dataesr/scientific-paragraphs-categorization huggingface dataset:
The Statistica software (Statsoft, version 12, Tulsa, OK, USA) was used to perform statistical analyses. Significance was set at p < 0. 05. Partial eta squared (ηp 2 ) was used as a measure of effect size (ES) for the factors in each ANOVA. Small, medium, or large effects were considered for ηp 2 ≥ 0. 01, ≥0. 06, and ≥0. 14, respectively. G*Power (version 3. 1. 9. 2, Germany) was used to compute Cohen's d ES for all significant pairwise comparisons. d ≥ 0. 20, ≥0. 50, and ≥0. 80 represent small, medium, and large effects, respectively. The ES of the Friedman ANOVAs corresponded to Kendall's W coefficients, and the ES for Wilcoxon matched-pairs tests were computed as the Z-score divided by the square root of the pair number. Kendall's W coefficient and Z score interpretation were shared. Small, moderate, and large ESs corresponded to values of ≥0.
Electromyographic Activity during LV Concerning both muscles, there was no effect from Condition (p > 0. 102) or Duration (p > 0. 142) or Condition × Duration interaction (p > 0. 336) for the RMS COND. M -1 (Table  3 ). Table  3. Electromyographic activity (root-mean-square) of the rectus femoris (RMS COND. M -1 RF) and vastus lateralis (RMS COND. M -1 VL) muscle expressed as a percentage of the maximal M-wave recorded during the MVIC prior to recording for the 14 subjects. The data are reported for each duration (i. e. , 1, 3, or 6 min) for the control (CONT) and local vibration (LV) conditions.
High quality HMW DNA was subjected to sequencing using 400 ng DNA input per isolate in a rapid library preparation (SQK-RBK004; ONT). A maximum of 10 isolates were multiplexed on a single R. 9. 4. 1 flow cell. Sequencing was done on a GridION device (ONT), allowing raw data demultiplexing and collection in the MinKNOW software (ONT). To evaluate nanopore sequencing quality and accuracy, DNA from an E. coli ATCC 25922 strain was isolated and sequenced in the same workflow as described before. This was done in triplicate to address final genome completeness and accuracy as determined against the E. coli K ATCC 25922 genome (NZ_CP009072. 1) using CheckM (based on 1,212 genes from 27 E. coli reference genomes) and pomoxis (ONT), respectively  (Vereecke et al. , 2020 (Vereecke et al. , , 2023;; Bokma et al.
Contigs were classified to chromosomes and plasmids using ViralVerify v1. 1. 3 Completeness and accuracy of final consensus genomes was assessed using ribosomal multi-locus sequence typing (rMLST)  (Jolley et al. , 2018)  and CheckM v1. 1. 0 (Parks et al. , 2015). A genome was considered complete based on 1,212 marker genes from 27 Escherichia genomes. When all marker genes were identified, a completeness of 100% was reported. Genome QC reports and associated NCBI accession numbers can be found in Supplementary Table  S1.
Identification of genetic virulence-and AMR-associated hallmarks Phylogenetic inference was done using the csi phylogeny tool to obtain a single-nucleotide polymorphism (SNP) alignment of all genomes  (Kaas et al. , 2014). The ETEC strain 1729 was used as reference. The SNP alignment was then used to build a maximum likelihood (ML) phylogenetic tree in IQ-tree2 v1. 6. 1 with 1,000 ultrafast bootstraps (-bb)  (Minh et al. , 2020). Tree visualizations were done in iTOL  (Letunic and Bork, 2021). Genomes were screened for virulence and ARG markers using Abricate v1. 0. 1 4  and most recent databases of virulence [ecoli_vf: virulence factor database (vfdb)] 5  and resistance genes [Resfinder:  (Bortolaia et al. , 2020) ]. Outputs are summarized in Supplementary Table  S3. E. coli-specific Resfinder searches were also performed on the center for genomic epidemiology v4.
