dataesr/scientific-paragraphs-categorization huggingface dataset:
All Salmonella-specific reads mapped on the branch leading to serovar Paratyphi C strain RKS4594. Ragna-specific reads in metagenomic libraries Reads were pre-processed using BBMERGE and BBDUK2 from the BBMAP package  [60]. Sequences specific to the Ragna genome were then identified by alignment against two sets of reference genomes using BOWTIE2  [61] : i) The ''ParaC'' set, consisting of all 219 modern genomes in the Para C lineage and ii) the ''outgroup'' set, consisting of representatives of all (4,441) non-Salmonella bacterial genomes in RefSeq, plus the human genome hg38. We assigned sequencing reads to Ragna if they yielded equal or better alignment scores to the ParaC set than to the outgroup, and differed in sequence from the most similar genome in the ParaC lineage by no more than 4%.
Sub-samples including the Ragna genomes were run with either of two dates for Ragna (1200 CE from archaeological dates and 1073 CE from AMS calibrated ages). Thus, our Bayesian phylogenetic analyses involved 12 datasets (Date estimation 3, see Key Resources Table ). The raw trees are presented in Date estimation 4 (see Key Resources Table ). Posterior distributions of parameters, including the MCC tree, were estimated using Markov chain Monte Carlo sampling. Samples were drawn every 5,000 steps over a total of 5 3 10 7 steps, with the first 10% of samples discarded as burn-in. Marginal likelihoods were used to compare two clock models (strict clock and uncorrelated lognormal relaxed clock) and three demographic models for the tree prior (constant population size, Bayesian Skyride coalescent  [104] , and birth-death process with serial sampling  [105] ).
The median values for their estimates of tMRCAs and mutational clock rates and the corresponding 95% confidence intervals are summarized in Table  S4. Temporal structure It is crucial to verify the existence of temporal structure in datasets used for date estimation. We therefore tested the reliability of the BEAST analyses by date randomization tests  [38]  which analyze multiple, date-randomized replicate datasets after randomly reassigning the ages of the sequences. Datasets are considered to have strong temporal structure when the 95% credible interval of the rate estimate from the original data does not overlap with those of the rate estimates from the date-randomized replicates  [107]. We used 10 date randomizations for each of the initial two sub-samples, with and without Ragna, and found strong temporal signals within all sub-samples of modern genomes analyzed with BEAST (Date estimation 1, see Key Resources Table ).
Root-to-tip regression We also used an alternative method to estimate temporal signal for dating on the non-recombinational SNPs in 116 Paratyphi C genomes and the 205 Para C lineage genomes with known collection dates, including Ragna but excluding Lomita. This consisted of a regression of root-to-tip distances with TEMPEST  [37]  (previously designated PATH-O-GEN) in which the degree of within-lineage rate heterogeneity is evaluated by calculating the correlation coefficient. A regression based on one aDNA genome (Ragna) plus multiple modern genomes is basically a regression line based on two points. We therefore also included two additional aDNA genomes, Tepos-14 and Tepos-35 from 1,545 CE in Mexico  [19]. The results (Data estimation 2, see Key Resources Table ) showed a clear temporal signal in both datasets, with an extrapolation to the root which was similar to the tMRCAs found with BEAST.
Inferring ancestral geographic sources A subtree of genomes with geographic source information was extracted from the Para C lineage phylogeny plus the outgroup Birkenhead with the ETE3 Python package  [66]. (Lomita is lacking from this subtree because its geographic source is uncertain). The ancestral geographic states of internal nodes in the subtree were inferred by three independent algorithms: i) the Maximum Likelihood comparison  [108]  in BayesTraits  [47] ; ii) the Markov chain Monte Carlo (MCMC) approach  [109]  in BayesTraits; and iii) Bayesian Binary MCMC in RASP  [75]. All three algorithms indicated a European origin for individual nodes within the Para C lineage (Ancestral reconstruction 1; Ancestral reconstruction 2, see Key Resources Table ), and this conclusion was not affected by the inclusion or absence of Ragna.
ACKNOWLEDGMENTS EnteroBase ( BBSRC BB/L020319/1 ) was developed by  N. -F. A. , M. J. S. , and Z. Z. (equal contributions) under guidance by  M. A. Additional  grant support was from the  Wellcome Trust  ( 202792/Z/16/Z ),  Medical Research Council  ( MR/M50161X/1 ),  Grundforksningfonden  ( DNRF 94 ) and  Lundbeckfonden  ( R52-5062 ), the  French government  ( ANR-10-LABX-62-IBEID ), the  Institut Pasteur ,  Sant e Publique France , and  Fondation Le Roch-Les Mousquertaires. We thank  A. Murat Eren  for assistance with implementation of Anvi'o in EnteroBase, and we thank  Siavash Mirarab  and  Tandy Warnow  for advice on Astrid and running Astral-II. We thank  Birgitte Skar  for permission to sample the skeletal material and the  Danish National High-Throughput DNA Sequencing Centre  for technical assistance with data generation.
