dataesr/scientific-paragraphs-categorization huggingface dataset:
The second was a 'counterfactual' ensemble driven with pre-industrial forcings and sea-surface temperatures adjusted downward by a climate change pattern obtained from the CMIP5 ensemble ('historicalNat' or NAT), and thus representative of the evolution of a climate system in a world without anthropogenic climate drivers. As all ensemble members were driven by the same SST the data are not independent. This is accounted for in the same way as the spatial dependencies were in the observational analysis. In section 2, we identified the synoptic situation during the 28 July 2014 event that contributed to the event's exceptionality. While not necessary conditions for a 2014-type event according to our definition, the features characterised by a north-south pressure dipole and anomalous temperature over Scandinavia highlight the importance of atmospheric circulation in transporting the moisture quantities that contribute to extreme precipitation events.
, , 2012) )  was used to downscale each of the 16 EC-Earth 2. 3 ensemble members over Western Europe for the period 1950-2100 at a resolution of 0. 11°, which is still not convection-permitting. Secondly, output was taken from the EURO-CORDEX project, a coordinated set of high-resolution (approximately 12 km × 12 km) climate projections for Europe and the North Atlantic. Data was taken from fourteen experiments using historical forcings up to 2005 and the RCP8. 5 scenario for the period 2006-2015. With the exception of the REMO2009 experiment driven by MPI-ESM-LR, which features two initialisations, all experiments feature only one ensemble member thereby reducing the number of annual maxima for GEV fitting. To counter this, a sub-ensemble was generated from a subset of seven bias-adjusted EURO-CORDEX  (Jacob et al.
31) (note that to ensure the fit to observed data is made with a number of data points similar in magnitude to that used in the fitting Table  1  Details for the GCM and RCM ensembles considered in the attribution analysis. The 7 models included in the CORDEX sub-ensemble are also detailed. for the model ensembles, the GEV is fitted with data from all observation points rather than spatial maxima). For EC-Earth 2. 3, the ratio σ/μ was far smaller (0. 18). It was concluded that, as the dispersion of the GEV fitted to EC-Earth 2. 3 output was not in agreement with that fitted to observations, any model bias in precipitation maxima could not be corrected using a simple multiplicative correction. The contribution of EC-Earth 2. 3 to further analysis was therefore rejected. The area-averaged model output has far larger decorrelation scales than the observations, showing only one or two degrees of freedom in the Netherlands rather than the 30 of the station data.
2a ). The time-course of OsHV-1 ORF expression is shown in Supplementary Fig. 2  for both oyster families. In S F11 oysters, the maximum viral load and transcriptional activity were reached at 48 h and remained stable until the first deaths (at 66 h). The viral load remained low in R F21 throughout the experiment without any remarkable mortality until the end of the experiment (330 h). Alignment of the Illumina reads to the OsHV-1 genome available in the NCBI database  30  revealed that the virus used in our experiments corresponded to an OsHV-1 µVar variant, as indicated by (i) the deletion of 3 ORFs (ORF36, ORF37 and part of ORF38), (ii) the deletion of an adenosine upstream of ORF43 and (iii) a 12-nt deletion in the microsatellite locus H10, which are shared characteristics of µVar variants  14, 20, 31.
These variants have been associated with mass mortalities of juvenile oysters since 2008  6, 14, 20, 32. Taken together, these results show that OsHV-1 µVar infection occurs in both families, but only R F21 oysters successfully control viral replication. Dysbiosis and bacteraemia occur in susceptible oysters. To investigate the dynamics of oyster microbiota in the two families showing contrasting resistance to the disease, we analysed total bacterial communities using 16S rDNA metabarcoding over the first three days of the experiment. Overall, 6,061,881 clusters were obtained from 42 libraries (2 families, 7 sampling times, in triplicate). After cleaning steps and singleton filtering, 4,238,989 sequences affiliated with 10,080 OTUs were kept for further analyses (Supplementary Data 1). A sufficient sequencing depth was confirmed by species richness rarefaction curves (Supplementary Fig.
