dataesr/scientific-paragraphs-categorization huggingface dataset:
While models with an allometric or linear covariate adjustment for brain size yielded similar significant effects, omitting brain allometry influenced reported sex differences in variance. Finally, we contribute to the reproducibility of research on sex differences in the brain by replicating previous studies examining cerebral sex differences. This large-scale study advances our understanding of age, sex, and brain allometry's impact on brain structure and provides data for future UK Biobank studies to identify the cerebral regions that covary with specific phenotypes, independently of sex, age, and brain size.
If neuroanatomical norms for a population were available, then comparing any clinical group to these norms would overcome the issue of sampling variability in the control group. However, neuroanatomical norms are not easy to establish, as they require large populations, and neuroanatomical measures often depend on magnetic resonance imaging (MRI) scanner characteristics and acquisition sequences. Valid norms would therefore only be established within a given study in a single scanner, or in a small set of comparable scanning sites with similar acquisition protocols as done by the UK Biobank. For this reason, the UK Biobank, the largest neuroimaging dataset available to date (N $ 40,000), is an ideal candidate to create neuroanatomical norms that could be reused for multiple studies of neurological and psychiatric disorders.
| Participants Participants were drawn from the UK Biobank, an open-access large prospective study with phenotypic, genotypic, and neuroimaging data from 500,000 participants recruited between 2006 and 2011 at 40-69 years old in Great Britain  (Sudlow et al. , 2015). All participants provided informed consent ("Resources tab" at https:// biobank. ctsu. ox. ac. uk/crystal/field. cgi?id=200). The UK Biobank received ethical approval from the Research Ethics Committee (reference 11/NW/0382) and the present study was conducted based on application 46 007. Currently, MRI data and imaging-derived phenotypes (IDPs) are available for about 41,000 participants. This study analyzed the IDPs from the first imaging visit generated by an image-processing pipeline developed and run by the UK Biobank Imaging team  (Alfaro-Almagro et al.
| Brain image acquisition and processing A standard Siemens Skyra 3 T running VD13A SP4 with a standard Siemens 32-channel RF receive head coil was used to collect data. The 3D MPRAGE T1-weighted volumes were analyzed by the UK Biobank Imaging team with pipeline scripts that primarily call for FSL and Freesurfer tools. Details of the acquisition protocols, image processing pipeline, image data files and derived measures IDPs of brain structure and function are available in the UK Biobank Imaging Protocols.
| Total brain volume TBV was calculated as the sum of the total gray matter volume (GMV; i. e. , sum of cortical and subcortical GMV, data-field 26,518), cerebellum white matter volume (WMV, data-fields 26,556 for left and 26,587 for right), and cerebral WMV (data-fields 26,553 for left and 26,584 for right) from the UK Biobank ASEG Freesurfer segmentations. Refer to Supplemental Info 1 for more on the choice of TBV. Individuals with missing data for these regions were excluded from the analyses, yielding 40,055 participants.
| Exploratory analyses These analyses were not preregistered unless otherwise stated (for more details see Supplemental Info 4). In brief, we first examined whether global and regional measures were allometric with the linearHypothesis function from the car R package  (Fox et al. , 2020)  with the null hypothesis: "The slope of log10(TCM) is equal to 1". Then, we examined whether the scaling coefficient of mean thicknesses with TBV differed from 1 /3 and whether the scaling coefficient of surface areas with TBV differed from 2 /3 with the linearHypothesis function  (Fox et al. , 2020)  and appropriate null (e. g. , null hypothesis for mean thicknesses: "The slope of log10 (TBV) is equal to 1/3"). We would expect these coefficients if brain growth was proportional (similar to a sphere) and if larger brains were scaled-up versions of smaller brains.
We additionally created neuroanatomical deviance markers that correspond to the standardized residuals of the full statistical models. These residuals reflect the extent to which the cerebral measures of an individual deviate from those of other individuals with the same sex, age, and total brain size. The global and regional neuroanatomical markers will be returned to the UK Biobank and made available to UK Biobank researchers for future use. When additional neuroimaging data becomes available, we recommend that future researchers run our code available on OSF to obtain neuroanatomical norms and regional and deviance markers for their participants to obtain more accurate estimates of the neuroanatomical norms and deviance markers of the UK Biobank participants. Finally, in addition to creating global and regional neuroanatomical norms for each of the 629 brain measures, we computed global neuroanatomical deviance markers, which reflect the deviance of an individual from the norm across all brain regions.
