dataesr/scientific-paragraphs-categorization huggingface dataset:
Homophone words Homophone words share the same pronunciation but differ in meaning. When they also differ in orthography they are called non-homographs (e. g. LEAK vs LEEK, or CONTE vs COMPTE in French). By presenting such words in spoken form, we wished to determine whether subtitles featured only a single spelling, possibly the most frequent one, or several concurrent spellings. We selected ten auditory word forms, based on 2 criteria: each should correspond to at least 3 words with different spellings, and one of those spelling should have a substantially higher frequency of occurrence than the others, according to the to the lexique. org database. We generated the corresponding audio files as before. In order to analyze how synesthetic spellings were selected among the possible spellings, we computed the percentage of participants who reported each spelling over the 10 stimuli, and modelized those values with a general linear mixed-effects model (GLMM), with log frequency and grammatical category (noun vs other) as fixed factors, and stimulus identity as random factor.
Predicting the epitope of a TCR sequence can be considered a straightforward machine learning problem. In the "seen" epitope setting, which is the focus of this study, the TCR sequence is used as the input of the model and a fixed number of epitopes are the target labels. The input features are therefore derived from the TCR sequence, however some models also include features derived from the epitope to possibly learn interactions between the two. Known TCR-epitope pairs, collected in databases such as VDJdb  [1]  or IEDB  [2] , can then be used to fit the epitope-TCR model. Within the "seen" epitope setting, only those epitopes that are contained in the epitope-TCR database are possible targets. In its simplest form, this can be a single epitope, reducing the problem to a binary classification.
The task of the model is to identify which, if any, epitope is the most likely target of the input TCR. During the past years, several solutions to unravel and predict the specificity and the cognate epitope target of a TCR have been proposed, ranging from a simple database look-up to deep learning-based prediction models. The advantages and disadvantages of these different approaches have not yet been systematically examined, most having only been evaluated within the context of their initial publications and data sets. In addition, the annotation of TCR-epitope pairs is a complex problem: the promiscuity of TCR-epitope binding and the technical but also experimental variations underlying paired TCR-epitope data make the identification of a clear signal difficult  [3]. There is thus a clear need to benchmark existing TCR-epitope prediction approaches, enabling the field to progress towards an understanding of the principles underlying T-cell specificity.
Here we report the findings of the first public TCR-epitope prediction benchmark performed in the context of the ImmRep 2022 TCR-epitope specificity workshop (https://www. pks. mpg. de/immrep22). Leading scientists in the field as well as junior researchers interested in the TCR specificity problem were invited to participate and were offered datasets to train and test a collection of existing prediction models. The aim of the workshop was to evaluate and compare the obtained outputs to classify the approaches and most importantly, to help identify an ideal dataset and optimal evaluation strategies for future follow-up efforts. Describing the outcome of the workshop, we attempt to group the selected and tested methods, both -based on the TCR feature input as well as on the underlying prediction algorithm -in an attempt to identify patterns within the performance results.
Construction of the training-and test data The benchmark data set was derived from the VDJdb database (downloaded on 23/06/2022). VDJdb is a curated database of TCRs with known antigen specificities  [4]. Only those TCR-epitope pairs with paired alpha-beta chain data were collected. Any duplicated TCR-epitopes were removed, as defined by V/J gene usage as well as the CDR3 (complementarity determining region) sequence for both the alpha-and the beta chain. Only TCR-epitope pairs obtained by tetrameror dextramer-sort were retained. Furthermore, all the dextramer-sort entries originating from the 10X technical report  [5]  were excluded because of the high reported cross-reactivity of these TCRs  [3]. Lastly, only those 17 MHC-epitopes that had at least 50 unique TCR sequences were retained. A full list of epitopes and the number of their associated TCR is provided in Table  S1.
To constitute the negative control data set, unpublished paired alpha-beta chain TCR sequences without peptide specificity information and obtained from 10x genomics sequencing of CD8+CD96+ T cells from 11 control individuals were provided by A. Eugster. The entire data was subsequently separated into "positive"/ "negative" training-and test data sets as follows. The "positive" set for each epitope under consideration was extracted from the VDJdb as described. The "negative" set for each epitope was constructed by randomly sampling a set of TCRs specific to any of the other 16 epitopes. The size of this negative set was three times larger than the number of the positive TCRs for the epitope in consideration. The negative set was further expanded by randomly sampling TCRs from the negative control dataset to obtain twice the number of positive TCRs.
