Florence Martin

Stéphane Torelli

Denis Le Paslier

Agnès Barbance

Fabrice Martin-Laurent

David Bru

Roberto Geremia

Gérard Blake

Yves Jouanneau
email: yves.jouanneau@cea.fr

Betaproteobacteria dominance and diversity shifts in the bacterial community of a PAH-contaminated soil exposed to phenanthrene

Keywords: 16S rRNA sequences, stable isotope probing, PAH degradation, phenanthrene, Betaproteobacteria

In this study, the PAH-degrading bacteria of a constructed wetland collecting road runoff has been studied through DNA stable isotope probing. Microcosms were spiked with 13 Cphenanthrene at 34 or 337 ppm, and bacterial diversity was monitored over a 14-day period.

At 337 ppm, PAH degraders became dominated after 5 days by Betaproteobacteria, including novel Acidovorax, Rhodoferax and Hydrogenophaga members, and unknown bacteria related to Rhodocyclaceae. The prevalence of Betaproteobacteria was further demonstrated by phylum-specific quantitative PCR, and was correlated with a burst of phenanthrene mineralization. Striking shifts in the population of degraders were observed after most of the phenanthrene had been removed. Soil exposed to 34 ppm phenanthrene showed a similar population of degraders, albeit only after 14 days. Our results indicate that specific Betaproteobacteria are involved in the main response to soil PAH contamination, and illustrate the potential of SIP approaches to identify soil PAH degraders.

Introduction

Among the environmental pollutants that perturb ecosystems and threaten human beings, polycyclic aromatic hydrocarbons (PAHs) are of special concern because they are persistent and accumulate along the trophic chain. To clean up contaminated sites, bioremediation strategies have been proposed based on the ability of particular microorganisms to degrade PAHs (Doyleet al., 2008). Numerous bacterial isolates able to utilize PAHs as carbon sources have been described, and the biochemical pathways responsible for their oxidative degradation have been investigated (Penget al., 2008). Based on their frequent occurrence on polluted sites and their metabolic potential, members of the Sphingomonadaceae and Actinobacteria are considered potent PAH degraders in soil or sediments (Leyset al., 2004;Leyset al., 2005;Alonso-Gutierrezet al., 2009). However, in the last decade, cultureindependent studies highlighted the great bacterial diversity in environmental ecosystems such as soils, and showed that culturable bacteria represented less than 5 % of the existing species (Coleet al., 2010). Hence, PAH-degrading strains described so far might not be representative of soil bacteria that actually remove pollutants in situ. Consistent with this idea, investigations involving a selective labeling of the bacteria of interest through stable-isotope probing (SIP (Radajewskiet al., 2000)) led to the discovery of new bacteria involved in pollutant degradation [START_REF] Wackett | Stable isotope probing in biodegradation research[END_REF]. SIP-based methods permit the exploration of uncultured bacteria present in natural or contaminated environments and reduce biases associated with selection in artificial media [START_REF] Dumont | Stable isotope probing -linking microbial identity to function[END_REF]. Using a field-based SIP strategy to track naphthalene degraders on a coal tar contaminated site, Jeon et al. identified a novel Polaromonas strain, as the major players (Jeonet al., 2003). In studies targeting naphthalene-, phenanthrene-or pyrene-degrading bacteria in a bioreactor treating PAH-contaminated soil, different bacterial taxa were detected depending on the PAHsubstrate used as probe (Singletonet al., 2005;Singletonet al., 2006). An Acidovorax strain, which was found to be dominant among phenanthrene-degrading bacteria, was later isolated and characterized as a novel PAH degrader (Singletonet al., 2009). Recently, bacteria related to the Pseudoxanthomonas and Microbacterium genera were identified as the main phenanthrene degraders in soil but diversity changes were observed in soil also treated with root exudates (Cebronet al., 2011). In another SIP-based study targeting anthracene-degrading bacteria, dominant soil degraders were found to be affiliated to the Sphingomonadales and Variovorax taxa (Joneset al., 2011).

In the present study, we have implemented a SIP strategy to explore the diversity of a PAHdegrading community in a constructed wetland collecting the road runoff from a highway.

Using 13 C-phenanthrene as tracer, we examined changes in the soil PAH-degrader population as a function of the dose and time of exposure to the tracer. For this purpose, soil DNA was extracted after various SIP treatments, separated by isopycnic centrifugation, and labeled DNA were used to identify phenanthrene degraders based on 16S rRNA gene sequences. . Moreover, dose-and time-dependent changes in the composition of the phenanthrene-exposed bacterial community were monitored by a combination of molecular methods, including single-strand conformation polymorphism and quantitative PCR. Results revealed that, in addition to Burkholderiales, bacteria related to the Rhodocyclaceae and Thiobacillus taxa appeared as new potential phenanthrene degraders. Our data also provide new insights into the response of a large panel of specific degraders, mostly undescribed, to soil PAH contamination.

MATERIALS AND METHODS

Sampling site and determination of soil PAH concentration

Soil samples were obtained from a constructed wetland collecting road runoffs from a highway near Chambéry (France) in October 2008. The facility, administered by the AREA company, was colonized by Typha latifolia and Phragmites australis all over the structure.

Soil was sampled from the 10-cm upper layer, sieved to about 3 mm and stored in a closed plastic box at 4°C until use. The physico-chemical composition of the soil used will be given elsewhere.

Synthesis of [U-13 C]-phenanthrene

[U-13 C]-phenanthrene was prepared from [U-13 C] succinic anhydride and [U- 13 C] naphthalene (both from Sigma-Aldrich), according to a previously described method [START_REF] Singleton | Stableisotope probing of bacteria capable of degrading salicylate, naphthalene, or phenanthrene in a bioreactor treating contaminated soil[END_REF] and references cited therein) with minor modifications. The product was obtained in five steps with a yield of approximately 44%. The purity and homogeneity of the final preparation were checked by 1 H NMR in CDCl 3 (Fig. S1) and GC-MS (m/z = 192 (M + )). A detailed description of the preparation procedure is available upon request.

Microcosm setup and incubation conditions

Incubations were performed in 250 ml sterilized glass Erlenmeyer flasks, closed with rubber stoppers. Microcosms consisted of 20 g wet soil (water content, 59 %, vol/wt) and 5 ml of a salt solution (7.5 mM (NH 4 ) 2 SO 4 -20 mM KH 2 PO 4 -30.6 mM Na 2 HPO 4 -0.18 mM CaCl 2 -3.6 10 -2 mM FeSO 4 -0.81 mM MgSO 4 ), added to favor bacterial metabolism. [U-13 C]phenanthrene was supplied as a 54.9 mM stock solution in dimethylsulfoxide (DMSO) to give final concentrations of either 33.7 or 337 ppm (on a dry wt basis), equivalent to levels 200-or 2000-fold as high as that of phenanthrene initially found in soil. Relevant microcosm experiments were referred to as SIP200 and SIP2000, respectively. Two microcosms supplied with 337 ppm labeled phenanthrene were inoculated with approx. 2 10 8 cells (0.2 ml) of a washed culture of Sphingomonas CHY-1 [START_REF] Willison | Isolation and characterization of a novel sphingomonad capable of growth with chrysene as sole carbon and energy source[END_REF]. Three types of control flasks were also prepared, which contained either unlabeled phenanthrene (added at 34 ppm as for SIP200), no additional carbon source, or sodium azide (7.7 10 -5 mol/g dry soil; abiotic control). Microcosms containing [U-13 C]-phenanthrene (SIP200 and SIP2000) were prepared in 9 replicates (triplicates at 3 time points), whereas controls with unlabelled phenanthrene or microcosms inoculated with strain CHY-1 were performed in 6 replicates (duplicates at 3 time points). Other control flasks were incubated in duplicates. Flasks were incubated at 25°C in static mode in a dark room.

PAH extraction from soil and quantification

Soil samples (1g) were mixed with 1g anhydrous sodium sulfate and 10 mL hexane in 30-mL glass tubes closed with Teflon-sealed screw-caps. Suspensions were homogenized by Vortex mixing and treatment in an ultrasonic bath for a total time of 1 h. Soil samples were extracted once more with hexane and organic phases were combined, centrifuged for 12 min at 7400 g before evaporation to dryness under argon. Dry extracts were dissolved in 0.5 ml acetonitrile.

Residual 13 C-phenanthrene was extracted in the same way.

Quantification of the 16 priority PAHs was performed with a HP6890 gas chromatograph coupled to a HP5973 mass spectrometer (Agilent Technologies). PAHs were separated on a MDN12 column (30 m by 0.25 mm, 25µm film thickness; Supelco) as previously described (Krivoboket al., 2003). The mass detector was operated in the single-ion monitoring mode.

PAHs were eluted between 8.9 and 32.7 min. Concentrations were determined using calibration curves obtained from dilutions of a standard mixture of the 16 PAHs (0.1 mg/ml, Agilent Technologies). 13 C-phenanthrene was quantified from the area of the peak detected at m/z : 192 using unlabelled phenanthrene as a standard.

Monitoring of mineralization by GC/MS analysis

Quantification of CO 2 evolved in the headspace of microcosms was performed by GC/MS analysis on 25 µl samples of the gas phase withdrawn with a gastight syringe. Analysis was carried out on the same apparatus as above equipped with an HP-PLOT/Q column (15 m by 0.32 mm, 20 µm film thickness; Agilent Technologies) with helium as carrier gas maintained at a flow rate of 36 ml/min. The GC was run in the isothermal mode at 40°C with a split ratio of 12:1. 13 CO 2 (m/z = 45) or 12 CO 2 (m/z = 44) were measured with the MS operated in the single-ion monitoring mode. Concentrations were determined from peak area using a calibration curve in the 0 to 200 µM range made with known mixtures of CO 2 in argon. Net 13 CO 2 produced in microcosm headspaces was calculated by subtracting background 13 CO 2 present in the air.

