dataesr/scientific-paragraphs-categorization huggingface dataset:
Augmentation performance While the size and the proportion of the class of the Ghosh dataset were the biggest factors of why the Ghosh dataset had a higher F-measure, the way the dataset is collected also contributed to the cause. Ghosh dataset is automatically generated, thus making it easier to create a dataset that was quite big in size and fairly balanced in class proportion. Furthermore, the way the Ghosh dataset labeled the data by detecting the "sarcasm" hashtag, maintain the quality of the label and made it more consistent. Nevertheless, because the label was added automatically, there is a high chance that the labeled sarcastic data was less precise, resulting in a lowerquality model. As for iSarcasm dataset, the lack of size, and the imbalanced nature of the dataset were the biggest factor in the low F-measure score.
However, this happen due to the fact that the iSarcasm dataset was manually collected, making it harder to collect many data, while maintaining the proportion between the sarcastic and non-sarcastic class. SemEval-18 dataset, was also collected manually, thus explaining the nature of the small size. However, it was quite balanced in proportion, resulting in F-measure that was slightly better than iSarcasm dataset. It should be noted while both the iSarcasm and SemEval-18 are manually collected datasets, they both have a different way of labeling. The iSarcasm dataset contains intended sarcasm from the participants of an online survey, while SemEval-18 contains perceived sarcasm that was labeled by third-party annotators. The different nature of how the data is labeled could also influence the F-measure score results.
When applying data augmentation, we consider the amount of generated data to make sure that we do not add too much sarcastic data and make our dataset unbalanced due to the generated data. Furthermore, we think that adding too much-augmented data creates bias, and it could reduce the performance of the model. In this case, we arrive at 3,098 generated data for Ghosh dataset, which balance our dataset. 147 for SemEval-18, because the dataset is already balanced, so we choose to take a few samples, and finally 106 generated data for iSarcasm dataset. The details can be seen in Table  3. Even if iSarcasm dataset is completely unbalanced with the non-sarcastic class, we cannot generate too much data for the sarcastic class, due to the bias effect. In fact, we only had 612 sarcastic sentences in the training dataset, so by adding 106 data, we already added 17,3% of the amount of sarcastic sentences.
However, when we further analyse in Table  6 , on the iSarcasm dataset, Using the SLOR filter in the SMERTBERT pipeline seems to improve the performance of Fmeasure by +0. 01, and for the SemEval-18 dataset, it increases by +0. 02 when compared to SMERT-BERT without SLOR filter. As for the SWORD pipeline, we can see that the only improvement of Fmeasure when using the SLOR filter is only in SemEval-18 dataset with an improvement of only +0. 01. We can see that choosing the best SLOR values increases results for both pipelines, but it is more effective for our SMERT-BERT pipeline. SLOR filter is not effective on the SWORD pipeline due to how the masking process works. SWORD pipeline only masks the word with the most positive or the most negative score only, thus there is only one word that is masked in the sentence.
Comparison with prior work. There are various similar work  [2, 14-16, 19, 20, 22]  that experimenting with data augmentation to improve the classification task in NLP. To provide an apples-to-apples comparison with our work, we are focusing on comparing our work with other, related research that uses data augmentation to solve the unbalanced data in sarcasm detection problem, specifically when using unbalanced dataset like iSarcasm. A comparison and discussion between our proposed model with other works in detecting sarcasm sentences can be seen in Table  7. Based on the previous work that we already discussed in section 2, we can conclude that data augmentation in NLP classification problem could significantly boost the performance of model. Although for the case of sarcasm data, building an augmentation approach which can maintain the sarcastic nature of the text proves difficult.
Previous work in the sarcasm dataset from Table  7  shows that recent work in augmentation of sarcasm data still lacks on the technique used to implement data augmentation and also on how to evaluate the generated sarcastic data. Thus in this research, we proposed SMERT-BERT and SWORD pipeline, where we use semantic text approach to create the augmented data, and also using SLOR metric to evaluate and filter our generated sarcastic text. Also, our approach only utilize the original dataset, without adding additional external dataset to further augment the data.