Evaluation of prediction performance Models were trained on the available training set for each epitope, or on all available training sets where appropriate for multi-label models. To decrease information leakage, the models were only allowed to train on the available data and any pre-training steps on TCR specificity were excluded. A decision value was then assigned to each TCR in the test data set for a given epitope with a blinded ground truth with respect to negative and positive samples. The ground truth of the test set was thus not available to workshop participants during model training and application. Two data set setups were utilized to evaluate the models. The first included a mixture of positive and negative test data for each epitope, with the target epitope being known. Thus, each model had to score the likelihood of the TCRs included in the dataset binding to the specified epitope.
From these results, the area under the ROC curve (AUC) was calculated. In the second setup, all positive test data from the previous setup were merged, and each method was challenged to provide predictions for every possible epitope seen during training. The epitopes were then ranked for each TCR from the most likely to the least likely according to the prediction scores of the model, and the rank of the true epitope was enumerated. As an epitope has multiple true TCRs, an average rank was calculated across all TCRs for one epitope. Within multi-label classification, this is equivalent to the coverage, i. e. the average number of labels to include to avoid missing the ground truth label  [16]. All prediction scores were then collected and analyzed with the same evaluation script, which calculated the AUC / the average rank for each epitope, as well as the average over all epitopes.
No relation between size of the training data and model performance When benchmarked on the 17 MHC-epitope test data, all methods reached a non-random performance (AUC > 0. 5) for most epitopes, as can be seen in Fig. S1. This demonstrates that independent of the method used, it is possible to classify unseen TCRs for a given epitope within this dataset. Only the SARS-CoV-2-derived epitope NQKLIANQF consistently scored poorly or even randomly across all methods (AUC 0. 539 mean ± 0. 065 s. d. ). In contrast, the easiest to predict epitope, NYNYLYRLF, also a known SARS-CoV-2 epitope, featured a near-perfect classification for most methods (AUC 0. 956 mean ± 0. 042 s. d. ). TCRs for both the NQK and NYN epitopes were derived from the same MHC-dextramer study  [17] , thus there is no experimental difference in how the TCRs were collected or from which individuals.
Utilizing unrelated, healthy repertoires has the disadvantage that there may be an experimental and/or biological bias in the data. This lends itself to the possibility that any model may learn the distinction between positive and negative based on such biases alone. In addition, healthy repertoire data is not free of potential misannotations, due to the presence of T cells specific for common dominant epitopes also present in the positive data. Simulated data with a known ground truth could not be used here as we wished to evaluate the contribution of CDR1/2, alpha/beta, which is a required assumption prior to simulation. Using the simulated datasets as negatives only, would have created more opportunity to bias. As highlighted by the difference in performance ranking based on AUC and epitope ranking, classifiers potentially do have a tendency to learn patterns within the negative data that changes how they will consider the problem.
This benchmark is far too limited to provide a solid answer to what negative strategy is most appropriate, and this remains an important topic for future research. Another key choice involved the lack of removal of similar TCRs from the test set compared to the training data set. This would have required a strict definition of what constitutes a sufficiently similar TCR. There does not currently exist a common consensus of how different a TCR can be before it can be expected to no longer bind the same epitope. Furthermore, whether to include or exclude similar TCRs from the datasets is a decision that depends fully on the downstream application. Mostly, the end goal of these methods is to identify epitope-specific TCR pairs among a large TCR sequence repertoire extracted independently. It is already known that public clonotypes do occur across different Fig.
