dataesr/scientific-paragraphs-categorization huggingface dataset:
This scaling relationship is often referred to as the 'growth law'. The combination of these 21 scores was used to classify a dataset composed of 240 wild-type replicates (controls) and the 797 mutant strains with a |s| ≥ 3 for at least one morphological or growth feature. To transform our 21-dimension clustering problem into a simpler two-dimensional (2D) similarity map (see Materials and methods), we used the machine learning "t-distributed stochastic neighbor embedding" (tSNE) algorithm  [58]. The principle of tSNE is to minimize distances between phenoprints with high mutual information. Taking advantage of the stochastic nature of tSNE, we generated 100 maps to identify stable clusters, or island using the density-based clustering algorithm dbscan  [19]. This combined tSNE-dbscan approach identified multiple isolated islands formed by the same strains in each map (Fig  4A ).
Long mutants, on the other hand, tended to lose their ability to maintain a narrow size distribution, as CV L increased with <L> (Fig  7A ). The origin for an increase in CV L may signify a loss of precision in the timing of division, but it may alternatively originate from an aberrant positioning of the division site (or both). The ∆minC mutant is an example of aberrantly large CV L (Fig 7C ) due to the mispositioning of the division site and not to a defective adder  [12]. This class of mutants can easily be identified in our dataset by their large variability in division ratios (CV DR ). Conversely, a high CV L associated with a normal variability in division ratios points to a mutant that has a more variable ∆L between divisions. We suspected that interesting cell size control mutants might be missed by only considering CV L.
Discussion In this study, we used a multi-parametric approach to quantitatively survey the role of all non-essential E. coli genes on cell shape, cell size, cell growth and the late cell cycle stages, nucleoid separation and cell constriction. The results provide a valuable resource of phenotypic references for both characterized and uncharacterized genes, as well as a rich dataset to explore the correlation structure between morphological, growth and cell cycle features at the system level. The large proportion of genes and the wide variety of functions impacting cell size and shape and the progression of late cell cycle stages (Fig  2  and 3 , Appendix Fig S4 ) underscore the degree of integration of cell morphogenesis and cell cycle progression in all aspects of E. coli cell physiology.
Data analysis Dataset curation -Support Vector Machine model. Due to the size of the dataset (> 1, 500, 000 cells detected globally), we adopted an automated approach to identify poorly (or wrongly) detected cells across the entire dataset. We developed an SVM model based on 16 normalized features: cell length, cell width, cell area, cell volume, cell perimeter, cell constriction degree, division ratio, integrated phase signal, integrated DAPI fluorescence signal, mean cell contour intensity in phase contrast, variability of cell width along the cell, nucleoid area, single cell nucleoid variability, circularity (2 × π × cellarea/(cellperimeter) 2 ), nucleoid intensity and number of nucleoids. We trained a binary classifier (positive or negative) over wild-type strain replicates as well as 419 mutants with the most severe morphological defects prior to data curation.
Data exploration, dimensionality reduction and clustering. A similarity measure between strains was needed to identify and separate different phenoprints. This measure was then used as an input for a dimensionality reduction algorithm to group strains together. Pearson correlations or Euclidean distances classically provide such similarity measures, and Principle Component Analysis (PCA) and/or hierarchical or k-means clustering are often used. However, PCA tends to explode datasets and Pearson correlations do not always reflect the desired type of similarity. As an extreme case, consider two strains with two phenoprints that are proportional, one with values within a very small score range, such as [-1 1], while the other with score values spanning the [-10 10] range. These two strains will get a maximal similarity measure through a correlation analysis, despite the fact that the first strain is wild-type-like while the other is an outlier.
Instead we chose to use a recently described algorithm, called t-distributed Stochastic Neighbor Embedding, or t-SNE  [58] , to project our multidimensional datasets in 2 dimensions and generate, at the same time, similarity measures between strains. t-SNE estimates low-dimensional space distances between points based on their similarity, as opposed to dissimilarity as in the case of PCA, thereby highlighting local similarities rather than global disparities. We used the stochastic nature of the t-SNE algorithm to evaluate the robustness of the resulting projection by repeating the procedure multiple times (n = 100 for each tSNE map). We coupled this dimensional reduction procedure with a density-based clustering algorithm, dbscan  [19]. The two input parameters of the dbscan algorithm, and minPoints, were optimized so as to generate a maximum number of islands without separating the bulk of WT strains in two or more islands.
