dataesr/scientific-paragraphs-categorization huggingface dataset:
Imaging was performed in Live-Cell Imaging buffer (Invitrogen) supplemented with 20 mM glucose. After being placed on the microscope stage, a perfusion pencil (Autom8 Scientific) was placed directly on top of the cell using a micromanipulator. Short bursts of imaging buffer (140 mM NaCl, 2. 5 mM KCl, 1. 8 mM CaCl2, 1. 0 mM MgCl2, 20 mM PIPES or HEPES) buffered to pH 5. 5 and 7. 5 with PIPES and HEPES, respectively, were alternatingly perfused in 5 s intervals using a gravity driven perfusion apparatus (Warner). All solutions were heated using an inline heating element (Warner). Perfusion intervals were controlled by an Arduino microcontroller using PyFirmata and custom Python scripts (available at https:// github. com/koschink/Arduino_Perfusion). Alternatively, instead of using pH changes, cells expressing the calcium sensor GCAMP6 were perfused with imaging buffers containing EGTA or Calcium using the same setup.
To this mixture, 100 μl of 75% Sucrose in liposome buffer was added, resulting in a 30% sucrose solution. This fraction was overlaid with 200 μl 25 % sucrose in liposome buffer and 100 μl of liposome buffer without sucrose. The sample was then centrifuged at 55,000 r. p. m. (240,000 g) in a Beckman swing rotor (TLS 55) for 1 h. Successful liposome flotation was verified by visualizing NBD-PE fluorescence using a "Safelight" gel imager, and fractions (250 μl (bottom), 200 μl (middle), and 100 μl (top)) were collected from the bottom. 25 μl of the top fraction was separated using SDS-PAGE and visualized by Coomassie Brilliant Blue staining. Gels were recorded using an "Odyssey" gel imaging device (LI-COR Biotechnology). Intensities were measured using Fiji and plotted using the Seaborn Python package.
MBP fusion was detected by western blotting with an anti-MBP antibody. Intensities were measured using Fiji and plotted using the Seaborn Python package. We quantified the intensity of the MBP fusion proteins in both the supernatant and pellet fractions ( = 100%) and plotted the fraction bound to actin in the pellet. Image pre-processing and data analysis. All live-cell images were deconvolved using Softworx (GE Healthcare) prior to analysis and presentation. All further image analysis and measurement steps were performed in FIJI-either by manual scoring or using custom Jython scripts. Measurement scripts are available at https://github. com/koschink/Phafin2. Quantification of macropinosome fusion. Macropinosome fusion was quantified by manually tracking individual macropinosomes from their formation to the endosomal phase and visually scoring fusion events.
Acknowledgements We thank  Dr. Frauke Ackermann  for critical reading of the manuscript and useful discussions. Dr. Gareth Griffiths  is acknowledged for useful discussions on macropinosome actin dynamics. We thank  Ulrikke Dahl Brinch  and  Eva Rønning  for expert technical support. The Advanced Light microscopy core facility and the  Flow cytometry Core facility at Oslo University Hospital  are acknowledged for providing access to relevant instruments. K. O. S was supported by a career development fellowship from the  Norwegian Cancer Society , a  Career grant  from the  South-Eastern Norway Regional Health Authority  ( 2020038 ), and a Research Grant from the  Research Council of Norway  ( 315103 ). C. C. is a young investigator of the  Research Council of Norway. H. S. was supported by an  Open Project Grant  from the  South-Eastern Norway Regional Health Authority  (2018081), a  Research Grant  from the  Norwegian Cancer Society  ( 182698 ), and an Advanced Grant from the  European Research Council  ( 788954 ).
Data availability All data shown and used to generate plots, as well as detailed statistical information, accompanies this manuscript in the source data file. Uncropped western blots and gels are shown in Supplementary Fig. S9. Underlying image data are available from the corresponding authors (H. St, K. O. S) upon request. Expression data for Phafin2 is available at the public BioGPS database (https://biogps. org/gene/79666/), the cancer copy number alteration data shown in Fig. 8d  and 8e are available at the cBioPortal database (https://www. cbioportal. org/). Source data are provided with this paper.
