dataesr/scientific-paragraphs-categorization huggingface dataset:
Our data show that for some sublineages, modern syphilis is a truly global disease with shared lineages, sublineages and indeed nearly identical strains found all over the world. The large expansions of highly related genomes, in particular sublineage 1, represent the bulk of our dataset, and the widespread sharing of major lineages suggests that we have sampled from a series of globally contiguous sexual networks. Furthermore, we find evidence of a striking change in the genetic diversity and effective population size of TPA genomes, suggestive of a possible population bottleneck occurring between the late 1990s and early 2000s. This was followed by a rapid expansion of certain sublineages, leading to the contemporary TPA population structure. This bottleneck, potentially a consequence of post-HIV safe-sex messaging, persistent antimicrobial prophylaxis in at-risk HIV-positive populations, and possibly HIV-associated mortality, appears to have led to a striking duality in the TPA dominating populations before and after its occurrence.
Sequence analysis and phylogenetics. We filtered Treponema genus-specific sequencing reads using the full bacterial and human Kraken 2 (ref. 39 ) v2. 0. 8 database (March 2019), followed by trimming with Trimmomatic 40 v0. 39 and downsampling to a maximum of 2,500,000 using seqtk v1. 0 (available at https://github. com/lh3/seqtk) as previously described  4. For publicly available genomes, raw sequencing reads were downloaded from the European Nucleotide Archive and subjected to the same binning and downsampling procedure. For five public genomes (Supplementary Data 1), raw sequencing reads were not available; for these we simulated 150 bp paired-end perfect reads at 50X coverage from the RefSeq closed genomes using Fastaq v3. 17. 0 (available at https://github. com/ sanger-pathogens/Fastaq).
For phylogenomic analysis, we mapped Treponema-specific reads to a custom version of the SS14_v2 reference genome (NC_021508. 1) after first masking 12 repetitive Tpr genes (Tpr A-L), two highly repetitive genes (arp, TPANIC_0470) and five FadL homologues (TPANIC_0548, TPANIC_0856, TPANIC_0858, TPANIC_0859, TPANIC_0865) using bedtools 41 v2. 29 maskfasta (positions listed in Supplementary Data 2). We mapped prefiltered sequencing reads to the reference using BWA mem 42 v0. 7. 17 (MapQ ≥ 20, excluding reads with secondary mappings), followed by indel realignment using GATK v3. 7 IndelRealigner, de-duplication with Picard MarkDuplicates v1. 126 (available at http://broadinstitute. github. io/picard/), and variant calling and consensus pseudosequence generation using samtools 43 v1. 6 and bcftools v1.
6, requiring a minimum of two supporting reads per strand and five in total to call a variant, and a variant frequency/mapping quality cut-off of 0. 8. Sites not meeting our filtering criteria were masked to 'N' in the final pseudosequence. After mapping and pseudosequence generation, we repeated the masking of the 19 genes on the final multiple sequence alignment using 'remove_block_from_aln. py' available at https://github. com/sanger-pathogens/remove_blocks_from_aln/ to ensure sites originally masked in the reference were not inadvertently called with SNPs due to mapped reads overlapping the masked region. These 19 regions of recombination and genomic uncertainty due to gene orthology or repetitive regions  3, 4, 6  accounted for 30,071 genomic sites (Supplementary Data 2). For basic lineage assignment of genomes, we excluded sequences with >75% genomic sites masked to 'N'.
7% (53,638 sites) of the genome over the 38 regions. We used IQ-Tree on the SNP-only alignment containing 901 variable sites, inputting missing constant sites using the '-fconst' argument, and allowing the built-in model test to infer a K3Pu+F+I model and 10,000 UltraFast bootstraps. To cluster genomes, we initially performed joint ancestral reconstruction 47 of SNPs on the phylogeny using pyjar v0. 1. 0 (available at https://github. com/ simonrharris/pyjar) and used this to determine phylogenetic clusters with a 10 SNP threshold in rPinecone 29 v0. 1. 0 (available at https://github. com/alexwailan/ rpinecone). We further investigated phylogenetic clustering by using IQ-tree to generate 100 standard non-parametric bootstraps on the maximum likelihood phylogeny and used the resulting 100 trees as independent inputs to rPinecone, as described in the rPinecone manuscript  29.
