dataesr/scientific-paragraphs-categorization huggingface dataset:
Data extraction Using a standardised data collection form, the following information was extracted for each study: first author's last name; publication year; study characteristics (country and region, design, recruitment, and mean follow-up periods); participant characteristics (sample size, number of cases, ethnic origin of participants, mean age, and percentages of pre-and postmenopausal women); blood cholesterol assessment (parameter assessed and serum or plasma); cholesterol level comparisons and corresponding HR or OR and 95 % CI: adjustment for covariates; results of stratified analyses (e. g. by menopausal status). Data regarding the association between cholesterol and the risk of breast cancer according to different breast cancer types (by histological type and hormonal receptor status) were also recorded.
Thus, residual confounding is not likely to entirely explain our findings. Second, the imperfections associated with published information may constitute limitations of the meta-analyses. Notably, lack of information about therapeutic strategies potentially influencing lipid biomarkers during the follow-up (such as treatments for metabolic disorders), as well as a single measurement of cholesterol biomarkers at baseline represent limitation of most individual studies included in these meta-analyses. However, it has been shown that lipids and lipoprotein biomarkers could be considered reliable enough to be used in cohort studies, based on measurements made at two different time points several years apart  (57). Third, only the MEDLINE database was searched. However, this approach is often used in meta-analysis on health-related topics  (58, 59)  , and in this field, the use of other databases such as Embase usually does not retrieve additional relevant articles compared with those already retrieved by the MEDLINE search.
Acknowledgements The present study was supported by the  French National Institute for Health and Medical Research  ( INSERM U1153 ), the  French National Institute of Agronomy Research (INRA   U1125 ), and  Paris 13 University. The authors' responsibilities were as follows: M. T. and P. L. -M. designed the research; M. T. , P. F. , M. H. , N. D. -P. and P. L. -M. conducted the research; P. F. analysed the data; M. T. and P. F. wrote paper; M. T. , P. F. , M. H. , T. N. , D. S. M. C. , J. B. , S. H. , P. G. , N. D. -P. and P. L. -M. contributed to the data interpretation and revised each draft of the manuscript for important intellectual content; M. T. had primary responsibility for the final content. All authors read and approved the final manuscript. The authors declare that they have no conflict of interest.
The effect of both tafamidis  (Bulawa et al, 2012)  and diflunisal  (Berk et al, 2013)  on fibrillogenesis of homotetramer S52P TTR (18 lM) was explored after incubating each protein sample at 37°C for 30 min in the presence of ligands at one-, two-, five-, eight-and tenfold molar excess, respectively. Protein/ligand complexes were then placed into 96-well black-wall plates and the fibrillogenesis procedure was carried out as described above. After the addition of trypsin to each well, ThT fluorescence was monitored for at least 10 h. The data were normalized to the signal plateau at 10 h after the initiation of each reaction. Cleavage and fibrillogenesis of other amyloidogenic TTR variants and wild-type TTR Fibrillogenesis of L55P, V122I, V30M, and wild-type alongside S52P and the non-amyloidogenic T119M TTR variant was carried out in glass vials stirred at 1,500 rpm (IKA magnetic stirrer) and 37°C using 18 lM TTR in PBS pH 7.
4 in the presence and in the absence of trypsin (5 ng/ll). The glass vial had an air/water interface of 1. 5 cm 2. Turbidity at 400 nm was monitored over time until it reached a plateau (72 h). Samples were analysed by SDS-homogeneous 15% PAGE (GE Healthcare) under reducing conditions, and bands corresponding to the intact TTR protomer were quantified with Quantity One software (Bio-Rad). After staining with alcoholic acid Congo red (CR), aggregated material was observed with polarized light microscopy. Morphology of wild-type TTR aggregates was characterized by electron microscopy as previously described (Mangione et al, 2014). Fibrillogenesis of V122I TTR was monitored by light scattering at 400 nm in the presence and the absence of 0. 5-, one-, two-and fourfold molar excess of tafamidis  (Bulawa et al, 2012) , diflunisal  (Berk et al, 2013)  and mds84  (Kolstoe et al, 2010).
