dataesr/scientific-paragraphs-categorization huggingface dataset:
[22]. In summary, LORDEC adopts a hybrid approach that takes advantage of the low error rate of short reads to correct the long ones. To avoid the computational bottleneck of all-against-all alignments, LORDEC builds the dBG of the short reads, and aligns long reads to the paths of the dBG. LORDEC exploits the fact that the dBG summarizes in a single structure the layout of short reads along the target DNA/RNA sequence. Hence, aligning a long read to the dBG allows the correction of erroneous sequence positions much more efficiently, and in a scalable manner. The dBG implementation of GATB allows LORDEC to process huge short reads libraries and to scale up to vertebrate or plant genome cases. The LORDEC software offers several programs: the main one for correcting the long reads, and others for trimming and splitting corrected reads into corrected regions if needed.
De novo identification of alternative splicing events in human RNA-seq data with KISSPLICE KISSPLICE was applied to a human dataset that consists of Fig. 6  Running GSV on Galaxy. The JSON graph generated by MAPSEMBLER2 can be navigated, filter parameters used to modify the visualization aspect, and results exported from liver from the Illumina Body Map 2. 0 Project (downloaded from the Sequence Read Archive, study accession number ERP000546, brain read accession numbers ERR030882 and ERR030890, liver read accession numbers ERR030887 and ERR030895). Even though KISSPLICE does not require a reference genome, we applied it to a case where an annotated reference genome is indeed available in order to be able to assess whether our predictions are correct. KISSPLICE ran in three hours using less than 8 GB RAM and was able to identify 2,336 bubbles corresponding to AS events.
Assessing DISCOSNP recall on real read sets from Saccharomyces cerevisiae DISCOSNP was applied to a set of biologically validated SNPs predicted from an artificial evolution study on S. cerevisiae  [26]. Twenty-four read sets (corresponding to three populations) were downloaded from the NCBI Sequence Read Archive (with the accession number SRA054922) and processed to remove barcode and adapter sequences as in the initial study. DISCOSNP was run independently on the three populations studied. For each population, DISCOSNP was applied to the eight read sets corresponding to eight time points, with the default parameters and c = 11. In this framework, DISCOSNP recall could be evaluated on real read datasets. As shown Table  3 , among the 29 reference-validated isolated SNPs, 27 were predicted by DISCOSNP, thus giving an estimated recall of 93.
Targeted assembly of S. cerevisiae using MAPSEMBLER2 MAPSEMBLER2 was applied to the S. cerevisiae dataset previously described in the DISCOSNP section. Biological validation of several identified SNPs is presented in a recent study  [26]. As a starter, we selected a sequence fragment of length 63 bp, occurring at position 1,014,600 on chromosome 4. This starter, GGGG TTTTTCAACTGAATGTTCTTCAATAAAGCCTTTTT CAGAAGCGATTTTGTTTCTGTGCT, occurs near a set of SNPs validated in the  [26]  study. The graph produced (Fig. 7 ) enables the retrieval of these validated SNPs, and also allows a check of the coverages of their two alleles in each of the 16 input read sets. Additionally, this graph also enables the detection of three SNPs and an indel that were not detected in the mapping pipeline used in  [26].
Metagenomics global similarity overview of five gut metagenomes with COMMET The MetaSoil study focuses on untreated soils of the Park Grass Experiment, Rothamsted Research, Hertfordshire, UK. One of the goals of this study is to assess the influence of depth, seasons, and extraction procedure on the sequencing  [27]. To achieve this, the 13 metagenomes from MetaSoil, two additional soil metagenomes, and a seawater metagenome were compared at the functional level using MG-RAST  [28]. This approach identified 835 functional subsystems present in at least Fig. 7  MAPSEMBLER2 output graph obtained from the Saccharomyces cerevisiae dataset visualized using GSV. A zoom is proposed for visualizing first nodes. The grey node is the starter. Node size depicts the length of the sequence stored by the node.
