dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectra were searched against two databases with L. pneumophila Paris proteins encoded by the chromosome (database containing 3142 protein sequences) and the plasmid (database containing 142 protein sequences) (http://genolist. pasteur. fr/LegioList/) with a mass tolerance for precursor and fragment ions of 4. 5 and 20 ppm, respectively, during the main search. Enzyme specificity was set as C-terminal to arginine and lysine, also allowing cleavage at proline bonds and a maximum of two missed cleavages. Oxidation of methionine residues was set as variable modification and carbamidomethyl formation of cysteine residues was set as a fixed modification. Only proteins with at least one unique or razor peptide were retained leading to the identification of 1662 L. pneumophila proteins (S8 Table ).
Proteins were quantified by the MaxLFQ algorithm integrated in the MaxQuant software  [83]. A minimum ratio count of two unique or razor peptides was required for quantification. Further data analysis was performed with the Perseus software (version 1. 5. 3. 0) after loading the protein group file from MaxQuant. Proteins only identified by site, reverse database hits and contaminants were removed and replicate samples were grouped. Proteins with less than three valid values in at least one group were removed and missing values were imputed from a normal distribution around the detection limit. After log 2 transformation of the LFQ intensity values, a t-test was performed (FDR = 0. 05 and S0 = 1) to compare both strains and reveal significantly up-and downregulated proteins (S2 Table ). After Z-scoring, the intensity values of each protein were also visualized on a heat map after non-supervised hierarchical clustering (Fig  2 ).
Genotyping The examined SNPs of TMEM39A were genotyped by applying polymerase chain reaction followed by restriction fragment length polymorphism (PCR-RFLP). Supplementary Table  1  shows primer sequences, annealing temperatures, and restriction enzymes used in this study. HLA-DRB1*15:01 status was determined in our earlier studies  (Wagner et al. 2013; Wisniewski et al. 2013 ) as described by Wisniewski et al. (Wisniewski et al. 2013) , and these data were taken into account in the analyses performed here. Accuracy of genotyping methods for the investigated SNPs was verified through direct sequencing of few samples representing homozygotes of two types and heterozygotes for each investigated SNP. These samples were used as the reference samples in following genotyping experiments.
Since, to date, very little is known TMEM39A gene, it is hard to predict what role it may play with regard to multiple sclerosis. As stated in GeneCards database (www. genecards. org), TMEM39A may be involved in positive regulation of antiviral response by host. Since a large body of evidence indicates that infection with the Epstein-Barr virus has a major role in the pathogenesis of MS  (Pender and Burrows, 2014) , one might expect that lower expression of TMEM39A mRNA (assuming positive correlation between the levels of mRNA and protein) may be associated with higher susceptibility to infection with Epstein-Barr virus and as a consequence with higher susceptibility to multiple sclerosis. However, further studies are necessary to verify this hypothesis, to find out in what way lower expression of this gene may influence the susceptibility to MS and what mechanism is responsible for decreased TMEM39A mRNA expression.
Acknowledgments We wish to thank  Izabela Nowak D. Sc. from the  Laboratory of Immunogenetics  and  Tissue Immunology  for great help in collecting DNA samples from healthy volunteers and  Andrzej Wiśniewski PhD  from the  Laboratory of Immunogenetics  and  Tissue Immunology  for providing part of HLA-DRB1*1501 data. We are also very grateful to our patients and healthy volunteers for blood donation and agreement to participate in this study.
Viral DNA (1 ml) from each sample was used to amplify circular DNA molecules using rolling-circle amplification (RCA) with the TempliPhi TM kit (GE Healthcare). The RCA DNA (5 ml aliquot from each sample) was pooled and sequenced on an Illumina 4000 (Illumina) sequencer at Macrogen Inc. (Korea). The paired-end reads were de novo assembled using ABySS v1. 9  (Simpson et al. 2009 ) and contigs >750 nucleotides were analyzed using BLASTx against a local viral database. A contig of 7,717 nucleotides was identified that encodes proteins that are homologous to those of papillomavirus. Further analysis of this contig revealed a 63 nucleotide terminal repeat and with the repeated region removed this contig represented a unit length papillomavirus. A pair of abutting primers (AdP2F 5'-CCA TGA GAA ACC TTT TAG TCT TCC CGG G-3' and AdP2R 5'-GGT ATG AAA GAG CCA GTG TCA GGC TTA TTG-3') were designed in the minor capsid protein (L2) open reading frame (ORF) to screen and recover the full genome of this papillomavirus.
