dataesr/scientific-paragraphs-categorization huggingface dataset:
Using GATK Haplotype Caller, Single Nucleotide Variants with a quality score >30 and an alignment quality score >20 were annotated with SNPEff (v4. 3)  (Cingolani et al. , 2012). Rare variants were identified by focusing on non-synonymous changes at a frequency of less than 1% in the gnomAD database. Copy Number Variants (CNV) were detected using two approaches based on read-depth analysis using Control-FREEC (v11. 4)  (Boeva et al. , 2012)  and based on read-pair anomalies combined with split-read detection using Lumpy (v0. 2. 12)  (Layer et al. , 2014). The resulting CNV and SV were annotated using inhouse python scripts and filtered in terms of their frequency in public databases (DGV, ISCA, DDD). 2. 3 High molecular weight (HMW) DNA extraction, quality control, and long-read genome sequencing and analysis HMW DNA extractions and sequencing were performed by the Centre National de Recherche en Génomique Humaine (CNRGH, CEA).
Library quality was checked on a LabGx (Perkin Elmer) for profile analysis and quantification. Sample libraries were pooled before sequencing to reach the expected sequencing depth. Sequencing was performed on an Illumina HiSeq4000 as paired-end 100 bp reads, using dedicated Illumina sequencing reagents. Libraries were pooled using four samples per lane. Fastq files produced after RNA-seq sequencing were then processed using in-house CNRGH tools to assess the quality of raw and genomic-aligned nucleotides. Aberrant splice events and expression outliers were identified using a computational platform of FHU Translad. Raw data quality was evaluated by FastQC software (v0. 11. 4. -see web resources). Reads were aligned to the GRCh37/hg19 human genome reference sequence using the STAR2 Aligner (v2.
Ultra-high molecular weight genomic DNA extraction, optical mapping and data interpretation Ultra-high molecular weight (UHMW) genomic DNA (gDNA) was extracted from 1. 5 million white blood cells or fresh cultured fibroblast cells via the SP Blood & Cell Culture DNA Isolation Kit (ref: 80030, Bionano Genomics) according to the Bionano Prep SP frozen human blood DNA isolation protocol or the Bionano Prep SP fresh cells DNA isolation protocol. After quantification, 750 ng of UHMW gDNA was labeled via a DLS DNA Labeling Kit (ref: 80005, Bionano Genomics) according to the Bionano prep DLS protocol. Labeled UHMW gDNA was then analyzed on a Bionano Saphyr instrument with a target output of 500 Gbp. Raw data from Saphyr were directly exported in a BNX molecule-information file format. The de novo assembly was performed by the Bionano Solve pipeline (v3.
6. 11162020). Short molecules (<120 kbp) or molecules with insufficient labels (<9) were filtered out. Molecules were then sorted using a numeric molecular identifier. Molecule-noise parameters (including missing or extra labels, sizing intervals, and stretch factors) were estimated during the auto-noise stage. The resulting corrected BNX file contained rescaled molecules, which were aligned with each other using a pairwise algorithm producing the basis of an overlap graph. A first assembly was created from the pairwise alignments by analyzing potential paths on the chart, resulting in a consensus map (CMAP file). The longest paths were selected by successive clean-up steps. RefAligner software was used to further refine the draft consensus map in several stages by adjusting the labels and splitting map regions, where necessary, due to poor molecule coverage.
Individual 2# AURKC Individual 2 was a 34-year-old male from a family with seven children (six boys) with no noteworthy family medical history, presenting with typical Spermatogenic Failure 5. Genetic investigations consisting of AURKC Sanger sequencing identified a single frameshift variant p. (Leu49Trpfs*23) in exon 3 of AURKC. The variant was in gnomAD (v2. 1. 1) and classified as pathogenic in ClinVar. GS identified a second ClinVar pathogenic hit, i. e. , a nonsense variant p. (Tyr248*) in exon 6 of AURKC (Supplementary Figures  S1E-F ). Revaluation of AURKC Sanger sequencing data APC AD NP - - - - - NP NP - 15 KBG syndrome, MIM 148050 ANKRD11 AD - NP - - - - NP NP - revealed the presence of a barely detectable peak, corresponding to the NM_01015878. 1:c. 744C>G variant identified by srGS.