One Codex, RIEMS, VirMet, and Taxonomer. Performance, characteristics, clinical use, and user-friendliness of these pipelines were analyzed. Results: Overall, viral pathogens with high loads were detected by all the evaluated metagenomic pipelines. In contrast, lower abundance pathogens and mixed infections were only detected by 3/13 pipelines, namely DNASTAR, FEVIR, and MetaMix. Overall sensitivity ranged from 80% (10/13) to 100% (13/13 datasets). Overall positive predictive value ranged from 71-100%. The majority of the pipelines classified sequences based on nucleotide similarity (8/13), only a minority used amino acid similarity, and 6 of the 13 pipelines assembled sequences de novo. No clear differences in performance were detected that correlated with these classification approaches.
Read counts of target viruses varied between the pipelines over a range of 2-3 log, indicating differences in limit of detection. Conclusion: A wide variety of viral metagenomic pipelines is currently used in the participating clinical diagnostic laboratories. Detection of low abundant viral pathogens and mixed infections remains a challenge, implicating the need for standardization and validation of metagenomic analysis for clinical diagnostic use. Future studies should address the selective effects due to the choice of different reference viral databases.
Introduction: Metagenomic sequencing is increasingly being used in clinical settings for difficult to diagnose cases. The performance of viral metagenomic protocols relies to a large extent on the bioinformatic analysis. In this study, the European Society for Clinical Virology (ESCV) Network on NGS (ENNGS) initiated a benchmark of metagenomic pipelines currently used in clinical virological laboratories. Methods: Metagenomic datasets from 13 clinical samples from patients with encephalitis or viral respiratory infections characterized by PCR were selected. The datasets were analyzed with 13 different pipelines currently used in virological diagnostic laboratories of participating ENNGS members. The pipelines and classification tools were: Centrifuge, DAMIAN, DIAMOND, DNASTAR, FEVIR, Genome Detective, Jovian, MetaMIC, MetaMix,.
Introduction Viral metagenomic next-generation sequencing (mNGS) is increasingly being used in virology laboratories for the diagnosis of patients with suspected but unexplained infectious diseases. The current main clinical application of viral metagenomics is for diagnosing encephalitis of unknown cause  [1, 2] , but metagenomic sequencing is considered useful in a growing number of other clinical syndromes  [3] [4] [5] [6]. Although many wet-lab challenges need to be faced as well  [14] , the performance of metagenomic methods is largely dependent on accurate bioinformatic analysis, and both classification algorithms and databases are crucial factors determining the overall performance of the pipelines  [7, 55]. A wide range of metagenomic pipelines and taxonomic classifiers have been developed, commonly for the purpose of biodiversity studies analyzing the composition of the microbiome in different cohorts.
Datasets To exclude differences in wet-lab procedures, the same raw, untrimmed metagenomic datasets were provided, so that the participants had standardized datasets for bioinformatic analysis. In total, 13 clinical metagenomic datasets from samples wellcharacterized by RT-PCR  [15] [16] [17] [18]  were selected from patients with encephalitis or respiratory complaints, including: cerebrospinal fluid (CSF, n = 4), brain biopsies (n = 3), nasopharyngeal swabs (n = 3), nasal washings (n = 1), bronchoalveolar lavage (n = 1), and a plasma sample (n = 1). RT-PCR panel results and Cq-values are included in the result section. The pathogens in the 13 datasets are depicted in Table  2. For samples processed at the Great Ormond Street Hospital, London (GOSH), mRNA from the three brain biopsy samples was sequenced on an Illumina NextSeq500 instrument using an 81 bp paired-run after library preparation using Illumina's TruSeq Stranded mRNA LT sample preparation kit (p/n RS-122-2101) according to the manufacturer's instructions  [19].
Data sharing The FASTQ datasets were and remain publicly available for userfriendly downloading at https://veb. lumc. nl/CliniMG (hosted by the dept. MM, LUMC, Leiden), and part of the datasets were additionally accessible via a COMPARE Data Hub at http://www. ebi. ac. uk/ena/path ogens (hosted by the European Bioinformatics Institute, EMBL-EBI)  [24].
Bioinformatic pipelines The datasets were analysed in a blinded fashion by the participants, with the (viral) metagenomic pipelines and classification tools (Fig. 1  and Table  1 ) used at their diagnostic laboratories: Centrifuge  [25] , DAMIAN  [26, 27] , DIAMOND  [28] , DNASTAR  [29] , FEVIR  [30] , Genome Detective  [31] , Jovian  [32] , MetaMIC  [33] , MetaMix  [34, 35] , One Codex  [36] , RIEMS  [37, 38] , Taxonomer  [39] , and VirMet  [40]. DAMIAN was run by two participants in combination with a different database (pipeline A and B), and one participant run both Centrifuge and GenomeDetective. Details of the algorithms are described in Table  1.
