dataesr/scientific-paragraphs-categorization huggingface dataset:
Population distribution of the diphtheria toxin gene To evaluate DIPHTOSCAN for its ability to detect the tox gene and to predict its toxigenicity, we used the 855 isolates for which data on tox qPCR and Elek test were available. DIPHTOSCAN detected that tox was located at the end of a contig and therefore incomplete in 3 cases (reported with a '$' suffix, indicating genomic assembly truncation). Of the 852 remaining isolates, 221 were tox-positive and 631 tox-negative by the reference qPCR method. DIPHTOSCAN detected the tox gene in 219 (99. 1%) of the tox-positive, and reported its absence in 2 isolates. Among the 631 tox-negative isolates, DIPHTOSCAN reported the absence of the gene in 625 (99. 0) isolates. 195 (98. 5%)  were predicted to be toxigenic by DIPHTOSCAN, whereas 1 was predicted to be non-toxigenic and for two isolates the tox gene was not detected.
Of the Elek-negative isolates, 11 (50. 0%) were predicted as non-toxigenic by DIPHTOSCAN. Thus, tox detection by DIPHTOSCAN was both sensitive and specific, whereas toxigenicity prediction was highly sensitive but not highly specific, likely due to unexplained non-toxigenicity in isolates with a full-length toxin gene. However, we did not observe non-toxigenicity-associated variation in the promoter region of the tox gene, nor on the DtxR protein sequence. In the France 2022 dataset, 45 genomes were detected as tox-positive and 44 of these were predicted as toxigenic, with 100% concordance with the Elek test. In comparison, within the global dataset, approximately one third of the isolates (331/976; 33. 9%) were tox-positive, as defined using DIPHTOSCAN, which detected a truncation and hence predicted non-toxigenicity in 16.
0% of these (52/331). Combining the global dataset with the France 2022 dataset (1077 genomes), DIPHTOSCAN identified 33 tox alleles. Among these, the most frequent are tox-2 (n=97, including the vaccine strain PW8), tox-3 (n=81) and tox-1 (n=41). These alleles are synonymous and thus result in the same amino acid sequence of the diphtheria toxin, implying complete match with the vaccine strain toxin. 25, 26, 27, 35, 36, 37 and 38  were predicted as NTTB by DIPHTOSCAN. The potential impact of protein changes deduced from tox gene sequence variation was previously analyzed  (Will et al. , 2021) ; we provide the correspondence of tox alleles in this previous study and ours in Figure  S8. The diversity of tox-positive isolates was evident from their distribution in the C. diphtheriae phylogenetic tree, but it was striking that the Gravis branch comprised much less tox-positive sublineages than the Mitis branch (Figure  3 ): in the Gravis lineage, there were only three main branches of tox-positive isolates: (i) an early-branching group of sublineages; (ii) a branch comprising SL377 and SL384 (two frequent sublineages in France-2022), and (iii) SL8.
NTTB isolates were only observed in the Mitis lineage (with one exception in Gravis-SL384) and this phenotype was acquired through multiple independent evolutionary events (Figure  3 ). A high diversity of tox-negative sublineages was also observed in the global dataset: whereas 173 of 245 (70. 6%) sublineages were entirely tox-negative, only 73 (29. 8%) of them had at least 1 tox-positive isolate. Of these, 50 sublineages were homogeneous for tox status (i. e. , they included uniquely tox-positive genomes), whereas 23 sublineages (9. 3%) included both tox-positive and tox-negative genomes (Table  S1 ; Figure  2 ), indicating that the gain or loss of the tox gene is not uncommon within sublineages. When considering the genomic clusters, almost all were either tox-positive or tox-negative in the global dataset.
Antimicrobial resistance DIPHTOSCAN includes a screen of C. diphtheriae genomes for the presence of antimicrobial resistance genes or mutations against 10 classes of antimicrobial agents. DIPHTOSCAN also computes a resistance score, defined as the number of antimicrobial classes for which at least one resistance gene or mutation is detected. The resistance score varied from 0 to 8 in the global dataset; 38. 2% non-redundant global isolates had at least one genomic resistance feature, and 118 isolates (12. 1%) were multidrug resistant (acquired resistance to ≥3 drug classes; Table  S1 ). Resistance feature frequencies are shown in Figure  4B  for the global dataset. The highest frequencies of resistance genes were observed for sulfonamides (exclusively gene sul1; rarely present in two copies; 260 non-redundant isolates; 26.
