dataesr/scientific-paragraphs-categorization huggingface dataset:
These two elements are interdependent and undividable. As much as possible, the whole process must be configured like a "plug and play" box. Experiments performed with high-throughput platforms (i. e. , microarrays and HTS) have to be archived following minimum information reporting recommendations, as suggested by work groups, such as MINSEQE  [105] , in order to adapt data submissions to specific guidelines. Bioinformatics pipelines must be standardized and designed to be easily used by non-experts. One way to achieve this goal is using virtual machines (VMs)  [101]. The first benefit of VMs is that they allow the user to be operating system (OS)-independent, which means that, for example, "Windows" users can run tools designed for a Linux environment. VMs can be distributed with pre-installed software, which dismisses the user of complex installation steps.
VMs can be used to make collections of HTS tools as a single downloadable unit or to encapsulate analysis pipelines, and they can be designed for cloud-computing or computational clustering. Integrating a graphical user interface with VMs would make them very easy to use. In addition, VMs can be distributed as a "snapshot" of the system with all the data and results, which would also enable researchers to more easily replicate the analysis. The possibility of developing a standardized and clinically-certified HTS-VM could dramatically reduce the complexity of integrating HTS technologies into diagnostic settings, allowing their broader adoption and use. The development of an HTS-VM by a hospital, with the help of a research center, and clinically validated for a specific application, could be downloaded via a specific repository of clinical HTS-VMs and used in another hospital.
In addition to HTS-VMs, curated databases would also be available containing a well-annotated reference genome to lead to a genome-based diagnosis. At the end of this first step, which includes sequencing and analysis and which must be efficient and fast, a genome-based diagnosis can be made and a pathogen-specific therapy can be adopted. The second step could take place outside the hospital, after the emergency step, producing a genome-based diagnosis. Data produced in the previous step can be exported as an e-case report, containing phenotypic data, the genome-based diagnosis and sequencing results (according to the MINSEQE guidelines). This data should be collected and centralized in order to be available for the scientific community. They would be used to improve or develop new HTS-VMs, which would be deposited in a specific repository to be available for all clinical centers.
To overcome these difficulties, some commercial IVD companies have developed strategies using HTS technologies to rapidly identify pathogens, without actually knowing the cause of the infection. For example, Base4 company (Cambridge, U. K. ) offers a novel, proprietary nanopore technology for single molecule analysis, pathogen detection and DNA sequencing. Pathogenica (Boston, MA, USA) provides a new technology consisting of probes and a proprietary quantitative pathogen sequencing software package to quantify and identify pathogens. PathoQuest (Paris, France) offers a game-changing approach using the combination of disruptive HTS and bioinformatics/database tools for the discovery and identification of new pathogens. The possible organization of HTS strategies to support a genome-based diagnosis, presented in Figure  2 , shows an efficient way to entirely use and optimize the data in the context of the P4-medicine (Predictive, Preventive Personalized and Participatory medicine) applied to infectious diseases in order to improve healthcare.
Data analysis and the integration of these data are another major bottleneck impeding the use of HTS as a standardized solution to obtain a genome-based diagnosis for the better care of infectious diseases. The absence of a standardized analysis pipeline and the lack of bioinformatics expertise could be solved by the use of a cloud-computing-based pipeline and virtual machines, providing an analysis solution accessible to a broader community. A virtual machine could be developed according to physicians' purposes and distributed for a better analysis pipeline homogenization to be able to compare data between several hospitals and to reduce the time devoted to analyses. The implementation of HTS approaches in routine laboratories can be considered arduous. Thus, IVD companies already offer workflow and analytical solutions, which are more and more integrated; these commercial solutions may facilitate HTS approaches to become considered as a powerful method to lead to a genome-based diagnosis.
