dataesr/scientific-paragraphs-categorization huggingface dataset:
(b) For the ResFinder orthologues, weighted bar plot shows that there are no fosfomicin, nitroimidazole, pseudomonicacid, fusidic acid, or glycopeptide orthologues in the 'both' location (yellow). Orthologues located in chromosomes (green) have a lower proportion compared to the other two genomic locations. (c) For the MUSTARD orthologues, there is a higher number of antibiotic resistance orthologues located simultaneously in both a plasmid and the chromosome ('both' location in yellow). There is a higher amount of blaa, rnam, and sul and orthologues. (d) For the MUSTARD database, there is a higher proportion of orthologues located in plasmids (blue). There is a lower proportion of orthologues located in 'both' a plasmid and the chromosome (yellow) than in a plasmid only or in the chromosome only.
Moreover, 15 out of 45 orthologues (e. g. , the orthologues of vgb, vat, or tetk) are present only in a plasmid or in the chromosome (i. e. , without a 'both' location). Overall, the results obtained with the two databases showed a high number of antibiotic resistance orthologues located in both genomic locations of the same genome, suggesting that ARs are carried in the bacterial mobilome. AR genes encoding beta-lactamases showed the highest numbers of orthologues in both the ResFinder and MUSTARD databases. This latter showed that the blaa category encodes Class A beta-lactamases, which are the predominant beta-lactamases found in the bacterial genome. All beta-lactamases can be found in plasmids and both plasmids and chromosome, except subclass B2 beta-lactamases, which are found only in the chromosome.
We have also generated boxplots of the antibiotic resistance orthologues per CDS for each genomic location (Figure  2 ). The boxplots do not show significant differences in the relative frequencies of ARs between the different genomic locations. The Kruskal-Wallis test was conducted to examine the differences between the relative number of antibiotic resistance orthologues according to the three types of genomic location. No significant differences were found (ResFinder: Chi square = 50. 00, p-value = 0. 2815, df = 16; and MUSTARD: Chi square = 116. 42, p-value = 0. 05838, df = 40). only or in the chromosome only. Moreover, 15 out of 45 orthologues (e. g. , the orthologues of vgb, vat, or tetk) are present only in a plasmid or in the chromosome (i. e. , without a 'both' location). Overall, the results obtained with the two databases showed a high number of antibiotic resistance orthologues located in both genomic locations of the same genome, suggesting that ARs are carried in the bacterial mobilome.
AR genes encoding beta-lactamases showed the highest numbers of orthologues in both the ResFinder and MUS-TARD databases. This latter showed that the blaa category encodes Class A beta-lactamases, which are the predominant beta-lactamases found in the bacterial genome. All beta-lactamases can be found in plasmids and both plasmids and chromosome, except subclass B2 beta-lactamases, which are found only in the chromosome. We have also generated boxplots of the antibiotic resistance orthologues per CDS for each genomic location (Figure  2 ). The boxplots do not show significant differences in the relative frequencies of ARs between the different genomic locations. The Kruskal-Wallis test was conducted to examine the differences between the relative number of antibiotic resistance orthologues according to the three types of genomic location.
Genomic Organization of Virulence Factor-Encoding Genes in Bacterial Genomes We have also computed the number of virulence factor orthologues as described in the previous section. We calculated the number of orthologues of virulence factors (VF) grouped into various categories of the VFDB database, and according to their genomic location: only in the chromosome, only in a plasmid, or in both simultaneously. Once again, we may expect that larger plasmids or chromosomes would contain more VF orthologues. To remove this effect, we proceeded by dividing the counts by the total number of CDS in each of the genomic locations, as we did for the analysis of the ARs. Although there are many virulence genes encoded on the plasmid, the chromosomal location is also very frequent in most bacteria (Figures  3  and 4 ).
For these calculations, we have considered that each protein of the dataset can be classified into different AR or VF categories. With this choice, we are assuming that the same protein can belong to different antibiotic resistance mechanisms. An example of this is proteins that are part of the efflux pumps that perform the nonspecific extrusion of antibiotics belonging to different classes. Heatmaps, as represented in Figures  6  and 7 , were generated for the co-occurrences between all classes of ARs and VFs. Values in each position of the matrix that are greater than zero represent the genomes for which there are a higher number of co-occurrences between ARs from a given category and VFs from another category than expected at random and may mean that there is co-selection of these two genetic determinants.
