dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources on Internet Consumer interaction on Internet is establishing a new digital channel between the brands and their audiences. Exabytes of data are created everyday as information based on data models that keep growing in volume and variety. Document-oriented data models assume documents encapsulate and encode data in some standard formats. Encodings in use include XML, YAML, JSON and BSON, as well as binary forms like PDF and Microsoft Office documents (old format). Whereas file types like XML allow different ways to define a schema, others like JSON have no explicit schema. Such documents have some kind of an implicitly defined schema. The main idea is to infer the schema from a sample file and provide a model to access any file which is conform to this schema. JSON illustrated in figure  1  using Open Weather API, is the most used technique for interchanging data on the web.
Available data processing engines Hadoop MapReduce is considered as an efficient processing technique for Big Data, since it provides better performance when dealing with complex high volumes of data. RDMS (Relational Database Management Systems) show a high performance indicator when processing small relational data, but are very limited in face of expanding data in volume and variety  [23]. MPP (Massively Parallel Processing) has slowly improved the performance indicator for complex volumes of data. Still, it could not be used to process BigData in a daily expansion. RDMS are unable to handle this task for the following reasons: -The primary constraining factor is database schema, because of the continuous changing structure of schema-less BigData. -The complexity and the size of data, overflows the capacity of traditional RDMS to acquire, manage and process data with reasonable costs (computing time and performance).
-Relation-Entity modeling of BigData does not easily adapt with fault-tolerant and distributed systems  [8]. Apache Spark which is based on Hadoop MapReduce technology provides an Ultimate Framework allowing to process different natures of data like text, graphs or real-time streaming. Spark is able to get an immediate increase in performance, using In-Memory processing feature and supports SQL queries with a dedicated command line shell. On the other hand, Spark is currently facing some limitations in terms of memory management (maxResultSize or frameSize), small-size files processing, resource consuming GZip compression and unstable processing of real-time streaming  [17]. For these reasons, Spark is not considered yet as a solution reaching the level of maturity. NoSQL (Non-relational SQL) is increasingly chosen as viable alternative to relational databases, particularly for interactive web applications and services  [9] ,  [21].
Non-relational databases The highly expending information nowadays contains complex and heterogeneous data types (text, images, videos, GPS data, purchase transactions…) that require a powerful data computing engine, able to easily store and process such complex structures. The four Vs definition (volume, velocity, variety, veracity) describing this expansion of data will then lead to extract the unnamed fifth V (value) from BigData. This so-called value is sought in big amounts of enterprise data to process in the daily life  [22]. Relational and non-relational data models are different. The relational model takes data and separates it into many interrelated tables that contain rows and columns. Tables reference each other through foreign keys that are stored in columns as well  [7]. When querying data, the requested information will be collected from many tables, as if the user asks: what is the answer to my question? Non-relational data models often starts from the application-specific queries as opposed to relational modeling  [2].
Modeling tools and operators There are four main families most used in non-relational database modeling: -Key-value store, the simplest non-relational model compared to a distributed hashmap. In this model, only PUT, GET and DELETE operations are allowed. Voldemort créé par LinkedIn is an example of this model. -Column-oriented model, a dataset that can grow to immense size with a column oriented layout, very effective to store sparse data as well as multi-value cell. Examples are Apache Cassandra, Amazon Dynamo  [3] , Google BigTable and Hadoop HBase. -Document-oriented model, designed for storing, retrieving, and managing documentoriented or semi structured data, such as XML or JSON. Examples are Apache CouchDB RavenDB (for. Net platforms) and MongoDB. -Graph data model, a schema-less non-relational database allowing the storage of information about the relationships between entries.
General modeling tools Non-relational database engines have limited transaction support. However, one can transactional behaviour in some cases only, by using one of the following modeling approaches: -The Atomic Aggregates can be applicable if the data store provides certain guaranties of atomicity, locks or test-and-set instructions. -It is possible to map multidimensional data to a simple key-value store or to another multidimensional data by using Dimensionality Reduction operator. -One can use Table Index operator for BigTable-style databases, as a simple or multidimensional index (using composite keys). The only thing is to create and maintain the special index table regularly or in batch-mode. -The Enumerable Keys operator allows avoiding unordered or complex key-value records, when using multiple server clusters.