Individual 3# TSC2 Individual 3 was a 2-year-old girl, the first child of unaffected, nonconsanguineous African parents. Tuberous sclerosis complex (TSC) was diagnosed prenatally. Prenatal and postnatal genetic investigations including screening for TSC in amniotic fluid and then blood and saliva samples using a specific panel were negative. GS analysis identified a frameshift variant p. (Gly654Alafs*44) in exon 19 of TSC2 (Supplementary Figure  S1G ). Reanalysis of TSC2 panel data revealed a bioinformatic pipeline anomaly that had filtered out this variant. In fact, it was artifactually detected in all patients from all batches but at an allelic frequency ranging between 0. 7%-25% and associated with a sequencing strand bias.
Individual 7# SGCG Individual 7 was a 33-year-old male, the third child of unaffected non-consanguineous French parents, with a clinical diagnosis of limbgirdle muscular dystrophy. Immunofluorescence on muscle biopsy showed a pattern consistent with a sarcoglycanopathy. Singleton ES identified only one heterozygous missense variant p. (Glu263Lys) in exon 8 of SGCG (Supplementary Figure  S3A ), previously described in the literature  (DiCapua and Patwa, 2014; Al-Zaidy et al. , 2015). ClinVar and HGMD databases have reported this variant as pathogenic. No additional single nucleotide, indels, or copy number variants were detected from ES data. GS confirmed the presence of the previous variant and identified a heterozygous intragenic deletion of about 6. 5 kb encompassing exon 7 (124 bp) of SGCG and expected to produce an out-of-frame variant p.
We performed GS in individual 13, and RNA-sequencing in individual 13 and his father (Figure  4B ). GS did not reveal any apparent structural variant (Figure  4C ). Transcriptome analysis identified the second hit, i. e. , an aberrant splice event involving exon 1 and promoter region of LSS (Figure  4D ). RNA-seq data also confirmed an altered allelic expression of the maternal allele in individual 13 and a monoallelic expression of LSS in the father (Figure  4C ). Reevaluation of GS at the promoter region indicated an unevenly distributed sequencing depth and the potential presence of an ill-defined SV. The predicted SV was too small to be characterized by optical mapping (i. e. , below 500 bp). Oxford Nanopore long-read sequencing was therefore used to resolve this region. A long-read data analysis confirmed the presence of a small deletion of 339 bp encompassing exon 1 and the intergenic region between LSS and MCM3AP (Figure  4E ).
Acknowledgments We are grateful to the families for their participation in this study. We thank the  "Centre de Ressources Biologiques Ferdinand Cabanne" (CHU Dijon)  for sample biobanking. We thank the  University of Burgundy Computing Cluster (CCuB). The study was performed within the framework of the  GAD ("Génétique des Anomalies du Développement ") collection and approved by the appropriate institutional review board of  Dijon University Hospital  ( DC 2011 ( DC -1332 )). We also thank  Dr Agnès Guichet  and  Dr Marine Tessarech  for their cytogenetic advice, and  Alexandre Plagos  for technical assistance.
Funding This work was supported by grants from  Dijon University Hospital , the  ISITE-BFC (PIA ANR ), the  European Union  through the  FEDER programs (PERSONALISE) , and the  Burgundy-Franche Comté regional council (INTEGRA). The sequencing performed at the CNRGH was supported by the  France Génomique National  infrastructure, funded as part of the  "Investissements d'Avenir" program  managed by the  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ).
The piriform cortex (PC) is located at the junction of the temporal and frontal lobes. It is involved physiologically in olfaction as well as memory and plays an important role in epilepsy. Its study at scale is held back by the absence of automatic segmentation methods on MRI. We devised a manual segmentation protocol for PC volumes, integrated those manually derived images into the Hammers Atlas Database (n = 30) and used an extensively validated method (multi-atlas propagation with enhanced registration, MAPER) for automatic PC segmentation. We applied automated PC volumetry to patients with unilateral temporal lobe epilepsy with hippocampal sclerosis (TLE; n = 174 including n = 58 controls) and to the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI; n = 151, of whom with mild cognitive impairment (MCI), n = 71; Alzheimer's disease (AD), n = 33; controls, n = 47).
segmentations overlapped with a Jaccard coefficient (intersection/union) of $0. 5 and a mean absolute volume difference of $22 mm 3 in healthy controls, $0. 40/ $28 mm 3 in patients with TLE, and $ 0. 34/$29 mm 3 in patients with AD. In patients with TLE, PC atrophy lateralised to the side of hippocampal sclerosis (p <. 001). In patients with MCI and AD, PC volumes were lower than those of controls bilaterally (p <. 001). Overall, we have validated automatic PC volumetry in healthy controls and two types of pathology. The novel finding of early atrophy of PC at the stage of MCI possibly adds a novel biomarker. PC volumetry can now be applied at scale. K E Y W O R D S Hammers Atlas Database, hippocampal sclerosis, MAPER, mild cognitive impairment, morphometry.