, 1995 (Tauch et al. , , 2003)). In the global dataset, 34 isolates (Table  S1 ; including strain BQ11 with three copies consistent with Forde et al. 2020) carried pbp2m and 35 carried ermX; 14 (1. 4%) isolates carried both genes. Sublineages SL297 and SL484 were the most common carriers of these genes, whereas the frequent multidrug resistant sublineages SL377, SL384 and SL301 did not carry ermX and pbp2m (Figure  S9 ). In France-2022, 8 (7. 9%) isolates carried both pbp2m and ermX. These were observed in patients with travel history from Mali (SL395, SL542, SL852) and Egypt (SL297-GC820). Antimicrobial susceptibility phenotypes were determined for the France-2022 dataset, and were highly concordant with the presence of resistance features (Table  S4 ). Resistance to penicillin and macrolides was associated with pbp2m and ermX, respectively, although some ermX-carrying isolates remained susceptible to erythromycin (Table  S4 ).
We included in DIPHTOSCAN a search for integrons, which may harbor multiple resistance genes in C. diphtheriae  (Barraud et al. , 2011; Arcari et al. , 2023). In the global dataset, we identified 45 (4. 6%) isolates carrying integrons (including integrase-less ones, i. e. , CALINs) (Table  S1 ), which were highly dispersed in the phylogeny (not shown). In France-2022, we found the presence of complete integrons in 9 isolates and integrase-less integrons in 9 additional isolates (18; 17. 8%). These structures were strongly associated with antimicrobial resistance, particularly to trimethoprim and sulfonamides (Figure  1 ; Table  S1 ).
Dual risk isolates: convergence of diphtheria toxin and multidrug resistance, including to first-line treatments The presence within the same isolates of multidrug resistance and toxigenicity could cause particularly threatening infections. We therefore explored the co-occurrence of these two genotypes (Figure  2 ). In the global dataset, 57 (5. 8%) isolates were both multidrug resistant and tox-positive. The majority of these isolates belonged to a few sublineages (Figure  2 ), including SL377, which comprised 9 tox-positive multidrug resistant isolates mostly from India (and also observed in France-2022). Eight convergent isolates of SL301 were also observed from India, Austria and Syria. SL453 had three tox-positive multidrug resistant isolates, which were isolated in Spain and France with links to Afghanistan  (Arcari et al.
, 2023). In metropolitan France, there were 22 tox-positive isolates that were multidrug resistant (21. 8%), with SL377 and SL696 being predominant among these (Table  S1 , Figure  1 ). Regarding resistance genes to first-line treatments, there was not a single isolate carrying at the same time tox, pbp2m and ermX in the global dataset (Table  S1 ). However, in France-2022, SL852 isolates (from two patients with travel history from Mali) were tox-positive and carried pbp2m and ermX. Furthermore, they carried other resistance genes including cmx, sul1, dfrA1, and in addition tet33 and aadA15 for isolate FRC1688. This latter isolate only lacked resistance features to quinolones and rifampicin. No other isolate of this particularly concerning sublineage (SL852) was found in the global dataset.
Nearly four in five (n=78) of the France-2022 isolates had a Mitis biotype (including 37 tox-positive), with 23 Gravis strains (8 tox-positive). To provide a population-level view of pathogenesis features in C. diphtheriae, we included in the DIPHTOSCAN database of searched genes, in addition to the tox gene, all virulence genes previously demonstrated or strongly suspected to be involved in diphtheria pathogenesis (see Table  S2  for pathogenesis involvement evidence). These include genes involved in iron and heme acquisition, fimbriae biosynthesis and assembly, and other adhesins  (Ott et al. , 2022). Screening for these genes in the global dataset revealed highly heterogeneous patterns of presence and phylogenetic distribution (Table  S1 ; Figure  S10 ). We found that a number of virulence factors are highly conserved within C.
