dataesr/scientific-paragraphs-categorization huggingface dataset:
The full genome sequence of the DENV-3-Jeddah-2014 isolate was then obtained as previously described  [12]. The sequenced length of this isolate was 10 635 bp with an open reading frame coding for 3390 aa. Phylogenetic analyses based on full genome and complete envelope gene of this strain and isolates representing diverse geographical locations were conducted using the maximum likelihood method in MEGA6 software  [13]. Full genome analysis showed close clustering of the DENV-3-Jeddah-2014 isolate with genotype III isolates collected between 2004 and 2007 from Sri Lanka and Singapore, with highest similarity to strain GU370053, which was isolated in 2007 from Singapore (Fig. 1(a) ). However, because of the temporal difference between the DENV-3-Jeddah-2014 isolate and closely related strains, as well as the limited number of available full genome sequences, a phylogenetic tree based on the envelope (E) gene was constructed using all available E gene sequences in the GenBank database within the clade of interest (Fig.
Disuse muscle wasting will likely affect everyone in his or her lifetime in response to pathologies such as joint immobilization, inactivity or bed rest. There are no good therapies to treat it. We previously found that allopurinol, a drug widely used to treat gout, protects muscle damage after exhaustive exercise and results in functional gains in old individuals. Thus, we decided to test its effect in the prevention of soleus muscle atrophy after two weeks of hindlimb unloading in mice, and lower leg immobilization following ankle sprain in humans (EudraCT: 2011-003541-17). Our results show that allopurinol partially protects against muscle atrophy in both mice and humans. The protective effect of allopurinol is similar to that of resistance exercise which is the best-known way to prevent muscle mass loss in disuse human models.
Allopurinol prevents muscle atrophy in ankle sprain (grade II) patients during immobilization. Results of a clinical trial: EudraCT 2011-003541-17. We performed a prospective, interventional, randomized study of the muscle atrophy determined by MRI during lower leg immobilization following ankle sprain (grade II) in young (aged between 25-40 years) male subjects. Following randomization, 13 patients were treated with allopurinol and 12 did not receive any treatment (control) during all the immobilization period (See subjects flow diagram in Supplementary Figure  4 ). We first determined uric acid levels in all patients to test the effectiveness of the allopurinol treatment. We did not find any difference between the uric acid levels in the control group before 5. 9 ± 0. 8 mg•dL -1 and after the immobilization 5.
The proteins of interest were normalized to the α-actin or to the GAPDH expression for each densitometry. We have included only a representative Western blot in each figure. Full-length blots are included in supplementary information. Electrophoretic gels and blots were analyzed with Image Gauge or ImageJ softwares, without any manipulation. Only, in case that it was needed, we change slightly the contrast of the image, but always in a linear way to all the samples and never getting a saturated image or deleting any band. For the figures, the blot image was open in Paint software to be cropped and, to assemble the final version of the image.
Malondialdehyde levels. Lipid peroxidation determination as MDA in plasma was performed as previously described  62. Histology. Cross sectional areas were measured by hematoxylin and eosin staining as previously described  24. Muscle fiber types were identified by immunohistochemical analysis using anti-myosin heavy chain I antibody (1:4000, Sigma Aldrich). Counterstaining was performed with hematoxylin. The minimum transverse diameter in type I fibers was determined measuring the minimum straight line segment that joins two opposite points of the fiber passing through its center. All the histological sections were visualized using a LEICA DMD 108 light microscope. Image analysis was carried out with the Image Pro. Plus. 6 software. Figure  1  (panels A and B) and Fig. 4  (panels C and D) were assembled with Paint software.
The images size was adjusted as a whole image (in all the samples) to the paper format (Photographs of 2048 × 1536 pixels). NF-κB p65 DNA binding activity. NF-κB was determined in nuclear extracts from gastrocnemius muscle according to a modified protocol by Dignam et al. 63  using an ELISA-based TransAM NF-κB p65 assay kit (Active Motif, Carlsbad, CA, USA) in accordance with the manufacturer's instructions. Sample absorbance was read at 450 nm on a multiwell microplate reader (Spectra Max plus 284, Molecular Devices). Wild-type and mutated consensus oligonucleotides were used as competitors for NF-κB binding to ensure specificity of the reaction as per the manufacturer's instructions. All the tests of the samples were run in duplicate, and the average value was used for data analysis. Assay of 6-keto-prostaglandin F1α.