SSCP fingerprint analyses of 16S rRNA genes

For each tested experimental condition, 1g of soil (wet weight) was sampled and divided into three 250-mg soil aliquots, thus making 3 replicates per flask. DNA was extracted using the Power Soil extraction kit (MO BIO Laboratories). DNA samples were adjusted to 10 ng/µl and used as templates to amplify the V3 region of the 16S rRNA genes. PCR conditions and subsequent capillary electrophoresis were performed as previously described (Zingeret al., 2007). SSCP profiles were computed and normalized. Nonmetric multidimensional scaling (NMDS) ordination was performed with the R software, using the vegan package (R Development Core Team, 2007). Environmental fitting was performed to evaluate the effects of time as a variable, and phenanthrene as a factor, on the bacterial community.

DNA extraction and CsCl gradient fractionation

For SIP200 and SIP2000 experiments, triplicate microcosms were sacrificed at day 5, 10 and 14. Soil samples (10 g) were transferred into 50-ml Falcon tubes and stored frozen at -80°C until use. Triplicate 10-g samples of untreated soil collected at the beginning of the experiment (day 0) served as controls. Duplicate soil samples were also collected at the endpoint (day 14) from control microcosms with no addition. DNA was extracted using the UltraClean Mega Soil DNA kit (MO BIO Laboratories) as recommended. DNA was eluted at 40-50 µg/ml in 8 mL.

For CsCl gradient fractionation, 2-ml portions of the DNA preparations were precipitated, and then adjusted to 700 ng/µl in H 2 O. Separation between 12 C-and 13 C-DNA was performed by isopycnic ultracentrifugation on a CsCl gradient (Luederset al., 2004). Gradients were adjusted to an average density of 1.725 g/mL in 3.3 ml OptiSeal TM polyallomer centrifuge tubes (Beckman Coulter), and loaded with 30 to 50 micrograms of soil DNA and 5 µL of SYBR Safe TM (Invitrogen) as DNA stain (Martineauet al., 2008). For each run, a tube containing equal amounts of 12 C-DNA from E. coli and 13 C-DNA Sphingomonas sp. CHY-1 (10 µg each) was processed as a means to control band separation and locate their position in the gradient. 13 C-labeled genomic DNA was prepared from Sphingomonas sp. CHY-1 grown on 13 C-succinate (Sigma-Aldrich) as sole carbon source. Preparation of genomic DNA from this strain (Demanecheet al., 2004) and from E. coli strain DH5a (Ausubelet al., 1999) followed published procedures. Ultracentrifugation was carried out at 413,000 x g AV for 17 h at 15°C, in a TLN-100 rotor using an Optima TM TLX Ultracentrifuge (Beckman Coulter).

Gradient fractionation was adapted from a published procedure (Manefieldet al., 2002), using a peristaltic pump operated at a flow rate of 0.18 ml/min and a Gilson fraction collector equipped with a home-made device for holding centrifuge tubes. Twenty-two fractions, 150 µl each, were obtained, from which DNA was precipitated according to [START_REF] Pumphrey | Field-based stable isotope probing reveals the identities of benzoic acid-metabolizing microorganisms and their in situ growth in agricultural soil[END_REF], using 1 µl of 20 mg/ml glycogen as a carrier (Fermentas). Each fraction was taken up in 20 µl H 2 O and DNA concentration was determined from UV absorbance measurement with a ND-100 spectrophotometer (NanoDrop Technologies, Inc.). In plots of DNA content versus fraction number, peak fractions of 13 C-DNA and unlabeled DNA were 4 fractions apart.

Construction of 16S rRNA gene libraries and DNA sequencing

Both the labeled and unlabeled DNA isolated from the SIP200 and SIP2000 experiments, as well as DNA extracted from untreated soil (control SIP 0D), was used as template to amplify the nearly complete sequences of bacterial 16S rRNA genes. PCR amplification was performed with primers 8F (AGAGTTTGATCCTGGCTCAG) and 1390R

(GACGGGCGGTGTGTACAA). PCR products were ligated into pCRÔ4 TOPOÒ TA vectors and electroporated into ElectroMAX™ DH10B™ T1 competent cells according to the manufacturer's recommendations (Invitrogen). Bidirectional Sanger sequence reads were obtained by standard procedures and assembled by PHRAP (http://www.phrap.org). Sequence data obtained from clone libraries have been submitted to DDBJ/EMBL/GenBank under accession numbers: FQ658499 to FQ660546 and FQ790244.

Sequence analysis

The 16S rRNA gene sequences were first affiliated to b acterial taxa using SeqMatch on the Ribosomal Database Project (RDP) website (http://rdp.cme.msu.edu/index.jsp ; (Coleet al., 2009)). Multiple sequence alignments and clustering into Operational Taxonomic Units (OTUs) of the 2049 sequences considered herein were performed with mothur (Schlosset al., 2009), using a 3% dissimilarity level between OTUs. Mothur was also used to generate rarefaction curves and calculate richness estimators and diversity index (Table 1). A phylogenetic tree was generated using the TreeBuilder software on the RDP website.

Selection of 16S rRNA sequences representative of phenanthrene degraders

In DNA-SIP analysis, the set of 16S RNA sequences recovered from 13 C-DNA may be contaminated by unspecific sequences due to some overlap between labeled and unlabeled fractions after isopycnic centrifugation. A control experiment is usually carried out involving DNA analysis of a sample incubated with unlabeled substrate. After CsCl gradient separation, a fraction equivalent to that containing 13 C-DNA in the labeled experiment (heavy fraction), is chosen to run control PCR. In our hands, the choice of the heavy fraction for control purposes was somewhat arbitrary due to small changes in gradient density between samples. In addition, the reliability of this type of control requires that incubation conditions be identical between the SIP and control samples, which is difficult to achieve due to the inherent heterogeneity of soil. Hence, in his study, 16S rRNA sequences from 13 C-DNA libraries were considered representative of phenanthrene degraders if they complied with the following criteria : (i) they had to be part of OTUs represented by a minimum of 3 sequences per library (ii) they were not detected or poorly represented in libraries made from unlabeled DNA from the same run of centrifugation.

Real time PCR quantification of phylum-specific 16S rRNA genes

The copy number of 16S rRNA genes in soil DNA samples was estimated by quantitative PCR (qPCR) using universal or taxon-specific primers according to (Philippotet al., 2010).

Amplification reactions were carried out in a StepOnePlus™ Real-Time PCR Systems (Applied Biosystems). Reaction mixtures contained 7.5 µl SYBRGreen ® PCR Master Mix (Absolute QPCR SYBR Green Rox Abgene), 250 ng of T4 gene 32 (QBiogene), 4 ng of soil DNA in a final volume of 15 µl. Fluorescence acquisition was performed during 80°C steps to avoid interference of unspecific products. For each of the eleven 16S rRNA targets, a standard curve was established using serial dilutions of linearized plasmid pGEM-T (10 2 to 10 7 copies) containing a relevant 16S rRNA gene. Melting curves were generated after amplification by increasing the temperature from 80°C to 95°C. qPCR results are averages of three replicates, and are expressed as copy numbers per nanogram of DNA. The relative abundance of each taxon was calculated as the ratio of copy number of this taxon to the total number of 16S rRNA sequences, determined using universal primers.

RESULTS

Mineralization rates of 13 C-phenanthrene in microcosms

The soil used in this study had been exposed to chronic hydrocarbon contamination from road runoffs. It contained ca. 4.0 mg/kg of PAHs, including 0.156 ± 0.05 mg/kg of phenanthrene.

For SIP experiments, fully labeled 13 C-phenanthrene was prepared and checked for purity and authenticity by GC-MS and NMR (supplementary Fig. S1). Experiments were carried out in microcosms containing 20 g of soil, and 13 C-phenanthrene supplied at 34 and 337 ppm, levels equivalent to approx. 200-fold (SIP200) or 2000-fold (SIP2000) the phenanthrene content of soil. Mineralization of the labeled C-source was monitored by GC-MS measurement of the 13 CO 2 released in the headspace over 14 days (Fig. 1). In SIP2000 experiments, the 13 CO 2 evolution reached a maximum rate after a 2-day lag phase, then leveled off after day 5. No lag phase was observed in microcosms inoculated with Sphingomonas CHY-1, a phenanthrenedegrading strain, suggesting that the delay reflected the time necessary for multiplication of soil PAH degraders. In SIP200 experiments, the rate of 13 CO 2 was just above the background level detected in control microcosms, which received unlabelled phenanthrene or no addition.

From the total amount of 13 CO 2 released in the SIP2000 experiment, it was calculated that ca.

20% of the 13 C-phenanthrene had been mineralized. This value is most likely underestimated as an unknown amount of the carbon dioxide remained trapped in soil as bicarbonate. The residual labeled substrate in soil of the SIP2000 experiment amounted to 20.9 ± 2.3 ppm at day 5 (6.7 %) and 13.9 ± 1.0 ppm at day 14 (4.5 %), meaning that a major part of the added hydrocarbon had been degraded during the early stage of mineralization between days 2 and 5. Discrepancy between the mineralization rate and the extent of degradation of 13 Cphenanthrene (95%) might be explained in part by underestimations of either 13 CO 2 (see above) or residual phenanthrene due to sequestration into soil particles (Johnsenet al., 2005).

Some of the labeled carbon was also incorporated into the organic matter of phenanthrene degraders.

Effect of phenanthrene on the overall bacterial community structure

To follow changes in the soil bacterial community upon incubation with phenanthrene, soil DNA was extracted at time intervals from SIP and control experiments, and 16S rRNA genes were first analyzed by SSCP fingerprinting. Electrophoresis profiles of PCR products targeting the V3 region were normalized and their distribution was analyzed by non metric multi-dimensional scaling (Fig. 2). Profiles were very similar suggesting that, at this level of observation, the bacterial community underwent little changes with the time of exposition (5, 10 or 14 d) and the dose of phenanthrene. Nevertheless, a clear trend emerged when comparing data sets at day 0, 5, 10 and 14, in that diversity profiles showed convergent timedependent variations. On the other hand, diversity profiles obtained from phenanthrenetreated microcosms were more closely related to each other than to untreated controls, suggesting that phenanthrene-dependent shifts occurred in the bacterial population, but these shifts were little correlated to the concentration of phenanthrene.

