dataesr/scientific-paragraphs-categorization huggingface dataset:
102, 103 Similar to the 2009 consensus survey, 17/25 (68%) believed that fasting was optional but at times had a role in the start of the KD (58% in 2009), often in situations in which a more rapid KDT benefit was desired (eg, status epilepticus). However, no center believed it was mandatory (vs 11% in 2009). Of note, despite this "optional" recommendation, 18/25 (72%) do not routinely fast children at their ketogenic centers, with the remainder (28%) individualizing on a case-by-case basis, reflecting a change in current practice. No center believed it was appropriate to fast a child younger than age 2 years. In 1996, The Charlie Foundation produced the program "KetoDietCalculator" to assist professionals and caregivers in managing dietary therapies. This online database calculates meal plans for all ages, from infants to adults, from solid food to liquid formulas, and, is available on the internet and on mobile devices (www.
Role of Food Composition to Decrease Intestinal URS Recently, large systemic studies revealed that a healthy diet including many fruits and vegetables, fish, legumes, whole grains, and fibers and also a cutting down on red meat, sodium, and refined sugar intake were associated with a decreased risk of incident CKD for participants consuming the highest quartile of vegetable proteins  [26]  and a lower mortality in CKD patients  [27]. In contrast, red meat intake increased the risk for developing end-stage renal disease  [28]. A kidney-friendly diet could help to protect renal function from further damage and a dataset seems to indicate that CKD and its related complications are the consequence of the modification of the intestinal microbiota and the production of URS. Unfortunately, despite the fact that the deleterious influence of certain types of diet/nutrients composition on URS generation has been identified, very few studies explored the impact of this specific diet on CKD progression and patient survival.
Feeding mice with a diet specifically deprived of a selected amino acid (e. g. , branched chain amino acids such as leucine, isoleucine, and valine) was sufficient to improve glucose tolerance and body composition to the same extent as a LPD  [78]. Therefore, we hypothesize that the selective reduction of amino acids recognized as precursors of URS (such as tyrosine and tryptophan), could exert a beneficial effect similar to that of LPD. There are some preliminary experimental data that observed a decrease of IS in mice fed with low-tryptophan diet  [79]. However, in a small cohort of hemodialysis patients, Brito et al. failed to prove an association between tryptophan dietary intake and IS levels. A major weakness of this study was that the tryptophan intake was only parameter evaluated by a 24-h dietary recall performed on 3 different days.
Plant Phenotyping Alexander staining for pollen viability was performed as described in  Alexander (1969). Meiotic chromosomal spreads were prepared and stained with DAPI as described in  Ross et al. (1996). Observations were made using a Zeiss Axio Observer epifluorescence microscope and photographs were taken using an AxioCam MRm (Zeiss) camera driven by ZEN 2 Software (Carl Zeiss Microscopy, GmbH). Plots and statistical analysis were made using the GraphPad software Prism6 1.
Whole Genome Sequencing and Mutation Analysis Genome sequencing was performed with Illumina Hiseq3000 HWIJ00115 with > 8X coverage. The resulting fastq files were analyzed using the Mutdetect pipeline (version 0. 0. 6-e3ef10e) 1 http://www. graphpad. com (Girard et al. , 2015)  using TAIR10 COL-0 genome as the reference genome. The FileMatch package was used to eliminate false positives by comparing each mutant line with another two mutant lines as controls. Mutations were considered after quality filtering (>80) and the presence of 0 or only one read with wild type allele was considered to indicate a homozygous mutations. Additionally, to differentiate real mutations from false positives, we compared the total number of reads with the coverage, discarding mutations that showed unmapped reads as a proxy for repetitive regions.
DNA replication is a fundamental cellular process necessary to ensure the faithful transmission of genetic information. In eukaryotes, three highly conserved DNA polymerases, polymerase epsilon, delta, and alpha, act in concert at the replication fork. [5] [6]  Microcephalic primordial dwarfism comprises a group of prenatal-onset extreme growth disorders characterized by intrauterine growth retardation, short stature, and microcephaly. ] [9] [10] [11]  As the molecular basis for many affected individuals remains to be determined, we performed whole-genome sequencing studies to identify further genes and facilitate more comprehensive diagnosis. Whole-genome sequencing (WGS) of 48 individuals with microcephalic primordial dwarfism identified heterozygous POLE (GenBank: NM_006231. 3) loss-of-function (LoF) variants in three subjects (P1, P3, P4; Table  1 ).