A melting curve analysis was performed to confirm that only the specific products were amplified. The threshold cycle was converted to a relative gene expression by using a standard curve. Primers for amplifying specific fragments of the genes were obtained from Thermo Fisher Scientific GmbH (Ulm, Germany): MAFbx-F, 5′-GCAAACACTGCCACATTCTCTC-3′; MAFbx-R, 5′CTTGAGGGGAAAGTGAGACG-3′; MuRF-1-F,5′-ACCTGCTGGTGGAAAACATC-3′; MuRF-1-R,5′-CTTCGTGTTCCTTGCACATC-3′; Cbl-b-F, 5′-GAGCCTCGCAGGACTATGAC-3′; Cbl-b-R,5′-CTGGCCACTTCCACGTTATT-3′; Cyclophilin-F, 5′-AGCATGTGGTCTTTGGGAAGGTG-3′; Cyclophilin-R, 5′-CTTCTTGCTGGTCTTGCCATTCC-3′. For each sample, the expression of the target gene was normalized with the cyclophilin mRNA content. Clinical Trial. This is a randomized, prospective, interventional study (EudraCT: 2011-003541-17.
Acknowledgements This work was supported by the following grants:  Fundación Grupo ERESA para el Desarrollo y la Investigación Médica, Instituto de Salud Carlos III  and co-funded by  FEDER  [grant number  PIE15/00013 ],  AICO/2016/076  from  Conselleria de Educacion, Investigacion ,  Cultura y Deporte ,  SAF2016-75508-R  from the  Spanish Ministry of Education and Science (MEC) ,  ISCIII2012-RED-43-029  from the  "Red Tematica de investigacion cooperativa en envejecimiento y fragilidad" (RETICEF) ,  CB16/10/00435  (CIBERFES),  PROMETEO2010/ 074  from  "Conselleria, de Sanitat de la Generalitat Valenciana  and  EU  Funded  CM1001  and  FRAILOMIC-HEALTH. 2012. 2. 1. 1-2. A. S. -P. was funded with a  FPU grant  from the  Spanish MECD. We also thank  Mrs Marilyn Noyes  for her kind help in reviewing the manuscript and all the patients who participated in the study.
Data analysis In Brazil, the interviews were conducted in Portuguese by the first author (PEP) and recorded using Audacity version 2. 1. 2. In France, the interviews were conducted by two investigators (SK and LG) and recorded by audiotape. Interviews were transcribed and analyzed in the original language using the qualitative content analysis method  [28]. First, two researchers independently read the transcripts to gain a contextualized impression of the interview. Then, codes were assigned to each sentence of the interview by each researcher independently, a process called coding. A code is a word or short phrase that captures and summarizes what the patient says in a particular part of the interview. In the sentences "I can't dress myself, tie my shoelaces and buttons due to psoriatic arthritis" and "Now I can't walk outdoors on flat ground and climb up steps", for example, the codes "dressing" and "walking" could be used, respectively, to summarize the sentences.
Results Twenty-eight PsA patients were interviewed: 15 in Brazil and 13 in France. The individual demographic data and disease characteristics of participants are described in Table  1. A comparison between the French and Brazilian samples is shown in Table  2. The samples from Brazil and France had participants with a wide range of disease duration, including patients with early PsA to those with more than 20 years of diagnosis. Brazilian patients had higher disease activity, a higher proportion of participants with current or previous use of biological therapy and a lower educational level compared to the French sample (Table  2 ). The analysis of the 28 transcripts found 67 different codes, grouped in 41 categories. Twenty-seven out of 41 categories (66%) were common to Brazilian and French participants (Table  3 ).
Demultiplexing was performed as previously described  (13). Leech control isolate Hm21 was sequenced in a different study as previously described  (10) , and leech-derived isolate Hv13-B-10d was sequenced using PacBio RS II. Quality filtering of reads, genome assembly, and annotation. For Illumina-sequenced strains, paired MiSeq fastq read 1 and read 2 files were imported into CLC Genomics Workbench software (CLC Bio-Qiagen, Aarhus, Denmark). Reads were trimmed, and those with Q scores of Ն15 were kept for the downstream assembly. Genome de novo assemblies were done with scaffolding using the same software; gene prediction and annotation of assembled genomes were performed using Prokka (66) for all strains except for the five leech control isolates, which were annotated using the RAST server (67) (Tables  S3  and S5 ).
