dataesr/scientific-paragraphs-categorization huggingface dataset:
Background We present in this Section the principles of gossip-based protocols and the algorithm for single-dimension slicing that we extend for supporting multi-dimensional slicing. This algorithm, which we name sort slicing is based on a self-organizing gossip-based distributed sort and was initially presented in  [13]. Gossip-based distributed systems allow implementing autonomous and selforganizing protocols. An early gossip-based protocol was proposed for the reconciliation of database replicas in the 1980s  [6]. Gossip came back into fashion in the 2000s as the complexity of using centralized or rigid management became clear with the increase of systems scales. A typical use of gossip is reliable applicationlevel broadcast  [7]. Another class deal with overlay management  [15, 16, 20, 21, 24].
Domination Relations Our approach for multi-dimensional slicing is based on the intuitive notion of quality of compromises that we presented in our problem definition. It does not require knowing the range of definition of any of the dimensions. It is based on a domination relation. We say that a node n a dominates another node n b when n a is strictly a better compromise than n b according to the value of their attributes over all dimensions. The definition of is as follows, where n i a denotes the value of the attribute of n a for dimension i and where d is the number of dimensions:   and in particular for the definition of SkyLine queries  [12]. A SkyLine query allows collecting the best-compromise points from a structured multi-dimensional database: the SkyLine points are the points that are not dominated by any other point, under the same definition as above.
The SkyLine of Figure  4  would be {n 7 , n 11 , n 2 }. The SkyLine operator has been extended towards the SkyBand operator  [22]. A k-SkyBand is a set of points that are not dominated by more than k points. The 2-SkyBand of Figure  4  would be {n 7 , n 11 , n 2 , n 6 , n 9 , n 10 , n 3 }. n a n b if ∀ i | 1 ≤ i ≤ d, n i a ≤ n i b. This relation is transitive: n a n b ∧ n b n c ⇒ n a n c and its inverse is : n a n b ⇔ n b n a. The slicing problem is however different than the k-SkyBand in several ways. First, there is no omniscient view of the data set as in a database. Nodes only know a small amount of other nodes in their view and have no global knowledge of the boundaries of the space, the number of nodes, or the distribution of values for each dimension. Second, the slices are not defined in terms of the number of dominating nodes but in terms of a size relative to the totality of the system.
Conclusion Distributed slicing is a fundamental primitive for resource allocation in large-scale distributed systems. Existing distributed slicing protocols did not offer support to perform this allocation considering nodes with multidimensional attributes. In practical settings, it is often desired to choose a best compromise between various characteristics (i. e. CPU, volatile memory, uptime, etc. ). We contribute a gossip-based distributed slicing protocol that fills this need. The proposed protocol exploits domination relations between nodes to define the membership of a node to a given slice. Our evaluation confirms the contributed solution as a sound approach to the problem. We support experimental reproducibility: source code and datasets are released under open-source and available at: https://github.
In particular, we have n 10 {n 1 , n 4 , n 5 , n 8 , n 12 } and n 10 {n 7 }. The relations define a partial order O between nodes. The partial order for nodes n 1 ,. , n 12 is shown on the right side of Figure4: we note that some pairs of nodes have no defined ordering according to and. For instance, n 11 and n 7 have no established or relation. We note that our notion of domination between nodes is similar to the notion of domination between data points that is used in the context of databases, Algorithm 2: Multi-dimensional domination-based slicing algorithm on node p (getSlice() is unchanged from Algorithm 1).
In particular, we have n 10 {n 1 , n 4 , n 5 , n 8 , n 12 } and n 10 {n 7 }. The relations define a partial order O between nodes. The partial order for nodes n 1 ,. , n 12 is shown on the right side of Figure4: we note that some pairs of nodes have no defined ordering according to and. For instance, n 11 and n 7 have no established or relation. We note that our notion of domination between nodes is similar to the notion of domination between data points that is used in the context of databases, Algorithm 2: Multi-dimensional domination-based slicing algorithm on node p (getSlice() is unchanged from Algorithm 1). 4 (left) gives an exam-ple of domination relations be-tween a node n 10 and a set of other nodes, over a 2-dimensional space. Relations that can be deter-mined based on transitivity are not // in addition to the ones in Algorithm 1 // local multi-dimensional attribute // view, close dominated nodes // view, close dominating nodes // active thread select partner q randomly from -view ∪ -view ∪ PSS.
The BFN consists of a sequence of forward and backward model integrations, both of them using a feedback term to the observations, as in the direct nudging. The BFN integrates the direct model backwards in time, avoiding the construction of the adjoint and/or tangent linear models needed by 4Dvar. Therefore, it uses only the fully non-linear model to propagate information forward and backward in time. The nudging gain, which has an opposite sign with respect to the forward case, has a double role: push the model toward observations and stabilise the backward integration, which is especially important when the model is not reversible. Back-and-forth algorithms have already been used in the past for initialisation and four-dimensional data assimilation  (Morel et al. , 1971; Talagrand, 1981) , but without nudging.
