dataesr/scientific-paragraphs-categorization huggingface dataset:
We also used Recombination Detection Program (RDP) version 4. 97  (Martin et al. 2015)  with default settings, except that we invoked the 'scan for reassortment and recombination' setting to identify any reassortment between the gene segments of Indian BTV strains. As evidence for reassortment, we took events detected by at least one of seven different recombination detection methods implemented in RDP: RDP, MAXCHI, and GENECONV methods in primary scanning mode and the Bootscan, CHIMAERA, SisScan, and 3SEQ methods in secondary scanning mode, each with a Bonferroni-corrected P-value cutoff of 0. 05. Distribution of inferred breakpoints of events detected by at least five methods was also analysed. Finally, linkage disequilibrium measures between pairs of single-nucleotide polymorphisms (SNPs) (within and among segments) were obtained using the D' statistic implemented in DnaSP v6  (Librado and Rozas 2009).
Conversely, negative Tajima's D values can be the result of purifying selection or a selective sweep (a beneficial allele has recently reached fixation due to strong positive selection), which both result in an excess of weakly divergent alleles. However, both positive and negative Tajima's D values can also result from demographical and epidemiological processes. Second, we tested for selection acting on particular amino acid sites by comparing the rates of non-synonymous (d N ) and synonymous substitutions (d S ), where d N /d S ratios are described by the parameter x. Values of x > 1 indicate positive selection, whereas a x < 1 indicates the operation of purifying selection  (Yang and Bielawski 2000). Analysis was performed with the program CODEML, which is part of the PAML package  (Yang 1997).
2006). Codons subject to episodic diversifying selection were identified with the mixed-effects model of evolution (MEME) method (Kosakovsky  Pond et al. 2011) , using a significance threshold of P 0. 05. Fast unconstrained Bayesian approximation (FUBAR) was used to rapidly detect negative and positive selection by Bayesian MCMC analyses to robustly account for parameter estimation errors  (Murrell et al. 2013). Codons with posterior probabilities !0. 9 were reported as being either negatively or positively selected. All methods were implemented in the HyPhy package  (Kosakovsky Pond, Frost, and Muse 2005) , combined with the best-fit model of nucleotide substitution based on Akaike information criterion. All these analyses were accessed through the Datamonkey webserver  (Delport et al. 2010 ).
TMRCAs and evolutionary rates Regression of root-to-tip genetic distance against sampling time (Supplementary Fig. S2 ) exposed sufficient temporal signal in each dataset (segment) to proceed with phylogenetic molecular clock analyses. BEAST  (Drummond et al. 2012 ) estimates of the coefficients of variation (CoV) were consistently >2 for each BTV segment (Table  1 ), indicating that strict clock-like evolution could be rejected and validating our choice to use a relaxed molecular clock. However, due to this high degree of clock rate variability seen, we examined whether TMRCA estimates were affected by this. Exploratory analyses revealed that three isolates of BTV-10 (IND2014/BTV1_2014-12-06, IND2014/ BTV2_2014-12-06, and BTV10IND2003K3_2003-08-01) in particular contributed to the high CoVs observed (MCC trees available at doi: 10.
Indeed, we detected fifty-eight recombination/reassortment events by at least one of the seven methods used, forty-six being supported by five or more methods and inferred breakpoints  on Seg-2 for ninety-two Indian BTV isolates, obtained using Bayesian phylogenetic inference in program BEAST  (Drummond et al. 2012). Seg-2 sequences were assigned to amino-acid based clusters  (Pessia et al. 2015) , as highlighted on the tree, which corresponded almost perfectly to serotype (shown on the very right). The same clustering approach was used for all other segments in the genome, for which cluster memberships is shown as vignettes on the right side of the tree. Colours chosen to represent clusters are used consistently across all figures of the manuscript. always fell on or very close to concatenation sites between segments (Supplementary Fig.
2008) , where overlap between observations is indicative of shared evolutionary history (i. e. linkage) between segments. In contrast, segments broken up by reassortment are expected to occupy different regions in the plot. Temporal estimation was done separately for each of the ten BTV segments. Clouds of points reflect phylogenetic uncertainty based on 500 trees sampled in program BEAST  (Drummond et al. 2012)  for each segment, with pairwise comparisons to other segments being limited to viruses sampled in the same year. Only the first two dimensions of the scaling are shown. because it is associated with another gene that is undergoing a selective sweep-and genetic drift. Deviation from neutrality tests also showed two contrasting patterns; Seg-2, Seg-6 as well as Seg-7 (which encodes for the intermediate capsid protein VP7) displayed positive D values, whereas other segments displayed negative D values or values close to 0.
