dataesr/scientific-paragraphs-categorization huggingface dataset:
Data processing of ATAC-seq and OmniATAC-seq samples Paired-end or single-end reads were mapped to the human genome (hg19-GENCODE v18) using Bowtie 2 (v2. 2. 6)  (Langmead and Salzberg 2012)  or STAR (v2. 5. 1b)  (Dobin et al. 2013 ) to speciesspecific genomes, which were downloaded from UCSC (https:// hgdownload. soe. ucsc. edu/downloads. html) (for human: hg19-GENCODE v18; for dog: canFam3; for horse: equCab2; for pig: susScr11; for mouse: mm10; for zebrafish: danRer10) and by applying the parameters --alignIntronMax 1 and --alignIntronMin 2. For the human data, we used hg19 as genome assembly instead of the more recent GRCh38 assembly because i-cisTarget  (Herrmann et al. 2012; Janky et al. 2014; Imrichová et al. 2015)  and  GREAT (McLean et al. 2010 ) are or were not (yet) available for GRCh38 at the time of the analyses.
Branch length scoring across species Conserved accessible ATAC-seq regions were identified as described above, and for each of the species, the set of conserved accessible regions was converted to the coordinate system per species and FASTA sequences were retrieved. All sequences were scored with the cisTarget motif collection (v8) (http://iregulon. aertslab. org/collections. html) (Herrmann et al. 2012; Janky et al. 2014; Imrichová et al. 2015)  containing 20,003 TF position-weight matrices (PWMs) using Cluster-Buster  (Frith et al. 2003)  with parameters -m 0, -c 0, and -r 10000. For each motif, the highest cisregulatory module (CRM) score per conserved accessible sequence was used to calculate the branch length score (BLS) across species according to  Stark et al. (2007)  and  Jacobs et al.
Enhancer luciferase activity was normalized against the Renilla luciferase activity. Publicly available data used in this work SOX10 ChIP-seq and MITF ChIP-seq data on the 501Mel melanoma cell lines were downloaded as raw FASTQ files from the NCBI Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) through accession number GSE61965  (Laurette et al. 2015)  and were mapped to the human genome using Bowtie 2 (v2. 1. 0) (Langmead and Salzberg 2012)  and peaks were called by MACS2 (v2. 1. 1) (Gaspar 2018). TFAP2A ChIP-seq data on human primary melanocytes from neonatal foreskin were retrieved from  Seberg et al. (2017)  (GSE67555) as a BED file, which was converted to a bedGraph and bigWig using the peak height from the BED file. Histone H3 at lysine 27 (H3K27ac) and H3 monomethylation at K3 (H3K4me1) ChIP-seq data for MM001 (GSE60666); and RNA-seq data (for MM031, MM034, MM057, MM074, MM087, MM099, and MM118 downloaded from GSE60666; for MM029, MM116, MM0163, MM164, and MM165 from GSE134432) were processed as explained in  Verfaillie et al.
(2015). OmniATAC-seq data for the human lines MM001, MM011, MM029, MM031, MM074, MM057, MM087, and MM099 were obtained through GSE134432  (Wouters et al. 2020 ) and were processed as described above in "Data processing of ATAC-seq and OmniATAC-seq samples"; which was also the case for ATAC-seq data from normal human melanocytes on foreskin (NHM1), which were downloaded as raw FASTQ files from GSE94488 (GSM2476338)  (Fontanals-Cirera et al. 2017). The massively parallel reporter assay (MPRA) data on the IRF4 enhancer was downloaded from https://mpra. gs. washington. edu/satMutMPRA/ and was processed as described above.
