dataesr/scientific-paragraphs-categorization huggingface dataset:
It has previously been suggested that detecting senescence would be difficult in wild animals because high levels of mortality would remove individuals from the population before they start to senesce  [66]. However, by revealing a great diversity of senescence patterns, studies using capture-recapture data collected across a broad range of taxa showed that this assumption was untrue (  [13, 16, 23] ; see also  [19]  for a review). Furthermore, Colchero's model has proven to be particularly efficient for detecting senescence when it is actually present  [37, 38]. Simulations showed that the model is able to detect senescence when the study period is equal or longer than the mean lifespan in the population  [37] , which was the case in the four datasets considered in our study. A long lifespan, a high level of iteroparity and a low reproductive effort appear to be closely associated with negligible actuarial senescence in the three species of true salamanders considered in our study.
Data were collected for the 2y range from 51 to 701 (step size of 0. 011 and count time 1 second per step). Collected data were qualitatively examined using the Bruker diffract+ and EVA search-match software (search matched against the ICDD database). The functional groups of HA were analyzed by ATR-FTIR using a Shimadzu IRAffinity-1S (Shimadzu Corp, Japan) equipped with a Quest ATR GS10801-B single bounce diamond accessory (Specac Ltd, England). Data were acquired with 40 scans at a resolution of 4 cm À1 and processed using the LabSolution IR software by Shimadzu. In addition, compositional analysis of HA and Sr-HA samples was performed by X-ray fluorescence (XRF). The test was executed in accordance to ISO standard 12677. Samples were ignited at 1200 1C for 1 hour to examine the loss on ignition.
Data were acquired with 40 scans at a resolution of 4 cm À1 and processed using the LabSolution IR software by Shimadzu. Native chitosan scaffolds were used as controls in both analyses. 2. 5. 2 Cell culture. Homo sapiens osteosarcoma bone cell line (MG-63) was chosen for the preliminary evaluation of the cytocompatibility of the bone tissue engineering scaffolds hereby fabricated. Prior to test, cells were cultured in typical cell culture polystyrene flasks using Dulbecco's Modified Eagle Medium (DMEM) with 10% (v/v) of fetal bovine serum (FBS) and 1% (v/v) combined penicillin-streptomycin antibiotic (PenStrep) (all reagents purchased from Gibco s , Germany). A monolayer of cells close to confluence (B80%) was detached from the flask at passage 15 using a trypsin/EDTA solution (Life Technologies, Germany) in PBS.
49  Lattice parameters  obtained using Bruker's TOPAS software confirmed this observation. The software showed that the a-axis and c-axis parameters of Sr-HA were increased after the substitution of Sr 2+ , as presented in Table  3. This is likely to be due to the larger ionic radius of Sr 2+ compared to Ca 2+ , as confirmed by previously reported results. 50  In addition, a slight shift in diffraction peaks towards lower angles (2y), as compared to reference HA peaks, was observed, which can be ascribed to the substitution of calcium ions by strontium ions. 49  3. 2. 2 Functional group analysis by FTIR. The FTIR spectra of HA and Sr-HA are presented in Fig. 5. The characteristic bands related to phosphate, hydroxyl and adsorbed water can be observed. Notably, all the representative bands that characterize the phosphate (575, 610, 967, and 1014 cm À1 ) and hydroxyl (3568 and 639 cm À1 ) groups were still present in FTIR spectra after strontium substitution.
Acknowledgements This work has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Sklodowska-Curie (HyMedPoly project , Grant Agreement No. 643050 ). We acknowledge the important support of the whole HyMedPoly consortium. We would also like to thank  Mr Mike Salt  for its technical guidance with freeze-drying and of  Mr Lee Bullock  for mechanical testing consultancy.
Physico-Chemical Characterizations The physicochemical properties of the materials were evaluated after each steps of the synthesis. Diffractograms are recorded on a Bruker D8 apparatus, using the CuKα radiation (λ = 1. 54059 Å). Data were collected for 2θ between 10° and 80°, with an increment of 0. 05° and an integration time of 1. 0 s at each step. Diffractograms were indexed using references PDF database. Crystallite mean.