We illustrate how this novel tool provides clinically-relevant genomic profiling and evolutionary understanding of emergence, by placing the 2022 C. diphtheriae from France in the context of 1,249 global C. diphtheriae genomes. Our results provide an updated overview of the population diversity of C. diphtheriae based on currently available genomic sequences. As previously reported  (Berger et al. , 2019; Seth-Smith & Egli, 2019; Hennart et al. , 2020; Guglielmini et al. , 2021) , C. diphtheriae is made up of multiple sublineages that are related through a star-like phylogeny. We here uncovered 123 novel sublineages, for a total of 253 described ones. We observed that, compared to previous datasets, there was no sublineage fusion upon adding novel genomes, which indicated an excellent stability of C.
diphtheriae sublineage classification. The latter provides a broad classification of isolates that correlates strongly with classical MLST, and which facilitates a deep-level approach to C. diphtheriae diversity and evolution. The naming of sublineages by inheritance of ST numbers will facilitate continuity with classical MLST. Besides, sublineage classification is more congruent with phylogenetic relationships: whereas most (140/146; 95. 8%) non-singleton sublineages were monophyletic, only 134 of 167 (79. 8%) non-singleton STs were (data not shown). We therefore strongly recommend transitioning from MLST to the cgMLST-based nomenclature, which is available on the BIGSdb-Pasteur platform. Our phylogenetic analysis of reference strains of the historical ribotype nomenclature provides a first overview of their relationships, to our knowledge, and allows revisiting genealogical inferences that were made among ribotypes based on CRISPR spacer variation  (Mokrousov, 2009).
Still, some NTTB isolates were not identified by DIPHTOSCAN. These cases may be attributable to (i) a lack of detection by the Elek test due to a low level of expression of the toxin gene in some strains, or (ii) yet unknown genetic mechanisms that abort tox gene expression entirely (unexplained true NTTB). Future work is needed to define the genotype-phenotype links underlying toxigenicity and to improve our predictive capacity of toxigenicity from genomic sequences. In the non-redundant global dataset, 16. 0% of tox-positive isolates were predicted as NTTB, which provides a quantitative view of the relevance of differentiating mere tox gene presence from actual toxigenicity. The capacity to predict toxigenicity from sequences opens interesting perspectives as to the diagnostic of diphtheria based on rapid genomic sequencing.
Such isolates may pose serious clinical management difficulties, and multidrug resistant C. diphtheriae should therefore be closely monitored. The combined analysis of the France-2022 and global datasets using a unique pipeline provides context to the reemergence of diphtheria (Figure  S6 ). The occurrence of cases of diphtheria among migrants, the vast majority of whom are not up to date with their vaccinations, raises concerns of the emergence of cluster cases in accommodation facilities for migrants, refugees or asylum seekers  (Badenschier et al. , 2022; Kofler et al. , 2022). Professionals dealing with these populations need to be particularly vigilant in spotting clinical signs of diphtheria and ensuring that their vaccinations are up to date. Here, we found that some sublineages contributing to the reemergence were previously observed, whereas others are described for the first time.
For example, SL377, one of the major toxigenic and resistant sublineages observed in France-2022, had been circulating in India during 2016 and was reported in Europe (Spain and France) since 2015 (Table  S1 ). In contrast, SL698 was absent from the global dataset. Of the 10 tox-positive France-2022 sublineages, five were associated with travel from Afghanistan, and were recently described in other European countries too  (Badenschier et al. , 2022; Kofler et al. , 2022). The DIPHTOSCAN tool will facilitate the harmonized characterization of C. diphtheriae sublineages of concern. Several virulence-associated genes were largely conserved in the entire C. diphtheriae population analyzed; these genomic features may therefore be central for C. diphtheriae colonization and transmission among humans, as there appears to be a strong selective pressure to maintain them.
Another striking feature we uncovered is the distribution of gene clusters coding for fimbriae. Previous work reported SpaA as being largely conserved in C. diphtheriae, with SpaD and SpaH being more variably present  (Reardon-Robinson & Ton-That, 2014; Sangal & Hoskisson, 2016; Ott, 2018). We found that SpaA was largely present in our dataset, however, the complete gene cluster spaABC-srtA was mostly found in the Gravis branch. SpaD was also more common among Gravis genomes, although the complete cluster (spaDEF-srtBC) was only detected in a minority of genomes. None of the Mitis isolates were positive for SpaH. These three Spa systems were experimentally shown to be involved in adhesion to different human tissues: pharyngeal (SpaA), laryngeal (SpaD) and pulmonary (SpaH) epithelial cells  (Mandlik et al.