We added the region to the Hammers Atlas Database (www. brain-development. org)  (Faillenot et al. , 2017; Gousias et al. , 2008; Hammers et al. , 2003; Wild et al. , 2017)  and used the resulting labels to carry out automatic PC segmentations using MAPER (Multi-atlas propagation with enhanced registration)  (Heckemann et al. , 2010)  in MR images of healthy participants, of patients with TLE and HS, and of patients with MCI or AD. Using quantitative evaluations, we compared and characterized both segmentation methods.
| Subjects We segmented the PC manually on the T1-weighted MR images (voxel sizes $0. 8 mm 3 ) of the 30 healthy young adult participants included in the Hammers Atlas Database (www. brain-development. org)  (Faillenot et al. , 2017; Gousias et al. , 2008; Hammers et al. , 2003; Wild et al. , 2017). To evaluate our segmentation protocol and the 30 PC segmentations, we used the atlas data set to automatically segment two target data sets obtained in clinical neurological studies. The first target data set  (Keller et al. , 2015)   The second target data set consisted of MR images from the ADNI database (adni. loni. usc. edu; for details of ethical approval, see www. adni-info. org). We downloaded 199 image sets of 151 unique subject IDs of the 'ADNI cohort 1 Baseline 3 T'. The image sets consist of T1-weighted baseline MR images acquired at 3 Tesla from patients with AD or MCI, and from healthy controls (voxel sizes 1.
73 mm 3 ). We used the images labelled as 'processed' via each centre's ADNI pipeline, including gradient unwarping (geometric distortion correction) for General Electric (GE) and Siemens scanners and bias correction for GE, Philips, and Siemens scanners (see https:// adni. loni. usc. edu/methods/mri-tool/mri-analysis) as recommended by ADNI and to minimise differences between scans. Images and metadata of matching participants (n = 151) were accessed in April 2018. The sample consisted of 33 patients with AD (mean age in years 74. 0 ± 8. 1 (SD) [57-89] (range); 22 female), 71 with ; 26 female) and 47 healthy control participants (75. 1 ± 3. 9 [70-86]; 29 female)  (Yaakub et al. , 2020). We subdivided the MCI group into progressive (pMCI) and stable MCI (sMCI), based on whether or not their diagnosis record changed to AD at any follow-up visit up to 36 months.
| Manual segmentations of the PC Our PC outlining protocol builds on the work of  Pereira et al. (2005)  and  Galovic et al. (2019). According to  Pereira et al. (2005)  The Hammers Atlas Database (www. brain-development. org) has been described previously  (Faillenot et al. , 2017; Gousias et al. , 2008; Hammers et al. , 2003; Wild et al. , 2017). Briefly, it contains 3D T1-weighted images of 30 healthy young adults. We applied the protocol to patients from the target data sets (20 each, chosen randomly), enabling comparisons of automatically and manually determined labels in pathology.
| Intrarater and interrater analysis of manual PC segmentation We conducted a reliability analysis in test-retest fashion to validate the outlining protocol. Rater 1 (D. S. ) segmented the right and left PC twice at an interval of 1 week on the images of five participants from the Hammers Atlas Database. Additionally, we performed an objectivity analysis with two additional independent raters, M. S. (Rater 2) and L. G. (Rater 3). Both outlined the right and left PC of the same five participants who had been selected for the reliability analysis. Before the first round of segmentations, the second rater and the third rater were only provided with the written outlining protocol (supplement). One week later, the two additional raters independently underwent one practice session, where Rater 2 and Rater 3 practised segmenting the PC under guidance from Rater 1.
| Automatic segmentation of the PC Automatic segmentation of the PC was performed using MAPER  (Heckemann et al. , 2010)  (https://github. com/soundray/maper), an application for multi-atlas-based brain image segmentation. MAPER implements an ensemble machine learning approach to transfer anatomical knowledge represented in an atlas database to a target image. Each atlas is paired with the target and registered using free-form deformation  (Rueckert et al. , 1999) , initially considering tissue probability maps and subsequently T1-weighted images. The resulting geometric transformation is applied to the atlas labels, generating one segmentation label set per atlas in the target space. The individual label sets are then combined into a single output label set using voterule decision fusion  (Kittler et al.
