Philippe Pouletaut

Salem Boussida

Redouane Ternifi

Véronique Miette

Stéphane Audière

Céline Fournier

Laurent Sandrin

Fabrice Charleux

Sabine F Bensamoun
email: sabine.bensamoun@utc.fr

Nonalcoholic Fatty Liver Disease Using Vibration-Controlled Transient Elastography (VCTE) and MR Imaging Techniques: a Clinical Study

Keywords: attenuation, fat, imaging techniques (elastography, US, MRI), iron, liver, stiffness

Introduction

Hepatic fibrosis is routinely controlled in patients with chronic liver disease. The hepatic fibrogenesis can result from several causes, such as high alcohol consumption, chronic viral hepatitis or metabolic dysfunction [START_REF] Friedman | Mechanisms of Hepatic Fibrogenesis[END_REF][START_REF] Reeder | Quantification of Liver Fat with Magnetic Resonance Imaging[END_REF]. Steatosis (intracellular fat accumulation) and iron overload are commonly regarded as two important cofactors in liver disease that can lead to fibrosis and cirrhosis [START_REF] Reeder | Quantification of Liver Fat with Magnetic Resonance Imaging[END_REF]. Non-targeted percutaneous liver biopsy with direct histological visualization is the current gold standard to assess liver disease [START_REF] Gandon | Non-invasive assessment of hepatic iron stores by MRI[END_REF], but this technique is invasive and involve potential sampling errors [START_REF] Merat | Sampling Error in Histopathology Findings of Nonalcoholic Fatty Liver Disease: A Post Mortem Liver Histology Study[END_REF].

Thus, three main techniques were emerged for non-invasive detection and quantification of liver fat and iron content. First, a clinically used technique is to measure the stiffness of liver by the vibrationcontrolled transient elastography (VCTE) using FibroScan ® (FS) device [START_REF] Sandrin | Transient elastography: a new noninvasive method for assessment of hepatic fibrosis[END_REF][START_REF] Tapper | FibroScan (Vibration-Controlled Transient Elastography): Where Does It Stand in the United States Practice[END_REF] (Echosens, Paris, France). Ultrasound (US) attenuation measurement using controlled attenuation parameter (CAP) [START_REF] Sasso | Controlled Attenuation Parameter (CAP): A Novel VCTE TM Guided Ultrasonic Attenuation Measurement for the Evaluation of Hepatic Steatosis: Preliminary Study and Validation in a Cohort of Patients with Chronic Liver Disease from Various Causes[END_REF] is an additional parameter introduced in 2010 to non-invasively assess liver steatosis [START_REF] Audière | Improved Ultrasound Attenuation Measurement Method for the Non-invasive Evaluation of Hepatic Steatosis Using FibroScan[END_REF] and recently improved [START_REF] Yin | Prediction of nonalcoholic fatty liver disease (NAFLD) activity score (NAS) with multiparametric hepatic magnetic resonance imaging and elastography[END_REF]. CAP measures the attenuation of the ultrasound beam at 3.5 MHz that travels through the liver tissue.

Secondly, magnetic resonance elastography (MRE) was correlated with liver biopsy to validate the imaging method and are now used in clinical routines to evaluate the different stages of stiffness [START_REF] Bensamoun | Cutoff Values for Alcoholic Liver Fibrosis Using Magnetic Resonance Elastography Technique[END_REF].

Third, for iron overload and steatosis quantification, a specific MR sequence called IDEAL-IQ ® (Iterative Decomposition of water and fat with Echo Asymmetry and Least squares estimation) demonstrated its clinical relevance [START_REF] Reeder | Quantification of hepatic steatosis with MRI: The effects of accurate fat spectral modeling[END_REF][START_REF] Kühn | Effect of Multipeak Spectral Modeling of Fat for Liver Iron and Fat Quantification: Correlation of Biopsy with MR Imaging Results[END_REF].

The objective of the present work is twice: (i) to compare VCTE (stiffness & CAP) measurements to MRI (PDFF) and MRE (stiffness) values within a NAFLD clinical study and (ii) to evaluate the impact of hepatic iron levels on the measurements of these three non-invasive techniques.

Materials and methods

2.1

Study population Ninety four patients referred for a liver MRI examination were recruited at the radiology department located at the ACRIM-Polyclinique Saint-Côme (Compiègne, France). Each subject underwent 1) FibroScan ® examination, 2) MR imaging tests for fat quantification and iron overload, and 3) MR elastography test for viscoelastic characterization with no delay between all the tests (Fig. 1). Total elapsed time for all tests was 10 minutes. Exclusion criteria were claustrophobia, mental instability, or acute reversible liver problems. This study was approved by the Institutional Review Board of Amiens Hospital. All subjects had the experimental protocol explained and gave their informed written consent prior to admission into the study.

Among the study population, 4 were excluded because the CAP files were corrupted. Three patients were also excluded because MRI-PDFF and MRE were not performed. Finally, the study included a total of 87 patients.

2.2

Protocols for fat quantification (CAP) is measured with the FibroScan ® (FS) device using a probe triggered manually by the operator through a standard method [START_REF] Sasso | Controlled Attenuation Parameter (CAP): A Novel VCTE TM Guided Ultrasonic Attenuation Measurement for the Evaluation of Hepatic Steatosis: Preliminary Study and Validation in a Cohort of Patients with Chronic Liver Disease from Various Causes[END_REF].

FS is used with two different probes (M and XL) used in function of the thickness of the subcutaneous tissue around the ribcages. The measurement depths and center frequency with M and XL probes are 25-65 mm / 3.5 MHz, and 35-75 mm / 2.5 MHz, respectively.

During the MRI acquisitions the subjects lay supine in a 1.5T Signa HDx MRI machine (GE, Milwaukee, WI) with an abdominal coil. Fat quantification is clinically obtained by applying the standard IDEAL-IQ ® sequence, implemented by General Electric. IDEAL-IQ ® is a gradient-echo sequence (TR = 11.5 ms, FA = 7°) applying 6 TEs from 2.4 ms to 9.6 ms. The following parameters were applied: receiver bandwidth = 166.7 kHz, FOV = 26-33 cm, matrix = 160 × 160, slice thickness = 10 mm, 24 slices covering 24 cm in the superior inferior direction. The scan time was approximately 21 s with a single breath-hold. For this study, 32 proton density fat fraction (PDFF) maps were reconstructed from the calculated water-only and fat-only maps (PDFF = fat / (water + fat) × 100). MRI-PDFF region of interest (ROI) was placed in the superior area of liver, at the nearly same location than the ROI of the FibroScan ® examination [START_REF] Kühn | Effect of Multipeak Spectral Modeling of Fat for Liver Iron and Fat Quantification: Correlation of Biopsy with MR Imaging Results[END_REF], [START_REF] Bensamoun | Measurement of liver stiffness with two imaging techniques: Magnetic resonance elastography and ultrasound elastometry[END_REF], [START_REF] Ternifi | Improvements of Liver MR Imaging Clinical Protocols to Simultaneously Quantify Steatosis and Iron Overload[END_REF].

2.3

Protocols for fibrosis quantification FS device was used to obtain liver stiffness measurement (LSM), and considered as a reference of stiffness [START_REF] Eddowes | Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease[END_REF]. This imaging technique was preferred to magnetic resonance elastography (MRE) due to the susceptibility artifact in presence of iron. MRE experiments were performed with the MR-Touch ® device composed of a clinical acoustic driver for wave generation at 60 Hz, placed at the same level as the diaphragm and set in contact with the ribcage. The applied frequency was chosen according to the analysis by Leclerc et al. [START_REF] Leclerc | Characterization of a hyper-viscoelastic phantom mimicking biological soft tissue using an abdominal pneumatic driver with magnetic resonance elastography (MRE)[END_REF] who have characterized the material properties of the acoustic driver. Phase images were recorded in one slice with four temporal offsets, a motion sensitizing gradient-echo sequence applied in Z direction, a flip angle of 30 • , a field of view between 36 and 48 cm, a 256 × 64 acquisition matrix, a TE/TR equal to 21.7/50 ms. The total scan time was 16 s, corresponding to one breath-holding period.

Viscoelastic maps were generated using multi-model direct inversion (MMDI) [START_REF] Silva | Magnetic resonance elastography: evaluation of new inversion algorithm and quantitative analysis method[END_REF]. For each dataset, phase images were unwrapped using a minimum discontinuity algorithm and filtered with a Butterworth bandpass (from 2 to 128 waves per FOV) to remove longitudinal waves and noise. Assuming the liver is homogeneous, isotropic, and incompressible, the complex shear modulus G* = G' + iG'' (G': storage modulus, G'': loss modulus) was calculated with a liver density of 1000 kg/m 3 . The mean and the standard deviation of Young's modulus E = 3 |G*| and damping ratio (DR) = G'' / (2G') were estimated in each ROI. The damping ratio is a relative parameter (viscous to elastic behavior) that was demonstrated to be associated with inflammation [START_REF] Yin | Prediction of nonalcoholic fatty liver disease (NAFLD) activity score (NAS) with multiparametric hepatic magnetic resonance imaging and elastography[END_REF][START_REF] Yin | Distinguishing between Hepatic Inflammation and Fibrosis with MR Elastography[END_REF]. For the DR and E maps, confidence threshold masks were used to evaluate the validity of the pixel values in the ROI [START_REF] Silva | Magnetic resonance elastography: evaluation of new inversion algorithm and quantitative analysis method[END_REF]: the criterion of ROI placement (circular region of 316 pixels) was such that all pixels in the ROI have a confidence value higher than 80 %.

