dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, their position regarding an expected trend line (threshold) across trophic levels (pathways) gives the main direction for revisiting those initial estimates. In the present case study, deviations from the PREBAL trend lines, or threshold values, are more related to modeller choices or to underlying assumptions of the modelling software than to poor quality data or ecological incoherencies in the model. As an example, surface-feeder seabird biomass does not fit with the general biomass allocation pattern in ecosystems (moderate to sharp decline across TLs) (e. g. Gascuel, 2005; Gascuel et al. , 2009). Given the high quality of the biomass estimate, this below-trend value could be explained by an underestimation of the TL. Kittiwakes and gulls have a substantial part of their diet coming from discarded organisms  (Arcos and Oro, 2002) , which are considered in Ecopath as dead material with a TL arbitrarily set to unity  (Christensen and Pauly, 1992).
Conclusion In conclusion for the case study, a general level of input data reliability that is satisfying with regard to the model applications has been achieved. Recently,  Essington and Plagányi (2014)  conducted a comparative study on the capacity of 18 ecosystem models for evaluating the ecosystem effects of forage fish fisheries. The Bay of Biscay Ecopath model was also positively evaluated in terms of time and spatial matching to stocks and of data representativeness and availability. However, we believe improvements could be investigated in the future, particularly on the parameterization of pelagic and benthic cephalopods, and mackerel. Table  1. Food-web model diagnostics used for the evaluation of the Bay of Biscay continental shelf food-web model according to  Link (2010).
This study aimed to assess the motion and its deformation index (DI) of red blood cells (RBCs) flowing through a microchannel with a microstenosis using an image analysis-based method. For this purpose, a microchannel having a smooth contraction was used and the images were captured by a standard high-speed microscopy system. An automatic image-processing and analysing method was developed in a MATLAB environment to not only track the motion of RBCs but also measure the DI along the microchannel. The keyhole model, tested in this study, proved to be a promising technique to automatically track individual RBCs in microchannels.
2014; Rodrigues et al. 2014 ) of cells flowing through microchannels. However, the majority of both cell tracking and deformation measurements are often made by using manual methods. These methods are extremely timeconsuming and may introduce operator errors into the data. During the last years, several researchers have been developing different kinds of automatic particle tracking (APT) tools either as plugin for Image J  (Sbalzarini and Koumoutsakos 2005; Smith et al. 2011; Meijering et al. 2012)  or as a MATLAB module  (Meijering et al. 2012; Pinho, Gayubo, et al. 2013). Promising APT plugins for Image J are 'Particletracker'  (Sbalzarini and Koumoutsakos 2005)  and 'SpeckleTrackerJ'  (Smith et al. 2011). Although both plugins can perform automatic tracking in an extremely fast way, these methods are still under development as the tracking trajectories tend to fail, especially at high haematocrits (Hcts).
Recently,  Pinho, Gayubo, et al. (2013)  developed a MATLAB module to track automatically labelled RBCs flowing through a microchannel. Using fluorescent labelling, several individual RBCs were able to track automatically as bright object against a darker background. However, this method still requires some consuming time by the user to perform tracking and is not able to measure RBCs deformability flowing along the microchannel. Hence, it is crucial to develop a fast automatic method that is able to not only track individual RBCs but also measure their deformability. In this study, we propose an automatic image analysis technique based on the keyhole model to characterise the motion and deformation of RBCs flowing through a microchannel having a smooth contraction shape. In this geometry, the mechanical properties of RBCs are under the effect of both simple shear and extensional flows.