Sequencing was performed on an Illumina MiSeq using a 2-by 250-bp paired-end protocol. After sequencing, the reads were demultiplexed according to their sample indexes, and read pairs were merged using SeqPrep to form single, high-quality contigs which were then quality trimmed using a Q 30 cutoff over a 10-bp sliding window and minimum length cutoff of 375 bp. The primer sequences were removed with Cutadapt (https://cutadapt. readthedocs. io/en/stable/), and the reads were then formatted for analysis using QIIME. For QIIME analysis, the reads from all samples were clustered by Uclust based on 99. 5% sequence identity, which allows for less than a 2-bp difference between reads. Representative sequences were then selected and aligned against a set of reference gyrA sequences selected from Aeromonas strains for which the gyrase A sequence was available in GenBank, including sequences from isolates whose genomes have been fully sequenced.
This competitor strain was derived from the leech strain Hm21, which was isolated from the crop of the medicinal leech, Hirudo verbana  (10). Test strains were ciprofloxacin-resistant Aeromonas leech isolates that were also selected for spontaneous rifampin resistance. Growth rates of all strains were determined in LB at 30°C in order to confirm that the mutants did not exhibit any growth defects in vitro. Hm21RT was also competed against another commonly used competitor strain, Hm21RS, a spontaneous rifampin-and streptomycin-resistant mutant of Hm21, to verify that Hm21RT's growth in the leech did not vary from Hm21RS (data not shown). The following final concentrations of ciprofloxacin were also used in the blood meals: 0, 0. 0025, 0. 007, and 0. 01 g/ml. Leeches used were obtained from BBEZ (Bierbertaler Blutegelzucht GmbH, Bierbertal, Germany).
Samples were serially diluted and plated as described above for the in vivo competition assay. Statistical analysis. Data were analyzed in GraphPad Prism 6 (GraphPad, San Diego, CA). A Kruskal-Wallis one-way analysis of variance with Dunn's post hoc test was used to determine if the CIs differed from one another (P Ͻ 0. 05). Sample means in Fig. 2  were log transformed due to high variation and analyzed with a one-sample t test to determine if sample means were significantly different (95% confidence interval) from a CI value of 1. Detection of ciprofloxacin and enrofloxacin using HPLC. Compounds were quantified via ultrahigh-performance liquid chromatography electrospray ionization mass spectrometry (UHPLC-ESI-MS) with accurate-mass detection. HPLC was performed with a reverse-phase HPLC column: an Agilent PLRP-S PSDVB column with 3.
7 min, and enrofloxacin eluted starting at 2. 75 min. The mass spectrometer used was an Agilent 6538 quadrupole time of flight (QTOF) spectrometer with ESI source; resolution is approximately 20,000 and accuracy is 1 ppm. Source parameters were as follows: drying gas, 8. 0 liters/min; drying gas heat at 350°C; nebulizer, 55 lb/in 2 ; capillary voltage, 3,500 V; capillary exit, 100 V. Spectra were collected in positive mode as appropriate from 50 to 1,700 m/z at a rate of 2 Hz. Samples were quantified with the Agilent MassHunter Quantitative Analysis package, using centroid data mode and peak definitions of 332. 1377 and 360. 1687 m/z for ciprofloxacin and enrofloxacin, respectively. Both analytes used a Ϯ50-ppm window for the m/z definition, which was evaluated for lack of interfering background signals with the samples.
Multiple sequence alignments (MSAs) were generated by aligning the genes using MUSCLE (v3. 8. 31)  (71). In-house scripts created a concatenated alignment of all 16 genes. A model of evolution was determined by using the Akaike information criterion with correction for small sample size (AICc), as implemented in jModelTest 2. 1. 4 (72). A maximum likelihood (ML) phylogeny was generated from the concatenated MSA, and individual gene phylogenies from the individual gene MSAs were determined by using PhyML (v3. 0_360-500M)  (73). PhyML parameters consisted of a general time-reversible (GTR) model, estimated proportion of invariable sites (p-invar), 4 substitution rate categories, estimated gamma distribution, and subtree pruning and regrafting enabled with 100 bootstrap replicates. MLSA and genome alignment distance calculation.
