dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The work presented in this paper was initiated while  Bertrand Carissimo  was visiting CNR/ISAC in Torino on a CNR- Short Term Mobility Program  ( Year 2018) -CUP B56C18002200005. The authors would like to thank  Steve Hanna  for inspiring this work and pointing to us the data, and to  Michael Pirhalla ,  David Heist  and  Steven Perry  from  EPA  for sharing their processing of the JRII sonics. We are also indebted to  Tom Mazzola  who has coordinated this activity.
Study cohort In this retrospective multicentre study, the clinical information of individuals with confirmed pathogenic NR2F1 variants was reviewed to establish the phenotypic spectrum. The initial cohort of three NR2F1-positive families was identified at Moorfields Eye Hospital NHS Foundation Trust (London, UK), with the contribution of additional families from Great Ormond Street Hospital for Children NHS Foundation Trust (London, UK), the Children's Hospital of Philadelphia (Philadelphia, USA), Gangnam Severance Hospital (Seoul, South Korea), Hospital Sant Joan de Deu (Barcelona, Spain), University Hospitals of Nı ˆmes, Lille and Montpellier (France). The recruitment centres interrogated their institutional clinical databases for individuals with genetically confirmed NR2F1 variants. Two of these individuals, NR2F1_10 (South Korea) and NR2F1_4 (France), have previously been reported.
Clinical phenotyping All affected individuals underwent a comprehensive neuro-ophthalmological examination during the initial diagnostic workup or following the identification of a pathogenic NR2F1 variant. When feasible, fundus imaging and high-resolution spectral-domain Spectralis OCT imaging (Heidelberg Engineering Inc. , Heidelberg, Germany) was performed. Segmentation was obtained using the automated segmentation software for the Spectralis OCT device (Heidelberg Engineering, software version 1. 9. 10. 0). Each macular OCT scan was inspected to assess the accuracy of the automated segmentation in the determination of retinal thickness. Manual adjustments were made when needed. Retinal thickness maps were overlapped with macula grid (1. 0, 2. 22, 3. 45 mm diameters) on OCT scans (Fig. 1A-C ).
The central 1 mm diameter circle was defined as the central subfield, followed by the inner 2. 22 mm diameter ring and the outer 3. 45 mm diameter ring. Both rings were divided into the inner and outer superior, nasal, inferior and temporal sectors, and average values were calculated per sector. The built-in software was used to automatically calculate separate thickness values for the retinal nerve fibre layer (RNFL), retinal ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer nuclear layer (ONL), and retinal pigment epithelium (RPE) in the inner and outer (global) circles, and for each sector. Eyes with bad quality scans were excluded. The data obtained were compared with age-matched healthy controls. Macular normative data were generated from SD-OCT images of 7 normal eyes from 5 individuals with no known retinal or optic nerve disease, and best-corrected visual acuity (BCVA) of 0.
00 logMAR. ] [23]  White matter tractography was performed on one subject (NR2F1_10) to assess the functional connectivity of the visual pathway. Using the occipital pole and primary visual cortex as a seed point, the inferior frontal-occipital fasciculus, the superior longitudinal fasciculus, and the inferior longitudinal fasciculus were reconstructed. High angular resolution diffusion images were acquired using a spin-echo EPI sequence with 3 multi-slice acceleration (TE 65 ms, TR 3200 ms, 64 directions, Bmax 3000, Bmin 0, 2 mm isotropic voxel size). White matter fibre tracking and reconstruction were performed using DSI Studio software (http://dsi-studio. labsolver. org/ last accessed July 2021) with a sparse solution of fibre orientation distribution function by diffuse decomposition. Termination criteria were based on a threshold of quantitative anisotropy of 1.
A similar analysis was performed on whole-genome sequencing data from individuals in the Genomic England 100 000 Genomes Project to identify additional individuals carrying pathogenic NR2F1 variants. The search was performed in October 2019. Case NR2F1_2 (South Korea) was identified by wholeexome sequencing using Twist Human Core Exome kit (Twist Bioscience, San Francisco, CA, USA). A 429 targeted genes panel sequencing was performed in NR2F1_3 (South Korea) and NR2F1_10 (South Korea), with the latter case having been previously reported. 18 Cases NR2F1_1 (Spain), NR2F1_13 (France) and NR2F1_21 (France) were identified by next-generation sequencing (NGS). For NR2F1_1 (Spain), an NGS panel of 6710 genes associated with pathologies described in the Human Gene Mutation Database, GeneTest. org and the OMIM catalogue was used for screening.
