dataesr/scientific-paragraphs-categorization huggingface dataset:
Configurations that were dominated on all benchmark datasets are not displayed. Since tightening does not affect the maximal number of iterations, in the first row, we only show the results for the configurations without tightening if both the version with and the version without tightening were not dominated on all datasets. with a small time limit and still returns an estimated median graph of good quality. On aids, all configurations except GMG-BCU (II) with time limits of 1 and 2 min terminated regularly.
Runtime and scalability Fig. 4  shows how the compared methods performed w. r. t. runtime and scalability. On all datasets except the two variants of letter, GMG-BCU (III) was orders of magnitudes faster than all tested competitors. Moreover, the results for aids and aids-edit clearly show that also the other two configurations GMG-BCU (I) and GMG-BCU (II) scale better to large collections of graphs than the competitors. On muta, this is less visible, because here all tested algorithms except GMG-BCU (III) reached the time limit of 10 min already on pretty small collections. On the largest dataset s-mol, all competitors reached the time limit already on the smallest sub-collections. In contrast to that, the fast configuration GMG-BCU (III) terminated in around a minute even on the largest sub-collections containing 45 000 graphs.
The results for the two variants letter-eucl and letter-squared of the letter dataset are very different. Here, the tested competitors were significantly faster than the three configurations of GMG-BCU. There are two reasons for this. Firstly, the letter graphs are very small (cf. Table  3 ), which implies that computing node maps -the dominant operation in the state of the art approaches -is cheap. Secondly, the node labels of the letter graphs are Euclidean coordinates, i. e. , the node label domain L V is continuous. GMG-BCU optimizes over this domain, Fig. 3. Effect of time limit on SOD and on the number of runs which terminated regularly for configurations GMG-BCU (I), GMG-BCU (II), and GMG-BCU (III). The results for aids are not displayed, because here all configurations except GMG-BCU (II) with time limits of 1 and 2 min terminated regularly.
4. Performance of GMG-BCU configurations w. r. t. runtime in comparison to state of the art heuristics and the first prototype of our algorithm presented in  [33]. All heuristics were run with a time limit of 10 min. which leads to an increased number of iterations w. r. t. instances with symbolic node labels (cf. Fig. 2 ). In contrast to that, the state of the art approaches always select the labels of the estimated median graph as (interpolations between) labels that are present in the graph collection. This improves the runtime, but negatively affects the quality of the estimated median graph, as detailed below. When comparing the variants of GMG-BCU to the preliminary version PROTOTYPE presented in  [33] , we observe that PROTO-TYPE was faster than all three GMG-BCU variants only on the two letter datasets, but was slower than GMG-BCU (III) on all other datasets and scaled worse to large graph collections than all three GMG-BCU variants.
Quality Fig. 5  shows how varying the size of the graph collection affects the compared algorithms' deviations from the theoretically known optimal SOD (plot for aids-edit) or from the best SOD obtained by one of the tested algorithms (plots for all other datasets). As expected, the three configurations of GMG-BCU computed tighter SODs than the competitors on the two letter datasets with continuous node label domains. On the chemical datasets aids, muta, and s-mol, all computed SODs were similar with GMG-BCU (II) performing slightly better than the other algorithms and configurations, on average. However, as indicated by the missing or partial curves in Fig. 5 , all state of the art approaches failed to compute any results on the larger subcollections of muta and on all sub-collections of the large dataset s-mol, because they reached the time limit while computing the initial GED based graph embeddings.
In contrast to that, also the slower configurations GMG-BCU (I) and GMG-BCU (II) always returned an estimated median graph even if interrupted before regular termination, because GMG-BCU is an any-time algorithm. Moreover, on aids, muta, and s-mol, all three configurations of GMG-BCU always yielded SODs which deviated from the best SOD by at most 11 %. Fig. 5. Mean deviations from theoretically known optima (aids-edit) or best computed SODs (all other datasets). Missing or partial curves for the state of the art heuristics indicate that they reached the time limit of 10 min while computing the graph embeddings and hence did not return any candidate median. Similarly, missing curves for the prototype of our algorithm presented in  [33]  indicate that it reached the time limit during initialization and hence did not return a median graph.
