dataesr/scientific-paragraphs-categorization huggingface dataset:
This case is equivalent to case 1 with, in addition to, the exchange of the cloud fraction and the droplet number from CHIMERE to WRF. 4. cpl_case = 4: the aerosol direct and indirect effects. geodata, dgrb, wrfrestart, wrf_restart_dir, wrf_tstep_grid: these flags are linked to the use of WRF in online mode. They are not used in offline mode. geodata and dgrb are data paths for input data. wrfrestart and wrf_restart_dir are for the management of the restart files in the case of chained simulations. wrf_tstep_grid is a parameter to control the time step estimated for WRF and is mandatory in its namelist. In general, it is recommended to have a maximum time step, in seconds, equal to 6 × dx, with dx as the grid-cell size in kilometers. Here, the user can modulate this value. -nest_topconc: the users can select the way to use model data at the top of the domain in the case of nesting.
The observations used for validation The model was validated by using a vast dataset of atmospheric and meteorological in situ measurements. The European Environment Agency (EEA, https://www. eea. europa. eu, last access: 6 September 2021) provides hourly data for a list of atmospheric species and a substantial number of stations covering Europe, categorized by station type  (rural, traffic, etc. ). The data for particulate matter PM 2. 5 and PM 10 , ozone (O 3 ), and nitrogen dioxide (NO 2 ) are used for regular model validations and are presented in this article. The number of stations differ for each pollutant; for the year of 2014, the data from 322 stations for NO 2 , 431 stations for O 3 , 178 stations for PM 10 , and 69 stations for PM 2. 5 are used in our comparisons. We have also included aerosol optical depth (AOD) data taken from the AErosol RObotic NETwork global remote sensing network (AERONET, https: //aeronet.
gsfc. nasa. gov/, last access: 6 September 2021). Out of the 305 stations in the network having measurements for the year 2014, 75 are in the simulation domain and are used for the validation shown in this paper. We have also included validation for vertical profiles of pressure, temperature, and ozone using the data downloaded from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC, https://woudc. org/home. php, last access: 6 September 2021) resulting from ozonesonde measurements. The database used is the one of July 2021. From around 25 stations that are in our domain, only nine have consistent data for the year 2014. All nine stations use an electrochemical concentration cell (ECC) as the measurement instrument (https://woudc. org/data/instruments/, last access: 6 September 2021).
Meteorological parameters have also been validated using two datasets. The ECA&D project provides the E-OBS database containing measurements for a large number of stations over the European region. This dataset provides values for daily temperature (average, minimum, and maximum), daily average wind speed and direction, relative humidity, and precipitation; the data can be downloaded both in a gridded format or per station files. The data provided by the British Atmospheric Data Centre network (BADC, http://data. ceda. ac. uk/badc, last access: 6 September 2021) is also integrated into the validation chain, providing hourly values for temperature, wind speed, and precipitation. While these results are not presented in this article, they are checked in regular validation processes performed for all simulations.
All datasets are integrated into the evaltools Python package (https://opensource. umr-cnrm. fr/projects/evaltools, last access: 6 September 2021, version 1. 0. 6r) which is designed as a free assessment tool for atmospheric models versus in situ observations. Given the output for all simulations and all the observations, this tool provides a statistical comparison between the aforementioned simulations and the provided measurements. The tables and images presented in this paper are all produced by this tool.
Improvement of v2020r1 compared to v2017r5 In order to quantify the quality of this new version, two simulations are performed: one with v2017r5 and one with v2020r1. The meteorology used corresponds to the IFS/ECMWF fields, with a global 0. 25 • × 0. 25 • resolution and 3-hourly time frequency. For each day, we use an analysis day of this dataset. The new version is used in offline mode to be homogeneous with the v2017r5 version.
Dimethyl sulfide emissions The DMS emissions may now be calculated over the oceans. DMS is a major source of atmospheric sulfur. It contributes to cloud condensation nuclei (CCN) and has thus an impact on cloud formation. DMS atmospheric emission fluxes are parameterized and correspond to a fraction of aqueous DMS sea surface concentrations (nM). These concentrations are extracted from the global database of  Lana et al. (2011). The global database contains monthly mean global fields of oceanic DMS with a horizontal resolution of 1 • × 1 •. The aqueous DMS is produced by plankton under light exposure and concentrations are thus sensitive to the time of the year, the latitude, and the sea surface temperature. In CHIMERE, these monthly mean global fields are projected on the current simulation domain, using land-use information to realistically downscale the values.
