dataesr/scientific-paragraphs-categorization huggingface dataset:
lugdunensis database. Bioinformatic analysis and visualization were performed in Perseus. Three sample tests were performed using Student's t test with a Permutationbased FDR of 0. 05. RNA extraction. RNAs were extracted from cells of S. lugdunensis N920143 and DagrA mutant cultured in BHI harvested in the late-exponential phase (OD 600 of 1) as previously described  (16). Following incubation of the cell pellets for 12 h at 280°C, RNAs were extracted with the Direct-zol RNA miniprep kit (Zymo Research, Irvine, CA, USA) and residual DNA was removed using Turbo DNase (ThermoFisher) according to the manufacturer's instructions. RNAs integrities and quantifications were measured by the Agilent TapeStation system (Agilent, Santa Clara, CA, USA). Four independent samples for each strain were used.
RNA-Seq and reads mapping. cDNAs from the RNA samples were sequenced with the Illumina HiSeq 4000 platform generating reads of 50 pb (iGE3 Genomics Platform, University of Geneva, Swiss). For each condition, RNA-Seq analyses have been carried out using the four independent RNA samples. The fastqc software, the STAR algorithm (version 2. 7. 5b), and the package HTSeq (version 0. 13. 5) were used to perform the quality assessments, reads mapping to the S. lugdunensis genome (assembly: ASM27046v1 accession number: NC_017353. 1), and table of counts, respectively. For the identification of genes differentially expressed in the DagrA mutant versus the S. lugdunensis N920143 parental strain, normalized data of reads were compared using the R package edgeR (version 3. 26. 8) and correction of the P value was performed by the Benjamini-Hochberg method.
ncbi. nlm. nih. gov/assembly/GCF_000270465. 1). First, using a homemade script, the MicrobiolSpectrum. asm. org 13 coordinates of the non-coding regions and all the CDS were extracted. Then the coordinates of the putative AgrA-binding motif defined as "WMMGTTNRK" (8) were extracted from the fasta file of the genome using SeqUtils and SeqIO from Biopython, on both DNA strands. Finally, among genes whose transcriptions were modified in the DagrA mutant, those with a motif found in the non-coding regions corresponding to the putative promoter regions, were suspected to be directly under AgrA control.
ACKNOWLEDGMENTS The authors warmly thank  Sebastien Galopin ,  Sophie Castagnet  and  Mamadou Godet  for technical assistance and  A. Peschel  for providing the pRB473 plasmid. M. Aubourg  and F. G. works are supported by grants from the  Ministère de l'Enseignement Supérieur et de la Recherche  and the  Region Normandy , respectively. M. Aubourg ,  M. Auzou , and J-C. G. conducted the experiments and drafted the manuscript. P. L. and  M. Aubourg  constructed the mutant strains. M. P. and A. L. performed the biofilm experiments. B. B. conducted the proteomic analysis. F. G. and M. Aubourg performed transcriptomic and statistical analysis. M. C. , R. T. , C. M. , and M. S. conducted the experiments of survival in macrophages. M. Aubourg, A. D. , F. G. , A. L. , M. P. , and J-C. G. participated to the results interpretation and editing manuscript.
Data availability. The LC-MS/MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD028347 (https://www. ebi. ac. uk/ pride). All the RNA-Seq data are available in the GEO database with accession number GSE181857 (https:// www. ncbi. nlm. nih. gov/gds/).
cerevisiae, ranging from 100% (blue) to 76% (orange). A Actin expression levels shown by western blotting for strains expressing S. cerevisiae's actin protein from various coding sequences, with tubulin (  Tub1p  Pearson correlation coefficient r is considered nonsignificant if P > 0. 05. H Polarity indexes, defined to assess whether cell polarity is normal or affected (value is 1 when all patches of medium to large budded cells are present in the bud, and -1 refers when all patches are in the mother cell). Data are presented as mean AE SD (n = 30 for all conditions). **P < 0. 01, ***P < 0. 001 (Brown-Forsythe and Welch ANOVA tests, with Dunnett's T3 multiple comparisons tests). Data information: Abbreviations: ns -nonsignificant, Sc -wild-type S. cerevisiae cells, ScNI -S. cerevisiae cells where the actin gene has been replaced with the wild-type gene but without the intron, Sc[X] -S.
