Johann Leplat
email: johann.leplat@culture.gouv.fr

Bousta Faisl

Alexandre Ab

François

Mikaël Guiavarc'h Ab1

Jean-Didier Mertz

Didier Brissaud

The pink staircase of Sully-sur-Loire castle: even bacteria like historic stonework

Keywords: Cultural heritage preservation, stone biodeterioration, rosy discoloration, high-throughput sequencing, Nitriliruptor sp., climatic monitoring

Rosy discoloration has affected the stone steps in the main spiral staircase of the Sully-sur-Loire castle donjon for many years now. This study monitors the development of the coloration and the environmental climatic conditions in the staircase to understand the conditions favoring the presence of this rosy discoloration. High-throughput sequencing was performed on healthy stone and on pink pigmented stone to identify the agent responsible for this discoloration. The results suggest that the rosy discoloration is the mark of a former degradation process which is now inactive, and that bacteria were the main agent of the pink patina formation. Nitiliruptor was the main genus identified in the pink parts of the staircase. This is the first time that this bacterial genus has been linked to a cultural heritage biodeterioration process. There was no evidence that this bacterial genus was responsible for the development of the pink patina, but it could have replaced it as a subsequent evolution of the process.

Introduction

Stone is one of the main and oldest materials used for artistic expression in the field of cultural heritage. It was already used during the upper Paleolithic (between 50 000 and 10 000 years ago) as a support for engraving and sculpting in shelters and caves [START_REF] Abadía | Paleolithic art: a cultural history[END_REF]. The use of stone ranges from monumental constructions to the sculpture of small statues.

The different types of stone used in the field of cultural heritage include marble, limestone, sandstone and granite, and even man-made stones such as brick, mortar or concrete. Whatever the type of stone used, they are all subject to some degree of biodeterioration [START_REF] Warscheid | Biodeterioration of stone: a review[END_REF]. This process is defined as an undesirable change in the properties of material caused by the activity of biological agents [START_REF] Hueck | The biodeterioration of materials as a part of hylobiology[END_REF]. The intensity of the biodeterioration process on stone monuments depends on the intrinsic properties that define the bioreceptivity of each stone [START_REF] Guillitte | Bioreceptivity: a new concept for building ecology studies[END_REF][START_REF] Miller | Bioreceptivity of building stones: A review[END_REF], combined with the effects of climatic changes and air pollution [START_REF] Brimblecombe | Millennium-long damage to building materials in London[END_REF][START_REF] Caneva | Biodeterioration of monuments in relation to climatic changes in Rome between 19-20th centuries[END_REF][START_REF] Thornbush | Changing patterns of soiling and microbial growth on building stone in Oxford, England after implementation of a major traffic scheme[END_REF].

The detrimental effects of organisms on stone monuments may be aesthetic, chemical and/or physical [START_REF] Scheerer | Microbial deterioration of stone monuments-an updated overview[END_REF]. For example, the secretion of inorganic and organic acids by organisms on stones leads to a process known as biocorrosion. This results in the dissolution of the mineral matrix of the stone and the precipitation of salts, possibly causing physical stress in the stone [START_REF] Mcnamara | Microbial deterioration of historic stone[END_REF][START_REF] Warscheid | Biodeterioration of stone: a review[END_REF]. Organisms can also enter the stone via pores in the material, causing mechanical damage and changing the physical properties of the structural material [START_REF] Scheerer | Microbial deterioration of stone monuments-an updated overview[END_REF][START_REF] Sterflinger | Microbial deterioration of cultural heritage and works of arttilting at windmills?[END_REF].