The composition of the bacterial population in soil samples was then analyzed by real-time quantitative PCR (qPCR) using phylum-specific primers (class-specific for Proteobacteria), according to a method that has proven [START_REF] Philippot | Distribution of high bacterial taxa across the chronosequence of two alpine glacier forelands[END_REF]. Samples from the SIP2000 experiments showed, small time-dependent variations in 16S rRNA copy numbers (data not shown), suggesting that overall, the community structure was little affected by phenanthrene.

However, when abundances were expressed as ratios with respect to the total copy numbers of 16S rRNA genes, a significant increase was found for the Beta-and Gammaproteobacteria (Fig. 3, grey bars). In contrast, Alphaproteobacteria and Actinobacteria showed unchanged or diminished proportions.

Betaproteobacteria enrichment in soil spiked with phenanthrene

Soil bacteria likely involved in phenanthrene degradation were identified based upon sequence analysis of 16S rRNA genes amplified from 13 C-DNA. Soil DNA samples recovered from SIP200 and SIP2000 experiments at day 5 and day 14 were separated into labeled and unlabeled fractions, both of which were used to prepare 16S rRNA gene libraries (Table 1). Sequences were affiliated to bacterial taxa using the RDP resources.. A detailed compilation of the 2049 sequences analyzed in this study is given in supplementary Table S1.

As depicted in Fig. 4, the distribution of sequences among known bacterial phyla revealed that Proteobacteria were predominant in all libraries, the Beta class being the best represented.

However, the proportion of Betaproteobacteria sequences was significantly higher in libraries made from labeled DNA, especially SIP2000 at day 5 (67%). In comparison, the library made out of unlabeled DNA (SIP2000 5D 12C) contained around 30% of Betaproteobacteria sequences, similar to the library of the untreated control (Fig. 4A and data not show). In the 13 C-DNA extracted from soil dosed with 10-fold less 13 C-phenanthrene, a Betaproteobacteria enrichment was also detectable, but only after 14 days (Fig. 4C).

Since the PCR-based method used to generate 16S rRNA sequence libraries might introduce biases in the determination of the actual proportions of bacterial taxa in soil, we implemented the quantitative PCR method described above to assess the copy number of taxon-specific16S rRNA genes in 13 C-DNA fractions. Results illustrated in Fig. 3 showed that labeled DNA was specifically enriched in sequences affiliated to the Betaproteobacteria. The enrichment occurred within the first days of the incubation with 13 C-phenanthrene, and the proportion of Betaproteobacteria stayed above 10% throughout the 14-d incubation. Gammaproteobacteria were found to be significantly more abundant at day 10, suggesting that members of this class also accumulated at least transiently in response to phenanthrene. In comparison, Alphaproteobacteria, and Actinobacteria, as well as other taxa tested (data not shown) were less represented and their copy number did not show a clear trend upon incubation with phenanthrene. Hence, consistent with the sequence analyses above, our quantitative data demonstrated that Betaproteobacteria became the dominant taxon in response to phenanthrene contamination of soil.

Identification of main PAH degraders

Further analysis of sequences in 13 C-libraries indicated that, at day 5, the community of PAH degraders exposed to 337 ppm phenanthrene was dominated by a few genera, including Acidovorax, Rhodoferax, Hydrogenophaga and Polaromonas, all members of the Comamonadaceae (Fig. 5A). Quite a few sequences affiliated to Rhodocyclaceae were also identified, but they were not all representative of phenanthrene degraders. Some of them were likely related to degraders as they belonged to OTUs only found in 13 C-libraries (OTU0, 1 and 17), while other were not because they belonged to OTUs mainly found in 12 C-libraries (OTU28, 51, 71; Fig. 6A). Hence, only some members of the Rhodocyclaceae would be able to degrade phenanthrene. A very similar pattern of dominant taxa was observed in the SIP200 experiment at day 14 (Fig. 5B), indicating that a 10-fold lower concentration of phenanthrene elicited the same soil population of degraders, although at a slower pace. Consistent with this idea, comparison of the SIP2000-5D-13C and SIP200-14D-13C libraries showed that they shared the highest number of common OTUs (78 of 161 or 48%, Fig S2), three of which were dominant in both libraries (OTUs 6, 4 and 17; Fig. 6A).

Apart from the Betaproteobacteria, two OTUs related to the Sphingomonadales were significantly represented in sequences obtained from 13 C-DNA, while almost inexistent in sequences retrieved from unlabeled DNA (OTUs 80 and 201; Fig. 6A). Sphingomonadales accounted for 32 to 37% of the Alphaproteobacteria sequences in SIP200 13 C-libraries, and for 38 to 59% in SIP2000 13 C-libraries, respectively (Fig. S3). One well-represented OTU related to unclassified Gammaproteobacteria (OTU 2, 5% of the SIP2000-5D-13C library) was also detected in the set of sequences obtained from 13 C-DNA (Fig. 6A).

A phylogenetic tree was built with the best represented OTUs found in sequence libraries derived from 13 C-DNA (Fig. 6B). The analysis further highlighted that phenanthrene degraders were dominated by Betaproteobacteria. Except for OTU 4, OTU 6, OTU 12, OTU 14 and OTU 27, which were closely related to known isolates, most sequences were either associated to uncultured microorganisms or distantly related to known bacteria. In this respect, OTU 80 and OTU 201, affiliated to Sphingomonadaceae, showed sequences relatively distant from that of known PAH degraders in this bacterial family (Pinyakonget al., 2003;[START_REF] Demaneche | Identification and functional analysis of two aromatic-ring-hydroxylating dioxygenases from a Sphingomonas strain that degrades various polycyclic aromatic hydrocarbons[END_REF].

Diversity shifts in the PAH-degrading community as a function of the time of exposure to phenanthrene

Comparison of the SIP2000 sequence sets obtained from 13 C-DNA at 5 and 14 days showed that the proportion of Betaproteobacteria dropped from 67 to 35% (Fig. 4D andE). This change could largely be explained by a decline of the bacterial taxa that were identified as dominant PAH degraders at day 5 (Fig. 5A). At day 14, the best-represented Betaproteobacteria sequences were affiliated to Thiobacillus, a genus that has been seldom described for its ability to degrade aromatic hydrocarbons although it was recognized as a possible phenanthrene degrader (Bodouret al., 2003). A significant proportion of Thiobacillus-like sequences was also detected in the SIP200 sequence sets retrieved from 13 C-DNA. Similar to what was observed for the Betaproteobacteria, the OTU 2 related to the Gammaproteobacteria was detected in the SIP2000 library at day 5, but not at day 14.

On the other hand, Sphingomonads were detectable under all conditions tested, but their number tended to increase in the long run as exemplified by OTU 201, which was undetected at day 5 and represented by 11 sequences (50% of Alphaproteobacteria) at day 14 (Fig. 6A).

DISCUSSION

PAH pollution is persistent in various ecosystems including soils all around the world, and microbial biodegradation is considered the primary process responsible for its natural attenuation. This process has been extensively studied but for a rather limited range of culturable species [START_REF] Peng | Microbial biodegradation of polyaromatic hydrocarbons[END_REF], and little is known on bacterial populations that mainly contribute to PAH removal in situ. In the present study, we have implemented a SIP approach and culture-independent methods to conduct a thorough investigation of phenanthrene degraders present in soil.

Overall, the soil community was dominated by Proteobacteria, consistent with previous reports documenting the prevalence of this phylum in different soils (Roeschet al., 2007).

Moreover, our results brought convergent pieces of evidence that Betaproteobacteria play a major role in the degradation of phenanthrene in soil. This is supported by the increased proportion of ribosomal sequences representative of this class in 13 C-DNA in response to phenanthrene, as shown by both qPCR and sequence analysis of cloned 16S rRNA genes.

The main PAH degraders identified in this work mostly belong to the Burkholderiales, with Acidovorax, Rhodoferax, Hydrogenophaga and Polaromonas as the best represented genera.

Likewise, a SIP study targeting naphthalene degraders, revealed that Polaromonas, Rhodoferax and Acidovorax mainly contribute to in situ degradation [START_REF] Jeon | Discovery of a bacterium, with distinctive dioxygenase, that is responsible for in situ biodegradation in contaminated sediment[END_REF], suggesting that the same type of bacteria are responsible for the biodegradation of naphthalene and phenanthrene in soil. Based on 16S rRNA sequence comparison, the dominant Acidovorax identified in this study strikingly resembled members of the same genus previously identified in a bioreactor treating a PAH-contaminated soil [START_REF] Singleton | Stableisotope probing of bacteria capable of degrading salicylate, naphthalene, or phenanthrene in a bioreactor treating contaminated soil[END_REF]. A strain isolated from this bioreactor, Acidovorax NA3, has been recently described as a new PAH degrader [START_REF] Singleton | Characterization of a polycyclic aromatic hydrocarbon degradation gene cluster in a phenanthrene-degrading Acidovorax strain[END_REF].

Interestingly, most PAH degraders identified in the present study are related to poorly described taxa, consistent with the fact that a majority of soil bacteria are unknown. They differ from PAH-degrading isolates studied so far, and even members of well-known degraders such as Sphingomonadaceae [START_REF] Demaneche | Identification and functional analysis of two aromatic-ring-hydroxylating dioxygenases from a Sphingomonas strain that degrades various polycyclic aromatic hydrocarbons[END_REF][START_REF] Leys | Occurrence and phylogenetic diversity of Sphingomonas strains in soils contaminated with polycyclic aromatic hydrocarbons[END_REF], were found to be distantly related to described strains (Fig. 6). Gammaproteobacteria are represented by one OTU unrelated to known genera of this class, such as Pseudomonas (OTU2; Fig. 6), but very similar to a pyrene-degrading uncultured bacterium detected by SIP in a bioreactor [START_REF] Singleton | Identification and quantification of uncultivated proteobacteria associated with pyrene degradation in a bioreactor treating PAH-contaminated soil[END_REF]. Moreover, our survey highlighted bacterial taxa affiliated to Rhodocyclaceae and Thiobacillus, which appear as new PAH degraders. Among Rhodocyclaceae, denitrifying bacteria belonging to the Azoarcus, Aromatoleum, Denitratisoma and Thauera genera were shown to anaerobically degrade phenolic compounds (Sueokaet al., 2009) and alkylbenzenes [START_REF] Rabus | Anaerobic degradation of ethylbenzene and other aromatichydrocarbons by new denitrifying bacteria[END_REF]Rotaruet al., 2010), but no Rhodocyclaceae member endowed with PAH-degrading ability has yet been described.

