dataesr/scientific-paragraphs-categorization huggingface dataset:
Other datasets WH longitudinal data appropriate for these types of analyses are rare. We analysed several whole-genome longitudinal data (patient 9213 studied in  [17]  and four patients -PIC38417, PIC71101, PIC83747 and PIC90770studied in  [13] ) but none of the data matched that of patient PIC1362. Overall, the molecular clock signal (estimated using the root-to-tip divergence method and the coefficient of variation method) was low in many of the segments (see above for the list of the segments removed). Furthermore, in patient 9213, almost  There is an interaction between the level and the overlap: overlapping regions evolve more rapidly at the BH level but less rapidly at the WH level. P-value notations are identical to Table  1. none of the phylogenies converged in BEAST but in the few segments that did converge (e.
g. VPR-TAT), results were consistent with that obtained in PIC1362 (results not shown). As mentioned above, our results are consistent with earlier studies that have shown a significant difference in evolutionary rates in part of the env gene  [6]. To further investigate the robustness of our results, we estimated evolutionary rates in part of the pol genes for other WH and BH datasets. Estimates of WH and BH evolutionary rates from the POL region for 6 WH datasets and 4 BH datasets supported the differences in ER reported above (Figure  5 ): the median ER at the WH level was 2. 85 • 10 -3 vs. 1. 74 • 10 -3 at the BH level (a significant 1. 64 fold difference, t = 17. 3, d. f. = 1417. 7, p-value < 10 -4 ). Figure  5  also illustrates that there can be great variations amongst patients or amongst BH datasets.
Overall, only one WH dataset stood out (PIC90770) with an ER lower than all the ER measured at the BH level. However, the ER measured in other regions of the genome for this patient were not lower than expected (Additional file 1: Figure  S4 ). This illustrates the utility of working with whole virus genomes originating from the same patient. These additional results show that estimating withinhost evolutionary rates requires detailed datasets that span over several years, with several sequences per time step. Appropriate data that is publicly available is limited but it is likely that there exist private datasets from which further insight could be gained.
We are only aware of one other study that compared WH and BH evolutionary rates across a whole virus genome  [7]. This other study was conducted on hepatitis C virus (HCV). Since HCV has no overlapping reading frames, the authors could cut the virus genome into segments of similar size. The main difference between their study and ours is that the WH evolutionary rates were estimated by pooling data from 15 different individuals, who were all infected by the same source via blood transfusion. Our results corroborate these results on HCV in that evolutionary rates vary across the genome and that the difference between WH and BH evolutionary rates is more pronounced in the envelope region. However, contrary to HCV, there is a difference between WH and BH rates even outside env, which allows us to hypothesise that the nature of transmitted strains differ for these two viruses.
A notable limitation to the generality of our results is that we were only able to analyse full genome sequence data of few patients to estimate WH rates. In order to generalise these results, one should analyse more WH genomes (preferentially sampled from patients with infections progressing at different rates). Overall, this illustrates that estimating within-host evolutionary rates requires extremely good quality datasets that have both a long longitudinal coverage and a deep sampling at each time point. This limitation is not technical and in fact it might be that such data already exists. However, it is not publicly available so far.
ii) Removing recombinant sequences Each segment was analysed with 6 different methods to detect recombination using the RDP software  [28]. According to the designer of RDP, any sequence where at least one of the methods detects recombination can be considered as a recombinant. We applied this criterion here (with a p-value of 0. 05). We did not find any evidence for recombination in the WH dataset. In the BH dataset, some sequences were recombinant and were removed.
This provided us with an R-squared of the regression between root-to-tip divergence that indicates the amount of sequence divergence explained by the sampling date. To do so, we first generated phylogenies using a ML likelihood approach (using the software PhyML v. 3. 0  [29] ). We then estimated the clock-like behaviour of the data by performing a regression between root-to-tip distance in the ML tree and the date of sampling of each sequence using the software Path-O-Gen v1. 3  [30]. Trees were rooted at the position that was likely to be the most compatible with the assumption of the molecular clock. This method estimated the amount of variation in genetic distances that can be explained by the sampling time. Third, we built phylogenies using datasets with randomised sampling dates (in order to scramble the temporal structure) and then estimated the evolutionary rate (ER) on the C2V5 segment.