Different types of organisms can be involved in biodeterioration, with both macro and microorganisms observed. Phototrophic microorganisms such as algae and cyanobacteria are generally considered as the initial colonizers (pioneer organisms) and produce colored patinas and incrustations on stone monuments [START_REF] Crispim | Cyanobacteria and biodeterioration of cultural heritage: a review[END_REF][START_REF] Macedo | Biodiversity of cyanobacteria and green algae on monuments in the Mediterranean Basin: an overview[END_REF], whilst lichens grow much more slowly [START_REF] Hoppert | The succession of biofilms on building stone and its possible impact on biogenic weathering[END_REF]. The presence of these first colonizers facilitates colonization by chemo-organotrophic bacteria and fungi [START_REF] Gorbushina | Microcolonial rock inhabiting fungi and lichen photobionts: evidence for mutualistic interactions[END_REF][START_REF] Li | Distribution and diversity of bacteria and fungi colonization in stone monuments analyzed by high-throughput sequencing[END_REF], which are strongly involved in the physical and chemical deterioration process of stone monuments [START_REF] Pinheiro | Limestone biodeterioration: A review on the Portuguese cultural heritage scenario[END_REF][START_REF] Salvadori | The role of fungi and lichens in the biodeterioration of stone monuments[END_REF]. Some chemo-organotrophic organisms have also been found on stone monuments without the presence of pioneer organisms, and use organic matter derived from rock mineral, airborne particles or products used in preceding restorations [START_REF] Scheerer | Microbial deterioration of stone monuments-an updated overview[END_REF][START_REF] Warscheid | Biodeterioration of stone: a review[END_REF]. Higher plants are macro-organisms, and may thus play a role in the biodeterioration of stone monuments by an initial colonization of monuments by pioneer plants (herbaceous annuals and perennials) that cause minor damage and are then replaced by shrubs or trees, which can cause substantial physical damage [START_REF] Korkanç | Impact of the surface roughness of stones used in historical buildings on biodeterioration[END_REF][START_REF] Lisci | Lichens and higher plants on stone: a review[END_REF].

Within the biodeterioration process, the phenomenon of rosy discoloration of stones and wall paintings has attracted increasing amounts of interest over recent years [START_REF] De Felice | Genetic fingerprint of microorganisms associated with the deterioration of an historical tuff monument in Italy[END_REF][START_REF] Ettenauer | Halophilic microorganisms are responsible for the rosy discolouration of saline environments in three historical buildings with mural paintings[END_REF][START_REF] Gurtner | Comparative analyses of the bacterial diversity on two different biodeteriorated wall paintings by DGGE and 16S rDNA sequence analysis[END_REF][START_REF] Imperi | The bacterial aetiology of rosy discoloration of ancient wall paintings[END_REF][START_REF] Laiz | Isolation of five Rubrobacter strains from biodeteriorated monuments[END_REF]Piñar et al., 2014b;Tescari et al., 2018b). This type of discoloration has been described in subterranean and non-subterranean environments and has been widely reported in central and southern Europe (Piñar et al., 2014a). Actinobacteria belonging to Rubrobacter genus were found to be involved in discoloration in most of these cases [START_REF] Jurado | Rubrobacter bracarensis sp. nov., a novel member of the genus Rubrobacter isolated from a biodeteriorated monument[END_REF][START_REF] Laiz | Isolation of five Rubrobacter strains from biodeteriorated monuments[END_REF][START_REF] Nugari | Biodeterioration of mural paintings in a rocky habitat: The Crypt of the Original Sin (Matera, Italy)[END_REF][START_REF] Schabereiter-Gurtner | Rubrobacterrelated bacteria associated with rosy discolouration of masonry and lime wall paintings[END_REF]. In some other cases, other pink pigmented bacteria such as Arthrobacter, Rhodococcus or Methylobacterium were identified (Piñar et al., 2014b;Tescari et al., 2018a;Tescari et al., 2018b). The common link between the different monuments affected by the development of pink biofilms seems to be water infiltration (Piñar et al., 2014a). The crystallization of salts that occurs with these infiltrations may offer optimal growth conditions for halophilic microorganisms in addition to their own destructive effect on the stone.

In France, rosy discolorations are observable in Sully-sur-Loire castle. It is far from being the only monument affected by this phenomenon in France, but it is one of the first to have been intensively studied [START_REF] Berdoulay | Genetic characterization of microbial communities living at the surface of building stones[END_REF]. The castle is one of the monuments listed by UNESCO in the Loire Valley in 2000. It is located in the far eastern sector of the area listed by UNESCO in the Loiret region. There has been a castle at this location on the left bank of the Loire since the 12 th century, but the construction of the current castle began at the end of the 14 th century. Additions and transformations were carried out until the 19 th century.

