dataesr/scientific-paragraphs-categorization huggingface dataset:
ADNI cohort -[18F] Florbetapir Data for a population of 916 individuals who had had at least one Florbetapir scan were downloaded from the ADNI database. The AD patients fulfilled the NINCDS-ADRDA criteria  [27]  whereas the MCI patients had a clinical dementia rating of 0. 5, abnormal memory function as documented on the Logical Memory II subscale, and were not demented  [30]. After exclusion of 11 individuals without MRI scans or with poorquality Florbetapir scans, we composed two groups from the remaining participants. First, age, mini-mental state examination (MMSE) score and gender distribution were randomly matched in a relatively young group of 51 HC, 72 MCI and 84 AD patients to the participants of the DiMI cohort. Second, the total ADNI population was split into younger (aged 55-75 y) and older (aged 76-83 y) subgroups.
[18 F]Florbetapir Raw Florbetapir frames acquired between 50 and 70 min after injection of the tracer were downloaded from the ADNI database and summed. The 207 Florbetapir images used in the comparison with PIB data had an accompanying MRI image that was used for the spatial normalisation of the respective integral co-registered PET images to MNI space, similarly to the method used for the PIB data. An average Florbetapir image was generated from these 207 images and used as a sample-based Florbetapir-PET template in MNI space. All raw Florbetapir images used in this project (n=774) were subsequently spatially normalised to MNI space with the use of this template. A whole cerebellum region (based on the mean cerebellar uptake) was used as a reference for all 50-70 min Florbetapir images as it displayed lower variance of the HCs and larger effect size between HC and AD patients in comparison to the cerebellar GM region (Supplementary Table  1 ).
The same anatomical mask as that used for the PIB data was applied to the Florbetapir data in order to define GM ROIs on the standard digital atlas, resulting in the same 12 bilateral GM ROIs as well as a CCTXR, as described above. The pre-processing of the images (i. e. , segmentation, coregistration as well as spatial normalisation steps) for both tracers was performed using SPM5 (Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, UCL, London).
Visual assessment of [18 F]Flurodeoxyglucose (FDG) scans The ADNI database FDG-PET (30-60 min) images from the AD patients in both age groups whose Florbetapir scans were negative for amyloid using the quantitative method, according to the cut-off point described below, were visually assessed for the presence of an AD-like pattern of hypometabolism  [32]  by two independent, experienced nuclear medicine specialists (raters) who were blinded to the clinical and demographic backgrounds of the individuals.
The second objective of this paper was to investigate the effect of age on amyloid PET data from within the large ADNI population sample. The younger HCs had significantly less amyloid deposition than the older HCs only in the putamen, independently of their ApoE4 status, whereas no significant differences in deposition were observed in the composite neocortical region in younger or older HCs. The composite cortical uptake values, however, for the younger HCs appeared to be distinctly different for amyloid-positive and amyloidnegative subjects. Conversely, probably due to the agerelated accumulation of amyloid  [49] , distribution in the older HCs was more likely to be unimodal, shifted to the amyloidpositive side, without any clear border between amyloidpositive and amyloid-negative individuals.
Acknowledgments The authors thank the DiMI principal investigators ( Juha Rinne [Turku, Finland], Alexander Drzezga [Munich, Germany], David J. Brooks [London, UK], Rik Vandenberghe [Leuven, Belgium] and Karl Herholz [Mancheter, UK] ) for kindly providing PET images and clinical data for this study. The authors are grateful to  David J. Coope  for the R scripts that he provided to process the PET data. Compliance with ethical standards Funding This article was funded by the following grants:  Swedish Research Council  (project  05817 );  Strategic Research Program in Neuroscience at Karolinska Institutet ;  Swedish Brain Power Network ;  Old Servants Foundation ;  Gun and Bertil Stohne's Foundation ; the  Alzheimer Foundation in Sweden; Brain Foundation ;  Regional Agreement on Medical Training and Clinical Research (ALF) between Stockholm County Council  and  Karolinska Institutet ; EC- FP6 Network of Excellence on Diagnostic Molecular Imaging (DiMI ,  LSHB-CT-2005-512146 );  INMI ND  (grant agreement number  278850 ); resources of the European Union's  Seventh Framework Programme for Research and Technological Development  (FP7/2007-2013).
