dataesr/scientific-paragraphs-categorization huggingface dataset:
These deviations suggest that in unfavourable drift configuration plasmas the simple SOL model introduced in Section II to deduce the upstream E r in the SOL from outer mid-plane measurements is not sufficient and it neglects important mechanisms which could lead to a qualitative and quantitative agreement. Possible modifications could come from parallel currents in the SOL, which are assumed to be small in our analytical model, but could become more important at higher densities  26. Also, cross-field transport is not included in the simple SOL model. To capture these effects correctly, more sophisticated SOL modelling would be required as it is included in the SOLPS code package  91. figures/35753_8_SOL-Er. pdf  the three different phases. The predicted E r profiles reproduce qualitatively the changes in the experimental data with increasing plasma density, but a decent quantitative agreement between the experimental and the predicted E r profile is only found for the L-mode phase with highest density, for which v i,NEO × B ≈ 0.
ACKNOWLEDGMENTS The main author would like to thank  Gregor Birkenmeier ,  Raul Gerrù-Miguelañez ,  Ondrej Grover ,  Rachael McDermott ,  Ulrich Stroth  and  Elisabeth Wolfrum  for fruitful discussions and valuable input. This work has been carried out within the framework of the  EUROfusion Consortium , funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No  101052200 -EUROfusion ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the  European Commission  can be held responsible for them. Work of  A. Hubbard  was supported by  US Department of Energy  Awards  DE-FC02-99ER54512  and  DE-SC0014264.
A dataset of 912 BSI and 370 commensal isolates We compared the genomes of 912 strains from BSI in adults, originating from two prospective multicentric studies (Colibafi in 2005 and Septicoli in 2016-7  [12, 31] ) performed in the Paris area, to the genomes of 370 commensal strains gathered from stools of healthy adult subjects in  2000, 2001, 2002, 2010 and 2017  in Brittany and the Paris area (Fig  1A ). In-hospital death (or at day 28) was 12. 9 and 9. 5% in the Colibafi and Septicoli studies, respectively. Most of the BSI were community acquired (79. 6 and 54. 3% in the two collections, respectively). To avoid biases, all strains were isolated with similar protocols adapted to the sample origin (BSI and commensal) and sequenced in our laboratory using a similar approach (Illumina technology).
BSI strains are enriched in VAGs and antibiotic resistance genes (ARGs) as compared to commensal strains Using a targeted approach, we next focused on the frequency of known VAGs and ARGs in both collections. A global comparison in the number of VAGs classified in functional categories showed a significantly higher presence of VAGs coding for adhesins, iron acquisition systems, protectins and toxins categories in BSI strains (Fig 3A and S4 Table ). We found similar results when comparing against BSI strains with urinary portal of entry to commensals (Fig  3B ). However, only the iron acquisition systems category remained significant when comparing against BSI strains with digestive portal of entry (Fig  3C ). More precisely for the full dataset, the highest significance was observed for the pap genes with the papGII allele, followed by the sit, iuc and irp2/fyuA (HPI) genes, all with p-values << 10 -10 (S4 Table ).
Bacterial genetic factors explain a large fraction of the variation in the BSI phenotype We then computed the heritability, as the proportion of the variance of a phenotype explained by variable genetic factors  [41] , to estimate whether we could expect to find bacterial genetic variants associated with commensalism vs. BSI in our dataset. We first measured the heritability using the ST information alone, to measure the influence of the genetic background on phenotypic variability. We then computed the heritability emerging from the individual genetic variants (Fig  4A ). We found that STs could explain 24%, 28%, and 11% of the phenotypic variance in the full collection, the subset with BSI isolates with urinary tract as portal of entry and digestive tract as portal of entry, respectively.
A whole-genome machine learning model differentiates commensals from BSI strains We applied a machine learning model trained on both the core and accessory genome of the strains to differentiate between commensal and BSI strains and highlight the genetic variants that contribute the most to the discriminatory power of the model (whole genome wg-GWAS). We performed the analysis on three different datasets: the full strain collection, and bound of the CI because the estimated odds ratios are huge as none of the commensal isolates were resistant to these antibiotics. AMK, amikacin; AMP, ampicillin; CTX/CAZ, cefotaxime/ceftazidime; FEP, cefepime; FQ, fluoroquinolones; GEN, gentamicin; SXT, cotrimoxazole; TZP, piperacillin/tazobactam. (G) Repertoire of adhesins and (H) iron capture systems in BSI and commensal strains.