The number of differences between the sequences in the concatenated alignment was calculated using the R package Ape. The dna. dist function was called with the model parameter set to "N" and pairwise. deletion parameter set to "TRUE. " Average nucleotide identity analysis. Assembled contigs were reconstituted from the RASTgenerated GenBank files for all genomes by using the seqret function of the EMBOSS package  (74). All genomes were treated in the same manner to ensure that any biases were consistent across the entire data set. JSpecies1. 2. 1 (75) was used to analyze these contig sets for the ANI, using default parameters. We report here the averages of the reciprocal comparisons. Whole-genome phylogenetic reconstruction. A whole-genome alignment of the 18 members of the AvCp clade was generated from sequence files in GenBank format using the progressiveMauve algorithm of Mauve  (76).
Hm21, Hv221, Hv571, Hv13-B-10a, and Hv13-B-11a were included to serve as outgroups for rooting purposes. The XMFA alignment files were converted into FASTA format using in-house scripts. A model of evolution was determined by using the Akaike information criterion with correction for small sample size (AICc), as implemented in jModelTest 2. 1. 4 (72). Phylogenies were calculated using both RAxML v8. 1. 17 (77)  under GTR CAT and GTR plus estimated gamma plus invariable sites models (producing identical topologies) and MrBayes v3. 2. 4 x64 (78) under a GTR model, with estimated gamma. Data availability. All scripts used for analysis, along with the gyrA reference data sets, are available from http://github. com/joerggraflab/SARIS. Accession numbers. All gyrA sequencing data were deposited under BioProject PRJNA296880 to the INSDC SRA.
The URL is https://www. ebi. ac. uk/ena/data/view/PRJNA296880. All new genome sequencing data presented in this study have been deposited to the European Nucleotide Archive under the study accession number PRJNA297409, except for one genome which is deposited under the study accession number PRJEB6940. The URL is https://www. ebi. ac. uk/ena/data/view/PRJNA297409. More information regarding sequencing metadata and individual sample accession numbers is shown in Table  S5.
ACKNOWLEDGMENTS We thank  Susan Janton  for excellent technical assistance;  Brian Nussenbaum ,  Carmen Giltner , and  Romney M. Humphries  for providing strains;  Rudy Rosenberg  for arranging a shipment of leeches, and  Jeremiah Marden  for helpful comments on the manuscript. We acknowledge the  Mass Spectrometry Facility at Montana State University (Murdock Charitable Trust  and  NIH 5P20RR02437  of the  CoBRE program ), the  UConn Bioinformatics Facility  for providing computing resources, and the  Center for Environmental Science and Engineering at the University of Connecticut  for analyses. Illumina sequencing was performed at the  Microbial Analysis, Resources and Services Facility of the University of Connecticut. This research was supported by  NIH   R01 GM095390  to  J. Graf ,  P.
Here we present, to our knowledge, the most comprehensive study to date examining the impacts of a major sporting event on ED attendances. Focussing on one major tournament, the 2016 UEFA European Football Championship (Euro 2016), we use a consistent methodology across four nations involved in the tournament (England, France, Northern Ireland [NI] and Wales). Syndromic surveillance data were used (where available) as a source of ED attendance data from multiple, geographically distinct EDs, collected using standardised methodology. Differential impacts were explored by country, time of day, age, gender and type of diagnosis. Alcohol-related and myocardial ischaemia (MI) attendances, increases in which have been linked to sporting events previously  [8, 9] , were readily identifiable as syndromic indicators (groupings of diagnoses codes).
Data sources In England and NI the ED Syndromic Surveillance System (EDSSS) is a voluntary network of sentinel EDs covering around 10% of all emergency care attendances in England and over 30% of attendances at type 1 (major) EDs in NI during Euro 2016  [11]. The OSCOUR 1 network in France, initially created as a voluntary network of EDs, became a mandatory national ED syndromic surveillance network during 2014. During Euro 2016 86% of all EDs in France and its overseas territories reported to OSCOUR 1  [12]. EDs which reported to EDSSS or OSCOUR 1 throughout the Euro 2016 period and the previous 2 years were eligible for inclusion in this study. Attendance data for all EDs in Wales were obtained using a bespoke query of the ED dataset held by the National Health Service Wales Informatics Service  [13].
Overall attendance data (for any condition) were obtained for 1 June-14 July 2016, encompassing the entire period of the tournament, as well as for corresponding periods in 2014 and 2015 (matched by day of the week: 3 June-16 July 2015 and 4 June-17 July 2014), subdivided by gender and age (0-4, 5-14, 15-44, 45-64, 65+ years). The data from all EDs, were subdivided by hour of arrival. Using the syndromic surveillance indicators available in EDSSS (restricted to EDs recording ICD-10 [14]/ SNOMED CT [15] diagnosis codes) and OSCOUR 1 (all EDs report ICD-10 diagnosis codes), daily data on ED attendances with a diagnosis of MI or related to alcohol surveillance data for England and Northern Ireland are collected and held by PHE for surveillance purposes and are not available for access: the authors had special access to the data for England and Northern Ireland.
