dataesr/scientific-paragraphs-categorization huggingface dataset:
Profiles were compared with hhsearch  [42]. All tools can be found in the HH-suite package-version 2. 0. 15. For CLADE, we considered the profile constructed from the test-set of n -1 aligned sequences in the HMMScan experiment, and we constructed additional profiles, one for each sequence in the n -1 set. This was done by using PSI-BLAST and the NR database (see details in Methods). To combine the outputs of the n profiles, we trained a SVM by using as positive set the same n -1 sequences and as negative set other sequences never used in training nor test. The negative sequences were chosen randomly into the same SCOP dataset of the positive ones, and by avoiding sequences sharing the same SCOP fold. The SVM was trained with an equal number of positive and negative sequences. The third step of CLADE, handling architectures with DAMA, was not used.
We call this model "1-mer". The second random model takes a protein and generates 20 different reshuffling of kmers in the original sequence, for k = 4. We call this model "4-mer". The idea behind this last model is that small k-mers within a protein sequence might be more likely to occur than random k-mers, since protein sequences might contain repetitive patterns (for instance, blocks of hydrophobic amino-acids or other compositional biases). Given the original set of protein sequences P and its associated shuffled sequences S, let P + S be the set of concatenated sequences. Note that the set P + S is a set containing 20 times more sequences than P because from each sequence in P, we generated 20 sequences. Then, we computed the number of CLADE domain predictions within the P-portion (saying R) and the number of predictions within the S-portion (saying A) of the P + S sequences, and set the FDR = A/R for the dataset.
This calculation is repeated 20 times, with respect to 20 different reshuffling and the FDR for the 1-mer experiment is considered to be the average of the FDRs of the 20 datasets. The same reaching its best value at 1 and worst score at 0. TP, FP and FN are defined as follows: let s be a protein sequence, A be its domain architecture and T be the evaluated method; a true positive is a domain in A that is correctly predicted by T, a false positive is a domain detected by T that overlaps a different domain in A, and a false negative is a domain in A that is not detected by T. The method T can detect other domains along s that do not overlap domains in A, and we shall refer to them as "additional" domains. Time complexity of the system and construction of the CLADE models library CLADE is a pipeline that involves several different tools, and its formal time complexity is hard to establish.
Data and software availability CLADE software and the entire library of CCMs used for the applications presented in this article are available at the address: http://www. lcqb. upmc. fr/CLADE. The CCMs that we generated can be used for annotating any genome and they avoid running the first step of CLADE again. For P. falciparum, CLADE website provides access to a downloadable xls file containing the full list of annotations for the 5542 P. falciparum proteins (AllDomains. xls). The file also contains the annotations obtained with HHblits and HMMScan, and for each hit, it reports its position, the PlasmoDb accession number, the Pfam domain name, the Pfam clan (if any), and the E-value. The list of disagreeing hits between CLADE and HHblits/HMMScan is also given. A HHblits/HMMScan hit disagrees with CLADE for two reasons: 1.
either the hit does not overlap CLADE hits, 2. or the hit overlaps (with an overlapping of any size) a CLADE hit of a different domain and a different clan. S3 Fig. CLADE  predicts more domains over a range of FDRs. The y-axis is the number of predicted domains per protein ("signal"), while the x-axis is the FDR ("noise"), so better performing methods have higher curves (more signal for a given noise threshold). CLADE (red) outperforms HMMScan (black), HHblits (green) and DAMA (pink) on the two datasets, 1-mer (top) and 4-mer (bottom), obtained by randomly reshuffling P. falciparum sequences (see text). CLADE has been tested under several restrictions and the resulting FDR curves have been added to the plot: CLADE ALV (grey), CLADE BEv (blue) and CLADE BEv -no-cut-off (orange). The inset plot zooms the curves on small FDR values (< 0.
CLADE (red) outperforms HMMScan (black), HHblits (green) and DAMA (pink) on the two datasets, 1-mer (top) and 4-mer (bottom), obtained by randomly reshuffling P. falciparum sequences (see text). CLADE has been tested under several restrictions and the resulting FDR curves have been added to the plot: CLADE ALV (grey), CLADE BEv (blue) and CLADE BEv -no-cut-off (orange). The inset plot zooms the curves on small FDR values (< 0. 001).
