dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by  Ministry of Health of the Czech Republic , grant no. 16-34272A ; computational resources were provided by the  CESNET LM2015042  and the  CERIT Scientific Cloud LM2015085 , provided under the programme " Projects of Large Research, Development, and Innovations Infrastructures". Analyses in Prague (JT, EF and MS)  were supported by  Ministry of Health, Czech Republic , grant no. 00064203 , and by  PRIMUS/17/MED/11. Analyses in the Monza ( Centro Ricerca Tettamanti , SS, AG and GC) laboratory were supported by the  Italian Association for Cancer Research (AIRC)  and  Comitato Maria Letizia Verga.
Trait-predictive DNA test development: monitoring major genetic factors for breeders Rapid advances in apple breeding were projected in the publications of both the first and second iterations of the apple WGS  1, 8. Indeed, progress is being made in the development and application of genomic technologies in the area of trait-predictive tests, although perhaps not as quickly as hoped for by earlier reviewers  171, 250. The use of SNP arrays  [10] [11] [12]  and GBS to efficiently screen mapping populations and construct relatively dense genetic maps  13, 251  has made the mapping of trait loci much less challenging and correspondingly the number of QTLs in the Genome Database for Rosaceae  252  has risen rapidly since the availability of the apple WGS. The recent development of a tool utilizing apple resequencing data from breeding germplasm, for the purpose of identifying SNPs that are unique to the accession carrying an attribute of interest, has both increased the efficiency of genetic mapping of trait loci and provided a source of SNPs for use in designing transferable highthroughput markers  253  in combination with accurate pedigree records or PBA  74, 75.
Highly reliable diagnostic markers derived from candidate genes for traits can now be readily developed by examining the DNA sequence at MTLs such as major resistance loci and the sequence underlying QTLs  254, 255. Early adopters of trait-predictive tests mainly employed simple sequence repeat and SCAR markers  [256] [257] [258] [259] [260]  and highly informative SSR markers are still used for tracing pedigrees  67, 69. Currently, high-throughput SNP-based markers have become the first choice for use in traitpredictive tests for parental and seedling selection by breeders and many programs have moved to develop and introduce the use of such tests  80, 254, [260] [261] [262] [263]. As markerassisted selection (MAS) was introduced into the apple breeding program at the New Zealand Institute for Plant & Food Research Limited (PFR) before the advent of the apple WGS  257  , PFR researchers across several disciplines were in a sound position to exploit the new knowledge to increase efficiencies and speed up progress towards the PFR goal of multi-resistant new cultivars with pyramided alleles across multiple loci for durable resistance.
The PFR scion breeding program employs diagnostic SNP markers to combine alleles at MTLs for resistance to apple scab. Currently, in order to be cost-effective, a phenotypic screen for Rvi2 is followed by MAS for Rvi6. In some families, MAS is applied for additional quality traits, such as red flesh (Myb110a). Both breeding parents and stage 2 selections are routinely screened with a range of markers including those for resistances to scab (Vh8/Rvi8 and Vr/Rvi19), powdery mildew (Pl2), fire blight (Fb-R5), European canker (Rnd1), and woolly apple aphid (Er1, Er2, Er3). The PFR apple rootstock breeding program has employed MAS since 2012, to reduce the population for detailed phenotyping for time-consuming horticultural traits to less than 10% of the initial numbers  253, 264. Screens applied vary according to year and genetic background of seedlings, but include dwarfing (Dw1, Dw2) resistances to fire blight (FB-R5), European canker (Rnd1), and woolly apple aphid (Er1, Er2, Er3), as well as adventitious rooting  265.
Genome database for Rosaceae: the researcher's toolbox Apple researchers exploiting the apple WGS are well provisioned with data and tools housed in the Genome Database for Rosaceae (GDR). The GDR  305  (https://www. rosaceae. org) is the publicly accessible, central repository and data-mining resource for genomics, genetics, and breeding data of the Rosaceae family, which includes apple as well as other economically important crops. The GDR is built using Tripal  306, 307  , a resource-efficient, open-source platform for online biological database construction. Prior to the availability of the first WGS in 2012, the GDR contained mostly EST data, genetic markers, and genetic maps. The new data types currently included in the GDR include multiple WGSs, reference transcriptomes, QTLs, SNP array data, and phenotypic and genotypic data.
