dataesr/scientific-paragraphs-categorization huggingface dataset:
Database with Verification Results To ease the analysis of the verification results, in addition to the textual verification reports, verification results are now stored in a database. To achieve this, the Monitor class has been extended with a member which is a vector of database entries (see Fig. 2 ). During a simulation, the monitor stores here the information about every assertion activation: start time, end time, status (pass or fail). This information is ultimately committed to the database. A post-processing tool extracts a concise and easily analyzable tabular representation of the results. Other post-processing tools e. g. , for statistical analysis, are under development.
Avionics Flight Control Remote Module This case study (from Airbus) is an avionics module dedicated to processing data from sensors and controlling actuators according to predefined flight laws. Certification issues and specific safety requirements must be taken into account. Properties are associated with the joint behaviour of the software and the DSP coprocessor (see Figure  4 ). This component stores and uses data that represent digital filter coefficients and analog input (ANI) calibration coefficients. Data integrity must be checked, to protect the device against SEU (Single Event Upset, change of state caused by energetic particles). Among the requirements: -A 1 : Checksum computation must be performed every CHECKSUM PERIOD ms (the corresponding result is stored in the register STATUS of the DSP).
-A 2 (ensures detection of potential checksum errors): The software must read the content of STATUS every CHECK PERIOD ms. -A 3 : When a checksum error is detected (wrong value in STATUS), the DSP function must be deactivated (within LIMIT ms). Those properties are clearly intercorrelated. Table  1  summarizes some results, for the processing of 25000 analog data coming from the ANI port, with configurations that can induce missed deadlines. Here full monitoring is not really time-consuming (about 2% CPU time overhead), therefore the benefit rather comes from the fact that the number of assertions triggerings significantly decreases, hence making the results more conveniently analyzable because avoiding irrelevant trigerrings means that only the most useful information appears in the simulation trace.
Database and pipeline Data are stored in a PostgreSQL relational database. The database contains three categories of data: (i) primary data such as genomic annotations that are extracted from complete genomes files (Genbank or EMBL format), (ii) secondary data corresponding to the cross comparisons, using BLASTp, of all the Coding DNA Sequences (CDS) of the stored bacterial genomes and (iii) tertiary data such as synteny regions computed from the secondary data. Blast alignments are performed at the protein level and results with an e-value less than 0. 01 are stored in the database. We define two genes as being orthologous if they give rise to a Bi-Directional Best Hit (BDBH) in Basic Local Alignment Search Tool (BLAST) comparisons of the corresponding genomes. More information about the concept of homology and orthology in bioinformatics is available on the website.
In the homologue browsing view, each column represents a gene from the user-constituted gene set and each row represents a compared organism. The presence or absence of homologue is shown in each cell. If multiple compared genes are homologous to a given reference gene, the user can browse amongst them. The genes are background-coloured according to which synteny they belong to. tween genes displayed by Insyght are 'orthologues', some are 'paralogues' and others correspond to genes of the reference genome that have several 'homologues' in the compared genome (see the database and pipeline section). For the sake of simplicity, we will use the general term 'homologue' to describe all evolutionary relationships thereafter. The user can either transfer genes from the genomic organization view or freely build up a combination of any genes from the same organism using the 'Search' tab.
To facilitate the retrieval of genes of interest and allow phylogenetic profiling, different types of filter have been developed (Supplementary Figure  S1 ); some are based on the intrinsic properties of the gene (genomic location, presence of domains or motifs), others on functional annotations (identifiers, functions, GO terms, EC number, cross-reference to public databanks such as EMBL or Uniprot) or, again, on the presence or absence of homologues in a given set of species. An unlimited number of filters can be combined together with either the operator AND (intersection) or OR (union). Therefore the users have the ability to formulate relevant biological questions, such as finding genes in species A that have homologues in species B and/or C but not D and/or E and that match a few particular functions or biological processes.
Our database contains the four complete genomes of E. faecalis isolates from human source to date, of which three are pathogenic to human according to the Genomes OnLine Database  (44). The first step in the analysis was performed using Insyght search ability and the orthologues table view. The dispensable genome of V583 was retrieved by searching for different combination of presence/absence of homologues with the 62, OG1RF and Symbioflor1 strains. Supplementary Table  S3  contains 20 loci of interest regarding horizontal genomic transfers that have been mentioned in various studies  (42, (45) (46) (47). The number of genes from the dispensable genome that belong to those loci is reported as well as the P-value calculated according to the binomial law. This result shows that the distribution is very significantly biased towards those loci.
We believe those innovative designs and functionalities will assist biologists in performing fast and efficient data mining of the conserved synteny, homologues and distinctive genomic regions. By downloading the virtual machine, biologists can work on their private sequences and focus on a group of closely related genomes of their choice. We plan to continue to develop Insyght by integrating more complete prokaryote genomes in our public database e162 Nucleic Acids  Research, 2014, Vol. 42, No. 21  PAGE 8 OF 9 and implementing links to complementary tools such as our genome annotation platform, AGMIAL  (60).