Graph processing Batch graph processing technique related to graph databases can be done using MapReduce routines  [4] , in order to explore the neighborhood of a given node or relationships between two or a few nodes. This approach makes key-value stores, document databases and BigTable-style databases suitable for processing large graphs  [13]. Adjacency list representation can be used in graph processing. Graphs are serialized into key-value pairs using the identifier of the vertex as the key and the record comprising the vertex's structure as the value. In MapReduce process represented in figure  3 , the shuffle and sort phase can be exploited to propagate information between vertices using a form of distributed message passing. In the reduce phase, all messages that have the same key arrive together and another computation is performed.
BigData workbench There is a high impact on the use of the modeling tools on non-relational data processing. In order to implement a new abstraction based on model driven architecture, we thought about creating new automatic programming software allowing the users / developers, based on drag-and-drop features, to do the following: -Add one or more components from available data sources (data files, social networks, web services). -Apply one or more of non-relational data modeling tools by connecting the components together. -Apply predefined analysis on sample data in order to dynamically define the structure of the files / messages. -Select a Hadoop processing engine available on a local or distant network. In the example of figure  5 , we capture the data from several available data sources (Facebook, Twitter, Open Weather API, Here Traffic API) all related to keyword Storm.
Conclusion In this paper, we discussed the available modeling tools and techniques for BigData. We mentioned the advantages and disadvantages, so that we provide a state of the art able to fill the gap in the domain of BigData processing approaches. We discussed some concepts which are not used on a wide scale, such as Multi-model Storage. We also discussed common concepts such as Graph Processing. In the last section, we shared an interesting experience combining several modeling tools, using BigData Workbench solution on which our study in next level will be oriented. Our goal is to provide the users in general and the scientific community in specific, a new technique of data processing for BigData, with better performance in terms of data calculation and business costs  [10]. The IT technology is moving forward very fast, especially on this field.
No study has yet demonstrated that all instances of any sense codon can be removed from essential genes. These insights are crucial for unambiguously reassigning sense codon translation function. We chose to study the rare Arg codons AGA and AGG [termed "AGR" according to International Union of Pure and Applied Chemistry (IUPAC) conventions] because the literature suggests that they are among the most difficult codons to replace and that their similarity to ribosome-binding sequences (RBSs) underlies important noncoding functions  (8, (27) (28) (29) (30). Furthermore, their sparse use (123 instances in the essential genes of E. coli MG1655 and 4,228 instances in the entire genome) (Table  1  and Dataset S1) made replacing all AGR instances in essential genes a tractable goal, with essential genes serving as a stringent test set for identifying any fitness impact from codon replacement  (31).
Results To construct this modified genome, we used coselection multiplex automatable genome engineering (CoS-MAGE)  (32, 33)  to create an E. coli strain (C123) with all 123 AGR codons removed from its essential genes (see Fig. 1A  and Dataset S1 for a complete list of AGR codons in essential genes). CoS-MAGE leverages Lambda Red-mediated recombination  (34, 35)  and exploits the linkage between a mutation in a selectable allele (e. g. , tolC) to nearby edits of interest (e. g. , AGR conversions), thereby enriching for cells with those edits (Fig. S1 ). To streamline C123 construction, we chose to start with E. coli strain EcM2. 1, which was previously optimized for efficient Lambda Red-mediated genome engineering  (33, 36). Using CoS-MAGE on EcM2. 1 improves allele replacement frequency by 10-fold over MAGE in nonoptimized strains but performs optimally when all edits are on the same replichore and within 500 kb of the selectable allele  (33)   1 ), revealing considerable flexibility of codon use for most essential genes.