For example, Figure  1  The GNB1 beta-propeller. In this JSmol  (Hanson et al. , 2013)  ribbon diagram view of a GNB1 beta-propeller ortholog (PDB:1TBG; GenPept:P62871), the blades are displayed as seven triangles organized in a circle, like a propeller. Each blade is composed of four strands, labeled A, B, C, and D starting from the inner one. Reading the protein sequence from N-terminus to C-terminus, the first strand is the D strand of the upper right blade (D 1 ), followed by the A, B, C strands of the upper left blade. The A, B, C strands of the upper right blade (A 1 , B 1 and C 1 ) are the last strands of the protein sequence. This implies that, sequence-wise, blades are shifted with respect to the corresponding 3D units. the number of W-D repeats in a domain typically ranges from 4 to 16 blades  (Li & Roberts, 2001) , making up a complex and interesting class of domains whose evolutionary relationships remain unresolved.
A GNB1 ortholog tandem repeats dataset We selected a set of GNB1 orthologs using the Ensembl  (Flicek, Amode & Barrell, 2014)  database, and BLAST  (Altschul et al. , 1990)  in the case of Arabidopsis. Table  1  gives the accession numbers and the positions of the WD40 tandem repeat in each sequence. Figure  2  shows the current consensus of the phylogeny of the six species  (Dunn et al. , 2008). We used the structure-guided alignment of GNB1 given in  Chaudhuri, Soding & Lupas (2008)  to identify insertions and deletions of the human sequence within the WD40 motif. We then aligned the human sequence with A. thaliana and N. crassa sequences in order to transfer the insertions and deletions from the human, and to identify further deletions in A. thaliana and N. crassa. The alignment is shown in Fig.
The remaining sequences, DAN, DRO and CEL, have exactly the same editions as the HUM sequence, since they align without gaps. The six resulting edited sequences have all the same length, 288 aa. The nucleotide sequences coding for these sequences were then extracted, to yield the main dataset of six sequences of length 864 bp. uses phylogenetic trees of ortholog domains to distinguish mutations that occurred before all speciation events, from mutations that occurred after speciation.
Algorithms and complexity The complete procedure is composed of the following steps, taking as input a motif of known length p and a set of n ortholog tandem repeats of the motif: 1. Create a dataset of n tandem repeats of the same length, using insertions and deletions within copies of the motif. If a structure-guided alignment exists, use it. 2. Find the values of (pos,k * p),k ≥ 1 that minimize, or nearly minimize, the contraction cost C(pos,ℓ), as defined by Eq. (  1 ) or Eq. (  2 ). 3. Decide whether there is sufficient evidence to confirm the position of a further contraction. If not, stop. 4. Perform the contraction, and go to Step 2. Only Steps 2 and 4 can be solved by algorithms, and, fortunately, efficient algorithms are available for these tasks. The computation of contraction costs as defined by Eq.
9. All reconstructions left undisturbed both the first two repeats-positions 1-251-and the last 39 positions (see Fig. S1 ). All reconstructions agreed on the following translated middle part sequence (see Fig. The pattern was not found in the six sequences that were chosen for the current experiment. However, there were 32 hits in the TrEMBL protein database  (Bairoch & Apweiler, 2000)  using ScanProsite  (de Castro et al. , 2006). Of these hits, two were for the slime molds Dictyostelium discoideum and Dictyostelium purpureum, whose divergence is presumed to date back to 400 million years  (Parikh et al. , 2010). The rest of the hits are all Basidiomycota, which is a fungus phylum: these hits include mostly rots and smuts, with some yeasts, and two edible mushrooms, the Shitake and the Champignon de Paris.
Taking an a posteriori look back at Fig. 5 , in which we studied the species separately, one can see that the NEU sequence is a very good predictor for the consensus position of the most recent duplication. It is also the most simple species in our dataset, and the common motif that we found points to early branching or slow evolving organisms, such as slime molds. Given the fact that the first repeat interacts with the last one in order to close the circular structure of the beta-propeller, as seen in Fig. 1 , it seems very plausible that expansion of the number of blades in propellers should the beginning and end of the sequence undisturbed, since they probably need co-evolution to properly glue the structure. This may explain why we could not reliably predict any contraction overlapping the first two repeats, or the last 12 residues.