The node and edge colors depict the read coverage (here for one among all datasets) of the sequence stored by the node. The 'bubbles' seen on the right of the starter witness the presence of SNPs and small indels in the datasets. Note that by changing the choice of the read set selected for visualizing the coverage (node and edge colors), one can deduce the heterozygous or homozygous nature of these variants one of the metagenomes that were used for clustering samples. This study was reproduced with COMMET on all available metagenomes. The generated bit vectors total 68 MB, while the explicit representation of the FASTA results requires 6. 4 GB. The storage footprint is thus divided by a factor of 100. This ratio is even higher if using FASTQ format or if dealing with larger read files. The COMMET computation time was 828 min.
LORDEC: impact of read correction on genome assembly To summarize, experiments on real data taken from bacterial and yeast species, up to the case of a vertebrate genome, show that LORDEC achieves a quality at least as high as that of available state-of-the-art methods, while usually being an order of magnitude faster. Exploiting the GATB implementation of the dBG makes it by far the most scalable and economical option in terms of memory usage: LORDEC can process large datasets on a standard computer. To assess the impact on the assembly quality of the PacBio reads correction performed with LoRDEC, we compared the assemblies obtained from corrected reads to those computed from uncorrected reads. For this purpose, we used public datasets from the Escherichia coli and S. cerevisiae genomes (see Table  4 ).
Moreover, when aligning (using BLASTN, NCBI-BLAST-2. 2. 29+, with a reward of 1 and a penalty of -3) the contigs longer than 1 kbp against the reference genome, only 2. 6 % of yeast contigs lack similarity, while all contigs of E. coli could be aligned. The genome coverage and N50 values show that ABySS did not succeed in assembling any of the uncorrected PacBio datasets, while it yields satisfactory assemblies (i. e. sets of contigs) with the same PacBio reads that were corrected with LoRDEC  [7]. Without correction, an assembly obtained from a traditional program is useless; clearly, LoRDEC correction has a strong impact on the assembly quality of PacBio reads. Moreover, the correction is faster than the assembly and simplifies the latter. Importantly, the results suggest that hybrid correction using LoRDEC makes PacBio reads amenable to a classical de Bruijn graph assembly approach.
LoRDEC has also been applied For the short read data of yeast, we used only half of the available reads. The reference yeast genome is available from  [40]  to MinION reads obtained with Oxford Nanopore technology and we observed a strong improvement in the mapability of the reads once corrected. More precisely, mapping the reads of an E. coli dataset on the reference genome with NucMer/Quast  [31] , we found that, while none of the raw MinION reads could be fully aligned, 2383 out of 2749 corrected reads were fully aligned on the genome, thereby covering 96 % of the genome. In The genome coverage accounts only for contigs longer than 1 kbp. With uncorrected reads, the N50 remains close to the k-mer length (whatever the value of k); this strongly suggests that ABySS fails to assemble uncorrected reads.
Galaxy integration Tools integration was made following Galaxy  [8] [9] [10] [11]  team recommendations on tool configuration syntax  [32]  as well as on tool shed administration and use  [33]. For each Galaxy tool repository, two packages are defined, one for dependencies, the other for descriptor and wrapper if required. We used the GenOuest Galaxy development tool shed and development Galaxy instance to create and test the tools. GSV was originally a standalone web tool for MAPSEMBLER2 output graph visualization. Adding this tool as a visualization tool on Galaxy  [34]  was done following Galaxy  [8] [9] [10] [11]  team instructions. Briefly, an XML configuration file is first created for the visualizer to define a link between a dataset and GSV. This GSV. xml file calls a Python GSV. mako template allowing dynamic generation of HTML and javascript codes.
Tool suite sharing GUGGO Tool Shed  [12, 13]  was used to disseminate Colib'read Galaxy repositories. Corresponding tools are installed on our production Galaxy instance  [35, 36] , allowing scientists to use Colib'read tools freely after registration on the GenOuest core facility  [37]. As we join a dependencies package to our tools, Galaxy instance administrators can easily install either Galaxy tools (i. e. description files and wrappers) or Colib'read binaries and dependencies without any command line typing.