On the contrary, cells containing aggregates rich in desmin are softer, with a median value of 1/A at about 145 Pa. In order to test a possible correlation between desmin network reinforcement and cell stiffness increase, desmin-GFP was imaged in individual cells during mechanical measurements. We then divided the C2C12-WT-GFP data in two subgroups, depending on the GFP fluorescence intensity level, which is correlated to the amount of desmin-WT-GFP expressed by the cells: cells with a low to medium fluorescence intensity ("C2C12-WT-GFP low") and cells with a high fluorescence intensity ("C2C12-WT-GFP high"). Indeed cells of the second subgroup tend to be stiffer than those of the first subgroup, but the difference is below significance (P = 0. 23): median value of 1/A of about 455 Pa versus about 345 Pa (see Figure  4B ).
Growth phase: A. gossypii reveals high flux through β-oxidation and TCA cycle Measured 13 C labeling data from the cell protein, the determined anabolic fluxes from medium ingredients and from pathway precursors, and measured extracellular fluxes provided a rich data set for the flux calculation. The network was fully determined so that the fluxes could be obtained through the established set of mass balances (see above and Additional file 1). Growth of A. gossypii on vegetable oil and complex medium resulted in a strong carbon flux through the ß-oxidation pathway as well as the TCA cycle (Fig. 5 ). Vegetable oil was cleaved into three fatty acids (average chain length of 17. 3 carbon atoms, denoted as FA in Fig. 5 )  [24]  and glycerol (See figure on next page. ) Fig. 3  Relative contribution of 13 C tracers to amino acids from hydrolyzed cell protein and glycogen of A.
gossypii B2 grown on complex medium with rapeseed oil. The 13 C labeling patterns of the amino acids were obtained at the end of the growth phase after 36 h. Respective contribution of [U 13 C] yeast extract (purple), [ 13 C 5 ] glutamate (light blue), and other media components (grey) are depicted by the bar next to the respective amino acid. The full length bar represents 100%. Data represent summed fractional labelings (SFL corr ), corrected for natural labeling and dilution effects through unlabeled pre-culture medium. Data were obtained from three independent replicates. Note that the conversion of citrate to isocitrate via aconitase most likely does not occur in the peroxisome  [68]  in the extracellular space  [38]. Fatty acids were then taken up by the cell (0. 430 ± 0. 056 mmol g -1 h -1 ) and oxidized to acetyl-CoA through the ß-oxidation pathway, located in the peroxisome  [39, 40].
gossypii B2 grown on complex medium with rapeseed oil as main carbon source. MIDs are presented for medium with naturally labeled substrates (grey), [U 13 C] yeast extract (purple), and [ 13 C 5 ] glutamate (light blue). Data were obtained from three individual replicates (See figure on next page. ) Fig. 5  Carbon fluxes during growth of A. gossypii B2 on rapeseed oil and complex medium. Fluxes are given in mmol g -1 CDW h -1 and are normalized to the substrate uptake rate (0. 43 ± 0. 06 mmol g -1 h -1 ). Flux calculations were derived from parallel 13 C tracer studies with [ 13 C 2 ] glycine, [ 13 C] formate, [ 13 C 5 ] glutamate, and [U 13 C] yeast extract (Additional file 1: Tables  S2, S3 ). The arrow thickness is proportional to the corresponding flux. The direction of net fluxes is indicated by size of arrow head.
8  Carbon contribution of medium ingredients to riboflavin based on combined parallel 13 C-labeled tracer studies using [ 13 C 2 ] glycine, [ 13 C] formate, [ 13 C 5 ] glutamate, and [U 13 C] yeast extract. Riboflavin was synthesized by A. gossypii B2 grown on complex medium with rapeseed oil. Riboflavin was obtained at the end of the growth phase of riboflavin producing A. gossypii after 144 h. Data are derived from positional 13 C enrichment obtained from 13 C NMR measurements (Table  3 ), corrected for natural labeling and dilution effects through unlabeled pre-culture medium. Values denote carbon atoms and are normalized to 17 carbon atom influx into riboflavin. All values were multiplied by the number of carbon atoms of the reactants. Note that the model is simplified and cannot distinguish between carbon flux through e.