Conclusion and Perspectives In this work, we have presented and validated two variants of a parallel HITS to solve the QAP. We have evaluated our approaches on 15 benchmark instances from the QAPLIB. It demonstrate high-quality results on the set of well-known benchmark instances from QAPLIB. The comparison between the variants has revealed the potential of exchange of information in the parallel and it has shown the efficiency of the UX to solve the QAP. As a future work, there are several possible ways to extend this work. One possibility to experiment other parameters to get better Another possibility is to explore the two other parallel designs (the iteration level and the solution The GPU platform is appropriate for the implementation of these two levels thanks to its single instruction multiple data architecture.
Computational prediction of nucleic acid binding sites in proteins are necessary to disentangle functional mechanisms in most biological processes and to explore the binding mechanisms. Several strategies have been proposed, but the state-of-the-art approaches display a great diversity in i) the definition of nucleic acid binding sites; ii) the training and test datasets; iii) the algorithmic methods for the prediction strategies; iv) the performance measures and v) the distribution and availability of the prediction programs. Here we report a largescale assessment of 19 web servers and 3 stand-alone programs on 41 datasets including more than 5000 proteins derived from 3D structures of protein-nucleic acid complexes. Well-defined binary assessment criteria (specificity, sensitivity, precision, accuracy.
Further, previous reviews  [3] [4] [5] [6]  only assessed at small scale the available datasets. Currently, RNA-and DNAbinding residue predictions are always treated as different problems, or trained with different data sets within the same model  [7] [8] [9] [10] [11]. However, whether RNA-and DNA-binding proteins (Supplementary Note 2 in S1 Text) exploit different driving forces is not established and it is known that some proteins do bind both types of nucleic acids. Very recently, Yan and coworkers noticed also that prediction programs are unable to distinguish between DNA and RNA binding proteins and concluded that one should compare RNA-and DNA-binding site predictors together  [6]. The definition of a nucleic acid binding residue is not standardized with definitions ranging from distance cutoffs  [8, 9, [12] [13] [14] [15]  to the enumeration of non-covalent contacts  [16] [17] [18] [19] (Supplementary Note 3 in S1 Text).
This leads to ambiguities goal in the problem and variations in prediction accuracy. Besides, tens of training and test sets of variable sizes have now been curated by developers during the development of computational approaches. How to avoid bias in a dataset is nontrivial. Further, the assessment criteria are still arguable, e. g. whether all residues from different proteins should be compared together (Supplementary Note 4 in S1 Text). In addition, programs differ in their approaches (Supplementary Note 5 in S1 Text) making a fair assessment difficult. Finally, the distribution and ease-of-use of the programs greatly determine their help to the users in the biological community. In this report, we present a large-scale assessment of 19 currently available web servers and 3 stand-alone prediction programs in nucleic acid binding site prediction, which is 24 predictors in total, on 41 different datasets derived from structures of protein-nucleic acid complexes in the PDB, including more than 5000 proteins.
Results The prediction of nucleic acid binding sites is usually determined by three main factors: the definition of a binding site, the assessment criteria and the datasets. Currently, there is no universal definition of a binding site and a minimum distance cutoff between interacting residues is most frequently applied. However, different distance cutoffs lead to accuracy variations while a single cutoff biases certain prediction programs. In Fig 1A is displayed a real-world case where a distance cutoff of 6. 0Å leads to a two times higher number of binding sites than that obtained with a cutoff of 3. 5Å. S1 Table and S12 Fig show that this difference is a general distribution rather than a rare case. And, in S2 Table, the data show that with a cutoff of 3. 5Å used for prediction and 6Å for the definition of the binding sites, the final specificity is 100% but the sensitivity is as low as 51-62% (high false negative rate) with a total accuracy (ACC) decrease down to ~90% (details are discussed in Supplementary Note 6 in S1 Text).