Performance characteristics Both qualitative and quantitative performance of the pipelines were analysed with real-time PCR results as gold standard. The following parameters available for all pipelines were considered: pathogen detection, taxonomic classification level and target read count. Additionally, horizontal genome coverage (if available), computational time, user-friendliness and output formats were considered. Since EAV and PhHV were added as internal controls and not reported by the participants (due to default reporting criteria, or absence in the database)they were not included in the comparative analysis.
Metagenomic pipeline characteristics In total 13 different metagenomic pipelines and classification tools were in use in the 13 participating diagnostic laboratories. Clinical use, classification and output characteristics of the pipelines and tools utilized are shown in Fig. 1  and Table  1. The majority of the pipelines were developed or adapted at a local site, while four pipelines were commercially available and web-based: DNASTAR (Madison, WI, USA), Genome Detective (Emweb bv, Herent, Belgium), One Codex (San Francisco, USA), and Taxonomer (Utah, USA). DAMIAN and Centrifuge are publicly available as an open source software. Both classification tools and reference databases differed among participants (and were fixed for end-users of the commercially available pipelines); (adapted versions of) NCBI's nucleotide and RefSeq databases were most commonly used to generate reference databases.
Detection of PCR targeted viral pathogens; sensitivity The qualitative and quantitative results of the pipeline benchmarking for viruses detected by RT-PCR are shown in Table  2  and Fig. 2. Overall, higher abundance viral pathogens (Cq-value < 28) were detected by all metagenomic pipelines evaluated. In contrast, viral pathogens with RT-PCR Cq-value of 28 and higher including mixed virus infections were only detected by 3/13 pipelines, namely DNASTAR, FEVIR, and MetaMix. Although participants analysed the same FASTQ files, read counts of the target viruses varied from one to several orders of magnitude across pipelines. Also, read counts (all datasets combined) achieved by participants did not correlate well with the viral load as measured by RT-PCR (R = -0. 07, P-value 0. 5), however it must be noted that wet lab procedures varied per set of samples, including protocols with and without viral enrichment, which had potential impact on the viral read counts and thus on correlation with Cq-values.
Taxonomic level of classification The taxonomic levels of classification and typing of pathogenic viruses by the metagenomic pipelines with the settings used and reported Fig. 1. Workflow of bioinformatic analysis of (viral) metagenomics data with the pipelines and classification tools used by participants in the current study. by the participants are shown in Fig. 3  and Supplementary Table  3. The classification level is dependent on the database used, algorithm settings (classification of reads to the lowest common ancestor, LCA, in case of multiple hits), and the participant's default reporting levels based on either in-house validation data or clinical relevancy. Species level classification was the most common level reported. Serotype and strain level were identified by tools that were combined with NCBI's nt database without the LCA setting.
RPKM; reads per kilobase of genome per million mapped reads. Fig. 3. (Taxonomic) level of classification and typing of the pathogenic viruses identified using the combination of tools and databases, as reported by participating diagnostic laboratories. Depicted are the number of target viruses per classification level. RefSeq; NCBI's RefSeq data base (or an adapted version), NT; NCBI's nucleotide database (or an adapted version); LCA; lowest common ancestor. *Taxonomic assignment method described in  [25, 26]  consistent between different pipelines: human Adenovirus type 31 (DIAMOND, Jovian, DNASTAR, VirMet), type 12 (DAMIAN), type 31 or 61 (metaMIC), indicating that type classification was not always correct. Type 12 and 31 are both from subgroup A Adenoviruses, whereas type 61 is a type 31 recombinant virus.
Additional virus hits and positive predictive value Additional viruses, either not tested for by RT-PCR or RT-PCR negative were reported by 11 out of 13 pipelines, and in one or more samples (Supplementary Table  4 ). The following additional viruses were reported by multiple pipelines and absent in the negative run control (dataset not available for the participants): human retrovirus RD114 (2-2102 reads, up to 28% genome coverage), feline leukemia virus (2-1406 reads), torque-teno virus (TTV) (18-66 reads, up to 7% genome coverage), polyomaviruses (5-41 reads, up to 37% genome coverage), Bovine viral diarrhea virus (BVDV) (6-220 reads, likely FBS contaminants), human metapneumovirus (HMPV) (15-21 reads, 9% genome coverage), human rhinovirus (HRV) (2-4 reads, up to 5% genome coverage), human parainfluenzavirus-4 (PIV-4) (2-6 reads) and Dengue virus (18-370 reads).
