dataesr/scientific-paragraphs-categorization huggingface dataset:
Threshold dose (Td): A Td value is statistically defined by applying a bilinear dose-response model to a dataset, for which the lower limit of the confidence interval must be positive (nonzero)  (Lutz and Lutz, 2009). The statistical package compares fit for the bilinear (Td) model to a linear model and determines which one is a better statistical fit for the data being modelled, and when appropriate, the Td value. Indeed, the available information has grown substantially in the past 15+ years and has revealed the ubiquitous presence of endogenous/background DNA adducts, including pro-mutagenic adducts. In conjunction with new data on Abbreviations: 4NQO, 4-nitroquinoline-1-oxide; 8-oxo-dG, 8-oxo-7,8-dihydro-2 0 -deoxyguanosine; AAG, 3-alkyladenine DNA glycosylase; ACC, American Chemistry Council; alkyl-FAPy, N 5 -alkyl-2,6-diamino-4-hydroxy-5-formamidopyrimidine; alkylG, alkyl/hydroxyalkylguanine; AP site, abasic or an apurinic site; APNG, alkylpurine DNA N-glycosylase; BER, base excision repair; BLEO, bleomycin; BMD, benchmark dose; BMD/L, benchmark dose lower bound; BPD, break point dose; BPDL, break point dose lower bound; Cefic, European Chemical Industry Association; DR, direct reversal; dsDNA, double-stranded DNA; E, ethylene; ECETOC, European Centre for Ecotoxicology and Toxicology of Chemicals; EMS, ethyl methane sulfonate; ENU, ethyl nitrosourea; EO, Ethylene Oxide; FAPy, N 5 -alkyl-2,6,-diamino-4-hydroxy-5-formamidopyrimidine; GTTC, Genetic Toxicology Technical Committee; HE, hydroxyethyl; HR, homologous recombination; ILSI/HESI, International Life Sciences Institute/Health and Environmental Sciences Institute; IVGT, In Vitro Genetic Toxicity Committee; JIG, Joint Industry Group; LMW, low DNA adduct chemistry and fate, including DNA repair, these provide important new perspectives for understanding the biological consequences and biological significance of specific adducts and how to incorporate this information into risk assessment.
Dose-Response Analysis of Mutations and Chromosome Damage Induced by Direct-Acting Alkylating Agents Doak and colleagues  (Doak et al. , 2007)  described in vitro gene mutation and chromosome damage doseresponse data for MMS, MNU, EMS, and ENU. These were evaluated using the AHH-1 human lymphoblastoid cell line and measuring responses with the hypoxanthineguanine phosphoribosyl transferase (HPRT) forward mutation assay and the in vitro cytokinesis-blocked micronucleus (MN) assay, all with a large number of cells and doses (e. g. , 8-20 doses tested and up to 5-fold more cells analyzed than typical for certain doses) tested to maximize the sensitivity of detection of a response. This dataset provided a detailed look at the dose-responses for induction of genotoxic effects by these DNA-reactive chemicals.
The resulting dose-response data demonstrated quantifiable noobserved-genotoxic-effect-levels (NOGELs) for MMS and EMS with both assays. However, while neither ENU nor MNU had doses with no increases, statistical NOGELs were defined in both assays (HPRT and MN) for both ENU and MNU (for example, see Fig. 3 ). These doseresponse data were further analyzed using the bilinear/ threshold dose-response modelling approach as described by  Lutz and Lutz (2009) , as published by  Johnson et al. (2009). This dose-response modelling approach uses statistics to compare the fit of a dataset to a bilinear/threshold model with its fit to a linear model, thus allowing for a statistical determination of a better fit between these two models and, where appropriate, defining a Threshold dose (Td) value. The modelled results of the Doak et al.
datasets indicated that the bilinear/threshold model provided a better fit to the MMS and EMS dose-response data both for gene mutations and MN induction, while the MNU and ENU data were mixed. ENU HPRT results were a better fit for the bilinear dose-response model, while the MN results for ENU were better described by a linear one; MNU results could not exclude a linear dose-response model for either endpoint. Complementary work conducted by Swenberg and colleagues provided some adduct dose-response data to match the doses evaluated for genotoxic effects by  Doak et al. , (2007). Both endogenous and exogenously-induced N7-MG and O 6 -MG were quantified in AHH-1 cells treated with stable isotope-and deuterium-labelled MMS ([ 13 C 2 D 3 ] MMS) or deuterium-labelled MNU ([ 2 D 3 ]-MNU) to increase the specificity and sensitivity for detection of these adducts and, importantly, to differentiate between endogenously present adducts (not labelled) and the exogenously induced ones (labelled).
