dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials availability The videos used in this paper can be obtained on request to {martinelli, men-cattini}@ing. uniroma2. it. Data and code availability All MATLAB codes and videos associated with this paper can be freely accessed and downloaded via https://cloudstore. bee. uniroma2. it/index. php/s/ eGaeonyFZg95Wf8, password: SLA_Patterns. MATLAB codes can be also downloaded at: https://github. com/ Arianna1974/NeuriTES. git.
60 GHz Processor, 16 GB RAM, Windows 10 Professional 64 bits, MATLAB 2020b. Table  3  lists the computational overhead of each step in the NeuriTES platform and related memory storage for a single motor neuron sequence using the described settings. Mouse model, cell culture, and sample preparation for the motor neuron case study: TgN (SOD1-G93A)1Gur mice expressing the G93A mutant SOD1 (G93A) originally obtained from The Jackson Laboratory (Bar Harbor, ME, USA) are housed and bred in the C57BL/6J background at the Fondazione Santa Lucia animal facilities. For the primary cultures, a male mouse carrying the mutant SOD1G93A gene was mated with a C57BL/6J female to obtain mixed embryos, processed separately, and then screened for the presence of the human transgene. 49 Motor neuron cultures were divided into two groups: transgenic mutant G93A and their corresponding non-transgenic littermates (WT), which were used as controls.
PC3 were also acquired under control conditions (i. e. , no drug). Imaging and image analysis Time-lapse microscopy image acquisition A customized microscope was used  31  : the prototype consisted of a smallscale inverted microscope suitable to work in high-humidity environments (incubators) with factory standard optics, custom aluminum structure, and fully sealed electronics. The whole-microscope dimensions were less than 400 mm in height and 350 mm in length and depth. Furthermore, a custom firmware was implemented in the MATLAB2017a environment to have full control on acquisition methods and light exposure. For case study 1, acquisition was at 1 frame/min, with 1 day of total experiment time for two different sessions (at the 6 th and 8 th days). For case study 2, acquisition was at 1 frame/ min, for a total duration of 6 h.
Using ASS we not only segment the motor neuron in each video frame but adapt the general contrast of the actual frame to that of the previous one, with the aim to increase the stationarity of the segmentation results along time. The small set of manually segmented motor neurons is a limiting factor for the machine learning model's accuracy and may cause overfit to the training sample and lack of generalizability. To address this aspect, we applied an augmentation procedure aimed at artificially increasing the dataset of training by randomly rotating the images with an angle in [0,360] degrees, reflecting images, translating images up to 10 px in both directions horizontally and vertically. After the SS network was trained on the 150 frames labeled, all the frames of the 79 videos were processed through the use of the ASS approach.
, 2016). Harmonisation methods that reduce or compensate for the variability due to acquisition differences and/or processing discrepancies are being developed to enable comparisons between or pooling of MRI-derived measures from different datasets  (Bordin et al. , 2020). Notwithstanding, the lack of a clear definition on what should be segmented as a WMH and whether some WMH sub-classes are more clinically relevant than others warrant further investigation  (Alber et al. , 2019; Frey et al. , 2019; Murray et al. , 2010; Smith et al. , 2019; Tate et al. , 2008; Wardlaw et al. , 2013). Periventricular WMHs are more strongly associated with concurrent cognitive deficits than deep ones  (Bolandzadeh et al. , 2012). This is in line with longitudinal studies on regional baseline WMH volumes and their association with the risk of transition from intact cognition to mild cognitive impairment and dementia  (De Groot et al.
MRI data were acquired at the Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging (University of Oxford), using a 3-T, Siemens Magnetom Verio (Erlangen, Germany) scanner with a 32-channel receive head coil from April 2012 to December 2014 (N = 550 participants) and a 3-T Siemens Prisma (Erlangen, Germany) with a 64-channel receive head-neck coil from July 2015 to December 2016 (N = 250 participants) due to a scanner upgrade. Details of acquisition protocols are shown in  (Filippini et al. , 2014)  and  (Zsoldos et al. , 2020)  and are reported in Supplementary Table  S1. For the purpose of this study we used high-resolution T 1weighted images, FLAIR images and diffusion weighted images (DWI). All images were processed and analysed using FMRIB Software Library (FSL) v.
