dataesr/scientific-paragraphs-categorization huggingface dataset:
The DTCWT has solved these problems by its non-oscillating property with approximate invariant shift amplitude where this property is very important for the detection of epileptic seizures in EEG signals. In conclusion, we have proposed a robust automatic approach for epileptic seizure detection and classification that was assessed on two EEG datasets generated from the CHB-MIT database. The proposed approach is based on two denoising signal methods (i. e. , S-Golay and MSPCA) combined with three popular feature extraction techniques (i. e. , DTCWT, DWT, and EMD). Three different machine-learning techniques (namely SVM, k-NN, and LDA) have been used to distinguish between the seizure-free and seizure segments based on the feature extraction methods already mentioned. The experimental results have shown that the employment of DTCWT has been very successful because it outperforms the other two methods (DWT and EMD).
Only the most robust and reliable modifiers of Tau toxicity were included. For REP modification imaging, adult flies were frozen then thawn at room temperature prior to light microscopy using a Leica APO Z6 macroscope (Leica, Wetzlar, Germany) equipped with a Leica DFC320 digital camera controlled with the Leica LAS V4. 8 software. Z stacks were generated for each sample to record images at different focal planes. Focus stacking was performed using Zerene Stacker (Zerene Systems, Richland, WA, United States) with the PMax stacking method, and images were then converted in gray scale, cropped and orientated using Fiji environment  (Schindelin et al. , 2012)   1  (RRID:SCR_002285).
We used the following antibodies: goat anti-horseradish peroxydase (HRP) (1:500; Sigma-Aldrich; RRID:AB_1840055), rabbit anti-Tau (1:1000; Agilent Dako, Santa Clara, CA, United States; RRID:AB_10013724), donkey anti-rabbit (ThermoFischer SCIENTIFIC; RRID:AB_2535792), and donkey anti-goat (ThermoFischer SCIENTIFIC; RRID:AB_2534105). Images were acquired using a Axioplan2 fluorescence microscope (Carl-Zeiss, Oberkochen, Germany) equipped with a digital AxioCam MRm camera (Carl-Zeiss) controlled by AxioVision software (Carl-Zeiss).
Genetic and Physical Interactors Retrieval Easy networks  (Bean et al. , 2014)   3  database was used to automatically retrieve genetic and physical interactors of Drosophila gene lists gathering comprehensive interactions evidences from FlyBase and BioGRID databases. For this particular task and in our hand, the choice of EsyN was dictated by its better ability to retrieve the highest number of interactors compared to the number collected with STRING described below.
Network Analysis Search tool for the retrieval of interacting genes/proteins (STRING)  (Szklarczyk et al. , 2019)   4  (RRID:SCR_005223) was used to construct physical and functional interaction networks among the genetic modifiers of Tau toxicity. Active interaction sources were restricted to "Textmining, " "Experiments, " and "Databases. " Note that, in our hand, STRING, based on specific features not included in EsyN (computational prediction, knowledge transfer between organisms and interactions aggregated from other primary databases), was more adapted in this task, leading to the construction of a denser network. Only interactions with confidence score over 0. 5 were mapped to the network which was imported into the Cytoscape V3. 7. 1 software platform  (Shannon et al. , 2003)   5  (RRID:SCR_003032).
Functional Enrichment Analysis The functional annotation enrichments of gene lists were calculated using Database for annotation, visualization, and integrated discovery (DAVID)  (Huang et al. , 2009a,b)   6  (RRID:SCR_001881) and querying for the biological processesrelated gene ontology (GO) Direct terms (GO mappings excluding parent terms). An EASE score of 0. 05 (a modified Fisher Exact Test) was used for hypergeometric testing, followed by the Benjamini correction for multiple hypothesis test adjustment. The threshold of significance was set to p-value ≤ 0. 05.
Human Genes Constraint Metrics Retrieval To depict the intolerance to haploinsufficiency/inactivation of the human orthologs of the genetic modifiers of Tau toxicity identified in Drosophila, the observed/expected ratios of predicted loss-of-function (pLoF) variations (o/e), continuous constraint metrics for the human genome, were downloaded from the Genome Aggregation Database (GnomAD)  (Lek et al. , 2016; Karczewski et al. , 2019)   9  (RRID:SCR_014964). The LoF o/e ratio is a stringent metric correlating with biological relevance (protein-protein interactions, gene expression, and disease associated) and robustly distinguishing genes based on their sensitivity to genetic disruption.