Acknowledgments This work was supported by a  European Research Council   Consolidator Grant  to S. A. (No. 724226_cis-CONTROL), the  Catholic University of Leuven  (Grant No. C14/18/092  to S. A. ), the  Foundation Against Cancer  (Grant No. 2016-070  to S. A. ), a  PhD fellowship  from the  Fonds Wetenschappelijk Onderzoek  (to L. M. , No. 1S03317N ), and a postdoctoral research fellowship from  Kom op tegen Kanker  (Stand up to Cancer; the  Flemish Cancer Society)  and  Stichting tegen Kanker (Foundation against Cancer ; the  Belgian Cancer Society)  (to J. W. ). We thank  Odessa Van Goethem  and  Véronique Benne  for their contribution in establishing and providing the mouse melanoma cell line and  Leif Andersson  for sharing the horse melanoma cell lines. We thank  Catherine André  ( National Centre for Scientific Research-University of Rennes 1 ,  UMR6290 ,  Institute of Genetics and Development of Rennes, Faculty of Medicine, Rennes, France)  and  Cani-DNA Biological Resource Centre (BRC)  ( Biosit, Rennes, France ) for sharing the in-house canine oral and uveal melanoma cell lines.
The  Cani-DNA BRC  (https://dog-genetics. genouest. org) is funded through the  BRC-Anim   PIA1  funding ( 2012-2022 )  ANR-11-INBS-0003. In addition, we thank  Austin George  for his help with the hyperparameter optimization. Computing was performed at the  Vlaams Supercomputer Center , and high-throughput sequencing was done via the  Genomics Core Leuven. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data access All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) under accession number GSE142238. This includes OmniATAC-seq data of human melanoma cell lines (MM029, MM034, MM052, MM116, MM118, MM122, MM163, MM164, MM165; data for the other lines used in this study were published before [see "Publicly available data used in this work"]), two dog melanoma cell lines, two horse melanoma cell lines, one pig melanoma sample, one pig melanocyte cell line, and one mouse melanoma cell line; ATAC-seq data of four zebrafish cell lines; and OmniATAC-seq data of SOX10 and TFAP2A knockdown in the human melanoma cell line MM001. The DeepMEL model was deposited in Kipoi  (Avsec et al. 2019 ) (http://kipoi.
Supervised methods aim at expanding human knowledge automatically by identifying the intrinsic differences between labeled points in a dataset. This knowledge is then generalized to other unlabeled datasets. Such machine learning techniques show extremely promising (often state-of-the-art) results in various tasks, such as image and speech recognition, analyzing particle accelerator data, or natural language understanding  (LeCun et al. , 2015). They have already been used specifically in heliophysics: to classify time series into several categories defined by the solar origin of the wind  (Camporeale et al. , 2017) , to detect magnetospheric ultra-low frequency waves  (Balasis et al. , 2019) , to forecast solar flares  (Nishizuka et al. , 2017) , or in particular to identify different regions of the near-Earth space  (Olshevsky et al.
, 2019; Nguyen et al. , 2019). Nevertheless, these models remain usually region-or mission-specific because the process of human-labeling is a time-consuming and tedious task and there is still a blatant lack of human labels to cover the whole near-Earth space. However, the Magnetospheric Multi Scale mission (MMS)  (Burch et al. , 2016) , launched in 2015 and whose orbit covers most of near-Earth space, has enabled such a dataset to be built. This mission uses the so-called Scientist In The Loop (SITL) system (see the following section for more details) to human-label phenomena and regions of interest. The SITL process involves that an expert scientist is designated to select data of interest that will be transmitted to the ground as high-time resolution (burst) data. The selected intervals are tagged with timestamps and a comment, which are reported in the SITL reports.
This system has been active since July 2015 and thus provides more than 4 years of commented data as of now. This makes it the biggest dataset of commented events and regions to our knowledge. We developed an automatic parser to convert all the comments, written by different scientists, in a unified list of labels that can be used by a machine learning algorithm. All SITL comments are associated with a time interval, therefore the temporal variations of the measurements can be used to predict the label of the signals. Thus, each data instance can be interpreted as a time series and the near-Earth classification using SITL data can be seen as a time series classification (TSC), a very challenging problem in data mining and machine learning  (Esling and Agon, 2012; Fawaz et al. , 2019). The main difference with classical classification problems comes from the ordering of the data.
