dataesr/scientific-paragraphs-categorization huggingface dataset:
Phase 3. Extracted features constitute the input of the recommendation engine, designed using the Formal Concept Analysis (FCA) framework. This method is an incremental association rules mining technique that exploits incoming annotations to incrementally produce better recommendations. Phase 4. Features of the IO port pair, alongside the workflow documentation and the recommendations, are the input of the user that is requested to select a set of annotations from a fixed vocabulary (the Datanode ontology). In this section we focus on the first three phases of the approach: the workflow to data graph transformation (Section 3. 1); the features extraction method (Section 3. 2); and the recommendation engine (Section 3. 3), leaving the last one to Section 4.
We name these arcs "IO port pairs" (input-output port pairs), and these are the items we want to be annotated. In this example, the IO port pair 1 → 3 could be annotated with the Datanode relation ref actoredInto, while the IO port pair 2 → 3 would not be annotated as only referring to a configuration parameter of the processor and not to an actual data input. For the present work we translated 1234 Workflows from the My Experiments repository, resulting in 30612 IO port pairs (altough we will use a subset of them in the user evaluation).
Extracting features from workflow descriptions As described in the previous Section, the workflow description is translated in a graph of IO port pairs connected by unlabelled links. In order to characterize the IO port pair we exploit the metadata associated with the components of the workflow involved: the input and output port and the processor that includes them. For each of these elements we extract the related metadata as key/value pairs, which we use as core features of the IO port pair. Applying this approach to the My Experiments corpus we obtained 26900 features. Table  1  shows an example of features extracted for the IO port pairs described in Figure  3. However, the objective of these feature sets is to support the clustering of the annotated IO port pair through finding similarities with IO port pairs to be annotated.
Implementation of the approach The approach described in the Section 3 has been implemented in the Dinowolf (Datanode in workflows) tool 15 based on the SCUFL2 worfklow specification 16  and the taxonomy of data-to-data relations represented by the Datanode ontology. While Dinowolf has been implemented leveraging the Apache Taverna 17 library, it can work with any input following the SCUFL2 specification. When a workflow is loaded, the system performs a preliminary operation to extract the IO port pairs and to precompute the related set of features following the methods described in Sections 3. 1 and 3. 2. In order to expand the feature set with derived features -bag of words and entities from DBPedia -the system relies on Apache Lucene 18 for sentence tokenization (considering english stopwords), DBPedia Spotlight 19 for named entity recognition, and the DBPedia 20 SPARQL endpoint for feature expansion with categories and entity types.
The tool includes three views: 1) a Workflows view, listing the workflows to be annotated; 2) a Workflow details view, including basic information and a link to the external documentation at My Experiments; and a 3) Annotation view, focused on providing details of the features of the IO port pair to annotate. The task presented to the users is the following: 1. Choose an item from the list of available workflows; 15 Dinowolf: http://github. com/enridaga/dinowolf. 16 SCUFL2 Specification: https://taverna. incubator. apache. org/documentation/ scufl2/. 17 Apache Taverna: https://taverna. incubator. apache. org/. 18 Apache Lucene: https://lucene. apache. org/core/ 19 DBPedia Spotlight: http://spotlight. dbpedia. org/. 20 DBPedia: http://dbpedia. org/. 2. Select an IO port pair to access the Annotation view; 3.
Experimental evaluation Our main hypothesis is that the approach presented can boost the task of annotating workflows as data-to-data annotated graphs. In particular, we want to demonstrate that the quality of the recommendations improves while the annotated cases grow in number. In order to evaluate our approach we performed a user based evaluation. We loaded twenty workflows from "My Experiments"  21 in Dinowolf and asked six users to annotate the resulting 260 IO port pairs. The users, all members of the research team of the authors, have skills that we consider similar to the ones of a data manager, for example in the context of a large data processing infrastructure like the one of  [4]. In this experiment, users were asked to annotate each one of the IO port pairs with a semantic relation from a fixed vocabulary (the Datanode ontology), by exploiting the workflow documentation, the associated feature set and the recommendations provided.