On aids-edit, the SOD of the best performing algorithm GMG-BCU (I) exceeded the theoretically known optimal SOD by a factor of around 2 (i. e. , deviated from the optimum by around 100 %). The SODs computed by the other algorithms and configurations were significantly looser. To correctly interpret these results, recall that, by construction, aids-edit is a very special dataset, since all contained graphs are extremely similar and at most one edit operation away from the optimal median. Because of this, already one incorrect node assignment in each of the node maps leads to a relative error of at least 2, even if the optimal median is found. Therefore, the empirical approximation factor of 2 is actually a pretty good result. The special structure of the aids-edit dataset also explains the poor performance of GMG-BCU (II), which yielded the tightest SODs on all but one of the other datasets.
Performance of GMG-BCU in application scenarios In a third set of experiments, we evaluated how GMG-BCU performs when used for computing representatives for microbiome data graphs, and when used as a sub-routine within higher-order algorithms for graph clustering, classification, and indexing. The prototype computation tests were carried out on the ibd dataset, the tests for clustering, classification, and indexing on the benchmark datasets letter-eucl, aids, and muta. Just as the expositions in Section 6. 2 to 6. 4, the latter tests are intended as proofs of concept. Carrying out in-depth empirical evaluations is the task of future work, whose dedicated focus is the development of graph clustering, classification, or indexing techniques. For the ibd prototype tests, we used the configuration GMG-BCU (I), which yielded the best empirical approximation ratio on aids-edit.
Computation of representatives for ibd graphs Table  4  shows the quality of the representatives for the three sub-groups of the ibd dataset computed by GMG-BCU and state of the art heuristics. For each sub-group, we show the obtained SODs and the deviations in percent from the best SOD. GMG-BCU computed the best representatives on all sub-groups; the SODs of the representatives computed by best competitor BEST-TRIANGULAR were between 10 % and 15 % looser. Like on the letter datasets, GMG-BCU outperformed the state of the art in terms of SOD, because the ibd graphs have continuous (edge) labels, and, unlike the competitors, GMG-BCU optimizes the labels of the estimated median over the continuous domain. Since the ibd dataset only contains 139 graphs, all heuristics terminated within a couple of seconds on all sub-groups.
Clustering To evaluate the clustering performance, we clustered the 90 % sub-collections of letter-eucl, aids, and muta with K -medians clustering (cf. Section 6. 2). We used K -means++ initialization and set the maximum number of iterations of Lloyd's algorithm to 10. For all datasets, we set K to the number of different classes, i. e. , K = 2 for muta and aids, and K = 15 for letter-eucl. We recorded the runtime, the SOD, and the adjusted Rand index (ARI) w. r. t. the ground truth clustering. ARI measures the similarity between two clusterings  [82]. Its range is [-1, 1], and it evaluates to 1 if the two clusterings are identical. To have a baseline against which to compare, we also recorded these metrics for the very first clustering obtained by the K -means++ initialization. Table  5  shows the mean outcomes of the experiments across the five test collections.
On all datasets, K -medians clustering based on our BCU reduced the overall SOD by more than 30 % w. r. t. the initial clustering obtained by K -means++ initialization. On letter-eucl and aids, K -medians clustering significantly improved the ARI. This was not the case on muta, which can be explained by the fact that, on this dataset, tightness of bounds for GED is only very weakly correlated with clustering and classification performance  [10]. Finally, the overall runtime was a couple of hours on letter-eucl, a couple of minutes on muta, and a couple of seconds on aids. This is due to the facts that we computed 15 classes on letter-eucl, but only 2 on aids and muta, and that computing the median labels is much more expensive than on aids and muta (cf. Section 7. 2 above).
Classification and data reduction Next, we tested how our algorithm performs when used for classification and data reduction as explained in Section 6. 3. First, we split the benchmark datasets into disjoint collections of data and query graphs that we constrained to be balanced w. r. t. the classes. For each dataset and each class, we then computed 10 representatives of the data graphs via 10-medians clustering. Finally, we ran the following 1-NN query specifications (cf. Definition 6) for each query graph: (a) determine the nearest neighbor of each query graph among all data graphs; (b) determine the nearest neighbor from the set of representatives. Both specifications were set up to use the upper bound computed by BRANCH-FAST as the proxy GED for GED. Table  6  shows the mean classification coefficients (i.