The significant local enrichments were considered at a threshold of 0. 05 after a false discovery rate correction that used the Benjamini-Hochberg-Yekutieli algorithm, taking into account dependencies between tests  [9]. Cluster of orthologous gene enrichment analysis. We associated E. coli BW25113 genes with COGs using the web server  [59]. The enrichment analyses were performed using a custom-built algorithm in MATLAB based on a two-tailed hypergeometric test to compute p-values, which were subsequently adjusted with the Benjamini-Hochberg False Discovery Rate procedure  [8]. Because the COG categories are largely independent, we did not consider any correction for the dependence between tests. Gene ontology analysis. We used ontologies from the Gene Ontology website (http://www. geneontology.
org/ontology/gene_ontology. obo, version 2016-05-27)  [2] , and annotations were obtained from EcoCyc for E. coli strain MG1655  [35]. Analysis was performed using a MATLAB custom-built algorithm that includes a hypergeometric test to compute p-values that were subsequently adjusted with the Benjamini-Hochberg-Yekutieli False Discovery Rate procedure  [9]. Bayesian network. The Bayesian network presented in Fig  8  was generated in R with the bnlearn package  [49] , using the ARACNE algorithm as described in  [39]. The network was bootstrapped 200 times, and all the edges were identified in more than 70% of the networks. We assessed the strength and the origin of collinearity among features using Belsley diagnostic method  [7] , with the in-built collintest. m function in MATLAB. We excluded features associated with a 'condition number' above the classical threshold of 30.
Data representation. All graphs were generated using MATLAB, except for the networks in Fig  4C  and  Fig 8A panels, which were created using Cytoscape v3. 2  [51]  and the Rgraphviz package in R  [34] , respectively. For Fig 4C, we used the edge-weighted, spring embedded algorithm in-built in Cytoscape. We considered the pairwise Euclidean distances between the 8 strains of island 17 as the weights of the edges connecting the nodes (or strains). The density scales in scatter plots represent the number of points around each point in a radius equal to the 0. 03 percentile of the pairwise distances distribution.
Figure1. Experimental approach and reproducibility. (A) Experimental workflow. Single gene knock-out strains from the Keio collection were grown in M9 supplemented medium at 30 • C in 96-well plates. DNA was stained with DAPI prior to imaging and 9 images were taken in both phase contrast and DAPI channels. The images were then processed with Oufti to identify the cell and nucleoid contours. In parallel, we recorded the growth curve of each imaged strain in order to extract growth parameters. (B) A SVM model was trained via visual scoring of 43,774 cells. (C) Confusion matrix of the SVM model based on a large validation dataset (102,137 cells), illustrating the distribution of the SVM classifier output in comparison to the visual classification. (D) Comparison of the average width of 192 strains obtained from two independent 96-well cultures of the 190 most remarkable Keio strains and 2 WT replicates.
Figure2: Distribution of morphological, cell cycle and growth phenotypes in the E. coli Keio strain collection. Bubble graphs representing, for each feature, the number of strains with a score value, s, beyond 3, 4, 5 or 6 times the interquartile range away from the median of the WT distribution (240 replicates). The size of the circles (bubbles) reflects the number of strains with a score beyond a specific, color-coded threshold for s, as indicated. The 'reference' bubble graph illustrates the expectations from a dataset of the same size (4,227 strains), assuming a standardized normal distribution of scores (with a mean of 0 and a standard deviation of 1).
Acknowledgements We are grateful to the  Yale E. coli Genetic Stock Center  for providing a large number of strains. We also thank  Pr. William Margolin  for the kind gift of the E. coli MG1655 strain and the ftsA* derivative. This work was partly supported by the  National Institutes of Health  ( R01 GM065835  to C. J. -W. ). We also thank the  Jacobs-Wagner laboratory  for fruitful discussions and for critical reading of the manuscript. C. J. -W. is an investigator of the  Howard Hughes Medical Institute.
