dataesr/scientific-paragraphs-categorization huggingface dataset:
Concordance Between the Typing Methods This analysis was performed on the 93 strains selected in the panel (Methods 3. 2 and Supplementary Data S2). The cgMLST INNUENDO, the cgMLST Oxford, and the wgMLST INNUENDO had an AWC of 1. 000 to the cgMLST SeqSphere+ schema, meaning that all strains clustering together using one of these three typing schemes are also classified together with the cgMLST SeqSphere+. The cgMLST Oxford had an AWC of 0. 948 with the cgMLST INNUENDO and, conversely, the cgMLST INNUENDO had an AWC of 0. 956 with cgMLST Oxford; 95% of the strains are clustered similarly using either cgMLSTs Oxford and INNUENDO. The majority (93. 7% and 94. 5%) of the strains that clustered with the cgMLST SeqSphere+ schema were also grouped by the cgMLST INNUENDO and the cgMLST Oxford, respectively.
In 2017, Cody et al. applied a maximum of 150 contigs covering at least 95% of cgMLST targets  (Cody et al. , 2017) , whereas the INNUENDO pipeline included a QC step requiring an assembled depth of coverage of 30x associated with at least 98% of scheme targets found in the cgMLST analyses  (Llarena et al. , 2018). The Draft Standard of International Standardization Organization (ISO/DIS 23418) suggests a depth of coverage of at least 20x for Illumina short-read raw data and 95% of the read lengths should be over 120 bp (International Organization for Standardization) depending on the application. In our analyses, we implemented stringent criteria for quality filtering to ensure robustness and minimizing potential biases related to missing targets generated by poor quality sequencing data.
, 2018). The cgMLST Oxford was built from loci occurring in 95% of the Campylobacter sp. reference panel (N = 2,472) to take into consideration variation in sequence quality and applied algorithms  (Cody et al. , 2017). They proposed a more relaxed core genome definition as some isolates may contain mutations, leading to the reduction of the core genome size as more isolates are selected, and that analyses conducted on incomplete draft genomes might constitute a source of missing data  (Cody et al. , 2017). The finalized cgMLST Oxford scheme represents thus 82% of the reference genome NCTC11168, which places this typing scheme as an intermediate between a core genome and a whole genome MLST scheme with a total of 1,343 loci vs. 637/678 for the two others. A sample-set independent approach was recently proposed to select a conserved-sequence genome as a novel core genome methodology to address this issue  (Van Aggelen et al.
, 2019). Further, the locus definition is different in the various schemes as well as allele calling algorithms. In the so-called gene-by-gene approach, a locus does not necessarily correspond to the complete coding sequence of a gene but can constitute a specific region. Thus, each schema includes target sequences of varying length ranging from 100 bp to several kb. Surprisingly, the sizes distributions of the targets in the three cgMLSTs tested are very similar with approximately: 21% below 500 bp, 40% ranging from 500 to 1,000 bp, 26% ranging between 1,000 and 1,500 bp, and 13% above 1,500 bp. Except for the SeqSphere+ commercial platform, the design of allele-calling pipelines from the two others WGS-based schemes were published  (Jolley et al. , 2018; Silva et al. , 2018). Both define alleles from sequence assemblies but perform a search by using nucleotide or translated sequences with BLASTN or BLASTP queries and by using "exemplar alleles" as reference or all alleles already recorded in the database.
jejuni genomes analyzed. Their results lead to the conclusion that the hierarchical approach is better suited to examine isolates epidemiologically related, while pairwise comparisons are preferable for the identification of outbreaks without initial suspicion  (Cody et al. , 2013). We assessed genomic clusters in our WGS data with goeBURST and we found that defined low cut-off values ranging from 6 to 11 AD and from 5 to 9 AD were appropriated to classify profiles generated with cgMLST Oxford and wgMLST INNUENDO schemes, respectively. By utilizing our newly established thresholds, the classification was consistent with the ones created by cgMLST methods that use a predefined threshold like SeqSphere+ (AD = 13 of 637 targets) and cgMLST INNUENDO (AD = 4 of 678 targets). Overall, a high concordance in clustering strains was observed between the three cgMLST typing schemes, although congruence is higher between the cgMLSTs Oxford and INNUENDO schemes (predictive of each other in 95% of the cases) compared to the SeqSphere+ scheme.
