dataesr/scientific-paragraphs-categorization huggingface dataset:
β-lactamase and MCR genes were cloned into the IPTG-inducible plasmid pDM1 using primers P1-P36. pDM1 (GenBank accession number MN128719) was constructed from the p15A-ori plasmid pACYC184  (65)  to contain the Lac repressor, the Ptac promoter, an optimized ribosome binding site and a multiple cloning site (NdeI, SacI, PstI, KpnI, XhoI and XmaI) inserted into the NcoI restriction site of pACYC184. All StrepII-tag fusions of β-lactamase and MCR enzymes (constructed using primers P1, P3, P9, P11, P13, P15, P17, P21, P23, P25, P27, P29, P37, P38 and P41-P50) have a C-terminal StrepII tag (GSAWSHPQFEK) except for OXA-4, where an N-terminal StrepII tag was inserted between the periplasmic signal sequence and the body of the protein using the primer pairs P7/P40, P9/P39 and P7/P8. Plasmids encoding ges-1, kpc-3 and mcr-3.
1) was used as a query in a blastp 2. 2. 28+ (77) search limited to Proteobacteria on the NCBI Reference Sequence (RefSeq) proteome database (21 April 2019) (evalue < 10e-5). 17,503 hit sequences were retrieved and clustered using the ucluster software with a 70% identity threshold and the cluster_fast option (USEARCH v. 7. 0 (76)). All centroid sequences were retrieved and clustered again with a 20% identity threshold and the cluster_fast option. Centroid sequences of all clusters comprising more than five sequences (809 sequences retrieved) along with the sequences of the five MCR enzymes tested in this study were aligned using MUSCLE  (78). Sequences which were obviously divergent or truncated were manually eliminated and a phylogenetic tree was built from a final alignment comprising 781 sequences using FastTree 2.
ACKNOWLEDGEMENTS: We thank J. Rowley for assistance with flow cytometry. SEM imaging was performed at the University of Texas Center for Biomedical Research Support core; we thank M. Mikesh for providing the training for the acquisition of SEM images. We are grateful to IHMA Inc. Schaumburg for the kind gift of the E. coli 1144230 isolate, T. Bernhardt for the kind gift of the pCB112 plasmid, and J. Beckwith, F. Alcock and V. Koronakis for the kind gifts of the anti-DsbA, the anti-AcrA and the anti-TolC antibodies, respectively. This study was funded by the MRC Career Development Award MR/M009505/1 (to D. A. I. M. ), the institutional BBSRC-DTP studentships BB/M011178/1 (to N. K. ) and BB/M01116X/1 (to H. L. P. ), the BBSRC David Philips Fellowship BB/M02623X/1 (to J. 1. Overview of the β-lactamase enzymes investigated in this study.
Enzymes GES-1, -2 and -11 as well as KPC-2 and -3 belong to the same phylogenetic cluster (GES-42 and KPC-44, respectively, see Supplementary Table  1 ). All other tested enzymes belong to distinct phylogenetic clusters (Supplementary Table  1 ). The "Cysteine positions" column states the positions of cysteine residues after position 30 and hence, does not include amino acids that would be part of the periplasmic signal sequence. All β-lactamase enzymes except L2-1 (shaded in grey; PDB ID: 1O7E) have one disulfide bond. The "Mobile" column refers to the genetic location of the β-lactamase gene; "yes" indicates that the gene of interest is located on a plasmid, while "no" refers to chromosomally-encoded enzymes. All tested enzymes have a broad hydrolytic spectrum and are either Extended Spectrum β-Lactamases (ESBLs) or carbapenemases.
MIC (µg/mL) / MC1000 + DSB system inhibitor MIC (µg/mL)) for β-lactamase-expressing E. coli MC1000 with and without addition of a DSB system inhibitor to the culture medium from two biological experiments, each conducted as a single technical repeat. Black dotted lines indicate an MIC fold change of 2. The aminoglycoside antibiotic gentamicin serves as a control for all strains; gentamicin MIC values (white bars) are unaffected by chemical inhibition of the DSB system (MIC fold changes: < 2). No changes in MIC values (MIC fold changes: < 2) are observed for strains harboring the empty vector control (pDM1) or expressing the class A β-lactamase L2-1, which contains three cysteines but no disulfide bond (PDB ID: 1O7E) (top row). The MIC values used to generate this panel are presented in Supplementary Table2.
| Statistical analyses All statistical analyses were carried out in R version 4. 0. 3 (R Core Team, 2021) and included two steps. First, we applied generalized linear mixed models (GLMMs) implemented in the lme4 R-package version 1. 1. 23 (Bates et al. , 2015)  to test for an effect of thermal stress on reproductive success separately for each sex and mating system. For the monogamous treatment, we defined the number of wild-type offspring as the response variable assuming a Poisson error distribution with a log link function. For the polygamous treatment, we defined the number of wild-type offspring and the number of RdHD offspring as response variables assuming a binomial error distribution with a logit link function. We fitted heat stress treatment as the only fixed effect together with block, incubator and observation level as random terms (effects of random terms are shown in Table  S3 ).
