dataesr/scientific-paragraphs-categorization huggingface dataset:
al. [27]  explored the utility of PSI-PRED  [28] ) and sequence alignment profiles like position specific scoring matrices (PSSMs) are used by LOCUSTRA  [21] , SVM-PB-Pred  [22]  and Dong and coworkers methodology  [24]. The currently available web-based tools that can predict local structure in terms of protein blocks are LocPred  [29]  and SVM-PB-Pred  [22]. The former implements a Bayesian methodology and the latter is SVM-based. In this work we describe PB-kPRED, a fragment and knowledge-based approach to predict local backbone structure of proteins in terms of protein blocks and a web-based tool that implements the method. In essence, it takes no other inputs than the amino acid sequence of a query and interrogates a database of pentapeptides extracted from protein structures, relying on evolutionary information only when available, hence useful in the context of proteins without any known structural homologues.
PENTAdb, a database of pentapeptides from protein structures A total of 68. 84 million pentapeptides obtained from the 0. 26 million protein chains and their corresponding local structure represented as one of the 16 PBs were obtained and stored in a dedicated database named PENTAdb. Out of the total number of theoretically possible 3. 2 million (20 5 ) pentapeptides, 2. 26 million (70. 9%) are found in PENTAdb. These 2. 26 million pentapeptides occur in different counts amounting to a total of 68. 84 million pentapeptides. PENTAdb had 8 different threshold of sequence identity, and can be directly used by PB-kPRED (see Table  1 ). A 32-fold decrease (from 68. 62 to 5. 13 million) in the number of pentapeptides in PENTAdb is observed when PDB chains not sharing more than 30% sequence identity are considered.
Not all possible tri-PB combinations are observed in known protein structures Out of all the theoretically possible 4,096 (16 3 ) tri-PBs, a total of 1,375 (i. e 33. 5%) were never observed in a non-redundant subset of PDB filtered at 30% sequence identity, subsequently termed as PDB30 dataset (see Methods section). Likewise, out of all the 1. 04 million (16 5 ) theoretically possible penta-PB motifs, only 40,130 (3. 8%) were observed in the PDB30 dataset. These results are indicative of the possibility that many combinations of three or five consecutive PBs are stereochemically unfavorable. The distributions of penta-PB motifs at other sequence identity cut-offs i. e. 40%, 50%, 70%, 90%, 95%, 100% and the entire PDB were also computed (see Table A in S1 File). Towards higher sequence identity cut-offs, a continuous increase in the penta-PB coverage is observed, but it comes at the price of the addition of redundant data.
Completeness of PENTAdb for knowledge-based prediction A quantitative assessment of how often the correct PB can be found in the list of all possible PBs reported for every query protein was performed. This assessment gives the highest accuracy attainable for a query protein using the proposed knowledge-based approach. To this end, for every query protein sequence, different portions of the pentapeptide database were made accessible to the prediction algorithm. This was possible thanks to the hierarchical clustering at different sequence identity levels by the BLASTClust algorithm  [30]. PDB30 dataset was used to benchmark the approach. For each of the 15,544 unrelated query protein sequences of this dataset, only pentapeptides coming from a subset of the PDB that shared sequence identities below an indicated cut-off values (from 30% to 100%) and excluding the query itself were used by PB-kPRED (see Table  1  for size of the database for each subset).
The results of the predictions are detailed in Table  2. At 30% sequence identity cut-off, the correct PB was found in the list of all possible PBs for 71. 4% of the case and the success rate increased to 77. 3% when only "homologues" sharing 100% sequence identity to the queries were filtered out. When full PDB was used (but excluding the query) as a database, the percentage times the correct PB is in the list of all possible PBs topped to 99. 93% (see Table  B  in S1 File). Shown is the percentage of pentapeptide queries for which the correct local conformation was found in the list of all possible PBs reported by the PB-kPRED algorithm after querying PENTAdb. A total number of 15,544 query proteins not sharing more that 30% sequence identity (PDB30 dataset) was used in this assessment.