Moreover, we find lysO and aqpZ in both subsets. Taken as a whole, we found the associated genes to be enriched in the L COG category (replication, recombination and repair) for the three subsets, and in the K COG category (transcription) for the full dataset only. We also performed a Gene Ontology (GO) term enrichment analysis and found that for the subset with BSI isolates with urinary tract as portal of entry, the relevant (depth > 1) enriched GO terms include different categories related to metabolic processes, ion binding and intracellular anatomical structure (S6 Table ). Similarly, to the targeted analysis described above, we found that the genes resulting from the three associations were enriched for VAGs and ARGs (Fig 4C ); when considering all VAGs and ARGs together we found a significant (p-value < 0.
coli BSI are a major cause of morbidity and death in Western countries. To investigate temporal trends in pathogenicity, we computed the pathogenicity score with the machine learning model (used to predict the commensal vs. BSI status of strains), in a dataset of commensals from 1980 to 2010 in France  [32]. We found that the proportion of commensal E. coli isolates predicted to be pathogenic isolates with our trained model increased over time, from 23% in the collection from 1980 to 31% in 2000, and then to 46% in the collection from 2010 (S5 Fig). Even though sample storage issues may slightly alter the predictions for samples from 1980 (S6 Fig,  panel B ), we see an even higher increase between 2000 and 2010. The signal of increased pathogenicity would be worth replicating in independent datasets.
Strain collections We studied the whole genomes of 1282 E. coli strains divided in two datasets, 370 commensals strains and 912 BSI strains. Commensal strains were gathered from stools of 370 healthy adults living in the Paris area or Brittany (both locations in the Northern part of France) between 2000 to 2017. These strains come from five previously published collections: ROAR in 2000 (n = 50)  [92]    ). In addition, a collection of 53 commensal strains sampled in 1980 from 53 healthy subjects in Paris (VDG collection)  [49]  was used to assess the temporal trend of pathogenicity. BSI isolates (Colibafi (n = 367) and Septicoli (n = 545) collections) were collected in 2005 and 2016-2017, respectively  [96]. In all studies, one single E. coli colony randomly picked was retained per individual after plating the blood cultures on non-selective rich medium, or the stools on Drigalsky plates.
05 level). Next, we compared the phylogenetic distribution of the commensal collection with the BSI collection. For all strains, we calculated the cumulative frequency distribution of STs in the commensal collection, and we compared it to the same distribution in 200 random sub-samples of 370 sequences from the BSI collection. We plotted the pangenome variation with the number of genomes analyzed (Panaroo output). We evaluated the pangenome variation between commensal and BSI isolates with Panstripe [105] using the output of FastTree (phylogeny of all strains) and Panaroo (gene presence absence matrix). We randomly subsampled 100 trees of 370 tips from the BSI phylogeny (n = 912) and compared the rate of gene gain and loss between those trees and the commensal tree (n = 370). To quantify the genetic diversity, we computed the pairwise nucleotide diversity (π) [106] in R (package ape)  [107].
The mean effect size was +0. 0012 per year (+3. 5% frequency over 30 years). This trend could be due to the loss of genes in older collections (possible if the bacteria replicate slowly, in particular in stab culture and for plasmid-borne genes), but also to changes in the phylogenetic composition of the population. To assess how this slight trend could have changed pathogenicity, we first identified the unitigs selected in the GWAS that reflect gene presence/absence. The criterion was a correlation > 0. 5 between gene presence/absence and unitig presence/absence. 13 unitigs out of 59 met this criterion. We then simulated two synthetic datasets of size 10000, one reflecting our main dataset (2000-2010) and one reflecting a hypothetical dataset of 1980 affected by the -3. 5% frequency of the 13 genes identified selected in the GWAS.
