dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013). Then properties over the Svalbard region are investigated and compared to those at regional scale. Section 2 describes the DARDAR retrieval algorithm and presents the dataset used and the method applied to detect MPC and determine cloud occurrences. In Sect. products is first compared to previous works in order to validate the methodology. Then in Sect. 4, focus is made on mixed-phase clouds variability at global scale and over the region of Svalbard. In Sect. 5, global and local MPC properties are compared and differences observed between the whole Arctic and the Svalbard region are discussed.
Data and method Cloud thermodynamic phase distributions over the Arctic region are studied using CALIPSO and CloudSat measurements. Active remote sensing observations are expected to provide a detailed characterization of the cloud vertical structure in the Arctic. Indeed, compared to passive instruments, active remote sensing is less disturbed by the important Arctic ice cover throughout the year or the lack of sunlight during the polar night  (Chan and Comiso, 2013; Liu et al. , 2010; Wang and Key, 2005).
Dataset and methodology First, it is necessary to precise the definition of mixed phase clouds used in the present study. All DARDAR pixels classified as mixing of ice and supercooled water (class 2, see Table  1 ) or all pixels classified as supercooled only (class 4, see Table  1 ) associated with the presence of ice or ice and supercooled water mixing in the vicinity of the pixel are assumed to belong to the mixed phase class (or are considered as mixed phase pixels). "Isolated" supercooled water pixels are not classified as mixed phase clouds. Figures.
Back Close Full The annual and seasonal variability of total cloud fraction and MPC is investigated using CALIPSO/CloudSat continuous measurements from 2007 to 2010. In this study the Arctic region is defined by all latitudes greater than 60 • N  (Liu et al. , 2012)  and the Svalbard region is defined as the area between 0 and 30 • E of longitude and 75 and 80 • N of latitude. The Spatial frequency of occurrence and vertical distributions are determined for both total cloud and MPC. The spatial distribution of clouds is studied for altitudes spanning from 500 m up to 12 km, according to the study of  Liu et al. (2012)  as well as for low-level altitude ranging between 500 m and 3 km (according to  Zygmuntowska et al. , 2012). A minimum altitude of 500 m is chosen to avoid surface contamination in CloudSat data  (Marchand et al.
As an example, in situ airborne measurements performed during ASTAR 2007 campaign, in spring, over the Svalbard region, provided data from several boundary layer mixed phase clouds, with typical MPC-IB structure  (Gayet et al. , 2009). Several ascents and descents in all of these clouds have been performed between clouds top and the sea level. Despite the small amount of data collected and the fact that they are very localized in time and space, these in situ measurements can be considered as a well representative dataset for studying MPC at small scale. Thereby, such in situ dataset will provide a more thorough characterization of MPC properties at small scale, and so by extension to typical MPC occurring in the Arctic. Such a study will provide in particular the quantification of ice and liquid particles microphysical and optical properties, vertical profiles of water droplets and ice crystals microphysical properties, giving an insight of microphysical processes responsible of particles growth within MPC.
Comparative genomics combined with phylogenetic reconstructions are powerful approaches to study the evolution of genes and genomes. However, the current rapid expansion of the volume of genomic information makes it increasingly difficult to interrogate, integrate and synthesize comparative genome data while taking into account the maximum breadth of information available. GenomicusPlants (http:// www. genomicus. biologie. ens. fr/genomicus-plants) is an extension of the Genomicus webserver that addresses this issue by allowing users to explore flowering plant genomes in an intuitive way, across the broadest evolutionary scales. Extant genomes of 26 flowering plants can be analyzed, as well as 23 ancestral reconstructed genomes. Ancestral gene order provides a long-term chronological view of gene order evolution, greatly facilitating comparative genomics and evolutionary studies.
Data sources As a starting point for comparative genomics and ancestral gene order reconstruction, we selected 18 species from the EnsemblGenome database release 16  (Kersey et al. 2014) , comprising 10 monocotyledons and eight dicotyledons. For these extant species, all protein-coding sequences were downloaded, together with their gene location and the gene family they belong to. Within these pre-existing families, we then integrated the data from eight other dicotyledons, downloaded from Phytozome8  (Goodstein et al. 2012)  or from specific servers (see Table  1 ). This leads to a total of 26 extant genome species, and their 23 inferred ancestors (see Fig. 1 ).
