dataesr/scientific-paragraphs-categorization huggingface dataset:
N. H. is funded by the  BBSRC ,  Core Strategic Programme Grant  at the  Earlham Institute  ( BB/CSP17270/1 ). F. -X. W. and A. T. -D. are supported by the  Institut Pasteur, Santé publique  and the  French government 's  Investissement d' Avenir programme, Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  ( ANR-10-LABX-62-IBEID ). K. S. B. is funded by a  Wellcome Trust   Clinical Research Career Development Fellowship  ( 106690/A/14/Z ). J. C. D. H. is funded by a  Wellcome Trust   Senior Investigator Award  ( 106914/Z/15/Z ). Genome sequencing was done by the  Earlham Institute  as part of the 10, 000 Salmonella  genomes project which is supported by the  Global Challenges Research Fund data  and resources grant ( BBS/OS/ GC/000009D ). Next-generation sequencing and library construction were delivered via the  BBSRC National Capability in Genomics  and  Single Cell  ( BB/CCG1720/1 ) at  Earlham Institute , by members of the  Genomics Pipelines Group.
QualiMap 65 v2. 0 was used to identify a mean sample depth of 35. 17× across all isolates. NATUrE MIcrOBIOLOGy Articles (https://10k-salmonella-genomes. com/). Illumina Nextera XT DNA Libraries were prepared (Illumina, FC-131-1096) and sequenced (Illumina HiSeq 4000) in multiplex (768) as 150-bp paired-end reads. Assembly and annotation of short reads. Sequence typing. Reference mapping to D23580. Trimmed sequencing data were mapped against the S. Typhimurium reference genome D23580 using BWA mem 62 v0. 7. 10-r789. The reference genome consists of the D23550 chromosome (GenBank accession: LS997973. 1), and four plasmids including pSLT-BT (accession: LS997974. 1), pBT1 (accession: LS997975. 1), pBT2 (accession: LS997976. 1) and pBT3 (accession: LS997977. 1). NATuRE MiCRoBioLoGY | VOL 6 | MaRCh 2021 | 327-338 | www.
EVA Software (Bruker) and the JCPDS (Joint Committee on Powder Diffraction Standards) database were used to identify crystalline phases. From the pyrolyzed material, a thin foil was prepared using focused ion beam (FIB, Strata DB 235, FEI). Then transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM, a high angle annular dark field-HAADF-detector is used) observations (Tecnai Osiris, FEI, acceleration voltage of 200 kV, point to point resolution of 2. 5 Å) coupled to energy dispersive spectroscopy analyses (EDS, Esprit, Bruker) using four quadrant silicon drift detectors (ChemiStem technology, FEI) were performed.
Because the intensity of the signal detected by a HAADF detector is proportional to the atomic number power 1. 7 (Z-contrast), a bright object is constituted by heavy elements. Accordingly, the dark second-phase is constituted by pores and/or lighter elements than the ones constituting the bright polycrystalline network. Figure  2b  shows a STEM/HAADF view at a higher magnification and Figure  2c-e  show EDS maps for the Si, C and O associated chemical elements, respectively. The grains constituting the polycrystalline network contain Si, C and O elements. Local chemical quantifications from EDS analyses carried out in point-mode have been performed using the Cliff-Lorimer method with theoretical k-factors calculated under Esprit software for an acceleration voltage of 200 kV (thin foil thickness assumed to be 75 nm).
Here, we first constructed nsCL/P PRS at a number of different thresholds using nsCL/P genome-wide association study (GWAS) summary data. We then evaluated the association between the nsCL/P PRSs and OC/OPC using individual participant data on OC/OPC cases and controls using data from the largest OC/OPC GWAS  (Lesseur et al. , 2016)  and UK Biobank. To differentiate between the two datasets, we refer to the samples as the International Agency for Research on Cancer (IARC) and UK Biobank samples, respectively. We then applied Mendelian randomization to both data sets to evaluate a potential causal effect of liability to nsCL/P, a latent measure of nsCL/P proxied by common nsCL/P variants, and OC/OPC. Finally, we used data from UK Biobank to explore associations between nsCL/P PRS and potential shared risk factors between nsCL/P and OC/OPC (alcohol/tobacco measures).