All the NR2F1 variants in the study cohort, and their segregation if available, were confirmed by Sanger sequencing. The Genome Aggregation Database (gnomAD, https://gnomad. broadinstitute. org last accessed July 2021) was used to assess for the rarity of the variants in the general population. In silico analysis was performed to evaluate the likely impact on protein function using Polymorphism Phenotyping v2 (http://genetics. bwh. harvard. edu/pph2/ last accessed July 2021) and Mutation Taster (http://www. muta tiontaster. org/ last accessed July 2021) predictive algorithms. The evolutionary conservation of the affected amino acid residues across orthologues was assessed using Uniprot sequence alignments (https://www. uniprot. org/help/se quence-alignments last accessed July 2021).
Statistical analysis All data were statistically analysed and graphically represented using Microsoft Office Excel software (Version 2003), IBM SPSS Statistics Software (Version 26) and GraphPad Prism (Version 7. 00). Quantitative data are shown as the mean 6 SEM. For cell percentage/number quantification after IF, measurements were performed on at least 5 sections coming from 2 to 3 different animals, unless otherwise stated. Fixed embryos/eyes with damaged tissues were excluded from any further analysis/processing. Microscope images were processed with Photoshop or ImageJ software, by randomly overlapping fixed-width (100 mm) rectangular boxes on the area of interest [e. g. sectioned neural retina (NR)], then quantifying positive cells inside the boxes. When calculating percentages over the total cell number, the latter was quantified by counting DAPI þ nuclei, unless otherwise specified.
Pathogenic NR2F1 variants are clustered within the DBD and LBD A total of 19 NR2F1 variants were identified in the study's patient cohort, all of which are absent from the gnomAD dataset (Table  1 ). Three variants, namely, c. 290 A > C p. (His97Pro), rs1554074673; c. 353 T > G p. (Leu118*), rs1561523796; and c. 1115T > C p. (Leu372Pro), rs1554075105, were found in ClinVar database. The majority of NR2F1 variants (13/19, 59. 1%) are located within the DBD and LBD (Fig. 2B ). Nine (40. 9%) individuals carried frameshift, stop-gain variants or entire gene deletion, whereas 13 (59. 1%) individuals carried missense variants or small deletions/ duplications. The identified NR2F1 variants were predicted to be disease causing based on in silico analysis. Multiple alignment of human NR2F1 orthologues confirmed the strictly conserved nature of the protein across different species (Supplementary Fig.
Visual function in individuals carrying pathogenic NR2F1 variants Clinical data were available for 22 patients from 19 independent families, with 20 patients not having been reported previously. The mean age at the last ophthalmological assessment was 15. 2 years (SEM ¼ 2. 7 years, range ¼ 1. 5-49. 0 years). There was an equal sex distribution with 11 women and 11 men. Vision impairment and optic nerve head pathology were identified when individuals were examined following referrals for nystagmus, strabismus, poor fixation and/or concerns about neurodevelopmental progress. Impairment of vision was noticed in early childhood in all individuals, except for case NR2F1_15 (UK), who was found to have subnormal vision in her 50 s (Table  2 ). However, that particular individual had other classical features described in BBSOAS, namely, delay in walking and speaking, and mildly dysmorphic facial features.
Discussion This study demonstrates that the optic neuropathy caused by pathogenic NR2F1 variants is of early neurodevelopmental origin, with limited evidence of progression in later life. This is in contrast with other inherited optic neuropathies caused by pathogenic gene variants in nuclear DNA, which are typically associated with progressive visual loss from early childhood. 27 In the current patient cohort, 22 individuals with pathogenic variants in NR2F1 were investigated, including familial cases. Despite the high prevalence of visual system deficits in previously reported individuals with NR2F1 variants,  9,14- 18,28  there are limited data on the ocular phenotype and the disease mechanisms that contribute to visual loss in affected individuals, since previous reports mainly focussed on the systemic clinical features.