(2019) , which included the generation of confusion matrix (i. e. , true positives, true negatives, false positives, false negatives) to be used in the meta v4. 20-2  (Schwarzer et al. , 2015)  and metafor v3. 8. 1  (Viechtbauer, 2010)  packages. The former allowed to log transform the data (sm = "PLN" in meta function) and determine Clopper-Pearson (CP) confidence intervals (method. ci = "CP" in meta function). The metafor package was used to generate forester plots using the forester function in R v4. 2. 1. An overview of these results is given in Supplementary Table  S4   (Shim et al. , 2019). Results of multiplex PCR and phenotypic resistance based on NRI-based ECOFF values were considered as "gold" standard in these calculations. To study the plasmid landscape, in-depth characterization and mobilization potential of virulence-and ARG-encoding plasmids was done using the MOB-suite v3.
In eight serovirotypes the stA and stB genes were present. While the stx2e genes were identified in 12 of these virotypes, EAST1 and AIDA were identified in one and eight serovirotypes, respectively. Within the CFA group, no hemolytic strains were observed. A total of ten different serovirotypes were detected, of which two showed no virulence genes (O9 and 058). The stx2e genes were identified in seven CFA-positive serovirotypes. In strains without known fimbriae, four out of 14 serovirotypes showed the presence of the non-fimbrial adhesin AIDA. Interestingly, only six serovirotypes showed hemolytic features and 11 showed the presence of the stx2e genes. Also, a wide variety of other virulence factors have been described. In the current dataset the eaeH, cah, and tia genes were identified, though their actual role in porcine disease is not clear yet (data not shown).
Adding the aac(3)-IId genetic marker for gentamicin AST prediction, showed a 67%/100% Sn/Sp. Lowering the NRI-based ECOFF to 19 mm, did not result in changes in the predictive measures here (Figure  3B , light blue). Next, kanamycin and paromomycin were grouped together for which the aminoglycoside-phosphotransferase aph(3′)-Ia gene was identified as best predictor (100%/97% Sn/Sp; Figure  3C , light blue). Even though for spectinomycin different reference databases, E. coli-specific Resfinder, gene-, and k-merbased GWAS identified various adenylyltransferase (aadA) genes, no clear associations could be made. The aadA1, aadA2, aadA5, aadA10, and aadA12 genes were identified in the dataset, but only aadA1 (n = 1), aadA10 (n = 1), and aadA12 (n = 1) showed apparent correlation with the observed phenotypes.
Even though the aadA2 gene was present in 14 genomes, only nine strains showed a spectinomycin resistance phenotype (Figure  3D , dark blue). Due to the antibiotic mechanism of aminoglycosides, 16S rRNA gene sequences were screened for known point mutations (data not shown). Still, no correlations with the observed phenotypes could be made. Nevertheless, a non-WT (resistant) population was observed for spectinomycin (Supplementary Figure  S1 ). Using the aad1, aad2, aad10, and aad12 genes and NRI-based ECOFF (WT ≥ 23 mm), Sn/Sp measures of 38%/85% were obtained. Lowering this to ≥22 mm, slightly increased both Sn and Sp to 50 and 88%, respectively (Figure  3D ; dark blue). Finally, predictive measures could be obtained for acquired resistance toward florfenicol, using the the presence of the plasmid-encoded floR gene (Figure  3E , light brown).
The aadA5 and dfrA17 gene were found on a hly(CABD) and fed(ABCEF) plasmid in strain 1737, although these genes were not associated with resistance in our dataset (Figures  4A, D ). Finally, in strain 4085 the hly(CABD) operon was located next to genes conferring resistance toward beta-lactam (bla TEM-1C ) and aminoglycosides (aph(6)-Id, aph(4)-Ia, and aph(3″)-Ib) among others. Virulence-encoding plasmids were categorized in a minimum of 36 different types, of which 27 were classified as conjugative. Three others were considered mobilizable and six non-mobilizable. The plasmid host range was "limited" to the Enterobacterales for all plasmids. Interestingly, two plasmids showed the lack of the fedA gene but the presence of all other F18 operon genes. On the contrary, four plasmids showed the presence of only the fedB gene.