0 WGS, however, a set of four pseudo-haplotype assemblies (primary and alternatives 1, 2, and 3) with chromosomal sequences, derived from the contigs of the original v1. 0 assembly, is available for download. The primary pseudo-haplotype data is also available in the Search Genes page and JBrowse 308 in the GDR. Researchers can readily examine in fine detail how the apple genome compares physically with those of other crops. The most recent apple WGS is being used in synteny analysis with eight other Rosaceae whole genome assemblies using MCScanX  309  , with the results available through the new Synteny Viewer. Another strong focus of the GDR is apple gene function. Since 2017, the community database has provided an apple reference transcriptome (M. domestica GDR RefTrans V1) that combines published RNA-Seq and EST data that are computationally annotated for homology to genes of other plant species and assignment of InterPro domains  310  and GO terms  311, 312.
The data can be accessed in various ways: through the Species page, the Search Genes and Transcripts page, the JBrowse 308 tool, or BLASTX  313  tool. Genetic linkage, marker and trait locus information needs are well served. The GDR contains 108 genetic maps for Malus, which can be viewed and compared through a new graphic interface, MapViewer. Currently, the GDR contains 2. 6 million genetic Malus markers, of which 99. 7% are SNPs while more than 7000 are other marker types. SNP data include those of the 9K 10 , 20K  11  , and 480K 12 apple arrays. Most of the remaining SNPs are those identified in silico from accession resequencing conducted during development of those SNP arrays. The SNP data are available as JBrowse tracks, downloadable files, and from the Marker Search page. The Marker Search page has a new Filter-by-Trait name feature, which enables researchers to find markers associated with QTLs.
In addition, the SSR and SNP genotype search pages contain data from ten genotyping projects. Trait locus data in the GDR includes 1528 QTLs and 36 MTLs for 143 horticultural traits of apple. Although impressive, some caution is necessary in considering these numbers. They are based on the accumulation of QTLs as independently reported in the literature and therefore might include redundancies across studies or treatments (including years) within a single study. In addition, some caution is be needed in using reported QTL information. Their assignment might arise from different levels of statistical significance; regrettably, it is often not specified whether a genome-wide or a chromosome-wide threshold was applied. Some reported QTLs were from an interval mapping procedure without subsequent co-factor analysis.
Acknowledgements The authors are grateful to the various funding agencies and projects that contributed to the presented work including, but not exclusively, the  FruitBreedomics  project no. 265582  "Integrated approach for increasing breeding efficiency in fruit tree crops" co-funded by the  EU Seventh Framework Program , the  USDA's National Institute of Food and Agriculture (NIFA)-Specialty Crop Research Initiative  funded projects " RosBREED: Enabling marker-assisted breeding in Rosaceae " ( 2009-51181-05808 ), " RosBREED: Combining disease resistance with horticultural quality in new rosaceous cultivars " ( 2014-51181-22378 ), and "Genome Database for Rosaceae: Empowering specialty crop research through big-data driven discovery and application in breeding ( 2014-51181-2237 ),  NIFA 's  National Research Support Program 10  "Database resources for crop genomics, genetics and breeding research", the  IDEEV, Laboratoire d'Excellence BASC , and the  Inserm ATIP-Avenir program.
Viral genomes collected at different time points, different hosts (human and their most closely related animal reservoirs) and different locations will be compiled. Extensive molecular evolutionary analyses will be carried out to infer gene expansion/contraction within groups, rates of evolution, and changes in selection pressure, including the detection of positive selected genes and sites (adaptive evolution). Positively selected sites will be mapped onto the viral protein structures to reveal their impact on function, and hence the location of potential virulence determinants. Virulence changes among particular viral strains and types will be defined and measured according to definitions based on an increase in: (1) transmissibility, (2) host tropism, (3) immune evasion, (4) morbidity and mortality, (5) drug resistance, and by the incorporation of epidemiological data to determine whether high or low virulence strains within different hosts and localities are spreading most efficiently in nature.