Similar populations of degraders were found upon soil treatment with 10-fold different levels of phenanthrene. This observation indicated that, at the highest concentration tested, no adverse effect could be noticed on any of the bacterial taxa involved in phenanthrene degradation. In contrast, different naphthalene degraders were identified in a ground aquifer, depending on the concentration of the hydrocarbon (Huanget al., 2009). In the latter case, the toxic or inhibitory effect of naphthalene might have influenced bacterial selection, in accordance with previous observations [START_REF] Jeon | Discovery of a bacterium, with distinctive dioxygenase, that is responsible for in situ biodegradation in contaminated sediment[END_REF].

Time-dependent changes in the population of degraders were observed upon incubation with phenanthrene. The rapid phase of phenanthrene mineralization during the first days coincided with the multiplication in soil of the main degraders discussed above. In the SIP2000 experiment, dominant degraders at day 5 drastically declined at day 14. During this time period, the soil bacterial community underwent little change as shown by SSCP and qPCR analyses. Hence, the decline appeared to specifically affect the subpopulation of PAH degraders, maybe because phenanthrene became less bioavailable. Although significant amounts of residual phenanthrene were detected in microcosms after 5 and 14 days, the time course of mineralization suggested that it became limiting at day 5, perhaps due to its sequestration or sorption on soil particles. In the SIP200 experiment, the lower dose of phenanthrene likely elicited slower growth, and might have sustained PAH degraders for a longer time. Hence, our results provide evidence that the subpopulation of degraders undergo relatively rapid dynamic changes in response to the level of PAHs available in soil. CO 2 was measured in the headspace of microcosms supplied with either 34 ppm ( ; SIP200) or 337 ppm ( ; SIP2000) 13 C-phenanthrene, at the beginning of the incubation. Two microcosms spiked with 337 ppm 13 C-phenanthrene were inoculated with Sphingomonas CHY-1 ( ). Other experimental conditions included addition of 34 ppm unlabelled phenanthrene ( ), no addition ( ) and abiotic control ( ). Data show net 13 CO 2 evolution calculated by subtracting natural 13 CO 2 present in the air. Data are averages of three replicate treatments for SIP200 and SIP2000, and two replicates for other experimental conditions.

Error bars indicate standard deviations.

Figure 2 :

 2 Figure 2: NMDS ordination of bacterial communities exposed to phenanthrene, based on

Figure 3 :

 3 Figure 3: Relative abundances of phylum-specific 16S rRNA genes in soil DNA during incubation with 13 C-phenanthrene. Template was either total soil DNA (grey bars) or 13 C-

Figure 4 :

 4 Figure 4: Prevalence of Betaproteobacteria-specific sequences in 16S rRNA gene libraries recovered from 13 C-labeled soil DNA.

Figure 5 :

 5 Figure 5 : Distribution of Betaproteobacteria into best-represented families and genera.Sequences were from 13 C-DNA libraries prepared at day 5 and 14 from the SIP2000 (A) and

Figure 6 :Figure 1 :

 61 Figure 6: Major bacterial taxa likely involved in 13 C-phenanthrene degradation.

  13

FigureFigure 2 :

 2 Figure Click here to download Figure: Figure 1.pdf

Figure

  Figure Click here to download Figure: Figure 2.pdf

Figure 3 :Figure 4 :

 34 Figure 3: Relative abundances of phylum-specific 16S rRNA genes in soil DNA during incubation with 13 C-phenanthrene. Template was either total soil DNA (grey bars) or 13 C-DNA (black bars) extracted from SIP2000 experiments at 5, 10 and 14 days (5D, 10D and 14D, respectively). A control DNA sample was obtained from soil incubated for 14 days

FigureFigure 5 :

 5 Figure Click here to download Figure: Figure 4.pdf

Figure

  Figure Click here to download Figure: Figure 5.pdf

Figure 6 :

 6 Figure 6 : Major bacterial taxa likely involved in 13 C-phenanthrene degradation.

Table 1 : Description of the nine 16S rRNA sequence libraries considered in the DNA SIP experiments.

 1 Figure 1: Time course of 13 CO 2 evolution during SIP experiments as measured by GC/MS.

	546
	547
	Library	Sequence	OTU	Richness estimation	Diversity Index
		number	number	Chao1	Ace	Shannon
	SIP1 0D	230	173	468	892	4.87
	SIP200 5D 13C	167	132	810	783	4.67
	SIP200 5D 12C	147	135	938	1897	4.82
	SIP200 14D 13C	353	161	748	2144	4.32
	SIP200 14D 12C	259	233	572	956	4.96
	SIP2000 5D 13C	423	173	536	942	3.89
	SIP2000 5D 12C	228	186	739	1981	5.01
	SIP2000 14D 13C	212	135	476	877	4.20
	SIP2000 14D 12C	28	27	118	182	3.23

Acknowledgments

We thank N. Duraffourg for assistance in recording RMN spectra, A. Monier and J. Roy for their help in SSCP experiments, E. Pelletier for submitting DNA sequences. F. Martin received a grant from the Rhône-Alpes region. This work was supported by the CNRS and the University of Grenoble.

Supplementary Files

Click here to download Supplementary Files: Table S1.xls Supplementary Files Click here to download Supplementary Files: Figures S1-S3.pdf


Antoine Herbelin
email: antoine-externe.herbelin@edf.fr

Thierry Couvant
email: thierry.couvant@edf.fr

Laurent Legras
email: laurent.legras@edf.fr

David Delafosse
email: david.delafosse@emse.fr

Gabriel Ilevbare
email: gilevbare@epri.com

Oxidation of austenitic stainless steels in PWR primary water

Keywords: Oxidation, austenitic stainless steels, SCC, PWR, TEM

304L and 316L SS samples are SCC-tested in PWR water for various conditions: strain level, duration, pH, surface finish. The passive Chromium-rich oxide layer, the consistent oxidation feature, is characterized using Transmission Electron Microscope analysis. EDX analysis and Energy-filtered images are used to map the oxide penetration at the surface of the sample and at contiguous grain boundaries. Statistical data obtained by these characterizations are exploited to considerate the effects of deformation, pH and duration on oxidation. Complementary analyses of intergranular crack initiations allow to invetigate the oxidation phenomenon at grain boundaries and its influence on intergranular cracking.

Background

Oxidation is a key phenomenon in the stress corrosion cracking (SCC) mechanisms of stainless steel (SS) in primary water of pressurized water (PWR) reactors: the passive chromium-rich oxide formed at the surface of SS forms a protective barrier from the environment, but also constitutes a boundary where the crack initiation could occur. Previous studies [START_REF] Couvant | Effect of strain path on SCC of AISI 304L stainless steel in PWR primary environment at 360[END_REF] have demonstrated the deleterious effect of plastic deformation on susceptibility of 304L to IGSCC in PWR primary water at 360°C. TEM observation [START_REF] Couvant | Investigations on the mechanisms of PWSCC of strain hardened austenitic stainless steels[END_REF] allowed to identify specific interactions between plasticity and oxidation. Oxidation phenomenon needs to be quantified in order to develop predictive models involving interactions between mechanical and environmental effects.

Materials

Austenitic alloys tested in this work (304L and 316L) were sampled from 30-mm-thick plates. Chemical compositions and mechanical properties of the materials as manufactured are given in Table 1 and Table 2, respectively. Unlike 316L, 304L contains less than 5% of residualferrite and is subject to strain-induced martensite transformation: Ms = -133°C, Md 30 = -3°C, as predicted by Angel [START_REF] Angel | Formation of martensite in austenitic stainless steels[END_REF]. In the 316L heat XY183, Ms = -133°C, Md 30 = -40°C. The estimated staking fault energy (SFE) of 304L is 24 mJ.m -2 and that of the 316L is 33 mJ.m -2 [START_REF] Lacombe | Structures et diagrammes d"équilibre des diverses nuances d"acier inoxydables. Conséquences sur leurs traitements thermiques[END_REF]. Non-sensitized 304L and 316L were solution-annealed at 1050°C and water quenched. The resulting microstructures, showing no evidence of carbide precipitation, neither in the matrix, nor along the grain boundaries, were characterized using EBSD. The average grain diameter was 27 µm for 304L and 90 µm for 316L. The distributions of misorientation angles were comparable for 304L and 316L, in the range 6° < < 59°, including twin boundaries ( > 54°). In both materials, 30 to 50% of boundaries were twin boundaries.

Experiments

Specimens

Most of the TEM characterizations were performed on specific cross-shaped specimens (Figure 1). This specimen geometry and testing procedure allows the testing of SCC in PWR primary water while inducing strain localization and complex loading paths. The details of these specimen and the associated tests have been described in [START_REF] Couvant | Development of understanding of the interaction between localized deformation and SCC of austenitic stainless steels exposed to primary PWR environment[END_REF][START_REF] Couvant | Development of understanding of the interaction between localized deformation and SCC of austenitic stainless steels exposed to primary PWR environment[END_REF].

Cross tests were used to promote strain localization. This design was optimized to obtain strain localization in the cross-area of the specimen during pre-straining and subsequent deformation in primary water. The specimen is not symmetrical through its thickness: only the front face is perfectly plane, designed to allow for easy mechanical polishing, material characterization and local strain evaluation. All characterizations and observations were done on 1 mm 2 area at the center of the specimen"s front face.