Prediction accuracies Two methods, further termed as majority rule method and hybrid method, were explored to predict the optimal PB sequence within the list of all the possible PBs obtained after querying the database (see Methods section for details). The average prediction accuracies for the PDB30 query proteins using the majority rule method and the hybrid method using the classic scheme for querying the database are given in Table  3. When homologues sharing !30% sequence identity with each of the queries were removed from the database, PB-kPRED performed with an average accuracy of 39. 2% and 40. 8% for the majority rule method and hybrid method respectively (Table  3 ). Surprisingly, the effect of enlarging the database to include closer homologues sharing <95% sequence identity with the queries improved only marginally the prediction accuracies reaching on average 40.
4% and 42. 4% for majority rule method and hybrid method respectively. Accuracy topped to 58. 0% and 54. 6% respectively when full PDB (excluding the query itself) was used as database for prediction. This overall gain in accuracy is due to an incremental increase of accuracy across all the 16 PBs. Shown are the accuracies for the PDB30 dataset using both the majority rule method and hybrid method. For each of the 15,544 query protein sequences, the portion of PENTAdb accessible for prediction was dynamically determined using MySQL queries: only pentapeptides coming from protein chains in PDB that shared sequence identities below the indicated cut-off values were accessible to PB-kPRED for prediction of local structures in terms of PBs. To improve the prediction rates, the hybrid method was tested using the noise filtering scheme whereby, for each query pentapeptide, data in PENTAdb coming from closest homologues was queried in preference to data from more distant homologues (see also Text B in S1 File).
Results are detailed in Table  4. When compared to the without noise filtering scheme (Table  3 ), the prediction rates improved to reach a maximum of 66. 3%. Interestingly, for experiments B2 to B7 where closest homologues to be queried first are in the range of <40% to <95% sequence identities, the prediction accuracies remained high at a level of about 61. 6%. Only in experiment B8 the prediction accuracy rate dropped to 40. 8%. It must be noticed that this experiment is identical to the one featured for <30% threshold (see Table  3 ) using the hybrid method and the without noise filtering scheme for querying the database. For each query protein, the portion of the database accessible to the algorithm is first restricted to the closest homologues and if no hits were found, only then the more distant homologues are made accessible progressively.
Eight results shown here correspond to the eight experiments described further below (see Methods section). Shown are the prediction rates averaged over 15,544 query proteins from the PDB30 dataset that was used in this assessment. All results further detailed hereafter are concerned with data obtained in experiment B1 where hybrid method was applied using the noise filtering scheme for querying PENTAdb and where best predictions were obtained. The distribution of the prediction accuracies for experiment B1 (Table  4 ) is bimodal (Fig 1 ). A spike in frequency is observed at the >80% range representing the set of queries which have closely related proteins of known structure available in the PDB and for which the method is able to perform extremely well. Many queries in this set indeed had prediction accuracy values above 90%.
Case studies Here were considered the predictions for 5 specific cases to look at the strengths and limitations of the PB-kPRED algorithm namely in presence or absence of homologues of known structure (see Table  6 ). These case studies correspond to counter intuitive prediction instances where (i) prediction accuracy is high despite not having any close homologues and (ii) prediction accuracy is low despite having sequences of PBs of closely-related proteins in PENTAdb. Query PDB chains with known homologues and with no known homologues are featured. The hybrid method with noise filtering scheme for querying the database was applied for the prediction whereby the conditions were identical to experiment B1 (see Methods section) as featured in Table  6. The queries themselves were excluded from the database prior to prediction.
Shown here are the accuracies of the predictions and the numbers of known homologues for different sequence identity thresholds. Regarding prediction in employing information from homologues of known structure, three contrasting cases were studied. The first case relates to chain A of a hypothetical DNA binding protein from Salmonella cholera (PDB id 2HX0_A) which has a homologue from Salmonella typhimurium (PDB id 2NMU) that is 100% identical, 100% accuracy was achieved as shown by the high accuracy score of 2. 81. Both structures aligned very well with a Root Mean Squared Deviation (RMSD) of 0. 14 Å (Figure B in S1 File). The second case relates to an energy-coupling factor transporter transmembrane protein EcfT from Lactobacillus brevis (PDB id 4HUQ_T) which has two other "homologues" (PDB id 4RFS_T and 4HZU_T) that are 100% identical to the query.