The RBC flowing in the centreline and far upstream of the contraction region has a mean diameter of about 25 pixels. In addition, the maximum velocity at the contraction region is around 12 mm/s. Deformation index DI is a dimensionless number which represents a degree of deformation of the circular object in 2D. Commonly used DI is obtained by the following formula: DI ¼ ða 2 bÞ ða þ bÞ ; ð6Þ where a and b are the major and minor axes lengths of the circular object. DI values are between 0 and 1, and a perfect circle means DI ¼ 0. When the object is strongly elongated, DI shows a high value. In order to obtain necessary axes lengths, the 'regionprops' function of MATLAB was used in the present method. Given the scalar 'MajorAxisLength' and 'MinorAxisLength', this function returns the major and minor axes lengths of the ellipse that is fitted to the object to measure.
The Kepler Project data validation (DV) process routinely checks for such displacements, ruling them false positives. We have carefully measured this maximum exclusion radius for all stars using the method described in detail by  Bryson et al. (2013). The exclusion radii are listed in Table  3  in column 2, in arcsec from the target star. The exclusion radii range from 0. ′′ 01 -4 ′′ , with a median value of 0. ′′ 30. For 10 bright target stars, namely  93, 102, 25, 37, 68, 96, 131, 408, and 409  no formal exclusion radius could be computed due to saturation of the Kepler CCD detector. For them, a reasonable exclusion radius of 4 ′′ was adopted corresponding to a position displacement of a full Kepler pixel that can be excluded despite the saturation. The exclusion radius for each of the 22 KOIs establishes a circular area on the sky (i.
ACKNOWLEDGEMENTS Kepler was competitively selected as the tenth NASA Discovery mission. Funding for this mission is provided by the NASA Science Mission Directorate. Some of the data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The Keck Observatory was made possible by the generous financial support of the W. M. Keck Foundation. Some of the asteroseismology analysis was performed by the Stellar Astrophysics Centre which is funded by the Danish National Research Foundation (Grant DNRF106 authors would like to thank the many people who gave so generously of their time to make this Mission a success. All Kepler data products are available to the public at the Mikulski Archive for Space Telescopes http://stdatu.
stsci. edu/kepler and the spectra and their products are made available at the NExSci Exoplanet Archive and its CFOP website: http://exoplanetarchive. ipac. caltech. edu We thank the many observers who contributed to the measurements reported here. We gratefully acknowledge the efforts and dedication of the Keck Observatory staff, especially Scott Dahm, Hien Tran, and Grant Hill for support of HIRES and Greg Wirth for support of remote observing. This work made use of the SIMBAD database (operated at CDS, Strasbourg, France) and NASA's Astrophysics Data System Bibliographic Services. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program.
In the last 50 years, hantaviruses have significantly affected public health worldwide, but the exact extent of the distribution of hantavirus diseases, species and lineages and the risk of their emergence into new geographic areas are still poorly known. In particular, the determinants of molecular evolution of hantaviruses circulating in different geographical areas or different host species are poorly documented. Yet, this understanding is essential for the establishment of more accurate scenarios of hantavirus emergence under different climatic and environmental constraints. In this study, we focused on Murinae-associated hantaviruses (mainly Seoul Dobrava and Hantaan virus) using sequences available in GenBank and conducted several complementary phylogenetic inferences. We sought for signatures of selection and changes in patterns and rates of diversification in order to characterize hantaviruses' molecular evolution at different geographical scales (global and local).
05 significance level. Codon analyses also revealed between 85% (SLAC, Single Likelihood Ancestor Counting model), and 98% (FUBAR, Fast Unbiased Bayesian AppRoximation model) of negatively selected aa sites, (p = 0. 05 or Bayes Factor > 0. 9) (Table  1 ). Results were consistent for both the small and the large dataset but 10 supplementary aa sites were detected by the MEME method with the large dataset (Table  1 , see Experimental Section for description of the datasets). For the M segment, we were unable to obtain evidence of positive selection for some sites by at least three of the five methods. Still, the MEME method identified 30 aa sites as being potentially subjected to episodic diversifying selection events (Table  1 ). Except for seven of them (aa 461, 462, 540, 544, 635, 642 and 643) they all corresponded to aa located in the predicted ectodomains of the Gn and Gc glycoproteins  [51] [52] [53].
