dataesr/scientific-paragraphs-categorization huggingface dataset:
The evolution of acute CHIK included 2 stages: the 'viral stage' (day 1-day 4) was associated with rapid decrease of viraemia and improvement of clinical presentation; the 'convalescent stage' (day 5-day 14) was associated with no detectable viraemia but a slower clinical improvement. Women and elderly had a significantly higher number of arthralgia at inclusion and at day 300. Based on the study clinico-biological dataset, scores for CHIK diagnosis in patients with recent febrile acute polyarthralgia were elaborated using arthralgia on hands and wrists, a minor or absent myalgia and the presence of lymphopenia (,1G/L) as major orientation criteria. Finally, we observed that CHIKV intra-host genetic diversity increased over time and that a higher viral amino-acid complexity at the acute stage was associated with increased number of arthralgia and intensity of sequelae at day 300.
Study design Patients and procedures. The main details of the CuraChik trial have been reported elsewhere  [14]. Curachik (http:// clinicaltrials. gov/ct2/show/NCT00391313) was a randomised double blind, placebo-controlled, prospective trial aiming at evaluating the efficacy and safety of chloroquine as therapeutic treatment of CHIK (study protocol available on File S1). This trial included adult patients (18-65 years old, men and women), who volunteered to take part in the study, residing in Reunion Island, having a typical presentation of acute CHIK (defined by acute febrile arthralgia) diagnosed within less than 48 hours. Exclusively general practitioners (GPs) enrolled the eligible patients. Clinical data were collected from three sources: (i) a daily autoquestionnaire from day 1 (D1) to day 14 (D14); (ii) three consultations with a GP on D1, day 7 (D7) (mean 6.
4, SD = 1. 4) and day 25 (D25) (mean 26. 5, SD = 9. 8); (iii) a telephone questionnaire on day 100 (D100) (mean 130. 5, SD = 28. 1) and day 300 (D300) (mean 300. 7, SD = 13. 8). Biological data were collected from the analysis of blood samples on D1, D3, D6 and D16. The extraction of nucleic acids and CHIKV specific RT-PCR  [15]  were carried out from all samples (D1, D3, D6 and D16). Tests for the presence of CHIKVspecific immunoglobulin G (IgG) and IgM were performed at D1, D6 and D16 by an indirect immunofluorescence assay (according to a procedure previously described  [16] ). The case definition of CHIKV positive patients relied on the association of a CHIKV specific positive RT-PCR on D1 and seroconversion on D16. CHIK negative patients tested negative for CHIKV genome on D1 and showed no evidence of seroconversion on D16.
This fragment includes amino acidsspanning position E1/226. The mutation A226V has been reported to confer a virus adaptation to Aedes albopictus  [17, 18]. Analysis was also performed for samples collected at D3 for 2 patients still viraemic at this time. Amplification products were purified (QIAquick PCR Purification Kit), subjected to 39 end adenosine addition (Taq polymerase, Invitrogen), ligated into the cloning vector pCR 2. 1 and transformed into TOP10 competent cells, according to the manufacturer's protocol (TA Cloning, Invitrogen). An average of 45 clones per serum sample was sequenced directly from the plasmid using the T7 promoter primer (59-CCCTATAGTGAGTCGTATTA-39). Sequences were analysed with the Sequencher software and aligned with ClustalX  [19]. For a given sample, the sequence of each clone was compared with the most common sequence using the Mega 4.
Quality of life (assessed by visual analogical scales (VAS)) was severely impacted with the capacity to perform normal activities, health status and quality of sleep assessed on average at 33/100 (SD = 26), 28/100 (SD = 25) and 27/100 (SD = 13), respectively. More than half of the patients scored ,30/100 in all aspects of quality of life evaluation (Figure  2 ). Demographic and biological data were compared as functions of clinical presentation at D1 using multivariate analysis. A higher number of arthralgic joints was independently associated with women (p,0. 001, SIR = 1. 361, CI (1. 191;1. 554)), with an increase of age (p,0. 001, SIR = 1. 017, CI (1. 011;1. 022)) and a decrease of leukocytosis (p,0. 001, SIR = 0. 881 CI(0. 841;0. 923)). Patients with important arthralgia were significantly older than others (p,0.