Discussion This study aimed to benchmark the combination of bioinformatic tools and databases currently in use in diagnostic virology laboratories from the ESCV ENNGS network. The data presented here support bioinformatic selection and optimization of software for the implementation of viral metagenomic sequencing for pathogen detection in clinical samples. To our knowledge, this is the first large-scale international benchmarking study using datasets from clinical samples and pipelines currently applied in a large series of clinical diagnostic laboratories. The study showed that the pipelines of all the participating laboratories succeeded in detecting viral pathogens with relative high viral loads (Cq-values <28), whereas lower abundant pathogens and mixed infections were only detected by some of the pipelines, namely DNAS-TAR, FEVIR, and MetaMix.
These results are in line with other reports  [7]. With regard to mixed infections, the less abundant viruses were generally missed, possibly due to the low number of reads, or reporting considerations. For the missed CoV-HKU1 virus, potential primer cross-reactivity with CoV-NL63 viruses was excluded by in silico analysis. The databases used in the pipelines were mostly custom-made, based on either NCBI's RefSeq  [41]  or nt database  [42]. All of the participants used different classification tools, though no selection of laboratories using different tools was made in advance. Given the inclusion of different types of pipelines including commercially available ones with fixed databases, it was not feasible to compare the different tools with one standardized database at the local sites. Two of the three pipelines that reached 100% sensitivity included NCBI's nt database but this was also seen using a pipeline with NCBI's RefSeq database.
Pipelines with NCBI's nt database scored both low and maximum precision. The design did allow for comparison of the complete pipeline in use for clinical diagnostics, from QC to reporting algorithms including posterior probability scores. No clear differences were observed in terms of performance based on nucleotide-based classification versus amino acid-based classification and de novo assembly-based algorithms versus read based classification: whereas amino-acid based classification may be more sensitive for detecting variants, two of the three pipelines with 100% sensitivity used nucleotide-based classification (DNASTAR, FEVIR). High precision was reached by pipelines that used de novo assembly but this was not essential: 3/8 pipelines with 100% precision did not use de novo assembly (Centrifuge, Taxonomer, One Codex).
Reported read counts and genome coverage varied between pipelines up to several orders of magnitude (for read counts), explaining in part the differences observed in limits of detection for samples with very low viral load. Possibly, differences in reporting of unique versus nonuniquely mapped sequence reads may be related to this difference. Sensitivity and positive predictive value were measured, conveniently avoiding the proportion of true negative findings given the immense but unknown number of negative mNGS hits without RT-PCR data needed for specificity calculations. This aspect remains a limitation intrinsically linked to mNGS validations with clinical datasets. Datasets from negative matrix samples would have been of use for specificity calculations. Positive predictive value calculations were hampered by the intrinsic inability to distinguish between sequences actually present in the dataset that might be undetected by RT-PCR because of, for instance, primer mismatches, index hopping or contaminant sequences introduced during library preparation.
This may partially be overcome by defining mNGS consensus results as alternative golden standard, however in diagnostic settings e. g. index hopping reads should not be labeled positive despite being actually present in the dataset. A study design using synthetic data for example spiked into real-data mimicking reallife situations would enable accurate estimation of the specificity and PPV whilst taking into consideration interfering real-life factors such as sequences with switched indices and 'kitome' sequences, present in every single dataset. Future studies could address specificity analysis using artificial datasets that take into account the index hopping phenomenon. It is important to note that participants likely have optimized their interpretation algorithm including cut-offs for their specific workflow from library preparation to sequencing.
A different wet lab procedure (sequencer with or without index hopping, preparation with or without probe enrichment) will require new validation and indexing of the determined cut-off values and probability values. Because this was a dry lab comparison exercise, the participants could not follow their routine wet lab workflow and confirmatory PCR steps, which may have affected the reporting of results. Therefore no conclusions can be drawn on the limit of detection of the full metagenomic workflows used in each specific laboratory. The two labs that distributed the datasets may have been advantageous with regard to the analyses since the cut-offs for defining a positive result likely have been adjusted to the specific wet lab procedure used. However, the scores from the pipelines in use in these labs were not higher for the specific datasets delivered, in contrast, all pipelines detected all samples distributed by lab A, and other participants were more successful in detection some of the most challenging samples distributed by lab B.
