dataesr/scientific-paragraphs-categorization huggingface dataset:
Distribution of the 83 unique genomic variants in the NR2F1 database (compact view). Complete deletions of the gene that extends beyond the exons are shown as an extended bar with rafters, substitutions as black bars, deletions as blue bars, and the duplication as an orange bar. From the top are reported: an ideogram showing the cytogenetic localization (5q15); the genomic coordinates on human chromosome 5(region shown extending over 10,846 bp, between positions 92,918,993 and 92,929,838  according to assembly GRCh37/hg19); and NR2F1 gene structure including exon numbering and domains. The full view detailing the names of each variant is available in FigureS1. NR C4-type domain: DNA-binding domain that is composed of two C4-type zinc fingers; NR LBD domain: nuclear hormone receptor ligand-binding domain.
Adapted from UCSC Genome Browser (http://genome. ucsc. edu) with the NR2F1 database custom track; data as of October 7, 2021T A B L E 1 Uniquevariants listed in the NR2F1 database (count: 83) Data as of May 7, 2021; for more information, please refer to the database using Database-ID. a Mutational data are described using the nomenclature of the Human Genome Variation Society (http://www. hgvs. org/mutnomen). Nucleotide numbering reflects cDNA numbering with "+1" corresponding to the A of the ATG translation initiation codon in the NR2F1 reference sequence (RefSeq:NM_005654. 4), according to journal guidelines. The initiation codon is codon 1. The notation out of nomenclature "c. -1687_*240{0}" indicates the absence of the entire NR2F1 gene with limits not precisely defined. b Predicted effect on protein based on clinical consequences.
c Classification of variant based on clinical consequences, using standardized criteria: pathogenic (disease-associated), likely pathogenic (likely disease-associated), VUS (variant of unknown significance), likely benign (likely not disease-associated), benign (not disease-associated); including inheritance (dominant or recessive) if applicable. d Affected domain of the protein: zinc finger, nuclear hormone receptor-type (NR C4-type; from amino acid 83-158, InterPro:IPR001628); nuclear hormone receptor, ligand-binding domain (NR LBD; from amino acid 184-410, InterPro:IPR000536). e Number of times variant has been reported in the database. f Identifier of variant in the NR2F1 database (https://www. lovd. nl/NR2F1).
with a pathogenic variant in NR2F1 almost always harbor at least one abnormality of the eye (HP:0000478; 79 patients or 95%), most often an optic atrophy (HP:0000648; 67 patients or 81%), associated with at least one abnormality of the nervous system (HP:0000707; 76 patients or 92%), most often manifests as delayed speech and language development (HP:0000750; 48 patients or 58%). The vast majority of these phenotypes have been reported isolated (sporadic, HP:0003745; 64 patients or 77%), with this HPO term being used to specify the apparent absence of transmission of the patient's Distribution of effects of variants on the protein classified as pathogenic or probably pathogenic in the NR2F1 database. (a) Comparison for each region of the protein of its size on the protein sequence in amino acids (amino acids), of the count of unique variants (i.
e. , by counting only once several reported cases of the same variant; Unique variants), and of the count of variants reports in the database (i. e. , by counting each of the reported cases of the same variant; Reported variants). (b) Effects of the unique variants on the protein. NR C4-type domain: DNA-binding domain that is composed of two C4-type zinc fingers; NR LBD domain: nuclear hormone receptor ligand-binding domain; Nonspecific: regions of the protein that are not domain specific. Data as of October 7, 2021 phenotypic profile from one generation to the next. Only two independent familial cases in which the variant segregates with the phenotype have been recorded as such: patients #00311064 (father).
Abbreviations: HPO, Human Phenotype Ontology; LSDB, locus-specific database; OMIM, Online Mendelian Inheritance in Man database; N/A, not applicable; =, confirmed; +, suggested addition; -, suggested deletion. a HPO term identifier. b Section clinical synopsis of OMIM entry #615722. Var. /nonspecif. : dysmorphic features, variable, nonspecific; "✓": term listed in the synopsis. c Section clinical signs and symptoms of orphanet entry #401777. d Frequency in the NR2F1 LSDB (data as of May 7, 2021). e Proposal to refine the clinical synopsis in relation to the OMIM reference.