This was not expected, at first glance, as cgMLST schemes from SeqSphere+ and INNUENDO have a close number of targets (637 vs. 678 targets, respectively) and share 68% of loci. The concordance between the cgMLST schemes Oxford and INNUENDO, both defined from a large collection of strains, suggests a more representative and stably defined core genome. It is noteworthy that in this study, the added value of the number of loci in the cgMLST Oxford cannot be truly attributed on its discriminative power as the datasets contain several clonal population. A largest test population, reflecting the genetic diversity within the C. jejuni species, would have been more appropriate for evaluating the resolution of the different typing schemes. As expected, cgMLST profiles could not be mapped with confidence to the wgMLST INNUENDO profiles including a significant larger number of targets.
, 2010; Perez-Boto et al. , 2012; Rauber-Würfel et al. , 2019; Jaakkonen et al. , 2020). The lineage B (ST2254-gyrA9-porA1) arose unexpectedly from our national surveillance with an epidemic curve between March and April 2014 (>70 campylobacteriosis cases). Interestingly, after this episode, clinical isolates of this lineage were still collected but at a much lower frequency during the following four years. To put things into context, this particular ST was singular in 2014 and by querying the pubmlst. org database  (Jolley et al. , 2018) ; only a dozen strains had been recorded at that time including two from poultry origin. Interestingly, the same "clone" was finally isolated in the framework of the official controls conducted by the state veterinary laboratory in Luxembourg and supported chicken as a possible source of this outbreak.
ACKNOWLEDGMENTS The authors express their gratitude to  Dr. Mirko Rossi  ( European Food Safety Authority, Parma, Italy ) for his help in the INNUENDO analyses and for his advices during manuscript preparation. The authors would like to thank  Univ. -Prof. Dr. med. Dag Harmsen  ( Hygiene Institute, Münster University Hospital, Münster, Germany ) for sharing details of the cgMLST SeqSphere+ scheme. The authors are thankful to  Fatù Djabi  and  Jessica Tapp  ( Microbiology Department, National Health Laboratory, Dudelange, Luxembourg ) for their help during the molecular analyses.
Experimental design In order to refine machine learning and to define quality criteria for reliable classification, a discovery cohort (N = 56) was compiled, containing 15 prospective cases and 41 retrospective cases from previously published datasets  [2, 15]. The local ethics committee (Charité -Universitätsmedizin Berlin, Berlin, Germany; EA2/041/18) approved generation of prospective data in the context of this study. Robustness of classification was then assessed in an independent validation cohort (N = 111) generated during routine clinical application at the Division of Neuropathology, Institute of Pathology, Basel, Switzerland (as an in-house validated diagnostic test) (Suppl. Fig. 1 ). All cases underwent routine diagnostic procedures, including microarray-based analysis  [1]  in 109/111 cases, and were classified in accordance with the 2016 World Health Organization Classification of Tumors of the Central Nervous System  [10]  (Table  1 ).
Nanopore whole-genome sequencing Library preparation with barcode labelling was performed with ~400ng input of genomic DNA using the Rapid Barcoding Kit (RBK004, Oxford Nanopore Technologies, UK) according to manufacturer's instructions. During library preparation, input DNA is fragmented while simultaneously attaching barcodes using a time-efficient transposase-based approach. The final library was loaded onto a R9. 4. 1 flow cell (FLO-MIN106D, Oxford Nanopore Technologies, UK; alternatively, FLG-0001 cells sharing the architecture with FLO-MIN106D, were used) and whole-genome sequencing was performed for 6 to 24 hours on a MinION Mk 1B device (Oxford Nanopore Technologies, UK). FAST5 files containing the raw data were obtained in real-time using the manufacturer's software MinKNOW (v. 1.
Sequencing data processing Base calling of raw data was performed using the manufacturer's proprietary software (guppy v3. 1. 5, CPU-based, fast mode, or v3. 4. 3, GPU-based, Oxford Nanopore Technologies, UK). Reads were then aligned to the hg19 human reference genome using minimap2 v. 2. 15  [9]. Copy number profiles were generated using R/Bioconductor and the QDNAseq package v. 1. 20 [14]  using data from NA12878 reference DNA  [5]  for pseudo-germline subtraction. Methylation status of CpG sites (5mC) was called using nanopolish v0. 11. 1  [16]. All workflows were implemented using snakemake v5. 4. 0  [8]  for parallelization and deployment to a HPC cluster. When not using HPC, the pipeline was deployed to a single x86_64 workstation with 120 cores and 2TB RAM, augmented with an RTX2070 consumer-grade GPU (NVIDIA, Santa Clara, CA, USA) for base calling, running a Linux operating system (Ubuntu 18.