The sample's surface of interest was mechanically polished down to 0.25 µm abrasive finish in order to remove the superficial cold work due to machining. Electrolytic polishing was then performed to eliminate residual cold work after mechanical polishing. The solution used was a Struers A2 electrolyte containing 73% ethanol, 10% butoxyethanol, 9% water, and 8% perchloric acid. This treatment leaves a chemically-affected layer enriched in chromium in the first few nm from the surface. A plasma cleaning step was thus used to remove this layer possibly affecting subsequent oxidation reactions. The layer was removed using 4.5 kV argon ions at low incidence for 2 minutes to minimize the irradiation damage to the surface.

EBSD characterizations were carried out on the 1 mm 2 area at the center of each specimen. Gold grids (1 mm 2 , mesh size = 5 µm) were deposited by electron micro-lithography at the center of the specimens, allowing the quantification of local deformation using image correlations on grid images acquired in a SEM.

Figure 1 -Cross-shaped specimen

Pre-deformation and SCC tests on cross-shaped specimens

Pre-deformation of the cross-shaped specimens was achieved through a monotonous tensile deformation along the X axis. After the acquisition of deformed microgrid images in a SEM to characterize the prior deformation, the specimens arms used for pre-straining were cut off and the reduction in section thickness ensured that deformation was localized at the center of the specimen during the second loading stage in simulated PWR primary environment at 360°C.

Specimens were rinsed ultrasonically in ethanol and in distilled water before testing was continued. Tests were carried out in Hastelloy (C-276) autoclaves. The specimens were insulated from the autoclave by oxidized Zircalloy to avoid galvanic coupling. Experiments were conducted under open circuit conditions. The environment was primary water (1000 ppm B as boric acid, 2 ppm Li as lithium hydroxide) at 360°C. The solution was previously de-aerated by evaporating 20% of the initial volume at 125°C, then hydrogen overpressure was introduced (30 cm 3 .kg -1 ) and controlled using a Pd-Ag probe. The chemistry was analyzed and validated before each test (B, Li, Cl -, SO 4 2-, F -). Finally, images of the surface of interest were acquired in a SEM in order to quantify the second deformation and identify SCC initiation sites.

Tests on U-bends and tensile specimens at different pH, temperatures and time durations

Other specimens were used: U-bends and "classical" tensile specimens cut in a 316L tube with an as-received grinded surface. Hardness measurements allowed to estimate the strain level on grinded surfaces to = 0.025. For U-bends tests, TEM samples were cut in non-strained parts.

Testing conditions are summarized in Table 4. A short exposure test was carried out in a specifically designed capsule placed in an autoclave (Figure 2). A specimen was introduced in the capsule, after tensile pre-deformation at room temperature (specimen 1171-13, 304L, heat T2575, strained to 40%). The test in primary water at 320°C lasted 6h (2h heating and 4h at 320°C), then a rapid decrease of temperature was obtained by the injection of cold pure water in the capsule (Figure 3). After testing, thin foils were cut to quantify the surface oxides. Three of the specimens were tested [START_REF] Couvant | Investigations on the mechanisms of PWSCC of strain hardened austenitic stainless steels[END_REF] at higher levels of alkalinity in order to simulate a possible occluded chemistry. At variance with nominal conditions (1000 ppm B as boric acid, 2 ppm Li as lithium hydroxide), 10 ppm Li was used in a first test, and 400 ppm Li in two other tests. Multeq TM simulations estimated that pH 320°C = 7.2 under nominal conditions (2 ppm Li), pH 320°C = 8 for the first alkaline test and pH 320°C = 9.1 for the other two.

TEM Analysis

A FEI dual-beam Helios Nanolab was used for preparing cross section thin foils on specifically deformed locations and SCC initiation sites on cross specimens. This dual beam has a Schottky field emission gun column, Ga Focused Ions Beam column, Pt and C gas injection system, carbon selective etching system, EDX detector, Autoprobe internal micromanipulator and 3D EBSD system.

Cross-sections from other specimens were prepared by cutting sections using a slow-speed diamond saw and gluing surfaces of interest with thermosetting epoxy under vacuum. The samples were adjusted and assembled in an insert, and thin foils were cut and polished. Electron transparency was achieved using a dimple grinder down to 20 µm and ion micromilling to perforation.

TEM observations of specimens were performed using a FEI TECNAI G2 FEG STEM equipped with Gatan Imaging Filter, EDAX energy dispersive spectrometer and HAADF detector. Besides conventional TEM and STEM imaging, oxides were characterized using EDS for concentration profiles across layers and EFTEM for elemental mapping.

Results

Oxide formed at pH 320°C 7.2 and 360°C on cold-worked material

Several sampling locations were chosen at the surface of the two cross-shaped specimens 1594-T112-06 and 1594-T217-01, based on the deformation measurements and SEM observations of crack initiation. Figure 4 and Figure 5 give an example of such a correlation for the site 1 on 1594-T217-01. The site was centered on an intergranular crack between two grains exhibiting different levels of deformation. The level of pre-deformation E xx in ambient air and second deformation E yy in primary water corresponding to the sampling for TEM examinations are reported in Table 5. Figure 6 shows a typical oxide formed on strain hardened stainless steels exposed to primary water at 360°C (T112-06, 304L, 7.2 pH 320°C ). The oxide-metal interface was clearly positioned below the grid-oxide interface, indicating the anionic growth of the inner Cr-rich oxide layer in the metal. A preferential oxidation can be also noticed within pre-established shear bands and microtwins from pre-deformation in air. Spinels grew on the inner Cr-rich oxide layer and on the grid. Magnetite was identified. Despite the presence of the inner Crrich oxide layer, spinels displayed an epitaxial relationship with the metal substrate. The important decrease of the Fe content indicates that oxidation essentially affects Fe. Within the range 130 nm -90 nm, Fe and Cr contents are equivalent (45%) and significantly higher compared to Ni content (10%). Therefore, a significant Cr enrichment is observed in this inner oxide layer. It can be assumed that Fe diffusion is more effective than Cr diffusion in this layer. Within the range 90 nm -45 nm, the oxide is essentially composed of Fe (60%), even if Cr (25%) and Ni (15%) are present. This outer oxide layer is probably essentially composed of Fe spinels. The total thickness of the oxide is approximately 150 nm, including 40 nm of Cr-rich oxide. EFTEM maps of O, Fe and Cr are presented on Figure 8. These maps were collected at a site where no prior strain was measured, but where a significant strain (0.075) was expected during exposure to primary water. The oxygen map indicates the position of the superficial oxide and magnetite. The iron map shows the presence of an outer thin (a few nm) Fe-rich oxide layer. The chromium map reveals possible variations of Cr concentration in the inner oxide layer. The interface between the inner oxide and the metal is irregular. The areas highlighted by red rectangles on Figure 8 suggest an anisotropic oxidation at the metal-oxide interface.

Cracked GB FIB sampling site

Figure 8 -EFTEM maps on site 2 at the surface of specimen 1594-T112-06 (304L). Focus (red rectangle) on anisotropic oxidation on the internal oxidation front.

Figure 9 displays a STEM observation showing possible differences in density between the outer and the inner part of the Cr-rich oxide layer. The outer part, at the interface with the spinels appears regular and dense, whereas the inner part seems to be composed of small "grains" growing inhomogeneously in the metal. The typical size of "grains" observed in the inner oxide may be compared with the characteristic length of fine dislocation structures in the underlying metal. Additionally, Figure 11 shows a significant porosity in the oxide layer at the intersection with slip bands, indicating an effect of the deformation occurring after oxidation on the metal/oxide interface and raising questions about the effects of plastic deformation on further oxidation and SCC processes. Detailed analyses were carried out in order to evaluate the effect of strain localization on oxide penetration and on metal-oxide interface. Figure 11 shows an example of EFTEM Crmap, with the associated binary map obtained after filtering. Image analysis of this binary map allowed to quantify the thickness of the inner Cr-rich oxide layer and perform a statistical analysis over the analyzed area (Figure 12). The three samples from specimen T217-01 (316L) were focused on IG crack initiation sites. This allowed the characterization of the oxide layer on both sides of the grain boundary, both grains having experienced distinct deformation levels. Figure 13 shows the crack initiation and Cr-layer analysis carried out on both grain around the cracked boundary on site 1 of specimen T217-01. The results obtained by image analyses of inner oxide layers are summarized in Table 5.

Oxides formed on material with machined surfaces

Specimen 1171-13 was a tensile specimen (grinding finish) strained to 40% deformation at room temperature and tested in the capsule during 4h at 320h. Figure 14 shows the analysis on site 1. The Cr-rich layer appeared fairly regular and thin (around 20 nm), only showing a significant penetration in one zone. Figure 16 shows the analysis of site 2, adjacent to site 1.

A thin and regular layer can be observed on the right hand side, but on the left, the thickness and morphology are different: the oxide layer penetrates deeper (around 80 nm), as seen on the O map, and seems more irregular, as seen on the Cr map. The two zones probably belonged to different grains, the left one having undergone a higher level of deformation. This illustrates the complexity of real surface conditions, particularly when a significant deformation is added. Secondly, oxide penetration in the "constant thickness" zone is lower than in the cross-shaped specimen (520h and 720h exposition), indicating that oxidation processes are still active after 4h. However, maximum penetration levels are comparable. This could be an effect of the surface finish and deformation level, but would also indicate that local maxima of penetration are reached earlier and that defects possibly acting as diffusion shortcuts also enhance the oxidation kinetics. 6 summarizes the analyses performed on specimens with machined surfaces.

Observations on intergranular crack initiation sites

Each of the three samples from specimen T217-01 was located on an intergranular crack initiation site. It was thus possible to conduct the analysis along the crack together with the adjacent free surfaces. Observed intergranular crack tips were most likely active, as the cracks only affected on boundary (no branching). Cracks usually initiated where discontinuities of deformation across grain boundaries were the most prevalent, as illustrated with local deformation measurements in Figure 4 and Crack depths did not exceed a few microns and the typical crack mouth opening displacement was about 200 nm. Open cracks were filled with Fe oxide (Figure 27). Crack walls exhibited a regular Cr-rich oxide layer whose morphology and thickness were comparable to that of the surrounding free surfaces. This would indicate that the local environmental conditions were essentially unaffected by the presence of iron oxide and that the time to stabilize the Cr oxide layer was probably significantly shorter than the testing time. The opening of the cracks was dissymmetric with respect to the grain boundary. This may be due to stress relaxation along the grain boundary after cracking, with plastic deformation in the "softer" grain possibly enhancing further dissolution in this grain by a local destabilization of the passive film.

