dataesr/scientific-paragraphs-categorization huggingface dataset:
Phase II: In Depth Analysis In Phase II, the results of Phase I were used to select samples for more detailed analysis. The selection was performed quite arbitrarily, based on at least one of the following criteria: relatively high number of non-viral, non-plant reads; at least one potential plant pathogen or pest present among the most prevalent rRNA hits per organism category; or overall high biodiversity of the sample. Typically, we aimed for keeping more or less half of the datasets, ideally retaining at least one dataset per participant. Next, participants were invited to transfer the raw data of the selected samples to ILVOThe raw data of each sample was further processed by a bioinformatics expertand checked for presence of adapters using FastQC (https://www. bioinformatics. babraham.
ac. uk/projects/fastqc/, accessed on 2 May 2020). If present, adapters were removed using cutadapt  [45]. Next, direct taxonomic classification of the reads was performed with the k-mer based Kraken2 software  [46] , using the lowest common ancestor method (LCA) for assignments at different taxonomic levels. For the database for the Kraken2 analysis, a local version of the complete Genbank non-redundant Nucleotide database converted to a Kraken2 index was used  [47]. The resulting taxonomic classifications were visualized using Krona  [48]. All detailed analyses of selected samples are made available through reports per participant on https: //gitlab. com/ilvo/PHBN-WP4-RNAseq_Community_Screening. This repository was also submitted to Zenodo under DOI 10. 5281/zenodo. 7974743.
Phase III: Feedback and Confirmation In Phase III, the participants received a report on the detailed analyses including the Krona plots (to interactively check the detailed taxonomic classification results of their samples in a web browser) and some suggestions of pathogens or pests that might be present in their dataset(s). Participants were asked to carefully review the results and give feedback on which observed non-viral pathogens or pests could potentially be present, taking into account host plant species, symptoms, sampling location, etc. Next, based on the feedback from the participants, we set a general cut-off value to classify the presence of nonviral pathogens per sample. For 7 samples for which material was still available, specific molecular confirmation tests, as described in literature, were conducted to confirm the presence of Pectobacterium sp.
[55] , 'Candidatus Phytoplasma pruni'  [56] , Botrytis cinerea  [57] , and/or Alternaria sp. [58]. This was performed on DNA extracted from the same samples for which the RNA-seq dataset was available. Furthermore, participants were also asked to complete an online questionnaire where their opinion on the usefulness of the analysis was evaluated, whether they were aware of such type of analyses, etc. The questions of the questionnaire can be found in File S2. Overall results were summarized in tables and downstream statistical tests and visualizations were performed in R v. 4. 0. 3  [53]  with RStudio v. 1. 3. 1093 [54]  using dplyr v. 1. 0. 2  [59]  and ggplot2 v. 3. 3. 3  [52].
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/plants12112139/s1, Figure  S1 : Overview of the datasets per participant (101 in total) that entered the "RNA-seq community effort"; File S1: Overview of the participants and datasets; File S2: Questionnaire sent to the participants at the time they received the results of the detailed analysis of their sample(s); File S3: Answers from participants to the questionnaire.
Data Availability Statement: The results of the RNA-seq community effort can be checked and downloaded from https://gitlab. com/ilvo/PHBN-WP4-RNAseq_Community_Screening. This repository was also submitted to Zenodo under DOI 10. 5281/zenodo. 7974743. Raw sequencing data generated during this study was submitted to the NCBI Sequence Read Archive (SRA) under BioProject number PRJNA961939.
Data accessibility. The Python code of the model and of the figures can be found at https://github. com/regicid/model_deprivation_violence. An online notebook to manipulate the model online without installing Python is available at: https://colab. research. google. com/ drive/1wf3KBd95YO_WTluGztaR-8l-1zOD-e0o?usp=sharing. The data are provided in the electronic supplementary material  [62].
| Immunoblots Immunoblots were performed as previously described. 21 Primary antibodies were anti-FSCN1 mouse monoclonal (sc-46 675, Santa Cruz Biotechnology; 1:1000), rabbit polyclonal anti-SF-1 (07-618, Millipore; 1:1000) and mouse monoclonal anti-GAPDH (CB1001, Sigma-Aldrich; 1:2000) antibodies. Secondary antibodies were HRP-conjugated antimouse (NA-931, GE Healthcare; 1:5000) and anti-rabbit (NA-934, GE Healthcare; 1:5000) antibodies. FSCN1 and SF-1 band intensities were quantified by the Image J software (https://imagej. net/ij) after GAPDH normalization.
