dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. TROPOMI level 2 CO retrievals for 7 November 2017 to 27 June 2018 were downloaded from https://s5pexp. copernicus. eu/ (last access: 27 November 2019), ESA, 2018a. ; retrievals for dates after 28 June 2018 were downloaded from https://s5phub. copernicus. eu/ (last access: 27 November 2019). TROPOMI reference profiles were obtained from ftp://ftp. sron. nl/pub/jochen/ TROPOMI_apriori/tm5_co/ (last access: 2 September 2020;  Landgraf, 2020). MOPITT data can be downloaded from https://doi. org/10. 5067/TERRA/MOPITT/MOP02T_L2. 008 (Ziskin, 2019a)   (TIR), https://doi. org/10. 5067/TERRA/MOPITT/MOP02N_L2. 008 (Ziskin,  2019b)   (NIR), and https://doi. org/10. 5067/TERRA/MOPITT/MOP02J_L2. 008 (Ziskin, 2019c) (TIR + NIR). ATom-4 data from the 7 September 2019 version were downloaded from https://doi.
Introduction: 5' splice site GT>GC or +2T>C variants have been frequently reported to cause human genetic disease and are routinely scored as pathogenic splicing mutations. However, we have recently demonstrated that such variants in human disease genes may not invariably be pathogenic. Moreover, we found that no splicing prediction tools appear to be capable of reliably distinguishing those +2T>C variants that generate wild-type transcripts from those that do not. Methodology: Herein, we evaluated the performance of a novel deep learning-based tool, SpliceAI, in the context of three datasets of +2T>C variants, all of which had been characterized functionally in terms of their impact on pre-mRNA splicing. The first two datasets refer to our recently described "in vivo" dataset of 45 known disease-causing +2T>C variants and the "in vitro" dataset of 103 +2T>C substitutions subjected to full-length gene splicing assay.
Results: Comparison of the SpliceAI-predicted and experimentally obtained functional impact assessments of these variants (and smaller datasets of +2T>A and +2T>G variants) revealed that although SpliceAI performed rather better than other prediction tools, it was still far from perfect. A key issue was that the impact of those +2T>C (and +2T>A) variants that generated wild-type transcripts represents a quantitative change that can vary from barely detectable to an almost full expression of wild-type transcripts, with wild-type transcripts often co-existing with aberrantly spliced transcripts.
Specifically, combining data derived from a meta-analysis of 45 human diseasecausing GT>GC variants and a cell culture-based Full-Length Gene Splicing Assay (FLGSA) of 103 GT>GC substitutions, we estimated that ~15-18% of GT>GC variants generate between 1% and 84% wild-type transcripts  [16]. During this analysis, we found that none of the four most popular splicing prediction tools, namely SpliceSiteFinderlike, MaxEntScan, NNSPLICE and GeneSplicer (all included within Alamut ® Visual; https://www. interactivebiosoftware. com/), were capable of reliably distinguishing those GT>GC variants that generated wild-type transcripts from those that did not  [16] ; for all variants tested, SpliceSiteFinder--like tended to predict a slightly reduced score whilst the other three invariably failed to yield any score.
The root of this problem is two-fold: Firstly, these splicing prediction tools (in common with many others) focus exclusively on short local DNA sequence motifs and secondly, GC is used instead of GT as the wild-type 5' splice site dinucleotide in ~1% of U2 type introns in the human genome  [17, 18]. Recently, SpliceAI, a novel deep residual neural network tool, has been developed for splicing prediction  [14]. Methodologically distinct from previous approaches that have either relied on human-engineered features and/or focused on short nucleotide windows adjoining exon-intron boundaries, SpliceAI learns splicing determinants directly from the primary sequence by evaluating 10,000 nucleotides of the flanking sequence context to predict the role in splicing of each position in the pre-mRNA transcript and achieved a top-k accuracy of 95% for pre-mRNA transcripts of proteincoding genes and 84% for long intergenic noncoding RNAs (lincRNAs) in the test dataset.