The frequency of allele replacement (in this case, AGR→CGU codon substitution) varied widely across these 110 permissive codons, with no clear correlation between the frequency of allele replacement and the normalized position of the AGR codon in a gene (Fig. 2A ). The remaining 13 AGR→CGU mutations were not observed, suggesting codon substitution frequency below our detection limit of 1% of the bacterial population (Materials and Methods and Dataset S2). These "recalcitrant codons" were assumed to be deleterious or nonrecombinogenic and were triaged into a troubleshooting pipeline for further analysis (Fig. 1 ). Interestingly, all except 1 of the 13 recalcitrant codons were colocalized near the termini of their respective genes, suggesting the importance of codon choice at these positions: seven were at most 30 nt downstream of the start codon, and five were at most 30 nt upstream of the stop codon (Fig.
2A , Lower and Dataset S3). Because of our unbiased design strategy, we anticipated that several AGR→CGU mutations would present obvious design flaws, such as introducing nonsynonymous mutations (two instances) or RBS disruptions (four instances) in overlapping genes. For example, ftsI_AGA1759 overlaps the second and third codons of murE, an essential gene, introducing a missense mutation (murE D3V) that may impair fitness. Replacing ftsI_AGA with CGA successfully replaced the forbidden AGA codon while conserving the primary amino acid sequence of MurE with a minimal impact on fitness (Fig. 3A  and Dataset S2). Similarly, holB_AGA4 overlaps the upstream essential gene tmk, and replacing AGA with CGU converts the tmk stop codon to Cys, adding 14 amino acids to the C terminus of tmk. Although some C-terminal extensions are well tolerated in E.
S5D ) while changing both RBS motif strength and amino acid identity. Our analysis of all four optimized gene sequences showed reduced deviation in computational mRNA folding energy [computed with UNAFold (40)] compared with the unsuccessful CGU mutations (Fig. 4,  x axis, orange asterisks, and green dots). Similarly, the predicted mRNA structure [computed with different mRNA folding software, NUPACK  (41) ] for these genes was strongly changed by CGU mutations and was corrected in our empirically optimized solutions (Fig. S4 ). Troubleshooting these 13 recalcitrant codons revealed that mutations causing large deviations from natural mRNA folding energy or RBS strength are associated with failed codon substitutions. By calculating these two metrics for all attempted AGR→CGU mutations, we empirically defined a safe replacement zone (SRZ) within which most CGU mutations were tolerated (Fig.
Developing in silico heuristics to predict problematic alleles streamlines the use of in vivo genome engineering methods such as MAGE to identify viable replacement codons empirically. Therefore, these heuristics reduce the search space required to redesign viable genomes, raising the prospect of creating radically altered genomes exhibiting expanded biological functions. Once we had identified viable replacement sequences for all 13 recalcitrant codons, we combined the successful 110 CGU conversions with the 13 optimized codon substitutions to produce strain C123, in which all 123 AGR codons have been removed from all of its annotated essential genes. C123 then was sequenced to confirm AGR removal and analyzed using Millstone, a publicly available genome resequencing analysis pipeline (https://github.
com/churchlab/millstone). Two spontaneous AAG (Lys) to AGG (Arg) mutations were observed in the essential genes pssA and cca. Although attempts to revert these mutations to AAG were unsuccessful-perhaps suggesting functional compensation-we were able to replace them with CCG (Pro) in pssA and CAG (Gln) in cca using degenerate MAGE oligos. The resulting strain, C123a, is the first strain completely devoid of AGR codons in its annotated essential genes (https://github. com/churchlab/agr_recoding) (Dataset S4). Although some AGR codons in nonessential genes could prove unexpectedly difficult to change, our success in replacing all 123 instances of AGR codons in essential genes provides strong evidence that the remaining 4,105 AGR codons can be completely removed from the E. coli genome, permitting the unambiguous reassignment of AGR translation function  (23).