Libraries were prepared according to the manufacturer's protocol (Illumina) and sequenced on a NovaSeq instrument at 2 Â 150 bp paired reads. The sequencing coverage and quality statistics for each sample are summarized in Table  S3. Data analysis steps were performed on the Galaxy web platform. 33 The FastQC tool was used for quality control of raw fastq data. Only reads with quality value >30 were further processed. Adapter sequences were removed by Trimmomatic and reads were mapped on the human genome (hg38) using RNA STAR. The number of reads mapping to NCBI RefSeq features were calculated by the featureCounts tool with parameters: stranded, reverse; minimum mapping quality, 10; feature type, exon. Counts were normalized using the DESeq2 tool. Differentially expressed genes (DEG) were identified using the iDEP.
| Proteomics For proteomic analysis, duplicates of each H295R clone (ctrl #1 and 2 and FSCN1 KO #1 and 2) were processed. 100 μg protein per sample was digested with trypsin (2 μg). Peptides were labeled with TMTPro 16Plex (Thermo Fisher) and pooled. The labeled peptide samples were fractionated in reverse phase into seven fractions using a nano-flow HPLC (RSLC U3000, Thermo Fisher) coupled to a mass spectrometer equipped with a nanoelectrospray source and FAIMS Raw data were then imported in Compound Discoverer (CD) software (Version 3. 3, Thermo Fisher) where they were processed using the default untargeted metabolomics workflow. Statistical analysis of modulated metabolites was performed using the CD software, where a P value <. 05 was considered as significant.
However, treatment with Dox, which increases SF-1 (F) Positive correlation of expression between FSCN1-CTNNB1 and β-catenin target genes AXIN2, LEF1, AFF3, FAM194A, ISM1, PXYLP1 and NKD1 in ACC from the TCGA cohort. Data were retrieved and analyzed using GEPIA (http://gepia. cancer-pku. cn/detail. php?clicktag=correlation). F I G U R E 1 [Color figure can be viewed at wileyonlinelibrary. com] expression (Figure  2A ), induced no significant increases in the number of those cytoskeletal structures in FSCN1 KO cells, in contrast to control cells (Figure  4A-C ). Consistent with those data, SF-1 dosagedependent invasion through Matrigel was significantly impaired in FSCN1 KO cells compared with control cells (Figure  4D ). A series of small molecule FSCN1 inhibitors has been developed recently, which have improved potency compared with the first inhibitor described, G2.
G2-044 is an orally available FSCN1 inhibitor which has an improved potency compared with previous inhibitors. 17 A phase I clinical trial that employed this molecule showed efficacy in some patients with advanced, treatment-refractory solid cancers. 46 Another trial is presently ongoing to study the effect of G2-044 monotherapy or in combination with an anti-PD-(L)1-immune checkpoint inhibitor on metastatic cancers (NCT05023486). In addition to its direct involvement in the metastatic process, recent studies have highlighted the effects of FSCN1 on the antitumoural immune response. In the TCGA ACC cohort, FSCN1 expression is negatively correlated with intratumoural effector and memory T cell signatures  47  and with PDL1 expression. 48 In our study, FSCN1 KO in H295R cells strongly upregulated expression of NLRP1, a gene encoding a key protein in the inflammasome  49  (Table  S5 ).
ACKNOWLEDGEMENTS sgCtr-LentiCRISPRv2 was a gift from  William Kaelin  (Addgene plasmid # 107402 ). We thank  D. Vignjevic  ( Institut Curie, Paris, France ) for providing the FSCN1 promoter-luciferase reporter and the ΔN-TCF4 expression plasmids;  F. Luton  ( IPMC, Valbonne, France ) for the gift of the anti-paxillin antibody;  E. Pallesi-Pocachard  ( Molecular and Cellular Biology Platform, INMED Marseille, France ) for DNA sequencing;  J. Cazareth  ( IPMC, Valbonne, France ) for flow cytometric analysis;  M. Decourcelle  ( Functional Proteomics Platform of the Montpellier Proteomics Platform, BioCampus Montpellier, France ) for proteomic analysis;  E. Turtoi  ( PLATON platform, MAMMA facility, UAR Biocampus Montpellier, France ) for metabolite analysis.
