dataesr/scientific-paragraphs-categorization huggingface dataset:
DLBCL patients: Training & Validation cohorts DLBCL tumors were required to be de novo and treated with Rituximab-CHOP (R-CHOP). All cases were diagnosed and reviewed by experienced hematopathologists and patient clinical information were obtained from the Hopital Saint Louis, Centre Hospitalier Princesse Grace and Centre Antoine Lacassagne institutions. DLBCLs were classified according to the WHO classification. Clinical data including gender, age, and age-adjusted International Prognosis Index (IPIaa) (scored (0-1, 2 and 3) based on i) the Ann Arbor stage (II-II vs III-IV), ii) the level of lactate dehydrogenase in the serum (LDH ≤ normal vs ≥ normal) and iii) the Performance status (PS 0-1 vs 2-4)) were collected in all cases and are summarized in Table  S2. These cases were anonymized and processed in accordance with a protocol approved by scientific committee of the LYSA (The Lymphoma Study Association).
Western blot analysis Cells were washed in PBS and lysed in laemmli buffer. Tissue samples were collected and lysed in Precellys 24 (Bertin Instruments) homogenizer (3x30 s, 6500g) in laemmli buffer. After quantification sheath gas flow was set to 20 units, the auxiliary gas flow was set to 5 units, and the sweep gas flow was set to 0 unit. The metabolites were detected across a mass range of 75-1,000 m/z at a resolution of 35,000 (at 200 m/z) with the AGC target at 106, and the maximum injection time at 250 ms. Lock masses were used to insure mass accuracy below 5 ppm. Data were acquired with Thermo Xcalibur software (Thermo Fisher Scientific). The peak area of metabolites was determined using Thermo TraceFinder software (Thermo Fisher Scientific), identified by the exact mass of each singly charged ion and by known retention time on the HPLC column.
For each spectrum, 256 free induction decays of 20 800 data points, were collected using 8000 Hz of spectral window. The FIDs were multiplied by an exponential weighting function corresponding to a line broadening of 0. 3 Hz and zero-filled prior to Fourier transformation. The calibration on the alanine doublet (δ=1. 47 ppm) and the phase and baseline corrections were performed manually for all the spectra. The identification of the NMR signals was carried out using 1 H-1 H TOCSY, 1 H-13 C HSQC NMR experiments, online databases (HMDB, BRMB) and literature  (Nicholson et al. , 1995; Wishart et al. , 2013). Each spectrum was divided into 0. 005 ppm-width integrated buckets and normalized to total spectrum intensity after having removed the presaturated water signal region (4. 85 -5. 10 ppm) using AMIX software (Bruker Biospin GmbH, Karlsruhe, Germany).
The water region must be removed due to inconsistent signal suppression. A matrix of 42 samples and 1700 buckets was then created and exported to the software Simca-P v13 (Umetrics, Umea, Sweden) for statistical analysis. Principal component analysis was first performed in order to detect intrinsic clusters and outliers within the dataset. Since expected discrimination was not achieved using PCA, the data were analyzed with orthogonalized projections to latent structures discriminant analysis (OPLS-DA). The OPLS algorithm derives from basic partial least-squares (PLS) • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Pvalue<0.
Acknowledgements We gratefully acknowledge the C3M animal facility, the  Tumorothèque of Hôpital Saint-Louis  and the  Lymphoma Study Association (LYSA). We thank the technician of the  Laboratoire Central d'AnatomocytoPathologie (LCAP) ,  CHU de Nice ,  S. Destrée  and  A. Borderie. We thank I. Nemazanyy from the  Platform for Metabolic Analyses (Neckert-Enfants Malades Institute, Paris). We thank  A. Vandenberghe ,  N. Alet ,  N. Jourdan-Desrayaud ,  F. Larbret ,  F. Reinier ,  N. Nottet ,  M. Baia ,  N. Vailhen ,  S. Roulland ,  M. Zala , and  I. Ben-Sahra  for reagents and input. This work has been supported by the  Fondation ARC pour la Recherche sur le Cancer  ( PGA1 RC-20170205463 ), the  Agence Nationale de la Recherche  ( LABEX SIGNALIFE ANR-11-LABX-0028-01 ), the  Fondation de France ,  Centre Scientifique de Monaco , the  Cancéropôle PACA  and INSERM transfert.
