dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing coverage statistics were calculated using the VarAFT tool. Genetic variants, with a read depth<20X and a mapping quality score<30, were discarded. On the assumption that significant disease-causing mutations are rare, a frequency filter was applied to only include variants with a Minor Allele Frequency (MAF) threshold of 0. 1 according to 1000Genomes (https://www. internationalgenome. org/), Exac (http://exac. broadinstitute. org), GnomeAD (https://gnomad. broadinstitute. org/), and GME (Greater Middle East) Variome (http://igm. ucsd. edu/gme/) databases. In the subsequent set of variants with a MAF<0. 1, we selected those located in both non syndromic (238) and syndromic (70) deafness genes by referring to two specific hearing loss databases: Hereditary Hearing Loss Homepage (HHLH, https://hereditaryhearingloss.
org/) and Deafness Variation Database (DVD, http://deafnessvariationdatabase. org/) as well as the general database Online Mendelian Inheritance in Men (OMIM, https://www. omim. org/). We have also targeted genes involved in sensorineural signalling pathways according to the Kyoto Encyclopaedia of Genes and Genomes database (KEGG, https://www. genome. jp/kegg/). The functional effects of genetic sequence variants were evaluated by in silico prediction tools including SIFT (sift. bii. a-star. edu. sg/), MutationTaster (mutationtaster. org/); PolyPhen (genetics. bwh. harvard), FATHMM (http://fathmm. biocompute. org. uk), PROVEAN v1. 1 (provean. jcvi. org/), MutationAssessor 1. 0 (mutationassessor. org/r3/), and VarSome (https:// varsome. com). Genetic variants, for which the CADD-Phred score is less than 15, were filtered out.
Copy number variation (CNV) detection and analysis. CNVs were called from ES data of the index case TNDF182-3 using the ExomeDepth R package that detects CNVs from exome experiments based on a read depth approach. The tested exome was compared to a matched aggregate reference set that combines five control exomes generated by identical NGS sequencing and bioinformatic analysis procedures. Filtering strategy and identification of pathogenic variants in TNDF182-6 patient. The aim of the ES analysis performed in TNDF182-6 patient was to identify variants of clinical significance related to cognitive impairment, in addition to the MAN2B1 gene mutation. Indeed, we consulted the OMIM database to extract gene lists associated to four disease categories: neuropathology (182), ID (185), cognitive impairment (187), and metabolic diseases (181).
We also carried out a variant screening in the genes involved in MAN2B1 gene (NM_000528. 4) biological pathways and network interactions according to GeneMania prediction web server (https://genemania. org/), stringdb 11. 0 (https://string-db. org/), and Phenolyzer (https://phenolyzer. wglab. org/) databases. The public databases Reactome (https:// reactome. org/), UniprotKB (https://www. uniprot. org/uniprot/), GO central (http:// geneontology. org/), and Genecards (https://www. genecards. org/) were used to retrieve the biological pathways implicating the selected genes of interest. The multiple genomic alignments tool (https://www. ensembl. org/Homo_sapiens/Gene/Compara_Alignments) in Ensembl genome browser (https://www. ensembl. org/) was used to align homologous nucleotide sequences to human SLC19A3 and GHR genes.
The UniprotKB database (https://www. uniprot. org/align/) was also used to align homologous protein sequences to human SLC19A3 and GHR proteins with the Clustal omega tool. The degree of conservation was retrieved from the Conserved Domain Database (CDD, https://www. ncbi. nlm. nih. gov/cdd). Sanger sequencing. Exons 14, 18, and 20 of the MAN2B1 gene (NM_000528. 4) were screened by Sanger sequencing in order to identify the genetic aetiology of AM in the first index case TNDF617-1. Sanger sequencing was performed for the index case (TNDF182-3) to screen for variants in the three most frequent deafness related genes in the Tunisian population: namely GJB2 (# 220290, NM_004004. 6), SLC26A4 ( � 605646, NM_000441. 2), and TMC1 ( � 606706, NM_138691. 2) genes. Following ES analysis, Sanger sequencing was used to validate the selected variants in MAN2B1, GHR ( � 600946, NM_000163.