, 2007; Reardon-Robinson & Ton-That, 2014). The Gravis/Mitis dichotomy in Spa-type fimbriae may have important implications regarding a possible differential ecology, transmission, tissue tropism and pathophysiology of these two major C. diphtheriae lineages. In conclusion, we developed and applied to a large dataset, the bioinformatics tool DIPHTOSCAN. Its public availability and ease of use will enable to conveniently extract and interpret genomic features that are relevant to the clinical and public health management of diphtheria cases, to understand the microbiological determinants of (re)emerging sublineages, and to future research on the genotype-clinical phenotype links in C. diphtheriae. This dedicated tool is also applicable to the other members of the C. diphtheriae complex, such as C.
Acknowledgements We thank  Martin Maiden  and  Keith Jolley  ( Oxford University ) for maintaining the previous MLST data from Oxford's PubMLST database and for providing the data for import into the BIGSdb-Pasteur C. diphtheriae species complex database. Preprint version 3 of this article has been peer-reviewed and recommended by Peer Community In Infections (García-Contreras, 2023; https://doi. org/10. 24072/pci. infections. 100080 ).
Funding MH was supported financially by the PhD grant "Codes4strains" from the  European Joint Programme One Health , which has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 773830. This work used the computational and storage services provided by the  IT department at Institut Pasteur. The  National Reference Center for Corynebacteria  of the Diphtheriae Complex is supported financially by the  Ministry of Health (Public Health France)  and  Institut Pasteur.
Methods First, the design and structure of the commercial lithium-ion pouch cell is discussed, which serves as the application case for this work. A section of this structure (unit cell) is modeled in detail using the finite element method. This unit cell is analyzed for three different load cases. These increases in complexity differ in terms of multiaxiality. For the solution, however, no conventional procedure is used but an approach based on PGD was utilized. For this, the linear global system of equations is exported using FEA software. The reordering of the degrees of freedom is then carried out on this. Subsequently, the problem is solved iteratively in-plane and out-of-plane until convergence occurs. By summing out several modes, the solution can be approximated as a finite sum of function products.
Unit cell A section of the described cell is used as an application example. A stack of 42 separators, 21 anodes, and 21 cathodes is utilized for this procedure. The commercial software VPS-Pam-Crash was used for modeling. The in-plane dimensions are 10 × 10 mm. The thickness of the stack results from the number of layers and the respective layer thicknesses. This leads to an overall dimension of 10 × 10 × 7. 14 mm for the section under study (Figure  1.
This consists of the global stiffness matrix K, the global external force vector F, and the global displacement vector U. Once the global system is assembled using the commercial solver, it can be solved quickly by applying the PGD-based separation of space approach. Python 3. 8 is used for this approach. The global displacement vector U computed via VPS is exported to compare the PGD-based solution. Moreover, an a posteriori separated approximation of U is built via the POD to be able to compare the PGD in-plane and outof-plane displacements.
The linear static calculation is carried out in parallel  (DMP 16 cores). This comparison shows that on average, only about half the computing time of the industrial solution is needed to calculate the first PGD mode. In addition, the PGD routine in Python is not optimized in terms of computing time, unlike the solver. The resulting error ε over the number of modes used is shown in Figure  4E. This shows that the first mode is already sufficient to achieve a deviation of only 2. 1 10 -6. With the following modes, this value can be minimized to 0. 25 10 -6. Figure  5  shows the results obtained with PGD compared to the POD results from the procedure illustrated in Section 2. 3. The left half shows the three normalized components of the first PGD mode, and the right half shows the same components with POD.
Conclusion In this work, an alternative modeling and simulation framework is proposed in the field of lithium-ion batteries, which are sensible toward out-of-plane loading. From one side, the study provides a useful in-plane/out-of-plane characterization of the mechanical behavior, preventing simplification hypotheses coming from shell-based formulations. On the other side, the methodology circumvents fully solid-based solutions, which are unaffordable for large-scale systems involving extremely thin structures. Moreover, an additional faced challenge is the minimal intrusiveness of the proposed procedure, which should be easily integrated into commercial FEA software. The algorithm exploits standard FEA software for the model definition, structure discretization, and finite element assembly.