4. Average performance by CDR region usage. From left to right, methods using only CDR3 amino acid sequence, methods that use CDR3 and V/J genes, and methods that use CDR1,CDR2,CDR3 amino acid sequences as input. The lines denote those methods that use the same architecture but with different input. individuals, and that small subsets of TCRs can be successfully annotated with their epitopes just by matching their TCR sequences to annotated TCR sequences of a database  [21] [22] [23]. However, public clonotypes are the exception rather than the rule. Removal of the highly similar TCRs from the test set does allow evaluation to what degree these methods are capable of linking distant TCRs to the correct epitope. This results in a far more limited positive test set, with more focus on the classification of the negative samples.
Finally, many methods are hampered in their success by the current limited training data set size. Thus their measured performance is not representative of their theoretical potential with an unrestricted training data set. For example, several methods have shown improvements by utilizing transfer learning or pre-training steps, which are not possible with this limited benchmark  [8]. In addition, methods using only beta-chain data can typically rely on a larger dataset than methods with both alpha and beta chains, which may explain their poorer performance in this instance. The performing data set curation from paired chain data also had an impact on the size of the training and the test set of a few epitopes. For instance, two TCRs would be considered for evaluation if their alpha chains are different, implying that they are overall different but they may collapse to a single TCR if their beta chain is identical.
Other prediction-focused fields have embraced the idea of an open competition where methods are benchmarked on the same never-beforeseen data set. This is a way to independently evaluate the wide variety of methods that are available, but also to drive the field forward and enable it to reach new heights. As a conclusion of the ImmRep TCR-epitope specificity workshop, we strongly encourage the organization of similar competitions focused on the TCR-epitope prediction problem. The most essential choice will be the origin of the test data as many options are available, from a stratified approach as highlighted here, to the integration of simulated datasets with a known ground truth  [24]. The ideal data set for such a challenge would unequivocally be an unpublished independent data set with both TCRs with known epitope-specificity, as well as "negative" TCRs that do not bind these epitopes.
As highlighted in this study, it would ideally involve paired alpha-beta TCR sequence data, and would therefore likely be derived from a single cell sequencing experiment. In addition, the use of oligo-tagged multimers would enable both identification of those TCRs that are specific for an epitope, along with those that are likely not. Furthermore, this data set should contain multiple previously studied epitopes, so as to compare the epitope rank beyond the straight-forward classification problem. The technology to create such a dataset is currently available, and therefore only requires the willingness of either funders, institutes or companies to provide it.
Acknowledgments We wish to thank the  Max Planck Institute for the Physics of Complex Systems in Dresden  and the  Max Planck Society  for their generous financial and physical support enabling the ImmRep 2022 TCR-epitope specificity workshop. We wish to thank the  VDJdb resource  for providing the dataset that formed the basis of this paper. In addition, we wish to thank the valuable input from  Prof. Victor Greiff  on the manuscript.
Materials and methods Raw single-cell sequencing from Syrian hamster and African green monkey lungs was downloaded from the INSDC with the following dataset identifiers: SRR13151627, SRR13151628, SRR13151629, SRR13151633, SRR13151634, SRR13151635, SRR13151639, SRR13151640, SRR13151641, SRR13151645, SRR13151646, SRR13151647, SRR13151651, SRR13151652, SRR13151653, SRS7251411, SRS7251412, SRS7251413, SRS7251415, SRS7251416, SRS7251417, SRS7251418, SRS7251419, SRS7251420, and SRS7251421. According to the original publications  (42, 43) , AGM modalities consisted of two and four samples at 3 dpi, for which either a live or an irradiated virus inoculum was used, respectively, as well as four samples at 10 dpi inoculated with live viruses. Eight adult African green monkeys (four males and four females; body weight, 3.
All animal experiments were approved by the respective animal care committees. Raw data were processed with Salmon-Alevin 1. 4. 0 (63) using GCF_017639785. 1_BCM_Maur_2. 0 and GCF_015252025. 1_Vero_WHO_p1. 0 as transcriptomes and decoy genomes for Syrian hamsters and African green monkeys, respectively. To allow the quantification of viral reads, positive and negative genomic sequences of the Wuhan strain of SARS-CoV-2 were added to both transcriptomes. Similarly, constant sequences of immunoglobulin heavy and light chains as well as of TCR alpha and beta were first assembled and added to monitor both antibody synthesis and T-cell response (GenBank accession Nos. : OQ624913-OQ624932 and OQ624937-OQ624954). To allow the integration of count matrices from Syrian hamsters and African green monkeys, orthologous genes were determined using tBLASTx of NCBI-BLAST+ package 2.
