dataesr/scientific-paragraphs-categorization huggingface dataset:
profunda global data  52 Time series analysis. In order to detect astronomical and orbital periodicities within the past productivity and oceanographic records, a continuous wavelet transform was performed using a Mortlet wavelet  72  in PAST version 3. 25. Prior to the analysis, the unevenly-spaced data was interpolated based on the average resolution of each dataset: 1 kyr for Ca/Fe ratios; 1. 5 kyr for the coccolith assemblage; 2 kyr for SST and alkenones; and 3. 5 kyr for diatoms. To test the significance of the spectrum peaks against the red-noise background, we performed spectral analysis for unevenly-spaced data using the REDFITX software  73. The REDFIT package allows analysis of an unevenly-spaced dataset without interpolating the raw data to generate an evenly-spaced time series.
Acknowledgements This work used samples and data provided by the IODP. We are thankful for the support from the crew of the  R/V JOIDES Resolution and IODP  staff. This work is funded through the  Universidad de Salamanca  Postdoctoral Contract supported by the  Ministerio de Ciencia, Innovación y Universidades  Grant  RTI2018-099489-B-I00  and the  German Science Foundation (DFG) Research Center/Cluster of Excellence  'The Ocean in the Earth System' (MARUM; Grant No. 49926684 ). We acknowledge financial support from the  National Science Foundation  of the US under Award No. 1737218  (M. A. B), the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Sklodowska Curie Grant  Agreement No. 799531  (M. S. P. ), and the  Spanish Ministry of Science and Innovation  Grant  CTM2017-89711-C2-1-P , co-funded by the  European Union  through  FEDER  funds (F.
Participants A total of 1179 adults took part in the study. The group was a representative sample of the Polish population in terms of sex, age, and place of residence. In order to ensure the quality of the data, 64 outliers were removed: 27 participants using only the highest or lowest scale levels in the whole survey, 25 participants using only the midpoint of the scale, 12 participants who had scores on the psychological scales under or over 3 SD from the mean. Finally, data from 1115 participants were analyzed. The vast majority (99%, n = 1108) of participants declared that Poland was their country of origin. The sample included 563 females (50. 5%) and 552 males (49. 5%), in the age range from 18 to 85 (M = 44. 64, SD = 15. 84). Over one-third (37%) of participants lived in a village or the countryside, 12.
MS analysis of proteins from subcellular fractions Proteins present in Nucl/Cyt fractions and isolated ribosomes, or R246A/R249A-mutated RPL3-GFP isolated by GFP-Trap ® immunoprecipitation, were resolved by SDS-PAGE, stained with Coomassie, and the portion of gel containing protein of interest was excised and subjected to in-gel proteolytic digestion using trypsin (Promega), chymotrypsin (Roche), Lys-C (Roche) or proteinase K (Sigma-Aldrich). Peptides resulting from proteolytic digestion were alkylated by adding 5 mM iodoacetamide, desalted using Nucleic Acids  Research, 2021, Vol. 49, No. 6 3189  ZipTips, and analyzed by liquid chromatography system coupled to a QExactive Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo Fisher Scientific), similarly as described previously  (35). MS data were analyzed using in-house maintained human protein sequence databases using SEQUEST™ and Proteome Discoverer™ (Thermo Fisher Scientific), similarly as described previously  (42).
After UV crosslinking, nylon membranes were pre-hybridized for 1-2 h, at 45-48 • C, in 6× SSC, 5× Denhardt's solution, 0. 5% SDS and 0. 9 g/ml of tRNA. Membranes were incubated overnight with 5'-radiolabeled oligonucleotide probes, whose sequences are indicated in Supplementary Table  S2. After hybridization, membranes were washed twice, for 10 min, in 2× SSC, 0. 1% SDS and once in 1× SSC, 0. 1% SDS and then exposed to PhosphorImager Screens (Fuji), which were then scanned using the Typhoon Trio Imager System (GE Healthcare), and quantified using the MultiGauge software.
Human METTL18 is an evolutionarily conserved 7BS MTase containing hallmark motifs The function of METTL18 has hitherto remained elusive, and it has been unclear whether METTL18 is an ortholog of yeast Hpm1. Thus, we have taken an unbiased approach to elucidate the role of this enzyme. The crystal structure of human METTL18 in complex with Sadenosylhomocysteine (PDB ID: 4RFQ) shows the presence of seven ␤-strands forming a twisted ␤-sheet, characteristic of 7BS MTases. Protein sequence searches indicated that putative METTL18 orthologs are present in all eukaryotic organisms, including animals, fungi and plants (Supplementary Figure  S1 ). An alignment of METTL18 sequences from various species revealed conserved motifs that are hallmarks of 7BS MTases (Motif I, Post I and Motif II), as well as the so-called 'DXXY-motif' (consensus D/E-X-X-Y/F) shared by MTF16 members  (19).
