dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion & Conclusion have presented YJIT, an open source JIT compiler built inside CRuby. This compiler is based on an LBBV JIT compiler architecture, and, although it is still in early stages of development, our empirical data suggests that it succeeds in delivering fast warmup times and significant performance improvements over both the CRuby interpreter and MJIT. YJIT is nearly 100% compatible with existing Ruby code and is able to match CRuby on all the tests in the Shopify and GitHub backend test suites. It has very fast and predictable warmup characteristics, delivering speedups on the first iteration, even for short-running benchmarks. Speedups from 15% to 19% over the CRuby interpreter were achieved on sizeable benchmarks based on real-world software. Given that there are multiple obvious improvements that we can make to the design of YJIT, we have a clear path forward in terms of directions to pursue to enable YJIT to deliver much bigger speedups in the future.
First, the quality controls and reference plasmas were run during this study. After successful completion of these runs, the plasma samples of the healthy donors were thawed. Thawing occurred by putting the small plasma tubes into a warm water bath at 37°C for 5 minutes. Immediately thereafter, the plasma tubes were gently mixed and TG was measured. The fluorescence measurement occurred every 15 seconds at an absorption and emission wavelength of 377 and 450 nm, respectively. The samples were run in duplicate, and the ST Genesia embedded software analyzed the data automatically and gave a mean result. For each run and each trigger, a specific freeze-dried normal human plasma, STG-RefPlasma, was run in parallel to samples, as also described earlier. 3 This reference plasma came with specific assigned ranges provided on a barcoded flyer and helped to normalize results.
3, 22 If we then compare this ETP% inhibition to the data obtained in this study, we indeed observed an overall ETP inhibition of 48. 3%. The ST Genesia is based on its precursor the CAT assay. They both use the same thrombin-sensitive substrate, but one of the major differences between the CAT assay and the ST Genesia is the method used for calibrating the samples. For the CAT assay, a thrombin calibrator is used (α2-macroglobulin-thrombin complex) that is added to every sample. Calibration of samples on the ST Genesia occurs in two steps: (i) Once daily a calibration curve is obtained using purified human thrombin in buffer with the fluorogenic substrate and also the activity of a fluorophore product is measured in the same buffer; and (ii) every plasma sample is spiked with a fluorophore product and measured.
By taking the ratio between the fluorophore product measured in the plasma sample and in the buffer system, the plasma sample can be calibrated correctly. The CAT Note: The reference ranges are obtained from the whole population of healthy donors excluding outliers. Data are expressed as raw and normalized values of the 2. 5% -97. 5% range for all three reagents. Abbreviations: ETP, endogenous thrombin potential; TM, thrombomodulin. assay remains a very important tool in the hemostatic research field, as it can be modified to investigate a specific part of the coagulation cascade, for example, by using a specific activator or by including the presence of the platelets. 30, 31 The ST Genesia also offers the possibility of using an in-house activator, although this could result in the same problems with the other TG assays: no standardization of the reagent and therefore the data will not be comparable between laboratories.
Currently, contradicting data are published, whereby Tan et al  36  did not find differences in TG, while other research groups did. 37, 38 From our previous TG survey, we have learned that not all research groups store their plasma samples at -80°C but also at -20°C. 19 Therefore, it could be interesting for future studies to investigate the effect of long-term sample storage at -80°C and -20°C, as well as the freeze-thaw effects on the reproducibility of the ST Genesia. 22 Douxfils et al 3 tested the effect of long-term sample storage at -70°C with the STG-DrugScreen and concluded that samples were stable up to at least 11 months. As this was not tested with the other reagents, it thus generates another study limitation. To conclude, we determined the reference ranges of the ST Genesia for the reagent kits available at the moment (STG-BleedScreen, STG-DrugScreen, and STG-ThromboScreen).
When data on the degradation of a chemical substance have been collected in a number of environmental media (e. g. , in different soils), two strategies can be followed for data evaluation. Currently, each individual dataset is evaluated separately, and representative degradation parameters are obtained by calculating averages of the kinetic parameters. However, such averages often take on unrealistic values if certain degradation parameters are ill-defined in some of the datasets. Moreover, the most appropriate degradation model is selected for each individual dataset, which is time consuming and then requires workarounds for averaging parameters from different models. Therefore, a simultaneous evaluation of all available data is desirable. If the environmental media are viewed as random samples from a population, an advanced strategy based on assumptions about the statistical distribution of the kinetic parameters across the population can be used.
