dataesr/scientific-paragraphs-categorization huggingface dataset:
STEP 2: Data visualization and processing Even though the provided script is customized to our dataset, with minor adjustments it enables the complete analysis of any dataset. 1. Go back to the R script. 2. Load the dataset by running the script lines entitled "raw data loading". 3. To visualize data, the provided script ( Fig. 10 A ) uses the {ggplot2} package to display the ratio curves colored according to the experiment date and replicate. H 2 O 2 addition time is indicated by vertical bars. The facet_grid function is used to split the response curves as a function of the 1 st qualitative variable: H 2 O 2 concentration ( Fig. 10 D ). These settings can directly be modified in the script (see https://ggplot2. tidyverse. org/ for more details). 4. If the time base is not the same for all cells, run the part of the script enabling to interpolate your data ( Fig.
10 B ) using the spline function from the {stats} package (R Core Team 2019). The number of knots can be set to the number of data points ( = 131 in the provided example). 5. Check that the interpolation does not noticeably alter the shape of the curves by plotting the interpolated ratio curves ( Fig. 10 E ). 6. Spread this interpolated dataset using the spread function of the {tidyr} package. This step changes the dataset from a long table, in which all the cells are one above the other ( Fig. 9 E ), to a wide table with one cell per column ( Fig. 10 C ). 7. Export the spread dataset as an excel file in the working directory, using the write. xlsx ({xlsx}) function ( Fig. 10 C ). STEP 3: Choosing kinetic parameters 1. Save the provided MATLAB script in the same folder as the excel file generated at previous step.
2. Open the script in MATLAB. 3. Adjust the following variables at the beginning of the script ( Fig. 11 A ): -The imported file name if required -The number of time points before H 2 O 2 addition -The smoothing parameters if required Indeed, the curves are smoothed before the kinetic parameters are calculated. In our case, the slope of the curve increased sharply upon addition of H 2 O 2. We therefore chose to smooth separately the two parts of each curve (before and after H 2 O 2 addition) to take this into account. For this reason, the number of time points before H 2 O 2 addition is required. 4. Run the script by clicking "run" ( Fig. 11 A ). The following parameters, summarized in Fig. 11 C , are calculated on smoothed curves: -The maximum ratio, rmax -The time of the maximum ratio in minutes, tmax -The slope at the maximal inflexion point in minutes -1 , noted infl -The time of the maximal inflexion point in minutes, tinfl -The ratio value at the maximal inflexion point, noted rinfl -The lag time, in minutes, noted tlag , is estimated by extending the tangent at tinfl to the initial baseline ( y = rinit, where rinit is the median ratio before H 2 O 2 addition).
Except the maximal ratio, these parameters cannot be estimated for flat curves, as observed in absence of treatment. The tmax, infl, tinfl, rinfl and tlag parameters are calculated for curves whose maximum ratio is arbitrarily at least two-fold the initial ratio and reached within 30 min. This filter can directly be customized in the script. 5. The calculated parameters are automatically saved as an excel spreadsheet in the working folder ( Fig. 11 B ). 6. From the R script, run the script lines entitled "Analysis of the parameters calculated by MATLAB". This part of the script merges the kinetic parameters with the complete dataset and plots each parameter as a function of qualitative variable 1 (H 2 O 2 concentration in our example). As our samples are small (less than 30 individuals per H 2 O 2 concentration tested), we chose to superimpose boxplots and dotplots to visualize the parameters as a function of H 2 O 2 concentration.
STEP 4: End-point ratio analysis 1. Run the R script section entitled "Analysis of the final median ratios". The median ratio between 60 and 65 min is calculated for each curve and this new variable, named r_end, is added to the dataset ( Fig. 11 C ). Final ratios are then plotted as a function of H 2 O 2 concentration ( Fig. 13  ). We observed that the final ratio increased with H 2 O 2 concentration, reaching a plateau at 40 μM H 2 O 2. Therefore, in our cell model, HyPer ratio returned to its basal state more efficiently for cells treated with 10 μM H 2 O 2 than for cells treated with 40 and 75 μM H 2 O 2. These results are consistent with results of step 3 ( Fig. 12  ).
