dataesr/scientific-paragraphs-categorization huggingface dataset:
5% antibiotics (penicillin/streptomycin). Cells were seeded at 0. 6 x 10 5 per well in a 12-well plate coated with collagen type 1. Two days after seeding, at approximately 90% confluence, GM was changed to differentiation medium (DM) (DMEM/F12 with 2% horse serum, 1x ITS solution (Sigma), and 0. 5% antibiotics (PC/ST). After three days in DM, PMOs at 10 μM were added in individual wells, with 6 μM of endporter transfection reagent. Two days following PMO transfection, the DM containing PMO was replaced with regular DM. Cells were harvested at day 8 of differentiation (5 days after PMO transfection). Skipping percentage was calculated as ( skipped transcript skipped transcriptþnative transcript ) using Ima-geJ software (NIH). In exon 44 skipping, an unknown top band above the native band, and an intermediate band, were excluded from the RNA quantification calculation.
Database creation: curation of existing data on previously tested sequences Survey of the literature identified five large-scale studies in each of which tens of oligonucleotide sequences had been tested for exon skipping  [28] [29] [30] [43] [44] [45]. These studies differed in the oligonucleotide chemistries and cell types used, and in their reporting of skipping efficacy information and of associated experimental details. Some reported various computed parameters including physical descriptions of the oligo (e. g. length, percentage GC content), binding energies (oligo to its target sequence, or oligo dimerization), the situation of the target sequence (distance from splice acceptor and/or donor site), and local target site characteristics (predicted openness or accessibility of target site conformation; predicted binding of splicing factors).
Skipping efficacy was always based on the ratio of skipped to native transcript observed following nested reverse transcription polymerase chain reaction (nested PCR) analyses, and was reported either as a percentage ( skipped transcript skipped transcriptþnative transcript ) or as a percentage classed into one of 2 to 4 grades (e. g. grade 1: <10%; grade 2: 10-30%; and grade 3: >30%). In general, the authors of these studies defined a 'good' level of exon skipping to be greater than 25-30%. A summary of the studies used is shown (table  1 ). These were combined to create two datasets (one for PMO oligos, and one for 2'O-Methyl oligos) detailing a total of 358 oligonucleotides, with care taken to correctly interpret the different numbering systems used by each study (S1 and S2 Tables). Where absent in the original publication, basic descriptors such as the length of the oligo, or the distance of the target site from the exon acceptor site, were calculated.
It has been shown in siRNA studies that target site accessibility correlates with efficacy  [46] , and particularly the accessibility of bases towards the 3' end of the target site  [47]. Specifically, for a test dataset of oligos targeted across various regions of mRNAs, the accessibility of the 8 base pairs at the 3' end of the target site gave the strongest association with efficacy. Since target site accessibility will also influence exon skipping efficacy, we calculated accessibility for each base of each target exon. From this we derived several different accessibility scores by summing across bases at the following sites: (1) the target; (2) the target (score normalized to oligo length); (3) the 3' end of the target (last 15 bases); (4) the 3' end of the target (last 8 bases); (  5 ) the site of maximum accessibility within the target (the eight contiguous bases of the target having the greatest summed accessibility scores).
These are represented in Fig. 1B. In addition, an attempt has been made to use thermodynamics calculations to model cotranscriptional pre-mRNA folding  [35]. The authors represented folding by defining a window of 1500 nucleotides and moving this window in single base increments over a region anchored by the exon skipping target site. For each increment of the window they computed the RNA secondary structure, allowing them to measure the proportion of structures for which a given base is bound or unbound, and thereby to derive an 'engagement' score for that base. We included into our database, scores for (L1) the average proportion of predicted structures for which each target base is unbound, and (L3) the average engagement of the target bases during virtual transcription, that had been calculated by  [35]  for two of the exon skipping studies  [43]  and  [45].
