dataesr/scientific-paragraphs-categorization huggingface dataset:
Contrary to what expected, we did not detect traces of interspecific recombination and genetic exchanges between the different species. Finally, these results should have consequences on the taxonomy, on our understanding of the epidemiology and on the therapeutic management of these infections. occurrence of genetic exchanges in our dataset, MLSA data were first analyzed using a network representation with the aim of replacing the bifurcating tree model with a ''reticulating tree'' model, in which the reticulations represent possible evolutionary processes other than lineal descent with modifications, such as horizontal gene transfers  [27]. Maximum Likelihood (ML) trees were constructed using PhyML, version 3. 0  [28, 29]. The bestfitting model for nucleotide substitution was identified using the Corrected Akaike information Criterion (AICc) and Bayesian Information Criterion (BIC) implemented in JModelTest  [28, 30].
These distantly related strains were selected to prevent derived characters to be wrongly considered as common ancestral characters. Non-parametric Shimodaira-Hasegawa (SH) tests implemented in the PAUP*4. 0b10 package were used to test the tree topology congruence  [36]. For a given dataset, the SH test uses the difference in log likelihoods of competing topologies as the test statistic. The null distribution of the test statistic (differences in log likelihoods) was obtained by using 1,000 replicates of nonparametric bootstrapping of re-estimated log likelihoods. To avoid potential bias toward higher levels of significance due to small numbers of topologies, 100 random topologies where added to each test. Tree congruence was also assessed by using the topological supertree approach (PhySIC_IST)  [37].
However, linkage analysis (RDP3, PHI-test) to test the possibility of recombination between neighboring alleles did not show any recombination signal, except between the genotypes LST0045, LST0032 and LST0115. Nevertheless, the signal was weak and was not possible to identify without ambiguity which of the three genotypes would result from the hybridization of the two others. Surprisingly, in 30 (38%) of the remaining heterozygous positions, the second parental allele was not found in the same cluster or in the other six clusters. Overall, of the 78 heterozygous positions identified in our dataset, 29 (37%) were orphan of one allele and therefore probably resulted from mutations that remained in the current populations rather than from recombination events.
This inconsistency was confirmed by the SH test (p-value,0. 05; Table  S5 ) except for cluster II (p-value = 0. 28). To assess whether the genetic structure within clusters was similar between MLSA and MLEE, the supertree approach was used to combine in a supertree all the Table  2. Statistical assessment of congruence between the tree topologies of the seven housekeeping genes. MLSA and MLEE sub-trees corresponding to the main individual clusters. This method showed that the genetic structure within clusters was not consistent between the two classifications (CIC values lower than 40%). Only cluster II (L. tropica) exhibited a well conserved genetic structure between MLSA and MLEE as indicated by the many sub-groups that were conserved in the supertree (Figure  S8 ; CIC value above 78%). The juxtaposition of the genetic data to the geographical data showed that the geographical origin of the genotypes was not randomly distributed (Figure  3 ).
More evolution by mutation than recombination? We did not detect any recombination signal whatever the approach used, except between the genotypes LST0045, LST0032 and LST0115. Conversely, we identified heterozygous positions in which only one of the two parental alleles was detected in the dataset (orphan allele), probably as a result of mutation rather than Figure  3. Geographical distribution of cluster II, VI and VII genotypes. The neighbor-Net analyses of clusters II, VI and VII (defined in Figure  1 ) are represented. The genotypes are color-coded according to their geographical origins (detailed in Table  S1 ). doi:10. 1371/journal. pntd. 0002255. g003 recombination events. Accumulation of independent mutations on each of the two homologous chromosomes has been observed in organisms with asexual reproduction, such as Bdelloids, and is usually described as the Meselson effect  [56].
In addition, some genotypes included in our dataset were identical, although the corresponding strains were collected over a long period of time. For example, the strains with the genotypes LST0001, LST0002, LST0010, LST0008, LST0005 and LST0007 were collected over 30, 29, 27, 26, 25 and 23 years respectively. This temporal stability is in agreement with results presented by Boiteet al. on South American Leishmania strains belonging to the Viannia sub-genus and might reflect limited genetic recombination  [13].
