dataesr/scientific-paragraphs-categorization huggingface dataset:
RT-PCR and G gene sequencing Primers for RT-PCR and sequencing were designed based on the published sequence of VHSV in the GenBank database under the accession number Y18263 (S2 Table ). The sequence of the complete G gene was amplified using the primers V2782for and V4664rev (S2 Table ). The predicted 1883-bp RT-PCR product (VHSV genome: nucleotides 2782-4664) encompasses the 1524-bp-long G gene, which is localized between nucleotides 2959 and 4482 of the VHSV genome (accession number: Y18263)  [25]. RT-PCR was performed using the One Step RT-PCR Kit (Qiagen). The 25-μL reaction mixture consisted of the one-step RT-PCR buffer (Qiagen, including 12. 5 mM MgCl 2 ), 40 mM deoxynucleosidetriphosphate (dNTP) mix (10mM of each dNTP), 10 pmol each of the forward and reverse primers, 1. 0 μL of the enzyme mix (Qiagen), 20U of RNasin (Promega), and 10-100 ng of total extracted RNA.
1 Cycle Sequencing kit (Applied Biosystems) according to the manufacturer's instructions. The sequencing product was purified using either Sigma Spin Post-Reaction purification columns (Sigma Aldrich) or Nucleo Spin Columns (Machery Nagel). After denaturation with Hi-Di Formamide, samples were analyzed on an automatic sequencer (ABI 377, Applied Biosystems), and nucleotide sequences were evaluated using the sequencer's Scanner software version 1. 0 (ABI) and Geneious software version 7. 1. 7 (Biomatters Ltd. )  [26]. The full-length G gene sequence was deposited in GenBank and the Fish Pathogens Database  [27].
Determination of the haplotype The haplotype of a VHSV isolate was determined based on the substitution differences within the complete G gene sequence. Specimens that featured a variation of two possible nucleotides at a single position of the G gene sequence were split into two haplotypes and phylogenetically handled as though they were two isolates. However, if the specimen varied at more than one nucleotide position of the G gene sequence, they were excluded from the dataset. Multiple sequence alignment was performed using Geneious Pro version 7. 1. 7 (Biomatters Ltd. )  [26]. The number of haplotypes was calculated using DnaSP version 5. 10. 01  [28].
Phylogenetic subdivision of the Ia subgenogroup (Ia lineage) into Ia clades Following the principle the lower the bootstrap support value, the less reliable the grouping, we divided the haplotypes of the Ia subgenogroup into subordinate Ia clades on the basis of a Maximum Likelihood (ML) bootstrap support value of !75%. The bifurcating ML tree, with each node splitting into exactly two descendant branches, was constructed by the computer program MEGA version 5. 2  [29]. The best-fit nucleotide substitution model was selected using the Bayesian Information Criterion score with Find Best DNA Model in MEGA version 5. 2  [29]. As a result, the general time reversible (GTR) model with gamma rate heterogeneity and invariant sites was chosen, and 250 bootstrap replicates were generated to assess the reliability of the clades obtained in the tree.
g. , geographic regions). After this, different local scenarios of selection and genetic drift result in the development of individual sublineages (clades) with a common time of origin. Therefore, a hard polytomy can be interpreted as a rapid radiation event. It is widely recognized that polytomy relationships are relatively common in intraspecific gene phylogenies  [32]. Although an extensive dataset of Ia isolates is available for this study, a sampling bias cannot generally be excluded. Therefore, detected polytomies can be interpreted as a potential indicator for a hard polytomy rather than evidence for such. To discover nodes with a tendency toward polytomy we adopted the following procedure. First, we created a phylogenetic Median Joining (MJ) network using the computer program NETWORK version 4.
6. 1. 2 (http://www. fluxus-engineering. com) [33]. This method is well suited to represent the polytomy structure of a node. The program's default setting of Epsilon (0) was chosen and the transition/transversion bias (R) was based on a maximum likelihood estimate obtained using MEGA version 5. 2  [29]. Due to the lack of a robustness measure for the MJ network in the NETWORK program, we sought to enhance the reliability of the MJ network topology by examining: (1) if the haplotypes of each ML Ia clade are also grouped in the MJ network; and (2) if they have a phylotemporal structure. In case of the latter, the expectation would be that older isolates (earlier collection dates) would cluster closer to the ancestral node of the lineage than isolates with more recent collection dates. Secondly, we created a NeighborNet (NN) network  [34]  using the program SplitsTree version 4.