These LoF variants were significantly enriched in our cohort compared to a control WGS dataset (GnomAD, 12 p ¼ 5. 1 3 10 À5 , Fisher's exact test, Table  S1 ). As these variants were present in the unaffected parents, the WGS data were further evaluated and a second rare intronic variant in POLE identified, c. 1686þ32C>G (dbSNP: rs762985435). This was present in trans with the LoF mutation in all three probands (Table  1 ). Targeted sequencing of POLE and interrogation of existing whole-exome sequencing (WES) data in additional cases of primordial dwarfism identified five additional subjects compound heterozygous for LoF alleles and the c. 1686þ32C>G variant (P5-P9, Table  1 ). Notably, a clinical diagnosis of IMAGe syndrome (GeneReviews in Web Resources) (MIM: 614732) had been considered in individuals P1 and P3, with adrenal failure also reported in P5, P6, and P7.
Acknowledgments We thank the families and clinicians for their involvement and participation; the  Potentials Foundation and Walking with Giants Foundation ;  D. Fitzpatrick ,  N. Hastie , and  W. Bickmore  for discussions;  E. Freyer  for assistance with FACS analysis; IGMM core sequencing service; and  Edinburgh Genomics  ( Clinical Division ) for WGS sequencing. We thank  Penny Jeggo  for sharing cell lines. This work was supported by funding to the Jackson lab from  European Research Council   ERC Starter Grant HumGenSize ,  281847 ;  ERC  Advanced Investigator Grant GrowCell, 788093; by a  UK Medical Research Council Human Genetics Unit  core grant ( MRC ,  U127580972 ), and the  Scottish Genomes Partnership. The Rios lab is supported by  Texas Scottish Rite Hospital for Children and the Children's Medical Center Foundation.
Research reported in this publication was supported by the  National Center for Advancing Translational Sciences  of the  National Institutes of Health  under award number  UL1TR001105. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH. Boulton lab work  is supported by the  Francis Crick Institute , which receives its core fund-ing from  Cancer Research UK  ( FC0010048 ), the  UK Medical Research Council  ( FC0010048 ), and the  Wellcome Trust  ( FC0010048 ); a  European Research Council (ERC)   Advanced Investigator Grant  (TelMetab); and  Wellcome Trust Senior Investigator  and Collaborative Grants. R. K. S. is funded by the  Wellcome Trust  ( 210752/Z/18/Z ). The Scottish Genomes Partnership is funded by the  Chief Scientist Office of the Scottish Government Health Directorates  ( SGP/1 ) and  The Medical Research Council Whole Genome Sequencing for Health and Wealth Initiative.
Bacterial genome sequences 2733 genomes of the K. pneumoniae complex, including isolates collected from diverse sources and geographical locations were analysed in this study (see Table  S1 ). The genomes represent a convenience sample of our own isolate collections from clinical and species diversity studies  (5, 8, 22, 32) , as well as sequences that were publicly available in GenBank or via the NCTC 3000 project (https://www. sanger. ac. uk/resources/downloads/bacteria/nctc/) at the commencement of the study. The majority of these genomes were also included in our previous genome study screening for yersiniabactin and colibactin  (8). For n=1847 genomes (see Table  S1 ), Illumina short reads were available and these were used to generate consistently optimised de novo assembly graphs using Unicycler v0.
3. 0b with SPAdes v3. 8. 1  (33, 34). The remaining n=886 genomes were publicly available only in the form of draft genome assemblies. All genome assemblies were re-annotated using Prokka  (35)  to allow for standardised comparison. All genomes were assigned to species by comparison to a curated set of Enterobacteriaceae genomes, using mash (implemented in Kleborate, https://github. com/katholt/Kleborate); this confirmed 2503 K. pneumoniae, 12 K. quasipneumoniae subsp. quasipneumoniae, 59 K. quasipneumoniae subsp. similipneumoniae, 158 K. variicola and 1 K. quasivariicola (Table  S1 ).
Multi-locus sequence typing (MLST) Chromosomal sequence types were determined for each genome assembly using the BIGSdb-Kp seven-locus MLST scheme (37) screened using Kleborate (https://github. com/katholt/Kleborate). A novel ST (ST3370) was identified and added to the BIGSdb-Kp MLST database. To facilitate the development of MLST schemes for the aerobactin and salmochelin biosynthesis loci iuc and iro, alleles for genes belonging to each locus (i. e. iucABCD, iutA; and iroBCDN; respectively) from strains with 'typeable' loci (defined as those in which all genes in the locus had high quality consensus base calls when mapping with SRST2) were extracted by comparison to known alleles in the BIGSdb-Kp virulence database (http://bigsdb. pasteur. fr/klebsiella/klebsiella. html)  (25) , using SRST2 v0.