In conclusion, a wide variety of viral metagenomic pipelines with overall high sensitivity are currently used in the ESCV ENNGS participating clinical diagnostic laboratories. Detection of low abundance viral pathogens or mixed infections remains a challenge, implicating the need for standardization and validation of metagenomic analysis for clinical diagnostic use  [44]. The algorithm for defining positive results and rejecting false positive results is critical and should be evaluated individually for every workflow, which includes genome extraction, library preparation, sequencer and bioinformatic pipeline. Identification of deeper taxonomic levels is challenging, dependent on the individual types present in the reference database, and should be validated separately to prevent misidentification.
viruses, fungi; Environmental, viruses, fungi, (complete) Based on nt (selection of viral/bacterial fungi, archae; v2019-05- protein V2020-01- [46] RefSeq o. (compressed v2. 1. 1 bacteria, Viruses archaea, v2019-3-16 NCBI's nt (v viral full (version) RefSeq 17, PFAM database/ 08 v90v_2018_11 (filtering: index) based on NCBI's RefSeq protein protozoa, One May 2019) genomes without (compressed 30. 0 pipeline 3B: Swissprot nt (complete) v2017 Codex DB compression) index) NCBI's nt Uniref 90) v2019-5-1 v224 V2019-04-04 V0. 9. 22 v2018-11-14 Paired reads as Y Y Y Y Y Y Y Y Y Y Y N input option N Trimming and QC Trimmo- Trimmo- Trimmo-matic Included in Included in Trimmomatic, HMN Trimmer TrimGalore!, Cutadapt Roche 454 N Seqtk, prinseq tools matic matic [47] DNASTAR virusscan [48] fastqc, multiqc prinseq newbler 1.
Experiments were initialized from biogeochemical fields created from an extensive spin-up of 5000 years under repeat physical forcing, followed by a 300-year simulation under the preindustrial control scenario. The preindustrial control simulation used in analysis was therefore the final 300 years of a 5600-year spin-up involving two repeat simulations of the preindustrial control scenario. We utilized a global compilation of δ 15 N NO3  20  supplemented with recent data to assess the isotopic routines in the model and conducted a thorough model-data skill assessment at replicating observed patterns in space (Supplementary Note 2 and Supplementary Figs. 1 2 3 ). Anthropogenic nitrogen deposition. The effect of increasing aeolian deposition of nitrogen was assessed in our simulations. Preindustrial nitrogen deposition was prescribed as the preindustrial estimate at 1850, whereas the historical to future deposition was created by linear interpolation between preindustrial (1850) and modern/future fields (2000, 2030, 2050 and 2100).
Acknowledgements P. J. B. , A. T. and C. M. were supported by the  ARISE  project ( NE/P006035/1 ), part of the  Changing Arctic Ocean programme , jointly funded by the  UKRI Natural Environmental Research Council (NERC)  and the  German Federal Ministry of Education and Research (BMBF). O. A. acknowledges support from the  LEFE   PISCO  project. L. B. acknowledges support from the  MERTOX  ( ANR-17-CE34-0010 ) and  CIGOEF  ( ANR-17-CE32-0008 ) projects. Simulations were undertaken on Barkla, part of the High-Performance Computing facilities at the  University of Liverpool. We acknowledge use of the  Ferret program  (http://ferret. pmel. noaa. gov/Ferret/),climate data operators (https:// code. mpimet. mpg. de/projects/cdo/), NetCDF Operators  (http://nco. sourceforge. net/), Python  (www.
Code availability The version of NEMO-PISCES used in this study can be downloaded with the following command: svn co https://forge. ipsl. jussieu. fr/nemo/svn/NEMO/releases/release-4. 0 NEMO4. 0. Additional development to include the isotopes can be merged into this version by contacting the lead author. Analyses and code to produce graphics were conducted using both Ferret and Python, and are available on GitHub at https:// github. com/pearseb/PISCESiso_Ncycle_analysis and are citable https://doi. org/10. 5281/ zenodo. 5543510.