dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The X-ray model and structure factors of RNase J data generated in this study have been deposited in the RCSB Protein Data Bank (PDB) database with accession code 7PCR. The atomic coordinates and cryo-EM density map of RNase J have been deposited in the PDB with the accession code 8CGL and in the Electron Microscopy Data Bank (EMDB) with the accession code EMD-16647. The mass spectrometry data generated in this study are available at the PRIDE repository with the dataset identifier PXD027670. Source data are provided with this paper.
Sequencing was carried out on the Illumina HiSeq 2500 platform with paired-end reads of 125-bp length. Bustard was used for Illumina standard base calling, and leeHOM was used to trim Illumina adapters and merge completely overlapping paired sequences  [35]. Then, deML was used to demultiplex the pooled sequencing data  [36]. The alignment and post-processing pipeline of the sequencing data was carried out as previously described  [5].
For MSY, combining the 20 new sequences of southern Thai_TK from this study (S5 Table ) with 1,674 sequences from previous studies  [23] [24] [25] [58] [59] [60]  brings the total to 1,694 sequences from 55 populations (S6 Table ). For mtDNA, haplogroup assignment was performed by Haplogrep  [61]  with PhyloTree mtDNA tree Build 17 (http://www. phylotree. org)  [62]. The mixture proportions of mtDNA samples and most likely detected mitochondrial haplogroups for each sample were estimated and identified by mixEMT software  [63]. Using yHaplo  [64] , MSY haplogroups were assigned to the maximum depth possible given the phylogeny of the ISOGG Y-DNA Haplogroup Tree 2015 (http://www. isogg. org/) and the available genetic markers in our target region. Summary statistics of the genetic diversity within populations and the matrix of pairwise genetic distances (F st ) were obtained with Arlequin 3.
5. 1. 3  [65]. To visualize population relatedness, the R package (R Development Core Team 2016)  [66]  was used to carry out nonparametric MDS analysis (based on the F st distance matrices for the MSY and mtDNA and using R function: isoMDS package: MASS) and to construct heat plots of the F st distance matrix and the matrix of shared haplotypes (R function: ape, pegas, adegenet and ggplot2 packages). STATISTICA 13. 0 (StatSoft, Inc. , USA) was used to carry out a correspondence analysis (CA) based on MSY and mtDNA haplogroup frequencies. Admix 2. 0 was used to estimate admixture proportions  [67, 68]. We used a simple admixture model consisting of two parental groups: East and Southeast Asian (ESEA) and South Asian (SA) (S2 and S6 Tables). Several analyses using 12 and 8 different parameters for mtDNA and MSY, respectively, i.
e. , admixture times (2000, 1000, 500, and 0 ya) and mutation rates (0, 1. 67 x 10 -8 and 2. 67 x 10 -8 for mtDNA and 0 and 8. 71 x 10 -10 for MSY) were performed (S7 Table)  [69] [70] [71] , with 10,000 bootstrap replicates. To construct Bayesian skyline plots (BSP) per population, based on Bayesian Markov Chain Monte Carlo (MCMC) analyses, we used BEAST 1. 8. 4  [72]. BEAST input files were created with BEAUTi v1. 8. 2 after first running jModel test 2. 1. 7 in order to choose the most suitable model of sequence evolution  [73]. For mtDNA, we executed BSP analyses per population with mutation rates of 1. 708 × 10 -8 and 9. 883 × 10 -8 for data partitioned between the coding and noncoding regions, respectively  [69]. For the MSY, we used an MSY mutation rate of 8. 71 × 10 -10 substitutions/bp/year  [71] , and the BEAST input files were modified by an inhouse script to add in the invariant sites found in our data set.
As discussed in more detail below, we thus find contrasting patterns of   2023 ), QGIS Geographic Information System. Open Source Geospatial Foundation (https://www. qgis. org) and was not taken from another source. and boundaries were adapted and modified from https://public. opendatasoft. com/explore/dataset/ worldadministrative-boundaries/export/. https://doi. org/10. 1371/journal. pone. 0291547. g006 mtDNA vs MSY SA admixture in practically all populations, except for Burmese, who have the highest level of SA ancestry for both markers.