1 parental strain (Fig. S6 ). Of particular interest was the mutation argU_G15A, located in the D arm of tRNA Arg (argU), which arose during CoS-MAGE with AGR set 4. We hypothesized that argU_G15A compensates for increased CGU demand and decreased AGR demand, but we observed no direct fitness cost associated with reverting this mutation in C123, and argU_G15A does not impact aminoacylation efficiency in vitro or aminoacyl-tRNA pools in vivo (Fig. S7  and Dataset S5). Consistent with the findings of Mukai et al. (25)  and Baba et al. (31) , argW (tRNA Arg CCU ; decodes AGG only) was dispensable in C123a because it can be complemented by argU (tRNA Arg UCU ; decodes both AGG and AGA). However, argU is the only E. coli tRNA that can decode AGA and remains essential in C123a, probably because it is required to translate the AGR codons for the rest of the proteome  (23).
To evaluate the genetic stability of C123a after removal of all AGR codons from all the known essential genes, we passaged C123a for 78 d (640 generations) to test whether AGR codons would recur and/or whether spontaneous mutations would improve fitness. After 78 d, no additional AGR codons were detected in a sequenced population (sequencing data are available at https:// github. com/churchlab/agr_recoding), and doubling time of isolated clones ranged from 22% faster to 22% slower than C123a (n = 60). To gain more insight into how local RBS strength and mRNA folding impact codon choice, we performed an evolution experiment to examine the competitive fitness of all 64 possible codon substitutions at each of the AGR codons (Dataset S6). Although MAGE is a powerful method for exploring viable genomic modifications in vivo, we were interested in mapping the fitness cost associated with less-optimal codon choices, requiring codon randomization depleted of the parental genotype, which we hypothesized to be at or near the global fitness maximum.
To do so, we developed a method called "CRAM" (Crispr-assisted MAGE). First, we designed oligos that changed not only the target AGR codon to NNN but also made several synonymous changes at least 50 nt downstream that would disrupt a 20-bp CRISPR target locus. MAGE was used to replace each AGR with NNN in parallel, and CRISPR/cas9 was used to deplete the population of cells with the parental genotype. This approach allowed exhaustive exploration of the codon space, including the original codon, but without the preponderance of the parental genotype. Following CRAM, the population was passaged 1:100 every 24 h for 6 d and was sampled before each passage using Illumina sequencing (Fig. 5  and Dataset S6). 53 )] versus deviations in mRNA folding [x axis, calculated at 37 °C by the UNAFold calculator  (40) ].
g. , synonymous codons with metrics outside of the SRZ) as compared with the naive replacement strategy (Materials and Methods, Fig. 7 , Fig. S10 , and Dataset S7). Furthermore, predicting recalcitrant codons provides hypotheses that can be tested rapidly in vivo using MAGE. Successful replacement sequences then can be implemented together in a redesigned genome. Encouragingly, because all newly predicted design flaws occur in nonessential genes, they would be less likely to impact fitness unless (i) despite the "non-essential" annotation, the gene is actually essential or quasi-essential (i. e. , inactivation would impair growth) or (ii) the codon in a nonessential gene impacts the expression of a neighboring essential gene (e. g. , impacts an RBS motif or RNA structure). Although incorrect genome annotations can only be addressed empirically (as demonstrated with gene dedD), further analysis reveals that AGR codons in nonessential genes should rarely impact annotated essential genes.
ColE1 agar plates were generated as described previously  (33). Doubling times were determined on a BioTek Eon Microplate reader with orbital shaking at 365 cycles/min at 34 °C overnight and were analyzed using a Matlab script available on GitHub (https:// github. com/churchlab/agr_recoding). Oligonucleotides, PCR, and Isothermal Assembly. A complete table of MAGE oligonucleotides and PCR primers can be found in Dataset S1. PCR products used in recombination or for Sanger sequencing were amplified with Kapa 2G Fast polymerase according to the manufacturer's standard protocols. Multiplex allele-specific PCR (mascPCR) was used for multiplexed genotyping of AGR-replacement events using the KAPA2G Fast Multiplex PCR Kit, according to previous methods  (22, 48). Sanger-sequencing reactions were carried out through a third party (GENEWIZ).
