dataesr/scientific-paragraphs-categorization huggingface dataset:
NeuAtlas, http://www. neuromorphometrics. com/?p=315)-although they are not always available to the public  4. In theory however, manually-generated data should only refer to volumes that are hand-drawn, from beginning to end, and are therefore free of software-related bias. Because the manual process is very tedious and time-consuming, such datasets are very rare (e. g. , MNI-HISUB25  7  ). Irrespective of the nature of segmentation, available datasets rarely provide population demographics, details on how the regions have been drawn or obtained, or whether anatomical variations are considered  1. Scarce attempts have been made in retrospect to address these issues  2. This situation proves to be challenging for end-users following such tools' parcellation protocols, particularly when it comes to anatomical variability, leaving lots of room for assumptions, misinterpretations, and inconsistent parcellations, all of which are undesirable.
We provide parcellation of 10 subjects' left and right SFG, SMG, and CG, which should prove useful to the brain imaging community given how rare manually-segmented datasets are. Although the sample size is small, these data are a good example of the morphological changes that an upper middle-aged healthy population undergoes, and a valuable ground truth for studies investigating the effects of ageing and/or disease on cohorts of similar age. We include complete population demographics and a detailed protocol that accounts for anatomical variability such as interruptions, connections and branching. This 60-gyrus dataset could also be used as a reference (rather than absolute truth) to assess inter-package parcellation differences or to validate a novel or improved parcellation tool, independently of the borders defined in the protocol, because the manually parcellated data allow checking for the anatomical variability we describe.
Dataset Methods Subjects. Ten healthy right-handed non-smoking subjects (5 male, 5 female, age range 55-64 years old), not on any medication, were randomly selected among a larger NIH-funded study (NIH grant R01 EB004155) involving 80 healthy subjects. MRI data were collected at the Western General Hospital (Edinburgh, UK) and structural scans were examined by a fully-qualified radiologist, confirming all subjects were in good health. Data acquisition. The scans and cognitive tests were acquired and administered in 2008-2012, (data summarized in Table  1 ) prior to the development of community reporting standards, however, all data were systematically collected and reported. The local ethics committee approved the study and informed consent was obtained from each patient. For each of the 10 subjects in this dataset, 4 MRI volumes were obtained: coronal high resolution 3D T1-weighted (T1w), axial T2-weighted (T2w), T2*-weighted and T2 FLAIR.
All scans were acquired on a 1. 5 T MRI scanner (General Electric, Milwaukee, WI, USA) at the Brain Research Imaging Centre in Edinburgh (UK). Further details can be found in Tables  2  and 3  of the Data Records section). A medical questionnaire and a battery of cognitive subtests from the 4 th edition of the Wechsler Adult Intelligence Scale (WAIS-IV  19  ) were administered to each healthy volunteer. Checks were made to ensure that they scored within the normal range (Table  1 ). The general practitioner (GP) of every volunteer was contacted twice throughout the study: once to inform them of the subject's participation in the study's details (along with the study's information sheet), and once more to inform them of the scan's outcome. Data preparation. Given the limited contrast between grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) at 1.
They were then revised for landmark consistency and accuracy by 2 experts, CP and GM, who were blinded from the protocol. When inconsistency was found across subjects, the protocol was amended, and the regions were redrawn and reviewed again. Consecutive revisions by the 3 authors and protocol  Table  5. The number of paired WM and GM segments varied across subjects and hemispheres depending on cortical variability and the degree of folding. updates continued until an agreement was reached on the dataset's anatomical accuracy and consistency as well as the comprehensiveness of the protocol with regards to variability. Because we did not seek to validate the tissue segmentation itself (grey matter-cerebrospinal fluid and grey matter-white matter borders), but the consistency of the parcellation scheme, the signal intensity (after enhancement) and spatial resolution of 1.
5 T MRI is adequate for this task. Columns 2-10 contain FreeSurfer-derived metrics, columns 11-19 contain Masks2Metrics-derived metrics, and columns 20-22 contain mean modified Hausdorff distance (MMHD, https://uk. mathworks. com/ matlabcentral/fileexchange/29968-modified-hausdorff-distance) metrics. Further metrics details can be found in the Technical Validation section.
