dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC IUT ,  PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI , and  FEDER (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
The southward-flowing deep limb of the Atlantic meridional overturning circulation is composed of both the deep western boundary current (DWBC) and interior pathways. The latter are fed by ''leakiness'' from the DWBC in the Newfoundland Basin. However, the cause of this leakiness has not yet been explored mechanistically. Here the statistics and dynamics of the DWBC leakiness in the Newfoundland Basin are explored using two float datasets and a high-resolution numerical model. The float leakiness around Flemish Cap is found to be concentrated in several areas (hot spots) that are collocated with bathymetric curvature and steepening. Numerical particle advection experiments reveal that the Lagrangian mean velocity is offshore at these hot spots, while Lagrangian variability is minimal locally.
c. SCVs Previous studies have found that material may leak from boundary currents via shedding of submesoscale coherent vortices (SCVs)  (McWilliams 1985; D'Asaro 1988; Bower et al. 1997). Bottom-reaching prograde boundary currents (propagating left of inshore in the Northern Hemisphere) can generally be expected to develop negative vorticity near the bottom boundary layer due to bottom drag  (Molemaker et al. 2015). If the prograde boundary current then separates from the slope, e. g. , at a bathymetric cape, the negative vorticity in the bottom boundary layer can cause a roll up into an anticyclonic SCV. Of the ExPath float dataset,  Bower et al. (2013)  indeed found that three floats became trapped within anticyclonic SCVs formed at the southern tip of the GB.
We combine a new regional model of the northwest Atlantic with historical observations to characterize the leakiness process in detail, and to investigate the mechanisms via which it occurs. In section 2 we describe the regional model, a particle advection code, and the observational datasets used in this study. In section 3 we diagnose the leakiness of the DWBC around FC, using both Lagrangian trajectories (section 3a) and Eulerian mean flow patterns (section 3b). We then quantify the variability in the patterns of leakiness (section 3c) and use budgets of potential vorticity (PV) (section 3d) and energy (section 3e) to investigate the relative roles of mean flows and variability in driving the leakiness. In section 4 we relate our results to the mechanism of leakiness (1-4) summarized in this section, and we put forward a hypothesis for the dependence of leakiness on the geometry of the continental slope.
b. Float datasets Two observational datasets of subsurface Lagrangian floats are used here. One is Export Pathways from the Subpolar North Atlantic Experiment (ExPath) dataset  (Furey and Bower 2009; Bower et al. 2011). In ExPath, RAFOS floats were seeded within the DWBC region west of Orphan Knoll (OKL; Fig. 1 ). These are isobaric (i. e. , approximately depth maintaining) floats that are tracked by acoustic sound sources and hence do not need to surface during their trajectory (unlike Argo floats, see below). Relative to float datasets used in prior analyses of DWBC leakiness in Newfoundland  (Lavender et al. 2000; Fischer and Schott 2002) , the ExPath dataset has the advantages that the floats used are not profiling (eliminating contamination of velocity from surfacing), and that the floats were all seeded within the DWBC and just north of the leakiness area, whereas previous floats were seeded farther upstream in the Labrador sea.
The second dataset consisted of a subset of Argo floats  (Riser et al. 2016). Argo floats drift at a set ''parking depth. '' After a typical period of 9 days, the float first descends to 2 km depth, and then ascends to the sea surface, while taking hydrographic measurements. At the surface the float transmits collected data via satellite communication. Then the float descends back to its parking depth, restarting the cycle. We compiled a dataset of all Argo floats that have ever crossed the DWBC cross section along which the ExPath floats were deployed. Specifically, the chosen area is west of OKL, between latitudes 49. 58 and 50. 58N and longitudes 49. 68 and 47. 78W. We find 67 floats that meet this criterion, with parking depths between 800 and 2000 m, between the years 1998 and 2017. Specifically, the number of floats that have parking depths of 800, 1000, 1500, and 2000 is 3, 43, 18, and 3, respectively.
c. Particle advection The phenomenon in question, Lagrangian leakiness, is most directly addressed in a Lagrangian framework. For that purpose, and for comparison with the float observations, we seed and track passive particles in the velocity fields obtained from the numerical model described in section 2a. We developed a FORTRAN code (named ''TrajInt,'' for trajectory integration) that allows three-dimensional (3D) integration of particle trajectories given their initial positions at a particular time. Particle advection experiments were performed offline, i. e. , after running GB B (section 2a). The main features of the code are described here. The particles are passively advected by solving the advection ordinary differential equation › t x 5 u, where x 5 x(t) is the particle position at time t, and u is the ROMS velocity field interpolated to time t and position x.
