dataesr/scientific-paragraphs-categorization huggingface dataset:
39) at the time of fMRI testing. Because our main goal was to find out as many disparate combinations of multiple asymmetries as possible, a large group of non-right handers was included to increase the likelihood of identifying rare individuals who either show right-hemisphere dominance for praxis and/or language, or their combinations with other (e. g. , bilateral) types of organization (e. g. ,  Johnstone et al. , 2020; Kimura, 1983; Knecht et al. , 2000). As indicated by the revised Edinburgh Handedness Inventory (EHI,  Oldfield, 1971) , our participants varied from strongly right handed, through ambidextrous (mixed handed), to strongly left handed: the mean laterality index, LI ¼ 10. 0, SD ¼ 75. 8; range ¼ 200, min. ¼ À100, max. þ100. To be quite conservative while identifying ambidexterity (e.
g. ,  Whitehouse, Badcock, Groen, & Bishop, 2009 , where scores !þ40 denote right-handed, À40 denote left-handed, and ambidextrous or mixedhanded individuals are placed in between), we adopted the !þ33. 3 and À33. 3 LI cutoffs for assigning volunteers to the right-handed and left-handed groups, with mixed-handed individuals located in between. With these criteria, the mean LIs for the three sampled groups were the following: righthanders (52) LI ¼ 89. 1, SD ¼ 16. 3, mixedhanders (31) LI ¼. 2, and SD ¼ 21. 4, and lefthanders (42) LI ¼ À80. 6, SD ¼ 16. 5. The required sample size was determined by percentages of atypical organization/lateralization of praxis and language functions from our earlier and/or source studies  (Kroliczak et al. , 2011; Kroliczak et al. , 2016; Kroliczak, Piper, et al.
fMRI data acquisition, pre/processing, and functional analyses All neuroimaging data were acquired with the use of a 3-T Trio MRI scanner (Siemens Medical Solutions), enabling echo planar imaging (EPI), with a 32-channel PA head coil, for Radio Frequency transmission/signal reception, courtesy of the Laboratory of Brain Imaging at the Nencki Institute of Experimental Biology in Warsaw. The echoplanar BOLD images were acquired using T2*-weighted segmented gradient-echo imaging sequence with the following parameters: field of view (FOV) ¼ 196 mm; 64 Â 64 matrix; in-plane resolution ¼ 3. 06 Â 3. 06 mm; flip angle (FA) ¼ 90 ; time to echo (TE) ¼ 30 msec; time to repetition (TR) ¼ 2,000 msec. Each volume contained 35 contiguous axial slices of 3. 1 mm thickness. The first four volumes in each scan series were always discarded.
Standard, anatomical scans were acquired in both sessions, with the use of a 3D T1-weighted magnetization prepared rapid gradient echo (MP-RAGE) pulse sequence, with the following parameters: TE/TR ¼ 3. 32/ 2,530 msec; inversion time (TI) ¼ 1,200 msec; FA ¼ 7 ; 176 contiguous axial slices; in-plane resolution ¼ 1. 0 Â 1. 0 mm; slice thickness ¼ 1. 0 mm; voxel matrix size ¼ 256 Â 176; FOV ¼ 256 mm. Finally, fast spin echo T2-weighted anatomical images were also collected, with the following parameters: TR ¼ 3,200 msec; TE ¼ 402 msec; FA ¼ 120 ; 512 Â 512 voxel matrix size; FOV ¼ 256 mm; 176 contiguous sagittal slices; inplane resolution ¼. 5 Â. 5; slice thickness ¼ 1 mm. The obtained DICOM images were converted to NIfTI (FSL) format using MRI-Convert software (http://lcni. uoregon. edu/ ~jolinda/MRIConvert/).
All anonymized raw data acquired in this project are publicly available at https://osf. io/63hjt/ (Kroliczak et al. , 2021). Both the pre-processing, and subsequent image processing steps were the same as in a pipeline described earlier by  Bidula and Kroliczak (2015). Namely, structural T1-, T2-, and functional T2*-weighted images were processed, co-registered, and analyzed using FSL (FMRIB's Software Library v6. 0. 1; http://fsl. fmrib. ox. ac. uk/fsl/fslwiki/), including FSL's FMRI Expert Analysis Tool (FEAT) v. 6. 00  (Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012). The non-brain tissues were removed with the Brain Extraction Tool (BET), and the FSL motion correction algorithm (MCFLIRT) was applied to attenuate signal changes due to small head displacements (despite the heads being largely immobilized with padding).
