dataesr/scientific-paragraphs-categorization huggingface dataset:
( D84G10, Cell Signaling Technology, 5053), anti-Ago2 (C34C6, Cell Signaling Technology, 2897), anti-Ago3 (4B1-F6, Active Motif, 39787), anti-Ago4 (D10F10, Cell Signaling Technology, 6913), anti-DDX6 (Bethyl, A300-460A), anti-Dicer (Cell Signaling Technology, 3363), anti-Flag (M2, Sigma-Aldrich, F1804), anti-GAPDH (14C10, Cell Signaling Technology, 2118), anti-GFP (D5. 1, Cell Signaling Technology, 2956), anti-PABPC1 (Atlas Antibodies, HPA045423), anti-phospho-IκBα (Ser32) (14D4, Cell Signaling Technology, 2859), anti-TNRC6A (Bethyl, A302-329A) and antia-Tubulin (DM1A, Cell Signaling Technology, 3873). Both antibodies were diluted 1000×. As secondary antibodies, anti-mouse (Cell Signaling Technology, 7076S) or anti-rabbit (Cell Signaling Technology, 7074S) were used and proteins were detected with Agrisera ECL SuperBright (Agrisera, AS16 ECL-S-100) using a LAS 4000 mini (GE Healthcare).
Background: Aberrant DNA methylation is a hallmark of cancer cells. However, the mechanisms underlying changes in DNA methylation remain elusive. Transcription factors initially thought to be repressed from binding by DNA methylation, have recently emerged as being able to shape DNA methylation patterns. Results: Here, we integrated the massive amount of data available from The Cancer Genome Atlas to predict transcription factors driving aberrant DNA methylation in 13 cancer types. We identified differentially methylated regions between cancer and matching healthy samples, searched for transcription factor motifs enriched in those regions and selected transcription factors with corresponding changes in gene expression. We predict transcription factors known to be involved in cancer as well as novel candidates to drive hypo-methylated regions such as FOXA1 and GATA3 in breast cancer, FOXA1 and TWIST1 in prostate cancer and NFE2L2 in lung cancer.
Furthermore, recent profiling of chromatin-accessible regions marking TF occupancy in primary cancer samples correlated them to hypo-methylated regions suggested to be driven by key TFs  [18]. However, investigating TF binding in primary cancer samples at a large scale remains challenging due to technical limitations. Here, we exploited the massive amount of primary DNA methylation datasets from The Cancer Genome Atlas (TCGA) to predict TFs driving aberrant DNA methylation in cancer. We first identified differentially methylated regions (DMRs) between cancer and healthy samples. We performed a TF motif enrichment analysis to predict TF binding in DMRs. We then integrated matching TF expression data to distinguish TFs expected to drive DNA methylation changes in cancer. Finally, we validated our predictions in breast cancer cells and showed that FOXA1 and GATA3 indeed mediate DNA hypo-methylation.
Processing of the TCGA methylation data To study DNA methylation changes in cancer, we retrieved 8425 raw methylation datasets, generated from Illumina Infinium HumanMethylation450 BeadChip (HM450), for 32 available cancer types from the TCGA resource. We processed the data with the ChAMP pipeline  [19, 20]  and performed normalization using noob  [21]  as implemented in minfi  [22, 23]. We trained a quadratic discriminant analysis aiming to classify the cancer and healthy samples in two groups for each cancer type and discarded samples that were misclassified (Fig. 1a ). Based on the number and dispersion of samples, we retained 13 cancer types for further analysis: bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), thyroid carcinoma (THCA) and uterine corpus endometrial carcinoma (UCEC) (Additional file 1: Table  S1 ).
Discussion In this study, we took advantage of the massive amount of primary DNA methylation datasets from TCGA and developed a computational approach to predict TFs driving aberrant DNA methylation in cancer. Due to the limited number of CpG probes present in the HM450 array covering 1. 7% of the human genome and their bias toward gene promoters  [26] , we identified a majority of hyper-methylated DMRs located in CpG-rich regions. However, thanks to the CpG probes designed in putative enhancer regions, we could identify a substantial number of hypo-methylated DMRs. To predict which TFs could drive those DMRs, we performed a comprehensive TF motif analysis. Since several GAPDH was used as a control for equal loading. b ChIP-qPCR analysis for GATA3 binding at two positive control regions CR1 and CR2 (regions unmethylated in hTERT-HME1 and HCC1954 cells with GATA3 motif and binding in HCC1954 cells), at two regions of interest ROI1 and ROI2 from Fig.