5), and SLC19A3 ( � 606152, NM_025243. 4) genes in the probands and to check the familial segregation when applicable. It was also used to perform cascade screening of the disease-causing mutation identified in exon 5 of the MAN2B1 gene in all affected members of TNDF182 family. Polymerase Chain Reaction (PCR) was performed on genomic DNA samples using primers (available upon request) designed by the primer3 (http://primer3. ut. ee) software. PCR products were sequenced with the Big Dye terminator v3. 1 cycle sequencing reaction kit on an ABI prism 3130 DNA Genetic Analyzer (Applied Biosystems, Foster City, CA, USA) in accordance with the manufacturer's recommendations.
It was not found in our in-house control database (92 exomes), nor in the other sample populations represented in GnomAD database and GME Variome Project (S1 Table ). TNDF182 family. The clinical presentation of the index case was firstly suspected as a hereditary HI fortuitously associated to skeletal malformations. After exclusion of the three most frequently mutated deafness genes in Tunisia: GJB2, TMC1, and SLC26A4 genes, ES was carried out for the index case TNDF182-3. ES results were interpreted following the variant prioritization steps described above. Clinically relevant variants were extracted by targeting more than 500 genes involved in non-syndromic and syndromic deafness as well as diseases including HI. Thirty-five variants were detected in 21 hearing loss related genes corresponding to 11 VUSs, 22 benign, and 2 pathogenic variants located in OTOG ( � 604487, NM_001277269) and MAN2B1 genes (S2 Table ).
314-5. It was not found in the exome data of our local control database (S1 Table ). Cascade screening targeting the MAN2B1 mutation p. (Arg229Trp) was then performed by Sanger sequencing for the index case's sister (TNDF182-5) and her two cousins (TNDF182-6 and TNDF182-7). The mutation was present at a heterozygous state in both parents of the index case (TNDF182-3) and in those of her cousin TNDF182-6 (Fig  2 ). As TNDF182-6 patient presented with an early onset of severe ID associated with autistic traits, we hypothesized that she might carry genetic alterations in disease loci involved in cognitive impairment. A total of 735 genes involved in all diseases and syndromes associated with ID and cognitive impairment were analysed. A total of 403 variants (Table  3 ) have been identified in the prioritized genes (S3 Table ) among which 64 variants were rare.
Two likely-pathogenic variants c. 329A>G; p. (Asn110Ser) and c. 74T>C; p. (Phe25Ser) were identified in GHR and SLC19A3 genes, respectively (Table  3 ). According to UniprotKB, Reactome, GO central, and GeneCards databases, both genes are involved in biological pathways that contribute to the development of the CNS (S4 Table ). Sanger validation of these two variants and evaluation of the familial segregation have been carried out; both variants were present at a homozygous state in TNDF182-6 patient. The parents were heterozygous for these variants and the other affected cousins had the WT alleles (Fig 2, S1 File). The two variants p. (Phe25Ser) and p. (Asn110Ser) respectively found in SLC19A3 and GHR genes were predicted as likely pathogenic according to VarSome tool whose classification was based on four ACMG rules PM1, PM2, PP2, and PP3  [14].
Both variants are located in mutational hotspots in a region of 25 base-pairs on either side of the variants. These mutational hotspots are within functional protein domains corresponding to the transmembrane helical domain of the thiamine transporter 2 (ThTr-2) and the extracellular domain (TOPO_DOM 19-264) of the Growth hormone receptor, encoded by SLC19A3 and GHR genes, respectively. The ratios of pathogenic variants in both domains are equal to 100% (greater than the 50% threshold). These arguments align with the ACMG rule PM1. Both variants were not found in 1000 Genomes Project, GnomAD, as well as the in-house local control database (Pathogenicity criteria PM2). In the SLC19A3 gene, 22 out of 34 missense variants are pathogenic (64. 7% which is more than the 51% threshold). Similarly, in the GHR gene, 32 out of 44 missense variants are pathogenic (72.