Conclusion We propose bioinformatics tools dedicated to raw NGS data analyses for DNA-seq, RNA-seq and metagenomics studies. Thanks to the Galaxy platform, we easily made this tools suite available to life scientists, regardless their level of programming skills. Colib'read tools thus inherit reproducibility and accessibility support from Galaxy. Moreover, with the growing number of bioinformatics core facilities hosting Galaxy servers, tool shed usage enhances tools descriptors, binaries and dependencies sharing. This tools suite allows life scientists to find candidates that cannot be found with classical assemblybased approaches. Moreover, the algorithm developments described in this paper enhance the optimization and the management of the use of computing resources, in a time where these resources can not match the pace imposed by the NGS data deluge.
Acknowledgements This work is supported by  ANR  Colib'read;  ANR-12-BS02-0008 ). ER, AZEA, BC acknowledge the support of  Défi MASTODONS SePhHaDe  from  CNRS ,  Labex NUMev , and  Projet Investissements d'Avenir France Génomique. LS is supported by the  Academy of Finland  (grant number  267591 ). GS was partially supported by the  ERC  program  FP7/2007-2013 / ERC  grant agreement no. [247073]10. YLB and OC were supported by the  Brittany and Pays de la Loire  regions through the  Biogenouest network. We also acknowledge the use of the  GenOuest bioinformatics platform at the University of Rennes 1.
We used Partial Least Squares Structural Equation Modelling to build, and relate, latent variables of environmental performance, intensification and self-sufficiency. Proxy indicators reflected the latent variables for intensification (milk yield/ cow, use of maize silage etc. ) and self-sufficiency (home-grown feed/total feed use, on-farm energy/total energy use etc. ). Environmental performance was represented by an aggregate 'eco-efficiency' score per farm derived from a Data Envelopment Analysis model fed with LCA and farm output data. The dataset was split into two spatially heterogeneous (biophysical conditions, production patterns) regions. For both regions, eco-efficiency was significantly negatively related with milk yield/cow and the use of maize silage and imported concentrates.
For instance, economic self-sufficiency is an indicator of the degree of economic dependence on external inputs, e. g. pesticides (a similar indicator is used by Lebacq et al. [21] ). On the other hand, land self-sufficiency indicates the extent to which a farm depends on 'imported' land but is unable to do so for other inputs such as pesticides and manure. Energy self-sufficiency accounts for, among others, energy required for manure for crop production, with the latter being either imported or recycled on-farm. At the same time, feed self-sufficiency alone fails to consider that farms also produce crops in addition to animal products. From this example it is evident that the four variables agree with the self-sufficiency definition adopted in this study and are complementary. The dataset used for the PLS-SEM exercise is summarized on Table  1.
See S1 File for the whole dataset. PLS-SEM comprises of two models (see  [41] ), the structural model and the measurement model (Fig  1 ). The structural model assumes a linear relationship between latent variables (also called constructs) and uses linear regression to estimate the path coefficients, representing the strength and direction of the relationships between the response latent variable, or target (endogenous) construct (ECO), and the predictor latent variables, or predictor constructs (INTENS-A, INTENS-F and SELF). In Fig  1  this relationship is represented by single-headed arrows from the predictor latent variables towards the response latent variable. In a similar manner, the measurement model uses linear regression to estimate the loadings, i. e. the correlations between a latent variable and its manifest variables.
In  is an ordinary least squares regression-based estimation and thus is especially useful for developing theories in exploratory research, that is, to search for latent patterns in the data when there is no or only little prior knowledge on how the variables are related (as in this study). It does so by focusing on explaining the variance in the dependent variables (ECO) when examining the model, i. e. it places emphasis on prediction and theory development. This is by contrast with other SEM methods such as covariance-based SEM, which follows a maximum likelihood procedure and is thus better-suited to confirming (or rejecting) theories, i. e. it focuses on causation. See  [30, 42, 43]. All calculations were performed in the R language  [44]. The PLS-SEM exercise and evaluation were performed with the R package 'plspm'  [41]  and, where necessary, the first author's own R functions.