After normalisation to reference temperature and moisture, the individual results such as degradation rate constants, half-lives or formation fractions of transformation products are then averaged to obtain so-called modelling endpoints that serve as input in environmental exposure models. These endpoints are considered representative for the degradation in a relevant environmental compartment such as the agricultural soils in EU member states. The variability of the results of the individual experiments is only taken into account if it can be attributed to a covariate such as soil pH. Other observed variabilities encountered in the environment are assumed to be covered by the standard scenarios used in the exposure scenarios  [7] [8] [9] [10]. Here, we show that the simultaneous evaluation of all datasets available for a certain type of media, as opposed to the separate evaluations described above, can make the endpoints more robust and can reduce the complexity of the model selection process.
When degradation is comparatively slow, i. e. , only little degradation occurs within the maximum incubation time, some experiments yield degradation rate constants very close to zero with large confidence intervals. If such parameters are tested for significant difference from zero, they often fail the test. Parameters affected by this problem are often rate constants for the second, slower degradation phase in biphasic degradation models or the rate constants for slowly degrading transformation products. Regulatory guidance for pesticide risk assessments in the EU recommends not considering such ill-determined rate constants when calculating geometric mean rate constants over the different soil types. However, ignoring such ill-determined rate constants in the average over individual datasets (e.
g. , for a metabolite (  [11] , p. 58/59)) may introduce a bias towards faster degradation. On the other hand, using them as obtained from nonlinear regression often leads to geometric mean degradation rate constants that appear to be unrealistically low in view of all available data. Therefore, it has been proposed to use default values for such datasets  [2, 3]  or to use surrogate values derived as a conservative percentile of the values derived from other datasets that do not suffer from this problem (  [12] , p. 12). Both approaches effectively discard information contained in the affected datasets and are therefore not satisfactory. Another problem associated with the separate evaluation of individual datasets is the necessity to select a model for each of these, which leads to a lengthy and, in combination with the expert judgement involved, unpredictable endpoint selection procedure.
A simultaneous evaluation of the available datasets using explicit assumptions about the underlying statistical distribution of the kinetic parameters can address both problems: ill-defined degradation rate constants in a subset of the datasets and the complexity of model selection. Here, we describe how such simultaneous evaluations can be achieved. Evaluations of synthetically generated data illustrate the advantages mentioned above for the case that the distributional assumptions about the parameters are known to be correct. Case studies of experimental data published in the course of EU pesticide risk assessment show how these assumptions allow us to avoid ignoring potentially relevant information or the use of somewhat arbitrary default parameters.
Theory and Methods Each individual degradation dataset consists of a series of residue measurements performed after dosing a specific chemical substance to defined experimental systems with one or more compartments in which degradation can take place. Each measured residue is characterised by the time after dosing and the residue type. The residue type is the chemical identity of the residue (parent compound or one of the transformation products), possibly differentiated by the compartment in which it was observed. Replicate measurements are often performed in order to reduce the influence of natural variability on the results. The measured residues are called observations in the following. Individual datasets are obtained under specific conditions, for example, in specific soils. For each individual dataset y i , let y (k) ij denote the jth observation for residue type k, measured at time t ij.
For other types of degradation parameters, such distributional assumptions have yet to be established. Degradation parameters that are bound to lie between zero and one such as formation fractions  [3]  cannot reasonably be assumed to follow a normal distribution or a log-normal distribution. After, e. g. , a logit transformation, the assumption of a normal distribution is tenable, and this is used here for such parameters. For degradation models where a chemical compound has several transformation products, the fractionation of the degradation pathway can be seen as compositional data, as the formation fractions sum up to one. For such cases, the isometric logratio transformation was used  [15, 16]. This transformation, constructed to avoid collinearity, was introduced as the default transformation to the mkin package almost ten years ago and can therefore be said to work well in practice.
Degradation Model Solutions For the degradation models describing the decline in only one residue type, analytical solutions are well-known  [3]. For some selected degradation models involving one parent compound and one transformation product, analytical solutions such as the one for the DFOP-SFO model (shown in the Supplementary Materials) were obtained using the Computer Algebra System Maxima  [17]. In all other cases, numerical solutions were obtained using the lsoda algorithm for stiff and non-stiff ODE systems  [18, 19]  as made available through the deSolve extension package  [20]  for the R software  [21].
