dataesr/scientific-paragraphs-categorization huggingface dataset:
In this previous study, ISs have been identified using a BLASTP-based approache against ISFinder and MITEs have been identified using BLASTN with the IS sequences previously identified (E value = 10e-5). All of these annotated transposons and their respective coordinates in the genomes were downloaded from the ISFinder database and compared with those identified with our pipelines. Additionally, we have randomly chosen 30 bacterial genomes available in the ISFinder database that cover the major bacterial phyla (Cyanobacteria, Firmicute, Deinococus-Thermus groups etc…) and compared the performance of our pipelines with the ISFinder annotations.
Results and Discussion Pipelines validation: test with 30 Archaeal and 30 bacterial genomes We constructed three different work-flows: two de novo pipelines that search for repeats sequences (called "Repeats search") and for Inverted Repeats (called "IRs search") and a library-based pipeline using HMM alignment profile searches. These methods were then tested using diverse genomic and metagenomic datasets. We first validated the performance of our different pipelines by detecting the presence of 190 different ISs and 26 different MITEs previously identified by BLAST in a set of 30 Archaeal genomes  [5]. The respective performances of the different methods are given in Table  1. The performances of the HMM pipeline fed with 28 transposase alignment profiles with the BLAST searches against ISFinder sequences are globally comparable.
Three known single copies have been lost during the HMM search process but 5 new multi-copies ISs have been identified. By contrast, our de novo pipelines improve significantly and MITEs annotation. Repeats search increased notably the number of multi-copy ISs compared to BLAST (10 new ISs, + 9%) and clearly outclasses the latter concerning MITEs identification (13 new MITEs, +50%). However, Repeats search generates a significant level of false positives that correspond to various other kind of mobile elements such as group-II Intron encoding a reverse transcriptase or conserved genes in prophage as integrase, in addition to several duplicate genes as rDNA 16S for example. Nevertheless as these false positive hits encoded for highly conserved and generally well annotated genes, they could be easily filtered with a simple BLASTX against a NR database at the end of the process.
Additionally, we have also tested a sample of 30 bacterial genomes annotated in the ISFinder database (Additional file 1: Table  S1 ). Performances of the different pipelines are remarkably similar with those obtained with Archaeal genomes. The best performances are provided by the Repeats search pipeline (with 8 additional new elements found) and the HMM pipelines. The IR search give even better results with the bacterial dataset (-16,5%) compared with the Archaeal datasets (-48%). This result is mainly due to the weaker proportion of IS families that do not displayed IRs in the bacterial dataset as IS605 and IS607 families. Finally, it should be noted that our de novo pipelines detected 21 potential MITEs in the bacterial genomes (with a total of 189 different ISs), a proportion roughly comparable with the Archaeal genomes (44 MITEs for 250 ISs).
Finally, de novo methods provide output files directly usable for transposon databases ie providing the complete transposon sequence, not only the transposase sequences (or the recombinase). Finding the complete sequence (IRs, the additional ORFs etc…) of a transposon detected on the basis of its transposase may appears trivial, but this task is in fact time-consuming and not easy to automate. Eye examination of the local alignment sequence bordering the transposase is generally required and the ISSaga software developed to find transposases do not provide options to find the complete corresponding IS sequences. By providing simple and fast methods to find the complete sequence of the transposon, de novo methods implemented in this study offer sensitive and reliable ways to find the complete sequence of the transposon.
It should be noted that ISFinder database already contains all the Sulfolobus ISs, consequently the result obtained by BLAST correspond to an optimum. Thus it's not surprising that the profile HMM searches do not fit exactly with the BLAST approches. In fact, all the transposases missed by the profile HMM pipeline with 1 kb and 250 bp belong to a single group of IS5 elements (ISC1212 and ISC1236 elements) which show only very weak similarities to other IS5 elements and also vary significantly among themselves. Moreover, the spacing of the DDE catalytic residues does not align with that of other IS5 family members  [5]  and this would probably explain the difficulties to identify them based of alignment profiles search methods. Finally, due to the lack of sequence similarities with very short input sequences, the performance of the HMM pipelines with 100 bp fragments decreases strongly.
