dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of Multi-Person Dataset MuPoTS The results of our system with the three improvements are listed in Table  1 , which can be compared to the literature results. We evaluated using the MuPoTS-3D dataset since it has been used to analyze 3D multi-person poses in both person-centric and camera-centric coordinates. Following  [31, 35] , the performance of person-centric 3D pose estimation was evaluated using AUC rel and PCK metrics, while camera-centric 3D pose estimation was evaluated using AP root 25 and PCK abs metrics. The detailed PCK abs results per sequence are shown in Table  2. We observed an improvement in the estimation accuracy in most of the sequences. According to both tables, all our strategies outperformed previous 3D multi-person absolute pose estimation approaches by a significant margin, even if the relative poses were weaker.
The average precisions throughout the entire dataset were then examined using various threshold settings ranging from 25 to 10 cm. AP measured the accuracy of the root key point; we only evaluated the Root-GAST system's performance using the GA approach since GR and GAR methodologies employed RootNet to predict the root joint. They produced the same result as the original paper. Table  3  displays the results. When compared to the state-of-the-art methodology, our method significantly achieves greater AP across all levels of thresholds. We deduce that our method estimates many more correct root keypoints even with a low distance threshold. To compare with most of the existing methods that evaluate person-centric 3D pose estimations on MuPoTS-3D using MPJPE , we report our results using the same metric in Table  4.
6M dataset, we employed the GA model that was retrained on MPII and the MuCo-Temp dataset, and we compared it to the RootNet model that was trained on the MuCo dataset to make a fair comparison. Our measurement error amounted to 158 mm, while that of  [31]  was 289. 28 mm. However, we could expect greater improvement if we train our model in the Human3. 6M dataset. The response time is the processing time taken by the algorithm to process its input; it depends on the material configurations. The Root-GAST-Net pipeline was implemented in C++ and executed on a machine equipped with Intel Core i5-9500, with a dedicated memory of 32GB, and the Nvidia GeForce GTX 1080, with a dedicated memory of 8GB. A comparative analysis of the response times of each network is shown in Table  6. The processing time was measured on batches of monocular images from the Human3.
6M dataset, each containing one person. Note that the processing time of the tracking step is negligible. Finally, the frame rate of the whole pipeline with each strategy is given in Table  7. The proposed Root-GAST-Net system can run at about 15 frames per second, which is suitable for real-time scenarios. Therefore, improving the metrics does not impact the real-time aspect of the pipeline. As the system follows a top-down approach, the final result depends on all previous outputs. If the detection is not correctly done, the 2D keypoints and depths will be wrongly estimated, which will impact the absolute pose. If there are numerous people inside the box or body parts that are partially outside the box's bounds, the full-body joint calculation is likely to be incorrect, as shown in Figure  5.
Conclusions In this work, we propose a top-down framework for 3D multi-person absolute pose estimation, reconstructed from 2D poses from a monocular camera. Our framework Root-GAST-Net can combine different models in three strategies. The GR strategy and GAR strategy, which integrate human detection, 2D pose estimation, 3D human root-relative single-person pose estimation, and root depth estimation. Moreover, the GA strategy integrates human detection, 2D pose estimation, and 3D absolute pose estimation. Experimental results on multiple datasets showed that our framework significantly outperforms the recent approaches in 3D absolute multi-pose estimation. In addition, the system can be used in real-time, as the execution time of each frame containing one person takes around 60 milliseconds using the Nvidia GeForce GTX 1080.
Data Availability Statement: We used the MuCo-Temp dataset, generated with the GitHub project at pose_refinement (https://github. com/vegesm/pose_refinement (accessed on 12 February 2022)) for training. For evaluation, we used the Human 3. 6M dataset, parsed and available at 3DMPPE_ROOTNET_ RELEASE github project https://github. com/mks0601/3DMPPE_ROOTNET_RELEASE (accessed on.
Introduction and objective: Nowadays, investigations of heart physiology and pathophysiology rely more and more upon image analysis, whether for the detection and characterization of events in single cells or for the mapping of events and their characteristics across an entire tissue. These investigations require extensive skills in image analysis and/or expensive software, and their reproducibility may be a concern. Our objective was to build a robust, reliable and open-source software tool to quantify excitation-contraction related experimental data at multiple scales, from single isolated cells to the whole heart. Methods and results: A free and open-source ImageJ plugin, Spiky, was developed to detect and analyze peaks in experimental data streams. It allows rapid and easy analysis of action potentials, intracellular calcium transient and contraction data from cardiac research experiments.
