dataesr/scientific-paragraphs-categorization huggingface dataset:
Network Simulation Scenarios: 20 and 30 LLN Nodes In this scenario, 20 and 30 battery-powered A-LLN sensor nodes are respectively simulated in a grid topology. The comparison is drawn between the standard RPL model and the RPAL model. The performance metrics of network lifetime are recorded when the outputs of the Powertrace model arrive at the predefined energy storage threshold. Whether a node is active depends on the existence of a connection to the sink node. Average data collection packet delay is measured as an average value representing the time cost of the sender node to send messages to the sink node successfully. The relevant distance to the sink node for each sensor node is presented as rank in the DODAG. Because rank is not a stable value in the procedures of DODAG maintenance, it is used as a snapshot value when the nodes run out of energy.
To simplify analysis of the results of this experiment, we provide enough energy to all the nodes for running a 40 min simulation. A JavaScript-based simulation script is used to give the runtime reconfiguration of the node state and generate random restart intervals for those misbehaving nodes. The obtained results regarding the packet loss, latency, number of failed co-operations, and packet transmission cost, are listed in Table  9. From the results, we can observe that the original RPL cannot solve the problems caused by the misbehaving nodes. Its packet loss increases very quickly if the number of misbehaving nodes is increased, and even though the ETX routing metric is able to alleviate the unreliable links its cumulative feature will still cause long-duration delays for carrying out these measurements.
Evaluation of RPL and RPAL in a Real World Environment To validate the results from the performed simulations, we performed a set of tests on a real-life testbed (IWoTCore plus Ext_MiLive extend board  [71] ). For these tests, the maximal number of retransmissions was increased to three since some links in the field test were less reliable and the link qualities are extremely dynamic due to obstacles (e. g. , trees, buildings or barriers). For the wireless multi-hop communication in these tests, radio transmitter modules were tuned using channel 19 and a transmit power of 3 dBm (the highest setting level in the RF230bb's driver).
Energy Usage In Figure  8b , the effects of two routing models on the energy consumption performance is illustrated. In comparison with the simulation results in Section 5. 2, we have to admit that the behavior is different from the expectations. This outcome can be explained by the following reasons: the nodes in the simulation tests have the same link conditions but the field test nodes do not have the same link conditions. In other words, the repair mechanism for detecting routing loops is initiated more often in the real world tests, and this could be responsible for an increase in the power consumption. Furthermore, the current prototype system cannot provide low MCU power mode due to the lack of an appropriate solution using Contiki process management to deal with the correlation between the AVR MCU and the NANO module.
Analyses of fruit set were performed by using a logistic regression (binomial distribution, log link function, proc GENMOD, SAS, Cary, NC), correcting for overdispersion (dscale option, proc GENMOD, SAS). Average seed weight, average seed number per fruit and pollination success were performed by using an ANOVA (proc GLM, SAS). In order to reach a normal distribution of residuals, we log-transformed the average seed number and the pollination success (Kolmogorov-Smirnov test of normality: P > 0. 15 for all shown analyses). All statistical analyses were conducted using SAS  (SAS version 9. 1. 3, 2002).
Outcrossing rates were determined using  Ritland (2002)  multilocus maximum likelihood estimation program (MLTR Version 3. 4, accessible at http://genet-ics. forestry. ubc. ca/ritland/programs. html). Standard deviations were determined based on 1000 bootstrap analyses; maternal genotypes were estimated as part of the maximum likelihood procedure. Within each patch, family estimates gave outcrossing rates per family, bootstraps using individual offspring as units of observation. We calculated selfing rate as s = 1 À t m. Differences in selfing rate between the two patches were analyzed by using a logistic regression (binomial distribution, log link function, proc GENMOD, SAS), correcting for overdispersion (dscale option, proc GENMOD, SAS). Moreover, we used these results to estimate the number of seeds produced by outcrossing in each focal plant.
Acknowledgments We are grateful to  J. Cuguen ,  J. F. Arnaud  and  I. De Cauwer  for helpful discussions and comments throughout the study and to  Lynda Delph  for her comments on the manuscript. We also thank  C. Glorieux  and  A. Bourceaux  for greenhouse work and  C. Gode ,  L. Frachon  and  C. Van De Paer  for help in genotyping. This work was funded by the  R egion Nord-Pas-de-Calais , the  Fondation pour la Recherche en Biodiversit  e ( GENEFRAG  project) and the  French Agence Nationale de la Recherche  ( ANR 11-BSV7-013-03 ). E. B. benefitted from a contract from the  University of Lille. E. L. 's PhD fellowship was funded by the  Centre National de la Recherche Scientifique  and the  R egion Nord-Pas-de Calais.