6 ). These data indicate that S F11 oysters mount a delayed inefficient antiviral response and have an impacted apoptosis regulation during the late phase response. OsHV-1 infection alters the antibacterial defence of oysters. Histological sections of S F11 oysters showed accumulation of OsHV-1 in the haemocytes (Fig. 7 ), which are oyster immune cells that play a major role in controlling bacterial infections  33. Interestingly, histological sections of R F21 oysters were devoid of OsHV-1 positive cells (Supplementary Fig. 11 ). As a proxy to monitor the antibacterial defence of oysters, we analysed the expression of genes encoding antimicrobial peptides (AMPs) in both oyster families. AMPs showed highly contrasted expression patterns in R F21 and S F11 oysters during the experiment (Fig.
Oyster transcriptome analyses. RNA was extracted from the powdered oysters using the Direct-Zol RNA Miniprep kit (Proteigene) according to the manufacturer's protocol. RNA concentration and purity were checked using a Nanodrop ND-1000 spectrometer (Thermo Scientific), and their integrity was analysed by capillary electrophoresis on a BioAnalyzer 2100 (Agilent). RNA-seq library construction and sequencing were performed by the Fasteris Company (Switzerland). Directional cDNA libraries were constructed using a TruSeq mRNA Stranded kit (Illumina) and sequenced on a Hiseq in paired-end reads of 2 × 75 bp. All data treatments were carried out under a local galaxy instance  49. Phred scores were checked using the Fastq-X toolkit  50  and were higher than 26 over 90% of the read length for all the sequences.
All the reads were thus kept for subsequent analyses. Mapping to the C. gigas reference genome (assembly version V9  51  ) was performed using RNAstar (Galaxy Version 2. 4. 0d-2  52 ). The HTSeq-count was used to count the number of reads overlapping annotated genes (mode Union) (Galaxy Version v0. 6. 1)  53. Finally, the differential gene expression levels were analysed with the DESeq2 R package  54. Fold changes between each time point of the kinetics and the T0 control condition were considered significant when the adjusted p-value (Padj) for multiple testing with the Benjamini-Hochberg procedure, which controls the false discovery rate (FDR), was < 0. 05. Because not all known C. gigas AMPs were present in the C. gigas reference genome (assembly version V9  51  ), read counts for all of them were specifically obtained by alignment against a protein database using DIAMOND 0.
7. 9  55. The AMP database was prepared by retrieving C. gigas sequences from GenBank that were manually inspected to discard irrelevant or incomplete sequences. The reads for each sample/replicate were compared with the database using DIAMOND blastx. Alignments were filtered for the best hit and e-value < 1e-6. Read counts for each AMP were normalized against both the transcript size and the total sequence number for each sample/replicate and used for the differential gene expression analysis with DESeq2. Gene ontology annotation and enrichment analysis. To work with current functional annotations of the C. gigas gene set, we performed a de novo functional annotation. Blastx comparison against the NR database was performed for the 28,027 genes annotated in the genome, with a maximum number of target hits of 20 and a minimum e-value of 0.
001. XML blast result files were loaded onto Blast2GO  56  for GO mapping and annotation with the b2g_sep13 version of the B2G database. These results were used as inputs for GO enrichment analysis, which was performed using adaptive clustering and a rank-based statistical test (Mann-Whitney U-test combined with adaptive clustering). The R and Perl scripts used  57  can be downloaded [https://github. com/z0on/GO_MWU]. The following parameters were used for adaptive clustering: largest = 0. 5; smallest = 10; clusterCutHeight = 0. 25. For the continuous value characterization of each gene in the data set, we used a strategy aiming to take into account both the level of expression and the significance of the differential expression. To combine these two factors, the log2 fold change was attributed to genes that were significantly differentially expressed (adjusted p < 0.
2  30 ) using bowtie2 with single-end global alignment and default parameters  60. The abundance of viral transcripts was calculated using HTSeq-count  53  and the viral genome GFF3 file. The counts for each ORF were normalized by dividing by the size of the ORF and multiplying by a library normalization factor (calculated as the average library size for all times and controls divided by the library size for the specific time point). C ¼ c orf s orf L L i , where c orf represents the raw counts for the ORF, s orf represents the size of the ORF, L represents the average reads for all the time points and L i corresponds to the number of total reads for the specific time point. Quantification of bacteria and viruses. Quantification of OsHV-1, total 16S rDNA Vibrio and Vibrio crassostreae was performed using quantitative PCR (qPCR).