The effect of tafamidis on V30M TTR fibril formation was also monitored as described above. For all the ligand inhibition experiments, the time of incubation of TTR with trypsin at 37°C was extended to 96 h. The ThT assay could not be used because mds84 strongly interferes with the fluorescence measurement. Quantification of the band of the full-length protomers in SDS-PAGE was performed as measurement of the susceptibility to proteolysis of TTR as described above. Experimental data were fitted to nonlinear regression curves using GraphPad Prism v5. Concentrations of ligand-inhibiting TTR fibril formation by 50% (IC 50 ) were determined from aggregation curves and expressed as mean AE SD of three independent experiments.
Acknowledgements We wish to thank  Ms. Alejandra Carbajal  and the  Electron Microscopy Unit ,  Royal Free Hospital, London , for imaging of amyloid fibrils. We gratefully acknowledge the use of the  University of Oxford Advanced Research Computing (ARC) facility. The study was supported by the  UK Medical Research Council  ( MR/K000187/1  to V. B. ); the  Italian Ministry of University and Research (MIUR)  Project  FIRB RBFR109EOS  (to S. G. ); the  Cariplo Foundation  Project  2014-0700  (to V. B. ); the  MIUR  Grant  17DPXLNBEK  and  MIUR Basic Research Investment Fund  Project  FIRB RBPR05JH2P "ITALNANONET"  (to C. C. ); the  Swiss National Science Foundation  Grant  P2ELP3_155339  (to M. T. D);  Istituto Nazionale di Biostrutture e Biosistemi  (to V. B and M. S. ); and the  UCL Amyloidosis Research Fund.
Reasoning with ontologies is one of the core tasks of research in Description Logics. A variety of reasoners with highly optimized algorithms have been developed to allow inference tasks on expressive ontology languages such as OWL (DL). However, unexpected behaviours of reasoner engines is often observed in practice. Both reasoner time efficiency and result correctness would vary across input ontologies, which is hardly predictable even for experienced reasoner designers. Seeking for better understanding of reasoner empirical behaviours, we propose to use supervised machine learning techniques to automatically predict reasoner robustness from its previous running. For this purpose, we introduced a set of comprehensive ontology features. We conducted huge body of experiments for 6 well known reasoners and using over 1000 ontologies from the ORE'2014 corpus.
To achieve our purpose, we proposed a set of valuable ontology features, likely to be good indicators of ontologies' hardness level against reasoning tasks. Then, we carried out a huge body of experiments using the widely recognized ORE'2014 Framework  (Bail et al. , 2014). Over 1000 ontologies were processed by 6 well known reasoners. Given these evaluation data, reasoner predictive models were trained by 5 of the most effective supervised learning algorithms. We have further improved the accuracy of our models by employing a set of feature selection techniques. Worth of cite, no prior work made use of the discretization to identify ontology relevant features. Then, we discussed and employed a variety of prediction assessment measures, well suited in the case of imbalanced datasets. Thanks to our established study, we unveiled a set of local and global ontology key features, likely to alter the reasoner robustness.
From an application point of view, an ontology should be classified regularly during its development and maintenance in order to detect undesired subsumptions as soon as possible. We recall some basic concepts of machine learning (ML)  (Kotsiantis, 2007)  for a better understanding of our study. In any dataset used by machine learning algorithms, every instance is represented using the same set of features. In our case, the instances are ontologies belonging to some corpus and the features are metrics characterizing the ontology content and design. Thus, each ontology is represented by a ddimensional vector i) ] called a feature vector, where i refers to the i-th ontology in the dataset, i ∈ [1, N], with N denoting the total number of ontologies and d standing for the total number of features.