To fully capture the accuracy variance resulting from a distance cutoff, a hierarchical definition with distance cutoffs ranging from 3. 5 to 6Å using 0. 5Å as step was used and the distributions of the accuracies were plotted. Besides, the prediction accuracy highly depends on the dataset used for testing. Normally, the largest the training set the better the prediction model, thus making it difficult to compare different programs. However, a good prediction program should show stable accuracy on all the datasets, a feature that cannot be achieved by biased predictions even with a large training set (Supplementary Note 7 in S1 Text). We used 41 datasets to accentuate the possibility of biased prediction by the programs. Finally, different criteria are measured to show different aspects of the programs.
The webservers assessed include BindN  [8] , BindN+  [9] , RNABindR  [20] , RNABindRPlus  [21] , DBS-Pred  [12] , DBS-PSSM  [13] , KYG  [14] , PRBR  [22] , PPRInt  [23] , DNABINDPROT  [24] , ProteDNA  [25] , DISPLAR  [10] , DR_bind1  [26] , aaRNA  [27] , RBscore  [28] , RBRDetector  [29] , DNABind  [30] , xypan  [31]  and RNAProSite (lilab. ecust. edu. cn/NABind/), while the programs are Predict_RBP  [17] , PRNA  [32]  and RBRIdent  [33]. Previously reported prediction approaches have been summarized in Table  1. Slow programs DR_bind1 and RBRDetector were only tested on part of the datasets and the results are   (Continued) Table  1. provided in the supplementary information. metaDBSite  [34]  shows same result as BindN and was not tested explicitly.
Detailed comparisons Some programs tested here are only designed to predict RNA binding sites while others DNA binding sites. However, when tested together, we find that some of the programs show predictive ability on both types of proteins. For instance, RNABindR, KYG, aaRNA, RNAProSite and RBscore are developed on RBP and never trained on DBP, but they also show predictive ability on DBP. In terms of sequence-based methods, RNABindR even demonstrates higher prediction accuracy than most DNA-binding site predictors. aaRNA and RBscore show even higher accuracies for DBP than for RBP. The RNA binding site prediction mode of BindN+ also shows prediction ability on DBP, but its DBP prediction mode has a much lower accuracy on RBP. Together with the problem of binding site prediction, it is very interesting to find out whether there is a program that can discriminate RNA binding residues from DNA binding residues.
This discrimination require three assumptions: i) residues from different proteins can be compared; ii) RNA and DNA binding is driven by different driving forces; iii) such driving forces have been explored by current programs. Previous work from Yan et al. [6]  analysed the cross-prediction between RNA-and DNA-binding residue predictors and concluded that they General accuracy distribution based on wAUC. wAUC as assessment criterion of all programs on all datasets with hierarchical definition of binding sites from 3. 5 to 6Å. wAUC is the weighted arithmetic mean of AUC and is considered as the criterion to assess the prediction accuracy of the predictors. It is plotted as rainbow colors from highest accuracy of red to lowest accuracy of blue. Each grid in the plot show the wAUC of a predictor (subtitle) on a certain are unable to properly separate DNA-from RNA-binding residues.
This result implies that these methods have similar prediction accuracies and similar preferences on datasets. Comparing the updated webservers with the old ones, we find obvious improvement over the years. BindN+ shows consistent improvement over BindN, when programs integrating an homologous search approach, RNABindRPlus, DNABind and RBRDetector show accuracy increase on some of the datasets. New upcoming webservers, aaRNA, RNAProSite and RBscore, show the top ranking performances. Still, xypan and RBRIdent, both improved versions of PRNA, do not display enough effectiveness in this large-scale test. The AUC is a criterion of assessment that presents a bias towards the score-based predictions rather than binary predictions (binding or not-binding). However, some programs, such as DISPLAR, include a predefined prediction process and give only binary results.
7, which can hardly be considered as meaningful predictions, since an AUC ~0. 5 is equivalent random guess. We find that aaRNA shows similar level of wAUC on dataset meta_R44 (0. 82) and Sung-wook_R267(0. 83), but its sensitivity on meta_R44 (0. 8) is much higher than on Sung-wook_R267(0. 52) while specificity shows the opposite, 0. 73 vs. 0. 89. Similar cases could also be found in many other programs. In fact, these programs show stable prediction accuracies on both of the sets, but the binary defined sensitivity is a trade-off of specificity and determined by a pre-set cutoff. DNABINDPROT and DISPLAR show top rank specificities and accuracies, which was not described by the AUC distribution. However, when regard to sensitivity, DNA-BINDPROT ranks at the bottom and DISPLAR is lower than median.