The images were then spatially smoothed, using a Gaussian kernel with a full width at half-maximum (FWHM) of 6. 2 mm (i. e. , twice the isotropic voxel size of 3. 1 mm), and temporally smoothed using high pass filter cutoff of 90. 0 sec (estimated from the model). Individual runs were analyzed separately at the first level. Prior to statistical analyses of the data, signal autocorrelations were corrected using a default pre-whitening procedure implemented in FSL, and hemodynamic responses were modeled using a double-gamma function. In each experiment, activity from the whole task intervals, and the whole rest blocks, was modeled, and critical contrasts (i. e. , praxis, language versus control tasks) from individual runs were first averaged, separately in each subject, using the fixed effects model.
In the group analyses, random-effects components of mixed effects variance were modeled and estimated with the default FSL method and settings: the FLAME Stage 1 procedure, with the resulting (Gaussianized t/F) Z-statistic images typically thresholded at least at Z > 3. 1, p ¼. 001, and corrected for multiple comparisons using additional clusterwise significance threshold of p ¼. 05, i. e. , the family-wise error rate (FWER) was always controlled at a ¼. 05. All processed data acquired in this project are publicly available at https://osf. io/ 63hjt/  (Kroliczak et al. , 2021). Neural activity from the following critical groups, and their direct contrasts, is displayed. First we show praxis-related activity (versus a control task) in its typical form, regardless of the organization of language, and language-related activity in its typical form, regardless of the organization of praxis.
Then, atypical representation of praxis is shown, regardless of language organization, and atypical representation of language is displayed, regardless of praxis organization. (When the typical and atypical groups are limited to individuals with both functions e praxis, language e similarly organized, largely the same networks of neural activity would be revealed. ) Finally, direct contrasts of praxis in its typical versus atypical form, as well as language in its typical versus atypical form are shown in both directions. Localization of clusters with significant brain activity was verified by projecting and visualizing the obtained volumetric outcomes with the use of the Connectome Workbench v1. 2. 3. (cf. Marcus et al. , 2011; Glasser, Smith, et al. , 2016). In this software, the group statistical imaging maps can be overlaid, e.
ROIs (regions of interest) Based on the earlier work, comparing the impact of ROI selection, and LI calculation methods (e. g. ,  Chlebus et al. , 2007)  to the outcomes of the Wada test  (McElligott, 2011)  used for the identification of the language dominant hemisphere, and its relation to the organization of praxis skills in individuals with different handedness status  (Kimura, 1983; Kroliczak et al. , 2011) , two sets of cytoarchitectonic maps for preliminary ROI analyses were utilized first. Namely, the left and right Brodmann areas [BAs]44/45 were chosen as critical for language production (healthy adults show fMRI signal increases in this vicinity during language and movement tasks, e. g. ,  Binkofski et al. , 2000; Amunts, Schleicher, & Zilles, 2004). The left and right [BAs]40, defined as divisions PF and PFm of IPL, were chosen as critical for praxis production (healthy adults show hand-independent signal increases in this vicinity during gesture planning and/or execution; e.
These parcellations are very handy in identifying e e. g. , quantifying or visualizing, and interpreting differences in functional organization of the cerebral cortex, including its variation across individuals. Their limitation is such that the 180 areas were not obtained on a volumetric basis, and a volumetric version (of the HCP-MMP1. 0 parcellation by  Glasser, Coalson, et al. , 2016)  had to be independently created. Based on its Freesurfer form, the atlas was first converted to volumetric space e its projection to the ICBM 152 2009a NLIN version (in MNI space) by Andreas Horn  (Horn, 2016). Subsequently, using FSLeyes (https://zenodo. org/record/3937147), these ROIs were resampled e with the use of the nearest neighbor interpolation e to the MNI152_T1_2 mm_brain space. Initially, we calculated LIs across the whole set of the 180 bilateral parcellations.