In general, global, and regional models use the dominant land-use and soil properties in their domain cells. For each cell, there is one characteristic and the emissions flux is thus calculated once. Using high-resolution (30 s) United States Geological Survey (USGS) databases, we implemented a matrix, providing for each model cell the percentage of each soil type, and for each one, the percentage of each land-use type. The USGS database has 16 soil categories and 24 land-use categories; the dust flux is calculated for a maximum of 384 combinations. The values are then accumulated to have the mean flux in the cell.
The CAMS pre-processor The pre-processor for ECMWF CAMS data formatting is a program distributed with the new CHIMERE version. The goal is to download the ECMWF model outputs of  Kaiser et al. (2012)  and adapt them to the CHIMERE format. For this formatting, several conversions are needed: projecting the data onto the CHIMERE simulation grid, converting the proposed chemical model species to the CHIMERE chemical mechanism species, and temporally refining the data by shifting the time resolution from daily to hourly fluxes. All available chemical species are downloaded and used to create the CHIMERE emitted species. Temporally, having no realistic information to move from daily to hourly emissions fluxes, an academic diurnal profile is applied to the daily data as F h = d w + (1 -d w ) 24 d s √ 2π × exp -0.
APIFLAME v2 APIFLAME was designed to calculate the emissions from biomass burning at high spatial and temporal resolution with flexibility in terms of the input data used. While the CAMS approach is based on fire radiative power (FRP) detection (active fires), APIFLAME calculates emissions based on the burned area and resulting biomass consumed (in terms of carbon), as described in  Menut et al. (2013)  and  Turquety et al. (2014). Maps of the burned areas and FRP on the user-defined grid are derived (with the corresponding geog file) from MODIS satellite observations (MOD14 and MCD64A1, respectively, version v006), combined with the information on the vegetation type (satellite observations or land-use databases). Depending on the type of vegetation burned, the amount of carbon consumed is calculated by multiplying the burned area with the fuel consumption derived from the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model  (Turquety et al.
Time management The time management in the model was already defined in  Menut et al. (2013). In this model version, some changes were done, mainly due to the need to manage variable time steps with the online version. Three time steps are defined: the hour, a physical time step called dtphys, and a chemical time step called dtchem. The hour is obviously a fixed time step of 1 h. This interval is mandatory since many input model data are provided at an hourly frequency: meteorological fields, emissions, and boundary conditions, for example. It is also the frequency used to read the data in offline mode. The physical time step dtphys is defined by the user in the CHIMERE parameter file. In offline mode, it indicates the value that the user wants for the transport calculation. The meteorological data are linearly interpolated between 2 consecutive hours; therefore, the number of steps may change the simulated chemical concentrations in the case of a rapid change in meteorology from one hour to the next.
To achieve this one-way nesting procedure, the ndown. exe WRF executable is used, permitting the use of meteorological outputs coming from the above domain to drive the nested domain. Therefore, for a given level of nesting, WRF and CHIMERE are always run using the downscaled information from the level above and never benefiting from the interior level, if it exists. A nested domain can use different numbers of vertical levels, as long as the top height of the nested domain is less than the top height of the parent domain. For the nudging, the CHIMERE-WRF coupled suite can use the WRF nudging option to avoid strong divergences inside the models. This used in coarse model simulations (typically above 25 km resolution) that are driven by global-scale simulations, commonly issued from Global Forecast System (GFS) or ECMWF datasets that have been re-analyzed with synoptic observations.
At higher resolutions with nested domains, model nudging should be not necessary to allow the model to accurately develop the local physics and dynamics. For the chemistry, model nudging is not used, but the assimilation of observation data (from satellite and ground stations) is an area of interest that is not yet fully operational in this version of CHIMERE. 12 Computing performance Computing performance and scalability are studied with a series of 1 d long run performed on the Irene Joliot-Curie machine of Très Grand Centre de Calcul (TGCC) with a varying number of cores dedicated to WRF and CHIMERE for coupling cases (cpl_case) 1 and 4. To increase the number of points and therefore allow the use of more cores, the MED20 domain (same domain as MED60 but at 20 km horizontal resolution) is used.