Large reconstructions occurred in the 20 th century after a great fire in 1918 and damage from bombings during World War II. The castle is made of white tufa, which was the stone typically used as a building material in the Loire Valley during the Renaissance period. Tufa is a yellowish-white, porous and very fine-grained siliceous limestone with a small amount of clayey minerals [START_REF] Janvier-Badosa | Gypsum and spalling decay mechanism of tuffeau limestone[END_REF]. The geometrical and symmetrical distribution of the rosy discoloration on the stones of Sully-sur-Loire castle made it a remarkable study case to compare and determine the conditions affecting its development.

The aim of this study was to confirm or exclude a biological origin for rosy discoloration, and, if applicable, identify the organisms responsible for the biofilm development. In a second step, we sought to compare the environmental conditions in areas of stone affected by rosy discoloration with those observed in healthy areas to widen our knowledge of the factors that lead to this phenomenon.

Materials and Methods

Study area

The rosy discoloration has only been observed on indoor stones and mainly in the "escalier d'honneur": the main spiral staircase of the donjon (Fig. 1a). Not all the steps are affected to the same extent by pink coloration, which is mainly located between the first and the second floor, on the underside of the steps and exclusively on the outer part of the steps (Fig. 1b). The steps range from those with no pink coloration to those with almost 75 % of their surface affected by pink coloration.

Two of the most severely affected steps between the first and the second floor were chosen for this study. This area had the further advantage of being closed to the public. The first step was used for colorimetric and climatic monitoring (Fig. 2), while the second was sampled for metagenomic microbial analysis.

Colorimetric monitoring

Colorimetric monitoring was used to characterize the initial differences between the white area (unaffected) and the pink area (affected) of a given stone step and then to monitor the progression of the coloration within and between these two sections through time according to different factors. The chosen step was first divided into two parts: white and pink (Supplementary fig. 1). Four measurement sections were then delineated within each of these two sections: i) section scratched with a scalpel to remove the outside layer of the stone then covered to hide it from direct light; ii) section scratched but not covered; iii) section covered but not scratched; iv) control section left unscratched and not covered. Scratching removed the coloration in the pink section and would thus enable us to observe any return of this coloration. Covering was carried out to check for a possible role of light in the development of the pink coloration. The first colorimetric measurements were made in September 2014 before and after scratching, then colorimetric progression was regularly checked over a threeyear period: in May 2015, January 2016, July 2017 and January 2018.

The measurements were made in each defined section with a Chroma meter CR-410 (Konica Minolta, Tokyo, Japan), which uses the L*a*b* CIE 1976 color space under D65 illuminant, and 10° observer. Each measurement returns three values assessed on a sample with a 5 cm diameter: the L* value for lightness (0=black, 100=white); the a* value for the green-red component (-120=green, 120=red); and the b* value for the blue-yellow component (-120=blue, 120=yellow). Each color value was obtained using the mean of two replicated measurements.

Raman spectroscopy

Raman spectroscopy was used on the pink powder obtained after scratching the stone to characterize the pigment involved. Analysis was performed using an InVia confocal Raman microscope (Renishaw, Wotton-under-Edge, UK) equipped with a 785-nm laser diode and a 50x lens at a power of 10 % (5.05 mW). Raman spectra were obtained by accumulating 50 acquisitions of 10 s.

Climatic monitoring

The climatic conditions were monitored on the same step as the colorimetric measurements (Supplementary fig. 1). The temperature inside the stone was monitored by drilling a hole in the pink and white sections and installing a Telog R-3300 probe (Trimble Inc., Sunnyvale, USA). This probe records temperatures between -20 and 60 °C with a resolution and an accuracy of 0.01 °C. Environmental conditions were monitored by hanging a Telog R-2162 probe (Trimble Inc., Sunnyvale, USA) between the two areas to measure air temperature and relative humidity. This probe records temperatures between -30 and 80 °C with a resolution of 0.12 °C and an accuracy of 0.4 %, and records relative humidity between 5 and 95 % with a resolution of 0.1 % and an accuracy of 2 %. Each probe recorded a measurement once every two hours. The environmental data collected in this study was then compared to reference data for outdoor temperature and relative humidity were collected from a website (www.historique-meteo.net).

