dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure5. Robustness of the IR for the true partners and the false ones for the 1BUH complex. A. Each partner of the 1BUH complex is coupled with one of the 168 proteins (including the monomer itself) of the dataset forming either a false (167 cases) or the native complex. For each complex, we computed the corresponding average IR and average AUC over 100 random sets of 40 proteins, obtained by using the experimental interfaces and the full exploration of the conformational space. These values are reported as a point in a plot. Each plot contains 168 points. The red circle in each plot corresponds to the values of the native complex. Dots are colored in a scale from black, blue, cyan to yellow. A color corresponds to the value of the standard deviation s of the distribution of 100 IRs computed for a complex: black if sƒ1, blue if 1vsƒ2, cyan if 2vsƒ3 and yellow otherwise (i.
e. sw3). B. The analysis in A is realized here with 1BUH coupled with 166 proteins (here we have not considered the complex 1ML0 of the Mintseris Benchmark 2. 0 because JET made no predictions and this turned out to provide no FIR value), with predicted interfaces and the full exploration of the conformational space. Dots are colored in a scale from black, cyan, green to yellow. A color corresponds to the value of the standard deviation s of the distribution of 100 IRs computed for a complex: black if sƒ1, cyan if 1vsƒ2, green if 2vsƒ3 and yellow otherwise (i. e. sw3). The structures of the native complex (red circle) and of four selected false complexes (orange circles) are shown to illustrate the conformations corresponding to the best II value. Notice that the II value is always the same for the 100 random runs while the NII varies with respect to the dataset of proteins used in a run.
Colors in the two lines of the matrix corresponding to the Enzyme-Inhibitor complex 1MAH are mapped on the dots of the plots for the receptor 1MAH_r and the ligand 1MAH_l (see legend of Figure 5 for the plots description). The black contour line on some of the proteins identifies bottom black dots in the IR analysis of Figures S38-S51 in Text S1. The red contour identifies the true interacting partner. 1MAH_r is a Mus musculus protein structure and 1MAH_l a Dendroaspis angusticeps' one, a highly venomous snake. The analysis of all proteins in the dataset is reported in Figures S60-S73 in Text S1. doi:10. 1371/journal. pcbi. 1003369. g007.
Interaction ranks distribution for the Mintseris' Enzyme-Inhibitors dataset. CC-D has been realized with MAXDo and HEX v6. 3 docking algorithms. Calculations based on predicted interfaces (fourth and sixth columns) are made with NIP+JET scores obtained with weights 0. 4 and 0. 6 for NIP and JET respectively. For each CC-D, over the 46 Enzyme-Inhibitors in the Mintseris dataset, we report the number of proteins whose native complex is identified within the top x% of complexes obtained by docking the protein with all 46 proteins in the environment. Cumulative counts and percentages (in parenthesis) are displayed. See legend of Table1.
Acknowledgments The CC-D data used in this work were obtained in phase 1 of the  Help Cure Muscular Dystrophy  project. The  World Community Grid  (www. worldcommunitygrid. org)adapted our docking program for use. We thank all members of the  WCG  team for their professional help along the project, in particular  Bill Bovermann  for his helpful interactions and scientific curiosity that made us to go easily through all stages of the project; the  WCG  volunteers for donating the computing time that made this largescale analysis possible;  Raphae ¨l Bolze and Frederic Desprez, of the Laboratoire d'Informatique du Parallelisme in ENS Lyon , to help with the transfer of the data from France to Seattle and back;  Richard Lavery  for reading a previous version of the article and providing many insightful remarks.