Funding This work was supported by  BBSRC/DBT  grant ( BB/L004690/1  to P. M. ) and by the  Government of India grant  ( BT/IN/Indo-UK/FADH/46/SM/2013  to S. M. ). S. Y. received support from an  ICAR   International Fellowship  awarded by  Government of India. Work at MRC-CVR, UK  was supported by  Wellcome Trust , grant ( 092806/B/10/Z ). Interactions between coauthors were also supported by the  EU H2020   PALE-Blu grant  (project No:  727393-2 ).
Sediment fingerprinting procedure Several analytical and statistical steps were used, using the model Sed_Sat-v1. 0 (Gorman Sanisaca el al. , 2017) to determine which tracers are most significant in defining tributaries and quantifing the relative contributions of tributaries to the suspended sediment samples. The model is written using the statistical software R (R Core Team, 2013) and Microsoft Access© is used as a user interface.
Range test A condition for sediment fingerprinting is that the tracer concentration values in the target dataset must be conservative and do not change during transport  (Walling et al. , 2002). A range test was used to determine if for any given tracer, the target samples lie within the range of the tracer concentration values in the source dataset. Any tracers that fail to satisfy this condition within the measurement error (10% of each fluvial sample's tracer value) are considered non-conservative and were removed from dataset  (Gellis et al. , 2013; Gorman Sanisaca et al. , 2017).
Efficiency of erosion control measures Information on the locations of erosion control measures (runoff retention pools, check dams, fascine, etc. ) in the Canche river watershed were provided by the Chamber of Agriculture using the Database RUISSOL (Chambre d'Agriculture Nord-Pas-de-Calais, 2013). For each tributary, the number of erosion control measure was extracted. We empirically defined an Erosion Control Index,   , using the number of erosion control measures installed on each catchment divided by their respective area: 𝐸𝐶𝐼 𝑖 = ∑ 𝐸𝐶𝑀 𝑖 𝑛 1 𝐴 𝑖 (7) With   presenting the number of erosion control measures installed on the catchment ; and   as the area of the catchment  in km². was then compared to the annual tributary sediment yield (in kt) calculated using the mixing model and Monte-Carlo simulations.
9 -42314 µg g -1 ; S: 1013. 4 -4723. 3 µg g -1 ) and trace elements (Ce: 29. 4 -55 µg g -1 ; La 16. 6 -30. 7 µg g -1 ). The element concentrations for the confluences are generally in the range of the values observed for the tributaries. Sometimes the range of the values for confluences is higher than the range for tributaries (Bi, Ca, Cu, Na, and Sr). Even though the Canche river catchment exhibits a homogeneous geology, major and trace elements seem to have a non-negligible potential for discriminating sources and quantify their contribution to the suspended sediment sampled at each confluence. Considering this first approach, robust statistical analyses are needed to explore the entire dataset.
Final composite fingerprint The first analysis consisted in the identification of outliers in the source dataset. Results showed no outliers for any tributary. Thus, all tracer parameters were kept for further analysis. Table  S3  presents the results of the bracket test and shows that different elements were considered conservative at each sub-basin outlet (spatial sediment sources). Tracers were considered conservative when, for any given tracer, no significant changes during transport between upstream sources and downstream sediment sampling sites occurred. At each confluence, more than twenty tracers were considered conservative and kept for further analysis except for the confluence C2 where only nine tracers were considered conservative. Cross validation using bi-plots of tracers of tributary and confluence samples confirmed the conservativeness/non-conservativeness of these tracers (Fig.
S2 ). For the pairs of tracers that are significantly correlated (R² > 0. 8) within the tributary sample dataset, the correlation is maintained when adding the confluence dataset suggesting a high degree of conservatism. For other tracer pairs, the correlation is not maintained when adding the confluence sediment samples, which suggests a non-conservative behaviour. These tracers were correctly identified by the range test and were removed from further analysis. With the remaining set of tracers, the stepwise DFA produced a final composite fingerprint at each confluence that provides sources discrimination (Table  2 ). The final composite fingerprint includes different chemical tracers that minimizes the Wilk's lambda criterion and that maximizes the source sample discrimination. At each confluence, the proposed final composite fingerprint correctly classified between 53 and 100% of the source samples in their correct affiliation.