The data demonstrated a linear dose-response for labelled N7-MG (both from MMS and from MNU), corresponding with exogenously induced N7-MG adducts, while the endogenous N7-MG did not increase over the 24-h exposure to either MMS or MNU  (Swenberg et al. , 2008 , Sharma et al. , 2014)  (see Figs. 4  and 5 ). A key finding from these data comes from comparison of the curves for exogenously induced adduct doseresponse to the curves for mutation/genotoxic effect doseresponse, where it becomes apparent that the shape of the mutation curves and the shape of the molecular dose of N7-MG are quite different, most notably for MMS. The labelled N7-MG adducts increase linearly over the dose range analyzed; however, the genotoxic effects (mutations, MN) reported by  Doak et al. (2007)  are nonlinear over that same dose range, with a low-dose section not showing any corresponding increase in genotoxic effects (flat slope).
Thus, increasing quantifiable levels of (exogenously induced) DNA adducts (lesions) did not correspond with an increase in genotoxic response. Together these datasets demonstrate that there are regions of the low-dose doseresponse where exogenously induced (labelled) N7-MG adducts are increasing, but with no corresponding quantifiable increase in genotoxic response (Figs. 3  and 4 ). The N7-MG adduct levels induced at the tested doses were not pro-mutagenic (did not induce increased mutations), and therefore do not contribute to increases in genotoxic events such as mutations or induction of micronuclei. It is likely that other DNA lesions, ones that are pro-mutagenic lesions such as the O 6 -alkylG adducts, are the cause of the genotoxic events. Those pro-mutagenic lesions comprise a smaller proportion of total adducts and may not even be present at such low doses.
The adduct profile data published by  Beranek, (1990)  would support this hypothesis, as O 6 -MG represents only a small proportion of the adduct profiles for all four of these mutagenic chemicals (from not detected to 16. 6% in cultured cells and in mammalian liver). The in vitro experimental work in AHH-1 cells was then extended at Swansea University, evaluating mutation doseresponse for lower doses, and using DNA repair knockdown techniques to manipulate the cell's repair capacity for more mechanistic analyses. For example, the DNA repair protein MGMT, which repairs the pro-mutagenic O 6alkyl-G adduct by removing the alkyl group, was predepleted with O 6 -benzylguanine pretreatment. The resulting reduction in DNA repair capacity led to a modified HPRT gene mutation dose-response curve following exposure to MNU.
Therefore, POD metrics (e. g. , NOGELs) can be defined for the potent alkylnitrosoureas, MNU and ENU, and when DNA repair is knocked-down the POD value decreases. The extensive genotoxicity dose-response data from  Doak et al. (2007)  were further analyzed, along with other examples of similar dose-response datasets, using a wider toolbox of approaches to define POD metrics described later on  (Gollapudi et al. , 2013 , Johnson et al. , 2014). Clear benchmark dose (BMD; BMDL) metrics were derived for each of these dose-response datasets, and bilinear/threshold model-derived Break Point Doses (BPD/L) were identified for EMS and MMS, based on the  Doak et al. (2007)  data; analysis of these data for ENU and MNU did not identify BPD/L values. Overall, the in vitro dose-response "titration" data of dose versus mutations demonstrated that threshold values or BPDs defined using bilinear/threshold models can be identified for genotoxic effects (mutation/MN) caused by DNA-reactive chemicals.
Implications from JIG-sponsored Work The ground-breaking analytical work built on Lindahl's much earlier efforts  (Lindahl, 1993) , with both stable isotopes and radioisotopes identifying and quantifying the many endogenously present DNA adducts. These included pro-mutagenic adducts and adducts that are chemically identical to specific chemical-induced ones, all found in tissues from untreated control animals under steady-state conditions as described earlier  (Swenberg et al. , 2008 , 2011 , Nakamura et al. , 2014). The growing database on endogenously present adducts has opened new avenues of perspective, leading to a paradigm shift that is still playing out in recognizing the impact of this information on background issues and on risk assessment. Some of the challenges are depicted in Figure  8.