Source of GT>GC Variants Three datasets of GT>GC variants, all of which have been characterized functionally in terms of their impact on splicing, were employed in this study. The first two datasets correspond to our previously described "in vivo" dataset of 45 disease-causing GT>GC variants and the "in vitro" dataset of 103 GT>GC substitutions  [16]. The third dataset comprised 12 GT>GC variants from the BRCA1 gene, which were extracted from a recent study that prospectively analyzed the functional impact of over 4000 BRCA1 variants by means of saturation genome editing  [19]. In the context of the first dataset (Supplementary Table  S1 ), the precise level of the variant allele-derived wild-type transcripts was available for four of the seven diseasecausing GT>GC variants that generated wild-type transcripts in the corresponding original publications (Table  1 ).
For the three remaining variants (i. e. , CAV3 c. 114+2T>C in  [20] ; PLP1 c. 696+2T>C in  [21]  and SPINK1 c. 194+2T>C in  [22] ), it is apparent from RT-PCR gel photographs in the original publications that all three were associated with the generation of both wild-type and aberrantly spliced transcripts. We employed ImageJ (https://imagej. net) to provide approximate estimates of the levels of the variant allelederived wild-transcripts for each of the three variants (Table  1 ).
SpliceAI Prediction GT>GC variants or substitutions as well as their corresponding GT>GA and GT>GG counterparts were processed (during October 2019) using the default settings of SpliceAI version 1. 2. 1https://pypi. org/project/spliceai/, with a custom gene annotation file containing NCBI reference sequence transcript start and end coordinates. Default settings, and instruction for use of custom annotation files, were taken from https://pypi. org/project/spliceai/.
Performance Testing Two statistical tests, a Matthews correlation coefficient (MCC) and a Receiver operating characteristic (ROC) curve, were carried out on the dataset 2 substitutions assessed by SpliceAI. MCC test is a correlation coefficient between the observed and predicted binary classifications. For a perfect prediction, the coefficient is +1; a coefficient of 0 is no better than random, and no correlation between observed and predicted yields -1  [24]. A ROC curve illustrates the diagnostic specificity and sensitivity of a binary classifier system as its discrimination threshold is varied; this enables the selection of an optimum threshold value. To assess the difference between the diagonal and the ROC curve obtained, the area under the ROC curve is measured (AUC). An AUC of 0. 5 would be a random prediction whilst a perfect predictor would score 1.
ROC analysis was carried out using the Rbased web tool easyROC  [25]. For the MCC test, a contingency table was derived from dataset 2 (Supplementary Table  S2 ) where a true positive is defined as a predicted splice altering substitution for which FLGSA produced no wild-type transcript and a true negative is a substitution not predicted to alter splicing and for which FLGSA produces wild-type transcript.
Accuracy and Reliability of the Experimentally Obtained Functional Assessment of the GT>GC Variants Analyzed Since the experimentally ascertained functional impact of the GT>GC variants analyzed was used as the starting point for our analysis, their accuracy and reliability were of critical importance. Regarding the first dataset of known pathogenic variants (Supplementary Table  S1 ), several points are worth highlighting. Firstly, all 45 disease-causing variants were either homozygotes, hemizygotes or compound heterozygotes, a prerequisite for determining the presence or absence of the variant allele-derived wild-type transcripts. Secondly, for each variant, patient-derived tissue or cells (pathologically relevant in about half of the cases) had been used to perform the RT-PCR analysis that had unequivocally demonstrated the presence or absence of variant allele-derived wild-type transcripts in the corresponding original publication.
Thirdly, the levels of the variant allele-derived wildtype transcripts in the seven disease-causing GT>GC variants that generated wild-type transcripts were very low (≤15% of normal; Table  1 ), potentially explicable by the ascertainment bias inherent to all disease-causing variants. Nonetheless, all seven of these variants were noted to be associated with a milder clinical phenotype than would have been expected from a functionally null variant  [16] , consistent with other findings that even the retention of a small fraction of normal gene function can significantly impact the clinical phenotype  [26] [27] [28] [29]. In the case of the second dataset (Supplementary Table  S2 ), the functional effects of all 103 engineered GT>GC substitutions (from 30 different genes) were analyzed by Full-Length Gene Splicing Assay (FLGSA) in transfected HEK293T cells  [16] , with all 19 substitutions that generated some wild-type transcripts (all confirmed by Sanger sequencing) being listed in Table  2.