DATA AVAILABILITY STATEMENT Data will be added and updated in the NR2F1 database, which is freely available in three different ways: (1) on the World Wide Web at: https://www. lovd. nl/NR2F1; (2) via an application programming interface (API); and (3) as a public beacon in The Global Alliance for Genomics and Health Beacon Project (Global Alliance for Genomics and Health, 2016). The data described in this study are also available on request from the corresponding author.
Thus, although policy requirements in the U. S. Water Resources Council's Principles and Guidelines of 1983 have illustrated the need for considering the future condition in ood risk management for the past four decades,  22  the state of the practice has not provided a consistent application of this on a national scale. The recent work of Bates et al. 23  addresses the limitations of existing ood models in the US, fusing the accuracy of local studies with the spatial continuity of large-scale models. They present a hydrodynamic ood model at 30 m spatial resolution accounting for all major ood drivers and built with a welldocumented ood protection database. The present and future impact of sea level rise, tropical cyclones, and changing weather patterns are all explicitly represented. Crucially, they benchmark their model against high-quality local ood maps, ood claims information, and, in Wing et al.
For the latter two constituents of risk, we employ detailed information from the U. S. government. The National Structure Inventory (NSI), a database of building locations and characteristics for residential and non-residential structures, was utilised for the representation of exposure. U. S. Army Corps of Engineers (USACE) depth-damage functions are utilised to describe the vulnerability of these buildings to ooding. Combining these three components (hazard, exposure, and vulnerability) yields a step-change in understanding of U. S. ood risk by providing the rst national-scale ood risk assessment using property-level residential and nonresidential asset data alongside spatially complete hazards maps of multiple frequencies (see Methods for further details). Estimates of annualised ood losses are compared to those recorded historically, and while we do not expect to replicate these precisely owing to uncertainties in those observations and their questionable relevance to present conditions (both in terms of hazard frequency and exposure availability), we use the comparison to demonstrate that the risk model provides sensible quanti cations at the U.
Properties within the SFHA are currently subject to AALs of $13. 0 billion (47. 6%), while AALs outside total $14. 4 billion (52. 4%). Proportional risk is much higher in the SFHA, with an AAL equal to 0. 399% of exposure; roughly 20 times the relative risk of non-SFHA properties (0. 021%). This is illustrative of the large number of low-or no-risk buildings outside the SFHA, yet it still remains that the majority of U. S. ood risk is unmapped by FEMA. Climate-induced risk changes in the SFHA are expected to be more intense than elsewhere. Within-SFHA AALs are projected to rise by 42. 5% to $18. 6 billion (or 0. 568% of exposure) by 2050, while the outside-SFHA increase is projected to be 31. 5% to $18. 9 billion (0. 027%). Flood risk is not borne equally by all. We use census-tract level data from the 2019 American Community Survey to assess the demographic characteristics of ood risk across the US.
33, 35 Crucially, it provides these answers much faster than full solutions or other common simpli cations of the shallow water equations (e. g. , the diffusive wave)  36  , owing to its linear scaling of stable time step with grid size. 33 Alongside vectorisation and parallelisation of code,  37  accurate computational hydraulics can thus be deployed at high resolutions over large spatial domains with practicable runtimes. The set of return period hazard maps at 1 arc second resolution used in this analysis took ~2 months on a ~2000 core compute cluster. The 1" grid is populated with elevation values principally obtained from the U. S. Geological Survey (USGS) National Elevation Dataset (NED). The NED consists of a plurality of high-accuracy LiDAR data, covering 39% of the contiguous U. S.
land area and two-thirds of its population. The NED is further in lled by sub-national LiDAR terrain data where available. River channels are represented in 1D, decoupled from the 2D grid to enable river channels of any size (including <1" width) to be modelled. 34 A ow accumulation grid was forged using the composite elevation data alongside the USGS National Hydrography Dataset, ensuring correct alignment of 1D channels with their 2D valleys. River bathymetry (particularly bed elevation) is mostly unobserved over large spatial domains, but since channels convey the bulk of ood ows the approximation of their bathymetric properties is essential. Channels are thus parameterised under the assumption that they can convey a certain return period discharge (generally the 2-year ow, rising to 5-year in arid regions), with their bed elevations thus estimated using an inverted gradually varied ow solver (which solves for water height rather than discharge).
