dataesr/scientific-paragraphs-categorization huggingface dataset:
The need for having joint and coordinated groups is obvious with so many different actors involved. The establishment of the Coordination Group on Green Data Centers (CEN-CENELEC-ETSI) helps to harmonize initiatives. d -Boosting or Slowing down: Without doubt, the role of standardization is globally, and on the long term, boosting the adoption of a technology and its spreading in the society. However, in the context of Green IT, and IT in general, the duration of the standardization process is often not compatible with the pace of innovation. One such example is the TCP/IP protocol stack never standardized but de-facto standards. In Green IT, the same applies with the PUE that is still under development in standardization bodies while already widely adopted (sometimes misused) in industry.
Governments a -Definition: At national or international level, governments can certify, regulate or enforce the usage of technologies, based on several factors. These factors can be related to Green IT and sustainability. b -Examples: The Energy Star program is a widely adopted mean aimed at offering customers the possibility to buy more efficient products than required by law. In US, the Department of Energy is regularly checking the compliance of the products to the Energy Star label, developed by the Environment Protection Agency (www. energystar. gov). The European Union Code of Conduct (EU CoC) is another such inter-state level initiative. Its aim is to inform and foster the improvement of energy efficiency in the planning and operation of data centers. It is also a voluntary initiative to help designing and operating and reporting datacenters power consumptions.
The N18 superalloy, powder metallurgy processed, is used for high-pressure turbine disks of the Snecma M88 engine and has been extensively studied. The size distribution of its γ precipitates was first modelled in the late 1980's using a binary alloy model  [1]. The precipitation model has been recently revised, upgraded in a pseudo-binary alloy model and used in a multi-scale mechanical model for the calculation of the fatigue life in low cycle fatigue conditions  [2]. We here present how the new calibration of the pseudo-binary model for the N19 superalloy has been accelerated through the use of the Thermo-Calc R software, its TCNI5 thermodynamic database and its MOBNI2 mobility database. On a more fundamental point of view, the influence of the γ > γ latent heat of precipitation on the microstructural parameters of quenched samples of N18 and N19 has been studied using this same model.
Precipitation model 2. 1. Brief description of the precipitation model Our precipitation model is based on N. Milhet-Gayraud PhD work  [1]  with recent modifications carried out by G. Boittin  [2]. This model is now implemented in Z-set software  [5]. We remind here shortly the approach, the main assumptions and the input parameters associated to the model. This is a class model, based on classical nucleation and LSW coarsening theory, where precipitates are distributed in classes of size. It calculates the size distribution of intragranular γ precipitates over the time, depending on the material thermal history (which is supposed to be given as an input of the model). The obtained distribution is most of the time bimodal or even n-modal (n > 2). It results in the knowledge of relevant parameters for the microstructure at the end of the heat treatment: mean sizes and volume fractions of each mode of the distribution (i.
Calphad-based calibration for the N19 precipitation model The N19 precipitation model was calibrated thanks to a Calphad approach using Thermo-Calc (version S) and Dictra softwares (version 26) with, respectively, TCNI5 and MOBNI2 database for the determination of both the N19 pseudo-binary phase diagram and an effective diffusion coefficient  [8] [9] [10]. Figure  1  shows the obtained phase diagram and Fig. 2  shows the diagonal interdiffusion coefficients for Al, Ti and Nb elements in the γ phase at its temperature-dependent equilibrium composition. It must be noticed that no coupling between Thermo-Calc (or Dictra) and Z-set is performed, but only a priori calculations for the input parameters. The obtained phase diagram allows the calculation of polynomial fits of the evolution of the equilibrium molar fractions in γ -forming elements of the γ phase and of the γ phase from room temperature up to the calculated γ solvus temperature (1153 • C).