A survey conducted in the UK has revealed that many patients were referred to several healthcare specialists before being directed to a metabolic specialist  [5]. This was the case for the patients studied by Beck et al. for whom the disease diagnosis took a decade or longer in almost half of the cases  [15]. Such observations are well illustrated in our report considering that the diagnosis of AM for patient TNDF182-6, who lives in Europe, was never established before its discovery in her affected cousin (TNDF182-3). Complexity of diagnosis is primarily due to the rarity of the disease. Indeed, according to the GnomAD database, the two variants c. 685C>T and c. 2402dup, identified in the present study within the MAN2B1 gene, have very low allele frequencies equal to 1. 314-5 and 3. 979-6, respectively.
Furthermore, both variants were not found in our in-house control database, nor in the other sample populations represented in GnomAD and GME Variome data, thus highlighting the ultra-rare character of the disease. The misdiagnosis of AM could also be related to the unpredicted progression of the disease course as well as the unawareness of certain healthcare practitioners of the clinical hallmarks of lysosomal storage disorders in general. In our context, the prevalence and incidence of AM in the Tunisian population as well as in other Arab countries is likely to be underestimated given the high consanguinity and endogamy rates characterizing these communities. This is shown by the fourfold higher prevalence of AM, estimated to 1. 51/100,000 live births in the Emirati population compared to European countries (Netherlands, Portugal, Czech Republic, and Australia)  [25].
Introduction Particles formed from geologic processes, from dust to boulders, typically have a scale-invariant and fractal frequency versus size relationship due to the natural process of fragmentation  [1]. This relationship can be modeled with a fractal (power-law), Weibull, exponential, or log-normal distribution, with varying success depending on the dataset. In planetary science, the methods for calculating particle size-frequency distributions (PSFDs) are similar to those for calculating crater size-frequency distributions, which have long been used to estimate the ages of many planetary surfaces (e. g. ,  [2, 3] ). The goal of determining PSFDs is often to assess candidate landing or sampling sites (e. g. ,  [4] [5] [6] [7] ) and to understand the geophysical processes responsible for shaping a planetary surface (e.
Here, we present local PSFDs from high-resolution images, in context with the global results, with particle sizes ranging in longest axis from 0. 3 cm to 95 m. Our results provide insight into the differing geologic processes at work on Bennu's surface. These data were also key in the selection of Nightingale crater as the OSIRIS-REx primary site. Lastly, we comment on improvements that could be made in the future by similar missions.
Furthermore, mapping performed on lower-spatialresolution data will miss particles that can only be identified in higher-spatial-resolution images (see Figure  1 ). This example falls within the Nightingale region. Illumination conditions and pixel resolution are included for context. We define incidence as the angle between the light vector and the surface normal (Sun-Bennu-surface normal), emission as the angle between the observation vector and the surface normal (OCAMS-Bennu-surface normal), and phase as the angle between the light vector and observation vector (Sun-Bennu-OCAMS).
Mapped Image Datasets Particle mapping was performed on images that were radiometrically calibrated  [27]  and registered to shape models of the asteroid and to each other  [28] , but not map-projected. A summary of the image datasets used is provided in Table  1. In most cases, multiple images were required to map all of the particles in a given scene, and the lowest emissionangle images were prioritized to minimize foreshortening and other projection effects. Particles were mapped on individual images first, then the particle measurement data were combined and cartographically projected onto an image mosaic for visualization. DellaGiustina and Emery et al. [24]  mapped particles at a global scale (~0. 42 m/px) on images taken by the OSIRIS-REx Camera Suite (OCAMS) PolyCam panchromatic imager  [29]  during the Approach phase of the mission  [20, 30]  (Figure  2a ).
This image dataset allowed the OSIRIS-REx team to identify ROIs on Bennu's surface that warranted more study before being classified as candidate sample sites. As the team obtained higherresolution imagery throughout the subsequent mission phases, the size and location of the ROIs used for particle mapping varied (Figure  2b ). These variations were partly driven by mission schedule-as image resolution increased, there were more particles to count. Refinements were also made to optimize sample collection and avoid unsafe terrain and large hazardous particles (boulders). Table  1. Summary of the image datasets used for particle mapping. Image datasets are grouped by global or sample site location. The number of unique image/user pairs (when greater than 1) denotes when images were mapped by more than one user.