The means of the volumetric, surface area, and neuroanatomical (all cerebral measures combined) global neuroanatomical deviance were larger in males, suggesting that male volumes and surface areas deviated more from their sex-specific norm than females. However, females deviated more from their norm in the mean thickness global allometry marker. Males additionally had more variable volumetric and neuroanatomical global allometric markers (Table  1 , Figure  1 ). Thus, investigations of global as well as regional neuroanatomical deviance should always take sex into account. With the world's largest neuroimaging dataset, we created neuroanatomical norms in the UK Biobank, to which any UK Biobank individual can be compared, in the same way that scores from intelligence tests or personality questionnaires can be compared to population norms.
In our replication of Mankiw et al. 's (  2017 ) study, we found that, instead of being greater in males, the cerebellum, flocculus, cerebellar lobule VIIb, VIIb, and VIIa, and crus II volumes were greater in females and that the flocculus volume did not vary between sexes. Although both studies used segmentation algorithms that have a better parcellation accuracy than the SUIT segmentation  (Diedrichsen, Balsters, Flavell, Cussans, & Ramnani, 2009)  of the present study  (Carass et al. , 2018; S. Han, Carass, et al. , 2020) , these discrepancies may also stem from differences in sample age (mean age = 12. 5 and 70 years old, respectively) and size (N = 116 and 2,023, respectively). In light of the difficulty of segmenting the cerebellum and the differences in regional specificity and accuracy across segmentations, we suggest that future studies take advantage of the large UK Biobank dataset to apply and compare cerebellar segmentation algorithms.
, 2020). When combining data from the Queensland Twin Imaging study (N = 727) and the Human Connectome Project (N = 960),  van Eijk et al. (2020)  similarly reported a larger hippocampal fissure in males. However, in contrast to our finding that females had a larger presubiculum in the left hemisphere, they found that males had larger presubiculum volumes. While males had larger fimbria volumes in their linear model, females in our study had a greater fimbria volume in the left hemisphere in both linear and allometric models. The hippocampal subfields from the UK Biobank and the Queensland Twin Imaging cohorts were extracted using the T1-weighted subcortical images, whereas T2-weighted images were also used for the Human Connectome Project. Although a recent study suggests that T1-weighted images may not be adequate enough to determine the internal boundaries of the hippocampal subfields  (Wisse et al.
Since interactions with brain size are often overlooked in studies examining sex and age differences (e. g. , Ritchie et al. , 2018; Vinke et al. , 2018) , the presence F I G U R E 2 Scaling coefficients of cortical surface areas, mean thicknesses, and volumes with total brain volume (TBV). Values are the scaling coefficients of a region with TBV and range from 0. 61 (volume of the right posterior ramus of lateral sulcus) to 1. 63 (mean thickness of the left paracentral gyrus and sulcus). The flat representation corresponds to the flattened image of the superior view with the midline of the circle reflecting regions within the sagittal plane and circle edges reflecting inferior regions. Figures made with https://neuroanatomy. github. io/cortex/ (Toro, 2020) [Correction added on 26 July 2021, after first online publication: Figure 2 has been replaced.
ACKNOWLEDGMENTS This work received support under the program " Investissements d'Avenir " launched by the  French Government  and implemented by l' Agence Nationale de la Recherche (ANR)  with the references  ANR-17-EURE-0017  and  ANR-10-IDEX-0001-02 PSL. Funding was also obtained from  Fondation pour l'Audition  ( FPA RD-2016-8 research grant ). This research has been conducted using the  UK Biobank Resource.
14 (right inferior circular sulcus of the insula) to À0. 02 (left posterior dorsal cingulate gyrus), and increased with age in 52 regions, ranging from 0. 02 (left middle occipital gyrus) to 0. 22 (right cuneus gyrus). Surface areas linearly increased with age in 36 regions, ranging from 0. 02 (left superior frontal gyrus) F I G U R E 4 Linear age effects across cortical measures. Age effects ranged from À0. 40 (mean thickness of the right inferior segment of the circular sulcus of the insula) to 0. 22 (mean thickness of the right cuneus gyrus, O6). The flat representation corresponds to the flattened image of the superior view with the midline of the circle reflecting regions within the sagittal plane and circle edges reflecting inferior regions. Figures made with https://neuroanatomy. github.