Here, the prediction accuracy is 73. 4% only with an accuracy score of 1. 31. 3-D structural alignment with these two "homologues" resulted in RMSDs 1. 41 Å and 1. 90 Å respectively displaying some structural variations (Figure B in S1 File) despite being 100% identical at the amino acid sequence level. These structural variations were due to rigid body movement. The third case is chain A of a pyrimidine deaminase / uracil reductase from Thermotoga maritima (PDB id 2HXV_A) which had only ten very distantly related proteins that shared less than 30% sequence identity in the PDB. Prediction rate is even lower here with accuracy reaching a value of 39. 1% as shown by the low accuracy score of 0. 30. As for predictions in absence of homologues of known structure, two contrasting cases were studied.
Implementation of the PB-kPRED methods as a web-tool The PB-kPRED methodology has been implemented as a web-tool that is freely available to the community at http://www. bo-protscience. fr/kpred/. Both majority rule and hybrid method without the noise filtering scheme for querying the database have been implemented. The tool provides a predicted PB sequence for each query amino acid sequence and also provides the accuracy score that serves as an a posteriori estimation of the prediction accuracy. In case the prediction score value is below -1, the prediction accuracy cannot be estimated and the user is notified. Users can provide multiple query protein sequences. All results are downloadable as FASTA formatted flat files. Optionally when submitting numerous query sequences, the user can provide an email address to which a notification will be sent when the job is completed.
Although all the methods evaluated their accuracies on non-redundant sets of proteins, an even comparison is hindered by difference in datasets, varying training regimes for the machine learning methods and different levels of sequence identity used as input in the prediction process. This motivated us to perform a battery of tests on the algorithm to estimate the prediction accuracy when incremental levels of sequence identities are made available in PENTAdb for the prediction (see Table  4 ). Importantly, to our knowledge, this is the first report of a querying scheme that dynamically filters out, on a per query basis, homologues at different cut-off values so that the portion of the PENTAdb that is made accessible for prediction is calculated on the fly. For each of the 15,544 query sequences of PDB30, 16 experiments were performed, hence amounting to a total dataset size of 248,704 proteins.
Using the noise filtering strategy, PB-kPRED was able to efficiently weed out the noise present in the database due to redundancy and hence to narrow down the search in the database to find the most appropriate local structure for a given pentapeptide. Therefore, filtering out from the database the pentapeptides from proteins that shared less than 30% sequence identity with the query indeed improves the prediction efficiency. In general, in those cases where pentapeptides from very close homologues were indeed present in the database, kPRED performed extremely well. Remarkably, the reported average accuracy (66. 31%) is very high considering the fact that for every pentapeptide, there are 16 possibilities. Indeed, if random PB predictions were made taking into account respective PB frequencies, prediction rate (Q16 value) would be very poor with an average value of about 7 to 8%.
This brings us to the conclusion that even though overall the hybrid method performs better, we cannot ignore the majority rule method all together. Nonetheless, this method still has room for improvement as it can be seen from the values in Table  2. The list of all possible PBs reported by the PB-kPRED algorithm after querying PEN-TAdb database indeed shows that the good PB was present in more than 70% of the cases. However, owing to the scoring functions S1 and S2 (see Methods for more details), both majority rule and hybrid methods failed to pick up these good PBs as predictions in several instances. Interestingly, once the local backbone of a protein was predicted in the form of a PB sequence, we were able to provide an a posteriori assessment of how accurate was the prediction. The method used here to achieve this relied on the simple idea that successions of PBs should follow the rule that not all combinations of PBs would be allowed.
Dataset All the protein chains from PDB  [1]  were segregated into clusters culled at 30% sequence identity using the BLASTClust algorithm  [30]  resulting in a collection of 15,544 clusters. The dataset comprises of 15,554 protein chains each corresponding to the best representative structure available from each of these clusters. It is hereafter termed as "PDB30 dataset". Preference was given to crystallographic structures over NMR and electron microscopy structures and also preferring better resolution and lowest R-value structures. Out of these 15,544 structures, 14,207 are crystallographic structures, 1,128 are from NMR experiments and 209 are solved by electron microscopy. Further, chains smaller than 100 residues were filtered out. We preferred to keep the NMR and EM structures, as we wanted to investigate if the experimental method impact on the quality of the predictions.