LI (lateralization index) calculations Custom-made scripts for LI calculations utilized "fslmaths" and "fslstats" functions implemented in FSL. As in the case of the ROI selection, our LI-calculation methodology was based on the results of a comparison of various methods of their computations  (Chlebus et al. , 2007)  to the outcomes of the Wada test  (McElligott, 2011)  in patients suffering from medically intractable epilepsy. Most importantly, our method of choice is conceptually identical with (but simpler than) an approach implemented in an LI toolbox  (Wilke & Lidzba, 2007) , running within other neuroimaging software environments. It capitalizes on threshold-independent estimates of cerebral asymmetries in individual participants as the most reliable method, and is therefore preferable (e.
LI network analyses and visualization All the main correlational analyses of praxis -language LI networks, performed separately in groups having either typically or atypically represented both functions, or their dissociations, utilized Spearman's rho non-parametric statistical tests. Two approaches were used. Seed-based connectivity analyses were performed first, with the following ROIs: PFm, PF, PFt, AIP, PH and FST for praxis (while looking for correlations with the 48 nodes during performance of the language task), and 6r, 44, 45, IFSa, MI, and AVI for language (while looking for correlations with the 48 nodes during the praxis task). Subsequently, LI network analyses were performed in nodes whose laterality was significantly different between the two e i. e. , typical, and atypical e groups (Bonferroni-corrected p <.
001). Additional seed-based connectivity analyses (with the same seeds) were also performed in individuals showing a dissociation of atypically represented e bilateral or right-lateralized e praxis, and typically represented (left-lateralized) language. The very rare cases of inverse dissociations of atypical language and typical praxis were not included. For more detailed information on the group sizes in LI network analyses, see Table  1. The final, Fig. 1  e The multi-modal parcellations of the human cerebral cortex serving as regions of interest (ROIs). The borders of all 180 areas per hemisphere that were delineated within the Human Connectome Project (HCP) initiative  (Glasser, Smith, et al. , 2016)  are shown, with a subset of 48 ROIs selected for further analyses color coded (as in the original atlas).
The surface-based "centroids" of these ROIs are also shown, as they were used for further visualizations in correlational analyses of laterality indices (LIs). Notably, because these areas were created using surface-based analyses, their volumetric versions obtained by their projections to the MNI space by  Horn (2016) , were used in this project. control, exploration was performed for nodes whose laterality did not differ between the typical and atypical groups. All these analyses were performed with IBM SPSS Statistics for Macintosh v. 26 (release 26. 0. 0. 1), Armonk, NY: IBM Corp. They were preceded by data preprocessing and extraction with the use of an interactive computational environment of the IPython Notebook (Jupyter Notebook), wherein custom-made scripts utilized Python functions from the following modules: scikit-learn & nilearn  (Abraham et al.
, 2014) , SciPy  (Virtanen et al. , 2020) , and pandas  (McKinney, 2010). Moreover, we utilized free and open source tools from the RStudio. The revealed significant between-task correlations were visualized with the use of BrainNet Viewer (v1. 7 of the 2019-10-31 release), a "Network Visualization Tool"  (Xia, Wang, & He, 2013). All the scripts and processing steps involved in LI calculation, including analysis codes, as well as the methods, and the layouts of component matrices, for network visualizations are provided at https://gitlab. com/mikbuch/lincalc and described by  Buchwald & Kroliczak, (Submitted) ; see also  Kroliczak and collaborators (2021).
e. , they are sorted by handedness, right to left, and then by praxis LIs, in the descending order e the cases of associations and dissociations of praxis and language can be easily spotted, as well. Their frequencies, including different combinations of handedness, praxis and language lateralization (i. e. , their disparate phenotypes) are summarized in Table  1  (and Supplemental Tables  2  and 3 ). Of course, these LIs would slightly differ, depending on the location and extent of the selected ROI, as well as the method used for LI calculation (cf. Bidula et al. , 2017). Our sample is not representative of a general population, as the research initially aimed to include a similar number of participants in each handedness group. The tasks turned out to be challenging for some mixedhanders, who upon closer testing ended up assigned to righthanders or lefthanders.
THE PRAXISeLANGUAGE LI CORRELATION NETWORKS. Analyses of the praxis-language LI correlations were performed on inputs from the following contrasts: praxis versus the control task, and language versus baseline (with the latter chosen for consistency with earlier comparisons to the Wada test; e. g. ,  Chlebus et al. , 2007). It should be added, though, that LIs from the language versus the control task contrast were nearly identical to the ones with baseline as a reference. Indeed, they were highly correlated: Pearson correlations of. 886** (two tailed), and Spearman's rho of. 737** (two tailed) were observed for VC, as well as. 886** (two tailed) and. 706** (two tailed), respectively, for SI. Figure  4  AB displays two sets of LI networks showing significant correlations in two groups of participants, i.