, 1998). We conducted a leave-one-out cross-comparison analysis to measure agreement (JC, volume error) between MAPER-generated PC labels and manually generated ones. Bland-Altman analysis was used to detect or exclude proportional bias on the volume measurements. We then used MAPER with all 30 atlases to segment the PC in the target data sets. We determined left/right asymmetry for each patient group (TLE with left/right sided HS; AD) and the healthy control groups. For method validation in patients with TLE and those with AD (cf. results Section 3. 2), volumes of PC, amygdala, and hippocampus were normalised by intracranial volume (ICV; region volumes divided by ICV, scaled by 10 4 for ease of reading). ICV was estimated using Pincram  (Heckemann et al. , 2015)  (https://github. com/ soundray/pincram).
Hammers Atlas Database Manual and automatic segmentations of the PC in the Hammers Atlas Database group overlapped with a mean JC of 0. 52 (right) and 0. 47 (left) (Table  2 ). For volume discrepancies, see Table  2. The relative volume differences between automatic and manual segmentations are shown in Bland-Altman plots (Figure  2 ). They illustrate moderate proportional bias between average volume and volume difference for right PC (r = 0. 38; p =. 04). For left PC, no significant correlation was found (r =. 27; p =. 15). Likewise, Bland-Altman plots for absolute volume differences (Figure  S1 ) show moderate proportional bias only for the right PC. Figure  3  shows two examples of a manual/automatic pair of PC labels, taken from the Hammers Atlas Database group, and illustrates the regions of agreement and mismatch within the chosen section.
Patients with TLE and patients with AD In the 20 patients with TLE and HS, on unflipped images (i. e. , not considering the lateralization of the epilepsy focus), the agreement between automatic and manual segmentation was slightly lower than in the Hammers Atlas Database group, with a mean JC of 0. 41 (right) and 0. 39 (left) (Table  2 ). For the group of 20 patients with AD, a mean JC of 0. 35 (right) and 0. 34 (left) was found. Volume discrepancy values are shown in Table  2  and volume differences are illustrated in Figure  S2. For PC volumes distinguishing between ipsilateral and contralateral sides in a larger patient sample, see the following section. One example of each patient group is illustrated in Figure  3.
Applied to patients with TLE with HS and to patients with AD, automatic segmentation of the PC yielded expected results, validating our method on a biological basis: PC volumes in TLE were lower ipsilateral to HS. In patients with MCI and in patients with AD, PC volumes were smaller compared to matched healthy controls. To our knowledge, this is the first study demonstrating PC atrophy in early (MCI) stages of AD. The proposed manual PC delineation protocol can be applied reliably by inexperienced raters once they have received some training. , 2004). With respect to SVR and region volume, overlap between manual and automatic segmentations of PC was comparable to similarly anisotropic structures like the temporal horn of the lateral ventricle  (Yaakub et al. , 2020). In patients with TLE with HS, the agreement between automatic and manual labels was as high as in the Hammers Atlas Database group.
In  Heckemann et al. (2011)  we had performed equivalence testing for Jaccard overlaps averaged across 83 regions defined with MAPER and compared those with TOSTs (two one-sided t tests, a type of equivalence test) across field strengths; this included three small regions in the vicinity of the PC (amygdala, hippocampus, and parahippocampal gyrus) which were also the most different between groups (controls, MCI, and AD). On this basis, we hypothesized that PC volumes would be equivalent between scanners, too. The results of the equivalence tests (TOSTs) in Table  S1  show that the null hypothesis of scanner differences can be rejected for all comparisons. In conclusion, PC volumetry results were independent of the manufacturer of the scanning equipment. A limitation is that the MR images in the Hammers Atlas Database had been acquired at 1.
| CONCLUSION The multi-atlas MAPER pipeline and manual delineations of PC integrated into the Hammers Atlas Database enable reliable automatic delineation of PC in healthy adult participants as well as in patients with TLE and HS or AD. As expected, PC volumes are unilaterally smaller on the side of HS and bilaterally smaller in MCI and AD. We found that atrophy of the PC in MCI and AD occurred early and exceeded that of hippocampus and amygdala, pointing to a potential role as a biomarker. The possibility to obtain PC volumes at scale will now allow such biomarker and functional studies.