To assess the impact of mapping to an alternative reference genome, we also mapped the 528 genomes to the Nichols_v2 reference (CP004010. 2), repeating the recombination masking and phylogenetic reconstruction as described above. Comparison of the resulting Nichols-mapped ultrametric tree to our SS14-mapped tree in a tanglegram also indicated equivalent topology and clustering of sublineages (Supplementary Fig. 8 ), with the exceptions of sublineage 1 (which has low diversity and thus low support for within-group topology) and sublineage 6 (which diverges from other strains close to the root of TPA and appears on either the Nichols-or SS14-lineage side of different midpoint rooted trees). For temporal analysis, our dataset was too large for robust model testing of all genomes, so we stratified our dataset by sublineage and country, then used the random sampler in R  48  to select a maximum of five genomes from each strata, plus all singleton strains, yielding a dataset of 138.
Acknowledgements We thank the sequencing team at the  Wellcome Sanger Institute , and  C. Puethe  and the  Pathogen Informatics team  for computational support; additional technical staff involved in sample diagnostics, DNA extraction and sample retrieval in laboratories at Public Health England and NHS laboratories, UK;  British Columbia CDC  and  Alberta Precision Laboratories, Canada; National Public Health Center, Budapest, Hungary; FRC Kazan Scientific Center, Tuva, Russia; National Institute for Communicable Diseases, Johannesburg, South Africa; Institute of Tropical Medicine, Antwerp, Belgium; Sahlgrenska University Hospital, Gothenburg, Sweden; Hospital Vall d'Hebron, Barcelona, Spain; Midlands Regional Hospital Portlaoise, Ireland; Pathology Queensland Central Laboratory, Australia; WHO Collaborating Centre  for Gonorrhoea and other  STIs, Sweden ;  G.
Tonkin-Hill ,  A. van Tonder  and members of the Thomson team for helpful discussions during analysis. M. A. B. and N. R. T. were supported by  Wellcome  funding to the Sanger Institute (# 206194 ). M. M. was funded by the  UKRI  and  NIHR  ( COV0335 ;  MR/V027956/1 ,  NIHR200125 ) and the  EDCTP  ( RIA2018D-249 ). D. M. W. was funded by a  Queensland Advancing Clinical Research Fellowship  from the  Queensland Government. D. A. W. was supported by an  Investigator Grant  ( 1174555 ) from the  National Health and Medical Research Council of Australia. S. A. L. was funded by the  National Institutes of Health  ( R01 AI42143  and  R01 AI123196 ). This research was funded in whole, or in part, by the  Wellcome Trust  (# 206194 ). For the purpose of open access, the authors have applied a CC-BY public copyright licence to any author-accepted manuscript version arising from this submission.
Data availability Sequencing reads for all novel genomes have been deposited at the European Nucleotide Archive (https://www. ebi. ac. uk/ena/browser/home) in BioProjects PRJEB28546, PRJEB33181 and PRJNA701499. All accessions, corresponding sample identifiers and related metadata are available in Supplementary Data 1. Map tiles were downloaded from http://maps. stamen. com using the ggmap interface. Publicly available syphilis incidence data are available for England at https://www. gov. uk/government/statistics/ sexually-transmitted-infections-stis-annual-data-tables and for British Columbia at http://www. bccdc. ca/health-professionals/data-reports/sti-reports. All sample metadata and intermediate analysis files are available at https:// doi. org/10. 6084/m9. figshare. 14376749 and https://github.
Cells were passaged every 3 to 4 days at cell concentrations of 0. 7 × 10 6 cell/mL. LLC-MK2 cells (ATCC CCL-7) were maintained in minimal essential medium (MEM, Life Technologies) supplemented with 10% fetal bovine serum (Wisent, St. Bruno, QC, Canada) and 1% penicillin/streptomycin (10,000 U/mL). The wild-type (WT) A1/C-85473 strain of HMPV (GenBank accession number: KM408076. 1) and two A1/C-85473-derived recombinant viruses were used in this study. Recombinant rC-85473-GFP (green fluorescent protein) virus, which is a GFP-expressing C-85473 WT counterpart virus, and the ∆SH-rC-85473-GFP virus (Metavac ® ), a recombinant virus from which the viral SH gene sequence is deleted, were generated by reverse genetics as previously described  [15, 27]. In order to constitute initial working viral stocks, both of these viruses were amplified onto LLC-MK2 monolayers in OptiMEM (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) in the presence of 1% penicillin/streptomycin and acetylated trypsin (T6763, Sigma-Aldrich, Saint Louis, MO, USA) and concentrated by ultracentrifugation as previously described  [15, 27].