This was done by randomly drawing unitig presence/absence for each sequence with the corresponding frequency, and neglecting linkage. We predicted that the synthetic dataset of 1980 counted 27. 5% of commensals, and that of 2000-2010 27. 1% of commensals. Thus, a bias in gene content in 1980 could not possibly have caused the observed trends in pathogenicity, both because the observed trend in gene content is weak, and because the GWAS model does not predict a strong net positive effect of more genes on pathogenicity. confounding (right; +100% increase in incidence of infection). The case-control study consistently has better power but is subject to over-estimating the effect size of a variant because of confounding. The case-crossover study has very poor power when the number of strains is small, maintains the same power regardless of confounding, and can estimate properly the effect size with large number of colonizing strains.
Funding: ED was partially supported by the " Fondation pour la Recherche Medicale " ( Equipe FRM 2016 , grant number  DEQ20161136698 ). GR was supported by a " Poste d'accueil " funded by the " Assistance Publique-Hopitaux de Paris " (AP-HP) and the " Commissariat à l'energie atomique et aux energies alternatives " (CEA) personal grant for his PhD. MG and JB were supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under Germany's  Excellence Strategy -EXC 2155  -project number  390874280. JB was further supported by the  Deutsche Forschungsgemeinschaft  grant number  GA 3191/ 1-1. F. B. was funded by the  ERC   StG 949208   EvoComBac. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Code availability Apart from the software packages mentioned in the previous sections, the following were used to run the analysis and generate the visualizations presented in this work: pandas v1. 3. 4 [114], numpy v1. 20. 3 [114], scipy v1. 7. 1 [115], matplotlib v3. 4. 3 [116], seaborn v0. 11. 2 [117], biopython v1. 80 [118] jupyterlab v3. 2. 1 [119]. Most of the analysis were incorporated in a reproducible pipeline using snakemake v7. 18. 1 [120] and conda v4. 10. 3 [121], which is available as a code repository on GitHub (https://github. com/jburgaya/2022_ecoli_commensal) under a permissive licence (MIT).
Simulated B 0 → K 0 S π 0 events are used to model signal decays and calculate the reconstruction efficiency. We use EvtGen  [19]  to generate ϒð4SÞ → BB with the subsequent B-meson decays and PHOTOS  [20]  to incorporate final-state radiation from charged particles. The simulation of q q background relies on the KKMC generator  [21]  interfaced to PYTHIA  [22]. The detector response for final-state particles is simulated with Geant4  [23]. Events are reconstructed using the Belle II software  [24, 25]. Candidate K 0 S mesons are reconstructed from pairs of oppositely charged tracks, which are assumed to be pions and fit to a common vertex. The resulting invariant mass is required to lie between 489 and 507 MeV, corresponding to a AE3σ range around the known K 0 S mass  [26] , with σ being the resolution.
We also apply a similar interaction-region constraint as that used for tracks on the signal side. We approximate Δt to be Δl=βγγ Ã , where Δl is the distance between signal-and tag-side vertices along the e -beam direction, βγ (≈0. 28) is the Lorentz boost of the ϒð4SÞ in the lab frame, and γ Ã (≈1. 002) is the Lorentz factor of the B 0 meson in the c. m. frame. We employ a BDT classifier that uses 32 event-topology variables to distinguish the q q background from B-meson decays. The following variables provide the most discrimination: modified Fox-Wolfram moments  [28] , CLEO  [29] , the thrust value  [30]  of the rest of the event, and the cosine of the angle between the thrust axis of the signal B and that of the rest of the event. The BDT is trained on samples of simulated e þ e -→ q q and signal events, each equivalent to about 3 times the size of the dataset.
We use a category-based multivariate flavor-tagging algorithm for this purpose  [31]. The algorithm outputs two parameters, the b-flavor charge q and r, which is an event-by-event tagging quality factor ranging from zero for no flavor discrimination to one for unambiguous flavor assignment. The dataset is divided into seven r bins that contain similar numbers of events, but have different signal-to-background ratios. We select events in which Δt is well measured by requiring jΔtj < 10. 0 ps and σ Δt < 2. 5 ps, where σ Δt is the uncertainty on Δt, estimated event by event. The Δt distribution of these events is fitted to determine C and S. For the remaining events, about 40%, the Δt distribution is not included in the fit. However, these events are still useful to constrain C, which is sensitive to the relative yields of B 0 and B 0 decays.