Acknowledgements. We would like to acknowledge  Douglas Keller  ( LMD ) for his complete review of this article, including an accurate correction of English. We also acknowledge  Blandine Rault  ( INERIS ) for the development and also support in the use of the evaltools Python package (https://opensource. umr-cnrm. fr/projects/evaltools, last access: 6 September 2021). This work was granted access to the HPC resources of TGCC under allocation 10274 made by  GENCI  ( Grand Equipement National de Calcul Intensif ). We thank the  OASIS modeling team  for their support with the OASIS coupler and the WRF developer team for the free use of their model. We thank the  National Aeronautics and Space Agency  for the availability of the MODIS data, the investigators and staff who maintain and provide the AERONET data (https: //aeronet.
gsfc. nasa. gov/,last access: 6 September 2021), the  University of Wyoming  for the availability of the atmospheric soundings, along with the  European Environment Agency  for providing the AirBase data. We acknowledge the E-OBS dataset from the  EU-FP6  project  UERRA  (http://www. uerra. eu,last access: 6 September 2021) and the data providers in the  ECA&D  project (https: //www. ecad. eu,last access: 6 September 2021). The  European Environmental Agency (EEA)  is acknowledged for their air quality station data that are provided and freely downloadable (https: //www. eea. europa. eu/data-and-maps/data/aqereporting-8,last access: 6 September 2021). We also thank the  World Ozone and Ultraviolet Radiation Data Centre (WOUDC) , the  Environment and Climate Change Canada, Toronto  and the  World Meteorological Organization -Global Atmosphere Watch Program (WMO-GAW)  for providing ozonesonde data, downloaded from the WOUDC site (https://woudc.
Basic Language Analyses Abdelali et al. presents a fast and accurate Arabic segmenter (Farasa)  (Abdelali et al. , 2016). This approach is based on SVM using linear kernels. To validate Farasa, the authors compare it (on two tasks MT and Information Retrieval IR) with two other segmenters: MADAMIRA  (Pasha et al. , 2014)  and the Stanford Arabic Segmenter (SAS)  (Monroe et al. , 2014). Farassa outperforms both segmenter for IR task and it is at par with MADAMIRA for MT tasks. Howevers, MADAMIRA offers many tasks (such as: feature modelling, tokenization, phrase chunking and named entity recognition) which are not included in Farasa. Moreover, MADAMIRA is not dedicated to MSA only but also handle the Egyptian dialect. Abainia et al. (2017)  presented ARLSTem, an Arabic light stemmer. The main idea of this stemmer is to remove the prefixes, suffixes and infixes (additional letter which is not original to the word root, present in the middle of the word).
Building Resources Selab and Guessoum (2015) construct the TALAA corpus, a large Arabic corpus (containing 14 million words), built from daily Arabic newspaper websites (57,827 different articles). Yousfi et al. (2015)  present the dataset ALIF dedicated to Arabic embedded text recognition in TV broadcasts. It is composed of a large number of manually annotated text images that were extracted from Arabic TV broadcasts. The ALIF corpus contains 6,532 Arabic text color images. El-Haj and Koulali (2013)  presented KALIMAT corpus (20,291 Arabic articles), collected from the Omani newspaper Alwatan The authors proceed to a set of actions on the collected data such as: (1) Summarization , (2) Recognition of the named entities and (3) Annotation of the data collection. Dima et al. (2018)  presented a small dependency treebank of travel domain sentences in Modern Standard Arabic.
34. In the context of SA system,  Badaro et al. (2014)  presents a nonlinear SVM implementation in MATLAB, with the radial basis function (RBF) kernel The best F1-score achieved by the authors is up to 64. 5%. To determine the usefulness of their Arabic sentiment lexicons,  Mohammad et al. (2016)  applied them in a sentence-level sentiment analysis system. The authors used a linear-kernel SVM (Chang and Lin, 2011) classifier. The best F1-score was up to 66. 6%. Al-Twairesh et al. (  2016 ) performed a set of experiments using a simple lexicon-based approach. Their results show the importance on handling negation and they achieved an F1-score up to 89. 58% Rushdi-Saleh et al. (  2011 ) have used crossvalidation to compare the performance of SVM and NB algorithms. The best accuracy was achieved with SVM classifier and it is up to 0.