This time period is comparable to the observed velocity autocorrelation time in subthermocline depths in the northwest North Atlantic  (Böning 1988; Lumpkin et al. 2002) , hence we consider the convergence satisfactory. We conduct several particle advection experiments. Floats are initialized along a line (Fig. 1c ) within the DWBC, west of OKL, close to the seeding locations of ExPath floats  (Furey and Bower 2009) , between the 1 and 2. 8 km isobaths, and with an initial depth at least 300 m above the bottom. The specified line is chosen since it is located upstream of Flemish Cap, where much of the leakiness occurs, and for comparison with the ExPath dataset (section 2b). The mean model velocity at the seeded depths along OKL is everywhere downstream (approximately southward) within the DWBC, except for a clockwise recirculation at Orphan Knoll.
a. Phenomenology Using two observational float datasets and a realistic, high-resolution, numerical model, we demonstrate that within the Newfoundland (Nfl) Basin, the DWBC has a few well-defined geographical hot spots of maximal Lagrangian leakiness (Figs. 2  and 3 ). At the leakiness hot spots, local maxima of time-mean Lagrangian and Eulerian offshore velocities occur in the numerical model, while Lagrangian EKE is minimal (Figs. 3 4 5 6 ). These hot spots are further characterized by convex curvature and/or downstream steepening isobaths (Fig. 9 ). The localized, and time-mean nature of the leakiness, and its apparent correlation with bathymetric variations, suggests that it occurs largely via an inertial separation mechanism (mechanism 2 in section 1). This contrasts with previous hypotheses that suggested that the DWBC leakiness was due to interaction with NAC eddies (mechanism 1 in section 1).
Typically, nudging model salinity to climatological values is required, although not always sufficient, to reduce or eliminate the bias in present models. A disadvantage associated with a nudging procedure may be reduction in frontal features and sharpness of boundary currents in high-resolution models, because the resolution of climatological datasets is generally coarser. We therefore did not apply such a nudging procedure. In our model the bias gradually appears during spinup and appears to be fully developed by year 9, without further increase in the bias amplitude in the following years. It is difficult to determine with certainty to what degree our key results are affected by the water mass bias. We expect, however, that such effects should manifest mainly indirectly, through the effects on the mean circulation and on EKE.
Acknowledgments. This material is based in part upon work supported by the  National Science Foundation  with Grants  OCE-1538702 ,  OCE-1751386 , and  OCE-1419450. The authors thank  Dr. Amy Bower  for providing the Export Pathways dataset, and  Dr. Christian Mertens  for providing the processed shipboard ADCP observations at 478N, east of Flemish Cap. The SLA altimeter products were produced by  Ssalto/Duacs  and distributed by AVISO, with support from  CNES  (http://www. aviso. altimetry. fr/duacs/). The Argo data were collected and made freely available by the  International Argo Program  and the national programs that contribute to it (http:// www. argo. ucsd. edu,http://argo. jcommops. org). The Argo Program is part of the Global Ocean Observing System. Argo float data can be downloaded from http:// doi.
, 2007). This framework has been incorporated into the canSNPer package for cladistic categorization and remains the current standard for B. anthracis classification across the globe  (Lärkeryd et al. , 2014; Van Ert, Easterday, Huynh, et al. , 2007). These methods have been used to document genotypic diversity in anthrax outbreaks occurring from local to regional scales  (Lienemann et al. , 2018; Rume, Affuso, et al. , 2016; Rume, Ahsan, et al. , 2016; Zhang, Zhang, He, Li, & Wei, 2018). More recently, whole-genome sequencing (WGS) technology has helped to resolve local differences in B. anthracis strains extending the aforementioned genotyping scheme  (Derzelle, Girault, Roest, & Koene, 2015; Girault, Blouin, Vergnaud, & Derzelle, 2014; Girault, Thierry, Cherchame, & Derzelle, 2014) , but currently no system based on WGS data, using high-resolution SNP detection, has been incorporated into a large-scale global framework, such as those produced for other monophyletic clades such as Salmonella enterica, Mycobacterium tuberculosis, and Yersinia pestis  (Coll et al.
, 2014; Morelli et al. , 2010; Wong et al. , 2016) , and no genotyping system defined by population genomic structure for this species currently exists. In this study, we developed a global genotyping system for B. anthracis using the WGS data of 356 B. anthracis isolates collected from 39 countries over the course of a century, available from the National Center for Biotechnology Information (NCBI) database (Table  1 ). We identified >14,500 chromosomal SNPs in the core genome, >580 SNPs in the pXO1 plasmid, and >320 SNPs in the pXO2 plasmid, all of which were used to construct comprehensive phylogenetic trees for each bacterial component. We then used this high-resolution WGS dataset to re-define the global systematics for B. anthracis using current methods for population genomic analysis, establishing a robust framework for primary cluster and clade designation based on hierarchical genomic structure.