Conclusions In this article we proposed a novel approach to support the semantic annotation of workflows with data centric relations. We showed through applying this approach on a set of workflows from the My Experiments repository that it can effectively reduce the effort required to achieve this task for data managers and workflow publishers. We plan to integrate the presented approach with the methodology described in  [4]  in order to support Data Hub managers in the annotation of the data manipulation processes required to compute the propagation of policies associated with the data involved. We have enough confidence to believe that the characteristics of scientific workflows as data intensive workflows  [16]  are equivalent, because they can be reduced to data centric representations, as demonstrated in Section 3.
1. The quality and consistency of the resulting annotations are not the subject of the present study, and we did not discussed the interpretation of the Datanode relations with the participants of our experiment. For this reason each user operated on a separate instance of the tool, to reduce the possibility that inconsistent usage of relations would negatively impact the quality of the association rules generated. However, we received feedback that encourages to better document the Datanode ontology, for example providing cases of the possible uses and misuses of each relation. In this work we only focused on the relations between input and output within workflow processors. It is possible to extend this approach to also cover relations between data items with other directions (input to input, output to input, etc.
Validation This new albedo simulation has been written as part of a new albedo module in ORCHIDEE-CAN  (Naudts et al. , 2015) , and integrates with the multi-level calculations of stomatal conductance, and the multi-level energy budget. For this set of tests, the model is run with six independent variables, that are fed directly into the albedo routine in order to access the capability of this scheme. Five of the variables also influence the single-layer scheme: the effective values of the total leaf area index (LAI), the single scatterer albedo (w l ), the forward scattering efficiency (d l ), the solar zenith angle (SZA -n. b. a value of 0 • corresponds to the sun directly overhead), and the background reflectance. In addition to the single layer case, we must also look at the distribution of the LAI between the two canopy layers.
For simplicity, we will use the terms 'LAI' and 'effective LAI' interchangeably below in discussing the sensitivity studies, as we are not using LAI from data and thus have no reason to convert to the effective LAI required by the model. As six independent variables is too many to perform an exhaustive sensitivity analysis, we selected just two parameter sets. One parameter set, denoted ALL, covers a wide range of possible values of the parameters. The second set, denoted REAL, focuses on a range of values which are more likely to be observed in nature. REAL is a subset of ALL, comprising almost an order of magnitude fewer points. The specific values used are given in Table  2. For each possible combination of parameters given in Table  2 , we computed the single-and two-layer solutions. The single layer case has been extensively validated  (Pinty et al.
These results display some expected behaviour, in particular that the fraction of simulations considered 'divergent' decreases as the threshold increases; fewer than 15% of points differ by an absolute value of more than 0. 01. This value was chosen to be an 'acceptable' deviation, in the sense that a global simulation with values for individual pixels varying by 0. 01 units from observed data is not considered cause for concern. At first glance, it may seem strange in 0. 01 from the single-layer solution, while REAL has only 43,800. This is expected as REAL is a subset of ALL. However, ALL has 2,594,592 total simulations while REAL only has 304,128. Therefore, the fraction of divergent points is clearly greater. A priori one might expect the deviations to occur for the extreme values, i. e.
The single scattering albedo (w l ), is the dominant input variable, followed this time by interactions between that variable and R bgd , the collimated background reflectance, as shown in Figure  6. The solar zenith angle is not an important factor, resulting in only small differences. These analyses show that as more light is scattered, for example, with a more reflective background or leaves, the number of steps required to converge can increase by a factor of five (shown in Figure  S5 ). Finally, Figure  S6  plots the marginal effects for a difference in albedo between the two-and one-layer model, for the both the REAL and ALL datasets. Figure  S7  is the corresponding graph for the number of iteration steps (n steps ), which shows little difference between the two value tests for this metric.
There has recently been an improvement in measurement capability within canopies, such as the development of a more portable LIDAR  (Hosoi and Omasa, 2007) , the increased availability (albeit gradually) of more detailed in-canopy datasets of canopy structure and the improved coordination and collation of time series measurements of in-canopy temperature, humidity and trace gases  (Chen et al. , 2015; Sellers et al. , 1997). Following this trend in data availability, the most recent generation of DGVM models include the simulation of canopy growth, vertical canopy profile information and leaf level resistances, such as stomatal conductance (e. g. in ORCHIDEE-CAN  (Naudts et al. , 2015)  and the CLM (Community Land Model)  (Bonan et al. , 2012 (Bonan et al. , , 2014)) ). This means that profile based models will increasingly couple not only with the atmosphere, but with other modules that provide more detail of canopy composition.
