dataesr/scientific-paragraphs-categorization huggingface dataset:
7: GEN T RANS(s,t,tgt(t)) For a transition t, GEN CHECK can generate single or multiple active state checking code. The latter occurs if the target of the transition is a pseudo state join because the transitions incoming to a join are fired if and only if all of their source states are active. The detailed discussion on these is not presented due to space limitation. Listing 4, lines 2-3 show a portion of the code with multiple checking generated for the completion event processing method. The transitions t14 and t15 incoming to Join1 are executed if S6 and S7 are active. In addition, the code portion checks the state associated with the current completion event emitted upon the completion of either S6's or S7's doActivity. In lines 4-6, the code concurrently exits the sub-states of S6 by using FORK and JOIN, which are respectively used to spawn and wait for a thread, for the region methods associated with S6's orthogonal regions, which actually exit S7 and S8.
EMPIRICAL STUDY The pattern is implemented in Papyrus Designer (LISE, ), which is an extension of the UML modeling tool Papyrus  (Gérard et al. , 2010). Papyrus Designer supports component-based modeling and code generation. The behavior of a component in Papyrus Designer is described by using UML State Machines. The tool allows to use some time notions from the MARTE profile to specify time events. C++ code is generated and runs within POSIX systems such as Ubuntu, in which Pthreads are used for implement-ing threads for concurrency. This section reports our experiments with Papyrus Designer on the semanticconformance and efficiency of generated code.
Benchmarks In this section, we present the results obtained through the experiments on some efficiency aspects of generated code to answer the following question. Research Question 2: Runtime performance and memory usage are undoubtedly critical in real-time and embedded systems. Particularly, in event-driven systems, the performance is measured by event processing speed. Are the performance and memory usage of code generated by our tool comparable to existing approaches? Two state machine examples are obtained by the preferred benchmark used by the Boost C++ libraries (Boost Library, 2016a) in  (Jusiak, 2016). One simple example only consists of atomic states and the other both atomic and composite states. We compared our tool with tools such as Sinelabore (which generates efficient code for Magic Draw  (Magic, 2016) , Enterprise Architect (SparxSysems, 2016)), Quantum Modeling (QM) (Quantum Leaps, 2016) (which generates code for event-driven active object frameworks  (Lavender and Schmidt, 1996) ), Boost Statechart (Boost Library, 2016d), Meta State Machine (MSM) (Boost Library, 2016b), C++ 14 MSM-Lite  (Jusiak, 2016) , and functional programming like-EUML(Boost Library, 2016c).
We used a Ubuntu virtual machine 64 bit hosted by a Windows 7 machine. For each tool, we created two applications corresponding to the two examples, generated C++ code and compiled it in two modes: normal (N), by default GCC compiler; and optimal (O) with GCC optimization options -O2 -s. 11 millions of events are generated and processed by the simple example and more than 4 millions for the composite example. Processing time is measured for each case.
However, as the results shown in  (Spinke, 2013) , these patterns require much memory because of an explosion of the number of classes and use dynamic memory allocation, which is not preferred in embedded systems. It is worth noting that none of these approaches provides implementation for all of state machine pseudo states as well as events. Tools such as (SparxSystems, 2016; IBM, 2016b) apply different patterns to generate code. However, as mentioned in Section 1, true concurrency, some pseudo-states, and UML events are not supported. FXU  (Pilitowski and Dereziñska, 2007)  is the most complete tool but generated code is heavily dependent on their own library and C# is generated. Umple  (Badreddin et al. , 2014b ) is a textual UML programming language, which supports code generation for different languages such as C++ and Java from state machines.
CONCLUSION We presented an approach whose objective is to provide a complete, efficient, and UML-compliant code generation from UML State Machines with full fea-tures. The design for concurrency of generated code is based on multi-thread of POSIX. The code generation pattern extends the IF-ELSE/SWITCH patterns and uses a hierarchical structure to preserve the state machine hierarchy. We implemented our pattern as part of the Papyrus modeling tool. We evaluated our tool by conducting experiments on the semantic-conformance and efficiency of generated code. The conformance is tested under PSSM: 62 of 66 tests passed. These results are a good hint that our tool preserves the UML State Machine semantics during code generation. For efficiency, we used the benchmark defined by the Boost library to compare code generated by our tool to other approaches.
