dataesr/scientific-paragraphs-categorization huggingface dataset:
diphtheriae genome assemblies that were fragmented into >400 contigs (Figure  S2 ). Overall, these results demonstrate high typeability across the phylogenetic breadth of C. diphtheriae and its two closely related species C. belfantii and C. rouxii. Based on the study dataset of 440 genomes, allele numbers ranged from 7 to 285 per locus, illustrating the high amounts of genetic diversity of the core genes within C. diphtheriae. As expected, the number of alleles per locus increased with locus size (Figure  S4 ). Locus-bylocus recombination analyses detected evidence of intra-gene recombination within a majority of loci (843; 64. 6%), whereas only 431 (33%) loci were deemed non recombinogenic. There were 31 (2. 4%) loci with too few polymorphic sites to be tested (Table  S2 ). The number of alleles per locus was higher for recombinogenic loci (Figure  S5 ), which reflects the contribution of homologous recombination to the generation of allelic diversity.
Definition of C. diphtheriae sublineages and their geographic distribution To guide the definition of C. diphtheriae sublineages, we performed an analysis of the mathematical properties of the clustering of cgMLST profiles at all possible thresholds (Silhouette analysis; Figure  S8 ). We observed optimal clustering indices on a large interval (~between 300 and 600 mismatches) with a maximum value at 554. As the Silhouette index was almost maximum at 500 cgMLST mismatches, for simplicity we chose this value to define C. diphtheriae sublineages (SL). Our dataset of 440 genomes comprised 114 sublineages, and a saturation curve analysis (Figure  2 ) indicates that future addition of C. diphtheriae genomes is expected to reveal the existence of a multitude of additional sublineages that remain to be sampled.
The additional dataset of 162 genomes uncovered 22 additional sublineages (Figure  S1 ). The average core genome nucleotide divergence among sublineages was 2. 4%. To create continuity in the numerical identifiers of sublineages with respect to MLST, we mapped the ST nomenclature on sublineages and incorporated the resulting sublineage tags into the BIGSdb C. diphtheriae genotype nomenclature database (Table  S3 ). Note that there was a strong concordance between MLST and cgMLST sublineages (Figure  S9 ) and that this correspondence was established up to ST750 (November 17 th 2020). All non-singleton cgMLST sublineages (66/66, 100%) were compatible with the phylogeny, whereas this was the case for only 53 of 66 (80. 3%) of 7-gene STs: 12 STs were polyphyletic and one was paraphyletic. The global distribution of C.
Frequently sampled sublineages of C. diphtheriae The most frequent sublineages in our dataset included 7-gene MLST genotypes ST5, ST8, ST76 and ST378; they were labeled as SL5, SL8, SL76 and SL378, respectively, by our inheritance script. These sublineages were previously reported from diphtheria outbreaks. SL378 comprised 19 tox-positive isolates (except one tox-negative) recovered during a 2015-1016 outbreak in the KwaZulu province of South Africa  [25]. Sublineage SL76 comprised isolates that were all tox-negative and belonged to ST76 or ST746 (a single-locus variant of ST76). Forty-five isolates were drawn from the Vancouver 2015-2018 outbreak of toxnegative C. diphtheriae  [21] , and five ST76 isolates were recovered from Belarus between 2004 and 2014 (Table  S1 ). Sublineage SL5 comprised 39 tox-negative genomes belonging to ST5 (all except 6), ST728, ST741 and 4 genomes for which the ST cannot be defined because they lack a leuA allele call (Figure  4A ).
Phylogenetic sister groups of the ST8 sublineage were ST123, ST451 and ST441, which comprised only tox-negative isolates. These data suggest a scenario of acquisition of the tox gene in the branch leading to the SL8 ancestor (before ~1967), and its subsequent loss in subbranch B (before ~1988). cgMLST variation within clusters or outbreak sets versus sporadic isolates and comparison with whole-genome single nucleotide polymorphisms. We aimed to assess the cgMLST variation within sets of isolates defined as epidemiologically related, i. e. , described as case clusters or outbreaks (hereafter, clusters) based on their provenance information. Fifteen such sets were collated, including four clusters identified by the French national surveillance system and sequenced for the purposes of this study (Table  1 ).