Concerning the "real" metagenomic datasets, the results vary according to the size of the reads (Table  2 ). With the 1 kb reads (Pacific Sand PBM and Sargasso Sea JCVI) the efficiency of the HMM pipeline is considerably better than BLAST (+ 40 and +51% respectively). In addition, BLAST tends to generate a lot of false positives displaying very short aligned regions (<150nt) (Table  2 ). For example, with the JCVI sample, the number of false positives corresponds to 114 hits for 44 apparent "true" ISs. To reduce the level of false positives, it's possible to decrease of the BLAST E values threshold to have a more stringent criterion of similarities. This option dramatically reduced the numbers of hits: for example, with E value = 10e-10 instead of 10e-5, the number of ISs identified in the JCVI sample is divided by a factor 5.
Absence of identification of these elements during the initial study with BLASTbased approach in 2007 is more puzzling. The IS200/IS605 is a loosely defined family that is clearly polyphyletic: it clustered together Serine Recombinase type and Tyrosine Recombinase type enzymes. These elements have been clustered together because they may share a second and non-essential ORF of unknown function  [4]. Until recently, this family has received little attention, but during the past few years many studies concerning its diversity, their mechanism of transposition and their roles on genome evolution were published  [23] [24] [25]. Many new sequences was probably identified and added to libraries, thus it's then possible that the sequence sampling in the ISFinder database increases enough to allow identification of more distant homologs in our archaeal genomes.
Additionally, we cannot rule out that these elements were missed as there is an important mass of IS200/IS605 elements present in these Archaeal genomes (36 distinct elements for a total of 210 copies). This situation well illustrates the fact that library based approaches critically depend on the quality and on the diversity of the sequence sampling in the database, representing a major weakness compare to de novo methods. Finally, it should be noted that we have identified with de novo methods 13 additional IS-like elements that display typical transposon structure (presence of IRs, multiple copies…). These IS-like elements show any The host genomes, the potential IS autonomous partner and its family when defined are indicated. Lenght (in bp), copy number and size of the DRs and IRs are also mentioned.
Conclusion In this work, we have developed three new alternative methods to improve ISs and MITEs identification in prokaryotic genomes. Compared to a routinely used BLASTbased approach, de novo methods based on repeats detection and identification of the IRs improve notably the detection of ISs and MITEs. These methods have led to the identification of more than 30 new transposons (+20%) in a reference dataset of 30 archaeal genomes. De novo methods appear powerful to detect ISs belonging to poorly defined families, highly divergent ones or emerging groups with no or few representatives in the sequence libraries. As MITEs display in prokaryotes very few sequence similarities with their autonomous IS partners (mainly in the IRs), the advantages of de novo methods are magnified. We also developed an alternative library-based method to detect transposases using profile HMM searches.
Acknowledgements We wish to thank all the members of the ELEGEM team and more precisely  Arnaud Le Rouzic  and  Jean-Michel Rossignol  for critical reading of the manuscript. We would also like to thank  Michael Chandler  and  Patricia Siguier  for kindly giving us full access to the ISFinder Database. Received: 7 February 2013 Accepted: 25 September 2013 Published: 11 October 2013.
The strong diurnal signal measured above the surface by HAMSTRAD (3-6 K) is consistent with all the other in situ data sets. The temporal evolution over the 12-day period in the PBL is also consistent with all other data sets (radiosondes, IASI, in situ sondes, and meteorological sensors). In the free troposphere and around the tropopause, the HAMSTRAD temporal evolution is consistent with that observed by radiosondes and IASI, although a cold bias exists compared with IASI and radiosondes around the tropopause. For heights less than 4 km above surface, HAMSTRAD correlates very well with radiosondes and in situ sensors (correlation better than 0. 8) but less well with IASI (0. 4). Below the tropopause, the IASI and HAMSTRAD correlation reaches 0. 9, whereas above the tropopause, the correlation of IASI and radiosondes with HAMSTRAD is rather low (< 0.