Bar graphs represent the percentage of cells with a given number of visible actin patches after CK-666 treatment. (n = 41 for ScNI, 55 for N2, 31 for Ca, 41 for Op, 55 for Hs). Data information: Abbreviations: ns-nonsignificant, Scwild-type S. cerevisiae cells, ScNI -S. cerevisiae cells where the actin gene has been replaced with the wildtype gene but without the intron, the other abbreviations correspond to cells expressing actins from other species (for the list of species see  However, verification that each of the actin structures was enriched by each of the variants is difficult to do in the absence of specific antibodies; nevertheless, our results indicate that defective actin functions in cells carrying a single actin variant were carried out more normally when the other actin variant was simultaneously expressed.
Our measurements of G/F actin ratios in Sc and N1 strains do not reveal any significant difference. However, we would like to point out that these measurements do not distinguish actin monomers and filaments from small oligomers. Several studies report the ◀ Figure  4. In vitro reconstitution of branched-and linear-actin networks assembly from purified actins. Standard conditions include Las17-(branched) and Bni1-(linear) coated beads, 8 µM F-actin assembled from different purified variants and 1% labeled with Alexa-568 rabbit muscle actin, 15 µM of profilin, 1 µM of capping protein, 500 nM of Arp2/3 complex and 600 nM of ADF/cofilin. Snapshots of representative actin networks were taken after 30 min. Scale bars: 6 µm. Data information: For all microscopy images, intensity levels were adapted for images of branched-and linear-actin networks separately as their brightness is different.
Reconstruction of ancestral protein sequences and selection of actins Actin amino acid sequences from 126 different species, selected from different branches of the eukaryotic and archeal tree of life to cover a wide range of variations, were collected from annotated and reviewed UniProtKB/Swiss-Prot entries. For species encoding more than one actin, the cytoplasmic actin with the most similar sequence to S. cerevisiae actin was selected. The resulting 126 selected sequences from different species were aligned using the Multiple Sequence Alignment program Clustal Omega  (Madeira et al, 2019)  in Pearson/FASTA format. The phylogenetic tree of the 126 species was created based on the NCBI taxonomy using the phylogenetic tree generator phyloT (https://phylot. biobyte. de/). Ancestral actin sequence reconstruction was performed from multiple sequence alignment and phylogenetic tree inputs using FastML  (Ashkenazy et al, 2012).
Actin cytoskeleton organization in yeast Yeast cell phalloidin staining and imaging Log phase cultures in YPD medium at 25°C were fixed with 4% formaldehyde for 2 h. For CK-666-sensitivity assays, cells were treated with the indicated concentration of CK-666 (Sigma-Aldrich SML0006) for 30 min before fixation. After fixation, cells were washed twice in PBS and stained overnight with 250-nM Phalloidin-Alexa568 (Invitrogen, ref. A12380) at 25°C. Samples were washed twice with PBS, resuspended in PBS-70% glycerol and directly mounted for imaging. Cells were imaged using a Leica TCS SP8 X White Light Laser confocal microscope equipped with an HC PL APO CS2 100×/1. 4NA Oil objective and a hybrid detector. Z-stack images were collected every 0. 3 µm with Las X 3. 5. 5. 19976 software.
Data analysis for live imaging Branched-and linear-actin network assembly in medium budded cells was assessed from the intensity of actin patches and cables, respectively. Total cell cable intensities were calculated from maximum intensity z-stack projections using Fiji v. 1. 53a. For total cell endocytic patch intensities, patches were identified using the Track-Mate plugin of Fiji  (Tinevez et al, 2017; Planade et al, 2019). Patch detection was corrected manually using the spot editing tool, and the integrated intensity for all patches was calculated from the analysis table of TrackMate. Fluorescence intensity of phalloidin labeling varied between strains expressing different actin variants. For this reason, the contrast of images showed in the figures was adapted from strain to strain so that both actin structures remained clearly visible.
RNA sequencing analysis The quality of the raw reads was assessed using FastQC v0. 11. 9 toolkit (https://www. bioinformatics. babraham. ac. uk/projects/ fastqc/). Adapters and low-quality reads were trimmed using Trimmomatic v0. 39  (Bolger et al, 2014). The reads were aligned with HiSat2 v2. 2. 1  (Kim et al, 2015)  using default options. Gene counts were quantified using feature Counts from the Subread v2. 0. 1 package  (Liao et al, 2019). Alignment and gene counts were generated against the reference genome Saccharomyces cerevisiae S288C (assembly R64). The low expressed genes, which did not have more than one count per million reads (1CPM) in at least three samples within each dataset, were removed from further analysis. Gene counts were then normalized and used for differential expression testing using DESeq2 v1.
