dataesr/scientific-paragraphs-categorization huggingface dataset:
ReGuard ReGuard  [90] , is a tool dynamic for detecting re-entrancy vulnerabilities through fuzzing. It automatically generates flagged reentrant errors. ReGuard takes solidity code or bytecode as inputs and then parses it into an intermediate representation (IR). The intermediate representation (IR) of source code is an abstract syntax tree (AST) and the bytecode is a control-flow graph. ReGuard has three main components: Contract Transformer, Fuzzing Engine, and Core Detector. ReGuard transforms the intermediate representation (IR) to C++ through the Contract Transformer. The fuzzing engine generates the random bytes iteratively using runtime coverage information as feedback. ReGuard interprets the bytes as transaction requests sent to the contract. Then, the tool executes the contract and dumps runtime trace of analysis-relevant operations.
Some papers explain how runtime verification can avoid the traditional complexity problems in model checking and theorem proving. But due to the cost and gas execution of smart contracts every time we use runtime verification as well as its dynamic behavior, it is actually not an efficient practical method for smart contracts. In table 2, for F*  [37] , the preliminary results and the suggested approach show that it cannot handle all the Solidity syntaxes. The design of Zeus  [30]  is similar to VERISOL  [29]  in that it translates Solidity to an intermediate language and uses SMT based solvers to discharge the verification problem. Seven out twelve tools use the static analysis and the remainder tools use dynamic analysis. F* and VERISOL can take the code or the bytecode of smart contracts as inputs.
As shown in table  3 , seven out of thirteen tools use the symbolic execution method. Four methods use the dynamic analysis, and the remainder tools use static analysis. Most of the tools take the smart contracts as bytecode level excepted Smartcheck  [73]  and ReGuard  [73] (ReGuard supports both solidity and bytecode level). The table  4  summarizes the vulnerabilities and related attacks that can be detected by the mentioned tools. More than half of the tools in table 4 (eight tools) can detect the re-entrancy vulnerability. To this day, and based on the existing tools the verification is successfully done only for simple smart contract. Thus a future research direction could include the development of approaches to address the complex smart contracts such as those that contain external calls or loops.
Non-dimensional variables resulting from the equations and time scaling were used. The results of model simulation with dimensionless variables were converted into physical variables. The simulation was conducted for a balanced motor. Thanks to that, it was possible to use the Steinmetz model to verify the obtained results. An original procedure for inverting the inductance matrix was developed. It allowed to present the equations in a form facilitating the solution of model equations in MATLAB-Simulink system. The operating diagram of the analyzed model is much smaller and simpler than the one presented in  [4, 13]  and allows to conduct simulation experiments to study power quality disturbances and dynamic mechanical loads of the motor.
The Model of the Motor in Simulink The motor electric and mechanical Equations (  44 ) and (  46   The physical parameters of the modeled motor are presented in Table  1. Physical variables are converted into dimensionless Simulink input variables and simulation output variables into physical variables of the object in the MATLAB script, which controls the simulation experiment. The electric currents and their time derivatives are used in the model in the form of twodimensional signal vectors. These signals lines are presented in the diagram as thick lines. The equations of the electrical part are integrated in the upper part of the model diagram. The mechanical part is solved in the lower part of the diagram. In the middle there is a block of the built-in MATLAB function, in which the equations of the state co-ordinates derivatives are formulated.
These derivatives for the electrical part are determined from (44) and for the mechanical part from (46) from dimensionless variables. The simulation was performed under zero initial conditions. The results of the simulation are placed in MATLAB workspace. Then they are used to draw up the waveforms of the model. The diagram of the model is very compact and clear. The return to physical quantities of the model variables is performed after the simulation. The rescaling calculations have been completed and then the figures are drawn. The simplicity of the final Simulink model ought to be emphasized. It results from the application of the transformation of the circuit equation to (44) and the use of vector-matrix notation. The simplicity of the model may be estimated by comparing it with the models presented in  [4, 13, 17].