In large Nidovirales, ExoN is bound to a processive replicative RNAdependent RNA polymerase (RdRp) and corrects mismatched bases during viral RNA synthesis. Over the last decade, a view of the overall process has been refined in Coronaviruses, and in particular in our lab  (Ferron et al. , PNAS, 2018). We have identified genetic markers of large RNA genomes that we wish to use to data-mine currently existing metagenomic datasets. We have also initiated a collaboration to sequence and explore new viromes that will be searched according to these criteria. Likewise, we have a collection of purified viral RdRps that are currently being used to generate RNA synthesis products that will be compared to existing NGS datasets of cognate viruses. We will be able to have an idea about how much genetic diversity is possibly achievable by viral RdRp ('tunable fidelity') versus the detectable diversity (i.
e. after selection in the infected cell) that is actually produced. A62 A major likelihood-based approach gives problematic estimates of diversification dynamics and rates Chenlu Di, 1 Andreas L. S. Meyer, The diversity of life is shaped by rates of speciation and extinction, and so estimating these rates correctly is crucial for understanding diversity patterns among clades, regions, and habitats. In 2011, Morlon and collaborators developed a promising likelihood-based approach to estimate speciation and extinction and to infer the model describing how these rates change over time based on AICc. This approach is now implemented in an R package (RPANDA). Here, we test the accuracy of this approach under simulated conditions, to evaluate its ability to correctly estimate rates of speciation, extinction, and diversification (speciation-extinction) and to choose the correct underlying model of diversification (e.
NUMA AWARE SPARK A Spark application comprises of:  A driver process, that controls the whole processing of the application. The application is modelled as a direct acyclic graph (DAG). The driver understands and interprets this model, and, when operating on a given dataset, splits the processing in stages and individual tasks that it schedules and distributes to executors that run in the cluster nodes.  Executor processes that perform the actual data processing, as instructed by the driver. The executor processes hold data parts in their memory: a task applies to a data part, and each executor receives (and runs) tasks retated to the data parts it holds. Executor processes run on cluster nodes, the so-called worker nodes. There may be one or more executors per worker node. Data processing may involve many executor processus spread on many worker nodes.
A Spark application consumes (large) data from various sources, processes it, then outputs (smaller) results to various sinks. Processing usually transforms the input data so that to build a dataset in the desired form, caches it in memory, then applies algorithms that repeatedly operate on the cached data. Unlike Hadoop -that stores intermediate results on storage (HDFS), Spark retains intermediate results in memory as far as possible, and the more memory it gets, the less pressure on the Input/output system. This makes Spark less sensitive to the input/output system than Hadoop. Spark will try to perform initial processing close to the input data location to consumes it at the highest rate, but further computing will be more dependent on the way it (re)partitions and retains data in the memory cache of the executors.
And as the processing time is usually far higher than the cumulated input and output times, the distribution of data among the executor processes (in their memory) is an important factor to consider. Spark still writes data to file systems during shuffling operation, when data is rebalanced between executors, e. g. on sort operations or data repartitioning. Shuffling thus may put high pressure on both I/O systems and network, the later incurring CPU consumption as data has to be serialized before being transferred through the network. One way to lower it is to use large memory executors: data analytics often reduce datasets size from a very large input to a far smaller output. The more filtering and transformations that lower the size can be done in a continuous space, the less the number and size of shuffling operations will occur.
THE TEST PROTOCOL The test protocol is to run the same benchmark against input datasets of various size onto a single BullSequana S800 server, with the NUMA awareness switched off, then to re-run it with the NUMA awareness switched on, then compare results of both: a) In terms of performance key indicator(s):  duration of the benchmark with NUMA awareness: T on.  duration of the benchmark without NUMA awareness: T off.  The improvement computed as: (1 -T on / T off.