Acknowledgments We would like to thank all participants who donated biological samples and  Sukrit Sangkhano  and  Suparat Srithawong  for coordinating sample collections. We greatly appreciate  Roland Schro ¨der  and  Alexander Hu ¨bner  for technical assistance. We thank the  Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany , for hosting laboratory facilities and the  Max Planck Society  for research support.
W. W. and W. K. were supported by  National Research Council of Thailand (NRCT) : ( N41A640275 ). W. K. , J. K. and M. Sr. were funded by the  Institute of Suvarnabhumi Studies, Thailand Academy of Social Sciences, Humanities and Arts: TASSHA, Ministry of Higher Education, Science, Research and Innovation. This study was also partially supported by the  Global and Frontier Research University Fund, Naresuan University  (Grant number:  R2566C051 ).
Concentrations were obtained by a two-fold serial dilution, starting at a 5. 93 mg/mL of compound (iridomyrmecin and hexane). Compound quantifications were then determined by calculating the area under the peak relative to the internal standard for the different samples and corrected by the calibration curve. Manual peak integration and the calibration curve were performed using GC-solution software v. 2. 3. Shimadzu 2010.
1973; Haight and Tschinkel 2003; Haight 2008; Fox et al. 2012 ). We performed a preliminary analysis to detect whether there is variability in the Argentine ant body size among supercolonies. We performed a linear mixed-effects model using the "lmer" function of the lme4 package  (Bates et al. 2015)  in R software (R Core Team 2022), with the nest as a random factor. To evaluate the supercolony effect, we used the "anova" function from the lmerTest package  (Kuznetsova et al. 2017 ) and we showed that there are significant differences in ant size among supercolonies (F = 3. 82, p = 0. 017, N = 660). Iridomyrmecin quantities were subsequently size-adjusted on the following analysis, by using the quantities found in each individual with respect to its size.
Linear models were fitted using the "lm" function in the R package stats (R Core Team 2022), linear mixed models were fitted with "lmer" function and general mixed models using "glmer" function, both in the R package lme4  (Bates et al. 2015). In order to test the effect of each independent variable, we used an anova for linear models (F statistic), "drop1" function for general linear mixed models, both from the stats package (R Core Team 2022), and "anova" function from the lmerTest package  (Kuznetsova et al. 2017). The "drop1" function compares the model including and excluding the variables based on the likelihood-ratio test statistics (LRT, option test = "Chi"). Finally, when significant differences were found, we performed pairwise posthoc comparison, using the lsmeans package  (Lenth 2016 ).
Acknowledgements  Ana Carvajal ,  Charlotte Hemingway ,  Sara Castro-Cobo ,  Miguel Lozano ,  Dailos Hernández-Brito ,  Sebastián Palacios ,  Roxana Josens ,  Nuria Morrow ,  Maria del Carmen Ramírez ,  Francisco Miranda  and  Isabel Afán. ICTS-RBD -CSIC  provided logistic support during fieldwork. We thank laboratory facilities at  Estación Biológica de Doñana (Laboratorio de Ecofisiología, LEF ;  Laboratorio de Ecología Acuática ,  LEA ;  Laboratorio de Procesado de Muestras, LPM ; and  Laboratorio de Sistemas de Información Geográfica y Teledetección, LAST). We thank two anonymous reviewers for their helpful comments.
Leaf area measurements Leaf area was assessed for photographs taken from the vertical perspective at three different times: two months after sowing, when the plants grew in the multipot trays, and three and four months after sowing, when the plants grew in single pots. Leaf area was analyzed with WinDIAS 3. 3 software (Delta-T Devices Ltd, UK). It was determined by counting the number of pixels that represented foliage and converting the obtained projection area to surface area expressed in cm 2. Total leaf area (la t ) was comprised of green leaf area (la g ) and discolored leaf area (la d ). The latter was represented by yellowish, reddish or brownish pixels, depending on the species and plant growth stage. The images taken two months after sowing were used to assess RF-EMF impact on all 10 plant species (see section 3.
Temporal trends in green and discolored leaf area observed in Trifolium arvense plants exposed to electromagnetic field Green and discolored leaf area (la g and la d ) in Trifolium arvense were determined from the images taken at two, three and four months after sowing, i. e. , one, two, and three months after germination of this species. These time series were used to investigate the changes occurring in the EMF-treated vs. control plants over the course of the experiment. The collected data represented a factorial model with two factors: treatment (radiation or control) and time (one, two or three months after germination). First, the means of la g and la d , averaged for the EMFtreated and control plants, were plotted against time. The size of the difference between the control vs. EMF treated plants was illustrated by error bars on the plots, calculated using between-subject (not withinsubject) comparisons  (Morey, 2008).