Methodology. As in most of the other related works, the restricted geometrical specifications imposed led to a numerically assisted design methodology. The suitable electromagnetic field simulator available for this work was Empire XCcel, from IMST  [20]. The whole process carried out may be described as follows. First, taking into account the PCB layout provided in Figure  1 , a few basic planar single-band radiator configurations were chosen, based on the available knowledge on compact antennas. In this work, meander line and IFA layouts were tested, simulating such structures directly on the PCB layout provided, with the aid of the field analysis software. Then, a fine tuning of these configurations to the lower frequency band (2. 45 GHz) was carried out, from which the IFA-based one presented the best performance, and, for that, it was selected for prototyping and a preliminary round of measurements.
The electromagnetic field simulator available for this work was the software Empire XCcel. The process should include the effects of the plastic casing of the dongle, as well as the influence of a typical laptop in use, in which the dongle was supposed to be plugged in during operation. While the laptop effect could be analyzed directly using the software, the lack of accurate information on the dongle case material electrical properties did not allow its inclusion in the simulations. The adopted solution came as an intermediate experimental step, in which a preliminary prototype tuned to the 2. 45 GHz band was assembled and measured with and without the case. From this procedure, the frequency shift on the  11 response could have been estimated, and, based on that, higher design target frequencies were considered on the simulations, to compensate the effect of the plastic casing.
Acknowledgments This work was supported in part by the  Brazilian Council for Scientific and Technological Development (CNPq)  under International Journal of Antennas and Propagation Grant  306819/2012-0  and the  Rio de Janeiro Foundation for Research Support (FAPERJ)  under Project  E-26/101497/2010. The authors would also like to express their gratitude to the European School of Antennas lecturers and technical staff from  IMST-GmbH  who managed the Industrial Antenna Design Course in April 2013, from which most of the present contribution was derived.
INTRODUCTION High-throughput sequencing technologies can generate millions of amplicons (or barcode sequences) in a single run, and are thus today our best approach to deeply assess the environmental or clinical diversity of complex microbial assemblages of archaea, bacteria, and eukaryotes. The millions, and soon billions, of raw reads produced in molecular ecology and metabarcoding projects need to be clustered into molecular operational taxonomic units (OTUs) before being used for diversity estimates or other statistical analyses. Because of the increasing sizes of today's amplicon datasets, fast and greedy de novo clustering heuristics are the preferred and only practical approach to produce OTUs  (Edgar, 2010; Ghodsi, Liu & Pop, 2011; Fu et al. , 2012). Shared steps in these current algorithms are: an amplicon is drawn out of the amplicon pool and becomes the center of a new OTU (centroid selection), this centroid is then compared to all other amplicons remaining in the pool.
Swarm can be defined as a fast and exact, two-phased, agglomerative, unsupervised (de novo) single-linkage-clustering algorithm. During the growth phase, Swarm computes sequence differences between aligned pairs of amplicons to delineate OTUs, using k-mer comparisons and a new and extremely fast global pairwise alignment algorithm. During the breaking phase, Swarm uses amplicon abundance information and OTUs' internal structures to refine the clustering results. In our view, amplicons can be seen as discrete coordinates in an abstract ampliconspace. Each position of this space represents a possible amplicon, which can be absent from the dataset (null abundance), or present (observed abundance). The direct neighbors of a given amplicon are all the possible amplicons with one nucleotide difference (a substitution, insertion, or deletion).
Speed and general behavior Swarm clustering is a non-linear process. For speed purposes, it is suggested to sort the input data by decreasing amplicon abundance (if applicable); however results will not change, only the speed. Abundant amplicons often occupy a central position in OTUs, and when used as seeds, rapidly capture large amounts of subseeds. Using this strategy, the first OTUs produced are generally among the largest. As OTUs are outputted as soon as they are closed, most of the amplicons can be quickly removed from the pool early in the clustering process, and can be passed to post-clustering analyses. The duration of a Swarm analysis depends on several parameters: e. g. , the d value used, the amplicon number and length, the number of CPU cores available, the molecular diversity of the dataset, and the amplicon abundance distribution.
