dataesr/scientific-paragraphs-categorization huggingface dataset:
In Fig. 14  we show a related experiment. Here we train the network on 4,430 animal meshes generated with the SMAL parametric model following the official protocol  (Zuffi et al. 2017). Given four low-resolution shapes X i as input, we first compute their spectra Spec(X i ), map these to the latent space via π(Spec(X i )), perform a bilinear interpolation of the resulting latent vectors, and finally reconstruct the corresponding shapes. We perform the same experiment on the human bodies category by exploiting the model O 30. In Fig. 16 , we consider two meshes from the SURREAL test set and two shapes from FAUST dataset. All the input shapes have been remeshed with different densities. The linear interpolation of the latent vectors obtained through π produces meaningful intermediate steps encoding the main intrinsic variation of the subjects involved.
2019; Rampini et al. 2019). These properties allow us to use our network for the task of mesh super-resolution. Given a low-resolution mesh as input, our aim is to recover a higher resolution counterpart of 123 Fig. 19  Qualitative and quantitative evaluation of point cloud spectra estimation. On the left we show the qualitative comparison for different samplings on three classes (animals, human faces and objects). We show the eigenvalues estimations alongside the input point cloud (depicted as surface samplings), and the ground truth spectrum (in red). On the last two columns, we report the average cumulative error curves evaluated on the FLAME dataset for the two different distributions (F1 and F2) and on ShapeNet (S) it. Furthermore, while the input mesh has arbitrary resolution and is unknown to the network (and a correspondence with the training models is not given), an additional desideratum is for the new shape to be in dense point-to-point correspondence with models from the training set.
2018 ) (middle) and ShapeNet watertight  (Huang et al. 2018 ) (right). To highlight its generalization capability, the network trained on COMA is tested on point clouds from the FLAME dataset, while on ShapeNet we consider 4 different classes (airplanes, boats, screens and chairs). We compute the cumulative error curves of the distance between the eigenvalues from the meshes corresponding to the test point clouds. The mean error across all test sets is also reported in the legend. Our method leads to a significant improvement over the closest state-of-the-art baseline  (Belkin et al. 2009).
Acknowledgements We gratefully acknowledge  Luca Moschella  and  Silvia Casola  for the technical support,  Nicholas Sharp  for the useful suggestions about pointcloud spectra. Parts of this work were supported by the  KAUST OSR  Award No. CRG-2017-3426 , the  ERC Starting  Grant No. 758800  (EXPROTEA), the  ERC Starting  Grant No. 802554  (SPECGEO), the  ANR AI Chair AIGRETTE , and the  MIUR  under grant " Dipartimenti di eccellenza 2018-2022 " of the  Department of Computer Science of Sapienza University  and  University of Verona.
Background: Venom-induced anaphylaxis (VIA) is a common, potentially life-threatening hypersensitivity reaction associated with (1) a specific symptom profile, 2) specific cofactors, and 3) specific management. Identifying the differences in phenotypes of anaphylaxis is crucial for future management guidelines and development of a personalized medicine approach. Objective: This study aimed to evaluate the phenotype and risk factors of VIA. Methods: Using data from the European Anaphylaxis Registry (12,874 cases), we identified 3,612 patients with VIA and analyzed their cases in comparison with sex-and age-matched anaphylaxis cases triggered by other elicitors (non-VIA cases [n 5 3,605]). Results: VIA more frequently involved more than 3 organ systems and was associated with cardiovascular symptoms.
Thanks to the large number of documented reactions in the European Anaphylaxis Registry, we were able to match the cases of VIA with non-VIA cases according to sex and age. When we analyzed a density plot of VIA cases according to patient age, we determined a bimodal distribution forming 2 subsets of patients with a cutoff age of 22 years (Fig 1  , B ). Subsequently, we compared the management in both groups and matched the control group according to the severity of a reaction. Cases were matched according to sex, age, and reaction severity to reduce the comparison bias by propensity score matching. Propensity scoring is a statistical approach to quantify the similarity between 2 unrelated cases. Propensity scores were calculated by using the MatchIt package for R software. 11 MatchIt uses logistic regression to reduce the bias due to multiple confounding variables (ie, sex and age) by weighing them and choosing cases with minimal differences in both groups.