The positions of the switches in diversification rates were very close to the tip of the trees. Singleand multiple-threshold models gave essentially similar results (Supplementary Figure  S1 ). The analyses lead to the identifications of many lineages affected by an increase in branching rates. The number of lineages identified by the method was very similar for both S and M segments; 60 putative lineages (confidence interval 53-67) and 66 putative lineages (confidence interval 54-81) respectively with the single-threshold analysis and 58 putative lineages (confidence interval 57-58) and 58 putative lineages (confidence interval 23-60), respectively, with the multiple-threshold analysis. Analyses performed with the large dataset indicated estimates of 32 lineages (confidence interval  with the single threshold method (2ΔL = 11.
Geographic Structuration Analyses Parsimony reconstructions as implemented in PhyloType, yielded nearly identical results for both S and M segments phylogenies independently of the optimization method used (ACCTRAN or DELTRAN) (Figure  3 , Table  S2 ). S2. These phylotypes were also observed when using the larger dataset for the S segment, which allowed reaching the minimal size selection criteria for the detection of several additional phylotypes on the whole tree. In particular, a phylotype corresponding to the Korea annotation (Pi 22) including the common ancestor of AMRV/SOOV and HTNV strains, suggested a Korean ancestor for the considered HTNV strains, which is consistent with the known history of the HTNV discovery in 1976 in Korea  [56]. The use of the large dataset also allowed obtaining two additional phylotypes among the SEOV clade associated with the Beijing annotations (Pi 393 and 423) and inferring the origin of the Zhejiang phylotype (Pi 318) earlier in the phylogeny, at an ancestral position compared to Hubei (Pi 319) and Beijing phylotypes.
Shifts in Diversification Rates Previous studies suggested that high rates of lineage births and deaths are a dominant feature of RNA virus evolution  [57, 70]. Our findings fully support this view, as the BD model is preferred to other models when considering the murine-associated hantaviruses phylogeny as a whole. However, we also detected several shifts to the Yule (pure-birth) model in certain tips of the phylogenetic trees. These shifts were detected in both S and M segments phylogenies and their localisations in the trees were congruent for both segments. In particular, two shifts were identified with both S (small and large dataset) and M segments, both shifts corresponded to a change of the diversification process from a BD model to a Yule model. These changes were accompanied by a strong increase in diversification rates.
Sampling Effects In this work, we chose to use only strains for which relevant genetic data (complete segment sequences) were available. This probably results in strong geographic and lineage biases, because not all geographic regions and genetic lineages have been studied equally. For example, strains from some Chinese provinces or from other Asian or European countries are underrepresented in our dataset compared to Guizhou and Hubei provinces. Such bias could result in underestimation of the number and localisation on clades experiencing drastic changes in patterns of diversification  [85]. Indeed, we detected an additional clade experiencing an increase in diversification rate when we used the large S dataset. Moreover, the number of geographic phylotypes detected is much more important with the large S dataset, probably because a minimal number of strains is needed to be able to extract significant information when using the method of Chevenet et al.
[55]. Another limitation of our study is that our analyses are dependent on the metadata linked with each deposited sequence, such as the precise time and place of sampling (latitude and longitude) that are often missing or incomplete in GenBank  [30]. Indeed, more precise geographic data could help to better refine the geographic areas where changes in evolutionary dynamics occur. Nevertheless, despite these obvious limitations we provide here a strong indication of the existence of different evolutionary patterns across lineages. For instance, though a large dataset was available for the DOBV/SAAV clade (29 sequences available in the large dataset vs. 15 in the small dataset), we were not able to detect any change in diversifying rates. Such a result contrasts with what was observed for HTNV and SEOV clades in China.
