dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods All women with singleton pregnancies who attempted a vaginal birth and delivered infants weighing at least 4000 g, in two French tertiary care centers of Paris suburbs (Hôpital Antoine Béclère, Clamart and Centre Hospitalier Intercommunal de Poissy-Saint Germain, Poissy) from January 2005 through December 2008 were included. Demographic characteristics, obstetrical history, pregnancy and neonatal data were registered prospectively in hospitalization databases (approval by CNIL, the French Data Protection Authority, under the notification number 1181076). Three types of maternal complications were considered: 1) the occurrence of a third-or fourth-degree perineal laceration; 2) a severe postpartum hemorrhage defined as persistent bleeding more than 500 cc requiring the use of prostaglandins (sulprostone), uterine artery embolization, internal iliac artery ligation or haemostatic hysterectomy; 3) the need for blood transfusion.
We chose to exclude women with elective cesarean delivery (defined as cesarean deliveries scheduled 8 hours or more before delivery and performed as intended  [18] ) since they were mainly planned due to maternofetal dystocia. However, we included other cesarean deliveries since they reflected a prolonged and difficult labor that may implicate maternal complications. To validate our selection of cesarean sections, we performed two sensitivity analyses on all cesarean sections, both elective and non-elective: the first with the same composite criteria as MC, and the second including elective cesarean delivery for macrosomic suspicion as a supplementary criteria for MC. Statistical analyses were performed using STATA W v. 11 (Stata Corporation, College Station, TX, USA) software.
Experimental Crystal data Table  1  Hydrogen-bond geometry (A ˚, ). (i) x À 1; y; z; (ii) x; y þ 1; z. Data collection: APEX2  (Bruker, 2009) ; cell refinement: SAINT-Plus  (Bruker, 2009) ; data reduction: SAINT-Plus; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012) ; software used to prepare material for publication: PLATON  (Spek, 2009)  and publCIF  (Westrip, 2010). Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: IM2438).
Computing details Data collection: APEX2  (Bruker, 2009) ; cell refinement: SAINT-Plus  (Bruker, 2009) ; data reduction: SAINT-Plus  (Bruker, 2009) ; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012) ; software used to prepare material for publication: PLATON  (Spek, 2009)  and publCIF  (Westrip, 2010). where P = (F o 2 + 2F c 2 )/3 (Δ/σ) max < 0. 001 Δρ max = 0. 18 e Å -3 Δρ min = -0. 15 e Å -3 Special details Geometry. All s. u. 's (except the s. u. in the dihedral angle between two l. s. planes) are estimated using the full covariance matrix. The cell s. u. 's are taken into account individually in the estimation of s. u. 's in distances, angles and torsion angles; correlations between s.
Nycteribiidae Phylogenetic Analyses All sequences were automatically aligned using MAFFT implemented in Geneious Pro version 5. 3. 4 [14]. Multiple sequence alignments were subsequently adjusted by visual inspection, using as a reference nycteribiid species sequences available on GenBank. COI sequences were easily aligned as no insertions or deletions were detected. 18S alignments were cleaned from problematic alignment blocks using Gblocks 0. 91  [15]  with the following parameters: minimum number of sequences for a conserved position = 38; minimum number of sequences for a flanking position = 38; maximum number of contiguous nonconserved positions = 8; minimum length of a block = 5; allowed gap positions = with half. Phylogenies were constructed separately for mitochondrial (COI) and nuclear (18S) datasets using homologous sequences available in GenBank as outgroups and ingroups (Table  S1  in Tables  S1 ).
Host Phylogeny and Cophylogenetic Analysis We used ParaFit in order to test potential coevolution between nycteribiid flies and their bat host species  [19]. Implemented test used a matrix of parasites/hosts associations (absence/presence of a parasite species on a bat species) together with two matrixes of patristic distances obtained from the reconstructed phylogenies of parasites and hosts. We used the nycteribiid COI generated phylogeny and bat species cytochrome b (cyt b) phylogeny based on available GenBank sequences (Table  S2  in Tables  S1 ) according to the best model of sequence evolution proposed by jModelTest  [17]. Then, the two matrixes patristic distances were obtained with Patristic software  [20]. Finally, the test was performed using ParaFit implemented in the package APE version 3.