At the crack tip, oxide penetration was deeper along the grain boundary than below the free surfaces. This may be due to the grain boundary acting as a short-circuit for oxygen diffusion.

In addition, the non-oxidized grain boundaries ahead of the crack tip showed significant compositional changes over more than 100 nm ahead of the intergranular oxide. This can be compared to what was seen at the metal-oxide interface over only a few nm: a significant enrichment in nickel, with associated depletion in iron and chromium. This phenomenon may also be related to the free volume of the grain boundary, allowing the oxidation phenomenon to operate deeper and promoting a faster diffusion of Cr and Fe. The implications on the oxidation mechanisms, the mechanical behavior of the boundary and the cracking could be considerable. Table 7 summarizes the analysis performed for each crack, taking into account the crack length, the oxidation of the crack tip, the affected zone ahead of the tip and the structure of the crack walls. Table 7 -Analysis of the cracks initiated at the surface of of specimen T217-01 (316L -720h -pH 7.2)

Discussion

Effect of deformation

Figure 29 shows the evolution of chromium-rich oxide penetrations as a function of cumulated deformation. The mean oxide penetration increases for higher deformation levels as well as the extrema. A sharp increase appears around 10 % cumulated deformation. This phenomenon could be explained by the features observed in Figure 6 and Figure 9. Oxidation phenomena seem to interact with plastic deformation by different mechanisms. The preferential oxidation along the deformation structures could explain this behavior, from the dislocation network increasing the mean penetration (Figure 6) to slip bands setting local maxima of penetration. Defects may act as diffusion sort-circuits for oxidizing species and allow the oxidation to penetrate deeper within the metal surface.

Effect of the pH of the environment

Figure 30 shows the effects of pH on the oxide growth, using the results from tests on specimen with machined surfaces. First, a decrease on the oxide penetration can be seen at pH 8 and a significant increase at pH 9.1. Secondly, comparing the durations of the various tests at pH 7.2 and pH 9.1, pH could also have a significant impact on the oxide penetration kinetics. At pH 7.2, stabilization of the Chromium passive layer seems to occur after a few hours of exposition ; 40 nm after 4h, 50 nm after 17000h (50 nm after 500h for cross test specimen on low deformation areas). On the other hand, at pH 9.2 the oxide is still actively penetrating the metal surface after hundreds of hours (150 nm after 240h, 550 after 5000h).

Cracks analysis

The crack tip in site 1 exhibits a different morphology; closer to the tip, the oxide layer on the crack walls gets thinner and the crack tip doesn't show any preferential oxidation along the grain boundary. The crack is twice longer (almost 1 µm) than the other so this could be seen as an effect of the crack length: the transport of the oxidizing species is less effective the longer the crack is. Additionally, the grain boundary ahead is also the most affected (almost 200 nm): the tip and affected region sum up to reach an equivalent level the other cracks. This fact could mean that the crack was still active at the end of the test, the oxidation processes not yet stabilized, and would indicate that the cationic diffusion of the metallic species (chromium and iron) is precursory to the oxidation (anionic diffusion of oxygen). Figure 33 shows the evolution of oxidation depth along the crack walls and crack length as a function of local deformation during the test (second E yy ). Observations are consistent with the surface observations, indicating a deeper penetration in areas with higher levels of deformation. Crack length seems to be correlated to local deformation in a similar way. Again, the crack at site 1 stands out, as seen on the EFTEM image (Figure 26). At a higher level of deformation, it is the longest crack and highest maximum oxide penetration along the crack walls. However, as indicated above, closer to the tip, the oxide thickness decreases. This may indicate that the crack was still active at the end of the test, achieving a deeper penetration and passivity where the crack initiated and thinner penetration in later exposed surfaces after a propagation step. From this case, and assuming the other two cracks experienced only one cracking event, this would indicate that the typical intergranular cracking step is between 200 nm and 500 nm. This information is valuable for the general understanding of SCC mechanisms, as it defines an area where investigations about the cracking factors should be led, and sets a representative length scale for future mechanistic modeling of intergranular cracking.

Assessed cracking mechanism

Based on previous considerations, the following basic mechanism is assumed to explain both initiation and propagation of SCC in stainless steels exposed to primary water :

1. Oxidation, leading to the formation of a superficial Cr-rich passive layer with a mean thickness of 50 nm. The maximum oxide penetration does not exceed 90 nm at 360°C, when 7 < pH320°C < 8, if the material is not strain hardened.

2. When sufficient, local stresses lead to the fracture of the oxide or metal-oxide interface. Strain hardening, loading fluctuations, intergranular strain incompatibilities and irregular metal-oxide interfaces promote stress concentrations and, as a consequence, initiation or crack tip extension.

Judging from observations, transgranular cracks are supposed to initiate on intense slip bands preferentially oxidized and strained during exposure while intergranular cracks initiate after preferential oxidation along grain boundaries and stress concentrations appearing between grains.

Conclusion

Oxidation of free surfaces and IGSCC cracks of cold-worked austenitic stainless steels exposed and strained in PWR water have been characterized by ATEM of cross-sections samples. Oxidation at free surfaces consists in an outer Fe-rich oxide layer and an inner Crrich oxide, and appears clearly below the original surface of the material. The oxide layer seem to penetrate deeper along deformation features and microstructural defects such as the dislocation network, slip band or grain boundaries. The assumed growth mechanism is by anionic processes, diffusion of oxygen allowing penetration of the oxide and defects acting as diffusion short-circuits. The sparse magnetite spinels found at the surface of the oxide layer most likely grow by a cationic mechanism. Judging from local deformation measurements, oxide penetration seem to increase with deformation of the material with a strong increase around 10 % local deformation. Alkalinity seems to affect oxide penetration: compared to the nominal pH 7.2 water, pH 8 shows a reduced penetration while pH 9.1 exhibits a significant penetration as well as altered kinetics.

Intergranular crack initiation was also characterized by ATEM. Initiation occurred at boundaries separating grain with seemingly different levels of plastic deformation. Cracks were filled with Fe-rich oxide and crack-wall exhibited a Cr-rich oxide penetration similar to that of the free surfaces. Grain boundaries at the crack tip exhibited preferential oxide penetration and a zone ahead of the oxidized tip revealed significant depletion in both Fe and Cr, with an according enrichment in Ni.

Results on machined surfaces emphasized the complexity of the phenomenon in real conditions. An upcoming study of surface finish effects is being performed on a set of samples oxidized in the same conditions (nominal PWR, 150h), with different surface preparation.

Short time exposure in the capsule and crack initiation analysis also revealed that the stability of the Cr-rich passive layer is reached after a few hours and set a higher bound for the investigation of oxidation kinetics. In this perspective, a device allowing short-time exposures (from a minute to tens of hours) is currently under development.

Figure 2 -

 2 Figure 2 -Capsule for short exposure to primary water.

Figure 3 -

 3 Figure 3 -Evolution of the temperature during testing in capsule.

Figure 4 -Figure 5 -

 45 Figure 4 -Pre-deformation Exx (in ambient air) on initiation site 1 at the surface of specimen 1594-T217-01 (316L) Figure 5 -Deformation Eyy (in primary water) on initiation site 1 at the surface of specimen 1594-T217-01 (316L)

Figure 6 -

 6 Figure 6 -Oxide observation (TEM, bright field) at site 1A on the surface of specimen 1594-T112-06 (304L).

Figure 7 Figure 7 -

 77 Figure 7 is an EDS scan corresponding to the previous site (at the location of the remaining gold grid). Oxygen content is plotted in arbitrary units, while Fe, Cr, Ni and Au+Pt contents are plotted in weight %. O and Ni contents slightly increase within the 200 nm -130 nm depth range. Conversely, Fe content decreases and Cr content does not change. It can be noticed also that Ni maximum correspond to minima in Fe and O content. This observation suggests that only a limited fraction of Ni (noble element) is affected by oxidation, contrary to Fe and Cr.

Figure 9 -

 9 Figure 9 -TEM on site 1B at the surface of specimen 1594-T112-06 (304L).

Figure 11 -Figure 12 -Figure 13 -

 111213 Figure 11 -EFTEM and analysis image of Cr-rich inner oxide layer at the surface of specimen T112-06 on site 1A (304L)

Figure 14 - 1 Figure 15 - 1 Figure 16 -Figure 17 -

 1411511617 Figure 14 -EFTEM and analysis image of Crrich inner oxide layer at the surface of specimen 1171-13 on site 1

Figure 18 -CrFigure 20 -

 1820 Figure 18 -EFTEM map of Cr-rich inner oxide layer at the surface of specimen T112-35 on site 2 (pH 7.2 -17000h) Figure 19 -EFTEM map of Cr-rich inner oxide layer at the surface of specimen 1171H4 (pH 8 -600h)

Figure 5 .

 5 STEM images of the cracks, seen in Figure 22, Figure 23, Figure 24 and Figure 25, clearly exhibit cracks along grain boundaries separating grains with different densities of dislocations.

Figure 22 -Figure 23 -Figure 24 -Figure 25 -

 22232425 Figure 22 -Bright field image of the crack initiated at site 1 of specimen T217-01 Figure 23 -Bright field image of the crack initiated at site 2 of specimen T217-01

Figure 26 -Figure 27 .