This definition of conserved gene adjacency may be more or less constrained. For example, it may request that the Table  1  Additional species protein-coding gene content and comparison with other monocotyledons or dicotyledons from the EnsemblGenome database two adjacent genes also conserved their transcriptional orientation, or it may tolerate that some intervening genes separate the two genes as long as they did not exist in the common ancestor (i. e. lineage-specific genes). AGORA first filters the two genomes to retain only genes present in their last common ancestor. It then intersects the two genomes to retain the pairs of adjacent genes in the same transcriptional orientation, which will be considered as potentially ancestral. Finally, it assigns each potential ancestral pair not only to the last common ancestor of the two species, but also to each ancestor along the branches that connect the two genomes, as long as no lineagespecific gene has been inserted between the two genes considered.
After extraction of the linear paths for each ancestor (that we call contigs), a second round of AGORA is made, by considering contigs as units of comparison. The algorithm then compares contig adjacencies in each pairwise extant genome that is informative for an ancestor, and builds a graph of contig adjacencies. The graph is then linearized to obtain final blocks (or scaffold) of ancestral gene order. Reconstructed ancestral genomes vary in completeness (see 'statistics' link on the GenomicusPlant front page). More fragmented ancestral blocks or missing ancestral genes are often due to longer divergence time, to the quality of sequence assemblies and to the gene annotations of the extant genome species used to infer the ancestor. Divergence times displayed in GenomicusPlants were extract from the Timetree database  (Hedges et al.
Web Interface, Usage and Graphical Outputs Homepage The home page of GenomicusPlants is divided into three parts. The left part allows an access to links about the server (the help menu that links a page with tutorial videos, Examples menu, Statistics and site history). The top of the right page is dedicated to the search panel. This search panel allows querying for a gene of interest by its name or by its ID (Ensembl Gene IDs or plantspecific gene IDs). The database can also be queried by a description word, and a selection of specific species. Once the user activates the 'Go' button, GenomicusPlants will load the default PhyloView, described in the next section. The home page allows a direct access to two pairwise genome comparison views, MatrixView and KaryoView.
Pairwise genome comparison: MatrixView and KaryoView The two different modules of pairwise genome comparison available in GenomicusPlants interactively display chromosome-scale synteny either between two extant genomes, between two ancestral genomes or between an ancestral and an extant genome. Fig. 2  describes the main top menu available in MatrixView or Karyoview to select the two genomes to compare. In this example, we selected the Capsella rubella genome against the reconstructed ancestor of all the Brassicaceae genomes present in the database. The MatrixView is a classical dotplot module showing the relative order of genes between two plant genomes. It is a twodimensional interactive image designed on the fly by requesting all orthologous genes between the two species, which are deduced from the computed gene trees stored in the database.
Different actions can be performed on the matrix: (i) a user can hide or show one or more specific lines or columns of interest by a right mouse click on the name of the chromosome; (ii) each cell of the matrix can be zoomed-in by a simple mouse click; or (iii) once in the maximum zoomable box, the user can switch to the Phyloview module by selecting a gene. The KaryoView module displays the karyotype of one species according to the chromosome color of the orthologous genes from another species. As for MatrixView, selecting or removing specific chromosomes is possible using a contextual menu available by a right mouse click. Note that while colors make it possible to show at once the chromosomescale regions that share an evolutionary relationship between the two selected genomes, individual genes are also displayed as black dots along the chromosome axes, forming diagonals when their orders are conserved.