iv) Balancing datasets In order to maximise the 'clock-likeness' of the data, it helps to have a balanced dataset, i. e. a similar number of sequences from each time point and as many time points as possible  [20]. This was obtained by removing samples (randomly) from the most overrepresented time points. For the WH dataset, we kept up to 13 sequences for each time point and for the BH dataset up to 4 (these numbers were chosen to maximise the signal in the C2V5 segment).
v) Determining the substitution model The substitution model was chosen using the software jModelTest v. 0. 1  [32]. We selected the HKY+ model, which had the advantage to often provide a good (if not the best) fit to the data without being too complicated (Additional file 2: Table  S1 ). This model also has the advantage to allow for comparisons with other studies, such as  [15]. Note that for the WH data, a GTR substitution model sometimes fitted the data better than an HKY model. However, as we show, our results were not influenced qualitatively by the substitution model.
vii) Building the phylogenies Phylogenies were inferred using BEAST v. 1. 6. 2  [10]  with default parameters. Simulations were run until convergence (i. e. an effective sample size greater than 200 for all parameters) and the results were summarised using Tracer v. 1. 5. Evolutionary rates were first estimated on each posterior tree distribution using Tracer. Approximately 10% of the output was used as a burn-in. We also estimated the coefficient of variation of the evolutionary rate for each region. For each data set, there was one tree posterior distribution for each genome segment and each of these http://www. retrovirology. com/content/10/49 posterior distributions were inferred using two different substitution models (GTR and HKY+ ).
viii) Analysing substitution rates on internal and external branches Using a relaxed molecular clock in BEAST allows us to estimate ER on different parts of the phylogeny. We obtained final estimates of ER on internal and external branches for 200 trees from the posterior distribution using the program RateAnalyzer. jar [15]. Note that even though part of the trees of the posterior distribution (200) were used in RateAnalyzer. jar, the results obtained were consistent with that obtained with Tracer, which used the full posterior distributions.
The data We selected full genomes from untreated US patients infected by HIV-1 subtype B from the Los Alamos HIV database http://www. hiv. lanl. gov/. The GenBank accession numbers of all the sequences we used are provided in Additional file 3. There was only one excellent quality longitudinal dataset that fitted our criteria (subject PIC1362, a homosexual caucasian male who refused treatment during the whole infection  [11, 12] ). The dataset consisted of 65 full genome sequences with sampling dates ranging from 1998 to 2002. Earlier studies show that, within a constraint of subsampling sequences that have been collected at a set of distinct sampling times, having an equal number per distinct time is best to maximise the molecular clock signal  [20] , this is why here we kept up to 13 sequences from each time point and ended up with a dataset of 65 sequences.
The Los Alamos HIV database did contain two other studies with longitudinal data of full genome sequences. With one of these datasets  [17] , the analyses were largely unsuccessful, probably because of a lack of molecular signal. More precisely, this dataset was based on 11 sequences sampled from a German patient (patient 9213) from 2004 to 2008 at 4 different time points. Four datasets from the study by Herbeck et al. [13]  could be analysed. A common feature of these sequences is that they were all obtained during the acute phase of the infection (the longest longitudinal timespan was 11 months). This means that the unit for the estimation of the evolutionary rates was months instead of years. The patient codes were PIC38417, PIC71101, PIC83747 and PIC90770. Finally, we measured evolutionary rates in the POL segment (a coverage of at least 900 nucleotides between positions 2300 and 4000 of the HIV genome).
This looser selection criterion allowed us to include data from two other studies: patient WC3 from a study by Kemal et al. [21]  and patient 1005 from a study by Kearney et al. [22]  (other patients were analysed in this study but there was no molecular signal in their sequence data, data not shown). For the BH dataset, we applied the same selection criteria (sequences had to be from HIV-1 subtype B, sampled in drug naive individuals from the US, with known sampling dates). Many sequences came from a study conducted in Boston  [14]  and additional sequences came from other studies  [23] [24] [25]. As for the WH dataset, we homogeneised our sampling by keeping up to 4 sequences from each time point and ended up with a dataset of 30 sequences with sampling dates ranging from 1985 to 2007. As for the WH level, we analysed other BH datasets for the POL segment.