Complementary Efforts Substantial complementary research efforts have generated many published datasets, including some extensive ones, to measure mutation dose-response curves and, in several cases, incorporating exposure biomarkers of internal dose and dose-response. Key results from several research groups, including both in vitro and in vivo work, are briefly described below. The dose-responses for genotoxic effects from a series of DNA-reactive chemicals were evaluated in vitro for induction of micronucleus with flow cytometry measurements, and large numbers of doses and cells were evaluated to strengthen the statistical evaluations; these included vinblastine sulfate (VB), EMS, MMS, ENU, MNU, and bleomycin (BLEO)  (Bryce et al. , 2010). The authors concluded that all the test chemicals except BLEO had dose-responses that demonstrated a better statistical fit with a nonlinear dose-response model than a linear one.
Several recent in vivo datasets on nitrosamines and other direct-acting mutagens focused on dose-response and contributed additional weight-of-evidence for non-linear doseresponse with DNA-reactive chemicals. Perhaps the strongest dataset came from the Hoffmann-LaRoche researchers, as it included internal dosimetry (hemoglobin adducts) in addition to gene mutation (Muta™Mouse) and MN induction as measures of in vivo genotoxic events, over a range of doses of EMS and ENU (see Fig. 9 )  (Gocke and Müller, 2009). The hydroxyethylvaline adducts clearly increased steadily with increasing EMS dose, as would the predicted N7-EG adducts based on Murthy and colleagues  (Murthy et al. , 1984) , representing an increasing internal dose. However, the markers of genotoxic effect did not increase above control levels until the EMS dose was above 20-50 and 80 mg/kg bw, for lacZ mutations and MN, respectively  (Gocke and Müller, 2009).
Several lower EMS doses did not result in any quantifiable increase in either gene mutation (lacZ) or MN levels. The data for ENU did not show the same threshold/BPD dose-response result, although the authors discuss how lower doses might have demonstrated a dose-response similar to EMS. Indeed later work did demonstrate such a threshold (and BPD) in rats for the in vivo induction of pig-A mutations in erythrocytes following 28-d repeated dosing with either EMS (21. 9 mg/kg bw) or ENU (0. 88 mg/kg bw), based on a better statistical fit for a threshold dose-response model  (Dobo et al. , 2011). Additional work with MNU and pig-A demonstrated no increases of CD59 -reticulocytes or erythrocytes for repeated doses (28 d) of MNU (0. 1 to 1. 25 mg/kg), whereas the MN frequency was induced at doses above 0.
3 mg/kg bw, again demonstrating a low-dose region with no increases in genotoxic effects for in vivo tests  (Lynch et al. , 2011). Adding to the weight of evidence for non-linear/threshold dose-responses, a similar dataset was obtained with 4-nitroquinoline-1-oxide (4NQO), a directacting genotoxicant, and pig-A, where repeated exposures (28 d) to 4NQO at the lowest dose of 1. 25 mg/kg bw also did not result in statistically significant increases in CD59 - reticulocytes or erythrocytes, nor in increased peripheral blood micronuclei  (Stankowski et al. , 2011). In fact, this kind of work was expanded beyond DNA-reactive chemicals with the work of Olipitz and colleagues  (Olipitz et al. , 2012) , who evaluated exposure of mice to low-dose radiation and measured subsequent mutation induction, along with gene expression changes in several marker genes for DNA repair and cell cycle effects.
The ILSI/HESI GTTC carried out extensive dose-response analyses on all adequate in vitro and in vivo gene mutation and micronucleus dose-response datasets for MMS, EMS, MNU, and ENU that were published pre-2012. In a report by Gollapudi and colleagues  (Gollapudi et al. , 2013) , data available for EMS and MMS were assessed, and clear POD metrics were defined for each dose-response, with NOGEL, BPD/L, and BMD/L metrics being defined for each dose-response dataset. Available, adequate MNU and ENU dose-response data were then assessed in a second effort, using a wider range of statistical approaches  (Johnson et al. , 2014) , in addition to those employed previously. This analysis showed that the bilinear modelling approach  (Lutz and Lutz, 2009)  had some major drawbacks (e. g. , required extensive data), such that BPD/L metrics could only be defined in ~5% of the cases.