A Labcyte Acoustic Automated Liquid Handling Platform (ECHO) was used for pipetting into the 384-well plate (Roche). A LightCycler® 480 Instrument (Roche) was used for qPCR with the following program: enzyme activation at 95 °C for 10 min, followed by 40 cycles of denaturation (95 °C, 10 s), hybridization (60 °C, 20 s) and elongation (72 °C, 25 s). A subsequent melting temperature curve of the amplicon was performed to verify the specificity of the amplification. Absolute quantification of viral and bacterial DNA copies were estimated by comparing the observed Cq values to a standard curve of the DP amplification product cloned into the pCR4-TOPO vector for OsHV-1 and from total DNA extraction of V. crassostreae J2-9 for total vibrio 16S rDNA. For total bacteria and V. crassostreae 16S rDNA, we used the relative quantification calculated by the 2 -ΔΔCq method  63  with the mean of the measured threshold cycle values of two reference genes (Cg-BPI, GenBank: AY165040 and Cg-actin, GenBank: AF026063).
Giemsa staining and in situ hybridization. Crassostrea gigas tissues were fixed for 24 h in Davidson fixative  46. Tissues were embedded in paraffin wax, serially sectioned to a thickness of 5 μm and collected on polylysine-coated slides (performed by Histalim Company, France). To visualize bacteria that had infiltrated the tissues, tissue sections were stained using Giemsa, which coloured the different tissues and cells in shades of pink to purple and most bacteria in deep blue (performed by Histalim Company, France). The presence of OsHV-1 in tissue sections was detected by in situ hybridization following a previously published protocol  64. The slides were hybridized with 5 ng µl -1 of OsHV-1-specific digoxigenin-labelled (DIG) antisense probes designed based on the C2-C6 fragment of the ORF4 of the OsHV-1 reference genome (GenBank: NC_005881.
DNA concentration and purity were checked with a Nanodrop ND-1000 spectrometer (Thermo Scientific). For each sample, 16S rDNA amplicon libraries were generated using the 341F-CCTACGGGNGGCWGCAG and 805R-GACTACHVGGGTATCTAATCC primers targeting the variable V3V4 loops for bacterial communities  61. Paired-end sequencing with a 250-bp read length was performed at McGill University (Génome Québec Innovation Centre, Montréal, Canada) on a MiSeq system (Illumina) using v2 chemistry according to the manufacturer's protocol. The FROGS pipeline (Find Rapidly OTU with Galaxy Solution) implemented on a galaxy instance [http://sigenae-workbench. toulouse. inra. fr/galaxy/] was used for data processing  65. In brief, paired reads were merged using FLASH  66. After denoising and primer/ adapter removal with cutadapt  67  , clustering was performed using SWARM, which uses a novel clustering algorithm with a threshold (distance = 3) corresponding to the maximum number of differences between two OTUs  68.
Chimeras were removed using VSEARCH  69. We filtered out the data set for singletons and performed an affiliation using Blast + against the Silva 16S rDNA database (release 128, Sept 2016) to produce an OTU and affiliation table in the standard BIOM format. Rarefaction curves of species richness were produced using the R package and the rarefy_even_depth and ggrare functions  70. We used phyloseq to obtain relative abundances at different taxonomic ranks (from genus to phylum) (tax_glom function). In our analyses, we only kept taxa that had a true annotation for each corresponding taxonomic rank (from genus to phylum). Statistical analyses. Statistical analyses were performed using R v3. 3. 1 (R: a language and environment for statistical computing, 2008; R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria [http://www.