 2627 Figure 26 -EFTEM analysis of Cr-rich inner oxide layer and Ni enrichment in grain boundary ahead of the tip in the crack initiated at site 1 of specimen T217-01

Figure 29 -

 29 Figure 29 -Evolution of the Cr-rich oxide penetration as a function of cumulated deformation

Figure 1 Figure 30 -Figure 31 -

 13031 Figure30shows the effects of pH on the oxide growth, using the results from tests on specimen with machined surfaces. First, a decrease on the oxide penetration can be seen at pH 8 and a significant increase at pH 9.1. Secondly, comparing the durations of the various tests at pH 7.2 and pH 9.1, pH could also have a significant impact on the oxide penetration kinetics. At pH 7.2, stabilization of the Chromium passive layer seems to occur after a few hours of exposition ; 40 nm after 4h, 50 nm after 17000h (50 nm after 500h for cross test specimen on low deformation areas). On the other hand, at pH 9.2 the oxide is still actively penetrating the metal surface after hundreds of hours (150 nm after 240h, 550 after 5000h). Figure31illustrates the possible evolution of the average oxide penetration as a function of time for pH 7.2 and pH 9.1, fitting the result and assuming a saturating law (Evans type). This would indicate a critical change in chemical behavior, bringing into question the passivity of the chromium layer at pH 9.2, as well as pointing out possible effects of occluded environments of high alkalinity on oxide growth and SCC cracking.

Figure 32 -

 32 Figure 32 -Comparison of crack structures

Figure 33 -

 33 Figure 33 -Oxidation of the crack walls and crack length as a function of deformation

Table 1 -

 1 Chemical composition (wt. %).

	Material	Heat	C (%)	S (%)	P (%)	Si (%)	Mn (%)	Fe (%)	Ni (%)	Cr (%)	Mo (%)	N (%)	Al (%)
	316L	XY183	0.026	0.004	0.033	0.42	1.81 65.75 12.00	17.34	2.57 0.050	-
	AISI Requirement for 316L	< 0.03	< 0.03	< 0.045 < 1	< 2		10-13	16.5-18.5 2-2.5 < 0.11
	304L	T2575	0.026	0.002	0.027	0.52	1.49 68.68 9.45	19.23	0.24 0.064	0.033
	AISI Requirement for 304L	< 0.03	< 0.03	< 0.045 < 1	< 2		8-10	18-20		< 0.11

Table 2 -

 2 Mechanical properties in air at room temperature.

	Material	Heat	Ys0,2 (MPa)	UTS (MPa)	El. (%)	HV30
	316L	XY183	255	553	63
	304L	T2575	247	582	60	160
	RCCM-M220 requirement	> 170	> 500	> 45	126-179

Table 3 -Summary of pre-deformation and test in PWR water for cross-shaped specimen Material Specimen Pre-deformation E xx Second deformation E yy in PWR water Duration of the test in PWR water (h)

 3

	304L	T112-06	0.09	0.03	519
	316L	T217-01	0.07	0.02	720

Table 4 -Summary of non-cross specimens and testing conditions Material Heat Name Surface finish Estimated strain level pH 325°C Temperature (°C) Exposure time (h)

 4

	304L	T2575 1171-13	Grinding	0.4	7.2	320	4
	304L	T2575 T112-35	Grinding	0.025	7.2	360	17000
	316L	XY183 1171H4	Grinding	0.025	8	360	600
	316L	XY183 1512-10	Grinding	0.025	9,1	360	240
	304L	T2575 1512-22	Grinding	0.025	9,1	360	5000

Table 5 -

 5 Analysis of Cr-rich inner oxide penetrations at the surface of cross-shaped specimens.

	Specimen Material Site	Local strain in the sample Prior E xx Second E yy	Cumulated strain level	Analysed length (nm)	Cr-rich inner oxide penetration (nm) Mean Median Min. Max. SD
			1A	0.10	0.02	0,12	1386	102	96	31	172	30
	T112-06	304L	1B	0.00	0.075	0,075	694	49	49	23	87	12
			2	0.02	0.07	0,09	679	81	82	54	101	9
			1L	0,05	0,05	0,10	1188	72	71	12	124	27
			1R	0,08	0,1	0,18	988	90	88	59	135	17
	T217-01	316L	2L 2R	0 0	0,02 0,02	0,02 0,02	2077 890	46 73	48 74	6 40	101 105	25 14
			3L	0,1	0,01	0,11	2941	96	94	41	141	16
			3R	0,1	0,01	0,11	880	61	61	19	92	16

Table 6 -

 6 Analysis of Cr-rich inner oxide penetrations at the surface of the specimens without surface preparation

	Material Specimen	Site	Estimated strain	Test duration	Temperature (°C)	pH320°C	Analysed length (nm)	Cr-rich inner oxide penetration (nm) Mean Median Min. Max. SD
			site 1					718	29	25	4	82	19
	304L	1171-13	site 2	0,4	4	325	7.2	455	22	24	3	38	9
			site 3					879	58	55	10	131	35
	304L	T112-35	site 1 site 2	0.025	17000	360	7.2	1787 1243	59 35	53 27	13 11	140 81	30 20
	316L	1171H4	-	0.025	600	360	8	534	33	22	9	95	23
	316L	1512-10	site 1 site 2	0.025 0.025	240	360	9.1	1303 465	151 139	121 114	79 80	279 246	63 57
	304L	1512-22	-	0.025	5000	360	9.1	4383	552	542	383	725	66


Mickaël Garnier
email: garnier.mickael@gmail.com

Maya Alsheh Ali

Johanne Seguin

Nathalie Mignet

Thomas Hurtut

Laurent Wendling

Grading cancer from liver histology images using inter and intra region spatial relations

Histology image analysis is widely used in cancer studies since it preserves the tissue structure. In this paper, we propose a framework to grade metastatic liver histology images based on the spatial organization inter and intra regions. After detecting the presence of metastases, we first decompose the image into regions corresponding to the tissue types (sane, cancerous, vessels and gaps). A sample of each type is further decomposed into the contained biological objects (nuclei, stroma, gaps). The spatial relations between all the pairs of regions and objects are measured using a Force Histogram Decomposition. A specimen is described using a Bag of Words model aggregating the features measured on all its randomly acquired images. The grading is made using a Naive Bayes Classifier. Experiments on a 23 mice dataset with CT26 intrasplenic tumors highlight the relevance of the spatial relations with a correct grading rate of 78.95%.

Introduction

Histology images are becoming more and more used in computer science. This particular modality of imaging preserves the underlying tissue architecture through its preparation process, making the use of histology images the gold standard in diagnosing a considerable number of diseases, especially cancers [START_REF] Gurcan | Histopathological image analysis : a review[END_REF]. The automated quantitative analysis of histology images can help to understand the reasons behind a diagnosis, as well as to reduce the inter and intra expert variations and to bring a better understanding of the growth of a disease.

Depending on the objects of interest, histology images are acquired on specimens stained by one or several dyes that highlight their particular features and give contrast to the tissue. The best solution for histopathologists is to analyze whole slide images, however it is very costly thus limiting their introduction and diffusion in research. Alternative solutions are used, either relying on analyzing regions of interest [START_REF] Mikalsen | Quantification of angiogenesis in breast cancer by automated vessel identification in cd34 immunohistochemical sections[END_REF], or using images taken randomly over all the slide [START_REF] Heldmuth | In vivo monitoring of elastic changes during cancer development and therapeutic treatment[END_REF].

Biologists, using histology images in cancer studies, are interested in analyzing both primary tumors and secondary metastases. In this paper, we propose a framework to grade cancer development in metastatic liver histology images. This work investigates the relevance of the spatial relations to recognize the advancement of cancer. The spatial relations are measured at two scales, between tissue types and between biological objects inside each type of tissue.

The most common framework for automatic analysis of multi-stained histology images follows three steps. First a segmentation of biological objects is performed, then these objects can be quantified, usually based on first-order measures [START_REF] Heldmuth | In vivo monitoring of elastic changes during cancer development and therapeutic treatment[END_REF], and biological object-based features can be extracted [START_REF] Swamidoss | Automated classification of immunostaining patterns in breast tissue from the human protein atlas[END_REF]. These methods are usually used for detection rather than grading, the latter being a more complex issue. Other methods focusing on texture analysis are applied to grade histology images for several cancers, such as lymphoma [START_REF] Sertel | Histopathological image analysis using model-based intermediate representations and color texture: Follicular lymphoma grading[END_REF] and carcinoma [START_REF] He | Histology image analysis for carcinoma detection and grading[END_REF].

Our description focuses on the encoding of the pairwise spatial relations which are already used in histology images analysis, usually between pairs of elements after a segmentation process. The spatial information is extracted using second order statistics, the most common being Ripley's K function, the Pair Correlation Function and Besag's L function, see [START_REF] Illian | Statistical Analysis and Modelling of Spatial Point Patterns[END_REF]. They are used in various biomedical applications such as the description of tumors in breast [START_REF] Mattfeldt | Statistical analysis of labelling patterns of mammary carcinoma cell nuclei on histological sections[END_REF] or brain [START_REF] Jiao | Spatial organization and correlations of cell nuclei in brain tumors[END_REF]. These functions aims at representing the distribution of distances between pairs of point objects but are not able to handle regions as we need.

Other methods of spatial relations measurement are proposed in computer vision. They can be split into quantitative and qualitative approaches. Qualitative approaches correspond to symbolic relations such as positioning [START_REF] Freeman | The modelling of spatial relations[END_REF]. Since we are interested in a measure between possibly unconnected subset of pixels, we cannot use a symbolic representation. Fuzzy quantitative approaches are commonly used in multiple application domains, such as medical imaging [START_REF] Bloch | Fuzzy spatial relationships for image processing and interpretation : a review[END_REF]. In this study, we use and extend the Force Histogram Decomposition (FHD) method [START_REF] Garnier | Object description based on spatial relations between level-sets[END_REF] to accurately describe the spatial organization of objects.

Materials

CT26 tumor intrasplenic implantation CT26 is an undifferentiated murine colon adenocarcinoma cell line syngeneic to the BALB/c mice strain. CT26 tumor was very tumorigenic in vivo and often produces pulmonary and hepatic metastases in mice. To develop a reproducible model of hepatic metastasis, tumor cells have been implanted into the spleens of mice. From this site of injection, tumor cells access to the liver via the blood vessels and proliferate to form secondary tumor. Using the model described by Wai [START_REF] Wai | Osteopontin silencing by small interfering rna suppresses in vitro and in vivo ct26 murine colon adenocarcinoma metastasis[END_REF], Female BALB/c mice, 6 to 7 weeks old were acclimated for 1 week while separated in groups of five. All animal studies were carried out according to the Institutional Animal Care and Use Committee of Paris Descartes. Splenectomy were performed 5 min after intrasplenic injection of 5x10 4 cells in culture media.