Conclusion This article shows that data augmentation applied for sarcasm detection can give better performance, especially for small and imbalanced datasets. Our two proposed augmentation methods SMERT-BERT and SWORD manage to improve the baseline results of small datasets, such as iSarcasm and SemEval-18 by 0. 04, 0. 03 and 0. 03, 0. 01 respectively. Nevertheless, the biggest factor that impacts the performance of the model is the quality of the data itself. Bigger and balanced datasets like the Ghosh dataset, which collected and labeled automatically using the "sarcasm" hashtag offer better results in performance, with F-measure of 0. 79 in both SMERT-BERT and SWORD pipeline, compared to a small dataset like iSarcasm or SemEval-18 with only 0. 42, 0. 69 and 0. 41, 0. 67 in SMERT-BERT and SWORD pipeline rerspectively.
We also found that SLOR metric contributes to better performance in SMERT-BERT methods compared to SWORD method. This happens due to the different amount of masked words in each method. SWORD method masks only one word in sentences, making the structure of the generated sentences have no significant distinctiveness compared to the original one, resulting in the ineffectiveness of the SLOR metric. Further analysis on the impact of sarcasm dataset characteristics like perceived or intended sarcasm could also be investigated to see if there is a significant impact on the model performance. International Journal of Intelligent Engineering and Systems, Vol. 16.
However, their technique incorporated new dataset that was used to be combined with the iSarcasm dataset. Handoyo [21] Model: F1 Score: BERT, RoBERTa, DistilBERT iSarcasm: 40. 44% Ghosh: 81. 08% Data Augmentation: Ptacek: 87. 41% Synonym Replacement SemEval-18: 67. 46% The research show that using data augmentation in sarcasm detection could increase the F1 performance of the model. However, due to the very basic technique that was used in the research, using more augmented data resulting in the decrease of performance. Our proposed method Model: BERT F1 Score: Data Augmentation: SMERT-BERT iSarcasm: and SWORD Pipeline 42% for SMERT-BERT Dataset: iSarcasm 41% for SWORD Novelty: data augmentation using SMERT-BERT and SWORD pipeline, which utilise semantic text exchange method to augment the sarcastic data while still maintaing the quality of the sentence by incorporating SLOR metric to filter the augmented result.
Data that were identified as outlawyers were excluded. Also, 4 popliteal lymph nodes from the in vivo experiments (one non-treated DTH, one treated with murine MSC, one with siRNA against LDH and one with galloflavin) were lost during the experimental processing due to poor lymph nodes cells recovery. The p-values were generated by non-parametric analysis using the Kruskal-Wallis test for multiple comparisons and the Mann-Whitney test to compare two groups for all the data that did not fit with normal Gaussian distribution. Two-ways ANOVA and t tests were used for normal distribution. p < 0. 05 (*), p < 0. 01 (**) or p < 0. 001 (***) were considered statistically significant. Data were analyzed with the GraphPad Prism TM 6 software (GraphPad Software, San Diego, California, USA).
Statistical analysis Quantitative variables are expressed as mean ± standard deviation and were compared using Student's t-test or Mann-Whitney U test, as appropriate. Qualitative variables are expressed as numbers and percentages and were compared using Chi-2 test or Fisher's exact test, as appropriate. The Kruskal-Wallis test was used to search for a possible imbalance in the anatomical distribution of different lesions. Cohen's kappa coefficient, simple or weighted if appropriate, was used to assess inter-observer agreement and was interpreted according to Landis and Koch  23. A value of p < 0. 05 was considered significant. Analyses were performed using SAS version 9. 4 (SAS Institute Inc. , Cary, North Carolina, USA).
2%/25. 8%) (Figure S1A and S1B). Therefore, consistent with previous studies, the mode of ISC division appeared to reflect the degree of intestinal damage, the symmetric mode being preferentiality adopted when the damage was greater (i. e. , when cell death occurred). We next monitored the number of ECs using the fly strain myo1A>GFP allowing the expression of GFP in all ECs. Btk Cry spores induced an excess of ECs at days 1 and 3 postingestion; the right number of ECs was recovered 5 days after ingestion (Figure  1D  and Figure  S1C ). Along with the absence of EC apoptosis (Figure  1A ) and the moderate induction of ISC proliferation (Figure  1B ), our data strongly suggest that Btk Cry spores weakly damage the intestinal epithelium, reminiscent of the "cell renewal" process previously described after infection with poorly virulent bacteria  (Loudhaief et al.