Genetic toxicology data have traditionally been employed for qualitative, rather than quantitative evaluations of hazard. As a continuation of our earlier report that analyzed ethyl methanesulfonate (EMS) and methyl methanesulfonate (MMS) dose-response data  (Gollapudi et al. , 2013) , here we present analyses of 1-ethyl-1nitrosourea (ENU) and 1-methyl-1-nitrosourea (MNU) dose-response data and additional approaches for the determination of genetic toxicity point-of-departure (PoD) metrics. We previously described methods to determine the noobserved-genotoxic-effect-level (NOGEL), the breakpoint-dose (BPD; previously named Td), and the benchmark dose (BMD 10 ) for genetic toxicity endpoints. In this study we employed those methods, along with a new approach, to determine the non-linear slope-transition-dose (STD), and alternative methods to determine the BPD and BMD, for the analyses of nine ENU and 22 MNU datasets across a range of in vitro and in vivo endpoints.
Data Selection The database employed, referred to as the G4 database, was developed by the GTTC QAW, and details of the quality criteria used for data screening are in  Gollapudi et al. (2013). In addition to the EMS and MMS datasets, the database contains a total of 45 datasets for ENU and MNU, including endpoints for gene mutation and chromosomal damage (measured as micronucleus formation) in vitro and in vivo. Since the goal of this work was to analyze datasets from which four PoD values could be derived (i. e. , NOGEL, BMD, BPD, and STD), only datasets with data supporting PoD derivation were evaluated. Based on the recommendations of  Lutz and Lutz (2009) , we further restricted the analyses to datasets with 5 doses (including the negative control) to ensure that we could use the bilinear modeling approach (i.
Benchmark Dose Analysis The benchmark dose (BMD) is defined in the U. S. Environmental Protection Agency's (EPA's) risk assessment glossary as 'A dose or concentration that produces a predetermined change in response rate of an adverse effect (called the benchmark response or BMR) compared to background' (http://www. epa. gov/riskassessment/glossary. htm). For BMD determination, the latest versions of the PROAST BMD software and EPA's Benchmark Dose Software (BMDS) were employed  (EPA, 2013).
RIVM-PROAST Benchmark Dose Analysis BMD analysis was conducted using PROAST, the dose-response modeling software developed at the National Institute for Public Health and the Environment (RIVM) in The Netherlands [http://www. proast. nl; version 36. 9  (Slob, 2002) ]. The nested set of models used included the exponential and Hill models that are recommended by the European Food Safety Authority  (EFSA, 2009). Note that the bilinear models used to define BPD metrics are not included, and thus are not assessed in these BMD packages  (Slob and Setzer, 2013). The BMR examined was 10%, which corresponds to an increase equal to 10% of the background (negative control) level, as estimated by the fitted model used for continuous endpoints such as genotoxicity. This is an arbitrary choice, but is one that has been frequently employed in the literature and that provides a conservative PoD that is close enough to the observable range of dose-response data to allow reliable estimates to be derived from fitted curves without uncertain extrapolation.
, 2011a). The model with additional parameters is accepted only if the difference in loglikelihoods exceeds the critical value at P 5 0. 05. In addition, the loglikelihood is used to compare the "full" model (geometric means of the observations at each dose) to the selected model, to provide an indication of the goodness-of-fit. The distribution of the residual errors in PROAST is similar to that derived using the EPA's BMDS (discussed below). For continuous data, the residual errors are assumed to be lognormally or normally distributed. There is an option in both software packages to choose one or the other distribution. In PROAST, the default assumption is that the standard deviation is proportional to the mean, and thus a log-normal distribution is applied to the continuous data, whereas in BMDS the default setting is the normal distribution.
U. S. EPA Benchmark Dose Software BMD analysis also was conducted using the latest version of the EPA's Benchmark Dose  Software [i. e. , BMDS v2. 4 (EPA, 2013) ]. The standard suite of continuous models (Hill, exponential, polynomial, linear, and power) was used along with constant and non-constant variance model assumptions. As was the case for RIVM-PROAST, the bilinear models used to define BPD metrics are not included, and thus are not assessed in this BMD package. The BMR chosen for the BMDS software was an increase equivalent to one standard deviation above the spontaneous (control) value; this was used in the calculation of a onesided 95% lower confidence limit for this BMD value and designated BMDL 1SD. This BMR is recognized to be equivalent to 10% excess risk for individuals below and above the 2nd and 98th percentiles, respectively  (Crump, 1995).