ACKNOWLEDGMENTS This work was partially funded by the  King's -Charité joint  research seed fund. This work is supported by the  Wellcome EPSRC Centre for Medical Engineering at King's College London  ( WT 203148/Z/16/Z ) and the  Department of Health  via the  National Institute for Health Research (NIHR)  comprehensive  Biomedical Research Centre award  to Guy's & St Thomas'  NHS Foundation  Trust in partnership with  King's College London  and  King's College Hospital NHS Foundation Trust. Siti N. Yaakub  (grant number  MR/T023007/1 ) and  Simon S. Keller  (grant numbers  MR/S00355X/1  and  MR/K023152/1 ) are supported by the  Medical Research Council UK. The Alzheimer's disease data collection and sharing for this project was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defense  award number  W81XWH-12-2-0012 ).
The images are rotated to position the anterior and the posterior commissure in common axial and sagittal planes (AC-PC alignment). Separate, spatially corresponding 3D label images contain labels of 95 anatomical regions obtained by manual segmentation. PC segmentation was part of a larger, ongoing initiative to extend the database to 120 regions in total. Using ITK-SNAP Version 3. 8 (Yushkevich et al. , 2006) , each image was displayed in three orthogonal planes, with the corresponding segmentation added as a colour overlay. After identifying the approximate location of the PC, the most rostral coronal plane intersecting with the PC was selected to draw the first outline. The outlining then proceeded section by section in caudal direction. Existing regions were partially relabelled to create the new PC regions, notably: background (region label 0), amygdala (3, 4), anterior temporal lobe, medial part (5, 6), parahippocampal and ambient gyrus (9, 10), insula anterior pole (92, 93) and posterior long gyrus (20, 21), as well as posterior orbital gyrus (72, 73).
Image orientation is identical in all panels (markers in Panels g and h indicate R = right, L = left, A = anterior, P = posterior, S = superior, I = inferior). Outline colours: white indicates label agreement (M \ A), red outlines surround regions included by the manual segmentation only (M\A), green outlines surround regions included by the automatic segmentation only (A\M). In Participant 16 of the Hammers Atlas Database (Panels a and b), label agreement is strong (JC right 0. 61, JC left 0. 56, ΔV right/ left = 2/23). In Participant 9 of the Hammers Atlas Database (Panels c and d), label agreement is moderate (JC right 0. 36, JC left 0. 38, ΔV right/left = 19/1). Panels e and f pertain to a patient with temporal lobe epilepsy with left-sided hippocampal sclerosis (JC right 0. 44, JC left 0.
manual segmentation protocol for delineating the human piriform cortex on MR images. We updated the Hammers Atlas Database with 30 instances of such segmentations and validated them via a four-pronged approach: by ascertaining reliability and objectivity of the procedure in health and disease, by demonstrating feasibility of automatic PC segmentation when using the segmented images as training data, and by showing morphometric correlates of disease processes (temporal lobe epilepsy, Alzheimer's disease, and mild cognitive impairment) in the PC. Availability of the protocol, the segmentations, and the automatic segmentation procedure (MAPER) facilitates independent reproduction and replication of our work and enables users of the resources we share to conduct PC morphometry studies on their own imaging data.
Mean volumes and mean volumes normalised by ICV of the ADNI cohort (N = 151), differentiated by scanner manufacturer. F I G U R E 4 PC volumes in patients with temporal lobe epilepsy with hippocampal sclerosis. PC volume normalised by ICV comparing ipsilateral and contralateral side in relation to hippocampal sclerosis. **p <. 001. For ease of reading, values were multiplied by 10 4. F I G U R E 5 Volume decline in patients with mild cognitive impairment and Alzheimer's disease. Volumes normalised by ICV (right and left side) of PC (a), hippocampus (b), and amygdala (c), comparing healthy controls, patients with mild cognitive impairment, and patients with Alzheimer's disease. *p <. 05. **p <. 001. For ease of reading, values were multiplied by 10 4. Using the interrater measures of agreement (JC and volume discrepancy) as benchmarks, automatic delineation of PC was similarly accurate when applied to healthy participants in the Hammers Atlas Database.
by the different termination of PC segmentation. Our study defines the last slice by first appearance of the hippocampus andPereira et al. define the last slice by the opening of the hippocampal fissure (i. e. , immediately adjacent landmarks), whereasIqbal et al. described    the complete fusion of the cerebral peduncle with the pons as the landmark for the caudal end of the PC. Considering the Yakovlevian torque with the right hemisphere extending further anteriorly, the latter PC definition with an external and remote landmark that is the same for both sides posteriorly will lead to a right/ left asymmetry. The MAPER algorithm reproduces segmentations from a database of multiple atlases on an 'unseen' (not contained in the reference) target image. Algorithms of this class perform best when applied withincohort (as in the leave-one-out cross-comparison).