Conversely, a value below zero means that co-occurrence between a given AR category and another given category of VF is rarer than what would be expected to happen by chance, perhaps due to counter-selection. However, we have only considered as relevant those that are above 1 (represented in the yellow to red color shades) or below -1 (represented in blue shades). For these calculations, we have considered that each protein of the dataset can be classified into different AR or VF categories. With this choice, we are assuming that the same protein can belong to different antibiotic resistance mechanisms. An example of this is proteins that are part of the efflux pumps that perform the nonspecific extrusion of antibiotics belonging to different classes. The co-occurrences that vary most according to genomic location involve T7SS, which frequently co-occur with fusidic acid resistance genes, either on the chromosome or on the plasmid, but never on both simultaneously (Figures  6  and 7 ).
Discussion This work aimed to study the evolutionary genomic dynamics of antibiotic resistance and virulence in bacteria. To have large-scale data and to perform a comprehensive study, we collected 16,632 reference genomes in the RefSeq database to address the correlation between the genomic locations of genes encoding for antibiotic resistance and bacterial virulence at two different scales. We first observed that the number of AR orthologues of all categories taken together does not differ significantly between the three locations (on the chromosome only, on a plasmid only, or on both). We reached the same conclusion for orthologues of VFs. In a second approach, which is novel, we analyzed the co-occurrences of each AR category with each VF category, by genomic location. This latter approach revealed co-occurrence patterns that are worth being addressed in more detail, and this will pave the way for further studies.
For this study, we have used the dataset of bacterial genomes from RefSeq, the NCBI Reference Sequence database, which consists of a non-redundant, well-annotated set of reference sequences including genomic and protein sequences. This dataset, however, does not represent a random sample of bacterial genomes. In fact, we expect this collection to be skewed towards bacterial genomes of human and medical interest. In this sense, an overrepresentation of human pathogens would explain the overrepresentation of VFs that we have found. The asymmetrical distribution per replicon of the ARs and VFs can result from the fact that we have 37-fold more coding sequences (CDS) at the chromosomal location than the plasmid one, and 51-fold more CDS in plasmids than in 'both' locations. This is also because we have as twice as many chromosomes as plasmids on our dataset, but also because most of the plasmids are indicated to encode around 60 proteins and are thus much smaller than chromosomes  [30] , and ICE are even smaller than plasmids.
Conclusions With this work, we aimed to test whether there would be an association between antibiotic resistance and bacterial virulence at the genomic level. To do this, we analyzed an exceptionally large set of reference bacterial genomes. Although the dataset studied here was expected to be enriched with pathogenic bacterial genomes, we did not identify an overall trend towards the existence of a genetic linkage between virulence and antibiotic resistance at either the chromosomal or plasmid level. However, there are patterns of association between some virulence categories and antimicrobial resistance factors at different genomic locations that point to the evolution of bacterial genomes dictated by antibiotic use.
Data Gathering and Preparation As a first step to begin this large-scale analysis, 16,632 complete bacterial genomes were retrieved from the National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database, which provides a non-redundant collection of sequences representing genomic data, transcripts, and proteins, in fasta format. Our dataset is composed of protein sequence fasta format files derived from every complete bacterial genome present in the repository, downloaded via command line, from https://ftp. ncbi. nlm. nih. gov/genomes/refseq/bacteria/ (accessed on 19 October 2020).
Blast against Selected Databases The BLAST+ executables package (ncbi-blast-2. 9. 0+ version) was downloaded from the NCBI website (ftp://ftp. ncbi. nlm. nih. gov/blast/executables/blast+ (accessed on 11 November 2019))  [41]. MUSTARD is a database of antimicrobial resistance determinants that was generated using a 3-dimensional modeling-based approach, called pairwise comparative modeling (PCM), that accurately predicts functions of proteins that are distantly related to proteins with known functions from the human intestinal microbiota. This database is structured in 41 categories. MUSTARD was downloaded from http: //mgps. eu/Mustard/db/all_ard. zip (accessed on 26 October 2020)  [25]. ResFinder is a comprehensive and up-to-date database of acquired genes and chromosomal mutations mediating antimicrobial resistance in total or partial DNA sequences of bacteria of major public health relevance.