Indication of rapid population growth A rapid increase in the size of an Ia clade population (a sudden demographic expansion due to increased population growth) was indicated using the following methods. (1) Visual inspection for a major star-like pattern in the MJ phylogenetic network constructed by NETWORK version 4. 6. 1. 2 [33]. (2) A coalescent Bayesian skyline analysis  [37, 38]. The Bayesian skyline plot was constructed using the program BEAST version 1. 8. 2  [39]  and was visualized with the program Tracer version 1. 5 (from BEAST). A further output of that method was the meanRate (substitution rate) of the sequence used for this calculation. The BEAST input gene files were generated using BEAUti version 1. 8. 2. Our analyses used the random starting tree for the Markov chain Monte Carlo (MCMC) search, selecting a general time GTR model along with an uncorrelated lognormal relaxed molecular clock.
Results We successfully sequenced 452 VHSV isolates, and they fell into the previously published phylogenetic classification of genogroups on the basis of their haplotypes (1 I (unclassified) isolate, 442 Ia isolates, 1 Ib isolate, 6 Ic isolates, and 2 Ie isolate) (S1 Table ). The GenBank and Fishpathogens database accession numbers of the additional sequences obtained for this study are also listed in S1 Table. In total, the dataset (GenBank samples and samples sequenced for this study) consists of 734 VHSV isolates (4 I (unclassified) isolates, 651Ia isolates, 27 Ib isolates, 11 Ic isolates, 32 Id isolates, 6 Ie isolates, and 1 each of isolates II, III, and IV). Genogroup Ia isolates can be subdivided into 398 haplotypes (S1 Table ). Approximately 95% of these 651 Ia isolates were obtained from Oncorhynchus mykiss, 3% from Salmo trutta, 1% from Esox lucius, and 1% from 6 further fish species (isolates from an unknown host species are excluded) (S1 Table ).
doi:10. 1371/journal. pone. 0164475. g001 only taxon in the Ia dataset with varying clade-membership when comparing the ML, MJ, and NN Ia phylogeny. In the ML tree, isolate X clustered with clade 6 (Fig  1b ), but with a very low bootstrap support value of 19%. In contrast, in the MJ network, the lineage of X originated from the common ancestor of clade 2 (Fig 2 ), whereas in the case of the NN network, it originated close to the ancestral node of the Ia lineage (node 1), which was approximately between the origins of the lineage of clade 2 and the lineage of clade 6 (Fig  1d ). However, when isolate X is excluded from the dataset, the bootstrap support value of node Y increased from 40% to 96% (Fig  1a  and 1b ). In addition, the number of splits was significantly lower in the NN network of  ).
Moreover, we confirmed a low value for the raggedness index and statistically significant negative values in tests for Tajima's D and Fu's Fs for both datasets (Table  1 ). This was also the case when we examined separately either the Danish 5i haplotypes or the German 9i haplotypes (Figs  3  and 4 , Table  1 ). However, when we calculated the mismatch distribution of clade 9i haplotypes exclusively from Switzerland, Italy, or Poland (Fig  3 ), and also of the haplotypes from 5ii, 8ii, 8iii, and 9ii (graphs not shown), the data only poorly fit the unimodal curve. Instead of a smooth unimodal shape, these curves exhibit a more ragged shape. Accordingly, the raggedness index was significantly increased and a negative non-significant P value was also obtained in tests for Tajima's D and Fu's Fs (Table  1 ).
Discussion Since the first Ia isolate was detected in France in 1971, the Ia population has become widely distributed in Europe, especially where rainbow trout is intensively produced in freshwater aquaculture  [9]. This extensive spread is clearly confirmed by our phylogenetic results. First, the Ia lineage underwent a considerable phylogenetic radiation, since we were able to divide this subgenogroup into nine Ia clades (Fig  2 ). Normally, such radiation is based on different local scenarios of selection and genetic drift resulting from the spread events. In addition, isolate X may even indicate-as a tenth Ia clade-the existence of further clades which remain, however, undetected using this dataset. Secondly, each of the three polytomies (nodes 1-3) (Fig  2 ) that were found among the Ia clades could be an indicator for a rapid radiation, as they reflect a simultaneous spread from one single place to several places.