Predictive statistical modelling We carried out preliminary data exploration to select only a subset of each parameter category to apply forward into predictive modelling, and we also tested the cross-terms describing relationships between whole exon descriptors (e. g. the GC contents of the exon and upstream intronic sequence) and target site descriptors. We found that an energetics parameter, dG50 (the energy of oligo binding to the target plus 50 base flanks), showed strong correlation with efficacy in the two larger datasets we investigated (Aartsma-Rus and Popplewell), and for modelling we used this parameter in favour of other energetics parameters such as the binding of the oligo to its target site only or to the entire exon sequence. The dG50 was considerably more predictive than these other parameters for the Aartsma-Rus dataset (p = 3 x 10 -4 for dG50, 0.
01 for oligo::target, and 0. 03 for oligo::exon) and, for the Popplewell dataset, only slightly less predictive than binding to the whole exon (p = 9 x 10 -5 for dG50, 0. 001 for oligo:: target, and 1 x 10 -5 for oligo::exon). Data for the two chemistries (2' O-Methyl RNA and PMO) were modelled separately, with the aim to identify parameters that were robustly predictive across both chemistries. We focused our analysis on the PMO data  [29]  since it included many data-points (66 oligos), had good coverage of four exons (8-23 oligos tested for each of exons  44, 45, 46, and 53)  and partial coverage of a fifth (2 oligos tested for exon 51), and the authors reported percentage values for efficacy that could be more powerfully leveraged in modelling than could the stratified levels of efficacy reported for 2'O-Methyl oligos.
As shown (Fig. 2A ), K-fold R 2 identified three parameters that had strong predictive power for the Popplewell dataset: (1) the dG of binding for the oligo to the target plus 50 base flanking regions; (2) the distance of the target site from the splice acceptor; and (3) the Malueka category for splice-factor related characteristics of the exon. It can be seen in Fig. 2A  that the K-fold R 2 score could be marginally increased with the addition of more parameters but we elected to be conservative in our modelling, using only these three strongest parameters, in order to diminish the likelihood of over-fitting the model to the data, which is a danger when modelling sparse datasets and can lead to a model which fails to be predictive of new data. A second round of stepwise regression on these three parameters and their factorials indicated that the inclusion of the cross-term of distance from acceptor with binding energy added to the predictive power.
Skipping (both observed and predicted) was generally more effective in exons belonging to Malueka splice type C (exons  44 and 46) , than those of type A  (45, 51 and 53) , especially towards the donor site end of the exon. Predictive modelling for PMO oligonucleotides. (A) Descriptive parameters are added consecutively to the model based on their predictive power (except parameter 1, which is the intercept). K-fold R 2 (green triangles), an indicator of the likelihood of the model to be predictive of new datasets, increases markedly with the addition of dG50 (binding energy of the oligo to a region encompassing the target site and 50 base flanks), DfA, the distance of the target from the upstream splice acceptor site, and Malueka type, the category of the exon based on splice-related descriptors defined by  [38].
g002 We tested whether the predictive formula derived from the PMO data could distinguish efficacious from non-efficacious oligos in the 2'O Methyl datasets (Fig. 4 ). Average values of predicted skip varied markedly between studies, partly following the authors' varying choices of oligo length. Average values were lower for the 2'O Methyl studies than for PMOs because PMO oligos have lower binding affinity, requiring longer sequences. In general, efficacious oligos had higher predictions than non-efficacious oligos, except for the Wilton dataset. However, this dataset had notable peculiarities that may limit its use for predictive analyses: (1) a single most successful oligo is reported per exon; (2) efficacy for exons in the range 55-70 was dramatically diminished relative to other exons (S1 Fig.
). Among our test parameters we could find none that would explain this latter observation, and we opted in subsequent analyses to omit this dataset. To identify the factors influencing skipping efficacy in the 2'O Methyl datasets we carried out stepwise regression. As for the PMO dataset, dG of binding of the oligo to the target site, and distance from the acceptor site (or ACP, which gave very similar results) were strongly predictive parameters, and it was useful to also include their cross-term. Unlike the PMO dataset, an oligo length parameter (categorizing into > 22 or < 22 bases) was selected, whereas the exon Malueka splice type was not. These same parameters were used to generate an ordinal logistic model that correctly placed 75. 6% of 2'O Methyl oligos into 'good' or 'bad' efficacy (defined as greater or less than 25%, 27.