Funding Information This work was funded by the  R egion Nord-Pas-de-Calais , the  Fondation pour la Recherche en Biodiversit  e ( GENEFRAG  project) and the  French Agence Nationale de la Recherche  ( ANR 11-BSV7-013-03 ). E. B. benefitted from a contract from the  University of Lille. E. L. 's PhD fellowship was funded by the  Centre National de la Recherche Scientifique  and the  R egion Nord-Pas-de Calais.
5%) in the microsporidian species Encephalitozoon cuniculi  [24]. Here, we investigated the presence of unannotated sCDSs genes in Microsporidia using as reference models the publicly available genomes from four distinct species belonging to the genus Encephalitozoon. These genomes were chosen because of their evolutionary and medical importance and extremely compact state. Indeed, the Encephalitozoon genomes are both very small (2. 3-2. 9 Mbp) and compact (120 bp intergenic spacers on average), and encode fewer proteins than their eukaryote counterparts (~1900 CDSs). Their genomes are also highly syntenic with large blocks of genes arrayed identically, and we hypothesized that the conserved sCDSs located therein would be easier to distinguish from regions with lower functional constraints (e.
Sequence analyses To accurately identify and delineate coding and intergenic regions, each predicted protein sequence from the four Encephalitozoon genomes was used as query for BLASTP and TBLASTN analyses  [41]  against the three remaining proteomes and genomes, respectively. Protein and nucleotide alignments between orthologs were performed with MUSCLE 3. 8. 31 [42]  and Clustal Omega  [43]  respectively. Their proper start/stop codons were then curated manually using the Artemis  [44]  annotation platform. Intergenic regions were extracted from the curated annotations using the custom Perl script and module intergenic_extract. pl and CDS. pm, respectively (https://github. com/EACIDAM/perl_script/blob/master/). Small proteins were detected using an "all-versus-all" TBLASTX approach (BLOSUM45, word size: 2 aa, lowcomplexity filter disabled) whereas putative transcriptional signals were manually searched for in the upstream and downstream regions of each predicted CDS.
Multiple sequence alignments between newly predicted orthologs from these four genomes were performed with MUSCLE 3. 8. 31. Orthologous proteins from other species were retrieved by a PSI-BLAST approach (three iterations, BLOSUM45)  [45]  using custom microsporidian and fungal databases. The microsporidian database was built from the genome sequences of 13 species extracted from the NCBI database: Nematocida parisii, Anncaliia algerae, Nosema bombycis, Hamiltosporidium tvaerminnensis, Ordospora colligata, Vittaforma corneae, Nosema apis, Spraguea lophii, Edhazardia aedis, Vavraia culicis, Nosema ceranae, Mitosporidium daphniae, and Trachipleistophora hominis. The fungal database was created from the NCBI RefSeq release version 01/2015 (ftp:// ftp. ncbi. nlm. nih. gov/refseq/release/fungi/).
ORFs of at least 69 nt were extracted with Getorf from the EMBOSS 6. 6. 0. 0 package  [46]. Conserved domains in the small protein-coding genes were predicted using InterProScan 5  [47] , Pfam 27. 0  [48] , SignalP 4. 1  [49]  and TMHMM2. 0 [50]. Maximum likelihood phylogenetic inferences based on the gene coding for the small ribosomal RNA subunit were performed under the HKY85 model of nucleotide substitution as implemented in PhyML 3. 0  [51]. For this analysis, the orthologous sequences were first aligned with MAFFT version 7  [52]  and the ambiguous regions in the alignments were filtered out with TrimAL version 1. 3 using the automated1 parameter  [53].
Results To facilitate the detection of small functional open reading frames, previously overlooked in Microsporidia we first performed a thorough curation of the available Encephalitozoon genome annotations. Using data from the four available Encephalitozoon genomes  [15, 19, 23, 24] , a total of 2, 2, 82, and 75 CDSs were added to the E. cuniculi, E. intestinalis, E hellem and E. romaleae annotations, respectively (S1 Table ). Using these comparative extrinsic data, we also identified 57, 51, 44 and 139 translation initiation sites (TISs) in E. cuniculi, E. intestinalis, E. hellem and E. romaleae, respectively (S2 Table ). Thereafter, using the curated annotations described above, we searched for the presence of short protein-coding gene candidates. Specifically, we searched for transcriptional and/or translational signals in intergenic regions that flanked small open reading frames, with the condition that both signals and ORFs were conserved across the Encephalitozoon genomes.