Code availability The code and the run environment are open source (http://forge. ipsl. jussieu. fr/orchidee). Nevertheless readers interested in running ORCHIDEE-CAN are encouraged to contact the corresponding author for full details and latest bug fixes. Step 1 Step 2 Step 3 top vegetation level bottom vegetation level sequence when initial light stream does not collide with vegetation sequence when initial light stream does collide with vegetation 100% 100% Step 1 Step 2 Step 3      integrated over all view angles as described in  Pinty (2006). LAI is in units of leaf area per square meter of land, SZA is the solar zenith angle in degrees, and the rest of the variables are unitless. ground level 1 R (uncoll) veg,Z=Ztop 2 R (coll, uncoll) veg, Z=Zbottom 2 T (uncoll, uncoll) veg, Z=Zbottom 2 T (coll, uncoll) veg, Z=Zbottom 3 T (uncoll, uncoll) veg, Z=Ztop 3 T (coll, uncoll) veg, Z=Ztop 3 R (coll, uncoll) veg, Z=Ztop 3 R (coll, coll) veg, Z=Ztop 3 T (coll, coll) veg, Z=Ztop 3 T (uncoll, coll) veg, Z=Ztop 2 T (coll, uncoll) veg, Z=Zbottom 2 T (uncoll, uncoll) veg, Z=Zbottom 2 R (coll, coll) veg, Z=Zbottom 1 T (coll) veg, Z=Ztop 1 R (coll) veg, Z=Ztop.
To examine the population structure based on genome-wide polymorphism data, we analysed single nucleotide polymorphisms (SNPs) that were defined by comparison of 61 T. gondii strains to the reference ME49 genome and filtered this set to include positions where reliable data were available for all strains (a total of 802,764 positions in each genome (Supplementary Data 8)). Generation of a neighbour network  39  for these data revealed that the 62 strains group closely with haplogroups and major clades that were previously defined by lower resolution genotyping (Fig. 4a )  16. Importantly, similar groupings were defined using admixture  40  (Supplementary Fig. 9 ) and principal components analysis (Supplementary Fig. 10 ). Collectively, these findings support a population structure consisting of a small number of clades that show strong geographic segregation, as described previously  16, 37.
Methods Propagation of strains and isolation of gDNA. Sixty-two representative strains of T. gondii were selected from different haplogroups from around the world (Supplementary Data 1)  16. Strains were cultured in human foreskin fibroblast cells, as described previously  16. Genome sequencing of T. gondii reference strains. Sequencing of 16 reference strains of T. gondiii, and of one isolate of H. hammondi, was conducted using a combination of 454, and Illumina PE sequencing technologies (Supplementary Table  1 ). Sequence reads were screened for contamination and reassembled using Celera Assembler software  54  or Newbler v2. 6 (ref. 54). Scaffolds were then aligned with MUMmer  55  to T. gondii ME49 chromosome sequences from ToxoDB v8. 0 (http://ToxoDB. org) to generate super-scaffolds spanning entire chromosomes.
Annotated genomes were deposited into National Center for Biotechnology (NCBI). Genome sequencing for SNP discovery. For each of the remaining 46 nonreference strain (Supplementary Data 1), a single Illumina PE barcoded library was prepared from tachyzoite gDNA. Libraries were then pooled into groups of nine samples and sequenced multiplexed in a single lane of an Illumina HiSeq 2000 machine. Sequencing reads were deposited in the Sequence Read Archive repository at NCBI. Sequencing of tachyzoite messenger RNA samples. To aid in the curation of ME49 gene models, two tachyzoite-specific Illumina complementary DNA libraries were constructed from mRNA isolated from tachyzoite cultures from ME49 and GT1 strains. Each library was then sequenced in a single lane of an Illumina Genome Analyzer II machine.
Structural and functional annotation of the ME49 genome. Gene annotations were derived by comparison of the existing ME49 reference genome (http://ToxoDB. org) using a combination of evidence from RNASeq data, cDNA/EST sequences and a variety of software tools to predict potential protein-coding genes using an in-house pipeline at J. Craig Venter Institute (JCVI) (Supplementary Methods). Predicted proteins were run through JCVI's autonaming pipeline, that assign product names based on a number of sequence similarity searches including blastp searches against the previous T. gondii ME49 proteome (ToxoDB v8. 0; http://ToxoDB. org ) and the GenBank non-redundant protein database, HMM searches against Pfam and TIGRfam 56 databases, and RPS-Blast searches against the NCBI-CDD database  56. Proteins without any significant hit to other proteins or protein domains were flagged as 'hypothetical protein'.