9  Intracellular carbon fluxes of riboflavin production with A. gossypii B2 on rapeseed oil and complex medium, which were determined by four parallel 13 C-labeled tracer studies using [ 13 C 2 ] glycine, [ 13 C] formate, [ 13 C 5 ] glutamate, and [U 13 C] yeast extract. The carbon fluxes were normalized to the specific riboflavin production rate (8. 7 ± 2. 3 µmol g -1 h -1 ), which was set to 100%. Riboflavin was obtained at the end of the growth phase of riboflavin producing A. gossypii after 144 h. Data are derived from positional 13 C enrichment obtained from 13 C NMR measurements (Table  3 ), corrected for natural labeling and dilution effects through unlabeled pre-culture medium. Note that the model is simplified and cannot distinguish between carbon flux through e. g. gluconeogenesis or lower PP pathway as well as pyruvate dehydrogenase.
In addition, a carbon flux map could be calculated for the riboflavin biosynthetic phase of the process using an industrial cultivation set-up. The data showed that while yeast extract was the main carbon source for growth (Fig. 3 ), its impact on riboflavin biosynthesis was still significant with an overall contribution of 8 ± 0%. In addition, glycine as two-carbon unit, which is readily incorporated into the vitamin, also made up 9 ± 0% of the product. By far the greatest impact had rapeseed oil (81 ± 1%). This highlights the importance of this carbon source for riboflavin production  [45]. Although various other nutrients were present, the cells efficiently incorporated the oil into the product.
Processing of RNA-Seq data and differential expression analysis Paired-end reads were generated using an Illumina NextSeq 500 and processed using the RNA-seq pipeline implemented in the bcbionextgen project [https://bcbio-nextgen. readthedocs. org/en/latest/]. Reads were aligned to the human genome version hg38 using the Hisat2 aligner  [12]. Counts of reads aligning to known genes are generated by featur-eCounts  [13]. Differential expression analysis was performed using DESeq2  [14]. All data are MIAME compliant, and the raw data have been deposited in a MIAME compliant database (GEO, accession number GSE103383).
Analysis of published transcriptomes Raw data from GSE25724 microarray study was downloaded from the GEO database. Differential gene expression was assessed as previously described  [15, 16]. The moderated t-statistics within the limma package were used to obtain p-values for differential expression of genes under investigation  [17]. If multiple probes mapped to the same gene, only the probe with smallest p-value was retained. Given the small number of individual hypothesis tests, we did not further adjust p-values for multiple testing. For the single-cell RNA sequencing studies (E-MTAB-5061), we downloaded fastq files from the GEO database and aligned them to human genome version hg38 using the hisat2 aligner  [12] , followed by quantitation using the Salmon algorithm  [18]  and cpm (counts per million) normalization.
Zinc staining EndoC-bH1 cells were seeded in 96 wells plates and allowed to attach for 24 h before treatment with 100 ng/mL FGF2. After 72 h, cells were treated with 30 mM KCl for 15 min to induce acute degranulation. They were rinsed, and fresh media containing 100 ng/mL FGF2 was added for another 72 h. Following treatment, cells were rinsed in PBS and stained with the zinc-specific fluorescent probe Zinpyr-1 (5 mM, Cayman Chemical), which stains primarily zinc-enriched secretory granules in beta cells  [22]. Cells were then observed using confocal microscopy using a 488 nm laser line for excitation and 525/50 nm emission wavelength. Image analysis was performed using the Colombus software (PerkinElmer). 2. 12. Statistical analysis Data are expressed as mean AE SD. Analyses were carried out using SigmaStatÒ 3.
Upregulation of the FGF2 pathway and dedifferentiation markers in human type 2 diabetes Finally, we analyzed previously published transcriptomic data to ask whether the pancreatic FGF pathway and dedifferentiation markers were activated in patients with type 2 diabetes. Remarkably, single cells RNA-Seq data  [27]  revealed an increase of FGF2 expression in   4 : Dedifferentiation is a reversible state. EndoC-bH1 cells were cultured for 3 or 14 days in control conditions (white columns), with FGF2 (gray columns) or for 3 days with FGF2 followed by 11 days without FGF2 (dashed columns). INS, MAFA, SOX9 and MAFA levels were analyzed by RT-qPCR at day 3 and day 14. Data are represented as mean AE SD. n ¼ 5 biological replicates. **p < 0. 01, ***p < 0. 001. pancreatic ductal, endothelial and pancreatic stellate cells from type 2 diabetic patients (Figure  7A ).