While being slower than Usearch, the fastest greedy heuristic for amplicon clustering, Swarm is fast enough to deal with datasets with millions of unique amplicons in a few days. For example, the clustering of an unpublished BioMarKs (http:// biomarks. eu/) dataset of 1. 5 million raw reads (312,503 unique amplicons, 382 nucleotides on average) takes less than 7 or 6 min (d = 1 or d = 7, respectively) using a single thread, while about 3 min (d = 1 or d = 7) using 16 threads. These timing experiments were performed with Swarm version 1. 2. 12 on a machine running Linux 2. 6 with dual Intel Xeon E5-2670 processors (2. 6 GHz) having a total of 16 physical cores and 64 GB RAM.
Mock-communities As Swarm was designed to analyze extremely large high-throughput sequencing datasets, we compared it to other stand-alone, fast, de novo clustering methods. Other clustering OTU and are from the same species, but adjusting for the expected proportions through random chance  (Rand, 1971; Hubert & Arabie, 1985). The results were synthesized in Fig. 2  (uneven community) and Fig. S1  (even community) using R (R Development Core  Team, 2014)  and the ggplot2 library  (Wickham, 2009). The command lines and scripts we used to analyze the data, and to visualize the results are provided in the File S1.
RESULTS AND DISCUSSION For both uneven (Fig. 2 ), and even communities (Fig. S1 ), almost all clustering thresholds for CD-HIT-454 and Usearch (with or without presorting) show extreme variability due to strong input-order dependency, while Swarm and DNAclust do not. Because of the pervasive variability in other methods, we then compared the median output values (see Table  S1 ). Using the adjusted Rand index for both the uneven and even populations, Swarm outperforms the other methods at small d or t values (1-2); these small clustering thresholds are of critical importance when making fine-scale partitions of amplicon datasets, and strongly indicate that Swarm is more robust than the other methods to sequencing noise. At medium values (3-5), Swarm is either better, tied, or within 0. 011 points to DNAclust.
Swarm outperforms all other methods at larger values (6-20). These superior results for Swarm reflect its ability to conserve good precision and good recall over a wide range of clustering thresholds, which is critical as the true threshold for species-level assignments will in general not be known in advance and will vary with the choice of the sequenced marker or genomic region. Additionally, the adjusted Rand index results show that Swarm results are little affected by the choice of d; that is, Swarm limits the effect of the choice of clustering threshold and is adaptative across a large array of organisms and genes. Swarm is efficient enough to deal with today's largest datasets, and several new optimizations are now in development to handle even larger future datasets. For example, with the 256-bit integer SIMD instructions of the new Intel Haswell CPUs (released in late 2013) Swarm's pairwise alignment throughput can double.
We are also testing more efficient parallelization strategies, as well as new filters to avoid further un-needed pairwise alignments. These hardware evolutions and software optimizations will improve Swarm's scalability even further. In parallel, improvements to our abundance-based chain breaking model will increase Swarm's capacity to produce robust OTUs and meaningful biological results. In summary, Swarm is a novel and robust approach that solves the problems of arbitrary global clustering thresholds and centroid selection induced input-order dependency, and creates robust and more natural OTUs than current greedy, de novo, scalable clustering algorithms. Swarm is a scalable C++ program able to handle many millions of amplicons. It is freely available at https://github. com/torognes/swarm under the GNU Affero General Public License version 3.
methods, such as average linkage, complete linkage or hierarchical clustering, do not scale up to these large datasets, and were therefore not compared. Swarm's performance was tested on two mock communities each comprising the same genome isolates from 49 bacterial and 10 archaeal species: one with even populations yielding 143,163 unique 16S amplicons (1,576,869 raw reads of average length 254. 6 bp), the other with uneven populations yielding 55,622 unique 16S amplicons (637,693 raw reads of average length 253. 9 bp). These 59 genomes derived from 57 species, as two strains of Shewanella baltica and two strains of Methanococcus maripaludis were used (See Table  S1  for a complete list). For the clustering algorithm comparisons, these strains were collated as we only used the species-level taxonomic classifications.