This work was supported by a grant from  DECRYPTHON program , set up by  CNRS  ( Centre National de la Recherche Scientifique ),  AFM  ( French Muscular Distrophy Association ) and  IBM. The  MAPPING  project ( ANR-11-BINF-0003 ), supported by the  Program of Excellence ''Investissement d'Avenir'' in Bioinformatics , sustained the late stages of this analysis. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
For each cluster, an audio-visual model is trained using the corresponding audio-visual data. The first step in the synthesis stage is the computation of the class that the novel audio data belongs to. The trained audio-visual model associated with the selected class is then used to estimate the corresponding visual parameters. In these approaches, the performance of the whole system is directly affected by errors derived by these classification procedures. For instance, in  [8]  a static mapping between phonemes and visemes was proposed, where a phoneme recognition engine is used to segment the input speech, and then the recognized phonemes are directly mapped to lip shapes using a viseme codebook. In the study by Beskow et al. [13] , in the context of the SynFace project  [14] , different rule-based and data-driven articulatory control models were proposed to animate the movements of the talking head.
This system takes time-stamped recognized phonetic symbols as input and produces articulatory control parameter trajectories to drive the face model. In  [15] , word-based and phoneme-based methods were proposed, where for each cluster an HMM model is trained using audio-visual data, and then, the HMM model associated with the recognized phoneme (word) is used to compute facial animation parameters. Kshirsagar and Thalmann  [16]  proposed a syllable-based approach, where captured facial motions are categorized into syllable motions and the new speech animation is achieved by concatenating syllable motions optimally chosen from the syllable motion database. Gutierrez et al. [5, 17]  used a training audio-visual database to generate a lookup table of audio-visual dynamic pairs. Given a new speech signal, they find the examples in the audio-visual lookup table of the k closest audio parameters using the Euclidean distance, then, the visual parameter is computed as the average of the visual parameters associated with the k closest neighbors.
Given a set of AAM parameters estimated from audio data, novel frames of the animation are generated by first reconstructing the shape and texture separately and then warping the texture to the shape. The limitation of this approach is that it does not actually animate 3D head models, but rather the resulting animation consists of a sequence of synthetic face images generated from the AAM. Another approach not requiring a priori classification, was introduced in the late 90's by Massaro and colleagues in  [24] , based on their text-to-speech driven talking head Baldi  [25]. An artificial neural network (ANN) is trained with an only audio database and the associated visual parameters, where the visual parameters are not extracted from real videos but are computed from the corresponding audio transcriptions.
In the synthesis stage, the input phoneme (word) is recognized and its associated HMM is used to compute the visual parameters by applying HMMI. Hence, this approach is susceptible to errors related to the classification procedures in the training and synthesis stages. To assess the performance of the different speech-driven approaches proposed in the literature, quantitative and perceptual evaluations are usually employed. In the case of quantitative evaluations, the estimated visual parameters are compared with their associated ground truth parameters extracted from the database  [6, 11, 15, 18, 29]. On the other hand, in the case of perceptual evaluations, the quality of the generated animations is judged by a group of persons. For instance, in  [29, 31]  the participants were asked about how natural and realistic are the animations in a five-point scale, and in  [17]  the individuals were asked to choose the more realistic animation between two different ones, generated by different methods.
Speech driven facial animation system A block diagram of the proposed speech driven animation system is depicted in Figure  1. An audiovisual database is used to estimate the parameters of a joint AV-HMM. This database consists of videos of a talking person. In the training stage, feature parameters of the audio-visual data are extracted. The audio part of the feature vector consists of Mel-Cepstral Coefficients  [35] , while the visual part consists of parameters related to a set of facial movements. In the synthesis stage, the trained AV-HMM is used to estimate the visual features associated with a novel speech signal. These visual features, corresponding to different facial movements, allow the animation of a simple (small number of vertices) face model synchronized with the speech signal, which in turn is used to animate an arbitrary complex head model.
AV-HMM training The training of the AV-HMM is carried out using an audio-visual database consisting of videos of a talking person. As described in Section 3, audio-visual features are extracted from videos. Then, the audio-visual observation sequences O av are used to estimate the parameters of an ergodic AV-HMM, as it is usual in HMM training, resorting to the standard Baum-Welch algorithm  [38].