Physico-Chemical Characterizations The physicochemical properties of the materials were evaluated after each steps of the synthesis. Diffractograms are recorded on a Bruker D8 apparatus, using the CuKα radiation (λ = 1. 54059 Å). Data were collected for 2θ between 10 • and 80 • , with an increment of 0. 05 • and an integration time of 1. 0 s at each step. Diffractograms were indexed using references PDF database. Crystallite mean sizes are calculated using the Scherrer equation: D cryst = (k•λ)/(β•cosθ), where k and β are respectively the shape factor (~0. 9) and the corrected full width at half maximum. N 2 -physisorption experiments were collected on a Micromeritics Tristar II porosity instrument. A known mass of catalyst was degassed at 150 • C under vacuum for 6 h. Isotherms were registered at a temperature of -196 • C and SSA were determined from the adsorption branch in the 0.
7-numerical-aperture (NA) or a 10ϫ/0. 4-NA lens objective. EGFP, dTomato/mCherry, and infrared fluorescent proteins were detected by laser/optical filters for excitation/emission at 488 nm/505 to 525 nm, 543 nm/560 to 620 nm, and 635 nm/655 to 755 nm, respectively. Images were collected with FluoView (Olympus) software. Dissected swimbladders. After live imaging, chosen zebrafish were euthanized with a Tricaine overdose at 25 to 27 hpi, and swimbladders were removed with fine forceps as described previously  (55). Swimbladders were transferred to 0. 4% low-melting-point agarose in PBS on a 25-by 75-by 1. 0-mm microscope slide and covered with an 18-by 18-mm no. 1. 5 coverslip. Preapplied dabs of high-vacuum grease (Dow Corning, Midland, MI) at the corners of the coverslip prevented crushing and deflation of the swimbladder.
3 M primer concentration, on a CFX96 thermocycler (Bio-Rad). Threshold cycle (C T ) values and dissociation curves were analyzed with Bio-Rad CFX Manager software. The change in gene expression was normalized to the gapdh level (ΔC T ) and then compared to the value for vehicle-injected controls (ΔΔC T ) using the 2 Ϫ⌬⌬C T method (135). Primers (Integrated DNA Technologies) are listed in Table  S3. Image analysis. The percentage of the swimbladder covered by Candida at 24 hpi was determined using Fiji software (ImageJ environment [136]) applied to maximum-projection images from stacks of 15 to 25 z-slices. Images were taken with identical acquisition settings to ensure comparability. The swimbladder area was delineated, and the percent coverage of Candida fluorescence above a set threshold (corresponding to background fluorescence) was calculated.
Images of the swimbladder areas of Tg(NF-B:EGFP) and TgBAC(tnfa:GFP) fish were analyzed using Fiji software. Images covered the swimbladder from midline to skin in 5-m z-slices. The number of slices per image ranged from 12 to 22, depending on the size of the fish. Time-lapse images were processed in Fiji using descriptor-based registration (137). Neutrophils and macrophages were outlined and counted in FluoView (Olympus), from images taken at 24 hpi. Statistical analysis. Statistical analyses were carried out using GraphPad Prism 7 software (GraphPad Software, Inc. , La Jolla, CA). All significant differences are indicated in the figures. When data failed to pass the D'Agostino-Pearson test for normal distribution of data, or when the number of samples was too small to determine normality, nonparametric statistics were used (Fig.
 Ensemble classifiers have a better robust capability in predicting the toxicity of NP based on physicochemical properties, quantum-mechanical, toxicological attributes and in vitro experimental conditions compared to base classifiers.  RF and NN combined with another base classifier have not the best performance. Combining lower rank classifiers can help to catch the outliers.  Copeland Index based on datasets, validation processes and performance metrics can be used to rank base and ensemble classifiers.  RF, Bayesian Network (BN) and ensemble classifiers show high performances with missing values while NN did not.
used to combine base models to optimize the classification prediction of toxicity. To facilitate inter-comparison, a Copeland Index was applied that ranks the classifiers according to their performance and suggested the optimal classifier. Neural Network (NN) and Random forest (RF) showed the best performance in the majority of the datasets used in this study. However, the combination of classifiers demonstrated an improved prediction resulting meta-classifier to have higher indices. This proposed Copeland Index can now be used by researchers to identify and clearly prioritize classifiers in order to achieve more accurate classification predictions for NP toxicity for a given dataset. NPs with enhanced properties and as such, they are produced for a wide variety of applications for example cosmetics, drugs and medications, biomedical devices, microelectronics and energy harvesting.