AVAILABILITY Insyght is an open source project under the CeCILL-B licence. The home page of the project is http://genome. jouy. inra. fr/Insyght. To facilitate the analysis of private data, a virtual machine can be downloaded and installed locally. It contains a pre-installed version of the pipeline, the database with a dozen example genomes and the visualization tool. The documentation on how to run the perl scripts of the pipeline to create a user-tailored database and visualize the results is provided. Creating a database of 20-30 organisms takes 1 or 2 days on a laptop Intel Core i7-2620M 2. 7Ghz 8 Go RAM. As the number of comparisons grows exponentially with the number of genomes to compare, the insertion of a non-trivial amount of organisms is consuming in terms of computer resources.
Computational prediction The microRNA that post-transcriptionally regulated LXR was identified using publicly available prediction software suite, TargetScan (http://www. targetscan. org/). The software suite, RNAhybrid (http:// bibiserv. techfak. uni-bielefeld. de/rnahybrid/) was used further to identify the microRNA that had the most favorable binding energy to the LXR mRNA. For predicting microRNA promoter interaction MicroPIR prediction software was used.
miR-206 targets the conserved 3′UTR of LXRα in humans and several mammals To identify post-transcriptional regulators of LXRα, we performed in silico analysis of LXRα mRNA sequence using the TargetScan microRNA prediction tool. TargetScan analysis revealed miR-206 to be a putative miR that potentially targets the 3′UTR of LXRα mRNA. Fig. 1a  shows the stem miR-206 loop structure, the mature miR-206 sequence and the conserved binding site of miR-206 on the LXRα 3′UTR of human and several mammals. We further examined the feasibility of the predicted interaction using the RNAhybrid program that predicts the minimum free binding energy (mfe) of miR206-LXRα interaction. This analysis revealed that the interaction between miR206-LXRα has a favorable mfe of (-) 23. 5 kcal/mol. LXRβ lacked miR-206 binding sites and therefore was not among the predicted miR-206 targets.
Acknowledgements The authors highly appreciate the technical assistance of  Anton Ibovnik ,  Etrika Bulqiza  and  Silvia Povoden. This work was supported by the  PhD Program in Molecular Medicine of the Medical University of Graz , the  Austrian Science Fund (DK-MCD   W1226 ,  SFB-LIPOTOX F30 ,  P22832 ) and the  Marietta Blau Scholarship (OeAD). We thank  Drs Rhonda Bassel  and  Eric Olson ,  Department of Molecular Biology, University of Texas SW-Dallas Medical Center  for providing us with the miR-206 KO mice. BS is a member of the  Institut Universitaire de France.
This paper analyzes the scanning process in IEEE 802. 11 networks in an urban setting characterized by a high Access Point (AP) density. Most of these APs belong to a community network, known as a collection of APs announcing the same network name (Service Set Identifier or SSID). The owner of an AP can optionally configure the community network of his/her AP, resulting in an irregular topology for each community network as there is no central planning authority. We investigate the relationship between the time spent in each channel while scanning for available APs and the number of AP actually detected. In particular we show that, in order to discover all available APs at a given location, we need to combine the results of multiple scans. Based on this result we argue that the efficiency of the scanning process could be greatly improved by using a database shared by all the users of a community network, containing the available APs at different locations.
Note that an MS may also receive beacon frame while waiting for Probe Responses. The Probe Request -Probe Response exchange follows the Distributed Coordination Function (DCF), which controls the medium access by assigning a random backoff to each of the contending station. Active and passive scanning are also confronted to the random variations in the wireless channel (signal degradation, fading, interference and noise). All this can lead to an unpredictable scanning behaviour, especially when the number of APs increases. Modern devices hide the implementation of the scanning algorithms. To the best of our knowledge, PCs running Linux are the best alternative as a research platform since most of the scanning implementation code is open. Until recently, most of the IEEE 802. 11 Medium Access Control were implemented within the hardware of the wireless card.
Recent versions of the Linux kernel, instead, use a software implementation of MAC so that it is possible to modify several of the scanning parameters, by generating a customized mac80211. ko kernel module. We have implemented a mechanism that can accurately measure the response delay and that can also modify the Channel Time parameter (see  [5]  for more details). In this paper, using the above mentioned implementation, we conducted a measurement campaign in a dense network scenario in the city of Rennes (France). The contribution of this paper is twofold. First, it shows the dynamics of the scanning in a crowded scenario. Counter-intuitively, as it seems to be generally understood (based on existing implementations), we show that the scanning process may not be as efficient as supposed when using long values for the timers.