METTL18 mediates 3MH modification of RPL3 Next, we set out to determine which isomer of methylhistidine, 1MH or 3MH, is introduced by METTL18. To this end, we isolated total and ribosomal proteins from WT and METTL18 KO HAP1 cells, and then subjected these to acid hydrolysis into amino acids, which were analyzed and quantitated by LC-MS/MS. For total protein, WT and KO cells showed no significant differences in the content of 1MH and 3MH (Figure  5A  and data not shown). For the ribosomes, however, the level of 3MH was dramatically reduced in the KO relative to the WT, while 1MH was not detected (Figure  5A  and data not shown). Since we had already identified RPL3 as target of METTL18, we determined the 3MH content specifically in ribosome-associated RPL3. Clearly, the relative content of 3MH was much higher (>10×) in isolated RPL3 compared to bulk ribosomes, directly demonstrating that RPL3 is 3MH-modified.
ACKNOWLEDGEMENTS We thank  Ingrid F. Kjønstad  and  Marit Ledsaak  for technical assistance. We would like to thank the  PRO-MS Danish National Mass Spectrometry Platform for Functional Proteomics  and the  CPR Mass Spectrometry Platform  for instrument support and assistance. We thank  Oslo NorMIC Imaging Platform  ( Department of Biosciences, University of Oslo ) for the use of cell imaging equipment.
FUNDING Norwegian Cancer Society  [ 107744-PR-2007-0132  to P. Ø. F. ];  Research Council of Norway  [FRIMEDBIO-Downloaded from https://academic. oup. com/nar/article/49/6/3185/6163085 by guest on 07 April 2021 3202 Nucleic Acids Research, 2021, Vol. 49, No. 6 240009  to P. Ø. F. ];  Max Planck Society  and  DFG Grant  [ LE 3260/3-1  to S. A. L. ];  Lundbeck Foundation  [ R231-2016-2682  to M. E. J. ];  Novo Nordisk Foundation  [ NNF16OC0022946  to  M. E. J. ,  NNF14CC0001  to J. V. O. ]. Agence nationale de la recherche  [ ANR-16-CE11-0029-01  to P. E. G]. Funding for open access charge:  Norwegian Cancer Society.
8-B ), in agreement with our in vitro data (Figs. 7-C, S3-C). Thus, consistent with previous experiments, trans overexpression of AsflhD in vivo reduces the abundance of flhD mRNA but it is less effective than AsflhD expressed from the flhD locus in cis and this effect is mostly independent of RNase III activity. To further confirm that AsflhD can impact flhD expression in vivo, we measured the expression of the flhD-lacZ reporter fusions when AsflhD is overexpressed from the plasmid. The overexpression induces about a 30-40% decrease in the expression of flhD both from the P flhD -flhD-lacZ and the P tet -flhD-lacZ fusions (Fig. 8 -CD, dark grey). However, overexpression of AsflhD from the plasmid in the strain where expression of AsflhD is already upregulated, by the presence of P AsflhD + mutation in cis, has no or little additive effect on the final repression (Fig.
Acknowledgements We thank  C. Beloin  and  J-M Ghigo  for providing strains and  A. Kolb  for the kind gifts of purified RNAP core, σ 70 and  CAP. This work was supported by the  Centre National de la Recherche Scientifique  ( UMR8261 ),  Université de Paris ,  Agence Nationale de la Recherche  (asSUPYCO,  ANR-12-BSV6-0007-03  to E. H. ), ( RIBECO ANR-18-CE43-0010  to E. H. ) and the " Initiative d'Excellence" program  from the  French State  (Grant " DYNAMO ,"  ANR-11-LABX-0011 ).
Second, the V OI represents a smaller proportion of V 0 [only 10%, compared to values between 7 and 50% in other studies  (36, 44, 45) ]. As the V OI is obtained from a linear fit, the range of data and the formula used for the fit can strongly change the V OI. We tested this possibility by changing the range of data used for the fit and show that using smaller ranges always provides larger V OI (Materials and Methods). One may argue that the larger range of osmotic shocks we used may artificially give lower values of V OI. To have an independent estimate of the V OI , we sought to measure the protein mass of the cell and compare the hydrated minimal volume of this mass to our V OI values. For this, we used optical diffraction tomography, a 3D tomographic label-free technique, to measure the cell's refractive index (RI)  (46) , hence giving a direct access to changes of cell mass and protein concentration.