Separate Evaluations by Nonlinear Regression When each group of degradation data is evaluated separately, the statistical model is fully described by Equation (2) or, alternatively, using the reparameterisation shown above, by Equation  (5). Depending on the parameterisation of the degradation model, a parameter vector ψ i or φ i is estimated for each individual dataset, with associated standard errors. These parameter estimates can be found by maximising the likelihood L i of ψ i given the data y i , which for error models based on the normal distribution, can be expressed as the product of likelihoods over all observations in this individual dataset. L i (ψ i ; y i ) = K ∏ k=1 n i ∏ j=1 1 2π g (k) (ψ i , t ij , ξ) e -1 2 y (k) ij -f (ψ i ,t ij ) 2 g (k) (ψ i ,t ij ,ξ) (6) If the reparameterised functions f and g are used in this equation, this likelihood can be expressed as a function of the transformed, unbounded parameters φ.
When the error model is constant variance, parameter estimates can be found by ordinary least squares regression. For other error models, different optimisation strategies can be used  [22]. For variance by variable, iteratively reweighted least squares (IRLS) regression is commonly used in the context of degradation kinetics  [13, 23, 24]. For fitting the two-component error model, a two-fold strategy has been developed for and implemented in the R extension package mkin  [25]. One optimisation is performed by maximising the likelihood with respect to all parameters (degradation and error model parameters) at the same time. An alternative optimisation is performed in three steps, starting with unweighted least-squares optimisation of the degradation model parameters, followed by optimisation of the error model parameters with fixed degradation parameters and followed by the combined optimisation of all parameters.
Simultaneous Evaluations Using Nonlinear Mixed-Effects Models Nonlinear mixed-effects models explicitly include assumptions about the distribution of the parameters over the population in the statistical model being fitted. Thus, our nonlinear mixed-effects model combines the model for a single group given in Equation (  5 ) with the parameter distribution given in Equation (  4 ). We are no longer interested in estimating the individual parameters ψ i or φ i for each group, but instead, we estimate θ = (µ, Ω, ξ) as the set of parameters maximising the likelihood of θ given the complete, grouped dataset y.
In this equation, p(y ij |φ i , t ij ) has the same form as the right-hand side of Equation (  6 ), but as the φ i are unknown, we integrate over their distribution. As a result, the likelihood in Equation (  8 ) can generally not be calculated using a closed-form expression and iterative procedures need to be used. A multitude of such procedures has been developed, but only two of them are used here: the First Order Conditional Estimation (FOCE) as implemented in the R extension package nlme  [27] [28] [29]  and the more recent Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm  [30] [31] [32]  as implemented in the R extension package saemix  [33]. As SAEM uses a conditional likelihood during the optimisation, the overall likelihood still needs to be computed at the end of the estimation.
In this work, the numerical approximation of the multidimensional integral by importance sampling is used for this purpose in our analyses using saemix. In nonlinear mixed-effects models, the individual parameter vectors ψ i for each experiment are not of primary interest. However, in order to be able to plot predictions for each individual dataset and to obtain residuals to visually check the error model, individual parameter estimates are obtained by using a Bayesian approach after the population parameters have been estimated, by computing the conditional distribution p(ψ i |y i , θ) and by using the mode (maximum a posteriori or MAP estimator) or the mean (conditional mean) of this distribution  [31]. In this work, we use the FOCE algorithm as implemented in nlme and the SAEM algorithm as available in saemix.
Synthetic Datasets Synthetic data were generated using two different degradation models: SFO to demonstrate the impact of slow degradation and DFOP-SFO to demonstrate the ability of the methods to recover biphasic degradation of the parent compound as well as slow degradation of a transformation product. For all synthetic data, typical sampling times for an aerobic soil degradation study according to OECD guideline 307  [35]  were used, with nine sampling times and an experimental duration of 120 days. To demonstrate the behaviour of the different methods in a setting where degradation is increasingly ill-defined, four variants of grouped SFO datasets were generated using geometric mean half-lives of 15, 120, 500 and 800 days. For each variant, 100 grouped datasets were generated. Each dataset has five groups of data, each generated with a half-life that was drawn from a log-normal distribution around this value, simulating data for five soils with different degradation activity.
In every group, i. e. , for each virtual soil, random deviations from the predicted degradation curves were added for two replicate "observations" according to the two-component error model, so that the deviations were larger at the start of the degradation curve, where residues are still around 100%, and decreased with decreasing residues. The parameters of the two-component error were selected to mimic typical variance behaviour of real data. The population parameters used for generating the SFO datasets are summarised in Table  1. For the DFOP-SFO model, one variant of parameter distributions was defined as shown in Table  2. For this variant, another 100 datasets were generated, each consisting of eight groups (corresponding to eight virtual soils) with different degradation parameters.