| UK Biobank UK Biobank is a large-scale cohort study of 502,655 participants aged between 40 and 69 years, who were recruited from 22 recruitment centres across the United Kingdom between 2006 and 2010. In UK Biobank, we identified OC/OPC cases and controls using secondary care data from Hospital Episode Statistics (HES) as well as the death and cancer registers. For secondary analyses, we used data on self-reported alcohol consumption and tobacco smoking which were collected at baseline using a questionnaire. More information on the study design, genotyping and phenotyping is contained in the Supporting Information Methods. For analyses, we used a subset of the study of 750 OC/ OPC cases and 336,319 controls, after restricting to individuals of self-reported "White British" descent and using kinship coefficients to remove individuals related to the greatest number of other individuals.
| The association of nsCL/P PRS with OC/OPC in UK Biobank The UK Biobank data set was used as a follow-up data set for analyses in the OC/OPC GWAS data set. For the analyses in UK Biobank, we used the nsCL/P PRS most strongly associated with OC/OPC in the OC/ OPC GWAS data set, tested the association of this PRS with OC/OPC. The limited number of OC/OPC cases (N = 750) in our sample meant that it was not possible to stratify by OC/OPC subtype, so all cases were analysed together. All analyses were adjusted for the first 10 genetic principal components, age, and sex.
| Mendelian randomization analysis of liability to nsCL/P on OC/OPC Mendelian randomization was then applied to evaluate a causal relationship between liability to nsCL/P and OC/OPC. As genetic instruments for liability to nsCL/P, we used six genome-wide significant single nucleotide polymorphisms (SNPs), as in a previous study  (Howe et al. , 2018; Ludwig et al. , 2012 ; Table  S2 ). Chosen variants have large effects on nsCL/P risk  (Ludwig et al. , 2012)  suggesting that they can be utilised as effective genetic instruments. Although recent nsCL/P GWAS have identified additional genome-wide significant markers in European populations  (Leslie et al. , 2017; Ludwig et al. , 2016) , we were limited by available summary data. SNP data for liability to nsCL/P were extracted from the nsCL/P meta-analysis GWAS summary statistics.
SNP data for OC/OPC were extracted from a metaanalysis of the IARC GWAS sample (European and North American subsamples) and UK Biobank  (Lesseur et al. , 2016). The GWAS in the IARC sample has been previously described in detail  (Lesseur et al. , 2016). In UK Biobank, the GWAS of 750 cases and 336,319 controls was conducted using logistic regression in PLINK v2. 00  (Purcell et al. , 2007). Sex, age, and the first 10 principal components were fitted as covariates in the model. The GWAS summary data from all data sets were metaanalysed using METAL  (Willer, Li, & Abecasis, 2010)  to generate combined summary statistics for OC/OPC. We then used the TwoSampleMR  (Hemani et al. , 2018 ) package in R to conduct Mendelian randomization analyses; reporting the inverse-variance weighted estimate as the primary analysis.
, 2017; Mostowska et al. , 2012; Vogelaar et al. , 2012)  because of the reduction in the number of statistical tests, and the potential to extend the evidence of genetic overlap at specific loci to examine genome-wide genetic overlap. Although the nsCL/P GWAS data set was modestly sized, the construction of nsCL/P PRS in a much larger OC/OPC GWAS data set meant that analyses were well-powered to detect a modest genetic overlap. Nevertheless, there are several limitations of this study. First, the lack of a convincing replication of the PRS analysis in UK Biobank weakens the argument for genetic overlap between the two phenotypes. However, this may be because of the modest number of OC/OPC cases in the UK Biobank, with confidence intervals overlapping between the two studies. Second, the OC/OPC GWAS data set was highly ancestrally heterogeneous and included samples from 12 different epidemiological studies, including a case-only study (the Head and Neck 5000).
, 2016; Sharp et al. , 2017). This complicates the interpretation of the results because the OPC includes the soft palate while the OC includes the lips and hard palate. It is therefore, possible that the different nsCL/P subtypes may have different mechanistic relationships with the OC/OPC subtypes. Finally, we were unable to explore the effects of somatic mutations in the oral tissues which could influence both orofacial cleft risk and localized cancer. To conclude, we found some evidence of shared genetic influences on nsCL/P and OC/OPC unrelated to alcohol or tobacco intake. Follow-up analyses, potentially using additional datasets such as the Cleft Collective (www. bristol. ac. uk/dental/cleft-collective/), are required to investigate the possible common biological pathways between nsCL/P and OC/OPC, and to evaluate possible mechanistic relationships between nsCL/P and cancer subtypes affecting distinct areas to orofacial clefts.