Acknowledgements We warmly thank the staff of the  Ifremer stations of Argenton (LPI, PFOM) and Sète (LER) , and the  Comité Régional de Conchyliculture de Méditerranée (CRCM)  for technical support in the collection of the oyster genitors and reproduction. We also thank  Fabrice Pernet ,  Marie-Agnès Travers ,  David Mouillot ,  Marion Richard  and  Franck Lagarde  for fruitful discussions. The authors are grateful to  Philippe Clair  from the qPHD platform/Montpellier genomix for useful advice and  Yannick Labreuche  for the Vibrio crassostreae-specific primers. This work, through the use of the GENSEQ platform [http://www. labex-cemeb. org/fr/genomique-environnementale-2]from the labEx CeMEB, benefited from the support of the  National Research Agency  under the ' Investissements d'avenir' program  (reference  ANR-10-LABX-04-01 ).
The authors also thank the Montpellier RIO imaging platform (https:// www. mri. cnrs. fr). The present study was supported by the  ANR  project  DECIPHER  ( ANR-14-CE19-0023 ), by the  EU  funded project  VIVALDI  ( H2020 program , n°678589) and by  Ifremer ,  CNRS ,  Université de Montpellier and Université de Perpignan via Domitia.
Data availability RNA-seq data and amplicon sequences for microbiota analysis have been made available through the SRA database (BioProject accession number PRJNA423079 with SRA accession SRP130264). RNA-seq data are available under SRA accessions SRR6679052-SRR6679093. Amplicon sequences for microbiota analysis during 'natural' experimental infections are available under SRA accessions SRR7786101-SRR7786142. Amplicon sequences for microbiota analysis during rationalized experimental infections are available under SRA accessions SRR6675783-SRR6675803. Other data analyzed during this study are included in this published article and its supplementary information files. Complementary information is available from the corresponding authors on reasonable request.
I. INTRODUCTION Nowadays, Big Data applications are employed in a wide range of industrial and research fields to extract meaningful information from large datasets. The adoption of user-friendly technologies like the MapReduce programming model  [1]  has allowed non-expert programmers to develop large-scale distributed applications without needing to implement lowlevel functionalities such as data movement and parallelism. This enables them to focus on the actual data processing needed to calculate the desired result. However, MapReduce applications do not always leverage the computational capabilities of the underlying system. This is often caused by performance drawbacks in the design of popular MapReduce frameworks like Hadoop  [2] , which incurs limited efficiency on resource usage and data pipelining.
Moreover, non-expert programmers can introduce inefficiencies in their applications due to the unawareness of certain framework functionalities (e. g. custom data formats). Although more advanced alternatives like Spark  [3]  and Hama  [4]  would allow improving the performance of existing Hadoop workloads, they require to rewrite the source code completely, and so are not always a feasible option. To solve this problem, new inmemory frameworks have been developed to transparently improve the performance of existing Hadoop applications, such as Flame-MR  [5] ,  [6]. This framework allows accelerating such applications without changing their source code. In our previous work  [6] , the acceleration of Flame-MR was experimentally demonstrated by conducting performance evaluations with synthetic benchmarks.
However, the performance of these benchmarks may not always correspond with the one that can be obtained in practical scenarios. This paper aims to overcome this limitation by presenting an in-depth analysis of the performance benefits provided by Flame-MR using three real-world Big Data applications. The main contributions are: • The identification of significant differences between realworld applications and standard benchmarks, which underscores the importance of using real cases when evaluating Big Data frameworks. • A detailed description of the techniques used by Flame-MR to adapt to the characteristics of real-world applications, like custom input and output formats and data objects. These techniques provide portability while maintaining performance optimizations. • An improved version of Flame-MR that includes a new load balancing mode to speed up the processing of skewed datasets.
• The optimization of three real Hadoop applications with Flame-MR to justify its efficiency by alleviating code inefficiencies and load balancing problems. Performance improvements higher than 40% are obtained in all cases. The rest of the paper is organized as follows: Sections II and III introduce the background and related work, respectively. Sections IV, V and VI analyze the optimization of three use cases based on the MapReduce model. First, Section IV describes the optimization of VELaSSCo, a visualization architecture for simulation data. Second, Section V analyzes CloudRS, a bioinformatics application for error removal in genomic datasets. Third, Section VI presents MarDRe, another genomic application that removes duplicate and near-duplicate reads. Finally, Section VII provides some general conclusions about the results gathered in the previous use cases.