Observation-level identity was added to account for overdispersion. In addition to tests for an overall treatment effect, we performed post-hoc tests to evaluate pairwise differences between the three heat stress levels. In addition to the analysis of relative reproductive success under polygamy, we also report GLMMs on the absolute number of wild-type offspring counted for the polygamy treatment in the Supporting Information (Figure  S1 ; Table  S4 ). Moreover, in an additional series of GLMMs we combined the data obtained from males and females and included the heat stress treatment, sex and their interaction as fixed terms to test whether sexes were differently affected by the heat stress (Table  S5 ). Second, we computed a selection coefficient to compare the effect of the heat stress treatment between mating systems, sexes and fitness assays.
Results The cell volume, genome size, and gene number distributions of prokaryotic and eukaryotic cells In this manuscript, we use theoretical models to assess the respiratory membrane requirements and DNA investments of mitochondrion-less and mitochondrion-bearing cells. These models might help explain, from an energetic point of view, the differences observed between modern prokaryotes and eukaryotes and thus inform our discussions of the prokaryote-eukaryote transition. We start by presenting the distributions of cell volume, genome size and gene number from a comprehensive survey of phylogenetically disparate prokaryotes and eukaryotes (Fig. 2 ; Dataset S1). The cell volume distributions of prokaryotes and eukaryotes point at two main conclusions. First, the ranges for each grade of organization (~10 -2 -10 2 µm 3 for prokaryotes and ~10 0 -10 8 µm 3 for eukaryotes) do not overlap for the most part: their medians (vertical dashed lines in Fig.
2 ; calculated over the full distribution of cell volumes) largely fall outside of each other's distributions (Fig. 2A ). This is most obvious when giant bacteria like Beggiatoa spp. and Thiomargarita namibiensis are excluded (Fig. 2A ; Dataset S1). Giant bacteria reach absolute volumes >10 6 µm 3 but these are mostly inert as they contain a large central vacuole or numerous intracellular inclusions made of sulfur or calcium carbonate (some exceptions are large cyanobacterial cells; Dataset S1). Thus, most prokaryotes are smaller than most eukaryotes. Second, prokaryotes and eukaryotes mostly overlap at cell volumes of ~10 0 -10 2 µm 3 (Fig. 2A ). This overlap includes large bacteria with entirely active cytoplasms composed of energy-demanding macromolecules (e. g. ,.
Azotobacter chroococcum, Magnetobacterium bavaricum, 'Candidatus Uab amorphum', and Chromatium okenii; Dataset S1), picoeukaryotes which are relatively reduced (e. g. , algae such as Chaetoceros calcitrans, Micromonas pusilla, Nannochloris sp. , and Nannochloropsis geditana; Dataset S1), and phylogenetically diverse nanoeukaryotes (e. g. , heterotrophic flagellates such as Andalucia godoyi, Mantamonas plastica, Bodo saltans, Malawimonas jakobiformis, Palpitomonas bilix, Ancyromonas mylnikovi, Reclinomonas americana; Dataset S1). Many small eukaryotes (both parasitic and free-living) can thus have sizes similar to those of many bacteria. The histogram for genome size follows a similar pattern to that of cell volume: prokaryotes and eukaryotes have distinct but overlapping distributions (Fig.
2B ). The genome size range for prokaryotes is <1-16 Mbp, whereas that of eukaryotes is ~8-10,000 Mbp. This suggests that there is an upper genome size constraint to prokaryotes based on the currently available data. Prokaryotes and eukaryotes also overlap at genomes sizes of ~8-16 Mbp if the genomes of highly reduced eukaryotic parasites are excluded (Fig. 2B ; Dataset S1). Many eukaryotes (protists) thus have genome sizes smaller than those of some prokaryotes. For example, prokaryotes such as myxobacteria, actinomycetes, cyanobacteria, and planctomycetes may have genomes of up to 16 Mbp in size (Dataset S1). The smallest genomes for free-living eukaryotes are those of some small green algae, red algae, and yeasts (8-13 Mbp); some parasitic eukaryotes have genome sizes of just 2 or 6 Mbp (e.