43 The pressure and wind elds from the above hurricane event sets were used to drive the GeoCLAW coastal ood model to produce 55,000 years of synthetic hurricane extreme water levels. 44 At coasts, ood inundation occurs due to the joint probabilities that riverine and oceanic ooding co-occur. Using the stochastic model of Quinn et al. ,  45  we link the return periods of univariate hurricane and non-hurricane uvial and coastal oods to the return period of the synthetic multivariate event. Flood inundation maps at coasts thus represent the compound impact of uvial and coastal oods at each return period. Flood defences are represented in a variety of ways, wrought from a painstaking scouring of national and sub-national databases of ood adaptations across the US. Levees from the USACE National Levee Database, as well as other projects identi ed locally, were incorporated explicitly into the model.
For the 5-year ood -more di cult to model owing to its modest size and thus sensitivity to channel   Wing et al. 51  posit that vulnerability functions are an underappreciated source of uncertainty in risk modelling, principally due to a lack of data with which to characterise the relationship between hydrological variables (e. g. water depth) and their consequences (e. g. building damage). They found the residential depth-damage curves used in this analysis matched poorly with empirical data contained in the NFIP claims record, which illustrated a highly variable relationship between depth and damage. However, while Wing et al. demonstrated low skill at the property level, when results were aggregated the local biases generally cancelled each other out. Since this study focuses on large-scale ood risk, rather than that of individual locations, this inventory of residential and non-residential functions is a suitable (and the only) choice.
Census tract data Demographic data split by census tract were obtained from the 2019 American Community Survey 5-year rollup. The proportion of census tract populations which fell into a speci c socio-economic grouping was computed by dividing the counts of each group by the total census tract population. The groups examined were: (i) below 100% of the poverty level (B06012_002), (ii) not Hispanic or Latino, White alone (B03002_003), (iii) not Hispanic or Latino, Black or African American alone (B03002_004), (iv) Hispanic or Latino (B03002_012), (v) not Hispanic or Latino, American Indian and Alaska Native alone (B03002_006), and (vi) other racial identities which do not fall into the groups mentioned here (ii-v) were combined into an 'other' group, calculated from the remaining census tract population yet to be allocated a group (B01001_001).
50r49eterisation and microtopography -the similarity between Bates et al. and the Iowa Flood Center was 69%. Wing et al. 24furthered the validation of the Bates et al. model by simulating historical ood events and comparing them to observations. They found roughly 87% similarity between modelled inundation and observations, and a mean bias of 0. 17 m compared to observed ood depths. These studies thus demonstrate the tness for purpose of the Bates et al. hazard model in a national-scale risk calculation framework. NSI) de ned the exposure in this assessment. The NSI is a database developed by the USACE to support their dam and levee safety programs, as well as real time consequence assessments and planning functions for risk mitigation. The NSI is designed to represent every structure in the United States as a point, as accurately placed and attributed as possible.
The product is described in more detail in USACE documentation. 46Thisstudyused the updated NSI V2 which is restricted by a license, therefore no results are shown below the census block level. This version of the NSI was developed in 2019 by coordinating many datasets such as the Microsoft building footprints layer, the CoreLogic parcel database, ESRI business layer, Census data, and many other datasets to derive a best-of-breed inventory t for evaluating natural disasters. The inventory is described by occupancy types, which are linked to standard depth-damage relationships to understand their vulnerability to ooding. Valuations are based on a variety of sources and are designed to represent depreciated replacement value. , which we intersected with the multi-frequency hazard data to estimate average annual ood exposure.
The EPA data were generated via reallocating 2010 U. S. census block populations to 30 m cells based on maps of land cover and slope. The projections of future populations are drawn directly from the U. S. EPA Integrated Climate and Land-Use Scenarios (ICLUS) projections. 47Thisinvolvestheuse of a 90 m resolution spatial allocation model to assign county-level population change estimates (from a demographic model) to housing units. Fertility, mortality, and migration are the key variables of the demographic model, used to project county-level cohorts split by gender, ethnicity, and age. Datasets from population, housing units, and non-residential land-use, to groundwater availability and transport infrastructure form inputs to the spatial allocation model, which requires parameters such as household size, land-use demand, and travel times to downscale county-level populations to the 90 m grid.