DATA AVAILABILITY STATEMENT Anonymized clinical and genetic data of patients with ACC are publicly available in the Xena Functional Genomics (https://xenabrowser. net) and cBioportal (https://www. cbioportal. org) websites. RNA-seq data are available as GSE216008 in Gene Expression Omnibus (https:// www. ncbi. nlm. nih. gov/geo). The raw mass spectrometry proteo-.
In this survey, we focus on the applications of graph representation learning to the detection of network-based and hostbased intrusions, with special attention to GNN methods. For both network and host levels, we present the graph data structures that can be leveraged and we comprehensively review the state-of-the-art papers along with the used datasets. Our analysis reveals that GNNs are particularly efficient in cybersecurity, since they can learn effective representations without requiring any external domain knowledge. We also evaluate the robustness of these techniques based on adversarial attacks. Finally, we discuss the strengths and weaknesses of GNN-based intrusion detection and identify future research directions.
We believe that the application of GRL and GNNs to intrusion detection is a promising research direction and hope that this survey will offer a concise and normalized view of recent techniques for future research. Our contribution is threefold: • We first introduce the different types of graphs used in graph-based intrusion detection, along with the random walk and GNN models commonly employed. We also provide a general architecture that summarizes most approaches based on GNN for intrusion detection. • To facilitate the easy comparison of existing works in the field, we have classified the state-of-the-art papers and provided a comprehensive review of current methods and datasets for GNN-based intrusion detection. For both network and system data, we also present the graph structures that can be used, along with their strength and weaknesses.
4) SCALING TO LARGE GRAPHS WITH GRAPHSAGE In traditional ML, the samples selected from the dataset are usually considered statistically independent. This assumption makes it possible to train models on large datasets by dividing the dataset into chunks that can be trained independently using mini-batches. Contrarily, nodes in a graph are inherently connected and are thus not considered independent and uniformly distributed. This makes the GNNs harder to be scaled on very large graphs with billions of nodes or edges. Indeed, models like GCN and GAT require the storage of the whole adjacency matrix with corresponding features into memory, which makes these models unusable on very large graphs. GraphSAGE  [42]  was the first model to address the scalability issue by sampling a fixed number of nodes s from the neighborhood of a given node u, rather than using the full neighborhood.
A. REPRESENTING NETWORK DATA AS GRAPHS A network is by definition a system made of interconnected entities. This is why networks have intuitively been represented as graphs since the beginning of the Internet era. Nodes usually represent hosts (e. g. computers, printers, IoT and physical devices) and edges model the interactions between those hosts (e. g. requests, authentications, communications). Literature commonly relies on three types of network data to model network traffic as a graph: Packet Graph: A packet is a layer-3 normalized unit of data transmitting on the network, notably composed of a payload that contains the actual data to be transmitted, and a header storing the necessary information to deliver the packet, such as the source and destination IP addresses and ports. The packet payload may contain sensitive information such as authentication credentials or visited website content.
1) NETWORK INTRUSION DETECTION WITH FLOW GRAPHS Against botnet attacks, some researchers attempt to leverage the graph representation of network flows to detect complex botnet communication patterns between hosts. Zhou et al. [65]  propose a supervised approach based on a GCN and network traces. Here, only the topological structure of the graph is considered. Indeed, they omitted edge features and initialized node features as a vector full of ones. The graphs used for training are significant and contain in average more than 140k nodes and 700k edges. A GCN model first computes the node embeddings across all the graph and malicious botnet nodes are then classified using a neural network at node-level. The performance of the model has been evaluated using a synthetic dataset presented in Section III-B4.