Menus and Tips The top menu of PhyloView and AlignView provides information on the reference gene or on the selected gene in the graphical display. The user can then change the reference gene or switch to a different reference genome. The menu allows users to switch between the two types of multispecies comparison views, focusing only on paralogs of the reference gene, hiding all ancestral species or all outgroups of a specific species. Data can be exported in text format or as an SVG (Scalable Vector Graphics) image that can be edited in Inkscape for editing. EnsemblGenome-Plant database, as is done for the vertebrate Genomicus server. Other clades would then be available in GenomicusPlants, such as gymnosperms and bryophytes. Additional information would then easily be added such as sequence similarity between orthologs and paralogs and dN/dS information, both shown as a color gradient as in the Genomicus server dedicated to Vertebrates.
Materials and Methods The alignments for 39UTRs of TBFV, ISFV and pestiviruses were constructed using ClustalX available in the BioEdit suite of programs  [18]  and further edited manually as described  [11, 13, [15] [16] [17]. Briefly, the regions of homology between distantly related virus species were used as anchors to align other less related sequences. This approach reflects an evolution of untranslated region via numerous deletions, insertions, reiterations and duplications, due to the copy-choice activity of RdRp  [19]. Nucleotide sequence homology was established using the Sequence Identity Matrix option of BioEdit  [18]. Viruses used for alignments are specified by appropriate abbreviations and accession numbers as indicated in Figures. The secondary RNA structures for individual virus 39UTRs were predicted using MFOLD 3.
2 accessed at http://mfold. rna. albany. edu/?q = mfold/RNA-Folding-Form  [20, 21]. Two MFold parameters, i. e. ''maximal distance between paired bases'' (MDBPB) and ''percent suboptimality'' (%S) were calibrated manually. An MDBPB of 60-100 and a %S up to 50% were empirically established to produce comparable images between viruses of distantly related groups and were therefore used for routine analysis. The use of an MDBPB value outside the 60-100 range predicted different structures even between closely related virus strains (not shown) and the limited sequence database of 39UTRs for each recognised species of the TBFV precluded the use of statistical measures of significance used previously  [22, 23]. RNA conformations observed as stem-loops (SLs) and Y-shaped structures were enumerated and annotated with the features revealed by alignment and by experimental data when available.
Duplicated 39UTR RNA structures in the ISFV The ISFVs include two subgroups of viruses that have been shown to replicate only in mosquitoes, or mosquito cell cultures. Thus far, they appear to reproduce either in Aedes species cells  [7, 8]  or Culex species cells  [9, 51, 52]. Kamiti River virus (KRV) and Cell Fusion Agent virus (CFAV) 39UTRs have two almost identical DRs  [7] , 67 nts long, designated R1 and R2, homologous to LRS4/LRS3 that form DB1/DB2 of the MBFV  [12, 13]  and TBFV (Figure  S1 ). The R1 and R2 in KRV are separated by ,500 nucleotides, whereas they occur as tandem repeats in the CFAV 39UTR  [17]. As no previous predictions of secondary RNA structures for the ISFV have been performed, structures in the ISFV 39UTR were predicted using MFold as shown in Figure  S3. As with TBFV, the limited sequence database of ISFV 39UTRs excluded the application of statistical methods of analysis  [22, 23].
However, in contrast to other flaviviruses, none of the ISFV showed a fold corresponding to a short SL2 in close proximity to the 39LSH, indicating some differences in the organisation of ''canonical'' regions of the flavivirus promoter. Remarkably, a significant secondary RNA structural duplication of the 39LSH-Y1 region was observed precisely between the two ''halves'' of the KRV 39UTR. The 39CPN of the 39LSH top loop, CACCG, was homologous to the sequence CUUCG localised in an apical position of SL16. The Y1 structure was homologous to Y5, with notably similar short signals UUU and AUG exposed at the top of the loops. These data confirm the previous suggestion that the KRV 39UTR was formed as a result of self-duplication of a sequence of ,600 nts  [17]. The most striking feature noted for each ISFV was the unusually large number of duplicated short signals, about 3-4 nucleotides in length, exposed as top loops.