Eco-efficiency, intensification and self-sufficiency: relationships, overlaps and recommendations As noted earlier, a possible confusion could arise from the HTMT results in that they suggested that the higher the (animal and farm-level) intensification, the lower the self-sufficiency. This finding resulted from the available dataset, but it is not always the case. Counterexamples include (i) the 'average' New Zealand dairy farm that is intensive per ha on-farm land, however imports less than 10% of total feed offered  [5] ; and (ii) intensive grazing systems in Belgium with high milk yields per cow and ha  [22]. Both cases suggest a synergy between intensification and self-sufficiency, contrary to what was found in this study. Nevertheless, in the case of this study's 185 French specialized dairy farms, the negative relationship between intensification and self-sufficiency indicated by the HTMT results is fairly intuitive.
ADS); 3. Physiologie Animale et Système d'Elevage division of the Institut National de la Recherche Agronomique (French National Institute for Agricultural Research; http://www. inra. fr/en) (ADS); 4. Project "Efficience Environnementale et productions animales pour le developpement durable", ANR-09-STRA-01-01, Agence Nationale de la Recherche (French National Research Agency; http://www. agence-nationale-recherche. fr/ en/project-based-funding-to-advance-frenchresearch/) (MB); 5. Scottish Government Strategic Research Programme 2011-16 (http://www. gov. scot/Topics/Research/About/EBAR/ StrategicResearch/future-research-strategy) Consultancy for Theme 4 WP4. 1 Adaptation to change in land-based and other rural industries (JL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
( ADS); 3. Physiologie Animale et Système d'Elevage division of the Institut National de la Recherche Agronomique (French National Institute for Agricultural Research; http://www. inra. fr/en) (ADS); 4. Project "Efficience Environnementale et productions animales pour le developpement durable", ANR-09-STRA-01-01, Agence Nationale de la Recherche (French National Research Agency; http://www. agence-nationale-recherche. fr/ en/project-based-funding-to-advance-frenchresearch/) (MB); 5. Scottish Government Strategic Research Programme 2011-16 (http://www. gov. scot/Topics/Research/About/EBAR/ StrategicResearch/future-research-strategy) Consultancy for Theme 4 WP4. 1 Adaptation to change in land-based and other rural industries (JL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
A cell has connections and directions to adjacent cells (neighbouring cells). Simply, directions can be organized as pairs of number, shown in Table  1. Table  1. An organization of directions in a cell system. Direction Value East (E) (1,0) West (W) (-1,0) North (N) (0,-1) South (S) (0,1) In this study, we suggest using two common types of two dimensional neighborhoods Von neumann 1 (4 neighbours: E, W, S, N) and Moore 1 (8 neighbours: E, W, S, N, NW, NE, SW, SE)  [2] , as presented in Figure  1. Table  2  formally shows an example of a cell system, which is generated from geographic data except for its local state being "pollution density". Notably, the number of neighbours of each cell is determined by the chosen CA and they are typically equal for all cells. But, in this case, cells' neighbourhood can differ from cell to cell since their original positions in the physical system.
Wireless sensor network model To represent a sensing component of CPS, a wireless sensor network (WSN)  [12]  is added to the context. It consists of a set of wireless sensor nodes that are scattered on target regions. Its featuring is to collecting environmental data and sending those data to centers (base stations) for processing. In WSN, connections between nodes are ensured via their waves. The maximum distance waves of one node can cover is determined by its communication range. Hence, nodes lie within communication range one node can connect to it. We assume that a collection of sensors is deployed in the forest. Each sensor node is represented by one cell in the context of the cell system. Then, the cellular space of the model is formed by links among those cells. Figure  6    • state: The cell state holds values of sensing data, which is collected from the forest (fire or normal).
Significant instances of these environments are CAM  [28] , CAMEL  [29] , StarLogo  [30] , and NEMO  [31]. These environments allow the exploitation of the inherent parallelism of the cellular automata model for the efficient simulation of complex systems that can be modeled by a very large number of cells with local interaction only. To carry it out, we propose to use the graphic processing unit (GPU)  [7] [8] in hopes of accelerating very timeconsuming simulations. The GPU provides hundreds of threads running in parallel. In this case, a cellular automaton is instanced as a SIMD (Single Instruction, Multiple Data) program. In fact, CA implemented as a number of processes mapped on threads that execute the same code on different data simultaneously. According to this approach, the transition function of a single cell of the system must be specified.