By comparison to the commonly used minigene splicing assay, FLGSA preserves better the natural genomic sequence context of the studied variants  [30, 31]. The accuracy and reliability of the FLG-SA-derived data can be inferred from the following three lines of evidence. Firstly, 10 GT>GC substitutions that gen-erated wild-type transcripts and 10 GT>GC substitutions that did not generate wild-type transcripts in transfected HEK293T cells were further analyzed in transfected HeLa cells using FLGSA, yielding entirely consistent findings in terms of whether or not wild-type transcripts were generated  [16]. Secondly, HESX1 c. 357+2T>C and SPINK1 c. 194+2T>C were the only variants common to both the first and second datasets. The functional effects of these two variants in vivo -HESX1 c. 357+2T>C generated no wild-type transcripts whereas SPINK1 c.
194+2T>C generated some wild-type transcripts (Supplementary Table  S1 ) -were faithfully replicated in FLGSA (Supplementary Table  S2 ). Thirdly, a GT>GC variant that was not present in either dataset, HBB c. 315+2T>C, had been reported to be associated with a milder hematological phenotype and it was suggested that it might have a limited impact on splicing  [32]. Using FLGSA performed in HEK293T cells, we found that it generated a low level of wild-type transcripts  [16]. Importantly, the orthologous variant of HBB c. 315+2T>C in the rabbit Hbb gene has also been experimentally shown to be capable of generating wild-type transcripts  [33, 34]. These notwithstanding, tissue-or cell-specific factors have on some occasions impacted splicing  [14, 35] , an issue that was not extensively addressed in our previous study  [16].
The bottom line here is that (i) the 30 genes used for FLGSA analysis were selected using a procedure that did not take into consideration the gene's function or expression, (ii) all 30 genes underwent normal splicing in the context of their reference mRNA sequences as specified in Supplementary Table  S2  and (iii) the generation (or not) of wild-type transcripts from the engineered GC allele was observed under the same experimental conditions as for the wild-type GT allele  [16]. The third dataset was obtained courtesy of a perusal of the literature (Table  3 ). Recently, the functional impact of all possible single nucleotide substitutions within 13 exons and a Nomenclature in accordance with Human Genome Variation Society (HGVS) recommendations  [23]. b Expresed as the level of the variant allele-derived wild-type transcripts relative to that of the wild-type allele-derived wild-type transcripts.
c Expression level determined here by ImageJ using gel photos from the original publications. d Expression level as described in the original publications. e Identical to the SPINK1 IVS3+2T>C substitution in Table  2. In accordance with the traditional IVS (InterVening Sequence; i. e. , an intron) nomenclature as previously described  [16]. b Expression level (in parentheses), determined by quantitative RT-PCR analysis, was available for all +2T>C substitutions that generated only wild-type transcripts under the experimental conditions described in  [16]. c Identical to the SPINK1 c. 194+2T>C variant in Supplementary Table  S1  and Table  1. b "Non-functional" was interpreted as meaning that no wild-type transcripts were generated whereas "functional" and "intermediate" were held to imply the generation of wild-type transcripts.
Selection and Interpretation of SpliceAI Delta Scores for Analysis We processed GT>GC variants using the default settings of SpliceAI as detailed in https://pypi. org/project/spliceai/. SpliceAI provides Delta scores (ranging from 0 to 1) for each variant, thereby providing a measure of their probability of altering splicing in terms of either splice donor gain, splice donor loss, splice gain, and splice acceptor loss. SpliceAI also provides Delta position that conveys information specifying the location where splicing differs from normal relative to the position of the associated variant. Since the GT>GC variants or substitutions under study invariably affected the +2 position of the canonical 5' splice site GT dinucleotides (in the context of the specified mRNA reference sequence), we focused our analysis exclusively on the Delta scores of donor loss although other scores may provide clues as to the nature of the resulting aberrantly spliced transcripts of splice-altering variants.
Encouraging Findings from a Quick Survey of the Three Datasets of GT>GC Variants As mentioned in the introduction, none of the four most popular splicing prediction tools, SpliceSiteFinder-like, MaxEntScan, NNSPLICE and GeneSplicer, were found to be able to distinguish those GT>GC variants that generated wild-type transcripts from those that did not  [16]. As described below, a quick survey of SpliceAI-predicted scores yielded encouraging results across all three datasets of GT>GC variants. Firstly, in the context of dataset 1, the level of variant allele-derived wild-type transcripts associated with the seven disease-causing GT>GC variants was at most 15% of normal (Table  1 ). Although this low-level increase in the generation of wild-type transcripts may make prediction a daunting task, it is interesting to see that the two lowest Delta scores of donor loss, 0.