This work undertaken (partially) in the framework of CALSIMLAB is supported by the public grant  ANR-11-LABX-0037-0  from the " Investissements d'Avenir" program  ( ANR-11-IDEX-0004-02 ). Experiments were carried out using Grid'5000 (https://www. grid5000. fr)and the  UPMC MESU  machine financed by the project  Equip@Meso  ( ANR-10-EQPX-29-01 ) of the " Investissements d'Avenir" program. Funds from the Institut.
S iÃ ¼ fs iÃ 1 ; 1. ; s iÃ m g. Each sequence in FULL, and therefore in SEED, is associated to a protein in the UniProtKB database and provides a NCBI taxonomy for it. A simplified Pfam flowchart, showing the library of SCMs generated by HMMer, one for each domain D i , is illustrated in S1 Fig (solid lines).
9 0. 99 0. 95 PPV 0. 91 0. 99 0. 90 Sen 0. 88 1 1 HHblits CLADE_HHblits CLADE BEv HHblits TP 3934 8537 7947 FP 1679 96 686 FN 3020 0 0 F-measure 0. 63 0. 99 0. 96 PPV 0. 7 0. 99 0. 92 Sen 0. 57 1 1 SCOP datasets ASTRAL95, ASTRAL30, ASTRAL10 contain sequences with at most 95%, 30%, 10% of sequence identity. doi:10. 1371/journal. pcbi. 1005038. t001.
Many of them are based on protein sequence information, using gene clustering or phylogenetic profiling. 20-26 However, although these methods can predict interactions they do not provide any atomic-level information on the conformation of the complex or on the origins of its formation and stability. Another approach has been to develop socalled template methods, which predict interactions between pairs of proteins that are homologous (either globally, or at the interface region) to pairs of proteins within known binary complexes. These methods have achieved good results, but are naturally limited by the quality and coverage of the available template database. 27-29 Still, important aspects of PPI, such as the influence of the crowded cellular environment, 30-32 or the time-dependence of these networks, 33-35 cannot be addressed via any of these approaches.
The collection of docking poses between two protein partners can be used to derive a consensus of predicted interface residues. Following the NIP (Normalized Interface Propensity) approach that was originally developed by Fernandez-Recio et al. , 42 docking calculations have been used for binding sites predictions both in simple docking studies, 43-47 i. e. studies involving only protein partners that are already known to interact, and complete cross-docking (CC-D) studies, which involve performing docking calculations on all possible protein pairs within a given dataset. 48, 49  After an early CC-D study on a reduced test set, 48 the computational power of the public World Community Grid, has allowed us to carry out CC-D calculations on the complete Mintseris Docking Benchmark 2. 0, 50 which comprises 168 proteins forming 84 known binary com-plexes.
Protein dataset All simulations were performed using the unbound conformations of the proteins from the Docking Benchmark 2. 0 of Mintseris et al. 50 with the exception of 12 antibodies for which the unbound structure is unavailable and the bound structure was used instead. Any further reference to these proteins uses their name, or the Protein Data Bank (PDB) code 52 of the experimental complex they belong to with the r or l extension denoting a receptor or a ligand protein respectively. For example, 1AY7_r and 1AY7_l refer to barnase (receptor) and barstar (ligand) in the barnase-barstar complex 1AY7. The coordinates for the bound and unbound structures of both receptor and ligand proteins are available in the PDB and can also be found at the following address: http://zlab. bu. edu/zdock/benchmark.
shtml. The 84 binary complexes listed in the Docking Benchmark 2. 0 cover three broad biochemical categories and three difficulty categories related to the degree of conformational change in the protein-protein interface upon complex formation. They are classified as Enzyme-Inhibitors (E, 23 complexes, enzymes with a r extension and inhibitors with a l extension after the PDB code), Antibody-Antigen (A, 10 complexes, antibodies with a r extension and antigens with a l extension after the PDB code), Bound Antibody-Antigen (AB, 12 complexes, bound-antibodies with a r extension and antigens with a l extension after the PDB code) and Others (O, 36 complexes). Note that for three cases in the AB category, namely 1IR9, 1KXQ and 2HMI, there was an inversion in the pdb files names, the antigen protein has a r extension and the bound antibody has a l extension after the PDB code.