Discussion In the current study, we applied long-read nanopore sequencing on 94 porcine ETEC/STEC field isolates to determine the predictive power of existing and future sequencing-based diagnostic workflows for combined virulence and genetic AST. Virulence and AMR-associated genes obtained from WGS data were evaluated against data from a virulence multiplex PCR and phenotypic disc diffusion tests, respectively. In addition, our data allowed to assess serovirotypes, along with in-depth characterization of plasmids since complete circular assemblies were obtained from longread data. Predictive estimates for alpha-hemolysis, fimbriae (F4 and F18), and enterotoxins (LT, STa, STb, and Stx2e) were high. A sensitivity of 100% was observed for hemolysis (hly(CABD)), F18 (fed(ABCEF)), LT (eltA and eltB), and Stx2e (Stx2eA and Stx2eB genes).
g. , F18/CFA, F4/CFA, and F4/F18) might be because of mixed populations  (Francis, 2002; de Lagarde et al. , 2021)  or hybrid ETEC/STEC strains as previously reported in Sweden  (Bai et al. , 2019). Overall, up to 17 different serovirotypes were identified, including the common O139:Stx2e:AIDA  (Fairbrother and Nadeau, 2019). Most common serogroups were also present in our dataset as observed in recent studies from Australia, Canada, and South-Korea  (Abraham et al. , 2012; de Lagarde et al. , 2021; Byun et al. , 2022; Do et al. , 2022). For the F18 group, all strains showed alpha-hemolytic features as previously reported  (Fairbrother and Nadeau, 2019). The non-fimbrial adhesin AIDA was also identified in a wide variety of serovirotypes, which seemed not to be limited to the STb or STb:EAST1 virotypes as previously reported  (Mainil et al.
, 2002; Fairbrother and Nadeau, 2019). Our dataset confirms the controversial role of EAST1 as it was never found alone in any of the virotypes. It was suggested that EAST1 on its own is unable to result in disease phenotypes, but requires the LT enterotoxin  (Dubreuil et al. , 2016). We observed LT and EAST1 to be co-occurring in F4 virotypes, though other combinations with EAST1 were observed in F18 and virotypes with unknown colonization factors. To the authors knowledge, this is one of the few reports on pig ETEC/STEC strains that includes the cfa(ABCEF) operon in pathotyping. While most studies perform targeted multiplex PCRs to characterize pathogenic E. coli strains, WGS facilitates the identification of a wider variety of adhesins and enterotoxins (e. g. , eae and cfa(ABCEF)), without prior PCR selection  (Botteldoorn et al.
, 2003; Casey and Bosworth, 2009; Abraham et al. , 2012; Byun et al. , 2022; Do et al. , 2022). So far, sequencing has not been encouraged in AST since timeconsuming phenotypic alternatives are still considered the "gold" standard. Nevertheless, implementing sequencing-based (meta) genomics could speed up diagnostics of enteric colibacillosis significantly by delivering strain-level identification, pathotyping, and genetic AST. Our data showed promising results for most of the relevant antimicrobial drugs. Over the last years, a multitude of studies reported a variety of resistance genes without proper associations with phenotypes  (Eyre et al. , 2017; Nguyen et al. , 2018). Nevertheless, ARG database searches often result in extensive lists of genes, which do not necessarily contribute to phenotypic resistance.