This database is divided into 17 categories. The ResFinder database was downloaded from https://bitbucket. org/genomicepidemiology/ resfinder_db. git, accessed on 30 October 2020  [24]. The Virulence Factor Database (VFDB) is a collection of bacterial virulence factor protein families with current and in-depth coverage of the major virulence factors of the bestcharacterized bacterial pathogens. This database contains a total of 13 functionally classified fasta files of bacterial virulence factor protein sub-families. Files were downloaded from http://www. mgc. ac. cn/VFs/Down/VFDB_setB_pro. fas. gz, accessed on 6 November 2020  [42]. Protein sequence fasta files pertaining to the databases were formatted in the bash shell, using the BLAST manual as a guideline. The BLAST program makeblastdb was run to set up the database.
Observed/Expected Ratio Calculation To calculate the extent to which each AR orthologue and each VF orthologue colocalized more often or less often than expected, we proceeded as follows. First, we built a matrix where, in each position, we had the number of times each AR orthologue and each VF co-localizes (in the chromosome of the same bacterial cell, in plasmids of the same bacterial cell, or in both). Then, we calculated another matrix, but with the expected values. These expected values were calculated for ResFinder, MUSTARD, and VFDB orthologues, from the observed count of orthologues per category multiplied by the total number of orthologues in that genomic location (of all categories) divided by the total number of orthologues found pertaining to the respective database. From this table, we constructed another matrix, for each database, with the observed/expected ratios of antibiotic resistance gene orthologues in each database category per genomic location.
Orthologues located in chromosomes (green) have a lower proportion compared to the other two genomic locations. (c) For the MUSTARD orthologues, there is a higher number of antibiotic resistance orthologues located simultaneously in both a plasmid and the chromosome ('both' location in yellow). There is a higher amount of blaa, rnam, and sul and orthologues. (d) For the MUSTARD database, there is a higher proportion of orthologues located in plasmids (blue). There is a lower proportion of orthologues located in 'both' a plasmid and the chromosome (yellow) than in a plasmid.
Orthologues located in chromosomes (green) have a lower proportion compared to the other two genomic locations. (c) For the MUSTARD orthologues, there is a higher number of antibiotic resistance orthologues located simultaneously in both a plasmid and the chromosome ('both' location in yellow). There is a higher amount of blaa, rnam, and sul and orthologues. (d) For the MUSTARD database, there is a higher proportion of orthologues located in plasmids (blue). There is a lower proportion of orthologues located in 'both' a plasmid and the chromosome (yellow) than in a plasmid only or in the chromosome only. Moreover, 15 out of 45 orthologues (e. g. , the orthologues of vgb, vat, or tetk) are present only in a plasmid or in the chromosome (i. e. , without a 'both' location).
(c) The number of co-occurrences in both the chromosome and plasmid is low. Consequently, we detected no co-occurrences involving six out of thirteen orthologues of virulence genes. There is a high number of co-occurrences of disinfectant orthologues with type IV and type VI secretion systems. The highest number of co-occurrences isFigure 6. Heatmaps of the number of antibiotic resistance orthologues of the ResFinder database (x axis) and virulence factor orthologues of the VFDB database (y axis) in each genomic position. The values correspond to the Log 2 of the quotient of observed/expected values. The yellow to red shades represent a higher-than-expected number of co-occurrences; the blue shades represent a lower-than-expected number of co-occurrences; and the grey represents values close to the expected.