Again, parameters for the individual groups were drawn from normal distributions around the desired population mean parameters, with a standard deviation of 0. 5 in transformed space, with the exception of the initial concentration of the parent for which a normal distribution with a standard deviation of 2% was assumed. Parameters λ 1 and λ 2 are the rate constants for the biexponential decline of the parent, γ 1 is the fraction of the parent declining with λ 1 , γ 2 is the fraction of the degrading parent compound forming the transformation product (formation fraction) and λ 3 is the degradation rate constant of the transformation product. The correspondence to the variable names used in the R scripts is also shown in Table  2 , as the latter are more easily recognised and were therefore used in the graphs showing the results for these data.
Experimental Datasets Experimental data on the two pesticide active substances 2,4-D and dimethenamid-P were evaluated in order to demonstrate the algorithms with real data and to perform first checks of their usability in the regulatory process. For this purpose, the results of separate and simultaneous evaluations are compared. The experimental aerobic soil degradation data reported in the EU risk assessment review of 2,4-D from 2014 consists of data obtained in laboratory studies with five different soils  [36]. In one of the soils, only parent compound residues were observed (compound code D24 used here for technical reasons) and the occurrence and identity of transformation products remained unclear  [37]. In the other four soils, residues of the transformation products 2,4-dichlorophenol (compound code DCP) and 2,4-dichloroanisol (compound code DCA) were reported as well.
In the case of the 2,4-D datasets, correction factors were derived using this method. For the first dataset conducted at 25 • C, a temperature factor of 1. 606 can be derived based on a reference temperature of 20 • C and assuming a factor of 2. 58 for a change of 10 • C  [38]. The same factor was also used in the EU pesticide risk assessment. No moisture normalisation was possible for this soil, as the moisture content during the study is not reported. The other datasets were obtained at 20 • C so only moisture normalisation was performed. As a result of these calculations, a normalised dataset with five groups for the five different soils was obtained, with data for the two transformation products DCP and DCA available for four of these soils. As a second example, the aerobic soil degradation data on dimethenamid and dimethenamid-P as documented in the RAR from 2018  [39]  are evaluated.
These two substances differ in their relative content of stereoisomers. As dimethenamid and dimethenamid-P were found to degrade in a comparable way in the EU assessment, no difference was made between the two substances with regard to degradation in soil. Here, the compound code DMTA is used for both of them and the data available for the same soils with different stereoismers were pooled. Again, normalisation factors derived according to the FOCUS guidance  [3]  were used in order to approximately scale the sampling times in each individual dataset to reference conditions. A grouped dataset with data for six different soils was obtained, with data for three transformation products with acronyms M23, M27 and M31. Both datasets described above are part of current versions of the mkin package  [25].
Evaluation Software and Variants All evaluations were performed using the R software  [21]. For separate evaluations of individual datasets, mkin  [25]  was used. For nonlinear mixed-effect model fits, the current release of the nlme extension [28] and a development version of saemix  [33]  were employed. The version of mkin used was also a development version, as it contains adaptations that make it possible to automatically set up nonlinear mixed-effect model fits from a set of separate evaluations. This includes the calculation of suitable starting parameters for nlme and saemix derived from the separate evaluations, as documented in the respective mkin methods. The saemix and mkin versions used in the evaluations are available in the Supplementary Materials. For the calculation of the starting parameters for saemix, it was found helpful only to consider rate constants that are significantly different from zero.
This method was employed for evaluating the synthetic SFO data with an input half-life of 800 days, for the synthetic DFOP-SFO data as well as for the experimental datasets. While all three error models can be fitted with mkin and nlme, the variance by variable error model is not available in saemix. For all evaluations, a reparameterised degradation model was used, where log-transformed rate constants and logit transformed fractions (g parameter of the DFOP model and formation fractions) are estimated. As the saemix package offers to perform these transformations internally, the original degradation models were generally used and parameter transformations were handled internally in saemix. However, for the models with more than one transformation product, the reparameterised models (as used per default in mkin) were used for parameter estimation with saemix as this could be implemented in a way that is applicable to all degradation models supported by mkin.
Synthetic Datasets When synthetic data are evaluated, the type of the parameter distributions, the transformations and the error model structure used to generate the datasets are known so that the ability of the different approaches to recover the correct input under these conditions can be checked. For the evaluation of synthetic SFO decline data, the SFO degradation model was combined with two different error models (constant variance and two-component error). In addition to the separate evaluations with mkin, nonlinear mixed-effects models were fitted with nlme as well as with saemix. All SFO fits were performed with default parameters. The synthetic DFOP-SFO decline data were also evaluated with the known degradation model and two error models (const and tc). Nonlinear mixed-effects model fits were conducted with nlme and saemix, but only saemix fits are reported because too many nlme fits did not converge.