HAMSTRAD was first deployed at the Pic du Midi station (42  the nonhydrostatic mesoscale atmospheric model (MESO-NH). In the very dry conditions such as the ones encountered in February 2008, the radiometer behaved consistently with the other data sets, whereas in standard and wet conditions such as the ones encountered in March-June 2008, a wet bias in comparison to the radiosondes and IASI was detected in the lower troposphere  [22]. After this successful validation campaign in Europe, the HAMSTRAD radiometer was sent and deployed at Dome C in January 2009. Since the shelter that ought to house the radiometer was not fully equipped, we installed the instrument outdoors (mean outdoor surface temperature of -35 • C) and performed measurements of water vapor and temperature over a limited period of 12 days from January 22 to February 2, 2009.
g. ,  [25] ) and helped to characterize the quality of integrated water vapor (IWV) observations at Dome C. This paper deals with the first measurements of H 2 O and temperature measured by HAMSTRAD at Dome C from January 22 to February 2, 2009. In order to assess the quality of these measurements, we compared the HAMSTRAD data sets with coincident measurements of H 2 O and temperature performed by several instruments launched from Dome C (meteorological radiosondes), deployed at Dome C (PT100 and Humicap in situ sensors) along the vertical extent of a 45-m tower, and orbiting above Dome C (the spaceborne IASI instrument). This paper is structured as follows: In Section II, we present the data sets used in our analysis. In Section III, we show the measurements of IWV and H 2 O profiles from HAMSTRAD and assess their quality as compared with all the other data sets in terms of temporal evolution and statistical analysis (mean, standard deviation, bias, and correlation).
The standard deviations calculated over the 12-day period (Fig. 16 , top right) in the PBL (0-200 m above surface) from HAMSTRAD and in situ (PT100 LGGE and CEA and meteorological sensor from HAMSTRAD) data agree very well and tend to show the strong diurnal cycle in temperature (3-6 K) that is indeed absent in the IASI standard deviation and radiosonde databases (2-3 K) because they do not sample the entire diurnal cycle. From 200 to 3000 m above surface, radiosonde and HAMSTRAD standard deviations are in good agreement (2-3 K), whereas IASI standard deviation is slightly less (about 2 K). From 3 to 5 km above surface, HAMSTRAD and IASI standard deviations are consistent with each other (about 2 K), whereas the radiosonde standard deviation is greater (∼3 K). Above the tropopause (∼5 km above surface), the standard deviations from the three data sets are not consistent at all (no sensitivity of HAMSTRAD in this region).
ACKNOWLEDGMENT The authors would like to thank all the  French and Italian  staff (technicians, engineers, and scientists) who have worked at the Concordia Base over the first (2009) and second (2010) summertime campaigns and the  Institute Paul-Emile Victor (IPEV)  staff for doing their best in taking care of the logistics of the missions and the  Australian Antarctic Division  staff for the Australian logistics in 2009. IASI has been developed and built under the responsibility of the  Centre National d'Etudes Spatiales (CNES). It is flown onboard the MetOp satellites as part of the EUMETSAT Polar System. The IASI L2 data are received through the EUMETCast NRT data distribution service. IASI vertical profiles of water vapor and temperature were extracted from the  Ether French atmospheric database  (http://ether.
Load injectors : • send requests, wait for replies, measure response times • according to a given scenario defining the workload  generating the workload and measuring response times, while the latter measure the usage of given resources: CPU, memory, network adapter or equipment, database, middleware, etc. Load injectors and probes are bound to a central control and monitoring component, namely the Supervisor, and a central Storage component that will collect all measures once a test execution is complete (Fig. 1 ). They are deployed on local or remote hosts. All these components are contained in the top-level, distributed Clif Application (ClifApp) composite component. The component based developement of CLIF facilitates its adaptation. We present in the nexts sections the implementation a scalable load testing framework based on CLIF.