Technical Validation As mentioned in the 'Parcellation Details' section, we validated the protocol using a blinded review of the data checking between subjects consistency whilst still considering of all known forms of cortical variability. A scanner strength of 1. 5 T was sufficient as we did not seek to validate the grey mattercerebrospinal fluid and grey matter-white matter border segmentation itself, but the consistency in parcellation and border identification despite all sorts of cortical variability, across hemispheres and subjects. To quantitatively assess the data parcels, we compared the average thickness, volume and surface area of each gyrus/sulcus from our parcellation to that of FreeSurfer (version freesurfer-Linux-centos4_x86_64-stable-pub-v5. 1. 0). Although anatomical variations are not accounted for as much as in our protocol  1  , it still provides a valuable comparison, as variations should not be substantial, in particular for average thickness  30.
0002], Fig. 6c ), with no association using either parcellation scheme. It is understandable that when dealing with large datasets it is not possible to manually segment the entire ground truth. However, from what we have observed with our small cohort, cortical variability is not rare. It is therefore crucial to be aware of the variability details of any regions of interest, and when working with automated tools to check for variability considerations to best assess the implications this may have on the results, if any. The data presented here provide in that sense a good testing ground for automated MRI parcellation.
Acknowledgements Data acquisition and preparation was funded by  NIH  grant  R01 EB004155  (MB, SM), SINAPSE-SPIRIT (a  Scottish Funding Council   HR09021  grant), the  Tony Watson Scholarship  and  Canon Medical Research Europe (SM) , and the  Row Fogo Charitable Trust  (Grant No. BRO-D. FID3668413 , MVH). We would like to thank  Ian J. Deary  ( Department of Psychology, University of Edinburgh, United Kingdom ) for providing the WAIS-IV kit and reaction time device, and  Alison Pattie  ( Department of Psychology, University of Edinburgh, United Kingdom ) for providing detailed training on the kit and device.
Usage Notes All previously described data is freely available at the Edinburgh DataShare repository (Data Citation 1) under the CCBY license. Masks2Metrics is a tool that is freely available on GitHub, at https://github. com/Edinburgh-Imaging/ Masks2Metrics, under the GNU General Public License (archived version used for the results presented available at Edinburgh DataShare repository  27  ). The tool has also been published in the Journal of Open Source Software (JOSS)  28. Results for the Technical Validation section were derived by running our Matlab code (Matlab_code_to_derive_stats_and_figs. m, Data Citation 2) which uses the parcel metrics of Data Citation 2 (SuperiorFrontalGyrus. tsv, SupraMarginalGyrus. tsv, and CingulateGyrus. tsv).
Validation of V-ASAP on Zika virus To validate our analytical method, we used an available public data on Zika virus  (Quick et al. , 2017). In this study, 35 primer pairs were designed and the PCR products were sequenced with Illumina overlapping paired-end reads leading to 35 amplicons of 400 bp covering the entire genome of Zika virus (around 10,300 bp). These data were comparable with the data produced in our protocol and the complete genome sequences obtained by the authors were available making this study a good validation dataset. We applied V-ASAP to this dataset and compared the results with the sequences obtained from the classical method consisting in aligning reads against a reference sequence and extracting a consensus sequence from this alignment (see section "Material and methods -Validation on Zika virus" for more details about the selection of reference sequences and parameters for both approaches).
Only one mismatch was observed for the consensus sequence generated with SRR5121076 dataset. This error in consensus calling was due to a misalignment of several reads in this region: the ends of a number of reads at this position did not align, probably due to an incorrect trimming of these reads in the original dataset. This example showed that the correct alignment of reads is necessary to call the correct consensus and that the alignment step has to be very carefully performed, an incorrect trimming or an incorrect parameters tuning could have a strong impact on results. When the reference sequence was divergent from the studied sequence, the quality of the obtained consensus sequence rapidly fall down. The coverage decreased (around 90% with HQ234499. 1 and only around 20% with the more distant sequences HQ234501.