The results of the propensity score matching are illustrated in The final database included 3612 cases of VIA reported from allergy centers in 11 countries and a sex-and age-matched control group. We compared the frequency of various symptoms-cofactors known to increase the risk of severe anaphylaxis  12  -and management in both groups. On the basis of the severity and symptom profile and the previous reports, 6 we defined subelevated baseline serum tryptase (BST) values as those between 8 and 11. 5 ng/mL. We used the R statistical package  13  for statistical analysis. A simple comparison of categoric variables was performed by using either the chi-square test or Fisher exact test (if there were <10 observations in a bin). Continuous variables were analyzed by using the Mann-Whitney U test.
In the case of comparisons with 2 or more independent variables, we used factorial ANOVA or generalized linear models. We defined statistical significance as a 5 0. 05. The data, along with the analysis script, can be accessed online at https://github. com/wolass/venomanaphylaxiscompendium. We developed a random forest classifier (using the randomForestpackage for R  14  ) to find those therapeutic approaches that varied the most between the VIA and non-VIA groups and presented the results as Gini importance. 15 oreover, association analysis of therapeutic interventions and symptoms was performed. The resulting 4 values were scaled and presented in a heatmap with automatic clustering by using Ward agglomerative hierarchical clustering with euclidean distances. 16.
Limitations Because of the design of the European Anaphylaxis Registry, our analysis was restricted to cases of anaphylaxis only. Patients experiencing milder hypersensitivity reactions, as well as healthy controls, are not included in the database. Although the European Anaphylaxis Registry is ideal for investigating anaphylaxis phenotypes, it might give an incomplete perception of the population distribution of hypersensitivity reactions and restrict us to comparing only various forms of anaphylaxis. Nevertheless, because the European Anaphylaxis Registry has thus far gathered more 12,000 cases of anaphylaxis, it was possible to perform a case-controlled analysis on a relatively large number of cases and investigate many aspects of VIA. It is worth underlining the important function of international registries, especially in diseases in which targeted studies are not possible.
scoring function called Convex-PL  16  demonstrated excellent results in the pose prediction tests but turned out to perform rather average in virtual screening exercises, as illustrated in Figure1 (c). Indeed, we can see that SASAs of about half of the ligands predicted to be the best binders are considerably higher than those of the true binders. This clearly indicates a bias of Convex-PL towards larger binding interfaces. The current work revisits the derivation of empirical scoring functions and presents a reworked Convex-PL model. Firstly, we retrained it on a more diverse structural dataset containing cofactors and modified residues to improve the support of interactions between ligands and molecules other than standard amino acids. Then, motivated by the general thermodynamics principles, we augmented Convex-PL with several physics-based terms that aim to penalize the bias of large interfaces.
While the inclusion of the entropic terms in most cases had a clear enhancing effect, the incorporation of solvation terms turned out to be more challenging and, for Convex-PL, lead to controversial results. Throughout the manuscript, we will keep referring to the initial version of Convex-PL as to Convex-PL, the newer version, trained with the regression model, will be called Convex-PL R. Convex-PL R does not require any specific preparation and parametrization of input structures. It is designed for relatively small molecules of less than 100 heavy atoms. Although we support ligand-cofactor interactions, the representation of metals is still limited. Currently, Fe has the most trustworthy representation driven by its presence in a considerable amount of heme cofactors. Although the dataset also included Zn, Mg, and other metals, we are not fully confident in them.
Our general aim is to approximate the configurational integrals ratios using only a single conformation of the protein-ligand complex instead of rigorously sampling the conformational ensemble. Firstly, we suppose that the ratio Z N,AB Z N,0 of the complex and the solvent configurational integrals can be approximated with the original Convex-PL knowledge-based potential,  16  which can be represented as a dot product between a structure vector x and a scoring vector w, E Convex-P L = M 1 k M 2 l (x kl • w kl ). (3) In this equation, the Convex-PL potential is considered to be a sum of pairwise interactions between the protein and the ligand atoms of M 1 and M 2 atom types, correspondingly. The structure vector x consists of number density functions extracted from the structures of protein-ligand complexes, and the scoring vector w contains weights for each contribution that are optimized by solving a classification convex optimization problem on a training dataset.