Random forest classification 5mC signals at sites overlapping with sites probed by the Illumina BeadChip 450K array were then used to train a random forest classifier using the Heidelberg reference cohort of brain tumor methylation profiles  [1]. Beta-values from the training set and sample set were binarized using 0. 6 as a threshold value. When more than 50,000 features, the 50,000 most variable features were selected by standard deviation. Random forest classification was implemented in R using the ranger package v. 0. 12. 1  [17]. The following parameters were modified from default: ntree = 20,000. Stratified sampling (to match the smallest class size, i. e. , eight) was used to account for class imbalance. While random forests (RF) have been shown to be effective for many classification tasks, they typically report poor estimates of class probabilities.
Prospective cases and cross-laboratory testing As a pilot phase for a planned multicentric clinical trial (Universal Trial Number: U1111-1239-3456), a small series of prospective cases (N = 15) were evaluated regarding turnaround time and concordance to results from routine diagnostic workup. Prospective samples were obtained from the local neurosurgery department after written informed consent was given by the patient. Pseudonymized study data (start of surgery, time point of tissue receipt, DNA extraction, library preparation, sequencing metrics, classification results) was collected and managed using REDCap software  [4] , which was provided by the Berlin Institute of Health's Clinical Research Unit in a certified computing environment.
Statistics Data analysis was performed using R v. 4. 0. 2. Receiver operator characteristics (ROC) were analyzed with the ROCit v. 2. 1. 1 package. Figures were mainly visualized using ggplot2 v. 3. 3. 2 and ComplexHeatmap v. 2. 6. 2 packages  [3]. in this manuscript will also be available at https://gitlab. com/pesk/nanoBenchmark. Raw sequencing data from the discovery cohort will be deposited at the European Genome-phenome archive (accession tba). Methylation microarray raw data and methylation calls from the validation cohort will be deposited at ArrayExpress (accession tba).
Cross-laboratory validation Next, to assess reproducibility across laboratories, a series of N=13 tumors were profiled independently by nanopore whole-genome sequencing at two sites using the same DNA sample. Mean correlation of methylation calls of individual CpG sites over all 13 samples was 0. 55 (SD = 0. 046) and matched pairs always showed the best correlation across the series (Fig. 2a ). Classification of matched samples was identical in 13/13 cases (Suppl. Table  2 ) with very similar raw vote distributions (Figure  2b ). In addition, DNA methylation microarray (Illumina Infinium BeadArray 850K chip) data were available for 11/13 samples. Concordant results across all three tests were obtained in 10/11 cases. In the one mismatch case, the nanopore-based classification at both sites was concordant (Meningioma) but did not match the array-based classification (CNS Ewing sarcoma family tumor with CIC alteration).
Acknowledgements We thank  Aydah Sabah  for expert technical assistance. Computation has been performed on the  HPC for Research cluster of the Berlin Institute of Health. N. B. and P. E. are participants in the  BIH-Charité Clinical Scientist Program  funded by the  Charité -Universitätsmedizin Berlin  and  BIH. The project is funded by the  The Brain Tumour Charity, UK.
Emissions of aerosols and their precursors were obtained from CMIP phase 6 (CMIP6) emission datasets  (Hoesly et al. , 2018; van Marle et al. , 2017). We ran the coarse resolution EAM configuration (i. e. , ne30np4, which corresponds to approximately 1-degree horizontal grid spacing) with 1) Present-day (Year 2000 here) forcing; 2) Pre-industrial (Year 1850 here) forcing; 3) Present-day forcing, except for pre-industrial aerosol emissions; 4) Pre-industrial forcing with SST elevated by 4 K uniformly; 5) Present-day forcing with SST, sea ice, and solar constant set to pre-industrial conditions. We compute the effective radiative forcing (ERF) from these prescribed-SST-and-sea-ice experiments  (Hansen et al. , 2005). Forster et al. (  2016 ) compared different methodologies for computing the ERF and recommend the prescribed-SSTand-sea-ice method.