This article is organised as follows. In Sect. 2 the BFN and the 4Dvar are described. Section 3 describes the model physics and the model set-up. Section 4 discusses some practical aspects of the backwards integration. Section 5 presents the BFN and the 4Dvar set-up and the designed data assimilation experiments. Finally, the data assimilation results are presented in Sect. 6, in which we discuss the impact of the length of the data assimilation window on the method performances as well as the sensitivity of each method to the observation network and the initial condition.
Observation network In this article, every 4 days an observation network simulating a Jason-1 satellite density sample is available. The data are perturbed with white Gaussian noise with standard deviation equal to 3 cm. With this observation network a new set of 5000 observations is available every 4 days. The data assimilation problem we proposed to solve is to recover the full model state at the beginning of the assimilation window. The model state space is composed of five variables: sea surface height (η), meridional and zonal velocities (u and v), temperature and salinity (temp and salt). Since we have a horizontal mesh of size 81 × 121 and 11 vertical layers the total size of the state space is 441 045. Therefore, the problem is undetermined, since the observations represent only a 1.
Conclusions and perspectives This study used the NEMO general circulation model in a double gyre configuration to investigate the diffusive backand-forth nudging performance under different configurations of the data assimilation window, observation network and initial conditions, and to compare it with 4Dvar. It has been shown that the reliability of the backward integration should be carefully examined when the BFN/DBFN is applied to non-reversible systems. This should support the choice of the assimilation window and identify whether the available observations are sufficient to control the errors induced by the non-reversible terms of the model equations. In this article we have shown that the DBFN might be used for the assimilation of realistically distributed ocean observations, despite the limited accuracy of the backward integration.
Acknowledgements. This work was supported by CNRS/INSU through the LEFE/MANU programme. This work was granted access to the HPC and visualisation resources of the Centre de Calcul Interactif hosted by Université Nice Sophia Antipolis. Calculations were also performed at the IDRIS computational facility centre. The authors thanks Pierre-Antoine Bouttier for his help with the set-up of the 4Dvar algorithm. We also thank the editor and the reviewers for their interesting comments that have enabled the improvement of the manuscript. Edited by: O. Talagrand Reviewed by: two anonymous referees.
The recent progresses of high-throughput sequencing (HTS) technologies enable easy and cost-reduced access to whole genome sequencing (WGS) or re-sequencing. HTS associated with adapted, automatic and fast bioinformatics solutions for sequencing applications promises an accurate and timely identification and characterization of pathogenic agents. Many studies have demonstrated that data obtained from HTS analysis have allowed genome-based diagnosis, which has been consistent with phenotypic observations. These proofs of concept are probably the first steps toward the future of clinical microbiology. From concept to routine use, many parameters need to be considered to promote HTS as a powerful tool to help physicians and clinicians in microbiological investigations. This review highlights the milestones to be completed toward this purpose.
HTS can be applied in the two major areas of clinical microbiology: diagnostic microbiology (management of infected patients) and the epidemiology of infectious diseases. Obtaining and combining results from time-consuming and heterogeneous methods into a single one is the promise of HTS, which can be foreseen as the future standard tool for a genome-based diagnosis. In this review, we will briefly come back on the advent of pathogenomics and the impact of HTS in this domain. Then, the current use of HTS data in genome-based diagnosis will be detailed. Existing current limitations, difficulties and challenges to overcome will be announced and discussed before considering a future HTS integration in clinical microbiology.
The Advent of High-Throughput Genomics: A Huge Boost for Pathogens Research In 1995, obtaining the first whole sequenced 1. 8 Mb genome of Haemophilus influenza  [1]  required 13 months of work and cost more than 1. 5 million U. S. dollars. In February 2014, the Genomes On Line Database (GOLD  [2] ) included 12, 280 complete bacterial genome projects, and the de novo sequencing of a 1. 8 Mb genome has a cost of around only ten U. S. dollars. In 1995, the era of high-throughput genomics began with microarray technologies, which allowed the studying of thousands of genes in parallel. Bioinformatics and statistical approaches have accompanied this technological evolution. In order to take into account the caveats in reliability and reproducibility of the data generated by microarray experiments, the Food and Drug Administration (FDA) decided, in 2005, to launch the MicroArray Quality Control (MAQC) project  [3].