Training data We were using two separate datasets for the classification and regression parts of the model. Complexes used in the two CASF benchmarks were excluded from both datasets. The original Convex-PL potential is trained on PDBBind 2016. It uses a 10 Å cutoff for the pairwise interactions between its 42 ligand, and 23 protein atom types. In the reworked Convex-PL R model, we use another version of the original potential, Convex-PL cof , with a 5. 2 Å cutoff, 39 ligand and 33 protein atom types listed in Tables S1, S2 of SI. Truncation of the cutoff radius alone already decreases the virtual screening test bias towards bigger ligands by reducing the variance and the total number of pairwise interactions. We also augmented the training decoys generation procedure by adding decoys with hydrogen atoms randomly substituted by heavy atoms.
To obtain Convex-PL cof , we collected a dataset consisting of PDBBind 2019,  66  Binding MOAD 2020,  67  and a set of complexes from PDB that contained cofactors or modified residues. The latter were chosen based on the PDBe-KB  68  annotations, with the exclusion of some potential solvents and buffers provided by VHELIBS. 69 We chose to use biological assembly submissions as the representatives of the complexes to be consistent with PDBBind and BindingMOAD, although in some cases of binding at the protein-protein interfaces such an approach can result in wrong binding site configurations. Finally, we filtered out complexes with small molecules having low occupancy, RSCC values, and extreme bond length outliers. RSCC values and geometry outliers were taken from the PDB validation reports.
This procedure resulted in 29,763 protein-ligand pairs that were used for training. The dataset creation pipeline is described in more detail in Table  S3  of SI and is available at https://github. com/chmnk/pl_binding. Some rare ligand-cofactor atom type pairs, however, are not present in the training set. In such cases, we set these interactions to zero and output a warning. We then trained Convex-PL R on a subset of the PDBBind 2019 general dataset. The subset was chosen to contain complexes with Convex-PL cof scores that do not correlate sufficiently with the affinities. To do this, we built a simple regression model and removed complexes with scores at a 0. 5 threshold distance from the trend line. We have also excluded complexes satisfying a number of criteria listed in Table  S4  of SI.
Scoring and ranking Convex-PL R obtains scores with rather high correlation to the experimental binding constants, which are plotted in Figure  4. Below we discuss several outliers present in the two benchmarks. The binding site of the 3kwa complex includes a zinc atom. Although we included some metals into the cofactor-containing dataset, this example may indicate that interactions with zinc are still not fully supported. The binding site of the 4dew complex is located on an interface between the two monomers. This assembly was constructed by applying a crystallographic symmetry operator to the monomeric asymmetric subunit. This resulted in unnaturally small distances (up to 2. 5 Å) between the heavy atoms of the ligand aromatic ring and the lysine amino group of the reconstructed monomer, and, in the underestimation of the binding constant.
Below we would like to discuss the reasons for the poor performance of Convex-PL R on two clusters. The 2zcq complex of the 2zcq cluster contains the magnesium ions interacting with the 0 10 20 30 40 50 60 70 80 90 100 Success rate (%) DScore@SYBYL SAS GScore@SYBYL Jain@DS ASE@MOE PMF@SYBYL PMF04@DS ChemScore@SYBYL London-dG@MOE X-Score-HM Affinity-dG@MOE LUDI2@DS GoldScore@GOLD ASP@GOLD GlideScore-XP Alpha-HB@MOE LigScore2@DS PLP1@DS ChemScore@GOLD GlideScore-SP ChemPLP@GOLD KORP-PL w AutoDock Vina PMF@SYBYL London-dG@MOE GlideScore-XP PMF@DS Jain@DS GlideScore-SP LigScore2@DS Affinity-dG@MOE GoldScore@GOLD LUDI3@DS KORP-PL w Alpha-HB@MOE D-Score@SYBYL ChemScore@GOLD ASE@MOE ASP@GOLD AutoDock Vina G-Score@SYBYL PLP1@DS.
Convex-PL ChemPLP@GOLD ChemScore@SYBYL Vinardo SAS GlideScore-SP GlideScore-XP ASE@MOE Jain@DS London-dG@MOE PMF@SYBYL PMF@DS ChemScore@GOLD ASP@GOLD D-Score@SYBYL SAS AutoDock Vina KORP-PL w LUDI1@DS LigScore1@DS Alpha-HB@MOE G-Score@SYBYL Affinity-dG@MOE ChemScore@SYBYL PLP2@DS VinaRF20 GoldScore@GOLD ChemPLP@GOLD X-Score HM AGL-Score SAS G-Score@SYBYL D-Score@SYBYL X-ScoreHM ASE@MOE PMF04@DS Alpha-HB@MOE ChemScore@SYBYL PMF@SYBYL Jain@DS PLP1@DS GoldScore@GOLD London-dG@MOE Affinity-dG@MOE.