By applying combined searches of databases (e. g. , abricate and Resfinder) in combination with GWAS tools (e. g. , gene-and k-mer based) a more comprehensive output can be generated  (Jaillard et al. , 2018; Farhat et al. , 2019; Bokma et al. , 2021; Weber et al. , 2021; Cardenas-Alvarez et al. , 2022). Here, good predictive power was shown for 13 different antimicrobial drugs for which a non-WT population (ECOFF) was determined based on the NRI method for their AMR phenotypes. Interestingly, a combination of 25 ARGs and seven point mutations allowed genetic AST of ETEC/STEC strains. Sensitivity measures ranged from 50% (spectinomycin: aadA genes), to 67% (gentamicin: aac genes), to 85% (trimethoprim-sulfa; dfrA genes) 97% (doxycycline: tet genes) up to 100% (kanamycin/paromomycin: aph genes; colistin: mrc genes).
mL -1 ), even in isolates with MIC values ≤0. 25 μg. mL -1  (Pogue et al. , 2020). This could be the case for the two false-negative samples, which is an additional advantage for the use of NGS in bacterial AST. Even though point mutations in regulators associated with colistin resistance mechanisms (e. g. , pmrA, pmrB, phoP, phoQ, mgrB, and prmHFIJKLML) were shown to be important, colistin resistance in our dataset seemed to be majorly mediated through the presence of mcr genes  (Gogry et al. , 2021). Lowered predictive values can generally be explained by the limited number of strains belonging to the non-WT population (colistin, n = 8; gentamicin, n = 9; florfenicol, n = 6; apramycin, n = 5; paromomycin, n = 4; cefalexin, n = 2; and kanamycin, n = 4), which represents a limitation of current study.
Still, our predictive measures for amoxicillin (94%/95%), colistin (100%/98%), apramycin (100%/100%), gentamicin (67%/100%), kanamycin/paromomycin (100%/97%), florfenicol (83%/100%), doxycycline (97%/95%), tetracycline (86%/92. %), enrofloxacin (76%/95%), flumequine (93%/96%), marbofloxacin (80%/99%), and trimethoprim-sulfa (85%/87%) are highly satisfactory. The available literature showed comparable genetic associations in E. coli and other bacterial species (e. g. , Pseudomonas aeruginosa)  (Zankari et al. , 2012; Wohlwend et al. , 2015; Nageeb and Hetta, 2022; Olorunleke et al. , 2022; Peng et al. , 2022). These predictive measures were higher than when only ARG databases, such as the comprehensive AMR database (CARD) or Resfinder, were used  (Mahfouz et al. , 2020). They were also better than reported genetic predictions for Klebsiella pneumonia and Neisseria gonorrhoeae  (Eyre et al.
, 2017; Nguyen et al. , 2018). Important to note is the species-specific nature of current hallmarks as different species might harbor other mechanisms of resistance  (Bortolaia et al. , 2020; Nageeb and Hetta, 2022). Genetic AST harbors an interesting future as it has been shown to aid in rapid identification and genetic AST in a preterm point-of-care-setting, but also the importance of pathogen and AMR surveillance was put forward. Nevertheless, improvements in both sequencing technologies (e. g. , further increase in ONT raw read accuracy to use single read instead of consensus sequence information) and bioinformatic software were stipulated to be important for its future broader implementation  (Leggett et al. , 2020; Li et al. , 2022). Hence, the current gene marker dataset provides a solid base for sequencing-based diagnostics to address genetic AST of ETEC/STEC.
Using a third method (e. g. , protein-based detection via ELISA) would allow to perform a more general and unbiased prediction of predictive measures using a Bayesian latent class model (BLCM) approach  (Bokma et al. , 2020 (Bokma et al. , , 2021b)). This would also deliver extra functional characterization of the identified genetic markers, which would be of great interest to further characterize and understand the observed discrepancies. Though this was not the scope of current manuscript. Still, performing functional studies will be an important part in the future implementation of sequencing-based diagnostics as it will contribute to the proper biological understanding of identified mechanisms and the curation of ARG databases. In-depth characterization of virulence-and AMR gene-encoding plasmids was performed on plasmids obtained from complete circular genomes (Trycycler) as these plasmids underwent proper circularization.