STEP 5: Assigning a global score to each curve using functional principal components analysis In this section, each curve is replaced by a single score using functional Principal Components Analysis (fPCA). This approach enables to preserve almost the entire variability of the initial dataset, allowing a fast and accurate comparison of the responses obtained under the different experimental conditions. Moreover, in contrast to the previous kinetic parameters, this approach can be used for Fig. 14. Functional principal components analysis of the dataset. A: Script for the functional data object generation from the spread dataset. Adjust the numbers framed in red according to your dataset and desired smoothing level. B: Visualization of the dataset 1 st two principal variation modes. The 1 st harmonic (left) is a pharmacokinetic-type function whose score quantifies the amplitude of the variation and the overall height.
This is the overwhelming mode of variation in our dataset as it accounts for 99% of its variability. The score on the second harmonic (right) indicates whether the curve tends to have a high maximum ratio and a low final ratio. This mode of variation only accounts for 0. 4% of the variability in our dataset, so we chose to neglect it. C: Curves colorization according to their score on harmonic 1. The lower the score of a curve, the closer its color is to light orange. The higher the score of a curve, the closer its color is to dark blue. The higher the score of a curve, the higher it is and the greater its amplitude. D: Curves score on the 1 st harmonic of fPCA after cells exposure to 0, 10, 40 and 75 μM H 2 O 2 (Kruskal-Wallis and Mann-Whitney tests). all curves including flat shaped curves.
According to fPCA principle, each curve is decomposed into a sum of components called harmonics representing their main variation modes. 1. Go to the R script section entitled "functional PCA". 2. Load both the stacked and spread datasets by running the appropriate script lines. 3. Convert the spread dataset into a functional data object by executing the corresponding section of the script ( Fig. 14 A ). A spline basis is created, whose parameters need be adjusted: rangeval must be set to the time range and nbasis to the number of spline functions of order norder that are summed to fit the curves. The higher the values of these two parameters, the lower the smoothing will be ( Fig. 14 A ). 4. Perform the functional PCA and plot the types of variation represented by the 1 st two harmonics ( Fig.
14 B ). The 1 st harmonic, namely the function type that is able to best describe the set of curves, is a pharmacokinetics-like function with a lag time followed by a growing phase and by a slow decrease ( Fig. 14 B, left ). This harmonic accounts for 99% of our dataset variability. The 2 nd harmonic only accounts for 0. 4% of the variability and can thus be neglected ( Fig. 14 B ). 5. Plot all ratio curves colored according to this score ( Fig. 14 C ). The plot unambiguously shows that the higher the score of a curve on the first harmonic, the higher the overall ratio: cells with an overall high cytoplasmic H 2 O 2 concentration have a high score and cells with an overall low cytoplasmic H 2 O 2 concentration, e. g. cells treated with 0 μM H 2 O 2 , have a low score. 6. Plot the score on the 1 st harmonic of fPCA as a function of the qualitative variable of your choice (the H 2 O 2 concentration in our example) ( Fig.
STEP 6: Principal components analysis of all parameters To get an overview of the above kinetic parameters and scores, we performed a Principal Components Analysis (PCA) on all these variables. PCA is a procedure enabling to simplify a dataset by replacing an important number of variables by a limited number of new variables. These new variables are called axes and are computed to account for the variability of the original dataset  [34]. 1. Go to the R script section entitled "PCA with every qualitative parameter". 2. The individuals factor maps ( i. e. the projection of each cell in the plane formed by the 1 st two axes) and the correlation circle ( i. e. the projection of the variables in the same plane) are automatically displayed. 3. The {factoextra} package enables to plot these data in a more esthetic and customizable way.
Individuals and variables were colored according to the H 2 O 2 concentration ( Fig. 15 A ), and to the quality of their representation (cos 2 ), respectively ( Fig. 15 B ). The closer to 1 the cos 2 value is (green vectors), the better the corresponding variable is represented in the plane formed by the 1st two axes of the PCA. Variables with a cos 2 close to 0 (red curves) are poorly represented. 4. Individuals and variables factor maps can also be superimposed ( Fig. 15 C ). From these plots, we note that the 1 st two axes account for 78. 39% of the total variability. The projection of the variables onto the plane formed by these two axes is thus a good image of the complete dataset Fig. 15. Excepting the ratio at the inflexion point, all the variables are well represented on this projection ( Fig.