The obtained model is also more convenient for the arrangement of simulation experiments. The physical parameters of the modeled motor are presented in Table  1. Physical variables are converted into dimensionless Simulink input variables and simulation output variables into physical variables of the object in the MATLAB script, which controls the simulation experiment. The electric currents and their time derivatives are used in the model in the form of two-dimensional signal vectors. These signals lines are presented in the diagram as thick lines. The equations of the electrical part are integrated in the upper part of the model diagram. The mechanical part is solved in the lower part of the diagram. In the middle there is a block of the built-in MATLAB function, in which the equations of the state co-ordinates derivatives are formulated.
Grape downy mildew (GDM) is a major disease of grapevine that has an impact on both the yields of the vines and the quality of the harvested fruits. The disease is currently controlled by repetitive fungicide treatments throughout the season, especially in the Bordeaux vineyards where the average number of fungicide treatments against GDM was equal to 10. 1 in 2013. Reducing the number of treatments is a major issue from both an environmental and a public health point of view. One solution would be to identify vineyards that are likely to be heavily attacked in spring and then apply fungicidal treatments only to these situations. In this perspective, we use here a dataset including 9 years of GDM observations to develop and compare several generalized linear models and machine learning algorithms predicting the probability of high incidence and severity in the Bordeaux region.
More specifically, we develop different statistical and machine learning models to predict the risk of high GDM incidence or severity on leaves and bunches at the end of the season, in untreated Bordeaux vineyards. The models tested are generalized linear models  [43] , regularized regression models (LASSO)  [44] , and two machine learning algorithms, i. e. gradient boosting  [45]  or random forests  [46]. These models are implemented with three sets of inputs, namely field scouting observations, climate inputs, and both types of inputs. Model performances are assessed by cross-validation using a large dataset of observations collected during 9 years in 153 vineyards in the Bordeaux regions. The most accurate models are then used to evaluate the potential reduction in the number of fungicide treatments achieved when model outputs are used to trigger fungicide applications.
Data Grape downy mildew (GDM) incidence and severity data were collected in several vineyards located in the Bordeaux region by the French vine and wine extension service (Institut Français de la Vigne et du Vin, IFV). Data have been collected with the agreement of the winegrowers. Different vineyards were included in the dataset each year. A site-year is a unique combination of vineyard site and year. The total number of site-years included in the dataset was 153. Each monitored site-year consisted in an untreated row of vines including from 6 to 165 plants and further referred to as "plot". Each untreated row was surrounded by two other untreated rows, to ensure that they were not unintentionally sprayed with fungicide. In the monitored central row, weekly visual inspections were performed on grape stocks, leaves and bunches in order to assess disease incidence and severity.
The proportion of vine stocks, leaves and bunches displaying symptoms (incidence) and the average percentages of leaves and bunches necrotic area (severity) were recorded. In total, 1 to 19 visual inspections were conducted in each vineyard. Observations were conducted from budburst (i. e. week 10, early March) until at least bunch closing (i. e. week 29, mid-late July) or stopped when the proportions of infected vine stocks and bunches were close to 100%. For each plot, the level of GDM (i) leaf incidence, (ii) bunch incidence, (iii) leaf severity and (iv) bunch severity at the end of the season were derived from the last epidemiologic observations. Among the plots, the median GDM incidence in the dataset was 16. 7% and 21. 4% on leaves and bunches, respectively. The median GDM severity reached 3.
0% and 4. 0% on leaves and bunches, respectively. Contrasted outbreaks were observed between 2010 and 2018 seasons. For example, the disease incidence on leaves ranged from 0 to 54. 8% in 2011 and ranged from 2. 2 to 100% in 2018  (Fig 1A). Dates of GDM onset were estimated by analyzing incidence data on vine plants, i. e. the proportion of infected plants in a plot. GDM onset was defined as the first week in which the proportion of infected vines stocks exceeded 1%. The number of weeks between the first week of the year and this date was estimated for each plot by survival analysis in order to deal with censored data  [47]. Censored GDM onset dates were found in 95 plots (right censored: 38. 9%, left censored: 38. 9% and interval censored: 22. 1%). Censored data were imputed by a semi-parametric survival model  [48]  including the average rainfall between March and June as covariate  [47].