An interesting case where both Convex-PL R and the top-ranked scoring functions, including ∆ Vina RF 20 , show near-zero correlation is the 4rfm cluster consisting of the 4rfm, 4qd6, 3qgy, 4m0y, and 4m0z PDB structures corresponding to the interleukin-2 inducible T-cell kinase (ITK) inhibitors. Here, most of the scoring functions, including Convex-PL, disfavor the 4qd6 ligand, although its experimental binding affinity is high and should be ranked second. It seems that the structures of the complexes from the benchmark correspond to those marked as a biological assembly in the Protein Data Bank. However, the biological assembly deposited for 4qd6, along with the published analysis of its interactions with the ligand,  73  does not include or describe the beta-sheet, which seems to be a part of domain swapping and is present in the original crystallographic structure.
D3R In addition to the CASF benchmark, we evaluated pose and affinity predictions on a dataset collected from user submissions in the D3R Grand Challenges 2,  75  3, 76 and 4. 77  The derivation of this test can be found elsewhere. 29 igure  6  demonstrates the high performance of Convex-PL R in both pose and affinity prediction parts of the Grand Challenge 2 test. In the Grand Challenge 3 exercise, both Convex-PL R and Convex-PL cof outperform their predecessor in pose prediction. During the D3R Grand Challenge 3 timeframe, only a few protocols that did not use visual inspection and ligand-based approaches were successful,  76  meaning that this particular case could be a challenge for many scoring functions. Convex-PL tended to prefer incorrect poses that were buried deeper in the binding pocket and had tighter binding interfaces.
DUD To additionally measure the virtual screening performance of Convex-PL R , we assessed it using the Directory of Useful Decoys (DUD)  78  dataset specifically designed for virtual screening benchmarking. It contains 40 targets with a total of 2,950 active compounds. For each active compound, the dataset provides 36 decoys with similar physics, but various chemical topology. In this test, we assessed Convex-PL R , Convex-PL cof , and Convex-PL scoring functions together with a few other ones, namely, KORP-PL w , AutoDock Vina, and Smina 79 with Vinardo chosen as the scoring function.
We have tested several ways to define the docking binding boxes. We achieved the best virtual screening results for all three protocols with the binding box determined with the following procedure: (i) Target co-crystal ligand dimensions, namely the co-crystal box, were measured. (ii) Dimensions of the input ligand 3D conformation provided in the dataset, namely the ligand box, were measured. (iii) The input ligand box was aligned with the co-crystal box. (iv) Finally, for each dimension, the maximum size of the two boxes was chosen and multiplied by an arbitrarily chosen factor of 1. 6. Notably, virtual screening results of both Vina and Vinardo achieved with such a box outperformed those reported previously. 17 igure  7 , Table  1 , and the Tables S16-S17 of SI summarize virtual screening powers evaluated by measuring the ROC AUC, enrichment factors, and BEDROC α=20.
This could be explained by the fact that VinaCPL, which we used for docking, does not have the cofactor support yet. Table  1 : Average ROC AUC scores, 5%, 1%, 0. 5% enrichment factor, and BEDROC  82, 83  values computed for the DUD dataset. Per-target evaluation results can be found in Tables S16-S17 of SI. Cofactor-containing targets are listed in Table  S15   compounds found in biological assay databases. Here we compare only AutoDock Vina and Convex-PL R. We used the same docking protocols as in the case of DUD. 3D ligand conformers were generated using RDKit's EmbedMolecule with ETKDGv3 parametrization. 83, 85 Prior to docking, we removed duplicating ligands having different PubChem substance IDs but encoded with the same SMILES strings. We also used only a part of the target representative proteins listed in Table  S18 , selected from their binding pocket alignment and visual analysis.