Nor is the opposite true, i. e. , short timers report only a partial view of the network. Second, we analyze the effect of the aggregated scanning on the AP discovery to finally come up with a discussion of a collaborative system where a centralized data base could gather several MS scanning results to build an accurate view of the network. The remainder of the paper is organized as follows. In Section II we present previous work on the IEEE 802. 11 network discovery process from two different perspectives, from the client and from the AP. In Section III we describe the methodology used for collecting the data and compare Linux and Android client platforms. In Section IV we highlight the main parameters that impact the AP discovery. In Section V we study the effects of repeated scannings and discuss about the need for a central system for assisted scanning.
A. Methodology and tools To collect the network deployment information we have designed and implemented a platform that is able to continuously scan for IEEE 802. 11 networks while collecting beacons and Probe Response frames, together with the following network information: Service Set Identifier (SSID), Basic Service Set Identifier (BSSID), security mode, operating channel and signal strength. The main components of this platform are: • Laptop: Hewlett-Packard, model nc-2400, operating system Debian GNU/Linux Jessie, with the modified mac80211. ko (see  [5]  for details on the configuration). • NIC: Intel Corporation, model PRO/Wireless 3945ABG-Golan. • iw tool, version 3. 13  [19]  • Mobile: Smartphone Galaxy S3 (GT-I9300) running Android OS 4. 3. 1. We used this platform to collect AP information in several locations in the downtown area of Rennes, France.
B. Dependency on the platform In order to draw better conclusions about the AP discovery, we made sure that observations were not platform dependent. While the scanning algorithm is different from one terminal (i. e. , network card and its driver) to another  [20] , we compare the scanning results obtained with the Linux platform (see above) against an Android platform. For the Android client, we have used an application called Wi2Me  [21]  which periodically triggers a scanning and logs their results in a database. Contrary to the Linux platform, we leaved untouched the default scanning behaviour in Android. We observed that the timer is around 250ms per channel, corresponding to a full scanning latency of 3. 5 seconds, for the 13 channels. Once we got the scanning results, we launched a new scan after an inactivity timer of 3 seconds.
Thus, a new scanning is launched approximately every 7 seconds. We have noticed that the Android scanning behaviour is more aggressive than the Linux one: in Android, several Probe Requests (4 on average) are sent in a channel, while in Linux a single Probe Request is sent on each channel. The aggressive sending of Probe Requests increases the chances for APs to receive them. Furthermore, this behaviour supports one of our claims in this paper: for dense topologies, a single scanning is most likely to discover only part of the available APs. Fig. 3a  and 3b  show how many times each AP has been discovered after 100 scannings. The APs that were discovered in all scannings are illustrated in black. We can see in both figures that most of APs are intermittently discovered: only 12% and 20% of the available APs are discovered in all scannings for Android and Linux respectively.
The total number of discovered APs is different between the two platforms: Linux is able to discover a total of 75 APs, while Android discovers 53 APs. We believe that this may be due to differences in hardware configuration (such as antenna, chipset, etc. )  [20]. In Fig. 4a  and 4b  we can observe an approximate difference of 5 dBm favouring the received power on Linux versus Android. While we can observe that there is a poor correlation Fig. 2 : Sequential Scannings Impact on the Discovery Rate between the power of an AP and the number of times it is discovered, some APs have relative good power and still are discovered a few times, and some other APs have a bad power, and are often discovered. This is further discussed in Section IV-D. Fig. 4c  finally shows the CDF of the number of discovered APs per scanning.
For each platform, the Xaxis is normalized with the total number of discovered APs. We can see that in Linux, the number of APs is higher, and that there is more APs per scanning (in median almost 90% of the APs while around 80% in Android). But these distributions are close. Also note that both systems almost reach the total number of APs in one scanning (for few scannings), which seems to indicate that the memory size may not be an issue. The Android curve is less exponential than the Linux one, meaning that there are more scannings with fewer APs in Android. This may be due to the plaform performance regarding the radio reception. Globally, we can also say that the number of APs per scanning has a small variance.
A. On the need for a Central Database Given the results presented in this paper, we argue that an open data system could opportunistically assist mobile users to better discover and control the Community Network topology and determine their link quality. As we saw, within a single scanning, only a subset of the availabe APs are discovered, and usually a client does not have the time to scan multiple times. However, with a centralized database system where users could share their information about their vision of the topology, offline servers could compute up-to-date map of the topology, proposing a global and more precise view of the AP deployment. Users would act as both data producer and consumer: user would push their vision of the topology from time to time, with a simple post on central servers.
The complex, rapidly evolving field of computational metabolomics calls for collaborative infrastructures where the large volume of new algorithms for data pre-processing, statistical analysis and annotation can be readily integrated whatever the language, evaluated on reference datasets and chained to build ad hoc workflows for users. We have developed Workflow4Metabolomics (W4M), the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy webbased platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation.