Indexing Finally, we tested how median based MBSTs as introduced in Section 6. 4 perform at processing GED range queries in the sense of Definition 8. We again split the benchmark datasets into disjoint collections of data and query graphs. Subsequently, we computed median based MBST indices for the data graphs. Finally, for each query graph, we answered GED range queries for thresholds τ p ∈ {τ 0. 01 , τ 0. 025 , τ 0. 05 , τ 0. 075 , τ 0. 1 } (a) with the help of the pre-computed indices, and (b) via linear scans against all data graphs. To define the thresholds, we first estimated the GEDs between all query graphs as the means between the lower and the upper bounds returned by BRANCH-FAST. Subsequently, the estimated edit costs were sorted in increasing order, and τ p was defined as the mean of the entries at positions ⌊p • length(a)⌋ and ⌈p • length(a)⌉ in the sorted array of estimated edit costs a.
This construction mirrors the fact that, when issuing range queries, one is typically interested in retrieving objects that are close to the query object. All configurations of the range queries were set up to use the upper bound computed by IPFP and the lower bound returned by the linear programming based algorithm ADJ-LP suggested in  [45]  as, respectively, the upper and lower bounds ⌈GED⌉ and ⌊GED⌋ for GED (cf. Section 6. 4. 4). We selected these methods, because in  [10]  they are reported to produce very tight bounds for GED at affordable computational costs. Fig. 6  shows the results of the indexing experiments. On all datasets, using median based MBSTs significantly reduced the mean query time. We also see that the runtime gain is especially high for small thresholds -that is, the thresholds one is typically interested in.
Configuration of the proposed algorithm Initialization. The best tradeoff between runtime and quality is achieved if GMG-BCU is initialized with a small number of randomly generated candidates. Termination criteria. Running GMG-BCU with a maximal number of iterations is unnecessary, because GMG-BCU typically converges after a small number of iterations which is independent of the size of the graph collection G. In contrast to that, the execution time of one iterations increases linearly with the size of G. To control the maximal runtime of GMG-BCU, it is hence better to run it with a time limit. Since GMG-BCU quickly finds solutions of good quality, this time limit can safely be set to a small value, if necessary. Heuristic used during BCU. The choice of the GED heuristic used during the BCU has only a small effect on the SOD but, on some datasets, a tremendous effect on the runtime.
Comparison to state of the art Runtime. Whenever computing node maps between the input graphs is computationally more expensive than computing median labels, GMG-BCU can be configured to run orders of magnitudes faster than all competitors. State of the art approaches are faster only when used on relatively small datasets that contain very small graphs with continuous labels, but then compute looser SODs than our algorithm (cf. next paragraph). Quality. If used for graphs with continuous labels, GMG-BCU computes tighter SODs than all evaluated competitors. If used for graphs with symbolic labels and constant edit costs, all obtained SODs are similar.
GMG-BCU. Clustering. On all test datasets except muta, K -medians clustering based on GMG-BCU yields a much better ARI than a randomized baseline that uses K -means++ initialization only. The poor performance on muta can be explained by the fact that, as reported in  [10] , the muta dataset seems to be poorly suited for GED based clustering techniques. Classification and data reduction. On all test datasets, 1-NN can be sped-up by orders of magnitude if run against classrepresentatives obtained via GMG-BCU based K -medians clustering. At the same time, the classification coefficient drops only on muta and stays unchanged or even improves on the other test datasets. Indexing. On all test datasets, GMG-BCU based MBSTs significantly speed up processing of GED range queries. This result is especially interesting, because, to the best of our knowledge, all existing indexing techniques that are designed to support GED range queries accept only uniform edit costs.
Moreover, we provided proofs of concept of how to beneficially employ generalized median graphs for applications such as differential microbiome analysis, clustering, classification and data reduction, and indexing. Extensive experiments showed that our algorithm GMG-BCU clearly outperforms state of the art median graph estimators and also yields promising results when used for the aforementioned applications. In future work, we will further explore how to beneficially employ GMG-BCU for differential microbiome data analysis. Here, the idea is to systematically compare the obtained generalized median graphs for the classes of the clinical variable of interest and hence individuate features that are predictive and/or causative for specific conditions. Moreover, we will build a compression tool for graph collections on top of GMG-BCU.