Similarly, Zhang et al. [66]  suggest using a GCN for botnet detection. In order to capture the long-term dependencies present in large botnet architectures, the authors used 12 GCN layers. However, the use of very deep GCN models is subject to over-smoothing  [80]  and dedicated methods are usually required to deal with these deep networks. The model was evaluated on the same dataset proposed by Zhou et al. [65]. To prevent the previous over-smoothing problem, XG-BoT  [67]  leverages grouped reversible residual connections  [81]  along with a GIN model to capture hidden topological patterns of botnets while maintaining more stability during the training of very deep models. XG-BoT was also evaluated on the dataset by Zhou et al. [65]  and outperforms the two previous works  [65] ,  [66]  for the detection of botnet nodes.
Forensics analysis is also considered by leveraging GNNExplainer  [82]  to highlight a relevant subgraph along with corresponding node features, ultimately resulting in an explainable graph that security teams can analyze. On a different dataset built from background traffic and synthetic botnet topologies, the authors in paper  [68]  suggest to leverage Inferential SIR_GN  [83] , a model able to generalize on unseen and very large graphs and that privileges node structural similarity. Indeed, the authors suggest that GNNs may not be fitted for botnet detection as they consider the node proximity similarity an important indicator in the learning process. They further explain that proximity-based similarity generates node representations that are close in embedding space when the nodes are near or highly connected in the graph space.
Knowledge is extracted from this graph using 10 hand-designed meta-paths, which capture useful symmetries hidden in botnet topologies (see Section II-C6 for the definition of meta-path). Weighted similarity graphs are built by calculating the similarity between pairs of nodes and GCN layers are applied on these graphs to characterize discriminative features of bots. Malicious nodes are then detected using a standard feed-forward network at node-level, trained with cross-entropy loss in an end-to-end manner. The proposed model outperforms other graph-based baselines such as a HAN and a standalone GCN, on private network data and on the CTU-13 dataset. A more general approach to detect intrusions with an heterogeneous graph is proposed in paper  [54]. The graph is built based on network flows and for each flow, three nodes are created: the source host node, destination host node and the flow node, where two undirected edges exist between source host and flow nodes and between flow and destination host nodes.
Note that σ type and δ type respectively represent a fully connected network and a GRU. A final MLP is used at node-level to classify among multiple attack classes using softmax along with the categorical cross-entropy loss for training. The custom MPNN has been tested against the CIC-IDS2017 dataset, after dropping 90% of benign examples for unbalanced classes reasons, and achieves a similar accuracy compared to benchmarked ML methods such as MLP, random forest or AdaBoost. However, the robustness of their model has been demonstrated by modifying flow features like packet size and inter-arrival times via multiple adversarial attacks  [84]. The results show that their accuracy remains good despite the attacks, whereas other ML algorithms see their performance diminished. This demonstrates that GNNs offer more robustness and generalization capabilities than traditional ML techniques.
For the detection of IoT-based network attacks, Protogerou et al. [70]  place multiple agents into a network to collect flow statistics that are then attributed to nodes and edges in a graph built from IP communications. For instance, a node may store the average number of packets sent from this node and an edge can contain the average number of packets sent between a pair of nodes. To propagate information in the graph and compute embeddings, a Graph Network (GN)  [85]  is used. This is a generalization block built from various GNNs, including MPNNs and Non-Local Neural Networks (NLNNs). Two Multi-Layer Perceptrons (MLPs) are then used for attack detection, one at node-level and another at edge-level, that are both leveraged to create a final prediction. The authors built a synthetic dataset containing benign and attack examples based on the CTU-13 dataset and Mirai attack's distribution.
The proposed solution has been evaluated against this custom dataset and outperforms the  VOLUME 11, 2023  compared ML techniques such as SVM and random forest, while consuming less energy. E-GraphSAGE  [71]  is another method for network intrusion detection in IoT environment. When building the graph, nodes are identified by an IP address and a port, whereas edges represent the flow communication between two nodes. The source IP addresses are randomly mapped to local addresses on a given range to avoid unintentional label for attack traffic. The actual flow features are stored in edges, unlike the original GraphSAGE model that only works with node features. For these reasons, they tuned the GraphSAGE model to work with edge-level features. The aggregation and update functions are similar to the ones used in GraphSAGE (See Eq.