Sequence analysis Viral ORFs were predicted using a custom, papillomavirus-specific annotation tool as described previously  (Van Doorslaer et al. 2013). Genome-wide identity analysis was performed using default settings within SDT v1. 2  (Muhire et al. 2014). Pairwise sequence comparisons with PaPV1  (Varsani et al. 2014)  were performed within Geneious version 10. 2  (Kearse et al. 2012). The annotated sequence of PaPV1 was downloaded from the PaVE database  (Van Doorslaer et al. 2013; Van Doorslaer et al. 2017). The L-INS-I algorithm within MAFFT v7. 3  (Katoh et al. 2002; Katoh and Standley 2013)  was used to align the translated protein sequence for each individual ORF (using the Blosum62 scoring matrix). Translated protein sequences were back translated to obtain codon-conserving nucleotide alignments.
Lognormal priors were used to calibrate nodes 2, 9, 25, and 26 based on the divergence estimates of the hosts. Calibration means and 95 per cent confidence interval were downloaded from TimeTree (www. time tree. org; Hedges et al. 2006; Hedges et al. 2015). The MCMC chains were run for 1 Â 10 8 generations. Samples were collected every 5,000th generation. The analysis was repeated for a total of six independent runs. The resulting log files and trees were combined using LogCombiner and resampled (every 10,000th generation) while discarding a 20 per cent burn-in. An estimated sample size of >200 was achieved for most parameters. The reported estimates (Fig. 2 ; Supplementary Table  S1 ) are based on the combined dataset. A posterior distribution of potential phylogenetic trees for the E1-E2-L1 concatenated alignment described above was estimated using MrBayes 3.
Matching divergence time estimates (T) associated with the host species were downloaded from TimeTree (www. timetree. org)  (Hedges et al. 2006; Hedges et al. 2015). The values of S and T were log-transformed (base 10), and the average 6SD was plotted. Generalized Deming regression  (Ripley and Thompson 1987)  was performed using the Deming function in R 3. 2. 1 (Team RC 2014). The linear model was then extrapolated to estimate the timescale of other nodes from their S estimates. Data, Python, and R scripts to perform these analyses are available through FigShare.
Acknowledgments The fieldwork for this study was supported by the  US National Science Foundation (NSF)  under grant  ANT-0944411  awarded to D. G. A. , K. M. D. and G. B. , with logistics supplied by the  US Antarctic Program. K. V. D. is supported by a  State of Arizona Improving Health TRIF  and by the  National Institute of Food and Agriculture, U. S. Department of Agriculture, Hatch NC229. The molecular work described in this study is supported by  Center of Evolution and Medicine Venture Fund (Center of Evolution and Medicine, Arizona State University, USA ) grant awarded to A. V. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  US Government.
Methods study design and participants This analysis was nested in the Northern Shanghai Study (ClinicalTrials. gov Identifier: NCT02368938) under the registered name "Prognosis Factors of Mortality and Cardiovascular Diseases in the Elderly Chinese: the Northern Shanghai Study", which was a prospective, ongoing and multistage community-based study. 19 This study was approved by the Shanghai Tenth People's Hospital Institutional Review Board. The study design was described in the protocol already. 19 etween June 2014 and August 2015, 1,721 subjects were invited, of whom 1,599 (92. 9%) were enrolled. In 1,599 participants, 1,586 participants' serum creatinine concentrations were measured. Among these 1,586 participants, 1,579 (99%) had intima-media thickness (IMT), 1,553 (98%) had anklebrachial index (ABI), 1,577 (99%) had left ventricular mass index (LVMI), 1,537 (97%) had urine albumin to creatinine ratio (UACR) and 1,533 (97%) had pulse wave velocity.