Data quantification for biomimetic assays Fluorescence intensity of actin networks and fluorescent ABPs was quantified using Fiji (Version 1. 52p), and fluorescence of the background was subtracted. To avoid any measurement bias, all actin networks present around the beads are quantified, independently of their apparent density. Similarly to the in vivo actin network deviation index, a linear-to-branched ratio was calculated to compare the efficiency of actin assembly in vitro for both branched-and linearactin networks of actin filaments for a given biochemical condition. This index measures how actin assembly between branched and linear networks deviates from the values obtained when S. cerevisiae actin is used. It is defined as follows: In vitro Actin Network Deviation Index ¼ where I branched is the average intensity of the branched network for all Las17 beads with a given actin, I Sc;branched is the same value for S.
Single filament preparation and imaging Monomeric actins purified from strains expressing Act_Sc, Act_N2, Act_Ca were precleared by centrifugation at 156,000 g for 1 h at 4°C, and mixed with Alexa568-labeled monomeric rabbit muscle actin to reach a final labeling percentage of 1%. The top 2/3 fraction was kept for the experiment and 3 µM of actin was polymerized for 1 h by addition of 1x KMEI, in the presence of 3 µM of phalloidin. Filaments were then carefully diluted 300 times in Fluorescence Buffer (10 mM of imidazole-HCl, pH 7. 0, 50 mM of KCl, 1 mM of EGTA, 1 mM of MgCl 2 , 70 mM of DTT, 2. 5 mg/ml of glucose, 15 µg/ml of catalase, 70 µg/ml of glucose oxidase, 0. 1% of BSA, and 0. 3% of methylcellulose 1500 cP) and loaded onto plasmacleaned coverslips. Filaments were observed using a Nikon Eclipse Ti microscope, equipped for TIRFM (Total Internal Reflection Fluorescence Microscopy) with a 60× NA 1,49 objective, a Prime 95B scientific CMOS camera (Photometrics), and using Metamorph software.
Actin-ABP contact analysis The amino acid positions of the substitutions between Saccharomyces cerevisiae (Act_Sc), Node 2 (Act_N2), and Candida albicans (Act_Ca) actin sequences were inspected within high-resolution Xray crystal and cryoEM structures of complexes containing actin. The PDB accession codes are: G-actin (1YAG)  (Vorobiev et al, 2003) , F-actin (6DJN)  (Chou & Pollard, 2019) , ADF/cofilin (5YU8 and 1CFY)  (Fedorov et al, 1997; Tanaka et al, 2018) , Arp2/3 daughter filament (6W17)  (Shaaban et al, 2020) , profilin (1YPR and 3CHW)  (Eads et al, 1998; Baek et al, 2008) ; CP/Arp1 (5ADX) (Urnavicius et al, 2015), WH2 (5YPU), formin (1Y64 and 4EAH)  (Otomo et al, 2005; Thompson et al, 2013) , and tropomyosin (5JLF) (von der Ecken et al, 2016). Actin residues that are within 5 A of the binding protein were identified in the CCP4 program  CONTACT (Winn et al, 2011) , with the exception of the Arp2/3 mother filament, which were within 10 A since the coordinates were not released when this study was performed  (F€ aßler et al, 2020).
Data reproducibility All experiments were repeated at least two times. For each mutant, a minimum of three independent clones were generated and compared for reproducibility of phenotypes (biological replicates). The results presented in this study are those obtained for one of these mutants, which is kept in our collection (i. e. , technical replicates). Western blot analyses show technical replicates from two different biological replicates, and RNAseq data are presented from three different biological replicates. For the colony area measurements, colonies were measured from 2 plates, results were normalized to control and pooled together. For cell doubling time measurement, experiments were repeated 3 times in different days.
Acknowledgements The authors thank  Isabelle Sagot  and  Emilia Mauriello  for their invaluable advice on the project; Sarah W€ urbel and  Ahmed Fatmi  for their technical help. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement n°638376/Segregactin) to A. M. , from the  Labex INFORM  ( ANR-11-LABX-0054 , funded by the " Investissements d'Avenir French Government program "), and from the  Fondation pour la Recherche M edicale (FRM)  to  M. B. S. under the program Fin de th ese (ref. FDT201904008021). R. C. R. thanks  Vidyasirimedhi Institute of Science and Technology (VISTEC) ,  RIIS  and  JSPS (KAKENHI  grant number  JP20H00476 ) for support. We acknowledge the  France-BioImaging  infrastructure supported by the  Agence Nationale de la Recherche  ( ANR-10-INSB-04-01 ).