MRI protocol for iron overload quantification

The originality of the present study has been to use the IDEAL-IQ ® fat sequence to quantify iron volumetrically through the 32 R2* liver slices which are not used in clinical practice. The advanced IDEAL-IQ ® protocol used the 32 R2* (1/T2*) liver slices, recorded with a scale unit in Hz, and transformed in iron overload concentration (IOC) in µmol/g with the Wood equation applied to each pixel (IOC = 0.4535/T2* + 3.607 with T2* in second [START_REF] Gandon | Non-invasive assessment of hepatic iron stores by MRI[END_REF]. This advanced IDEAL-IQ ® protocol thus provides a volume representation of IOC values in μmol/g. A ROI was placed at the nearly same location than the ROI of the FibroScan ® .

2.5

Statistical analysis and staging levels For descriptive statistics, continuous variables were expressed as medians (interquartile range [IQR]) followed by Mann-Whitney test. Categorical data were summarized as counts. In order to estimate the diagnostic accuracy of VCTE measurements, the analysis was focused on fibrosis threshold F  F2 with gradation obtained by VCTE as reference [START_REF] Eddowes | Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease[END_REF], and on steatosis S  S1 with gradation obtained with MRI-PDFF as reference. The F2 threshold is set to 8.2 kPa for the Young's modulus (E) according to the stiffness thresholds used in VCTE [START_REF] Eddowes | Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease[END_REF]. The S1 threshold is set to 5 % as specified by Tang et al. 2015 [START_REF] Tang | Accuracy of MR Imaging-estimated Proton Density Fat Fraction for Classification of Dichotomized Histologic Steatosis Grades in Nonalcoholic Fatty Liver Disease[END_REF]. Moreover, the patients were classified in two groups according to the IOC level [START_REF] Merat | Sampling Error in Histopathology Findings of Nonalcoholic Fatty Liver Disease: A Post Mortem Liver Histology Study[END_REF]: hemochromatosis stage H0 for IOC < 36 µmol/g and stage H1 for 36 µmol/g  IOC < 80 µmol/g. An area under the receiver operating characteristic curve (AUROC) is calculated to evaluate the diagnostic performance. The correlation between the different variables (LSM, E, DR, CAP, PDFF) was analyzed using the Spearman's correlation. The conventional level of statistical significance of 0.05 was used for all the analyses.

All statistical computations were performed using the R software version 4.0.3 (The R Foundation for Statistical Computing, Vienna, Austria) and the graphics, stats, pROC, ggplot2 packages.

Results

Patients characteristics and relationships between VCTE and MRI/MRE measurements

The characteristics of the study cohort are provided in Table I. Seventy patients have no hemochromatosis. The IOC value is significantly different between H0 group and H1 group. The Young's modulus (E) value estimated from MRE is also different between these two groups.

The correlation between VCTE and MRI/MRE measurements are summarized in table 2 for the two groups H0 and H1. Of note, for H0 group, there is a significant correlation between LSM and E or DR, whereas there is no corresponding correlation for H1 group. For H0 group, CAP value is correlated to PDFF whereas it is not correlated for H1 group. CAP value is correlated to DR for H1 group.

Analysis of VCTE measurements of patients without hemochromatosis

The subgroup H0 of patients without hemochromatosis (n=70) include patients with different levels of fibrosis classified as followed: F0-F1 (n = 56), F2 (n = 4), F3 (n = 4) and F4 (n = 6). Among the cohort, there are 29 patients with S0 steatosis stage and 41 patients with S1 steatosis stage.

The results of VCTE measurements are presented in Fig. 2. No significant difference in LSM measurements was found between the two types of probes (Fig. 2A), and the LSM values increase in function of the level of fibrosis stage. CAP measurements with the probe XL are significantly higher for patients with steatosis grade S  S1 (Fig. 2B). This finding results from the specific design of this probe for obese patients. The CAP values increase with the level of steatosis.

Fig. 3 shows ROC curves for the MRE-E and CAP measurements. Young's modulus E identified patients with fibrosis with an AUROC of 0.78 (95% confidence interval [CI] 0.63-0.93) for F  F2 with VCTE as reference. CAP identified patients with steatosis with an AUROC of 0.80 (95% [CI] 0.70-0.91) for S  S1 with PDFF as reference. These findings as well as the significant correlations for LSM vs E and CAP vs PDFF (Table 2), demonstrate the good performance of VCTE measurements for patients without hemochromatosis.

Fig. 4 shows the cartographies of IOC, E, DR and PDFF for the H0 group with different stages of fibrosis and steatosis. It can be observed that the PDFF cartographies show a sparse diffusion of the steatosis for the stage S1 compared to the stage S0.

Analysis of VCTE measurements of patients with hemochromatosis

The subgroup H1 of patients with hemochromatosis (n = 17) include patients with F0-F1 (n = 14), F2 (n = 1) and F4 (n = 2) fibrosis stage. Among the 17 patients, there are 4 patients with a S0 steatosis stage and 13 patients with a S1 steatosis stage.

Fig. 5 shows the distribution of LSM and CAP measurements. Due to the lack of hemochromatosis patients with a severe fibrosis, the effect of the probe has not been tested on the VCTE parameters.

Table II has revealed no significant correlation for LSM vs E and low correlation for CAP vs PDFF (p = 0.07) for H1 subgroup. Thus, no ROC analysis was performed for fibrosis evaluation. CAP identified patients with steatosis with an AUROC of 0.71 (95% [CI] 0.45-0.98) for S  S1 with PDFF as reference.

Fig. 6 shows the cartographies of IOC, E, DR and PDFF for patients with different stages of fibrosis and steatosis. The IOC cartographies well represent the heterogeneous distribution of iron compared to the IOC cartographies for the H0 group (Fig. 4). Moreover, the PDFF cartographies illustrate the spatial localization of the steatosis even though for high IOC level.

Discussion

The originality of this study was to show the impact of iron on the in vivo liver measurements to monitor the fibrosis and / or the steatosis. While it is difficult to recruit hemochromatosis patients, these results will increase radiologist's awareness on the effect of IOC level on the diagnostic result.

The present findings indicate that MRE does not significantly correlate with LSM measurements for patient with hemochromatosis H1 stage. This may be due to the fact that MRE technique is less precise when iron level is too high. Indeed, studies have demonstrated that the gradient-echo sequence used in MRE technique is inaccurate for T2* < 10 ms [START_REF] Ghoz | Hepatic iron overload identified by magnetic resonance imaging-based T2* is a predictor of non-diagnostic elastography[END_REF]. In this study, H1 patients have an IOC level superior to 36 µmol/g, corresponding to a T2* value less than 14 ms. As a consequence, from our measurements, we cannot evaluate the effectiveness of MRE for patients with hemochromatosis. A solution would be to use a spin-echo or spin-echo echo-planar imaging MRE sequences [START_REF] Mariappan | Application of Modified Spin-Echo-based Sequences for Hepatic MR Elastography: Evaluation, Comparison with the Conventional Gradient-Echo Sequence, and Preliminary Clinical Experience[END_REF][START_REF] Wang | Assessment of Advanced Hepatic MR Elastography Methods for Susceptibility Artifact Suppression in Clinical Patients[END_REF] to give more reliable measurements of stiffness. But this sequence is less widespread in clinical routine due to the high time of acquisition. Otherwise, it may therefore be necessary to resort back to liver biopsy to assess fibrosis in such patients.

In this study, biopsy, considered as the clinical gold standard, was impractical to obtain and VCTEassessed LSM was chosen as the reference of fibrosis. This imaging biomarker may be controversial as many studies tend to outline a better performance of the MRE technique compared to ultrasound based elastography, including transient elastography [START_REF] Imajo | Magnetic Resonance Imaging More Accurately Classifies Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease Than Transient Elastography[END_REF][START_REF] Park | Magnetic Resonance Elastography vs Transient Elastography in Detection of Fibrosis and Noninvasive Measurement of Steatosis in Patients With Biopsy-Proven Nonalcoholic Fatty Liver Disease[END_REF]. Thus, another ROC analysis has been undertaken with MRE as the reference of fibrosis: the F2 threshold is set to 9.9 kPa for the Young's modulus (E) according to the stiffness thresholds used in MRE [START_REF] Silva | Magnetic resonance elastography: evaluation of new inversion algorithm and quantitative analysis method[END_REF]. The results were similar with an AUROC value of 0.77 (95% confidence interval [CI] 0.62-0.92) compared to 0.78 (95% confidence interval [CI] 0.63-0.93) obtained with VCTE technique as the reference. For the present cohort, the results of the ROC analysis of fibrosis are equivalent.