1 and KF268948. 1). Moreover the assembly became very fragmented with an increasing number of contigs. Another interesting feature was the number of unaligned contigs, i. e. contigs produced by the method but which did not align against the true sequence. This number increased dramatically when the reference sequence was more divergent. This was due to the well-known over-fitting bias of the alignment-based method  (Archer et al. , 2010) : some bases of the reference sequence were selected in place of the true bases from reads because of incorrect alignment of reads. The reference-free method used in V-ASAP resulted in sequences close to the expected ones with metrics similar to the one obtained with the closest reference sequence. The assembly covered more than 95% of the sequence with only 2 or 3 contigs and without any errors for the three datasets.
Primer design and grouping for PCR multiplex To generate a reliable consensus sequence on which to design primers, 47 HCoV-OC43 genome sequences were aligned using CLC GenomicsWorkbench (Qiagen), the accession numbers of the 47 genomes used are available in Supplementary material part 3. The primers were designed in conserved regions to obtain amplicons of 500-550 bp with a minimum overlap of 16 bp between each amplicon (after trimming of primer sequences) leading to a list of 99 primer pairs (see Supplementary material for the list of primers). In order to decrease the number of PCR reactions, we pooled PCR primer pairs in 12 groups each containing between 3 and 12 PCR primer pairs. The primer pairs were grouped according to their positions on the genome avoiding too short distance between concomitant pairs.
Sequencing of HCov-OC43 genomes from clinical samples The complete protocol was applied to 11 clinical samples from patients infected with HCoV-OC43. The sequencing data were analyzed with V-ASAP. The sequencing results and metrics are shown in Table  2. In comparison with previous studies and despite a longer genome sequence, the sequencing of coronavirus genome was very specific. The number of reads that mapped against a HCoV-OC43 sequence was very high (more than 92% for 9 of the samples), showing that the primer design and the biological protocol led to few noise in sequencing data. Only MDS5 and PR2 samples showed very low percentages of mapped reads, 11. 30% and 9. 39% respectively, due to the fact that the major part of reads was locally aligned against human sequences. For the 11 samples, the number of merged reads was comprised between 1,811,280 and 2,309,880 providing enough sequencing data for reliable analysis.
V-ASAP Several public tools are used in V-ASAP. CASPER was used for read merging (option -j). PhiX reads were filtered out with bowtie2 (parameter-local). Reads were then sorted by amplicon with cutadapt (option-discard-untrimmed) and primer sequences were trimmed. For each amplicon, reads were then clustered using CD-HIT (parameters -c 1 -d 0 -s 1). Finally the representative reads for each amplicon were assembled with CAP3 (-o 16 -s 251 -j 31 -p 66 -i 21). V-ASAP is freely accessible on https://github. com/caboche/V-ASAP as a docker container.
Validation on Zika virus Zika datasets (SRR5121076, SRR5121078, SRR5121079) were obtained from SRA (NCBI). Reads were already pre-processed: primers were trimmed and a quality control was probably performed. In our method, primer sequences are used to assign each read to its amplicon of origin, but in these datasets primer sequences were already trimmed and we had to adapt our method for this step. The 22 nucleotides following the true forward primers designed in the original study was considered as "fake forward primers" (the list of these 35 primers is given in Supplementary material part 5). The reverse primers could not be used because the read ends in SRA were trimmed for quality. The "fake forward primers" were used as input of V-ASAP analysis with a change in parameters of CASPER (-g 0.
Data availability Sequencing reads are available from BioProject PRJNA507154. Genome sequences were deposited in GenBank with accession numbers: MK303620 for MDS2, MK303621 for MDS4, MK303619 for MDS6, MK303622 for MDS11, MK303623 for MDS12, MK303624 for MDS14, MK327281 for MDS15 and MK303625 for MDS16.