However, when the BMD-1SD and BMD-10% approaches were used, BMD/L metrics were defined in all cases. These findings indicate that the 'threshold' dose-response modelling provided within the bilinear approach is less suitable for robustly defining a POD with a standard dataset. This along with other information, resulted in the ILSI/HESI GTTC and International Workshops on Genotoxicity Testing (IWGT) 2013 expert groups  (MacGregor et al. , 2015a (MacGregor et al. , , 2015b) )  recommending the BMD/L approach as best for defining POD metrics for genotoxic effects. One other major recommendation was that, although statistically defined 'thresholds' can be determined using large datasets with bilinear dose-response modelling, the BMD/L model allows for smaller numbers of animals to be used, and more precise POD metrics to be defined; however, this is done with acceptance that low levels of even DNA-reactive genotoxicants pose negligible concern to the human population.
Thus, with the additional site-specific mutagenesis data demonstrating that N7-HEG and N7-HPG are not mutagenic, and all the additional in vitro and in vivo mutation datasets demonstrating clear NOGELs for direct-acting mutagens (MNU, ENU, MMS, EMS, 4NQO, etc. ), with more in vivo transgenic mutation data coming, we are ready to reach a consensus on a level of DNA adducts-perhaps it would only be for LMW N7-alkylG adducts-that would be considered of negligible concern. This might even be applied in a similar way to the application of concentration cut-off values with the Globally Harmonized System of classification approach, or possibly in potency determinations as discussed by  Hennes et al. (2014). Questions such as how broadly these examples and this growing database can be extrapolated, and how many data are enough, are critical to address as a scientific body.
In addition, we use the more stringent selection technique, stability selection  (20, 21) , to find a minimal stable set of predictors for each proportion. Stability selection selects predictors of each immune cell proportion that are consistently predictive in 100 subsamples of the dataset. We then build predictive models from the stability selected set of predictors using ordinary least squares. Compared with the elastic net, stability selection is more demanding of the predictors it selects. Consequently, it targets probes that mark differentiation events that are the most important for the cell. We therefore explore the biological functions associated with the stability selected probes, to improve knowledge of the epigenetic changes that characterize differentiated immune cells. A similar two-pronged approach is used to predict other conditions and traits collected within the Milieu Intérieur study, including age, smoking, height, BMI, routine chemical and hematological laboratory tests, and the serological responses to antigens of 13 common pathogens  (22).
Discussion Our study reports novel, accurate models to predict blood cell composition from whole blood DNAm profiles. Models were built using a unique dataset that comprises both the quantification of 70 blood cell proportions by standardized flow cytometry (  14 ) and blood methylomes established with the MethylationEPIC array  (15) , assessed in 962 healthy donors of western European ancestry. Predictive models are built using the elastic net method  (17) , a regularized linear regression model that has been recently used to predict age from MethylationEPIC array data  (18). The prediction accuracy, measured as the correlation between predicted and observed out-of-sample values and the MAE, is improved for our models, compared to the widely-used Houseman model, based on either the standard or improved IDOL reference libraries  (9, 12).
We are also able to accurately predict 35 subset frequencies, in contrast to the six that are currently possible to estimate by the Houseman model using either reference panel. These results suggest that our models should better prevent false positives in EWAS due to cellular heterogeneity, relative to existing gold-standard methods. Nevertheless, it must be noted that we assessed prediction accuracy based on cellular fractions estimated with the same flow cytometry technique, panel design and standardization steps as those used for the training dataset, which may disfavor the other methods trained on other types of cell enumeration techniques. We also show that it is possible to find predictive models of immune cell proportions that are comparable in terms of accuracy to elastic net models, and to the Houseman models with either reference library, using considerably fewer predictors.
This is done by employing the stability selection technique  (20, 21). Because of their much smaller size, such models can more robustly, flexibly and cost-effectively predict blood cell composition, age, and smoking consumption than previous models. to the exhaustive immunophenotyping performed in our training dataset, we can extend the number of blood cell subsets that can be accurately predicted from blood DNAm data. Notably, our models can accurately predict the blood frequencies of MAIT cells, eosinophils, basophils and T reg cells (R>0. 6; Table  1 ). Importantly, all these leukocyte subsets have previously been reported to vary with various disease conditions, and are thus expected to confound interpretation of EWAS. For instance, circulating levels of MAIT cells are known to be strongly altered during infection  (31)  and in systemic lupus erythematosus and rheumatoid arthritis patients  (32).