High-Throughput Sequencing: Toward Routine Use The most powerful existing HTS platforms deliver more raw data than required for most clinical investigations (a bacterial genome has a mean size of around only 5 Mb). These platforms could be considered for the moment too slow and too expensive for implementation and use in routine clinical practice. Conversely, the launch of second generation benchtop sequencers, cheaper platforms with performances in terms of throughput more adequate for investigating microbial genomes, could be a better way to enter clinical labs  [10]. The difficulty resides in how to handle the knowledge acquired from complete genome collections  [11, 12]. The early age of HTS soon represented the promise of the exploration of microbial diversity  [13] , then the dream of accessing the whole microbial genome rapidly became reality and will probably change the practices of diagnosis  [14].
Bioinformatics to Make Sense of Sequences HTS technologies are currently able to sequence millions or even billions of bases per run. Once sequences are obtained, the next steps of storing, analyzing and interpreting this huge amount of data requires bioinformatics skills and tools. The cost of sequencing dropped faster than what would have been expected by Moore's law. Unfortunately, this rapid decrease has not been matched by a comparable decrease in the cost of the computational infrastructure required to mine the data  [48]. Every new project in the field will require proportionally less money for the sequencing part, but will have to allocate more resources to the bioinformatics management and analysis of the data. That is the reason why bioinformatics, if not already, will become the bottleneck for a complete and rapid exploitation and interpretation of HTS data.
In this case, other bioinformatics tools are required. De novo assembly algorithms align and merge reads to obtain longer fragments in order to reconstruct the original sequence without a reference sequence. Assembly is useful for studying an emerging pathogen or identifying gene transfers by assembling reads that were not mapped onto a reference genome. Most assemblers are specific to one or a subset of sequencing technologies (see Table  2 ). In single-cell sequencing, the MDA amplification leads to non-uniform read coverage, as well as elevated levels of sequencing errors and chimeric reads. Some recent assemblers have been developed to deal with these specifications (see Table  2 ). In the field of pathogen discovery, another important task is to annotate the sequences obtained from mapping or assembly, which is often done by comparison, consisting of searching sequence similarity within current databases.
For example, PATRIC (PathoSystems Resource Integration Center) is the Bacterial Bioinformatics Resource Center, an information system designed to support the biomedical research community's work on bacterial infectious diseases  [74]. ViPR, the Virus Pathogen Resource, is a publicly available resource that supports the research of viral pathogens  [75]. A famous tool for similarity finding is BLAST  [76] , which is widely used in annotation tools and pipelines. Some annotation pipelines for prokaryotic sequences annotation are also available (see Table  2 ). In the case of public health applications, identification of specific sequences, such as virulence and resistance genes, is essential to adapt the medical treatment. Drug resistance genes can be detected using BLAST against specific databases, such as ARDB for antibiotic resistance gene screening  [77]  or using dedicated tool, such as resFinder  [78].
For the typing of bacteria species, the MLST scheme  [79]  can be used to identify sequence type directly from reads or from the obtained assembled sequence. Several free on-line tools exist for the identification of prophage sequences within bacterial genome (e. g. , PHAST  [80] ). For plasmid identification, one possible way is to BLAST the sequence against a plasmid database, such as the PATRIC plasmid database. Another possible analysis is to identify SNPs and DIPs (deletion insertion polymorphisms) suitable for downstream phylogenetic analyses (see  [81]  for a survey of tools for variant analysis). One part of culture-independent sequencing analysis is based on metagenomic approaches and requires specific bioinformatics tools. The analysis of metagenomics data represents a big challenge, as it relies on identifying each individual organism in a mixed sample.
Two main metagenomic approaches are used in microbial community analysis: 16S rRNA and whole genome shotgun metagenomics. In 16S rRNA metagenomic approaches, the main step is to assemble overlapping reads and to reduce the dataset complexity by determining operational taxonomic units (OTU) clustering (for example, using UCLUST /USERACH  [82]  or CD-HIT  [83] ). In whole genome shotgun approaches, reads are assembled using, for example, MIRA, MetaVelvet or IDBA-UD (see Table  2 ). In both approaches, the final step is to perform taxonomic classification and compute diversity metrics, which can be done using ARB  [84]  and the SILVA database  [85]  or the Greengenes database  [86]. Some programs integrate all the analysis steps (e. g. , QIIME  [87]  and mothur  [88]  for 16S metagenomics and MEGAN  [89]  for whole genome shotgun metagenomics).
The analysis of HTS data requires high-performance computational resources. Even if CPU speeds and memory capacities have increased, the huge amount of data to be handled in HTS analyses often requires adequate computational solutions. Several solutions are widely used for HTS programs, such as computer clustering and cloud computing. A computer cluster consists of a set of connected computers with a centralized management approach. With cloud computing, researchers have the option of simply paying for their computing requirements, rather than building and maintaining their own physical computing infrastructure. Most academic bioinformatics tools for HTS are technology-dependent, open-source and designed to be run in a UNIX environment with command lines (see Table  2 ). Computational skills are often necessary, and only a few of them offer a graphical user interface to make them easy to use  [90].