Comparison with popular de novo clustering methods Sequences were trimmed for quality and adaptors using Sickle (https://github. com/najoshi/sickle) and then forward and reverse reads were overlapped with Pan-daSeq  (Masella et al. , 2012)  insisting on a 50 bp overlap. Strictly identical amplicons were merged with the command described in Swarm's README, and the two datasets were subjected to five different stand-alone clustering methods: CD-HIT-454  (Fu et al. , 2012 (Fu et al. , , v4. 6 2012-11-12)-11-12) , DNAclust  (Ghodsi, Liu & Pop, 2011 , v64bit, release 3, 2013-08-12) , Swarm (v1. 2. 3) with OTU breaking, Usearch  (Edgar, 2010 (Edgar, , v7. 0. 1001 i86linux32) i86linux32)  with presorting of the amplicons by decreasing length (option -cluster fast) and without presorting (options -usersort -cluster smallmem).
ADDITIONAL INFORMATION AND DECLARATIONS Funding FM and CdeV were supported by the EU EraNet BiodivErsA program BioMarKs (grant #2008-6530) and the French government "Investissements d'Avenir" project OCEANOMICS (ANR-11-BTBR-0008) and the EU FP7 program MicroB3 (contract number 287589). FM and MD were supported by the Deutsche Forschungsgemeinschaft (grant #DU1319/1-1). TR was supported by a Centre of Excellence grant from the Research Council of Norway to CMBN. CQ is funded by an EPSRC Career Acceleration Fellowship -EP/H003851/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
First Case Study: multi-player and parallel scenarios In the Nuxil scenario, the players explore the environment and have to use linux commands (e. g. copy (cp), move (mv), edit file permissions (chmod),. ) in the activities proposed. The objective is to offer to players a concrete metaphor of linux commands through a visual and interactive environment. For instance, in the scenario, the command "mv" serves to move objects between the game's areas and "chmod" to provide permissions for opening a chest. Nuxil activities allow players to acquire skills (e. g. training on file commands). In this scenario, the players evolve separately and don't interact directly. In this paper, we selected the specification of three activities to illustrate our approach; their inputs and outputs are presented in table 2.
This study presents the advances on designing and implementing scalable techniques to support the development and execution of MapReduce application in pervasive distributed computing infrastructures, in the context of the PER-MARE project. A pervasive framework for MapReduce applications is very useful in practice, especially in those scientific, enterprises and educational centers which have many unused or underused computing resources, which can be fully exploited to solve relevant problems that demand large computing power, such as scientific computing applications, big data processing, etc. In this study, we propose the study of multiple techniques to support volatility and heterogeneity on MapReduce, by applying two complementary approaches: Improving the Apache Hadoop middleware by including context-awareness and fault-tolerance features; and providing an alternative pervasive grid implementation, fully adapted to dynamic environments.
MAPREDUCE/HADOOP BASICS MapReduce  (Dean and Ghemawat, 2008 ) is a parallel programming paradigm successfully used by large Internet service providers to perform computations on massive amounts of data. This model is currently becoming popular as a solution for rapid implementation of distributed data-intensive applications. The key strength of the MapReduce model is its inherently high degree of potential parallelism that should enable processing of petabytes of data in a couple of hours on large clusters consisting of several thousand nodes. A MapReduce computation takes a set of input key/value pairs and produces a set of output key/value pairs. The user of the MapReduce paradigm expresses the computation through two functions: MapReduce most known implementation is AH (2014a). This framework takes care of splitting the input data, scheduling the jobs' component tasks monitoring them and re-executing the failed ones.
Hadoop Schedulers Hadoop scheduling has evolved along its versions. On Hadoop 1. x, the default scheduler was designed for supporting a batch job submission, organizing jobs in a queue. Similarly, Fair Scheduler (AH, 2013), also proposed on Hadoop 1. x, considers with the same input data size, using a two level scheduling in order to distribute the resources equally: A superior level that allocates queues for each user, using a weighted fair algorithm; and a second level that allocates the resources inside each user queue. Hadoop 2. x adopts, as a default scheduler, a more sophisticated scheduler, the Capacity Scheduler  (AH, 2014b). This scheduler considers a shared Hadoop environment across multiple partners. It focuses on guarantees that a minimum share will always be available for each partner.