Accordingly, some clinical data regarding outpatients could be collected from cases notified through either the epidemiological surveillance system  [24, 25, 26] , or community based cross sectional survey  [27, 28, 29, 30, 31, 32, 33]. However, the unique aspect of the current study relies on the detailed clinical and virological follow-up of such patients. It included laboratory confirmation of CHIKV infection (based on RT-PCR and seroconversion), a clinical follow-up at the acute phase (associating daily auto-questionnaires and medical consultations), and late assessments (by telephone until day 300). The study took place at the end of the outbreak (i. e. , the enrolment was performed by practitioners who had previously managed approximately one third of the population infected by the virus), and there is no evidence that another arbovirus (e.
* Clinical assessment at D1 was obtained from all CHIKV+ve patients (N = 54) during the Reunion island outbreak 2005-2006. Only patients receiving placebo (N = 27) were included in D2-D14 clinical assessment. doi:10. 1371/journal. pntd. 0002004. g002 Deciphering these data indicated that the acute disease includes 2 distinct stages (i) the first (D1-D4, 'viral stage') was associated with viraemia, i. e. , clinical symptoms that reflect the viral 'burst' and the initiation of innate immunity, associated with high levels of pro-inflammatory cytokines  [42, 43, 44]  such as interferon-a and IL-6 but also IL-1Ra, IL-12, IL-15, IP-10 and MCP-1. This is in coherence with results from Chow et al. , who extended the 'acute phase' of the disease until D4  [44]. In all cases in the current study, this response was associated with a rapid decrease of viremia from D1 to D3 (39.
Clinical data collection and definitions We obtained clinical data by standardized questionnaires sent to the treating physicians and extracted relevant data from the SHCS database. We collected socio-demographic data (age, sex, origin of birth, citizenship, legal status, immunosuppressive therapy, risk factors for TB such as recent TB within family or immediate social surroundings in the last two years), laboratory parameters (CD4 cell count and plasma HIV RNA in HIV-infected cases) and clinical information (site of disease, radiography findings). Chest radiography parameters were consolidation, cavitations, enlarged intrathoracic lymph nodes and pleural thickening. Any drug resistance was defined as any resistance to isoniazid, rifampicin or ethambutol as reported to the FOPH. Most TB cases in Switzerland are treated under the guidance of experienced infectious and respiratory disease specialists, and the clinical data were of high quality.
Sensitivity analyses In sensitivity analyses, we excluded patients with a history of frequent travelling to remove its influence on the association between HIV infection and allopatric lineages. In addition, we repeated the analyses using fully probabilistic Bayesian methods using weakly informative prior distributions  [71]. The CIs reported from these analyses are 95% credible intervals and correspond to tail probabilities of the coefficient's posterior distributions. Bayesian statistics are less sensitive to errors when calculating estimators and CIs in small datasets.
Acknowledgments We thank all tuberculosis patients included in this study. We are grateful to the  Swiss HIV Cohort Study  and their cohort centers, to the treating physicians for providing clinical information, and to the  Microbiology Laboratories  for providing strains. We are indebted to the  National TB Surveillance Registry at the Federal Office of Public Health  and the  Bernese Lung Association  ( Christa Butz  and  Yvonne Bongni ). We also thank  Joel Ernst ,  Douglas Young , and  Peter Small  for critical review of the manuscript. The members of the study groups:  Swiss Molecular Epidemiology of Tuberculosis Study Group Central  coordinating team:  Lukas Fenner  and  Matthias Egger ,  Institute of Social and Preventive Medicine, Bern ;  Sebastien Gagneux  and  Marcel Tanner ,  Swiss Tropical and Public Health Institute, Basel ;  Hansjakob Furrer ,  Inselspital Bern.
This work was supported by the  Swiss National Science Foundation  (grant number  324730-12544 ), the  Swiss HIV Cohort Study  (grant number  588 ), and the  Federal Office of Public Health  (grant number  09. 007368  and the  National Center for Mycobacteria). L Fenner  and  M Egger  are supported by the  National Institute of Allergy and Infectious Diseases (IeDEA Southern Africa , grant number  5U01-AI069924-5 ) and  S Gagneux  by the  National Institutes of Health  (grant number  R01-AI090928-01  and  HHSN266200700022C ) and the  Swiss National Science Foundation  (grant number  PP0033-119205 ). EC Bo ¨ttger was supported by the  University of Zu ¨rich , the  European Community  (PAR,  FP7-HEALTH-2009-241476 ) and the  Federal Office of Public Health (National Center for Mycobacteria).