ACKNOWLEDGMENTS We would like to thank  Sandra Guilmeau ,  Cochin Institute Paris , for careful reading of the manuscript. This work was supported by the  European Diabetes Research Programme in Cellular Plasticity Underlying the Pathophysiology of Type 2 Diabetes from EFSD-AstraZeneca  (RS), and an  Aviesan-AstraZeneca program  (RS and FC), the  Fondation pour la Recherche Médicale  (MD and MO) and the  Aide aux Jeunes Diabétiques (MD). This project has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  115881  (RHAPSODY). This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA. This work is supported by the  Swiss State Secretariat for Education' Research and Innovation (SERI)  under contract number  16.
The photons resulting from the de-excitation of the dyes molecules, i. e. , fluorescence, were separated from the excitation laser beams with a dichroic mirror and spectrally splitted in two channels, e. g. , donor and acceptor that are imaged on the same electron-modifying charge-coupled device camera (iXon, Andor, Irlande). For 100 ms ALEX illumination, i. e. , 200 ms frame time acquisition, the laser power measured preceding the dichroic mirror is ~0. 4 mW for donor excitation laser and ~0. 09 mW for the acceptor excitation laser. For 40 ms ALEX illumination (only used to acquire the data with the ΔP promoter and ATP starting substrate experimental condition), i. e. , 80 ms frame time, the laser power measured preceding the dichroic mirror is ~1 mW for donor excitation laser and ~0. 25 mW for the acceptor excitation laser.
The imaging buffer contained the reaction buffer completed with 1 mM Trolox, 1 mM COT, 1% (w/V) glucose, 0. 4 µg/ml of catalase, and 1 mg/ml of glucose oxidase (Sigma Aldrich). The catalase and the glucose oxidase were pre-mixed together in a solution of 50 mM KCl and 50 mM Tris-OAc buffer pH 7. 3 at 100 × concentration  57, 58. The data were acquired after immobilization of the RPo complex to the surface. After ~200 frames (~20 s), the imaging buffer is spiked with a 12. 5 × NTP solution and the reaction is observed for the remaining ~5800 frames (total time: 10 min). For the post-RNA synthesis rinsing experiments, the RP O was incubated with NTPs in the reaction buffer for 10 s before the reaction buffer was exchanged twice and finally replaced with imaging buffer, followed by the start of the acquisition.
The buffer exchange procedure takes ~40 sec to be completed before the start of the acquisition. All single-molecule FRET experiments were performed at 22 °C. Single-molecule data analysis. FRET pair localization and detection: The movies recorded on the camera were offline analyzed using the home-built Matlab routine Twotone-ALEX  59  to extract the intensities of co-localized donor and acceptor, i. e. , FRET-pair. The following parameters from Twotone-ALEX were used to select only the FRET pairs formed by a single ATTO647N acceptor dye and a single Cy3b donor dye: channel filter as DexDem&&AexAem&&DexAem (colocalization of the donor dye signal upon donor laser excitation, the acceptor dye signal upon acceptor laser excitation, and the acceptor dye signal upon donor laser excitation), a width limit between the donor and the acceptor between 1 and 2 pixels, a nearestneighbor limit of 6 pixels, and a maximal ellipticity of 0.
6 (ellipticity is defined as the ratio of the minor and the major axis of the ellipse). The traces extracted from the Twotone-ALEX analysis were then sorted to remove all the traces that displayed extensive blinking or multisteps photobleaching, i. e. , that contain more than one donor or acceptor dye in the same diffraction limited intensity spot. Calibration of the FRET sensor. We calibrated the FRET sensor by measuring E FRET for initial transcribing complexes in the presence of different subsets of nucleotides that allowed maximal transcript lengths of 6, 7, or 11 nucleotides, and compared their FRET profiles with RP O. These experiments allowed unambiguous assignment of the RP O and ITC11 FRET levels (E FRET ) as ~0. 5 and ~0. 76, respectively (Fig. 1c ; see also Supplementary Fig. 1 ).
The assignment of this E FRET level to an ITC6 pause state, or "PS," was based on our previous observations showing that ITC6 (but not ITC7) accumulates in significant amounts in conditions that allow continuous transcription past the 6-7-mer RNA and that ITCs do not pause again until reaching the position + 12 of the DNA template used here  27. FRET efficiency and hidden Markov modeling. The FRET efficiency dynamics for each FRET-pair was calculated with the standard formula E ¼ IDA IDAþIDD , with I DA and I DD being respectively the intensity of the acceptor and of the donor upon donor excitation  62. The traces were analyzed through a modified version of the hidden Markov model ebFRET software from ref. 63  (the modified code is available from the corresponding authors on reasonable request), where only steps longer than two frames and separated from the subsequent step by more than twice the Allan deviation estimated at five frames were conserved  64  to be assembled into dwell time.