2. 0  (38)  to screen Illumina read sets where available and BLAST+ v2. 2. 30 to screen assemblies. Incomplete, 'non-typeable' iro and iuc loci were excluded from the MLST scheme (marked NT in Table  S1 ). Each unique combination of alleles was assigned an aerobactin sequence type (AbST) or salmochelin sequence type (SmST), defined in Table  S4  and Table  S5. The AbST and SmST schemes, profiles and corresponding alleles are also available in the BIGSdb-Kp database and in the Kleborate Github repository (see links above).
Identification of other genes of interest, and genetic context of iuc and iro loci Capsule (K) loci were identified in the assembled genomes using Kaptive  (39). RmpA gene copy number was determined by BLASTn search of all genome assemblies using the rmpA and rmpA2 sequences from pK2044 (GenBank accession AP006726. 1) as queries. Similarly, BLASTn was used to screen the genomes assemblies for the IncFIB K repA sequence from virulence plasmids pK2044 and Kp52. 145 pII (GenBank accession FO834905. 1), with FIB K presence defined as >90% coverage and >80% nucleotide identity to these query sequences. FII replicons were identified using BLASTn search of the PlasmidFinder database  (40). Assemblies of all iuc+ or iro+ genomes were manually inspected to determine whether the loci of interest were located on the chromosome or on previously described virulence plasmids (pK2044 and Kp52.
145pII). This confirmed most to be located in the chromosome (iro3 in ICEKp1, or iuc4 in the subspecies rhinoscleromatis lineage) or one of the known plasmids. For the remaining genomes, annotated contigs containing the iuc and/or iro loci were checked for known chromosomal or plasmid features, aided by BLASTn searching against the NCBI nonredundant nucleotide database and inspection of the assembly graphs using Bandage v0. 8. 0  (41).
Phylogenetic analyses Maximum likelihood phylogenetic trees capturing the relationships between AbSTs or SmSTs were constructed by aligning the allele nucleotide sequences corresponding to each sequence type within each scheme using MUSCLE v3. 8. 31  (42) , then using each of the two alignments (one for AbSTs, one for SmSTs) as input for phylogenetic inference in RAxML v7. 7. 2  (43). For each alignment, RAxML was run fives times with the generalised time-reversible model and a Gamma distribution, and the trees with the highest likelihood were selected. Lineages were defined as monophyletic groups of AbSTs or SmSTs associated with the same MGE structure; STs within lineages shared ≥2 alleles (for SmSTs) or ≥3 alleles (for AbST), whereas no alleles were shared between lineages. Maximum likelihood phylogenies were similarly constructed for (i) aerobactin and salmochelin locus alignments populated by sequences extracted from BLAST hits amongst representatives of the wider Enterobacteriaceae family (representatives listed in Table  S6 ), and (ii) FIB K replicon sequence alignments constructed by mapping iuc positive (iuc+) and iro positive (iro+) genomes to a reference IncFIB K sequence (coordinates 128130 to 132007, spanning repA to sopB, of the pK2044 plasmid sequence; GenBank accession AP006726.
Plasmid comparisons Twelve representative plasmids (10 complete and 2 partial) were chosen for comparative analysis (these are available as a set in FigShare under doi:10. 6084/m9. figshare. 6839981; and see Table  S2  for list of sources and GenBank accession numbers). Six of these representative plasmids were sourced from the NCTC 3000 project (https://www. sanger. ac. uk/resources/downloads/bacteria/nctc/). The representative plasmid sequences were compared using Mauve v2. 4. 0  (44) , in order to identify homology blocks conserved amongst subsets of the plasmids. BLASTn comparisons of related plasmids were plotted using GenoPlotR v0. 8. 7 package (45) for R. All iuc+ or iro+ genomes were mapped against all 12 representative plasmids in order to calculate coverage of each plasmid in each genome.
This was done using Bowtie2 v2. 2. 9  (46)  to map Illumina reads where available, and 100 bp reads simulated from draft assemblies where raw sequence reads were not available, using the RedDog pipeline (https://github. com/katholt/RedDog). For every gene annotated within each reference plasmid, the proportion of strains within each group of genomes sharing the same iuc/iro lineage carrying the gene was calculated using the gene presence/absence table reported by RedDog (presence defined as ≥95% of the length of the gene being covered by at least five reads), and plotted as circular heatmaps using ggplot2 in R (using geom_tile to achieve a heatmap grid and polar_coord to circularize).