Conclusion After more than 20 years of debate, the question of genetic exchanges between parasites of the Leishmania genus remains open. Although interspecific exchanges could be a possible source of plasticity and adaptation to new life conditions and they have been described in natura, they do not seem to have left detectable traces in the current genomes, at least in our dataset (222 Leishmania strains from Africa and Eurasia). Different Leishmania species might evolve through progressive divergence, by accumulating mutations and being structured through distinct parasitic cycles. Concomitantly, the dispersion of some genotypes in wide geographical areas also indicates a process of active diffusion, probably linked to the mobility of some reservoirs, such as humans or dogs, and to the parasite capacity to jump from a reservoir species to another.
From a taxonomic point of view, the strong genetic structuration of the different species does not question the current classification, except for species causing visceral forms of leishmaniasis (L. donovani, L. infantum and L. archibaldi) for which the current classification does not seem valid. Although these taxa cause different clinical forms and are maintained in different cycles, they are not clearly distinct and form a continuum, in line with the concept of ''L. donovani species complex'' already suggested for this group a long time ago. Our data also suggest that L. killicki should not be considered as a valid Leishmania species. These results might have practical consequences regarding the molecular identification of Leishmania parasites and the subsequent therapeutic management. Finally, our system provides an improved resolution in comparison to MLEE and could contribute to both a novel MLSA/MLST system and future Leishmania subgenus whole genome sequencing projects.
Acknowledgments The authors are grateful to two anonymous reviewers for their comments that significantly improved the manuscript, to  Sylvain Brisse ,  Jean-Antoine Rioux  and  Jean-Pierre Dedet  for valuable discussions,  Yves Ballard ,  Patrick Lami  and  Loı ¨c Talignani  for their technical assistance and  Valerie Macioce  and  Elisabetta Andermarcher  for English assistance.
Results and Discussion Analysis of raw metabolite profile data of selected cyanobacteria (Table  1 ) led to the curation of 264 metabolites across the ten analyzed cyanobacteria (Supplementary Table  S1 ). The data analysis was performed combining the MathDAMP package  [34]  with iterative manual curation as described in the Experimental section. Annotations of metabolites were also visualized on density plots (Figure  1 ) to find any unannotated metabolites. Our aim was to annotate polar metabolites with at least one of its spectral features above 10,000 ion counts. Tandem mass spectrometry (MS/MS) was performed on characteristic ions (primarily [M + H] + in positive mode and [M -H] -in negative mode) of metabolites following spectral feature annotation (Figure  1 , Supplementary Table  S1 ).
Putative identifications of metabolites were based on our in-house database, built with identifications from our previous studies  [26, 29, 30]  and analysis of MS/MS spectra using public spectral libraries  [35, 36]. MS/MS spectra do not always provide sufficient information for full structural characterization of metabolites without corresponding true chemical standards, which limits the characterization power and overall degree of certainty of untargeted metabolomics  [37]. Out of 264 metabolites annotated in this study, we could assign chemical formulae to 157 and putatively identify only 105 metabolites. This further underscores the relative ease of detecting metabolites which are not included in databases of metabolism or spectral libraries using untargeted metabolite profiling.
Other Metabolites Similarly to the differences in the distribution of oligosaccharides, we also observed differences in the profiles of fatty acids, especially unsaturated ones (Supplementary Figure  S3 ). A large proportion of detected metabolites remained unidentified and their chemical formulae are not present in databases of metabolism KEGG  [56]  or MetaCyc  [57]. These metabolites represent a valuable resource for the characterization of novel metabolic capabilities. Having metabolite profile data of only ten cyanobacterial strains proved insufficient to link specific genes to individual metabolites as the number of candidate genes correlating with the presence or absence of specific metabolites was too large. Nevertheless, we believe that scaling up untargeted metabolite profiling to a larger number of strains would enable to both detect novel metabolites as well as to zoom in on a small number of potential corresponding biosynthetic genes.
LC-MS Analysis An Agilent capillary 1200 liquid chromatography system coupled to an Agilent 6520 ESI-Q-TOF mass spectrometer was used for LC-MS analysis. ZIC-HILIC column (3. 5 μm, 100 Å, 150 × 1 mm) for normal phase liquid chromatography using analytical conditions as described previously  [28]. Profile mode data (MS1) were acquired using a fast polarity switching mode. One LC-MS run was performed for a single sample of each strain. Fragmentation (MS/MS) spectra were acquired as two separate positive and negative polarity runs for each sample using data-dependent selection of precursor ions (Auto MS/MS) using collision energy of 10 V. Sample injection volume was 2 μL.