Functional PCA was introduced to simplify each curve by a single score yet retaining variability. This dataset can be analyzed using parametric or non-parametric statistical tests, depending on data size and distribution. Final PCA on all quantitative variables allows a global overview of the conclusions, through a clear and visual presentation of the results. As previously mentioned, our free method mainly uses open source softwares (FIJI and R) and associated packages, allowing easy access to all. Although some parameters were calculated using the MATLAB software, calculation can be performed using any curve analysis free software like Scilab or spreadsheet program like LibreOffice Calc. Moreover, scripts or macros are easily editable and intelligible thanks to many embedded commentaries.
Softwares used for macro development and subsequent data analysis -Macro development We used the FIJI distribution of ImageJ to develop the macro, that can be executed either with the simple ImageJ or with FIJI. FIJI is an open source project based on the ImageJ software  [38] [39] [40]. -R, RStudio and associated packages R is a powerful language for statistical computing and graphics based on an open source environment highly compatible with every operating system  [41]. The Rstudio interface was used for programming  [42].
Acknowledgments The authors wish to thank  Pierre Adenot  ( MIMA2, Microscopy and Imaging Facility for Microbes, Animals and Foods , https://doi. org/10. 15454/1. 55723482100 07727E12  ) and  Dorothée Baïlle  ( Institut Curie ) for helpful advice in setting fluorescent microscopy. This research was funded by regular funding from the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE)  and  Institut Curie. E. L. is supported by a doctoral research fellowship from the  Doctoral School ABIES, AgroParisTech. F. J. , D. L. , H. R. , V. B. , and D. M. are supported by  INRAE  and by the  Fondation pour la Recherche Médicale  (Equipe FRM  DEQ20150331689 ). L. V. is supported by the  Institut National de la Santé Et de la Recherche Médicale (INSERM).
DNA Sequencing We cultivated isolates on a Lowenstein-Jensen medium for 5 months. We performed DNA extraction as previously described  (16). We sequenced genomes by using either MiSeq or HiSeq sequencer (Illumina, https:// www. illumina. com) with Nextrera XT DNA preparation kit or Ion Torrent S5XL technology with IonXpress Plus Fragment library kit (Life Technologies, https://www. thermofisher. com/us/en/home/brands/life-technologies. html). We submitted generated reads to the National Center for Biotechnology Information Sequence Read Archive (BioProjectID no. PRJNA499075). (See additional methods in Appendix 1, https://wwwnc. cdc. gov/EID/article/26/3/19-0573-App1. pdf. ).
Variant Detection and Maximum-Likelihood Phylogenetics After checking quality with FastQC version 0. 11. 7  (17) , we performed a quality trimming by using Trimmomatic version 0. 36  (18)  and read mapping and SNP detection by using Snippy version 3. 2  (19). We used the Burrows-Wheeler Aligner version 0. 7. 12  (20)  with default parameters to map clipped read-pairs to the Agy99 reference genome (Genbank accession no. CP000325) and to the pMUM001 plasmid (accession no. BX649209)  (21). Agy99 is the only annotated strain from Africa available for M. ulcerans species. By using the alignment of core genome SNPs of the first 179 genomes, we generated a maximum-likelihood phylogenetic tree with PhyML 3. 020120412 using the general time-reversible model  (22). For the second set of sequencing of 29 strains, we generated another tree by using the alignment of all the sequenced genomes.
Statistical Analysis We tested for statistical significance by using either Fisher exact test or 1-way analysis of variance. We validated the model by calculating accuracy and Matthews correlation coefficient on the confusion matrices (Appendix 1). We performed data analysis and visualization in R 3. 4. 4 (26)  with the ade4, plot3D, seqinr, and ggplot2 packages and data-intensive computations by using a GenOuest computer cluster (https://www. genouest. org).