g. , Encephalitozoon and Babesia; Dataset S1). The small heterotrophic nanoflagellate Andalucia godoyi (Jakobea), which have the most ancestral-like mitochondrial genomes, has a nuclear genome size of ~20 Mbp  (27) , barely larger than the largest prokaryotic genomes. For gene number, there is an even wider overlap between prokaryotes and eukaryotes (Fig. 2C ). Prokaryotes with the greatest number of genes have 10,000-13,000 genes (Dataset S1), whereas eukaryotes with the lowest number of genes include intracellular parasites (~2,000 genes in Encephalitozoon), free-living fungi (~4,500 genes in Malassezia restricta or ~6,400 in Saccharomyces cerevisiae), and small algae (~5,300 genes in Cyanidioschyzon and ~7,800 genes in Ostreococcus tauri). Some of the closest relatives of animals, the free-living flagellate Monosiga brevicollis (Choanoflagellata) and the symbiotic amoeba Capsaspora owczarzaki have ~9,200 and 8,800 genes, respectively (Dataset S1).
, 2017) : three that are upregulated on DN3-to-DP transition (Bcl6, Nfatc3, Rag1), three that are downregulated (Cdh1, Il17rb, Pla2g4a), and two whose expression are unchanged (Cd3, Zap70)  (Fig S1A and Table S1). We also performed conventional Hi-C on DN3 and DP cells for genome-wide but lower-resolution assessment of any chromatin topology changes (overview of datasets in this study given in Table  S2 ). Capture Hi-C was additionally performed on ESCs to provide a reference point and comparison with high-resolution conventional Hi-C data  (Bonev et al. , 2017). As expected, the Capture Hi-C provided improved resolution at the target regions compared to previously generated Hi-C maps for thymocytes  (Hu et al. , 2018) , even though the latter dataset had ~4fold greater sequencing depth  (Fig 1A).
Biological replicates were highly correlated (Spearman correlation coefficient ≥ 0. 95; Fig  S1B-C  and Table  S3 ) and were pooled for improved resolution in subsequent analyses. The resultant maps were of sufficient quality to resolve chromatin interactions between promoters and putative enhancers (at Bcl6), and between CTCF-bound sites (at Rag1), further confirmed by 4C-seq analysis  (Fig 1B and S1D). Visual inspection of the Capture Hi-C maps showed that thymocytes seem to have better defined TADs and more heterogeneous structures than ESCs  (Fig 1B) , in line with observations that ESC chromatin is generally more open and "plastic" than differentiated cells  (Gaspar-Maia et al. , 2011). Observing the contact strength decay of the Capture Hi-C datasets with increasing genomic distance reinforces these trends by showing greater variability of contact strength at a given genomic separation within thymocytes  (Fig 1C).
Further, the cis-decay profiles for both Capture Hi-C and, at lower resolution, the genome-wide Hi-C, show that DP chromatin is globally more compact than the other cell types, in agreement with previous observations  (Rawlings et al. , 2011). Overall, these results validate the quality of the Capture Hi-C datasets for subsequent in-depth comparisons across cell types. TAD structure is largely conserved but with cell type-specific differences Visual inspection of Capture Hi-C maps around the target regions suggested that TAD structure was largely conserved across the studied cell types, and particularly in between the two thymocyte populations, in line with lower-resolution studies  (Dixon et al. , 2015; Hu et al. , 2018). To compare TAD organization more systematically, we computed insulation scores  (Crane et al.
, 2017)  and of a correlation between RNA polymerase occupancy and intragene contacts in ESCs  (Hsieh et al. , 2020)  in ultra-high-resolution Hi-C or Micro-C maps, although neither study had assessed whether domains were directly formed as a consequence of transcription. Analysis of the thymocyte Hi-C datasets, restricted to sufficiently long genes to accommodate for the resolution limit, also showed an overall positive correlation between RNA polymerase occupancy and intra-gene contact strength  (Fig 4D) , quantitatively very similar to what was observed in ESCs (Spearman correlation coefficient of 0. 60  (Hsieh et al. , 2020) , compared to DN3 (0. 63) and DP (0. 57) in this study). When clustering genes into four groups based on transcriptional output, metagene analysis confirms the gradual increase of intra-gene compaction by augmenting gene expression  (Fig 4E).
Hi-C analysis For convenience, Hi-C data were processed using the FAN-C suite of tools  (Kruse et al. , 2020) , entailing read mapping to the mm10 genome assembly, mating pairs, attribution to restriction fragments with filtering of self-ligated fragments and PCR duplicates, matrix balancing, and computing insulation scores. For compatibility with Capture Hi-C plots, the data for the relevant regions were extracted using the dump tool, and visualized as for the Capture Hi-C matrices.