To fill this gap, some frameworks have been developed with the famous example of Galaxy  [91] , an open webbased platform for accessible and reproducible results for genomic research. Some tools are available as web-based resources, which make them easy to use (see Table  2 ). Another way to make the tools accessible to a broader community is the use of virtual machines (VMs). A VM is a software implementation of a machine (i. e. , a computer) that executes programs like a physical machine. A snapshot of a given configuration can be taken and distributed to the community. For example, Cloud BioLinux is a publicly accessible VM containing more than 135 bioinformatics packages, along with documentation, a desktop interface and graphical software applications  [92]. Bioinformatics is playing and will undoubtedly play a central role in the development of HTS in medicine.
One of the many examples showing how bioinformatics impacts the medical management of infectious diseases is the German outbreak caused by the entero-hemorrhagic O104:H4 E. coli strain in 2011  [93, 94]. The genome sequence was rapidly available through HTS, and at the same time, the microbial characterization, including the clinically antibiotic susceptibility profile, was provided. The authors show that the antibiotic profile can be computationally identified. The German outbreak also used crowd-sourcing as a power tool to fight against pathogens. The genome sequence was released in open-access, and the scientific community was asked for help to annotate the genome. The crowdsourcing analysis allowed the obtaining of the first annotated version of the genome in a few days. The routine use of HTS data in clinical microbiology will depend on the availability of bioinformatics tools, which have to be integrated, i.
Current Limitations and Challenges The utility of HTS for genome-based diagnosis in clinical microbiology no longer needs to be proven; the challenge is more, nowadays, in the transfer from pilot studies to routine use in a clinical context. Although HTS is the more powerful method, PCR-based methods currently dominate the market in routine clinical laboratories, because they are more cost-effective, and their workflow and produced data are easier to manage. For the HTS technologies, steps upstream of the sequencing run are not trivial; to prepare a library is an expert user task, and that is why a majority of sequencing platform providers tries to simplify and automate this time-consuming step. Moreover, the newly-arrived HTS methods are facing regulatory hurdles, which may limit their routine use in genome-based diagnosis.
According to a survey among physicians, which has tried to enlighten existing barriers for the integration of personalized medicine into clinical practice  [96] , the access to specific training and clear guidelines were two major items that could influence this adoption of a new clinical practice. A consequence of a young technology, such as HTS, is the lack of academic training, which can result in less adhesion to the new technology. High throughput sequencers produce huge amounts of data. Long-term archiving of this data is not a trivial task, and it is evident that the HTS community is facing a storage problem  [97]. A reflection on how to store proprietary data concerning patients or research centers has to be made. A significant part of HTS analysis is based on the comparison with diverse databases.
An improvement of databases used to analyze HTS data in order to obtain well-annotated reference databases is urgently needed  [50, 98]. Even if some centers organized and distributed some data, a lot of databases exist, and there is a wealth of data, which is left mostly inaccessible and unexplored. Better centralization and data collection should be developed. Another limitation with HTS data is the lack of format standardization. Even if some formats are widely used in the HTS field, such as FASTQ , SAM or BAM formats, standardized formats and procedures are still lacking. The establishment of standards would help data sharing and connecting tools. In clinical applications, it is also important to integrate and standardize meta-data and include them in the analysis  [98]. The integration of results with other types of data, such as the sample collecting place or phenotypic data, is necessary for implementing personalized medicine  [99].
In general, regulatory agencies will not approve an IVD that uses a patchwork of ill-defined open source databases and software tools, so this is a requirement to gain regulatory approval for a diagnostic test to be marketed to clinical labs as true IVD. In the future, IVD companies will probably develop proprietary software and databases to address some of these challenges. More likely, in the transition phase, IVD companies will probably form their own "reference labs" and offer these tests and the associated data analysis on a fee for service basis. The current challenges and limitations are now known and well described. Some improvements, especially in organization, standardization and data analysis, have to be made in the future to allow the democratization of HTS in clinical applications.
A Possible Future To generalize the HTS use for genome-based diagnosis in hospitals, the global data flow has to be improved. Figure  2  shows a possible efficient organization, which could be based on two steps: a first emergency step, in the hospital, and a second step in academic research centers. The first step should consist of an emergency stage in a clinical center, leading to a genome-based diagnosis. Sequencing can be achieved by culture-dependent and/or -independent approaches. At this time, it can be difficult to know if HTS will avoid the need for pathogen isolation and culture. Maybe, parallelizing the culture-dependent and -independent methods could be effective for genome-diagnosis, but difficult to manage. Routine use of HTS in order to make a genome-based diagnosis involves standardizing the biological step and the data analysis.