| Bacterial isolates and WGS data A total of 422 B. anthracis isolates collected from both natural hosts (wildlife and livestock) and directly from the environment were compiled for this study. Paired-end Illumina raw read data included in the analysis were freely available for download from public databases and followed variable WGS protocols (Data S1). Sample isolation dates range from 1905 through 2018, encompass 39 countries, and span six continents (Africa, Asia, North and South America, Europe, and Oceania; Table  1 ).
| SNP detection and analysis SNP analysis was carried out using the RedDog pipeline (https :// github. com/katho lt/RedDog), which implements a workflow for short read length sequencing analysis  (Edwards, Pope, & Holt, 2016). Paired-end reads for a total of 422 B. anthracis isolates, a single Bacillus cereus isolate (chromosomal outgroup), and a single Bacillus biovar anthracis isolate (pXO1 and pXO2 plasmid outgroups), were mapped to the B. anthracis "Ames Ancestor" reference genome, and its accompanying pXO1 and pXO2 plasmids (GenBank: GCA_000008445. 1), using Bowtie2 version 2. 2. 9  (Langmead & Salzberg, 2012). SNPs were called using SAMTools version 1. 8  (Li, 2011). Sites for which conclusive allele calls could not be determined in at least 95% of all isolate sequences at a read depth >10 were not used, resulting in a final dataset of 356 B.
anthracis isolates. The resulting SNP dataset then underwent extensive postprocessing to (a) remove calls found in regions with large inexact repeats, (b) remove calls within prophage regions of the reference genome identified with PHAST  (Zhou, Liang, Lynch, Dennis, & Wishart, 2011) , and (c) remove calls from regions that were found to be invariable in all but the outgroup. We also removed calls from regions potentially resulting from recombination, identified by Gubbins  (Croucher et al. , 2014) , or stutter, identified as multiple calls occurring within a 3bp window, or 3 or more calls within a 10bp window. This resulted in a final set of 14,601 chromosomal SNPs in an alignment length of 5,227,419 for the global collection of 356 B. anthracis isolates, a final set of 582 SNPs in an alignment length of 181,677 for the accompanying pXO1 plasmid, and a final set of 328 SNPs in an alignment length of 94,830 for the accompanying pXO2 plasmid.
3  (Letunic & Bork, 2016). To compliment the whole-genome tree-building analysis described above, we also applied a population genomic approach to identify distinct clusters across the phylogenetic datasets. For this analysis, we employed hierBAPS  (Cheng, Connor, Sirén, Aanensen, & Corander, 2013; Tonkin-Hill, Lees, Bentley, Frost, & Corander, 2018)  which provides a method for hierarchically clustering DNA sequence data to reveal nested population structure. Two nested levels of molecular variation were fitted to the data, and the algorithm was run until it converged at a local optimum for each bacterial component. Clusters identified by hierBAPS were then added to the phylogenetic trees, again employing iTOL to visualize the nested population structure across all B. anthracis isolates (Figures  1, 2a,  and 3c ).
| SNP-based genotyping framework We distinguished a minimum set of 19 canonical chromosomal SNPs that can be used to rapidly categorize B. anthracis isolates into the six primary clusters and 18 nested clades described above (Table  S1 ). patterns, and identify how plasmid evolution may differ from that exhibited in the core phylogeny. Plasmid tree building followed the same methodology as the core genome, and the resulting datasets were again classified using a population genomic framework to elucidate two levels of nested structure across both the pXO1 and pXO2 plasmids (Figure  1b ,c, see methods). For both plasmids, a subset of samples failed to meet read depth criteria across the replicon due to absence or low count number, resulting in a total of 283 samples used to build the pXO1 tree, and 268 samples used to build the pXO2 tree.
| Geographical and temporal distribution of B. anthracis We also examined the geographical and temporal distribution of B. anthracis isolates based on our global genotyping framework. When examining the dataset with regard to continental distribution, most primary clusters spanned multiple continents, putatively reflecting the spread of B. anthracis relative to both ancient dissemination and contemporary transit in terms of agricultural trade practices (Figure  2 ). The only exception to this is primary cluster 1 (black; Figure  2 ), which contains a limited number of representative isolates (n = 5) and is all from North America (consistent with the previously described major lineage C). When examining all other primary clusters, some clusters were more heavily weighted with samples from certain regions than others.
Primary cluster 5 represents the most geographically and genetically diverse primary cluster examined (mean number of pairwise SNPs between clades >133). The majority of isolates from this cluster are from Asia (n = 31; 43%), but this cluster also contained multiple representatives from all other continental regions examined in this study. Primary cluster 6 is the largest cluster examined in this study and is dominated by isolates from North America (n = 62; 46%) and Europe (n = 54; 40%), but also contains several isolates from Asia (n = 18; 14%) and South America (n = 2; 1%). Clade 6. 3 from primary cluster 6 is made up entirely of isolates from North America (n = 56) and is the largest single clade in the dataset. Clade 6. 2 contains a single sample from Africa, the only such sample found across 135 isolates in primary cluster 6.