For each ligand, we took the top-scored predictions among the chosen protein representatives. Table  2 : Average ROC AUC scores, 5%, 1%, 0. 5% enrichment factor, and BEDROC  82, 83  values computed for the LIT-PCBA dataset. Per-target evaluation results can be found in Table  S19    Figure  8  and Table  2  demonstrate that Convex-PL R outperforms AutoDock Vina in this benchmark. However, their ROC AUC values and enrichment factors are, in general, rather low. Docking to 7 and 5 targets for Vina and Convex-PL R , respectively, resulted in zero 0. 5% enrichment. We suppose that such poor results can be partially related to the benchmark composition. For example, the kat2a target contains very different representatives with binding pockets located on two different domains, while the adrb2 representatives contain proteins that were specifically mutated for covalent binding.
Technical details Convex-PL R is written in C++ and is available as a standalone binary. It takes about 16 milliseconds on a single core of Linux Intel(R) Core(TM) i7-8565U CPU @ 1. 80GHz to score a protein-ligand complex from the CASF-2013 core set containing a single ligand pose of 25 heavy atoms on average. Convex-PL cof is about 1. 5 times faster, as it does not require the SASA computation.
Conclusion This paper presents Convex-PL R -a reworked Convex-PL protein-ligand potential, derived from thermodynamical considerations. Our model incorporates conformational entropic terms for the ligand and the binding pocket sidechain flexibility. For the solvation contributions, we have developed two models, either computing atom-mesh interactions, or approximating the solvation energy with SASA. To our surprise, the weights of these contributions turned out to be negligibly small after the training, and we removed them from the final model. We also developed a novel docking protocol by incorporating the Convex-PL scoring function inside Autodock Vina with a subsequent rescoring of the docking poses with Convex-PL R. We successfully validated Convex-PL R on CASF 2013 and 2016 benchmarks, on a dataset derived from the D3R Challenges, and on DUD and LIT-PCBA virtual screening tests, where it was ranked top-2 -top-3 in the majority of the CASF tests, top-2 in DUD, D3R, and outperformed AutoDock Vina in LIT-PCBA.
Acknowledgement The authors would like to thank  Ivan Gushchin  from  MIPT Moscow  for providing his expertise in crystallography,  Pablo Pablo Chacón  from  Rocasolano Institute of Physical Chemistry, Madrid  for the discussions of the representation of the desolvation free energy change in knowledge-based potentials, and anonymous reviewers for their comments and evaluation. V. Ch. is supported by the  Ministry of Science and Higher Education of the Russian Federation  (agreement #  075-00337-20-03 , project  FSMG-2020-0003 ).
Statistical Analysis Statistical analyses were performed using R Studio software (version 1. 4. 1103), R Markdown package  [51]. The statistical analyses to compare the distribution of MIC values for antimicrobial agents and relative abundance of ARGs in Aeromonas strains isolated from healthy, furunculosis-suffering and antibiotic-treated fish and their environment on two fish farms were realised using the Kruskal-Wallis nonparametric ANOVA test. The statistical analyses to compare the occurrence of NWT Aeromonas strains and the presence of ARGs between the groups studied were realised using univariate analysis and binary logistic regression test for each antimicrobial agent or ARG. A significant difference was expressed as a p-value below the 5% confidence interval.
Antimicrobial Susceptibility A total of 257 Aeromonas spp. were selected for antimicrobial susceptibility tests from farms A and B, including 189 Aeromonas from fish samples and 68 from environmental strains (49 and 19 isolates from water and biofilm, respectively). Among these strains, 153 environmental and fish isolates were considered as healthy Aeromonas strains, including 58 and 98 strains isolated from farms A and B respectively, when no episode of furunculosis and no antibiotic treatment were observed. Fifty-four isolates were collected from fish with furunculosis signs after the confirmation of furunculosis on farm A. Fifty isolates were considered as treated environmental and fish isolates with the Aeromonas strains isolated following an antibiotic treatment. For each antimicrobial susceptibility test, the MIC results obtained for the reference strains were in accordance with CLSI guidelines (data not shown)  [39].
Acknowledgments: The authors would like to thank " Le Gouessant Aquaculture Co", "Bretagne truite " and fish farmers for their organisation and participation in our on-farm research project. We show our gratitude to  ITAVI (Institut de l'Aviculture, Pisciculture et Cuniculture)  for their support in the biofilm research study. We are also grateful to  Anne Lehebel  and  Nadine Brisseau  for their help in conducting the statistical analysis.