Concerning the steatosis results, the CAP has low performances in differentiating the steatosis stages for H1 hemochromatosis patients. This observation may be biased by the low number of patients in each group (S0: n = 4; S1: n = 13) and highlight a limitation of the study. It would be of interest to test the new CAP measurements and see whether the findings are the same [START_REF] Audière | Improved Ultrasound Attenuation Measurement Method for the Non-invasive Evaluation of Hepatic Steatosis Using FibroScan[END_REF].

The perspectives will be 1) to increase the number of patients with hemochromatosis to confirm the steatosis and fibrosis analysis and 2) to improve the CAP and MRE methods of measurement.

Conclusions

The study demonstrates relative good correlations between VCTE and MRI-MRE measurements for patients without hemochromatosis. However, the correlations are degraded for H1 patients with hemochromatosis. This information is of importance for the radiologist and it can be concluded that these non-invasive techniques are well validated for patients with a low level of iron (e.g. IOC < 36 µmol/g).

Tables Captions List

Fig. 1 .

 1 Figures Captions List

Fig. 2 .

 2 Fig. 2. Distribution of VCTE measurements obtained with M and XL probes for patients without hemochromatosis with different stages of fibrosis (F) and steatosis (S) classified as followed : F < F2 (n = 56), F  F2 (n = 14), S < S1 (n = 29), S  S1 (n = 41). A. Liver stiffness measurement (LSM). B. Controlled attenuation parameter (CAP). The significant test of the measurement difference between probes is performed with a Mann-Whitney's t-test (**p <0.01).

Fig. 4 .

 4 Fig. 4. Representation of the different cartographies obtained with the MRI (A. IOC, D. PDFF) and MRE (B. Young's modulus, C. Damping Ratio map) acquisitions for the H0 group of patient at different fibrosis (F) stages and steatosis (S) stages. 80% confidence threshold masks are superimposed on Young's modulus and Damping Ratio maps.

Fig. 5 .

 5 Fig. 5. Distribution of VCTE measurements obtained with M and XL probes for patients with hemochromatosis with different fibrosis (F) stages and steatosis (S) stages: F < F2 (n = 14), F  F2 (n = 3), S < S1 (n = 4), S  S1 (n = 13). A. Liver stiffness measurement (LSM). B. Controlled attenuation parameter (CAP). The significant test of the measurement difference between probes is performed with a Mann-Whitney's t-test (**p < 0.01). In this distribution, only one patient was examined with XL probe for the stages F < F2 and S  S1.

Fig. 6 .

 6 Fig. 6. Representation of the different cartographies obtained with the MRI (A. IOC, D. PDFF) and MRE (B. Young's modulus, C. Damping Ratio map) acquisitions for the H1 group of patients at different fibrosis (F) stages and steatosis (S) stages. 80% confidence threshold masks are superimposed on Young's modulus and Damping Ratio maps.

Fig. 1 Fig. 4 18Fig. 5

 145 Fig. 1

Fig. 6

 6 Fig. 6

Table 1 . Patients characteristics.Table 2 . Comparison of CAP and LSM measurements versus MRI-PDFF and MRE (E, DR).

 12

	Table 1
	Hemochromatosis stage	H0	H1
	No. of patients	70	17
	No. of men	34	15
	Age, mean  SD (y)	57  15	57  15
	BMI, mean  SD (kg/m²)	27  5	25  5
	Normal weight/overweight/obese	23 / 17 / 20	11 / 1 / 5
	M probe / XL probe	47 / 23	16 / 1
	IOC, median (IQR) (µmol/g)	22.8 (6.6)	48.8 (18.4)***
	MRE-E, median (IQR) (kPa)	7.52 (3.70)	5.54 (2.78)**
	MRE-DR, median (IQR)	0.17 (0.07)	0.15 (0.05)
	LSM, median (IQR) (kPa)	5.30 (3.20)	5.20 (2.30)
	MRI-PDFF, median (IQR) (%)	5.7 (9.5)	6.9 (12.0)
	CAP, median (IQR) (dB/m)	257 (91)	278 (87)
	BMI = body mass index; CAP = controlled attenuation parameter; IOC = iron
	overload concentration; IQR = interquartile range; MRE-DR = damping ratio
	from magnetic resonance elastography; MRE-E = Young's modulus from
	magnetic resonance elastography; MRI-PDFF = proton density fat fraction
	from magnetic resonance imaging; SD = standard deviation. The significant
	test of the measurement difference is performed with a Mann-Whitney's t-test
	(**p <0.01, ***p <0.001).

Table 2

 2

	Hemochromatosis stage	H0	H1
	No. of patients	70	17
	Spearmann correlations
	LSM vs E
	LSM vs DR
	CAP vs PDFF
	CAP vs DR

  ). A. Liver stiffness measurement (LSM). B. Controlled attenuation parameter (CAP). The significant test of the measurement difference between probes is performed with a Mann-Whitney's t-test (**p <0.01). Receiver operating curve (ROC) analysis for the subgroup without hemochromatosis and for fibrosis (F) stage greater than or equal to F2 (8.2 kPa, n = 14) and for steatosis (S) stage greater than or equal to S1 (5%, n = 41). A. ROC analysis of Young's modulus (E) obtained with MRE. B. ROC analysis of CAP. AUROC (95% confidence interval CI): area under the receiver operating characteristic curve.

	Fig. 3.

Financial support

This study was funded by Echosens company (Paris, France).

Conflict of interest statement

All authors have no conflict of interest to disclose.


Jean-Marie Lagniez
email: lagniez@cril.fr

Emmanuel Lonca
email: lonca@cril.fr

Jean-Guy Mailly
email: jean-guy.mailly@u-paris.fr

Julien Rossit
email: julien.rossit@u-paris.fr

DESIGN AND RESULTS OF ICCMA 2021

Since 2015, the International Competition on Computational Models of Argumentation (ICCMA) provides a systematic comparison of the different algorithms for solving some classical reasoning problems in the domain of abstract argumentation. This paper discusses the design of the Fourth International Competition on Computational Models of Argumentation. We describe the rules of the competition and the benchmark selection method that we used. After a brief presentation of the competitors, we give an overview of the results.

Formal argumentation [START_REF] Baroni | Handbook of Formal Argumentation[END_REF][START_REF] Gabbay | Handbook of Formal Argumentation[END_REF] is a major topic in the domain of knowledge representation and reasoning. This formalism allows to reason with conflicting information, and has applications e.g. in automated negotiation [START_REF] Dimopoulos | Arguing and negotiating using incomplete negotiators profiles[END_REF] or decision making [START_REF] Amgoud | On the use of argumentation for multiple criteria decision making[END_REF]. The most classical reasoning tasks in this kind of formalism are intractable in the general case (see e.g. [START_REF] Dvorák | Computational problems in formal argumentation and their complexity[END_REF] for an overview of computational complexity in formal argumentation). This has conducted to the organization of the International Competition on Computational Models of Argumentation (ICCMA), that allows to compare the efficiency of the different algorithms that have been proposed for these reasoning problems. The previous competitions [START_REF] Thimm | The first international competition on computational models of argumentation: Results and analysis[END_REF][START_REF] Sarah | Design and results of the second international competition on computational models of argumentation[END_REF][START_REF] Bistarelli | A first overview of iccma'19[END_REF] have shown that, in spite of the theoretical hardness of argumentative reasoning, some powerful techniques allow to handle them efficiently.

After a short introduction to abstract argumentation in Section 1, we describe the rules of the competition in Section 2. In particular, we describe the various (sub)tracks as well as the scoring system and the benchmark selection. Note that, contrary to what was initially announced [START_REF] Lagniez | Introducing the fourth international competition on computational models of argumentation[END_REF], there has been no track dedicated to dynamic argumentation or structured argumentation at ICCMA 2021. However, there has been some interest in the community that conducted us to add a new track dedicated to approximation algorithms for reasoning with abstract argumentation frameworks. Then, we present the competitors and the results in Section 3.

Background: Abstract Argumentation

An abstract argumentation framework (AF) [START_REF] Minh | On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games[END_REF] Different semantics have been defined for evaluating the acceptability of (sets of) arguments.

Definition 1 Given an AF

F = A, R , a set of arguments S ⊆ A is conflict-free iff ∀a, b ∈ S, (a, b) ∈ R. A conflict-
free set S is admissible iff ∀a ∈ S, S defends a against all its attackers. Conflict-free and admissible sets are respectively denoted by CF(F ) and ADM(F ). Now, we formally introduce the extension-based semantics. For S ⊆ A,

• S ∈ CO(F ) iff S ∈ ADM(F ) and ∀a ∈ A that is defended by S, a ∈ S;

• S ∈ PR(F ) iff S is a ⊆-maximal admissible set;

• S ∈ ST(F ) iff S is a conflict-free set that attacks each a ∈ A \ S;

• S ∈ SST(F ) iff S ∈ CO(F ) and there is no S 2 ∈ CO(F ) s.t. S ⊕ ⊂ S ⊕ 2 ;

• S ∈ STG(F ) iff S ∈ CF(F ) and there is no

S 2 ∈ CF(F ) s.t. S ⊕ ⊂ S ⊕ 2 ; • S ∈ ID(F ) iff S ∈ ADM(F ), S ⊆ ∩PR(F ), and there is no S 2 ⊆ ∩PR(F ) such that S 2 ∈ ADM(F )
and S ⊂ S 2 .