The score of 0. 63 was observed for the DMD c. 8027+2T>C variant, for which the detection of wild-type transcripts was based upon RT-PCR analysis of disease-affected muscle tissue from a hemizygous carrier with Becker muscular dystrophy  [39]. As for the second dataset (Supplementary Table  S2 ), the four lowest Delta scores of donor loss (i. e. , 0, 0. 03, 0. 05 and 0. 08) were all found in substitutions that generated wild-type transcripts; and 63% (n = 12) of the 19 substitutions that generated some wild-type transcripts had a Delta score of <0. 80 (Table  2 ). As for the third dataset, the lowest Delta score, 0. 53, was observed in association with the only "functional" BRCA1 IVS18+2T>C variant (Table  3 ).
Statistical Comparison of Experimentally Obtained Functional Data with SpliceAI Predictions for the 103 Engineered GT>GC Splice Variants (Dataset 2) Dataset 2 comprised 19 substitutions that generated wildtype transcripts and 84 substitutions that generated no wildtype transcripts. We thus performed two statistical tests, a Receiver operating characteristic (ROC) curve and a Mat-thews correlation coefficient (MCC), on the 103 substitutions assessed by SpliceAI (Supplementary Table  S2 ) with a view both to identifying an optimum threshold value and to assessing the correlation between the FLGSA assay results and the SpliceAI predictions. Based on a ROC analysis of 103 variants from dataset 2 (Supplementary Table  S2 ), an optimum threshold point of 0. 85 was provided -similar to the threshold of 0.
80, recommended by SpliceAI for high precision results. A contingency table was constructed (Supplementary Table  S3 ) to calculate values for the false positive rate, specificity, sensitivity, accuracy and the Matthews correlation coefficient. These are summarized in Table  4 , along with the AUC result obtained from the ROC analysis, the curve from which is shown in Fig. (1). As can be seen from Table  4 , the AUC of 0. 79 and the MCC score of 0. 41 are indicative of a good correlation between predicted and actual results. There is also a low falsepositive rate whilst still maintaining high accuracy and sensitivity. These results show that for dataset 2, at a threshold of 0. 85, SpliceAI can accurately discriminate between those GT>GC substitutions which disrupt splicing and transcript production and those which do not disrupt splicing and produce the transcript.
Considerable Discrepancy between the Predicted and Experimentally Functional Impact Assessments of GT>GC 5' Splice Site Variants Employing 0. 85 as the threshold Delta score (donor loss) to define the generation of wild-type transcripts, rather variable performance between SpliceAI-predicted and experimentally demonstrated functional effects of GT>GC variants were observed across the three datasets: 33-84% of the variants that generated wild-type transcripts and 67-89% of the variants that generated no wild-type transcripts were correctly predicted by SpliceAI (Table  5 ). The poorest performance (43% (3/7) and 33% (1/3)) was observed with datasets 1 and 3 variants that generated wildtype transcripts (Table  5 ). In the context of the seven dataset 1 variants that generated wild-type transcripts (a qualitative property), the relatively poor performance of 43% might be related to the fact that the functional impact of these GT>GC variants actually manifested as rather small quantitative changes, generating between 1-15% normal transcripts (Table 1).
However, of the three +2T>A substitutions that were experimentally shown to generate some wildtype transcripts, two were predicted to have a Delta score of >0. 85, namely 0. 93 for HESX1 IVS2+2T>A (Supplementary Table  S2 ) and 0. 98 for BRCA1 IVS18+2T>A (Table  3 ). The other one, BRCA1 IVS16+2T>A, was predicted to have a Delta score of 0. 74; but an identical score was also predicted for BRCA1 IVS16+2T>C and IVS16+2T>G, both of which were classified as "non-functional" (Table  3 ). In short, SpliceAI appeared not to work as well for the +2T>A variants that generated wild-type transcripts as for the +2T>C variants that generated wild-type transcripts.