Antibiotics 2022 ,Figure 5. 20225 Figure 5. Relationship between antibiotic resistance gene orthologues of the ResFinder database (vertical axes) and virulence gene orthologues of the VFDB (horizontal axes). To correct for the size of the corresponding locations, the number of resistance and virulence genes in each genome was divided by the amount of coding DNA sequences (CDS) in that genomic location. Each point represents a genome. The colors indicate the genomic location: plasmid (blue), chromosome (green), or 'both' (yellow). (C,D) show the same data; however, (D) has the scale adjusted to better demonstrate the distribution of points. In (A), plasmid location (R = 0. 005, slope = 0. 032, p-value = ~0). In (B), both locations (R = 0. 109, slope = 0. 183, p-value = ~0). In (C,D), chromosome location (R = 0.
The ability to apply techniques used to investigate the structure of frequency-specific activity may be particularly useful for developmental EEG studies, both for clarifying the functional role of sensorimotor beta activity but also of other frequency bands in which activity may occur in bursts (e. g. gamma;  Cheyne and Ferrari, 2013; Lundqvist et al. , 2018). Therefore, the two main objectives of this paper are as follows: 1) to provide a tutorial for developmental researchers on how to apply techniques for evaluating the rhythmicity (i. e. periodicity, a primary characteristic of oscillatory activity) of frequency-specific activity and for detecting bursts of activity; and 2) to apply these methods to infant and adult EEG datasets collected during the same grasping task in order to provide evidence for infant sensorimotor beta bursts, and to compare burst properties and their modulation in infants and adults.
Specifically, we illustrate the use of power spectral densities combined with lagged coherence to determine frequency band limits and examine the rhythmicity of beta band activity, and a variant of the widely-used 'p-episode method'  (Caplan et al. , 2001; Hannah et al. , 2020; Little et al. , 2019; Lundqvist et al. , 2016; Sherman et al. , 2016; Shin et al. , 2017; Tal et al. , 2020; Wessel, 2020)  for the identification of beta burst timing, duration, and amplitude. All source code used for tutorial and subsequent analyses have been made publically available.
Tutorial In the following section, we demonstrate how to determine frequency band limits, how to evaluate the rhythmicity of activity within a frequency band, and how to identify transient burst events occurring in that band. These techniques can be used to analyze burst events within any frequency range, but here we focus on activity within the beta band, measured from electrodes over the sensorimotor cortex, as this signal is dominated by transient bursts of activity  (Little et al. , 2019). In this tutorial section, data are analyzed from an example adult participant, averaged within an electrode cluster centered on the C3 electrode (E16, E20, E21, E22). All source code for these analyses is available at https: //github. com/danclab/dev_burst_analysis. These analyses were run using EEGLAB v14.
Finally, the power spectra for all segments are averaged. The power spectrum includes both periodic and aperiodic activity, which can bias estimates of frequency band limits  (Donoghue et al. , 2020). The aperiodic activity typically has a 1/f-like distribution. We therefore fit a 1/f function to the spectrum and identify frequency bands using the residuals of this fit, representing the periodic component of the spectrum (see Discussion). Band peak frequencies are identified as local maxima, and the full width half maximum (FWHM) is computed for each band. The code for computing the PSD and identifying frequency bands for the C3 electrode cluster in an example adult participant can be found in the Github repository in the script tutorial_1. m (Code segment 1). The EEGLAB function spectopo is used to compute the PSD.
The MatLab function fit_lm with 'RobustOpts' on is used to fit the 1/f function to the spectrum using robust linear regression to reduce the influence of the periodic component peaks. The MatLab function findpeaks is used to find peaks in the periodic spectrum component. The resulting alpha and beta ranges for the C3 cluster in this example participant are 9-12 Hz and 19. 25-22. 75 Hz. Code segment 1. : Determining beta frequency band limits. Lines 31-86 of tutorial_1. m. The sampling rate of the EEG data is used to compute the window size and overlap. The EEGLAB function spectopo returns the spectra and vector of frequencies for each cluster. The MatLab function fitlm returns the fitted model including coefficient estimates and residuals, and findpeaks returns the local maxima of the spectrum residuals and the indices of the frequencies vector where they occur.