, 2018). Also, non-virulence or ARG-carrying plasmids often belong to the IncF group. Hence, it is 10. 3389/fmicb. 2023. 1139312 Frontiers in Microbiology 14 frontiersin. org thought these plasmids have been circulating in the family upon acquiring ETEC-associated virulence factors  (Bergquist et al. , 1986; Chaslus-Dancla et al. , 1991). The spectrum of different replicons was wider for plasmids carrying ARGs. The IncF plasmids have also been associated with the spread of ARGs, more specifically the extended-spectrum beta-lactams (e. g. , bla CTX-M )  (Coque et al. , 2008; Marcadé et al. , 2009). Indeed, also in our dataset, a plasmid carrying a bla CTX-M gene was presented on a multi-replicon IncF-IA/IncF-IC/lncN plasmid. This combination is not surprising as IncN plasmids are known to colocalize with IncF plasmids.
In addition, IncN plasmids are important players in the dissemination of bla CTX-M throughout Europe as they are broad host plasmids  (Rozwandowicz et al. , 2018). Interestingly, the quinolone resistance gene, qnrS1, was first identified on a IncN replicon plasmid in pigs in Europe  (Szmolka et al. , 2011). In our ETEC/STEC strains, the qnrS1 gene was identified on a IncX1/IncX3 multi-replicon plasmid in some strains. Even though beta-lactams are mostly associated with IncF group plasmids, our dataset showed a wide distribution of betalactams in various plasmids belonging to the IncH, IncI, IncK, IncQ, IncR, IncX, IncY groups and not predominantly in IncF type plasmids  (Rozwandowicz et al. , 2018). The presence of IncI-gamma/K1 plasmids is not surprising as these are predominantly found in Europe, carrying tetracycline resistance genes  (Pezzella et al.
, 2022). In our dataset, plasmids were identified which carry the floR gene (IncFIA or IncY rep types). This floR-carrying IncFIA plasmid also showed the presence of 11 other ARGs, including genes against tetracycline and aminoglycosides. The latter were shown to be present on IncF plasmids among others. Our data showed its presence predominantly on IncF, next to IncX1 and IncI-gamma/K1 plasmids, along with various multi-replicon plasmids. In conclusion, our data highlights the feasibility to accurately infer information on bacterial identification, virotyping, and AST from genetic information in the context of enteric colibacillosis. This will allow quicker and cost-efficient ETEC/STEC diagnostics as compared to default combined culture-based and multiplex PCR methods. Even though the current method relies on purified ETEC/STEC cultures, this knowledge can be exploited in all-in-one (meta)genomic workflows which are promising to revolutionize current routine diagnostics  (Theuns et al.
The inlet temperature during the measurements was (22 ± 1) • C. The calibration of the nozzles was carried out with six different gases (the purity of each gas is shown in parentheses): • dry air, • Ar -argon (99. 999 %), • He -helium (99. 9996 %), • H 2 -hydrogen (99. 9995 %), • N 2 -nitrogen (99. 999 %), • N 2 O -nitrous oxide (99. 5 %). All properties of the gases were defined using REFPROP v9. 0 database  [15]. Table  1  shows gas properties for all gases at average test conditions: absolute pressure 450 kPa and temperature 22 • C.
Results Study samples and pain traits examined. We examined two, partially overlapping, population samples. For the analysis of Neanderthal introgression in the SCN9A gene region we examined 5971 individuals from the CANDELA cohort (recruited in Brazil, Chile, Colombia, Mexico and Peru). For the analysis of genetic association to experimental pain we examined 1623 Colombians (hitherto referred as: the QST cohort). A total of 710 individuals from the QST cohort are included in the CANDELA cohort (these are Colombian CANDELA volunteers that were recontacted for the collection of pain data). The CANDELA individuals have a mean of 46. 0% Native American, 49. 6% European and 4. 4% African ancestry, but with considerable variation between individuals and countries (Supplementary Data 1 and Supplementary Fig.