5%, or 30% skipping, depending on the stratification of skip levels reported in the study; it was necessary here to omit 28 oligos from the Aartsma-Rus data, for which reported efficacies of >5% were not further stratified) (table  3 ). Correct placing was better for the Harding (79. 4%) and Dwi Pramono (82. 6%) datasets than for the Aartsma-Rus dataset (73. 4%). We applied the predictive formula for 2'O Methyl in an in silico screen across the five exons that have been most frequently targeted using  A complete list is given of the sixty parameters considered, indicating which were included into the two predictive algorithms (S3 Table ).
Observed protein levels also correlated strongly with prediction (average R 2 of 0. 73 for exon 44 and of 0. 78 for exon 53). Due to inter-study variation, our test sequences, despite being PMOs of length 30-mer, cannot be compared directly with the PMO dataset on which the predictive formula is based (for example, we amplified transcripts using a standard single step of PCR rather than nested PCR, we used unmodified bare-morpholinos as opposed to leashed oligos, and the end-porter transfection reagent as opposed to lipotransfection). Due to these differences, when plotting these data against distance from acceptor for each exon we normalized the predicted skip to the average of the observed skips or of the observed protein levels for the test oligos (Fig. 5D-E  and 5G-H ). For exon 44, a low level of native skipped transcript was observed in untreated controls-this level was subtracted to quantify skipping in the treated samples.
For both exons, a clear pattern is apparent matching observations with predictions across the length of the exon. Greatest efficacy was observed for the oligos targeting positions 0 and 2 (exon 44) or 26 and 30 (exon 53) bases away from the splice acceptor site, within regions of high predicted efficacy. Since our predictive model was constructed using data that included exons 44 and 53, we wished to guard against the possibility that our model was biased towards these exons, and that this would explain the strength of correlation between our experimental data and the predictive algorithm. For this we repeated statistical modelling of the PMO dataset, based on the same   A , upper panel), and categorization into > 30% skip or < 30% skip (using an ordinal logistic model) was also similar (correct placement for 96% and 86% of oligos, respectively; S7 and S8 Figs.
, A, lower panel) to the model for the full PMO dataset (89%). Likewise, very strong correlation of our experimentally observed levels of skipped transcript with predicted skipping efficacy was preserved (S7 and S8 Figs. ; average R 2 of 0. 89 and 0. 92, respectively). In silico screening tool: The process described above to screen across a DMD exon, predicting efficacy for each target site, can be reproduced for any target exon sequence. We provide instructions for how to implement such a screen (see materials and methods), using our Perl code, the RNAstructure software  [41] , and a spreadsheet program such as Microsoft Excel.
Our validation establishes this approach for DMD, but future studies will be required to determine whether predictions are as accurate for the exons of other genes. This will require large datasets from multiple pathologies and thus is a long-term challenge dependent on the continued output of the wider exon skipping community. Of the models for PMO and 2'O-Methyl data the former provides the better fit, so it will be important in future efforts to refine the 2'O-Methyl model using new data and/ or new predictive parameters. For the purposes of data modelling, we would encourage our colleagues in the field to report skipping efficacy as a continuous variable (e. g. the precise percentage of the skipped transcript, as reported in the Popplewell et al. PMO dataset and the Dwi Pramono et al. 2'O-Methyl dataset) rather than an ordinal variable (such as the skipping levels reported for the majority of 2'O-Methyl studies).