II. BACKGROUND The MapReduce programming model was originally proposed by Google in  [1]. This model allows developing largescale Big Data workloads by keeping some implementation details such as parallelization and data communication hidden to the programmer. The only thing that has to be defined are the data processing functions, map and reduce, that operate the input data represented in form of key-value pairs. The map function processes each input pair independently to extract the Nowadays, the de-facto standard implementation of MapReduce is Hadoop  [2] , an open-source Java-based framework. It mainly consists of two parts, the MapReduce data engine and the Hadoop Distributed File System (HDFS)  [7] , which distributes the storage of large datasets over the nodes of a cluster. Hadoop workloads commonly use the MapReduce model to process textual data stored in HDFS, following several steps: input, map, shuffle, merge, reduce and output.
These steps are depicted in Figure  1. As can be seen, the input dataset stored in HDFS is divided into many splits that are read by map operations to extract the relevant key-value pairs. These key-value pairs are partitioned, sorted by key and sent to the nodes where they will be merged to form the reduce input. Each reduce operation reads the pairs contained in its input partition, processing them to generate the output result that is written to HDFS. Hadoop can adapt its behavior to the particular needs of each application, providing a wide set of configuration options to do so. This includes the setting of some software components defined via Java interfaces, modifying their implementation according to the specific computation that the user needs to perform. For example, the user can configure a different input and output formatter class if the data is not in textual format.
Furthermore, the source code is not always publicly available, which precludes the users from rewriting it. Our previous work focused on the development of Flame-MR  [5] , an easy-to-use MapReduce framework that gives solution to this problem by accelerating Hadoop applications without changing the source code defined by the user. Flame-MR replaces transparently the underlying implementation of the Hadoop MapReduce data engine by an in-memory architecture that leverages system resources efficiently. The operation of Flame-MR is based on the deployment of several Worker processes over the nodes of a cluster. Each Worker is in charge of executing multiple map and reduce operations by using an event-driven architecture shown in Figure  2. The thread pool executes the operations concurrently, scheduling them to pipeline data processing and data movement steps.
The data pool allocates memory buffers in an efficient way, reducing the amount of buffer creations  [6]. Once the buffers are filled with data, they are stored into in-memory data structures to be processed by subsequent operations. Performance is further improved by minimizing the connections needed to read and write textual data to HDFS, working with full input splits in memory. Moreover, primitive data types of Hadoop are modified to avoid the use of redundant memory copies, using instead references to the serialized data to optimize pair copies and comparisons. The Hadoop data engine is also accelerated by using efficient sort and merge algorithms. Flame-MR has been assessed by means of synthetic benchmarks, showing significant performance improvement over Hadoop and other Hadoop-based alternatives  [5]  and also providing competitive results compared to Spark  [6].
Many papers in the literature have compared the performance of the MapReduce model when using different data processing engines (e. g. , NativeTask  [8] ), file systems (e. g. MARIANE  [9] ), and network interconnects (e. g. RDMA-Hadoop  [10] ). These works generally evaluate their proposals by executing popular Big Data benchmarks like TeraSort or K-Means. However, the lack of performance results with realworld applications makes it difficult to determine the actual performance benefit that a user can obtain when replacing Hadoop with any of the optimized alternatives. Regarding large-scale applications employed in real use cases, their optimization is often performed by translating their source code to a more efficient computing paradigm. For example, Kira  [11]  is a distributed astronomy image processing toolkit on top of Spark.
On the one hand, NativeTask is based on a native C++ implementation that replaces the task management of map and reduce functions, while also optimizing the cache awareness of the merge-sort mechanism  [19]. As these optimizations are highly dependent on the underlying system, they do not keep portability. On the other hand, M3R is an in-memory framework based on the X10 programming language  [20]. Although it accelerates Hadoop workloads by reducing the shuffling overhead and caching intermediate data, it is restricted to Hadoop jobs that can fit in memory. These characteristics prevent the utilization of M3R for real-world Big Data use cases. As commented in the previous section, the performance benefits of Flame-MR when executing synthetic benchmarks have already been assessed. However, the artificial nature of this kind of workloads makes it difficult to extrapolate these results to practical use cases.