cgMLST cluster classification uncovers cryptic transmission and cryptic duplicated cultures We classified the 440 C. diphtheriae genomes into single linkage groups using a threshold of 25 cgMLST mismatches, corresponding to the maximum value observed so far for well described clusters. We define these groups as genetic clusters. This analysis enabled to detect 43 cryptic clusters, i. e. , isolates belonging to genetic clusters but for which we have no evidence of epidemiological links. The cryptic clusters were genetically more heterogeneous than the epidemiological clusters (Figure  5 ). One cryptic cluster corresponded in fact to three distinct genome assemblies of the reference strain Park-Williams 8 (generally named PW8), which were deposited separately in public sequence databases (Table  S1 ).
Acknowledgements We thank  Melody Dazas  for assistance in the early steps of the project and  Annick Carmi-Leroy ,  Annie Landier ,  Nathalie Armatys  and  Virginie Passet  for technical assistance with the microbiological characterization and sequencing of the Corynebacterium diphtheriae strains from the National Reference Center. We thank  Vincent Enouf  and the  P2M core facility of Institut Pasteur  for genomic sequencing. This work used the computational and storage services (TARS cluster) provided by the  IT department at the Institut Pasteur, Paris.
Although no association was observed with the cumulative distribution of ROH, individual ROH were associated with the risk of childhood acute lymphoblastic leukemia  [19]. Hodgkin lymphoma has been inconsistently associated with specific ROH and overall homozygosity  [20, 21]. To our knowledge, no studies have examined ROH in association with adult NHL. The goal of the present study was to investigate the association of homozygosity with the risk of four major NHL subtypes: chronic lymphocytic leukemia/small chronic lymphocytic leukemia (CLL), diffuse large Bcell lymphoma (DLBCL), follicular lymphoma (FL), and marginal zone lymphoma (MZL). Several measures of homozygosity were tested against NHL risk using data from eight GWAS.
METHODS We used data from eight previous GWAS of NHL  [6, 8, 10, 11]  composed of cases and controls of European ancestry [Supplementary Table  1  and 2 ]. The National Cancer Institute (NCI) NHL GWAS included cases with one of four common NHL subtypes and controls from 22 studies of NHL: 9 prospective cohort studies, 8 population-based case-control studies, and 5 hospital-or clinic-based case-control studies or case series. These 22 studies comprising the NCI NHL GWAS were genotyped using the Illumina OmniExpress or Omni2. 5 arrays and analyzed as a single study. The other seven GWAS were the University of California at San Francisco Molecular Epidemiology of Non-Hodgkin Lymphoma study (UCSF2)  [23]  , the University of California at San Francisco Molecular Epidemiology of Non-Hodgkin Lymphoma study (UCSF1) combined with controls from the Nurses' Health Study (NHS)  [24]  , the Scandinavian Lymphoma Etiology Study (SCALE)  [25]  , the Groupe d'Etude des Lymphomes de l'Adulte (GELA) study combined with controls from the European Prospective Investigation into Cancer, Chronic Diseases, Nutrition, and Lifestyles (EPIC), the Mayo Clinic Case-Control Study of Diffuse Large B-cell Lymphoma (Mayo), the Genetic Epidemiology of CLL Consortium (GEC), and the Utah Chronic Lymphocytic Leukemia Study (Utah).
Acknowledgements ATBC -The  ATBC Study  is supported by the  Intramural Research Program  of the  United States National Cancer Institute, National Institutes of Health, Department of Health and Human Services. BCCA -Canadian Institutes for Health Research (CIHR) ;  Canadian Cancer Society ;  Michael Smith Foundation for Health Research. CPS-II -The Cancer Prevention Study-II (CPS-II) Nutrition Cohort  is supported by the  American Cancer Society. Genotyping for all CPS-II samples were supported by the  Intramural Research Program  of the  National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The authors would also like to acknowledge the contribution to this study from central cancer registries supported through the  Centers for Disease Control and Prevention National Program of Cancer Registries , and cancer registries supported by the  National Cancer Institute Surveillance Epidemiology and End Results program.