Several programs exist for the analysis of this type of data, but these are not fully open source and may require licensed software (e. g. , Matlab ® ). They may also therefore be difficult to adapt to the user's specific needs. Moreover, the types of image format that can be processed by these programs are often limited. Thus, we developed Spiky as an open source working interface dedicated to the analysis of functional data obtained from experiments going from action potentials to contractions, and from single cells to the whole heart. The final purpose of Spiky was to provide a tool which could be integrated to the data acquisition software (ImageJ) and be able to perform the whole pipeline from data acquisition to results interpretation. It avoids data conversion, exportation, importation and use of multiple software which make the analysis time consuming and possibly error prone.
Discussion To our knowledge, Spiky is the first fully open-source software to propose a wide range of analysis of experimental cardiac XT and XYT data, as well as analysis of optical mapping data. Indeed, despite the existence of two programs whose authors provided the software code, they only apply to optical mapping data analysis  [9, 10]. The code cannot be adapted in order to fit user's specific needs without a Matlab ® license. Moreover, Spiky does not require data conversion or programming skills to perform a detailed analysis of excitation-contraction parameters obtained with imaging techniques, since ImageJ opens almost all image file types. The software has been especially developed for a translational use at multiple scales, from single cardiac cell to the whole heart, and from fundamental to clinical research.
Though Spiky has been developed for cardiac experimental data, its use can be extended to other cell types and research applications which involve periodic phenomena or peak analysis. Spiky is free and works under ImageJ, which is a widely used image analysis software for Windows, Mac and Linux operating systems. Moreover, analysis can be performed on any data format that can be opened by ImageJ, from a single XT signal to XYT multichannel images or videos. For those with programming skills, the source code of Spiky is open and can be improved or adapted to fit the user needs. Limitations: Spiky has been designed for a wide range of applications with different types of data. Therefore, the parameters used for the detection of a peak (positive or negative) have to be entered, tested and validated by the user.
Spiky does not support a fully blind analysis and its use does not exempt the user from careful interpretation of the output results. For example, the decay constant map of the calcium transient close to the edge of the cell in Figure  4A  is impaired by the contraction of the myocyte, and the measurements of action potential duration on the margins of the tissue in Figure  4B  are distorted by edge effects. Finally, to make it applicable to the largest range of applications yet easy to use, some very specialized functions were not implemented. For example, processing of data from panoramic optical mapping systems. To perform such an analysis, software like RHYTHM would be more appropriate [9].
The trainee could interact in natural language with the VP. In our simulation approach (Figure  3 ), the WoZ operator (a geriatric expert) selects the verbal and non-verbal behaviors (facial expressions, posture, etc. ) to be generated in real time on the VP, based on the verbal and non-verbal behavior of the trainee who was expected to act as in a role-play with a real patient  (Benamara et al. , 2020). Each action performed by the WoZ to control the VP behavior is recorded and logged. After each session, the educational tool is assessed with questionnaires in terms of system usability, acceptability, VP realism and effectiveness of the educational tool. In addition, a key aspect of the VirtuAlz platform relates to the automatic evaluation of the trainee's non-verbal behaviors, that are captured by a front-facing camera during the interaction with the VP that lasted on average 6 min.
Acknowledgements This study is supported by  NWO-TTW  under project number  14715 , by the  French PIA  under project number  ANR-15-IDEX-04-LUE CAP-MAT , by the " FEDER-FSE Lorraine et Massif Vosges 2014-2020 ", and by the region  Grand Est  under the  DESOMIN  project. We thank  Dr. Ivan Bykov  and  Dr. Stanislav Leesment  from  NT-MDT Spectrum Instruments  for providing NTEGRA model AFM and technical support in the method development,  Dr. Kıvan¸c Esat  from  Zurich Instruments  for supplying MFLI model digital lock-in amplifier and assistance in the utilization,  Wijnand Dijkstra  for the contribution to the AFM sensor development.