Image Acquisition and Analysis for Flipper-TR Imaging. Membrane tension measurements were performed on the setup published in ref. 31. HeLa Kyoto cells were labeled with Flipper-TR (Spirochrome SC020) in Leibovitz medium. Flipper-TR was dissolved in DMSO at 1 mM stock solutions. Cells were labeled with a 1:1,000 dilution from the DMSO stock and incubated at 37 • C for 15 min, and slices were acquired every 25 s for 10 min without washing the probe. Osmotic shocks were applied 10 s before the second timepoint. Lifetimes of Flipper-TR were extracted from fluorescence lifetime microscopy (FLIM) images using SymPhoTime 64 software (PicoQuant) with the same method published in ref. 31. We selected full images instead of choosing a region of interest. Drug Treatment. Concentrations of drugs were kept constant throughout the experiment.
ACKNOWLEDGMENTS. We thank  K. Roux  and  R. Wimbish  for critical reading of the manuscript and  C. Tomba  for discussions. A. R. acknowledges funding from  Human Frontier Science Program Young Investigator  Grant  RGY0076/2009-C ,  Swiss National Fund for Research Grants   31003A_149975  and  31003A_173087 , Synergia Grant  CRSII5_189996 ,  European Research Council   Consolidator Grant   311536 , and Synergy Grant  951324-R2-TENSION. This work has been supported by  Institut Pasteur ,  CNRS , and  ANR (SeptScort)  (A. E. ). V. A. received a fellowship from the  Doctoral School Complexité du Vivant   ED515  and  La Ligue Contre le Cancer. M. L. was supported by  ANR  Grant  ANR-15-CE13-0004-03  and  European Research Council   Starting Grant   677532. M. L. 's group belongs to the  CNRS consortium Approches Quantitatives du Vivant.
Methods This study took place in the teaching hospital of Caen, a 1410-bed hospital located in Normandy, France. On 6 th August 2018, a fourth spatiotemporal case of S. marcescens SSI appeared in the digestive surgery ward. Furthermore, the clinical strains displayed the same antibiotic resistance profile. As a consequence, the hygiene and infection control department of the hospital was alerted on 7 th August 2018 by surgeons, microbiologists and intensive care seniors; this led to the immediate closure of the operating room. To screen unnoticed cases since the beginning of 2018, retrospective investigations were performed. Information was collected from the acts traceability booklet and medical files, as well as coding data transmitted by the hospital archive department, the microbiology laboratory and the hospital pharmacy.
marcescens strains, clinical and environmental, isolated throughout this investigation, as well as two supplementary not-linked strains. Moreover, these strains were subjected to whole-genome sequencing (WGS) using a NextSeq 500 platform (Illumina, USA), generating 150 bp paired-end reads. Genomes were de novo assembled using SPAdes v 3. 12 software. Every genome is available on the NCBI BioProject: PRJNA648275. All known acquired antibiotic resistance genes were screened thanks to the Resfinder database (www. cge. dk). Furthermore, point mutations were searched in order to detect alterations in the quinolone resistance-determining regions (QRDRs). Reads were mapped against the reference genome S. marcescens subsp. marcescens Db11 (Nucleotide accession number: NZ_HG326223) using the BurrowseWheeler Aligner, and single nucleotide polymorphisms (SNPs) were determined in order to evaluate the distance between all the sequenced strains using mpileup, as previously described  [10].
Acknowledgements. We thank  Gaëlle Tallec  and  Charles Perrin , for their careful review prior to article submission. Ludovic Oudin ,  Laurent Coron ,  Éric Martin  and  Nathalie de Noblet-Ducoudré  are gratefully acknowledged, for their advice on the doctoral research of the first author. Météo-France  and SCHAPI are acknowledged, for providing climatic and streamflow data, respectively. We thank  Isabella Athanassiou , for her cautious proofreading and help in making the English easier to read. The authors gratefully acknowledge the reviews by  James Craig  and by another anonymous reviewer. Financial support. This work has been funded by the project  AQUACLEW , which is part of  ERA4CS , an  ERA-NET  project initiated by  JPI Climate , and funded by  FORMAS (SE) ,  DLR (DE) ,  BMWFW (AT) ,  IFD (DK) ,  MINECO (ES)  and  ANR (FR) , with cofunding by the  European Commission  (grant no.
