dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported by  NIH  grant # 1U01EB021238  and  NSF  grant  1917206  to D. G. ,  NIH  grant #  U01CA200059  to C. S. D. C and D. G. ,  NIH  grant #  5R01GM126045-04  to R. C. and by grant # 2019-198155  (5022) awarded to C. S. D. C. by the  Chan Zuckerberg Initiative DAF , an advised fund of  Silicon Valley Community Foundation , as part of their  Imaging Scientist Program. D. S. was funded in part by  NIH/NCI  grants  U54CA209988  and  U2CCA23380. C. M. B. was funded in part by grant # 2020-225398  from the  Chan Zuckerberg Initiative DAF , an advised fund of  Silicon Valley Community Foundation. R. N. was funded by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) grant number  Ni 451/9-1 MIAP- Freiburg. C. S. S. was supported by the  Netherlands Organisation for Scientific Research (NWO) , under  NWO START-UP  project no.
740. 018. 015  and  NWO Veni  project no. 16761. T. G. is a member of  RTmfm  network and IMAG'IC core facility is supported by the  National Infrastructure France BioImaging  (grant  ANR-10-INBS-04 ) and  IBISA  consortium. S. W. -P. was funded by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) project -ID 267205415 -SFB 1208 , project INF. The  UNC Neuroscience Microscopy Core  (RRID: SCR_019060 ) is supported, in part, by funding from the  NIH-NINDS Neuroscience Center Support  Grant  P30 NS045892  and the  NIH-NICHD Intellectual and Developmental Disabilities Research Center Support  Grant  P50 H103573. M. S. I. was supported by grant number  2019-198107 , from the  Chan Zuckerberg Initiative DAF , an advised fund of  Silicon Valley Community Foundation.
• Project name: Micro-Meta App • Project home page: https://github. com/WU-BIMAC/MicroMetaApp. github. io Documentation: https://micrometaapp-docs. readthedocs. io/en/latest/index. html • Executable available at: ○ Desktop application (Javascript Electron): https://github. com/WU-BIMAC/MicroMetaApp-Example Microscope. JSON, Settings. JSON and associated image data file (Figure 5 use case): Electron/releases/latest • Source code available at: ○ Desktop application (Javascript Electron): https://zenodo. org/record/4750765 ○ Data-portal integratable application (Javascript React): https://zenodo. org/record/4751438 • https://doi. org/10. 5281/zenodo. 4891883.
e-BPRIM development and deployment The fourth phase of the AMME lifecycle focuses the development of a modeling tool. In the following, we will thus concentrate on the realization of a multi-view modeling tool for e-BPRIM, called adoBPRIM. For this purpose, we utilize the meta-modeling platform ADOxx  [2]. ADOxx provides an integrated development environment for developing graphical modeling tools. A comparative analysis of ADOxx and other meta-modeling platforms can be found in  [46, 72, 77]. The decision to choose ADOxx was motivated as follows: -ADOxx is a multi-user platform that provides a metamodel and model repository based on a relational database. -ADOxx enables the definition of modeling languages, their graphical representations, and required mechanisms & algorithms without any advanced programming experience.
In other scenarios, for example, the synthesis of the Risk Model, a meaningful initial positioning is proposed by using default horizontal and vertical offsets for all concepts. Although automated positioning might appear trivial or even unimportant, such functionality has a strong influence on efficiency and ease of use of the modeling tool. The last phase of the AMME lifecycle focuses the deployment of a modeling tool. In the e-BPRIM case, the prototypical implementation will be transitioned from the development environment into a stand-alone tool, which will be provided as an installation package through the OMiLAB 1.
Fig. 9  Excerpt of the implemented e-BPRIM modeling language (syntax and notation) 2 A comprehensive evaluation of the original BPRIM modeling method can be found in  [46, 70, 71]. 1 e-BPRIM project space within OMiLAB [online]: https:// austr ia. omilab. org/ psm/ conte nt/ BPRIM, last visited: 08. 10. 2020.
Acknowledgements The authors would like to show their gratitude to the  Ecole d'ingénieurs d'Informatique et de Sytèmes d'Information pour la Santé (ISIS) de Castres , which is an Engineering Departement of the  Institut National Universitaire Champollion , who participated to evaluate the two version of our adoBPRIM tool. We would also like to thank  OMiLAB members  for assistance with the ADOxx platform and for providing a collaborative space on their OMiLAB portal.