Presentation of Selected Strains and Areas We analyzed 179 strains isolated from patients diagnosed with and treated for BU at CDTLUB during 2007-2016. Patients originated from regions of Ouémé (111 [52%]) and Plateau (47 [26%]) in southeast Benin and from Ogun State in southwest Nigeria  (21 [12%] ). The proportion of genomes selected was in accordance with the geographic origin of patients visiting CDT-LUB (Figure  1 ). Moreover, the 158 bacterial strains selected from Benin (Ouémé and Plateau) were representative of the epidemiologic data of BU in patients in Benin (Appendix 2 Tables 1, 2, https://wwwnc. cdc. gov/EID/article/26/3/19-0573-App2. xlsx).
Statistical measurements of the performance of the multinomial models on a real dataset of newly sequenced genomes, Benin and NigeriaNigeria (Ogun State) 86. 25% 0. 371** †Sum of the true positive and true negative divided by the total population. ‡Contingency matrix method of calculating the Pearson product-moment correlation coefficient (27). Its value ranges from -1 to +1 with +1 a perfect positive prediction model, 0 no better than random prediction, and -1 being a model where predicted class is in total contradiction with observed class. Values indicated the strength of the relationship (*, strong positive relationship; **, moderate positive relationship).
Acknowledgments We thank  Laurent Marsollier  for proofreading and advice,  Tim Stinear  and  Jessica Porter  for support with DNA sequencing, all staff at CDTLUB-Pobè in Benin who performed diagnosis and treatment, and  David Goudenege  and  Vincent Procaccio  from the  Biochemistry and Genetics Department at Angers Hospital, France. This work was carried out with support from the  GenOuest bioinformatics platform. Funding for this work was provided by  Agence Nationale de la Recherche  (grants nos. ANR 11 CEPL 00704 EXTRA6MU  and  17-BSV3-0013-01 ),  Fondation Raoul Follereau, INSERM , and  Agence Nationale de la Recherche Région Pays de la Loire. Use of patients' data was approved by the  CDTLUB Institutional Review Board , the national BU control authorities of Benin (approval no. IRB00006860 ), and the.
We have proposed in  [4]  a correct-by-construction method (named Heñcher) dedicated to the construction of critical embedded control systems. This method, based on Event-B, is intended to guide step by step the specifier or the engineer to drive its development from requirements to concrete software, defining abstract models, and refining them in a systematic way. The current article is an extension of that previous work. We extend that work in two main directions: i) we extend the proposed method with an assistance tool dedicated to help the users to apply the Heñcher method step by step and to build quickly the preliminary Event-B models of her/his control systems. The tool is designed as a companion tool of the already existing Event-B frameworks such as Rodin or Atelier B for which we provide input models; ii) the Event-B models of the case study are now totally proved using the Rodin tool.
Experimentation with Rodin and statistics The main modelling steps of the Landing Gear System case study have been completely achieved; that is the modelling from very abstract level to more concrete ones, the refinements and the decomposition into hardware and software parts. Applying a rigorous method as we defined, was very helpful to master the complexity of the case study. The specifications are available online  2. The current version of the Event-B models is deliberately partial as we chose to focus on representative events instead of being exhaustive. We have used the version 3. 4 of Rodin in the last experimentations; the statistics on Proof Obligations are given in Table  2. The proofs discharged using the interactive prover are related to the structural refinement and specifically they are related to the binding invariants.
Using the Rodin tool we have modelled and refined the Landing Gear System until to take account of the main requirements about software part, physical part and some of the specific properties as explained in Section 3. 3. After several steps of vertical refinements we have a complete model of the Landing Gear system. For the experimentation purpose, using the Event-B decomposition technique  [22] , we decompose the last model of the system into two parts corresponding to the hardware part and the software or control part. The so-called A-style decomposition, based on the separation of events through different machines, and implemented as a Rodin plugin  [15] , was successfully used in this step. Managing very large models requires a rigorous slicing and several small steps of refinements. This is the reason why we have introduced many refinements, but it is still not enough, the slicing can be of finer grain.