Choice of summary statistic and fitting criterion. -The ABC inference method is based on the choice of a summary statistic that describes the data while performing a dimension reduction task. The latter is used to evaluate the quality of agreement (similarity) between the simulated data sets (the generated parasite trees) and the observed (the real parasite tree). In our case, the summary statistic will be based on the measured distances between the generated parasite trees and the real one. The distance of each simulated tree to the real parasite tree is therefore informative about the quality of the vector that generated it. Hence, the distance that will be used must take into account: (i) how well does the simulated tree represent the set of trees generated by a given vector, and (ii) how topologically similar is the simulated tree to the real parasite tree.
According to our experiments (see Supplementary Material available at, http://dx. doi. org/10. 5061/ dryad. 9g5fp), the most appropriate values are 1 = 0. 7 and 2 = 0. 3 but this can be set by the user. The main drawback of this distance is that it is not a metric; however, it achieves good results with respect to discriminating the trees as observed in our experiments. In COALA, we implemented two other distances, both of which are variations of the MAAC. A user can choose the most appropriate one depending on the case. In this article, we show only the results for the two-component distance, as this had the most discriminating power (data not shown). Given a parameter vector = p c ,p d ,p s ,p l , we generate M trees and for each of them we consider the distance of P from the real parasite tree P.
EXPERIMENTAL RESULTS AND DISCUSSION We evaluated our method in two different ways. First we designed a self-test to show that the principle underlying it is sound and to test it on simulated data sets. We then extended the evaluation to four real examples that correspond to biological data sets from the literature. This choice was dictated by: (i) the availability of the trees and of their leaf mapping; and (ii) the desire to, again, cover for situations as widely different as possible in terms of the events supposed to have taken place during the host-parasite coevolution. As a matter of fact, the first point drove the choice more than the second: there are not so many examples available from which it is easy to extract the tree and/or leaf mapping and that are big enough to represent meaningful data sets on which to test COALA.
All four examples were also analyzed in the original paper from which they were extracted by one or more of the existing algorithms that search for a most parsimonious (possibly cyclic) reconciliation (i. e. , for a reconciliation of minimum cost). Except in one case, which is a heuristic strategy and therefore does not guarantee optimality of the solution, all existing algorithms need to receive as input the cost of the events, which is thus established a priori and drives the conclusions on the results obtained. Finally, we applied COALA to a biological data set of our own, representing the coevolution of bacteria from the Wolbachia genus and the various arthropods that host them. This data set was selected because of its size: the trees have each 387 leaves.
In practical terms, we execute the following procedure: in order to get realistic data sets we choose a real host tree H (see Supplementary Material available at http://dx. doi. org/10. 5061/dryad. 9g5fp, for more information). Then, given a probability vector and H, we generate 2000 parasite trees using our model, without imposing any limit on the size of the generated trees. We then compute the median size of all generated trees and we filter out those whose size is far from this value (difference greater than 1 or 2 leaves from the median value). Finally, we select as typical tree P the one that shows the smallest 2 distance between the vector and the vector of observed frequencies of events. We generated in this way 9 data sets (H,P) associated with the following 9 probability vectors: 1 = 0.
70,0. 10,0. 10,0. 10 , 2 = 0. 80,0. 15,0. 01,0. 04 , 3 = 0. 75,0. 01,0. 16,0. 08 , 4 = 0. 70,0. 05,0. 02,0. 23 , 5 = 0. 60,0. 20,0. 00,0. 20 , 6 = 0. 55,0. 00,0. 20,0. 25 , 7 = 0. 45,0. 10,0. 15,0. 30 , 8 = 0. 40,0. 20,0. 10,0. 30 , and 9 = 0. 30,0. 20,0. 40,0. 10 (see the Supplementary Material available at http://dx. doi. org/10. 5061/dryad. 9g5fp, for more details). The choice of vectors was done with the aim to cover different patterns of probability. All data sets were generated with the same host tree H of 36 leaves. Self-test. -As concerns the self-test, we designed the following procedure. Let P denote the simulated parasite tree chosen in correspondence of the probability vector , as explained in the previous section. We recall that the host tree H remains the same during all the self-test experiments.