Integration of new DNA into cellular genomes mediates replication of retroviruses and transposons; integration reactions have also been adapted for use in human gene therapy. Tracking the distributions of integration sites is important to characterize populations of transduced cells and to monitor potential outgrow of pathogenic cell clones. Here, we describe a pipeline for quantitative analysis of integration site distributions named INSPIIRED (integration site pipeline for pairedend reads). We describe optimized biochemical steps for site isolation using Illumina paired-end sequencing, including new technology for suppressing recovery of unwanted contaminants, then software for alignment, quality control, and management of integration site sequences. During library preparation, DNAs are broken by sonication, so that after ligation-mediated PCR the number of ligation junction sites can be used to infer abundance of gene-modified cells.
We then describe methods for alignment, data management, and quantification of cell clones based on integration site data. The pipeline accommodates analysis of integration in both singlecopy and repeated sequences. We generated synthetic integration sites corresponding to known locations on the human genome and used them in tests to optimize performance of our pipeline, including quantifying the influence of error in sequence determination. Performance was then tested over several datasets ranging from experimental infections to human gene therapy samples, allowing analysis of the influence of repeated sequences on site capture. In our accompanying paper in this issue of Molecular Therapy: Methods & Clinical Development,  52  we describe a suite of analytical tools that draws on the data products described here.
PCR amplification and paired-end sequencing results in recovery of many different sites of linker ligation near the unique integration site in the expanded clone. Sites of linker ligation are recovered in read 1, and LTR-host junctions are recovered in read 2 (Figure  1 ). The number of these linker positions is tabulated, providing an abundance score (Figure  3 , bottom). For statistical analysis, the estimated abundance needs to be corrected to account for the frequency of identical linker ligation positions generated independently, which occurs with increasing frequency as the numbers of linker positions increases per integration site. 50 Numbers of linker ligation sites are recorded along with integration site positions and uploaded into our IntSiteDB database for analysis.
Processing and Aligning Integration Site Sequence Data INSPIIRED begins by parsing raw Illumina output files (FASTQ format) using both index and linker sequences. 7] [58]  Reads are subsequently trimmed to remove primer and LTR sequences (requiring exact matching to predicted sequences), yielding only genomic sequence data. A problem arises due to mispriming in the human genome, which can yield spurious integration sites. For this reason, we require a perfect match for the LTR segment extending between the 3 0 end of the amplification primer to the 5 0 -CA-3 0 sequence that defines the edge of the LTR. Reads are next filtered to remove sequences complementary to the vector or virus used, requiring at least 75% global identity, a value chosen based on results with empirical datasets, and aligning in the first 5 nt of the read.
Sequences are aligned to the reference genome using BLAT (parameters for alignment are found in the Materials and Methods). Alignment information is then paired between the reads, and the integration site position and DNA fragment breakpoints (linker positions) are returned and stored in the IntSiteDB database (described in detail in our accompanying paper  52  ). Read 1 and read 2 are joined based on location in the sequencing flow cell (encoded as the read name). Read pairs that map to identical sites are judged to be PCR duplicates and collapsed into single sites. To pass our quality filter, the genomic coordinates of these positions must lie within the range accessible by the sequencing chemistry-we allow a maximum of 2,500 base pairs (bp) as the default value (Figure  4A ). Integration sites for which the read 1 (linker side) and read 2 (integration site side) positions are unreasonably distant, or on different chromosomes, are judged to be chimeras formed during PCR and are removed from the output data (Figure  4B ).