CONCLUSION AND PERSPECTIVES In the present study, we attempted to correlate SpliceAIpredicted and experimentally obtained functional effects of GT>GC variants in the context of three independent and complementary datasets. Employing data from dataset 2 substitutions, we were able to propose a Delta score of donor loss, 0. 85, as defining the threshold of whether or not wildtype transcripts would be generated by GT>GC variants; whereas a score of ≥0. 85 defines the complete absence of wild-type transcripts, a score of <0. 85 defines the generation of at least some wild-type transcripts. Subsequent use of this threshold score to correlate SpliceAI-predicted and experimentally obtained functional effects of the GT>GC variants revealed that SpliceAI performed better than the popular prediction tools such as SpliceSiteFinder-like, MaxEntScan, NNSPLICE and GeneSplicer.
Introduction Motion sickness (MS) is a condition generated due to the perceptual difference between the expected and the actual motion. This condition can cause fatigue, cold sweat, disorientation, drowsiness, vomiting, among other symptoms  [1]. Simulator Adaptation Syndrome (SAS), or simulator sickness, is a form of motion sickness experienced in virtual simulators. SAS is associated with more oculomotor and head symptoms such as headache, dizziness, and eyestrain  [2, 3]  Most people suffer from this syndrome to a greater or lesser degree  [4] , even reaching dropout rates of 30% in simulator studies  [5]. The aetiology of these phenomena has been explained from several theories. Two main theories stand out from the rest. The sensory conflict theory  [4, 6] , postulates that SAS symptoms result from a mismatch between the visual, vestibular, and/or somatosensory systems.
The discomfort is created by tuning to multiple streams of conflicting information among afferent sensory systems, interrupting the integration of expected information according to the typical template processed by the central neural system. For example, in a simulator the visual stimulus indicates self-motion, which conflicts with the vestibular system because it did not signal body motion. Importantly, this theory emphasizes that SAS is not produced by a simple mismatch among the senses. It further highlights the symptoms reflect the failure to integrate sensory input with predicted activity, formed by past patterns of sensations which accompanied motor activity failing to predict the current sensations accompanying similar motor activity. Thus, the crucial factor in the genesis of SAS is the sensory conflict and the discrepancy between the current and the expected sensory patterns.
In this avenue, repetitive expose to SAS produces a sensory rearrangement which leads to a decrease in SAS symptoms. This theory has been extended and completed over time with a mathematical model  [6, 7]  and subjective vertical theory  [8]. In this last one, for example, the spatial orientation is capital to estimate our own motion and orientation and with regard to the Earth's vertical. Thus, the key sensory conflict in SAS is related to the conflict between the several ongoing sensations of the vertical and the organism's internal model of subjective vertical based on past experience. From a different avenue, the theory of postural instability  [6]  offers an alternative explanation for the genesis of SAS. It states that SAS is caused by an inability to maintain a "stable" pattern of movement, whether this stability is slowly degraded or outright lost.
Riccio and Stoffregen  [9]  postulated that sickness is due to an interference of the movements produced by imposed motion and the normal balance of the body. This interference increases the difficulty of maintaining posture stability, producing SAS symptoms. Several studies have supported that postural instability is correlated with MS  [10, 11]. For instance, Merhi et al. [12]  report that video gamers who performed more frequent head movements experienced greater MS symptoms. In another study by Bonnet, et al. [13] , observers were exposed to low frequency movements in a moving room while standing. The results showed that there were more changes in body sway in the group that suffered from more MS symptoms compared to the group with weak symptoms. It is concluded that increased difficulty in postural stability is the cause of MS.
Additional research supported that different body sway strategies are handled to stabilize postural control as a function of floor area requirements (e. g. , land or sea;  [14] ). See  [15]  for a full description of SAS theories. Different approaches have been tested to decrease MS and SAS symptoms. Beyond some drugs that, although effective at decreasing MS, can produce fatigue among other symptoms  [16] , behavioral approaches have been proved as suitable solutions to mitigate MS, such as biofeedback and cognitive methods  [17, 18] , and pleasant and relaxing music or smell for SAS  [19, 20]  among others. A countermeasure has proved to be especially effective in alleviating SAS, namely galvanic cutaneous stimulation (GCS). This technique delivers a short electrical impulse below the motor threshold and stimulates superficial skin nerve fibres of the neck muscles (usually the sternocleidomastoids) approximately 3-4 cm below the mastoid process.