Methods DNA methylation data. The Milieu Intérieur cohort includes 1,000 healthy donors who were recruited by BioTrial (Rennes, France) and were stratified by gender (i. e. , 500 women and 500 men) and age (i. e. , 200 individuals from each decade of life, between 20 and 70 years of age). Donors were selected based on stringent inclusion and exclusion criteria, detailed elsewhere  (13). DNAm data was retrieved for all donors from a previous study  (15) , where detailed methods are provided. In brief, the DNA methylome was profiled with the Infinium MethylationEPIC BeadChip on whole bloodderived samples. Raw fluorescence intensities of 866,895 methylation sites across the human genome were processed with the R (version 3. 5) Bioconductor package minfi. Values were corrected for probe color bias and differences in type-I and type-II probe distributions, using the single sample NOOB (ssNOOB) method implemented in minfi.
Because we wanted to use the methylation data primarily for prediction, which can easily be evaluated on out-of-sample observations and in validation cohorts, we wanted to exclude as few probes as possible. Therefore, we did not exclude probes from the X and Y chromosomes. We did neither exclude possibly cross-reactive probes. From the 866,895 initial probes, we only excluded probes that had a detection P ≥ 0. 01 for more than 3 samples. A total of 858,923 probes were kept for the analyses. We suppose in this study that DNAm levels are linearly related to cell proportions. We therefore use β methylation values instead of m values. Flow cytometry data. Flow cytometry data was retrieved for all Milieu Intérieur donors from a previous study  (14) , where detailed methods are provided. Briefly, whole blood samples were collected from the 1,000 healthy, fasting donors on Li-heparin.
Sample staining was performed within 6h of blood draw. Ten 8-color flow cytometry panels were developed. The acquisition of cells was performed using two MACSQuant analyzers, which were calibrated using Mac-sQuant calibration beads. Flow cytometry data were generated using MACSQuantify TM software. Among the 313 exported immunophenotypes, we only kept 70 cell proportions and 2 ratios as candidate measures for prediction. Houseman model using standard and IDOL reference libraries. We used the implementation of the Houseman model in the EstimateCellCounts2 function of the Bioconductor R package FlowSorted. Blood. EPIC to predict immune cell proportions for all our 962 samples with both the default and IDOL reference panels.
The pure LASSO penalty has a saturation property: it cannot select more predictors than the number of samples  (35). Note that for the pure LASSO penalty, all coefficients will be zero if λ > max X T y. ( ) 2 To target X C , we suppose that an α between zero and one will be optimal. To find this parameter, we employ our own cross-validation scheme, detailed in Algorithm 1. We fit the optimization problem Eq. (  1 ) by the glmnet package in R. Stability selected linear regression Elastic net regression with regularization parameters tuned by cross-validation will typically include predictors of weak predictive power as well as some false positives  (36). To target S D , we therefore use a more stringent selection scheme. As mentioned above, we suppose that D n. Therefore, we are now only aiming to select predictors to use in a linear model; we do not want Algorithm 1 Cross-validation for elastic net linear regres-Stated here using the correlation between out-of-sample predictions and observed values as performance estimate.
Therefore, we anticipate that prediction models of a form suitable for immune cell frequencies should work well also for traits related to them. For binary traits, code the classes as either 0 or 1. The procedure we use for binary traits follows the algorithms above verbatim, except that the least squares term y -Xβ 2  2 in the cost function in Eq. (  1 ) is replaced by the negative loglikelihood of the binomial distribution given a logit link function - 1 n n i=1 y i µ + β T x i -log 1 + e µ+β T x i. ( 6 ) Logistic regression with elastic net regularization is implemented in glmnet. For stability selection, we use the stabs R package with a custom built selection function based on glmnet.
ACKNOWLEDGEMENTS This work benefited from support of the  French government's Program Investissement d'Avenir , managed by the  Agence Nationale de la Recherche  ( ANR , reference  10-LABX-69-01 ). J. B. is a member of the  LCCC Linnaeus Center  and the  ELLIIT Excellence Center at Lund University  and is supported by the  ELLIIT Excellence Center.