S2A ). The human Tau protein reached its steady-state level (data not shown) and was located in the soma and in all the neural processes in our cellular model (Supplementary Figure  S2A ). As expected  (Li et al. , 2016; Bolós et al. , 2017) , Tau overexpression led to a significant reduction in the average length of the neural extensions compared to control primary cultures without Tau (Supplementary Figures  S2B, C ) [p-value = 0. 003, "control" mean = 59. 160 ± 5. 142 µm, "Tau" mean = 30. 884 ± 2. 416 µm, 95% CI  (14. 185; 42. 368) ]. In order to determine gene expression dysregulations induced by Tau overexpression in these primary neuronal cultures, we carried out a transcriptional analysis using 2-color microarrays. A total of 908 genes were found differentially expressed (p-value logRatio < 0.
001, 95% CI (0. 025-0. 049)] and that the Tau toxicity modifiers were over-represented among the 908 differentially expressed genes. Therefore, we reasoned that the dysregulation of the expression of each of these 32 genes could have functional consequences on the biology of their physical and genetic partners, and therefore that some of these partners could act as genetic modifiers of Tau toxicity. Using the easy networks database (esyN), we identified 740 physical and/or genetic interaction partners of these 32 genes. Interestingly, 48 of these 740 partners (6. 5%) were part of the list of the 283 genetic modifiers of Tau toxicity identified using the REP, which validates our approach (Figure  1 ). Again, a proportion comparaison test (reference: 2%) showed a significant enrichment of Tau genetic interactors among these 740 partners [p-value was < 2.
To summarize all these data, the reevaluation of our genetic screen allowed us to identify 59 novel Tau genetic modulators (section "A genetic Screen Identifies 59 Novel Modifiers of Tau Toxicity in Drosophila"). Then, combining genetic analyses and transcriptomic analyses, we identified 18 additional Tau genetic interactors (section "Combined Genetic and Transcriptomic Analyses Allow the Identification of 18 New Genetic Modifiers of Tau Toxicity"). Added to the 224 genetic interactors identified in previous studies, these new data bring to 301 the number of genetic modifiers of human Tauinduced toxicity identified so far in flies using REP as read-out (Supplementary Table  S5 ). This combined strategy also allowed us to show that 50 (32 and 18) of these genetic modulators were dysregulated upon Tau expression in vitro and that their misexpression in vivo modulates Tau induced toxicity, suggesting FIGURE 1 | Unbiased combinatory approach crossing genetic and transcriptomic datasets.
On the top, Venn diagram showing logical relations between the set of genetic interactors of human Tau identified in Drosophila ("Genetic screens," in blue) and the set of genes whose expression was dysregulated (fold change > 1. 5) upon human Tau expression in Drosophila primary nerve cells ("µArray," in red). The intersection of these two datasets encompassed 32 genes, which are listed in the corresponding stamp. On the middle, identification of 740 physical and genetic partners of these 32 genes using the easy networks database (esyN). On bottom left, Venn diagram showing logical relations between the set composed of these 740 protein partners (in yellow) and the set of genetic interactors of human Tau (in blue). The intersection of these two datasets is composed of 48 genes, which are listed in the corresponding stamp.
On bottom right, Venn diagram showing logical relations between the set composed of the 740 protein partners (in yellow) and the set of genes dysregulated upon human Tau overexpression in primary nerve cells (in red). The intersection of these two datasets encompassed 44 genes, which are listed in the corresponding stamp. In stamps, genetic interactors validated experimentally are indicated in bold. that they may participate in Tau-driven toxicity in vivo in Drosophila (Figure  1 ).
Network Analysis Highlights Non-random Interconnectivity Between the Genetic Modifiers of Tau Toxicity To analyze the biological connections between these 301 genetic Tau-modifiers, we constructed an interaction network using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database. We restricted the analysis to the following categories of interactions: protein-protein interactions (PPI) documented by co-immunoprecipitation or yeast two-hybrid, functional interactions documented by data gathered from curated databases and text-mining that detects co-occurrence of gene names in literature. Furthermore, we considered only interactions with confidence scores over 0. 5. This led to a connected network of 229 genes (Figure  2 ). Note that 72 of the 301 genetic Tau-modifiers identified so far were not found in this network.