Deep WMHs were then defined by subtraction. We finally combined the two criteria and obtained four WMH masks for the following sub-classes for each participant: periventricular T 1hypointense WMHs; periventricular non T 1 -hypointense WMHs; deep T 1 -hypointense WMHs; deep non T 1 -hypointense WMHs. Then, each mask was used to derive the corresponding WMH volume, which was adjusted for the total brain volume and log transformed for statistical analysis. We performed univariate multiple linear regression using the univariate general linear model function type III sum of squares on SPSS version 25. 0 (IBM Corp. Armonk, NY). The following measures of participants' cognition were selected as indices of global functioning, executive function, processing speed, working memory and word retrieval, in line with  (Bolandzadeh et al.
To better investigate the meaning of T 1 -hypointensity in our sample, we performed two post-hoc analyses. First, we studied WMH microstructure using DTI-derived metrics, given the lack of histopathological data for this dataset. Accordingly, we compared T 1 -hypointense and non T 1 -hypointense WMHs in terms of average fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) using paired t-tests to evaluate potential differences in the underlying microstructure. Second, since we noticed that a WMH often includes both T 1 -hypointense and non T 1 -hypointense voxels, we adopted an alternative T 1 -weighted intensity-based classification of WMHs by dividing the total WMH map into WMH clusters with and without T 1hypointense voxels. This sub-classification was then used to look at the prevalence of WMH clusters with a T 1 -hypointense component and to better interpret the results of the main analysis.
, 2002; Griffanti et al. , 2018) , where periventricular WMHs were found to be more strongly associated with participants' cognition than deep WMHs. Increased volume of periventricular WMHs in the Alzheimer Disease Neuroimaging Initiative dataset was also associated with evidence of beta-amyloid deposition in the brain  (Marnane et al. , 2016) , suggesting a synergistic damage driven by concurrent SVD and Alzheimer's pathology in these areas. Periventricular regions are also characterised by high density of long associating fibres which link the cortex to the deep grey matter and other distant brain territories  (Filley, 1998). For this reason, they are potentially susceptible to pathologies that damage cortical arteries and eventually provoke distal hypoperfusion  (Moody et al. , 1990). Disrupted cholinergic activity is related to periventricular (and not deep) WMHs and may be involved in the physio-pathological pathway that underpins the observed cognitive scores  (Bohnen et al.
This would in turn explain the positive relationship with cognition observed in our results. Further investigation would be necessary to explore the potential cascade of events leading to change in T 1 intensity in a longitudinal setting, since the hypothesis of rims as WMHs associated with healthy cognitive aging is very speculative. Our study has some limitations in terms of the data and the methodology. The Whitehall II imaging sub-study dataset shows a narrow age range (60-84 years) and a strong gender imbalance, skewed towards men, since it reflects the demographic of British civil servants at the time of recruitment in the main study. Moreover, our finding that higher periventricular WMHs are linked to poorer cognitive scores is based on the definition of periventricular WMHs as the WMHs that are contiguous with the margins of each lateral ventricle.
Moreover, we performed linear and non-linear registrations between images with different resolutions (FLAIR, T 1 and MNI space) and the interpolation process could have slightly affected the segmented volumes. Finally, our hypothesis on the evolution of WMHs should be interpreted cautiously and prompt further longitudinal studies. For example, it would be very valuable to follow up participants and study how WMH sub-classes evolve over time to validate the proposed theory. Another interesting future development would be looking at how the different WMH sub-classes are related to incidence of diseases, such as stroke and dementia, using risk models in well-balanced longitudinal datasets. If results are confirmed, our classification system could ultimately be translated into the clinic. Despite these limitations, this study presents some novel theoretical and methodological insights that can contribute to better understanding of the role of WMHs in cognitive aging.
Acknowledgements We thank all Whitehall II participants for their time, the  Whitehall II  staff at the  University College London ,  Mandy Pipkin  and  Barbora Krausova  for assisting with recruitment and data collection, the FMRIB Radiographers team for data acquisition, IT and support teams at the  Wellcome Centre for Integrative Neuroimaging  for their helpful collaboration. The study follows MRC data sharing policies (https:// www. mrc. ac. uk/research/policies-and-guidance-for-researchers/datasharing/). Data will be accessible via the  Dementias Platform UK  (https://portal. dementiasplatform. uk/)after 2020.