The latter ones may be continuous, categorical or binary. The learning is called supervised when the dataset ontologies are given with known labels. In our context, a label would describe a given reasoner performances when processing the considered ontology, for instance a time-bin. The vector of all labels is specific to one reasoner and denoted Y , where y i is the label of the i-th ontology. Thus, for each under study reasoner a dataset is built in and de- x (i) = [x 1 (i) , x 2 (i) ,. , x d ( signed by D = [X N,M | Y T ]. Later, the dataset is provided to a supervised learning algorithm in order to train its data and establish a predictive model for its corresponding reasoner. Roughly speaking, a model is a mapping function from a set of features, to a specific label. It would be a mathematical function, a graph or a probability distribution, etc.
Features, identified in the previous step, will be computed for each ontology O i ∈ C (O), to obtain its corresponding d-dimensional feature vector X (O i ) ∈ F d. Step 3. Reasoner Selection. Any reasoner would be enough for the study; no knowledge about its algorithm neither details about its internal working mechanism are needed for the training process. Thus, given a set of reasoners S(R ), a reasoner R k ∈ S(R ) will be iteratively picked to carry on the process. Step 4. Dataset Building. At this step, each ontology belonging to the corpus O i ∈ C (O) will be processed by the previously selected reasoner R k to achieve the classification task. Then, the termination state of this task will be retained l R k (O i ) ∈ {C,U, T, H}. Thus, the final training dataset of the selected reasoner R k , designed by D R k , is built in by gathering the pairs (feature vector, label ), i.
e. D R k = {(X (O i ) , l R k (X (O i ) )), i = 1 • • • N}, where N = |C (O)|. Step 5. Feature Selection. A huge amount of features does not necessarily improve the accuracy of the prediction model. Commonly, feature selection or dimensionality reduction techniques are applied to identify the relevant features. In our study, we will compare three different methods of feature selection, described in Section 7. Consequently, three variants of the initial reasoner dataset are established, called featured datasets, each with a different and eventually reduced subset of ontology features. The initial dataset is also maintained and called "RAWD". Step 6. Learning and Assessing the Models. Each reasoner dataset D ( j) R k established in the Step 5 is provided to a supervised machine learning algorithm.
The latter train the data and build a reasoner predictive model, M R k (section 2): M R : X ∈ F d → lR (X) ∈ Y (1) Worth to cite, we investigated 5 well known supervised machine learning algorithms. Therefore, this step will be repeated as far as the number of algorithms and the number of datasets for a given reasoner. Then, the established models will be evaluated against a bunch of assessment measures. Further, we introduced a method to compare these models and figure out their "best" one. Details about the learning algorithms, the assessment measures and the selection procedure are given in Section 8. Step 7. Unveil the Key Features. Steps 3-6 are repeated K times to cover all reasoners in the set S(R ), with K = |S (R )|. As a result, K best predictive models are identified each for a reasoner, and each having its own most relevant feature subset.
DATA COLLECTION (STEPS 2-4) To ensure the reliability of reasoner evaluation results, we have chosen to reuse the ORE'2014 evaluation Framework 3  , as well as, its test set ontologies. Under this experimental environment, we conducted new DL and EL classification evaluations, for 6 reasoners and using over 1000 ontologies. The motivation behind our choice is multi-fold: first, the event is widely recognized by the Semantic Web community; the ontology corpus is well established and balanced throughout easy and hard cases; and finally the description of reasoner results is consistent with the specification of the robustness criterion, designed in the previous Section 4. 1. Ontologies. In overall, we retained 1087 ontologies from the ORE'2014 corpora 4. The testing ontologies fall into both the OWL 2 DL and the OWL 2 EL profiles.
4GHz and having 32GB RAM, where 12GB were made available for each reasoner. We set the condition of 3 minutes time limit to classify an ontology by a reasoner. This tight schedule would be consistent with the chosen scenario, i. e. the online classification of the ontology. Table  2  summarizes the new classification challenge results 7. We did not distinguish between results of DL and EL ontology classification. Reasoners are listed based on their robustness rank, that is the number of correctly processed ontologies within the fixed cutoff time. For each reasoner, Table  2  shows the number of ontologies within a specific robustness bin (Section 4. 1) and the range of processing time of the correct cases. Building the Reasoner Datasets. Each testing ontology was examined to compute and to record its features.