Altogether, the data indicate that loss of Nr2f1 expression affects the organization of the optic nerve head region, further reinforcing the notion that visual loss in individuals carrying pathogenic NR2F1 variants arise from pathological events in early development. Furthermore, given the highly conserved evolutionary role of NR2F1,  29  we hypothesize that the molecular domain shift at the optic nerve head border could account for the ONH described in a proportion of affected individuals. Finally, electrophysiological recordings were used to evaluate visual system spatial resolution, a surrogate of visual acuity, in Nr2f1 mutant mice. Nr2f1 haploinsufficiency led to delayed signal transmission and reduced VEP amplitude, in keeping with the observed optic nerve abnormalities and the retinal alterations in bipolar and RGC cell populations.
These observations are particularly relevant given that half of all patients carrying pathogenic NR2F1 variants develop ONH as a prominent ocular feature. Furthermore, the longitudinal visual data collected in our NR2F1 patient cohort indicate that the visual loss arises due to an insult occurring early in development with no evidence of marked progression later in life. A prospective study involving a larger cohort of patients assessed using a standardized protocol and spanning a longer follow-up period is needed to confirm that visual loss is non-progressive in BBSOAS, which has important implications for genetic counselling and patient management. Taylor Tavares, E. R. A. Thomas, S. R. Thompson, A. Tucci, M. J. Welland, E. Williams, K. Witkowska, S. M. Wood.
NR2F1 variants identified in the patient cohort Annotation Dependent Depletion; DBD, DNA binding domain; Het, heterozygous; LBD, ligand binding domain; NA, not available; WGD, whole gene deletion. a Previously reported variants. b Variants listed in Clinvar database. c $599 kb deletion includes entire NR2F1, FAM172A genes and partial NR2F1-AS1 and last exon of KIAA0825. Ocular spectrum of NR2F1 disease BRAIN COMMUNICATIONS 2021: Page 9 of 21 | Summary of ophthalmological features in individuals carrying heterozygous NR2F1 variants Subject OA.
Acknowledgements This research was made possible through access to the data and findings generated by the  100 000   Genomes  Project. The  100 000  Genomes Project is managed by  Genomics England Limited  (a wholly owned company of the  Department of Health and Social Care ). The  100 000   Genomes Project  is funded by the  National Institute for Health Research and NHS England. The  Wellcome Trust ,  Cancer Research UK  and the  Medical Research Council  have also funded research infrastructure. The 100 000 Genomes Project uses data provided by patients and collected by the National Health Service as part of their care and support.
Allowing user-free processes and high reproducibility, it appears promising for procedural planning, when a description of each cardiac chamber and structure is required. Several works evaluating WHS from computed tomography (CT) imaging have already been performed  [17] [18] [19] [20] [21]. However, some of these studies were based on multi-atlas segmentation, which seems to be less accurate and more time consuming than deep-learning-based methods  [20]. Furthermore, owing to the computing power required for the algorithms, to date none of the previously published work integrated the automatic segmentation process in a readily useable and standalone software, which may impede the use of these methods in daily practice, despite the good results reported in these studies. In the present work, we developed and validated a deep-learning-based WHS segmentation process from a dedicated CT database, which was fully integrated in a routinely used procedural planning software.
Data acquisition Imaging data used in the present work were ECG-gated CT performed during the preprocedural work-up of patients undergoing transcatheter aortic valve implantation (TAVI) at our institution. CT of consecutive TAVI recipients between May and September 2019 were analyzed. Exclusion criteria were image quality not allowing precise manual segmentation of the structures of interest, either because of artifacts or poor image contrast, which was assessed by the expert physician performing manual segmentation, and patients with prior surgical aortic valve replacement or TAVI. All patients gave written informed consent for the procedures and anonymous collection of their data, which were prospectively gathered in an electronic database as part of a national registry  [3]. The present study was not pre-specified, observational and retrospective.
CT manual segmentation protocol For each CT, an expert interventional cardiologist with a cardiac imaging degree and extensive experience in the field of cardiac CT and TAVR performed a manual segmentation of the heart and surroundings large vessels using the Endosize© software (Therenva, Rennes, France) a CE-and FDA-marked medical device for planning and sizing of endovascular procedures  [22]. The following ten structures were segmented: pulmonary veins (PV), left atrium (LA), left ventricular cavity (LVC), left ventricular myocardium (LVM), aorta (Ao), coronary sinus (CS), superior vena cava (SVC), right atrium (RA), right ventricular cavity (RVC), pulmonary artery (PA). The segmentation method of the different elements has been previously described  [20, [23] [24] [25]. LA and RA segmentation included the appendage.