Audio-to-visual conversion evaluation In order to evaluate the proposed strategy for estimating the visual parameters from the speech signal, two audio-visual databases were compiled. One consists of videos of a person pronouncing the digits, and the other one consists of videos of the same person pronouncing a set of 120 phonetically balanced sentences. In both cases, the videos were recorded without compression, at a rate of 30 frames per second, with a resolution of 320 × 240 pixels, and the audio was recorded at 11025 Hz synchronized with the video. Audio-visual features were extracted from these databases as described in Section 3, and then used to train the AV-HMM. In particular, in the experiments performed in this article, the audio feature vector a t is composed of the first eleven non-DC Mel-Cepstral coefficients, and the visual feature vector o vt is formed by four components of vector α related to mouth movements.
The first database (digits utterances) was used to evaluate the influence of the structure of the covariance matrices (full or diagonal) on the visual estimation. To evaluate the performance of the proposed conversion algorithm in a more complex and realistic scenario, AV-HMMs were trained using the second database (120 phonetically balanced sentences). The performances of the different models were quantified by computing the average mean square error (AMSE)(ϵ), and the average correlation coefficient (ACC)(ρ) between the true and estimated visual parameters, defined as ϵ = 1 4T 4 ∑ r=1 1 σ 2 v r T ∑ t=1 [ õv r t -o v r t ] 2 , (11) ρ = 1 4T 4 ∑ r=1 T ∑ t=1 (o v r t -µ v r )(õ v r t -μv r ) σ v r σv r , (12) respectively, where o v r t is the value of the rth visual parameter at time t and õv r t its estimated value, µ v r and σ 2 v r denote the mean and variance of the rth visual parameter in the time interval [ 1, T], respectively, and μv r and σ 2 v r denote the mean and variance of the rth estimated visual parameter in [ 1, T], respectively.
Influence of the covariance matrix structure To evaluate the influence of the structure of the covariance matrices on the audio-to-visual conversion algorithm, experiments were performed using AV-HMMs with diagonal and full covariance matrices, different number of states and mixtures in the ranges from 2 to 19, and from 3 to 20, respectively, and different values of the co-articulation parameter t c in the range from 0 to 7. This evaluation was performed using the digits database, where each digit was pronounced 40 times in different order. To train the AV-HMMs, digit utterances were randomly extracted from the database and then used to generate sequences of different duration, e. g. , "7 5 4" and "2 0 3 9". To maintain the natural coarticulation between digit utterances, the digits were segmented preserving silence frames at the beginning and at the end of the utterances.
Overall evaluation To evaluate the proposed audio-to-visual conversion algorithm in a more complex scenario, the second database, consisting of videos of a person pronouncing a set of 120 phonetically balanced sentences (uttered 3 times), was employed. Experiments were performed with AV-HMM with full covariance matrices, different number of states and mixtures in the ranges from 8 to 40, and from 2 to 7, respectively, and different values of the co-articulation parameter t c in the range from 0 to 7. These experiments do not reveal a particular trend about the values of the optimal parameters. The best values of ϵ (AMSE) and ρ (ACC) were obtained for 20 ≤ N ≤ 35 states and 3 ≤ M ≤ 7 mixtures. In these experiments, the optimal value of the co-articulation parameter was t c = 3, which corresponds to a co-articulation time a of 100 ms.
Compared to the previous experiment, where a reduced vocabulary (digits utterances) was employed, the number of states needed in this case increases considerably due to the fact that a larger vocabulary is being represented by the AV-HMM. The true and estimated visual parameters for the case of full covariance matrices with N = 28 states, M = 5 mixtures and co-articulation parameter t c = 3 are represented in Figure  8 , where a good agreement between them can be observed. This combination of number of states, number of mixtures and co-articulation parameter was among the ones that yielded the best results. The AMSE and the ACC are for this case ϵ = 0. 47 and ρ = 0. 81, respectively, over the testing audio-visual database. A fair comparison, in terms of AMSE(ϵ) and ACC (ρ), between the proposed audio-to-visual conversion algorithm and previous research is not an easy task since there is neither a common audio-visual corpus for evaluation, nor a common quality metric  [15, 17, 29, 31, 44, 45].