Having the evaluation results and the ontology feature vectors, 6 datasets were established each for a specific reasoner. It's important to notice that our reasoner datasets are imbalanced. Based on the description made by  (Hu and Dong, 2014) , a dataset is considered as imbalanced, if one of the classes (minority class) contains much smaller number of examples than the remaining classes (majority classes). In our context, the classes are reasoner robustness labels. We take as an example the Konclude's dataset described in the table 2, we can easily notice the huge difference or Timeout #T (the minority). However, its obvious that predicting the minor cases is much more interesting for both ontology and reasoner designers, since they describe a failure situation in processing the ontology.
FEATURE SELECTION (STEP 5) We started by performing some preprocessing steps on the reasoner datasets. Mainly, we applied feature selection methods. The latter ones were designed to recognize the most relevant features, by weeding out the useless ones. In our study, we tried to track down the subset of features, which correlate the most with the robustness of a given reasoner. Thus, we chose to investigate the utility of employing discretization  (Garcia et al. , 2013) , as a feature selection technique. Basically, discretization stands for the transformation task of continuous data into discrete bins. More specifically, we opted for the well known Fayyad & Irani's supervised discretization method (MDL). This technique makes use of the ontology label to achieve the transformation. If the continuous values of an ontology feature are discretized to a single value, then it means the feature is useless for the learning.
Consequently, it can be safely removed from the dataset. Seeking of more validity, we decided to compare the discretization results to further feature selection techniques. Thus, we carefully chose two well known methods representative of two distinct categories of feature selection algorithms: first, the Relief method (RLF), which finds relevant features based on a ranking mechanism; then, the CfsSubset method (CFS), which selects subsets of features that are highly correlated with the target label while having low intercorrelations. All of aforementioned feature selection methods are available in the machine learning working environment, Weka  (Hall et al. , 2009). Table  3  summarizes the feature selection results when applied to the feature space, F d , of each of the reasoner datasets.
The reported values are the sizes of the reduced feature subsets 8  computed by the respective method. We further investigated the possible presence of common features across these subsets, by computed the size of their respective intersections. Interestingly enough, in the all cases, we observe that the initial feature dimension was reduced. This would confirm that there are some particular features, which are more correlated to the reasoner robustness bins. However, the reduction level of the feature selection methods varies even for the same reasoner dataset. Nevertheless, it would be noticed that, for each reasoner dataset, feature selection methods agreed on the predictive power of some number of ontology features. Moreover, given a selection method, common features were identified across the datasets of the different reasoners, particularly when using MDL and RLF.
However, the CFS method delivered just one shared feature, this is the frequency value (CCF) of the OWL constructor hasSelf. Worth of cite, this feature also figures in the intersection set of MDL's feature subsets. Certainly, at this stage, it is hard to decide which feature subset is having the most relevant features for a given reasoner. This would be concluded only after training predictive models from these featured datasets. We believe that the subset leading to the most accurate predictive model, is the one having the key ontology features, likely to impact the reasoner robustness. As mentioned in the Section 4. 2, we will conduct a comparison between predictive models derived from reasoner initial datasets, RAWD, and those trained form the featured ones respectively by i. e. MDL, RLF and CFS.
Prediction Accuracy Measures. In previous works  (Kang et al. , 2012) , the accuracy standing for the fraction of the correct predicted labels out of the total number of the dataset samples, was adopted as main evaluation metric of the predictive models. However, accuracy would be misleading in the case of imbalanced datasets as it places more weight on the majority class(es) than the minority one(s). Consequently, high accuracy rates would be reported, even if the predictive model is not necessarily a good one. Thus, we looked for assessment measures known for their appropriateness in the case of imbalanced data. Based on the comparative study conducted by  (Hu and Dong, 2014) , we retained the following ones. Worth to cite, all of these measures are computed based on the confusion matrix, that we describe in what follows.