What a HiBench/Kmeans Run Looks like Whatever the input dataset size, a HiBench/Kmeans run exhibits the following behaviour: it runs N jobs in sequence, numbered from 0 to N-1. Job #0 triggers the reading of the input dataset and loads it in the spark cache, i. e. the executor memory. Further jobs from 1 to N-1 mainly operates on the data loaded in the cache. Figure  3  -extracted from the Spark web console, shows a timeline run of Hibench/Kmeans on a gargantua (401,6 GB) input dataset, with NUMA awareness on. The upper pane shows the launch of the executors at the beginning of the run. The lower pane shows the 19 jobs it runs sequentially, numbered from 0 to 18, some of which have been labelled for the sake of readability. Each job includes around 3300 tasks, distributed to the 8 executor processes, each being configured to potentially run 16 tasks in parallel, so that there is a potential of 128 parallel running tasks.
Moreover, the total heap is increased from 1,25 TB (NUMA on) to 2 TB (NUMA off). NUMA awareness thus increases dramatically the efficiency, as the Spark cluster will be able to process more job submissions in a given time interval. NUMA awareness shortens the benchmark duration by a factor of 2. 0 (approximately) or even more. Moreover, it dramatically increases efficiency by dividing by 2. 5 or even more the CPU consumption to complete a run. Figure  5  show efficiency and gain on duration for the various input dataset. These results apply to HiBench/Kmeans only. Other workloads may have a different sensitivity to NUMA awareness.
CONCLUSIONS Spark leverages High-End servers such as BullSequana S, each able to support hundreds to thousands Spark parallel tasks and up to several terabytes of in memory data, thus enabling to process large datasets within a single system or a few systems, thanks to NUMA aware scale up placement of spark executor processes. Beyond the benefits brought by the many-cores and large-memory features, the support of Apache Pass non-volatile RAM (NVRAM) and GPUs opens new perspectives the CloudDBAppliance project is yet exploring:  Though Spark intensively uses memory, it still writes data to file systems during shuffling operations, when data is rebalanced between executors, e. g. on sort operations or data repartitioning. Another case is for some cache management policies. The use of Apache Pass is expected to relax the high pressure it may put on the input/output system, as it exhibits higher throughput and lower latency than SSDs or NVMe.
The VAMDC e-infrastructure The Virtual Atomic and Molecular Data Centre (VAMDC,  (Dubernet et al. (2016) )) is a political and technical framework for operating and sustaining a worldwide digital research infrastructure, built over two European FP-7 projects (  (Dubernet et al. 2010) ;  (Zwölf et al. 2014)). The e-infrastructure federates in an interoperable way about 30 heterogeneous atomic and molecular databases. By providing data producers and compilers a large dissemination platform for their works, VAMDC succeeded in removing the bottleneck between data producers and the wide body of users of that data. The "V" of VAMDC stands for "virtual" in the sense that the e-infrastructure does not contain data: it is a wrapping for exposing in a unified way a set of heterogeneous databases. An ad hoc generic wrapping software, called the node-software  (Regandell et al.
(2018) ) transforms an autonomous database into a VAMDC federated database, called data-node. Each data-node accepts queries submitted in a standard grammar (VAMDC SQL Subset (VAMDC Consortium (  2012 )), a subset of SQL as it names indicates) and, by implementing an interoperable data access protocol  (Dowler et al. (2010) ) developed by the IVOA, 3  provides output formatted into a standard XML file (VAMDC XML Schema for Atomic Molecular and Solid Data, VAMDC-XSAMS). 4 The data-nodes are listed into a specific registry  (Benson et al. (2009) ), a sort of yellow pages service for discovering the VAMDC available resources. The current VAMDC registry implementation is derived from the AstroGrid project  (Walton (2004)). A user wishing to extract data from VAMDC: • may use a VAMDC client software: when the client forms a query, the client asks the registry about the availability and relevance of the data-nodes, and then dispatches the query to the nodes.