The genomic DNA of Btk Cry1Ab and Btk Btk SA11 was extracted using the KingFisher cell and Tissue DNA kit (ThermoFisher) and sequenced using Illumina technology at the Institut du Cerveau et de la Moelle Epinière (ICM) platform, as previously described  (Bonis et al. , 2021) , (NCBI accession numbers SAMN23436140 and SAMN23455549, respectively). The absence of cry genes in Btk Cry1Ab , with the exception of cry1Ab, was confirmed from raw reads using KMA  (Clausen et al. , 2018).
500 µL of this cell suspension were added to a well of a 24-well microplate. To allow cell-cell adhesion (aggregation), the microplate was placed under constant agitation on a rotary platform at 150 rpm at 25°C for the indicated time (1h, 2h and 3h) with or without protoxins (at a final concentration 35µg/ml). Cell aggregates formed in the wells were observed using an inverted Fluorescent microscope (Nikon, Eclipse TE2000-U). Images for the florescence of GFP was acquired using a CCD camera (ORCA ER, Hamamatsu Photonics). The same parameter settings were used to acquire images (objectives, gain, exposure time …). The area of fluorescent aggregates and individual cells were measured using Fiji software  (Schindelin et al. , 2012). The average area of a S2 cell in our condition is about 15. 5 µm 2.
Hence, quantification of the aggregates area was performed excluding all objects smaller than 15 µm 2. The mean area of aggregates were calculated after background subtraction. Three independent experiments were performed for each condition. Values in µm 2 were represented in GraphPad Prism 5 software as scatter-plot view. Statistical analysis was conducted using GraphPad Prism 5 software. The significance of the difference between CTR and exposed conditions was assessed using one-way ANOVA and Tukey's post hoc tests. Statistical parameters for each experiment can be found within the corresponding figure legends.
Acknowledgments We are grateful to all members of the  BES  team for fruitful discussions. We want to thank  Arnaud Felten  ( GVB Unit, Anses Ploufragan ) and  Pierre-Emmanuel Douarre  ( SEL unit, Anses Maisons-Alfort ) for their help regarding the WGS analysis of the mutant Btk Cry1Ab. We also thank  Laurent Ruel  for providing pWA-Gal4. We are grateful to  Hiroki Oda  for providing the pUAST-DEFL. We thank  Olivier Pierre  from the microscopy platform of the  Sophia Agrobiotech Institute  for his help. Our thanks to the  Université Côte d'Azur Office of International Scientific Visibility  for English language editing of the manuscript. We also thank the  Space, Environment, Risk and Resilience Academy of Université Côte d'Azur  for their financial support.
Funding This work has been supported by the  French government , through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01  and through the  ANR-13-CESA-0003-01 , by the  Région Provence Alpes Côte d'Azur , by the  Département des Alpes-Maritimes , by the  Institut Olga Triballat  ( PR2016-19 ) and by the  ANSES PNR-EST & ECOPHYTO II  ( EST-2017-2021 ). RJ was funded by the association  AZM & SAADE (Lebanon)  and  Université Côte d'Azur (ATER). RL was funded by the Ministère.
Conversely, in a B cell lineage tree, the root, the BCR IGH sequence of the unmutated B cell giving rise to the lineage, can be deduced by aligning the IG variable region sequences with sequences stored in a reference database  [9]. This alignment allows identifying the germline V, (D) and J genes in the ancestor cell from which the B-cell lineage derives  [10]. Another distinct point is that B cells with different BCR mutations can coexist; therefore, the observed BCR IGH sequences can be leaves or internal nodes in the tree  [11, 12]. Due to simultaneous divergence, multifurcations are also common  [13]. IG sequences are under intense selective pressure, and the neutral evolution assumption is invalid. Moreover, the context dependence of SHM violates the assumption that sites evolve independently and identically.