Molecular Diagnosis accords with Morphological Identification The morphology-based identifications were compared to a molecular phylogeny obtained with the nuclear and mitochondrial markers. The most suitable substitution models based on Akaike Information Criterion were GTR+I+G and HKY+I+G for both mitochondrial and nuclear data, respectively. Amplification of mitochondrial COI produced 44 sequences of 658 bp and the amplification of the nuclear 18S locus produced 45 sequences ranging from 826 to 2139 bp. All sequences are available on GenBank under accession numbers KF021491 to KF021528 for mitochondrial haplotypes, and under accession numbers KF156670 to KF156714 for nuclear haplotypes (Table  S1  in Tables  S1 ). The COI-based phylogeny comprises two distinct clades (1 and 2), both supported by posterior probabilities (P.
Acknowledgments We are grateful to  Departement de Biologie Animale, Universite d'Antananarivo ;  Direction du Systeme des Aires Protegees, Direction Generale de l'Environnement et des Fore  ˆts; Madagascar National Parks (Madagascar); and  Centre National de Documentation et de Recherche Scientifique  ( Union of the Comoros ) for kindly providing research and export permits (Nu  194/12/MEF/SG/DGF/DCB. SAP/SCB, Nu 032/ 12/MEF/SG/DGF/DCB. SAP/SCBSE, Nu 283/11/MEF/SG/DGF/ DCB. SAP/SCB, 067/12/MEF/SG/DGF/DCB. SAP/SCBSE ). We thank  Paul W Webala ,  Bruce D Patterson , and  Jake A Esselstyn  for additional fly specimens, and  Josselin Cornuault  and  Camille Lebarbenchon  for their help in the analysis. For comments on an earlier version of this paper, we are grateful to  Manuel Ruedi  and an anonymous reviewer.
). Meteorological conditions, including ambient temperature, wind direction and speed (corrected for the ship's movement), and RH were measured routinely on the R/V Point Sur, along with ship-specific data such as heading and speed. Additional visual observations (e. g. , fog, other ships) were noted in deck logs. Mass concentrations of oxalate and glyoxylate are reported for the smoke sampling events (described in Sect. 2. 6). PM 10 was collected on pre-baked 47 mm quartz fiber filters that were stored in a freezer prior to chemical analysis. The filter extraction procedure consisted of ultrasonication (15 min) of filter halves with 18. 2 MΩ Milli-Q water. Syringe filters (Acrodisc filter, 25 µm) were used to remove any remaining insoluble matter from the extracts after ultrasonication. Ion chromatography analysis (IC; Thermo Scientific Dionex ICS-5000 anion system with an AS11-HC 2 mm column) was conducted using a 38 min multi-step gradient program with sodium hydroxide eluent (1 mM from 0 to 8 min, 1 mM to 30 mM from 8 to 28 min, 30 mM to 60 mM from 28 to 38 min).
Data processing and quality control For the HTDMA data, an important source of uncertainty is the variability of RH in DMA 2  (Swietlicki et al. , 2008). For quality control, the RH in DMA 2 and its variability in time (over the duration of a scan) and space (along the DMA 2 column) were calculated from flow rates, temperatures, and measured RH in the sample flow out of the humidifier and Introduction.
Conclusions Figures Back Close Full in the sheath flow in DMA 2 at the beginning of the DMA 2 column (these two flows were humidified separately), and of temperature in the excess flow at the end of the DMA 2 column. Scans were accepted if they fulfilled the following criteria: (i) flow rates were within 25 % of the set point; (ii) combined humidifier and sheath air RH variability over the time of a scan did not exceed ±1. 5 % RH; and (iii) the temperature gradient over the length of the DMA 2 did not exceed ±1 • C. The raw count data from DMA 2 were inverted to produce the growth factor distributions: raw counts were shifted in time to correct for particle transit time between the DMA column and particle counter and de-smeared to correct for a finite particle counter response time. The de-smeared data were finally inverted using the diffusing form of the DMA transfer function  (Stolzenburg, 1988)  and assuming a single charge.