Collection of geographical data The geographical coordinates (latitude, longitude, and elevation) were obtained from three different sources. 1. The geographical coordinates of specimens conserved in six recognized Herbaria (Herbier National du Gabon (LBV); Herbier National du Cameroun (YA); Limbe Botanic Garden (SCA), Muséum national d'Histoire naturelle de Paris (P); Royal Botanic Gardens, Kew (K); Missouri Botanic Garden (MO) and Global Biodiversity Information Facility (GBIF) portal (www. gbif. org) were collected. Web sites including Google Earth, world-gazetteer. com and diva-gis. org were used to determine or complete geographical coordinates for samples with these data which are not indicated or are partially specified. The ecological notices indicated by collectors have been used to identify samples from wild individuals.
2. Specimens collected in natural forests mentioned in the literature  (Aubreville, 1962; Onana, 2008)  with their geographical coordinates were selected. 3. The geographical coordinates of specimens (leaves or cambium) collected in natural forests for DNA extraction (conserved in the laboratory of CEFE in Montpellier, France) during IFORA's prospection between 2006 and 2010 in Cameroon and in Gabon were considered. These leaf or cambium samples collected (with herbarium specimens in support) were identified using the identification key developed by  Lam (1932) , leaning on systematic reviews in the genera  (Pierlot, 1996; Onana, 1998, Onana and Cheek, 2003). All duplicate samples or already mentioned in one of the sources have been eliminated to retain one. In total, the geographical data (latitude, longitude and elevation) of 168 collections were compiled.
Geographic distribution and estimation of the spatial richness The geographical data of collections were entered into the software DIVA-GIS 4. 1  (Hijmans et al. , 2005). The natural geographical distribution was established from the geographical coordinates of each sample. The potential geographical distribution was modeled using nineteen climatic parameters extracted from BIOCLIM  (Busby, 1991)  and DOMAIN  (Carpenter et al. , 1993)  available in DIVA-GIS.
Ecological niche and adaptability The characteristics of ecological niche were studied based on the cover of the forest, climatic factors and elevation of collection sites. From these parameters, the map showing the ground cover in which the species could grow naturally was made following the spatial location of collection points. Graphs describing the ecological requirements from the data of the elevation and nineteen climatic parameters were made using the software STATISTICA version 6. Statistical processing was done by descriptive statistics and calculation of the correlation coefficient (r). Todou et al. 233.
Microarrays Experiments Genomic DNA was extracted using the genomic-tip 20/G isolation kit (Qiagen). Two micrograms of DNA from G1-arrested cells and samples was, respectively, labeled with Cy3 or Cy5 fluorescent dye (GE Healthcare -CyX-dCTP), using the Bioprime DNA labeling Kit (Invitrogen) and following the manufacturers' protocol. 500 ng of G1 and sample-labeled DNA was competitively hybridized on 2Â105k Agilent custom microarrays. Hybridization (40 h) and washing were performed using Agilent Oligonucleotide Array-Based CGH protocol. Microarrays were scanned using Axon 4000B scanner (5 mm resolution, PMT 700-500) and data were extracted with Genepix v6. 0. 1. 00 software. All microarray data are available from the GEO database with the accession number GSE37244.
For each probe, processed ratios were calculated from the scaled ratios by adjusting the lowest and the highest ratio values between 1 and 2, respectively, using the following formula: Rprocessed i, j ¼ 1+ Rscaled i, j À MinðRscaled i Þ Max Rscaled i ð ÞÀMinðRscaled i Þ , where Rscaled i, j is the scaled intensity ratio for probe i at time point j, and MinðRscaled i Þ and Max Rscaled i ð Þ are the minimum and maximum value, respectively, for all scaled intensity ratios for probe i. All processed ratios for the three replicate experiments are provided in supplementary table S0, Supplementary Material online, and deposited, along with raw microarray data, in the GEO database (accession number GSE37244). T rep were defined for each probe as the time where processed ratios equal 1. 5. Replication timing profiles were obtained by plotting the T rep as a function of the chromosome coordinates and by fitting a regression curve to the data (Loess) using a span of 30 kb.