Clouds )) shows similar results. Figure  5  shows cloud bias reductions in the Northern Hemisphere high latitudes, the stratocumulus regions, TWP, and over tropical lands. These improvements match our expectation, increasing our confidence in the model clouds. However, there are some differences between the comparisons in Figure  4  and 5. In Figure  4 , EAMv1    (Cesana et al. , 2019 ) and results will be discussed further in Section 3. 5. Finally, we find that the inverse relative variance of cloud water, which affects the enhancement factors of autoconversion, accretion, and immersion freezing  (Morrison and Gettelman, 2008) , are not sensitive to the parameter changes. Thus, there is a limited impact on these three processes from changes in subgrid in-cloud water variance. Next, we compare the estimated inversion strength (EIS)  (Wood and Bretherton, 2006)  between model simulations and a reanalysis dataset.
Acknowledgements. The model tuning was supported as part of the  Energy Exascale Earth System Model  ( E3SM ) project [https://dx. doi. org/10. 11578/E3SM/dc. 20180418. 36 ],funded by the  U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research ,  Earth System Model Development (ESMD) program  area. The analyses of effective radiative forcing and aerosol-cloud interactions were supported by the  Enabling Aerosol-cloud interactions at GLobal convection-permitting scalES (EAGLES)  project, funded by the  U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research ,  Earth System Model Development program  area. The development and evaluation of gustiness effects over land were supported as part of the Integrated Cloud,  Land-Surface , and  Aerosol System Study (ICLASS).
climate. copernicus. eu/cdsapp#!/home]. The CERES-EBAF cloud radiative effect data and the GPCP precipitation climatology data are obtained from the AMWG diagnostics package, available at [http://www. cesm. ucar. edu/working_groups/Atmosphere/amwg-diagnostics-package/]. The MODIS data is obtained from NASA GIOVANNI at [https://giovanni. gsfc. nasa. gov/giovanni/]. The MERRA-2 dataset is obtained from NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) at [https://disc. gsfc. nasa. gov/]. The GOCCP data is obtained from [https://climserv. ipsl. polytechnique. fr/cfmip-obs/]. The E3SM diagnostics package used in this study was archived at [https://doi. org/10. 5281/zenodo. 5555094].
SMF of the mouse genome Quantification of genomic binding events by SMF requires the sequencing of a large number of DNA molecules encompassing the binding regions of the factor of interest. Generation of highcoverage SMF datasets is challenging in mammalian genomes, which are a factor of 20 bigger than Drosophila genomes. To overcome this bottleneck, we took advantage of the fact that TF binding is mostly restricted to CREs, which represent only a small fraction of the genome  (Stamatoyannopoulos et al. , 2012). We employed DNA capture to enrich for CREs prior to sequencing (Figure  1A ). We used a library of RNA baits tiling 297,000 regions ($2% of the genome), covering a large fraction of CREs accessible in mouse embryonic stem cells (mESCs) (59. 7% of open regions as detected by DNase sequencing [DNase-seq]).
, 2015). This line enables the use of GC, as well as CG methyl-transferases, for footprinting, significantly increasing the spatial resolution (up to 7 bp). However, such analysis is limited to the stem cell state  (Sakaue et al. , 2010). We generated high-coverage SMF datasets in wild-type (WT) and DNMT TKO mESCs with highly reproducible methylation footprints between biological replicates (R > 0. 90; Figures  S1A  and S1B ). Moreover, footprinting levels were in close agreement between WT and DNMT TKO cells (R = 0. 90; Figure  S1C ), consistent with previous observations  (Domcke et al. , 2015). The high capture efficiency (>70% of reads were within bait regions) achieved coverage levels that allowed data interpretation at the single-molecule resolution for 78,807 CREs in mESCs. Detection of TF binding using SMF When inspecting SMF signal at CREs, we observed discrete footprints (<25 bp) around TF motifs (as exemplified in the Elp6 promoter; Figure  1B ).
These motifs are recognized by TFs that have ChIP-sequencing (ChIP-seq) enrichments at respective regions (Figure  1B ), suggesting that TF binding results in footprints detectable by SMF, consistent with previous observations  (Abdulhay et al. , 2020; Gal-Yam et al. , 2006; Levo et al. , 2017; Shipony et al. , 2020; Stergachis et al. , 2020). This prompted us to assess whether footprints at TF motifs can be found genomewide and ask whether their presence is consistent with orthogonal measures of TF binding. We used published TF ChIP-seq datasets (Table  S1 ) to identify the subset of motifs that are bound by their TF genome-wide. We plotted the SMF signal around these motifs and found that some TFs, such as the transcriptional repressor RE1-Silencing Transcription factor (REST), create short footprints over their bound sites (Figure  1C ), in contrast with unbound motifs (Figure  S1F ).