Domain Identification of T. gondii novel gene families. To identify known protein domains the T. gondii ME49 proteome was searched against Pfam and TIGRfam HMM profiles using HMMER3 ref. 57). Proteins matching a particular HMM profile were assigned to that domain and remaining peptides searched against each other using blastp to identify potential novel domains. The top five protein families containing novel para domains (TgFAMs A to E) were analysed using Phobius  58  to identify signal peptides and transmembrane domains. De novo identification of conserved protein domains across members of the same gene family was carried out with MEME  59. Expression levels for the TgFAMs were obtained from T. gondii Affymetrix Array data available from NCBI GEO records GSE32427 and GSE51780. Estimation of d N /d S ratios.
Coding sequences from each cluster of orthologous genes from the 16 T. gondii reference strains were used to estimate d N /d S ratios using a modified version of the Bioperl script bp_pairwise_kaks. pl (http://search. cpan. org/dist/BioPerl/scripts/utilities/bp_pairwise_kaks. pl). SNP identification. Illumina reads for each of the 61 other genomes were aligned using Bowtie2-end-to-end  60  against the ME49 reference genome assembly (release date 23 April 2013), identifying a total of 2,342,433 SNPs across all strains. Positions with informative base calls for all 62 strains were identified, generating a final list of 802,764 SNPs that were used for analysis. Analysis of orthologous genes. Annotated proteomes were analysed using OrthoMCL v2. 0 (ref. 61) to define orthologous groups. Clusters of orthologous groups were functionally annotated using GO Slim terms, which are designed to group the many different GO terms into smaller groups of related processes (http://geneontology.
org/page/go-slim-and-subset-guide). The proteomes were queried against the Pfam HMM database using HMMER3 to estimate the abundance of Pfam domains. Mapping metabolic differences. SNPs from the 16 T. gondii reference strains that correspond to the 382 proteins in the iCS382 metabolic pathway reconstruction of T. gondii ME49 (ref. 27) were downloaded from ToxoDB (http://www. toxodb. org/ toxo-release4-0/home. jsp). Network and principal components analyses. Genome-wide SNPs were saved as FASTA files and directly incorporated into SplitsTree v4. 4 (ref. 39) to generate unrooted phylogenetic networks using a neighbour-net method and 1,000 bootstrap replicates. Principal components analysis was performed by eigenanalysis of a co-ancestry matrix implemented in fineSTRUCTURE, as described in ref.
43. Chromosome Ia analysis. SNP data for ChrIa were plotted as a minimum spanning tree using SplitsTree v4. 4 (ref. 39) with 2,000 spring-embedded iterations. The 62 strains were clustered into four major groups denoted as monomorphic, divergent, 5 0 -chimeric and 3 0 -chimeric chromosome Ia. SNPs present in each cluster were calculated using a custom script over a 10-kb moving window and plotted using Excel. Admixture analysis. The population genetic structure of T. gondii was determined by an unsupervised clustering algorithm, ADMIXTURE  40  with ancestral clusters set from k ¼ 1 through 10. The number of ancestral clusters k was determined by estimating the low cross-validation error (CV error) for different k values using five-fold CV. Co-ancestry heatmap. We developed a co-ancestry heatmap by using the linkage model of ChromoPainter (http://www.
Phylogenetic trees for the conserved and non-conserved regions were constructed using maximum likelihood as implemented in RAxML version 7. 3. 0 with the GTR þ GAMMA model  63. Standardized Robinson-Foulds distances  64  were calculated between the conserved and non-conserved trees based on 500 bootstrap replicates. Trees were considered congruent if they had no conflicting branches with bootstrap support of 495%. Phylogeny. Phylogenetic trees were constructed for the conserved OrthoMCL OG5_0126701 using the Neighbour-Joining algorithm with 1,000 bootstrap replicates as implemented in Geneious ver. 7. 1. 5 (http://www. geneious. com (ref. 65)) and visualized with FigTree ver. 1. 4. 0 (http://tree. bio. ed. ac. uk/software/figtree/). Synteny. The OrthoMCL ortholog clusters (see above) were reformatted to represent each pair found in the cluster outside of self-matches and syntenic blocks were generated between all combinations of genomes as described in ref.