Genetic relationships of isolated influenza C genome segments. Partial genome segment sequences were obtained from deep sequencing for segments 1, 5, 6 and 7, encoding PB2, NP, M1/CM2 and NS1/NS2 respectively. Those greater than 200 bases are deposited in GenBank, accession numbers KY075640 -KY075642. Insufficient reads were available to assemble the other segments. Although breadth of coverage across segments is low (ranging from 22% in segment 5 to 32% in segment 6), there is sufficient genetic information to assign each fragment to a clade as defined previously  28  , using Bayesian phylogenetics. Plotting of the root-to-tip genetic distance on a neighbour-joining tree using TempEst showed that molecular clocks apply best to segments 2 and 7 (PB2 and NS1/NS2), but that both segments 5 and 6 (NP and M1/CM2) have lower root-to-tip distances for Clade memberships were determined by examination of Bayesian phylogenetic trees produced in BEAST, as previously  28  and then annotated onto the neighbour-joining trees used for the molecular clock analysis.
The M1/CM2 (Fig. 3 ) and NP segments (Supplementary Figure  3 ) for C/Lancaster/1/2015 have lower root-to-tip distances than expected under the assumption of molecular clock-like evolution. When this method is used on database-derived sequences, it is often taken as indicative of incorrect dating. However, given that we know precisely when our samples were collected, it is more likely to reflect a genuinely slower rate of evolution in these samples. The M1/CM2 segment of C/Lancaster/1/2015 is positioned in the phylogenetic tree near segments from the 1980s (Supplementary Figure  1 ) and the NP segment near segments from the 1990s and 2000 (Fig. 4 ). This same phenomenon of slowed molecular clock, and aberrant positioning with the phylogenetic tree, has been seen in some strains of Zaire ebolavirus  34  and also in the 1977 "Russian Flu" H1N1 outbreak  35  , and is thought be a consequence of the virus entering a host population where the serial interval -the time between infection of one host and the next in a transmission chain -is reduced and the virus therefore spends longer in a non-replicative state.
Enzyme-linked immunosorbent assay (ELISA) was performed on the serum samples using influenza C antigen as previously described, and using the same antigen preparation  38  , with goat anti-human HRP-conjugated secondary antibody (ab6858, Abcam ® ) and SureBlue ™ TMB Microwell Peroxidase Substrate solution. Absorbance was measured at 450 nM using a Wallac Victor2 ™ (Perkin Elmer) plate reader. Anti-influenza C IgG was quantified by calibration of the peroxidase reaction against a standard dilution series of IgG concentrations. The threshold for seropositivity was placed at 2 standard deviations above the mean level of the negative control serum. Genome segment sequence assembly. Illumina reads were trimmed of adapters and other non-genomic elements using CutAdapt 1. 1  39  (https://pypi. python.
org/pypi/cutadapt), fastq-mcf 0. 11. 3  40  (https://expressionanalysis. github. io/ea-utils), and trim_galore (http://www. bioinformatics. babraham. ac. uk/projects/trim_galore/), within the Read_cleaner pipeline (Gatherer, unpublished). Ethical approval required that no genetic material remain within the samples which could enable identification of patients. Therefore, human genome and transcriptome sequences were removed by iterative alignment onto the NCBI, Ensembl and UCSC human iGenomes (http://support. illumina. com/sequencing/sequencing_software/igenome. html), first using bowtie 1. 1. 1  41 (http:// bowtie-bio. sourceforge. net/index. shtml), then BWA 0. 7. 12-r1039  42  (http://bio-bwa. sourceforge. net) within the Valet pipeline (Gatherer, unpublished). Following each alignment, extraction of unaligned reads was achieved using samtools 0.