Acknowledgements We thank  I. Bravo ,  J. Herbeck ,  K. Lythgoe ,  Y. Michalakis ,  O. Pybus ,  G. Shirreff ,  T. Stadler  for discussion. We are grateful to  P. Lemey  for sharing his code for the estimation of substitution rates on internal and external branches of the phylogeny. SA is funded by an  ATIP-Avenir  grant from  CNRS  and  INSERM , by the  CNRS  and by the  IRD. CF is funded by a  Royal Society  fellowship.
Using the same approach, the implication of CD14 was also evaluated by using macrophages obtained from CD14 -/-mice. Unexpectedly, despite the absence of direct Tat-CD14 interaction (Figure  2A-B ), the presence of CD14 expression seems to be essential for the activation of TLR4-MD2 signalling pathway by Tat as shown by the absence of cytokine production (Figure  4E-F ). However, these data seem to be in apparent contradiction with those obtained with blockade anti-MD2 and anti-CD14 antibodies, which were unable to block Tat-induced TNF-α and IL-10 production (Figure  G-H ). As controls, and in agreement with previously reported data, the same antibodies completely blocked LPS-induced cytokine production (Figure  4G-H ). We also confirmed that stimulation with LPS at relatively high concentrations restored cytokine production in macrophages from CD14 deficient mice (data not shown).
After incubation with GST-Tat 1-101, GST-Tat 1-45, GST-Tat 30-72 or GST control (1 μM), interaction of Tat with coated rh proteins was analyzed by ELISA. The data represent OD at 450 nm and are representative of one of three independent experiments. B-C) GST-pull down experiments: GST-Tat 1-101, GST-Tat 1-45, GST-Tat 30-72 or GST control (1 μM) coupled to glutathione-agarose beads were incubated B) with recombinant TLR4-MD2, MD2 and CD14 (1 μg/ml) or C) with 500 μg of cellular extracts from HEK cell expressing TLR4-MD2-CD14 or not. After washes, retained and unretained proteins fractions were evaluated by SDS-PAGE and western blot using antibodies directed against CD14, MD2, TLR4 and GST. D) Evaluation of the capacity of recombinant TLR4 to interact with recombinant MD2. rh TLR4 was coated at 1 μg/ml in the wells.
After incubation with various concentrations of MD2 (1-10 μg/ml). TLR4-MD2 interaction was analyzed by ELISA using anti-MD2 monoclonal antibodies. E) rhMD2 and rhTLR4-MD2 compete for Tat-rhMD2 interaction: GST-Tat 1-101 (0. 1 μM) were pre-incubated for 1 h with PBS (control) or with increasing amounts of soluble rhMD2, rhTLR4-MD2, rhTLR4 or rhCD14 before incubation with the coated rhMD2. Binding of Tat to rhMD2 was analyzed as described above by measuring OD at 450 nm. The data represent OD at 450 nm and are representative of one of three independent experiments. showed the co-detection of Tat and TLR4 only in TLR4-MD2 expressing cells. Comparison of the dissociation constant values K 0. 5 of Tat-TLR4-MD2 (10 -9 M) and Tat-MD2 (4. 10 -9 M), showed that K 0. 5 of Tat-TLR4-MD2 was 2. 5 times smaller than that of Tat-MD2.
Acknowledgements This work was supported by  ANRS ,  SIDACTION , and ministère de la recherche et des technologies. We thank  Drs B Lbachir  and  N Chazal  for helpful discussion,  Pr B Ryffel  for the gift of TLR KO mice, the plateau technique INSERM U1043 for technical assistance and  S. Becker  for the reading of the manuscript.
Cells were treated or not with IFN-α (100 U/ml) for 24 h, then were incubated with empty or Vpx-containing VLP (X-and X+, respectively) for 2 h. Finally, the cells were transduced with a VSV-G-pseudotyped GFP-encoding HIV-1 virus for 2 h (MOI 1). Exogenous deoxynucleosides (dNs) were used as controls for further dNTP quantification. Three days post-transduction, the percentage of infected cells (% GFP + cells) was measured by flow cytometry. (B) dATP and dTTP levels were measured using the single nucleotide-incorporation assay (three donors, one representative donor data is shown). OR: out of range. unchanged under IFN-α or IFN-γ treatments, suggesting that the inhibition of Vpx-mediated SAMHD1 degradation was not the result of an alteration of the proteasomal degradation pathway (Figure  5A , bottom).