For a pair of host and parasite trees (H,P ), we ran COALA 50 times. In each run j, we computed the quality q j that corresponded to how well the method was able to recover the target vector used for generating the data set P. To do this, for each run j, we considered the representative vectors of the clusters produced as output. We computed the 2 distance for each of the representative vectors to the target vector and set q j to the smallest value among them. Figure  3  shows the distribution of the quality values which were obtained at the end of each round (from 2 to 5) for the simulated data sets 1 , 3 , 4 , and 7 (the results for the remaining data sets can be found in the Supplementary Material available at http://dx. doi. org/10. 5061/dryad. 9g5fp). Figure  4  shows the histograms of the event probabilities observed for the 50 parameter vectors with smallest 2 distance at the end of each round for data set 3 (again, the results for the remaining data sets are available in the Supplementary Material available at http://dx.
doi. org/10. 5061/dryad. 9g5fp). Up to a certain level of cospeciation probability (≥0. 50), our results (Fig. 3 ) show that in the rounds 2 and 3, COALA is able to select parameter vectors that are close to the target probability vector. Looking to the histograms of these two rounds, we can observe that in most of the runs, the closest parameter vector has low 2 distance to the target. After the third round, this tendency changes and the closest parameter vectors show high 2 distances indicating that COALA is mainly selecting vectors which are far from the target one. Because COALA is based on an ABC-SMC approach, the accepted vectors in one round have summary statistics (i. e. , average distance) smaller than the defined in the previous round. This means that at each new round, COALA is selecting parameter vectors that have more probability of explaining the pair of trees given as input because their simulated parasite trees are, on average, closer to the real one.
Column Opt indicates the cost of the optimal solution and columns #c, #d, #s, #l the numbers of each event type which are observed among the enumerated scenarios. Finally, columns #A and #C   (Conow et al. 2010) , which uses a genetic algorithm approach to produce only acyclic reconciliations. They thus started with the only cost vector obtained by CORE-PA associated with a feasible reconciliation, however transforming it into integer numbers (a requirement of the software), and then gradually changed the costs until a feasible reconciliation was produced (again using branch-length information). This procedure resulted in the cost vector c c = 1,c d = 1,c s = 1,c l = 2 and a reconciliation with 9 cospeciations, 0 duplication, 7 host switches and 1 loss, the same as obtained by CORE-PA. Running COALA on this data set, we obtain 3 nonsingleton clusters which are quite different from each other (Table  2 ).
Clusters 2 and 3 point to an opposite scenario. Similarly to the analysis performed for the small biological data sets, we transformed each one of the representative parameter vectors into a vector of costs that was then used to compute optimal reconciliations between the host and the parasite trees given as input. What is most striking with the results obtained for this data set is the absolutely huge number of optimal reconciliations that can be derived for all clusters. Because the total number of solutions makes impossible the enumeration of all the results, for this data set, we therefore only computed the costs of the optimal solutions and the total number of solutions. Additionally, for each cluster, we sampled 10,000 solutions and we checked for the presence of acyclic solutions. Table  5  summarizes the results obtained.
CONCLUSIONS We have developed an automated method that, starting from two phylogenies representing sets of host and parasite species, allows extraction of information about the costs of the events in a most probable reconciliation. It is clear that within a parsimony-based approach, an optimal solution strictly depends on the specific values attributed to these costs. However, there seldom is enough information for assigning those values a priori. Indeed, we observe in the results we obtained on a diverse selection of data sets that the costs inferred by our simulations may be very different across data sets, thus motivating the use of estimated instead of fixed costs. Such costs may even differ widely for a same pair of host-parasite trees, as is observed for the Wolbachiaarthropods data set.
Running Time The experiments were executed at the IN2P3 Computing Center (http://cc. in2p3. fr/). For the simulated data sets, each pair of trees was processed with 3 threads for speeding up the simulation process. The time necessary to complete 5 rounds for all 50 runs varied from 1 to 2 days depending on the size of the trees. For the biological data sets 1 to 4, we also used 3 threads. The observed execution times for 5 rounds were between a couple of hours for the smallest data set (Data set 1) and 1 day for Data set 4. Due to its size,.