dataesr/scientific-paragraphs-categorization huggingface dataset:
A letter of consent was signed by at least one parent before initiating the study, expressing their consent to record the assessment and the intervention sessions and to use the data and recordings for scientific purposes in an anonymous fashion. The clinical study where this data was collected received prior ethical approval from the Scientific Council of Babes-Bolyai University in Cluj-Napoca, Romania, where the trial was conducted (record no. 30664/ February 10th, 2017). The clinical trial was pre-registered to the U. S. National Library of Medicine database (ClinicalTrials. gov) under the number NCT03323931. The therapy environment followed two configurations illustrated in Fig 1. The two configurations (RET and SHT) were designed to be as similar as possible, with the interaction partner constituting the primary difference.
Sensors and setting All therapy sessions were recorded using the same sensorized therapy table  [24]. The table was equipped with three high-resolution RGB cameras and two RGBD (Kinect) cameras that, in combination with state of the art sensor interpretation methods, provide information about the child's position, motion, eye-gaze, face expressions, and verbal utterances. In addition, the table captured the presence and location of objects used in the therapy. A range of different algorithms were employed to compute these perceptions. A complete list of sensor primitives and associated methods is provided in Table  1. Note that only a subset of these features are included in the public dataset, see Section 4 for details. The data gathered by the sensorized table was collected in real time with a temporal resolution of 25 Hz and was used to guide the behavior of the robot in the RET condition, following the session script.
In addition, recorded data was used off-line to support analysis and assessment of the child's progress through therapy, in both RET and SHT conditions. The table works without sensors placed on the child and without individual calibration, both of which are problematic to employ in therapy with autistic children. While sensor data was used to guide the robot's behavior on-line, robot responses were kept consistent throughout the study, i. e. , the robot did not learn from previous interactions.
Open dataset The dataset resulting from the clinical evaluation presented above comprises a total of 306 hours of therapy. 41 out of 61 children finished all 8 interventions and the final diagnosis. The remaining participants finished an average of 4. 5 interventions, i. e. , just above half of the complete protocol. The total length of each intervention varied from a 3 to 87 minutes as a result of script length and child behavior, with a median duration of 32 minutes. Average intervention durations for each of the tow conditions (RET, SHT) are presented in Fig 3. To our knowledge, this is the largest dataset of autism therapy involving robots and probably also the largest recorded data set of children interacting with robots in general. As mentioned in the introduction, this dataset does not comprise any direct measurements, i.
e. , raw data, from the conducted therapies. However, given that a RET framework involves processing of sensory data into higher level perceptions (see Section 3. 1), we have access to comprehensive secondary data from these recordings. Any variables related to the clinical evaluation can however not yet be released, concerning for example the child's performances during therapy. This type of information may be included in a later release of the dataset. Other variables, such as face expressions, have relatively low reliability and were excluded from dataset for this reason. Finally, any variables that may reveal the child's identity have been excluded from the dataset. The following data has been selected for inclusion in the public release of the DREAM dxataset: 1. Child ID (numerical index), 2.
Child's gender, With the ambition of releasing the dataset in an easily accessible, well-specified, and commonly used file format, JavaScript Object Notation (JSON) was selected. JSON is a stripped form of the JavaScript programming language, intended for data representation, https://www. json. org. JSON has many of the attractive attributes found in XML, including standard libraries for most programming languages, validation patterns, and human readability. However, JSON is less verbose than XML and includes standard notation for arrays, making it much more suitable for storing numeric data. An example structure of the DREAM dataset JSON format is included in Table  2. The complete format is specified by the JSON Schema found in Appendix A. An important attribute of.
16 " participant " : {" id " : 37 , "gender" : "male" , "ageInMonths" : 47} , 17 "date" : "2017 ¡04 ¡23T18 : 25 : 43. 511Z" , 18 " condition " : "RET" , 19 " task " : {"index " : 18 , " ability " : "TT" , " difficultyLevel " : 1 , start : 0 , end : 10279} , this dataset is that all attributes are defined in a common frame of reference using a Cartesian coordinate system. The orientation of the Cartesian space in relation to the therapy environment is visualized in Fig  2.