Presentation of the results, software used in the analysis Significant differences between the control and EMF-treated plants detected in all analyses of variance were marked on the plots of the means using star or dot symbols. Detailed ANOVA results were presented in Appendix B. The entire analysis was performed in R, version 4. 1. 0 (R Core Team, 2021). R code provided by  Mangiafico (2015)  was applied to build the contrasts. Image processing and the calculation of h and s indices was performed using the "bwimage" R library (Biagolini-Jr and Macedo, 2020).
Temporal changes in green and discolored leaf area in Trifolium arvense exposed to electromagnetic field The response to RF-EMF detected in Trifolium arvense was analyzed using time series data on green leaf area and discolored leaf area at one, two and three months after germination. Trifolium arvense was the only species where this data for the three months was available. Two-way ANOVA performed to estimate general effects of the studied factors indicated that for both la g and la d the interaction between treatment and time is significant (F 1,5 = 12. 69, p = 0. 0005 and F 1,5 = 9. 86, p = 0. 002, respectively) (Table  1, Table 2 ). The plots of temporal changes in green leaf area and ANOVA with planned contrasts (performed as a complement to the plots) showed that green leaf area was significantly larger in the EMF-treated plants compared to the control plants after one month, F 1,5 = 6.
Acknowledgements We thank  Iwo Gałecki  and  Roman Malik  who provided access to the research area and hosted the experiment. Roman Malik  helped us also with the set-up of the study, gave us many constructive comments and provided his assistance in the field. We are also grateful to  Agnieszka Jaros  for valuable discussion and advice on statistical analysis. This study was funded by the  National Science Centre Poland (NCN) , under research project " The impact of electromagnetic fields used in wireless communication on the growth and development of herbaceous plants " no  2022/04/X/NZ8/01792. Publication was co-financed within the framework of the  Polish Ministry of Science and Higher Education 's program: " Regional Excellence Initiative " in the years  2019-2023  (No. 005/RID/2018/19 ).
Literature search and inclusion criteria We searched the PubMed database using terms 'fitness' and 'Escherichia coli' or 'E. coli' and 'antibiotic resistance' or 'antimicrobial resistance' or 'drug resistance'. Search results were limited to peerreviewed studies in English available online by the 29th September 2022. No start date was specified. To be included, studies had to report relative fitness findings numerically in the text, in a table or a figure. Relative fitness measurements should meet the following criteria. The relative fitness had to be measured in competition assays in an approximate proportion of 1:1 between a resistant strain (mutant) and a wild-type (susceptible or ancestral) strain only differing by the absence of resistance. Studies in which the data were reported in an unstandardized protocol without competition with a control strain were excluded.
Studies comparing two resistant strains were also excluded. The competition assays had to be conducted at 37°C in a stable antibiotic free environment. Experiments involving a modification of environmental conditions were excluded. Finally, relative fitness had to be estimated using one of the three methods W r , W s or W t described above. Estimations based on relative growth rates, competitive indices or uncommon methods were excluded. Of 335 studies matching the search criteria, 127 reported relative fitness values and 46 were included in the final dataset (Figure  1 ), representing a total of 783 resistant strains. Details of all eligible studies and reasons for exclusion where applicable are given in the Supplementary Data S1. For each resistant strain and competition assay included in the final analysis, we collected the duration of the assay, the culture medium, the relative fitness estimation method, the mean and standard error of the relative fitness, the number of experiment replicates, the nature of the susceptible strain (either isogenic, or ancestral), the nature of its differences relative to the resistant strain (such as plasmid loss, mutation) and the number of AMR genes or mutations in the resistant strain.
06% per kbp (95% CI, 0. 03-0. 09%; Figure  6A ). In strains with one or more plasmids, however, the number of plasmids had no significant effect on relative fitness (Figure  6B ). The conjugative nature of the plasmid, compared to non-conjugative plasmids and to plasmids engineered in vitro as vectors of AMR genes, had no significant impact on relative fitness (Supplementary Figure  S5A ). Nine major incompatibility groups of plasmids were represented in our dataset. The relative fitness did not significantly differ between groups (Supplementary Figure  S5B ), although possible trends were observed. The most frequent incompatibility group was IncF (n = 88 strains), with an average relative fitness of 0. 928. Strains with IncA/C and IncX plasmids appeared more costly than the other Inc. groups (0.
