dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments:  Agnes Biehlmann ,  Josiane Bourgeay ,  Sandrine Cottin ,  Christian Labro ,  Catherine Mouclier ,  Claire Olivier ,  Clemence Roby-Gensel ,  Sandrine Soupirot ,  Marie-Pascale Souteyrat , and  Nadine Tache  from the  Hospices Civils de Lyon. Thomas De Charentenay ,  Yannick Albizu ,  Rachel Sembeil ,  Samy Chellali ,  Alireza Mafi ,  Rossana Juarez ,  Sorin Abrudan ,  Catherine Labarre  and  Jean-Yves Moreau  from  Sanofi. Manuscript editing services were provided by  Alireza Mafi  from  Sanofi.
Efficiently detecting genomic structural variants (SVs) is a key step to grasp the "missing heritability" underlying complex traits involved in major evolutionary processes such as speciation, phenotypic plasticity, and adaptive responses. Yet, the SV-based genotype/trait association studies are still largely overlooked mainly due to the lack of reliable detection methods. Here, we present a random forest (RF) method for accurate deletion identification: RF4Del. By relying on the analysis of the mapping profiles, data already available in most sequencing projects, RF4Del can easily and quickly call deletions. Several classic and ensemble learning strategies were carefully evaluated using proper benchmark data. RF4Del was trained and tested on simulated data from the model species Drosophila melanogaster to detect deletions.
1 ). At the end of this first round of model testing, Linear Discriminant Analysis, Decision Tree, AdaBoost, RF and GBM reached the best results in all three metrics (see Supp Fig. 2 ). Thus, to perform the subsequent testing, the three ensemble models (AdaBoost, RF, and GBM) were selected mainly because of their robustness when running with complex classification datasets. To compare the ensemble models against each other, 60% of the balanced dataset was used for training and 40% for testing. Running time was also added to the evaluation metrics. All three models reached almost 100% in all metrics, RF being the best model when considering the execution time (see Supp Table1). To assess the impact of deep learning compared to neural learning, the RF model was then tested against neural networks with the same balanced dataset and with F1 score, Recall, and Precision as evaluation metrics.
Stratified cross-validation (10 k-folds), and the train and test strategy were used to perform this final evaluation. Although the neural network models achieved high F1 score, Recall, and Precision scores, the RF model with equivalent scores outperformed in execution time (see Supp Table1, Supp Table2). The deletion caller method has been devised on the basis of a RF model. We have explored the trade-off between model complexity and performance  [9]. Therefore we have tried several RF models having distinct numbers of trees and features. No significant differences were detected between RF models configurations (see Supp RF4Del can detect all deletions with accuracy. Testing SV callers using ground truth datasets is essential to assess a caller's performance and correctness. To test the veracity of our approach, we start by choosing a set of highly accurate validated deletions of different sizes.
We used a set of 8 962 deletions detected in 39 lines derived from short-read DNA sequencing in a natural population (the "Drosophila melanogaster Genetic Reference Panel," DGRP)  [10]. And then we generated a simulated D. melanogaster genome using the reference genome (dm6). In order to be close to what we can expect in real nature populations, we launch the simulation taking into account the size of the 8 962 previously detected deletions from 50 bp to 165. 32 kb length. However, with a high prevalence of deletions smaller than 1 kb (37,6% : 3 376 out of 8 962) observed in the real data (see Supp Fig. 4. a), we decided to simulate a set with larger deletions to assess accurately the deletion size effect on the detection. Comparison of the prevalence of deletions as a function of size between the "real" data from the study by Zichner et al.
[10]  and those from the simulation shows a sizebiased prevalence in the real data and a more homogeneous prevalence in the real data and a more homogeneous prevalence in the simulated data (see Materials and Methods). We ended up with a simulated genome of 99,29 Mb with 8 962 deletions with the same length range but with a higher density of long deletio ns (see Supp melanogaster populations. The reads were then mapped on the reference genomic sequences (dm6; see Materials and Methods). 93% of the (8 309 out of the 8 962) deletions were correctly detected. The approach did not show a detection accuracy bias according to the deletion length.