The probabilities of atypical praxiselanguage associations and the outcomes of mediation analyses We found that atypical praxis is 2,773 times more likely to occur with atypical language than the null effect (of no chance for them to occur together). The chance factor for this case is OR ¼ 28. 9  [CI. 95 ¼ (5. 6, 241. 8) ]. This means that if we find that in a given individual the language organization is atypical, there is a 28. 9 times higher chance for the praxis organization to be atypical too (rather than typical). The links between praxis and language are weaker and more diffuse in the typical group. When both functions are atypically represented, the tighter links were observed between several temporal, parietal and frontal nodes. (B) Praxis-language LI correlation analyses in nodes differing significantly in their laterality between the typical and atypical group.
This particular relation seems to be affected by handedness, as defined by EHI. The correlation between the lateralization of praxis and language is r tet ¼. 70 (with the splitting of both indicators at LI of 33). The 95% highest density interval is from r tet ¼ À. 10 to r tet ¼. 80, because the distribution of possible (assuming a different point for dichotomization) correlation coefficients is very skewed. The outcomes of the subsequent analyses were obtained with the assumption that handedness can affect language laterality only indirectly, e. g. , by influencing the praxis network, which is, in turn, more closely related to the language network. The alternative model of indirect relationships between the variables was also considered. Consistent with the layouts seen in Fig. 5 , we took it into account that the relationships are curvilinear.
In fact, the LI correlation networks disclosed in this study point to intricate interrelationships in numerous praxis-language related nodes of the three phenotypes e typical or atypical for both praxis and language, and with praxis dissociated from typically lateralized language. (The fourth group, with language dissociated from typically lateralized praxis was too small to be studied in isolation. ) What these outcomes mean to developing a more comprehensive model of praxis representation in the brain, and its links to language representation and handedness will be discussed next. These findings, once again, favor the interpretation that the observed relationships between praxis and language are contingent on a common cerebral specialization. Possibilities that we consider include the ability to construct symbolic representations, and/or to represent motor sequences, or sequential hierarchies  (Kimura & Archibald, 1974; Greenfield, 1991; Bradshaw & Nettleton, 1982 ; but see also  Arbib, 2008; Corballis, 2003; 2017; Rizzolatti & Arbib, 1998).
Acknowledgments The authors declare no conflict of interest. This work was funded by  National Science Centre  grant  Maestro 2011/02/A/ HS6/00174  to GK. The equipment used for data acquisition and analyses was funded by the  Ministry of Science and Higher Education  grant  6168/IA/128/2012  to GK. The CePT research infrastructure was purchased with funds from the  European Regional Development Fund  as part of the  Innovative Economy Operational Programme , 2007e2013. GK, MB, WP and AMN were supported by the Maestro  2011/02/A/HS6/00174  grant to GK. We thank  Lukasz Przybylski ,  Piotr Styrkowiec  and  Magdalena Reuter  (also supported by the Maestro  2011/02/A/ HS6/00174  grant) for help with data acquisition.
Shigella sonnei is the most common agent of shigellosis in high-income countries, and causes a significant disease burden in low-and middle-income countries. Antimicrobial resistance is increasingly common in all settings. Whole genome sequencing (WGS) is increasingly utilised for S. sonnei outbreak investigation and surveillance, but comparison of data between studies and labs is challenging. Here, we present a genomic framework and genotyping scheme for S. sonnei to efficiently identify genotype and resistance determinants from WGS data. The scheme is implemented in the software package Mykrobe and tested on thousands of genomes. Applying this approach to analyse >4,000 S. sonnei isolates sequenced in public health labs in three countries identified several common genotypes associated with increased rates of ciprofloxacin resistance and azithromycin resistance, confirming intercontinental spread of highly-resistant S.