15mmary of genome features for T. gondii and representative apicomplexans. All sequence reads were deposited in the National Center for Biotechnology (NCBI) sequence read archive under accession SUB554996 ref. 14. wReid et al. 15. zGenBank Assembly ID GCA_000258005. 2. yGenBank Assembly ID GCA_000006565. 2. ||Scaffolds 410,000 bp.
Acknowledgements Sequencing, assembly and annotation for the Tg and Hh genomes reported here were supported by the  National Institute of Allergy and Infectious Diseases (NIAID) ,  National Institutes of Health (NIH) ,  Department of Health and Human Services  under contract number  HHSN272200900007C. Additional analyses were provided by community-based efforts and included support from  NIH  grants  AI059176  and  AI036629  (L. D. S. ) and the  Canadian Institutes for Health Research  grant  MOP 84556  (J. P. ). Data for the Sn annotation was generously provided by M. E. G. and J. P. prior to publication and was supported by the  Intramural Research Program of NIAID  at the  NIH  (M. E. G. ). Additional computing resources were provided by the  SciNet HPC Consortium. We acknowledge ToxoDB  (http://toxodb.
5% 54. 8% 52. 5% 52. 2% % Protein-coding sequence ww 50. 9% 59% 57. 3% 60. 5% Average length of protein-coding genes zz (bp) 9,121 4,892 4,868 4,778 Average number of exons per protein-coding gene 5. 5 12 11. 7 11. 5 NA, not available. *z960 Scaffolds, any size. #Contigs 42,000 bp. **GenBank Assembly ID GCA_000208865. 2. wwExons and introns, without UTRs. zzWithout UTRs.
Results: The system was tested for a three field (11k spot) base of skull treatment plan computed in a patient CT dataset. Simulations were split into 40 calculations over a 10 quadcore CPU cluster, requiring <30 minutes to achieve dosimetric uncertainties (within the 90% isodose volume) of <1%. The figure demonstrates the broad agreement between the TPS (left) and the MC simulation (right). The local gamma pass rate between the two (bottom) is 97% at 4%/4mm (green voxels pass, red / blue voxels fail). This should be interpreted in the context of this being a highly inhomogeneous target site: Differences occurred only in heterogeneous regions where the TPS's analytical dose calculation would be expected to model dose deposition less accurately than MC systems. For example, the MC simulations predict a lower dose around the sinus air cavities than the TPS.
Material and Methods: The simulated beam parameters of a Varian CLINAC 2300 were adjusted based on measurements in a water tank for 6 MeV energy and 10x10 cm² field. The water tank was divided in 81x81x155 voxels with dimensions of 2x2x2 mm³. The Gamma Function (GF) was used for agreement assessment and a phase-space was obtained above the MLC. A solid water phantom with a PTW OCTAVIUS® 729 2D ionization chamber array inserted was imaged by a CT scan and used in PRIMO. A dynamic IMRT plan was calculated by the Eclipse™ TPS and irradiated. The LINAC DynaLog files were analysed and the dynamic delivery was divided into series of static fields in PRIMO. MATLAB was used to analyse the PRIMO output and to create images of dose distributions at specific locations. The simulated dose at the ion chamber matrix position in the phantom was compared with the matrix measurement using the 2D GF through the PTW Verisoft program.
Conclusion: This preliminary study shows that an IMRT plan was successfully simulated through PRIMO with acceptable concordance with the experimental results. Even though further studies on more complex treatments are still required, the results confirm PRIMO as a promising tool for IMRT simulation in clinical environment. Purpose or Objective: The aim of this paper is to evaluate the EPID detector sensitivity and specificity for in vivo dosimetry of VMAT treatments to identify dosimetric and geometric errors and anatomical variations. Material and Methods: Measurements were performed by using TrueBeam STx accelerator equipped with EPID aSi1000 (Varian, Palo Alto, CA) and PerFraction (PF) software (Sun Nuclear Corporation, Melbourne, FL). PF is a commercial EPID-based dosimetry software, which allows performing transit dosimetry, to provide an independent daily verification of the treatment.