Initial Statistical Evaluation All data sets were imported into R (R Development Core Team, 2011) and the following analyses conducted: Shapiro-Wilk Normality Test, Bartlett Test of Homogeneity of Variances, Jonckheere-Terpstra Test of Monotonic Trend (asymptotic version), and a Bonferroni test for outlier identification. Data were transformed in order to achieve normally distributed data and homogeneity of the within dose variances. In most cases where the original data were not normally distributed and/or the variances were heterogeneous, one of the data transformation processes (square root or logarithmic) performed on the response data resulted in datasets with satisfactory distributions and variances, and the transformation essentially eliminated any significant outliers identified by the Bonferroni test.
No Observed Genotoxic E¡ect Level (NOGEL) The NOGEL was defined as the highest tested dose at which there was no statistically significant increase in genotoxic effect compared to the control. Means from datasets that met the criteria for being normally distributed with homogeneous variances across dose groups were compared using a post-hoc Dunnett's Test (alpha 50. 05). Means for datasets that were normally distributed but with heterogeneous variances were compared using a post-hoc Dunnett's T3 test  (Field, 2009). Means from datasets that were non-normally distributed and with heterogeneous variances were compared using the non-parametric post-hoc Dunn's Test  (Laws et al. , 2000).
Breakpoint Dose (BPD) Modeling Two similar bilinear methodologies were used to identify a discontinuity or breakpoint (below which the slope was zero) in the doseresponse function:  Lutz and Lutz (2009) , and a package in the R system called 'segmented'  (Muggeo, 2008; Wood, 2011). The Lutz and Lutz model, referred to here as the L&L model, is the bilinear approach discussed and applied by  Gollapudi et al. , (2013). The R segmented package  (Muggeo, 2008)  is similar to the L&L model-i. e. , it determines the best-fitting two segment linear function where the first segment from zero dose to the breakpoint is horizontal (i. e. , has zero slope) and the second segment has a positive slope. However, the segmented approach has several advantages: it is based on an open source, peer reviewed package available in R, and, unlike the L&L model, it does not require removal of top doses due to supra-linearity (saturation or high-dose toxicity).
Smoothing Regression Spline We also used penalized smoothing splines to analyze the doseresponse relationships. Penalized smoothing splines are a family of flexible techniques for estimating a continuous functional relationship without the need to assume linearity or any specific non-linear functional form. Wood (2006)  has extensively developed the underlying theory. The 'mgcv' (Mixed GAM [generalized additive model] Computation Vehicle) package in R  (Wood, 2006 (Wood, , 2011) )  was used to estimate the dose-response function using a default thin plate smoothing regression spline with degrees of freedom determined by generalized cross validation. Unlike the bilinear model used with the 'segmented' algorithm, 'mgcv' is commonly regarded as semi-parametric because it can be applied to non-normally distributed data with heterogeneous variances, and still provide an optimal solution from a cross-validation perspective.
At all lower doses, the slope of the doseresponse function is not statistically distinguishable from zero, therefore represents a flat line. When the lower bound CI on the STD (i. e. , STDL) is 0, the hypothesis that the slope is increasing significantly at dose 5 0 (i. e. , slope is above zero) cannot be rejected. This is conceptually different from the L&L and segmented bilinear models, where a BPDL 0 means that linearity cannot be rejected (regardless of the slope of that linearity). The smoothing regression spline approach arrives at a PoD by directly assessing the slope of the dose-response relationship throughout the dose range to determine the dose where it initially becomes positive. In addition to deriving potential PoDs, the smoothing regression spline model also was used to test the overall linearity of the dose response of a given dataset.