CO, PR, ST, SST, STG and ID stand (respectively) for the complete, preferred, stable [START_REF] Minh | On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games[END_REF], semi-stable [START_REF] Martin | Semi-stable semantics[END_REF], stage [START_REF] Verheij | Two approaches to dialectical argumentation: admissible sets and argumentation stages[END_REF] and ideal [START_REF] Phan | Computing ideal sceptical argumentation[END_REF] semantics. We refer the interested reader to [START_REF] Baroni | Abstract argumentation frameworks and their semantics[END_REF] for more details about these semantics.

For σ ∈ {CO, PR, ST, SST, STG, ID} a semantics, an argument a ∈ A is credulously (respectively skeptically) accepted in F = A, R with respect to σ iff a ∈ S for some (respectively each) S ∈ σ(F ).

We are interested in various reasoning problems:

• CE-σ: Given an AF F = A, R , give the number of σ-extensions of F .

• SE-σ: Given an AF F = A, R , give one σ-extension of F .

• DC-σ: Given an AF F = A, R and a ∈ A an argument, is a credulously accepted in F ?

• DS-σ: Given an AF F = A, R and a ∈ A an argument, is a skeptically accepted in F ?

Most of these problems are computationally hard in general, under the semantics from Definition 1. For instance, the decision problems DC and DS might be complete for the first or second level of the polynomial hierarchy (see [START_REF] Dvorák | Computational problems in formal argumentation and their complexity[END_REF] for more details).

2 Rules of the Competition

Tracks and Subtracks

The competition is made of two main tracks. The first one, the most "classical" one, is dedicated to exact algorithms for reasoning with abstract AFs. The second track has been introduced for the first time at ICCMA 2021, and is dedicated to approximation algorithms for abstract argumentation.

Exact Algorithms The first track is made of six subtracks, each of them corresponding to one of the six semantics under consideration. Each subtrack is made of several reasoning tasks. More precisely, for σ ∈ {CO, PR, ST, SST, STG}, all the problems CE-σ, SE-σ, DC-σ and DS-σ must be solved. For σ = ID, since any AF possesses exactly one ideal extension, CE-σ is trivial, and DC-σ is equivalent to DS-σ. So, only SE-σ and DS-σ are considered.

Approximate Algorithms

The second track is also made of six subtracks corresponding to the six semantics. However, the problems CE-σ and SE-σ are not included in the subtracks this time, i.e. only the decision problems DC-σ and DS-σ appear. This means that every subtrack is made of two problems, except σ = ID which is made of only one task.

Input/Output and Environment

The input/output formats are the same as the formats from ICCMA 2019 for all the reasoning tasks that were considered then; only CE-σ requires the definition of a new format. Each benchmark is provided in two different file formats: trivial graph format (tgf) and ASPARTIX format (apx). For the following examples, we use the AF F = A, R with A = {a 1 , a 2 , a 3 } and R = {(a 1 , a 2 ), (a 2 , a 3 ), (a 2 , a 1 )}, depicted at Figure 1.

a 1 a 2 a 3 Figure 1: The AF F
The Trivial Graph Format describes a graph by giving a list of identifiers for nodes, then a list of edges, separated by a # symbol. See https://en.wikipedia.org/wiki/Trivial_Graph_Format for more details. We give below the content of myFile.tgf that corresponds to the AF depicted at Figure 1.

1 2 3 # 1 2 2 3 2 1
The ASPARTIX format (named after the ASP-based argumentation solver ASPARTIX [START_REF] Egly | Answer-set programming encodings for argumentation frameworks[END_REF]) describes the argument names as rules arg(name)., and attacks as rules att(name1,name2). We give below, as an example, the content of myFile.apx that corresponds to the AF depicted at Figure 1.

arg(a1). arg(a2). arg(a3). att(a1,a2). att(a2,a3). att(a2,a1).

For both tracks, solvers must write the result to standard output exactly in the format described below.

• DC-σ and DS-σ, for σ ∈ {CO, PR, ST, SST, STG, ID}. The output must be either

YES

if the queried argument is (respectively) credulously or skeptically accepted in the given AF under σ, or NO otherwise.

• SE-σ, for σ ∈ {CO, PR, ST, SST, STG, ID}. The output must be of the form

[a1,a2,a3]
meaning that {a 1 , a 2 , a 3 } is a σ-extension of the given AF, where a 1 , a 2 and a 3 are arguments. If σ = ST, there may be benchmarks that do not possess any extension. In that case, the output must be NO

• CE-σ, for σ ∈ {CO, PR, ST, SST, STG}. The output must be of the form

k
where k ∈ N is the number of σ-extensions of the given AF.

The solver interface is also inspired from ICCMA 2019. The new problem CE has a similar command line to the previous EE problem. 1The competition has been run on a computer cluster where each machine has an Intel Xeon E5-2637 v4 CPU and 128GB of RAM. The runtime limit for each instance is 600 seconds for the "exact" track, and 60 seconds for the "approximate" track. The memory limit is the machine's memory, i.e. 128GB.

Scoring Rules

There is one ranking for each sub-track, i.e. six rankings for the "exact" track and six rankings for the "approximate" track. To be ranked, a solver must participate to the full subtrack (but without any obligation to participate to all the (sub)tracks). The scoring system is slightly different between both tracks.

For the "exact" track, any wrong result on an instance i in a subtrack conducts to the exclusion of the solver from the said subtrack. It does not prevent the solver from being ranked for other subtracks if there is no wrong results for these other ones. Then, the score of a solver S on the instance i is Score(S, i) =

1 the correct answer is given in the runtime limit 0 timeout or non-parsable output On the contrary, wrong results do not lead to an exclusion in the "approximate" track:

Score(S, i) =
1 the correct answer is given in the runtime limit 0 wrong result, timeout or non-parsable output

Then, the score of the solver S for the task T is

Score(S, T ) = i∈T Score(S, i)
and the score for the subtrack ST is

Score(S, ST ) = T ∈ST
Score(S, T ).

In the case where two solvers have the same score for a given subtrack, the cumulated runtime over the instances correctly solved is used as a tie-break rule (the fastest is the best).

Benchmark Selection

ICCMA 2019 Instances We have selected 165 instances from the previous competition. The goal is to observe the evolution of the algorithmic techniques for abstract argumentation during the last two years. These instances are the hardest ones from ICCMA 2019, with respect to two criteria:

• the number of times some solvers have reached the timeout on these instances;

• the average runtime for solving these instances.

New instances

We have defined a new method for generating challenging benchmarks. This method has been used for generating 422 new instances. For creating an instance, we use this procedure:

1. Generate a (meta-)graph G following a classical generation pattern (e.g. Erdos-Renyi, Barabasi-Albert,. . . ).

2. For each node n i in this graph, generate a new graph F i . 3. For each edge (n 1 , n 2 ) in G, pick some arguments a 1 in F 1 and a 2 in F 2 , and add an edge (a 1 , a 2 ).

Intuitively, this method is used to create graphs with "communities of arguments".

Query argument selection

The last part of the benchmark selection is the choice of an argument to be queried for skeptical or credulous acceptance (DS, DC). Simply, for each AF, one argument is randomly chosen, and this argument is used for all the DS and DC queries on the same AF.

3 Participants and Results

Competitors

There were 9 solvers participating to ICCMA 2021, 7 in the exact track, and 2 in the new approximate track.

Exact solvers

• A-Folio DPDB (Fichte, Hecher, Gorczyca and Dewoprabowo) [START_REF] Fichte | A-Folio DPDB -system description for ICCMA[END_REF] • ASPARTIX-V21 (Dvorák, König, Wallner and Woltran) [START_REF] Dvorák | ASPARTIX-v21[END_REF] • ConArg (Bistarelli, Rossi, Santini and Taticchi) [START_REF] Bistarelli | ConArg: A constraint-programming solver for abstract argumentation problems[END_REF] • FUDGE (Thimm, Cerutti, Vallati) [START_REF] Thimm | Fudge: A light-weight solver for abstract argumentation based on SAT reductions[END_REF] • MatrixX (Heinrich) [START_REF] Heinrich | The MatrixX solver for argumentation frameworks[END_REF] • µ-toksia (Niskanen and Järvisalo) [START_REF] Niskanen | µ-toksia: An efficient abstract argumentation reasoner[END_REF] • PYGLAF (Alviano) [START_REF] Alviano | The PYGLAF argumentation reasoner (ICCMA2021)[END_REF] Observe that 3 solvers are new submissions (A-Folio DPDB, FUDGE and MatrixX) , while 4 of them (ASPARTIX-V21, ConArg, µ-toksia and PYGLAF) are updated versions of solvers submitted to previous editions of ICCMA.

Various techniques have been used, like translations into SAT, Constraint Programming or ASP, and dedicated algorithms.