Capture Hi-C preprocessing To ensure that the captured regions were treated independently of the non-captured regions, the datasets were processed with custom scripts, as entailed in  (Ben Zouari et al. , 2019). The initial steps are essentially the same as FAN-C, entailing read mapping with  Bowtie (Langmead et al. , 2009)  to the mm10 genome assembly, mating pairs, and attribution to restriction fragments with filtering of self-ligated fragments and PCR duplicates. Custom perl scripts filter the data to include only those where both ends of a paired read correspond to regions targeted by capture oligonucleotides, and to bin these submatrices to fixed genomic bins. Each captured sub-region was treated separately for matrix balancing with the Knight-Ruiz method  (Rao et al. , 2014 ; R code kindly shared by  Alexandra Pekowska).
All plots in this study visualizing (Capture)-HiC matrices alongside ChIP-seq tracks and insulation scores were generated by custom R scripts. 4C-seq analysis 4C-seq data were mapped to the mm10, processed, normalized and visualized using 4See  (Ben Zouari et al. , 2020) , as in  (Taylor et al. , 2022). Insulation scores were essentially computed as in  (Crane et al. , 2015). determined by counting interactions along sliding windows off the Hi-C diagonal, with lower counts/weaker interactions implying greater topological insulation and thus a greater likelihood of the existence of a TAD border. This has the additional advantage of giving a TAD border "score" for all genomic intervals, rather than simply calling TADs using a more complex set of (often arbitrary) parameters. The only tunable parameter required in computing insulation is the sliding window size, altering the sensitivity to TADs of different sizes within the folded hierarchy  (Zhan et al.
, 2017). Local insulation score minima are candidate TAD boundaries, and their boundary scores are computed as the difference in insulation score between the minima at the boundary and the adjacent bins (the delta in  Crane et al. , 2015) , with higher scores indicating sharper, stronger TAD borders. For 10 kb Hi-C data, insulation score was computed with the insulation tool of FAN-C, using sliding windows of 70, 100 and 150 kb (7, 10 and 15 bins). Local minima were subsequently identified with the FAN-C tool boundaries, using a window of 3 bins for computing the boundary score and with no initial threshold filter for minimal boundary score. For Capture Hi-C, insulation score was computed with custom R scripts on the balanced sub-matrices at 5 kb (for individual replicates) and 2 kb (for pooled data) resolutions, using sliding windows of  15, 25, 35, 50 and 75 kb (3, 5, 7, 10 and 15 bins), and 30, 40, 50, 80 and 100 kb (15, 20, 25, 40   Virtual 4C plots were derived as one row from the relevant Capture Hi-C normalized interaction score submatrix, applying smoothing with a running mean over three bins.
10 kb and 5 kb resolution, respectively. Positions of genes are denoted underneath the maps. B) Pooled Capture Hi-C interaction maps for a ~600 kb region around the Rag1 gene are shown for ESCs, DN3 and DP cells at 5 kb resolution. Below, in order, are shown positions of genes, and ChIP-seq tracks for CTCF and H3K27ac in DP cells. Bottom: 4C-seq profiles, using the Rag1 promoter as bait, performed in DN3 (blue) and DP (red) cells. Dashed lines denote position of Rag1 promoter and downstream CTCF site, which form an interaction in thymocytes, as shown on the Capture Hi-C and 4C maps. C) Plot of median normalized interaction score against genomic separation from the pooled Capture Hi-C (top) and Hi-C (bottom) datasets, at 5 kb and 10 kb resolution, respectively, for ESCs (green), DN3 (blue) and DP (red) cells.
kb (15 bin) window, on ESCs (green), DN3 (blue) and DP (red) cells. Whether analysis is restricted to the region targeted in the Capture Hi-C (top) or applied to the whole genome (bottom), ESCs have apparently more homogeneous insulation scores than thymocytes. B) Scatter plots comparing insulation scores from pooled Hi-C data at 10 kb resolution. Top: comparing ESC and DP cells using a 70 kb (7 bin) window; bottom: comparing DN3 and DP cells using a 150 kb (15 bin) window. SCC values are given on the graph. C) Plots of first two principal components for insulation scores computed on biological replicates of Capture Hi-C datasets at 5 kb resolution, using a 25 kb (5 bin) window (left) or a 75 kb (15 bin) window (right) for DN3 (blue), DP (red), untreated ESCs (green), and ESCs after either homozygous deletion of the major CTCF site at the Bcl6 promoter (yellow) or CRISPRa ectopic induction of Bcl6 (black) or Nfatc3 (gray).