This phase aims at evaluating the load injection capacity of an injector VM, in terms of greatest number of clients it is able to emulate (vUsers). In order to evaluate the capacity of an injector, the Calibrator uses a CLIF extension module called Selfbench  [5]. Selfbench results from research work on automating performance modelling of black boxes. Part of this work consists in a self-driven workload ramp-up, looking for the maximum number of vUsers a SUT can serve until its resources are considered as insufficient. Since Selfbench makes no assumption about the SUT capacity, it starts with a single vUser. From the response times and throughput the load injector gets, Selfbench computes the SUT's theoretical maximum capacity, with minimal assumptions in terms of parallel processing capability (single-threaded).
Grid'5000 is organized in "sites" (a site represents a city), which in turn are organized in clusters. For our experiments, we configure two Grid'5000's clusters (Chicon at Lille, north of France; and Pastel at Toulouse, south of France) to provide separately the SUT cloud and the Injector Cloud (as shown in the BaaSP architecture in Figure  2 ). The two clusters run OpenStack  [9]  in order to provide virtualized cloud. The virtualization system is KVM version 2. 0. 0. We start each RUBiS VMs with 1GB of memory while injectors and the others BaaSP VMs used 256MB of memory. Each VM is pinned to one processor. They run the same operating system as the nodes which host them, which is Linux Ubuntu 10. 04 distribution with a 2. 6. 30 kernel, over a gigabit connection. In this environment, we have calibrated the deployment time TTSVM.
Let nbVMRubis be the number of VM used to run RUBiS. Let nbVMBaaSP be the number of VM used to run BaaSP components. Let nbInj be the maximum number of injectors used during the test (5 in our case). The cost of running the test in the cloud without dynamic provisioning (P olicy 0 ), noted Cost 0 , is given by the following formula: Cost 0 = [nbInj * T T T + (nbV M Rubis + nbV M BaaSP ) * (T T T + T T S)] * Cost tu Workload scenario 1 We compare the cost of P olicy 0 to the two others policies: P olicy 1 (injectors addition is done without a "just ahead of time" provisioning), and P olicy 2 (injectors addition is done in a "just ahead of time" provisioning). Figure  4 (1) presents actual results of the execution of this first workload scenario. It shows two types of curves: (a) the variation of the CPU load of the RUBiS database tier (remember that it is the bottleneck of our RUBiS configuration), and (b) the injector provisioning rate during the test.
Related work Very few works are interesting on adaptive benchmarking tools. However, we study some work situated around this topic. Unibench  [12]  is an automated benchmarking tool. As our BaaSP, Unibench is able to deploy remotely both the SUT and benchmarking components in a cluster. It is adaptive in the way that it is able to identify a modification in the SUT and then achieve another benchmarking process related to this modification. To do this, Unibench is supposed to know the source code and the programing language of the SUT. Unlike our BaaSP, the SUT is not considered as a black box. [13]  presents research challenges for implementing benchmarking tools for self-adaptative systems. Except the definition of metrics and some principles to be considered when defining workload, it does not care about the self-adaptation of the benchmarking tool itself.
The first stage essentially performs an analysis of the visual input into universal features; it is often unsupervised and can use deep networks or any of the above mentioned codes (e. g. ,  Serre, Oliva, & Poggio, 2007) , whereas the later stage is one of feature selection specific to the task, which uses supervised classifiers that are most commonly of the generalized linear type known as support vector machines (see for instance  Pinto, Barhomi, Cox, & DiCarlo, 2008). Following on the procedure outlined in recent instanciations of state-of-the-art visual object recognition models like HMAX (hierarchical model and X;  Serre et al. , 2007) , the LAM/perceptron's input code could be upgraded to the product of an unsupervised learning process that turns images into a high-dimensional vector of frequently occurring feature combinations detectors, as determined by the statistics of a training base of natural images.
01, which required 323 epochs of training for lowercase letters and 242 for uppercase letters. In both conditions, the model achieved 100% recognition by the end of training. For each letter unit in the output layer, all the trained weights afferent to it were resized as a 2-D image (of sizes 188 • 188 or 128 • 128, depending on the lowercase or uppercase training condition). These were normalized and thresholded in order to be compared to the global human classification image for the same letter. The global classification image was the sum of the classification images over the five different bubble sizes tested in  Fiset et al. (2008). All programs were written in Python and the Neurolab package was used to implement the networks. The Neurolab package can be found at https://pypi. python.