Presentation For each condition, the observer was presented with the utterances of the 27 syllables in random order. Through a graphical user interface, the person had to indicate the perceived syllable within the set of 27 syllables. The natural and synthetic talkers are shown in Figure  10. The visual stimulus, for both the natural and synthetic talkers, were presented in the center of the graphical user interface, by means of uncompressed videos with a resolution of 640 × 480 pixels. The auditory speech was taken from the natural talker videos. The synthetic face animations were generated using the original speech signal from the natural speaker as input of the facial animation system proposed in this article. The system was trained using the phonetically balanced sentences database.
The Carbohydrate-Active Enzymes database (CAZy; http://www. cazy. org) provides online and continuously updated access to a sequence-based family classification linking the sequence to the specificity and 3D structure of the enzymes that assemble, modify and breakdown oligo-and polysaccharides. Functional and 3D structural information is added and curated on a regular basis based on the available literature. In addition to the use of the database by enzymologists seeking curated information on CAZymes, the dissemination of a stable nomenclature for these enzymes is probably a major contribution of CAZy. The past few years have seen the expansion of the CAZy classification scheme to new families, the development of subfamilies in several families and the power of CAZy for the analysis of genomes and metagenomes.
Thus a modular CAZyme can be assigned to several families if its constitutive modules belong to separate families. The third important feature is that we only analyse systematically protein sequences released in the daily releases of GenBank (ftp://ftp. ncbi. nih. gov/genbank/daily-nc), to avoid analysing unfinished sequences that may change accession number. As early as 1991, it was noted that the sequence-based families of glycoside hydrolases grouped together enzymes of different substrate specificities (i. e. enzymes with 'different' EC numbers)  (1)  demonstrating that the acquisition of novel specificity has been commonplace during evolution. This feature was subsequently noted for the other classes of CAZymes  (4, 6). The processes by which a novel substrate specificity was acquired from a common ancestor leave detectable traces in the sequence of contemporary proteins.
This can sometimes be overcome by the definition of subfamilies within families (9,10) (see below), but our current knowledge of the sequence-to-specificity relationships in CAZymes families is still largely insufficient and unevenly distributed for many families to allow unsupervised automated substrate prediction. The Carbohydrate-Active Enzymes database (CAZy; http://www. cazy. org) was launched in 1999 to provide online and constantly updated access to the family classification of CAZymes. Coupled to the CAZypedia encyclopaedic resource (http://www. cazypedia. org), CAZy is the only comprehensive resource that correlates the sequence, structure and molecular mechanism of CAZymes. CAZy was presented in this journal in 2009  (11)  and the present article outlines the changes that have been implemented in CAZy during the past 5 years.
WEBSITE DESIGN In March 2011, the website interface was deeply redesigned both in appearance (new layout, new colours and new logo) and in content. Thus new sections and new links have been added to commercial providers that list their products following the CAZy nomenclature. Other additions cover scientific meetings relevant to CAZymes, positions available and a 'what's new' section that provides news on changes in the CAZy database. More interactivity in the display of information associated with each family was introduced (Figure  1 ). In particular, each family has now a specific tab, which lists those individual CAZymes that we believe have been experimentally characterized. Because the number of entries in several families had become impractical, the display was modified to just show the header for each family along with a series of tabs for access to subsets (All, Archea, Bacteria, Eukaryota, unclassified, Structure, Characterized).
DATABASE GROWTH At the date of submission of this article, CAZy reports sequence information on almost 340 000 CAZymes, a staggering 225% increase compared with 5 years ago (Table  1 ). During the same period, the number of biochemically characterized CAZymes has grown by only 30% to 12 700 and the number of CAZymes with 3D structures has grown by 78% (Table  1 ). Despite this growth, only 1400 (0. 4%) of the 340 000 CAZymes have a 3D structure solved to date. The past 5 years have seen the number of families covered by CAZy grow slowly to >330 at present. Five years ago the number of genome sequences analysed in CAZy was 750  (11). This number is now greater than 2800 (see below), representing a 3. 8-fold increase. The continuously growing gap between the number of sequences and the number of biochemically or structurally characterized CAZymes is a direct consequence of the avalanche of genome sequences resulting from modern sequencing technologies combined with the much lower pace of experimental characterization of gene products.