Calculation of Mean Fraction of Replicated Probes, Discrete, and Normalized Slopes at Origins from Microarrays The fraction of replicated probes at a given time point was estimated from data on the average DNA content x, as x -1. Indeed, x ¼ ' 1 ð Þ + 2'ð2Þ, where ' 1 ð Þ is the fraction of probes with copy number equal to 1 and ' 2 ð Þ is the fraction of probes with copy number equal to 2, which needs to be estimated. As ' 1 ð Þ + ' 2 ð Þ ¼ 1, one has this result. This procedure yielded a fraction of replicated probes ' (for each locus and time point), which was averaged over a sliding window of 10 kb. The initial discrete slope was defined as (' i (30 min) À ' i (25 min))/5 min and plotted as a function of genome coordinate to perform origin detection under minimal interference. For the estimate of fork velocity, we measured the decay of the discrete slope with the distance d of a sliding window from an origin.
Deep Sequencing Experiments Genomic DNAs from six samples covering S-phase (from 80 to 150 min) were extracted using the genomic-tip 20/G isolation kit (Qiagen). One microgram of DNA from each sample was sequenced using the Illumina technology ("GAIIx" device and "36 cycles Sequencing Kit v5" ). The six libraries were multiplexed before polymerase chain reaction amplification to avoid coverage heterogeneity between samples and sequenced in two channels producing 10 M reads (single-end) per sample. Fastq files were generated using CASAVA-1. 8. 2. Sequencing data are available from the SRA database (NCBI) under the accession number SRA059086. Sequence reads were mapped on the L. kluyveri CBS3082 reference genome  (Souciet et al. 2009) , using BWA 0. 5. 9, allowing no mismatch and no gap. Mapped reads were subsequently filtered to keep only unique match and high quality mapping scores (MAPQ > 37, i.
Generation of Replication Timing Profiles from Sequencing Data Changes in DNA copy number were measured by calculating the ratios of the number of reads between S-phase samples taken every 10 min and the first S-phase sample which correspond to G1-arrested cells. For each time point, the number of reads for every 500 bp window along the genome was calculated using the AMADEA Biopack platform developed by ISoft (http://www. isoft. fr/bio/biopack_en. htm, last accessed February 6, 2013). These ratios were processed as were processed the microarray intensity ratios (discussed earlier). All processed ratio from sequencing data are provided in supplementary table  S1 , Supplementary Material online. T rep were defined for each 500 bp window as the time where processed ratios equal 1. 5. Replication timing profiles were obtained by plotting the T rep as a function of the chromosome coordinates and by fitting a Loess regression curve to the data, using a span of 60 kb.
Evaluation of the differences between two motif logos (http://weblogo. berkeley. edu/, last accessed February 6, 2013) was performed with Two Sample Logo  (Vacic et al. 2006). Two motifs were considered to be similar if they were shifted at most by one position and if they differed in base composition at less than 10% of the positions (P value of difference 0. 05). Perl scripts are provided on request.
GC-Content and Compositional Skews All calculations were performed with the use of the AMADEA Biopack platform. To calculate compositional skews between the leading and the lagging strands, we selected a set of 207 replication origins corresponding to clear peaks flanked on both sides by clear valleys on the replication timing profile. Normalized positions along the 207 replicons were obtained by dividing the size of each replicon into 50 equal bins of 2%. The position of the origin (Ori) was set at 50% and the position of the flanking termination regions (Ter) were set at 0% and 100% in each replicon. For each 2% section, we pooled the data from all 207 replicons and compositional skews were calculated at third codon positions as S GC3 ¼ (G À C)/(G + C), S TA3 ¼ (T À A)/(T + A) and S 3 ¼ S GC3 + S TA3.
S1 ). A number of previous simulation studies have reported a reduction in lipid diffusion adjacent to TM proteins  [25]. Lipid diffusion coefficients for the 10HA simulations (Fig. 4 ) were analyzed in three different regions: lipids within the cluster; lipids outside the cluster; and bulk lipids. This revealed that diffusion of DLiPC within the cluster is considerably slower than that of DLiPC outside the cluster and even more so than that of bulk DLiPC. In contrast, only a small effect is seen for DPPC. It therefore appears that the decrease in DLiPC diffusion coefficient in proximity to the protein is amplified by increased local protein concentration, while the smaller effect on DPPC remains relatively insignificant. Lipid tail ordering was also analyzed, and found to be essentially unaffected by position inside or outside the cluster (data not shown).