Data Description This dataset contains the RNA-Seq analysis of nine tissues of Sardina pilchardus. Nine tissues from two female and one male sardines were dissected onboard and immersed immediately in RNAlater. Sequencing was performed using the Illumina HiSeq 20 0 0 platform, yielding 56 million single-stranded paired-end reads of length 101 base pairs, a median quality value per sequence of 37, 5. 6 million reads per sample on average, resulting in a total of 5. 70 Gbp ( Table  1  ). Reads were preprocessed with Trimmomatic, which slightly reduced the dataset to 98,09% of the reads, and the mean read length to 100. 67 base pairs. Clean reads were assembled with Trinity. To measure the quality of the assembly, cleaned reads were back-mapped to the reference, and transcripts were searched for Actinopterygii Single-Copy Orthologs (SCOs).
Transcripts were annotated with TransDecoder and Trinotate. Results of the sequencing effort and read cleaning are available in Table  1  , while the ones of assembly, quality control and annotation are in Table  2. Fig. 1  shows the most frequent Gene Ontology annotations received, and the coverage of the metabolome based on the KEGG annotations. Finally, each library was quantified with kallisto and prepared for differential downstream analysis with sleuth to obtain the expression patterns for each transcript in every tissue. The raw reads for the nine tissues of Sardina pilchardus have been deposited at the European Nucleotide Archive, under the umbrella project PRJEB18441, while each experimental run is deposited under accession numbers ERR5925802 to ERR5925811 ( Table  1  ). To our knowledge, this is one of the widest datasets not only in Clupeiformes but also in fish in general, only surpassed by the ones in  [1].
Acknowledgments The authors are thankful for the technical and human support provided by  Fernando Rendo ,  Irati Miguel , and  Irantzu Bernales  from the  Genomics Service (SGIker)  at the  UPV/EHU. We also thank the crew of the R/V THALASSA, as well as  E. Duhamel  and  P. Gatti  for the onboard sampling.
In addition, the same two protocols were followed using blood agar medium (Columbia agar plus 5% sheep blood). A MALDI Biotyper Microflex LT controlled by Compass flexControl software (version 3. 4; Bruker Daltonics, Billerica, Massachusetts, USA) was used to generate mass spectra for all isolates. Mass spectra were acquired using automatic mode and default settings (2000 to 20 0000 Da; linear positive mode; 240 laser shots).
Identification of phylogroups based on MALDI-TOF data in Klebsiella MALDI TypeR. The KpSC and the KoSC encompass species and subspecies that are defined as phylogroups. As such, we aimed to distinguish them on the basis of MALDI-TOF data, using information from the spectra, defined as peak shifts  [15]. Identification of phylogroups is therefore based on a small selection of biomarkers for which one (or more) protein weight modifications have been previously reported  [4, 5]. In fact, each phylogroup could be seen as comprising MALDI-Types, as described by Bridel et al. 2020  [12] : a specific combination of protein m/z values of these biomarkers. Klebsiella MALDI TypeR was run on a 4 CPUs 16-GB RAM CentOS Linux 7 (Core) virtual machine.
Results and Discussion Development of Klebsiella MALDI TypeR and its web interface. Klebsiella MALDI TypeR was developed as a web application with R (version 3. 4. 0) and R Shiny (version 1. 4). A user can upload either a single spectrum or multiple spectra (a maximum number of spectra is not defined, but we recommend working with batches of up to 100 spectra per analysis). The uploaded spectra must be in zip archive format. The zip archive must contain the raw data from any spectrometer. This includes the tree structure, which must sample spectra overlaid on the reference spectra (1 to 3 spectra for each phylogroup). Predefined windows (one for each biomarker) allow the user to directly investigate specific spectral regions. This feature could also enable the detection of new isoforms or biomarkers.