Ancestral isolates from primary clusters 1 and 2 which result from deep branching originating in close proximity to the root of the B. anthracis whole-genome tree were rare in the collection (n = 18) and mostly found in North America (n = 13; 72%). Nevertheless, it should be noted that this dataset is heavily weighted by isolates collected in North America, accounting for more than half of the isolates examined in study (n = 186; 52%). Finally, the whole-genome tree was examined at the level of individual countries to assess temporal patterns in genomic structure across the genotyping framework, for samples for which dates were available. In this analysis, branches within a designated clade specific to a country that contained more than one isolate were collapsed and plotted across years of isolation for each phylogeographical branch in the tree (Figure  3a.
When comparing the classification framework with the previous system, major differences in resolution are easily discernable, ultimately leading to higher specificity in sample designation for the revised approach. Primary cluster 1 (consistent with the previously described major lineage C; Figure  S1 ) was unclassifiable using the older canSNPer-based scheme, whereas the new framework allows for both primary cluster designation, as well as clade designation within this group. When examining primary cluster 2 (consistent with the previously described major lineage B; Figure  S1 ), we see that the previous system allowed for only two subgroups within our dataset, where as a test of population genomic structure detected and designated 3. In addition, when viewing the new framework in the context of previously described major lineage A, we were able to distinguish 4 major clusters, and 10 nested clades, whereas the previous scheme allowed for only 4 subgroups and 3 additional sublineages based on rare alleles in our dataset.
Lastly, the older genotyping scheme exhibited an instance of misclassification for a single sample falling on the edge of A. Br. 009 (classified as A. Br. 004; Figure  S1 ), suggesting homoplasy and indicating that further misclassification upon expansion of the dataset has the potential to produce erroneous results. This updated genotyping scheme therefore allows for a far more robust system for tracking, distinguishing, and tracing the evolutionary origins of B. anthracis based on global genomic structure. As a result of the geographic clustering exhibited at the clade level, the incorporation of additional B. anthracis isolates may lead to an even more robust predictive genotyping scheme, helping to identify the geographic origins of regional outbreaks at finer scales. For example, two samples that fall between primary clusters 3 and 4, from the United States and Indonesia, were designated to clade 6.
In addition, the results of this study suggest that some clades may have potentially evolved to fill ecological niches based on subgroupings that appear to be biogeographically suited to specific regions. With the expansion of this genotyping system, a more robust understanding of B. anthracis evolutionary specificity may be possible, especially with the inclusion of isolates from the many regions that are currently under-represented in this dataset. Over the past several decades, many public health institutions and wildlife disease laboratories have replaced PCR-based methods with WGS, given the falling price of sample throughput and high-resolution data available from such technology  (Wyres et al. , 2014). This provides a fantastic opportunity to utilize and expand the global framework presented here, with potential benefits for both local and broader regional agencies seeking to understand both outbreak origins and recent introductions of this bacterium.
Geographical persistence and diversity of Bacillus anthracis lineages. (a) Maximum likelihood tree for the chromosomal SNP dataset with clades containing branches from a single country collapsed into nodes (circles), sized to indicate the number of isolates in the sub-population and colored by region of isolation. Branches are also colored to indicate the country of origin and correspond to the nodes. (b) Years of isolation for each phylogeographical cluster in the tree. Isolation dates are indicated by rectangles and lines spanning the earliest and latest years of isolation for each cluster are colored to indicate the country of origin. The two adjacent color strips correspond to the clade and primary cluster designations from Figures1 and 2.
In this paper, a new analytical approach to object oriented modeling is presented for the predesign of a multibody system. We investigate the dynamic behavior of a system of interconnected components using the modeling language Modelica. In engineering, beam-masses are often used as design models. In fact, the considered system is composed of a flexible beam subjected to a moving load and supporting one or more translating elastic subsystems. Each subsystem is controlled by a vibration absorber and the structure is affected dynamically only through the moving character of the load. The problem of calculation of the dynamic response of this system is very important in many engineering applications such as in the predesign and analysis of a robotic portal systems, machine tools and bridge crane systems.
Introduction Multibody dynamic analysis represents a powerful tool to investigate the dynamic response of flexible systems of arbitrary complexity and topology  [1, 2]. The multibody system modeling is a common approach for representing mechanical systems as interconnected components  [3, 4]. In fact, a flexible multibody system can be composed of elastic and rigid components which are connected by force and/or joints elements as springs, dampers, and actuators  [5, 6]. Alessandro et al. [7]  propose two new Modelica libraries which are the Engines libraries and the Powertrain Dynamics (PTDynamics). The libraries enable the modeling and the simulation of a multibody system in one simulation environment (Dymola) based on Modelica language. Indeed, Modelica is very appropriate for building muli-domain models  [8].