91. Aly and Atiya (2013)  used Multinomial NB, Bernoulli NB and SVM. The best F1-score (up to 0. 91) was achieved with the unbalanced dataset combining unigrams, bigrams and trigrams, by using TF-IDF and with SVM classifiers. Dahou et al. (2016)  introduce a method based on word2vec which evaluates polarity from product reviews. A convolutional neural network (CNN) model was trained on a model of Arabic word embeddings. More recently,  Badaro et al. (2018)  proposed EMA system (Emotion Mining in Arabic) The authors proceed to a set of treatments including: Diacritics, normalisation, stemming (using ARLSTEM  (Abainia et al. , 2017) ), etc. To extract features, the authors used the two embedding vectors AraVec  (Soliman et al. , 2017)  and fasText  (Joulin et al. , 2016). The authors used SVM, Random Forest (RF) and a set of deep neural network algorithms such as CNN with LSTM LSTM layer.
They relied on 200,000 words selected from Gumar  (Khalifa et al. , 2016a). For the annotation, the authors used MADARi interface  (Obeid et al. , 2018). MADARi is a web-based interface supporting both morphological annotation and spelling correction during all the process of annotation. MADARi was initially using MADAMIRA (for EGY dialect). However, it was extended with CALIMAGLF for more coverage. Zalmout et al. (2018)  recently presented a neural morphological tagging and disambiguation model for the Egyptian dialect, with various extensions to handle noisy and inconsistent content. these authors relied on LSTM and CNN model for generating their character embedding. Kwaik et al. (2018)  present the construction of the Shami corpus, a LEV Dialect Corpus. This corpus covers data from the four dialects spoken in Palestine, Jordan, Lebanon and Syria and contains 117,805 sentences.
Jarrar et al. (2017)  present the construction of Curras, a morphologically annotated corpus of the Palestinian Arabic dialect. Curras consists of more than 56,000 tokens, which were annotated with rich morphological and lexical features. Al-Twairesh et al. (2018)  proposed SUAR, a semiautomatically Saudi corpus which was morphologically annotated automatically using the MADAMIRA  (Pasha et al. , 2014). The generated corpus was manually checked The resulted corpus contains 104,079 words. Alsarsour et al. (2018)  presented DART (The Dialectal ARabic Tweets dataset), a large manually-annotated multi-dialect corpus of Arabic tweets This corpus contains around 25,000 tweets annotated via crowd-sourcing. Erdmann et al. (2018)  proposed different word embedding models for the four dialects EGY, LEV, MAGH and GLF.
To build a corpus as large as possible, the authors combined different earlier corpora  (Almeman and Lee, 2013; Khalifa et al. , 2016a; Bouamor et al. , 2018). The resulting corpus contains 5. 6 million sentences Suwaileh et al. (Suwaileh et al. , 2016)  presented ArabicWeb16, a multidomain, very large corpus containing 10. 8 TB. 4 Khalifa et al. (2016a) focus on GLF Dialects where they proposed a large-scale corpus consisting of 110 million words from 1,200 novels. This corpus contains many domains such as; romance, drama and tragedy. More recently Abdul-Mageed et al. (  2018 ) built a large-scale dataset for a variety of Arabic dialects. This corpus contains 234,801,907 tweets. Saad and Alijla (2017)  propose the construction of a comparable Wikipedia corpus between the Arabic and Egyptian dialects.
The authors used acoustic and linguistic features on the Arabic dialectal speech dataset. The authors use the MGB-3 (Multi-Genre Broadcast) dataset  (Ali et al. , 2017). Tachicart et al. (2017)  focused on an Identification system distinguishing between the Moroccan Dialect and MSA. The authors relied on two different approaches: (1) rule-based and (  2 ) statistical-based (using several machine learning classifiers). However the statistical approach outperforms the rule-based approach where the SVM classifier is more accurate than other statistical classifiers. The work of  Salameh and Bouamor (2018)  bring a new idea by presenting the first results on a fine-grained dialect classification covering 25 specific Arabic cities (such as Morocco, Egypt, Iraq, Gulf, etc), in addition to MSA. To do so, the authors relied on MADAR, the parallel corpus of 25 Arabic dialects  (Bouamor et al.