ACKNOWLEDGEMENTS We would like to thank all of the participants involved in this study as well as the funders and supporting organisations. L. J. H. , G. H. , G. D. S. and S. J. L. work in the  Medical Research Council Integrative Epidemiology Unit at the University of Bristol  ( MC_UU_00011/1 ). S. J. L. is funded by the  HEFCE  and by a programme grant awarded by  CRUK  ( ICEP, Principle Investigators: Martin and Relton ), G. H. is funded by the  Wellcome Trust  ( 208806/ Z/17/Z ). Funding support for the nsCL/P ICC study was provided by several previous grants from the  National Institute of Dental and Craniofacial Research (NIDCR). Funding for individual investigators include:  R21-DE-013707  and  R01-DE-014581  (Beaty);  R37-DE-08559  and  P50-DE-016215  (Murray,  Marazita ) and the  Iowa Comprehensive Program to Investigate Craniofacial and Dental Anomalies (Murray) ;  R01-DE-09886  (Marazita),  R01-DE-012472  (Marazita),  R01-DE-014677  (Marazita),  R01-DE-016148  (Marazita),  R21-DE016930  (Marazita);.
DATA AVAILABILITY STATEMENT Research Data are not shareThe nsCL/P family data set used to construct summary data described in this manuscript were obtained from dbGaP at https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id=phs000094. v1. p1 through dbGaP accession number (phs000094. v1. p1). Individuals interested in accessing summary data from the nsCL/P Bonn GWAS are encouraged to contact Elisabeth Mangold (e. mangold@uni-bonn. de) or Kerstin Ludwig (kerstin. ludwig@uni-bonn. de). The OC/OPC GWAS data is available from dbGaP at https://www. ncbi. nlm. nih. gov/ projects/gap/cgi-bin/study. cgi?study_id=phs001202. v1. p1 through dbGaP accession number (phs001202. v1. p1). Summary data from the UK Biobank GWAS of OC/OPC, described in this manuscript, will be made available in the MR Base platform and the GWAS catalogue before publication.
Artificial intelligence (AI) in imaging of COVID-19 Machine learning techniques have the potential to dramatically transform medical imaging  [46] [47] [48]. Several deep learning and radiomics based tools are currently being developed for automated diagnosis, quantification of lesion extension and prognostic estimation of COVID19 pneumonia. They show great promise for making the positive diagnosis of COVID-19 pneumonia, for automatically quantifying pulmonary lesion extension and for identifying biomarkers that could be of prognostic interest. In a study from the city of Wuhan in China, carried out on a database of 4356 CT examinations (30% with COVID-19, 40% with community pneumonia and 30% with non-infectious pneumonia), a deep learning model diagnosed COVID-19 pneumonia with a sensitivity of 90% and a specificity of 96%, allowing reliable differential diagnosis with community acquired pneumonia  [49].
Huang et al. trained a deep learning algorithm on an annotated database of 842 COVID-19 patients, and tested it on an independent series of 126 patients. The percentages of lung involvement were significantly different for mild, moderate and severe clinical conditions, and automatic quantification also allowed longitudinal follow-up of patients  [50]. AI certainly has an important role to play in assessing the prognosis of COVID-19 and identifying biomarkers predictive of patient outcomes. For this purpose, several national and international databases are currently being built to enable AI models to be trained on massive data.
Egg-clutches are laid in March, and metamorphosis generally occurs in late spring-early summer (June-July). Temperature is the main driver of development rate in amphibians  31, 71  and previous studies have shown that lowland and foothill populations are exposed to different climatic regimes and show local adaptation to the extant climatic gradient  25. We considered tadpoles originating from multiple foothill and lowland populations, and covering the whole altitudinal range of the species within the study area. To compare climatic regimes between foothill and lowland habitats, we calculated the mean spring temperature (March-June) for all the collection sites (see Fig. 1b ) from the CHELSA climatic data set at a resolution of 30 arc-s (roughly 900 × 650 m within the study area; ref. 72  ).
5 l of aged tap water (total: 250 tadpoles reared). Tadpoles were maintained under common laboratory conditions (12-h light-dark cycles at constant temperature of 20 °C) and fed ad libitum with lettuce and rabbit pellets. Development time was calculated as the number of days between hatching and metamorphosis (Gosner's stage 45; fully-developed forelimbs and almost complete reabsorption of the tail  76  ). Tadpole survival until metamorphosis was similar between lowland and foothill populations (χ 2 1 = 0. 084; P = 0. 771). The results of the 2003 experiment have been published in a previous paper  25  , but we here re-analyse these data with updated statistical tools for a better comparison with the 2017 experiment. Tadpole rearing after the crayfish invasion. To assess the response of frogs to the selective pressure posed by the alien crayfish, we repeated the analysis of larval development 14 years after the onset of the invasion.