Network Clustering Highlights Biological Processes Related to Tau Toxicity Using network-clustering algorithms to detect densely connected subgroups in the network, we identified 8 main modules (Figure  4 ). Downstream analysis of these sub-networks using DAVID bioinformatics resources revealed highly significant functional enrichments (Supplementary Table  S6 ). The subnetwork 1 is highly enriched in genes involved in protein phosphorylation, actin organization and signal transduction. Sub-network 2 genes are robustly associated with proton transport. The module 4 is enriched in genes involved in microtubule-based movement, and the cluster 7 with hits associated with neurotransmitter secretion and vesicle dynamics. Tagging modules with GO terms also outlined cell cycle activities (module 6), RNA metabolism (clusters 3 and 5) and Heparan sulfate proteoglycan (HSPGs) biosynthesis (module 8).
Human Orthologs of the Genetic Modifiers of Tau Toxicity Are Less Tolerant Than Average to Haploinsufficiency/Inactivation Based on the analysis of 125,748 exomes and 15,708 human genomes, the Genome Aggregation Database (gnomAD) provides for all human genes the ratio of observed over expected loss-of-function variations (LoF o/e). This ratio constitutes a continuous constraint metric describing the spectrum of tolerance to loss-of-function for each protein-coding gene. It has been observed that essential genes for human cell viability are far more intolerant to haploinsufficiency/inactivation than non-essential genes that are more likely to be unconstrained  (Lek et al. , 2016). Moreover, genes responsible for mendelian diseases are significantly more intolerant to functional genetic variation than genes that do not cause any known disease  (Petrovski et al.
The mean LoF o/e for the orthologs of the genetic modifiers of Tau toxicity was significantly lower than the mean LoF o/e of the rest of the genome [p-value = 2. 2E-16, "reference" mean = 0. 448 ± 0. 004, "Tau interactors" mean = 0. 292 ± 0. 017, 95% CI (0. 121; 0. 190)] (Figure  5A ). Looking at the LoF o/e distribution for the two datasets, we found a higher density of weak LoF o/e values for the human orthologs of the genetic modifiers of Tau toxicity compared to reference genes (Figure  5B ). These data indicated that the human orthologs corresponding to the Tau genetic modifiers identified in flies were more intolerant to inactivation/loss-of-function than average. Thus, they are likely critical for cellular functions, and possibly disease-causing.
Human Orthologs of Genetic Modifiers of Tau Toxicity Involved in Neurological Disorders Next, we used DIOPT-Diseases and Traits (DIOPT-DIST) to determine if these interactors were associated with human diseases. Among the 364 human orthologs of genes coding for Tau interactors, 87 are involved in the genetic determinism of human diseases according to the OMIM database, which is consistent with their intolerance to inactivation. Among them, 38 are involved in the determinism of neurodevelopmental or neurodegenerative diseases (Table  3 ). In most of these diseases, no Tau pathology has been described to date. However, 2 of these genes are risk factors for AD, a condition in which Tau pathology is a well-recognized feature of the disease. First, the BIN1 locus is a firmly established susceptibility AD locus, with a frequent variant located outside the coding sequence conferring AD risk  (Lambert et al.
ACKNOWLEDGMENTS We thank  Christophe Antoniewski ,  François Lecoquierre ,  Laetitia Miguel ,  Gaël Nicolas , and  Anne Rovelet-Lecrux  for helpful discussion and their critical reading of the manuscript. We also thank the  Bloomington Drosophila stock center  for providing fly stocks. Light microscopy was performed at the  PRIMACEN imaging platform (Rouen University, France ).
Cross-lingual extension Most data sets for NEL are available only in English. Among them, the AIDA data set is the main data used to train NEL system on the state of the art. Unfortunately, there are few data sets for lowresourced languages, with the notable exception of the WikiANN corpora. In order to extend GH's system to a cross-lingual setting, we made a number of modifications to their approach. Instead of using the Word2Vec embeddings, we used the pre-trained multilingual MUSE embeddings 5   [3]. These embeddings are available in 30 languages (including Croatian, Estonian, Finnish, Slovenian, to mention a few) and they are aligned in a single vector space. Therefore, words like "house" and "talo" ("house" in Finnish) have similar word representations. One of the main goals of using these embeddings is to generate multilingual entity embeddings that can provide entity representations for mentions in several languages.