E. Zs, K. P. E. and S. S. were supported by the  European Union 's  Horizon 2020 programme 'Lifebrain'  ( 732592 ). S. S. was also supported by an  Alzheimer 's  Society Junior Research Fellowship (Grant ref :  441 ). V. S. and M. J. were supported by the  Wellcome Centre for Integrative Neuroimaging , which has core funding from the  Wellcome Trust  ( 203139/Z/16/Z ). M. J. was supported by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) , and this research was funded by the  Wellcome Trust  ( 215573/Z/19/Z ). A. S. -M. receives research support from the  US National Institutes of Health  ( R01AG056477 ). M. K. was supported by  NordForsk , the  UK Medical Research Council  ( MRC S011676 ), the  Academy of Finland  ( 311492 ), and the  US National Institutes on Aging  ( NIA R01AG056477 ,  RF1AG062553 ).
Pathogenic variants of the nuclear receptor subfamily 2 group F member 1 gene (NR2F1) are responsible for Bosch-Boonstra-Schaaf optic atrophy syndrome (BBSOAS), an autosomal dominant disorder characterized by optic atrophy associated with developmental delay and intellectual disability, but with a clinical presentation which appears to be multifaceted. We created the first public locusspecific database dedicated to NR2F1. All variants and clinical cases reported in the literature, as well as new unpublished cases, were integrated into the database using standard nomenclature to describe both molecular and phenotypic anomalies. We subsequently pursued a comprehensive approach based on computed representation and analysis suggesting a refinement of the BBSOAS clinical description with respect to neurological features and the inclusion of additional signs of hypotonia and feeding difficulties.
| Nomenclature All names, symbols, and Online Mendelian Inheritance in Man (OMIM) database numbers were checked for correspondence with current official names indicated by the Human Genome Organization (HUGO) Gene Nomenclature Committee  (Gray et al. , 2013)  and the OMIM database  (Hamosh et al. , 2000). The phenotype descriptions are based on the Human Phenotype Ontology (HPO), indicating the HPO term name and identifier  (Köhler et al. , 2019). NR2F1 variants are described according to both the NCBI genomic reference sequence (RefSeq:NG_034119. 1) and transcript reference sequence (RefSeq:NM_005654. 4), including the reference sequence for three exons encoding a protein of 423 aa (Re-fSeq:NP_005645. 1) (O'  Leary et al. , 2016). The numbering of the nucleotides reflects that of the complementary DNA (cDNA), with "+1" corresponding to the "A" of the ATG translation initiation codon in the reference sequence, according to which the initiation codon is codon 1, as recommended by version 2.
| Implementation of the database The NR2F1 database belongs to the Global Variome shared Leiden Open-source Variation Database (LOVD), currently running under LOVD v. 3. 0 Build 26c (I. F. Fokkema et al. , 2011) , following the guidelines for LSDBs  (Vihinen et al. , 2012)  and hosted under the responsibility of the Global Variome/Human Variome Project  (Cotton et al. , 2008). The database reviews clinical and molecular data from patients carrying NR2F1 variants published in peer-reviewed literature as well as unpublished contributions that are directly submitted. If there are inaccuracies or an obsolete convention is used, the "DNA published" field of the page dedicated to each variant indicates whether the published name has been modified by the curator. The NR2F1 LSDB website requires full compliance with the rules set out above for the description of sequence variants to provide uniform and comparable data.
| Data collection and analysis The causative variants were collected from the literature published to date (May 2021) using the NCBI PubMed search tool  (Sayers et al. , 2010). The positions of variants in the reference transcripts were determined and updated according to the HGVS nomenclature version 2. 0  (den Dunnen et al. , 2016). Correct naming at the nucleotide and amino acid levels was verified and reestablished when necessary using the Mutalyzer 2. 0. 34 Syntax Checker  (Wildeman et al. , 2008). Information on the number of patients carrying each causative variant, as well as their geographical origins and the homo-or heterozygosity, was taken from the original or review publications, as well as from data collected from our clinical laboratory. If the same patient or variant is reported in more than one article, then it is recorded only once in the database with reference to the first publication.