Briefly, 10 µL of the suspension was diluted in trypan blue and analyzed using a Countess™ II FL Automated Cell Counter. We then harvested and centrifuged a minimal sample of 1 × 10 6 cells in suspension, supernatants were titrated as TCID 50 /mL, and pelleted cells were fixed in a 2% formaldehyde solution to be analyzed by flow cytometry (FACS CantoII analyzer, Becton Dickinson). Percentages of infected GFP-positive cells in a minimum of 1 × 10 4 total cells were measured with FACS Diva software. To constitute concentrated DuckCelt ® -T17-produced viral working stocks, the whole suspension of cells was harvested after 7-8 days of production, clarified by centrifugation at 2000 rpm, and the supernatant was then concentrated by ultracentrifugation as previously described  [15, 27]. The obtained pellet was resuspended in OptiMEM and stored at -80 • C for further use.
Acknowledgments: We thank  Elisabeth Errazuriz-Cerda  for her technical contribution to electron microscopy observations and the microscopy service of the  Centre d'Imagerie Quantitative Lyon-Est (CIQLE) in Lyon , the flow cytometry service of  Plateforme de Cytométrie en flux at the Centre de Recherche en Cancérologie de Lyon (CRCL ), and the animal care services of the  Plateau de Biologie Expérimentale de la Souris in Lyon. We thank  Fortune Bidossessi  for her technical contribution to in vivo experiments (INRAE, UVSQ, VIM, 78350 Jouy-en-Josas, France) and  Fanny Salasc  for her technical contribution to cell culture and viral production ( CIRI, Team VirPath U1111, Lyon, France ).
A statistical test, based on Desolneux, Moisan and Morel's a contrario theory  [8] , is used to decide when a significant JPEG grid is found while controlling the number of false detections. The method described here derives from the one in  [16]  but includes some improvements such as the fact that it can detect more types of forgeries. When the image to be analyzed is encoded in a JPEG file, there is no need to detect the JPEG grid as all the relevant information is contained in the header of the JPEG file itself. Nevertheless, even in that case, it may be interesting to detect traces of a previous JPEG compression. More importantly, in forensic applications one wants to analyze an image in any format and study whether one or more JPEG compressions were applied. For these reasons, the method described here takes as input only the pixel values of the image and uses no metadata information that may be contained in the image file.
Algorithm 1 provides a pseudo-code of the method applied to an image. Each step of this algorithm is described in the following subsections. For the sake of simplify, the reference code does not include an implementation of the JPEG compression algorithm, as would be required to perform step 6 of Algorithm 1; instead, the code takes as a second input the JPEG compressed version of the input image with QF = 99. In terms of the pseudo-code, the image I ′ is expected as a second input, to be computed externally using standard tools (such as the ImageMagick package https://imagemagick. org). In the case where this second input is not provided, the analysis of missing grids is not performed and only the F mask is produced.
1%). All statistical analyses were performed at Charles River, using SAS software, version 8. 2 (Cary). Gd content extrapolated at t = 1 day and elimination halflives were compared pairwise by applying a two-sample z-test for comparing two values μ1 and μ2, with errors σ1 and σ2. Errors were estimated by the fitting procedure (for half-lives) and through error propagation rules for Gd content at 1 day. The null hypothesis was that the two values are equal, while the alternative hypothesis was that the two values are different. The z-value was calculated as: z-value ¼ μ1 À μ2 ð Þ =sqrt σ1 2 þ σ2 2 À Á and compared with critical values 1. 96, 2. 57, and 3. 29, corresponding to a significance of 5%, 1%, and 0. 1%, respectively. Statistical analyses were performed using Mathematica software, version 12.
Results All animals successfully underwent all aspects of the study and were sacrificed as foreseen in the study protocol. No unexpected changes in bodyweight were noted and no adverse signs or symptoms were observed for any animal. No relevant gross pathological tissue changes related to GBCA administration were noted at sacrifice. The mean (AE Standard Deviation, SD) Gd contents across groups and tissue types are presented in Table  1  and Figure  2. Among the 720 samples, data points from six (0. 008%) samples were considered outliers based on the Dixon test. No more than a single outlier was identified in each 15-sample group of animals and organs. These data points were excluded from further elaboration. Measurable amounts of Gd were detected in all tissue samples up to 5 weeks after the last injection.