047 for C and AE0. 040 for S. In summary, we have measured the CP-violating parameters C and S in B 0 → K 0 S π 0 decays using a sample of 387 × 10 6 BB events recorded by Belle II in e þ e -collisions at the ϒð4SÞ resonance. Based on a signal yield of 415 þ26 -25 events, we obtain C ¼ -0. 04 þ0. where the first uncertainties are statistical and the second are systematic. This constitutes the first Belle II measurement of CP asymmetries in the decay. Our results agree with previous determinations  [13, 14] , and the precision obtained for S is better than (similar to) that achieved at Belle (BABAR), despite using a data sample only 60%-80% the size of the samples used in those experiments. The results are consistent with SM predictions and can provide useful constraints on non-SM physics. This work, based on data collected using the Belle II detector, which was built and commissioned prior to March.
2020. 02/0257 , and  Ministry of Education and Science of Ukraine ; the  U. S. National Science Foundation  and Research Grants No. PHY-1913789  and No. PHY-2111604 , and the  U. S. Department of Energy and Research  Awards No. DE-AC06-76RLO1830 , No. DE-SC0007983 , No. DE-SC0009824 , No. DE-SC0009973 , No. DE-SC0010007 , No. DE-SC0010073 , No. DE-SC0010118 , No. DE-SC0010504 , No. DE-SC0011784 , No. DE-SC0012704 , No. DE-SC0019230 , No. DE-SC0021274 , No. DE-SC0022350 , No. DE-SC0023470 ; and the  Vietnam Academy of Science and Technology (VAST)  under Grant No. DL0000. 05/21-23. These acknowledgements are not to be interpreted as an endorsement of any statement made by any of our institutes, funding agencies, governments, or their representatives. We thank the  SuperKEKB team  for delivering high-luminosity collisions; the  KEK cryogenics group  for the efficient operation of the detector solenoid magnet; the KEK computer group and the  NII  for on-site computing support and SINET6 network support; and the raw-data centers at  BNL ,  DESY ,  GridKa ,  IN2P3 ,  INFN , and the  University of Victoria  for off-site computing support.
Acknowledgements We acknowledge the  DFG-CRC 1573 "4  f for Future" and the  Karlsruhe Nano Micro Facility  (KNMF, www. kit. edu/knmf) for the provision of access to instruments at their laboratories. EMP thanks the  Panamanian National System of Investigators (SNI)  and  SENACYT  (project  PFID-FID-2021-60 ) for support. WW thanks the A. v. Humboldt Foundation  and the  ERC  grant MoQuOS No. 741276.
Data availability Supplementary information is available in the online version of the paper. Full crystallographic details for the complexes can be found in CIF format: see the Cambridge Crystallographic Data Centre database (CCDC 2192851-1547623). The experimental data for Figs. 4a, c  and 5  generated in this study have been deposited in the Figshare database under accession code https://figshare. com/s/af2dc483e82240f8d835. Source data are provided with this paper.
In general, the sequence-to-function mapping of structured RNAs is a complex problem. In the course of evolution, sequence patterns necessary for function are conserved, suggesting that large sequence datasets can shed light on this mapping. Comparative analysis of RNA sequences sampled during evolution and collected in Multiple Sequence Alignments (MSA)  (46)  have been applied to predict RNA structures from sequence only  (5, 42, 53). Computational methods exploiting sequence co-variation have been successful in predicting RNA secondary structure, even before experimental probing  (18, 24). The majority of the computational algorithms introduced to predict the structure of RNAs rely on strong simplifying assumptions that are enforced for purely technical reasons (mainly computational efficiency  (53, 75) ), such as ignoring pseudo-knots and other tertiary contacts, which however are known to be biologically important.
Multiple sequence alignment of SAM-I riboswitches The RF00162 family from the Rfam database (31) groups sequence homologs of the aptamer domain of the SAM-I riboswitch. We downloaded a manually curated seed alignment from Rfam (version 14. 7), containing 457 purported aptamer sequences supported by literature evidence. These seed sequences are aligned to a consensus secondary structure (shown in Figure  1B ) that has been informed by the holoform of SAM-I riboswitch crystal structures  (36, 43). After removing extended stems and variable loops, labeled as insertions in the alignment, we obtain 108 matched positions (including gaps that mark deletions) spanning four helices that interleave around a central four-way junction. We trained a covariance model (CM)  (17)  on this seed alignment using Infernal  (46)  with default settings.