This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  766214  ( Meta-Can ). C. T. is funded by the  Nella and Amadeus Barletta foundation (FNAB). BN is funded by  Cancéropole PACA, la Fondation ARC  ( PGA120150202381 ) and Plan cancer 2015 ITMO cancer ( C15077AS ). The IPMC functional genomics platform is a member of the " France Génomique "  consortium  ( ANR-10-Infra-01 ), with financial support from the  Cancéropôle PACA. E. Vil. is supported by  La Ligue contre le Cancer ; J. C. is supported by foundation Tourre and  France Lymphoma Espoir , G. B is supported by la  Fondation pour la Recherche Médicale  (FRM,  FDM20150734500 ) and  J R-M. is supported by  INCa.
Results Assay Design. The analysis of multiple-sequence alignments of CaPV genomes, including a SPPV vaccine from Morocco, enabled the identification of a region with a 21-nucleotide and 27-nucleotide deletion, unique to the SPPV vaccine. This region, within the CaPV homolog of the variola virus B22R gene, also harbored specific nucleotide differences between SPPV field isolates, GTPV and LSDV (Fig. 1 ). To investigate the uniqueness of these deletions to SPPV vaccines, we sequenced and analyzed the corresponding region in SPPV vaccines from Algeria, Senegal and the Pan African Veterinary Vaccine Center of the African Union Commission (AU-PANVAC). The nucleotide sequences generated in this study have been deposited in GenBank under accession numbers MK005931 to MK005967. The comparative analysis of the nucleotide sequences showed the presence of these two deletions in only the SPPV vaccines, as compared to SPPV field isolates, GTPVs and LSDVs (Supplementary Fig.
S1 ). The sequencing results also confirmed the presence of species-specific nucleotide differences between the SPPV field isolates, GTPV and LSDV. We identified 6 single-nucleotide differences when comparing SPPV field isolates to LSDVs: C/G, T/C, C/T, G/A, A/T, G/A, of which only four nucleotide changes (C/G, G/A, A/T, G/A) can influence the melting temperature. Comparing GTPVs with LSDVs, 7 nucleotide differences were found: G/A, T/A, A/G, C/G, G/A, A/G, G/T, including three aggregate changes that could be exploited during the melting curve analysis: T/A, C/G, G/T. This region was then targeted to design another set of primers for the amplification of a 110 bp fragment in SPPV vaccine strains and a 158 bp fragment in SPPV field strains, GTPV and LSDV. Subsequently, we tested the expected melting profiles and melting peaks of the predicted amplicons for SPPV vaccine strains, SPPV field isolates, GTPVs and LSDVs using the uMelt software.
90 ± 0. 13 for SPPV vaccine, 80. 27 ± 0. 11 for SPPV field isolates, 81. 58 ± 0. 06 for GTPV and 82. 29 ± 0. 07 for LSDV. Improved Data Analysis using HRM Software. To enhance and clarify the data analysis, we used the HRM software to further analyze the melting curves of the PCR amplicons. The HRM analysis of the normalized melting (Fig. 3a ) and differential curves (Fig. 3b ) were clearly distinct between SPPV vaccine strains, SPPV field isolates, GTPV and LSDV, producing four independent clusters, readily and consistently resolved (Fig. 3a, b ). The HRM analysis results were consistent with the conventional melting curve analysis; however, the processed graphs displayed by the HRM software were easier to interpret.
The comparative analysis of the partial B22R sequences of SPPV field isolates, GTPVs and LSDVs generated in this study with those retrieved from full genomes in public databases also confirmed the presence of species-specific signatures that were well-conserved within each of these 3 species, with minor sequence variability noted for both SPPV field isolates and GTPVs (Supplementary Fig. S1 ). The HRM typing results were in full agreement with sequence results of the PCR amplicons for all 37 samples that were sequenced and the sequence variability had no impact on the melting temperatures within each genotype. Thus, classification of the CaPV isolates into SPPV, GTPV and LSDV using the current HRM assay was consistent with previous methods  [18] [19] [20]  , for all 61 samples in this study.
2 ) with similar shapes to the uMelt predicted melting curves (Supplementary Fig. S2 ), with different Tm values, thus illustrating the usefulness of the uMelt simulation software  28  for the design of HRM-based assays. The HRM assay displayed good sensitivity, comparable to a previously reported dual-hybridization probe assay for CaPVs' classification  20. It was highly specific, with no misidentification of genotypes and no reactivity with other ruminant pathogens tested in this study. The current HRM assay is easy to perform, interpret and is compatible with various qPCR platforms. Indeed, with all four qPCR instruments tested in this study, the analysis of the melting curves was sufficient to differentiate SPPV vaccines from SPPV field isolates and discriminate between SPPV, GTPV and LSDV.