In cases where this value does not match the total number of images mapped, a modified DBSCAN clustering algorithm was used to combine and reduce the dataset. The Detailed Survey phase  [20, 30]  of the mission included the Baseball Diamond (BBD) imaging campaign, which provided global image data at ~0. 05 m/px  [25, 31]. We performed detailed mapping of 16 ROIs. Analyses of these particle measurements, together with other data products and logistical information, informed the selection of four candidate sample sites, named Nightingale, Osprey, Sandpiper, and Kingfisher  [30]. The particles mapped during BBD for each of the candidate sites are shown in Figure  2c.
Particle Mapping Team and GIS Environment For each mission phase, particles were manually identified by their longest axis dimension with polylines using ArcMap, part of the Environmental Systems Research Institute's (ESRI)  [32]  geospatial information system (GIS) software. The GIS environment enabled us to explore the geospatial connection between the mapped particles and the geometry layers, included as backplanes in a multilayer image cube file, using the Integrated Software for Imagers and Spectrometers 3 (ISIS3)  [33]. Pixel backplane values were assigned using ISIS3 s spiceinit routine and the approved shape model version in use in each mission phase. The values were refined using manual and automated photogrammetric registration  [28]. Particle long-axis length was calculated using the Cartesian distance formula with the endpoints determined from the geometry backplanes.
Where there was overlap between mappers on images, we employed a modified DBSCAN  [36]  clustering algorithm to combine and reduce the dataset. We used the GIS environment to calculate the reference surface area of each mapped region from shape models of Bennu. This value allows us to derive a cumulative frequency of particles normalized by area, providing the ability to make meaningful comparisons among regions with varying sizes of mapped areas. The shape models created for Bennu represent the surface with triangular facets where each vertex has a body-fixed Cartesian coordinate  [37, 38]. The high-resolution (5-cm grid spacing) regional models are uniformly spaced grids of height values above a planar surface averaged over the region. They were converted to triangular facets using the gridded heights as vertices.
e. , Sun-Bennu-OCAMS) to provide a test data set for remote measurements. Nearly every mapper involved in our study  (13 total)  mapped particles in at least one of the trays to determine the accuracy of their remotely sensed particle measurements as compared to ground-truth caliper measurements (Figure  3 ). The team mapped the particles in these trays at different points in the mission, resulting in varying levels of detail. An initial group tested mapping and data flow processes with one of the tray images prior to the OSIRIS-REx spacecraft's arrival at Bennu. A second group of mappers performed this task after mapping particles on Bennu. The two groups differed only in the total number of mapped particles identified per mapper, where experience with mapping on Bennu led to an increased number of particle identifications in the rock tray.
Results The global set of particle measurements from the OSIRIS-REx Approach phase presented in DellaGiustina and Emery et al. [24]  included 3136 unique particles (Figure  2a ). We identified between 269 and 556 unique particles for each of the candidate sample sites during BBD (Figure  2c ) and between 5111 and 17,867 unique particles during Recon A (Figure  2d ). During Recon C (Figure  2e ), we identified 26,171 unique particles at Nightingale and 28,929 unique particles at Osprey. Using the approach described in Section 2, we calculated the PSFDs and identified the best-fitting power laws, which involved determining the completeness of each dataset. Table  2  summarizes the particle mapping effort performed during each mission phase and the resulting best fit power-law parameters. Figure  6  presents the cumulative PSFDs of the four candidate sample sites as calculated for BBD, Recon A, and (only Nightingale and Osprey) Recon C.
The power-law fit to the global set of measurements consistently overestimated the number of small particles present at each of the candidate sample sites. This observation is not surprising given that power-law fits are only applicable for particle sizes larger than the completeness limit and these four candidate sample sites were selected for their relative scarcity of large (hazardous) particles. However, the global power-law index provides an upper bound for all four-local power-law indices. The uncertainties in the power-law indices obtained for particles mapped during BBD are relatively high, likely as a consequence of the proportionally fewer counts; less than 600 particles were identified for each candidate sample site. At the time, these datasets were sufficient for mission operational decisions by making relative comparisons and assuming consistent mapping coverage.