In plots where few observations were collected, the imputed dates of disease onset were close to the median onset date of the dataset, which corresponded to week 23, i. e. early-mid June. Between 2010 and 2018, GDM onset date varied across years. For example, less than 25% on the monitored plots displayed symptoms by week 23 (i. e. early-mid June) in 2010, 2011 and 2016, whereas 100% of the plots were infected at this date in 2014, 2015 and 2018 (Fig  1B ). Climatic variables were computed from the SAFRAN database, produced by the French national meteorological service (Météo-France). SAFRAN data covers France in the form of an 8 by 8 km grid  [49]. For each plot and each year, mean amount of rainfall (in mm day -1 ) and mean temperature (in ˚C) were calculated in March, April, May and June from the weather data of the grid cell including the considered plot (Fig  2 ).
Models predicting occurrence of high levels of disease incidence and severity Different models were considered to calculate the probability to reach a high level of contamination at bunch closing stage, i. e. higher than the median value reported in the dataset. Four types of output were considered in turn, i. e. , incidence on leaves, incidence on bunches, severity on leaves and severity on bunches. For each output, four types of models were developed in the version 3. 5. 1 of the statistical software R  [50]  and compared to predict occurrence of high level of GDM (i. e. , higher than median): generalized linear models (binomial-logit models), further denoted as GLM  [43] , binomial LASSO regression  [44] , random forest  [46]  and gradient boosting  [45]. Depending on the considered output, the predictions returned by these models correspond to predicted probabilities of high levels of incidence or severity of GDM on leaves or on bunches.
Here, this model was fitted with the version 2. 0 of the R package glmnet  [52]  and the most relevant model inputs were selected by cross-validation using the R function cv. gmlnet. Random forest and gradient boosting are ensemble learning algorithms; they are based on the combination of multiple learning simple algorithms to improve prediction performance. Random forest is a bagging method developed by Breiman  [46]. The algorithm builds an ensemble of independent deep decision trees (500 in our study) from bootstrapped samples. Deep trees have the properties to have low bias but high variance, and when combined together, produce an output with lower variance. In the case of gradient boosting, an ensemble of successive shallow trees are built (100 in our study), such as each new tree predicts the residuals of the previous one.
Model assessment and sensitivity analysis The ability of the fitted models to predict occurrence of high level of GDM was assessed by year-by-year cross validation using the area under the ROC curve as a measure of classification performance  [55, 56]. Here, we used a year-by-year cross-validation to account for the strong "year" effect on the disease intensity. As data collected during the same year in different plots are not independent, it was safer to remove all the data collected a given year at each cross-validation step. This is equivalent to a group-wise cross-validation based on 9 groups corresponding to the 9 years of data included in the dataset. A separate ROC analysis was conducted for each output and each model separately. The 153 plots were divided into two subgroups depending on whether the final disease observation (Y) was above the regional median value (Yt), computed for the 2010-2018 period, or less than or equal to this threshold.
The sensitivity of the probability of high disease severity to temperature is consistent with the partial dependence plots obtained for Each graphic shows the effect of precipitation change (from -15% to +15%) for a fixed level of temperature increase (from +0˚C to +4˚C) on predicted probability that GDM severity on leaves will be higher than regional median at the end of the season. Probabilities are forecasted by a gradient boosting algorithm that includes all climatic features. Each boxplot represents the distribution of the probability values over the vineyard plots of our dataset; the shaded boxplot corresponds to initial precipitation and temperatures (precipitation and temperature kept unchanged compared to actual conditions) and the median probability obtained with this scenario is indicated by a red dotted line.
This work received funding from the  French Ministry of Agriculture (CAS DAR ,  SMART-PIC  project), the  Institut Carnot Plant2Pro  (project L-i-cite, see https://www. instituts-carnot. eu/en/carnot-institute/plant2pro) and from the  Bordeaux Vine Council (CIVB). This work is part of the # DigitAg  project ( ANR-16-CONV-0004 , see https:// www. hdigitag. fr/en/who-are-we/). The work of D. M was partly funded by the  CLAND Institute of Convergence  ( 16-CONV-0003 , see https://cland. lsce. ipsl. fr/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgements The authors wish to thank  Laura Cobus ,  William Lambert ,  Paul Balondrade  and  Serge Meimon  for fruitful discussions. Funding: The authors are grateful for the funding provided by  Labex WIFI (Laboratory of Excellence  within the  French Program Investments for the Future)  ( ANR-10-LABX-24  and  ANR-10-IDEX-0001-02 PSL* ). A. B. acknowledges financial support from the  French "Direction Générale de l'Armement" (DGA). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreements nos. 610110  and  819261 , HELMHOLTZ* and REM-INISCENCE projects, respectively). K. I. acknowledges financial support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No.