For example, in the case of even a single delivery of VHS-infected trout, one single Ia haplotype can be spread to various regions of Europe. However, this is only true, if these polytomies are hard polytomies and not the result of a potential taxon sampling bias in our dataset. Third, the Europe-wide distribution of clades 8 and 9, in particular, indicates a strong mixing of Ia isolates through the trout trade. ). Finally, the indications for the two large-scale rapid population growth events in the Ia population could also point to, at least temporally, a sudden extensive demographic expansion, which is normally the result of the spatial spread of a species  [42, 43]. However, a clear reconstruction of the spread routes of ancestral lineages of individual Ia clades was largely impossible.
The reason for that was that most Ia clades include several nodes with weak ML bootstrap support (not shown), a result of the low resolving power of mutational differences among their closely related haplotypes, and apparently due to homoplasy (parallel  or back mutation events)  [33]. This poor phylogenetic resolution was also apparent in the MJ network through the many polytomy structures, and several circle structures within the Ia clades (Fig  2 ). Furthermore, this reconstruction was additionally complicated by an almost complete absence of a country-specific distribution for most of the Ia clades (Fig  2 ). As mentioned above, it seems that the Ia isolates were permanently and complexly changed due to the Europe-wide mixing of Ia isolates via the trout trade. And finally, the reconstruction of the value of the raggedness index (r), nucleotide diversity (PI), Tajima's D, and Fu's Fs of each dataset.
The Bayesian skyline plot shows changes of the 9i population size between 1999 and 2015. The plot was generated using all 213 9i sequences (complete G gene sequence). X axis: time in years, Y axis: population size. The middle solid line is the median estimate, and the area between the blue lines shows the 95% highest probability density (HPD). spread routes was further complicated, by a sampling bias in our dataset. Although an extensive dataset of Ia isolates was used here, spatiotemporal sampling bias may exist in the case of Ia isolates obtained from Austria, the Czech Republic, France, Georgia, Italy, Poland, Slovenia, Switzerland, and the United Kingdom, and from Germany and Denmark before 1996 (S1 Fig). This is possible, as outbreak samples from European trout farms (let alone from farms with latent infections) were collected using different levels of strictness depending on individual national surveillance of VHS.
In addition, the determination of the place of origin is further complicated because our dataset exhibits a spatiotemporal sampling bias of Ia isolates from the period when the first Ia isolates were collected (S1 Fig). Due to the particularly high spatiotemporal density of Danish and German Ia isolates in our dataset from approximately the last 20 years, we particularly focused our phylogenetic analysis on those two countries during this period. Thus, in comparison to the German situation, we observed an instance of rare gene flow of Ia isolates between Denmark and other European countries. This was supported by the following three findings. ). Accordingly, the nucleotide diversity of the German Ia population (304 used sequences) was almost more than twice as high as the nucleotide diversity of the Danish Ia population (244 used sequences) (not shown).
Nevertheless, we found 9i isolates in eight further European countries (Fig  1 ). Accordingly, their participation in the population growth was not excludable, because of the mentioned sampling bias of Ia isolates in our dataset. In particular, the role of Italy, Poland, and Switzerland was at issue, as their jagged mismatch curves still exhibited a discernible unimodal shape that may indicate their participation (Fig  3 ). In the case of Switzerland, however, we know that the trout farming industry did not sell life trout abroad during this period; instead, they bought many life trout from surrounding countries. We only exclude Danish participation, as our dataset comprised no Danish 9i isolates, even though our dataset has a high spatiotemporal density of Danish isolates from that period.
At first sight, this Europe-wide spread and mixing of Ia isolates seems to be surprisingly inconsistent with another outcome of this study, namely that only about 4% of the Ia haplotypes occurred in more than one European country (S1 Table ). However, we think that this can most probably be explained by the observed high substitution rate of VHSV  [13, 17]. Our dataset indicates that the mean period of occurrence of a single Ia haplotype is typically not longer than one calendar year. Furthermore, distinct isolates from the same VHS outbreak occasionally fall into different but very closely related haplotypes (e. g. , isolates V43-14bb, V44-14bb, and V45-14bb) (S1 Table ). However, the observation that one haplotype in our dataset (number 140) was re-isolated (as isolate V01-90he) 19 years after its first isolation (as isolate FR-0771) can be best explained by laboratory contamination or a confusion event.