This implies that these programs sacrifice sensitivity to gain specificity and accuracy, leading to low true positive rates. data set (x-axis) assessed according to the binding sites defined by a certain distance cutoff (y-axis). For each subplot, DBP data sets and RBP data sets are separated by bold line. The last two data sets are mixed with DBP and RBP. RBscore_P627 is a non-redundant data set by removing cases of sequence identity >25%. All_P5114 is a mixture of all data sets. doi:10. 1371/journal. pcbi. 1004639. g002 This demonstrates that proteins in different environments can have different affinities to nucleic acids and vary in the size of their binding interface. Thus, the use of fixed cutoffs to define binary prediction may misinterpret the binding reality (Supplementary Note 4 in S1 Text).
Tests on newly solved structures Another way to show the predictive ability is to test the newly solve complex structures, since most of the predictors were developed on datasets before 2013. We, thus, collected the proteinnucleic acid complexes solved after 2014, 31 DBP and 15 RBP, as test sets. Because these cases are non-homologous with all the training and test sets of all the programs, they can be taken as an independent test set. This test is similar to the blind tests of CASP  [65] , RNA-Puzzles  [66, 67]  and CAFA  [68]. If a prediction program really has predictive ability rather than the ability of interpolation, it should show high accuracy on these new data. The homologous search approaches of some programs, such as RNABindRPlus and DNABind, could be excluded by this assessment.
According to Fig 2 , we find that some of the programs show much lower accuracies on these new data than expected according to previous performance (Supplementary Note 7 in S1 Text). But, overall, the results on the newly published data sets are similar to the performance on some 'difficult sets' such as meta_R44 and RBscore_R117. The values presented in Table  2  indicate also that many machine learning based methods show their minimum performance in these two new datasets. This highlights the data set bias problem of the machine learning approaches that can only demonstrate predictive ability in terms of interpolation. For this reason, a fair comparison indeed require for blind tests independent datasets that do not have any relation to the known datasets.
Discussion Rather than achieving a high level of accuracy on existing datasets, both an increase in knowledge and understanding of the driving forces and mechanisms for protein-nucleic acids binding are required in order to improve the accuracy on all datasets. Although most of the programs have been validated with curated datasets, we find that some of the programs do not consistently show high predictive ability on all the datasets. That is to say, these programs are poor at recapitulating the main factors key to protein-nucleic acid binding. According to results on all the programs, some RNA binding site prediction programs (RBscore, RNAProSite, aaRNA and BindN+) show predictive ability in DNA binding site prediction, but hardly any DNA binding site prediction program demonstrates high level of accuracy in RNA binding site prediction.
Thus, the key features of RNA binding residues and DNA binding residues are similar and can be better captured by RBP-based datasets than by DBP-based datasets. Besides, we find the most stable and accurate programs are aaRNA, RNA-ProSite and RBscore, all programs that take the advantage of protein structure, while other sequence-based programs are less stable in terms of both data set and distance cutoff, implying that important structural features cannot be fully captured by sequence-based programs making them less accurate. The previous assessments of the predictive ability were mainly focused on two approaches, comparisons between previously reported results  [4]  and with small tests  [6]  (Supplementary Note 8 in S1 Text). However, as shown by this large-scale test, datasets can be biased and tests on one or a few test sets cannot testify the bottom line of predictive ability of a program.
Datasets All of the data sets were built based on protein-nucleic acid co-crystal structures extracted from the PDB. 23 RNA binding protein datasets and 16 DNA binding protein datasets were collected from previous studies and listed in Table  3. Some unreasonable cases were excluded from the assessment datasets: 1) the presence of a DBP in a RBP set (PDB ID 1a1v); 2) superseded PDB structures; 3) peptides shorter than 20 residues; 4) weak and uncertain nucleic acid binding proteins including those with less than three binding residues; 5) PDB chains containing only Cα atoms; 6) proteins constituted by two separate short peptides. Other two data sets of RBP and DBP after 2014 have been curated. Sequence similarity of 25% have been used as cutoff to remove redundancy from other data sets by PISCES  [69].