The coupling between the states suggests a possible competition between NO and REST at its binding sites. To further test this possibility, we investigated how binding states redistribute upon perturbation of REST expression levels. During the early stages of neuronal differentiation, REST expression levels are reduced by $4-fold (Figure  S2F ) without significant redistribution of REST target sites  (Arnold et al. , 2013b). We generated a SMF dataset in in vitro-derived neuronal progenitors (NPs)  (Bibel et al. , 2004)  (Figures  S1D  and S1E ). Upon reduction of REST expression, we observed a global decrease of $4-fold in REST occupancy at its motifs (Figure  2E ), indicating that REST abundance correlates with its binding frequency in the cell population. We also observed a concomitant increase in NO at REST sites (Figure  2E ), whereas there was very little effect on CTCF binding frequencies at CTCF binding regions in NPs (Figures  S2G-S2I ).
Quantification of the molecular co-occupancy of TFs Having established the ability of SMF to quantify the binding of TFs, we developed a strategy to quantify their degree of molecular co-occupancy (see STAR Methods; Figure  S4A ). Here, we used the dual-enzyme footprinting dataset generated in DNMT TKO cells, which enables quantification of approximately five times more binding events than in WT cells (Figure  S4B ). We did not observe major differences in TF binding frequencies when comparing DNMT TKO cells with WT cells (R = 0. 94; Figure  S4C ). In our analysis, we distinguished co-occupancy at dimeric motifs (Figure  4A ) from TFs occupying distinct motifs lying in the vicinity of each other (15-140 bp; Figure  4B ). When applying this strategy to the TFs creating quantifiable footprints by SMF (Figure  3A ), we obtained reproducible, high-confidence co-occupancy measurements for 1,238 TF dimers and 381 TF pairs (Figures  S4B  and S4C ).
Together, these data provide evidence that NRF1 binds as an obligatory homodimer in vivo and suggests that binding dependency between neighboring NRF1 binding sites varies substantially from one CRE to another. SMF is performed on permeabilized nuclei, in the absence of protein-DNA cross-linking. The residence times for TFs in living cells range from a few seconds to a couple of minutes, which are much shorter than the time nuclei are incubated with methyl-transferases in vitro. Thus, it is possible that the co-occupancy patterns detected by SMF could reflect TF retention on chromatin in vitro, rather than binding dependencies between TFs occurring in vivo. To address this question, we developed an independent SMF protocol where we fixed protein-DNA interactions in vivo using formaldehyde prior to methylation footprinting (X-link SMF; see STAR Methods).
DISCUSSION Here, we demonstrate that SMF can quantify the binding of multiple TFs and nucleosomes at mouse CREs. We use this property to systematically quantify the degree of co-occupancy of neighboring TFs. We find widespread evidence of co-occupancy for most of the tested TFs and demonstrate that high co-occupancy identifies loci with binding cooperativity between TFs. Our data suggest that TF co-occupancy is an important component of the mechanism used by TFs to outcompete nucleosomes in order to access their binding sites. We detected quantifiable footprints for more than half of the TFs we tested in mESCs. Differences of in vivo footprint patterns between TFs has been previously reported for other DNA footprinting technologies, such as Assay for transposase-accessible chromatin (ATAC-seq) or DNase-seq  (Karabacak Calviello et al.
CviPI and 128pmol of SAM before a second incubation round of 37 C for 7. 5 minutes. For ES samples, reactions were stopped at this point by adding a SDS containing buffer (20mM Tris, 600mM NaCl, 1%SDS 10mM EDTA). For DNMT TKO samples, where dual enzyme footprinting was applied, 10mM MgCl2, 128pmol of SAM and 60U of M. SssI (NEB-M0226L) were added for a third incubation round of 37 C for 7. 5 minutes followed by stoppage of the reaction by adding the same SDS containing buffer. For all samples, material was digested with Proteinase K (200 mg/ml) overnight at 55 C, followed by phenol/chloroform purification of DNA, which was resuspended in water and treated with RNase A at for 1 hour 60 C and quantified using Qubit 1x dsDNA High Sensitivity kit. Bait-capture of footprinted DNA Genome-wide data were obtained using Agilent SureSelectXT Mouse Methyl-Seq Kit.