5 × 31. 5 × 20 cm), positioned to the right of a screen, about 50 cm away from the midline of the participant. The manipulandum restricted the movement of the index finger to the horizontal plane and allowed a full-range of friction-free flexion-extension motion about the metacarpophalangeal joint. The output of the potentiometer was sampled at 1000 Hz in step with the EEG, using a National Instruments A/D converter. The signal was down-sampled offline to 500 Hz for online computational efficiency, and used as a continuous input into a computer, which ran the HDC program implemented on C++ using the cross-platform IDE Code Blocks and the open source library OpenFrameworks (code available at https://github. com/ GHFC/SoNeTAA). A java script version of the HDC is available on GitHub: https://github.
com/crowd-coordination/web-vpi and can be tested online at http://www. morphomemetic. org/vpi/. The velocity of the human finger was numerically computed using a three-point differentiation algorithm and together with position data plugged into the VP equation (cf. ,  Dumas et al. 2014a  for details). The differential equation returned instantaneous VP acceleration, which was integrated using a fourthorder Runge-Kutta method at 500 Hz to provide VP velocity and position. We compared digital timestamps and analog triggers with an oscilloscope to ensure that a maximum delay of 2 ms occurred between data acquisition and computation of the model output (0. 3% of typical movement cycle length). To create the animation of VP finger movement used in the experiment, a series of position-indexed images was created and stored (cf.
After the interaction period, the participants were reminded of the Task instruction ("your task was in-phase. " or "your task was anti-phase. "). Furthermore, they were asked to judge the VP using rating scales presented on the screen. In details, participants reported the degree to which the VP was cooperative or competitive early and later on during the trial (questions: "early on, the partner was. ?" and "then, later on, the partner was. ?"; continuous value from 0: competitive to 1: cooperative). At the end, they judged the humanness of the VP (question: "overall, how was the partner like?"; binary choice with 0: machine and 1: human). All reports were done using a scale on the screen which was controllable by the finger motion of participants. Since the resulting values were in a limited range and non-Gaussian, such data were analyzed by means of a Wilcoxon rank test.
Artifact Correction and Data Preprocessing Following visual inspection, any noisy EEG channel was marked as bad (average = 4. 5, min = 0, max = 10) and interpolated using a spherical spline algorithm  (Perrin et al. 1989 ) with an interpolation order m = 3, a Legendre polynomial order n = 50, and a regularization parameter λ = 10e-8  (Kang et al. 2015). Correction of eye blink artifacts in the EEG data was performed using a classical PCA filtering algorithm  (Wallstrom et al. 2004 ) on 800ms windows with 400-ms overlap. A Hamming window was used to control for artifacts resulting from data splicing. EEG signals were then visually checked to exclude from analysis all trials contaminated by residual eye blinks, unwanted swallowing, coughing, or movement artifacts. Following correction, EEG data were re-referenced to a common average reference.
In our case, we observed an increase of low-frequency connectivity across a large-scale network modulated by both sensorimotor and sociocognitive factors, suggesting that the integration of socially relevant components during social interaction influences global cortical activity. Conversely, activity in the gamma band is often associated with the local processing of information  (Fries 2009). Much previous research has reported a coupling of theta and gamma activity over similar areas thought to form a neural code for representing sequential order among numerous elements  (Canolty and Knight 2010; Lisman and Jensen 2013). Here, the colocalization of the theta hub between self-and other network and the gamma activity associated with active social coordination points toward a functional cross-frequency link uniting the two processes.
In this analysis we included individuals of European (all previously used data sets), East Asian (CLHLS), and African American (CHS) ancestry. However, with the exception of APOE and rs2069837, located in IL6, which has previously been associated with longevity in CLHLS  9  , this analysis did not identify additional genome-wide significant loci (Table  4 , Fig. 3  and Supplementary Fig. 3 ). The observed association of the genetic variant in IL6 in the transethnic meta-analyses was mainly driven by the association in the East Asian population. The other variant previously associated with longevity in CLHLS  9  , rs2440012, located in ANKRD20A9P, did not pass quality control in the large majority of the included cohorts from populations of European descent and was thus not analysed in the trans-ethnic meta-analyses.
05) with higher odds of surviving to the 90th and/or 99th percentile age. Since the phenotypes used in our study (i. e. , cases surviving at or beyond the age corresponding to the 90th/99th survival percentile) were different from the one used in the previous study (i. e. , parental lifespan), we performed an additional look-up of these variants in one of the UK Biobank data sets we created for validation of our findings (i. e. , the 90th percentile cases versus all controls data set). With the exception of the variant in HTT, all variants showed a nominal significant association in this data set (Supplementary Table  2 ), indicating that the lack of significant replication of these loci in our discovery phase data set is not likely to be due to a difference in the used phenotype. Gene-level association analysis.