Here, we describe the global population structure for S. sonnei and (i) propose a hierarchical SNV-based genotyping scheme, which we define using 1935 globally distributed genomes; (ii) implement the scheme within the free and open-source Mykrobe 32 software alongside detection of genetic determinants that are highly predictive of fluoroquinolone susceptibility phenotypes in S. sonnei  28  ; and (iii) validate this approach to genotyping using an additional 2015 genomes that were sequenced in public health laboratories and deposited in the publicly available GenomeTrakr database. Code is available at https://github. com/ katholt/sonneityping. By applying this novel genotyping framework to S. sonnei WGS data generated in public health laboratories on three continents, we demonstrate the utility of the new scheme for tracking and reporting emerging AMR clones both within and between jurisdictions.
Results Defining phylogenetically informative genotypes for S. sonnei. In order to define the global population structure and identify clades and marker SNVs, we collated 1935 publicly available S. sonnei genomes from eight studies  4, 7, [10] [11] [12] [13] 15, 16  as our "discovery" dataset (see Supplementary Table  1 ). These genomes represent isolates from 48 countries, collected between 1943 and 2018 (Fig. 1c, d , Table  1 ). The majority originate from Asia (32. 4%), Europe (29. 3%), Australia (18. 8%), or Latin America and the Caribbean (17. 4%) (Table  1 ). The data set is diverse in terms of acquired AMR genes (median 9 per genome, range 0-21), and includes 150 (7. 8%) genomes known to be associated with MSM. The recombination-filtered core-genome maximum likelihood phylogeny inferred from these genomes (Fig.
1 ). Pairwise distances within and between genotypes of all levels are shown in Fig. 2b. Development and validation of SNV-based scheme for assigning genotypes. We identified marker SNVs unique to each genotype (147 SNVs in total, see Supplementary Data 2) and implemented code to assign new genomes to genotypes based on presence of these markers (see "Methods"). To validate this approach, we downloaded and genotyped 2015 additional S. sonnei genomes from GenomeTrakr (referred to as validation set, see Supplementary Data 1). These genomes originate from public health laboratories in three countries (n = 609 USA, n = 1325 UK, n = 11 Israel, n = 70 country unknown), with isolation dates between 2015 and 2019. We identified 17 different genotypes, all belonging to clades 3. 6 or 3. 7 (Supplementary Table  2 ).
Application to public health surveillance data from Australia, England and USA. To demonstrate how the S. sonnei genotyping framework can facilitate the rapid tracking and reporting of emerging AMR trends across jurisdictions, we applied it to genomic surveillance data from Victoria, Australia (n = 644), England (n = 2867) and USA (n = 711) generated over a 4-year period (2016-2019) (data in Supplementary Data 1). The data represents all cultured isolates submitted to the Microbiological Diagnostic Unit Public Health Laboratory in Australia (42% of all S. sonnei notifications in Victoria)  4  and all those sent to the Public Health England Gastrointestinal Bacteria Reference Unit  28  (provided direct from the reference laboratories for the present study); and ~5% of those notified in the USA (sourced from the public GenomeTrakr database  33  ).
Discussion Here, we provide a global framework for S. sonnei, and identify marker SNVs that can be used to easily position newly sequenced isolates into this framework, without the need for timeconsuming comparative genomics or phylogenetic analysis. We demonstrate that the population structure of S. sonnei can be represented by a robust maximum likelihood phylogeny and define within it 137 subtrees on the basis of pairwise divergence and epidemiological coherence, which we designate as hierarchically nested genotypes. Furthermore, we provide a software package implemented within the Mykrobe 32 code base, which can identify both the S. sonnei genotype and QRDR SNVs direct from short-read sequence files in a few seconds. The genotyping scheme was constructed with a view towards stability, prioritising as markers SNVs that are found in highly conserved core genes that are not under adaptive selection in the population.
Importantly, our approach provides genotype definitions and nomenclature that are stable and transparent, not dependent on comparative analysis or on any specific sequencing assay or software package, although we provide an implementation in the Mykrobe v0. 9. 0 software for convenience. The Mykrobe implementation was tested on all Illumina reads used in this study, and matching long (ONT) reads for eight of these genomes. Correct genotypes were returned in all cases (providing the --ont flag to Mykrobe when using the long reads as input). This overall approach is designed to facilitate easy communication between laboratories and jurisdictions, and straightforward comparison of pathogen populations over time, allowing for rapid identification of clonal dissemination and AMR trends. To demonstrate the utility of the genotyping scheme for public health applications, we applied it to summarise large genomic surveillance datasets from three jurisdictions in Australia, England and the USA.