FESI tool We have developed the FESI (FEderation of SImulation) tool by using C/C++ language. It enables to develop parallel simulations and federate them as distributed simulations. Cell systems generated by PickCell open source software  [11]  are used as inputs. The framework CERTI  [17]   [18]  was used in this project as the role of RTI. Meanwhile, the X Window System  [24]  was employed to provide a GUI (Graphical User Interface) environment for displaying simulation results and interacting with users. The computations of the parallel simulations run on the NVIDIA card GeForce GTX 680 1. 15GHz with 1536 CUDA Cores (8 Multiprocessors x 192 CUDA Cores/MP). This board was connected to the Intel CPU 3. 4GHz (8 CPUs x 4 cores/CPU).
Acknowledging that rule-following behavior always takes place inside some specific community whose very identity corresponds to the rules that its members are following, it is not farfetched to claim that rule-following is essentially community-based in the sense developed above. In some way, this is nothing but a generalization of Lewis's account of the role of salience in the working of conventions to all kinds of institutional objects: social and moral norms, legal rules, customs, and so on. I would therefore argue for the following characterization of rule-following in an s. e. m. : Rule-following behavior-The players in a game G are following a rule according to some s. e. m. I if and only if, for the behavioral pattern defined by the strategy profile s = (s 1 ,. , s n ) corresponding to the event [s], there is at least one objectively salient event [e] such that w* ∈ [e] ∩ B N [c] ⊆ B*[s].
Background An emerging field in materials sciences is the introduction of novel data-driven approaches for mining materials knowledge from the large collections of experimental, modeling and simulation datasets being produced today. This field is termed "materials informatics" in  [1]. In this paper, large simulation data are generated and converted into hyper-reduced order models in order to calibrate mechanical parameters of finite element models. These parameters are calibrated when observed data are replicated by simulation outputs. The calibration of complex in-situ experiments, such as mechanical experiment under X-ray tomography  [2] [3] [4] , demand ever longer numerical simulations. They also generate a huge amount of numerical data that are temporary stored in computer memory. Because in-situ experiment are getting more and more complex, there is a need for simplified calibration protocols allowing to preserve all the original mechanical parameters of the specimen finite-element model.
The numerical method has been implemented in the research software named Z-set (http://www. zset-software. com). We have selected a blade involving two grains. The Euler angles of each grains are (-13. 9, 5. 6, 0. 9 • ) and (64. 2, 17. 8, -84. 0 • ). As shown in Fig. 2 , slots have been designed and machined on the turbine blade in order to amplify the stress concentration factor around the grain boundary. The position of the slots, the boundary conditions and the magnitude of the mechanical loading have been chosen by recourse to FE simulations. This preliminary work, before doing experiments, including the machining of the slots, took almost 3 months. This gave us time to conduct the numerical simulation S2 and the data mining. Both exact locations of the crack initiation and the grain boundary around the crack are revealed at the end of the fatigue test.
The constitutive equation of AM1 follows the crystal plasticity theory proposed in  [48]. It accounts for the thermal expansion and thermal sensitivity of plasticity. The expected life duration of the specimen in the framework of oligocyclic fatigue should be around 10,000 cycles. This is a constrain to account for, when choosing the magnitude of the load. Before the experiment, the life duration of the specimen was estimated by neglecting the effect of the grain boundary on the life duration criterion. Data observed during the experiment are the normal displacements at three points A, B, C. The loading magnitude is both imposed during the experiments and the numerical simulations. The exact location of the load could not have been measured accurately. This location has a significant impact on stresses around the grain boundary.
Similarly, initial studies reporting a significant genetic association with various pathologies are often shown to report inflated effects when compared to the results of subsequent studies  [13] [14] [15]. When the effect sizes associated with biomarkers reported in 35 highly cited studies were compared to those reported in the corresponding meta-analyses  [16] , 14 were initial studies reporting a significant association, of which one was disconfirmed by the corresponding metaanalysis and seven reported an effect size at least twice as large as that the one indicated in the corresponding meta-analysis. The methods used in the four studies listed above were different. Two selected initial studies, and then searched for subsequent studies on the same topic  [14, 16]. The other two exploited a database of 36 meta-analyses  [13]  that was extended to 55 meta-analyses  [15].