6236 ) and  Navarra, ISCIII RETIC  (( RD06/0020 ) (Spain);  Swedish Cancer Society, Swedish Research Council and Country Councils of Skåne and Västerbotten (Sweden) ;  Cancer Research UK  [ 14136  to  K. T. Khaw ,  N. J. Wareham ;  C570/A16491  to  RCT  and  C8221/A19170  to  T. Key  ( EPIC-Oxford )],  Medical Research Council  [ 10001433  to  K. T. Khaw ,  N. J. Wareham ,  MR/M012190/1  to  T. Key  ( EPIC-Oxford )] ( United Kingdom). EpiLymph -European Commission  (grant references  QLK4-CT-2000-00422  and  FOOD-CT-2006-023103 ); We thank  CERCA programme/Generalitat de Catalunya  for institutional support. This work was supported by  Spanish Ministry of Economy and Competitiveness -Carlos III Institute of Health  cofunded by  FEDER funds/European Regional Develpment Fund (ERDF)  -a way to build Europe (grant references  CIBERESP ,  PI17/01280 ,  PI20/00288 ) with the support of the  Secretariat for Universities and Research of the Ministry of.
Availability of data and materials NCI NHL GWAS data used in this manuscript has been deposited in the dbGaP repository with accession code phs000801. v2. p1. Other relevant data used in this study are available upon request. Financial support and sponsorship This project was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH and in part by the Intramural Research Program of the NIH, Frederick National Lab, Center for Cancer Research. It has also been funded in whole or in part with federal funds from the Frederick National Laboratory for Cancer Research, under Contract No. HHSN261200800001E.
Experiments To study the movement initiation of millimetre particles with different size and materials, the experimental set up schematically presented in Figure  3  was designed. The selected rough surface is mounted on the platform of the mechanical wave oscillator which can vibrate it vertically with a sinusoidal motion with controlled amplitude and frequency. Two methods are employed to record the movement, by a FULL HD camera recording and by direct observation with a loupe. The camera is connected to a PC for data recording and later processing. The rough surfaces employed were fabricated by gluing glass beads of a given size on a flat circular plate using an EPOXI type adhesive. Two main different configurations were built. The multilayer configurations (6 cm plate diameter) were made with 250 m and 500 m glass beads, respectively, with corresponding size dispersions of 44 and 85 m.
Then, a new set of grains was distributed on the surface and a new run with a different amplitude value was started. The procedure was repeated five times for each surface and bead size. For a given run, the critical frequency,  c , was defined as the one at which half of the vertically vibrated grains had horizontally moved a displacement of the order of the grain radius. To determine  c , each film is analysed with an open source image processing program ImageJ. Although not critically important, humidity effects were present in most of the experiments. It was verified that inside a range of 30% to 50% of ambient humidity the reproducibility of the results was achieved. Figure  4  shows typical results for the 500 m multi-layered substrate and mobile grains of glass, (a), and stainless steel, (b), with several size ratios, i.
g. proportion of ingested amorphous material and Δ 15 N; Table  1 ) with Collembola life-forms. By contrast, fungi and bacteria are ubiquitous in soil and parameters indicating fungivory and bacterivory, respectively, will little correlate with life-form. To test our hypotheses, we compiled available published and unpublished data on trophic parameters of Collembola derived from the four above-mentioned methods in a single dataset, and conducted meta-analyses using species names to connect different studies.
| MATERIAL S AND ME THODS We compiled trophic data on Collembola from field studies that used visual gut content, digestive enzyme, FA and stable isotope analyses. Data were collected from the personal libraries of the authors and complemented with searching for published literature in the Web of Science using the keywords 'Collembola' and specific terms of the instrumental methods. A complete list of studies is found in Supplementary materials. Most of the published studies applied only one method; only two used a combination of two methods  (Ferlian et al. , 2015; Haubert et al. , 2009). For each study, we averaged individual measurements by species and ecosystem for 15 trophic niche parameters derived from the four methods (Table  1 ).
| Visual gut content Primary screening of literature yielded 31 studies that reported data on the gut content of Collembola. We selected those that provided quantitative estimates from natural environments, mostly temperate forests and grasslands. We built on the most commonly reported categories and defined gut content parameters as (a) particles of fungal origin, including hyphae and spores, (b) particles of plant origin, mostly coarse plant detritus (excluding pollen and algae) and (c) amorphous material of unknown nature (i. e. fine detritus such as soil organic matter). The final dataset for the three gut content parameters included 77 records on 56 species from 15 studies (Table  S1 ). Raw data were expressed as proportion of a certain type of particles among the total particles ingested.