However, including the PGD-based system solution in implicit computations appears as an appealing alternative. Indeed, the total complexity of the PGD algorithm is expressed as O(nK(m + L)), while that of classical methods, as O(3mL). Since n and K are usually small (in the present study, n ≤ 10 and K < 10 allow an accurate enough solution) and mL ≫ m + L, the PGD complexity is much lower than classical inversion, making the algorithm particularly attractive. The method is exemplified over three different load cases of a linearized static problem defined for a lithium-ion cell. The model is defined using the industrial software application VPS-Pam-Crash, and the structure is discretized using solid elements, obtaining a laminate with a large number of degrees of freedom along the thickness.
The results show that the deviation decreases with the number of modes used. For example, in load case I, one mode is sufficient to achieve a deviation of only 2. 1 10 -6. For the other load cases, which are more complex, several modes are needed to achieve a sufficiently small deviation below 1%. Thus, the load influences how many modes are required. By comparing PGD and POD, it was shown that almost the same results were obtained. The main advantage of the PGD method presented here is that no a priori database is required, while the POD-reduced model is built only after solving the full system. The computing time of the commercial FEA software application VPS-Pam-Crash (parallel computing) is compared with the needed time of the PGD routine. It has been shown that on average, it takes about the same time to calculate two modes using Python as it does for the classical FEM solution in VPS-Pam-Crash.
Phylogenetic Analysis The TEF-1α sequences of isolates were amplified by PCR. These PCR products were confirmed with aimed bands by agarose gel electrophoresis and purified using the DNA Gel Extraction kit TSP601 (Tsingke, Beijing, China). PCR products were then cloned and sequenced in the pMD18-T vector. The TEF-1α sequences were showed in Supplementary Table  S1. Additionally, TEF-1α sequences of other F. oxysporum isolates were obtained from the publicly available genomes (https://www. ncbi. nlm. nih. gov/, accessed on 30 July 2021). The analysis involved 31 isolate sequences that were aligned with MUSCLE  [29] , and the phylogenetic analyses were performed using maximum likelihood (ML). The phylogenetic tree was constructed using the MEGA 6. 0 software with the Kimura 2-parameter model  [30].
The pathogen was re-isolated from the inoculated, symptomatic plants and confirmed with the original one using microscopic observation and PCR. The pathogen was re-isolated from the inoculated, symptomatic plants and confirmed with the original one using microscopic observation and PCR. Values are the means of three replications. Data were transformed using log 10 (1 + data), the presented means are original, and the values inside the parentheses indicate transformed data; ANOVA was conducted on the transformed data. The mean values in the same column with the same letter are not statistically significantly different (p < 0. 05).
Acknowledgments: We are thankful to banana growers in the Chitwan and Nawalparasi districts for their active participation during field survey and interaction. We thank  Elizabeth Aitken , the  University of Queensland, Australia  for providing nit M mutants of the tester. We are also grateful to  Thirtha Devi Ghimire ,  Bindu Laxmi Shrestha , and  Pushpa Shrestha  for their contributions during laboratory works and to Bikram Datt Pant from the  Noble Research Institute, USA  for reading the manuscript.
s u m m a r y Background: Booster COVID-19 vaccines have shown efficacy in clinical trials and effectiveness in real-world data against symptomatic and severe illness. However, some people still become infected with SARS-CoV-2 following a third (booster) vaccination. This study describes the characteristics of SARS-CoV-2 illness following a third vaccination and assesses the risk of progression to symptomatic disease in SARS-CoV-2 infected individuals with time since vaccination. Methods: This prospective, community-based, case-control study used data from UK-based, adult (≥18 years) users of the COVID Symptom Study mobile application, self-reporting a first positive COVID-19 test between June 1, 2021 and April 1, 2022. To describe the characteristics of SARS-CoV-2 illness following a third vaccination, we selected cases and controls who had received a third and second dose of monovalent vaccination against COVID-19, respectively, and reported a first positive SARS-CoV-2 test at least 7 days after most recent vaccination.