Extrapolated values were used for blood samples at the 5-week timepoint, when the amount of Gd was below the LLOQ. Extrapolated values corresponded to at least three times the background, based on instrument response to a reagent blank sample. Significant differences in elimination Values presented as mean AE Standard Deviation (SD). The significance of differences between groups is shown as *P ≤ 0. 05, **P ≤ 0. 01, ***P ≤ 0. 001 vs. group 1 and P ≤ 0. 05; P ≤ 0. 01; P ≤ 0. 001 vs. group 2. Data for blood below the lower limit of quantification (LLOQ) were extrapolated. a 3/15 value below the quantification limit. b 10/15 values below the quantification limit. c 1/15 value below the quantification limit. kinetics were noted for the three GBCAs in all tissues and at all timepoints.
The questionnaires were divided into 4 pages, namely, the consent form, demographic and screening items, primary outcomes, and secondary outcomes. The questionnaire pages were limited to around 50 items per page. Cookies were enabled, and participants could resume their questionnaire on the same device. Participants were required to input their research ID in the questionnaire, and duplicated data with the same research ID were eliminated. The postintervention questionnaires were sent to participants in both wait-list control and intervention conditions on the 22nd day. To adopt an intent-to-treat approach, all participants were invited to complete the self-report questionnaires at every time point regardless of whether they had completed the questionnaires at previous time points. Wait-list control participants received the intervention after completion of the 3-month follow-up questionnaire.
Analysis Significance tests such as the independent 2-sided t tests and chi-square test of independence were employed to explore any baseline difference between completers and dropout participants. Intent-to-treat analyses were used in this study. Data of all the participants were included in the analysis regardless of their treatment adherence or attrition. Missing data were treated using multiple imputations, and 100 imputed data sets were generated. van Ginkel and Kroonenberg's  [76]  method of repeated measures analysis of variance (ANOVA) using imputed data was used in this study. Specifically, missing values were estimated and 100 plausible complete versions of the data sets were created. Results from these 100 data sets were then pooled into 1 analysis by applying Rubin's  [77]  pooling procedures.
The pooling procedure was carried out using an SPSS macro by van Ginkel  [78]. Treatment condition (intervention condition vs wait-list control) and time were entered as the fixed between-group and within-subject factors, respectively. To examine the mediating effects of mindfulness and reperceiving (ie, dysfunctional beliefs and attitudes about sleep, pain catastrophizing, power of food) on the relationship between condition and the primary and secondary outcomes, path analyses were conducted using Mplus 7  [79]. In the path analyses, condition was dummy coded (with the wait-list control condition coded as the reference group) and was treated as the independent variable. Mindfulness and condition-specific reperceiving mediator at postintervention assessment were treated as the mediators. Primary and secondary outcomes at 1-month and 3-month follow-up assessments were treated as the dependent variables.
Primer sequences for each cDNA were designed using either Primer Express Software (Applied Biosystems) or qPrimer depot (http://primerdepot. nci. nih. gov) and are available upon request. Detection of RPL0 gene was used to normalize the results. Primer sequences for each cDNA were designed using either Primer Express Software (Applied Biosystems) or qPrimer depot (http:// primerdepot. nci. nih. gov), and these sequences are available upon request.
Proteomics analysis and nano-HPLC-MALDI-TOF/TOF analysis Proteins from parental or MYC/DDK-FBXO32-expressing 501Mel melanoma cells were extracted in buffer containing TRIS-HCl pH7. 5 50 mM, NaCl 15 mM, Triton X-100 1%, supplemented with protease and phosphatase inhibitors. Cell lysates (2 mg) were immunoprecipitated with anti-DDK antibody, washed and eluted with DDK peptide. Then, samples were immunoprecipitated again with anti-Myc antibody and separated by SDS-PAGE. Proteins contained into gel slices were reduced/alkylated and digested by a treatment with DTT/IAA and trypsin. Peptides extracted were separated using a nano-HPLC (250 mm column, Ultimate 3000, ThermoFisherScientific). Nano-HPLC was coupled to Q-exactive plus mass spectrometer (ThermoFisherScientific). Data were reprocessed using Proteome Discoverer 2.
Gene expression profiling Total RNAs from three different melanoma cell lines (501Mel, SKMel28, and WM9) and one short-term culture treated with control or FBXO32 siRNA were extracted using the RNeasy kit (Qiagen), according to the supplier's recommendations. mRNA expression profiling was performed with 8 × 60 K high-density SurePrint G3 gene expression human Agilent microarray, in accordance with the protocol described by the manufacturer. Microarray data analyses were performed using R (http://www. rproject. org/). The quality control was performed using the Bioconductor package ArrayQualityMetrics and custom R scripts. Additional analyses were performed using Bioconductor package Limma. Briefly, data were normalized using the quantile method. Replicated probes were averaged after normalization and control probes were removed.