However, data processing and chemical characterization remain difficult despite recent developments. Chemical reference standards and spectral data enable us to confirm the structure of observed characteristics, but reference standards, especially metabolites and by-products, are rarely available for thousands of characteristics in non-target analysis (NTA) and suspect screening analysis (SSA)  (McEachran et al. , 2018) , and having these thousands of standards can also represent a considerable cost. Since the appearance of HRMS, the interest in improving confidence in the identification of small molecules increase, such as pesticides, from putative positive samples based on detection to confirmation  (Bade et al. , 2015a; Schymanski et al. , 2014). SSA studies are those in which observed but unknown features are compared against a database of chemical suspects to identify plausible hits.
Dataset Initially, the dataset included 843 RTs of pesticides collected from the article of  Wang et al. (2019). Ultra-high-performance liquid chromatography (UHPLC) gradient conditions, column temperatures, mobile phases, columns, and instruments used to generate the data presented in detail in  Wang et al. (2019). Three free software applications have been used in order to compute the pesticide's MD. These applications are free, can calculate a large number of descriptors and are widely available. The ACD software (Advanced Chemistry Development, Toronto, ON, Canada) was used to calculate LogP and LogD. The Toxicity Estimation Software Tool (TEST, Cincinnati, OH, USA) was used to compute Hy, Ui, IB, BEHp1, BEHp2, GATS1m, and GATS2m. The rest of the molecular descriptors (1834 MD) were calculated using the ChemDes online platform (http://scbdd.
com/ chemdes/). Once the MDs were computed, the dataset was cleaned in order to remove constant and missing values (Figure  1 ). Indeed, constant values are useless in order to develop QSRR models and missing values make learning and prediction impossible. The missing values are due to the softwares and their inability to generate, depending on the molecules, the MD. At the end of this curation process, 792 pesticides, their RTs, and 1545 MDs remained in the final dataset. The dataset containing the MDs for each pesticide was then ready to build QSRR models (Table  S1 ).
QSRR model development The dataset constituted previously and containing the pesticides (792), their MDs (1545), and RTs was used in order to select among them the best MDs inherited from the literature review (Model 1). Importantly, in order to find the best set of MDs, a literature review was done by selecting the most recent and pertinent papers with the following criteria: the prediction of retention times measured by RPLC and for pesticides or similar compounds (pharmaceuticals, veterinary drugs). At the end of this literature review, seven articles, their MDs, and models were selected (shown in Table  1  with their performances) and compared in term of performance measured principally through the percentage of error, which is the ratio between the root mean square error (RMSE) divided by the maximum retention time measured on the last eluted compound.
a4 the water solubility described by the logarithm of water solubility in mg/L at 25 C. Regardless of the MD dataset used, the following procedure was used. The MD datasets, and the corresponding values of pesticide RTs, were divided into three subsets: a training, a test and a validation dataset (Figure  1 ). The training dataset was composed of 445 pesticides chosen randomly, their corresponding MD (input) and experimentally measured pesticide RTs (output). The test dataset was composed of 148 pesticides chosen randomly, their corresponding MD (input) and experimentally measured pesticide RTs (output). The training and a test set have a size ratio of three to one, respectively. The validation dataset was composed of 198 randomly chosen pesticides never used before, their corresponding MDs, and experimentally measured pesticide RTs.
Initially, the training dataset was used to train the DNN, here an MLP, by tuning the hyper-parameters through a gridsearch and a crossvalidation process, where the training dataset was divided in five equal size sub-datasets (cv ¼ 5). The hyper-parameters tuned were: -Number of hidden layers constituted each by a number of neurons equal to the number of MD used as inputs Geron (2017): from 1 to 5 hidden layers constituted each by 16 neurons -The activation function among: ReLu, tanh and logistic -The alpha value: 10 or 1 -The solver function among: Adam, SGD and Lbfgs. The data were standardized (mean-centered) in order to accelerate and enhance the training and the predictions, and also to simplify interpretation of the importance of the features/MDs. All the models were developed with Python 3.