Performance of the Pipeline Analyzed Using Synthetic Data The performance of the pipeline was analyzed by generation and analysis of synthetic integration site data. Reads were generated with lengths of 179 and 143 nt corresponding to read 1 and read 2, respectively, including addition of the Illumina sequencing primers, DNA barcodes, primer landing pads, and flanking host DNA. A total of 5,000 sites were simulated. The distances between reads 1 and 2 were chosen randomly from a distribution of distances modeled to match empirical data, with 100 different distances between pairs sampled for each of the 5,000 integration sites. Four sets of the 5,000 integration sites were studied, containing no error, 1% error (roughly that expected from the Illumina sequencing method), 2% error, and 4% error.
Integration site datasets were trimmed, aligned, and quality filtered using the INSPIIRED pipeline. Results are tabulated in Table  1. Initially we asked whether each integration site could be recovered from at least one of the 100 read pairs (Table  1 , top), and we then asked how many of the read pairs were recovered (Table  1 , bottom). For 0% error, 99. 6% of sites could be recovered. Twenty-one sites were not aligned, and 87 sites were incorrectly aligned. These latter sites mapped to regions annotated as "low alignability," as defined by the GEM mappability program. 62 By visual inspection, these regions were rich in multiple repetitive element classes that were often nested within each other. Overall, of the 100 simulated sequence reads for each integration site, on average 98 could be mapped correctly.
Analysis of Experimental Integration Site Data We next assessed the performance of the pipeline over three empirical datasets (Table  2 ). The first consists of human HAP1 cells infected with an HIV-based vector and then grown for only 24 hr. A total of 38,967 integration sites were recovered, with an average of only 1. 36 cells per site (SonicAbundance estimate). The second dataset consists of five purified 293T cell lines, each with a single lentiviral integration site. Cells were pooled and integration sites were determined from DNA purified from the mixture. Thus, only five sites were recovered by sequencing, with an average of 3,582 cells per site. The third dataset was a specimen of blood cells from a patient successfully treated for severe combined immunodeficiency (SCID-X1) using a gammaretroviral vector.
33 A total of 755 integration sites were recovered, ranging from 1 to 9,325 cells per site (average SonicAbundance of 40). This emphasizes that after gene correction, different progenitor cell clones delivered quite different proportions of cells to the periphery. These data allow us to investigate the effects of human repeated sequences on integration site recovery using Illumina paired-end data. Multihits accounted for 6. 6% of sites in the HAP1 cells and 11. 8% in the SCID gene therapy specimen. Thus, multihits were roughly in the range expected from tests with the in silico-generated data. Quantification was tested using the 293T cell clones with known integration sites mixed in different ratios. Figure  5  shows the expected and observed values (R = 0. 852). In all cases, the expected ranking by frequency was observed, although there was some departure from the exact value expected.
This is evident in the analysis of in silico data (Table  1 ), in which more multihit sites were identified in paired reads (R1 plus R2) compared to those using the integration site sequence read only (R2). Multihit reads are grouped based on alignments and may have multiple linker attachment sites, as with unique integration sites. Thus, multihits can also be quantified by the SonicAbundance method and queried for possible clonal expansion. The output data tables generated with INSPIIRED make possible the generation of a series of analytical products. These include (1) interactive heatmaps summarizing relationships of integration site data to genomic and epigenetic features; (2) reproducible reports on genecorrected patient samples summarizing numerous features of integration site populations, including expansion of clones with integration sites near cancer-related genes; and (3) data frames suitable for statistical analysis based on the SonicAbundance method.
Read 1 sequences were generated by obtaining the 179 nt following the sequencing primer 1 after the reverse complementing the template. Should fewer bases exist in the template strand than are required, the ends of the reads are filled with poly(T) sequence, as is observed on the Illumina instrument. Index reads were generated as random 12 nt or a specified Golay DNA sequence. Sequencing primer, PCR primer, and LTRbit sequences are specific to each study. For the synthetic dataset, 5,000 integration sites were generated from the human genome, and 100 lengths between read 1 and read 2 were simulated for each site. Simulated template lengths followed a normal distribution with a mean of 70 (SD of 250), while only keeping lengths greater than 30. In total, there are 500,000 read pairs in the simulation set.