, 2009) , as described by  Delouis et al. (2004) , located at 36. 83 • N, 3. 65 • E; and (2) a potential regional source of landslide-triggered tsunami in the Ligurian Sea located at 44 • N, 8. 5 • E. Tsunami travel times are computed using the TsuDIG software with a 2 arcminute bathymetric grid (Fig. 1 ). The computation shows travel times of 1 h 30 min for the first scenario and of 18 min for the second one. et al. (1996)  distinguish two main approaches to model and simulate evacuations: (1) macro-simulators compute travel times in open areas or networks, describing each portion of space by attributes that pressure the velocity of pedestrians, enabling simulations on a large scale; (2) microsimulators try to simulate the loss of velocity generated by the interactions between pedestrians  (Helbing and Molnár, 1995)  on the individual scale.
A graph-theory and network-based macro-simulator: RouteFinder © Once people at stake on beaches are quantified, the RouteFinder © approach consists in (1) identifying potential shelter areas, (2) mapping accesses to the beaches and quantifying the people depending on them, (3) identifying the most accessible shelter areas following the shortest evacuation routes, and finally, (4) quantifying the access time and number of people depending on each shelter area. RouteFinder © software (http://www. routeware. dk) is based on Euler's graph theory  (Deo, 2004)  estimating the accessibility from a point of a network to another. The road and path network is represented as a graph, in which intersections are associated with nods and roads are associated with arcs. The graph has to consider the road hierarchy  (Appert and Chapelon, 2006 ) and be valued depending on the width and slope of the road as both parameters influence pedestrians' velocity.
A multi-agent micro-simulator: SimWalk © SimWalk © was designed to assess pedestrian behaviour in normal or stressed conditions  (Zainuddin et al. , 2009)  inside buildings. Based on a social force model  (Helbing and Molnár, 1995) , it makes each agent act individually according to his/her destination, environment, level of stress, and depending on the other agents' behaviour. The level of service (i. e. the space needed for each agent to walk freely at his/her optimal velocity) is configured according to the FGSV (2001). Antibes' technical services provided the spatial data used for the micro-simulation, including dense altimetry points, buildings and parcels. The available urban environment data were completed by fieldwork, which consisted in collecting and mapping objects that could potentially influence pedestrian tracks (obstacles).
This paper describes the process followed in order to make some of the public meterological data from the Agencia Estatal de Meteorología (AEMET, Spanish Meteorological Office) available as Linked Data. The method followed has been already used to publish geographical, statistical, and leisure data. The data selected for publication are generated every ten minutes by the 250 automatic stations that belong to AEMET and that are deployed across Spain. These data are available as spreadsheets in the AEMET data catalog, and contain more than twenty types of measurements per station. Spreadsheets are retrieved from the website, processed with Python scripts, transformed to RDF according to an ontology network about meteorology that reuses the W3C SSN Ontology, published in a triple store and visualized in maps with Map4rdf.
Introduction Governments and their associated agencies worldwide are making some of their data sources available under open data licenses, so as to ensure consumption by the general public and other public and private organisations. In this context, AEMET 1 , the Spanish Public Weather Service announced on November 2010 a major change in its data policy, offering a gradual, free and public access to its data in electronic format. As a first step AEMET made publicly available in its website meteorological and climatic data registered by its weather stations, radars, lightning detectors and ozone soundings. These data are currently published as spreadsheets in the AEMET FTP server, linked from its website and detailed in section 3. Our work aims at facilitating the use of these data by processing them and offering them as Linked Data.
00 c 2011 -IOS Press and the authors. All rights reserved Web, Section 3 describes the data sources selected to be converted as Linked Data. Section 4 presents the design decisions for the URIs, while Section 5 describes the RDF generation process. Section 6 explains the development of the Ontology Network for Weather Data. Next, Section 7 presents the exploitation of the metereological Linked Data, and Section 8 describes some recommendations for publishing Linked Sensor Data. Finally, Section 9 presents the conclusions and future lines of work.