Data Analysis Raw datasets from profile mode (MS1) analysis were exported to mzdata format using Agilent MassHunter Qualitative analysis software (B. 05. 00) and preprocessed by MathDAMP package  [34]  to unit mass resolution for comparative analysis. Differences among preprocessed datasets of the ten analyzed cyanobacteria were identified using MathDAMP by direct comparisons identifying outliers using quartile analysis  [34]. Redundant spectral features potentially corresponding to a single metabolite (adducts, multimers, fragments) were grouped by correlation of their peak shape along the chromatographic dimension as described previously  [29]. Resulting groups of spectral features were manually curated using Agilent MassHunter Qualitative Analysis software for chemical formula calculation.
Genome Analysis Candidate genes and synthesis pathways were identified using the data and tools in Integrated Microbial Genomes database (IMG)  [59]. GenBank accession numbers of 10 cyanobacterial genomes are as follows: CP003943 (Calothrix sp. PCC 7507), (Chroococcidiopsis sp. PCC 6712), CP003591 (Geitlerinema sp. PCC 7407), CP003945 (Halothece sp. PCC 7418), CP003946 (Leptolyngbya sp. PCC 7376), CP003548 (Nostoc sp. PCC 7107), CP003590 (Pleurocapsa sp. PCC 7327), AP008231 (Synechococcus elongatus PCC 6301), CP000951 (Synechococcus sp. PCC 7002). Putative orthologs of experimentally characterized proteins in cyanobacterial genomes have been identified as bi-directional best BLASTp  [60]  hits using e-value cutoffs of 1. 0 × 10 -5. Experimentally characterized proteins included in the analysis were ergothioneine biosynthesis proteins EgtB-E from Mycobacterium smegmatis MC2 155  [39] , ovothiol biosynthesis protein OvoA from Erwinia tasmaniensis  [61] , GSMT and SDMT from Ectothiorhodospira halochloris  [41] , ggpPS from Synechocystis sp.
The same argument applies for a combination of group A markers with group C or D markers, linking clinical relevance with mechanistic insight. Such approaches will also result in new datasets that may be used to establish correlations of particular group C or D markers with clinical endpoints, which may lead to their evolution into group A markers within a reasonable time frame. In contrast, measuring numerous group B markers alone will lead to results that may be very clear regarding the clinical relevance, but remain inconclusive on the involvement of (altered) immune functions. Likewise, studies that focus exclusively on group D markers may very convincingly demonstrate that particular immune functions are altered, but the clinical relevance of these changes will remain largely elusive.
Acknowledgements This work was conducted by an expert group of the European branch of the  International Life Sciences Institute (ILSI Europe). The authors would like to thank  Prof. Philippe Moreillon  ( University of Lausanne ) who chaired this expert group in its early stages, as well as all participants of the workshop held on 16 -17 April 2012 in Nice, France, for their active contribution to this work. This publication was coordinated by  Marie Latulippe ,  Scientific Project Manager at ILSI Europe. The expert group received funding from the  ILSI Europe Nutrition and Immunity and Probiotics Task Forces. Industry members of this task force are listed on the ILSI Europe website at www. ilsi. eu. For further information about ILSI Europe, email info@ilsieurope. beor call þ  32 2 771 00 14.
Besides, we thought that these ESEs could be modified to further increase their strength and add force to the trans-splicing activity. This was achieved by using the ESEfinder software, which guided the choice of several silent nucleotide changes in ESEs at the level of the exon 23 of the PTM (Supplementary Figure  S4A )  (43, 44). In particular, two nucleotide changes-nucleotide 15 from E23 beginning (UCU > UCC, both codons encoding serine) and nucleotide 16 (UUG > CUG, both codons encoding leucine)-were introduced to increase the score value for SC35 protein binding from 2. 7 to 5. 2 (E23 ESE1, Supplementary Figure  S4B ). In parallel, we constructed a PTM with an isosemantic change at nucleotide 57 in exon 23 (E23 ESE2: ACU > ACG, both codons encoding threonine) that increased the score for SRp40 binding from 2.
ACKNOWLEDGEMENT We are grateful to George Dickson for supplying the murine optimized microdystrophin plasmid, Vincent Mouly (Human Cell Culture Platform of the Myology Institute, Paris, France) for access to human immortalized DMD myoblasts, France Pie´tri-Rouxel for technical advice, the imagery platform 'Plate-forme d'Imagerie Cellulaire Pitie´Salpeˆtrie`re', Etienne Mouisel and Christel Gentil for technical support and William Duddy for critical reading of the manuscript. Conflict of interest statement. None declared.