In the same way, the other specific (reachability and non functional) properties listed in P n are integrated into the refined model M 1 using the algorithm described in Fig. 14. Assistance is provided to the user for selecting the specific properties to be integrated into the abstract model. The remaining steps of the method, that is the refinement of the control software (Step 6. 1 of the method) and the refinement of the controlled environment (Step 6. 2) of the methods should be achieved within Rodin (or a dedicated Event-B framework). However we propose some assistance as described in the following section. Assistance in the vertical refinement process (Step 4, 5) There is no specific tool for the Step 4. Refining the global abstract model. This steps consists in describing the behaviour of the devices involved in the case study.
RT 3 Pattern based substitutions and automatic refinements. Some plugins propose to add (de-)composition features into Event-B/Rodin. The Feature composition plugin  [17]  enables one to merge Event-B machines and their seen contexts with the facilities to highlight multiple declarations of variables or events and to resolve conflicting elements. RT 4 Collaboration: working in parallel, versioning, decision traceability. There exists a plugin called "team-based development"  [19]  which allows Event-B models to be stored in a SVN repository. That plugin allows to share the Event-B specifications but not the proof effort. The Modularisation plugin 5  may also help here by allowing separate development of part of the a global system as sub-modules and then by combining them. Indeed, this plugin enables one to weave together modules composing a model so that they work on the same global problem.
We provided preliminary assessment in  [4] ; in this paper the case study is dealt with more details and we covered all the steps of the proposed method. The Landing Gear case study is representative of large systems involving the control and the interaction between software and physical parts. Before going into the presentation of the assistance tool that we have developed, we emphasised the motivations and the requirements for the needed tools. Among the identified tool requirements, we detailed a tool to assist the specifiers in starting the modelling process, and to assist them during the development process with Rodin. Yet our Heñcher assistance tool is a prototype developed as a standalone web application. We experimented with Rodin but, it can be used not only for Rodin. We have studied other available Rodin plugins that can help in putting into practice or to implement the remaining identified tool requirements.
Finding the Best 7-round Differential Characteristics for E 0 and E 1 As a starting point, we look at the setting where E m covers 2 rounds and search the best characteristics over 7 rounds for E 0 and E 1. To find these, we use the two-step strategy described in  [GLMS18] : • In the first step, we abstract all the nibble differences by Boolean variables (if a nibble is active then its associated Boolean value is 1, else it is 0) and we look for the truncated differentials with the minimum number of active Sboxes. We implement this step using a high-level modeling language called MiniZinc [NSB + 07]. MiniZinc models are translated into a simple subset of MiniZinc called FlatZinc, using a compiler provided by MiniZinc. Most existing constraint programming solvers (including SAT solvers and MILP solvers) have developed FlatZinc interfaces (there are currently fifteen solvers with FlatZinc interfaces).
This article presents a dataset obtained from the deployment of an IEEE 802. 15. 4g SUN (Smart Utility Network) single-hop network (11 nodes) in a large industrial scenario (110,044 m 2 ) for a long period of time (99 days). The dataset contains ∼11 M entries with RSSI (Received Signal Strength Indicator), CCA (Clear Channel Assessment), and PDR (Packet Delivery Ratio) values. The analyzed results show a high variability in the average RSSI (i. e. , between -82. 1 dBm and -101. 7 dBm) and CCA (i. e. , between -111. 2 dBm and -119. 9 dBm) values, which is caused by the effects of multi-path propagation and external interference. Despite being above the sensitivity limit for each modulation, these values result in poor average PDR values (i. e. , from 65. 9% to 87. 4%), indicating that additional schemes are needed to meet the link reliability requirements of industrial applications.
More recently, in  [9, 10] , the authors studied the suitability of IEEE 802. 15. 4g in smart building and environmental monitoring scenarios, respectively. However, none of these studies have conducted a deployment in an industrial scenario to evaluate the real-world performance of these technologies for a long period of time to demonstrate their benefits and drawbacks. In this paper, we fill this gap by presenting a dataset obtained from the deployment of 11 nodes using the IEEE 802. 15. 4g SUN modulations for 99 days in a warehouse with an area of 110,044 m 2. The dataset contains 10,710,868 measurements of RSSI (Received Signal Strength Indicator), CCA (Clear Channel Assessment), and PDR (Packet Delivery Ratio) values. The results show large variability in the average RSSI (i. e. , between -82.