Thus, we exclude age and sex from the confounders to avoid excessive computation. Maximum likelihood parameter estimates of odds ratios with 95% Wald confidence intervals were calculated. C-statistics were used to assess the discrimination abilities of adjusted logistic models (Table  S2 ). Akaike information criterion was used to compare the goodness-of-fit among the different models (Table  S3 ). The area under the curve (AUC) of the receiver operator characteristic (ROC) curves was used to indicate the detection value of eGFR equations. Z-test was used to compare the AUCs. Statistical analyses were performed using the SAS software version 9. 4 (SAS Institute, Inc. , Cary, NC, USA). The two-sided significant level of 5% was considered as the level of statistical significance.
strengths and limitations The present analysis has several strengths. First, this study is the first study comparing the eGFR equations in their associations with preclinical TOD in elderly Chinese. Second, we had a good population consistency of various measurements. We analyzed five major hypertensive TOD in. 97% (97%-99%) of the final 1,586 participants in our analysis. There are also limitations in this study. First, in this study, the serum creatinine concentrations were measured by the enzymatic method, so that we excluded the Cockcroft-Gault equation, which is based on the Jaffe method. Second, our target was to compare the associations between equationderived eGFRs and TOD; we did not have the data of GFR from inulin clearance. Thus, the superiority is only for the associations with preclinical TOD.
Publish your work in this journal Submit your manuscript here: http://www. dovepress. com/clinical-interventions-in-aging-journal Clinical Interventions in Aging is an international, peer-reviewed journal focusing on evidence-based reports on the value or lack thereof of treatments intended to prevent or delay the onset of maladaptive correlates of aging in human beings. This journal is indexed on PubMed Central, MedLine, CAS, Scopus and the Elsevier Bibliographic databases. The manuscript management system is completely online and includes a very quick and fair peer-review system, which is all easy to use. Visit http://www. dovepress. com/testimonials. php to read real quotes from published authors. Clinical Interventions in Aging 2017:12.
Acknowledgments We would like to express our great gratitude to all the people who participated in the  Northern Shanghai Study. We also thank  Yuyan Lu ,  Yiwu Zhou  and  Henry Xu  for the dedication and precious advice they provided. This analysis is nested in the Northern Shanghai study which is under the financial support from the  Shanghai municipal government  (Grant IDs  2013ZYJB0902  and  15GWZK1002 ). Dr Yi Zhang  was supported by the  National Nature Science Foundation of China  (Grant IDs  81300239  and  81670377 ).
Acknowledgements The authors wish to thank  Sue Vize ,  Kevin Petrini ,  Anne Meldau ,  Leauga Tamasoalii Saivaise ,  Tapu Tuailmafu ,  Cathy Nunn ,  Christopher Bartlett ,  Filomena Nelson ,  Ali Hobbs  and  Carol Young. Institutions  which provided support included:  In Samoa , the  Ministry of Education, Sports and Culture (MESC) , the  Global Climate Change Alliance (GCCA) of the University of the South Pacific (USP)  and the  Journalists Association of Samoa (JAWS). In Fiji , the  Department of Environment , the  Ministry of Education, Natural Heritage, Culture and Arts , the  Ministry of Information and National Archives  and  Vodafone Fiji. In Vanuatu , the  Ministry of Education, Vanuatu National Advisory Board on Climate Change and Disaster Risk Reduction , the  Vanuatu Cultural Centre  and the  Media Association of Vanuatu (MAV).
5). We recruited 30 participants for a lab based experiment, in which participants carried the experiment on computers in the Psychology department building. One participant was excluded from analysis as she used only one advice level. Lab participants included 13 males (ages 26. 5 ± 6) and 16 females (ages 26. 2 ± 6). Finally, 34 participants were recruited for the neuroimaging part of the experiment. These corresponded to the expected effect size of activity in previous social cognition neuroimaging studies, e. g. , see refs  25, 31, 43. Of these, two participants were excluded from analysis due to head movements and data corruption. Neuroimaging participants therefore included 18 males (ages 24. 7 ± 6. 6) and 14 females (ages 23. 78 ± 4. 6). Finally, in the fully interactive experiment we collected data from 19 triplets (57 participants, 24 males aged 25.