Licence The Open DREAM dataset is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4. 0 International Licence  [50]. This licence permits copying and redistribution of the material in any medium or format and states that the licensor cannot revoke these freedoms as long as you follow the licence terms. The material here refers to all secondary data and metadata included in this public release of the dataset, excluding its underlying recordings or direct measurements. This freedom is given under the following terms: 1. Attribution-You must give appropriate credit, provide a link to the licence, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. 2. NonCommercial-You may not use the material for commercial purposes.
Data visualization An important aspect of any public dataset is to make it accessible, and understandable, for a larger audience. In addition to the technical specification provided in Appendix A, we release a visualization tool named DREAM Data Visualizer. This tool runs directly in the web-browser and comprises a 3D environment where a user can playback each intervention, view the child's movements in relation to the interaction partner. The DREAM Data Visualizer is released as open source under GNU GPL and available for download at github. com/dream2020/DREAM-data-visualizer. An example screenshoot from the DREAM data visualizer is presented in Fig  4.
Discussion Although the global prevalence of autism is difficult to assess  [51]  and its exact origin has not been determined  [5, 52] , autism is becoming an increasingly common diagnosis worldwide. This affects not only all the people receiving diagnosis, but also constitutes a significant cost for society  [30]. RAT/RET has been put forward as a potentially cost-effective treatment, but still lacks large-scale clinical trials and longitudinal studies in order to assess its effects  [12, 15, 17]. In the present work, we present a dataset covering behavioral data recorded during therapeutic interventions with 61 children. The dataset comprises a rich set of features which we believe are essential for understanding and assessing childrens' behavior during therapy. By providing a large set of data, comprising 61 children with varying degree of ASD taking part in more than 3000 therapy sessions, we hope that the DREAM Dataset can constitute an important asset in future studies in the field.
The dataset may for example be used by studies employing machine learning or artificial intelligence to find patterns in behavioral data. Such patterns may guide further clinical studies by providing new insights into how to appropriately select between RET and traditional ABA therapies or constitute input to new therapeutic methods. "type" : " object " , 7 " properties " : { 8 "date" : {"type" : " string " , "format" : "date¡time"} , 9 "ados" : { 10 " type " : " object " , 11 " properties " : {" i n i t i a l " : {" type " : "number"}} 12 } , 13 " condition " : {" type " : " string " , "pattern" : "^(RET|SHT)$"} , 14 " task " : { 15 " type " : " object " , 16 " properties " : { 17 " index" : {" type " : " integer "} , 18 " ability " : {"type" : " string "} , 19 " difficultyLevel " : {" type " : " integer "} , 20 " start " : {" type " : " integer "} , "end" : {"type" : " integer "} 21 } 22 } ,.
The complete dataset is available for download through the Swedish National Data Service, https://doi. org/10. 5878/ 17p8-6k13. Sample data and usage instructions can be accessed at https://github. com/dream2020/ data. In addition, source code for the DREAM RET System, with which the present dataset was gathered, is made available at https://www. doi. org/ 10. 5281/zenodo. 3571992.
Data Availability The complete dataset is available for download through the Swedish National Data Service, https://doi. org/10. 5878/17p8-6k13. Sample data and usage instructions can be accessed at https://github. com/dream2020/data. In addition, source code for the DREAM RET System  [24] , with which the present dataset was gathered, is made available at https://www. doi. org/10. 5281/zenodo. 3571992. Fig 4. Visualization of the DREAM dataset, including 3D skeleton of upper body and head rotations. https://doi. org/10. 1371/journal. pone. 0236939. g004.