Experimental Integration Site Analysis Workflow After sequencing, Illumina output FASTQ files are used as inputs for INSPIIRED, along with sample information (which linkers and barcodes were used with which samples) and vector sequence information. As Golay DNA barcodes have large edit distances, the index sequence file that contains the barcode sequences for each read is subjected to error correction (up to two bases of correction). With the corrected barcodes, information from read 1 (containing the linker sequence) is used with the barcodes to demultiplex the samples, creating independent FASTQ files for read 1 and read 2. Sequences are then subjected to quality trimming (only keeping information with Q-scores greater than Q30). Following quality trimming, respective reads are filtered and trimmed for linker, primer, LTRbit, and vector-related sequences to yield only genomic DNA.
The genomic sequences are then aligned to the host reference genome using BLAT, yielding an output PSL file with the alignment information. The alignment information is then used to determine the locations of alignments and read information is filtered and paired, as previously discussed. Alignments for integration sites are then placed in four output files, including allSites (containing all uniquely mapped integration sites and their corresponding breakpoints), sites. final (contains a condensed form of allSites), multihitData (contains all properly paired and filtered integration sites that could not be uniquely mapped to the reference genome), and chimeraData (contains integration sites that failed proper pairing and are considered artifacts). These outputs are generated for each sample given a specific linker and barcode in the input sample information file.
ACKNOWLEDGMENTS We are grateful to members of the F. D. B. laboratory for help and suggestions. All authors were supported by grants from the  NIH  ( AI052845 ,  AI104400 ,  AI082020 ,  AI045008 ,  AI117950 , and  HL113252 ), a grant from the  European Research Council  ( ERC Regenerative Therapy 269037 ), and an award from the  French National Agency for Research on AIDS and Viral Hepatitis. We also acknowledge support from the  Penn Center for AIDS Research  ( P30-AI045008 ) and the  PennCHOP Microbiome Program.
IV. ANALYSIS METHODS AND TOOLS Two new tools were developed for the analysis of quench temperatures and voltages: QuenchProtection spreadsheet (QP-sheet) and code for current decay calculation based on known protection efficiency (Coodi)  [16]. QP-sheet is used for fast feedback of the worst case hot spot temperature. It is implemented as a Microsoft Excel spreadsheet and was used by the magnet designers. If the temperature in the magnet was not within the limits with the 40 ms quench delay, it provided fast feedback how, for example, modifying the Cu content in the cable impacts the temperature. Coodi is a Fortran 90 code and it enables a more detailed analysis of the temperature distribution, and failure scenarios, such as asymmetric heater failures, as well as voltages. Both use a very similar method for the temperature calculation.
This needs to be further investigated. Lowering the Cu/non-Cu ratio in the high-field cable helps in reducing these temperature gradients. Analysis of the CLIQ implementation in the block design was done using the TALES software, showing that the peak temperature could remain below 350 K with a CLIQ protection system. However, also this analysis showed that the voltages are too large. This paper discussed the quench analysis which was performed to ensure that the magnets are not impossible to protect, and guide the direction of the next iteration in the design. The next step in the quench protection analysis is to sketch the protection scheme considering both heaters and CLIQ and the technical implementation of both  [23].
The algorithm accomplishes the temporal constraints that are defined for DFORE. It is proved in a formal way that the algorithm satisfies such constraints. A computational cost analysis and a simulation are presented to show the viability of our solution. We compare our proposed algorithm to MULES  [10] , adapted to the foraging reconnaissance task, which is a random walk with uniform distribution algorithm to collect data from the environment. MULES has similarities with our proposed mechanism, they both use indirect communication through an intermediary in our case artificial pheromones and in MULES the use of a mobile data relay. We measured the number of trips by each algorithm in different conditions. The comparison with MULES is justified since this proposal is the baseline algorithm for indirect communication among mobile sensors.