Statistical analysis No statistical methods were used to determine sample size. Sample size was determined to be adequate based on the magnitude and consistency of measurable differences between groups. The data are presented as the means ± SD and analyzed using two-sided Student's t-test with Prism or Microsoft Excel software. For xenograft studies, sample size was determined using ClinCalc assuming a standard deviation of 30% in the control group and 50% of difference in FBXO32 group (α = 0. 05, β = 0. 1). No randomization was used, and no blinding was done. The data were analyzed using the non-parametric Mann-Whitney test. The difference between conditions was statistically significant at p < 0. 05.
Analysis of another cutaneous melanoma cohort (GSE19234) showed that patients with high FBXO32 expression had a decreased survival (Fig. 1B ), whereas no significant association of HERC5 expression with survival (Supplementary Fig. 1E ) was observed in the same cohort. Note that MITF expression is also associated with a poor survival in this cohort. This is not the case in the TCGA cohort. Additional analysis of FBXO32 expression in the GSE12391 cohort, containing different stages of the disease, including nevi, dysplastic nevi, primary, and metastatic melanomas, showed that FBXO32 expression was increased in metastatic melanoma as compared to earlier stages (Fig. 1C ). Taken together these observations prompted us to investigate the role of FBXO32 in melanomas.
Acknowledgements We thank  Alain Mauviel  for the gift of the TGFb reporter. We thank  Marie Irondelle , head of imaging facility, and all the staff of animal facility. Funding NH was a recipient of  ARC  fellowship. NE-H was a PhD student of the  Labex Signalife program   ANR-11 LABEX-0028-01. FS was supported by  INCa -10573  to CB and NN by  Team ARC  grant  2019  to RB. We also thank the  "Fond de Dotation de la Socièté Française de Dermatology "  AO-2019  and the  "Ligue Contre le Cancer "  N°AAPEAC2019. LCC/STD  for their financial supports to BB-dP.
Differential protein expression of the exocytic machinery in human pheochromocytoma Since catecholamine release is altered in Pheo, we asked whether proteins of the core machinery of secretory granule exocytic process could be deregulated. To do so, we performed a mass spectrometry analysis comparing 5 pairs of human Pheo with their respective matched adjacent non-tumor tissue. To increase detection and sensitivity, we conducted the proteomic analysis on purified subcellular fractions in place of total homogenate. Two main subcellular fractions were isolated from each tissue sample, a membrane fraction enriched for organelles and vesicles derived from the exocytic pathway and a fraction enriched in cytosolic proteins. From our proteomic data set, we selected proteins from the regulated exocytosis (#BPGO:0045055) and secretory granule (#CCGO:0030141) GO terms and focused on proteins significantly up or downregulated in the tumor by comparison with the corresponding non-tumor tissue, with fold change values greater than or equal to 2.
This specific dataset of proteins corresponding to the proteins involved in the machinery of secretory granule exocytotic process clearly differentiate, by their modulation of expressions, the Pheo samples from the non-tumor samples. The functions of the deregulated proteins linked to secretory granule exocytosis include mainly: secretory granule organization and biogenesis, hormone processing, vesicular trafficking, docking, priming, membrane fusion, actin cytoskeleton organization, and small GTPases (Supplementary Table  2  and Figure 5D ). Hence, we observed that numerous secretory granules cargos, as well as different proteins involved in the control of the frequency and the dynamic of the exocytic events are up-regulated.
The color scale illustrates the relative level of protein expression: red, higher expression; blue, lower expression. (D) Functional enrichment analysis of identified proteins. Each differentially expressed protein selected for their potential role in calcium-regulated exocytosis (Table  4 ) has been classified in one or several indicated biological functions. To further assess the relevance of our data, we have compared our proteome dataset to a published gene expression analysis of 60 Pheo and 6 normal adrenal medulla tissues ((Lopez-Jimenez et al, 2010) https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE19422). Using this analysis, we have extracted 170 genes that belong at least to one of the 2 GO terms used in the proteomic analysis (regulated exocytosis and secretory granules) and that are significantly differentially expressed by at least two-fold between Pheo and normal adrenal tissues.