Following standard protocols  (30) , we then acquired 6161 additional sequences from Rfam, collected from genome databases search fetching for significant matches to the CM model. We constructed a multiple sequence alignment (MSA) containing these sequences, that we will refer to below as the full MSA, to distinguish from the seed MSA that consists only of the 457 manually curated seed sequences. The sequence conservation logo of the full MSA is shown in Figure  1A.
Infernal pipeline Infernal (  46 ) is a set of computational tools to facilitate modelling RNA sequence families under a profile stochastic context-free grammar (pSCFG) formalism, also known as covariance models (CM)  (17). A CM is capable of modelling the conservation profile of important sites along the sequence, as well as correlations between distant sites required by the complementarity of base-pairs in a given secondary structure. Infernal is routinely used in the maintenance of alignments in the Rfam database  (30, 31). We employed Infernal to construct the RF00162 full MSA that we use to train the RBM with the procedure described in the previous section, as explained above. Fundamental assumptions at the core of the CM enable implementation of efficient dynamical programming algorithms to train, sample, and scan large genomes for sequences that score highly under the CM  (17).
However, these assumptions also imply that the CM is unable to include additional constraints in the probabilistic sequence model, such as pseudoknots and other tertiary contacts in the 3dimensional fold of the RNA molecule. Rfam CM. The Rfam database associates a CM model to each family, trained on the seed alignment, that is used to efficiently scan large genomes for significant sequence matches to the family (the hits). The raw CM model downloaded from Rfam is significantly regularized so that it is more effective in fetching far homologs of a family in deep genome searches  (45). We will refer to this CM model as the Rfam CM. Refined CM. Since the Rfam CM is strongly regularized, in this work, we also trained another CM model on the full MSA, with no regularization, that we call hereafter the Refined CM.
Accordingly, we proceeded to train a new CM model on the rearranged full MSA. We call the resulting model the Untangled CM in what follows. Sampling the CMs. To better understand the limitations of CM models and the advantages of RBM, we sampled 10000 sequences from each of the three CMs described above. For the Untangled CM, the rearranged columns are permuted back to their original positions after sampling. We used Infernal's cmemit program with default parameters, and without insertions. Infernal computes a score of sequences aligned to the CM, related to the likelihood of the CM to emit the a given sequence. We computed this score using cmalign, with -g (global) option to avoid local approximations  (45).
Biophysical energy calculations We computed biophysical binding energy predictions for formation of P1 and the pseudoknot of various sequences using the Turner energy model, as implemented in the ViennaRNA package  (35) , with the RNAeval program. For the P1 helix, we computed the energy difference of each sequence in the consensus secondary structure, which has P1 paired (shown in Figure  1B ), and in a conformation where P1 is unpaired (Figure  1A ). To estimate the binding energy associated to the pseudoknot, we used RNAeval on a secondary structure that includes only the pseudoknot base paired sites, with all other sites unpaired. We then considered only interior loop contributions to the resulting folding energy. Note that in both cases, intrinsic limitations of the ViennaRNA implementation imply that we cannot model the pseudoknot with other structural elements (and other tertiary contacts) simultaneously.
SHAPE-MaP experiments Selection of a representative set of natural SAM-I aptamers sequences. SAM-I aptamer natural sequences were downloaded from the RFAM database  (31). Then CD-hit clustering program (https://github. com/ weizhongli/cdhit-web-server) was used to select a set of representative sequences  (34). An identity cut-off of 85% and 70% was applied for sequences extracted from the seed alignment and the full SAM-I sequence database respectively. In total, 206 clusters were generated with a representative sequence corresponding to each one (151 sequences from to the seed MSA and 55 sequences from to the full MSA). RNA preparation. DNA oligonucleotides representing the 206 sequences of the natural aptamers and the 100 sequences of the artificial aptamers preceded by the T7 promoter (5'CGGCGAATCTAATACGACTCACTATAGG3') and followed by a tag sequence representing a 10 nucleotide barcode unique for each aptamer and a primer biding site were purchased as an oligonucleotide pool (Twist bioscience®).