Introduction Oxygen saturation measured in blood (S a O 2 ) has been assessed non-invasively by pulse oximetry for decades. Pulse oximetry is a relatively easy technique that acquires photoplethysmographs (PPG), which are important bio-signals for physiological assessment. This technology, employed in intensive care units as well as in ambulatory procedures, enables the monitoring of the respiratory condition of the patients and the evaluation of the state of their cardiovascular systems over time. Among its advantages, this technique requires few low-cost components and can give results using a minimal setup  [1]. Its ease of use and the derived related patient data (respiratory rate, blood pressure, arterial stiffness, etc. ) open the way for advanced applications regarding neurovascular coupling  [2] [3] [4] , thus making pulse oximetry an essential tool in hospital routine for rapid patient diagnosis.
Experimental Setup The experimental setup is illustrated in Figure  5a. Each device under test (DUT) is placed at the tip of the optical fiber using a customized holder to ensure a precise axial alignment with the core of the optical fiber FC-UVIR1000-2-ME (Avantes, Inc. , Apeldoorn, The Netherlands). The light sources are driven with a stable current source, following the conditions in the respective datasheet, if mentioned; if not, the standard forward current I F = 20 mA is used as the driving parameter value. The emitted photons are collected by the AvaSpec-ULS2048XL-USB2 spectrophotometer. This equipment has a spectral resolution of 0. 6 nm and covers the spectral range from 333 nm to 1100 nm, which suits our purposes for the near infra-red (NIR) range. Figure  5b  shows the companion software Avasoft used to configure the acquisition, with an example of an acquired spectrum presented, plotted along with the wavelength with the visible spectrum in the background for reference.
S5F ). Next, we performed NMR analysis of cell supernatants (media) collected from MCF-7/Cas9 and MCF-7/ CD73-KO cells to evaluate the catabolism of the supplemented NAD + and NAD + -precursors. The cell supernatants were collected after 24 hrs of incubation with vitamin B 3 precursors as well as the FK866 (FK) inhibitor. Overall, the distribution of precursors between the Cas9 (control) and CD73-KO cell lines showed high similarity. Specifically, supplementation with NR or NR+FK, showed ~50% of the NR being metabolized into NAM, while supplementation with NMN and NMN+FK gave about 40-50% NAM, with NR being the second largest component and a small portion of NMN present in all conditions, with the exception of MCF-7/Cas9 treated with NMN alone. These data suggest robust conversion of NMN to NR and further, NR to NAM, in both CD73 positive and negative cell lines.
Nicotinamide adenine dinucleotide (NAD + ) was purchased from Sigma (St. Louis, MO). Nicotinamide riboside (NR), nicotinamide (NAM), nicotinamide mononucleotide (NMN) were synthesized as described previously  16, 51. All NAD + precursors were dissolved in water right before the experiments (NR) or dissolved and stored as aliquots at -80 °C (NAM, NMN, NAD + ). CometChip analysis. The CometChip Platform is described in detail in our earlier report  62. The CometChip Platform is comprised of disposable 30-micron CometChips (glass-backed CometChip cassettes with 1 mm agarose and micro-patterned micro-wells each at 30-micron width), a well-former that creates the 96-wells when assembled, the CometChip Electrophoresis System (CES) and the Comet Analysis Software (CAS), all described previously in detail  62.