Simulated lineage trees To create simulated lineage trees, we used the B cell lineage simulator provided by GCtree. The simulator produces a B cell lineage by randomly selecting IGHV, IGHD, and IGHJ germline genes from the IMGT database  [9]. Then, nucleotide(s) can be added to or removed from the junction region: IGHV-IGHD and IGHD-IGHJ. Next, it performs a branching process, and point mutations can be included in the descendants. Somatic hypermutations are simulated by a sequence-dependent process, where mutations are preferentially introduced within specific hot-spot motifs  [34]. We kept the simulator default parameters and generated 92 artificial lineage trees. The sizes of simulated trees ranged from 6 to 99 nodes, the number of observed sequences between 20 and 200, the degree of root nodes varied from 1 to 42, and depth trees from 2 to 7, see Table  1.
Experimental data We used a public data set from a previously reported experiment  [11] , where the authors combined multiphoton microscopy and single-cell mRNA sequencing to obtain IGH sequences extracted from germinal B cells of a lineage sorted from the mouse germinal centre. The data set, labelled as TAS-42, contains 65 IGHV sequences and 42 genotypes. For this data set, a genotype is a group of identical sequences. The ancestor IGHV gene was inferred with Partis  [35] , and the final data set contains 66 sequences: 65 from the original data set plus one representing the reconstructed germline sequence. The second experimental data set was generated by sampling sequences from the most abundant clone of a BCR repertoire associated with a CLL patient. The data set contains 3406 sequences, annotated with IGHV4-34*01/IGHD3-3*01/IGHJ4*02 genes using IMGT/HighV-QUEST software  [24].
Computational tools used for comparisons There are only a few B cell lineage reconstruction softwares; some are not available for download  [15] , others are specific for Windows operating system  [40] , and others are very time consuming. Among the available methods, we selected two state-of-art tools to compare with ClonalTree: GCtree  [19]  and GLaMST  [22]. GCtree is an exhaustive solution; it uses dnapars  [20]  to find a parsimony forest and then ranks parsimonious trees according to genotype abundance information. On the other hand, GLaMST  [22]  is a time-efficient algorithm that uses MST but does not consider genotype quantities. The authors have shown a higher performance of these tools in the respective publications, with GCTree outperforming IgPhyML  [13] , and GLaMST surpassing IgTree.
Results To reconstruct B-cell lineage trees, ClonalTree starts placing the root (the inferred ancestral sequence) and iteratively adds nodes presenting minimal edge cost and maximum genotype abundance; therefore, it optimises two multi-objective functions rather than a single one based only on edge costs as implemented in other approaches. We first validated our method with several artificial lineage trees that simulate the affinity maturation process. Then, we used two data sets containing experimental B cell lineages for biological validations. (3) MRCA(T 1 , T 2 ) = i,j MRCA(i, j) C n 2 (4) COAR(T 1 , T 2 ) = i∈T 1 COAR(i) N L.
However, COAR distance values indicated that ClonalTree had better reconstructed entire evolutionary paths; we observed significantly lower COAR values for ClonalTree than GLaMST. Since ClonalTree achieved a lower GED path-based distance, it was expected to present lower COAR distances. COAR also considers sequence dissimilarities as GED path-based distance rather than only differences in the paths. Both measures confirmed that ClonalTree produces trees that are closer to the GCtree ones. The CLL-20 data set was obtained from the blood samples of a patient with CLL (Chronic lymphocytic leukaemia). We only analysed the most abundant clone, which contains 3406 sequences and 20 different genotypes. For these sequences, IMGT/HighV-QUEST  [24]  inferred ancestor IGHV, IGHD, and IGHJ genes using germline sequences from the IMGT  [9]  database.
The 4th and 5th columns of Table  3  show tree distances for ClonalTree and GLaMST, respectively. ClonalTree GED tree-based distance was the smallest among experimental data sets, showing that ClonalTree generated a tree with a very similar topology to the one generated with GCtree. Likewise, GED path-based distance was smaller for ClonalTree. GLaMST presented an extremely high GED pathbased distance; it added many unobserved nodes, causing several mismatches when comparing tree paths. We also evaluated tree path agreement without considering unobserved nodes. ClonalTree presented only two different paths against 13 of GLaMST. As observed in the data set TAS-42, we noted a slight difference between ClonalTree and GLaMST MRCA values, indicating that both tools reconstructed the most recent ancestral properly.