Then, GH's approach will be able to analyse documents in the languages of these embeddings and link them to an English KB. Therefore, we generate the entity embeddings using the English version of Wikipedia and train this system on the AIDA data set using the MUSE embeddings. In this scenario, GH's approach analyses English documents and links their mentions to an English KB. Moreover, we extend the training process for some low-resourced languages by using the previous English model and continue the training process with data on other languages. This tuning procedure optimises our model to analyse better the documents on low-resourced languages and link their mention to an English KB. More precisely, we initialized the weights of the neural network model with the weights of the English model, and we reduced the learning rate to tune our model for the target languages.
EXPERIMENTAL SETUP In order to analyse the impact of using multilingual embeddings on the representation of entity embeddings, we used the entity relatedness data set of Ceccarelli et al. [1]  to compare the quality of entity embeddings produced by the Word2Vec and multilingual embeddings. This data set contains 3319 and 3673 queries for the test and validation sets. Each query consists of one target entity and up to 100 candidate entities with gold standard binary labels indicating if the two entities are related or not. The associated task requires ranking of related candidate higher than unrelated ones. Following GH's work, we used the normalised discounted cumulative gain (NDCG) and mean average precision (MAP) measures to evaluate them. We also performed candidate ranking based on cosine similarity of entity pairs.
We then trained and tested GH's approach with the following benchmarks: AIDA-CoNLL  [7] , AQUAINT  [6] , ACE2004  [6, 15] , WikiANN  [13] , CWEB  [4]  and WIKI  [15]. The WikiANN data set was split into 2 separate data sets, 70% of the corpus for training and 30% for testing. For the training process, we use AIDA data set to train the NEL system for English using the MUSE embeddings. Then, we use the WikiANN training data set to optimise the English model for each low-resourced language. Finally, we tested our model on the WikiANN test data sets. Following previous works, we evaluate the performance of our approach by analyzing the precision, recall and F1-measure. Precision is the fraction of correctly linked entity mentions that are generated by a system. Recall considers all entity mentions that should be linked and determines how correct linked entity mentions are concerning the total entity mentions that should be linked.
EXPERIMENTAL ASSESSMENT Entity embeddings performance: Table  1  shows the entity relatedness results using Word2Vec and MUSE embeddings for the English data set  [1]. Both embeddings have the same dimensional space (300 dimensions) but different vocabulary sizes: Word2Vec (3 million tokens) and MUSE (200 thousand tokens). This large difference helps Word2Vec to achieve the best results for all entity related measures. More precisely, the Word2Vec embeddings provide a better analysis of the Wikipedia documents because it has less out-of-vocabulary words than the MUSE embeddings and can represent better the meaning of sentences and entities. Despite this performance drop, GH's approach using MUSE embeddings achieved better results than  [17]  and  [12]  for all metrics. NEL analysis for mono-and multilingual embeddings: Advancing our analysis of GH's system, we compared the F1-measure results for this system on English corpora using the Word2Vec and MUSE embeddings (Table  2 ).
As expected, the small vocabulary and lower performance in the entity relatedness measures reduced the performance of GH's system in the NEL task. These factors reduced the quality of the attention and the context embeddings, and prioritised the relevance of entity priors (log p(e |m)) to disambiguate the mentions in a document. Despite this drop, GH's system using MUSE achieved identical or very close perfomance for most data sets. XEL analysis: Table  3  presents the F1-measure results for the NEL on four languages of the WikiANN corpora. We tested the NEL system using only the AIDA training data set to train GH's model in order to link mentions to the English version of the Wikipedia; and using the AIDA training data set in a first step and, then, the WikiANN training data set for each language (second line of Table  3 ).
The tuning process on the WikiANN data set improved the performance of GH's for the WikiANN test data sets. Unfortunately, the WikiANN data set is composed of short sentences with little contextual information. This characteristic makes the context analysis of GH's system less relevant and implies that the disambiguation process mainly consists in pairwise matching between mentions and entities using log p(m|e). Another limiting factor is the small MUSE vocabulary. Finally, the English version of Wikipedia does not have all entities listed on the Croatian, Estonian, Finnish, and Slovenian Wikipedia versions, which reduces the number of entities that can be linked to the KB. XEL is a fundamental tool for search engines in digital libraries to retrieve documents where their contents (including named entities) are in different languages and contexts.