After parsing the entire volume, distances can be converted to parabolic curves using polynomial regression, where the minimum is searched for each axis. The center of the aortic valve (defined as the intersections of the three commissures) was defined as the anatomical landmark to be found. 3D images were converted into three sets of 2D images for the axial, sagittal and coronal axis, respectively, and preprocessed with cropping and padding operations. For each axis, a regression CNN was trained with the 2D image slice as input. Specifically, the VGG-16 CNN architecture  [28]  was adapted to output quantitative values by modifying the softmax loss layer with a Euclidean loss layer. All other weights were initialized from pre-training on the ImageNet database (accessed at http://www. image-net.
org). The three networks were trained separately and did not share weights. Convergence was obtained after 100 epochs. Following this approach, the exact position of the aortic valve was estimated. After this automatic detection of the aortic valve, in the segmentation step, 3D data were resized to a 320x320x320 ROI and resampled to 0. 7x0. 7x1 mm by voxel to include all structures of interest. These preprocessed volumes were then used as the input dataset of the Dense V-Net 3D segmentation CNN  [29]. Briefly, the volume is cropped into voxels batches and a succession of convolutional 3D 3x3x3 kernels, dense connections, and batches normalizations are applied to extract 3D spatial information from each batch. The resultant feature maps are then down-sampled by 2 and the operation is repeated 3 times.
CT automatic segmentation testing The dataset was randomly split in a training set (n=55 patients, 17 600 slices), a validation set (n=8 patients, 2560 slices), and a test set (n=8 patients, 2560 slices). For testing purposes, the segmentation method was integrated in the Endosize® software (Therenva, Rennes, France). The automatic segmentations in the test set were obtained directly from this routinely used software using a standard 2GHz workstation with 2GB of RAM to evaluate the clinical applicability of our method. WHS obtained using the deep-learning approach were compared with the manually segmented reference from the test set. The image-based performance metric was the Dice index  [30]. Dice similarity score quantifies the voxel-wise degree of similarity between the model predicted segmentation mask and the ground truth, and ranges from 0 (no similarity) to 1 (identical).
DISCUSSION In Our overall Dice score of 0. 920 (IQR: 0. 906-0. 925) is comparable with values reported in these previously published state-of-the-art works. Nonetheless, we demonstrated significant discrepancies according to the segmented structures, i. e. the model was not sufficiently accurate for small structures such as CS, PV and SVC, and was marginally less accurate for right-sided structures and the LVC. Several reasons may explain these observations. First, we used CT data obtained during the pre-procedural work-up of TAVI recipients, for which acquisition parameters intend to optimize the contrast in the aorta and peripheral vascular structures. This may explain the sub-optimal results obtained for right-sided structures. Similarly, mixing of the non-contrasted blood from the inferior vena cava and the contrastsaturated blood from the SVC results in an inhomogeneous enhancement of the RA and beam ACCEPTED MANUSCRIPT -CLEAN COPY As expected among TAVI candidates, more than one-fourth of our population suffered from atrial fibrillation, which negatively affects CT images quality and is a known contributor to sub-optimal results of deep-learning-based WHS  [20].
Thus, it makes sense to consider the main papillary muscles as myocardium to provide an appropriate description of the LVM shape and mass. From a segmentation standpoint, it likely complicated the automatic delineation of the LVC border, which was far less predictable than when papillary muscles are included in the LVC. In keeping with this point, CT data of the present study were acquired in systole, in patients with varying degrees of cardiac remodeling induced by their aortic stenosis, which may have resulted in different patterns of left ventricular hypertrophy, heterogeneously affecting the global geometry of LVC. These elements might have significantly participated in degrading the results of the automatic WHS explaining the lower Dice score values observed for LVC in the present study.
