dataesr/scientific-paragraphs-categorization huggingface dataset:
Following Cohen's benchmarks,  20, 21  effect sizes of 0. 2, 0. 5 and 0. 8 were characterized as small, medium and large, respectively. TAMC results were interpreted using previously established minimal clinically important differences (MCIDs). The MCID was the smallest difference in a patient-reported outcome score interpreted as clinically important or meaningful to the patient; these have been defined for FACT-P (and its subscales), FACT-G and EQ-5D (Table  1 ). In our analyses, the lower end of the range indicated a clinically meaningful change. TAMCs were considered statistically significant if the 95% CI excluded zero. Data were analyzed using SAS version 9. 3 (SAS Institute, Cary, NC, USA). Adjustments for multiple comparisons were not made because these were exploratory analyses.
28 he more pronounced effect of spinal cord compression on HRQoL likely reflects not only its magnitude of effect on HRQoL but also the timing of HRQoL measurement. As HRQoL data is only measured every 12 weeks, SREs may occur up to 3 months beforehand. Thus, for example, patients undergoing radiotherapy for pain may experience some resolution of symptoms and some level of recovery and relief before their next HRQoL assessment. Also, for example, in the case of a moderate fracture, many of its effects also may have resolved when HRQoL is next measured. In contrast, spinal cord compression has a longer duration of impact and sequelae are often still present when patients undergo HRQoL assessment. Radiation or surgery to bone was associated with statistically significant and clinically meaningful declines in FACT-P and FACT-G total scores in AFFIRM; however, in PREVAIL, none of the changes in FACT-P outcomes were clinically meaningful, although several showed statistically significant changes.
10, 13 In mCRPC, delaying or preventing SREs is an important treatment goal  10, 32, 33  and reflects preservation of HRQoL and functionality. 11 Our analysis provides quantitative data linking the most commonly observed adverse consequence of advanced metastatic prostate cancer-complications from skeletal involvement-with quantitative HRQoL measures. Some limitations of our analyses should be considered. A higher proportion of enzalutamide patients were available for HRQoL assessment in both trials because of disease progression occurring earlier with placebo, at which time HRQoL data collection ceased. Absence of HRQoL data after disease progression is a recognized drawback of trials using HRQoL as a secondary end point. Additionally, our SRE categories did not include 'change of antineoplastic therapy to treat prostate cancer-related bone pain'; however, few patients had SREs in this additional category (AFFIRM, 3-4%; PREVAIL, 6-9%) (Astellas data on file, 2014).
13 Finally, the study design may have led us to underestimate changes in HRQoL outcomes caused by SREs. The recall period of FACT-P is 7 days, but we collected HRQoL data every 12 weeks; therefore, a patient experiencing a moderate fracture may not have had an HRQoL assessment until 2-3 months after the event, by which time many of the fracture's effects may have resolved. In conclusion, our analyses show an association between SREs and clinically meaningful functional declines in the daily lives of patients with mCRPC. As expected, spinal cord compression had the largest impact on HRQoL. Moreover, the impact was larger in chemotherapy-naïve mCRPC patients. Treatments that reduce or delay emergence of SREs may slow HRQoL decline in mCRPC. In AFFIRM and PREVAIL, both time to first SRE and time to HRQoL deterioration were delayed with enzalutamide versus placebo.
Questionnaires Among the 80 patients included in this study, only 68 answered questionnaires (12 patients did not respond). All patients responded to two questionnaires by phone. The Mini Mental State Examination (MMSE) questionnaire was used to confirm their mental health state after the surgery, and the Global Physical Activity Questionnaire (GPAQ) was used to measure the recreational PA levels of 68 of the 80 patients (13 symptomatic patients and 55 asymptomatic patients). The GPAQ was originally designed by the World Health Organization (WHO) to assess PA patterns in western countries  (Bull et al. , 2009). The data in this study were analyzed according to the GPAQ Analysis Guide provided by the WHO (retrieved 2015). Recreational PA was expressed in min/day. For data analysis, all patients (symptomatic and asymptomatic) in the cohort were ranked in increasing order of duration of daily PA, and divided by 3 to obtain three PA tertiles corresponding to duration of daily PA (T1: 3.
Statistical Analysis Biological results are expressed as mean ± SEM. A nonparametric Mann-Whitney test was used to compare biological parameters between symptomatic and asymptomatic patients. A nonparametric Kruskal-Wallis test followed by Dunn post-hoc's test was used to compare biological and clinical parameters between PA level tertiles. Spearman correlation was used to test the relationship between the hemorheological parameters and the percent stenosis, and between the hemorheological parameters and the PA level. A linear multivariate model was used to test the independent predictors of blood viscosity and RBC aggregation. Significance level was defined as p < 0. 05. Analyses were conducted using GraphPad Prism software (v. 6, San Diego, CA, USA).
Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks (PBs), is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of PBs. Thus, predicting the local structure of a protein in terms of protein blocks is a step towards the objective of predicting its 3-D structure. Here a new approach, kPred, is proposed towards this aim that is independent of the evolutionary information available. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) apply a purely knowledge-based algorithm, not relying on secondary structure predictions or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures.