We produced a data set from the NR2F1 LSDB version NR2F1:211007 (last updated on October 7, 2021) to carry out the statistical analysis. The HPO terms have been checked and prepared using the suite of R packages ontologyIndex version 2. 5 and ontolo-gyPlot version 1. 4  (Greene et al. , 2017) , within R version 4. 0. 5 (R Core Team, 2020), to read in the OBO file version hp/releases/2021-04-13  (Köhler et al. , 2019). Hierarchical clustering is performed using the hclust function from the R-Core package (R Core Team, 2020). Our data were crossed with the following external sources: gnomAD v. 2. 1. 1  (Karczewski et al. , 2020) ; OMIM version updated May 20, 2021  (Hamosh et al. , 2000) ; Orphanet version 5. 46. 0  (INSERM, 1997). BILLIET ET AL. | 129.
| Data access and submission The NR2F1 database is an open database allowing any researcher or clinician to consult the contents freely without prior registration or to contribute new data after registration to ensure traceability. The database can be accessed on the World Wide Web at: https://www. lovd. nl/NR2F1 (through the Global Variome shared LOVD server or through the MITOchondrial DYNamics variation portal at: http://nr2f1. mitodyn. org/). The data can also be retrieved via an application programming interface (API), that is, a web service allowing simple queries and retrieval of basic genes and variants information (documentation available on the web page of the database), as well as serving as a public beacon in The Global Alliance for Genomics and Health Beacon Project (Global Alliance for Genomics and Health, 2016).
| RESULTS AND DISCUSSION The NR2F1 database contains three main interconnected tables: the "Individuals" table contains details of the patient examined, including gender, geographic origin, and patient identification, if applicable; the "Phenotype" table indicates the clinical phenotypic features, described according to the root of the phenotypic abnormality subontology (HPO# HP:0000118); and the "Variants" table includes information about the sequence variations at the genomic (DNA) and transcript (cDNA) levels, as well as the clinical classification for each variant.
| Genotypic data To date, the database contains 112 sequence variants records. Excluding duplicates, 83 unique variants are listed (Figures  1  and S1  and Table  1 ), of which 83% (69) are considered pathogenic sequence variants in a dominant condition, 12% (10) are of unknown significance and 5% (4) are benign  (Al-Kateb et al. , 2013; Balciuniene et al. , 2019; Bertacchi et al. , 2020; Bojanek et al. , 2020; Bosch et al. , 2014 Bosch et al. , , 2016;; Brown et al. , 2009; Chen et al. , 2016; Dimassi et al. , 2016; Eldomery et al. , 2017; I. Fokkema et al. , 2019; Hino-Fukuyo et al. , 2017; Hobbs et al. , 2020; Jezela-Stanek et al. , 2020; Kaiwar et al. , 2017; Martín-Hernández et al. , 2018; Mio et al. , 2020; Park et al. , 2019; Rech et al. , 2020; Starosta et al. , 2020; Vissers et al.
Fokkema et al. , 2019) , HI17  (Hino-Fukuyo et al. , 2017) , HO20  (Hobbs et al. , 2020) , JE20  (Jezela-Stanek et al. , 2020) , KA17  (Kaiwar et al. , 2017 ), MA18 (Martín-Hernández et al. , 2018) , MI20  (Mio et al. , 2020) , PA19  (Park et al. , 2019) , RE20  (Rech et al. , 2020) , ST20  (Starosta et al. , 2020) , VI17  (Vissers et al. , 2017) , WA20  (Walsh et al. , 2020) , ZO20  (Zou et al. , 2020) ; the variants referenced FO19 are classification records from genomic diagnostic laboratories, that is, there is no associated patient information; the character "-" indicates a submission to the database without publication. h 3ʹ-untranslated region. BILLIET ET AL. | 135 mRNA decay surveillance pathway; 10% lead to haploinsufficiency, either due to the complete deletion of the whole gene or as a consequence of a variant in the translation initiation codon on one allele; two variants are a deletion of a single aa (Figure  2b ).
Although only a few variants are recurrent, the large deletions of NR2F1 encompassing the cDNA boundaries have been significantly more frequently reported, with 11 records (Table  1 )  (Al-Kateb et al. , 2013; Bosch et al. , 2014; et al. , 2009; Chen et al. , 2016; Rech et al. , 2020). The data from the Genome Aggregation Database (gnomAD), which is the aggregation of the high-quality exome (protein-coding region) DNA sequence data for about 140,000 individuals  (Karczewski et al. , 2020) , has been integrated into the Global Variome shared LOVD server. However, it was decided to only indicate the frequency reported in gnomAD for each variant present in the server, so as to not flood the LSDBs with data not related to a phenotype. This information is particularly useful at the time of curation for assessing the variants' classification.