Auxiliary data Three-day HYSPLIT back-trajectories  (Draxler and Rolph, 2012)  ending at the ship's location and at an altitude of 50 m were calculated for every third hour of the R/V Point Sur cruise. GOES visible satellite images (every 15 min) were collected and overlaid onto plots of the R/V Point Sur track to confirm and complement the visual observations of clouds from the R/V Point Sur. To analyze synoptic weather conditions, maps of surface temperature, wind, pressure, and 500 mb geopotential height were obtained from the website of the NOAA/OAR/ESRL Physical Science Division (http://www. esrl. noaa. gov/psd/data/composites/day/).
fMRI PREPROCESSING Preprocessing and analysis of fMRI data was performed using BrainVoyagerQX 2. 1 (Brain Innovation, Maastricht, Netherlands). Primarily, all volumes were realigned to the first volume in order to minimize effects of head movements on data analysis. Further data preprocessing comprised spatial (6 mm full-width halfmaximum isotropic Gaussian kernel) and temporal smoothing (high pass filter: 15 cycles per run; low pass filter: 2. 8 s; linear trend removal). The anatomical and functional images were co-registered (Figure  A2  in Appendix) and normalized to the Talairach space  (Talairach and Tournoux, 1988).
, 2010). According to  Beissner et al. (2010) ,"pricking" is an indicator for the first pain, while "pressing" or "dull" are indicators for the second pain. Thus, subjects were requested to choose the appropriate perception(s) from this list of three adjectives for the previous stimulation. Twenty-three healthy right-handed subjects (3 male, 20 female, 19-28 years) volunteered in Experiment 2. Subjects were paid C5 for completing the experiment. For the analysis of the data of Experiment 2, odds ratios (OR) were calculated separately for each of the three descriptors If "pricking" will be chosen more often for shallow SSH (OR < 1), then we might conclude that this stimulation preferentially activates Aδ-fibers. Accordingly, if "pressing" and/or "dull" will be chosen more often for steep SSH (OR > 1), then we might conclude that this stimulation preferentially activates C-fibers.
Following Raftery  [40] , we assumed C total has a Poisson distribution with mean m: C total *POI(m) We used a gamma prior distribution on m with variance = 100 and mean = the number of observed carcasses (c). To determine the effects of the prior gamma distribution, the analysis was repeated with a different gamma prior distribution with variance 50 and the same mean and results compared. For the second approach (following a suggestion by an anonymous reviewer) rather than treating the total number of carcasses as an unknown parameter, we modeled the number of un-stranded carcasses (u) as a negative binomial random variable: where u is the number of carcasses that were not stranded. For this approach, C total = c+u. To apply drifter stranding probabilities to turtle carcasses, we used the carcass data that spanned a 15-day period centered at the middle of drifter deployment dates.
For example, if drifters were deployed between 15 th and 18 th of July 2010 at PSL, the total deaths were estimated for the period between 9 th and 23 rd of July 2010. We used a Bayesian approach with vague prior distributions on all parameters to estimate stranding probabilities. To obtain a joint posterior distribution of the parameters, OpenBugs  [41]  was run through matbugs (available from http://code. google. com/p/ matbugs/) in Matlab (v. R2011b; MathWorks). Five independent chains of at least 20,000 steps were used to tune the Markov chain Monte Carlo sampling parameters, followed by 100,000 to 500,000 steps of sampling. These chains were thinned every five steps to reduce auto-correlations. Summary statistics were computed from the remaining samples.
North and south latitudes correspond to the northern and southernmost latitudes at which drifters were recovered. Estimates of p and their 95% PI for 15-day periods are from the dataset in which drifter deployments were grouped into deployment (upper row) and retrieval (lower row) boxes (b = 0. 02u60. 02u, c = 0. 03u60. 03u, d = 0. 04u60. 04u). These estimates are from the binomial-Poisson model but other models provided similar results. The point estimate is the mode of the hyperdistribution of p's for all boxes. See text for details. doi:10. 1371/journal. pone. 0056776. t001 probability at PAO may be explained by the occurrence of southerly swells in August 2010 (Mancini, pers. obs. ). The change in species dominance from south to north was also reported by Mancini and Koch  [19] , with loggerhead turtles being more abundant in the southern areas (see above) while green turtles are distributed more uniformly along the Pacific coast with a strong association to coastal lagoons, of which several are found further north (Figure  1 )  [43].