C: We consider the application of diffRBM to modeling peptide immunogenicity or T-cell receptor (TCR) antigen specificity, whereby the background dataset consists, respectively, of all antigens presented by a given Human Leukocyte Antigen class I complex (HLA) or of generic TCRs from the bulk repertoire. D: The selected sequences correspond to HLA-specific antigens validated to be immunogenic or to TCRs that are antigen-specific responders. The inferred parameters associated to the diffRBM units allow one to identify putative contact positions in the peptide-HLA-TCR structure (E) and more generally to assign scores that distinguish the selected from the background sequences (F). E is an example of a peptide-HLA-TCR structure for the CMV peptide NLVPMVATV (PDB-ID:3GSN), where the contact points along the peptide and the TCR are highlighted in different colors (image obtained with Mol*  [Sehnal et al.
, 2018] ). pre-trained on the larger background dataset as a starting point for learning the diffRBM units, reduces the amount of parameters and data features to infer from the selected dataset (typically much smaller than the background dataset). A second advantage, particularly relevant to the interpretable modeling of biological data, is the fact that the diffRBM units focus on the differences relative to the background, disentangling in this way the data features that make them a selected subset with distinctive properties. As a result, scores based on the diffRBM units can distinguish selected data from the background data (Figure  1F ). In the context of modeling antigen immunogenicity and TCR specificity, the diffRBM units and their parameters can also identify antigen-TCR contact points in the three-dimensional molecular structure (Figure  1E ).
It relies on the simple assumption of position-independence of the main biophysical properties underlying immunogenicity. Some of these approaches are based on a preliminary choice of peptide positions  [Schmidt et al. , 2021]  or properties (such as hydrophobicity  [Chowell et al. , 2015] ), known or assumed to be important for recognition by TCRs. The diffRBM approach we propose 3 is a sequence-based method that does not require an ad hoc selection of residue positions or properties and does not rely on the assumption of peptide position independence. We collected from the Immune Epitope Database (IEDB)  [Vita et al. , 2019]  sets of peptides that elicited a T-cell reaction in T cell assays (referred to as 'immunogenic') and sets of peptides that were not T-cell-reactive ('non-immunogenic').
The peptide-presenting MHCs are specialized proteins coded by highly polymorphic human genes called Human Leukocyte Antigen (HLA) gene. We selected only peptides presented by 3 HLA-I alleles (HLA-A*02:01, HLA-B*35:01, HLA-B*07:02). We chose these HLA-I alleles since they are associated to at least 200 immunogenic peptides in IEDB and at least one TCR-pMHC structure in the Protein Data Bank (Methods). We trained diffRBM for each set of HLArestricted immunogenic peptides in two steps, by training first a background RBM on samples of antigens presented by one specific HLA via the RBM-based algorithm RBM-MHC  [Bravi et al. , 2021b] , and next by training the diffRBM units on the set of immunogenic antigens of the same HLA type (Figures  2A, S2 ). Background RBM can predict scores of presentation on the specific HLA under consideration, while the diffRBM units predict scores of immunogenicity.
Differently from some existing approaches to modeling immunogenicity  [Calis et al. , 2013 , Chowell et al. , 2015 , Schmidt et al. , 2021] , we train HLA-specific models. Preliminary inspection of the datasets revealed that patterns of amino acid enrichment differ between immunogenic peptides presented by different HLAs, apart from some general trend in terms of dominant amino acid properties  [Calis et al. , 2013 , Chowell et al. , 2015 , Riley et al. , 2019 , Schmidt et al. , 2021 , Łuksza et al. , 2022]. This is true also when we restrict to the peptide positions known to be relevant for immunogenicity  [Schmidt et al. , 2021 , Rudolph et al. , 2006 , Calis et al. , 2012 , Schmidt et al. , 2021 , Łuksza et al. , 2022]  (Figure  3A ). For instance, the extent to which enrichment in hydrophobicity can discriminate immunogenic from nonimmunogenic peptides was observed to vary across HLAs  [Buckley et al.