The PB with lowest RMSDA is assigned to the central residue of the pentapeptide region. The choice of fragment size as 5 and library size as 16 for the PBs was because 5 consecutive residues capture well the local contacts in regular secondary structures (α-helices and β-strands) and 16-library size is a good balance between the specificity and sensitivity of predictions  [13]. All the 15,544 protein chains from PDB30 dataset were encoded into their corresponding protein blocks sequences (PB sequences) after comparing their backbone φ and ψ torsion angles with the corresponding standard torsion angles for the 16 PBs  [12]  using an in-house developed Perl script. Sequence of PBs as observed in crystal and NMR structures were later used as a reference to assess the accuracy of predicted PB sequences.
Database of pentapeptide conformations from protein structures A database of pentapeptide conformations (PENTAdb) was developed using known 3-D structures of proteins. PENTAdb is essentially the entire structural information contained in the PDB, broken down into chunks of pentapeptides. A sliding window of 5 residues was used to extract structural features for every overlapping pentapeptide of a polypeptide chain. The dihedral vector associated with the five consecutive residues that is required to assign PBs as described in the previous section was obtained from the DSSP  [39]  program. All the information was stored as a MySQL relational database. PENTAdb is maintained up-to-date; the update frequency corresponds to the weekly updates of PDB. The protein chains from which the pentapeptides are extracted are filterable at 30%, 40%, 50%, 70%, 90%, 95% and 100% sequence identity thresholds.
Prediction scheme The overall scheme for predicting the local structure in terms of PBs is based on querying the PENTAdb database for every constitutive pentapeptides of a query protein sequence using a sliding window of 5 residues (Fig  4A ). Hits from the database are reported as predicted protein blocks (PBs). Predicted PBs are assigned to the central residue of each query pentapeptide. The prediction results are presented at different levels of refinement. The prediction in the coarsest form consists of the list of all the possible PBs for a particular pentapeptide of the query protein sequence. This is the case when multiple hits from PENTAdb database are obtained for a particular query pentapeptide (Fig  4B ). The multiple hits correspond to the different conformations, which the pentapeptide has been seen to adopt in protein structures (Fig  4B ).
When the query pentapeptide is not found in PENTAdb, the information available for the tetrapeptides covering the first four residues with a wildcard for the fifth position was used (Fig 4B ) to identify the list of possible PBs with first 4 amino acid residues matching this query. The position of wildcard did not influence the outcome of the results (data not shown). The list of hits thus obtained is referred as all possible PBs. This list serves as a framework from which the most probable PB sequence is predicted. Two methods were explored to predict the optimal PB sequence within the list of all the possible PBs obtained after querying the database (Fig  5 ). The first method, termed as majority rule method, is purely probabilistic and consists of simply picking up the most frequently observed PB for each query pentapeptide.
It corresponds to the PB that has highest S1 score, where S1 scores are simply the raw counts of all possible PBs reported by PENTAdb database for the query pentapeptide. In cases when there is no decisive majority (two or more equiprobable PB), both of them are reported as predictions. However, it is known that the structure adopted by a short peptide can be highly dependent on its local environment 3. A second method that integrates contextual information was hence developed and is hereafter termed as hybrid method. Here, to predict the local structure of a pentapeptide, the information about the structural status (in terms of PBs) of the two immediately adjacent and overlapping pentapeptides (preceding and succeeding) is also taken into account (Fig  5 ). It requires a normalized frequency look-up table for observed motifs of 3 consecutive PBs also termed as tri-PBs (see Text A in S1 File for more details).
Evaluating PB-kPRED using different subsets of PENTAdb Two evaluation schemes were developed to benchmark the PB-kPRED methodology. As mentioned above, the query dataset used here constituted of the 15,544 proteins from the PDB30 dataset. The schemes relied on the ability to control which subsection of PENTAdb will be accessible to the prediction algorithm for every query. For example, allowing only pentapeptides in PENTAdb from non-homologues to be accessible by the prediction algorithm emulates a scenario of attempting to predict the local structure of a protein with no known homologues. On the other hand, as in the case of other local structure prediction methods  [13, [21] [22] [23] 27] , it can be advantageous to have the ability to privilege information from homologous structures when these are available to predict the local structure of a query protein.