Approximate solvers

• AFGCN (Malmqvist) [START_REF] Malmqvist | AFGCN: An approximate abstract argumentation solver[END_REF] • HARPER++ (Thimm) [START_REF] Thimm | Harper++: Using grounded semantics for approximate reasoning in abstract argumentation[END_REF] AFGCN is mainly based on neural networks, while HARPER++ uses the grounded semantics as a tool for approximating the results of the various decision problems. 1: Participation of the solvers to the various subtracks

Results of the Exact Track

Results for the track dedicated to exact algorithms are described in Table 2. Half of the subtracks are won by new solvers (A-Folio-DPDB for the complete and stable semantics, and Fudge for the ideal semantics), while updated versions of existing solvers perform well in the other subtracks (PYGLAF for the preferred and semi-stable semantics, and ASPARTIX for the stage semantics). An interesting point is the global correctness of the solvers. Only two solvers presented minor bugs during the running of the competition (Fudge on the stable semantics, and PYGLAF on the stage semantics). We gave some feedback to the competitors, which allowed to correct the mistakes, and only PYGLAF had to be excluded from one subtrack (the one dedicated to the stage semantics). Detailed results (ranking for each subtrack, with the cumulated runtime over the successfully solved instances) are given in Appendix A.

Results of the Approximate Track

The results for the various subtracks of the Approximate Track are described in Table 3. AFGCN wins most of the subtracks, but it is dominated by HARPER++ for the complete and ideal semantics. Detailed results (ranking for each subtrack, with the cumulated runtime over the successfully solved instances) are given in Appendix B.

Conclusion

This paper presents a short overview of the organization of ICCMA 2021 as well as the results of the competition. Detailed results and benchmark descriptions will be available soon at http://argumentationcompetition.org/2021/index.html. Several ideas could be interesting for the organizers of the future editions of the competition. The first one consists in reviving the dynamic track that was introduced at ICCMA 2019. Unfortunately, the lack of participants for this track conducted us to remove it from ICCMA 2021, but it seems to us that real world application of argumentation requires techniques dedicated to dynamic scenarios, which makes it an important feature of next ICCMA editions. The same comment applies to structured argumentation, which was also removed from ICCMA 2021. On the contrary, two interesting topics were introduced to ICCMA 2021 thanks to suggestions from the community. The first one is the track dedicated to approximate algorithms. For this first introduction of approximate algorithms at ICCMA, we chose to focus on the decision problems which are quite easy to evaluate. For the other reasoning tasks, it is not so easy to determine a relevant metric for evaluating the answer of an approximation algorithm. For instance, if the task is to determine a preferred extension of an AF, should we rank equally a solver that returns a (possibly non-maximal) admissible set and a solver that returns a conflicting set? This question, and similar ones, conducted us to exclude CE-σ and SE-σ for the approximate track. Metrics for evaluating approximate algorithms for these problems are thus an interesting question for the future of ICCMA. The other question that arose during the organization of ICCMA 2021 is the issue of parallel computing. One solver (µ-toksia) was submitted in two versions, one single-threaded and one multi-threaded. While we decided to run the multi-threaded versions without ranking it, a special track dedicated to parallel computing could be interesting in the future.

Rank

For concluding this short description of ICCMA 2021, we thank all the participants, as well as the ICCMA steering committee for providing some valuable feedback during the preliminary phases of the organization.

B.3 Semi-Stable Semantics

Table 12 shows the results for the semi-stable semantics.

B.4 Stable Semantics

Table 13 shows the results for the stable semantics.

B.5 Stage Semantics

Table 14 shows the results for the stage semantics.

B.6 Ideal Semantics

Table 15 shows the results for the ideal semantics.

  is a directed graph F = A, R , where A is the set of arguments, and R ⊆ A × A is the attack relation. For a, b, c ∈ A, we say that a attacks b if (a, b) ∈ R. If in turn b attacks c, then a defends c against b. Similarly, a set S ⊆ A attacks (respectively defends) an argument b if there is some a ∈ S that attacks (respectively defends) b. For S ⊆ A a set of arguments, S + is the set of arguments that are attacked by S, formally S + = {b ∈ A | ∃a ∈ S s.t. (a, b) ∈ R}. The range of S is S ⊕ = S ∪ S + .

  Participation to SubtracksThe solvers are registered to ICMMA 2021 (sub)tracks as described by Table1.

		Exact Track	Approximate Track
	Solver	CO PR ST SST STG ID CO PR ST SST STG ID
	AFGCN
	A-Folio DPDB
	ASPARTIX-V21
	ConArg
	FUDGE
	HARPER++
	MatrixX
	µ-toksia
	PYGLAF
		Table

Table 2 :

 2 Rankings for the Exact track

		Rank		Solver	Score	Rank		Solver	Score
		1	HARPER++	747	1		AFGCN	637
		2		AFGCN	668	2	HARPER++	457
		(a) Complete Semantics	(b) Stable Semantics
	Rank	Solver		Score	Rank	Solver		Score Cumulated Runtime
	1	AFGCN		567	1	HARPER++	108	9.848397
	2	HARPER++	438	2	AFGCN		108	470.655630
	(c) Preferred Semantics		(d) Ideal Semantics
		Rank		Solver	Score	Rank		Solver	Score
		1		AFGCN	522	1		AFGCN	392
		2	HARPER++	351	2	HARPER++	349
		(e) Semi-Stable Semantics	(f) Stage Semantics

Table 3 :

 3 Rankings for the Approximate track

  The competition was run on the CRIL computer cluster, that was funded by the French Ministry of Research and the Région Hauts de France through CPER DATA.

	Rank	Solver	Score	Runtime	Rank	Solver	Score	Runtime
	1	PYGLAF	107	283014.802853	1	FUDGE	587	931.517987
	2	µ-TOKSIA	107	288298.145252	2	A-Folio-DPDB	587	5706.033478
	3	FUDGE	107	288718.386884	3	µ-TOKSIA	587	7540.231083
	4	ASPARTIX-V	107	288844.420419	n/a	µ-TOKSIA-parallel	587	12981.315567
	5	ConArg	106	206539.159533	4	MatrixX	587	22954.152336
	n/a	µ-TOKSIA-parallel	105	287803.928120	5	ASPARTIX-V	587	30644.831330
	6	A-Folio-DPDB	80	224853.575249	6	PYGLAF	586	56647.132293
	7	MatrixX	57	240888.182199	7	ConArg	107	199182.937802
		(a) CE-CO			(b) SE-CO
	Rank	Solver	Score	Runtime	Rank	Solver	Score	Runtime
	n/a	µ-TOKSIA-parallel	587	21217.410508	1	FUDGE	587	928.161398
	1	A-Folio-DPDB	584	19690.986367	2	A-Folio-DPDB	587	6614.674043
	2	PYGLAF	557	75189.484641	3	µ-TOKSIA	587	8540.708610
	3	µ-TOKSIA	522	62550.500735	n/a	µ-TOKSIA-parallel	587	13997.941173
	4	ASPARTIX-V	506	107024.507674	4	ASPARTIX-V	587	30766.565703
	5	FUDGE	414	127037.443418	5	PYGLAF	585	56820.945178
	6	ConArg	108	201311.092994	6	ConArg	107	199660.489916
	7	MatrixX	58	240710.947344	7	MatrixX	57	242088.917976
		(c) DC-CO			(d) DS-CO
			Rank	Solver	Score	Runtime
			n/a	µ-TOKSIA-parallel 1866 336000.595368
			1	A-Folio DPDB	1838 256865.269137
			2	PYGLAF	1835 471672.364965
			3	µ-toksia	1803	366929.58568
			4	ASPARTIX-V21	1787 457280.325126
			5	FUDGE	1695 417615.509689
			6	MatrixX	759	746642.199855
			7	ConArg	428	806693.680245

(e) Overall Results for CO

Table 4 :

 4 Result for the Exact Solvers on the Complete Semantics

See http://argumentationcompetition.org/2021/SolverRequirements.pdf for more details.

A Results of the Exact Track

A multithreaded version of µ-toksia has been submitted, and was run out of competition. Its results are presented here with those of single threaded solvers, although it was not eligible for an ICCMA award.

A.1 Complete Semantics

Table 4 shows the results for the complete semantics.

A.2 Preferred Semantics

Table 5 presents the results for the preferred semantics.

A.3 Semi-Stable Semantics

Table 6 presents results for the semi-stable semantics.

A.4 Results for the Stable Semantics

Table 7 presents results for the stable semantics.

A.5 Stage Semantics

Table 8 shows the results for the stage semantics. PYGLAF was removed from the ranking because of wrong results on CE-STG.

A.6 Ideal Semantics

Table 9 shows the results for the ideal semantics.

B Results of the Approximate Track

B.1 Complete Semantics

Table 10 shows the results for the complete semantics.

B.2 Preferred Semantics


C Jolivalt
email: jolivalt@ext.jussieu.fr

A Raynal

E Caminade

C Mougin

B Kokel

F Le Goffic

Transformation of N',N'-dimethyl-N-(hydroxyphenyl)ureas by Laccase from the White Rot Fungus Trametes versicolor

Transformation of N',N'-dimethyl-N-(hydroxyphenyl)ureas was assayed in the presence of purified laccase produced by the fungus Trametes versicolor. The para-and orthohydroxyphenyl derivatives were enzymatically transformed, whereas the meta derivative was not.

The performance of laccase-mediated transformation depended on the pH with an optimum for the para-derivative degradation rate at pH 5. The pH also influenced the nature of the reaction products. The chemical was exclusively oxidised into p-benzoquinone at pH 3 and into mainly N',N'-dimethyl-N-[(2,5-cyclohexadiene-1-one)-4-ylidene]urea at pH 6. The ortho-derivative was transformed essentially into insoluble purple compounds, probably appearing as polymers resulting from coupling of the parent compound.