Histological analyses At 7, 11, 13 and 17 days after implantation, liver were removed, frozen, cut and stained with the same protocol for all animals. Each slice of liver was counterstained with Hematoxylin and Eosin. From this biological material, 246 histology images were digitized (Leica DM6000B) into 90 images from healthy liver and 156 from metastatic tumor model: day 7 (5 mice) and days 11, 13, and 17 (3 mice each). The mean number of images per mouse is 10, these images were taken randomly from all over a single slide. The resolution of the images is 2592 × 1944 pixels.

Mice cancer grading

The liver slices were analyzed by an expert and graded according to three criteria. First, the number of metastases, which was done with a macroscopic observation. The second criterion is the number of metastases per square millimeters and finally the area of metastases per square millimeters. The final grade of the mice is defined accordingly to these three criteria. The dataset is composed of 9 sane mice, 3 of grade 1, 7 of grade 2 and 4 of grade 3.

Methods

We use texture analysis and spatial relation measures in order to describe histology images. The analysis of texture is performed to detect the presence of metastases inside the tissue section while the final description only relies on the spatial relations. The different measures of every images are then aggregated to accurately describe each mouse.

Metastases detection

Our aim is to detect if there is metastases in each image. As shown in Figure 1, the textures of sane tissue and metastases are quite different, both stains have different hues and the shapes of the nuclei are more random in tumorous tissue. Thus, a texture analysis method is carried out to detect these variations. We use a multiresolution texture analysis method introduced in [START_REF] Garnier | Automatic multiresolution age-related macular degeneration detection from fundus images[END_REF]. The multiresolution is achieved using a wavelet filter-bank which also gives us access to high frequency information. All the information resulting from this decomposition is then described using the completed model of Local Binary Patterns (cLBP). A Linear Discriminant Analysis is conducted and used for classification. This detection serves as a pre-processing to induce a semantic information into the spatial relations measurement step.

Spatial relations measurement

We seek to describe the spatial organization of the tissue at two scales: between types of tissue and between biological objects inside each tissue type. All the spatial relations are here represented using FHD.

Force Histogram Decomposition

The FHD is an object recognition method where the description relies on both the shapes and the spatial relations underlying the object structure. These two information are acquired using histograms of forces [START_REF] Matsakis | A new way to represent the relative position between areal objects[END_REF]. The histogram of forces computed between two objects A and B with a force ϕ along a direction θ is defined as:

F AB (θ) = C θ c A c B ϕ(||ab||) db da (1)
where C θ is a set of θ-oriented lines spanning the image and, c A and c B are respectively a single line from C θ cutting through the objects A and B, see Figure 2. In order to access the object structure, the FHD first requires to decompose the object into several layers. Histograms of forces are then computed between every pairs of layers for the spatial relation information, and on every layer with itself to get a shape description. The FHD is defined as the set of all these histograms of forces. This method presents the advantage of being invariant to both scale and position, and can easily achieves rotation invariant using a circular dissimilarity measure. The FHD requires the definition of a force, we choose to set a constant force in all our measures, i.e. ϕ = 1. Thus our description does not take the distance between objects into consideration but allows for objects overlapping. This description is also robust to the accuracy of the decomposition method.

In this study, we are interested in reducing the amount of information contained in each histogram of forces. Thus, we use a signature based on the Fourier series. The final description retains only the S first amplitude values which are not considered as noise. This signature was introduced and its efficiency was assessed in [START_REF] Tabbone | Matching of graphical symbols in line-drawing images using angular signature information[END_REF]. This signature also has the advantage of ensuring that our description is rotation invariant.

Inter regions spatial relations An adaptive multi-channel wavelet representation [START_REF] Lee | Learning non-homogenous textures and the unlearning problem with application to drusen detection in retinal images[END_REF] is applied to separate the different tissue types. This method aims at separating the image into meaningful texture sets based on a wavelet packet transform using Lemarié's wavelet. Each pixel of the image is classified into a layer according to both its high and low frequencies wavelet coefficients envelopes. The Figure 3 shows the outcome of this decomposition on a representative image of our dataset.

This method is used to separate the image into three layers: sane tissue, metastastic tissue and the remaining image content. In the case of an image without metastases, we only decompose two layers, the metastases layer being forced to a blank image. The shapes of these three regions and their spatial relations are measured using the FHD.

Original image

Sane tissue

Metastases Vessels and gaps Fig. 3. Representative image from our dataset decomposed into three layers.

Intra regions spatial relations We aim at describing the biological objects organization inside each type of tissue. In order to keep this measure relevant and as disjoint as possible from the shape of the region, we define a circular patch inside the most attractive set of connected components of this region. This set of connected component has to be wider than the size of the patch and possess both the highest solidity and the largest area possible. If no group of connected components fits these criteria, the one with the largest area is chosen. The resulting three patches are shown in the first row of figure 4. Once the patches are defined, we apply a color deconvolution [START_REF] Ruifrok | Quantification of histochemical staining by color deconvolution[END_REF] to separate the hematoxylin from the eosin. This operation allows access to the cells' nuclei, the stroma and the remaining objects. The outcome of the color deconvolution is rather noisy and the FHD requires binary layers, therefore, every stain channel is thresholded using the Otsu method with three classes, the background, the noise and the dye. The resulting layers for the three previous patches are displayed in the second row of Figure 4. As for the inter regions spatial relations, we apply the FHD to describe the shapes of each object and the spatial relations between them.

Mice signature and classification

Since each mouse is represented by several images, we have to aggregate their descriptors in the classification process. Thus we use a Bag of Words model [START_REF] Sivic | Video google: A text retrieval approach to object matching in videos[END_REF] with a coding-pooling scheme. This method requires the computation of a visual codebook composed of prototype descriptors, called visual words. A mouse is then described by counting the occurrences of each visual words within its images. The visual codebook is generated using a k-means algorithm on the training data to group the descriptors. The description computed on a single image correspond to a single word.

The coding-pooling scheme consists in dividing the mapping in two steps. Each descriptor is first mapped to the closest one in the visual codebook, a binary signature, where a 1 corresponds to the closest visual word, is then obtained. The second step aggregates these signatures into a single description by adding up all the mapped visual words. This method is more complicated to implement than the simple Bag of Words but allows to keep a small signature size. The final classification is made using a Naive Bayes Classifier on the outcome of the coding pooling scheme.

Experimental results

Metastases detection

This step requires to set both the base parameters of the cLBP and the depth of the wavelet decomposition. We chose a LBP radius of 1 with a number of neighbors of 8 and a depth of 4. This step provides good results, see Table 1, especially in terms of false positive with using only one mouse per grade as training data. Almost all the images presenting metastases are correctly detected. We prefer to have a very good sensitivity rather than a good specificity so that the grading method does not miss metastases related spatial relations.

Grading of cancer

In this step, we have to set several parameters. The first parameter is the number of directions of the computed histograms of forces. We chose to use 180 directions, thus giving an angular resolution of one degree. The Fourier series signature length (S) is set to several values (4, 8, 16 and 32) in order to measure the influence of this parameter.

The next parameter is the size of the patch (r), its effects are also studied by using four different values (50, 100, 200 and 400 pixels). Our last parameter is the size of the visual codebook (C), we conducted our experiments using 8, 16 and 32 different visual words in our codebook. The results of our grading method, with the same training data as the metastases detection, for the different sets of parameters are summarized in Table 2. It achieves good results and is rather robust to the choice of the parameters with the exception of the patch radius. This shows that the intra-regions spatial relations hold useful information for the cancer grading but they require enough materials in order to accurately describe the tissue. The size of the visual codebook seems to have little influence on the grade recognition. However, the length of the Fourier series signature has to be chosen wisely since a length of 8 appears to bring a loss of more than 20% in recognition.

Conclusion

In this paper, we proposed a framework to grade cancerous histology images. After a metastases detection using texture features, the method aims at describing the spatial relations between and inside the different types of tissue. We first decompose each image into several layers corresponding to the three types of tissue: sane, metastatic, and others such as blood vessels and gaps. Then, to access the biological organization inside each type of tissue, we further decompose a representative circular patch per tissue into its three types of biological objects: nuclei, stroma and gaps. The spatial organization at both scales are then described using an extended Force Histograms Decomposition.

Our work shows correct grading recognition of 79.0% despite our dataset being composed of random images taken from a single whole slide per mice. Thus, it could provides a better description in the case of whole slide images. This work does not take into consideration a description of the pairwise spatial relations between biological objects which seems to hold additional information. In the future, we aim at integrating this statistical information. We are also interested in studying the relevance of this method for grading other types of cancer.

Fig. 1 .

 1 Fig. 1. Images showing the variations in texture between sane tissue and metastases. (a) without metastases despite being of grade 1 while (b) with metastases and of grade 3.

Fig. 2 .

 2 Fig. 2. Principle of computation of histograms of forces.

Fig. 4 .

 4 Fig. 4. Three patches extracted on a representative image and their decomposition into biological objects. RGB channels respectively represent the nuclei, the stroma and the remaining tissue.

Table 1 .

 1 Confusion matrix obtained on our dataset. It shows a specificity of 76.2%, a sensitivity of 98.3% and a recognition rate of 87.1%.

	Output	Healthy 93 Metastases 29 Healthy Metastases 2 116 Target	Specificity Sensitivity Recognition Rate 76.2% 98.3% 87.1%

Table 2 .

 2 Tables showing the cancer grade recognition rates (%) of our method according to the size of the intra-region patch (r), the length of the Fourier series signature (S) and the size of the visual codebook (C).

	S	32	S 4	8	16 32	S	32
	C	8	C	8		C 8	16 32
	r 50 100 200 400	r	400	r	400
	% 52.6 57.9 63.2 79.0	% 79.0 57.9 79.0 79.0	% 79.0 73.7 73.7

Acknowledgement

This work was supported by the ANR SPIRIT #11-JCJC-008-01.