| Digestive enzymes To our knowledge, cellulase, trehalase and chitinase activities in Collembola were reported only in three studies  (Berg et al. , 2004; Parimuchová et al. , 2018; Urbášek & Rusek, 1994). Despite using the same conceptual method, these studies used different chemical protocols and ways of glucose detection, which resulted in evident differences in absolute mean values of substrate production per unit of animal body mass. Thus, we excluded the study of  Urbášek and Rusek (1994). The final dataset included 45 records on 27 species (Table  S2 ). Raw data of digestive enzyme activity were expressed as mg of reaction products per g of animal mass per hour. as relative fungal biomarker, 18:1ω9 (in addition 21:0, 22:0, 23:0, 24:0) as relative plant biomarkers and several gram-positive bacterial biomarkers (including i15:0, a15:0, i16:0, i17:0, a17:0) and gramnegative bacterial biomarkers (including 2-OH 10:0, 2-OH 12:0, 3-OH 12:0, 2-OH 14:0, 3-OH 14:0, 2-OH 16:0, cy17:0, cy19:0).
If reported, we also used 16:1ω5 as the biomarker related to feeding on arbuscular-mycorrhizal fungi  (Ngosong et al. , 2012)  and the sum of 20:1ω9, 22:6ω3, 22:2ω6, 22:1ω9 and 24:1 as FAs in metazoan animals (e. g. nematodes;  Chamberlain & Black, 2005; Chamberlain et al. , 2005; Chen et al. , 2001; Tanaka et al. , 1996). Raw data were compiled as proportions of the total neutral lipid FAs of the organism. Individual biomarker FAs were summed up to generate seven conventional FA parameters (Table  1 ). The final dataset included 130 records on 47 species from 13 studies (Table  S3 ).
| Stable isotopes The dataset was based on the previous compilation of Potapov et al. (  2016 ) and complemented with data on grassland and forest communities, including recent publications. For each study, isotopic baseline (i. e. plant litter) was used to calculate littercalibrated Δ 13 C and Δ 15 N values of Collembola  (Potapov, Tiunov, & Scheu, 2019). The final dataset for the two stable isotope parameters included 378 records on 96 species from 10 studies (Table  S4 ).
| Data analysis For the 15 trophic niche parameters derived from the four methods (Table  1 ) we in total included 125 species, each analysed with at least one method (Tables ; Dryad Digital Repository, https://doi. org/10. 5061/dryad. gtht7 6hm6). All calculations were based on these datasets and conducted in R 3. 5. 3 (R Core Team, 2019). Data on digestive enzyme activity were added with 0. 02 μg product mg -1 hr -1 (the minimum positive value observed) for all values and then log 10 -transformed. We normalised data of chitinase activity separately for  Berg et al. (2004)  and  Parimuchová et al. (2018)  by subtracting mean and dividing by standard deviation to account for the differences in chemical protocols. Proportional data (gut content and FA) were logit-transformed, with 0 and 1 proportions adjusted to 0.
025 and 0. 975, respectively, using the logit function in the car package. Focusing on interspecific variability across trophic parameters, we conducted species-based analyses using the species names following GBIF to link potential taxonomic synonyms (https://www. gbif. org/tools/ speci es-lookup). For each trophic parameter and each species, we averaged data across ecosystems and studies. To have the same data representation across all 15 parameters, each parameter was scaled between 0 (lowest observed value of the parameter) and 1 (highest observed value of the parameter). All the following analyses and results were based on the scaled data. We performed three analyses: First, we tested pairwise correlations among all trophic niche parameters. Spearman rank correlation was applied using the cor function.
The number of points for each correlation varied among parameters according to the number of shared species of the paired parameters (Table  S5 ). Correlation tests were conducted for those with a minimum of six data points. Second, we visualised interspecific differentiation in multidimensional trophic niches using principal component analysis (PCA) with the prcomp function. We selected species to maximise the number of common trophic parameters available. Only two species had data across all 15 parameters. Thus, we excluded parameters which were recorded only for few species, and finally chose six species that had data across seven parameters, with a focus on gut content and fatty acid parameters. Third, we tested the effect of taxonomic affiliation (as the proxy of phylogenetic group) and life-form (as the proxy of microhabitat preference) on each of the 15 trophic niche parameters using linear models (the lm function).