The inability of other parameters to much improve K-fold R 2 suggests that the remaining variance of the exon skipping system is highly stochastic and inherently unpredictable or that we do not understand it sufficiently to define and compute additional predictive parameters. The Malueka exon type C is characterized by strongly defined but low GC content acceptor sites, and a low density of ESS motifs, and was associated in the PMO dataset with greater efficacy than type A which is characterized by a high presence of SF2/ASF ESE motifs. In contrast, the Malueka exon type parameter was not predictive of efficacy in the 2'O Methyl datasets, and none of our splice motif parameters (including scores for both SF2/ASF ESEs and ESSs), nor our calculation of upstream GC content improved our predictive models for either chemistry.
e. the length parameter serving as a subsidiary index of binding energy). In other words, length may be a useful predictive parameter whilst also acting as little more than a crude approximation of binding energy. It should be understood that, in this type of analysis, other parameters may be strongly predictive but are not selected because they do not add to the predictiveness after the inclusion of related but more strongly predictive parameters. For example, in modelling of the PMO dataset: length of the oligo was not useful after the dG of binding had already been modelled, and distance from acceptor to the centre of the target (the ACP value;  [30] ) was not useful after distance from acceptor to the nearest base of the target had already been modelled. Both binding energetics and distance from acceptor site have previously been identified to discriminate effective from non-effective oligos  [28, 29].
In our modelling to identify the strongest predictors of efficacy, when applied to the Aartsma-Rus dataset alone and to categorize as those authors did in their study into >5% or <5% skipping, we identified the same parameters (binding energy, the number of RESCUE-ESE hexamers, Tra2β and SC35 scores) as those authors did (except that in our models, the inclusion of SC35 scores had no effect on the percentage correct classification; data not shown). However, when we modelled the categorization of these oligos into their reported skipping levels (>25%, 1-25%, undetected) neither the number of RESCUE-ESE hexamers, nor Tra2β scores, nor SC35 scores improved the Bayesian information criterion (BIC) of the model. Thus we did not consider these parameters useful to include into our predictive algorithm.
Similarly, none of these parameters were predictive for the Popplewell PMO dataset. Certain other parameters were also not additive to the predictive power of our models despite being capable to distinguish efficacy levels in previous studies: these included hybridisation peaks and SF2/ASF splicing motif scores  [29] , and co-transcriptional RNA accessibility prediction  [35]. As with the Aartsma-Rus dataset, we could confirm that these parameters were predictive in the context of their original studies, but in our modelling analyses they were overshadowed by more powerfully predictive parameters such as binding energetics and the proximity of the target site to the splice acceptor. In fact, we identified no splice motif related parameter that was useful in this sense. As noted previously, the algorithms used to predict splicing motifs do not take into account the local RNA secondary structure, nor the effect that local oligo binding might have on this structure  [29] -perhaps if some aspect of folding could be incorporated into future forms of these algorithms then they may become more strongly predictive.
Likewise, improved understanding of structural recognition on the part of specific splicing motifs may also aid prediction. The scores derived from computations of co-transcriptional folding dynamics were previously shown to discriminate certain levels of efficacy in the Aartsma-Rus and Wilton datasets  [35] , but when applied collectively to those two datasets in our modelling analyses those parameters were entirely overshadowed by dG of binding, and their inclusion did not improve the Bayesian information criterion (BIC) of the model. Notably, it was recently found that local predictions of RNA folding are more accurate than global predictions, the authors giving an optimal window of 150 bases as a reasonable balance between maximizing accurate prediction of base pairing, while minimizing effects of incorrect long-range predictions  [36].
This may explain why co-transcriptional RNA accessibility scores, which consider a folding window of 1500 bases, were not additive over local computations of binding energetics. We note that the datasets on which our models are based are dominated by oligos targeting exonic sequences, and a dataset describing many oligos that target the splice site boundary, Wilton et al. [45] , is omitted for reasons described. For this reason, we limited predictive analyses to exonic regions, which is in keeping with the observation of Aartsma-Rus et al. that exonic regions give greater efficacy  [31]. Our predictive algorithms should not be applied to intronic sequences because certain parameters may behave quite differently in the intronic context (such as the distance from acceptor, which changes its sign, becoming negative in the upstream intron, thereby inverting its contribution to the predictive formula).