Implementing the Query Store for the VAMDC infrastructure The RDA Data Citation recommendation is meant for standalone data-repositories and/or for warehouses. It was both technically and politically challenging to implement the RDA recommendation in the case of the distributed VAMDC infrastructure. The solution had to deal with a lot of constraints: • any evolution of the infrastructure automatically impacts all the connected databases (there are about 30 connected databases nowadays). • as a consequence, the majority of the VAMDC Consortium members must validate any technological evolution of the infrastructure. Any adopted solution must lessen the load on the existing infrastructure members and have minimal implementing costs for each data-node owner. These constraints suggested to embed part of the solution into the node-software (cf.
par. 2. 1). Our implementation of the Query Store consists of two distinct software elements: • an overlay to (and embedded into) the existing VAMDC node software, thus independent from any specific database. • a set of centralized asynchronous web-services, which may be seen as a smart log-service. In what follows we will call this element Query-Store service. 8 Concerning the data versioning and time-stamping, we have two different mechanisms: • a coarse-grained one: a modification of any publicly available data at a given data-node induces an increment in the version of the data-node. We have indeed a mechanism for informing that something has changed on a given data-node: in other words, we know that the result of an identical query may be different from one version to the other. • a fined-grained one: based on the introduction of the Version element into the VAMDC-XSAMS standard, as described in  (Zwölf et al.
The functioning of the Query Store For extracting data from VAMDC, the users may query directly a given known data-node or use one of the centralized query-clients (e. g. the VAMDC portal, https://portal. vamdc. eu). In the latter case, the centralized client software asks the registries what are the data-nodes able to answer and dispatches to them the query. Any centralized client acts as a relay. This is complitely transparent from the data-node perspective and a datanode acts in the same way regardless the source of the query it is serving: when a data-node receives a query: • it generates a unique query-token (this can be seen as a session token associated to the incoming query); • it answers the query by producing the VAMDC-XSAMS output file, which is returned to the user together with the generated query-token.
This information is kept for different reasons: • statistics: it is interesting for database owner to know which queries are submitted and how many times a given query is re-submitted. This information is used for reporting to our founders and stakeholders. • coherence of the human-interface: a user who has just re-submitted a query which was played for the first time long time ago by another user, may believe that there is some bug on the system if only the original timestamp is returned. By returning all the re-execution timestamp we avoid any ambiguity. • troubleshooting and technical support: if something goes wrong on the Query-Store service before it issued the final UUID, we may use the token for identifying the query who generated the problem. Indeed the token is the first element generated into the query-notification pipeline.
During the query-submission phase the user has no direct interaction with the Query-Store service (as we explained before, the data-node that answers the query, notifies directly its action to the Query-Store). When the user receive the data from the data-node he/she has no information about the UUID the Query-Store service assigned to his/her query. The user may recover the final UUID assigned to his/her query by sending the query token to a specific service endpoint of the Query-Store (plus further optional information, e. g. the user e-mail and/or ORCID, information about the used client, etc…). This mechanism is implemented into the VAMDC-client software and its complexity is transparent to the scientific-user. The functioning of the Query-Store is asynchronous. This was a mandatory constraint in order to avoid slowing down the VAMDC-infrastructure with a central bottleneck service.
Interlinking the Query Store with Zenodo As we highlighted in the remark 2, most of the queries are not cited by published works, and after an arbitrary time the underlying data are deleted from the Query-Store service. On the other hand, we have queries generating data used and cited in published works. A lifetime access must be provided to these data. The interconnection with Zenodo provides the Query Store with Scholix functionalities and with lifetime access to the query-generated data. The link between the Query-Store service and Zenodo is implemented using, on the Query Store side, the Zenodo public REST API. 14  As we see in Figure  1 , when a user uses the Query-Store service for displaying the information related to a given query, a button "Get a DOI" is displayed (if the query has not already been assigned a DOI).