In addition, they have a high proportion of non-specific bacteria FAs, suggesting that they may graze on freshly fallen litter and assimilate it with the help of bacterial symbionts. Clarification of their trophic niche could be advanced with enzyme analysis. The only species Tomocerus minor in our database has the highest cellulase activity among all records, confirming its grazing on fresh plant litter. Entomobryidae include many species that morphologically resemble Tomoceridae; however, these two families differed in a number of trophic niche parameters. Entomobryidae have more fungi in the gut, remarkably high trehalase activity, but a lower proportion of non-specific bacteria FAs. Overall, these differences suggest that Entomobryidae are likely browsers, rather than grazers. Lepidocyrtus lignorum is a good illustration-this species prefers fungi (Figure  2 ), having high trehalase but low cellulase and limited chitinase activity (Table  S2 ).
This study was funded by the Russian Science Foundation (project 19-74-00154) to A. M. P. Research visit of T. -W. C. was supported by the Czech Academy of Sciences with the MSM project for research and mobility of starting researchers (MSM200962001). M. M. P. was supported by the German Research Foundation (DFG, MA 7145/1-1). Joint discussions among T. -W. C. , V. S. , M. M. P. and A. M. P. were supported within the CAS-DAAD mobility project.
The aim of this work is to calculate for the first time the spectral index of the large-scale diffuse radio emission. Combining LO-FAR LBA and HBA data we could create a spectral index map of the whole diffuse emission in this cluster. To understand the origin of the diffuse radio emission inside and outside the cluster core, we also analysed archival radio and X-ray data, looking for a possible curvature in the radio emission and a spatial correlation between radio and X-ray surface brightness. A composite image of the source, created with our new LOFAR LBA data, is shown in Fig. 1. The paper is organized as follows: in Section 2 we describe the data used in our analysis and their reduction. The results of the radio data are presented in Section 3. A comparison between radio and X-ray data is reported in Section 4.
DATA REDUCTION In this paper we present new LOFAR LBA (54 MHz) observations of the galaxy cluster RXJ1720. 1. These observations are complemented by LOFAR HBA (144 MHz), GMRT (610 MHz) and VLA L-band (1480 MHz) and C-band (4860 MHz) observations. In the following sections, we describe the data reduction procedures of LOFAR LBA and HBA and VLA observations. For GMRT data reduction instead we refer to  Savini et al. (2019). All radio observations used in the paper are summarized in Table  1.
LOFAR LBA observations The galaxy cluster RXJ1720. 1 was observed with LOFAR LBA on 2019 September 26 and November 20, for a total observing time of 10 h. The observations were performed in the frequency range 30 -78 MHz in a dual-beam mode, with one beam continuously pointing at the calibrator (3C295) and one beam at the target. We used the LBA_OUTER antenna mode, where the outer 48 dipoles of the station are used to minimize the coupling between dipoles, using 24 Core Stations and 14 Remote Stations. The data were taken at 1s integration time and at a frequency resolution of 64 channels per 0. 192 MHz sub band and subsequently averaged to 2 s and 16 ch/sb. The calibrator data were reduced following  de Gasperin et al. (2019) , to isolate the systematic effects of the polarisation alignment, the bandpass and the clock drifts.
A spatial correlation has been observed in several clusters. In the case of giant radio haloes a sub-linear or linear scaling is generally found (e. g. Govoni et al. 2001; Botteon et al. 2020; Rajpurohit et al. 2021) , whereas mini haloes present a super-linear scaling between radio and X-rays  (Govoni et al. 2009; Ignesti et al. 2020) , suggesting an intrinsic difference in the nature of these radio sources. We performed a point-to-point analysis of the emission in our source, considering its radio components separately. For each component, we constructed a square grid with cell sizes equal to the radio image FWHM and computed the mean surface brightness inside each cell. To determine the best-fitting parameters of the I R -I X relation, we used the Linmix 2  package (Kelly 2007). Linmix performs a Bayesian linear regression taking into account measurement uncertainties on both variables, intrinsic scatter, and upper limits.