Using this approach, a total of 31 small but highly conserved CDSs were identified in the four Encephalitozoon species (Fig   1  and S3 Table). Another sCDS was also found to be shared between E. cuniculi (ECU04_1635) and E. romaleae (EROM_041665). However, its presence Table  1. Predicted small protein-coding gene orthologs in the four Encephalitozoon species. Orthologs in other microsporidian genomes were predicted using PSI-BLAST and manual validation. Additional functional inferences were performed using InterProScan 5 (conserved amino-acids motifs), TMHMM (transmembrane helices) and SignalP (signal peptides). Bold: Genes present in independent RNA-Seq datasets  [48].
cuniculi, E. intestinalis, E. romaleae and E. hellem contain  2126 , 1927 , 1904 and 1955 CDSs, respectively. CDSs, respectively. Despite the high rate of sequence evolution prevalent in Microsporidia, we were able to discern putative homologues in non-Encephalitozoon microsporidian species for 24 of the 32 newly identified sCDSs (Fig 4 ; Table  1 ; S3 Table ). Putative orthologs of ECU02_1495, ECU07_1385, ECU08_1555 and ECU11_1205 were also found in non-microsporidian fungi. A hemaglutinin glycoprotein domain (IPR013829; ECU03_0255) potentially involved in pathogenicity and host invasion, a Zinc finger domain (IPR024766; ECU07_1385) involved in proteinprotein or protein-DNA interactions and a nucleolar protein NOP10-like domain (IPR007264; ECU08_1555) involved in 18S rRNA production or rRNA pseudo-uridylation were found in the predicted proteins.
cuniculi RNA-Seq experiments  [55] , thus providing external confirmation that these were not procedural artefacts. While the remaining 23 genes were not found in this external dataset, these may simply correspond to genes that are either lowly expressed or expressed under conditions that differ from the performed RNA-Seq experiments, in which the RNA was isolated at three specific post-infection time points  [55]. Another possibility is that these transcripts were present but discarded by the pipelines used due to the filtering schemes involved (e. g. the removal of transcripts shorter than a specified cutoff). Some of the sCDS identified here in this manuscript were accurately predicted in the N. bombycis and T. hominis genomes with ab initio gene prediction methods  [22, 27]. However those methods also likely lead to numerous false positive predictions, for the large number of genes in N.
Functional inferences based on homology are only as good as their reference datasets, and while small CDSs have been identified in animals, plants, yeasts, and bacteria, their functions have been rarely addressed  [56]. Microsporidia currently lack a viable genetic characterization system, unlike many model organisms, and the RNA interference machinery is absent from Encephalitozoon species, preventing identification through silencing. Other Argonaute/PIWI-bearing microsporidian species do exists, but RNA interference assays have yet to be implemented in Microsporidia. That said, the presence of a hemaglutinin glycoprotein potentially involved in pathogenesis among the putative functions that we were able to infer suggests that exploring these sCDS further will likely yield profitable insights into the parasitic cycle of these organisms.
Conclusion This study underlines the usefulness of associating classic gene prediction and fine genome exploration (e. g. synteny, transcriptional signals) to improve annotation in Microsporidia. Recently, a similar approach has been successfully used to perform identification of sCDSs from the re-sequencing of eight isolates of N. ceranae species  [57]. Both independent studies highlight the value of sequencing very phylogenetically closely-related species to reveal their complete gene repertoires, an essential step towards the understanding of an organism physiology and adaptive capabilities. This is especially true when the species involved are fast evolving organisms and/or hard to culture such as microsporidia. Finally, the current study provides an important framework for future studies and datasets that can be used to better train and evaluate new computational methods dedicated at detecting ultra-small genes.