A long-term ozone loss time series is necessary to understand the evolution of ozone in Antarctica. Therefore, we construct the time series using ground-based, satellite and bias-corrected multi-sensor reanalysis (MSR) data sets for the period 1989-2010. The trends in ozone over 1979-2010 are also estimated to further elucidate its evolution in the wake of decreasing halogen levels in the stratosphere. Our analysis with ground-based observations shows that the average ozone loss in the Antarctic is about -33 to -50 % (-90 to -155 DU (Dobson Unit)) in 1989-1992, and then stayed at around -48 % (-160 DU). The ozone loss in the warmer winters (e. g. 2002 and 2004)  is lower (-37 to -46 %), and in the very cold winters (e. g. 2003 and 2006) it is higher (-52 to -55 %). These loss estimates are in good agreement with those estimated from satellite observations, where the differences are less than ±3 %.
The ozone trends based on the equivalent effective Antarctic stratospheric chlorine (EEASC) and piecewise linear trend (PWLT) functions for the vortex averaged ground-based, Total Ozone Mapping Spectrometer/Ozone Monitoring Instrument (TOMS/OMI), and MSR data averaged over September-November exhibit about -4. 6 DU yr -1 over 1979-1999, corroborating the role of halogens in the ozone decrease during the period. The ozone trends computed for the 2000-2010 period are about +1 DU yr -1 for EEASC and +2. 6 DU yr -1 for the PWLT functions. The larger positive PWLT trends for the 2000-2010 period indicate the influence of dynamics and other basis functions on the increase of ozone. The trends in both periods are significant at 95 % confidence intervals for all analyses. Therefore, our study suggests that Antarctic ozone shows a significant positive trend toward its recovery, and hence, leaves a clear signature of the successful implementation of the Montreal Protocol.
Satellite observations To compare with the ozone loss estimates from the groundbased observations, version 8. 5 total column ozone measurements from TOMS onboard Nimbus-7, Meteor-3, and Earth Probe are used  (Bhartia and Wellemeyer, 2002). The uncertainty of the TOMS ozone column data is 3. 3 %, and the bias among ozone products from the different TOMS platforms is 1-2 %  (Kroon et al. , 2008). Since 2005, the OMI data are used as the continuation of the TOMS series and the uncertainty of the OMI ozone column is 2-5 % for SZA < 84 •  (Levelt et al. , 2006). Therefore, a continuous series comparable to the ground-based series is available from TOMS and OMI from 1979 to 2010, with the exception of 1994 and 1995. However, as shown by  Hendrick et al. (2011)  there is still some bias of the order of 2 % between TOMS/OMI and SAOZ observations at Dumont d'Urville, Antarctica, with a strong seasonal dependence.
As this bias was random, it was not possible to correct here for the TOMS/OMI data. In addition, we have used a bias-corrected reanalysed ozone data set, the Multi-Sensor Reanalysis (MSR), compiled from various satellite observations during the period 1979-2008. The Global Ozone Monitoring Experiment 2 (GOME-2) total column data  (Eskes et al. , 2005)  are used to extend the MSR data to 2010 for the trend analyses. These data sets have a bias of about 1±2 % compared to various satellite observations  (van der A et al. , 2010). Previous studies have successfully used these data in various scientific studies (e. g. de Laat and van Weele, 2011).
Multi-variate regression of vortex averaged ozone Since it is difficult to accurately estimate trends directly from ozone observations, we have constructed and applied a multivariate regression model to the ground-based, TOMS/OMI and MSR total column data. Note that the 1994-1995 data gap in TOMS/OMI is filled with corresponding overpass analyses using the MSR data. We consider the deseasonalised September-November ozone average over 1979-2010. Apart from using various data sets for the trend analysis, we have also used various vortex definitions to group each data set, i. e. the data averaged inside the vortex, vortex Table  2. The cumulative ozone (O 3 ) loss estimated from 1 June averaged between 26 September and 5 October. The loss is computed as the ground-based O 3 -passive O 3 tracer in DU and 100 × (ground-based O 3 -passive O 3 tracer)/passive O 3 tracer in %.