8 and Eq. 9). However, the newly created neighborhood aggregator embeds sampled neighbor edges instead of neighbor nodes, as explained in Eq. 18 and 19. h t N s (u) = AGG t e t-1 uv , ∀v ∈ N s (u), uv ∈ E , (18) h t u = σ W t • h t-1 u , h t N s (u) , (19) where e t-1 uv is the embedding of edge (u, v) at the previous layer and E is the set of edges. No node features are used here (i. e. a node is just a all-one vector). The model is composed of two E-GraphSAGE layers to build the embeddings, meaning that edges are aggregated up to a 2-hop neighborhood. After message-passing, edge embeddings are used for supervised edge classification. The performance has been evaluated on the BoT-IoT, ToN-IoT datasets along with NF-BoT-IoT and NF-ToN-IoT. These are the Netflow normalized versions of the datasets, described in Section III-B4.
For the detection of peer-to-peer botnets, LGANet  [72]  proposes to consider each node in the network graph as a centroid and builds a local graph from its 1-hop neighborhood, on which the correlation between nodes is modeled using attention. Indeed, P2P bots usually communicate with peers to provide malicious commands and attention is leveraged to capture such relations between hosts. The performance of this custom model has been demonstrated on a dataset created by merging multiple public datasets such as CTU-13, PeerRush  [86]  and MAWI  [87]. Based on the work of E-GraphSAGE  [71] , Chang et al. [55]  propose an improved version of the model by leveraging residual connections to train deeper models and to deal with the high class imbalance of most current intrusion detection datasets.
Node features are initialized with all-one vectors with the same dimension as edge features storing flow statistics. Edge features are then updated by concatenating node embeddings from both endpoints along with the initial edge features as a residual connection: z uv = [h K u , h K v , e uv ], (21) where z uv is the final edge embedding between nodes u and v, h K i is the final embedding of a given node i after K iterations, e uv is the initial feature vector of edge (u, v). Here, (u, v) ∈ B where B represents a batch of edges. In a second part, the E-ResGAT model is presented. It consists of a GAT model with additional residual connection and operates on the same line graph as the first method. The authors evaluate the performance of both models on UNSW-NB15, CIC-DarkNet, CSE-CIC-IDS and ToN-IoT datasets.
However, only the original GAT is used as a baseline for the benchmark. Another improvement to E-GraphSAGE is provided by Lan et al. [73] , by introducing a pre-sampling step before training the model, resulting in smaller graphs and better scalability. The performance of this model has been compared to the original E-GraphSAGE along with other baselines on the UNSW-NB15 dataset where the accuracy and F1-score are slightly improved on both binary classification and multi-class classification tasks. However, the performance of this model has not been demonstrated on other datasets such as the ones originally used in the E-GraphSAGE paper. Authors of E-GraphSAGE also contributed to an important improvement to the model, by applying it to a self-supervised approach. This new method named Anomal-E  [50]  leverages the same graph structure (i.
These scores are finally used in the binary cross-entropy loss to train the model: L = - 1 2n n i=1 E G log D z K uv , ⃗ s +E G log 1 -D zK uv , ⃗ s , (25) where D z K uv , ⃗ s is a trainable bilinear binary classifier that takes as input an edge embedding along with a graph summary. Anomal-E can then classify malicous flows based on the embeddings using unsupervised classifiers. On both Net-Flow datasets presented in  [88] , Anomal-E achieves close performance compared to the original supervised baseline  [88] , using an extra tree classifier. However, the current approach does not require any label, which makes this solution more applicable to real-world scenarios. Against DDoS attacks, a graph classification approach is proposed by Li et al. [74]. In this work, the presented GraphDDoS model aims to detect both low-rate and high-rate DDoS attacks by considering the relationship of packets from a single flow and the relationship between flows.
The model has been evaluated on the synthetic dataset against CNN, GCN and SVM baselines, and the ST-GCN achieves an important accuracy increase and false positive rate reduction. Against intrusions in IoT environment, paper  [76]  leverages the Auto-Metric Graph Neural Network (MAGNN)  [91]  along with meta-heuristic optimization methods based on HTTP requests data. The MAGNN was initially introduced to be used for the diagnosis of Alzheimer's disease, where it outperforms most graph-based methods like GCN or GAT on biomedical datasets  [91]  by using a meta-learning paradigm  [92]. Here, the authors apply this model to IoT node classification using 2 datasets: CSIC-2010 and ISCXIDS-2012. On both datasets, the proposed model outperforms the compared baselines.