Limitations A number of this study's limitations have been discussed above. First, CT were acquired during the pre-procedural work-up of TAVI recipients using a dedicated protocol in accordance with an international expert consensus  [8]. Whether the feasibility and performance of our algorithm, especially for the identification of right-sided structures, significantly differ according to the CT acquisition protocol or the underlying pathology will be addressed by our future works on a larger, more diverse database. In keeping with this point, this analysis was performed at a systolic phase in accordance with current guidelines for the measurement of aortic annulus. Future works will also have to determine the performances of our algorithm at a diastolic phase. Second, the training and validation sets of 63 patients encompassed a largely sufficient number of images to train a medical image deeplearning system to reach high accuracy  [32].
WHS remains a challenging task for which emerging deep-learning methods appear as innovative and appealing tools, especially from a computational cost standpoint, compared with previously described methods [12, 17, 20]. Zhuang et al. reported the results of a worldwide challenge of multimodality WHS [20]. In this work, twelve algorithms from twelve different teams were evaluated for the automatic segmentation of seven cardiac structures (Ao, LA, LVC, LVM, PA, RA, and RVC) from CT and magnetic resonance imaging data. For the CT dataset, composed of 60 cardiac CT volumes with only 20 for training, the best Dice score was 0. 908 ± 0. 086 and was obtained with a mean 104s computing time on an Intel i7-4820K 32GB CPU with a Nvidia GTX TITAN X 12GB GPU. Similarly to the proposed model in the present work, the best algorithm in this challenge used two separate CNNs to first localize the ROI in the volume and then perform the pixel-wise segmentation using a volumetric kernels equipped 3D CNN.
Another interesting contribution to the field recently came from Baskaran et al. who trained, validated and tested in a 70:20:10 split dataset of 166 CT, a U-Net-inspired, deep-learning model [21]. The authors identified five cardiac structures: LA, LVC, LVM, RA, RV. They reported an overall Dice score of 0. 925 (IQR: 0. 887 to 0. 948) for the identification of these structures by their model with Dice score for LA, LVC, LVM, RA, RV being 0. 934, 0. 938, 0. 920, 0. 915, and 0. 927, respectively. They demonstrated a good correlation and agreement between volumes and mass predicted by the model compared with their manual ground-truth in a test set encompassing 17 patients with 1477 images. The mean computing time was 13. 13 s/patient but the authors did not report the characteristics of the workstation they used for this work.
As part of the EUREC 4 A field campaign which took place over the tropical North Atlantic during January-February 2020, 1215 dropsondes from the HALO and WP-3D aircraft were deployed through 26 flights to characterize the thermodynamic and dynamic environment of clouds in the trade-wind regions. We present JOANNE (Joint dropsonde Observations of the Atmosphere in tropical North atlaNtic meso-scale Environments), the dataset that contains these dropsonde measurements and the products derived from them. Along with the raw measurement profiles and basic post-processing of pressure, temperature, relative humidity and horizontal winds, the dataset also includes a homogenized and gridded dataset with 10 m vertical spacing. The gridded data are used as a basis for deriving diagnostics of the area-averaged mesoscale circulation properties such as divergence, vorticity, vertical velocity and gradient terms, making use of sondes dropped at regular intervals along a circular flight path.
, 2017). The dropsondes are thus essential in characterizing the atmospheric environment within which many complementary measurements took place during EUREC 4 A. The purpose of this paper is to describe the resultant dropsonde dataset, which we call the Joint dropsonde Observations of the Atmosphere in tropical North atlaNtic meso-scale Environments, or JOANNE, in honor of Joanne Simpson's seminal contributions to our field of research. Recent instances of dropsonde datasets from tropical field campaigns include ones by  Konow et al. (2019)  in the north Atlantic summertime tropics and by  Vömel et al. (2021)  in the tropical east Pacific and Caribbean. Konow et al. (2019)  published dropsonde data from the second phase of Next Generation Remote Sensing for Validation Studies (NAR-VAL2). They provide data on a uniform vertical grid at 30 m.
Raw data and initial processing The raw data collected on the aircraft by AVAPS and the subsequent processing with the Atmospheric Sounding Processing Environment (ASPEN;  Martin and Suhr, 2021)  software constitute Levels 0 and 1 of JOANNE, respectively. The data included as part of these two levels involve no external adjustments other than the standard processing and quality control by AVAPS and ASPEN -both state-of-the-art tools for dropsonde measurements.