BACKGROUND In supervised machine learning, a relationship exists between model complexity and the volume of training data; too few training examples and the model will over-fit, resulting in an unnecessarily complex model that produces poor results. Therefore, securing sufficient and representative data is a limiting factor in model development and performance  [7]. In practice, accessing and/or generating sufficiently large and representative training examples is a complex challenge and may involve significant manual effort and processing time  [8]. Nonetheless, there are publicly available data sets for training IDS systems, notably the CICIDS2017 and the NSL-KDD sets. These data are used to pre-train the Siamese Network, subsequently, in the evaluation of the performance of the model in identifying a new class of attack after a limited number of that class' samples has been recorded.
e. selecting similar and dissimilar pairs) or during evaluation of the new class (i. e. selecting the labelled instances that best represent this class). Furthermore, the uniqueness of the pairs -no duplicates -is ensured. A "set" data structure is used. it is added to the batch of pairs unless that pair is already contained within the set. This is demonstrated in Algorithm 2. During evaluation, an instance i is paired with one random instance from each class. The instances are drawn out of the pool of testing instances, resulting in N pairs. The similarity is then calculated for the N pairs. Instance i is classified (labelled) based on the pair with the highest similarity (i. e. least distance). As discussed in Section 5, to determine the trade-off between the number of labelled instances of the new attack class and accuracy, the process is repeated j times for each instance i.
DATASETS Three data sets are used to evaluate the proposed models; two benchmark IDS data sets, specifically, CICIDS2017 and NSL-KDD and KDD Cup'99. The latter is used in comparison to the NSL-KDD to demonstrate the effectiveness of clean data when generating training pairs and also, when introducing new attacks to the trained model. Each data set contains N classes. K classes are used to train the network, such that K = N -1. The K classes include normal/benign and K -1 attack classes. The instances of each of the K class act as a pool used to generate similar and dissimilar pairs. Furthermore, one class is used to simulate a new attack, mimicking the situations in which little/limited data is available for a new attack. The pair generation details and the experiments are further discussed in Section 3.
KDD Cup'99 The KDD Cup'99  [44] , although old, is still considered as the classic benchmark data set used in the evaluation of IDS performance. More than 60% of the research in the past decade (2008 -2018) has been evaluated using KDD'99  [3]. KDD Cup'99 covers 4 attack classes alongside normal activity. The attacks contained in the data set are; Denial of Service (DoS), Root to Local (R2L), User to Root (U2R) and probing. The KDD Cup'99 data set is relatively large, however, the provider has made available a reduced subset of ˜10%  [45]. For the purposes of evaluation here, only the smaller subset is used. Table  3  shows the number of instances per class for the KDD Cup'99 data set.
NSL-KDD The NSL-KDD  [46]  data set was proposed by the CIC to overcome the problems of the KDD Cup'99 set discussed by Tavallaee et al. [47]. Similar to KDD Cup'99, NSL-KDD covers 4 attack classes alongside normal activity. NSL-KDD is used for evaluating the effect of enhancing and filtering a data set on the similarity learning and performance. Recent surveys examined the use of ML for IDS  [48]. Furthermore, Thomas and Pavithran  [49]  study the recent ML techniques evaluated using the NSL-KDD data set. While, Panwar et al. [50]  evaluate the usage of ML on CICIDS-2017 data set. Although there are various manuscript using ML for IDS, comparing the proposed model with recent IDS models is not applicable. This is because the proposed model leverages One-Shot learning, therefore, it cannot be in comparison with classical classification models.
On inspection of Table  6  and Table  8 , it is evident that using five labelled instances of the new attack class results in an increase in both the overall accuracy and the TPR together with a drop in the FNR. Using only 1 labelled instance demonstrates a comparably poorer performance owing to the instance selection randomness, which could result in either a good or a bad class representative. However, using 5 random labelled instances boosts performance, reinforcing the importance of having distinctive class representatives. The remainder of the CICIDS2017 performance evaluation results are characterised by similar behaviour and are listed as follows. DoS (Hulk) results are presented in Table  9  and Table  10 , while DoS (Slowloris) in Table  11  and Table  12. The CMs of the KDD Cup'99 and NSL-KDD data sets One-Shot, excluding the DoS attack from training are presented in Table  13  and Table  15 , respectively; the overall accuracies are 76.