Lagged coherence involves splitting the data up into segments, computing the Fourier coefficients within those segments, comparing the phase of the signal between segments, and then summarizing the consistency of phase differences across segment pairs  (Fransen et al. , 2015). The script tutorial_2. m computes lagged coherence within the C3 electrode cluster across a range of frequencies and lags for an example adult participant (Code segment 2). It uses FieldTrip functions, therefore the EEGLAB-formatted data must first be converted to FieldTrip format (see the create_ft_data function). For a range of frequencies and cycle lags, the script uses the FieldTrip functions ft_freqanalysis to compute the complex Fourier spectra, and ft_connectivity_laggedcoherence to compute lagged coherence. Code segment 2.
m (Code segment 3). In order to avoid filterrelated edge artifacts, the data are padded using the DC offset before applying the filter and Hilbert transform. This padding is then removed from the resulting amplitude envelope. The FieldTrip function ft_pre-proc_bandpass_filter is used to perform bandpass filtering, and the Hilbert transform is applied using MatLab function hilbert. Code segment 3. The filter-Hilbert method for computing signal amplitude. Lines 31-80 of tutorial_3. m. The DC component of the data for each trial (data) is computed, and then used to pad the data on either side. The FieldTrip function ft_preproc_bandpassfilter is used to bandpass filter the data within the frequency range, the amplitude is derived from the Hilbert transform, and the padding is removed. Fig. 2. Lagged coherence localizes beta activity to central electrode clusters.
This threshold is computed based on multiple standard deviations above the median beta amplitude, with the median representing a robust measure of centrality for skewed distributions. This measure is subject-specific, with the absolute threshold set per subject based on their amplitude distribution. More specifically, for each subject we compute the nonparametric correlation (Spearman's ρ) between the number of bursts per trial (number of threshold crossings) and the mean amplitude per trial. This is done using a range of thresholds computed from the median and standard deviation of beta amplitude in all time points across all trials for that subject. We then determine the standard deviation multiple that maximizes the mean of this correlation (Code segment 4). For the C3 cluster in this example participant, this results in a threshold of 1.
Extracting beta bursts for further analysis Given the beta amplitude envelopes and threshold, beta bursts can be extracted from the data. The script tutorial_5. m (Code segment 5) extracts the duration, peak amplitude, and peak time of each burst for every trial from an example adult participant. First, times at which beta amplitude crosses the absolute, subject-specific threshold are identified. Second, for each threshold-crossing, the time at which beta amplitude returns below the threshold is found. The difference between these two times points gives the duration of the burst, and from within these, burst onset and offset times, peak amplitude, and the time at which this peak is reached can be identified. The timing of the bursts can then be compared to changes in beta amplitude and between epochs to determine if it is modulated by the task.
Participants Forty-six full-term 12-month-old infants (26 females, age range 11. 2-12. 93 months) and 23 adults (10 females, age range 18-22 years) were recruited for a study about the neural bases of reaching and grasping. Sixteen infants were excluded due to unusable EEG data before preprocessing (N = 8), being distressed shortly after applying EEG electrodes net (N = 7), and recording failure (N = 1). One adult was also excluded due to an error in data recording. Therefore, the final sample included 30 infants and 22 adults. All infants were typically developing with no known or suspected neurodevelopmental or medical diagnoses. Prior to an infant's participation in the study, informed consent was obtained from the infant's parents. All adults had normal or corrected to normal vision and did not have any neurological disorder.
EEG recording and preprocessing EEG was recorded using a 65-channel HydroCel Geodesic Sensor Net (Electrical Geodesics, Inc. , Eugene, OR). The vertex (Cz) electrode was used as online reference. EEG data were sampled at 500 Hz using EGI's Net Station (v4. 5. 4) software. Impedances were kept below 100 kΩ. After recording, EEG data were exported to a MatLab compatible format using Net Station software for offline processing. Both infant and adult datasets were preprocessed using the MADE pipeline (https://github. com/ChildDevLab/MADE-EEG-preprocessing-pipeline;  Debnath et al. , 2020)  and MatLab 2015b. The data were highpass filtered at 0. 3 Hz and lowpass filtered at 50 Hz using EEGLAB v14. 1. 1  (Delorme and Makeig, 2004 ) FIR filters. Artifact-laden channels were identified and removed using the EEGLAB plug-in FASTER (infants: 0-5 channels, M = 1.