1. 19  43 (http://samtools. sourceforge. net) and the next alignment commenced. Bowtie, BWA and samtools were co-ordinated using the Vanator pipeline  44  (https://sourceforge. net/projects/vanator-cvr). The resulting trimmed and cleaned reads are available from the Sequence Read Archive (https://www. ncbi. nlm. nih. gov/sra/: BioSamples SAMN05954290 and SAMN05954291, Runs SRR4733498 and SRR4733494). Influenza C genome C/Victoria/2/2012 (Genbank ref. KM504282) was selected as a representative of recently circulating influenza C and alignment of cleaned reads carried out using bowtie within the Valet pipeline. Consensus sequences were constructed using samtools 0. 1. 19 (bcftools and vcfutils functions). C/Victoria/2/2012 was used to fill gaps in the consensi and the bowtie alignment repeated.
This cycle was performed until a stable consensus was obtained for each genome segment. The same process was repeated using BWA and combined consensi obtained. Alignment of reads to the final consensi was examined with Tablet 45 (https://ics. hutton. ac. uk/tablet). Resulting assemblies of more than 200 bases were submitted to GenBank (references KY075640 -KY075642). The new strain of influenza C identified was designated C/Lancaster/1/2015. Phylogenetics and genome constellations. Sequence alignments of composite partial segments with full influenza C genomes from GenBank, were performed using Muscle  46  in MEGA  47  (http://www. megasoftware. net) and neighbour joining trees  48  constructed. Clock-like behaviour in sequence evolution on those trees was checked using TempEst  49  (http://tree.
bio. ed. ac. uk/software/tempest). Bayesian phylogenetic analysis was performed in BEAST v. 1. 8. 3  50  (http://tree. bio. ed. ac. uk/software/beast/). A Tamura 3-parameter (T93 + G) substitution model  51  , coalescent constant size tree prior and relaxed lognormal clock were run for 100 million iterations in BEAST, with a burn-in of 25%. Genome constellations were determined by establishing the clade, as previously defined  28  , in which each genome segment was located. Statistical analyses. on volunteers and ELISAs, BAM files and reference genomes for genome assemblies, genome fragments too short for inclusion in GenBank, BEAST inputs and outputs, TempEst inputs and outputs and pipeline Perl scripts, are available from: doi://10. 17635/lancaster/researchdata/111. This work is licensed under a Creative Commons Attribution 4.
In this paper, we present a novel framework for semi-automatically creating linguistically challenging microplanning data-to-text corpora from existing Knowledge Bases. Because our method pairs data of varying size and shape with texts ranging from simple clauses to short texts, a dataset created using this framework provides a challenging benchmark for microplanning. Another feature of this framework is that it can be applied to any large scale knowledge base and can therefore be used to train and learn KB verbalisers. We apply our framework to DBpedia data and compare the resulting dataset with Wen et al. (2016)'s. We show that while Wen et al. 's dataset is more than twice larger than ours, it is less diverse both in terms of input and in terms of text. We thus propose our corpus generation framework as a novel method for creating challenging data sets from which NLG models can be learned which are capable of handling the complex interactions occurring during in micro-planning between lexicalisation, aggregation, surface realisation, referring expression generation and sentence segmentation.
We start by reviewing the main existing types of NLG benchmarks and we argue for a crowdsourcing approach in which (i) data units are automatically built from an existing Knowledge Base (KB) and (ii) text is crowdsourced from the data (Section 2). We then propose a generic framework for semi-automatically creating training corpora for NLG (Section 3) from existing knowledge bases. In Section 4, we apply this framework to DBpedia data and we compare the resulting dataset with the dataset of Wen et al. (2016) using various metrics to evaluate the linguistic and computational adequacy of both datasets. By applying these metrics, we show that while Wen et al. 's dataset is more than twice larger than ours, it is less diverse both in  1  We ignore here (Lebret et al. , 2016)'s dataset which was created fully automatically from Wikipedia by associating infoboxes with text because this dataset fails to ensure an adequate match between data and text.
We manually examined 50 input/output pairs randomly extracted from this dataset and did not find a single example where data and text matched. As such, this dataset is ill-suited for training microplanners. Moreover, since its texts contain both missing and additional information, it cannot be used to train joint models for content selection and micro-planning either. terms of input and in terms of text. We also compare the performance of a sequence-to-sequence model (Vinyals et al. , 2015) on both datasets to estimate the complexity of the learning task induced by each dataset. We show that the performance of this neural model is much lower on the new data set than on the existing ones. We thus propose our corpus generation framework as a novel method for creating challenging data sets from which NLG models can be learned which are capable of generating complex texts from KB data.