Introduction

Laccases (EC-1-10-3-2) are copper-containing oxidases widely distributed in woody plants [START_REF] Mayer | Polyphenol oxidases in plants -Recent Progress[END_REF] and fungal species which carry out the delignification process, such as white rot fungi [START_REF] Hatakka | Lignin-modifying enzymes from selected white-rot fungi: production and role in lignin degradation[END_REF][START_REF] Thurston | The structure and function of fungal laccases[END_REF]. Typically, the enzyme catalyses the one-electron oxidation of phenolic substrates or aromatic amines. It has been also shown that in the presence of appropriate low-molecular-weight compounds called mediators, laccase was able to oxidise a wide range of other aromatic compounds. Mediators were identified among compounds naturally synthesised by the fungus [START_REF] Eggert | A fungal metabolite mediates degradation of non-phenolic lignin structures and synthetic lignin by laccase[END_REF] and among xenobiotics [START_REF] Bourbonnais | Reactivities of various mediators and laccases with Kraft pulp and lignin model compounds[END_REF] such as 2,2'-azinobis(3-ethylbenz-thiazoline-6-sulfonic acid)(ABTS). Laccase catalyses oxidations, leading in most cases to coupling reactions and the formation of conjugates or polymers.

Phenylurea herbicides are widely used for pre-and post-emergence selective control of grasses and broad-leaved weeds in winter cereal crops. These chemicals undergo oxidation reactions in soil, plant and microbial systems, including N-dealkylation, aryl or ring substituent hydroxylation [START_REF] Gross | The metabolic behaviour of chlorotoluron in wheat and soil[END_REF][START_REF] Ryan | The metabolism of chlortoluron, diuron, and CGA 43057 in tolerant and susceptible plants[END_REF]. Most of these reactions are followed by sugar conjugation, in spite of free oxidised compounds being detected. Enzymes principally involved in phenylurea transformation were identified among P450 mono-oxygenases [START_REF] Mougin | Hydroxylation and N-demethylation of chlorotoluron by wheat microsomal enzymes[END_REF] and glucosyl transferases [START_REF] Hatzios | Metabolic reactions and pathways of herbicide metabolism[END_REF]. Hydroxylated metabolites formed by plant and microorganism metabolism appear as phenolic compounds and behave like substrates of laccases. In this paper, we report the results of our studies on the transformation of hydroxylated metabolites of N',N'-dimethyl-N-phenylureaknown as fenuron -by partially purified laccases of the white rot fungus Trametes versicolor.

Materials and Methods

Chemicals

Unless otherwise stated, all chemicals and reagents used for synthesis were purchased from Sigma (St Quentin Fallavier, France). Solvents used for HPLC analysis came from Carlo Erba (Val de Reuil, France).

Synthesis of model compounds

R 1 R 2 N',N'-dimethyl-N-(2-hydroxyphenyl)urea (2-HF) 1 [R1: 2-OH, R2: NH-CON(CH3)2]
The synthesis procedure for 2-HF was adapted from Hadjieva et al. (1988). Triethylamine (25 mL) diluted in ethanol (25 mL) was added dropwise to a stirred solution of benzoxasolone (2.7 g, 0.02 mol) and dimethylamine hydrochloride (10 g, 0.123 mole) in 50 % aqueous ethanol. The resulting mixture was heated at 80°C under reflux for 6 hours. After cooling to room temperature, diluted aqueous hydrogen chloride was added until neutralisation and evaporated under reduced pressure. The product was purified by flash chromatography on a silica gel column using ethyl acetate as a mobile phase. Yield: 585 mg; 15 %.

N',N'-dimethyl-N-(3-hydroxyphenyl)urea (3-HF) 2 [R1: 3-OH, R2: NH-CON(CH3)2]
Dimethylamine hydrochloride (1.6 g, 20 mmoles) was added to a solution of 3hydroxyphenylurea (2.5 g, 15 mmoles) in water (200 ml) and heated under reflux for 5 hours.

After cooling to room temperature, the clear mixture was saturated with sodium chloride, extracted with ethyl acetate (4 x 100 ml) and the combined organic layers dried over sodium sulfate. The solvent was then removed in vacuo and 500 mg of the crude residue (total: 2.35 g) was chromatographied over a silicagel column using a mixture of cyclohexane and ethyl acetate 2/1 as eluant to afford pure 2. Yield: 123 mg, 21.5%. then with diethyl ether (3 x 20 ml) and dried in vacuo at room temperature. Yield: 14 g; 91%.

Azide 3b [R1: 4-OH, R2: CON3]. Pure acetic acid (8.4 ml, 0.14 mole) was added dropwise to a stirred mixture of 3a (10 g, 0.066 mole) and sodium nitrite (10 g, 0.145 mole) in water (200 ml) over a period of 30 min. The solution was maintained at 0°C in an ice bath. The resulting mixture was then allowed to stand under stirring at 0°C for 3 hours, warmed to room temperature and filtered using a glass filter. The crude residue was mixed with diethyl ether (50 ml), while the insoluble residual starting material was separated by filtration. The solvent was removed under reduced pressure and the azide 3b dried in vacuo at room temperature. Yield: 10.2 g; 95 %.

Protected azide 3c [R1: 4-OCOCH3, R2: CON3]. Acetyl chloride (2.85 g, 36.5 mmoles) in diethyl ether (10 ml) was added dropwise to a stirred solution of 3b (5 g, 30.5 mmole) and triethylamine (3.7 g, 36.5 mmole) in dry diethyl ether (50 ml) at room temperature over a period of 30 min.

The resulting heterogeneous mixture was then allowed to stand under stirring at room temperature for 2 hours. The insoluble triethylamine hydrochloride was filtered using a glass filter. The filtrate was treated first with a 2% aqueous potassium hydrogen carbonate solution (4 x 40 ml) followed by water until neutralisation. The organic layer was dried over sodium sulphate. The solvent was removed under reduced pressure and the residue dried in vacuo at room temperature to afford the protected azide as a white solid substance. Yield: 5.2 g; 83%.

Protected isocyanate 3d [R1: 4-OCOCH3, R2: NCO]. A stirred suspension of the crude protected azide 3c (5.2 g, 25.4 mmol) in toluene (50 ml) was heated to 80°C until nitrogen evolution had ceased (4 hours). The solvent was then removed in vacuo and the oily residue mixed with pentane to afford the protected isocyanate as a white solid substance. Yield: 4 g; 89%.

Protected dimethylphenylurea 3e [R1: 4-OCOCH3, R2: NHCON(CH3)2]. Triethylamine (11.4 g, 0.113 mol) in dichloromethane (50 ml) was added dropwise to a suspension of dimethylamine hydrochloride (9.2 g, 0.113 mol) and 3d (4 g, 22.6 mmol) in dichloromethane (100 ml). The mixture was heated at 40°C for 3 hours. After cooling to room temperature, it was treated in turn with a 5% aqueous hydrogen chloride solution (4 x 20 ml), a 5% aqueous sodium hydrogen carbonate solution (4 x 20 ml), and water until neutralisation. The organic layer was then dried over sodium sulfate and the solvent removed in vacuo. Yield: 3.06 g; 61%.

N',N'-dimethyl-(4-hydroxyphenyl)urea 3. A mixture of the crude protected dimethylphenylurea 3e (1.7 g, 7.65 mmol) and potassium hydroxyde (1.5 g, 26.8 mmol) in 1% aqueous methanol (100 ml) was heated at 40°C for one hour. The solvent was then removed in vacuo and the oily residue neutralised to pH 5 with HCl acidified water. The water was evaporated and ethanol was added to the residue. The insoluble part was removed by filtration and the solvent evaporated.

The product was then purified by precipitation in diethyl ether, filtered and dried. Yield: 1.06 g; 77 %.

The overall yield of the multi-step procedure was 30%.

Enzyme Production and Purification

The organism used for laccase production was Trametes versicolor. It was obtained from the culture collection of the Unité de Phytopathologie (INRA Versailles, France).

For biomass production, the fungus was pregrown on the medium previously described (Lesage- et al., 1996) using glucose (5 g l -1 ) as a carbon source. Biomass production was achieved in Roux flasks containing 200 mL culture medium, developed from five agar disks (5mm), and cultured at 25°C for 7 days. Then, three mat mycelia were filtered from the growth medium and resuspended in sterile, deionized water to a final volume of 300 mL. The resulting mycelia suspensions were fragmented for 1 minute with an Ultra Turrax homogeniser (10 000 rpm). The pounded mats were used to inoculate the bioreactor.

Meesen

The air-lift bioreactor used for laccase production contained 5 litres of the mineral culture medium described by [START_REF] Collins | Oxidation of anthracene and benzo(a)pyrene by laccases from Trametes versicolor[END_REF]. The culture, shaken by continuous air bubbling, was grown for a further 7-day period. Laccase production was induced with 2,5-xylidine (0.2 mM) at the beginning of the culture. The laccase-containing exocellular culture fluid was filtered through

glass-wool or a cheese cloth and frozen at -80°C. The solution was then filtered successively on miracloth and glass filters (Whatman) with decreasing pore diameters (2.7 to 0.7 m) in order to eliminate polysaccharides. A final filtration step was performed on a 0.22 m Durapore (Millipore) membrane before concentration by ultrafiltration on an YM10 Amicon membrane.