5f  (regions methylated in hTERT-HME1 cells, unmethylated in HCC1954 cells, with GATA3 motif and binding in HCC1954 cells, and gaining methylation in GATA3 KO clones), and at two negative control regions NC1 (region methylated in hTERT-HME1 cells, unmethylated in HCC1954 cells with no GATA3 motif and binding in HCC1954 cells), NC2 (intergenic region unmethylated in hTERT-HME1 and HCC1954 cells with no GATA3 motif and binding in HCC1954 cells). ChIP was performed with antibody against GATA3 or a control IgG antibody. ChIP signals are represented as percentage input. Data represent mean ± SEM (n = 3 biological replicates). c Patterns of CpG methylation of ROI1, ROI2 and NC1 by bisulfite sequencing analysis in HME1 WT and HME1 overexpressing GATA3 (HME1-GATA3). White circles represent unmethylated CpGs and black circles represent methylated CpGs TFs recognize similar motifs, we used TF gene expression to predict which specific TF could act in trans to mediate DMRs: we searched for downregulated TFs whose motifs were identified in hyper-methylated DMRs and for upregulated TFs in hypo-methylated DMRs.
Conclusions We developed a computational approach to identify TFs driving DNA methylation changes and applied it to TCGA cancer methylation data to predict TFs regulators in 13 different cancer types. This approach could be applied to a wide range of other DNA methylation datasets to infer TF regulators. We validated two TFs, FOXA1 and GATA3 in breast cancer cells, and found that their binding indeed mediates focal hypo-methylation. Altogether this demonstrates the crucial role of TFs in shaping the DNA methylation patterns of a genome and how their deregulation leads to aberrant DNA methylation changes in cancer.
Identification of DMRs in TCGA data Differentially methylated cytosines were called using the R package limma  [24]  at an FDR threshold of 0. 05 and cytosines located in exons were excluded (using the ENSEMBL annotation for Homo sapiens version GRCh38. 87). DMRs were then called using a modified version of DMRcate  [25]. The original implementation of DMRcate smoothes a t 2 statistic and computes p-values using a χ 2 distribution. This design implies that DMR detection is not sensitive to the sign of methylation change and DMRs could contain a mixture of hypo-or hyper-methylated cytosines. We therefore modified the DMRcate approach to smoothe a t statistic, compute p-values using a normal distribution and used a p-value threshold of 0. 001 and parameter λ = 1000. We then selected DMRs or sub-regions of DMRs containing at least 2 consecutive CpGs that were constantly hypo-or hyper-methylated.
Webserver for results visualization The TCGA DMR, TF motif and expression analyses can be visualized on our webserver http:// bardet. u-stras bg. fr/ cance rmeth tf/. It was implemented in R using the shiny package (https:// shiny. rstud io. com/). It was deployed using the open-source Shiny Server, was containerized using Docker (https:// www. docker. com/) and uses Traefik as load-balancer (https:// docs. traefi k. io/). R shiny servers are optimized for Safari or Microsoft Edge web browsers.
e. , number of sources that published the news, and (iii) fact-check, i. e. , opinion of well-known fact-checking websites about the news, i. e. , true or false. The proposed system only employs eight features, which is less than most of the state-of-the-art approaches. Also, the evaluation results on a fake news dataset show that the proposed system employing both types of features can reach an accuracy of 94. 4%, which is better compared to that obtained from separately employing linguistic features (i. e. , accuracy=89. 4% ) and fact-verification features (i. e. , accuracy=81. 2%).
e. , factcheckers) to verify the content of the given news item  [86]. Expert-based fact-checking is often performed by a small group of highly credible fact-checkers, which could lead to very accurate results. However, they require continuous and manual updates, and cannot perform automatic learning. Through consultations and extraction of data from different sources, automated fact-checking aims at automatically verifying claims. Then, a classification based on the stance and strength of reputable sources regarding the claim is assigned  [16]. Despite this technique is still in progress, it is very promising. The major challenges that hinder the efficiency of the existing fake news detection solutions are related to the highly versatile nature of deceptive information. Indeed, it is very difficult to obtain a generalized dataset for fake news detection.