Sequences Acquisition We used a dataset of 124 Hantavirus strains belonging to the hantaviruses carried by the Rodentia subfamily Murinae. We selected only strains for which complete coding sequences for the two segments (S, ORF, 1293 bp, and M, ORF, 3408 bp) were available. Segment L was not included in the analyses due to the paucity of the data available (8 SEOV, 1 SANGV, 9 HTNV and 11 DOBV). For some analyses we also used a larger dataset of 225 complete S segments for which the M segment was not available. Sequences were obtained from the NIAID Virus Pathogen Database and Analysis Resource (ViPR) online through the website in  [86]. The list of strains and their geographic origin are displayed in Table  S1  and Figure  S1.
Molecular Signature of Selection To detect and/or determine selection acting on both segments, sequences were analyzed using HyPhy software package  [87] [88] [89]  implemented in the Datamonkey web server  [90]. Datamonkey includes several codon-based maximum-likelihood methods described in  [87, 88] , inferring both positively and negatively selected sites: the Single Likelihood Ancestor Counting (SLAC) model, the Fixed Effect Likelihood (FEL) model, the internal branches FEL (iFEL). SLAC is the most conservative of the three methods. We also employed two recently developed models, the Mixed Effects Model of Evolution (MEME)  [62]  and the Fast Unbiased Bayesian AppRoximation (FUBAR)  [91]  methods. MEME is a branch-site model, which is an extension of FEL that permits a proportion of branches at a site to evolve neutrally or under negative selection, while the remainder can evolve under diversifying selection.
Phylogeny and Congruence between M and S Segment Trees Sequences were aligned with the Clustal Omega alignment program implemented in SEAVIEW v4. 4. 2  [92]. The optimal substitution model for both segments was identified as the GTR +G +I model (General Time Reversible) using a function implemented in Mega v5. 1  [93]. Phylogenetic reconstructions were conducted with the ML approach using PhyML v3. 0  [94]. We tested the congruence between the S segment phylogeny and the M segment phylogeny using the on-line calculation of the congruency index Icong  [47]  see  [95]. This index is based on topological congruence assessed using MAST (Maximum agreement subtrees) measurement. A large number of random trees are generated, with a wide range of number of leaves and the topological congruence is then determined as the minimum number of leaves that have to be removed in each phylogeny to render the trees identical.
Phylogenetic Analyses Diversification analyses require the use of ultrametric trees as input trees. Ultrametric trees for both segments were obtained using the Metropolis-coupled Markov Chain Monte Carlo (MCMC) method in BEAST package v1. 7. 4  [98]. BEAUTi v1. 7. 4  [98]  was used to generate BEAST. xml input files. The two datasets were analysed under the GTR +G +I model and with a relaxed clock allowing branch lengths to vary according to an uncorrelated lognormal distribution. Inferring ultrametric trees with BEAST for species-level phylogenies requires specifying a speciation model prior  [99]. Two most used speciation models are available in BEAST, the pure-birth (Yule) process, which governs branching times assuming a constant rate of diversification and no extinction  [100] ; and the birth-death (BD) process, which is a special case of Yule process allowing lineage extinction.
In order to search for the model that best fits to our data sets, we used the fitdAICrc function (which compares models AIC scores) implemented in the R package Laser  [101]  on the S and M ML phylogenetic trees previously made ultrametric by the chronos function implemented in the R package Ape  [102]. As the birth-death (BD) process was selected for both segments S and M (Table  2 ), we then used a BD prior for ultrametric tree inference with BEAST, and all other priors were left to the default settings. A random tree was used as the starting tree. Prior ages to lineages were arbitrarily assigned to 1 (meaning that the time separating the root from the present was arbitrarily assigned to 1). We ran two analyses of 10 million generations each for both segments with parameters sampled every 1000 generations.