DATABASE CONTENT: SUBFAMILIES The occurrence of enzymes that act on different substrates in the same family prevents the straightforward functional annotation of CAZyme-related genes. The division of CAZyme families into subfamilies based on phylogenetic analysis has been explored as a possible approach to improve the relationship between sequence and specificity. Subfamily classification of GH5, GH13, GH30 and all of the PL families has shown that the majority of the defined subfamilies are monospecific, thus indicating that the correlation of substrate specificity with sequences is significantly better at the subfamily level than the family level  (9, 10, 13 ). An additional benefit of the division into subfamilies is the identification of currently uncharacterized subfamilies that can subsequently be analysed experimentally to unveil potential new activities.
DATABASE CONTENT: GENOMES The collection of carbohydrate-active enzymes encoded by the genome of an organism ('CAZome') provides an insight into the nature and extent of the metabolism of complex carbohydrates of the species. The CAZomes of free-living organisms typically correspond to 1-5% of the predicted coding sequences. Extremely reduced CAZomes are characteristic of species with a strict intracellular parasitic lifestyle. Because of the massive chemical, structural and functional variability of carbohydrates, CAZome comparisons can highlight the adaptation of the CAZymes repertoire of species to their environment  (14, 15). Since 2011, in addition to giving the family distribution, the new CAZy website displays the complete list of putative CAZymes (with accession numbers) of each genome that was analysed.
At present, CAZy covers >2800 genomes in the following kingdoms: Bacteria (2351), Archea (158), Eukaryota (73), Viruses (240). The CAZomes listed in the CAZy website correspond to protein models of finished genomes, i. e. with proteins released in the daily releases of GenBank (ftp://ftp. ncbi. nih. gov/genbank/daily-nc). In a few cases, genomes with protein models not released as finished entries in GenBank but publically available, have been analysed and are presented in CAZy. However, for these few cases, the display only shows the number of proteins in each family, but does not feature the actual list of proteins. Genomes are analysed using the CAZy pipeline, which combines Blast and HMM tools to compare protein models, respectively, with sequence and profile libraries created from the sequences of the catalytic and noncatalytic modules of the CAZy database.
This is followed by a manual inspection by expert curators to resolve borderline cases  (11). Our methodology provides coherent, expert and comparable sets of annotations. In this respect, one should note that the correspondence between CAZy families and those in PFAM (  16 )/INTERPRO  (17)  or DBCAN  (18)  is far from perfect. This is due to a variety of reasons that include different strategies, different thresholds, different goals, different methods, different training sets and different degrees of expert curation. An unfortunate consequence is that the CAZyme analysis of a genome performed with one method usually cannot be compared with that done with another. There are two ways to get a genome analysed by CAZy: if the genome and encoded proteins are deposited as finished entries in GenBank (or EMBL or DDBJ) they will be analysed by our daily routines.
DISPLAY OF STRUCTURAL INFORMATION The CAZy database is not only used by those who wish to analyse genomes, but also by structural biologists who study the molecular details of substrate recognition by CAZymes. Until September 2013, the only information available in the structure pages of CAZy was the accession and macromolecule chain name(s) in the PDB (http:// www. rcsb. org) (19). We have made a series of developments to provide additional information relevant to the 3D structure of CAZymes such as the resolution (for crystal structures) and a description of the carbohydrate ligands found in the CAZyme binding sites. The resolution information is straightforward to generate, as it is present in the PDB files of structures solved by x-ray crystallography. When the resolution information is unavailable in the PDB file, the type of experimental method by which the structure was solved is given instead (powder diffraction or nuclear magnetic resonance).