5 https://ia802304. us. archive. org/27/items/xvmf6/xvmf6. pdf. 6 https://glosbe. com/. 7 A word or element is treated by language users as having a fundamentally similar semantic content in more than one language or dialect  (El Haj et al. , 2017). combination with the constructed lexicon (NileULex). The results show that the integration of NileULex improved the results of classification (F1-score up to 79%). Guellil et al. (2017d)  propose a sentiment analysis algorithm dealing with Algerian dialect morphology and handling negation and opposition. The best F1score that they achieved is up to 78%. To evaluate the performance of their corpus,  Medhaffar et al. (2017)  relied on three classifiers such as SVM and NB and MultiLayer Perceptron classifier (MLP). The input of each sentiment classifier is the set of feature vectors from the Doc2vec  (Le and Mikolov, 2014)  toolkit.
van der Wees et al. (  2016 ) built an SMT system. They began with the unsupervised alignment of words in hand-aligned sentences. The best results achieved by this approach are up to 18. 4 (manual transliteration) and up to 15. 1 (automatic transliteration). In the context of SA,  Duwairi et al. (2016)  constructed a small annotated corpus manually (containing 3,026 messages) containing Arabizi messages transliterated into Arabic. For classification, these authors used NB and SVM classifiers where SVM gave better results. Some other works try dealing with Arabizi without the transliteration step  (Medhaffar et al. , 2017; Guellil et al. , 2018b). However,  Guellil et al. (2018b)  clearly stated that the low F1-score that they obtained for the Arabizi dataset (up to 0. 66) is principally related to the fact of handling Arabizi without transliteration.
2 URL: http://www. textminingthequran. com/apps/similarity. php. 3 URL: http://corpus. quran. com/. 4 URL: http://www. textminingthequran. com/wiki/Pronoun_Reference_in_the_Quran. 5 URL: http://www. textminingthequran. com/apps/pron. php. 6 URL: https://github. com/karimouda/qurananalysis. 7 URL: http://www. qurananalysis. com/. 8 URL: http://tanzil. net/trans/. 9 URL: https://github. com/minijoomla/Tanzil. 10 URL: https://sourceforge. net/projects/tashkeela/.
(2016) 2016 SA SA model Badaro et al. (2018) 2013 EM EM model Kim et al. (2016) 2016 LM LM model 18 1 URL: http://qatsdemo. cloudapp. net/farasa/. 2 https://camel. abudhabi. nyu. edu/madamira/. 3: https://www. nltk. org/_modules/nltk/stem/arlstem. html. 4URL:https://nyuad. nyu. edu/en/research/centers-labs-and-projects/computational-approaches-to-modeling-language-lab/resources. html. 5URL:http://camel. abudhabi. nyu. edu/resources/. 6 URL: https://github. com/UniversalDependencies/UD_Arabic-NYUAD. 7 RL: https://conllul. github. io/. 8 URL: https://github. com/saidziani/Arabic-News-Article-Classification. 9 URL: https://cactus. orange-labs. fr/ALIF/. 10 URL: https://conferences. unite. un. org/uncorpus. 11 URL: http://opus. nlpl. eu/OpenSubtitles2016. php. 12 URL: http://el. tufs. ac. jp/tufsmedia-corpus/.
13 URL: http://www. oma-project. com/. 14 URL: http://saifmohammad. com/WebPages/ArabicSA. html. 1 URL: https://github. com/nora-twairesh/AraSenti. 16 U L: http://sinai. ujaen. es/en/?s=oca&submit=Search. 17 URL: https://github. com/mahmoudnabil/labr. 18 URL: https://github. com/yoonkim/lstm-char-cnn. 2 http://www. opensubtitles. org. a.
Neat1 pull down LncRNA pull-down (  17 ) is a hybridization-based strategy that uses complementary oligonucleotides to purify lncRNA together with its targets from reversibly cross-linked extracts. In cross-linked extracts, it is expected that some regions of the RNA will be more accessible for hybridization than others due in particular to secondary structure. To design oligonucleotides that target these regions and then can hybridize specifically to lncRNA Neat1, we modeled the secondary structure of Neat1 RNA using the RNAstructure software  (20). Two pools (A and B) of 6 antisens DNA oligonucleotide probes that target accessible regions throughout the length of the lncRNA Neat1 were designed and used for the specific Neat1 RNA pull-down (Figure  1 ; Supplemental Table  1 ). All these probes were biotinylated at the 3' end (IDT, Coralville, Iowa, USA) Twenty-four hours minimum after the fresh medium replacement, GH4C1 cells were rinsed one time with cold PBS with Ca ++ and Mg ++ , then incubate with 0.
Then, FeatureCounts  (22)  was used to quantify the number of counts for each gene. In order to assess the specificity of the Neat1 RNA pull-down we crossed the results obtain with the two pools of oligonucleotides to generate the list of transcripts associated to the lncRNA Neat1. The RNA sequencing data are available at Gene Expression Omnibus (GEO) (accession number n°GSE160069).