Each session consisted in three consecutive jumping trials per individual during which the distance covered with each single jump was measured with a ruler  64. Two froglets were excluded from jumping trials due to a hindlimb malformation, which hampered their normal locomotion. The repeatability of individual jumping performance across the three trials was high (repeatability tested using the rptR package 79 : R = 0. 62, 95% CI = 0. 51-0. 71, p < 0. 001). As a measure of jumping performance we considered the maximum distance, since in frogs, maximum jumping ability is more strongly related to feeding and escape ability than average jumping length  [80] [81] [82]. Statistical analyses. For both the 2003 and the 2017 experiments, we used linear mixed models to assess the factors affecting development time of tadpoles.
We therefore built three separate SEMs using block, population of origin and clutch identity as clustering parameters. In the main results we present the SEM including block as clustering parameter, as this was the analysis showing the most conservative results. Results were highly consistent when using site of origin or clutch identity as clustering parameters (see Supplementary Table  2 ). We performed analyses in R environment (version 3. 4. 1), using lmerTest, lme4 and MuMIn packages for linear mixed models and lavaan package for SEM analysis  84, 85. In all mixed models, sample size was not homogeneous among groups, thus the degrees of freedom were approximated and can be non-integers  85. Furthermore, we used visreg package  86  to generate conditional regression plots. Ethical statement.
Acknowledgements We thank  L. S. Pollock  for insightful comments on a previous draft of the paper. We are grateful to  J. Measey  and  J. Baxter-Gilbert  for their assistance in the final revision of the paper. R. Pennati  and  F. De Bernardi  provided valuable suggestions across multiple phases of the study;  N. Ancona  and the staff of the  Acquario Civico di Milano kindly  allowed the use of Aquarium facilities. A. Urso ,  S. Robino ,  I. Mazzoleni  and  L. Nudo  greatly helped during tadpole rearing and behavioural experiments. Several students and colleagues helped during sampling activities:  E. Lo Parrino ,  S. Pitton ,  S. Romagnoli ,  B. Barzaghi. ,  F. Bernini ,  A. Gentilli ,  E. Razzetti  and  A. Bonardi.
Data availability All the experimental data are available in the fisgshare repository with the identifier [https://doi. org/10. 6084/m9. figshare. 12452600. v2]. All meteoclimatic data used are available on the websites of the regional network of the Regional Environmental Agency (www. arpalombardia. it) and of the CHELSA high-resolution climate data set (http:// chelsa-climate. org/). Source data are provided with this paper.
a. Irradiations The energy of 490 keV was chosen as the energy of the Fe + ions to bombard the samples. This energy corresponds to the mean energy of Primary Knock-on Fe Atoms produced from 14 MeV fusion neutrons. The penetration of 490 keV Fe + is around 400 nm. For depths larger than 100 nm implantation effects start to become significant, whereas for film thickness up to about 80 nm radiation damage effects are predominant. In order to define the film thickness and the damage produced by 490 keV Fe + ions simulations using the SRIM-2008 software  [39]  have been performed on Fe-5at%Cr and Fe-10at%Cr targets. For the displacement energy the value of 40 eV [ASTM standard, 40] has been used. There are two main parameters which characterize the interaction of ion with matter: the damage produced and the number of ions implanted.
Discussion and Conclusions Films of Fe-5at%Cr and Fe-10at%Cr of thickness around 70 nm have been irradiated by 490 keV Fe + ions to different damage levels from 3 to 50 dpa. The energy of 490 keV Fe + ions corresponds to the mean energy of the primary Fe knock-ons produced by 14 MeV fusion neutrons. The films before and after irradiations have been characterized by XRF spectroscopy (chemical composition), XRR (film structure and density) and XRD (crystal structure). The Cr total content in the film as determined by XRF spectroscopy after irradiation remains the same as the initial one. XRD data show that the crystal structure remains unaltered even after 50 dpa irradiation. Structural stability is also observed by XRR measurements from which it is concluded that both the density and the film thickness are not influenced by the irradiation.
Acknowledgments We would like to thank  Prof. Sergei Dudarev  for fruitful discussion. This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme 2014-2018 and 2019-2020 under Grant Agreements No. 633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. Experiments at the  ISIS Neutron and Muon Source  were supported by a beamtime allocation from the  Science and Technology Facilities Council. The use of the  VSM Laboratory Network Unit of the University of Ioannina  is gratefully acknowledged.