CONCLUSION AND FUTURE WORK The paper presents an Intrusion Detection Siamese Network framework capable of classifying new cyber-attacks based on a limited number of labelled instances (One-Shot). The evaluation of the model was performed on three different data sets; CICIDS2017, KDD Cup'99 and the NSL-KDD, an enhancement of the KDD Cup'99. Results of the evaluation re-confirm that particular consideration must be given on creating the training set, ensuring an equal number of training pairs for every class combination. The core requirement, in turn, presents a challenge of an exploding number of combinations between all instances. Thus, distinct pairs are chosen to create large batches in the region of 30,000 pairs to mitigate the growth. During evaluation, similarity comparison using a single point for each class resulted in noisy predictions due to randomness obviated through the selection of multiple (j) random instances from each class and aggregation using majority voting.
Results demonstrate the ability of the proposed architecture to classify cyber-attacks based on learning from similarity. Moreover, the results highlighted the need for representative instances for the new attack class. Furthermore, evidence is provided to confirm the ability of One-Shot learning methodologies to adapt to new cyber-attacks without retraining when only a few instances are available for a new attack. An overall accuracy of between 80% -85% for the CICIDS2017 data set was evaluated, demonstrating acceptable accuracy in detecting previously unseen attacks. The overall accuracy reached above 75% for the KDD Cup'99 and NSL-KDD data sets. Further and also important to the application is that the overall accuracy was achieved at a low FNR for the new classes.
end function is. For class e (the attack class that Trained Siamese Network Evaluation Ensure: dataset = {c 1 , c 2 ,. , c n } s. th. n ≥ 3 1: train batch size, test batch size ← 30, 000 excluded from training), the first half acts as the pool of labelled and the second half act as the pool of new unlabelled instances. The model evaluation yields a Confusion Matrix (CM) that visualises the performance. A sample CM is presented in Table 1. Each row of the CM represents a class; True Positive (TP) is the number of attack instances correctly classified as attack; True Negative (TN) is the number of normal instances correctly classified as normal; False Positive (FP) is the number of normal instances wrongly classified as attack; False Negative (FN) is the number of attack instances wrongly classified as normal.
The overall accuracy is calculated as shown in Equation 4. True Positive Rate (TPR) and False Negative Rate (FPR) for each class are shown in Equation 5 and Equation 6 respectively; finally, True Negative Rate (TNR) and False Positive Rate (FPR) are calculated using Equation 7 and Equation 8 respectively. Algorithm 1 Train and Test Siamese Network Input: Attacks Dataset Output: Algorithm 2 Generate Training Batch Input: Dataset of K(N -1) classes, Batch Size Output: Batch of similar and dissimilar pairs and associated labels (0: dissimilar, 1: similar) 1: function GETTRAININGBATCH(batch size) 2: num similar pairs = batch size/2 3: num dissimilar pairs = batch size/2 4: num similar pairs per class = num similar pairs/K 5: all combinations = combinations(K) 6: num dissimilar pairs per combination = num dissimilar pairs/len(all combinations) 7: pairs set ← {} 8: for c in K do 9: for i = 0 to num similar pairs per class do 10: (ins 1 , ins 2 ) ← 2 random instances ∈ c i 11: if (ins 1 , ins 2 ) ∈ pairs set then 12: go to 10 13: end if 14: 16: end for 17: end for 18: 23: go to 20 24: end if 25: 27: end for 28: end for 29: targets[0.
th. e ∈ dataset 4: training classes = dataset -e 5: training = 50% c i ∀c i ∈ training classes 6: testing = dataset ∩ training 7: batch ← GETTRAININGBATCH(train batch size) 8: Build Siamese Network with Random Weights 9: for i = 0 to n iterations do 10: Update Siamese Network Weights based on batch 11: end for 12: EVALUATE(test batch size) pairs[i] ← {ins 1 , ins 2 } 15: pairs set. add({ins 1 , ins 2 }) for c 1 , c 2 in all combinations do 19: for i = 0 to num dissimilar pairs per combination do 20: ins 1 ← random instance ∈ c 1 21: ins 2 ← random instance ∈ c 2 22: if (ins 1 , ins 2 ) ∈ pairs set then pairs[i] ← {ins 1 , ins 2 } 26: pairs set. add({ins 1 , ins 2 }) accuracy = n correct * 100/batch size 24:.