Multiple subjects. In order to evaluate a more realistic (routine) scenario, 42 different groups worldwide with varying levels of expertise each scanned six different healthy participants (3 males, 3 females), aged between 19 and 56 y. o. , median age 28 y. o. , resulting in 260 data sets. Participant-specific list of age and sex distribution is available at https://github. com/spine-generic/data-multi-subject/blob/r20201130/participants. tsv. Each group used the spine generic protocol and SOP, and obtained consent to scan and upload participants' anonymized data onto a publicly-available repository. Anatomical scans (T1w, T2w) where facial features are visible were "defaced" before being released to the public domain using pydeface  6. Some centers equipped with more than one system were asked to scan 6 different participants on each scanner, although they were also given the possibility to scan the same 6 participants across systems for convenience.
In the latter case, this could slightly bias the inter-site coefficient of variation (COV), however it would not affect the inter-manufacturer COV. The list of centers and scanner models is shown in Online-only Table  1. Data from each participant was then entered into the processing pipeline described in the next section. COVs were computed within site and manufacturers, and compared across manufacturers. Data processing. Data were processed using Spinal Cord Toolbox (SCT) v5. 0. 1  7  and spine-generic v2. 6 (https://github. com/spine-generic/spine-generic/releases/tag/v2. 6). The processing pipeline is illustrated in Fig. 1  and is fully documented at https://spine-generic. rtfd. io. Briefly, for the T1w scan, the SC was segmented using deep-learning  8  , vertebral levels were identified  9  and the SC was registered to the PAM50 template  10  using the C3 and C5 vertebrae as labels.
Magnetization transfer ratio (MTR) and MTsat  12  were computed and their values extracted for the white matter (WM) between C2 and C5 using the WM probabilistic atlas  13. For the diffusion weighted imaging (DWI) scan, the time series of diffusion-weighted scans were averaged and the SC was segmented so that a mask could be created around it. The time series were motion-corrected using slice-wise 2D transformations regularized along z  7  , with time-adjacent volumes grouped together for increased robustness  14. The PAM50 template was then registered to the DWI dataset using the initial transformation from the T1w scan and diffusion tensor imaging (DTI) metrics were computed using the weighted least squares fitting algorithm implemented in Dipy  15  and wrapped in SCT's function sct_dmri_compute_dti.
Data Records The two datasets associated with this publication are: • The Spine Generic Public Database (Single Subject)  16  • The Spine Generic Public Database (Multi-Subject) 17 Dataset management. Figure  2  illustrates the data management workflow. Datasets are managed using git-annex (https://git-annex. branchable. com/); git-annex is built on git technology and enables the separation of large files (NIfTI images, hosted on Amazon Web Services, AWS) from small files (metadata and documentation, hosted on GitHub). This decision was based on the modularity of git-annex (multiple mirrors can be added) and its compatibility with Datalad  18. The documentation for contributing to the repositories is hosted on a wiki (https://github. com/spine-generic/spine-generic/wiki). To facilitate data aggregation across centers, we used the Brain Imaging Data Structure (BIDS) convention  19.
BIDS notably features JSON metadata files as a sidecar for each NIfTI file, which includes relevant acquisition parameters, making it easy to assess how well each site followed the generic protocol and which parameters were modified. Parameter verification (within a specified tolerance) as well as file and folder naming is automated (https://spine-generic. rtfd. io/en/latest/data-acquisition. html#checking-acquisition-parameters) such that, every time new participants are added to the database, a notification is sent to a continuous integration system (e. g. , https://github. com/spine-generic/data-multi-subject/runs/2730553396?check_suite_focus=true) that downloads the dataset and runs custom scripts to verify the validity of the dataset. For example, if a flip angle for a particular volume exceeded the tolerance range, the BIDS validator would fail and the data would not be merged.
In that case, the management team would reach out to the data contributors asking if they can reacquire the data. If not, the data would not be added to the dataset. Another (less problematic) example: if a file was incorrectly named (sub_amu01_T2w. nii. gz instead of sub-amu01_T2w. nii. gz), the BIDS validator would fail. In that case, the management team would manually correct the file name, commit and push the change to the working branch and wait for the BIDS validator to pass before being able to merge the new data on the main (master) branch. Below is an example of the "BIDS Validator" script output (only showing part of it): WARNING: Incorrect FlipAngle: sub-amu01_T2w. nii. gz; FA=180 instead of 120 WARNING: Incorrect RepetitionTime: sub-amu02_T2w. nii. gz; TR=2 instead of 1. 5 WARNING: Incorrect FlipAngle: sub-amu02_T2w.