Acknowledgments We would like to thank  Markus List  and  Monica Matchado  for pointing us to the ibd dataset. This work has been supported by  Région Normandie, France  under the project RIN AGAC, and by  Agence nationale de la recherche, France  under the project  ESIGMA  ( ANR-17-CE23-0010 ). This work was supported by the  Open Access Fund of the Free University of Bozen-Bolzano, Italy.
0  (Tian et al. 2017)  with Maize AGPv4 (Maize-GAMER) as custom reference background. Enrichment was assessed using hypergeometric tests with Yekutieli adjustment for multiple comparisons (p value ≤0. 05). For KEGG annotation, the R package biomaRt  (Durinck et al. 2009 ) was used to map KEGG terms to the transcripts, and the R package clusterProfiler  (Yu et al. 2012 ) was used to perform enrichment analysis of KEGG terms. Clustering of genes based on expression across different N-treatments was performed using a fuzzy clustering approach with the R package mfuzz  (Kumar & Futschik 2007). Shell scripts and R functions used for the present study are made freely available on GitHub (https://github. com/ fabiomarroni/mais_DE_2020).
The lipid binding loop of BoNT/B binds to GT1b and SYT Our molecular modeling data suggest that the LBL could physically interact with SYT and GT1b. In order to corroborate the accuracy of these in silico predictions, we experimentally investigated BoNT/B LBL binding to GT1b and SYT. We used the Langmuir monolayer method and recorded surface pressure changes in GT1b or SYT2-TM monolayers induced by a water-soluble BoNT/B peptide p1242-1256 encompassing the LBL (fig. S1 ). Injection of p1242-1256 underneath a monolayer of GT1b yielded an increase in surface pressure (Fig. 6A ), whereas limited interaction was found with lyso-LacCer (a lipid with an inverted conic shape resembling gangliosides) or sphingomyelin (a major sphingolipid component of lipid rafts). The absence of interaction with the ceramide domain of sphingomyelin indicates that the LBL of BoNT/B interacts preferentially with the sugar part of GT1b.
Acknowledgments We thank  Raymond Miquelis  for constructive discussions. Funding:  Institut National de la Santé et de la Recherche Médicale (INSERM) Aix-Marseille Université (AMU) Agence Nationale de la Recherche (ANR)  (grant  ANR-17-CE16-0022 ) for the postdoctoral financial support of  JRF Ministère des Armées (AID)  and  Aix-Marseille Université AMU  for the PhD thesis of FO.
Structural Modeling of the Nb -TNC Interaction For the Nb -TNC structral interaction modeling the Rosetta Antibody application  (57, 58)  from "ROSETTA 3. 8" 1  was used. Selection of the top 10 Model was done according to Rosetta scoring based on system energy. Structural information about the 5th TNC fibronectin type III repeat (TN5) was extracted from the protein data bank deposited under the PDB code: 1TEN. The TN5 structure was refined using the ROSETTA relax application which then was used to map potential interaction sites in the selected Nb through the ZDOCK docking tool, version 3. 0. 2 (59). Structure and complex interactions were then visualized via the molecular visualization PyMOL software  (60).
Three Dimensional Topology of the Nb3/TN5 Interaction Complex We adopted a computational structure analysis strategy in order to identify amino acid residues involved in the interaction of Nb3 with TN5. The 1f2x (chain L), 3l95 (chain B), 5ocl (chain G) and 5vak (chain B) structures were used as principal template for Nb3-frameworks, CDR1, CDR2 and CDR3, respectively. The number of generated Nb3 models was set to 1,000. The top ten model best scores, ranked according to an energy-based scoring were selected from 10 clusters composed of 100 structures per cluster then visually double checked (to detect structural anomalies) using the molecular visualization PyMOL software. The TN5 structure (1TEN) was included in the Nb3 molecular docking simulation. The positively and negatively charged residues are in blue and red, respectively, whereas the neutral side-chains are indicated in white, showing clearly separated charges on the TN5 surface.
