dataesr/scientific-paragraphs-categorization huggingface dataset:
POPS incorporates knowledge on geographical and environmental covariates, X, by considering the following distribution P(x i |p, X) = K k=1 P(z i = k| X)P(x i |z i = k, p, X). More specifically POPS considers a particular class of latent class models called concomitantvariable latent class or latent class feed-forward models  (Dayton and Macready 1988; Vermunt and Magidson 2003). Such models were implemented in the R (R Core Team 2015) package poLCA  (Linzer and Lewis 2011). They assume that the covariates have no influence on the distribution of the data in a given class, i. e. , the right term in the previous sum can be simplified as P(x i |z i = k, p, X) = P(x i |z i = k, p). In POPS, the conditional probabilities P(z i = k| X) are computed using a probit regression model  (Jay, François, and Blum 2011).
Label switching In this section, we briefly discuss label switching that is a common issue for STRUCTURE-like models. Label switching refers to the invariance of the likelihood function under relabeling of the mixture components. The likelihood is unchanged when permuting the labels  (Stephens 2000). Because of this invariance to permutation, the marginal posterior distribution of an individual's membership coefficient, z i , is given by P(z i = k|x, X) = 1/K, for k = 1,. , K. Label switching in POPS can be addressed using the software CLUMPP  (Jakobsson and Rosenberg 2007 ) that provides alignments of distinct clustering results, and allows users to compare these results. Once distinct clustering solutions are aligned with CLUMPP, we recommend to use a model averaging approach based on the outputs of several MCMC runs.
Updating σ 2. POPS uses a Gamma distribution for updating the hyperprior parameter φ k = 1/σ 2 k for each k in {1,. , K} given by φ k |y ∼ Ga   N 2 , 1 2 N i=1 j =i w ij y ik y jk   , ( 22 ) where Ga(a, b) denotes the Gamma distribution with shape a and rate b. Updating ρ. Let e = (e 1 ,. , e N ) be the N eigenvalues of the weight matrix W , and e max the largest eigenvalue. POPS uses a uniform hyperprior distribution in the range (0, e -1 max ) on ρ. POPS updates ρ k independently for each k by using a Metropolis-Hastings step. A new value ρ * of ρ k is proposed from a Gaussian random walk with a fixed variance equal to 0. 05. POPS rejects the proposed value when the value is not within the interval (0, e -1 max ). Otherwise, the program accepts it with probability min   1, N i=1 1 -ρ * e i 1 -ρ k e i 1/2 exp   - 1 2σ 2 k y ik N j=1 w ij y jk (ρ k -ρ * )    .
Outputs The program POPS provides textual and graphical summaries of MCMC runs, including estimates of model parameters, log-likelihood histories of each run for convergence diagnostics, DIC values for model selection, and visualizations of vectors of posterior probabilities for cluster labels (models without admixture), or matrices of admixture coefficients (admixture models). These graphical outputs use barplot representation of ancestry coefficient matrices, as commonly displayed by other ancestry estimation programs. When geographic information is provided, POPS displays hard-clustering assignments where each individual is assigned to the cluster for which the membership or admixture coefficient is maximal. An additional feature of POPS is to compute predictions of population genetic structure for new environmental data.
Supplementary scripts are provided on the POPS web page to display actual or predicted population genetic structures on geographic maps using the fields package for R  (Nychka, Furrer, and Sain 2015). Finally, correlation coefficients evaluating the correlation between matrices of inferred and predicted coefficients are computed and returned to users for facilitating the interpretation of results. Output results are accessible by double-clicking on the corresponding items in the tree widget (Figure  2A ). In addition, the "Summarize Runs" button allows the user to quickly access the summaries of all runs (K, trend degree, model used, DIC, average log-likelihood, correlation score). POPS also provides a tool to export runs to CLUMPP that can average membership and admixture coefficients estimated from multiple MCMC runs  (Jakobsson and Rosenberg 2007).