CONCLUSION This paper is the first step to analyze the impact of multilingual embeddings to extend monolingual NEL to XEL. The next step is to investigate the impact of degraded documents on this cross-lingual task. Despite the small multilingual vocabulary on the word embeddings and the poor context quality of training data sets for lowresourced languages, our experiments showed a worst drop of 5. 6% on F1-measure on the English test data set (and the same performance of monolingual embeddings in the best case) and a small improvement with the tuning procedure on low-resourced languages. Therefore, we intend to build training data sets on the target languages that are composed of long sentences with rich context information to improve our XEL model. Further work is under progress to develop and analyze the performance of end-to-end XEL systems on OCRed data sets.
Acknowledgements. I. B. was supported by the  Hungarian National Research, Development and Innovation Office NKFIH  Grants  K 111827  and  K 116769 , and by the  Bézout Labex  ( ANR-10-LABX-58 ). X. G. was supported by  Institut Universitaire de France. The authors are grateful for the hospitality during the  ASPAG  ( ANR-17-CE40-0017 ) workshop on geometry, probability, and algorithms in Arcachon in April 2018.
INTRODUCTION The modern IoT technologies have profoundly transformed classical "vehicular ad-hoc networks" (VANETs) into the "Internet of Vehicles" (IoV) (F. Yang, S. Wang, J. Li, Z. Liu and Q. Sun, 2014). More precisely, the IoV is defined as the real-time data interaction between vehicles and between vehicles and infrastructures through information platforms, mobile communication technology, smart terminal devices and vehicle navigation systems. Vehicles are incorporated into the IoT by being connected to the Internet, to the other vehicles nearby as well as to traffic information systems. Yet, there are many challenges related to this concept due to high connectivity and exchange of sensitive data, compromising of security and privacy and leave the vehicles susceptible to malicious entities.
Although the mechanism of action is still unclear, these results suggest competition at the S protein receptor binding domain (RBD). Following the findings of a host-virus interactome network analysis of various viruses including SARS-CoV  [15 ] , emodin has emerged as one of 16 most repurposable agents for COVID-19 with least expected adverse effects and highest target specificity  [16]. Molecular docking and dynamic simulation studies predict polyphenols from plants such as Citrus and Curcuma species to have a potential inhibitory effect on SARS-CoV-2 infection by interacting with the S protein RBD. A study has shown stronger interactions of polyphenols from Curcuma spp. (curcumin and derivatives) and Citrus spp. (tangeretin, hesperetin, hesperidin) to the S protein (PDB: 6LXT) than nafamostat  [8] , the reference antiviral  [17].
Hesperidin was predicted to target the binding interface between S protein and ACE2 by positioning on the middle shallow pit of the surface of the S protein RBD  [18 ]. Naringenin, found in a variety of herbs and fruits, had a stronger binding energy with the spike glycoprotein (PDB: 6VSB) than remdesivir  [19] , an antiviral temporarily approved by the FDA in the treatment of COVID-19  [20]. Epigallocatechin gallate, abundant in tea, as well as herbacetin from Rhodiola spp. (golden root) and other flavonoids also interacted strongly with S protein RBD (PDB: 6VXX) in silico  [21].
Screening by molecular docking of 33 molecules including natural products, antivirals, antifungals and antiprotozoal agents revealed that rutin (a citrus flavonoid) could bind to the active site of the SARS-CoV-2 3CL pro (PDB: 6Y84) with the highest affinity among the molecules screened  [44 ]. Other citrus flavonoids such as tangeretin and naringenin and polyphenols from Curcuma spp. were also reported to bind strongly to SARS-CoV-2 3CL pro substrate binding domain, while interacting with the S protein and ACE2 in silico, predicting stronger antiviral potential of these polyphenols compared to lopinavir and nafamostat  [8]. Several polyphenols were also found to have a synergistic effect on 3CL pro and PL pro. In cell-free and cell-based assays, chalcones isolated from Angelica keiskei exhibited competitive inhibition of the SARS-CoV serine protease 3CL pro , whereas noncompetitive inhibition was observed with the SARS-CoV cysteine protease PL pro  [47].