RF4Del can detect deletions with low coverage data We further simulated paired-end reads using this simulated genome with lower depth coverage (1. 2X, 6. 3X and 12. 7X, 25. 3X). The short-reads from the D. melanogaster were then mapped on the reference genome (dm6). The final RF model was then loaded to predict new datasets with different sequencing coverage, 1. 2X, 6. 3X, 12. 7X, and 25. 4X corresponding to 1 million, 5 million, 10 million, and 20 million reads respectively (see Supp Fig. 5 ). As expected the 1. 2X sample returns the worst result with only 1 729 out of 8 962 deletions detected (19. 3%). RF4Del shows a higher accuracy for the 6. 3X (75. 9% ; 6 808 out of 8 962 deletions) and 12. 7X (75. 6% ; 6 777 out of 8 962 deletions) samples than the 25X sample (67. 8%; 6 078 out of 8 962 deletions).
RF4Del outperforms established SV callers. We then test the RF4Del veracity by comparing the detection results with those from well-known and commonly used variant callers: Pindel  [12]  and DELLY  [13]. In contrast with RF4Del, the amount of detected deletions using DELLY and Pindel appears to be correlated with the amount of sequencing data. While DELLY detected around three-quarters of the deletions (73%; 6 542 out of 8 962), Pindel detected 54 190 deletions, suggesting a high number of false positives (see Fig. 2A ). With 6. 3X coverage data, the amount of deletions detected using Pindel is more than twice the amount of expected deletions (see Fig. 2A ). The F1-score estimated for each dataset and tool confirms the low detection accuracy of Pindel. This analysis also highlights the higher sensitivity and precision of RF4Del with low coverage data compared with DELLY (see Fig.
2B ; see Supp Fig. 6 ). Taken together, RF4Del appears as the best approach to detect most of the deletions with accuracy. A depth analysis allows estimating the F1 score using the 25. 4X dataset using each tool per deletion size. The accuracy for the three approaches slightly decreases with the deletion length. Pindel fails to detect very short deletions (<100bp) and deletions from 500kb to 1. 5kb length fail to be correctly detected by both DELLY and Pindel (see Fig. 2C ; see Supp Fig. 7 ). The manual curation of the mapping in the vicinity of deletions of around 1kb reveals some mapping issues due to the insert size of the paired-end data. Compared to other ML SV-callers, RF4Del is a good compromise for accurate deletion detection. Other similar tools which are also using a deep learning-based approach as RF4Del were recently developed: DeepSVFilter  [14]  and AquilaDeepFilter  [15].
Also, a similar method was published in the preprint bioRxiv, called Scotch  [16]. DeepSVFilter's authors propose a deep learning-based approach for filtering SVs in short-reads sequencing data. In the training step, they use some state-of-the-art SV detection approaches, such as DELLY  [13] , Lumpy  [17] , and Manta  [18] , to detect SVs from data samples. Then DeepSVFilter encodes all SVs as images and trains CNN models. In the prediction module, they use the same strategy as the training module to generate SVs candidates and run CNN models to predict each candidate using a score between 0 and 1, representing the probability of the classification as the true SV. They adopt transfer learning with five state-of-the-art pre-trained CNN models on the ImageNet dataset  [19]  for training and prediction, including Inception-ResNet v2  [20] , MobileNet v1  [21] , MobileNet v2  [22] , NASNet-A Mobile  [23]  and PNASNet-5 Mobile  [24] *.
However, this may not be the best approach as it takes considerable time in training and prediction steps with generated images. PNASNet-5 Mobile took about 4. 87 hours and Inception-ResNet v2 took about 42. 59 hours of training. Also, they run all CNN models on the server with the specifications: Intel(R) Xeon(R) Gold 5220R CPU 2. 20GHz, 24 cores and 284 GB RAM, while RF4Del can run easily in a personal computer with AMD Ryzen 5 3500U 2. 10GHz and 8GB RAM in few minutes (see Supp Table1). To demonstrate the classification accuracy of DeepSVFilter  [14] , they trained the model with 90% split of the dataset and used another 10% split to validate the trained model. In RF4Del, we trained our model with 60% split of the dataset and used 40% split to predict and validate the trained model, and reached almost 100% in F1 Score, Recall and Precision metrics.