Medical Research (INSERM, France) and labex GR-Ex. The labex GR-Ex, reference ANR-11-LABX-0051 is funded by the program "Investissements d'avenir" of the French National Research Agency, reference ANR-11-IDEX-0005-02. AdB acknowledge supports by University Paris Diderot, Sorbonne, Paris Cite(France), FC acknowledge supports by Universitede La Reunion, Faculty of Sciences and Technology. NS and AdB acknowledge to Indo-French Centre for the Promotion of Advanced Research / CEFIPRA for collaborative grant (number 5302-2). Research in NS laboratory is also supported by Department of Biotechnology, Government of India. NS is a J. C. Bose National Fellow.
A Matlab package implementing this algorithm is available at http://www. public. asu. edu/~jye02/ Software/CCA/. • The sparse CCA presented in  [12]  where the sparse canonical projectors are computed by solving two 1 -minimization problems by using the Linearized Bregman iterative method  [19]. This algorithm is denoted CCA LB (Linearized Bregman). We re-implemented the sparse CCA algorithm proposed in  [12]  using Matlab. For the proposed sparse CCA algorithm, we have used F x (z) = F y (z) = z 0 as penalty functions. We solve the sparse coding problem by using orthogonal matching pursuit (OMP) algorithm  [20, 21]. For proposed smoothed CCA algorithm, we chose x = y and given by Eq. (  20 ).
SNR (dB) The single subject (id 100307) rsfMRI dataset used in this section was obtained from the Human Connectome Project Q1 release  [33]. The acquisition parameters of rsfMRI data are 90 × 104 matrix, 220 mm FOV, 72 slices, TR = 0. 72 s, TE = 33. 1 ms, flip angle = 52 • , BW = 2290Hz/Px, in-plane FOV = 208 × 180 mm with 2. 0 mm isotropic voxels. The obtained data was already preprocessed with the preprocessing pipeline consisting of motion correction, temporal pre-whitening, slice time correction and global drift removal, and the scans were spatially normalized to a standard MNI152 template and were resampled to 2 mm × 2 mm × 2 mm voxels. The reader is referred to  [33, 34]  for more details regarding data acquisition and preprocessing. The second data set obtained by a single sample delay was used for CCA.
Cartographic products of the Martian trough system, Valles Marineris, are useful to identify the diversity and complexity of geological activity that has occurred there. A huge fraction of the processes that have shaped the surface of Mars are also concentrated there. A geomorphological map of Ius Chasma in western Valles Marineris is presented. The map is published in three sheets at 1:260,000. It was drawn on the basis of 100 Mars Reconnaissance Orbiter's Context Camera images of 12 m/pixel resolution, mosaiced using the USGS ISIS Planetary Image Processing Software, and subsequently mapped and interpreted for geomorphology in ArcGIS. The map displays 52 main geomorphological units of which some are further subdivided. They include both well-established features (e. g. spurand-gully morphology on trough walls, landslide scars, and deposits), and newly reported landforms (e.
Data CTX images were used to create the background mosaics for geomorphological mapping. One hundred panchromatic CTX images with an original spatial resolution of 6 m/pixel and width up to 30 km  (Malin et al. , 2007)  were obtained from the Mars Orbital Data Explorer website (NASA PDS Geoscience Node) and subsequently processed and mosaiced with Integrated Software for Imagers and Spectrometers, ISIS  (Gaddis et al. , 1997). The spatial resolution of resultant images was decreased to 12 m/pixel in order to optimize the mapping procedure in Esri ArcGIS. HiRISE images provide discontinuous coverage at the higher spatial resolution of up to 25-32 cm/pixel  (Ebben, Bergstrom, Spuhler, Delamere, & Gallagher, 2007) , and were incorporated into the project as a supplementary dataset in order to verify interpretations of ambiguous areas.