Sample sizes differ in panels C,D because of the exclusion of strains with missing information on either the no. of AMR genes or the no. of drug family resistances. meta-analysis by  Melnyk et al. (2015)  found that the relative fitness assessed by the W r method (ratio of Malthusian parameters) exceeded by a factor of 1. 7 those assessed by the W t method  (Dykhuizen and Hartl, 1983; Dean and Dykhuizen, 1988; Dykhuizen, 1990). Yet, in our study this bias did not translate into a measurable difference of fitness estimations across studies, suggesting that other sources of variation outweighed the influence of the estimation method for biologically relevant values of relative fitness. Indeed, variations of culture media or competition assay duration were common in our dataset, but the observed influence on the estimation of fitness cost was at most moderate (Supplementary Figure  S4 ), enabling for the joint analysis of those experiments in a multi-level analysis framework.
Biological Significance of the Electrostatic Surface Potential Technically, the electrostatic potential can be generated by solving the Poisson-Boltzmann equations, using the partial charges of all the atoms belonging to a given area of a molecule  [13, 14] , or by using Coulomb's law  [15]. In this review, we generally used Molegro Molecular Viewer (http://molexus. io/molegro-molecular-viewer/, accessed on 14 January 2023) to visualize the surface electrostatic potential. The electrostatic potential measured and illustrated by Molegro Molecular Viewer is the sum of the Coulomb potentials for each atom of the considered molecule, with a distance-dependent dielectric constant. Alternatively, the biomolecular simulation program CHARMM.
Biological Significance of the Electrostatic Surface Potential Technically, the electrostatic potential can be generated by solving the Poisson-Boltzmann equations, using the partial charges of all the atoms belonging to a given area of a molecule  [13, 14] , or by using Coulomb's law  [15]. In this review, we generally used Molegro Molecular Viewer (http://molexus. io/molegro-molecular-viewer/, accessed on 14 January 2023) to visualize the surface electrostatic potential. The electrostatic potential measured and illustrated by Molegro Molecular Viewer is the sum of the Coulomb potentials for each atom of the considered molecule, with a distance-dependent dielectric constant. Alternatively, the biomolecular simulation program CHARMM (http://www. charmm-gui. org, accessed on 14 January 2023) proposes the PBEQ-Solver module to solve the finite-difference Poisson-Boltzmann equation of submitted proteins.
PBEQ-Solver gives the calculated elec-trostatic potential on the solvent-accessible surface as well as iso-electrostatic potential contours  [16, 17]. Biomolecules present immense diversity at the level of their surface potential, which is expressed not only by well-delimited zones presenting a positive or negative potential, but also by distribution gradients of charges visualized by the shades of the color code blue-redwhite. However, there are major trends among biomolecules. Nucleic acids are negatively charged because of the phosphate groups of the 3 -5 -phosphodiester bonds  [18]. Lipid rafts, which are rich in glycosphingolipids  [19] , are globally electronegative, especially when these glycosphingolipids are gangliosides  [20]. Proteins are more ambivalent, as they can have anionic, cationic, and most often both amino acids in infinitely varied proportions.
The kinetics of the reaction are controlled by attractive electrostatic forces between the electronegative (red) surface of the raft and the electropositive (blue) surface of the virus protein. Molecular dynamics simulations performed with the Hyperchem software  [50]  showed that the conformational rearrangements needed to fit the raft surface concerns chiefly the amino acid side chains of the virus protein that interact with gangliosides, rather than the secondary or tertiary structure of the protein. In other words, it is the lipid raft that adapts its shape to the viral protein surface, not the reverse. This phenomenon is well illustrated by comparing the raft surface before (Figure  4A ) and after binding to the V3 loop of HIV-1 gp120 (Figure  4B ) or to the SARS-CoV-2 Omicron Spike protein (Figure  4D ).