The toxicity of NPs is an increasingly important research area worldwide in recent times  [3]  through a wish to ensure the sustainability of this new technology and not repeat mistakes of the past  [40]. ML has been an essential tool for predicting the toxicity surrogating A C C E P T E D M A N U S C R I P T the relationship between input and output. Classifiers are one of the most common ML tools exploiting experimental data such as physicochemical properties, quantum-mechanical attributes and toxicological outputs for nanotoxicity prediction  [13, 28, 41]. Despite the wide variety and selection of classifiers and modelling approaches, no optimal classifier can be identified so far  [32, 42]. Instead the predictability of the classifier depends on the dataset characteristics (missing values, training size, input variables) or the methods used to assess classifier performance  [32].
Summary of base and ensemble classifiers used in WEKA. The eight base classifiers were selected to represent different categories of supervised classifiers such as rules, trees, lazy, functions and bayes. The Vote ensemble method combines the results of base classifiers to provide an optimized prediction. Rules as a classifier category comprises algorithms that break down the dataset according to rules. Decision table (DT) classifiers hold all links of input data and their outcomes and in case of unknown inputs they use majority value for estimating the outcome, or, as in our application, a nearest neighbour algorithm  [49, 50]. Bayes classifiers are probabilistic classifiers  [51]. A Bayesian Network (BN) is a directed acyclic graph model that represents variables as nodes and their connections as arcs.
Model Evaluation Internal validation was performed using 10-fold cross-validation for BD and ID training sets. In 10-fold cross-validation the data is divided randomly into 10 equal sized parts. One part is 1 The number of k-combinations of n-elements is computed by the binomial coefficient (n¦k) =n!/k!(n-k)!. To evaluate the model predictivity, several performance metrics were considered in addition to correctly classified instances  [32]. Those metrics, as seen in Table  3 , included the percentage of correct classification for positive rates -toxic (sensitivity, SENS) and non-toxic In our study, ACC was used to be consistent with Choi et al. [1]. ACC is synonymous with a confusion matrix that shows the number of instances classified correctly as well as A C C E P T E D M A N U S C R I P T details of misclassifications, or complemented with SENS and SPEC  [31].
Missing Values One of the biggest challenges in predicting NPs toxicity is lack of proper and sufficient information. The lack of standard protocols for NP in vitro toxicity studies  [80, 81]  results in heterogeneity of data in the literature and, consequently missing values in compiled datasets. Choi, Ha  [1]  provided a complete dataset having filled the gaps of physicochemical and quantum data in their original source by elaborately combining manufacturer data, theoretical values, and literature data  [16]. In order to investigate the robustness of the models when values are missing, we generated versions of the dataset removing values artificially  [82, 83]. Three additional training datasets were created from the S2NANO data training set removing values randomly. Probabilities of missing values (0.
Copeland Index Copeland's method or Copeland's pairwise aggregation method ranks candidates which are ordered by the number of pairwise victories (true predictions of toxicity), minus the number of pairwise defeats (false predictions of toxicity)  [84] [85] [86]. Each classifier was ranked according to its performance on different datasets (BD, ID), validation processes such as internal (INT), external (EXT) and realibility (REL) and performance metrics such as The performance value from each metric individually, except DP, were discretized into bins of equally frequenced values. DP bin-ranges were defined according to their evaluation meaning as explained in 2. 4. Bins were used to filter out small differences that the Copeland Index would enhance. Each bin got a score in the same range (0.