Acknowledgements The authors greatly acknowledge  M. Chazal  for his technical assistance. The authors acknowledge  K. Labroquère  and  M. Andrieu  of the Cytometry and  Immunobiology Facility  and  B. Durel  and  P. Bourdoncle  from the  Microscopy Facility of the Institut Cochin. This work was supported by grants from the  "Agence Nationale de la Recherche sur le SIDA et les hépatites virales" (ANRS) ,  SIDACTION ,  Fondation de France  and  US National Institutes of Health  ( GM1041981  and  AI049781  to B. K). LD received a fellowship from the  French.
5%, 35. 6% and 36. 2% of patients respectively. This improvement is clinically relevant compared to scores at diagnosis, corresponding at least to a minimal clinically important difference (MCID). Factors such as sex, age, platelet count, bleeding scores, bone marrow aspiration and persistence of ITP at 6 months were not significantly associated with HRQL scores. However, preceding viral infection was identified to have an impact on HRQL. Conclusions: This first longitudinal study assessing HRQL in children with ITP reveals a global improvement in PedSQL™ 4. 0. However, these results should be considered with caution since our data also confirm that self-report HRQL scores are not influenced by any analyzed biologic or clinical parameters. Others tools, such as Kids' ITP Tools, would probably be required to assess the HRQL of this population.
Excellent agreements between the SEM and MCID were shown in the literature  [25, 26]. Improvement in quality of life was defined as the score at M6 corresponding to the score at diagnosis with an improvement at least equal to the value of the MCID score. Potential predicting factors that impact improvement in quality of life at M6 as previously defined were determined by univariate logistic regression. The associations are presented as beta coefficients with standard error (SE) derived from logistic regression. Given the exploratory nature of this analysis, we did not perform multivariate analyses. Statistical analyses were conducted using Stata software (version 12. 1; StataCorp, Texas).
The fact that children enrolled in the French reference population were older than those of our study could be one of the possible explanation for this difference, as younger children are less likely to worry. But this does not explain all the differences observed in parent-proxy reports. These differences were not sufficient to be considered as an impairment, nor a minimal clinically important in terms of quality of life. We did not observe any difference of HRQL between self or parentproxy-reports and the original norm data after 6 months of ITP evolution either. The results are not surprising as ITP is a blood disease which causes considerable worry about both rare but serious bleeding and having a more serious condition. Over the following months, most patients (children and parents) experienced an improvement of HRQL and at M6 quality of life was the same as the healthy population.
Conclusions This longitudinal study assessing HRQL in children with ITP reveals a global improvement in PedSQL™ 4. 0 scores from diagnosis up to six months of follow up, but these variations are not sufficient to indicate significant changes in quality of life. Our data also confirm that self-reported HRQL scores are not influenced by any analyzed biological or clinical parameters. However, further studies using a disease-specific measure, such as the Kids' ITP Tools, will certainly provide more information on HRQL.
Acknowledgements The authors thank the nurses and physicians at the participating centres for dedicated patient care. We are also grateful to parents and children without whom this study would not have been possible. The Quality of Life study described in this paper was carried out using the PedsQL, developed by  Dr. James W. Varni. Contact information and permission to use: MAPI Research Trust, Lyon, France. E-mail: PROinformation@mapi-trust. org -Internet: www. mapi-trust. org. We thank the author who has given permission for the use of  PedsQL via Mapi Research Trust.
Acknowledgments We thank  A. Courtois ,  M. Dubois  and  B. Muller , ( CRCTB, University Bordeaux Segalen ) for their help and for access to the EPR spectrometer. We thank  Z. Ivanovic  and  J. Chevaleyre  ( french blood transfusion service, Bordeaux ) for kindly providing leukodepletion filters and helpful advices with neutrophil purification from leukodepletion filters, respectively.