Our dataset suggests that Ia clades become extinct after a period, since the mean period of occurrence of a clade is approximately 11 years (Fig  2 ). We consider that this cyclic pattern of newly arising formations and the disappearance of clades have been mainly caused by anthropogenic influences such as trout farmers and traders, since the host of the Ia population mainly been the European farmed rainbow trout. Approximately 95% of the Ia isolates of our dataset are from farmed rainbow trout. This close ecological interaction is supported by the fact that Ia isolates have, in contrast to isolates from other subgenogroups of the genogroup I (Ib-Ie) and the genogroups II-IV, a particularly high virulence for farmed rainbow trout  [12, 46]. We believe that careless breeding and trade practices when dealing with VHS-infected fish have unintentionally supported the geographical spread of the virus and the establishment of new clades, while sanitation and eradication measures have led to their extinction.
4 years (σ = 6. 5 years) (Fig 2 and S1 Table). Notably, German Ia haplotypes from 2005 to 2015 fall into four distinct Ia clades (4, 5, 8, and 9), whereas Danish Ia haplotypes from 1999 to 2009 fall into two Ia clades (5 and 9). By comparison, the mean value was 4. 7 Ia clades per calendar year (σ = 1. 658 per calendar year) for the entire dataset, calculated over the last 20 years (1996-2015). On the basis of the phylogeographic pattern of the Ia population (Fig 2), clades 1, 3, 4, and 6 indicate a trend toward Germany and France, whereas clades 2 and 7 suggest a trend toward Germany and Denmark. In case of clades 8 and 9, the phylogeographic pattern was particularly difficult to interpret, as each of them included isolates from an especially large number of European countries (Fig 2 and S1 Table).
Acknowledgments Special thanks to  Peter-Joachim Enzmann  for his substantial work regarding the establishment of the German VHSV isolate database. Furthermore, the skillful technical assistance of  René Grenkowitz ,  Günter Strebelow ,  Kersten Biebl  and  Troels Secher Rundqvistis  is highly appreciated. We also would like to thank all colleagues from the German health services and regional laboratories for providing samples and background information, as well as  Henrik Korsholm , the  Danish Food and Veterinary Administration  and  Morten Sichlau Bruun ,  DTU Vet  for information and encouragement. Special thanks also to  Eva Schellenbeck  for editing this manuscript.
, 2003). In Nicaragua, manual versus chemical weed control in pastures, pruning of trees in live fences, harvesting branches and trees for firewood, posts and timber, and land use change were the main factors determining the degree of tree cover retained in pastures and live fences. Both studies use Netica BBN software (www. norsys. com) to model a number of underlying farm practices, ecological and socio-economic factors that in turn determined these main effects. Sadoddin et al. (2005)  use BBNs to evaluate biophysical, social, ecological and economic factors determining the dryland salinity effects of different management scenarios on terrestrial and riparian ecosystems in the Darling Basin, Australia. They argue that BBNs are particularly useful for communicating risk and uncertainty and providing a framework for analysing cause and effect relationships in natural systems.
Data structure Fig. 2  illustrates the conceptual structure of the BBN and types of knowledge that were combined (colour legend). Primary data to populate the BBN come mainly from structured surveys of farmers, follow-up interviews of farmers with a paddock simulation board, interviews with scientists, and several secondary sources. Mosquera (2010) conducted semi-structured interviews of 76 farmers on their local knowledge of functional traits of tree species and their perceived benefits and disadvantages. Salazar (2012) conducted a structured survey of 55 of the farmers from Mosquera's sample collecting complementary information on farmers motivations, limitations and solutions for managing trees in paddocks, and knowledge of trees' costs and benefits. Structured survey questions were complemented by a semi-structured paddock simulation exercise with farmers, and semi-structured interviews with scientists on trees' functional traits and ecosystem services.