Evaluation of the identification tool. We first tested fifty KpSC isolates grown overnight on lysogeny broth (LB) agar (37°C, 18h) and subjected to ethanol/formic acid extraction following the manufacturer recommendations (Bruker Daltonics, Bremen, Germany). For each strain, a total of 3 spectra from the same spot were acquired (Bruker Microflex LT mass spectrometer, Paris). This dataset yielded correct assignments to species complex for 50/50 (100%) isolates, 47 (94%) of which had an identification value >50% (Tables  S2  and S3 ). The same samples prepared using different experimental conditions (two media: LB and Columbia agar plus 5% sheep blood; each with both extraction and direct application in the MALDI-TOF target) also yielded correct complex assignments for all isolates, albeit with lower confidence (Table  S2 ).
Regarding phylogroup assignment, using LB-extraction 90% of samples were correctly identified at phylogroup level (Table  S3 ), while the other sample preparation methods yielded better results overall (Table  S4 ). To validate robustness of the tool to instrument and user variation, we tested an additional dataset comprising 248 KpSC isolates prepared using the LB-extraction method (176 isolates with a single spectrum, 26 in duplicate and 68 in triplicate); these spectra were generated on a different instrument (Bruker MALDI Biotyper microflex LT-SH) in a different laboratory (Melbourne, Australia). 244/248 isolates (98%) were assigned to the correct species complex, and 219/244 (90%) were correctly identified at the phylogroup level (84% to 100% for each phylogroup, see Table  S5 ). The KoSC assignments were tested using 28 isolates prepared using LB-extraction and triplicate spectra generated on the Bruker Microflex LT mass spectrometer instrument (Paris).
Isolates and outbreak sets included in the study A total of 440 isolates (Table  S1 ) were included into the study dataset. First, we included and sequenced 8 isolates of the French National Reference Center (NRC) collection corresponding to four available pairs of C. diphtheriae infection cases defined as epidemiologically linked (Table  1 ). The first pair (Mayotte set 1) included two isolates from a single sample, isolated at one-month distance (FRC025 and FRC030). The second pair (Mayotte set 2) included isolates from one patient and one of his contacts; the two isolates were isolated 15 days apart. The third pair (Carcassonne set 1) included isolates from two members of the same household, sampled 14 days apart. The fourth pair (Bron set 1) included two isolates from a pair of siblings, sampled on the same day.
Second, we included and sequenced a random selection of 21 isolates from the French NRC collection, selected among isolates that were cocirculating temporally or geographically with each outbreak (Table  S1 , 'sporadic' with FRCxxxx isolate names). Third, 162 isolates from previously published genomic epidemiology studies in South Africa  [25] , Germany-Switzerland  [5] , Germany  [20] , France  [22]  and Canada  [21]  were included. These genomic investigations of outbreaks (or clusters of cases) together comprised 11 sets of isolates defined as related based on epidemiologically and genomic evidence (Table  1 ), and also included isolates considered as non-related to the outbreak sets. Finally, all other publicly available genomic sequences from GenBank available on May 30 th 2019 were included (Table  S1 ).
Definition of the cgMLST scheme We first selected 171 genomes representing a diversity of C. diphtheriae sublineages, including 104 genomes present in Table  S1  and 67 genomes from the French NRC  [11]. These included 14 C. belfantii isolates, but no C. rouxii isolates were included at this step, as this species was not identified at that time. From this set of genomes, we inferred the species core genome using the CoreGeneBuilder pipeline (https://zenodo. org/record/165206#. WVpT7I55EQU) as in  [33] , using the NCTC13129 strain genome (GenBank accession no. NC_002935) as reference  [34]. CoreGeneBuilder automatically removed genomes for which the size was too divergent compared with the whole set. We applied the following quality filters: a maximum of 500 contigs and a minimum N50 of 10,000 bp.
To detect those potential paralogs, we compared each allele of each locus against all the alleles of all the other loci using the software BLAT  [38]. If a single hit was found between 2 different loci (more than 70% amino acid sequence identity between two alleles), we removed both. A total of 8 loci were discarded by this process. We also removed genes that belong to the classical 7-gene MLST scheme  [14]  and the ribosomal protein genes that are used in the ribosomal MLST approach  [39] , so that they could be analyzed independently. Next, we removed loci whose length varies too much among alleles, which is useful to reduce ambiguities during the genotyping process. We aligned the amino acid sequences and removed loci for which the alignment contained more than 5% of gaps (total number of gaps compared with the total number of character states).