In particular, TNC-specific antibodies were shown to be a means for tumor targeting. In the past, several TNC-specific monoclonal antibodies were developed as well as aptamers and antibody fragments (scFv) that are currently undergoing clinical evaluation  (37, (74) (75) (76) (77) (78). The anti-human TNC G11 antibody (79) was used for targeting TNC in glioma xenografts upon coupling with 18 F-fluorodeoxyglucose  (37). Phase I and II clinical trials were performed with the F16 anti-TNC antibody in glioma patients  (80, 81) , breast cancer (82, 83) and Hodgkin's lymphoma  (35). Coupling the TNC specific F16 antibody to IL-2 (Teleukin R ) was used to deliver IL-2 into the cancer tissue  (82, 84). Moreover, since 2013, a phase II clinical trial using Teleukin R labeled with 131 Iodine is in progress in melanoma patients (EudraCT 2012-004018-33)  (35).
ACKNOWLEDGMENTS We like to thank  Dr. H. Bannour ,  M. Vet  for his invaluable help on dromedary immunization. We thank  Prof. Lotfi Hendaoui  and  Prof. Ahlem Lahmar  for providing the CGB hepatic metastasis FFPE tissue and the associated clinicopathological diagnosis. We like to acknowledge  Agnes Neuville  ( Department of Pathology, HUS Hautepierre ) for providing the UC specimens and  Tristan Rupp  for having generated the U87MG tumors. We are sincerely sad that we cannot share the accomplished work with RC-E who had initiated this project together with BB-Z.
FUNDING This work was funded by grants from  Institut Pasteur Tunis ,  PRF D4P1  project, Tunisia, to BB-Z,  Friedrich Miescher Institute, Basel, Swizerland  to  RC-E and ANR-AngioFib ,  ANR-ACKITEC ,  INCa/Ligue contre le Cancer ECMpact ,  University Strasbourg  and INSERM to GO and personal fellowships to WE ( French Ministry of Research MRT ) and DM (Association pour la recherche sur le cancer ARC).
Acknowledgements The authors would like to thank  Dr. Kenneth Street  for his help in developing FIGS subset and  Mr. Amer El-Omrani , the technician at  Sidi Allal Tazi experimental station  for his help with field experiments. Funding This study is supported financially through GIZattributed funding to ICARDA genebank and the  CAIGE-GRDC - ICA00010  project.
Statistical analyses This analysis calculated the efficacy of the 8-aminoquinoline used in each clinical trial from the output collected in each respective simulation. It calculated efficacy using multiple metrics in order to examine how different data collected during trial follow-up resolved biases in the efficacy estimates. Consistent with previous clinical trials  [7, 9] , freedom from LM-detectable recurrent infection was used as the default efficacy metric in the simulation studies. Efficacy was calculated using the Cox proportional hazards model  [34] , which computes the hazard ratio between the treatment and control arms based on the times to first LM-detectable recurrent infection. Following the DETECTIVE trial protocol  [7, 9] , recurrent infections that occurred before 32 days post-enrolment were not included.
This analysis next considered whether a method using time-to-event and genotyping data that are capable of distinguishing between different types of P. vivax recurrent infections at different sensitivities and specificities could reduce or correct the bias in the efficacy estimates. Then, it measured the extent to which the duration of prophylaxis provided by the treatment regimen biases estimates of efficacy by preventing reinfections in the treatment arm only. Finally, it considered how the efficacy estimates that was obtained varied with the choice of efficacy metric and infection endpoint. A supplementary analysis exploring a sub-set of these effects was also performed using a simpler, hazard-based transmission model. For each simulation setting, a clinical trial with 1000 participants in each arm was simulated.
Conclusions This analysis predicts that site-specific biases are likely to occur in clinical trials for radical cure, and these results suggest that care should be taken in the planning of future trials and the interpretation of trial data. Mathematical modelling that accounts for site-specific biases can aid in the interpretation of clinical trial data and may be useful for identifying future trial sites where biases may be less severe  [53]. As always, the utility of the insights from mathematical models is improved with additional data, so modelling should be integrated into clinical trial design to identify data needs and inform data collection in order to reduce these biases and improve understanding of radical cure's potential to control P. vivax malaria.