-sp Special data format: 1 individual = 1 row (-spy: yes, -spn: no, default). Execute pops|more and popsAdm|more or see POPS manual for extra options. The command pops (or pops. exe on the Windows operating system) runs models without admixture, whereas popsAdm (or popsAdm. exe) runs admixture models. For example, suppose that the data set is stored in a file named example. txt in a folder Example. This file is provided with the software. The data contain 268 haploid individuals (-N268 -A1) genotyped at 86 loci (-L86), without any environmental data (-XL0 -X0), no spatial coordinates (-T-1), 1 extra row -r1 and 4 extra columns -c4. Assuming there are at most 3 clusters (-K3), we set the parameter of the Dirichlet allele frequency model to 1. 0 (-D1. 0). To run the MCMC algorithm for a total of 1,000 sweeps (-S1000) with the first 200 sweeps discarded as burn-in period (-B200), we use the following command $ pops -Fexample.
Examples In this section, we illustrate several features of POPS using a data set of 377 individuals from plant species Rhododendron ferrugineum L. , with each individual genotyped at 111 loci (INTRABIODIV database,  Gugerli et al. 2008). Rhododendron ferrugineum L. is a small and evergreen shrub present in European mountains. With the genetic data, we consider geographic and environmental covariates consisting of latitude, longitude, average minimum and maximum annual temperatures, average precipitations, and an additional set of topographic variables measured at each sampled site.
R scripts based on kriging methods allow us to display admixture coefficients spatially (Figure  3 ). Though substantial admixture occurs within contact zones between clusters, the results are consistent with hard-clustering assignments. To evaluate the improvement on the estimation of population genetic structure obtained with models using environmental covariates, we computed DIC for models that do not use any environmental information. We find that DIC decreases from 19565 to 19525 when adding environmental covariates.
Conclusion POPS is a software package that can be used to estimate population structure from individual multilocus genotypes and external covariates without assuming predefined populations. POPS jointly infers population genetic structure and the effect of environmental covariates on this structure. POPS users can use environmental covariates to predict genetic structure or for changing environmental conditions. POPS can be used either from a graphical user interface or from a command-line engine. R scripts for post-processing results and displaying membership or admixture coefficients are available in the POPS package. POPS can be downloaded from http://membres-timc. imag. fr/Olivier. Francois/pops. html.
N. massiliensis is Gram negative, aeroanaerobic, indole negative, nonmotile, and coccobacillus. This bacterium was cultivated as part of the implementation of matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (MALDI-TOF) in Hôpital Principal, Dakar, aiming at improving the routine laboratory identification of bacterial strains in Senegal  [5]. The current taxonomic classification of prokaryotes relies on a combination of phenotypic and genotypic characteristics  [6, 7] , including 16S rRNA sequence similarity, G+C content and DNA-DNA hybridization. However, these tools suffer from various drawbacks, mainly as a result of their threshold values, which are not applicable to all species or genera  [8, 9]. With the development of cost-effective high-throughput sequencing techniques, tens of thousands of bacterial genome sequences have been made available in public databases  [9].
Five species validly published names in the Pasteurellaceae family were selected to make a phenotypic comparison with Necropsobacter massiliensis (Table  2 ). Extended features descriptions MALDI-TOF protein analysis was carried out as previously described  [12, 13]  using a Microflex LT (Bruker Daltonics, Leipzig, Germany). Twelve individual colonies were deposited on a MTP 384 MALDI-TOF target plate (Bruker). A total of 2 μL of matrix solution (saturated solution of alpha-cyano-4hydroxycinnamic acid) in 50% acetonitrile and 2. 5% trifluoroacetic acid were distributed on each smear and air dried for 5 minutes at room temperature. The 12 individual spectra from strain FF6 T were imported into MALDI BioTyper software 2. 0 (Bruker) and analysed by standard pattern matching (with default parameter settings) against the main spectra of 6252 bacterial spectra.
The scores previously established by Bruker Daltonics allowing (or not) validating the identification of species compared to the database of the instrument were applied. Briefly, a score 2. 000 with a species with a validly published a IDA, inferred from direct assay; TAS, traceable author statement (i. e. a direct report exists in the literature); NAS, nontraceable author statement (i. e. not directly observed for the living, isolated sample but based on a generally accepted property for the species or on anecdotal evidence). These evidence codes are from the Gene Ontology project (http://www. geneontology. org/GO. evidence. shtml) [33]. If the evidence is IDA, then the property was directly observed for a live isolate by one of the authors or an expert mentioned in the acknowledgements. name provided allows the identification at the species level; a score of 1.