Acknowledgments We thank the  Secretarı ´a para el Medio Ambiente y los Recursos Naturales (SEMARNAT)  for providing the permits under which this study was conducted. We thank all of the PROCAGUAMA team especially Victor and  Vladimir de la Toba  and other fishermen from the communities at our sampling sites. We would also like to thank  Azucena A ´lvarez ,  Eduardo Resendiz  and  Susana Tobar  for their help in the field, and  Asuka Ishizaki ,  Candan Soykan  and  Todd Jones  for their comments on earlier versions of the manuscript.
Materials and Methods This study re-analyzes data from Davison et al. [14]  and O ¨pik et al. [18] , and therefore plant and AM fungal identification details are described in Davison et al. [14]. Briefly, three separate 10610 m forest plots in the Koeru forest area in central Estonia were sampled within two different successional stages: three old growth spruce forest (old successional stage), and three spruce forest clearcut and planted 25 years prior to sampling (young successional stage) for a total of 6 plots sampled at three times during the growing season. Plant roots were sampled from each plot in early June, late July, and early October 2003 (see sampling diagram, Figure  1 ) for molecular analysis of the AM fungal communities colonising roots. Plants were identified prior to collection of roots, and samples belonging to 11 of the more common plant species (Fragaria vesca, Galeobdolon luteum (synonym for Lamiastrum galeobdolon), Hepatica nobilis, Oxalis acetosella, Trifolium pratense, Geranium pratense, Geum rivale, Hypericum maculatum, Paris quadrifolia, Veronica chamaedrys, and Viola mirabilis) were collected in each plot at each time point where possible.
Not all plant species were present at each sampling time in each plot, and T. pratense occurred only in the young successional stage plots while G. luteum only occurred in the old successional stage plots. These sampled species were spread among ten different plant families at the study sites, and included 55% of the total understorey plant cover of the sampled plots  [31]. Details of the molecular analysis can be found in O ¨pik et al. [18]  and Davison et al. [14]. Briefly, the AM fungal nuclear SSU rRNA gene was targeted using the primers NS31 and AM1 (as described in  [18] ), and resulting PCR products were cloned. Forty-eight of the resulting colonies per individual plant were picked and stored, and 16-32 of these colonies were used for plasmid isolation and Sanger sequencing. Single strand sequences were generated and compared using TOPALi  [36]  along with known taxa from the MaarjAM database of Glomeromycota sequences  [37]  on a neighbour joining phylogenetic tree.
Sequences were grouped into phylogroups based on the tree at $97% sequence similarity. In the subsequent analyses, the respective virtual taxon (VT) nomenclature of the MaarjAM database of Glomeromycota sequences is used. VT are phylogroups which are defined on the basis of bootstrap support and sequence similarity $97% across data originating in individual case studies  [37] , and used as a proxy for species throughout the remainder of this paper. To determine how well our sampling method estimated the diversity of AM fungal species in each host plant, successional stage, and sampling time we produced rarefaction curves using the 'exact' method within the specacum() function  [38]  uisng the package 'vegan' in R 2. 15. 3 (R Development Core Team, 2013)  [39]. Using the specpool() function in 'vegan' we also calculated the Chao total richness estimates for each group (plant species, successional stage, or time point)  [40].
We used Generalised Linear Models to examine the effects of successional stage and sampling time on each network descriptor following Tylianakis et al. [1]. H2' data was arcsin square root transformed prior to analysis. By observing the networks we could discern changes in species associations throughout the growing season. To determine whether seasonal or successional dynamics influenced these changes we used the metric turnover rate (t)  [44]. This metric has previously been used to determine the consistency in network structure between years, but here we adapt it to examine the difference in network structure between sampling times within each successional stage. Turnover rate is calculated for each partner group (plants and AM fungi) as well as the links between species. Finally, we calculated the number of unique interactions for old and young successional stage networks by pooling data and comparing 'robustness'  [45]  to species extinction.
Results For all successional stages and sampling times as well as the majority of plant species, sample rarefaction curves approached an asymptote and estimated (Chao) total richness did not greatly exceed observed species richness (Table  1 ; Figure  2 ). The ratio of observed:estimated (Chao) richess for the entire dataset was 0. 94, and ratios of observed:estimated richness for sampling times and successional stages were also high (Table  1 ). This suggests that our sampling effort was relatively effective and that, in most cases, our sampling scheme appropriately estimated the diversity of AM fungal species present in plant roots. Similarly, network descriptors were generally consistent between replicate plots within each successional stage (Figure  3 ), and tended to exhibit relatively low variation (Figure  4 ).