Here, the τ i are the experimentally measured dwell times and N is the number of collected dwell times τ i. The error bars for each fitting parameters are one standard deviation extracted from 1000 bootstrap procedures  68. The ebFRET software  63  was also used to extract the peak positions of each FRET level, subsequently fitted with a Gaussian function, with the peak center and the SD as free parameters (Supplementary Figs. 1d  and 3a-d ).
Acknowledgements We thank  Dr. Francesco Pedaci ,  Dr. Mohamed Fareh , and  Tao Ju Cui  for carefully reading the manuscript. We also thank  Dr. Jan Lipfert  for discussion and  Dr. Jun Fan  for experimental support during the revision process. This work was supported by grants to ANK from the  European Research Council  ( 261227 ), the  Wellcome Trust  ( 110164/Z/15/ Z ), and the  UK BBSRC  ( BB/H01795X/1  and  BB/J00054X/1 ). D. L. V. B. was supported by an  US National Science Foundation  grant ( 1309306 ) and by an  EPA Cephalosporin Junior Research Fellowship  at Linacre College, Oxford. D. D. was supported by the  Interdisciplinary Center for Clinical Research (IZKF)  at the  University Hospital of the University of Erlangen-Nuremberg. A. M. M. was supported by the  Instrumentarium Science Foundation ,  Finnish Cultural Foundation and Alfred Kordelin Foundation.
Statistical Analysis. To compare extinction probability between the different treatments, we recorded the number of weeks that each parasitoid species persisted in each of the 40 mesocosms. Species that persisted in mesocosms until the end of the experiment were treated as censored data. We used the Cox proportional hazard model from the survival package  (30)  in the opensource software R 3. 1. 0 (31) to test for the impact of the complexity and harvesting factors and their interaction on parasitoid species persistence in the communities. We further included parasitoid species identity of the nonharvested specialist parasitoids (L. fabarum and A. ervi) and interaction with the treatments harvesting and aphid distribution in the model to test for species-specific differences in response to the treatments.
Proportional hazard assumptions were tested by inspecting Kaplan-Meier survival curves and with a cox. zph test  (32). Single treatments were compared with Kaplan-Meier survival curves and a log-rank test as implemented in the R-function survdiff  (33). Aphid and parasitoid population densities were analyzed with linear mixed effect models with log-or square root-transformed density data as the dependent variable with a normal error distribution. Harvesting treatment, community complexity treatment, and the interaction between the two treatments were included as fixed factors. To account for systematic trends over time, we included week and week squared as covariates. Mesocosm identity nested in a block was used as a random intercept in the model. Because the residuals of these models showed a significant partial temporal autocorrelation, we included a firstorder autoregression for the residuals.
This was done using the function lme from the package nlme  (34). The parasitism rate of M. viciae was tested with general linear mixed effects models, including harvesting, complexity, and week as fixed factors and block and mesocosm as random factors assuming binomial error structure. The response variable was a binary variable containing the number of mummified aphids and the number of live aphids per mesocosm. For the latter analyses, we used glmer from the r-package lme4  (35). The hyperparasitism rate of primary parasitoids (A. megourae, A. ervi, and L. fabarum) was similarly analyzed using a combined variable with hyperparasitized and nonhyperparasitized mummies. We also tested for treatment effects of the species richness and diversity of hyperparasitoid communities. Hyperparasitoid community composition between treatments was compared with nonmetric multidimensional scaling plots using the r-package vegan  (36).
The reported data used in the above analyses are presented in the Datasets S1-S3. Food Web Modeling. As in previous food web simulations on extinction cascades  (17) , community dynamics were modeled using the generalized Lotka-Volterra equation: dN i dt = N i b i + X n j=1 a ij N j ! , where N i is the density of species i, n is the number of species in the community, b i is the intrinsic growth rate of species i, and a ij is the per capita effect of species j on the per capita growth rate of species i. Basal species (i. e. , plants) have a positive intrinsic growth rate (b i > 0) and are self-limited (a ii = -b i /K i < 0, where K i is plant species i carrying capacity). For consumer species (i. e. , aphids, aphid parasitoids), b i < 0 and a ii = 0. When species j consumes or attacks species i, a ij < 0 and a ji = -a ij (a ij = 0 otherwise).