2 ± 4. 76, and 33 females aged 21. 2 ± 2. 25) and excluded 3 triplets from analysis. These included two triplets in whom advisers used only the highest confidence levels (4 and 5) and one triplet in whom the client chose only one adviser throughout the experiment. We therefore analysed the data from 32 advisers in the live interaction experiment. Client and rival adviser algorithms. All participants in the main experiment played the role of an adviser, whereas the client and the other adviser were played by computer algorithm. The other adviser's advice were calculated on each trial according to the probability of the coin being in the black urn (between 0-1), plus noise (~N (0,0. 08)), to range between [5W 5B], just like the participants' advice. After outcome is revealed on each trial, both advice's prognostic value is calculated by multiplying the confidence level (1-5) by accuracy (indicating the correct coin location, i.
Therefore, lower values of the cost function indicate better fit of the model to the data. We used a Markov-Chain Monte Carlo (MCMC) Metropolis-Hastings algorithm for model fitting and estimation for each participant  [60] [61] [62]. For model comparisons we calculated individual Deviance Information Criterion 61 , which uses the distribution of likelihood obtained and penalizes for increased number of parameters (Supplementary Figure  S2 ). We used in house Matlab code and the MCMC toolbox for Matlab by Marko Laine (http:// helios. fmi. fi/~lainema/mcmc/#sec-4). Parameter estimation was done individually by integrating the marginal distribution of the parameter values, uncovered using the Markov process chain  62. The learning rate parameters estimated using the Interaction model were used in the aggregated analysis to determine the trial by trial relative merit and separate trials to positive and negative relative merit.
The mean parameter estimations for all models are reported in table ST1 in the supplementary materials. MRI data acquisition. Structural and functional MRI data were acquired using Siemens Avanto 1. 5 T scanner equipped with a 32-channel head coil at the Birkbeck-UCL Centre for Neuroimaging. The echoplanar image) sequence was acquired in an ascending manner, at an oblique angle (≈ 30°) to the AC-PC line to decrease the impact of susceptibility artefact in the orbitofrontal cortex  63  with the following acquisition parameters: volumes, 44 2 mm slices, 1 mm slice gap; echo time = 50 ms; repetition time = 3,740 ms; flip angle = 90°; field of view = 192 mm; matrix size = 64 × 64. As the time for each block was dependent on the participants' reaction time, overall functional blocks changed in length and approximately 250 volumes were acquired in about 15 min and 40 s.
A structural image was collected for each participant using MP-RAGE (TR = 2730 ms, TE = 3. 57 ms, voxel size = 1 mm 3 , 176 slices). In addition, a gradient field mapping was acquired for each participant. fMRI data analysis. Imaging data were analysed using Matlab (R2013b) and Statistical Parametric Mapping software (SPM12; Wellcome Trust Centre for Neuroimaging, London, UK). Images were corrected for field inhomogeneity and corrected for head motion. They were subsequently realigned, coregistered, normalized to the Montreal Neurological Institute template, spatially smoothed (8 mm FWHM Gaussian kernel), and high filtered (128 s) following SPM12 standard preprocessing procedures. We carried two complementary data analyses. Using the computational behaviour analysis, we labelled trials according to selection by client (chosen/ ignored) and relative merit (positive/negative).
Acknowledgements U. H. and B. B. are supported by the  European Research Council  ( NeuroCoDec 309865 ). S. P. was supported by a  Marie Sklodowska-Curie Individual European Fellowship (PIEF-GA-2012 Grant   328822 ) and is currently supported by an  ATIP-Avenir grant  ( R16069JS ) and a  Collaborative Research in Computational Neuroscience ANR-NSF  grant ( ANR-16-NEUC-0004 ). C. D. F. is supported by the  Arts and Humanities Research Council  (grant  AH/M005933/1 ).
Background: The wide availability of the Internet and the growth of digital communication technologies have become an important tool for epidemiological studies and health surveillance. Influenzanet is a participatory surveillance system monitoring the incidence of influenza-like illness (ILI) in Europe since 2003. It is based on data provided by volunteers who self-report their symptoms via the Internet throughout the influenza season and currently involves 10 countries. Objective: In this paper, we describe the Influenzanet system and provide an overview of results from several analyses that have been performed with the collected data, which include participant representativeness analyses, data validation (comparing ILI incidence rates between Influenzanet and sentinel medical practice networks), identification of ILI risk factors, and influenza vaccine effectiveness (VE) studies previously published.