Infrequent and rare genetic variants in the human population vastly outnumber common ones. Although they may contribute significantly to the genetic basis of a disease, these seldom-encountered variants may also be miss-identified as pathogenic if no correct references are available. Somatic and germline TP53 variants are associated with multiple neoplastic diseases, and thus have come to serve as a paradigm for genetic analyses in this setting. We searched 14 independent, globally distributed datasets and recovered TP53 SNPs from 202,767 cancer-free individuals. In our analyses, 19 new missense TP53 SNPs, including five novel variants specific to the Asian population, were recurrently identified in multiple datasets. Using a combination of in silico, functional, structural, and genetic approaches, we showed that none of these variants displayed loss of function compared to the normal TP53 gene.
However, the analysis of somatic or germline TP53 variants faces a range of challenges. In tumors, the somatic origin of mutations can be inferred via comparison with matched normal DNA from the same patient. Unfortunately, this material is often missing, necessitating indirect assumptions made using databases that include the most frequent germline variants observed in the human population. For the analysis of germline variants, data are directly compared to population databases. In both cases, the accuracy of the results will depend on the characteristics of the population databases. Among those characteristics are not only the increasing number of rare variants found in the human population but also the ethnic diversity of this latter. The advent of massively parallel sequencing (next-generation sequencing, or NGS) has shown that the human genome includes far more genetic variation than anticipated, with many variants detected at frequencies far below the formerly used 1% limit  [4].
To illustrate, the most frequent build (151) of dbSNP includes 335 million variants. However, several studies have questioned the quality of the entries, among which both pathogenic and nonpathogenic TP53 variants are included  [5, 6]. In contrast, data from the 1000 Genomes Project have been highly curated, but the small number of individuals (2504) included in the project does not enable the full coverage of low-frequency variants. To circumvent this issue, the Exome Aggregation Consortium released the ExAC database, which compiles whole-exome sequencing data from 60,706 individuals  [7]. ExAC includes not only data from the 1000 Genomes Project but also constitutional data from unrelated individuals sequenced as part of various disease-specific and population genetics studies. In 2016, ExAC became the Genome Aggregation Database (gnomAD) with an enlarged dataset providing information from 123,136 exome sequences and 15,496 whole-genome sequences from unrelated individuals (http://gnomad.
broadinstitute. org/). Both ExAC and gnomAD have been widely used as a substitute for or a complement to dbSNP and are currently used in multiple analytical pipelines. Two missense SNPs in the TP53 gene have been identified and extensively characterized. SNP rs1042522 (p. P72R) is common in all world populations but the ancestral allele (Pro) shows a north-south gradient ranging between 0. 2 and 0. 7  [8]. The second SNP, rs1800371 (p. P47S), has been shown to be specific to the African population, albeit at a lower frequency (0. 01-0. 02)  [9]. Both SNPs are included in ClinVar and considered benign according to American College of Medical Genetics and Genomics (ACMG) criteria. In the present study, using a combination of in silico, in vitro, structural and genetic approaches, we addressed a two-tiered question: (i) is it possible to identify novel and infrequent TP53 SNPs in the human population and (ii) what is the potential pathogenicity of these variants? Using aggregated and population-specific databases, in silico and functional studies, and clinical data, we identify 19 new exonic TP53 SNPs including five variants specific to the Asian population.
Identification of new TP53 SNPs in the human population To identify novel infrequent TP53 SNPs in the human population, fourteen independent datasets compiled from individuals without neoplastic disease (202,767 individuals) were screened for variants located anywhere in the entire TP53 gene (Fig. 1A  and Table  S1 ). Three datasets included aggregated data from multiple studies, whereas 11 comprised data from specific countries. These latter were globally distributed to increase the identification of population-specific variants. Furthermore, data overlap between these datasets was minimal, which avoided excessive redundancy in the analysis ("Materials and methods"). We thus uncovered 6001 different TP53 variants at frequencies ranging from 0. 8 to 10 -6 , and, as expected, most of them were located in noncoding sequences of the TP53 gene (5628 noncoding and 373 coding) (Table  S2a  and Fig.