In total, only five of the unique variants in our database (6%) have a frequency assigned in gnomAD: all four variants considered benign that are recorded in the database, with a low frequency ranging from one allele in 248,414 to less than 0. 02%; as well as a single variant classified as likely pathogenic with a frequency of 0. 04%. This additional information reinforces the importance of the LSDB approach and data sharing to improve the classification of variants for genetic diagnostics.
| Phenotypic data To date, the database includes 84 patient records (44 females, 38 males, and two records of unspecified gender), 70 retrieved from publications, along with data from 11, to date, unpublished cases relating to our Molecular Genetics Laboratory and three from another external source. Of these patients' reports, 83 have an extended set of full clinical description; only one case lacked phenotypic description  (Balciuniene et al. , 2019). For the exhaustive description of phenotypes, use is made exclusively of a standard vocabulary for referencing phenotypic abnormalities, the so-called Human Phenotype Ontology (HPO)  (Köhler et al. , 2019). A total of 381 unique HPO terms annotate the patients, with each assigned from 1 patient to 67 patients for the most frequent term, optic atrophy (HP:0001138).
| Clinical synopsis The comparison of our data set, which reflects the state of the knowledge in the literature as well as recent unpublished patients, with the reference knowledge concerning BBSOAS in disease databases (MIM# 615722 and ORPHA:401777; Figure  S4 ) confirms some clinical signs associated with NR2F1 and suggests that some others should be added or removed (Table  2 ). Compared to the OMIM synopsis, we first corroborate that ocular involvement predominates (95% of patients), with mainly optic atrophy (HP:0000648) and visual impairment (HP:0000505) in 81% and 72% of patients respectively. The absence of optic disc pallor (HP:0000543) in both our data set and Orphanet is explained by the fact that this subjective clinical sign (discoloration of optic nerve head, as interpreted by the clinician) represents de facto a form of optic atrophy; some authors explicitly mention it  (Bosch et al.
, 2014; Chen et al. , 2016; Rech et al. , 2020; Zou et al. , 2020)  while the majority do not. Ultimately, optic disc pallor is a milder form of optic atrophy. We also confirm that the brain physiology (HP:0012638) is primarily affected (90% of patients), but with emphasis on the presence of delayed speech and language development (HP:0000750), motor delay (HP:0001270), and seizure (HP:0001250) in about half of the patients registered in the database. Interestingly, autistic behavior (HP:0000729), which is tagged "in some patients" in OMIM, appears as frequently as the previous phenotypes in the NR2F1 database (42%). Furthermore, we confirm the variability and nonspecificity of the dysmorphic features. Finally, we suggest considering, in addition to what is already well established, two independent phenotypes: hypotonia (HP:0001252), which is found in 55% of patients in the database, and feeding difficulties (HP:0011968), which is found in 34%.
This impaired ability to eat, related to problems gathering food and preparing to suck, chew, and swallow it, may be secondary to hypotonia  (Rech et al. , 2020). Overall, our meta-analysis of the state of molecular and clinical knowledge of BBSOAS, representing over 5000 ontological terms in 83 patients, is inspired by big data methodologies, by retaining only Only strong discordance with the OMIM reference guided the suggestion of signs to be added or removed from the clinical synopsis (Table  2 ). The benefit is to eliminate punctual biases or diagnostic errors and provide a global profile. In contrast, it does not finely reflect the specificities of each individual. The database contains direct submissions from unpublished patients (17%), a practice that is becoming more frequent in our experience as curators.
| CONCLUSION Genomic medicine calls for the precise definition of phenotypic variations  (Biesecker, 2004; Deans et al. , 2015; Robinson, 2012). Thus, descriptions of human diseases using HPO annotations are key elements of our novel NR2F1 clinicobiological database. Meanwhile, specialized databases reporting pathogenic variations, the so-called LSDB, have proven to be the most complete  (Brookes & Robinson, 2015) , as they benefit from the participation of a curator who is a referent specialist for the gene or disease in question. Our systematic and rational approach, based on the computer representation and analysis of data, has led us to propose a refinement of the description of the clinical signs of BBSOAS, particularly with respect to neurological features and the suggestion of additional hypotonia that may result in impaired feeding ability.