Neurons or activated microglia were visualized by staining with streptavidin-biotin-conjugated horseradish peroxidase (AbCys, Paris, France) for 60 min at room temperature. Peroxidase was developed for 3 min with diaminobenzidine at room temperature. Slices were analyzed under a light binocular microscope (Leica, Wetzlar, Germany) and histological analysis was performed with Histolab imagery software (US Histology Laboratories, Rockville, USA). Neuron survival and microglia activation were analyzed in 2 different areas for each brain: the area of QA injection (bregma +0. 7 mm) and the cortical area at a distance from the site of injury (bregma -3 mm). The neurons were visually counted and the percentage of neuronal loss in the ipsiversus contra-lateral hemisphere was calculated in matching areas of analysis.
For Ox-42, the total surface area occupied by activated microglia was automatically measured by the imagery software and the percentage of increase in ipsi-versus contralateral hemisphere was measured for each section. Five striatal slices were made within the QA injection site (from Bregma +0. 6 to Bregma +0. 8). Three areas per hemisphere were then randomly selected and neurons and surface occupied by activated microglia were counted by 2 independent operators. The surface area of tissue destruction in striatum was measured using Beta-Vision Plus software (Biospace Lab, Paris, France) and the percentage of tissue loss in ipsi-versus contra-lateral hemisphere was calculated on 5 slices per animal.
(2004)  conceptual model. In this study we use the meteorological and chemical data collected during the time period covered by the simulation from the Sonne Research Vessel (RV), local boats, at Bukit Atur, and from the Falcon. We use meteorological parameters collected by the radiosonde launches from Kuching and aboard the RV Sonne, and precipitation rates derived from the MMD weather radar network. All of the data that we use are summarised in Table  2. Figure  1  shows the location of the RV Sonne and local boat cruises, of Bukit Atur, Miri, and Kuching in context relative to the locations of Flights A and B.
, 2013)   4 ). They also show that the background tends to be higher at 1. 2 pmol mol Finally, the model exhibits various small transient over and underestimations of CHBr 3 in the comparisons close to the CHBr 3 source regions, and, in the case of the latter, this could be due to unrepresented inhomogeneity in the real emissions (see Fig. 13d ). We also note differences in the observed CHBr 3 mixing ratios from WASP and GHOST from coincident measurements of both instruments; these two datasets will be examined in further detail in  Sala et al. (2013). Note that part of the difference between the observed means during Flight A for both the WASP and GHOST instruments, and between the relevant simulated means and backgrounds, can be explained by the different sampling altitudes of each instrument.
Many of the expanded families appear to function at the exterior or cell surface of the pathogens, such as proteins containing CBM1 (Carbohydrate Binding Module Family I according to the CAZy database (http://www. cazy. org/; [46] ), ricin B lectin, Notch domains, and also numerous peptidases. Among the proteins that are unique to S. parasitica compared to plant pathogenic oomycetes, the largest families have similarities to animal-pathogenesis-associated proteins, such as disintegrins, ricin-like galactose-binding lectins and bacterial toxin-like proteins (haemolysin E). Oomycetes contain an unusually large number of proteins with novel domain combinations, recruited from common metabolic, regulatory and signaling domains  [47, 48]. S. parasitica contains in total 169 novel domain combinations that are specific to this pathogen (Table  S9 ).
Bioinformatic searches with the de novo motif-finding program MEME did not identify other putative host-targeting motifs. Despite the absence of large RXLR effector families, S. parasitica does have a small family of host targeting proteins related to SpHtp1, which do contain an N-terminal RXLR sequence. Interestingly one of these proteins was shown to translocate into fish cells  [28]  and entry required the N-terminal leader sequence  [30]. The lack of sequence similarity between any part of SpHtp1 and RXLR-proteins from plant pathogen oomycetes, except for the three proximally located amino acid residues, suggests that the presence of the RXLR-sequence in SpHtp1 is currently unclear. No significant matches were found to Crinkler effectors, suggesting they are absent from S. parasitica. Using search criteria that do not rely on sequence homology, such as induction of expression during the pre-infection stage, presence of secretion signals, and signatures of fast evolution, several candidates (Table  S10 ) for host-targeting proteins (including SpHtp1) were identified in S.