HA model The model of HA (Fig. 1 ) was based on the X-ray structure (PDB code: 1MQM) of the protein from the A/duck/Ukraine/1/63 (H3N8) influenza strain  [75] , which was converted to the CG representation using the standard MARTINI tools  [31, 32]. The Xray structure includes the ectodomain, but excludes the TM and cytoplasmic domains, and a short linker between the ectodomain and TM domain. This missing sequence was modeled as a-helix and added to the ectodomain crystal structure; palmitoyl chains were added at the three sites towards the IC side of the TM domain (Cys555, Cys562 and Cys565; see Supporting Information for further details). The model of the intact HA trimer was then simulated in a bilayer patch, allowing for relaxation of the added structure. A clustering algorithm was then used to select the most representative conformation.
Simulation details All simulations were conducted using the Gromacs package (www. gromacs. org) [79]  and the MARTINI CG force field  [31, 32]. The integration time step was 10 fs. Lennard-Jones and Coulomb interactions were shifted to zero between 0. 9 and 1. 2 nm, and 0 and 1. 2 nm respectively. The Berendsen thermostat  [80]  (coupling constant of 1. 0 ps) and barostat (coupling constant of 1. 1 ps; compressibility of 1. 0610 26 bar 21 ; reference pressure of 1 bar) were used. Visualization was performed with VMD  [81] , and analysis with the MDAnalysis Python library  [82]. All reported simulation times and time-dependent data have been adjusted to account for the faster sampling of the MARTINI model; times have thus been multiplied by a factor of 4. The comparison of time-dependent data to experimental work should be considered semi-quantitative.
Segura et al. described high levels of leptospires in the lung, liver, muscle and kidney tissues of patients who died and found that the presence of at least 10 4 leptospires/mL of blood can be considered a critical threshold for fatality  [36]. These findings agreed with those of Truccolo et al. [37]. We considered it relevant to define a leptospiremia threshold associated with disease severity. In our study we determined the leptospiremia in patients who had been diagnosed by quantitative PCR, but we only kept the values for blood samples taken before antibacterial therapy was started. This forced us to exclude data if the exact date of sampling was not available, and led to a substantial number of missing values in our model. We therefore performed a MI analysis, which is a well-established method for analyzing data sets with missing values, and showed by multivariate analysis that a threshold of 10 3 leptospires/mL of serum strongly correlated with severe forms.
Acknowledgments We are grateful to  Suzanne Chanteau  for her constant support for this study and for critical reading of the manuscript. We acknowledge the statistical support of  Noemie Baroux. For their assistance, we thank the directors, the clinicians, the biologists  Dr Yann Barguil  and  Dr Sylvie Tardieu  and the staff of the archive department of the  Centre Hospitalier Territorial of Noumea  and the  Centre Hospitalier du Nord. We are grateful to the  Pole Integrede Recherche Clinique (PIRC)  for their assistance. The authors thank the health authorities, Direction des affaires sanitaires et sociales de  Nouvelle-Cale  ´donie for their support for the leptospirosis surveillance activity.
Sequence analysis The mtDNA sequences have been deposited in GenBank, with accession numbers JX152783 -JX154074. To analyze the mtDNA sequences, we first compared all complete sequences with the reference sequence (rCRS). A summary of all identified mutations is shown in Table  S2. The analysis of single mutations did not highlight any specific mutation that could consistently account for an advantage in longevity. Thus, we analyzed the possibility that nonsynonymous mutations may alter specific genes and affect the chances of attaining extreme longevity. We analyzed mtDNA sequences of ultranonagenarians and controls to find an aggregate mtDNA variation associated with longevity. To avoid false-positive results as much as possible, the analysis of mtDNA sequences was carried out using a gene-based kernel approach  (Wu et al.
Each amplification step was followed by purification with EXOSAPit (U. S. Biochemical, Cleveland, Ohio). The sequencing reaction was conducted using the BigDye Kit, version 3. 1 (Applera), and M13 universal primers (forward and reverse), followed by purification of the sequences by ethanol precipitation. Electropherograms were analyzed with SEQSCAPE, version 2. 5 software (Applera), that allows the assembly of all 46 fragments belonging to the sample and to align and compare the obtained consensus sequence with the revised Cambridge Reference Sequence (rCRS, NCBI: NC_012920. 1 gi:251831 106)  (Andrews et al. , 1999). Two independent operators manually verified sequences for phantom mutations by reads of both strands. The remaining samples were sequenced in Beijing Genomics Institute (BGI) as previously described  (Wang et al.