Discrete events simulation is a data-processing tool widely used in industry and by the researchers to reproduce the dynamic behaviour of a manufacturing system studied  [10, 11]  in order to subject them to predictive experimentation without risk or disturbance of the real life system. More specifically, our proposed approach consists of the following four successive steps, shown in Fig. 2 : 1. Data collection and preparation step: it is the starting point of any process mining technique. It consists in looking at historical process data and generating a dataset in the form of so-called event logs that can be used as input for the process mining step. 2. Process mining step: the aim of this step is to discover and construct, from the generate event logs, the process model of the processing of incoming calls of the Medical Assistance Centre.
So, the output of this step is the As-is model 3. Model Transformation step: Following an approach grounded in Model-Driven Engineering (MDE), this step aims at generating a computer simulation model from the As-Is knowledge model. The resulting model will be executed on the selected simulation tool. 4. Simulation step: It seeks to ascertain and to assess a couple of scenario for process improvement regarding to the performance targets requested by the French government. The development of the simulation model of our process is carried out with the software Witness 1. It is worth noting that this proposed approach is not limited to analyse and to enhance an emergency call centre but could be used for any reengineering process needed since a significant time-stamped and valuable data are tracked and closely related to the process execution.
Although these states are interesting to discover the incoming call regulation process, it remains a blur to discover milestones between the moment where the call is picked up and the moment where it is hung up. Indeed, a hold call can generate one or several movements corresponding to periods of communication on distinct phone line services. To overcome this issue, we have enriched our dataset by other useful time-stamped data related to the emergency regulation file states. In fact, when the AO holds the incoming call, he opens a regulation file (RF) and if necessary a medical regulation file (MRF), localizes the request and saves the phone number, evaluates the gravity of the emergency in differentiating a real emergency from a felt one, transmits the call to the regulator doctors. Then, he has to pass the intervention requests on, either after a practitioner's regulation or directly (Reflex Departure) during very few cases according to the service protocols (sudden death, cardiac arrest, death by hanging…).
In the IT support system, there also are many data related to the lifecycle of theses emergency regulation file, as depicted in Fig. 3. Once these data have been collected, we conducted a first cleaning which allowed us to exclude all outgoing calls-related data and to complete some missing information. After we have proceeded to the merger of the two dataset into one log file by linking together information of both dataset that belong to the same process execution. This linking is based on the identification of the phone number and the date of creation of each regulation file and incoming call and several others data as the line service number or the kind of resource (AO, GP, RD) as show in Fig 3.
Displaying the Mined Model The next step was to run the process mining algorithm within Disco tool 2  , namely the Christian's Fuzzy miner, to discover the control flow of the incoming call regulation process. The resulting mined model is shown in Fig. 4. It is based on the sequence and timing of the activities in the generate event log dataset. We can see that there are 267 cases which represent the total incoming calls covered by the chosen timeframe which corresponds on the most critical day (Sunday). We can easy notice that all these cases start with the activity reception call and are served according to the first-come first-served discipline. In this section, we present the "As-is" simulation model built with the information discovered from the process mining tool. We decided to focus our simulation study on the (AO) Assistant Operator's activities because the performance of the emergency call centre is mainly dependent on the answer speed of this resource.
is the objective for this kind of service  [4] ), particularly for severity index =1. The simulation study has been carried on the most critical day (Sunday), as it is the day when the number of incoming calls is the highest. Fig. 6  illustrates the distribution of 267 incoming calls on 48 slots of 30 minutes. As far as the incoming call distribution is concerned, the simulation model is based on statistics from data collected by the SAMU 81. As presented in Table  1 , the processing times of the different activities follow a uniform distribution and have been established thanks to data collected and the outcomes of the process mining step. The simulation runs for 24 hours from 00:00 to 24:00. At 00:00, the call centre is empty. The statistics are recorded between the 26 th and the 263 th call after a warm up period.