Vortex averaged ozone trends during 1979-1999 The regression functions applied to the ground-based measurements show a trend of about -4. 1 to -5. 2 DU yr -1 from both regressions over 1979-1999 and are significant at 95 % confidence intervals. As presented in Table  3  (top panel), these results did not change significantly when the data were analysed with respect to different vortex criteria (i. e. inside vortex, vortex core, and over the EqLs 65-90 • S). The trends derived from ground-based observations are in very good agreement with those found from TOMS/OMI and MSR data. The similar trends deduced from both EEASC and PWLT regressions imply that the ozone decrease over 1979-1999 is dominated by the increase in halogens during the period, consistent with the results of previous studies (e.
Vortex averaged ozone trends during 2000-2010 The results for the 2000-2010 period show a trend of about +1 DU yr -1 from the EEASC and +2. 3 to +2. 9 DU yr -1 from PWLT functions and are significant at 95 % confidence intervals for the ground-based data averaged with respect to various vortex criteria. These trends are also in very good agreement with those estimated from TOMS/OMI and MSR data. The EEASC-based results are consistent with those derived from the CCM/CTM simulations for the 1997-2009 period, which exhibit an EEASC-based ozone trend of around +1 DU yr -1  (Austin et al. , 2010; Kiesewetter et al. , 2010). The trends derived from EEASC regression are smaller than those obtained from PWLT. This implies that the ozone increase during 2000-2010 cannot be explained by the reduction in ozone depleting substances alone, but there are strong influences from the dynamics and other parameters.
Conclusions A comprehensive analysis of chemical ozone loss in the Antarctic vortex from 1989 to 2010 is presented using total ozone observations from ground-based Brewer, DOAS, Dobson, and SAOZ, and space-borne TOMS/OMI and MSR data. The passive technique is applied to find the ozone loss at each station, and then averaged to find the mean loss. The loss in  1989 -1991 and 2002 is about -110 to -140 DU or -33 to -40 %, and during 1992 -2010 (except 2002) )  is around -160 DU or -48 %. In general, the ozone loss in the Antarctic starts by mid-June and intensifies in August-September, peaks by the end of September/early October, and ozone recovers thereafter, consistent with the results of previous studies. The ozone loss estimated from TOMS/OMI and MSR data also exhibits a proportional progress of ozone and ozone loss as for the ground-based measurements throughout the period , where the differences with the groundbased estimates are less than ±3 %.
The piecewise and EEASC-based trends estimated from the September-November vortex averaged ground-based ozone column show a trend of about -4. 1 to -5. 2 DU yr -1 for the period 1979-1999 and a trend of around +1 DU yr -1 with EEASC and +2. 3 to +2. 9 DU yr -1 with PWLT functions during 2000-2010. These trend analyses are significant at the 95 % confidence levels in both periods for all vortex averaged data clusters. The ground-based analyses are well supported by those of the TOMS/OMI and MSR data. In 1979-1999, both piecewise and EEASC-based ozone trends show very similar values, corroborating the dominance of stratospheric halogens on the ozone decrease in that period. However, the larger values derived from the PWLT regression for the 2000-2010 period suggest the greater influence of dynamics plus other regression indices not considered here on the increase of ozone during the period.
Acknowledgements. J. K. thanks  Marion Marchand  and  Slimane Bekki  for their encouragement and help to complete this work. The authors thank  Eric Nash  ( NASA GSFC ) for the vortex edge data for the 1988-2009 period and  Paul Newman  ( NASA GSFC ) and  Murry Salby  ( Macquarie University, Australia ) for the EEASC data and the discussions with them. The authors thank  Cathy Boonne of Centre for Atmospheric Chemistry Products and Services (ETHER)  for providing the REPROBUS model code and data. The authors are grateful to two anonymous referees and the Editor for their critical and constructive comments that helped to improve the quality of this work. The authors are also grateful to principal investigators of various ground-based and satellite ozone observation programs:  U. Friess ,  University of Heidelberg, Germany ;  J.
woudc. org). We take this opportunity to thank the respective national governing bodies, research establishments and station scientists, who maintain the stations. This work was partly supported by the  EC  as a part of the  FP7  project  RECONCILE , Grant number:  RECONCILE-226365-FP7-ENV-2008-1. This work was also supported by the  French CNES/ACE  and  ESA/MULTI-TASTE  projects. Edited by:  M. Palm.