This in turn allows the activation of CD4+ T cells, which promote the selection and the full differentiation of cognate plasmablasts and activated B cells into plasma cells. The absence or late arrival of anti-fusogenic and neutralizing antibodies leads to a continuous release of chemoattractants constitutive of cytokine storms. In this model, the balance between binding-only antibodies, on the one hand, and anti-fusogenic and neutralizing antibodies, on the other hand, determines the onset and magnitude of this pro-inflammatory cytokine build-up, constituting the phenomenon of antibody-dependent enhancement (ADE). This figure was created with BioRender. com. Cell types were determined using differentially expressed genes identified using the FindAllMarkers function of the Seurat package and cell type markers mentioned in the literature.
Differentially expressed genes in hamsters were identified using the following RNA-seq datasets: SRR13151598, SRR13151599, SRR13151600, SRR13151604, SRR13151605, SRR13151606, SRR13151610, SRR13151611, SRR13151612, SRR13151616, SRR13151617, SRR13151618, SRR13151621, SRR13151622, and SRR13151623. For AGM and for some analyses referred to in the text for hamsters, the scRNA-seq mentioned previously was used as pseudobulk RNA-seq. After reading pseudoalignment on respective reference transcriptomes using Salmon, differentially expressed genes and gene ontology enrichments were performed in R using the Edger (70) and clusterProfiler  (71)  packages. This study received funding from Prevor SPRL. The funder was involved with the study conceptualization and supervision.
Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www. ncbi. nlm. nih. gov/ nuccore/OQ624913; https://www. ncbi. nlm. nih. gov/nuccore/ OQ624914; https://www. ncbi. nlm. nih. gov/nuccore/OQ624915; https://www. ncbi. nlm. nih. gov/nuccore/OQ624916; https://www. ncbi. nlm. nih. gov/nuccore/OQ624917; https://www. ncbi. nlm. nih. gov/nuccore/OQ624918; https://www. ncbi. nlm. nih. gov/nuccore/ OQ624919; https://www. ncbi. nlm. nih. gov/nuccore/OQ624920; https://www. ncbi. nlm. nih. gov/nuccore/OQ624921; https://www. ncbi. nlm. nih. gov/nuccore/OQ624922; https://www. ncbi. nlm. nih. gov/nuccore/OQ62492; https://www. ncbi. nlm. nih. gov/nuccore/ OQ624924; https://www.
Viral titer The viral titer (i. e. concentration of infectious viral particles) was measured using the most probable number (MPN) assay  (Reed and Muench, 1938) : The viral titer was obtained by dilution to extinction of the virus sample in an exponentially growing host suspension in a 48-multiwell plate. Therefore, a 50 μL sample was inoculated in 450 μL of the host culture, mixed and serially diluted in 10-fold increments (from 10 1 to 10 14 ). Dilution series were made in triplicate. The Most Probable Number was calculated using online software (mostprobablenumbercalculator. epa. gov).
Automated image acquisition A confocal microscope (Leica SP8, Germany) equipped with 63× oil NA 1. 4 objective was used. For high-content screening of many individual cells, a dedicated experimental "Mark&Find" template was designed and pre-tested with the Leica MatrixScanner automation software. The imaging experiment consists of three essential steps: (1) Acquisition of a reference focus map, (2) a fast low-resolution scan to detect cell positions, and (3) a 3D high-resolution scan at selected positions only. During the high-resolution scan, cell morphologies postinfection automatically according to the following predefined settings: 68 z-planes were recorded over a total height distance of 20 μm for each cell. At each of these z-planes, fluorescence was read out in 3 fluorescent channels and one transmission light channel: Table  1.