This is further discussed in Section 4. 2. [24]  is included as a reference. Light gray points indicate particle measurements that were smaller than the completeness limit. For discussion purposes, we also present merged datasets in the cumulative PSFD plot type. We present this in two stages for clarity: a merged dataset for each mission phase (Figure  9 ) and a single merged dataset (Figure  10 ). To obtain a merged dataset for each mission phase, we combined the counts made for each of the candidate sample sites and normalized by their cumulative area. Then, to obtain a single merged dataset, we combine counts as follows: the minimum measurement from Recon C to the largest measurement smaller than the completeness limit for merged Recon A, the completeness limit for merged Recon A to the largest measurement smaller than the completeness limit for the global counts, and the completeness limit for the global counts to the Roc (95 m).
We exclude merged BBD as the counts in this size range are better captured by the merged Recon A data. [24]  is included as a reference. Light gray points indicate particle measurements that were smaller than the completeness limit. The goal of this presentation is to simplify variation among the different sites and present a single, representative PSFD of Bennu. Though interesting for discussion, this PSFD likely represents only the lower bound of Bennu's full surface particle frequency distribution as each of the sample sites was chosen for its scarcity of large particles. Notably, this dataset is lacking in measurements between 3 to 7 m, which can be attributed to the way data was culled from the four mission phases in our analysis to produce the single merged dataset, which prioritized the completeness of measurement data.
The power-law index of the single merged dataset is -2. 5 ± 0. 1. Particle densities also vary across each of the four sites. In Recon A we observed particle densities (particles per square meter) for Nightingale, Osprey, Sandpiper, and Kingfisher to be 1. 4, 2. 0, 2. 1, and 2. 9, respectively, for particles greater than 30 cm. For Recon C, Nightingale and Osprey had particle densities of 6. 0 and 24. 7, respectively, for particles greater than 15 cm. Particle density also varied within each of the four sample sites, as evidenced in Figure  2b. For example, the area directly south of the Recon C ROI for Nightingale would have increased particle density based on the presence of the four large particles alone. The team calculated particle densities and localized power-law fits within each of the sample sites in order to optimize potential sample coordinates to prioritize spacecraft safety and the likelihood of sampling success.
Using the merged datasets presented in Figures  9  and 10 , we can also attempt to translate these PSFDs based on particle diameter into PSFDs based on particle surface area assuming a notional boulder ellipticity of 0. 7  [65]. The goal of this presentation is to understand what the average boulder surface area size would be on Bennu. This value also has implications for interpreting cratering regimes on asteroids  [66]. In Figures  12  and 13  we present the merged PSFDs of particle surface area with the y-axis scaled to fractional coverage. Average particle measurements (where cumulative surface coverage reaches 50%) is between 12 to 18 cm in longest axis based on the merged datasets of Recon A and Recon C (Figure  12 ) or is 23 cm based on the single merged dataset (Figure  13 ). These results could be refined by determining the true average ellipticity of particles on Bennu.
For Recon A, this would roughly translate to 4-cm particles. In every case, however, this minimum resolvable feature size is much smaller than the completeness limits we identified with the Clauset et al. [46]  technique. This is not surprising as the Clauset et al. [46]  technique aims to maximize the goodness of fit between the power-law and the data set above a given particle size; however, it means we cannot differentiate between identification bias and a possible small particle depletion mechanism.
g. , lag deposits) on larger planetary bodies. Recently, Grott et al. [68]  proposed that Ryugu's PSFD is better described by a Weibull distribution of the form N(D) = N T exp(-3(D/λ) β /β), where D is the mean horizontal particle diameter, N T is the total number of particles, and λ and β define the Weibull shape. A Weibull distribution provides an improved fit to the small diameters and is better able to capture changes in the PSFD over a larger range of particle sizes. Other studies have also proposed Weibull distributions or an exponential variation as opposed to a power-law fit (e. g. ,  [4, 5, 50, 51, 69]. Using the merged dataset presented in Figure  10 , we fit a Weibull distribution (Figure  15 ) and, similarly, find this fit very suitable. In terms of applicability for future missions undertaking similar particle mapping work, the power-law fitting technique provides a superior estimate for gauging the completeness of measurements by providing an upper bound or conservative estimate of the completeness limit based on the fractal nature of particles; however, a Weibull fit may be a better predictor of overall particle frequency.