Evaluation of cgMLST allele call rate Because loci with high numbers of missing alleles were filtered out during cgMLST scheme construction, the allele call rate of the resulting scheme was assessed using an independent set of genomes, which had not been used for the above-described definition of the scheme. This entirely distinct dataset of C. diphtheriae isolates was made of 162 genomes published after cgMLST scheme definition and described in the first Methods paragraph. Missing alleles were defined as being not called using 90% identity and 90% of length coverage.
Constitution of a BIGSdb database for C. diphtheriae The cgMLST scheme was incorporated into a BIGSdb seqdef database  [41]. Classification schemes were created at the level of 25 and 500 mismatches. The isolates provenance data and genome sequences were imported into a linked isolates database (https://bigsdb. pasteur. fr/diphtheria). A publicly available BIGSdb project (i. e. , a browsable list of isolates) entitled 'cgMLST set-up study' corresponds to the dataset of 440 genomes and was created to facilitate retrieval of the data used in this work.
Inheritance of classification identifiers from 7-gene MLST MLST alleles and profiles of the 440 genomes were defined in the PubMLST C. diphtheriae database. We then mapped the sequence type (ST) identifiers onto the obtained 500-mismatch cgMLST single linkage classification groups, and attributed the dominant ST identifier to the group, using an inheritance algorithm (https://gitlab. pasteur. fr/BEBP/inheritance-algorithm).
Phylogenetic and population structure analyses Phylogenetic analyses were performed by first inferring a tree using JolyTree v1, an alignment-free distance-based procedure  [43]. The resulting tree was used as input for ClonalFrameML  [44] , which also requires an alignment. The alignment was obtained by translating allelic sequences of cgMLST loci of each genome into proteins. We aligned the proteins of each loci using MAFFT v7. 467  [45]  with default parameters, and then backtranslated the protein alignments to codon alignments using Goalign (https://github. com/evolbioinfo/goalign); the individual gene alignments were then concatenated into a supermatrix, also using Goalign (the nucleotide positions of missing alleles were replaced by gaps in the concatenate of alignments of individual loci).
We used this alignment and above defined input tree to run ClonalFrameML with default parameters. We used the software PHIPack v1. 0  [46]  to calculate the PHI (Pairwise Homoplasy Index) statistic and perform a locus-by-locus test of recombination with a p-value threshold of 0. 05. Nucleotide diversity was calculated both for the entire dataset of each species, and among sublineages, using an in-house script relying on the p-distance calculation provided by Goalign. The overall average Silhouette width, which estimates the overall clustering quality based on maximizing inter-group distances and minimizing intra-group distances, was used to define the clustering consistency  [47]. Dating ST5 and ST8 phylogenetic tree nodes was performed using the LSD (Least-Squares Dating) algorithm  [48]  as implemented in IQ-TREE v2.
0. 6  [49]. The sampling dates were used to calibrate the trees. The subtrees were extracted from the entire tree (Figure  1 ). For both analyses, the closest outgroup was also extracted and included in the analysis. This allowed for the dating of the root of the ST8 phylogeny, whereas for ST5 this analysis did not allow dating the root. The clade compatibility index of STs or other groups was calculated using the ETE Python library (http://etetoolkit. org/docs/latest/tutorial/tutorial_trees. html#checking-the-monophylyof-attributes-within-a-tree), in order to define whether their constitutive genomes formed a monophyletic, paraphyletic or polyphyletic group within the recombination-purged sequencebased phylogeny of the core genome. We estimated clade compatibility as the proportion of non-singleton STs, sublineages or clonal groups that were monophyletic.
Saturation analysis of the number of sublineages and genetic clusters Following the definitions of genetic clusters and sublineages (25 allelic mismatches for the genetic clusters, 500 for the sub-lineages), we simulated 1,000 samples of 1 to 440 profiles (the 440 profiles of our initial dataset), with a step of 1. For each threshold and each number of profiles, we then calculated the mean number of clusters, and used the minimum and maximum values to define the range. To extrapolate the previously-obtained curves, we defined the species accumulation curves using the R package vegan. We chose the Lomolino function  [51]  for its excellent fit with our data.