S1 ). For the present work, we focused our analysis on missense variants. Other variants such as intronic, synonymous, or specific TP53 isoform-targeting variants are fully described in the Supplementary Information. The particular case of rs138729528, (p. R175C), a passenger mutation found at high frequency in human tumors and in the normal population is also discussed in the Supplementary Information. We identified 247 missense variants in the 14 datasets, with 4, 6, and 14 variants found in more than 5, 4, and 3 datasets, respectively (Figs. 1A, S1b and c, and Table  S2c ). Their frequencies ranged from 0. 8 to 4 × 10 -6. The two most common benign TP53 SNPs found in the human population, i. e. , rs1042522 (p. P72R) and rs1800371 (p. P47S), are included in this list (found in 14 and 7 datasets, respectively) (Figs.
1B, C  and S1b  and c ). The statuses of the remaining variants were unknown, but their recurrences in various independent datasets suggested that they could also be rare constitutional TP53 SNPs. The gnomAD dataset includes population data that enables subdivision into eight classes according to ancestry. rs1042522 (p. P72R) was found in all ethnic groups at a frequency ranging from 0. 4 to 0. 7 (Fig. 2A ). Our analysis confirmed previous observations of a lower frequency for rs1042522 in the African population (0. 4 in our analysis). Those observations have led some authors to hypothesize that the frequency of this polymorphism may be latitudedependent  [8]. In the present analysis, we also confirmed the quasi-exclusivity of rs1800371 (p. P47S) in the African population, as previously observed in other studies  [10]  (Figs.
2A  and S2 ). Unexpectedly, our analysis uncovered five novel variants that were present in only the East Asian (EAS) population of gnomAD (rs201753350; (p. V31I), rs587780728; (p. D49H), rs201382018; (p. E11Q), rs121912665; (p. A189V), and rs773553186; (p. E358V)) (Fig. 2B ). This specificity was confirmed by the analysis of datasets specific to Asian populations (Taiwan, Korea, China, and Japan) (Figs. 2B  and S2 ). These five variants are also included in such cancer mutation databases as Cosmic, UMD, and IARC. Because normal DNA is usually not available, the constitutional origin of these variants may have been missed. The UMD_TP53 database included 146 entries for these five variants published in 64 reports. For all of these latter, the ethnicity of all patients was carefully checked and shown to be highly predominantly Asian (139 out of 146 patients) (Fig.
2C  and Table  S3 ). Notably, in two American multicenter studies that included patients from various geographical regions, these variants were only identified in Asian patients. We also noticed that the nine cell lines expressing any one of these variants originated from Japanese patients (Table  S3 ). Taken together, these results clearly showed that the five novel constitutional variants were indeed specific to the Asian population. Whether any of these infrequent variants originated from a single event, i. e. , a founder mutation, is currently unknown. A sixth rare variant, rs72661119; (p. N263D), was identified specifically in the South Asian (SAS) population of gno-mAD with multiple reports originating from India (Fig. 2C  and Table  S3 ). The various TP53 mutations databases include both somatic variants from multiple types of cancer and germline variants from a range of families or patient cohorts with Li-Fraumeni syndrome or hereditary breast and ovarian cancer syndrome.
As discussed above, normal tissue was not available for many of the 80,000 tumors included in the UMD_TP53 database; thus assessments of variant origin had often been done by comparison with dbSNP, which includes a limited number of TP53 population variants. It is therefore likely that infrequent germline variants were mistakenly identified as somatic variants, leading to database contamination. We therefore evaluated the germlineto-somatic ratio for each TP53 variant in the UMD_TP53 database. As shown in Fig. 2D , the distribution of this ratio for the majority of TP53 variants, including all TP53 mutation hot spots, was quite low, ranging from 0. 03 to 0. 056. Ten outlier variants displayed a high ratio, suggesting that they may be undetected germline variants (Fig. 2E ). Indeed, nine of these outlier variants are included in the 247 missense variants described above, with seven of them identified in three or more datasets.