The important features helping to discriminate the labels are mainly found in the ordering of the values while classical methods only consider the values at a given time. We employ two different techniques to solve this multivariate and multicategory classification task: the multilayer perceptron is applied naively on instantaneous data and is considered as a baseline for comparison and the convolutional neural network is applied on time series. In this article, we use the magnetic field and particle measurements of the MMS mission to establish a vast and reproducible automatic detection of 8 plasma regions that cover almost the entire near-Earth space. First, we introduce the MMS and Cluster missions, and how we used the SITL reports to build a large labeled dataset of magnetic field, plasma moments and plasma distributions from the 2016-2019 period.
The labels datasets One of the most important innovation brought by the MMS mission is its burst data management and selection system. The MMS spacecraft collect a combined volume of ∼ 100 gigabits per day of particle and field data. On average, only 4 gigabits of that volume can be transmitted to the ground. With nested automation and "Scientist-in-the-Loop" (SITL) processes, this system is designed to maximize the value of the burst data by prioritizing the data segments selected for transmission to the ground. Concretely, the SITL system consists of a manual selection process by a scientific expert designated to eyeball daily survey (low-time resolution) data and pick time intervals of interest that will be transmitted to the ground as burst (high-time resolution) data. The selected intervals are tagged with timestamps and a comment, which usually includes the type of event selected and eventually the near-Earth region in which it occurred.
For each orbit, these selections are written up in a "SITL report", in the form of a text file notably. An example of these reports can be found in  Argall et al. (2020). We developed a Python code which parses these text files to extract the timestamps and comments by identifying keywords associated with the near-Earth region where the event occurred. Each time interval was then associated with a label indicating where the spacecraft was located as follows: If a region cannot be identified from the SITL comment, then the time interval is rejected. If FGM or FPI data is not available during a labeled time interval, then the time interval is also rejected. Using this technique, from the date at which the SITL reports are available as text files, i. e. , April 2016, to the end of 2019, we collected 7, 832 labeled time intervals relevant for our study.
We note here that the number of occurrences for each regions were somewhat unbalanced, so we added a total of 605 time intervals labeled by hand to undersampled regions (examples of typical plasma parameters for the different magnetospheric regions can be found in textbooks, e. g. Baumjohann and Treumann (1996) ), bringing the total to 8, 437 labeled time intervals of various lengths. We resampled the label data to the same cadence as FPI data, i. e. , 4. 5s, representing a total of 1, 331, 133 labeled data points. This constitutes the biggest dataset of labeled time intervals for near-Earth space to our knowledge.
Final data set Once the labels are defined, we build the final dataset that will feed the ML models. In Earth's magnetosphere, the plasma is dominated by its core magnetic field, thus the first feature we took into account is the magnetic field vector B. Additionally, in each magnetospheric region the plasma has a rather typical distribution function. However, the full particle distribution function constitutes an enormous amount of data when dealing with several years of data (as done here), in particular with the MMS mission (several TB of data). Furthermore, important differences exist between different heliospheric missions in the specificities of the distribution functions, e. g. energy and angular ranges as well as energy and angular resolutions. Therefore, we chose not to consider those products and focused on moments only (density, bulk velocity and temperature), to keep our model lightweight and as general as possible (i.
e. , applicable to different heliospheric missions such as Cluster). For this latter reason, we also decided not to use the spacecraft location as a feature, because different missions cover different locations in near-Earth space (e. g. Cluster has a polar orbit while MMS has an equatorial orbit; apogees/perigees are different, etc). We start with loading the raw MMS magnetic field and plasma moments data from FGM and FPI instruments for the 2016-2019 period using the aidapy package 1  , and resample the data to the FPI cadence, i. e. , 4. 5s. The resulting dataset contains 12 variables: the magnetic field magnitude B tot and its components B x , B y , B z , the ion density N i , the bulk velocity magnitude V itot and its components V ix , V iy , V iz , the parallel and perpendicular temperatures T , T ⊥ with regards to the ambient magnetic field and the total value T tot.