2 , Table  S2 ). These include six complete K. pneumoniae plasmid sequences identified from finished genomes: iuc1/iro1 (n=1), iuc2/iro2 (n=1), iuc2a (n=3), iuc3 (n=1); three novel complete K. pneumoniae plasmid sequences that we generated for this study, carrying iuc2a (n=2) and iro4 (n=1); and 2 large contigs that we identified from public K. pneumoniae genome data representing partial sequences for additional plasmids carrying iuc2a (n=1) and iuc3 (n=1) (Fig. 2 ). The K. pneumoniae genomes in which iuc5/iro5 were identified were available only as draft assemblies deposited in public databases, and the associated plasmid sequences were fragmented in these assemblies, hence we used E. coli strain PCN033 plasmid p3PCN033 (47) as the representative for iuc5/iro5. The representative plasmid sequences differed substantially in their structure and gene content (Fig.
5 ). pINF078-VP harboured multiple tandem copies of a 17,129 bp region containing iroBCDEN and 12 other genes of unknown function (Fig. S4 ). Long read sequences (up to 70 kbp) spanning the non-repeat and repeat region of pINF078-VP confirmed at least n=3 copies of the 17 kbp repeated sequence, whose mean read depth in the Illumina sequence data was 13. 3 times that of the rest of the plasmid sequence, suggesting approximately 13 tandem copies.
Iuc/iro lineage 5 loci are associated with plasmids originating from E. coli Four K. pneumoniae isolates carried the E. coli variant iuc5; two of these also carried the E. coli variant iro5 (see species trees in Fig. S2 ). Three iuc5+ isolates (including one with iro5) belonged to the globally-disseminated, carbapenemaseproducing K. pneumoniae CG258 (ST258, KPC+; ST437, KPC+; ST833, KPC-) and carried several AMR genes. Unfortunately all four iuc5+ genomes were sourced from public databases and were available in draft form only, and the complete plasmid sequences could not be resolved. However the iuc5+ contig sequences from K. pneumoniae share close homology (99% identity, >65% coverage) with iuc5+iro5+ FII conjugative plasmids from E. coli that also carry AMR genes (e. g. p3PCN033, CP006635.
This highlights the need to raise awareness of the iuc and iro loci as potentially clinically relevant hypervirulence factors, and to screen for them in isolates and genome data. The latter we aim to facilitate via the genotyping schemes established here, which can be used to easily screen new genome assemblies using Kleborate (https://github. com/katholt/Kleborate/) or BIGSdb-Kp (http://bigsdb. pasteur. fr/klebsiella/klebsiella. html), or new short read data sets using SRST2 (https://github. com/katholt/srst2). PCR primers suitable for screening for iro and iuc can be found in Lee et al  (53). Notably many studies rely on the hypermucoidy phenotype to identify hypervirulent strains, however this is dependent on growth conditions  (54) , and recent studies indicate that aerobactin synthesis is a more important virulence determinant  (16, 13, 14).
The system and dataset considered in this study concerns the full process of bacteria production and stabilisation, with 49 variables measured at 4 di↵erent steps (fermentation, concentration, freeze-drying and storage), at 4 di↵erent fermentation conditions (22 C and 30 C, evaluated at the beginning of the stationary growth phase and 6 hours later). The considered variables range from transcriptomic data to fatty acid membrane composition, from acidification activity to viability  [15]. The paper is organised as follows: Section 2 provides background on complex systems approaches in food science, on symbolic regression and on the modelled process. LIDeoGraM is detailled in section 3. Section 4 describes experimental results from a preliminary user evaluation, comments, conclusions and future developments are given in sections 5 and 6.
Symbolic regression Symbolic regression, hereafter based on a genetic programming approach, is a technique able to extract free-form equations that expose correlations in a given 1 Complex Systems Society, see http://cssociety. org or http://www. mathinfo. inra. fr/en/community/complexsystems/presentation for an introduction to the topic. Comment citer ce document : Chabin, T. , Barnabe, M. , Boukhelifa, N. , Fonseca, F. , TONDA, A. , VELLY, H. , LEMAITRE, B. , Perrot, N. , LUTTON, E. (2017). LIDeOGraM : an interactive evolutionary modelling tool. In: Artificial Evolution 2017 (EA-2017) 13th Biennial international conference on artificial evolution. Presented at Biennial International Conference on Artificial Evolution (EA-2017), Paris, FRA. experimental dataset. The original idea is presented in  [6] , and the technique has been applied to a vast array of real-world problems  [2, 12, 5].