To build a fake news detection model, Pérez-Rosas et al. [55]  combined morphological, syntactic, understandability, psychological, and n-grams patterns. The authors observed that authentic news in newspaper and entertainment magazines are likely to begin with the first-person pronouns, besides containing positive feeling words. However, fake contents commonly utilize the second-person pronouns and negative feeling words, and focus on the present actions. Mrowca and Wang  [52]  applied different deep neural network models on the Fake News Challenge (FNC)  1  dataset to solve the stance detection problem. The goal was to determine if there is any relationship between the headline and the news article. The results show that Word Embeddings Conditioned Bidirectional Long Short-Term Memory (LSTM) achieves the best classification accuracy.
They applied four neural models namely, Bag of Words (BoW), LSTM, LSTM with attention, and conditional encoding LSTM with attention on Fake News Challenge dataset. The results show that the attention models achieve better results than BoW and LSTM with respect to F1 Score. Jiang et al. [38]  proposed a fake news detection system, which applied BiLSTM and text embedding Glove on linguistic-based features. The proposed approach was able to achieve an accuracy of 99. 82%. In a more recent work  [39] ,the same authors introduced a new stacking method for fake news detection that consists of training nine (09) different machine learning and deep learning algorithms on a set of linguistic features that were extracted using Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. Choudhary et al.
[14]  introduced a fake news detection system based on linguistic features. The authors extracted four different linguistic features, namely, syntactic, grammatical, sentimental, and readability features. In order to classify news into either fake or real, the authors used a sequential neural network (SNN), which was trained on different combinations of features, and thus building various linguistic-features-based SNN models. Experimental results showed that the classification model, which was trained on combined features, achieved the highest performances with 86% of accuracy using a dataset composed of 250 news. Some metaheuristics algorithms have been proposed to deal with the fake news detection issue. In  [53] , two metaheuristic algorithms, i. e, salp swarm optimization (SSO) and grey wolf optimization (GWO) were proposed.
C. SOCIAL CONTEXT ANALYSIS The social context-based approaches  [30] ,  [45] ,  [78] ,  [80] ,  [85]  typically analyze the spreading patterns and the diffusion on social networks to distinguish misleading substance. Yang et al. [80]  proposed an unsupervised approach for fake news detection on social media. The authors investigated the veracity of news and credibility of users, and utilized a probabilistic graphical model to capture the complete generative spectrum. They evaluated the model on two different datasets (i. e. , LIAR and BuzzFeedNews), and obtained an accuracy of 75. 9% and 67. 9% respectively. Zhou and Zafara  [85]  proposed a network-based patterndriven approach for fake news detection in social networks. The main idea behind this work is to focus on the credibility of the news source, covering both the sources that create and publish the news, as well as the sources that spread the news.
The method was evaluated on PolitiFact and BuzzFeed datasets showing good performance compared to the state of the art, with an accuracy of 93. 30%. Wu and Huan  [78]  proposed an approach for social media news classifying using diffusion traces in social networks. They first inferred embeddings of social media users with social network structures to classify news items. To this end, they utilized a new Long Short-Term Memory networks (LSTM-RNN) model to represent and classify the propagation pathways of a message. They evaluated the proposed model on real-world datasets, and the experimental results demonstrated its effectiveness on the task of fake news detection and categorization. Yu et al. [45]  used a combination of recurrent and convolutional neural networks to model news diffusion pathways as multivariate time series, where each tuple of a news story is a numerical vector representing characteristics of a user who engaged in spreading the news.
The method was evaluated on three real-world datasets and experimental results showed that the proposed model was able to effectively identify fake news content with an accuracy of 92. 3%. Gupta et al. [30]  proposed a PageRank-like credibility propagation algorithm on a multi-typed network by encoding users' credibility and tweets' implications. Further, they enhanced the basic trust analysis by updating event credibility scores and exploited event graph-based optimization to assign similar scores to similar events. They evaluated the model on two tweet feed datasets, and the proposed approach achieved an accuracy of 86%. In general, context-based approaches achieve better performance compared to both linguistic and knowledge-based ones. However, they work only a posteriori, disregarding the actual news content and requiring large amounts of data.