Model validation The validation of QSRR models is probably the most significant and critical part of model evaluation in order to prevent overfitting in particular. For this reason, we carried out the validation step using the validation dataset never used for the training and testing parts  (Noreldeen et al. , 2018)  (Figure  1 ). The coefficient of determination (R 2 ) and the RMSE were used to evaluate and compare the models extracted from the literature review and were measured on the test set (Table  1 ). These parameters were also used for the models developed in this study in order to determine the error between the experimental and predicted RTs in the QSRR models, especially in terms of their ability to be generalized to new pesticide substances with unknown RTs. The lower the RMSE and the higher the R 2 value, the better the model.
The number of layers and neurons is also called the "depth" and "width" of DNN, respectively. Larger numbers of layers and neurons mean deeper and wider DNNs, which often have more powerful fitting ability and can achieve better accuracy on the prediction. However, too many layers and neurons can lead to an overfitting problem, which is an accurate prediction on the training set but poorer prediction on the test set. It is crucial for the DNN to be able to generalize on a dataset never seen before. For this last reason, we split the dataset into a training, test and validation datasets, in order to evaluate the capacity of the DNN to generalize. The model development process is hence to develop an optimum architecture of the DNN with an appropriate fitting ability. In this study, our DNN was composed of an input layer, several hidden layers, and an output layer.
The RMSE and the R 2 alone are not sufficient to determine which MD set and QSRR model is the most efficient. For this reason, we calculated the percentage of error (Eq. (  3 )), which was not done in the recent article of  Parinet (2021)  where all the references selected, and their corresponding MD datasets were applied directly on the pesticides dataset in order to make the prediction of RT. The percentage of error was between 5. 4% and 9. 4%. The lowest value for the percentage of error was obtained for the QSRR developed by  Bade and colleagues (2015)  on 544 emerging contaminants and by the use of 16 MDs (nDB, nTB, nC, nO, nR04-nR09, UI, Hy, MLogP, ALogP, LogP, LogD) and a DNN (MLP). Based on these results, we retained for our QSRR development, the Bade and colleagues (2015) MD set and the MLP as the best ML algorithm to use (model 1) with a percentage of error equal to 5.
4%. Then, we used the MD listed by  Bade and colleagues (2015)  on our dataset and through a MLP (Bade-MLP -Model 1) as described before in the text. By this approach we got a R 2 on the training and test set equal to 0. 95 and 0. 90, respectively (Table  2 , Figure  S1A  & S1B). The RMSE obtained on the training and test set were equal to 0. 43 and 0. 63. On the validation set, never used for the learning and optimizing process, the R 2 was equal to 0. 82 and the RMSE equal to 0. 67 (Table  2 , Figure  S1C ). These past results are similar to those obtained by  Parinet (2021)  with the McEachran 3 MDs, on the validation dataset, and by the use of SVM and MLP as machine learning algorithms where the R 2 were between 0. 85-0. 89 and the RMSE between 0. 64-0. 69, respectively. The percentage of error obtained thanks to these molecular descriptors and with a MLP was around 6%, which is close to the 5.
4% got by  Bade and colleagues (2015)  on their compounds. To develop the most efficient QSRR model specifically for pesticides, we compared the performances obtained for Model 1 (Bade-MLP) with those of Model 2 to 8 (no a priori approach). The performances of Model 2 (Lasso-MLP) applied on our pesticide dataset gave R 2 on the training and test set equal to 0. 60 and 0. 50, respectively (Table  2, Figure S2A  & S2B ). The RMSE obtained on the training and test set were equal to 1. 19 and 1. 27. On the validation set, the R 2 was equal to 0. 49 and the RMSE equal to 1. 36 (Table  2 , Figure  S2C ). The percentage of error obtained thanks to these molecular descriptors and with a MLP was around 12%, which is twice as much as Model 1 (Bade-MLP) with 6% on the same compounds. The performances of Model 3 (Pearson-MLP) applied on our pesticide dataset gave R 2 on the training and test set equal to 0.
79 and 0. 79, respectively (Table  2 , Figure  S3A  & S3B). The RMSE obtained on the training and test set were equal to 0. 86 and 0. 83. On the validation set, the R 2 was equal to 0. 78 and the RMSE equal to 0. 88 (Table  2 , Figure  S3C ). The percentage of error obtained thanks to these molecular descriptors and with a MLP was around 8%, which is less good as Model 1 (Bade-MLP) with 6% on the same compounds but much better than Model 2. The performances of Model 4 (RFE-MLP) applied on our pesticide dataset gave R 2 on the training and test set equal to 0. 69 and 0. 60, respectively (Table  2 , Figure  S4A  & S4B). The RMSE obtained on the training and test set were equal to 1. 04 and 1. 15. On the validation set, the R 2 was equal to 0. 63 and the RMSE equal to 1. 16 (Table  2 , Figure  S4C ).