Image processing for visualisation For 2D visualisation of multi-colour 3D datasets from confocal microscopy, datasets were batch-processed in FIJI  (Schindelin et al. , 2012). The full annotated script can be found on https://github. com/mariescopy/Guinardia_ ViralInfection. In brief, for each z-stack recorded, a 2D maximum projection was calculated for each fluorescence channel and a minimum projection for the transmission channel. For an overview collection of individual cells (Figures  1, S2 ), the dominant cell orientation in each image was detected by Fourier component analysis  (Tinevez et al. , 2018) , and cells were aligned along the vertical axis and cropped to their bounding box. In case of ambiguities in the image, we rotated them by hand.
The volume of chloroplasts was estimated from the sum of voxels showing chlorophyll autofluorescence. As a metric for the shape of chloroplasts, we analysed the sphericity of their 3D renderings. It is a measure of how closely their shape resembles that of a perfect sphere. Sphericity Y is defined as the ratio of the surface area of a sphere (with the same volume V O as the given object) to the actual surface area A O of this object: Y = p 1 3 6V O ð Þ 2 3 A O A sphere has a Y=1, while elongated objects have lower values between 0 and 1. Usually, several chloroplasts were detected inside the same cell. We then took their mean sphericity. 3D renderings and quantifications of organelle morphologies were conducted with custom macros in FIJI based on the 3D ImageSuite plugin  (Ollion et al. , 2013)  and cross-checked with Imaris 3D image visualization and analysis software (Oxford Instruments, UK).
Morphological and physiological changes From the 3D image data of each cell, we monitored morphological changes in control and infected cultures (Figure  1 ) and quantified the deformation of their subcellular components throughout the viral infection (Figures  3A-C ). Complementary physiological measurements of F v /F m were taken in parallel to relate them to observed morphological changes. Cells in all control samples had irregularly lobed chloroplasts pressed against the cell wall and a single nucleus (stained by the DNA dye Hoechst 33342) located at the centre of the cell (Figure  1 ) all along the experiment. Cells with these characteristics were further considered "healthy". Chloroplast median sphericity of these cells remained between 0. 3 and 0. 4 throughout the experiment while the median volume of chloroplasts per cell was between 600 and 1100 μm³ with the highest values recorded at the end of the experiment (92 hpi).
We did not detect RNA spots inside these auxospores. From 68 hpi until the end of the experiment (92 hpi) most G. Representative Guinardia delicatula cells from samples infected with GdelRNAV-04 (left) and a non-infected control culture (right). Each cell is the depicted as the maximum projection of a corresponding 3D confocal dataset, with blue showing DNA and nuclei, green single-stranded RNA (including large viral accumulations, green arrows) and magenta autofluorescence from chlorophyll. Dead cells are highlighted by a white cross, and the increased formation of auxospores by purple dot. delicatula cells were dead or severely damaged and colonised by bacteria in the infected culture (while few dead cells were detected in the control culture (Figure  2B ). The photosynthetic machinery in infected cultures maintained an optimal F v /F m value for 26 hours postinfection.
Data availability statement All image analysis code is made available under an open-source license (github. com/mariescopy/Guinardia_ViralInfection). Data plots shown in report were created in RStudio. All R scripts and intermediate Excel tables are also available on GitHub. All raw and processed images are publicly available at EBI-BioStudies (accession ID: S-BIAD534). The taxonomically and ecologically annotated projections will also be released on EcoTaxa (ecotaxa. obs-vlfr. fr) for public exploration. Further inquiries should be directed to the corresponding author.
Funding MW was supported by a Benjamin Franklin Fellowship (project 464344344) from the German Research Council (Deutsche Forschungsgemeinschaft, DFG) as well as a Strateǵie d'attractivitédurable (SAD) fellowship from the Reǵion Bretagne. A-CB has been supported by the French Agence nationale de la recherche (ANR) with a research grant (project ANR-22-CE01-0018-01-BONUS).
Acknowledgments The authors are grateful for the insightful comments from the editor and two anonymous reviewers that help improve the paper greatly. Thanks are also due to all CMIP6 and CFMIP participants, as well as to  NCAR  for the free access to the NCL software. BM acknowledges support by the  U. S. Department of Energy  under Award  DE-SC0022070  and  National Science Foundation  (NSF) IA 1947282; the  National Center for Atmospheric Research  is a major facility sponsored by the  NSF  under Cooperative Agreement 1852977.