Acknowledgements This work was financially supported by the  Mines-Telecom Institute of Lille-Douai , with additional funding provided by the  Artois-Picardie Water Agency  ( QUASPER  project). We would also like to acknowledge technical support from the  SYMCEA  and the regional Chamber of  Agriculture Nord-Pas-de-Calais, France. The authors are grateful to  L. Alleman  and  B. Malet  for the ICP-MS/AES analyses ( IMT Lille-Douai -SAGE department ). The authors thanks  A. Gellis  and  L. Gorman Sanisaca  ( USGS ) who provided the Sed_Sat-v1. 0 model and for their helpful discussions. The authors also thanks the four anonymous reviewers who provided constructive suggestions to improve the manuscript.
The 2D class averages from these micrographs further showed that the cones have domain-sized bulges and an opening at their bottom end (Figure  2c ). These features are also observed in the cryo-EM structure and they result from an opening of the monomers in the helical core and connector region of the trimer, as discussed below. For cryo-EM structure determination, freshly prepared 229E ectodomain was frozen on EM grids and analyzed with single-particle techniques (Figure  2b ). During data processing, extensive 2D and 3D classification were performed to remove a large number of small particles that did not represent the intact complex. The final dataset included ~71,000 particle images, most of them showing sideviews of the trimeric S-protein (Figure  2d , Figure  2 -figure supplement 1).
The 229E trimer can open at the bottom of the S2 subunit A unique feature of our 229E S-protein trimer is the existence of a large opening that separates the monomers at the C-terminal end of the S2 subunit in the helical core and CD regions (Figure  8a, b ). The opening results in distinctive protrusions on the sides of the trimer that are clearly visible in the 2D class averages from both the negative stain and cryo-EM images (Figure  2c, d ). Among other S-proteins only that of MHV (PDB:3JCL) shows a similar opening, albeit on a much smaller scale. For the closely related NL63 S-protein, the helical core and CD domains of each monomer are packed around the trimer axis for their entire length. A search of our cryo-EM images with a 229E homology model produced from the NL63 S-protein did not return particle images that lacked the opening.
Conversion from the pre-fusion to the post-fusion form requires major conformational changes in the S2 region and our analysis suggests that these hydrophilic interfaces may play a role in the process. In the pre-and post-fusion structures of the MHV S-protein (PDB: 3JCL, 6B3O)  (Walls et al. , 2016b; Walls et al. , 2017) , the CH helix, the helix closest to the trimer axis, undergoes a 60˚rotation along its long axis on conversion to the post-fusion form (Video 3). The rotation restructures the CH trimer interface and the transition is presumably facilitated by the hydrophilic interface as only a single apolar contact need be broken and formed in the process. Even larger conformational changes are associated with the formation of the post-fusion triple-helical HR1 coiled-coil (Figure  5b, c ).
Cloning and expression of hAPN and the 229E RBDs The RBD expression constructs and cell lines used were those described by  Wong et al. (2017). In that work, the Class I RBD was from sequence AAK32191. 1. For the Class II -Class VI RBDs the receptor binding loop regions were from the following: ABB90507. 1 (Class II), ABB90514. 1 (Class III), ABB90519. 1 (Class IV), ABB90523. 1 (Class V), and AFR45554. 1 (Class VI). The remainder of the RBDs comes from the Class I sequence. A sequence alignment of the six RBD classes can be found in Figure  1 -figure supplement 1. The cDNA sequences encoding the six RBD classes were cloned into an inducible piggyBac transposon-based stable expression vector  (Li et al. , 2013). Stable cell lines were generated by co-transfecting HEK293S-GnT1(-/-) cells (ATCC Cat# CRL-3022, RRID: CVCL_A785)  (Reeves et al.
X-ray diffraction data collection and structure determination The diffraction data for the Class V RBD-hAPN complex were collected on beamline 23ID-B at the Advanced Photon Source. Data for the Class III and IV RBD-hAPN complexes were collected on beamline 08ID-1 at the Canadian Light Source. Data reduction was performed using XDS  (Kabsch, 2010)  and HKL2000  (Otwinowski and Minor, 1997). The structures were solved by molecular replacement using a hAPN monomer (pdb id: 4FYQ)  (Wong et al. , 2012)  and the Class I RBD after removal of the receptor binding loops (PDB ID: 6ATK  (Wong et al. , 2017) , lacking residues  307-324, 348-359, and 403-410). The atomic models were built using Coot  (Emsley and Cowtan, 2004 ) and iteratively refined using REFMAC5  (Murshudov et al. , 2011)  and Phenix.