RESULTS Table  I  summarizes the individual study characteristics for the various genotoxicity datasets analyzed. A wide range of endpoints were analyzed, including in vivo and in vitro measures of micronuclei (MN), in various cell types and species. Similarly, gene mutations were assessed in multiple target genes including transgenes (e. g. , LacZ) and endogenous genes (e. g. , Hprt). The PoD metrics for nine ENU genotoxicity datasets are summarized in Table  II. NOGEL values were obtained for all datasets with the exception of the Dlb1 mutation analyses in the small intestine, where the lowest study dose was significantly different from control. BMDL 10 values were determined using PROAST for reasons stated in  Gollapudi et al. (2013) , whereas BMDL 1SD values were determined using BMDS.
The latter is the default metric used by the EPA for continuous data (U. S. EPA, 2012). In all cases, an exponential model provided the best fit in PROAST. In BMDS, the best fitting model was selected among the typical suite of continuous models. Overall, although not all PoD methods provided a good fit of the data to a statistical model, the two BMD modeling approaches provided estimates of the BMDL values that were lower than the corresponding NOGELs. PoD values were derived for four of the ENU datasets using at least one of the three models; L&L, segmented, and smoothing regression spline. In all four cases, the slope below the LOGEL was not significantly different from zero, suggesting a bilinear dose response. Two of the datasets for which BPD values were derived were not predicted to have an STD by mgcv  (van Delft et al.
, 1998; Doak et al. , 2007). Examination of the mgcv plots of these datasets reveals an apparent lack of bi-linearity, consistent with the model results failing to identify an STD (Table  II ). In contrast, the two datasets from  Bryce et     NOGEL. There were two MNU datasets for which both the bilinear and smoothing regression spline methods provided BPDLs and STDLs, respectively  (Monroe et al. , 1998; Pottenger et al. , 2009). The PoDs associated with these data, calculated by the different aforementioned methods, were in remarkably close agreement for a given dataset, and the slopes up to and including the NOGEL were not significantly different from zero, demonstrating a good fit for the bilinear models (Fig. 2 ). There were eight datasets that required analysis with non-parametric methods, and the application of the smoothing regression spline methodology yielded STD values for five of these datasets, with the slopes from the negative control up to and including the NOGEL not being consistent with a zero slope.
Therefore, these STD values should be interpreted with caution (see below). Eighteen in vivo MNU datasets are summarized in Table  III. Although the studies examined different endpoints in different species under different exposure scenarios, a quantitative comparison of the PoDs can still be conducted (Fig. 3 ). The medians (2. 5 mg/kg/day) and distributions of NOGEL and LOGEL values were very similar (Fig. 3A ). The BPD, STDL, and BMDL 1SD were also in close agreement, ranging from 0. 8 to 1. 2 mg/kg/day. The median BPDL, BMD 10 , and BMDL 10 were 0. 3, 0. 2 and 0. 1 mg/kg/day, respectively. To get a sense of how these PoD metrics compare to one another within each in vivo MNU dataset, each PoD was 'normalized' to the LOGEL of that dataset (if available). As expected, the ratio of the NOGEL to the LOGEL was less than unity (i.
DISCUSSION The current work, which follows that of  Gollapudi et al. (2013) , is focused on advancing the development and application of statistical approaches to define PoDs for genotoxicity dose-response data. This is a necessary step in the path forward for the use of genotoxicity PoD metrics to inform regulatory decision making and/or risk assessment. The earlier work focused on the following PoD metrics: Dunnett's approach for calculating NOGELs; a multi-step approach to define BPD values  (Gocke and Wall, 2009) ; and the BMD approach (i. e. , using PROAST and BMDS software). Here we present the next phase in these analyses, which involved a series of approaches (Table  IV ) to determine PoDs for the potent genotoxicants ENU and MNU. The data from the examined publications, in most cases, suggest that PoD values are generally lower and more difficult to define for ENU and MNU in comparison with EMS and MMS.
This   III ). The various software packages employed have only limited ability to adjust X-and Yaxes, and all plots shown are the default outputs of these programs. By default, PROAST provides plots with untransformed data. For both the Monroe and Pottenger datasets, log transformed responses provided the closest distribution to normal with homogeneous variance (see Table  III ). L&L, Lutz, and Lutz hockey stick/bilinear approach for defining a breakpoint dose (BPD); Segmented, bilinear model for defining a BPD; mgcv, smoothing regression spline for defining a slope transition dose (STD) using 'drsmooth' in R version 3. 0. 2; BMD 1SD (BMDS), Benchmark dose 1 standard deviation using BMDS; BMD 10 (PROAST), Benchmark dose 10 using PROAST. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.