The co-receptor switch (from CCR5 to CXCR4) is associated with the emergence of more aggressive viruses, inducing a more rapid decline of CD4 + lymphocytes  [56, 57]. The structural basis of this evolution is largely caused by an increase in the net charge of the V3 loop, due to an accumulation of mutations that dramatically affect its electrostatic surface potential  [10]. This mechanism can be explained by considering the electrostatic surface potential of CCR5 and CXCR4 (Figure  5 ), which must be complementary to the V3 loop. According to the analysis of Figure  5  performed with the ImageJ software  [58] , the isopotential contours of CXCR4 are 2. 12 times more electronegative than CCR5 (and 1. 65 times for the determinations based on the electrostatic surface potential). Correspondingly, the net charge of the V3 loop, and thus its electropositive surface potential, gradually increases as the virus evolves in an infected individual.
The structural basis of this evolution is largely caused by an increase in the net charge of the V3 loop, due to an accumulation of mutations that dramatically affect its electrostatic surface potential  [10]. This mechanism can be explained by considering the electrostatic surface potential of CCR5 and CXCR4 (Figure  5 ), which must be complementary to the V3 loop. According to the analysis of Figure  5  performed with the ImageJ software  [58] , the isopotential contours of CXCR4 are 2. 12 times more electronegative than CCR5 (and 1. 65 times for the determinations based on the electrostatic surface potential). Correspondingly, the net charge of the V3 loop, and thus its electropositive surface potential, gradually increases as the virus evolves in an infected individual. When it reaches a threshold value (+4 or +5), the virus becomes able to use both CCR5 and CXCR4.
Interestingly, López de Victoria et al. (2012) elegantly demonstrated that V3 loop subtypes with similar spatial distribution of electrostatic potential cluster together  [12]. Thus, for X4 and R5 viruses, the electrostatic surface potential of the V3 loop is a fundamental property that can be used to characterize and classify HIV-1 subtypes. This notion of a reference threshold value to categorize variants, quasi-species, and/or subtypes of HIV-1 is in fact fairly standard for this retrovirus. Indeed, variations in the genomic sequence of HIV-1 subtypes can also be detected retrospectively in RT and/or protease sequence databases, when the divergence with a reference subtype B virus (HXB2) exceeds the cut-off value determined by the algorithm  [73].
We generated a 3D structural model of this protein using data from the UniProt database (https://www. uniprot. org, accessed on 26 December 2022) and the Robetta server (https://robetta. bakerlab. org, accessed on 26 December 2022). By a dedicated molecular modeling approach adapted to the topology of E8L  [111] , we have determined a possible mode of interaction of this protein, with a cluster of gangliosides mimicking a lipid raft (Figure  10A )  [48]. The electrostatic surface potential at the level of the protein domain containing the ganglioside binding motif is strongly electropositive (Figure  10B ), in agreement with all the viruses previously studied by our team  [26]. This study allowed us to identify a new type of ganglioside binding domain, organized in an annular structure on the surface of the protein (Figure  10C ).
We generated a 3D structural model of this protein using data from the UniProt database (https: //www. uniprot. org, accessed on 26 December 2022) and the Robetta server (https://robetta. bakerlab. org, accessed on 26 December 2022). By a dedicated molecular modeling approach adapted to the topology of E8L  [111] , we have determined a possible mode of interaction of this protein, with a cluster of gangliosides mimicking a lipid raft (Figure  10A )  [48]. The electrostatic surface potential at the level of the protein domain containing the ganglioside binding motif is strongly electropositive (Figure  10B ), in agreement with all the viruses previously studied by our team  [26]. This study allowed us to identify a new type of ganglioside binding domain, organized in an annular structure on the surface of the protein (Figure  10C ).
The recent CASP15 competition highlighted the critical role of multiple sequence alignments (MSAs) in protein structure prediction, as demonstrated by the success of the top AlphaFold2-based prediction methods. To push the boundaries of MSA utilization, we conducted a petabase-scale search of the Sequence Read Archive (SRA), resulting in gigabytes of aligned homologs for CASP15 targets. These were merged with default MSAs produced by ColabFold-search and provided to ColabFold-predict. By using SRA data, we achieved highly accurate predictions (GDT_TS > 70) for 66% of the non-easy targets, whereas using ColabFold-search default MSAs scored highly in only 52%. Next, we tested the effect of deep homology search and ColabFold's advanced features, such as more recycles, on prediction accuracy. While SRA homologs were most significant for improving ColabFold's CASP15 ranking from 11th to 3rd place, other strategies contributed too.