The remaining genes are located on chromosome 5, 12, and 17. As depicted in Supplementary Data 2, distinct sets of genetic variants were used by MetaXcan for all significant tissuespecific gene expression associations with survival to the 90th and/or 99th percentile age. Genetic correlation analyses. LD score regression was performed to determine the genetic correlation between the different case definitions used for our meta-analyses (based on the results from the European cohorts only), and between longevity and other traits and diseases  24. The genetic correlation (rg) between the 90th and 99th percentile analysis, using all controls for both groups, was 1. 01 (SE = 0. 06, P = 3. 9 × 10 -66 ). Using LD Hub  26  , which performs automated LD score regression, we subsequently estimated the genetic correlation of our phenotypes with 246 diseases and traits available in their database.
Genetic correlation analysis showed that our longevity phenotypes are genetically correlated with father's age at death, CAD and T2D-related phenotypes. For the CDKN2A/B locus we have also reported the second most significant variant in this locus (rs2184061), since the allele frequency of the most significant variant (rs7039467) is not comparable between the meta-analyses and UK Biobank data sets due to difference in the reference panel used for imputation. The rsID is based on dbSNP build 150. The Chr:Position is based on Genome Reference Consortium Human Build 37 (GRCh37) EA effect allele, OA other allele, EAF effect allele frequency, OR odds ratio (i. e. , odds of parent(s) to become long-lived when carrying the effect allele), 95% CI 95% confidence interval Genetic variation in APOE is well known to be associated with longevity and lifespan, with the first report more than two decades ago in a small candidate gene study  27.
On the other hand, we were able to validate one additional locus, CDKN2A/B, in the parental lifespan data set. This is not surprising, since this locus had already been reported to associate with parental lifespan  20. However, it is unclear why our reported variants at this locus, rs7039467 and rs2184061, are not associated with parental longevity, given that the most significant parental lifespan-associated variant at this locus, rs1556516, also shows a nominal significant effect on parental longevity (see Supplementary Table  2 ). We hypothesise that this may be due to a difference in the LD structure of the reference panels used for imputation. We were able to detect significant genetic associations at two previously identified longevity/lifespan-related loci, FOXO3 and CDKN2A/B. For the other loci, we did not find evidence for replication (P > 7.
The cohort life tables were available through the Human Mortality Database (www. mortality. org)  56 , the United States Social Security Administration (https:// www. ssa. gov/oact/NOTES/as120/LifeTables_Tbl_7. html)  22 or National registries; https://opendata. cbs. nl/statline/portal. html?_la = nl&_catalog = CBS&tableId = 80333ned&_theme = 90; http://webarchive. nationalarchives. gov. uk/ 20160129121820/http://www. ons. gov. uk/ons/rel/npp/national-populationprojections/2012-based-extra-variants/index. html). For example, the 60th, 90th, and 99th percentile correspond to ages of 75, 89, and 98 years for men and 83, 94, and 102 years for women for the 1920 birth cohort from the US. For cohort life tables providing birth cohort by decade, linear model predictions were used to estimate the ages corresponding to survival percentiles at yearly birth cohorts.
ARTICLE NATURE COMMUNICATIONS | https://doi. org/10. 1038/s41467-019-11558-2 Quality control of individual cohorts. Quality control of the summary statistics from each cohort was performed using the EasyQC software and the standard script (fileqc_1000G. ecf) available on their website (http://www. uni-regensburg. de/ medizin/epidemiologie-praeventivmedizin/genetische-epidemiologie/software/)  58. The only difference was that we used the expected minor allele count (eMAC) instead of the MAC. To this end, we first calculated the 'Effective N' (2/(1/N cases + 1/N controls )) for each cohort. The use of the 'Effective N' instead of the 'Total N' leads to a more stringent filtering of genetic variants and decreases the chance of false positive findings due to an imbalance between the number of cases and controls  58.