Additionally, we found that using the HRM analysis software enables a better visual representation for the discrimination of different clusters, especially when several samples are analyzed in a single run. In SPP and GTP endemic countries, the Yugoslavian RM65, the Romanian and the KSGP 0240 strains are the most widely used live attenuated vaccines to protect small ruminants against SPP and GTP  12, 13. The KSGP 0240 vaccine strain which is used in several SPP and GTP endemic countries  12  was shown to be LSDV  29, 30  , therefore, it can be differentiated from SPPV field isolates using species-specific PCR methods  [18] [19] [20]. The Yugoslavian RM65 strain which is mainly used in the Middle East, Asia and the Horn of Africa  12  , and the Romanian strain, used in India and the Maghreb region  12  , are all SPPV derived vaccines.
Target Gene and Primers Design. In order to identify regions with unique differences between SPPV vaccines and SPPV field isolates, we scanned the sequence alignments of a draft full genome SPPV vaccine from Morocco and representative field isolates and vaccines of all three CaPV species. The sequences were retrieved from Genbank (accession numbers are indicated in Fig. 1 ) and aligned using MAFFT (http://mafft. cbrc. jp/alignment/server). To broaden the scope of the assay to enable the classification of CaPVs, we selected the final region based on the following: (1) the selected region offered markers for an unequivocal differentiation of SPPV vaccines, derived from the Romanian and Yugoslavian RM65, from SPPV field isolates; (2) the region presented enough nucleotide differences between the SPPVs, GTPVs and LSDVs that enabled the separation of these three species by HRM; (3) the differences between SPPV, GTPV and LSDV were well conserved within each CaPV species, thus representing strong species-specific signatures for genotyping.
A region within the B22R gene of CaPVs which contains two separate nucleotide deletions of 21 and 27 nucleotides in length was selected. The same region also presented species-specific signatures for SPPV, GTPV and LSDV. A primer pair (Table  1 ) flanking this region was selected (positions 122045 to 122202 in SPPV A genome) to amplify and sequence the targeted regions in all SPPV vaccines, and in some representatives of SPPV field isolates, GTPV and LSDV available in our laboratory. Another set of primers (positions 121952 to 122202 in SPPV A genome) was designed for evaluation in the HRM assay (Table  1 ). To confirm the suitability of these primers in the HRM assay to differentiate SPPV vaccines from SPPV field isolates with the simultaneous classification of CaPVs, we performed an in-silico simulation, with the predicted PCR amplicons for SPPV vaccines, SPPV field isolate, GTPV and LSDV, using the uMelt software  28.
uMelt is a flexible web-based tool for predicting DNA melting curves and denaturation profiles of PCR products. The above-mentioned primers were selected using Allele ID version 6 software (Premier Biosoft International, Palo Alto, CA, USA), synthesized and purified by reverse phase high-performance liquid chromatography by Eurofins, Germany. The specificity of the primer sequences were checked using the Basic Local Alignment Search Tool (NCBI/Primer-BLAST). From now on, we will refer to each of these four groups, SPPV vaccines, SPPV field isolates, GTPVs and LSDVs, as genotypes. Positive Controls. Positive control plasmids, containing the target gene fragment of the B22R gene of SPPV Morocco vaccine, SPPV Turkey/98 Denizli, GTPV Awi/O13/2011 and LSDV Guder/B5/2008 isolates were constructed by ligating the respective PCR products into the pGEM-T Easy Vector System (Promega, Madison, WI, USA) as per the manufacturer's instructions.
The PCR products were then denatured at 95 °C for 30 sec, cooled to 65 °C for 1 min, and melted from 65 °C to 90 °C at a rate of 100 acquisitions per °C. For each set of reactions, positive control plasmids and negative controls consisting of nuclease-free water in place of the template DNA were included. High Resolution Melting (HRM) curve analysis was performed using the LightCycler 480 Gene Scanning Software (Roche) to analyze the data and melting profiles of SPPV vaccines, SPPV field isolates, GTPVs and LSDVs. Normalized melt curves and difference plots were obtained by analyzing the active melt region and designating the pre and post melt regions.