Experimental Setup This experiment includes 5,000,000 flights since the number of simulations provided sufficient data to measure the average number of trips, such that the difference among several simulations was not significant. To determine if the proposed algorithm accomplishes the constraint on the average number of trips given by T h = 2p h + 2 ∑ h-2 i=1 (p h -i) + 1, the number of trips required to travel every DFG G h were measured. The number of steps was set in the range of [ h , 2 h ], with increments of two units, since a unitary increment does not change the behavior of the algorithm due to Restriction 2 (see Section 4. 4). Table  4  shows the results of this experiment. Figures  10 11 12 show the distribution of trips for every DFG considering the different number of steps that the MDF can make.
However, we found no convincing evidence of disrupted genes with known functions in any of the three lineages (Supplementary Note). Given the high level of gene conservation between lineages, we sought to identify rapidly evolving genes that might be involved in phenotypic differences between C. neoformans lineages. For each gene, we built a consensus sequence for each lineage and then calculated pairwise d N and d S of these fixed sites. As d S was uniformly low throughout the dataset due to limited genetic diversity, we identified differences in d N , which measures both the mutation rate and selection. The top 10 annotated genes with the largest d N for each pairwise comparison are shown in Table  2 , and the three comparisons in total include 18 unique genes. The set is dominated by transcription factors (GLN3, PDR802, SXI1α, YOX101, and ZNF2) and transferases (ATG2602, CDC43, GPI18, HOC1 and RAM1), many of which have already been implicated in virulence  (Esher et al.
Discussion This population genomic analysis of Cryptococcus neoformans var. grubii has revealed new biogeographic relationships and highlighted a complex history of hybridization events between groups. Analysis of genome-wide variation of 188 geographically diverse isolates greatly increases the resolution of the VNI, VNII, and VNB phylogenetic groups and precisely measures the level of genetic differentiation between isolates within each group and across geographic scales. This data supports a much higher diversity of isolates in the VNB group compared to VNI and VNII isolates. Notably, we show that hybridization between these groups can result in genome mixing suggestive of recent and ongoing meiotic exchange. A recent taxonomic proposal to divide the C. neoformans and C. gattii species complexes into seven monophyletic species did not subdivide C.
grubii  (Xu et al. , 2000) , South America could also represent a major diversity center of Cryptococcus neoformans var. grubii. Our data suggests that Cryptococcus neoformans var. grubii VNB isolates in both subgroups from South America have undergone ancestral recombination events, donating genetic material to all lineages across multiple geographical locations. Our study also provides clear evidence for recombination within the VNI and VNII lineages, where nearly all the isolates contain the MATα mating type. This suggests that mating likely occurs between MATα isolates, as is found in C. neoformans var. neoformans  (Sun et al. , 2014). Previous studies have hypothesized that Cryptococcus neoformans var. grubii can complete its sexual reproductive life cycle in environmental niches, such as plants  (Xue et al.
Funding Statement This project has been funded in whole or in part with Federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services , under grant number  U19AI110818  and by the  National Human Genome Research Institute  grant number  U54HG003067  to the  Broad Institute. Support to J. R. P. came from  Public Health Service  Grants  AI73896 ,  AI93257. JR and MAB were supported by a  UK Medical Research Council   Grant  awarded to MCF, TB and TH ( MRC MR/K000373/1 ). MV was supported by a  UK Natural Environment Research Council  PhD studentship. JH was supported by  NIH  grants  AI39115-19  and  AI50113-13. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Offline capability The product needs to be online/offline capable, or at least intranet-capable, since, while Possible can get computers electricity 95% of the time, the internet (despite serious investments) is only 85% at best. Bahmni (http://www. Bahmni. org) is an open source framework built as a javascript application on top of the OpenMRS (http://www. openmrs. org) data model and application program interface. Bahmni uses OpenERP (https://www. odoo. com/) for billing and inventory management and OpenELIS (http://www. openelis. org/) for laboratory management. ThoughtWorks has been developing Bahmni over the last four years for deployments in India, Uganda, Haiti, and other countries. We provide a diagram of the system architecture as supplemental material (See Supplemental Figure  1 ).