The goal of LIDeOGraM is to help experts build a global model of their complex process by characterising each non-input variable by a mathematical Comment citer ce document : Chabin, T. , Barnabe, M. , Boukhelifa, N. , Fonseca, F. , TONDA, A. , VELLY, H. , LEMAITRE, B. , Perrot, N. , LUTTON, E. (2017). LIDeOGraM : an interactive evolutionary modelling tool. In: Artificial Evolution 2017 (EA-2017) 13th Biennial international conference on artificial evolution. Presented at Biennial International Conference on Artificial Evolution (EA-2017), Paris, FRA. formula that depends on the other variables in the system. Finding the right equation in a context with high variability in the dataset is an ambitious task. Indeed, it is easy to come up with over-fitted equations that perfectly model a dataset including its noise.
For automatic learning purposes, the dataset is separated into training and test sets. Moreover, some constraints in the search are defined beforehand by the user, using the interface presented in Figure  2. This tool allows attributing each variable to a given class, and defining authorized links between them. This means that only the variables from a parent class can be used in the equations for determining the variables of the child class. This also means that dependencies will be searched only with variables of other classes and that no intra-class dependencies will be considered. This structure of classes can be used to distinguish between scales and steps in the studied process. Variables measured at a macro-scale, like the viability of the population of bacteria could, for example, be only explained with variables from a micro-scale, such as the composition in fatty acids.
The dataset The case study is based on the work of H. Velly et al. [15] [16] about the resistance of Lactococcus lactis subsp. lactis TOMSC161 to freeze-drying. This bacteria is used in the production of Tomme de Savoie, a french cheese, for its interesting texturing and acidification properties, but exhibits a high sensitivity to freeze-drying. The resistance of the bacteria is studied for 4 di↵erent conditions of fermentation: 22 C and 30 C, evaluated at the beginning of the stationary growth phase and 6 hours later. The dataset featured 12 data points, with 3 biological repetitions of each experimental condition. The dataset is made of 2 input variables, the temperature of fermentation and the time at which the fermentation is stopped and 49 variables measured at 4 di↵erent steps (fermentation, concentration, freeze-drying and storage) for 3 biological scales (Genomic, Cellular and Population).
Presented at Biennial International Conference on Artificial Evolution (EA-2017), Paris, FRA. The dataset is also separated into a training dataset(66%) made of two out of the three repetitions for each experimental condition, and into a test dataset(33%) with the remaining repetition. The authorised mathematical operators for the Symbolic regression using Eureqa are: Constants, Input variables, Addition, Subtraction, Multiplication, Division, Exponential, and the Natural logarithm. For each non-input variables, 3 minutes of computation were allowed on an Intel(R) Core(TM) i7-4790 CPU. A total of 232 equations were obtained for the variables.
Optimisation of the Global Model A (µ + )-evolutionary algorithm was taken from the Python DEAP package  [4]  to optimise the global model. The genome of a candidate global model is string of integers, of size equal to the number of variables in the process. Each gene is associated to a variable, and can assume a value between 1 and the number of equations available to describe that variable, thus representing an index for a candidate equation in that node. The parameters of the evolutionary optimization algorithm used for the global model are reported in Table  1. The mutation function takes complexity information into account. It has been experimentally proven to be more e cient than a mutation which randomply picks an equation in the list of candidate equations. The graphical model associated to one of the optimisation runs is shown in Figure  4.
Comment citer ce document : Chabin, T. , Barnabe, M. , Boukhelifa, N. , Fonseca, F. , TONDA, A. , VELLY, H. , LEMAITRE, B. , Perrot, N. , LUTTON, E. (2017). LIDeOGraM : an interactive evolutionary modelling tool. In: Artificial Evolution 2017 (EA-2017) 13th Biennial international conference on artificial evolution. Presented at Biennial International Conference on Artificial Evolution (EA-2017), Paris, FRA. The creation of a global model does not involve only an automatic optimisation, but also requires experts knowledge, obtained via interaction with the software. Feedback on the proposed local models was given by a researcher with 20-years of expertise in the bacteria freeze-drying process. The local models, presented in Figure  1 , were explored by the expert during 20 minutes. The expert chose to remove 5 equations.