Cloning and expression of the 229E spike protein ectodomain The Class I HCoV-229E sequence (AAK32191. 1) was chosen for the cryo-EM analysis. The cDNA sequence encoding the ectodomain of the S-protein (residues 17-1113) was cloned into an inducible piggyBac transposon-based stable expression vector  (Li et al. , 2013). A T4 fibritin trimerization motif, a TEV cleavage site, and a 6xHis tag was added to the C-terminal end of the 229E ectodomain. In addition, Thr 871 and Ile 872 were both mutated to prolines to stabilize the pre-fusion form of the S-protein as previously described  (Pallesen et al. , 2017). A stable cell line was generated by co-transfecting Freestyle-293F cells (Thermo Fisher Scientific, RRID:CVCL_6642) with the resulting expression plasmids with both a piggyBac transposase helper plasmid and an rtTA helper plasmid.
Cryo-EM data collection Holey gold film (Russo and Passmore, 2014) coated EM grids with 2 mm holes were prepared following a previously described procedure  (Marr et al. , 2014). The S-protein solution contained 0. 4 to 0. 8 mg/mL protein in 10 mM Tris, pH 8. 0 and 150 mM NaCl. Vitrification of the cryo-EM specimen was performed using a Gatan Cryoplunge 3 device. For each cryo-EM specimen, 3 mL of S-protein solution was applied to a glow-discharged (2 min, 15 mA) holey grid. The grids were blotted for 13 s at 100% humidity and 295 K and then plunge-frozen in a 50:50 ethane:propane mixture  (Tivol et al. , 2008)  at 77 K. Specimen screening and optimization was done with an FEI Tecnai TF20 electron microscope equipped with a Gatan K2 summit direct detector. The final high-resolution data collection was performed with a Thermo Fisher Scientific Titan Krios G3 300 kV microscope equipped with a Falcon 3EC direct detector.
The final high-resolution data were collected at 75000x nominal magnification, resulting in a calibrated pixel size of 1. 06 A ˚. Each movie was recorded in counting mode with a 60 s exposure and saved in 30 fractions. The exposure rate was adjusted to 0. 8 electrons per pixel per second, resulting in a total exposure of 42. 7 electrons per A ˚2. The data were collected with a 1. 8 to 2. 2 mm defocus. A total of ~4000 movies were collected for the final high-resolution dataset.
Cryo-EM data processing Full-frame motion correction of the cryo-EM movies were initially performed using an implementation of the alignframes_lmbfgs algorithm  (Rubinstein and Brubaker, 2015)  in cryoSPARC v2  (Punjani et al. , 2017). Local motion correction was later performed after initial particle processing in cryoSPARC v2 with an improved implementation of the alignparts_lmbfgs algorithm  (Rubinstein and Brubaker, 2015). CTF parameters were estimated using CTFFIND4  (Rohou and Grigorieff, 2015). Particle selection was performed using multiple methods in both RELION2. 2 (Fernandez-Leiro and Scheres, 2017) and cryoSPARC v2. The methods used included manual picking followed by template-guided automatic picking, and automatic picking with a Gaussian blob. For the templateguided auto-picking, templates were low-pass filtered to 20 A ˚.
The micrographs all contained many small particles that did not correspond to the intact S-protein ectodomain. The particle images were therefore processed by multiple rounds of 2D-classification and 3D-classification to remove these particles. During 2D-classification, only 2D classes with well-defined internal structures were selected for further processing. The final dataset included ~71,000 particle images. Initial map generation, homogenous 3D refinement, heterogenous 3D refinement and non-uniform 3D refinement were all carried out in cryoSPARC v2. The 3D refinements were carried out with either C1 or C3 symmetry imposed. Comparison of these results indicated that the 229E S-protein particles refined in C1 did not display significant non-three-fold symmetric features. Therefore, the maps generated from the C3 refinements were used for model building and analysis.