The crude enzyme concentrate (CE) was stored at -80°C.

The CE was then applied on a DEAE52 ion-exchange column (Whatman) equilibrated with a 10 mM citrate/phosphate buffer pH 6. The laccase was eluted with a linear gradient up to 0.75 M NaCl. The active fractions were pooled and concentrated by ultrafiltration on an YM10 Amicon membrane and ultradialized against 20 mM sodium phosphate buffer pH 7 before a further purification step by size-exclusion chromatography on a Sephacryl S200HR column (Pharmacia)

in the same buffer. This partly-purified enzyme solution (PE) was stored at -20°C in 30 % w/v glycerol.

Enzyme Assay

Laccase activity was measured as the oxidation of 2,2'-azinobis-(3-ethylbenz-thiazoline-6sulfonic acid) (ABTS, 420 = 36 000 M -1 cm -1 ) method [START_REF] Wolfenden | Radical cations as a reference chromogens in kinetics studies of one electron transfer reactions[END_REF]) using a Kontron UV-visible 860 spectrophotometer. The assay was conducted at 30°C in 1 mL of a solution containing 1 mM ABTS in 100 mM phosphate-citrate buffer pH 3. International units (mol min -1 ) of enzyme activity were used.

Enzymatic transformation of dimethyl-(hydroxyphenyl)ureas

Enzymatic transformation of (dimethyl-hydroxyphenyl)ureas was performed in 8 mL test tubes containing 4 mL of solution prepared as follows: ethanolic solutions of the three synthesised ureas (10 mg mL -1 ) were dissolved to final concentrations varying from 0.7 mM to 0.9 mM in 100 mM citrate/phosphate buffer at a chosen pH. All the experiments were carried out using airsaturated buffer solutions. The reaction started by adding PE laccase solution. Laccase titres in the assays were adjusted to 0.4 unit. Enzyme was omitted in controls containing the dimethyl-(hydroxyphenyl)urea at the same pH and concentration. The samples were stirred on an orbital table and temperature was kept constant at 30°C. Specific reaction conditions are detailed in the legends of the figures.

The enzymatic reactions were stopped by heating at 100°C for 5 min. The samples were cooled and used for HPLC and/or UV spectral analysis. Control samples were also heated to 100°C and analysed to check any modification of the substrate during incubation and heating.

Isolation of the reaction products

In order to identify the 4-HF reaction products, transformation of 5.9 10 -2 moles (11 mg) was performed at 30°C in 10 mL in the presence of 5U laccase at two pH values. Medium pH was set to 3 with phosphoric acid to avoid any interference from citrate protons in the NMR analysis. For pH 6, a 20 mM phosphate buffer was used. After 20 hours incubation, the reaction media was supplemented with NaCl to reach a 2M concentration, and then acidified to pH 1.5-2 with concentrated HCl. Organic compounds were extracted with dichloromethane (3 x 10 mL). HPLC analysis showed that the extraction efficiency had reached 95 %. The organic solvent was dried over Na2SO4 and evaporated under reduced pressure. The crude extract was then purified by chromatography on a silica column using ethyl acetate as an eluant. The main product was analysed by NMR and mass spectroscopy.

Isolation of the insoluble product formed from 2-HF was achieved by filtration over a GV 0.22 µm filter (Millipore). The insoluble part was dried at 60°C for 24 hours and dissolved in 2 mL dichloromethane.

High Performance Liquid chromatography (HPLC) separation

The concentrations of the dimethyl-(hydroxyphenyl)urea derivatives were determined using an HPLC system (Waters Associates, Milford, USA) equipped with a 486 UV detector and a 600E system controler. Ten-microlitre samples were analysed using a Nova Pack C18 column (1503.9 mm, Waters). The mobile phase consisted of water (solvent A) and acetonitrile (solvent B) at a flow rate of 1 mL min -1 . The following mobile phase gradient method was used: 100% A for 3 min, linear increase from 0% B to 50% B in the next 15 min, brought to 100% B in 2 min and kept constant for 7 min. The column was equilibrated to initial conditions for 13 min before a further injection. The total run time was 40 min. The samples were detected at 244 nm. The acquired data were analysed using the Waters chromatography system manager Millenium v2.00.

Spectroscopic methods

Mass spectrometry (MS) -The mass spectra of the separated products were obtained by electron impact (EI) mass spectroscopy analysis (70 eV) using a Ribermag R10-10 quadrupole mass spectrophotometer, with sample introduction by direct insertion probe. The molecular weight was checked by chemical ionisation (CI) using CH4 or NH3 as reactant gases.

GC/MS was also used. Gas chromatography was performed with a HP 5989A chromatograph fitted with a RTX 5MS column (length 30 m, 0.25 mm diameter, 0.5m deepth film, Restek).

The temperature of the injector was 260°C. The oven ramped from 80 to 300°C at 5°C per minute.

Nuclear magnetic resonance (NMR) -Proton 1 H NMR spectra of the analysed compounds dissolved in CDCl3 or DMSO were obtained using a Bruker AC-200 NMR spectrometer at 200

MHz. TMS was used as a reference compound.

Results

Synthesis of model compounds

Spectroscopic data for synthesised 2-, 3-and 4-HF are shown in Table 1. Molecular weight of the three synthesised ureas (determined by CI-NH3) was 180. Fragment ions for 2-and 4-HF were consistent with dimethyl-(hydroxyphenyl)urea structures. NMR data definitively confirmed the position of the hydroxyl group. Synthesis of 2-HF and 3-HF using one-step procedures gave rather low yields (15.0 and 21.5%, respectively), mostly because of the presence of by-products.

No attempt was made to optimise them since the amounts obtained were sufficient to undergo the degradation assays in the presence of laccase.

We attempted to use the transamination method outlined above for the synthesis of 4-HF. The reaction between aminophenol and potassium cyanate leading to the corresponding ureidophenol was assayed according to [START_REF] King | Intramolecular ureido and amide group participation in reactions of carbonates diesters[END_REF]. Unfortunately, high amounts of the corresponding amide were formed due to the presence of acetic acid in the reaction mixture. The overall yield (including the transamination) was therefore very low. As a consequence, we turned to another multi-step method adapted from the preparation of putrescine hydrochloride, as reported by [START_REF] Smith | Putrescine dihydrochloride synthesis[END_REF]. The overall yield for the 6-step synthesis procedure was about 30 %, and provided enough material to successfully carry out degradation experiments.

After the final purification procedure, the purity of each dimethyl-(hydroxyphenyl)urea was at least 95%, as checked by NMR and HPLC.

Preliminary transformation experiments

The ability of the laccase from Trametes versicolor to transform the three dimethyl-(hydroxyphenyl)ureas was tested in assays performed at 30°C with pH from 3 to 8 and with different incubation times. Incubation of 2-HF and 4-HF with the enzyme resulted in the decrease of their initial concentrations and the formation of new products, as shown on the chromatograms in Fig. 1. The concentrations of the 2-and the 4-substituted derivatives were reduced to 43% and 58 % of their initial value, respectively after 15-min incubation. Conversely, 3-HF was not transformed in the presence of the laccase at the pH generally known to be optimum for laccase activity. No transformation products were detected in the controls run without enzyme.

Influence of pH on N',N'-dimethyl-N-(4-hydroxyphenyl)urea degradation

The pH had a marked influence on the kinetics of 4-HF degradation. Firstly, laccase activity exhibited a bell shape as a function of the pH with a maximal transformation (91%) at pH 5.

Laccase catalysis decreased rapidly when the pH was raised above the optimum. At pH 6, only 12.2 % of the 4-derivative was converted after 1 hour in the presence of enzyme. At pH 7, no change in the initial chemical concentration could be detected by HPLC in incubation media over 6 hours. However, evidence for biological transformation of the para-derivative occurred between 6 and 96 hours of incubation, as 84 % of the chemical had degraded. Conversely, laccase remained active at acidic pH, as 40% of 4-HF had been transformed at pH 3. Whatever the pH value, the concentration of 4-HF remained unchanged in the controls without enzyme during 96-hour periods.

Another important aspect of the pH effect was the nature of the degradation products, as seen in Fig. 2. At pH 3 and 4, the main degradation product (called compound I) was more polar than the parent compound (RT 5.6 min versus 12.0 min). At pH 6, the 4-HF was mostly transformed into the apolar compound II (RT 12.9 min) plus a significant amount of compound I. Between these two pH values, there were equal amounts of compound I and II.

Isolation and identification of the N',N'-dimethyl-N-(4-hydroxyphenyl)urea transformation products

Degradation of 4-HF was accompanied by the formation of about seven products, as revealed by HPLC analysis. However, compounds I and II were predominant (Fig. 1A). The identification of the latter was based on the analysis of degradation experiments performed at pH 3 and 6, respectively. We took advantage of the pH specificity of the reaction to minimise the purification steps needed to prepare enough samples used to carry out the identification procedures. After a 20-hour incubation, the reaction solution colour was orange at pH 3 and yellow at pH 6. The products extracted with methylene chloride and purified by liquid chromatography were then analysed through MS and NMR (Table 1). CI-CH4 analysis of compound I revealed a molecular weight of 108. Its NMR spectrum only exhibited a singlet at 6.4 ppm, indicating a symmetric molecule. From these data, compound I was identified as p-benzoquinone. As further confirmation, HPLC and spectroscopic analysis (EI mass spectra and NMR data) of authentic pbenzoquinone showed similar results.