The ROC curve for the complete dataset (which contains >48,000 protein residues) with the variation of the sensitivity and specificity of the predictions is plotted in Figure  1 (a), while Figure  1 (b) shows the selection of residues potentially belonging to a protein interface as a function of a PIP cutoff comprised between 0 and 1. In Figure  1(a) , the dotted diagonal corresponds to a random sampling of surface residues and divides the ROC space into areas of correct (above the diagonal) and incorrect (below the diagonal) classification. The greatest distance between the ROC curve and the diagonal yields the lowest error estimate, which is indicated by a dashed arrow in Figure  1(a). With a minimum error of 0. 29 [see Supporting Information Fig. S3(a) ], the optimal PIP cutoff [0. 09, vertical dotted line in Fig.
1(b) ] enables us to select 32% of the residues with a sensitivity of 71% and a specificity of 71% for the interface residues. These results are comparable to those that were obtained in our previous work cross-docking the unbound structures of 12 proteins from a reduced dataset and which led to 30% coverage and 70% and 75% sensitivity and specificity, respectively. To measure the efficiency of our predictions independently of the cutoff value, we can also use the area under the curve (AUC) value, which is 0. 77 for the black line in Figure  1 (a) (while random predictions would yield an AUC value of 0. 50). Finally, Figure  1 (c) shows the variation of the precision with the PIP cutoff. Experimental interface residues represent only 11% of all the surface residues from our data set, which corresponds to the random precision value that would be obtained with a PIP cutoff set to 0.
73 and 0. 72 respectively. In contrast, interfaces from antibodies (both in the unbound, AB, and bound, BAB, groups), are better predicted than average, with ROC curves (dark and light blue lines) above the overall one, reduced minimum errors of 0. 16 and 0. 20 respectively, and increased AUCs of 0. 90 and 0. 85 respectively. Instead of using the classic Sen. /(1-Spe. ) ROC curve, one can also evaluate the performance of the PIP predictions with the Precision/Sensitivity (Recall) curve like what has been done by Hwang et al. for the prediction of binding interfaces from simple docking calculations. 47  In that case the random AUC is no longer a constant and will depend on the proportion of experimental interface residues in each dataset, which corresponds to the Precision column on the far right of Table  I.
The individual clustering profiles for all the proteins in the dataset are available in the Supporting Information Figure  S5. When false positives turn out to be true positives: the case of proteins with multiple interfaces Promiscuity, or multispecificity, is a frequent feature in the protein world 65,66 and a key issue when trying to predict proteic interfaces, since many proteins can actually present multiple binding sites, even when interacting with a single partner. 67 This is also the case for proteins from the docking benchmark 2. 0, and the apparent failure of cross-docking for predicting experimental binding sites can result from the detection of alternative interfaces formed by the protein with other biomolecular partners. For example, Figure  5 (a) presents the case of HIV1 reverse transcriptase (2HMI_r).
CONCLUSIONS In this work, we use high-throughput cross-docking calculations on a dataset of 168 protein unbound structures to develop a PIP index for the prediction of interface residues on the protein surface. The PIP index does not require any prior knowledge of a protein's potential interaction partners, and its predictive power depends on the protein functional group and is poorer for antigens. We also develop a clustering algorithm which permits to group together surface residues with high PIP-values. The resulting clustering profiles for the various biochemical categories show remarkable differences, especially between the antibody and antigen groups, thus that the binding ability of the surface residues actually holds some information regarding a protein's potential function. To the best of our knowledge, this is the first time such a clustering analysis has been performed on proteins surfaces, and that a close connection between a protein's function and its binding behaviour has been established.
Finally, the PIP index can also lead to the prediction of alternate interface that are not present in our original benchmark, but are still biologically significant. This means that the evaluation of its predictive power, that is based only on the prediction of a restricted number of proteic interfaces already present in the docking benchmark, is probably well underestimated. The regular finding of alternate experimental interfaces which concur with our cross-docking predictions also highlight the fact that defining «true negatives» when dealing with PPI is actually a difficult matter. In future work, we plan to apply the analysis tools that have been developed within the frame of this first experiment to the results from the cross-docking calculations subsequently performed on a much-larger protein database (made of 2246 proteins) that has been used in the second stage of the HCMD project.
