVI) time series data and extracted seasonality information from the NDVI time series for measuring phenology parameters. The potential of another less-known index, the Normalized Difference Phenology Index (NDPI), is exploited by  Gan et al. in 2020 [27]  in order to detect winter wheat green-up dates.
During the evaluation with three other indices (NDVI, EVI, and EVI2), the results indicate that NDPI outperforms the other indices with the highest consistency with the ground truth. Compared to the optical data, SAR data is less used in agricultural areas. Nevertheless, lately, with the emergence of a new generation of high-resolution SAR data, in particular since the Copernicus program Sentinel-1 C-band high spatial-temporal resolution images became available, SAR data has begun to draw interest, especially for its advantage of having its own source of energy, making it nearly independent of weather conditions  [8]. Thus, SAR backscattering coefficient time series data is now more frequently used for crop phenology monitoring. While optical data strongly depends on the chlorophyll content in the plants, SAR data can reveal the main changes in the canopy structure, identify significant phenological stages, and determine the main growing period with the signal that is received after interacting with the canopy of the plants.
Data The study was executed in the Finistère department in France during 2019, using openaccess high-quality satellite data from the Sentinel platform. It is worth noting that the latest version of the graphic parcel register was published in 2019 by the French National Institute of Geographic and Forest Information, and this information was relevant to our study. Due to the annual high-intensity precipitation there is frequent heavy cloud cover in the region, therefore, operable optical satellite images are very rare in the study area. Nevertheless, a Sentinel-2 optical satellite image was acquired in the spring, which is the growing season of the winter crops. In order to create a cloud-free time series of the study, the phenological phrases of winter crops from the SAR data were applied to the phenology monitoring process.
Sentinel-2 Optical Data Sentinel-2 is an satellite imaging mission that is implemented by the European Commission (EC) and the European Space Agency (ESA) as a part of the Copernicus program  [36]. The two identical satellites (Sentinel-2A and Sentinel-2B) provide continually open-access, multispectral, wide swath (290 km), high spatial resolution (four bands at 10 m, six bands at 20 m, and three bands at 60 m), and high revisit frequency (five days with combined satellites) image data  [36]. Due to frequent heavy cloud cover in the area, only one cloud-free level 2A atmospheric effect-corrected Sentinel-2 image from 20 April 2019 was acquired from the Theia platform (catalog. theia-land. fr) [37]  (Table  1 ). Ten spectral bands (Table  2 ) were extracted for further processing and analysis.
Sentinel-1 SAR Data The Sentinel-1 C-band SAR (Synthetic-aperture radar) is one of the ESA missions under the Copernicus program  [38]. Sentinel-1 possesses two polar-orbiting satellites (Sentinel-1A and Sentinel-1B) sharing the same orbital plane, which are able to operate day and night using their own energy source in order to perform high spatial resolution (10 m), wide coverage, high repeat cycle (generally five days with two satellites), C-band SAR imaging in all weather conditions  [38]. In this study, interferometric Wide Swath mode level-1 GRD (Ground Range Detected) Sentinel-1 data with an incidence angle ranging from 30 to 46 were acquired to create the time series of the growing period of the winter crops (winter wheat and winter barley) in 2019, from 1 October 2018 to 1 September 2019.
Auxiliary Data RPG (Graphic Parcel Register) was applied as the ground truth data in our study, used for creating training data and test data. RPG 2019 is the latest version of the very precise, geo-referenced agricultural land database that covers the entire France territory (except Mayotte) that was published by IGN. The databases show the precise crop types (e. g. , wheat, corn, vegetables, sunflower) or temporary and permanent grasslands in that are in the recorded agricultural lands in each year  [39].