or Adenine/Thymine rich signals for E. cuniculi Interpro TMHMM SignalP domain E. cuniculi E. E. hellem E. romaleae intestinalis ECU01_1065 Eint_010975 EHEL_010945 EROM_010865 73 Th a , Vc, Aa,Oc ECU02_0235 Eint_020155 EHEL_020165 EROM_020145 57 Ea, Na, Nb, Nc, Th a , Vco, Aa, Ht, Vc, Oc ECU02_0425 Eint_020355 EHEL_020345 EROM_020335 57 + (1) ECU02_0885 Eint_020835 EHEL_020805 EROM_020795 59 Oc + (1) ECU02_1495 Eint_021465 EHEL_021435 EROM_021405 56 Ea, Na, Nb, Th a , Vc, Ht, + Nc, Vco, Oc, Sl ECU03_0255 Eint_030145 EHEL_030135 EROM_030155 56 Oc IPR013829 ECU04_0123 Eint_040045 EHEL_040035 EROM_040065 55 Oc + (1) ECU04_0152 Eint_040082 EHEL_040072 EROM_040102 54 Th, Vc, Np ECU04_1622 Eint_041635 EHEL_041595 EROM_041652 28 Na, Nb, Aa, Ea, Nc, Th, Vc, Oc, Vco ECU04_1635 - - EROM_041665 55 ECU05_0087 Eint_050075 EHEL_050137 EROM_050055 71 Aa, Vc, Ea, Sl, Th, Oc, + + Na, Nb a , Ht ECU05_0115 Eint_050105 EHEL_050165 EROM_050085 65 ECU05_1185 Eint_051235 EHEL_051295 EROM_051225 51 Nc, Ea, Na, Nb, Oc ECU05_1275 Eint_051335 EHEL_051395 EROM_051335 42 Oc ECU06_0285 Eint_060185 EHEL_060205 EROM_060195 33 Eb, Na, Nb, Nc, Ea + (1) ECU07_0862 Eint_070802 EHEL_070832 EROM_070812 41 ECU07_1385 Eint_071345 EHEL_071365 EROM_071325 84 Aa, Ea, Eb a , Na a , Nb a , Nc, IPR024766 Np a , Sl a , Th, Vc, Vco, Ht a , Oc ECU07_1645 Eint_071493 EHEL_071625 EROM_071565 69 ECU07_1775 Eint_071493 EHEL_071755 EROM_071695 75 + + (1) ECU08_1445 Eint_071493 EHEL_081425 EROM_081445 60 Oc, Nc, Nb a , Na ECU08_1555 Eint_071493 EHEL_081525 EROM_081555 52 Aa, Ea Vco a , Oc IPR007264 ECU09_0465 Eint_090475 EHEL_090465 EROM_090475 42 ECU09_1255 Eint_091465 EHEL_091435 EROM_090625 25 ECU09_1665 Eint_091675 EHEL_091675 EROM_091655 49 Aa, Sl, Th, Vc, Oc ECU09_1755 Eint_091775 EHEL_091775 EROM_091755 43 Nc, Oc, Na, Ea ECU10_0635 Eint_100575 EHEL_100635 EROM_100505 68 Oc ECU11_0185 Eint_110055 EHEL_110065 EROM_110055 42 Nb, Nc, Eb a , Na, Vco, Oc ECU11_0525 Eint_110375 EHEL_110395 EROM_110385 25 Nb a , Oc ECU11_0575 Eint_110425 EHEL_110445 EROM_110435 49 Oc ECU11_1175 Eint_111055 EHEL_111055 EROM_111055 84 Oc ECU11_1205 Eint_111085 EHEL_111085 EROM_111085 43 Vco, Th a , Nb, Vc, Ea, Ht, Sl, Aa, Na, Oc ECU11_1725 Eint_111615 EHEL_111615 EROM_111615 68 Th, Vc + a Previously predicted (1) Accession numbers, positions and locus tags are listed in the S3 Table.
Then other constituents (lane, pedestrian crossing, markings, etc. ) are linked to this network. We base all our work on a Relational DataBase Management System (RDBMS), to store inputs, results, topology, and processing methods. This article follows the IMRAD format (  (Wu, 2011) ). In Section 2. we explain why we chose to base our work on a RDBMS, and explain the hypothesis, how we generate the road surface, and how the parameters of the resulting model can be edited. In Section 3. we provide results of street generation and results of editing. In Section 4. , we discuss the results and present limitations and possible improvements. The design of StreetGen is a result of a compromise between theoretical and practical considerations. StreetGen amplifies data using a simple, yet strong hypothesis.
Introduction to RDBMS We chose to use a RDBMS  ((PostgreSQL, 2014)  with  (PostGIS, 2014) ) at the heart of StreetGen for many reasons. First, RDBMSs are classical and widespread, which means that any application using our results can easily access it, whatever the Operating System (OS) or programming language. Second, RDBMSs are very versatile and, in one common framework, can regroup our input GIS data, a road network (with topology), the resulting model of streets, and even the methods to create it. Unlike filebased solutions, we put all the data in relation and enforce these relations. For instance, our model contains surfaces of streets that are associated with the corresponding street axis. If one axis is deleted, the corresponding surface is automatically deleted. We push this concept one step further, and link result tables with input tables, so that any change in input data automatically results in updating the result.