Financial benefits and costs Salazar (2012) interviewed farmers regarding costs, productivity and prices for t non-timber products from paddock trees (blue nodes; Fig. 2 ). A BBN sub-network was constructed to calculate costs of tree planting and the net annual benefits of tree products minus management costs for live fences and trees in pasture for the current paddock and 'ideal' SPS (Supplementary material Fig. S1 ). Gross income and management costs were derived from the financial questions in the survey by  Salazar (2012) , determining net annual income per tree and for the current and ideal paddock as a whole. We based tree crown diameter node on the SILPAS project database of 1821 observations of 21 different species in the study area. We determined the number of additional trees based on information from the paddock simulation exercise.
Discussion Our analysis demonstrates how BBNs can be used to join local and scientific knowledge databases on silvo-pastoral systems in support of agricultural extension work matching farmer stated needs with the tree species composition, densities, and traits that meet those needs. Baynes et al. (2011)  discuss how the ability of BBNs to accommodate imprecise estimates of qualitative variables is highly advantageous in extension work, which by definition is concerned with subjective human values and attitudes. For example, farmers' needs expressed through a semi-structured interviews could be classified using local terminology for functional traits and linked to e. g. quantitative information on tree cover. Landuyt et al. (2013)  argue that integrated models can become structured libraries of existing scientific knowledge enabling the identification of knowledge gaps.
Conclusions Farmers' knowledge is built on experience over long time spans and integrating multiple dimensions, which are difficult to cover in scientific surveys that address specific questions in isolation, with data collected in relatively short periods. BBNs are a generic modeling tool that enable the integration of different sources of knowledge complemented and updated over time. Collecting data from several studies from a single study area we use a BBN to describe farm and farmer characteristics, and functional characteristics, ecosystem services and disservices of trees in silvopastoral systems of Rivas, Nicaragua. We illustrate how the BBN can integrate different domains of scientific and farmer knowledge; both primary data collected in farmer interviews and previously published results.
Rather than a field validated expert system for SPS we demonstrate two desirable properties of such an 'expert system': a predictive, deductive analysis and a diagnostic, inductive analysis. The case study demonstrates how interactive farmer interviews can be integrated in a BBN. The 'paddock simulation exercise' used qualitative participatory landscape visualization techniques to provide probabilistic information for scenario analysis and Bayesian inference. We integrated a knowledge database on costs and benefits with biophysical assessment of SPS, which may be useful for extension services. The ability to differentiate advice by farmer typology can also make extension more context adapted. Our BBN model illustrates how linguistic differences in the definition of scientific knowledge and local farmer knowledge of tree functional traits may be one explanation for differences in understanding of the more abstract notion of ecosystem services.
Acknowledgement This research would not have been possible without the generosity and openness of farmers in Rivas,  Nicaragua. The research was supported by the  FUNCiTREE  Project (http://funcitree. nina. no/)grant no. 227265  co-funded by the  European Commission ,  Directorate General  for Research, within the  7th Framework Programme of RTD, Theme 2 -Biotechnology ,  Agriculture & Food , and  Multi-Functional Landscapes  grant no. 190134  funded by the  Research Council of Norway. Thanks to  Juan Guillermo Martínez Souza  for graphic design of the paddock simulation exercise.
We have seen important strides in our understanding of mechanisms underlying stroke recovery, yet effective translational links between basic and applied sciences, as well as from big data to individualized therapies, are needed to truly develop a cure for stroke. We present such an approach using The Virtual Brain (TVB), a neuroinformatics platform that uses empirical neuroimaging data to create dynamic models of an individual's human brain; specifically, we simulate fMRI signals by modeling parameters associated with brain dynamics after stroke. In 20 individuals with stroke and 11 controls, we obtained rest fMRI, T1w, and diffusion tensor imaging (DTI) data. Motor performance was assessed pre-therapy, post-therapy, and 6 -12 months post-therapy. Based on individual structural connectomes derived from DTI, the following steps were performed in the TVB platform: (1) optimization of local and global parameters (conduction velocity, global coupling); (2) simulation of BOLD signal using optimized parameter values; (3) validation of simulated time series by comparing frequency, amplitude, and phase of the simulated signal with empirical time series; and (4) multivariate linear regression of model parameters with clinical phenotype.