Acknowledgments We thank  Susan Chan  for sharing reagents and  Alexandra Pekowska  for sharing R code for Hi-C matrix balancing. Sequencing was performed by the  IGBMC GenomEast platform , a member of the  France Génomique consortium  ( ANR-10-INBS-0009 ). This study was made possible because of the IGBMC flow cytometry and molecular biology platforms. We thank  PSMN (Pôle Scientifique de Modélisation Numérique) of the ENS de Lyon  for computing resources. Work in the Sexton lab was supported by funds from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Starting Grant  678624 -CHROMTOPOLOGY ), the  ATIP-Avenir program , and the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02.
Materials and methods: Electronic medical records from the Observational Medical Outcomes Partnership (OMOP) database were analysed retrospectively using propensity score matching to compare therapy advancement with iGlarLixi or BI + RAI in US adults ≥18 years with T2D on BI who had ≥1 valid glycated haemoglobin (HbA1c) value at baseline and at the 6-month follow-up. The primary objective was non-inferiority of iGlarLixi to BI + RAI in HbA1c change from baseline to 6 months (margin 0. 3%). Results: Propensity score matching generated cohorts with balanced baseline characteristics (N = 814 in each group). HbA1c reduction from baseline to 6 months with iGlarLixi was non-inferior to BI + RAI [mean difference (95% confidence interval): 0. 1 (À0. 1, 0. 2)%; one-sided p =. 0032]. At 6 months, weight gain was significantly lower with iGlarLixi than with BI + RAI [À0.
| Study design SoliSimplify was a retrospective, observational, cohort study of anonymized patient data collected from the Observational Medical Outcomes Partnership (OMOP) US Ambulatory electronic medical records (EMRs) database comparing outcomes with iGlarLixi and BI + RAI in adults with T2D advancing therapy from BI. Data were collected between 1 July 2016 and 28 February 2021 (Figure  1 ). A participant's index date was defined by their first prescription of either iGlarLixi or RAI and their baseline period began 6 months (180 days) before their index date. The follow-up period duration was variable, starting from index date to the earliest date of death, health plan disenrollment, last record or 6 months (180 days) postindex.
| Participants The database contained 3 730 396 participants with T2D with records during the study period. After applying sample selection criteria, 819 individuals were eligible to be included in the iGlarLixi cohort and 7298 were eligible for the BI + RAI cohort (Table  S1 ). Before PSM, cohorts differed in several baseline characteristics (STDDIFF >0. 1; Table  S2 ). After PSM, there were 814 participants in each cohort, with no differences observed between participant demographics and baseline characteristics (STDDIFF ≤0. 1 for all; Table  1 ). Participants in both cohorts had a number of comorbidities, the two most common being hypertension ($75% in each cohort) and hyperlipidaemia ($65% in each cohort). Participants in each cohort had a mean age of $60 years, a mean weight of $100 kg, and almost 30% were obese.
| DISCUSSION Real-world data, such as electronic health records and health insurance claims datasets, are increasingly used as a complement to  F I G U R E 2 Efficacy outcomes in PSM-matched cohorts. (A) Mean HbA1c; (B) mean body weight; (C) proportion of participants achieving HbA1c <7% (<53 mmol/mol) without any hypoglycaemia and without body weight gain at the 6-month follow-up. *One-sided p-value assessing non-inferiority of iGlarLixi versus BI + RAI with non-inferiority margin = 0. 3% (3 mmol/mol); †Two-sided p-value assessing superiority of iGlarLixi versus BI + RAI. BI, basal insulin; CI, confidence interval; HbA1c, glycated haemoglobin; iGlarLixi, insulin glargine 100 U/ml and lixisenatide; MD, mean difference; OR, odds ratio; RAI, rapid-acting insulin; SD, standard deviation, SE, standard error randomized clinical trials to inform health care and regulatory decision making.
10 These data provide an index of interactions between patients and the health care system and as such provide a measure of the clinical courses of individual patients or effectiveness of a pharmacological intervention at scale. 11 SoliSimplify was designed to compare the real world effectiveness of iGlarLixi with a BI + RAI regimen in a propensity-matched population of people with T2D advancing treatment from BI using anonymized patient data collected from OMOP US Ambulatory EMRs. In this setting, we found that after 6 months, iGlarLixi was non-inferior to a BI + RAI regimen in terms of HbA1c reduction and did not result in weight gain, while weight gain was seen with BI + RAI. Sensitivity analyses revealed that the results were not greatly influenced by slight variations in starting parameters or endpoint definitions and similar findings were seen in two subgroup analyses (HbA1c ≥75 mmol/mol or age ≥65 years at baseline).