• Confusion Matrix it is a square matrix, L ×L, where L is the number of labels to be predicted. It shows how the predictions are made by the model. The rows correspond to the known labels of the data, i. e. in our case {C,U, T, H}. The columns correspond to the predictions made by the model. Each cell (i, j) of the matrix contains the number of ontologies from the dataset that actually have the label i and were predicted as with label j. A perfect prediction model would have its confusion matrix with all elements on the diagonal. • Precision (PR), Recall (RC), and F-Measure (FM): these are common measures of model effectiveness. Precision is a measure of how many er-rors we make in predicting ontologies as being of some label l. On the other hand, Recall assesses how good we are in not leaving out ontologies that should have been predicted with the label l.
A MCC value close to 0 indicate a model that performs randomly. Training and Selecting the Best Predictive Models. A reasoner dataset, D R , is trained by each of the above-mentioned supervised machine learning algorithms. Thus, five distinct predictive models are learned. We made use of the standard cross-validation technique for evaluating these models. We applied a stratified 10-fold cross-validation to ensure the "generalizability" of the results. For the sake of simplicity, we only retained the average values of the computed assessment measures over all the crossvalidation steps. We recall, our study covers 6 reasoners, for each 4 datasets were built in and then provided to 5 supervised learning algorithms, each of which have trained it separately and produced its corresponding predictive model.
MKS( M R ) = α * MCC(M R ) + β * Kappa(M R ) α + β (2) ,where (α + β) = 1. Thus, the best predictive model for a given reasoner is the one having the maximal value of the MKS score: M RBest = arg max M i R ∈M R ( MKS(M i R )) (3) ,where M R denotes the set of predictive models learned for a reasoner R. In the case where multiple maximal models are identified, the model with the highest F-Measure (FM) is selected. We believe that using MKS is the straightforward way to automatically determine the best robustness predictive model for a given reasoner. The rational behind this proposal is twofold: first, MCC and Kappa are widely recognized as powerful assessment measures, even more effective that FM, in the case of imbalanced datasets; second both measures are ranging in [-1, 1], which makes the aggregation coherent.
Best Models from RAWD Datasets For the sake of brevity, we only report the assessment results of the predictive models learned from reasoner RAWD datasets. Indeed in this type of datasets, ontology feature vectors are full ones, counting 101 distinct features. A number of important observations can be made from this table. Obviously, the RF algorithm is the most performing learning algorithm in training the RAWD datasets, since it derived the best predictive models for all the 6 reasoners. Moreover, it would be noticed that the maximal reported value of the F-Measure (FM) for the 6 reasoner ranges from 0. 86 by RF in the case of TrOWL, going to 0. 96 also by RF in the case of Konclude. These close to optimum FM values indicate that our proposed set of ontology features entails highly accurate predictive models, even when no feature selection or dimensionality reduction techniques are deployed a priori.
Thereby, the relatively high correlation between our ontology features and the reasoners' robustness is confirmed. In overall, the reasoner best models have achieved good MCC and Kappa values, as their MKS scores vary between 0. 5 (by RF for TrOWL) and 0. 86 (by RF for JFact). This finding proves the ability of the learned models to predict the minor classes (Timeout, Unexpected and Halt). Nevertheless, TrOWL's and Konclude's predictive models are less effective than the other reasoner models. At this stage, we can not conclude, which precise aspect is lowering the MKS scores of both models. It would be either the biased nature of the datasets, or probably some noisy features, which should be removed.
Best Models Across the Dataset Types In the table  5 , each reasoner best model given a dataset type is reported as well as its assessment values of MKS and FM. The last line of the table sum ups the comparison by revealing, the across datasets, reasoner best robustness predictive model, denoted by M RBest. The dataset type and the dimension of the feature space of the M RBest are also indicated. Not once the RAWD dataset, with its entire set of 101 ontology features, was listed in the final selection of the reasoners' best models. In most cases, the MKS and FM values of reasoner predictive models derived from featured datasets exceed the ones computed from RAWD models. Therefore, it is quite cer-tain, that restricting the full initial set of ontology features to some particular subsets would improve predictive power of the reasoner robustness models.