NLG Benchmarks Domain specific benchmarks. Several domain specific data-text corpora have been built by researchers to train and evaluate NLG systems. In the sports domain, Chen and Mooney (2008) constructed a dataset mapping soccer games events to text which consists of 1,539 data-text pairs and a vocabulary of 214 words. For weather forecast generation, the dataset of Liang et al. (2009) includes 29,528 data-text pairs with a vocabulary of 345 words. For the air travel domain, Ratnaparkhi  (2000) created a dataset consisting of 5,426 datatext pairs with a richer vocabulary (927 words) and in the biology domain, the KBGen shared task (Banik et al. , 2013) made available 284 data-text pairs where the data was extracted from an existing knowledge base and the text was authored by biology experts.
An important limitation of these datasets is that, because they are domain specific, systems learned from them are restricted to generating domain specific, often strongly stereotyped text (e. g. , weather forecast or soccer game commentator reports). Arguably, training corpora for NLG should support the learning of more generic systems capable of handling a much wider range of linguistic interactions than is present in stereotyped texts. By nature however, domain specific corpora restrict the lexical and often the syntactic coverage of the texts to be produced and thereby indirectly limit the expressivity of the generators trained on them. Benchmarks constructed from "expert" linguistic annotations. NLG benchmarks have also been proposed where the input data is either derived from dependency parse trees (SR'11 task, Belz et al.
That is, these benchmarks give very limited support for learning models that can handle the interactions between micro-planning subtasks. Crowdsourced benchmarks. More recently, data-to-text benchmarks have also been created by associating data units with text using crowdsourcing. Wen et al. (2016) first created data by enumerating all possible combinations of 14 dialog act types (e. g. , request, inform) and attribute-value pairs present in four small-size, hand-written ontologies about TVs, laptops, restaurants and hotels. They then use crowdsourcing to associate each data unit with a text. The resulting dataset is both large and varied (4 domains) and was successfully exploited to train neural and imitation learning data-to-text generator (Wen et al. ,  2016; Lampouras and Vlachos, 2016).
The path structure T 1 where B is shared by two predicates (mission and operator) will favour the use of a participial or a passive subject relative clause. In contrast, the branching structure T 2 will favour the use of a new clause with a pronominal subject or a coordinated VP. More generally, allowing for trees of deeper depth is necessary to indirectly promote the introduction in the benchmark of a more varied set of syntactic constructs to be learned by generators. To address these issues, we introduce a novel method for creating data-to-text corpora from large knowledge bases such as DBPedia. Our  method combines (i) a content selection module designed to extract varied, relevant and coherent data units from DBPedia with (ii) a crowdsourcing process for associating data units with human authored texts that correctly capture their meaning.
Example 1 shows a data/text unit created by our method using DBPedia as input KB. ( Our method has the following features. First, it can be used to create a data-to-text corpus from any knowledge base where entities are categorised and there is a large number of entities belonging to the same category. As noted above, this means that the resulting corpus can be used to train KB verbalisers i. e. , generators that are able to verbalise fragments of existing knowledge bases. It could be used for instance, to verbalise fragments of e. g. , MusicBrainz 3  , FOAF 4  or Linked-GeoData. 5 econd, as crowdworkers are required to enter text that matches the data and a majority vote validation process is used to eliminate mis-matched pairs, there is a direct match between text and data. This allows for a clear focus on the non content selection part of generation known as microplanning.
DBPedia To illustrate the functioning of our benchmark creation framework, we apply it to DBPedia. DBPedia is a multilingual knowledge base that was built from various kinds of structured information contained in Wikipedia (Mendes et al. , 2012). This data is stored as RDF (Resource Description Format) triples of the form (subject, property, object) where the subject is a URI (Uniform Resource Identifier), the property is a binary relation and the object is either a URI or a literal value such as a string, a date or a number. We use an English version of the DBPedia knowledge base which encompasses 6. 2M entities, 739 classes, 1,099 properties with reference values and 1,596 properties with typed literal values. 6.