700 to <2. 000 allows the identification at the genus level; and a score of <1. 700 does not allow any identification. Thus, scores ranging from 1. 2 to 1. 3 were obtained, suggesting that this isolate was not a member of any known species. The reference mass spectrum from strain FF6 T was incremented in our database (Fig. 4 ). Finally, the gel view showed the spectral differences with other members of the family Pasteurellaceae (Fig. 5 ).
Genome Sequencing Information Genome project history The organism was selected for sequencing on the basis of its 16S rRNA similarity, phylogenetic position and phenotypic differences with other members of the family Pasteurellaceae, and is part of a study aiming at using MALDI-TOF for the routine identification of bacterial isolates in Hôpital Principal in Dakar  [1]. It is the second genome of a Necropsobacter species and the first genome of Necropsobacter massiliensis sp. nov. A summary of the project information is shown in Table  3. The GenBank accession number is CDON00000000 and consists of 101 contigs. Table  3  shows the project information and its association with MIGS version 2. 0 compliance  [14] ; associated MIGS records are also summarized in Supplementary Table  S1.
Genome annotation Open reading frame (ORF) prediction was carried out using Prodigal  [15]  with default parameters. We removed the predicted ORFs if they spanned a sequencing gap region. Functional assessment of protein sequences was performed by comparing them with sequences in the GenBank  [16]  and the Clusters of Orthologous Groups (COGs) databases using BLASTP. tRNAs, rRNAs, signal peptides and transmembrane helices were identified using tRNAscan-SE 1. 21  [17] , RNAmmer  [18] , SignalP  [19]  and TMHMM  [20] , respectively. Artemis  [21]  was used for data management, whereas DNA Plotter  [22]  was used for visualization of genomic features. In-house perl and bash scripts were used to automate these routine tasks. ORFans were sequences which have no homology in a given database, i. e.
in nonredundant (nr) or identified if their BLASTP E value was lower than 1e-03 for alignment lengths greater than 80 amino acids. PHAST was used to identify, annotate and graphically display prophage sequences within bacterial genomes or plasmids  [23]. To estimate the nucleotide sequence similarity at the genome level between Necropsobacter massiliensis and another ten members of the Pasteurellaceae family, we determined the average genomic identity of orthologous gene sequences (AGIOS) parameter as follows: orthologous proteins were detected using the Proteinortho software  [24]  (with the following parameters: E-value 1e-5, 30% percentage identity, 50% coverage and 50% algebraic connectivity) and genomes compared two by two. After fetching the corresponding nucleotide sequences of orthologous proteins for each pair of genomes, we determined the mean percentage of nucleotide sequence identity using the Needleman-Wunsch global alignment algorithm.
Genomes from the genus Necropsobacter and closely related genera were used for the calculation of AGIOS values. The script created to calculate AGIOS values was named MAGi (Marseille Average genomic identity) and is written in perl and bioperl modules. GGDC analysis was also performed using the GGDC Web server (http://ggdc. dsmz. de) as previously reported  [25, 26]. Here, we compared the genome sequences of Necropsobacter massiliensis strain FF6 T (GenBank accession number CDON00000000) with those of N. rosorum strain P709T (CCMQ00000000), Pasteurella multocida subsp. multocida strain Pm70 (AE004439), Haemophilus influenzae strain Rd KW20 (L42023), Haemophilus ducreyi strain 35000HP (AE017143), Histophilus somnus strain 129PT (CP000436), Haemophilus parasuis strain SH0165 (CP001321), Haemophilus parainfluenzae strain T3T1 (FQ312002) and Aggregatibacter aphrophilus strain NJ8700 (CP001607).
Conclusions On the basis of phenotypic, phylogenetic and genomic analyses, we formally propose the creation of Necropsobacter massiliensis sp. nov. that contains strain FF6 T. The strain was isolated from a cervical abscess of a 4-year-old Senegalese boy. Necropsobacter massiliensis strain FF6 T is susceptible to amoxicillin, amoxicillin/clavulanic acid, ceftriaxone, gentamicin, nitrofurantoin, rifampicin, trimethoprim/sulfamethoxazole and ciprofloxacin but resistant to erythromycin, doxycycline and vancomycin. The G+C content of the genome is 46. 2%. The 16S rRNA and genome sequences of N. massiliensis strain FF6 T (= CSUR P3511 = DSM 27814) are deposited in GenBank under accession numbers HG428679 and CDON00000000, respectively. The type strain, FF6 T , was isolated from a cervical abscess of a 4-year-old Senegalese boy hospitalized in Hôpital Principal in Dakar, Senegal.