Validation of model predictions against TCR-pMHC structures Since the diffRBM units are inferred to capture the distinctive patterns of peptide immunogenicity, we hypothesized that the associated inferred parameters can be informative about the actual structural properties of the pMHC-TCR complex. To test this hypothesis, we collected a set of resolved crystal structures publicly available in the Protein Data Bank  [Berman et al. , 2000]  describing peptides in complex with the presenting HLA molecule and a cognate TCR. For each of these TCR-pMHC complexes, we estimated the peptide positions in contact with the TCR and the HLA (Methods, Table  S1 ). Figures 2B-C show the frequency of contacts at each peptide position with the HLA (B) and the TCR (C) in peptides presented by HLA-A*02:01 (the HLA allele to which the large majority of structures is available, 41 over 46 structures, see Methods).
We first focus on HLA-peptide binding. Anchor sites for the bond with the HLA constrain the amino acid 4 usage at those positions across peptides presented by that same HLA, increasing the frequency of the amino acids required for binding (e. g. , I, L, V at positions 2 and 9 of HLA-A*02:01 ligands). As a result, the sites occupied by the high-frequency amino acids at those positions are typically anchor sites (Figure  2B ). We can translate this simple observation into a recipe to predict peptide contacts with the HLA. For each peptide sequence from the TCR-pMHC-A*02:01 structures, we ranked sequence positions by the frequency of the amino acids estimated from the background dataset of HLA-A*02:01-presented peptides. We took the top ranking positions as putative anchor sites and we verified this prediction against the true contacts by calculating an average Positive Predictive Value (PPV), see the inset of Figure  2B  and Methods.
The diffRBM units outperform these predictions based on amino acid frequency ratios, as quantified by the average PPV (Figures  2D, S4 ). For the HLA-A*02:01 peptides, we also found that the magnitude of the diffRBM single-site factors across positions correlates with the pattern of contact frequency better than predictions based on amino acid frequency ratios (Figure  2C ). Finally, Figure  2D  shows that a prediction based only on the frequency of an amino acid in the dataset of immunogenic peptides, similarly to the dataset of all presented peptides, tends to identify as putative contacts the anchor sites of binding to the HLA, leading to completely incorrect predictions concerning the binding sites to the TCR.
, 2022 ,Buckley et al. , 2022]  (especially valine, denoted by symbol V); the abundance of glutamic acid (E)  [Calis et al. , 2013]  and, to a more moderate extent, of tryptophan (W)  [Schmidt et al. , 2021 [Schmidt et al. , , Łuksza et al. , 2022]] ; the depletion of the small polar amino acid serine (S) and the positively charged amino acid lysine (K), consistently with the observations respectively in  [Chowell et al. , 2015]  and  [Schmidt et al. , 2021]. We noted also a number of discrepancies, presumably due to the use of datasets of immunogenic and non-immunogenic peptides that were updated with respect to previously available ones and restricted to sequences of only three selected HLA types. The most striking is the under-representation of the aromatic amino acids phenylalanine (F) and tyrosine (Y), particularly severe in HLA-A*02:01, in contrast with the experiment-based observations in  [Schmidt et al.
, 2021 , Piepenbrink et al. , 2013]. (Note however that also other analyses performed on IEDB data, like  [Calis et al. , 2013 , Chowell et al. , 2015] , did not flag up a significant enrichment in Y). The diffRBM single-site factors recover the positive contribution to immunogenicity of key residues. We then verified whether these apparent discrepancies in terms of site-specific amino acid abundances in our IEDB-derived dataset persisted at the level of model predictions. We considered a few combinations of amino acids and positions along the peptide that were suggested to play a crucial role in T-cell reactivity and binding (in the context of HLA-A*02:01 epitopes) based on structural or functional analyses. For instance, W at position 6 and F at position 7  [Schmidt et al. , 2021] , as well as Y at position 8  [Piepenbrink et al.
The classifier architecture was optimized among several ones of different depth and width (Methods, Figure  S11 , Table  S2 ) and reaches a performance quantified by a cross-HLA average AUC = 0. 71 ± 0. 02 (see 'optimal classifier' in Figure  4B-D ; here and in the following uncertainties are estimated over several training-test partitions, see Methods). As a term of reference we considered a linear classifier, whose performance (AUC = 0. 66 ± 0. 02) shows that there is a gain in 10 resorting to a more flexible architecture in terms of depth and width. The AUC of the optimal classifier sets the maximal predictive performance that can be achieved, with the datasets under consideration, by a supervised method that is trained to discriminate immunogenic and non-immunogenic antigens. Its value (AUC = 0.