AquilaDeepFilter filters large deletions from Aquila  [25]  and Aquila_stLFR  [26]. Both tools are reference-assisted local assembly approaches to generate high-quality diploid assemblies and enable the genome-wide discovery and phasing of all types of variants including SNPs, small indels and SVs. However, they produce a substantial proportion of false positives, especially for large deletions. In this way, AquilaDeepFilter encodes the candidate variants from Aquila in RGB images splitted into two classes, candidate variants that are true deletions and candidate variants that are false-positive deletions, that will be the input of CNN Models. Then CNN models are trained in a supervised way and the prediction result of the model is stored in a BED file. In the evaluation step, they split the dataset in 90% for training and 10% for evaluation, and for all the experiments, a server with Intel(R) Xeon(R) CPU E5-2420 0 @ 1.
90GHz, 24 cores and two high-performance GPU RTX 2080 Ti was used. Using this server, the training step took about 7 hours until convergence. In the results section, it is possible to see that AquilaDeepFilter generally improves the performance of Aquila and Aquila_stLFR by 20% in F1 Score metric, and also is compared with the previous approach DeepSVFilter. Using the same 10x lib and stLFR lib datasets, AquilaDeepFilter outperforms DeepSVFilter in most experiments in Precision, Recall and F1 Score metrics, however, there are some optimizations to increase general performance  [14][15].
e. , mapping) and analyzes each base individually. Then, it creates a numerical profile of these positions, describing features of the aligned sequencing data like depth, base quality, and alignment to the reference. For the prediction, each position is classified based on the RF model as a non-indel or a type of indel breakpoint. The Scotch efficiency was evaluated with five other tools, such as DeepVariant  [27] , GATK  [28] , VarScan2  [29]  and two versions of Pindel using three benchmark datasets: simulated variants, Syndip  [30] , and NA12878  [31]. For the deletion detection, Scotch outperformed both versions of Pindel with simulated data. However, its accuracy is quite low, as Scotch reaches only 38. 2% of Precision and about 50% for F1 Metric  [16]. In the supplementary section, analyzing the hyperparameters employed, authors considered four possible values for ntree (350, 450, 550, 650), and two for mtry  (7, 8) , resulting in eight possible hyperparameter configurations.
In the RF4Del model, we considered the values 100, 200, 400, 800 and 1000 for ntree and 2, 4, 6, 8 and 10 for mtry, totalizing twenty-five different hyperparameter configurations. In Supplementary Figure  3 , our experiments show that the model achieves 99. 8% on F1 Score metric for each possible configuration. In addition, Supplementary Table  4  compares the running time of the RF4Del model with each ntree and mtry configuration, concluding that the faster took only 47 seconds with 100 ntree and 10 mtry. Scotch's runtime is approximately 24 hours when parallelized by chromosomes with their adopted data. In the results, even Scotch's performance in deletions with simulated data is better than both versions of Pindel, the accuracy is not satisfactory, as it reaches only 50% for F1 Score Metric.
Comparing all these tools, RF4Del appears thus as the most appropriate tool to investigate with accuracy the detection of a short and large deletion in a fast and easy way with a personal computer (see Table1). Table  1. Comparison with recent other ML-based approach tools: DeepSVFilter  [14] , AquilaDeepFilter  [15]  and Scotch  [16]. [10]. In this study, the authors used 39 D. melanogaster lines from the DGRP pilot data  [32]  (Illumina® technology) to call SVs. The SVs detected by different tools such as DELLY and Pindel were then merged. Then some random SVs were experimentally validated. In the SV catalog, they detected 8 962 deletions from 50 pb to 165. 32 kb (median 178 bp; see Supp Fig. 4 ). Simulated data. We simulated sequencing data from the D. melanogaster genome. Deletions were simulated using the simulateSV function of the RSVsim package (version 1.
34)  [33]. The size range and the number of simulated deletions were parameterized according to the SVs catalog. The estimationSV function of the RSVsim package (version 1. 34) was used to draw 8 962 random deletions, sizes within the range from 50 pb to 61 kb according to a beta distribution and homogeneously distributed along the genome. The deletions are thus cut from the rearranged genome and their uniform breakpoints placed. The inSilicoSeq software  [34]  was used to obtain simulated reads from the rearranged genome (99. 285 Mb).