Fungal isolates The isolate v23. 1. 3 of L. maculans 'brassicae' and the isolate IBCN84 of L. maculans 'lepidii' were used throughout the analyses  (Rouxel et al. , 2011; Grandaubert et al. , 2014). Fungal cultures were maintained as described previously  (Ansan-Melayah et al. , 1995). For chromatin immunoprecipitation and transcriptomic analyses performed during in vitro growth, mycelium of Lmb and Lml were grown on V8 Analysis of ChIP-seq data and identification of significantly enriched domains Analysis of ChIP-seq datasets was performed as described in  Schotanus et al. (2015). Quality of Illumina reads was analysed using FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Based on results of this analysis, ten bp were trimmed from the 5' end. Processed reads were mapped on the reference genome of Lmb  (Dutreux et al.
, 2018)  or Lml  (Grandaubert et al. , 2014)  using Bowtie2  (Langmead and Salzberg, 2012)  with default parameters (Table  S1 ). Peak calling analysis was performed on each ChIP sequencing dataset, to identify significantly enriched domains for either H3K4me2, H3K9me3 or H3K27me3, using RSEG  (Song and Smith, 2011). A domain was considered if identified in at least two out of the three biological replicates. The Integrative Genome Viewer  (Thorvaldsdóttir et al. , 2013)  was used to visualize location of each domain along genomes of Lmb and Lml according to other genome features (gene annotation, TE-annotation, GC-content). Coverage of the histone modifications was assessed in 10 kb non-overlapping sliding windows along the supercontigs and correlation analyses, using Kendall's Ƭ correlation coefficient, were performed between biological replicates to check for reproducibility (Tables  S2, S3 ).
, 2014)  to remove short reads (<30 bp) and eliminate sequencing adaptors. Cleaned reads were then mapped against each genome using STAR with default parameters  (Dobin and Gingeras, 2015 ; Table  S1 ). Gene expression was evaluated using the rpkm_count function of EdgeR  (Robinson et al. , 2010). Genes with RPKM ≥ 2 were considered expressed. Sequencing data are available under the GEO accession number GSE150127.
Analysis of GO enrichment Gene Ontology (GO) annotations of the Lmb and Lml genes were retrieved from  Dutreux et al. (2018)  and  Grandaubert et al. (2014)  respectively. GO term enrichment analysis of the H3K4me2-, H3K9me3-and H3K27me3-associated genes was performed with the plug-in Biological networks Gene ontology (BinGo; v3. 0. 3) of the cytoscape software  (Shannon et al. , 2003). List of genes submitted to BINGO were considered as significantly enriched for a given GO term with an associated False Discovery Rate (FDR) ≤ 0. 01 for the biological processes. A Chi² test, or a Fisher's exact test for small sized-population, were applied to identify significant enrichment of H3K4me2-, H3K9me3-or H3K27me3-domains for certain categories of genes: the expected proportion of a given category of genes across the entire genomes of Lmb or Lml was compared to the observed distribution of the gene category in the H3K4me2-, H3K9me3-or H3K27me3-domains.
A different genome organisation but similar epigenomic properties in both genomes To assess the genome-wide distribution of histone marks in Lmb and Lml during axenic growth, we performed ChIP-seq experiments using antibodies against histone modifications H3K4me2, H3K9me3 and H3K27me3 (Table  S1 ). Mapping of the ChIP-seq data was followed by identification of significantly enriched domains, for any of the histone modifications targeted, and reproducibility of the biological replicates was assessed (Tables  S2, S3 ). Based on the genomic coordinates of the H3K4me2-, H3K9me3and H3K27me3-domains, the number of bases associated with any of the three histone modifications was evaluated, for each genome, in 10 kb sliding-windows. In the genomes of Lmb and Lml, the proportion of H3K4me2 and H3K27me3 was similar (H3K4me2: 39% in Lmb vs.
Acknowledgments Authors wish to thank all members of the " Effectors and Pathogenesis of L. maculans" and "Environmental Genomics " groups;  M. Freitag  and  J. Grandaubert  for fruitful discussions;  R. O'Connell  for the English proofreading of this paper. J. L. Soyer was funded by a " Contrat Jeune Scientifique " grant from  INRA. The "Effectors and Pathogenesis of L. maculans" group benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ). Research on fungal histone modifications in the group of E. H. Stukenbrock is funded by the  Max Planck Society, Germany. was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted May 10, 2020. ; https://doi. org/10.