They may also be less preferred because of their dependence on the specific doses tested. As part of this effort to expand the number of data analysis tools for determination of PoDs, other approaches beyond the L&L and BMD approaches were examined and evaluated. The L&L and segmented models provided similar results (Tables  II  and III ). However, the segmented PoD may be viewed as a more reliable metric since it does not require dataset censoring at the highest doses to address saturation or high-dose toxicity, and moreover, it is a well-documented R procedure. The seg-mented package also contains functions that directly account for variance heterogeneity and non-normality of residuals via weights, and conducts these analyses within the framework of generalized linear models (GLMs). GLMs can directly model data sets such as cell or colony counts, incidence frequencies, and other types of data that do not generally adhere to the normality and variance constancy assumptions.
Therefore, we propose that the segmented package supersede the L&L package as the preferred bilinear modeling approach for assessing genetic toxicology data. The STD method, which uses a smoothing regression spline approach also within a GLM framework, directly assesses the slope of the dose-response relationship for a continuous non-linear dose-response function rather than for two linear segments (bilinear model); it provided PoD estimates that were consistent with those derived by other methods (Fig. 3 ). A potential advantage of the smoothing regression spline approach compared to bilinear modeling, is that it can be compared directly to different non-linear models such as the exponential, Hill and quadratic models. Thus we recommend the smoothing regression spline method applied here in preference to the segmented and L&L models.
However, this method shares a major disadvantage similar to the BPD approaches, in that it is frequently not possible to derive a PoD with this method. For example, a PoD was determined for only 15/34 using smoothing regression spline and segmented methods (Table  IV ), whereas PoDs were determined for 30/34, 31/34, and 34/34 datasets when using NOGEL, BMD 10 , and BMD 1SD , respectively (Tables  II  and III ). Similar to observations in the previous evaluations of EMS and MMS data  (Gollapudi et al. , 2013) , the BMD  When statistical power is reduced, the PoD is also reduced due to the increased variance in the dataset. Environmental and Molecular Mutagenesis. DOI 10. 1002/em was readily determined for almost all of the ENU and MNU datasets. Moreover, the BMD approach provides a number of advantages when compared to the NOGEL, BPD and STD methods.
For example, the BMD methodology generally requires fewer doses in comparison to the BPD methods, BMDLs are readily defined [i. e. , Tables  II  and III , and  (Gollapudi et al. , 2013) ], and BMDL 10 values, although generally lower and thus more conservative, are comparable to other PoDs for the datasets analyzed here. It is worth noting that the BMDL 10 value, which represents a lower confidence limit of a 10% increase above the estimated background, is often below the BPDL, which represents a 0% change above background [Tables  II  and III  and also in  (Gollapudi et al. , 2013) ]. This is a consequence of these two PoD metrics being defined using different statistical models and approaches  (Crump, 2011; Slob and Setzer, 2013 ). An essential feature of a PoD approach that can be broadly applied with ease and success is its ability to determine whether, and at what dose, there is a detectable increase in genotoxic effect above the spontaneous background level in a particular system.
As part of a related exercise, we employed Monte Carlo simulation to empirically assess the effect of dataset censoring (i. e. , varying number of doses and dose spacing) on the probabilistic distribution of PoD values for a given dataset. The results obtained to date, which will be published separately, indicate that BPD determinations (e. g. , the L&L BPD) are far more sensitive to dataset censoring than are the BMD determinations. For example, analyses of in vitro HPRT gene mutation doseresponse data for ENU  (Doak et al. , 2007)  indicated that BPD determination is optimized when the dataset contains responses for three or more doses below the PoD and three or more doses above the PoD. No such requirement was noted for the same dataset with respect to the determination of a BMD 10. Moreover, although not typically applied, censoring of data near the point of inflection can prevent successful determination of a BPD value.