Training The Hiseq (Illumina®) error model was used to generate read-pairs of 126 bp and four sequencing coverage (1. 2X, 6. 3X, 12. 7X, and 25. 4X). These reads were then aligned to the D. melanogaster reference genome (not rearranged) using the default parameters of BWA-mem alignment software  [35]. Preparing training data set. To build the training matrix, 12 features were extracted from the simulated bam files from 12. 7X sequencing data (see Supp tab 1). Bam files were converted to bed files using BEDTools (version 2. 30. 0)  [36]  suite sub-command BamtoBed with -cigare option to extract the CIGAR information for each mapped read. SAMtools bedcov (version 1. 13)  [37]  command was used to estimate the coverage for each mapped read position from the bed file. The remaining features were extracted from the bam files using SAMtools view command (version 1.
13)  [37]. The final training matrix was obtained by adding the variation event (deletion or not) for each position. Benchmarking. The benchmark of the ML methods was performed by the machine learning-dedicated Python library, scikit-learn (version 0. 22)  [38]  using default parameters. RF4Del performances were benchmarked against two conventional callers relying on short-read mapping signals, DELLY (version 0. 8. 1) (RP and SR)  [13]  and Pindel (SR)  [12]  using F1 score, Recall, and Precision as metrics. The RF deletion prediction model. The final prediction model was built with 1000 decision trees. The model was saved in a. sav file that can be loaded anytime to perform prediction with new data.
The in vitro experimentations generating large real datasets with specific features are tedious and cost-prohibitive  [31]. In silico simulations, on the other hand, are a low-cost, unbiased alternative and unlimited generating data allowing accurate Precision and Recall estimations of SV calling methods. Here, we design a new learning-based approach to detect all types of deletions that we call RF4Del. This tool is based on an RF method for accurate deletion identification starting with mapping data. Taking all together, RF4Del overcomes the main deletion detection limits as it can be used to call deletions using short-read data, with very low coverage in a fast way (see Table1). With the exponential increase of sequencing projects for which we are interested in the impact of deletions on evolutionary processes, we need tools to detect the SVs with accuracy and in a reasonable amount of time.
The final training data set has 13 features (see Supp tab 1) along with 10 015 384 instances. The 13 th feature corresponds to the class label. Labels are defined according to a dummy transformation for the class either, as 'No Deletion' or 'Deletion'. The data set mirrors biological reality, hence the predominant label being 'No Deletion' with 9 835 103 instances and the Deletion label representing 0. 5% of all instances with 180 181. To handle such an imbalanced data set, data corresponding to 'Deletion' and 'No Deletion' was first separated from the original dataset, then random samples from each group were selected. A final balanced data set summing up a total of 700 000 instances (150. 000 'Deletion' (21. 5%) and 550. 000 'No Deletion' (78. 5%)) was obtained. The CIGAR and label values were encoded using an one_hot_encoding strategy transforming each categorical value into a numerical one.
Rigshospitalet; The Memorial Sloan Kettering Cancer Center part of the study is supported by the Core Grant, Grant/Award Number: P30CA008748; Department of Anesthesiology & Critical Care Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA considerably. Therefore, we aim to provide contemporary epidemiological data on thrombocytopenia and platelet transfusion practices in the ICU. Methods: We will conduct an international inception cohort, including at least 1000 acutely admitted adult ICU patients. Routinely available data will be collected at baseline (ICU admission), and daily during ICU stay up to a maximum of 90 days. The primary outcome will be the number of patients with thrombocytopenia (a recorded platelet count < 150 Â 10 9 /L) at baseline and/or during ICU stay. Secondary outcomes include mortality, days alive and out of hospital, days alive without lifesupport, the number of patients with at least one bleeding episode, at least one thromboembolic event and at least one platelet transfusion in the ICU, the number of platelet transfusions and the indications for transfusion.