They also included three of the variants detected in the Asian population. In a study published by our group, p. N235S was detected in a patient with lung cancer and defined as somatic  [11]. However, when we reanalyzed this patient by sequencing normal DNA, we found that the variant was germline. ALL all populations, AFR African/African American, AMR admixed American, ASJ Ashkenazi Jewish, EAS East Asian, FIN, Finnish, NFE non-Finnish European, OTH other unassigned populations, SAS South Asian. C Ethnicity origin of the six variants described in the literature of the UMD_TP53 database. * and **: including two or three different cell lines, respectively. D, E Germline-to-somatic (GVS) ratio of TP53 variants in the UMD_TP53 database. D A box-plot analysis of the GVS ratio shows that the vast majority of TP53 variants had similar values.
Ten outlier variants above the 95% confidence interval had high frequency as germline variants in the database and could be benign SNPs. TP53 variants found below the 5% interval were never found as germline variants in the database; they corresponded to specific TP53 variants associated with carcinogen exposure, such as p. R249S found in aflatoxin B1-associated hepatocellular carcinoma, and p. V157F found in tobacco-associated lung cancer. The box-andwhisker plot shows the interquartile range (box), median value (horizontal line inside the box), and full-range distribution (whiskers) for the GVS ratio. E Detailed analysis of the germline ratio. The ten outlier variants are shown in red. Values for six hot spot TP53 variants are shown in black. This analysis was performed on 195 TP53 variants with at least five occurrences as germline variants in the database.
(Fig. S3a ). Similarly, reanalysis of normal DNA from chronic lymphocytic leukemia patients confirmed the germline origin of this variant as well as variants p. R290H, p. R283C, and p. G360A (Fig. S3b-f ). Our analysis suggests that the coding region of the TP53 gene includes far more ethnically distributed SNPs than was previously thought, and that some of them are mistakenly included as somatic variants in a number of databases. It is therefore essential to determine whether or not the p53 protein encoded by these SNPs is functional and how they must be classified according to ACMG criteria.
Predictive and functional analysis of TP53 variants; in silico analysis Computational algorithms to predict the functional effect of missense variants were performed on 27 SNPs (set 27) found in at least three datasets, and on the outlying germline  variants identified in the analysis of the UMD_TP53 database described in the previous section (Table  S2c ). First, we used the prediction scores from dbNSFP, a database that compiles scores from several popular algorithms along with a conservation score and other related information for every potential single nucleotide variant in the human genome  [12]  ("Material and methods"). Most of these algorithms are based on sequence homology and/or the physical properties of amino acids (Table  S4 ). They are widely used for a global estimation of the deleteriousness of missense variants but they are not specific to any protein.
We also used new in silico mutation prediction tools such as Revel or Envision that have been trained on known pathogenic and neutral nsSNVs. Among the 27 analyzed TP53 variants, 4 were shown to be included in the cancer shared dataset (CSD), including the hot spot variant p. R248Q (Fig. 3 ). Although the majority of individuals included in the various datasets were selected for nonneoplastic disease, it is known that the frequency of de novo germline TP53 mutation in the human population is high, ranging from 1/5000 to 1/20,000  [13, 14]. It is therefore not surprising that among the 138,632 individuals in gnomAD as well as those in other population datasets, some may have been carrying such de novo mutations, although without cancer at the time of inclusion  [15]. The rank scores of TP53 variants found in set 27 were always associated with a low pathogenic score, in contrast to the scores of the CSD-associated variants (Fig.
S10d ). This polar interaction network was lost in the p. N235S variant but may have been compensated for, at least in part, by alternative, potentially water-mediated hydrogen bonds formed by the serine hydroxyl. In the mouse structure, there is a lysine at this position, and a serine is found in various fish species. The other two DBD variants, p. R283C and p. R290H, were located on the C-terminal helix that binds to the major groove of the p53 response elements (Fig. 5B ). Arg283 sits close to the protein-DNA interface, but in most p53-DNA complexes, it does not interact directly with the phosphate backbone  [25, 26] , so no significant effect on DNA-binding affinity and specificity is expected. The closest distance between the guanidinium group and the phosphate backbone of DNA in Protein Data Bank (PDB) entry 3KZA, for example, is more than 6 Å.