The standard CoS-MAGE protocol for each oligo set was to insert tolC, inactivate tolC, reactivate tolC, and delete tolC. mascPCR screening was performed at the tolC insertion, inactivation, and deletion steps. All Lambda Red recombinations were followed by a recovery in 3 mL LB L followed by an SDS selection (tolC insertion, tolC activation) or ColE1 counterselection (tolC inactivation, tolC deletion) that was carried out as previously described  (33). General AGR Replacement Strategy. AGR codons in essential genes were found by cross-referencing essential gene annotation according to two complementary resources  (31, 52)  to find the shared set (107 coding regions), which contained 123 unique AGR codons (82 AGA, 41 AGG). We used optMAGE  (35, 51)  to design 90-mer oligos (targeting the lagging strand of the replication fork) that convert each AGR to CGU (Datasets S1 and S8).
We reduced the total number of AGR replacement oligos to 119 by designing oligos to encode multiple edits where possible, maintaining at least 20 bp of homology on the 5′ and 3′ ends of the oligo. The oligos then were pooled based on chromosomal position into 12 MAGE oligo sets of varying complexity (minimum: 7, maximum: 14) such that a single marker (tolC) could be inserted at most 564,622 bp upstream relative to replication direction for all targets within a given set. We then identified tolC insertion sites for each of the 12 pools either as intergenic regions or nonessential genes that met the distance criteria for a given pool. See Table  1  for descriptors for each of the 12 oligo pools. Troubleshooting Strategy. A recalcitrant AGR was defined as one that was not converted to CGU in one of at least 96 clones picked after the third step of the conversion process.
Sequences that failed to yield a wild-type amplicon were Sanger-sequenced to confirm conversion. We also measured doubling time of all clones in LB L to pair sequencing data with fitness data and chose the recombined clone with the shortest doubling time. Doubling time was determined by obtaining a growth curve on a BioTek plate reader (either an Eon or H1) and was analyzed using web-based open-source genome resequencing software available on GitHub at https:// github. com/churchlab/millstone. This genotype then was implemented in the complete strain at the end of strain construction using MAGE and was confirmed by mascPCR screening. Predicting optimal replacements for AGR codons reduces the number of codons that are predicted to require troubleshooting. (A) Empirical data from the construction of C123.
Synonymous codon replacement was possible for genes rffT and mraW but not for gene yidD. We then relaxed synonymous stringency by recombining with oligos encoding AGR-to-NNN mutations for gene yidD and found multiple alternative solutions, including CGA, UGA, GUG, GCG, and TAA. Importantly, the synonymous CGA alternative solutions were less disruptive than CGU to RBS strength and mRNA folding (Dataset S7), further confirming our rules as useful guidelines. mRNA Folding and RBS Strength Computations. A custom Python pipeline (available at https://github. com/churchlab/agr_recoding) was used to compute mRNA folding and RBS strength value for each sequence. mRNA folding was based on the UNAFold calculator  (40)  and RBS strength on the Salis calculator  (53). The parameters for mRNA folding are the temperature (37 °C) and the window used, which was an average between -30 to +100 nt and -15 to +100 nt around the start site of the gene and was based on ref.
12. The only parameter for RBS strength is the distance between the RBS and the promoter, and we averaged between 9 and 10 nt after the codon of interest based on Li et al. (20). Data visualization was performed through a custom Matlab code. For in silico predictions on the entire genome, all 3,222 AGR in nonphage genes were analyzed using this custom pipeline; data are presented in Dataset S7. Phage genes were not analyzed to reduce the complexity of the genome, inspired by other reduced genome efforts  (54). Whole-Genome Sequencing of Strains Lacking AGR Codons in Their Essential Genes. Sheared genomic DNA was obtained by shearing 130 μL of purified genomic DNA in a Covaris E210 ultrasonicator. Whole-genome library preparation was carried out as previously described  (55). Briefly, 130 μL of purified genomic DNA was sheared overnight in a Covaris E210 with the following protocol: duty cycle 10%, intensity 5, 200 cycles per burst, time 780 s per sample.