Discriminating Power and Specificity of the Assay. The discriminating power of the developed HRM assay was evaluated using DNA (n = 61) extracted from suspected capripox clinical samples and CaPV infected cell culture suspensions from sheep, goats and cattle from different geographical regions (Supplementary Table  S1 ). The accuracy of the assay results was confirmed by comparing the HRM genotyping results with the sequencing data of the amplicons. The specificity of the method was also evaluated by testing DNA (n = 13) extracted from Mycoplasma mycoides subsp. mycoides Small Colony (MmmSC), Orf virus (ORFV), Bovine herpes virus 1 and 2, and cDNA from Peste des Petits Ruminants virus (PPRV), (Supplementary Table  S2 ).
Statistical Analysis. All statistical analyses were performed in R (version 3. 4. 1) via Rstudio (version 1. 1. 383). A One-Way ANOVA test and Tukey multiple comparisons of means were performed in R to determine whether the average Tm differences between the genotypes were significant. In addition, box and whisker plots were constructed to illustrate the differences between the Tm of the four genotypes using the ggplot2 package in R. For LOD determination, the LC_probit function of the ecotox package in R was used to calculate the predicted limits of detection and their confidence limits (CL) using a probit analysis. The dose-response curves were plotted using ggplot2 in R. Cross Platform Compatibility Test. The cross-platform compatibility of this assay was evaluated using various real time PCR instruments.
58 °C for 30 sec, and 72 °C for 30 sec, and a final extension at 72 °C for 2 min. PCR products were cheeked by electrophoresis on a 2% agarose gel for 1 h at 100 V. The PCR products were purified using Wizard SV Gel and PCR Clean Up System (Promega) and sequenced commercially by LGC Genomics (Germany). The sequence data were edited, and the fragments were assembled using Vector NTI 11. 5 software (Invitrogen). Additional nucleotide sequences of the corresponding regions for SPPV, GTPV and LSDV isolates were retrieved from GenBank for comparative analysis. Multiple sequence alignments were performed using the CLUSTALW algorithm implemented in BioEdit 7. 5 software package.
Acknowledgements This study was funded by the  International Atomic Energy Agency (IAEA)  project " Improvement of Veterinary Laboratory Capacities in Sub-Saharan African Countries ". The authors would like to thank  Dr. Esayas Gelaye  ( NVI, Ethiopia ),  Dr. El Harrak M. (Biopharma , Morocco),  Dr. Velý Gulyaz  ( Pendik -VCRI, Turkey ),  Dr. Erdenebaataar Janchivdorj  ( IVM, Mongolia ),  Dr. Tajelser Idris Badri  ( VRI, Sudan ) and  Dr. Jacqueline Kasiiti Lichoti-Orengo J. ( CVL, Kenya ), for providing capripox virus samples and  Dr. Adi Steinrigl  ( AGES, Austria ) for providing the Bovine herpes virus samples.
Statistical analysis Data from each of the four experiments were analysed separately. Peakto-peak MEP amplitudes (mV) were measured off-line using custom-written Spike 2 or Signal scripts (Cambridge Electronics Design, Cambridge, UK). Trials were excluded if they were contaminated by muscle contraction or if their amplitudes were greater than or less than 1. 96 SDs from the mean of that condition for that subject. On average 16 (± 1. 1 SEM) trials were excluded for each subject. The mean MEP amplitude for each condition was then calculated. D'Agostino-Pearson omnibus tests were applied to verify if the data came from an approximately normal distribution. Since the data for some conditions were not normally distributed, a Friedman repeated measures, non-parametric rank test with one factor (ISI) was applied to the raw MEP amplitudes (mV) to compare the mean amplitudes across conditions.
Dunn's Multiple Comparison post-hoc tests comparing the control condition (TMS-only) with each ISI (15 to 85ms) were applied if the factor ISI was significant with a significance level of 0. 05. Data were analysed using Prism 5 (GraphPad Software, Inc. , California, USA). For each subject, mean MEP amplitude values for each ISI were normalized to the mean of the TMS-only condition and these normalized data. CC-BY 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted August 8, 2019. ; https://doi. org/10. 1101/719708 doi: bioRxiv preprint were used to graphically represent the results but all analyses were conducted on raw MEP amplitudes.