The percentage of error obtained thanks to these molecular descriptors and with a MLP was around 10%, which is less good as Model 1 (Bade-MLP) with 6% on the same compounds, and less good as Model 3. The performances of Model 5 (PCA1-MLP) applied on our pesticide dataset gave R 2 on the training and test set equal to 0. 75 and 0. 61, respectively (Table  2 , Figure  S5A  & S5B). The RMSE obtained on the training and test set were equal to 0. 94 and 1. 12. On the validation set, the R 2 was equal to 0. 64 and the RMSE equal to 1. 14 (Table  2 , Figure  S5C ). The percentage of error obtained thanks to these molecular descriptors and with a MLP was around 10%, which is less good as Model 1 (Bade-MLP) with 6% on the same compounds, and quite similar to Model 4. The performances of Model 6 (PCA2-MLP) applied on our pesticide dataset gave R 2 on the training and test set equal to 0.
42 and 0. 34, respectively (Table  2 , Figure  S6A  & S6B). The RMSE obtained on the training and test set were equal to 1. 44 and 1. 47. On the validation set, the R 2 was equal to 0. 38 and the RMSE equal to 1. 50 (Table  2 , Figure  S6C ). The percentage of error obtained thanks to these molecular descriptors and with a MLP was around 13%, which is less good as Model 1 (Bade-MLP) with 6% on the same compounds, and the worst model developed with performances quite similar to Model 2. The performances of Model 7 (PCA3-MLP) applied on our pesticide dataset gave R 2 on the training and test set equal to 0. 61 and 0. 53, respectively (Table  2 , Figure  S7A  & S7B ). The RMSE obtained on the training and test set were equal to 1. 18 and 1. 24. On the validation set, the R 2 was equal to 0. 56 and the RMSE equal to 1.
26 (Table  2 , Figure  S7C ). The percentage of error obtained thanks to these molecular descriptors and with a MLP was around 11%, a little better than Model 5 but which is less good as Model 1 (Bade-MLP) with 6% on the same compounds. The performances of Model 8 (PCA4-MLP) applied on our pesticide dataset gave R 2 on the training and test set equal to 0. 82 and 0. 75, respectively (Table  2 , Figure  S8A  & S8B). The RMSE obtained on the training and test set were equal to 0. 79 and 0. 91. On the validation set, the R 2 was equal to 0. 76 and the RMSE equal to 0. 93 (Table  2 , Figure  S8C ). The percentage of error obtained thanks to these molecular descriptors and with a MLP was around 8%, better than all the models developed thanks to the PCA approach and similar in term of performances to Model 3, but still less good as Model 1 (Bade-MLP).
Conclusions We compared a literature review approach to a no a priori approach in order to select, by diverse strategies, the best set of molecular descriptors among 1545 MD in order to predict, through a QSRR model, the RPLC retention times of 792 pesticides. The literature review approach yielded the best results when DNN was used as the ML algorithm, with an R 2 of 0. 82 and an RMSE of 0. 67 min (Model 1) on the validation set. However, it could be useful in future research to test some other no a priori selection strategies in order to determine new MD datasets and also to consider reducing the number of MD with the goal to simplify the models while obtaining good predictions.
Materials and Methods We handled experiments with long HRV time series obtained from two biological databases (rats and humans) and exhaustively applied CMSE, RCMSE, MMSE, MFE, CMFE, RCMFE, and MMFE to different sizes of segments. The minimum error compared to the original long-term MSE was considered as the maximum accuracy. The corresponding multiscale entropy algorithms are briefly detailed in the following subsections. For all methods, consider a time series u with N samples defined as u(1), u(2),. , u(N). We define m as the length of vectors (patterns) to be compared and r as the tolerance accepted between corresponding points within the vectors. This tolerance is defined as a percentage of the original time series SD. There are Nmδ + 1 template vectors x m (i) for {i | 1 ≤ i ≤ N -mδ + 1}, where x m (i) = {u(i + kδ) : 0 ≤ k ≤ m -1} is a vector with length m and δ is the delay considered between samples.