Based on the strategy used for scanning the database, the method was able to achieve efficient mean Q 16 accuracies between 40. 8% and 66. 3% for a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. The impact of these scanning strategies on the prediction was evaluated and is discussed. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R 2 of 0. 82). An online version of the tool is provided freely for non-commercial usage at http://www. bo-protscience. fr/kpred/.
Introduction Knowledge of protein structure considerably helps towards understanding protein function. The Protein Data Bank (PDB) that serves as the central repository of knowledge for the protein structural biology community contains more than 125,000 protein structures and its growth has been considerable in the past decade  1. This number is however still far below the ~70 million protein sequences referenced in UniProt database  2. Hence it is at stake to find methods to bridge this considerable gap. Computational methods for predicting protein secondary and tertiary structure have persistently tried to fill it. In this paper, we explore the ability of a structural alphabet based prediction method to fulfill in part this role. Since the seminal works by Kabsch and Sander in 1984  3  , one of the most popular and rewarding protein local structure.
Dataset All the protein chains from PDB  1  were segregated into clusters culled at 30% sequence identity using the BLASTClust algorithm  28  resulting in a collection of 15,544 clusters. The dataset which we set-up comprises of 15,554 protein chains each corresponding to the best representative structure available from each of these clusters and 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  12. 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 11 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  29  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 (Figure  1a ). 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 (Figure  1b ). The multiple hits correspond to the different conformations, which the pentapeptide has been seen to adopt in protein structures (Figure  1b ).
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 (Figure  1b ) 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. [Fig. 1  about here] Two methods were explored to predict the optimal PB sequence within the list of all the possible PBs obtained after querying the database. 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.
As shown in Figure  2 , 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 equi-probable 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 (see Figure  2 ).
This approach is called the hybrid method because it combines the majority rule method with contextual information in the prediction process. [Figure  2  about here] 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 homologue of known structure used. On the other hand, as in the case of other local structure prediction methods  12, [20] [21] [22] 26  , 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.
PENTAdb, a database of pentapeptides from protein structures A total of 68. 84 million pentapeptides obtained from the 0. 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 (Supplementary Table  1 ). Towards higher sequence identity cut-offs, there was a steady increase in the penta-PB coverage. But this comes at the price of the addition of redundant data. Still the entire PDB covered less than 10% of the total penta-PB space. For calculating the accuracy score, the penta-PB frequency table derived from the PDB30 dataset was used even if it contained only 40,130 penta-PB motifs.
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 represents the theoretical highest prediction rate 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 is manageable, thanks to the hierarchical clustering at different sequence identity levels by the BLASTClust algorithm  29. For each of the 15,544 unrelated query protein sequences (PDB30 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 made accessible to PB-kPRED for prediction (see Table  1  for size of the database for each subset).
The results are detailed in Table  2. At 30% sequence identity cut-off, the correct PB was found in 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%. The PB-wise breakdown of these values are further detailed in supplementary Table  2. [Table  2  about here].
Prediction accuracies 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 Q16 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. [Table  3  about here] As an attempt to improve the prediction rates, the hybrid method was tested using the noise filtering scheme for querying the database whereby, for each query pentapeptide, data in PENTAdb only coming from closest homologues was queried first (see Figure  3 ). 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 predictions remained high at a level of about 61. 6%. Only in experiment B8 the prediction accuracy rate dropped to 40.
8%. This experiment is in fact identical to the one featured for <30% threshold shown in Table  3  using the hybrid method and the without noise filtering scheme for querying the database. [Table  4  about here] 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 (see Table  4 ) shows a bimodal distribution (see Figure  4 ). 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. At the other end of the spectrum, there is an almost normal distribution with an average around the 35%-40% accuracy range.
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. Results are reported in Table  6  and further described below. 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 closelyrelated proteins in PENTAdb. [Table  6  about here] 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 RMSD of 0. 14 Å (Supplementary Figure  2a ). 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 (Supplementary Figure  2b ) 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.
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 a n d hybrid methods 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.
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 cutoff values so that the portion of the PENTAdb that is made accessible for prediction is calculated on the fly. For the each of the 15,544 query sequences of PDB30, 16 experiments were performed amounting a total of 248,704 datasets building. This is computationally intensive and was performed using extensive MySQL querying. Thanks to 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.
Hence, filtering out from the database the pentapeptides from proteins that shared less than 30% sequence identity with the query indeed improves the prediction efficiency. When the majority method and the hybrid method (Figure  5 ) were compared, two distinct clusters were noticed. Upon further investigating the reason for this distinct clustering, we note that, irrespective of the sequence identity cut-off, the points below the diagonal were found in more populated clusters while the points above the diagonal were found in least populated clusters Hence the hybrid method using the noise filtering scheme will perform better when there are some closely-related protein structures to look-up to in PENTAdb. In a real-life scenario, this will not be always the case. Indeed, proteins for which we want to predict the structure and which do not have any homologues even at 30% sequence identity are not so uncommon.
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 PENTAdb 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 (Figure  2 ), the decision rules implemented in 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.