2 Fold coverage 141× MIGS-30 Assemblers CLC 7 MIGS-32 Gene calling method Prodigal Locus Tag Not indicated GenBank ID CDON00000000 GenBank date of release 26 March 2015 GOLD ID Gp0102103 BIOPROJECT PRJEB4626 Source material identifier DSM 27814 Project relevance MALDI-TOF implementation in Dakar MALDI-TOF, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. [27]nded insightsThe draft genome of Necropsobacter massiliensis (2. 49Mb) has a lower size than that of N. rosorum (2. 52 Mb) but a larger size than those of P. multocida (2. 25 Mb), H. influenzae (1. 83 Mb), H. ducreyi (1. 69 Mb), H. somnus (2. 00 Mb), H. parasuis (2. 26 Mb), H. aphrophilus (2. 31 Mb) and H. parainfluenzae (2. 08 Mb). The G+C content of Necropsobacter massiliensis (46. 2%) was lower than that of N.
Pipeline Performance To measure the speedup obtained with our narrowphase pipeline, we compared the GPU computation time of the narrow-phase while using our pipeline reorganization and without it. In the scene 'rigid objects' we measured the performance of our pipeline reorganization when using only the full ray-tracing algorithm (BVH ray traversal) and when using both full and iterative ray-tracing algorithms. Result are shown in Figure  6. The first result is the speedup obtained when using our pipeline method with only the full ray-tracing algorithm. We get an average speedup of 1. 31 times when using our pipeline ray-tracing method. This speedup can be explained by the per-vertex step that guarantees that the ray-tracing step is executed on dense data even if the input of the per-vertex step is sparse.
We measured distance to shoreline as the minimum distance between the marsh patch and the nearest ocean shoreline (defined using vector layers from USGS National Assessment of Shoreline Change and National Geodetic Survey Coastal Mapping Program). Table  1. Sampling locations for nelsoni and caudacutus individuals. Table includes a site code for each marsh, the marsh name, coordinates, number of individuals sampled, and whether the marsh was considered allopatric nelsoni, sympatric, or allopatric caudacutus. Descriptive environmental features are included for each marsh (patch size in hectares, proximity index, and distance of marsh to the nearest ocean shoreline in meters). At the point-of-capture scale, we collected data on vegetation and distance to upland edge within a 5. 25-ha buffer around the bird capture locations that correspond to the average core home range size of a female Saltmarsh Sparrow (which have the smallest core area of both species, males and females;  Shriver et al.
Site code For ecological niche models, we included four environmental variables over a continuous spatial extent covering the entire study area: distance to shoreline, marsh isolation, NDVI, and vegetation zones, characterizing vegetation as high, mixed, and low marsh (Table  2 ). We calculated the distance to shoreline by creating a continuous Euclidean distance surface using the same shoreline vector layers (see above) as input in ARCMAP v10 (ESRI). Similarly, to quantify patch isolation, we created another continuous Euclidean distance surface that represented the distance between suitable habitats using all marsh patches within the study area as input. We also included vegetation maps and raw NDVI values for the entire study area in the niche models.
Genetic data and admixture analysis To evaluate the role of local habitat features in shaping the distribution of pure and admixed individuals, we used genotypes from 24 microsatellite loci from a previously published genetic data set  (Walsh et al. 2015)  to calculate a site-averaged genotype for correlation with measured environmental features. This final data set included genotypes from 290 individuals (Table  1 ). To obtain a siteaveraged genotype, we first characterized spatial variation in allele frequencies using principal components analysis Table  2. Habitat features quantified for each sampling location and included in this study, including marsh size, proximity to neighboring marshes, Normalized Difference Vegetation Index (NDVI), proportion of low (predominantly Spartina alterniflora), and high marsh (Spartina patens and Juncus gerardii; vegetation is expressed both separately for genotype-habitat associations or compiled as a vegetation map for the ecological niche models), and distance to shoreline and upland edge.