5 to 1 being an appropriate scale) so that different metrics can be compared and aggregated. This score was used to calculate the win-loss scores of the classifier for each case (e. g. BD-INT-SENS). The win-loss score, SW-L, is the count of wins minus the count of losses when comparing the classifier with the other classifiers. Copeland Index is the sum of win-loss scores for all datasets and validations for one classifier  [87] [88] [89]. In total, 20 values of SW-L (five validation processess for two different datasets) were added per classifier to provide the Copeland Index (Figure  2 ). Reliability validation was performed only with BD in order to compare the classifiers with Choi, Ha  [1]. A C C E P T E D M A N U S C R I P T.
Discussion In  In most existing databases several issues such as, data curation (quality and completeness), common ontology, format of datasets, missing values (grouping approaches) etc. , are not handled. Basei, Hristozov  [92]  provides an overview on databases, highlighting the importance of data curation showing that more efforts are needed to develop reliable datasets. In this scope, an integrated science-based framework under the GRACIOUS project (https://www. h2020gracious. eu/) is currently being built. The curation system of GRACIOUS is targeting existing data that will go through quality assessments. A dataset derived from the S2NANO database was selected for this study. S2NANO integrates data form different sources and handles the aforementioned data quality issues making the dataset fit for testing models.
The specific dataset is curated (quality data and completeness are assessed and handled) and has been used before for predicting toxicity using ML models  [1]. Results of the latter served as a reference point for testing and demonstrating our methodology. Using datasets from the GRACIOUS project database when available or testing the methodology on existing high quality datasets of different case studies including multiple toxicological outcomes  [10, 17, [93] [94] [95] , are the next steps to demonstrate the validity and.
Random Forest (RF) and Neural Network (NN) have the best performance compared to the other base classifiers. techniques have exploited large datasets and computation power to create breakthroughs in diverse fields from facial recognition to genomics. More recently, ML techniques have been applied to nanotoxicology with very encouraging results. In this study, categories of ML classifiers (rules, trees, lazy, functions and bayes) were compared using datasets from the Safe and Sustainable Nanotechnology (S2NANO) database to investigate their performance in predicting NPs in vitro toxicity. Physicochemical properties, toxicological and quantummechanical attributes and in vitro experimental conditions were used as input variables to predict the toxicity of NPs based on cell viability. Voting, an ensemble meta-classifier, was.
a) apply classification models investigating how appropriate they are for each analysis scenario (datasets, validation, performance metrics), b) generate integrated classifiers to increase the overall performance in all scenarios, c) develop an index capable of prioritizing the most appropriate classifier for NPs toxicity and d) investigate the performance of base and ensemble classifiers with different ratios of missing data. We demonstrate the efficacy of base and ensemble classifiers using different datasets such as balanced and imbalanced, diverse validation processes (internal, external and reliability validation) and several performance metrics (accuracy, sensitivity, specificity, F1score, discriminant power). A Copeland Index based on the above results ranks the most appropriate classifier, either base or ensemble.
The proposed index is recommended for scientists analysing the toxicity of NPs exploiting ML classifiers. The suggested index can be used as a tool to identify which base classifier combinations achieve more accurate classification predictions. The index clearly ranks the optimum classifier with the highest score. In this study we used a dataset with NPs physicochemical properties, toxicological and quantum-mechanical attributes and in vitro experimental conditions as inputs to predict the toxicity based on cell viability in vitro studies.
value 0 or 1 was used to indicate the absence or presence of the originally nominal attributes. The S2NANO dataset was randomly split into a training (60%) and a validation set (40%) for the classification training and evaluation. The S2NANO training dataset had a class imbalance problem, as it is dominated by experiments of nontoxic class [44]. We balanced the dataset by adjusting the relative frequency of toxic/non-toxic instances by resampling the training dataset applying the Synthetic Minority Oversampling Technique (SMOTE), a supervised instance algorithm that oversamples the minority instances using k-nearestneighbor (kNN) [45]. The training dataset comprised of 343 rows (Imbalanced Dataset, ID) and reached 575 rows (Balanced Dataset, BD) after applying SMOTE. Later operating SMOTE the training dataset had equally toxic and non-toxic instances.