However, conclusions drawn with these data are aggregated to the state and national level, where likely substantial local uncertainties will, to some extent, cancel each other out. The results demonstrate that plausible patterns of future development are the overwhelming driver of increased ood risk, which, in spite of projection uncertainty, are conclusions which cannot be ignored. For more information, the reader is referred to U. S. EPA report. 47VulnerabilityfunctionsThevulnerabilityfunctionsutilizedforthisreport were sourced from a curated database developed to support a variety of the USACE Hydrologic Engineering Center's ood risk modelling activities. The residential damage functions represent those required by policy -Economic Guidance Memorandum (EGM) 01-03 and EGM 04-01 -for use by USACE.
Data Availability The ood hazard data used in this analysis is available from  Bates et al. (2021) , with details on constituent data availability contained therein. Alternatively, the data can be viewed at https:// oodfactor. com/. The building data used in this analysis is currently restricted from public use. Vulnerability functions (and the code for computation of losses) are available at https://github. com/USACE/go-consequences. EPA EnviroAtlas and ICLUS population data are available from https://edg. epa. gov/. The R package tidycensus (https://cran. r-project. org/package=tidycensus) was used to obtain data from the American Community Survey.
48,49Non-residentialfunctions are selected from the HAZUS database from previous USACE projects, based largely on expert elicitation. 50Theseareshown in Table1. Occupancy Type Source Description RES1-1SNB EGM One floor, no basement RES1-1SWB EGM One floor, with basement RES1-2SNB EGM Two floors, no basement RES1-2SWB EGM Two floors, with basement RES1-SLNB EGM Split level, no basement Building data RES1-SLWB EGM Split level, with basement The National Structure Inventory (Population data The U. S. EPA EnviroAtlas programme produced a 30 m resolution dasymetric map of contiguous U. S. populations.
Measures In this study we combined eye tracking, EEG and self-reports data to measure users' cognitive states, traits, and emotional reactions (Westerman, Sutherland, Robinson, Powell, & Tuck, 2007) that might influence visuo-spatial decision-making. We triangulated eye tracking measures (i. e. , gaze entropy; see section 2. 3. 1 for more), self-reported engagement (extracted from the Short Stress State questionnaire;  Helton, 2004) , and engagement index, alpha power and frontal alpha asymmetry (extracted from EEG data;  Coan & Allen, 2004  Table  1  summarizes and defines the physiological and cognitive constructs and the related measures.
Tobii Studio Gaze data were collected with the Tobii TX300 eye tracker (Tobii Technology AB, http://www. tobii. com/, accessed: 21. 01. 2021), and calibrated, processed and analyzed with the software Tobii Studio 3. 4. As I-VT fixation filter, we selected a minimum fixation duration of 60 ms, and a velocity threshold of 30 • /s. The sampling rate was 300 Hz.
Missing data and data synchronization Due to low signal quality, 35% of the collected EEG data and 38% of the eye tracking data was missing and not further analyzed to avoid misinterpretation of the outcomes  (Dalrymple, Manner, Harmelink, Teska, & Elison, 2018; Wass, Forssman, & Leppänen, 2014 , Table  2  for a summary). A complete set of raw data (recorded from all the eye tracking, EEG, and questionnaire data sources) was only available for 19 participants (10 ATC experts and 9 novices). Specifically, the eye tracking data of 13 participants were not further analyzed because the quantity of gaze samples collected by the eye tracker was below a minimum value of 50%, likely due to head or body movements. The 19 remaining participants whose data was included in the multivariate analysis have a valid gaze samples mean of 81%.