Genetic Findings The genotypes of patients with double rings are listed in Table  1. All had a combination of a null and a missense mutation. Both missense mutations, p. Arg303His (c. 908G > A) (Patients 1 and 2) and p. Gly4032Arg (c. 12094G > A) (Patient 3) are predicted to be pathogenic by Polyphen2 (genetics. bwh. harvard. edu/pph2), SIFT (https://sift. bii. a-star. edu. sg/www/SIFT_aligned_seqs_submit. html) and MutationTaster (http://www. mutationtaster. org/) and have low allele frequencies in GnomAD database (p. Arg303His present in 11/281768 alleles (0. 00003904) and p. Gly4032Arg present in 1/250584 alleles (0. 000003991); gnomad. broadinstitute. org, accessed 18 June 2019). Among the comparator group, 7/24 (29%) had a combination of a null and a missense mutation among which none were p.
Effective food policies in Europe require insight into the environmental impact of consumers' diet to contribute to global nutrition security in an environmentally sustainable way. The present study therefore aimed to assess the environmental impact associated with dietary intake across four European countries, and to explain sources of variations in environmental impact by energy intake, demographics and diet composition. Individual-level dietary intake data were obtained from nationally-representative dietary surveys, by using two non-consecutive days of a 24-h recall or a diet record, from Denmark (DK, n ¼ 1710), Czech Republic (CZ, n ¼ 1666), Italy (IT, n ¼ 2184), and France (FR, n ¼ 2246). Dietary intake data were linked to a newly developed pan-European environmental sustainability indicator database that contains greenhouse gas emissions (GHGE) and land use (LU) values for ~900 foods.
These individual-level dietary data reflect a wide variety of realistic food choices in the consumer domain, and therefore allow for studying the variability in diet-related footprints of individual' diets across population (sub)groups. A number of studies have assessed the environmental impact of food intake using individual-level data  (Capone et al. , 2013; Germani et al. , 2014; Meier and Christen, 2013; Saxe et al. , 2013; Temme et al. , 2015; Vieux et al. , 2012). As these studies were conducted within single European countries, a European comparison of diet-related environmental impact is hampered, as these national averages may be biased by the ecological fallacy, lack of comparability of dietary assessment methods  (Bingham et al. , 1994)  and systematic differences in life cycle assessment (LCA) databases  (Garnett, 2008; Notarnicola et al.
, 2017a). Comparable individuallevel intake data and LCA databases allow to evaluate environmental impact at the level of consumers' food choices and allow to explain variation between-and within countries, between population (sub)groups and between subjects. The aim of this study was to analyse diet-related GHGE and LU using reported food intake data obtained from national dietary surveys from four European countries that reflect heterogeneity of diets in different European regions, i. e. Denmark (DK; Scandinavia), Czech Republic (CZ; Central East Europe), Italy (IT; Mediterranean) and France (FR; Western Europe). Moreover, the present study aimed to study the variability in diet-related environmental footprints between and within countries, and to explain this by energy intake, and by demographics and diet composition.
Study population and food intake data Individual-level dietary intake data were obtained from nationally-representative dietary surveys for each of the countries studied, and for each country adults aged 18e64 years were included. The National Survey on  Diet and Physical Activity (2005e2008)  in DK was based on a seven-day diet record on consecutive days and included 1739 adult men and women  (Pedersen et al. , 2009). The national  SISP04 (2003e2004)  in CZ was based on two replicates of 24-h recall spaced over three-to-five months and included 1666 adult men and women  (Leclercq C et al. , 2009). The national  INRAN-SCAI (2005e2006)  in IT was based on a three-day diet record on consecutive days and included 2313 adult men and women  (Ruprich J et al. , 2006). The national  INCA-2 Study (2006e2007)  in FR was based on a seven-day diet record on consecutive days and included 2276 adult men and women (Agence Française de S ecurit e Sanitaire des Aliments (AFSSA), 2009).
Surveys were organised throughout the entire year, covering all four seasons, and proportionally included weekand weekend-days. For comparison across countries, dietary intake data of two nonconsecutive days were used, hereby sampling two non-consecutive days in DK, IT and FR, and using both available days in CZ. Intakes of food groups and individual food items were classified according to the FoodEx2 classification that was developed by the European Food Safety Authority (EFSA) (EFSA (European Food Safety Authority), 2011; European Food Safety  Authority, 2015). Intake data coded by FoodEx2 were disaggregated in 287 FoodEx2-codes in DK, 338 in CZ, 423 in IT, and 662 in FR.