Concurrency and scaling One big query We emphasize that StreetGen is one big SQL query (using various PL/pgSQL and Python functions). The first advantage it offers is that it is entirely wrapped in one RDBMS transaction. This means that, if for any reason the output does not respect the constraints of the street data model, the result is rolled back (i. e. , we come back to a state as if the transaction never happened). This offers a strong guarantee on the resulting street model as well as on the state of the input data. Second, StreetGen uses SQL, which naturally works on sets (intrinsic SQL principle). This means that computing n road surfaces is not computing n times one road surface. This is paramount because computing one road surface actually requires using its one-neighbours in the road network graph.
Those case are, however, easily detected and could be solved individually. Failure 1 is caused by the fact that axis 1 and 2 form a loop. Thus, in some special cases, the whole block is considered an intersection. This is rare and easy to detect. Failure 2 is caused by our method of computing intersection surface. It could be dealt with using the variable buffer. Failure 3 is more subtle and is because one axis is too short with respect to the radius. It could be fixed by taking data so that it is closer to the reality, until a very good match is reached. Scaling The entire Paris street network is generated in less than 10 minutes (1 core). Using the exact same method, a single street (and its one-neighbour) is generated in ∼ 200ms, thus is lower than the human interactive limit of ∼ 300ms.
SQL set operations We illustrate the specificity of SQL (working on set) by testing two scenarios. In the first scenario (noset), we use StreetGen on the Paris road axis one-by-one, which would take more than 2hours to complete. In the second scenario (set), we use StreetGen on all the axis at once, which takes about 10minutes. Concurrency We test StreetGen with two users simultaneously computing two road axis sets sharing between 100% and 0% of road axis. The race condition is effectively fixed, and we get the expected result. Parallelism We divided the Paris road axis network into eight clusters using the K-means algorithm 3  on the road axis centroid. This happens within the database in a few seconds. Then K users use StreetGen to compute one cluster (parallelism), which reducez the overall computing time to about one minute.
DISCUSSION Street data model Our street data model is simple and represents well the roadway, but would need to be detailed in some Lanes are not a priority at the moment, and cannot have different width nor type (bus lanes, bicycle lanes, etc. ). Our model is just the first step towards modelling streets. In particular, we do not integrate any urban objects in it. We could easily extend our street data model to add street objects positioned regarding the distance to the roadway and a curvilinear abscissa along the street axis as well as oriented with respect to the street axis. Kinetic hypothesis Overall, kinetic hypotheses provide realistic looking results, but are far from being true in an old city like Paris. Indeed, a great number of streets pre-date the invention of cars. We attempted to find a correlation between real-world corner radius (analysing OpenDataParis through Hough arc of circle detection) and the type of road or the road's average speed (from a GPS database).
We did not created a Graphical User interface (GUI), but rather a set of automatic in-base behaviours so that editing input data or special interaction layers can interactively change the StreetGen results. Doing so ensures that any GIS software that can read and write PostGIS vector can be used as StreetGen GUI. In some cases, we may have observations of street objects or sidewalks available, possibly automatically extracted from aerial images or Lidar, and thus imprecise and containing errors. We tested an optimisation algorithm that distorts the street model from best-guess StreetGen to better match these observations. This subject is similar to Inverse Procedural Modeling, and we feel it offers many opportunities.
The first comprehensive database of fungal DNA barcodes was established for yeasts  (Kurtzman & Robnett 1998 , Fell et al. 2000 , Scorzetti et al. 2002)  and revolutionised the approach to species recognition in that group. Where once a single taxonomist could identify only a handful of yeast strains in a week because of the plethora of physiological tests required, now it is possible to process hundreds. The result has been a rapid increase in the number of recognised yeast species, and a wealth of ecological data  (Kurtzman et al. 2015). The broad acceptance of DNA barcoding today relies on public repositories such as the INSDC (http://www. insdc. org/), which accessions hundreds of thousands of sequence entries  (Schoch et al. 2012 (Schoch et al. , 2014)). In a concerted action with the Consortium for the Barcode Of Life (CBOL), the Fungal Barcoding Consortium  (Schoch et al.