Dataset Heart rate variability (HRV) series from rats and humans were obtained from previous studies  [19, 25]. The first group of ECG data was recorded in 18 healthy Wistar rats. The recordings were performed in the Cardiovascular Physiology Laboratory of Ribeirão Preto Medical Schools, University of São Paulo. Briefly, the rats had their ECG recorded for approximately 1 h (40 to 80 min) at baseline conditions. Computer software (LabChart, ADInstruments, Sydney, Australia) was used to create RR series from ECG recordings, sampled at 2 kHz. All RR series were visually inspected for artifacts and corrected when necessary. Since the time series's length varied from 15,892 to 32,333 points, all RR series were truncated to 15,892 points. The second group of HRV series consisted of 12 healthy human individuals, obtained from the Physionet MIT-BIH Normal Sinus Rhythm database digitized at 360 Hz per signal lead  [26].
The 12 ECG recordings were selected randomly from the database. The RR series were calculated using the ann2rr tool from the WFDB Physionet package, which uses the recordings' beat annotations to calculate the RR intervals. Only normal RR intervals were considered, that is, the intervals between two successive normal beats. Eventually, all RR series were truncated to 15,892 samples so that the series of rats and humans had the same length. The recording period of all series ran from 8 a. m. to 10 p. m.
Moreover, to assess the cost-effectiveness of fuzzy-based MSE variants, we measured each algorithm's average computation time. The analysis was performed on a desktop computer equipped with an Intel Core i7 930@2. 8 GHz processor and 16 Gb of RAM. To guarantee the isonomy of the results, all the tests were performed with the MATLAB software (The MathWorks, Inc. Natick, MA, USA) and the maxNumCompThreads = 1 command so that all methods used a single CPU. The average time consumed to process three randomly selected human HRV series is reported as a segment size function (from 400 to 12,000 points). The fuzzy exponent n adopted in this experiment followed the equation previously found for the choice of the best exponent according to the segment size (x)  [19] : n = 0. 82 + 0. 10 exp(-3 × x/10 4 ).
Results The accuracy of CMSE, RCMSE, MMSE, CMFE, RCMFE, MMFE, and MFE were evaluated as the error compared to the MSE calculated using the full-length series. Figure  1  shows the accuracy of MFE obtained with both rats and humans HRV series. The descriptive measures of the MSE for both the human and animal datasets can be found in a previous paper  [19]. The top left plot shows the mean squared errors as a function of the rat dataset's segment size. We illustrate different error curves for different fuzzy exponents ranging from n = 0. 8 to n = 1. 5. The magnification of the errors' curves for short segment sizes are shown at the bottom left corner, and the minimum error for each segment size is shown on the right side of the magnification plot. The mean squared errors are shown as a segment size function for the humans' dataset at the top right corner.
For segments sized up to 13,000 points, the MFE, CMFE, and RCMFE curves are superimposed because these methods have similar results. Mean squared error for all considered approaches, i. e. , CMSE, RCMSE, MMSE, CMFE, RCMFE, MMFE, and MFE. For fuzzy entropy-based approaches, only the minimum error is shown, obtained with the optimal fuzzy exponent for each segment size. Results are shown for both rats (left) and human (right) database. Note that the errors are calculated regarding the MSE of full-length time-series, i. e. , 15,892 beats. Figure  6  shows the optimal fuzzy exponents of fuzzy-based MSE variants for each segment size. These exponents were utilized to calculate the minimum error curves for multiscale fuzzy entropy-based variants (see Figure  5 ). The curves were fitted to exponential functions, which can be employed to find the best fuzzy exponent of those datasets according to the time series length.
Note that for the HRV series from rats, the optimal fuzzy exponent increases with the series length, decreasing the HRV series of humans. For the sake of comparison, Figure  7  shows the mean square error of MFE using the alternative fuzzy function exp(-0. 6931 × (d/r) n ). The errors are illustrated for the fuzzy exponent ranging from n = 1. 3 to n = 3. 0 (rats) and from n = 2. 0 to n = 5. 5 (humans). Results show that MFE with this alternative fuzzy function also presents dependence on the segment size, similar to the original fuzzy function (Figure  1 ). However, the values of optimal exponents for each segment size are markedly different from the original fuzzy function, and the best n increases with the segment size for both rats and humans HRV series. Best fuzzy exponents found for each segment size in both rats and humans HRV datasets.