III. PROPOSED FAKE NEWS DETECTION SYSTEM The proposed fake news detection system consists of two phases, namely training and testing. Both phases include a preprocessing task, which consists of cleaning and preparing the training and testing datsets of real and fake news. In the training phase, the feature extracting task extracts a set of relevant features from the training dataset, which are then fed to several machine learning algorithms to build a fake news detection model. In the testing phase, the detection model is applied on test data to decide whether the provided news articles are real or fake. Figure  2  presents the overall architecture of the proposed fake news detection system.
2) Linguistic features We list the following linguistic features and their corresponding extraction tools: • Title: It is obtained using two web-based software: Zyte 7  and BuzzStream 8. • N of words: It is obtained using different tools such as WordCounter 9. • Reading ease: It computes the reading ease of a text using the Readability Formulas tool, which provides the Flesch Reading Ease score, as shown in Equation  1. RE = 206. 835-(1. 015×ASL)-(84. 6×ASW ) (1) where : -RE : Readability Ease. -ASL : Average Sentence Length (i. e. , the number of words divided by the number of sentences). -ASW : Average number of syllables per word (i. e. , the number of syllables divided by the number of words). The Flesch Reading Ease Formula provides a score between 1 and 100, and 100 represents the best readability score.
-Mean Segmental Type-Token Ratio  [40] , denoted by M ST T R. It is computed by dividing the text into segments, compute the T T R of each segment, and average the obtained T T Rs. The above equations are computed using Quanteda tool  [4] , as shown in Figure  3. In our case, we define our lexical diversity as the sum of all values obtained from Equations 2 to 12, M AT T R, and M ST T R. • Sentiment: We used the SEO Scout's analysis tool  10 to get this feature, as it can effectively and rapidity estimate the dominant sentiment in the news. To select the best model, AutoAI initially applies the candidate algorithms on small subsets of the dataset, and ranks them. Then, it repetitively increases the size of subsets and executes the candidate algorithms until the best algorithm is found. AutoAI performs hyper-parameter optimization of the best selected algorithm by applying an optimization algorithm that allows fast convergence to a good solution, and generating the best model.
A. IMPLEMENTATION ENVIRONMENT To implement our learning models, we use IBM Watson Machine Learning software  12. This software can generate analysis models, which are trained on our dataset. Watson Machine Learning offers a full range of tools and services to generate, train, and deploy machine learning models. The following tools are available with the Watson Machine Learning service: • The AutoAI experimentation generator, which automatically processes structured data to generate model pipelines. The best performing pipelines can be saved as machine learning models and deployed for evaluation and the best algorithm is determined by the model selection and optimization during AutoAI training. • Notebooks that provide an interactive programming environment for working with data, testing models, and obtaining rapid prototyping.
A. EVALUATION METHODOLOGY Our experimental process consists of training and testing our system using only linguistic features, then using only factverification features. Finally, we train and test the proposed system using various combinations of linguistic and factverification features. In order to assess the effectiveness of the proposed fake news detection system, we used the holdout cross-validation technique, which consists in dividing the dataset into two subsets: training and testing. In our case, we choose to use 85% of the data for training, and 15% of the data for testing.
D. COMPARISON WITH OTHER APPROACHES Actually, a fair comparison with previous approaches is not possible due to many reasons including the use of different methods ( i. e. , machine learning, deep learning, neural Networks,. etc), as well as different datasets. However, we can only compare our work (Accuracy=94. 40%) with Horne and Adali  [36]  (Accuracy=77%), as the same data set, i. e. , Buzzfeed Political News Data, is used. In Table  4  and Figure  4 , we present the performances of our system and those of machine learning-based state-ofthe-art approaches with respect to accuracy and the number of features. Zhou and Zafarani  [85]  employed linguistic and social features, and achieved a detection accuracy of 93. 30%. Gupta et al. [30]  and Yang et al. [80] , which only used socialcontext features, obtained an accuracy of 86% and 75.