Ecological niche models We used ecological niche modeling to assess differences in the niche space of pure caudacutus, nelsoni, and  hybrids. Based on our predictions for a mosaic hybrid zone, we expected to see variation in niche space for pure and hybrid individuals and a patchy distribution of both upriver and coastal marsh habitat types and genotypes across the hybrid zone. We developed ecological niche models for each parental species and hybrids using a maximum entropy method implemented in the program Maxent v. multilocus genotypes (Appendix S2). Because both species are marsh specialists, the four environmental data layers (described above) were clipped to include only marsh habitat in our study area -Maine to Connecticut. Maxent uses environmental data from known occurrence points to predict the expected distribution of a species and produces a map where each grid cell represents the predicted suitability for each species.
For example, at Popham Beach, Maine (sampling location 8) we identified a mix of genotypes (pure individuals and both backcrossed and recent generation hybrids), while approximately 20 km away Maquoit Bay, Maine (sampling location 9) was comprised of only pure and backcrossed nelsoni. Based on habitat data (point-of-capture scale), both Popham Beach and Maquoit Bay were dry with an abundance of high marsh (100% and 62%, respectively) in areas where the birds were sampled. This translates into nesting habitat that is suitable for both caudacutus and nelsoni. One key difference between Popham Beach and Maquoit Bay, however, is the difference in size and degree of isolation. Popham Beach is larger and more connected (143 ha, proximity index of 0. 25) compared to Maquoit Bay (28 ha, proximity index of 0.
Acknowledgments We thank  T. P. Hodgman  of  Maine Department of Inland Fisheries and Wildlife, N. S. Pau of Parker River National Wildlife Refuge ,  K. M. O'Brien  of  Rachel Carson National Wildlife Refuge, E. King of United States Fish and Wildlife Service ,  P. Hunt  of  New Hampshire Audubon ,  K. Raposa  of  Narragansett Bay National Estuarine Research Reserve ,  C. Weidman  of  Waquoit Bay National Estuarine Research Reserve , and  K. E. Iaquinto  of  Monomoy National Wildlife Refuge  for their advice and help in facilitating sampling efforts. K. Ruskin ,  C. Elphick ,  C. Field , and  G. Mittlehauser  provided sample collection assistance. We thank the  Maine Department of Inland Fisheries and Wildlife , the  Trustees of Reservations , the  Essex County Greenbelt association , and the  Nature Conservancy  for allowing sample collection in protected marshes.
Sequence alignment is foundational to genomics and bioinformatics, where tools like BLAST (Basic Local Alignment Search Tool) are used to search for homologous (similar) gene or protein sequences across large databases. 12 To mention just one typical application, a biologist might want to track the presence or absence of a gene across many different species, so as to gain a better understanding of their evolutionary history. A crucial point is that homology between genes is never exact. Therefore, searching for identically matching sequences, although computationally easy, is not useful. The solution now universally adopted in bioinformatics is to do sequence alignment. Inexact matching, of course, is also central to the study of intertextuality. If the computational identification of intertexts is to become as routine and useful as finding homologous genes, methods that can flexibly account for the "fuzzy logic" of Latin intertextuality, to borrow Stephen Hinds' prescient metaphor, are likely to play an important role.
E-learning Resource Relations E-learning resources are always described under a standard format, which allows presenting their metadata (i. e. , title, abstract, keywords, disciplines, levels, etc. ) under a well-structured form. Among a number of standards (such as Dublin Core, MPEG-7, MODS, and so on), the IEEE Learning Object Metadata (LOM) is dominant in use  (Nilsson et al. , 2006). According to the LOM standard 1 , a resource can have different kinds of relations with other resources. These kinds 2 include: ispartof, haspart, isversionof, hasversion, isformatof, hasformat, references, isreferencedby, isbasedon, isbasisfor, requires and isrequiredby. Although the definition of resource relations was reported as a standard, there are still debates about its appropriateness and missing relationships.
For example,  (Seeberg et al. , 2000)  argued that the defined relations mix content-based and conceptual connections between resources, the 'requires/isrequiredby' is inappropriate, or there is no difference between a 'isbasedon' relation and a 'isrequired' one and so on. (Engelhardt et al. , 2006)  proposed additional relations, such as 'illustrates/isIllustratedBy', 'is-LessSpecificThan/isMoreSpecificThan', etc. , regarding to the semantic connections between learning objects. Or, the Open University of Humanities (UoH) 3 has defined a new relation, which is 'isassociatedto', in their LOM metadata to present the coherence between resources. In our approach, we suppose that there are generally k kinds of resource relation, numbered from r 1 to r k , regardless their meanings. Each kind signifies a specific meaning of the relation.