Laboratory Methods HIV-RNA was quantified with the Cobas Taqman HIV-1 v1. 5 assay (Roche Diagnostics, Meylan, France) as recommended by the manufacturers (threshold of detection of 20 copies/ml). Cell associated HIV-1 DNA in whole blood samples was quantified using the real-time HIV-1 DNA assay (Biocentric, Bandol, France) with a detection limit of 5 copies/PCR. Drug resistance was evaluated by amplifying and sequencing the HIV-1 reverse transcriptase (RT) and protease genes in plasma HIV-RNA samples obtained at enrolment  [19]. Resistance to nucleoside RT inhibitors, non-nucleoside RT inhibitors and protease inhibitors was defined according to the 2012 ANRS HIV-1 genotypic resistance interpretation algorithm (www. hivfrenchresistance. org). HIV-1 subtype was determined by phylogenetic analysis of the RT and V3 env sequences, based on sequence comparisons with previously reported representatives of group M including the reference sequences of subtypes and sub-subtypes, and all the CRF sequences available in the HIV database or genbank (up to CRF54_01B) (http://www.
DNA Sequencing Nucleotide sequences were determined by direct sequencing according to the manufacturer's instructions (Applied Biosystems, Foster City, CA, USA). Electrophoresis and data collection were performed on an ABI 3130 Genetic Analyser sequencerH (Applied Biosystems). Individual sequence fragments for each amplicon were assembled and edited using Sequence Navigator software  [23]. All chromatograms were inspected for sites of mixed bases (double peaks), which would be evidence of priming from more than one template or the introduction of errors in early cycles of PCR. Any sequence with evidence of double peaks was excluded from the subsequent analyses. Nucleotide sequences have been submitted to GenBank (accession numbers [KF142735-KF143426].
Estimation of the Date of Infection of the Donors The date of the donors' HIV-1 infection was unknown. Two methods were used to estimate the date. First, a serum sample was sent to the French National Reference Center for HIV and tested for recent infection by EIA-RI  [29, 30] ; this single indirect enzymelinked immunosorbent assay quantifies antibodies for TM (gp41) and V3 peptides and has been validated as being able to discriminate recently infected individuals from those with longlasting infection. Second, we used a probabilistic modeling approach. For each data set, the env sequences were aligned using MAFFT (L-INS-i option)  [31]. Sites with more than 75% gaps were removed, and we estimated the dates of infection of chronic patients as the date of the most recent common ancestor (MRCA) of his sequences, calculated using the Bayesian, MCMC-based program BEAST v1.
7  [32]. We assumed a GTR+I+C4 substitution model and a strict molecular clock with a fixed substitution rate of 6610 23 substitutions per site and per year (a standard value for env, see, for example,  [33] ). We used a Bayesian skyride tree prior as a coalescent demographic model with time-aware smoothing  [34]. MCMC simulations were run for 2610 8 chain steps with subsampling every 2610 5. Convergence of the chains and results were inspected using Tracer v1. 5. ESS values were larger than 200 for all parameters and all data sets, except for two parameters (prior and posterior) with the MRT data set. We also tested a lognormal relaxed molecular clock, but obtained poor results as nearly identical sequences were separated by large divergence times, most notably among recipient sequences. As a consequence, the infection date of the recipient was close to the infection date of the donor, thus contradicting clinical evidences.
An example of the output of this tool for one recipient (recipient#1) is given in Figure  3A ; it shows a remarkable degree of homogeneity of the env sequences in this Figure  1. Evolutionary relationships between the HIV-1 env genes in the eight donor/recipient pairs. The evolutionary history was inferred using the Neighbor-Joining method  [38]. The optimal tree with the sum of branch length = 2. 01912678 is shown. The tree is drawn to scale, with branch length in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Maximum Composite Likelihood method  [39]  and the unit is the number of base substitutions per site. Codon positions included were 1 st +2 nd +3 rd +noncoding. All positions containing gaps and missing data were eliminated from the dataset.
There were a total of 230 positions in the final dataset. Phylogenetic analyses were conducted in MEGA4  [40]. For each recipient, viruses isolated from PBMC-derived DNA ( N ) and plasma RNA (#) are represented, with a different color for each donor/recipient pair. Asterisks indicate branches with bootstrap values greater than 98%. doi:10. 1371/journal. pone. 0069144. g001 recipient, whose infection was estimated to have occurred 33 days prior to sample collection. Approximately 80% (47/59) of the sequences are identical despite each being amplified from a unique viral genome. Similar proportions of identical sequences were observed in sequences obtained from both plasma (32/39) and PBMC  (15/20). Compared to the reference amplicon, approximately 17% of the sequences exhibited a single nucleotide change, and these changes were randomly dispersed over the C2V5 region; 2% of the sequences carried two nucleotide changes.