Finally, we compared the discrimination performance of diffRBM to two established methods for immunogenicity prediction, the IEDB immunogenicity tool and PRIME  [Schmidt et al. , 2021]. Since these methods are not designed to be re-trained on custom datasets such as ours, the comparison cannot be fully consistent in terms of using the same training and test sets, as done for all the methods in Figure  4. The IEDB tool for immunogenicity prediction is based on the model by  [Calis et al. , 2013]  and can be downloaded from http://tools. iedb. org/immunogenicity/. By applying it to our set of immunogenic and non-immunogenic peptides, we obtained AUC=0. 54 for the HLA-A*02:01-specific peptides, AUC=0. 60 for HLA-B*07:02 and AUC=0. 57 for HLA-B*35:01, which are all lower than the diffRBM average AUC values (respectively 0.
66, 0. 65, 0. 67, see Figure  4 ). By applying PRIME  [Schmidt et al. , 2021]  to our set of immunogenic peptides, we found again poorer performance compared to the diffRBM units (average cross-HLA AUC = 0. 51, see Methods), despite not having excluded from our test sets the immunogenic peptides that are either in PRIME's or in the IEDB tool's training sets. Differences in the peptide datasets used may contribute to explain this result. For instance, our dataset consists of epitopes with experimentally validated positive T-cell responses from IEDB, mainly of microbial origin, while PRIME's training set  [Schmidt et al. , 2021]  was constructed in such a way as to contain a high proportion of neoantigens. As an additional point of comparison, Riley et al. [Riley et al. , 2019]  propose a neural network trained in a supervised way to classify immunogenic against non-immunogenic peptides using sequence as well as structural features of the peptide/HLA complex.
DiffRBM model for T-cell specific binding The idea of a differential learning of unsupervised models of TCR statistics, designed to describe the process of thymic selection acting on top of the receptor generation via V(D)J recombination, has first been put forward by  [Elhanati et al. , 2014]. This effort has culminated in the release of the software SONIA  [Sethna et al. , 2020]  and, most recently, of its deeper version soNNia  [Isacchini et al. , 2021]. Depending on the TCR dataset used for training, these unsupervised models are applicable also as tools to classify TCRs by type (helper vs killer) and by epitope specificity  [Isacchini et al. , 2021]. The computational prediction of the epitope specificity of a given receptor, in particular, is extremely relevant for therapeutic design, and has been attempted through several algorithms  [Cinelli et al.
A B C D Allele HLA-A*02:01 12 memory networks and autoencoders  [Springer et al. , 2020 , Lu et al. , 2021] ). These models are typically calibrated towards achieving high predictive power as classifiers of TCR specificity, while less attention is paid to the interpretability of their predictions apart from a few exceptions  [Papadopoulou et al. , 2022]. Our diffRBM approach differs from and complements these existing methods because, similarly to SONIA, it focuses on the unsupervised discovery of salient amino acid patterns that can allow for biological insight into the molecular properties determining binding specificity. d if f R B M u n it s f u ll R B M b a c k g r o u n d R B M d if f R B M u n it s ( d if f e To train diffRBM models of epitope specificity, we first collected from the VDJdb database  [Shugay et al.
, 2018 ,Bagaev et al. , 2020]  datasets of TCRs specific to 4 epitopes (Methods): the M1 58 peptide from the influenza virus (with sequence GILGFVFTL), the pp65 495 from the human cytomegalovirus (CMV, with sequence NLVPMVATV), the BMLF1 280 peptide from the Epstein-Barr virus (EBV, with sequence GLCTLVAML), the peptide from the Spike protein S 269 from Sars-Cov-2 (with sequence YLQPRTFLL). We limited the search to sequences of the β chain of the TCR (TCRβ), where the sites of binding to the antigen are concentrated in a region called complementarity determining region 3 (CDR3β). The background dataset, in this case, is meant as a typical bulk TCRβ repertoire in normal conditions (Figure  S13 ). In particular we take, to train background RBM, the repertoire of a hypothetical universal donor that was constructed by  [Isacchini et al.