VII. CONCLUSION In this paper, we have proposed a novel hybrid fake news detection system that employs two types of features: linguistic and fact-verification features. The proposed detection system employs only eight features, which less compared to the stat-of-the-art approaches. It operates in two phases: training and testing. In the training phase, the detection system runs four machine learning algorithms, i. e. , Logistic Regression (LR), Random Forest (RF), Additional Trees Discriminant, and XGBoost, in order to select the best classifier for the testing phase. Evaluation results on the Buzzfeed Political News data set show that the proposed detection system achieves an accuracy of 94. 4% under Random Forest. These results are better compared to those obtained from employing linguistic features, (i.
35The word ʾs¹kn is left untranslated in Macdonald, Al-Muʾazzin, and Nehmé(1996, 458)  and the ociana database. The translation 'settlement/settled areas' is based on Al-Jallad (2015, 341). 36 Note that all three inscriptions with the krs siglum seem to refer to the same event, s¹nt ws¹q ʿbdrb{ʾ}l lḥyn 'the year ʿbdrbʾl confronted lḥyn' (ociana, accessed 22-04-2018). 37 lḥyn is more commonly found as a personal name in the Safaitic inscriptions. Compare for example (krs 185) l-lḥyn bn s¹ny bn s¹lm bn s¹ʿd 'by lḥyn son of s¹ny son of s¹lm son of s¹ʿd' (ociana, accessed 22-04-2018). Kootstra -9789004512634 Downloaded from Brill. com 06/03/2024 04:48:07PM via Open Access. This is an open access title distributed under the terms of the CC BY-NC-ND 4. 0 license, which permits any non-commercial use, distribution, and reproduction in any medium, provided no alterations are made and the original author(s) and source are credited.
This paper is structured as follows. First, the synoptic-scale atmospheric state that accompanied the selected AR cases is discussed, followed by the analysis and quantification of the associated poleward transport of water vapour and dust. The radiative forcing is then estimated both using in-situ measurements at a weather station in the Alps, and satellite-and model-derived products for a larger-scale perspective. Finally, as these events may become more frequent in a warmer world (e. g. Espinoza et al. , 2018; Massoud et al. , 2020; Payne et al. , 2020) , the climatology and trends in ARs and dust storms over Europe is presented. The main findings of the work are outlined in the "Summary and Conclusions" section, while in the "Materials and methods" section, the observational and modelled datasets employed as well as the methodology followed in the analysis are described.
Summary and conclusions In this study, the identification of dusty atmospheric rivers (ARs) stretching from the Sahara all the way to Europe is achieved for the first time using a mixture of satellite-derived and ground-based measurements and state-of-the-art reanalysis datasets. The crucial role of dusty atmospheric rivers in the reduction of snow cover over the European Alps as well as their impacts on the surface meteorological fields over Europe is then investigated during two dusty atmospheric rivers in February 2021. The two ARs that affected Europe in February 2021 were linked with distinct atmospheric patterns. The first, from 04 to 08 February, occurred in association with a transient pattern, with a trough over the Iberian Peninsula and a ridge over southeastern Europe leading to a southerly flow in the region.
Materials and methods The four observational datasets used in this work are listed below: ➢ Red-Green-Blue (RGB) images from the Spinning Enhanced Visible and Infrared Imager (SEVIRI;  Schmetz et al. , 2002)  instrument on board the Meteosat Second Generation Spacecraft. These satellite images, available on a 0. 05 • grid every 15-min, are constructed from the 8. 7, 10. 8, and 12 μm brightness temperatures  (Martinez et al. , 2009). They allow for the identification of the dust plumes as they propagate away from the source region, as well as the clouds and moist air; ➢ Clouds and the Earth's Radiant Energy System (CERES;  Wielicki et al. , 1996)  surface radiation fluxes. The product used is "CERES FLASH_TISA Version4"  (Kratz et al. , 2014) , which provides daily averaged surface and Top of the Atmosphere (TOA) fluxes, and MODIS cloud properties on a 1 • × 1 • grid, within a week of the satellite observations; ➢ Vertical profiles of the vertical feature mask and aerosol subtype estimated from the measurements collected by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO;  Winker et al.