The EEG data of 13 participants was missing because of the inability to complete the calibration procedure. Task accuracy, completion time and questionnaire responses were complete (N = 37). Since an imputation analysis is not recommended with more than 10% missing data  (Lodder, 2014) , we performed the PCA analysis presented in this article only by considering the 19 participants with a complete dataset. Time-based data sources were collected using different sampling rates (eye tracking data at 300 Hz and EEG data at 128 Hz). This sampling rate difference should not compromise our statistical outcomes since the measured values, derived from the two data sources, have been aggregated over the whole animation trials as averaged values (e. g. , one averaged entropy or alpha power value across all the 16 animated displays for each participant).
, 2015). Stationary entropy ( Ĥs ) is calculated for individual stationary fixation distributions as shown in equation (  3 ): Ĥs = - ∑ i∈S π i log 2 π i (3) Higher Ĥs values suggest that participants' visual attention is more homogeneously distributed among AOIs, whereas low values indicate that eye fixations are concentrated on a few specific AOIs. Transition matrix entropies were calculated using an R script developed by  Krejtz et al. (2015). For further details about how transition and stationary entropy have been computed, see  Krejtz et al. (2015).
EEG data processing EEG raw data were collected using the Emotiv TestBench software (version 2. 0, https://www. emotiv. com/, accessed: 21. 01. 2021). Successively, EEG raw data were processed with EEGLAB (version 14. 1. 1, https://sccn. ucsd. edu/eeglab/index. php, accessed: 21. 01. 2021). First, EEG raw data and event information (i. e. , type and latency) were imported. Afterwards, continuous EEG data were filtered by applying a short non-linear (IIR) filter (i. e. , 0. 1-30 Hz), and then EEG artefacts, such as eye blinks or body movements, were removed (i. e. , signal segments containing artefacts were first rejected manually and then decomposed by independent component analysis algorithms to eliminate eye blink artefacts). Finally, the filtered data were segmented into epochs, which started 500 ms before and ended 4 s after the onset of each animation.
These binned EEG data were then used to calculate average alpha power, FAA, and the engagement index. Firstly, binned data were used to compute mean alpha (8-12 Hz) band power values for each participant to infer information about participants' cognitive load and determine frontal alpha asymmetry (FAA). Higher frontal alpha asymmetry indicates approach behavior for positive stimuli, but withdrawal behavior from negative stimuli   (Briesemeister et al. , 2013). Previous research demonstrates that FAA effects are limited to both frontal electrodes and alpha band  (Davidson, Schwartz, Saron, C. , Bennett, & Goleman, 1979; Ramsøy, Skov, Christensen, & Stahlhut, 2018; Weinreich, Stephani, & Schubert, 2016). To calculate both alpha power and FAA, we used a custom-made MATLAB graphical user interface called GIVA_EEGtoolbox (available at the following URL: https://gitlab.
Principal components analysis To further investigate which variables of this study predict task accuracy and completion time, a Principal Component Analysis (PCA) was conducted. Principal components analysis (PCA) was employed as an exploratory data reduction technique to summarize variables that share common variance into a single component to triangulate the variables in this study. The results of this technique are presented for 19 participants who had a complete dataset. For this reason, these results should be considered as an exploratory cross-validation analysis, and future studies will be required to confirm patterns in our findings. Prior to conducting the principal components analysis, we utilized a parallel analysis to determine the number of components to extract from our data using the psych package in R  (Watkins, 2006).
Acknowledgements We would like to express our gratitude to the air traffic controllers and students who participated in our user study. We are also grateful for  Dr. Jean-Paul Imbert 's and  Dr. Maxime Cordeil 's invaluable support during the design phase of the experiment and for the programming of the animated displays. We are also particularly grateful to  Dr. Tyler Thrash  and  Bingjie Cheng  for their critical feedback on earlier drafts of this manuscript. Thanks to  Dr. Jennifer Bartmess  for proofreading this manuscript.
Availability of data and materials The statistical data, R-code and animations (inclusive Processing codes) that support the findings of this study are available in OSF at htt ps://osf. io/d2jpg/. If you have any questions related to the data, please contact the corresponding author, Sara Lanini-Maggi at sara. maggi@g eo. uzh. ch. The MATLAB code of our EEG tool is available at the following URL: https://gitlab. uzh. ch/giva/geovisense/GIVA_EEGtoolb ox (accessed: 21. 01. 2021).