This database comprises a subset of >200 proteins that are known to be mutated and over-expressed in neuromuscular disorder. Combining data resulting from cross-docking simulations and evolutionary approaches, we aim to retrieve key information regarding the function, binding sites and potential binding partners of these target proteins. Globally it is quite remarkable how a simple coarsegrain rigid approach can still bring us many information on protein sociology. Just like we cannot expect every single individual in a group to behave the same way, a protein will not always behave and bind the way we expect it to, no matter how exact or detailed our protein model might be. Because proteins, like people, are complex systems, driven by a profusion of parameters that we cannot hope to identify in their entirety.
Figure1(a) gives us a quantitative view of the results using the sensitivity and specificity notions defined in the Methods section. The ROC curve for the complete dataset (which contains >48,000 protein residues) with the variation of the sensitivity and specificity of the predictions is plotted in Figure1(a), while Figure1(b) shows the selection of residues potentially belonging to a protein interface as a function of a PIP cutoff comprised between 0 and 1. In Figure1(a), the dotted diagonal corresponds to a random sampling of surface residues and divides the ROC space into areas of correct (above the diagonal) and incorrect (below the diagonal) classification. The greatest distance between the ROC curve and the diagonal yields the lowest error estimate, which is indicated by a dashed arrow in Figure1(a).
With a minimum error of 0. 29 [see Supporting Information Fig. S3(a)], the optimal PIP cutoff [0. 09, vertical dotted line in Fig. 1(b)] enables us to select 32% of the residues with a sensitivity of 71% and a specificity of 71% for the interface residues. These results are comparable to those that were obtained in our previous work cross-docking the unbound structures of 12 proteins from a reduced dataset and which led to 30% coverage and 70% and 75% sensitivity and specificity, respectively. To measure the efficiency of our predictions independently of the cutoff value, we can also use the area under the curve (AUC) value, which is 0. 77 for the black line in Figure1(a)(while random predictions would yield an AUC value of 0. 50). Finally, Figure1(c) shows the variation of the precision with the PIP cutoff.
World Community Grid (WCG, www. worldcommunitygrid. org) during the first phase of the Help Cure Muscular Dystrophy (HCMD) project. It took approximately six months to perform CC-D calculations on the complete dataset of 168 proteins. More technical details regarding the execution of the program on WCG can be found in Ref. 60. The resulting CC-D data is available for download at the following address: http://www. lcqb. upmc. fr/CCDMintseris/.
ACKNOWLEDGMENTS This work was originally carried out in the framework of the  DECRYPTHON  Project, set up by the  CNRS  ( Centre National de la Recherche Scientifique ), the  AFM  ( French Muscular Distrophy Association ) and  IBM. The cross-docking calculations were carried out on the  World Community Grid  (www. worldcommunitygrid. org) in phase 1 of the  Help Cure Muscular Distrophy  project. The authors wish to thank all the members of the  WCG team  for adapting our docking program for use on this grid, and also WCG volunteers for donating the computing time that made this work possible. S. S. -M. , A. C. and L. V. thank the  Mapping Project (Investissement d'Avenir )  ANR-11-BINF-003  for postdoctoral funding.
World Community Grid (WCG, www. worldcommunitygrid. org) during the first phase of the Help Cure Muscular Dystrophy (HCMD) project. It took approximately six months to perform CC-D calculations on the complete dataset of 168 proteins. More technical details regarding the execution of the program on WCG can be found in Ref. 60. The resulting CC-D data is available for download at the following address: http://www. lcqb. upmc. fr/CCDMintseris/ DATA ANALYSIS Definition of surface and interface residues.
1  and B. 2 for annotation), where the eastern side is darker. This feature could be a consequence of ADI artifact because a similar structure appears in forwards modelling of featureless synthetic dusty disks (see section 4 and Fig. C. 1). The clump and drop of intensity, however, are still detected in a spectral TLOCI reduction of the IFS data, which is not affected by the ADI bias. Moreover, the clump is slightly shifted with respect to the ansae of R3. Whether it could be associated with a real object requires more data that do not suffer from ADI biases (polarimetry for instance). Two other much fainter structures which that look like broken rings, R1 and R2, are visible at larger separations than R3 (Fig. 1  and B. 2 for annotations). All these three ringlets are recurrent patterns in the various datasets YJ, H2H3, and Ks, collected at two epochs (Fig.