Estimated metrics are shown in red. technical Validation Data quality. Overall, data quality was satisfactory based on qualitative visual inspection. Criteria included the correctness of field of view prescription, proper selection of receive coils, quality of shimming (assessed by looking at fat saturation performance and the presence of susceptibility distortions), and the presence and severity of motion artifacts. Figure  3  shows examples of good quality data for all sequences. A few operator errors occurred, including: mis-labeled MT0 for MT1 and MT1 for MT0, shim parameters changed between MT0 and MT1 scans (causing different signal intensities, and hence not suitable for MT-based metrics), change of FFT scaling factor between the MT1/MT0 scans and the T1w scan used to compute MTsat and T1 maps (causing different signal quantization and hence not suitable for MT-based metrics unless corrected for), and repositioning of the participants, causing mis-alignment between the images before/after repositioning and violation of the analysis pipeline assumptions (all images are supposed to be acquired with the patient in the same position).
These errors were not caught by the BIDS validator, but by the managing team during visual inspection of the data and interpretation of the qMRI metrics results. In future work, the data validator could be made sensitive to these issues. For example, the FFT scaling factor and shim coefficient are sometimes retrievable from the DICOM data and could be checked. Also, the qform (affine matrix present in the NIfTI header) could be checked to ensure consistency across data from the same series, e. g. MT1, MT0, GRE-T1w. Regarding the mis-labeling of MT1/MT0, training a deep learning model to recognize image contrast could address this issue. Figure  4  illustrates some of the image artifacts encountered during QC. A list of poor data quality scans is available on the Github's issues of the dataset under the label "data-quality" (https://github.
com/spine-generic/ data-multi-subject/labels/data-quality); most of these were caused by patient motion. Mosaics of images for every contrast and every participant are available in the supplementary materials (Figures  S1-S5 ). Additional examples of good quality data are also available in the spine generic website (https://spine-generic. rtfd. io/en/latest/ data-acquisition. html#example-of-datasets). Quantitative results: Single subject. Overall, data quality was satisfactory. All images were visually inspected to ensure that there were no significant errors in the masks used to average the signals in the SC, WM or GM, and any errors were manually corrected. A list of poor quality scans is available on Github in the issues for the dataset, under the label "data-quality" (https://github. com/spine-generic/data-single-subject/labels/ data-quality).
Complete metrics and statistical tests are available in the r20201130 release assets (https://github. com/spine-generic/data-single-subject/releases/download/r20201130/results. zip). Figure  5  shows the SC CSA data from the T1w scan, averaged between cervical levels 2 and 3 (C2 and C3), for the single participant across the 19 centers. Within each manufacturer, the inter-site standard deviation ranges from 0. 65 mm 2 (Siemens) to 1. 56 mm 2 (GE), which is remarkably small considering that the size of a pixel is 1 mm 2. The inter-site COVs were 2. 3% for GE, 1. 8% for Philips and 0. 9% for Siemens. The inter-manufacturer difference was significant (p < 0. 01), with the Tukey test showing significant differences between GE and Philips (p-adjusted = 0. 03) and between GE and Siemens (p-adjusted < 0.
g. FA >1 or <0). Average +/standard deviation (SD) and COVs for mean diffusivity were, respectively, (0. 62 +/-0. 03) mm 2 /s and 5. 6% for GE, (1. 00 +/-0. 06) mm 2 /s and 5. 71% for Philips, and (1. 01 +/-0. 05) mm 2 /s and 4. 83% for Siemens. Average +/-SD and COVs for radial diffusivity were, respectively, (0. 36 +/-0. 03) mm 2 /s and 7. 21% for GE, (0. 51 +/-0. 06) mm 2 /s and 11. 34% for Philips, and (0. 54 +/-0. 03) mm 2 /s and 6. 37% for Siemens. Quantitative results: Multiple subjects. As in the case of the single subject data, all images were visually inspected to ensure that there were no significant errors in the masks used to average the signals in the SC, WM or GM and any errors were manually corrected. Complete metrics and statistical tests are available in the r20201130 release assets (https://github.