Collecting Context Information To include context information on Hadoop, we integrated a collector module based on standard Java monitoring API (Oracle, 2014), which allows to easily access the real characteristics of a node, with no additional libraries required. The collector module, illustrated by Fig. 4 , allows collecting different context information, such as the number of processors (cores) and the system memory, using a set of interface and abstract/concrete classes that generalize the collecting process. Due to its design, it is easy to integrate new collectors and improve the resources available for the scheduling process, providing data about the CPU load or disk usage, for example. Context information is described by using a predefined name and a description. Such name corresponds to a given concept identified in the context ontology.
Experiments Description In order to evaluate our proposal, we compare the container allocation pattern in the original Capacity Scheduler with our context-aware Capacity Scheduler. We perform the analysis of the container allocation pattern when executing the TeraSort algorithm with a 5GB dataset to sort. By requesting enough containers (from nodes expressed capacities) and providing enough data to stress the cluster, we aimed at comparing how the context information influences the containers (tasks) allocation and the overall execution time. The experiments were performed in a cluster subset of the Grid '5000 http://www. grid5000. fr computing environment. The subset had five nodes, one master and four slaves, each node having the following configuration: 2 CPUs AMD@1. 7GHz, 12 cores/CPU and 47GB RAM.
Validating the Prototype with Docker-Hadoop To validate our solutions, however, we need to test different scenarios of node and network faults. Because these experiments require the execution (and reproduction) of well-defined scenarios, we rely on virtual machines (most specifically on container-based virtualization), which allow the researchers to control both as both system images and network interconnections. In our experiments, we used Docker-Hadoop https://github. com/vierja/docker-hadoop as a testbed to simulate different failure scenarios. Indeed, thanks to Docker-Hadoop dashboard, one can easily switch-off or restart nodes in the environment and reproduce the same scenario at will. Using Docker-Hadoop dashboard allowed us to test different failure scenarios like:.
Prototype Evaluation The experiments were conducted over 16 machines on the Helios cluster from the Grid'5000 network. Each machine is composed by 2 AMD Opteron 275 2. 2 GHz CPUs, totalizing 4 cores per node and a Gigabit Ethernet interconnects the nodes. For the experiments, we evaluate the performance of both CloudFIT and Hadoop solutions when varying the total amount of data and the number/size of input files. For each data size, we measure 3 different input splits: One single file, 1MB splits and 512kB splits. The reason for such approach is to analyze the impact of the input files on the map step from both solutions. For the input data, we chose the Gutenberg Project Science Fiction Bookshelf CD http://www. gutenberg. org/wiki/Gutenberg:The_CD_and _DVD_Project, which contains more than 200 books in text format.
With its shortest analysis cycle, a plate can be analyzed in 36 min, so during an 8 h period more than a dozen 96-well plates can be handled. 2 Thus, in a day, results for more than a thousand samples, generally corresponding to worms subject to different experimental treatments, can be generated. Keeping track of this number of results, especially in the context of quantitative genome-wide RNAi screens,  3  is not a trivial task and incited us to design and implement a database to ensure that information was stored in a user-friendly way.
Results We constructed a web-based application, ICeE, using PHP and Javascript, coupled to an associated MySQL database. While ICeE is designed to be compatible with the most commonly used web browsers, the use of Firefox TM is recommended. ICeE permits users to specify a series of pre-defined parameters to provide a complete description of an experiment, inspired by the MIAME guidelines (www. mged. org), and to associate these experimental criteria with a set of data. First, there are general identifiers, such as the date and the experimenter's name. Then, there is the rubric "protocol," which can contain free-text, or be linked to one or more separate files. Under the section, "Conditions," the experimenter adds precise details of the experimental conditions. Since it is designed for C. elegans, the application comes pre-loaded with a list of the nematode strains available from the Caenorhabditis Genetics Center (http://www.