S-protein ecto domain cryo-EM model building The atomic model of the ectodomain of the HCoV-229E S-protein was built manually into the C3symmetry map using Coot  (Emsley and Cowtan, 2004). A homology model based on the 3. 4 A cryo-EM structure of the HCoV-NL63 S-protein trimer (PDB id: 5SZS)  (Walls et al. , 2016a)  was produced using the PHYRE2 server  (Kelley et al. , 2015) , and used as a reference during model building. Except for a few loops and the linker between S1 and S2, we were able to trace the polypeptide chain for residues 38-958. In the connector domain the homology model was docked into the map. This resulted in an atomic model of the HCoV-229E ectodomain that included most of the residues between residue 38 and residue 1033, 24 residues before the start of the HR2 region. The atomic model was refined against a half-map using Phenix.
Comparison of the 229E and NL63 S-proteins (Side view). https://elifesciences. org/articles/51230#video2 Video 3. Comparison of the CH helix in the pre-fusion and post-fusion conformations of the MHV S-protein. Top view showing the morphing between the two helices. PDB IDs: pre-fusion, 3JCL; post-fusion, 6B3O. Rotation of the CH helix during the conversion from the pre-fusion to the post-fusion conformation breaks one apolar contact (Leu 1062, blue) and forms another (Val 1070, green). The rest of the residues in the interfaces are polar. https://elifesciences. org/articles/51230#video3.
Acknowledgements The work was supported by  CIHR  operating grants to PJT, JLR and JMR, and a  Canada Research Chair  to JLR. The  Canadian Light Source  and the  Advanced Photon Source at Argonne National Laboratory  are acknowledged for synchrotron data collection. The  Toronto High Resolution High Throughput cryo-EM facility , supported by the  Canada Foundation for Innovation and Ontario Research Fund , is acknowledged for Cryo-EM data collection.
Video 1. 1 Comparison of the 229E and NL63 S-proteins (Bottom view). Two views showing a morphing between the two S-proteins. PDB ID: NL63, 5SZS. An S1 subunit is colored cyan. The S2 subunit with which it associates is from another monomer and it is colored blue. https://elifesciences. org/articles/51230#video1.
Video 2. 2 Comparison of the 229E and NL63 S-proteins (Side view). https://elifesciences. org/articles/51230#video2 Video 3. Comparison of the CH helix in the pre-fusion and post-fusion conformations of the MHV S-protein. Top view showing the morphing between the two helices. PDB IDs: pre-fusion, 3JCL; post-fusion, 6B3O. Rotation of the CH helix during the conversion from the pre-fusion to the post-fusion conformation breaks one apolar contact (Leu 1062, blue) and forms another (Val 1070, green). The rest of the residues in the interfaces are polar. https://elifesciences. org/articles/51230#video3.
The expected benefits of Model-Based System Engineering (MBSE) include assistance to the system designer in finding the set of optimal architectures and making trade-off analysis. Design objectives such as cost, performance and reliability are often conflicting. The SysML-based method OOSEM and the ARCADIA method focus on the design and analysis of one alternative of the system. They freeze the topology and the execution platform before optimization starts. Further, their limitation quickly appears when a large number of alternatives must be evaluated. The paper avoids these problems and improves trade-off analysis in a MBSE approach by combining the SysML modeling language and so-called "decision points". An enhanced SysML model with decision points shows up alternatives for component redundancy, and instance selection and allocation.
The same SysML model is extended with constraints and objective functions using an optimization context and parametric diagrams. Then a representation of a constraint satisfaction multi-criteria objective problem (CSMOP) is generated and solved with a combination of solvers. A demonstrator implements the proposed approach into an Eclipse plug-in; it uses the Papyrus and CSP solvers, both are open-source tools. A case study illustrates the methodology: a mission controller for an Unmanned Aerial Vehicle (UAV) that includes a stereoscopic camera sensor module.
INTRODUCTION According to INCOSE  [1] , Model-based systems engineering (MBSE) is the formalized application of modeling to support system engineering activities, from requirements to validation. These activities have traditionally been performed using document-based approaches. The expected benefits of MBSE include better specification and design quality, reuse of design artifacts, and a coherent model of the system to be developed. Selecting a modeling language is a key issue for MBSE. Originally, mathematical formalisms (e. g. [2] ) were introduced, allowing analysis and optimization by means of specific tools. Over the past decade, joint efforts of OMG and INCOSE have lead to the standardization of SysML  [3] , a modeling language that addresses important issues such as requirements, architecture and behaviors.