For 8 classifiers in triads the number of possible combinations is (8¦3)=8!/3!(8-3)!=56. 72. Kreher, L. , D. , and D. Stinston, R. ,, Combinatorial algorithms : generation, enumeration, and search, ed. C. Press. 1999, London: CRC Press. withheld and the algorithm is trained on the remaining nine-tenths; then the error rate is calculated on the holdout set. Finally, the 10 error estimates are averaged to yield an overall estimate. This process aims to reduce randomness of the results and conduct a robust evaluation. The validation data set (40% of S2NANO data) was used for external validation [4]. The reliability dataset was used to test how the models built on data of different origin perform in new situations, predicting the classification of instances from different studies.
and LR have also very poor performances. LIR has a very robust behaviour competing with RF. Similar conclusions can be drawn for the external validation, with SIR having the best accuracy, preforming better than RF. Values were randomly removed in this study while in real-world datasets missing values correspond to omissions or limitations in the experimental protocols and designs. The robustness of the model performance on missing data should be tested in real datasets where no information for systematically missing for specific attributes instead of randomly distributed values in the dataset.
robustness of our concept. Classifiers used previously on the data can be included in the implementation and participate in the ranking process. In this study, we demonstrated the performance of many classifiers on extracts of a specific toxicological dataset. Ensembles of those classifiers were compiled based on combined performance. Major voting ensemble classifiers were applied in the same dataset and were found to outperform base classifiers. The classifiers were ranked according to a Copeland Index based on all basic criteria of the analysis, such a datasets, validation processes and performance metrics. Bagging, reducing the variance of the classifiers, results to better results in tree classifiers and all ensemble classifiers; the best ensembles included one accurate base classifier with simpler "satellite" classifiers that predict the outlying instances.
Classifier comparison on datasets with artificially removed values demonstrated that ensemble classifiers based on voting algorithm where still able to perform acceptably even when most values in a dataset were missing. -Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon & Hyung-Gi Byun, S2NANO data and S2NANO reliability validation data, Nature Scientific Reports, 2018.
Supplementing each other, IBk and RF are always the two best base classifiers in all best triad combinations. RF is already a strong bagging classifier able to predict the majority of instances. Combinations of a base classifiers with RF and IBk, predict specific instances that are unpredicted by the best base classifiers. In our case, LIR, that is, majority voting among a bagging classifier, RF, and two lazy classifiers, IBk and LWL, gives the best results, as reflected also in the Copeland Index. These results point towards combining more robust with simpler classifiers and using bagging (RF and Voting)[90]. did not performed well with the reliability dataset. For the three datasets, the best triad shows an improvement in classification performance in all cases, especially in external validation.
 Random Forest (RF) and Neural Network (NN) have the best performance compared to the other base classifiers. techniques have exploited large datasets and computation power to create breakthroughs in diverse fields from facial recognition to genomics. More recently, ML techniques have been applied to nanotoxicology with very encouraging results. In this study, categories of ML classifiers (rules, trees, lazy, functions and bayes) were compared using datasets from the Safe and Sustainable Nanotechnology (S2NANO) database to investigate their performance in predicting NPs in vitro toxicity. Physicochemical properties, toxicological and quantummechanical attributes and in vitro experimental conditions were used as input variables to predict the toxicity of NPs based on cell viability. Voting, an ensemble meta-classifier, was.
5 Conclusions 5 robustness of our concept. Classifiers used previously on the data can be included in the implementation and participate in the ranking process. In this study, we demonstrated the performance of many classifiers on extracts of a specific toxicological dataset. Ensembles of those classifiers were compiled based on combined performance. Major voting ensemble classifiers were applied in the same dataset and were found to outperform base classifiers. The classifiers were ranked according to a Copeland Index based on all basic criteria of the analysis, such a datasets, validation processes and performance metrics. Bagging, reducing the variance of the classifiers, results to better results in tree classifiers and all ensemble classifiers; the best ensembles included one accurate base classifier with simpler "satellite" classifiers that predict the outlying instances.