The dataset is matched with the labels points defined in the previous section. If data gaps are present in the specified time interval, then the whole interval is rejected. This method yields a total dataset of size 1, 331, 133 × 12. The TSC model requires as input arrays of equal sizes with one label per array. Thus the dataset is grouped as time series corresponding to the labeled time intervals, resulting in 6, 928 data blocks of various sizes. We decided to split these time series into equal chunks of 3 minutes, because this time length correspond roughly to the mean value of the data blocks (i. e. , it minimizes the padding) and yields enough points (40) in each block given the time sampling resolution. To do so, we apply the following scheme: if the data block is shorter than 3 minutes, it is padded with the wrap of the vector along the axis (i.
e. , the first values are used to pad the end and the end values are used to pad the beginning); and if the data block is longer than 3 minutes, then it is split into several time series of 3 minutes each (the last one being padded as described above if also shorter than 3 minutes). Table  2  gives the distribution of the occurrences for each class. This operation yields a dataset as an multidimensional array of size  (34159, 40, 12). Finally, to input this array to the TSC model (and to avoid temporal bias due to the spacecraft orbit), we shuffle and randomly split the time series into training (56. 25%), test (25%) and validation (18. 75%) datasets. These three classical categories are defined such as: • the training set is used to determined the weights of the model; • the validation set is an out-of-sample set and allows to evaluate the error for data which has not been observed.
In particular, the validation set is used for hyperparameter search and early stopping to avoid over-fitting ensuring a good generalization of the model; • the test set is also an out-of-sample set which has not been used during training. The data are only used after the training to assess the performance of the final model, after training and hyperparameter optimization. 3 Classification methods with a fully convolutional neural network In this section, we overview the machine-learning algorithm we use: the fully convolutional neural network (FCN). This algorithm is assessed with several evaluation metrics. FCN works with time-dependant inputs (time series) in order to learn dynamical features. The main purpose of classifying time series and not only the instantaneous values is to learn automatically different features across the time dimension.
The events can be characterized by dynamical insights at different scales learned by the model. We present a second model, called Multilayer perceptron (MLP), in the supplemental materials. It considers the input of the 12 variables as time independent. The main advantages of this method are its simplicity to prepare the machine learning pipeline and the possibility to work with any temporal resolution. However, the classification performance is much lower. The FCN belongs to the category of artificial neural networks. They are usually characterized by their architecture: number of layers, type of connection (feedforward, feedback, convolution, etc. ), and the activation functions. A layer is defined as a set of neurons which are not connected to each other. We selected neural network as they are able to learn non-linear models and can handle large datasets compared to kernel methods.
However, these points can be easily eyeballed as outliers or discriminated by their low quality flag. Figure  4  shows another example of the FCN classification method described above, but this time when MMS spacecraft flew through the nightside region. The MMS1 observations take place during the whole day of 2019-07-13, and the format is the same as in Figure  3. In this example again, the FCN model is able to label properly the regions spanned by MMS1: at the very beginning MMS1 exits the magnetosphere to the plasma sheet boundary layer, then goes in and out the lobe regions before reaching the plasma sheet. Again, the FCN model is also able to pick up the small-scale dynamics such as short plasma sheet crossings. The two examples shown in this section therefore support the good metrics obtained in the previous section, showing concretely that the FCN model is suited to properly label most of the MMS data with the regions spanned.
Low values mean the subsequence does not contribute to the decision of the classifier while high values mean the section contributes significantly. Figure  5  shows CAM examples for two different classes: the solar wind and the foreshock. For each class, a wellpredicted time series is drawn and colored by the value of the CAM, illustrating the results from MMS data given  On the other hand, the classifier detects the FS mainly thanks to a specific pattern around 65 seconds associated to extrema for B x , B y , N i , and V y and significant slopes for the other features. Only a few specific time intervals are involved in the decision of the classifier for FS. The CAM analysis strengthens the conclusion that temporal and dynamical analysis are fundamental to classify nearby regions with fluctuating behaviors.