1 ). The QST cohort has a median age of 22 (range 18-45), of which 56. 1% are women (Supplementary Data 2). Mean ancestry estimates for this cohort are: 31. 3% Native American, 59. 0% European and 9. 7% African (a similar ancestry distribution as in the Colombians of the CANDELA cohort; Supplementary Data 1 and Supplementary Fig. 1 ). Using the protocol described in Schmid et al. 18  , we measured thresholds to heat pain, mechanical pain and pressure pain. We also measured Wind-Up Ratio (WUR; a measure of central sensitization). In addition, heat and mechanical pain thresholds were measured after application of mustard oil (which increases pain sensitivity). We observed a low/moderate but significant correlation of sex with four traits, women manifesting greater pain sensitivity, consistent with previous studies  22, 23.
The bottom panel (c) shows association P values of Neanderthal introgression segments with POST_MPT (on n = 1552 independent samples). The threshold for significance is indicated by a dashed red line (P = 9. 75 × 10 -3 , corrected for multiple testing). Significantly associated segments are labelled (further details on these segments are provided in Supplementary Data 8). the three Neanderthal variants all impact on pain sensitivity. In single-locus tests, the strong LD between D1908G and V991L-M932L complicates an assessment of the possibility that both these loci could impact on POST_MPT (LD makes impossible a distinction between V991L and M932L in these tests). The phased data show that ~90% of the chromosomes carrying Neanderthal alleles at V991L-M932L also carry a Neanderthal allele at D1908G.
4c  and Supplementary Data 7). The contrast between the set including haplotypes carrying one or more Neanderthal alleles to the fully ancestral haplotype showed significant association (actually the most significant association of our study; see Supplementary Data 7), consistent with all haplotypes carrying Neanderthal alleles having an effect on POST_MPT. Although nominally significant, the triple-variant haplotype has a larger effect than the haplotype carrying only D1908G, thereby suggesting an additive effect of both loci. A direct way to evaluate the possibility that both D1908G and V991L-M932L impact on POST_MPT (accounting for the LD between the loci) is to test for an association with the total number of Neanderthal alleles carried by an individual (i. e. summing across loci). This test shows a negative, significant, trend (β = -0,075, P value = 7.
2 × 10 -4 , Figs. 4  and 5  and Supplementary Data 7), consistent with allelic effects being additive across D1908G and V991L-M932L. Finally, the regional introgression analyses described above allow an exploration of association of Neanderthal ancestry with POST_MPT across the SCN9A region, beyond the three known amino acid substitutions. We observe an increase in the significance of association for Neanderthal segments towards the right end of the region of introgression, variant D1908G being included in segment 26 and variants V991L and M932L in segment 30 (Fig. 2  and Supplementary Data 8). Interestingly, strongest association is seen for two short, non-coding segments (Fig. 2 ). In agreement with the single-variant and haplotype analyses, the effect of introgressed segments is consistently to increase pain sensitivity (negative Betas; Fig.
Discussion Zeberg et al. 7  reported that individuals carrying the three Neanderthal Nav1. 7 variants (M932L, V991L and D1908G) significantly over-reported a broad clinical pain phenotype (i. e. any of 19 forms of pain recorded in the UKBB questionnaire). However, individuals carrying these three variants are rare in Europe (frequency of 0% in 1000 Genomes and 0. 2 % in the UKBB data). The rarity of these variants in Europeans complicates further analysis of their potential impact on pain sensitivity in this population. Furthermore, the UKBB pain data does not provide information into specific sensory modalities that could be impacted by Neanderthal introgression in SCN9A. Here we aimed to overcome these limitations of the Zeberg et al. 7  study by: (i) examining a population with a relatively high frequency of the Neanderthal Nav1.