Acknowledgements. We acknowledge financial support from the  Programme National de Planétologie (PNP)  and the  Programme National de Physique Stellaire (PNPS)  of  CNRS-INSU. This work has also been supported by a grant from the  French Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ). The project is supported by  CNRS , by the  Agence Nationale de la Recherche  ( ANR-14-CE33-0018 ). This work is partly based on data products produced at the  SPHERE Data Centre hosted at OSUG/IPAG, Grenoble. We thank  P. Delorme  and  E. Lagadec  ( SPHERE Data Centre ) for their efficient help during the data reduction process. SPHERE is an instrument designed and built by a consortium consisting of  IPAG (Grenoble, France ),  MPIA (Heidelberg, Germany) ,  LAM (Marseille, France), LESIA (Paris, France), Laboratoire Lagrange (Nice, France) ,  INAF-Osservatorio di Padova (Italy) ,  Observatoire de Genève (Switzerland) ,  ETH Zurich (Switzerland) ,  NOVA (Netherlands) ,  ONERA (France)  and  ASTRON (Netherlands)  in collaboration with  ESO.
SPHERE was funded by  ESO , with additional contributions from  CNRS (France) ,  MPIA (Germany) ,  INAF (Italy) ,  FINES (Switzerland)  and  NOVA (Netherlands). SPHERE also received funding from the  European Commission Sixth and Seventh Framework Programmes as part of the Optical Infrared Coordination Network for Astronomy (OPTICON)  under grant number  RII3-Ct-2004-001566  for  FP6  ( 2004-2008 ), grant number  226604  for FP7 (2009)(2010)(2011)(2012) and grant number  312430  for FP7 (2013)(2014)(2015)(2016). V. C. acknowledges support from the  European Research Council  under the European Union's  Seventh Framework Programme  ( ERC  grant agreement No. 337569 ) and from the  French Community of Belgium  through an  ARC grant for Concerted Research Action. J. O. acknowledges support from the  Millennium Nucleus RC130007 (Chilean Ministry of Economy ).
Recent safety and economical concerns for modern nuclear reactor applications have fed an outstanding interest in basic nuclear data evaluation improvement and completion. It has been immediately clear that the accuracy of our predictive simulation models was strongly affected by our knowledge on input data. Therefore strong efforts have been made to improve nuclear data and to generate complete and reliable uncertainty information able to yield proper uncertainty propagation on integral reactor parameters. Since in modern nuclear data banks (such as JEFF-3. 1. 1 and ENDF/BVII. 1) no correlations for fission yields are given, in the present work we propose a covariance generation methodology for fission product yields. The main goal is to reproduce the existing European library and to add covariance information to allow proper uncertainty propagation in depletion and decay heat calculations.
To do so, we adopted the Generalized Least Square Method (GLSM) implemented in CONRAD (COde for Nuclear Reaction Analysis and Data assimilation), developed at CEA-Cadarache. Theoretical values employed in the Bayesian parameter adjustment are delivered thanks to a convolution of different models, representing several quantities in fission yield calculations: the Brosa fission modes for pre-neutron mass distribution, a simplified Gaussian model for prompt neutron emission probability, the Wahl systematics for charge distribution and the Madland-England model for the isomeric ratio. Some results will be presented for the thermal fission of U-235, Pu-239 and Pu-241.
Nowadays full uncertainty information are not available in the present data banks (such as JEFF-3. 1. 1 a e-mail: nicholas. terranova@unibo. it and ENDF/BVII. 1) and the nuclear community expressed the need for full fission yield covariance matrices in core and fuel cycle analysis, to take into account complete uncertainty data  [4]. Several efforts are being spent to generate reliable FY covariances by different organizations  [7, 8]. In the present work we propose a possible alternative methodology to be applied in FY covariance generation. We chose to adopt the GLSM and the analytical marginalization tools available in CONRAD  [5]  (COde for Nuclear Reaction Analysis and Data assimilation), developed at CEA-Cadarache. In the following sections we will describe the main features of the procedure we adopted to generate preliminary results on the thermal fission of U-235, Pu-239 and Pu-241.