PHE are data processors for attendance data reported to EDSSS; individual NHS Trusts are data owners. EDSSS governance agreements with each Trust state that ED attendance data collected through the EDSSS will not be shared with other parties to preserve the anonymity of Trusts and patients. The Real-time Syndromic Surveillance Team can be contacted directly at: syndromic. surveillance@phe. gov. uk. Information on the PHE Office for Data Release, including contact details, can be found here: https://www. gov. uk/government/publications/ accessing-public-health-england-data/about-thephe-odr-and-accessing-data. • Syndromic surveillance data for France are collected and held by SantePublique France for surveillance purposes. Bespoke emergency department data requests for public health research purposes can be formulated and will be reviewed on a case-bycase by the agency.
The syndromic surveillance unit can be contacted directly with requests for data used in this study at: sursaudhotline@santepubliquefrance. fr. • Emergency Department data for Wales are held by NHS Wales Informatics Service: http://www. infoandstats. wales. nhs. uk/page. cfm?orgid= 869&pid=40977. Data used in this study can be requested by contacting: pdit. requests@wales. nhs. uk. (EDSSS-acute alcohol intoxication; OSCOUR 1 -any alcohol related diagnosis) were obtained for each country, 1 June 2014-31 July 2016, plus corresponding periods in 2014 and 2015 (matched by day of the week). It is important to note that different codes were used in different countries (and indeed in different EDs), however this is most likely due to the diagnoses available for selection in the patient record, rather than a true difference between presentations.
Ethics approval and consent to participate Ethical approval and consent to participate for this work was not required. The anonymised EDSSS data used in this study were routinely collected and analysed as part of the public health function of PHE. The collection and analysis of data provided by the OSCOUR 1 network in the frame of public health surveillance and epidemiological studies has been authorized by the French National Commission for Data protection and Liberties (CNIL). The anonymised ED data used in this study were routinely collected by NHS Wales Informatics Service and analysed as part of the public health function of Public Health Wales.
Statistical analyses ED attendances are influenced by hour of day, day of the week, holiday periods and time of year  [16] , requiring statistical analysis to differentiate between changes in attendance levels related to Euro 2016 and other effects. Expected hourly numbers of attendances were estimated using negative binomial models, to account for possible over-dispersion in the syndromic surveillance data. The impact of matches was modelled with the inclusion of three Boolean variables (1/0) representing the 'pre-match' (4 hours before), 'during match' (2 hours: 3 hours for the final), and 'post-match' (4 hours after) periods. The pre-match and post-match periods were chosen based upon previous research  [7, 8, 17]. Models were developed to investigate their effects on the expected hourly number of attendances, for all data pooled and for each nation individually and: 1.
Analysis of hourly attendances did, however, show some indication of reduced ED attendances during, and possible increases immediately following matches, particularly in France. The France national team played four Sunday  Overall odds ratios (OR) for hourly ED attendances (all countries) compared to the number expected indicate a statistically significant impact, although small, with pre-match and duringmatch reductions in ED attendances (Fig  2 ). Across the whole dataset (all nations, all matches) there was a reduction in pre-match attendances (OR 0. 97, CI 0. 94-0. 99), with a stronger reduction observed on weekdays than weekends (Fig  2 ). Pre-match reductions were observed in each country individually and were statistically significant in France and Wales. During match periods statistically significant reductions in ED attendances were observed in all instances, though not statistically significant for Northern Ireland (Fig  2 ).
Acknowledgments We acknowledge the contribution and support from the ED clinicians and Trust staff; the ongoing support of the  Royal College of Emergency Medicine ; the technical support provided by  EMIS Health  and  L2S2  Ltd in developing the EDSSS system in England and  Northern Ireland. We also acknowledge the contribution from the ED structures and clinicians involved in the OSCOUR 1 network in France; the on-going support of the  Federation of the Regional Observatories of Emergencies  and the  Scientific Society of Emergency Medicine. We are grateful to  Anna Morris ,  NHS Wales Informatics Service , for providing ED attendance data for Wales.