| Study design and setting We will conduct an international inception cohort study with prospective data collection in general ICUs located in Europe and North America. We aim to enrol at least 1000 patients from 40 to 50 participating ICUs (planned sites are listed in Supplement S2). Patients will be included during inception periods of 14 consecutive days with a 90-day follow-up period. An overview of the study is presented in Figure  1. The steering committee will oversee the study and the management committee will handle the study on a day-to-day basis (Supplement S3).
| Data collection and management The local investigator or his/her delegate will collect routinely registered data from the medical and laboratory records. Baseline data will be collected at ICU admission and will include patient and admission characteristics, comorbidities, laboratory values including platelet count, treatments, acute conditions including sepsis and septic shock,  25  bleeding, platelet transfusions, and the Simplified Mortality Score in the Intensive Care Unit (SMS-ICU)  26, 27  (further details and definitions of baseline variables are provided in Supplement S7 and S14). Daily data will be collected during ICU stay at the participating ICUs to a maximum of 90 days, and will include the use of life-support, treatments, laboratory values including platelet counts, bleeding episodes graded according to the WHO classification (Supplement S5), thrombotic events (Supplement S6), and platelet transfusions (further details and definitions of variables registered daily during ICU stay are provided in Supplement S8-S10).
Data on discharges and readmissions from the ICU will also be registered (see Supplement S11). The 90-day follow-up data will be collected by assessing medical records, national registers, or by phone call as appropriate according to local or national regulations and will include vital status and hospital discharges and readmissions (details and definitions of follow-up variables are provided in Supplement S12). Lastly, we will collect data on participating sites including type of hospital, type and size of the ICU, and the presence of general or specific guidelines regarding platelet transfusions (see Supplement S13 for details). The local investigator or his/her delegate will be responsible for data accuracy and ensure the completeness of data after the follow-up period has ended. The study data will be collected and managed using the REDCap electronic data capture tool hosted at the Capital Region of Denmark.
28, 29 REDCap is a secure, web-based software platform that supports data capture for research studies and includes data-validation tools and audit trials. All included patients will be assigned a unique study identification (ID) number and data will be handled in accordance with all applicable regulations. Each investigator or his/her delegates will be assigned a personal login with access to enter and read data for patients enrolled at their site only. A detailed manual describing all aspects related to data-entry and management will be provided to the local investigators and educational meetings with members of the core research team will be encouraged prior to initiation of the study at each site.
| Descriptive data We will report results for the following secondary outcomes descriptively stratified on patients with 'any thrombocytopenia' (overall and within the subclasses defined above), 'thrombocytopenia at baseline' and 'thrombocytopenia during ICU stay': • Death within 90 days of ICU admission. • Days alive and out of hospital within 90 days of ICU admission. 3 | ADMINISTRATIVE ASPECTS.
ACKNOWLEDGEMENTS CTA has received funding from the  Ehrenreich's Foundation. CTA  and LR have received funding from the  Research Council of Rigshospitalet. The Memorial Sloan Kettering Cancer Center part of the study is supported by the  Core Grant  ( P30 CA008748 ) and the  Department of Anesthesiology & Critical Care Medicine, Memorial Sloan Kettering Cancer Center, New York, NY. The funders do not have any influence on the study design, conduct or reporting.
Thunderstorms, the main generator of lightning on earth, are characterized by the presence of extreme atmospheric conditions (turbulence, hail, heavy rain, wind shear, etc. ). Consequently, the atmospheric conditions associated with this kind of phenomenon (in particular the strike itself) can be dangerous for aviation. This study focuses on the estimation of the lightning strike risk induced by thunderstorms over the sea, in a short-range forecast, from 0 h to 24 h. In this framework, three methods have been developed and compared. The first method is based on the use of thresholds and weighting functions; the second method is based on a neural network approach, and the third method is based on the use of belief functions. Each method has been applied to the same dataset comprising predictors defined from numerical weather prediction model outputs.
In order to assess the different methods, a "ground truth" dataset based on lightning stroke locations supplied by the World Wide Lightning Location Network (WWLLN) has been used. The choice of one method over the others will depend on the compromise that the user is willing to accept between false alarms, missed detections, and runtimes. The first method has a very low missed detection rate but a high false alarm rate, while the other two methods have much lower false alarm rates, but at the cost of a non-negligible missed detection rate. Finally, the third method is much faster than the other two methods.