GC/MS spectrum of compound II suggested a molecular weight of 178 (Table 1). CI analysis gave no results, either with NH3 or CH4 as reactant gases. The NMR spectrum of compound II exhibited two doublets at 6.66 and 7.16 ppm, characteristic of para-substituted aromatic protons.

A singlet at 2.90 ppm could be assigned to the dimethyl protons of the substutited urea. These data as well as fragment ions obtained by EI suggested that compound II was N',N'-dimethyl-N-[(2,5-cyclohexadiene-1-one)-4-ylidene]urea.

Study of N',N'-dimethyl-N-(2-hydroxyphenyl)urea transformation products

Several transformation products of 2-HF were detected (Fig 1B). The solution rapidly became purple in the presence of laccase and a precipitate appeared. HPLC analysis of the filtered soluble fraction revealed eight different peaks with low intensity indicating small concentrations of these soluble products. The low concentrations prevented any identification of the metabolites by MS and NMR. All the peaks were eluted after the initial parent compound. The composition of the insoluble fraction checked by HPLC varied depending on the incubation time in the presence of laccases. After 15-min incubations, one main peak was detected whose retention time was very close to the void volume of the column (Fig. 3). Its area remained constant regardless of the duration of the experiment. Three hours after the beginning of the transformation, two additional peaks appeared. They were more retained by the column and their proportions were inverted after 3 days of incubation. The intermediate retention time product was extensively reduced (almost 5 times), while the product with the higher retention time became the main conversion product (a ten-fold increase). UV-visible spectra of the insoluble fraction of 2-HF degradation products established the appearance of an intermediate product absorbing at = 417 nm. However, none of these products could be identified. No interpretable results were obtained either by GC/MS analysis of a dichloromethane extract, nor through MS by direct insertion probing of the samples collected following HPLC.

Discussion

The three hydroxyphenyl derivatives of fenuron were treated with partially purified laccase from

Trametes versicolor. Under our experimental conditions, 3-HF was not transformed by fungal laccase whereas 2-and 4-HF were converted to several products. This paper clearly establishes for the first time, to our knowledge, that laccases are able to catalyse the direct attack of herbicide metabolites.

Enzyme activity towards hydroxyphenyl ureas followed this order: 3-HF<<4-HF<2-HF. The correlation between the amount of substrate transformed at the optimal pH and the position of the substitutents groups on the phenolic ring is reported by [START_REF] Shuttelworth | Soluble and immobilised laccase as catalysts for the transformation of substituted phenols[END_REF], who showed that 3-substituted cresol was poorly oxidised, while 4-cresol was oxidised to the greater extent.

Previously, [START_REF] Alberti | Enzymatic removal of dissolved aromatics from industrial aqueous effluents[END_REF] had shown that 3-chlorophenol was not efficiently removed by peroxidase while 2-and 4-chlorophenol were clearly removed. The absence of reaction of 3-substituted phenols could be due to the reaction mechanism. The initial step involves the formation of an aryloxy radical by the removal of one electron and one hydrogen atom from the hydroxyl group. The aryloxy radical generates resonance structures, which are stabilised in para and ortho positions, whereas a meta substitutent does not allow stabilisation, which implies a low reactivity of the meta-substituted phenols.

Our results also show that dimethyl-(hydroxyphenyl)urea transformation greatly depended on the pH of the medium, with respect to the kinetics of degradation and the nature of the metabolites.

The enzymatic transformation of 4-HF by laccase was greater at acidic pH (3 to 5) than at higher values. The bell-shaped dependence has been observed with different phenolic substrates such as 2,6-dimethoxyphenol, syringic or vanillic acid. Recently, Xu (1997) explained such a pH-activity profile as the balance of two opposite effects. Due to the deprotonation of their hydroxyl group, the apparent electrochemical potential of phenols decreased with pH, by enhancing the difference with the electrochemical potential of the T1 copper centre of the laccase. The oxidation reaction of the substrate was thus thermodynamically promoted. At higher pH, the activity of the enzyme decreased. This was due to the inhibition generated by the bonding of the hydroxide anion to the coppers of laccase. However, this physico-chemical description of the pH influence on enzyme activity could not explain the preferential p-benzoquinone formation at lower pH.

The pH also determined the nature of the reaction product. At pH 3, laccase catalysed both the oxidation of the hydroxyl group and the breakdown of the Caryl-Calkyl bond. p-Benzoquinone was the main product formed, whereas at pH 6 the mixture was mainly composed of N',N'-dimethyl-N-[(2,5-cyclohexadiene-1-one)-4-ylidene]urea. The formation of quinones from different substituted phenols such as benzoic and cinnamic acids by laccase from Trametes versicolor has been previously reported [START_REF] Leonowicz | The effect of pH on the transformation of syringic and vanillic acids by the laccases of Rhizoctonia praticola and Trametes versicolor[END_REF][START_REF] Katase | Transformation of trans-'-hydroxy-cinnamic acid by laccase of fungus Trametes versicolor: its significance in humidification[END_REF][START_REF] Lacki | Transformation of 3,5-dimethoxy, 4-hydroxy cinnamic acid by polyphenol oxidase from the fungus Trametes versicolor: product elucidation studies[END_REF]. This could also be expected with fenuron derivatives. The effect of pH on the nature of the product formed is not as well documented. [START_REF] Leonowicz | The effect of pH on the transformation of syringic and vanillic acids by the laccases of Rhizoctonia praticola and Trametes versicolor[END_REF] showed that the oxidation of vanillic acid by laccase from Rhizoctonia praticola led to the corresponding 1,4-benzoquinone at pH < 6, whereas the same quinone was formed with syringic acid in a pH range of between 3 and 8.

With 2-HF showing two substitutents groups in the ortho position, a purple flocculating precipitate was observed in the reaction medium. Unfortunately, the products could not be identified due to analytical difficulties. Nevertheless, their low solubility in water seemed to indicate a polymerisation process between the o-quinonic products.

Bound and conjugated pesticide residues are formed in plants and soils [START_REF] Khan | Bound pesticide residues in soil and plants[END_REF]. These are mainly oligomers of the parent compound or the degradation products of the latter. They can also result from a coupling with naturally occurring compounds. Phenylurea herbicides are degraded to give phenolic intermediates or amines. They give bound and/or conjugated residues in soils [START_REF] Gaillardon | Changes in the concentrations of isoproturon and its degradation products in soil and soil solution during incubation at two temperatures[END_REF] and plants [START_REF] Gross | The metabolic behaviour of chlorotoluron in wheat and soil[END_REF]. This paper shows that laccases allow the formation of insoluble compounds, probably as polymers of fenuron metabolites. These results might partly explain some of the reactions involved in bound residue formation in plants and soils. Moreover, laccases may also find a possible application in wastewater treatment [START_REF] Maloney | Transformation of trace of organic compounds in drinking water by enzymatic oxidative coupling[END_REF]. Particular attention should be paid to their effectiveness in removing soluble phenolic contaminants and transforming them into insoluble compounds.

Xu F (1997) Effects of redox potential and hydroxide inhibition on the pH activity profile of fungal laccases. J Biol Chem 272:924-928 Fig. 1: HPLC analysis of incubation media containing 4-HF and 2-HF at the beginning of the incubations (panels A and C, respectively) and after 15-min assays in the presence of laccase (panels B and D).

Reaction conditions: 0.1 M CPB buffer pH 5; concentration of fenuron derivatives: 0.75 mM; laccase activity: 0.1 U per mL medium.

N

  ',N'-dimethyl-(4-hydroxyphenyl)urea (4-HF) 3 [R1: 4-OH, R2: NH-CON(CH3)2] N-(4-hydroxyphenyl)hydrazide 3a [R1: 4-OH, R2: CONH-NH2]. Ethyl 4-hydroxy-benzoate (15.4 g, 0.1 mole) was added portionwise to an excess of hydrazine hydrate (50 g, 1 mole) under stirring over a period of one hour. The solution was allowed to stand overnight at room temperature. The resulting heterogeneous mixture was diluted with cold absolute ethanol and filtered using a glass filter. The white residue was washed first with cold ethanol (4 x 20 ml),

Fig. 2 :

 2 Fig. 2: Effect of pH on the formation of compound I () and compound II () from 4-HF in the

Fig. 3 :

 3 Fig. 3: HPLC separation of the insoluble fraction formed from 2-HF in the presence of laccase.

Table 1 .

 1 Spectroscopic data for synthesised N', N'-dimethyl-(hydroxyphenyl)ureas and transformation products of 4-HF by laccase from Trametes versicolor

	Identification	Abbrev.	m/z of fragment ions (% relative intensity)	1 H chemical shift

Acknowledgments

We thank Michael Dever for proof-reading the manuscript.