, 2008) , MetaboAnalyst  (Xia et al. , 2009)  and XCMS Online  (Tautenhahn et al. , 2012). There is, however, an unmet need for an open source and open development infrastructure which would enable developers to readily integrate new modules, compare their performances on reference datasets or download and modify the existing ones for their own research. We have thus developed a collaborative online research resource, Workflow4Metabolomics (W4M), for comprehensive metabolomics data pre-processing, statistical analysis and interpretation. W4M is a fully open-source virtual research environment (VRE;  Carusi and Reimer, 2010)  built upon the Galaxy environment  (Goecks et al. , 2010)  for bioinformatics developers and metabolomics users, with minimal wrapping burden of algorithms into modules, in addition to user-friendly functionalities for workflow management.
Framework The VRE integrates several digital resources over many layers (hardware, software, user interfaces, documentation, tools and workflows), and is based on a High Performance Computing environment (600 cores, 100 TB). The light-weight runner technology has been added to enhance interoperability and integration of components from heterogeneous environments (Linux, Windows, etc. ). Multiple workflows can be run in parallel and users can rapidly analyse large datasets: for example, the full pre-processing, statistical analysis and annotation of a cohort dataset (184 raw files, 11 Go) from liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) was performed in 5 h, using 1% of the computational resources. Modules include wrappers from existing open-source code, and innovative tools developed with standard languages (e.
g. R, Perl, Python or Java). Each tool has a web page including working examples. In addition, a help desk is provided for both users and developers. An 'app-store' based on the Galaxy native toolshed, ToolShed4Metabolomics, fosters the local deployment of modules, facilitates the management of developer contributions, provides wrapper templates and promotes best practice guidelines. Finally, a full W4M portable virtual machine is distributed for local installation (e. g. for development prototyping).
W4M currently contains 19 modules covering all steps of LC-HRMS data analysis: • Format conversion: raw data can be converted from commercial formats (e. g. Thermo Fisher. RAW) to open formats (including mzXML,  Pedrioli et al. , 2004, and mzML, Deutsch, 2008) , via a recently developed toolshed wrapper implementation of the ProteoWizard software  (Kessner et al. , 2008 ). • Pre-processing: all wrappers of the reference XCMS  (Smith et al. , 2006)  and CAMERA  (Kuhl et al. , 2012)  functions are available to perform peak extraction, retention time alignment and annotation of isotopes and adducts. (Trygg and Wold, 2002) , with all numerical and graphical results and diagnostics (optimal number of components estimated by cross-validation, variable importance in projection, model significance by permutation testing, outlier detection).
• Annotation: a formula generator based on the HiRes (High Resolution) algorithm  (Kind and Fiehn, 2007)  is provided, in addition to several modules for public database query which allow the user to define specific annotation strategies (e. g. by searching from general to more specialized resources). Metabolomics scientists can access W4M with a simple web browser, upload their data, select analysis parameters or choose the default settings, and run their workflows in batch mode. In addition, W4M provides functionalities for creating interactive web-based documents showing the results of the analyses, and sharing them with collaborators directly on W4M. To get started easily, preconfigured workflows and corresponding histories are publicly shared for pre-processing, statistical analysis and annotation, respectively.
Conclusion The W4M infrastructure enables both experimental users with no specific programming skills and advanced developers to perform cutting-edge and reproducible computational analyses from raw data to metabolite annotation. W4M can be further extended to integrate external workflows running on desktop platforms (e. g. Taverna, KNIME), or acquisition instruments. The statistical modules from W4M can be used to analyse other 'omics' data, or can be combined with existing Galaxy workflows (e. g. in transcriptomics), thus enabling multi-omics analyses in a global systems-biology approach. In the coming months, modules for NMR data preprocessing will be integrated into W4M and the infrastructure will be connected to MetExplore  (Cottret et al. , 2010)  for genome-scale network analysis. W4M is therefore an innovative open-source computational VRE bridging the data-intensive bioinformatics and metabolomics communities.
Below the mixed layer depth (MLD), stratification occurs and results in the presence of the seasonal and permanent pycnocline, i. e. , where the vertical density gradients are maximum. In temperature, this highly stratified zone is referred to as the thermocline, and in the case of salinity it is called the halocline. The base of the main pycnocline marks the depth limit of the upper ocean surface layer. In the absence of direct turbulent dissipation measurements, the different depths of the upper ocean layers are commonly derived from profile data using threshold or other proxy variables. The different methodologies often yield different values for the MLD depending on the source of data and definition used for the upper ocean features [e. g. ,  Thomson and Fine, 2003 ]. In the Tropics, another level of complexity in the vertical structure of the surface layer was revealed by large differences between the bases of the thermocline and halocline in the western equatorial Pacific  [Lukas and Lindstrom, 1991 ].