13 MHz for protons (9. 39 Tesla). TopSpin 3. 2 (Bruker BioSpin) was used for all NMR spectral acquisition (ns = 1024) and pre-processing and the automation of sample submission was performed using ICON-NMR (Bruker BioSpin). All samples were automatically shimmed and their acquisition time was 1 hr 18 minutes. 1 H NMR and data collections. The sensitivity of the 400 MHz instrument is low compared to NMR instruments currently used for similar experiments. However, the increased number of scans, the use of an internal standard, manual peak peaking and manual measurement of the individual peak intensity aimed to compensate to a large extent for this limitation. Upon acquisition of the 1 H-MR spectra and referencing to the acetone peak with a relative intensity of 6 (6 equivalent H/molecule of acetone), peaks which corresponded to NAM, NR, NMN and NAD + were manually identified and the relative intensity of these peaks to that of acetone were recorded.
5 minutes at 37 °C under gentle shaking. Then, the reaction was stopped by addition of the Green Malachite reagent (Phosphate assay kit, Sigma) and production of inorganic phosphate was quantified by reading the optical density at 630 nm on a plate reader (Tecan Sunrise). A standard phosphate concentration range (0-50 µM) for normalizing raw data was included and the results correspond to the average of three independent experiments (GraphPad Prism 8 was used for analyzing and plotting the data).
The array strip was immersed into hybridization mixture and incubated in the hybridization station at 45 °C for 16 h. After hybridization, the strip was washed and stained in the GeneAtlas Fluidics Station using the GeneAtlas Hybridization, Wash, and Stain Kit (#900720; Affymetrix), and the intensity of each hybridized probe was generated using the GeneAtlas Imaging Station. Raw. cel files from the Human U219 Array Strip were analyzed using the "affy" package in R Bioconductor. The raw data were normalized and summarized using the robust multichip average method (RMA). At this point, each gene is represented by one or more probe sets. The probe sets expressing <75 units for all samples were filtered out for the genes that have other probe sets that are being expressed (>75 units). The selective filtering was performed to avoid getting rid of any gene altogether.
Acknowledgements We thank the members of the  Sobol laboratory  for discussions and comments on the manuscript. This work was supported by  National Institutes of Health  Grants  CA148629 ,  ES014811  and  ES029518  (to R. W. S. ) and the  Abraham A. Mitchell Distinguished Investigator Fund  (to R. W. S. ). R. W. S. is an Abraham A. Mitchell Distinguished Investigator.
belari JU2817 and M. okuensis JU3147 (n = 6) or M. belari JU2817 and M. microbursaris PS1179 (n = 6)) also died during the first cell cycle (Figure  S4 ). Thus, the vast Fig. 4  Reduction of male body size and reproductive apparatus in auto-pseudogamous Mesorhabditis species. The raw data are in Table  S4. For each measure, between 8 and 20 individuals were analyzed. Sexual species are shown in black and pseudogamous species are in blue. M. sp: M. spiculigera AF72, M. l: M. longespiculosa DF5017, M. be: M. belari JU2817, M. p: M. paucipapillata JU3003, M. si: M. simplex JU2864, M. o: M. okuensis JU3143, M. bi: M. bifurcata JU2902, M. m: M. monhystera JU2855. a Boxplot of body size for females (in white) and males (in grey) for each strain. b Drawing of the male tail, to scale, in a sexual and a pseudogamous strain, in ventral view.
Sequencing and phylogenetic tree reconstruction ITS2 and 28S rDNA loci were amplified by PCR on single worm using the following primers: ITS2_forward 5'GCTGCGTTATTTAACGAATTGCARAC-3′, ITS2_reverse 5′-CACTTTCAAGCAACCCGAC-3′ (reverse), 28s_forward 5'AGCGGAGGAAAAGAAACTA-3′ and 28S_reverse 5'ACGATCGATTTGCACGTCAG-3′. Sequences are accessible on Genbank, accession number MT710227-MT710292. Using MEGA7 (Molecular Evolutionary Genetics Analysis version 7. 0 for bigger datasets  [36] ), we obtained sequence alignment (with Muscle); pair-wise distance calculation and phylogenetic tree reconstruction (with UGPMA method). The evolutionary distances were computed using the Maximum Composite Likelihood method, and are expressed in number of base substitutions per site (out of 1595 positions). All ambiguous positions were removed for each sequence pair (pairwise deletion option).