Data set D 1 corresponds to a simple simula- tion, containing 30 sequences and six genotypes. D 1 is the simplest case, and all tools took less than 1 second. D 2 is a data set provided by GLaMST, containing 684 observed sequences and 541 different genotypes. GCtree took 118 hours, GLaMST spent 55 minutes, and ClonalTree took less than 15 minutes to reconstruct a lineage tree for this data set. D 3 is the CLL-20 data set, containing 3406 sequences but just 20 genotypes. Clon- alTree achieved the lowest execution time (less than 1 second), while GLaMST took 1. 5 seconds. GCtree needed 118 hours to produce a tree for the D 3 data set. Although D 3 had more sequences than D 2 , it contained fewer genotypes, which explains why execu- tion times were lower for D 3 , compared to D 2.
Discussion We have explored 92 simulated B cell lineage trees, ranging in trees' depths, degrees of roots, number of nodes and leaves, and number of observed sequences, among other tree properties. Our simulations showed that ClonalTree reconstructed accurate trees, preserving the correctness of ancestral reconstruction, compared to ground truth trees. Our approach outperformed GLaMST, a method based only on the minimum spanning tree; ClonalTree systematically produced more maximum parsimonious trees than GLaMST. Compared to GCtree, an exhaustive sequence-based phylogenetic method, ClonalTree presented a comparable performance in most cases, and we observed few differences in the obtained trees. We also evaluated ClonalTree on experimental BCR sequencing data. As observed on simulated data sets, ClonalTree outperformed GLaMST, producing trees closer to GCtree.
Our approach obtained similar tree topologies for both experimental data sets, with minor errors in ancestral reconstructions. ClonalTree is time efficient, showing the lowest time complexity among the compared tools. Its computational efficiency is directly linked to the algorithms choices and programming language. ClonalTree employs multiple sequence alignments, which are faster to compute and preserve important evolutionary properties that would otherwise be lost when using pairwise sequence comparison. Moreover, it is coded in python and uses freely available libraries/software, without any operating system requirement, and is available for scientific community usage. On the other hand, GLaMST is coded in Matlab, a proprietary programming language, and only an executable for Windows is available for users that do not possess a Matlab licence, limiting its usage by the scientific community.
Due to its ability to process a high number of sequences within a reasonable amount of time, it is particularly adapted to large datasets obtained by high-throughput repertoire sequencing. ClonalTree may have a great potential for studying the dynamics of immune responses in various disease conditions, such as chronic infectious diseases, autoimmune diseases and lymphoid malignancies. It can compare trees generated in these situations to those generated in normal immune response dynamics. Moreover, it can help understand B-cell clonal lineage evolution by comparing trees of a specific B-cell lineage at different time points, for example, before and after vaccination or treatment. However, ClonalTree has some limitations; for instance, it does not consider hot spot mutated positions to define a distance between sequences; in the current version, all positions have the same weight.
Characterisation of surveillance programmes and mapping Surveillance programmes were characterised using a standardised grid adapted from the ECoSur matrix developed by Bordier et al. [13]. The grid included 28 variables of interest covering aspects related to organisation (e. g. regulatory status, ownership, steering and coordination activities), methods and operations (e. g. target population, coverage, sampling strategy, data collection and analysis, indicators used, dissemination of the results). Contribution to supra-national surveillance programmes was also recorded. The detailed descriptive grid is provided in Supplementary material S1 (Table  S1 ; List and characterisation of the 48 programmes retained in the study). For each included surveillance programme, the descriptive grid was pre-completed using information collected from the literature by four members of the research team, including two scientists from the human sector and two from the animal sector.
In addition, the French National Observatory for Epidemiology of Bacterial Resistance to Antimicrobials (ONERBA), existing since 1997, grouped together 10 voluntary programmes (nine in the human and one in the animal sector). Of these, four were bacterial speciesspecific (Table  S1 ), and two had regional coverage  [15]. Last, 16 were national reference centres (NRCs) coordinated by Santé publique France, with two of these also operating under ONERBA. Among the 48 programmes, the majority (n = 35) focused on ABR (Table  1 ). Three programmes in the human sector monitored both ABR and ABU. All 31 human ABR-related programmes collected data from clinical samples; two programmes also collected data from screening samples (colonisation). In the animal sector, most programmes focused on diseased or healthy food-producing animals (n = 12) and targeted multiple animal species (n = 7), horses (n = 3), pigs (n = 2), poultry (n = 1), veal calves (n = 1) or rabbits (n = 1).