Introduction Influenza is a global public health problem-whether seasonal, zoonotic, or pandemic-causing high general practice consultation rates, increased hospital admissions, excess deaths, and high absenteeism in schools and workplaces, including in health workers. Its high socioeconomic impact and burden is not just limited to the industrialized world but extends to lowand middle-income countries, encompassing multiple dimensions such as direct costs to the health service and households and indirect costs because of productivity losses, as well as broadly affecting the overall economy  [1]. Influenzanet  [2]  is a participatory monitoring system for influenza-like illness (ILI) based on data reported by Internet users among the general population who volunteer as participants. It was initially conceived to make scientific information accessible to a broad public and to promote students' enthusiasm for science  [3, 4].
It was first launched in the Netherlands and Belgium as "The Great Influenza Survey" (De Grote Griepmeting  [5] ) in the 2003-04 influenza season. In 2005, Portugal joined  (Gripenet [6] ). Subsequently, the system was adopted by Italy (Influweb  [7] ) in 2008, the United Kingdom (Flusurvey  [8] ) in 2009, Sweden  (Hälsorapport [9] ) in 2011, France (Grippenet  [10] ) and Spain  (Gripenet. es [11] ) in 2012, and Ireland (Flusurvey. ie [12] ) and Denmark (Influmeter  [13] ) in 2013. Switzerland joined in December 2016; however, at the moment of the writing of the paper, there were not enough data to be included in the analysis. In 2009, the Influenzanet consortium was established to foster collaboration and pool resources toward using this uniform system of participatory ILI surveillance across Europe.
Here, we describe the Influenzanet participatory surveillance system and provide an overview of the results obtained from different analyses performed with the data, including representativeness analyses, data validation (comparing ILI incidence rates between Influenzanet and sentinel medical practice networks), identification of ILI risk factors, and influenza vaccine effectiveness (VE) studies previously published. Additionally, we present new VE analyses for the Netherlands, stratified by age and chronic illness, and offer suggestions for further work and considerations on the continuity and sustainability of the participatory system.
Influenzanet Data Collection Any resident of a participating country can register on its national website by completing a simple Web-based intake questionnaire containing various sociodemographic, medical, and behavioral questions, in addition to the questions about postal code of residence and workplace (see Multimedia Appendix 1). Once registered, participants receive a weekly email newsletter with a reminder to complete a short symptoms questionnaire. In this questionnaire, participants are asked to report any symptoms that they have experienced since their previous visit to the Influenzanet website. If symptoms are reported, participants are asked to provide further information, including the date of onset, whether these led to a change of behavior (eg, missing school or work or taking medicines), and whether the participant visited a medical service, and if so, the outcome of the consultation.
Participation Rates Several thousands of participants have been engaged yearly in Influenzanet, with over 36,000 volunteers in the last season of 2015-16. On the basis of the 2015-16 season, the Netherlands had the highest number of participants who completed at least 3 symptoms questionnaires (13,821 participants corresponding to 0. 08% of the country's population), followed by France (6413; 0. 01%), the United Kingdom (5134; 0. 01%), Belgium (Dutch-speaking region: 4559; 0. 07%), Sweden (3245; 0. 03%), Portugal (1840; 0. 02%), Italy (1822; 0. 003%), Denmark (1541; 0. 03%), Ireland (575; 0. 01%), and Spain (487; 0. 001%). Data for Sweden are for the 2013-14 season, as after that Sweden started using Influenzanet through invitation only to improve the representativeness of the monitored sample and compare it with the previous seasons.
In 2011-12, Influenzanet launched a standardized common framework for data collection. A study of representativeness was then extended to all participating countries (7 at that time) to assess the representativeness of the sample in terms of a set of demographic, geographic, socioeconomic, and health indicators  [25]. The Influenzanet population was not representative of the general population in terms of age distribution, underrepresenting the youngest and the oldest age groups. However, all age classes were represented. The gender imbalance differed between countries, although higher female participation occurred in most countries (the Netherlands, the United Kingdom, Sweden, and France). Differences between gender-specific information-seeking behavior (more prominent in women) and Internet usage (with higher rates in male populations) may have been at the origin of these gender imbalances.