We demonstrate the good accuracy of the classification predictions on the test and validation datasets, but also on unlabeled data from the MMS mission. We also show the adaptability of the trained FCN model by applying the classification to the Cluster ESA mission data. The predictions showed good accuracy and enhanced dynamics in comparison with previous human-labeled dataset. These results show on one hand that the model can be applied to the whole MMS dataset, which would be of great interest to map the different magnetospheric boundaries and build empirical models of the properties and dynamics of the plasma in the near-Earth space. On the other hand, these results show that the model could be applied on different space missions orbiting around Earth (such as Cluster or THEMIS) or other planets to automatically label available data.
However, we recommend to build a small labeled dataset specific to each mission with limited retraining of the last layer. This process called transfer learning could help improving the generalization of our labeled dataset to other missions such as the JUICE mission, which will be launched in 2022 and for which selective downlink strategies are being discussed. As a matter of fact, the model could be used as a support to the scientific experts in charge of the data selection process (such as the SITL system), providing classification of the regions that would contribute to ease and speed up this time-consuming and tedious task. Such lightweight and easily-adaptable algorithms could also be important for the so-called SOC (Science Operation Center) of current and future spacecraft missions, both on ground and onboard.
For the latter case, these algorithms could be implemented within onboard spacecraft digital boards to automatically select regions and events of scientific interest, much reducing the complexity and the cost of science operations. The intended integration of this model into the aidapy package 2  , which allows to automatically load selected data from open-access databases, will be of particular interest by providing plasma data accompanied with region labels and quality flags on the fly for use case and statistical studies. The integration of such lightweight algorithms can be also generalized to other programs. In terms of modeling, we expect to improve the FCN results by generalizing the use of hyperparameter optimization for a higher number of hyperparameters and on larger ranges. We also plan to investigate the use of convolution methods directly on the omnidirectional ion energy fluxes.
Acknowledgments Data were retrieved using HELIOPY v0. 5. 3 (Stansby et al. , 2020) and processed using aidapy 3 , pandas  Wes McKinney  ( 2010 ), and scikit-learn (Pedregosa et al. , 2011). Figures were produced using matplotlib (Hunter, 2007). The model has been built with Keras from Tensorflow 2. 2 (Abadi et al. , 2016) and we modified scripts provided in the dl-4-ts repository 4. The hyperparameter optimization is performed with the Python library optuna (Akiba et al. , 2019). The authors would like to acknowledge  Gautier Nguyen (LPP)  for providing classifications on Cluster data,  Nicolas Aunai  (LPP) and  Alexis Jeandet  ( LPP ) for the helpful discussion and support.
Finally, the analysis of splicing changes in RED-depleted cells reveals a limited but significant overlap with the splicing changes in IAV-infected cells. This observation suggests that hijacking of RED by IAVs to promote splicing of the abundant viral NS1 mRNAs could partially divert RED from its target mRNAs. All our RNA-seq datasets and analyses are made accessible for browsing through a user-friendly Shiny interface (http://virhostnet. prabi.
Furthermore, we investigate to what extent hijacking of the RED splicing factor by IAVs to promote splicing of their own mRNAs (5) could account for some of the observed AS changes in infected cells. All our RNA-seq datasets are available in GEO (accession number GSE154596) and our analyses can be explored through a Shiny user-friendly interface (http://virhostnet. prabi. fr: 3838/shinyapps/flu-splicing).
, San Diego, CA), following the manufacturer's instructions. Libraries were checked for quality on Bioanalyzer DNA chips (Agilent Technologies, Santa Clara, CA). Accurate quantification was performed using the fluorescence-based quantitation Qubit dsDNA HS Assay Kit (Thermo Fisher). Based on a pilot experiment showing that in infected cell samples >50% of the reads aligned with viral sequences, the eight sample libraries derived from four biological replicates of mock-and virus-infected cells were randomly distributed into four lanes of a HiSeq2500 sequencer flow cell using a non-equimolar ratio of 1 (mock sample) to 2. 3 (infected sample) in each lane, and were sequenced in a paired mode (2 × 120 bases). Raw reads were quality checked using FastQC and mapped to the human genome (hg38, Gencode v27 annotation) and to the A/WSN/33 virus genome (accession numbers CY034132-CY034139) using STAR (v2.