Shifts in Diversification Rates and Patterns We ran several analyses to identify shifts in modes of lineage diversification in both S and M phylogenies. Analyses were performed in R (R-Development-Core-Team 2008) using the MCC trees and the Ape  [102] , Geiger  [104] , Laser  [101] , SPLITS  [54]  and iteRates  [105]  libraries. The Species' LImits by Threshold Statistics model of  Pons et al. , 2006 [54]  implemented in the package SPLITS seeks for transition between two branching patterns in a tree, corresponding to the Yule process and the coalescent process, respectively. The method tests for the fit of the data to the null model of general coalescence (in which the entire tree represents a single population) and to the alternative model, called general mixed Yule coalescent (GMYC) model, which combines models that separately describe branching within populations (coalescent process) and branching between species (a Yule model of stochastic lineage growth).
Analyses were conducted on the web server for PTP  [108]  using phylogenetic trees obtained with RAxML v7. 0. 3  [109]  under a GTR +CAT +I substitution model. The latter model is an alternative of the +G model of rate heterogeneity, which optimizes the evolutionary rate for each individual site using a fixed number of rate categories. The Modelling Evolutionary Diversification Using Stepwise AIC (MEDUSA) method implemented in the package GEIGER aims to detect and locate lineage-specific shifts in diversification rates in a tree. To this aim, it fits a series of birth-death (BD) and pure-birth Yule models to a phylogenetic tree (reconstructed by default under a BD process) with an increasing number of breakpoints (rate shifts)  [110]. These models are then compared using corrected Akaike information criterion (AICc) to choose the best-fit model.
We tested for significant variation in diversification rates by using the medusaSummary command and chose the model with the lowest AICc value. The threshold for retaining additional rate shifts was an improvement in AIC score of 5. 26 units or greater as determined as appropriate for a tree of 124 tips by MEDUSA. We also used the Parametric Rate Comparison (PRC) test implemented in the -iterates‖ package  [105]. The method is similar to the MEDUSA approach but does not make assumptions regarding the underlying evolutionary processes of diversification.
Analyses were performed with phylogenies inferred by maximum likelihood (ML) and with a LRT SH-like branch support as described in  [55]. Annotations in our dataset were the geographic origin of each strain. For all analyses, criteria chosen for phylotype selection were as follows: size ≥5, size/different ≥1, persistence ≥1, only nodes which ML bootstrap values were ≥75 were taken into account in the analyses. Both ACCTRAN and DELTRAN (recommended for geographic annotations) optimization were tested. Shuffling procedures were performed with 1000 iterations to test for phylotype significance. Only those phylotypes whose p-value was ≤5% were retained.
Acknowledgments This work was partly supported by the  French National Agency for Research project BioDivHealthSEA  (Grant Number  ANR 11 CPEL 002 ), and the European program  FP7-261504 EDENext. The manuscript is catalogued by the EDENext Steering Committee as EDENext193 [113]. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the  European Commission.
indicate sites identified as being under positive selection by at least two methods. Methods S segment (small dataset) S segment (large dataset) M segment positively selected aa sites percentage of negatively selected aa sites positively selected aa sites percentage of negatively selected aa sites positively selected aa sites percentage of negatively selected aa sites SLAC 43 * 85% 43 * 90% no site 86% FEL 43 * 89% no site 92% no site 90% iFEL 43 * 88% no site 91% no site 90% FUBAR 43 * 98% 43 * 98% no site 97%.
In this case, both types of cuts simply amount to conditions on whether a given generated event is considered in the analysis or not. However, the reinterpretation of an analysis in general requires ex novo implementation of the full set of cuts. Several frameworks  [1] [2] [3]  have recently been released with this aim, based on simulated collisions including an approximate modeling of the detector response. Although the description of the detector is highly simplified when compared to the full ATLAS or CMS software, public fast detector simulations, most notably the Delphes program  [4] , have been found to provide reasonably accurate results. Detector effects could instead be mimicked by scale factors derived from unfolded data as done in Rivet  [5]. While this is an excellent approach for Standard Model measurements, detector unfolding is however a Present address: Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, Norfolk Place London, W2 1PG, UK.