Microbiological analysis

Sampling

Samples were taken from the white and pink areas of the same step for high-throughput sequencing analysis of microbiological populations. Three samples were taken from the white area (W1, W2 and W3) and from the pink area (P1, P2, P3). Samples were collected by scratching a 25 cm 2 surface with a scalpel, providing a fine powder.

Total genomic DNA extraction and high-throughput sequencing

Bacterial, fungal and eukaryotic communities were identified for each sample. Total genomic DNA was extracted from the samples and from an outgroup (O) composed of non-filtered autoclaved water ("dead DNA" remained in the sample) using FastDNA TM Spin Kit for soil (MP Biomedicals, LLC, Santa Ana, USA), according to the manufacturer's instructions.

High-throughput sequencing library preparation and Illumina Miseq (Illumina, San Diego, USA) sequencing were conducted by Genoscreen (Lille, France). The Metabiote protocol (Genoscreen, Lille, France) was used to prepare and barcode the twenty-one libraries (6 samples + 1 outgroup x 3 microbial communities), and targeted the 16S ribosomal V3 and V4 DNA hypervariable regions, the ITS1/ITS2 DNA region and the 18S ribosomal DNA region for bacteria, fungi and eukaryotes, respectively. The twenty-seven libraries were then pooled for single-run sequencing. Sequencing was performed with 4 pM of the pooled library using a 300-bp paired-end sequencing protocol on the Illumina MiSeq platform.

Processing of high-throughput sequencing data

The data were demultiplexed using CASAVA software (Illumina, San Diego, USA). After primer removal, the sequences with a low Phred quality score (Q<30) were removed and the overlapping paired-end reads were assembled using FLASH software [START_REF] Magoč | FLASH: fast length adjustment of short reads to improve genome assemblies[END_REF]. Finally a QIIME 2 2018.11 pipeline was used to remove chimeric sequences, cluster the sequences in operational taxonomic units (OTUs; quality-filter and deblur plugins; q-score and denoise-16S/denoise-other functions) and taxonomically classify the obtained OTUs (plugin feature-classifier; function classify-consensus-vsearch; [START_REF] Bolyen | QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science[END_REF]). The taxonomic classification of bacterial, fungal and eukaryotic OTUs was achieved using Greengenes database [START_REF] Mcdonald | An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea[END_REF], the UNITE database [START_REF] Nilsson | The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications[END_REF] and the SILVA database [START_REF] Quast | The SILVA ribosomal RNA gene database project: improved data processing and web-based tools[END_REF], respectively.

Statistics

Colorimetric monitoring

Statistical analyses were carried out using R 3.2.5 with α = 5 % (R Core Team, 2014). The results of colorimetric measurements were analyzed by a generalized linear mixed model (GLMM). The GLMM is a suitable analytical tool for non-normal data collected from complicated designs involving random and fixed effects.

2018).

The GLMM for this study was:

Link(pijklm) = ηijklm = θ + αi + βj + γk + δl + αi:γk + αi:δl + γk:δl + αi:γk:δl + μijkl + εijklm
where θ is the general effect, αi is the effect of Color area, βj is the effect of Covering, γk is the effect of Scratching, δl is the effect of Measurement date, αi:γk is the effect of the interaction between Color area and Scratching, αi:δl is the effect of the interaction between Color area and Measurement date, γk:δl is the effect of the interaction between Scratching and Measurement date, αi:γk:δl is the effect of the triple interaction between Color area, Scratching and Measurement date, μijkl is the experimental error associated to each measurement and εijklm is the residual error. The effects of other interactions between variables were not included in the model because they do not provide supplementary information. α, β, γ, δ, α:γ, α:δ, γ:δ and α:γ:δ were considered as fixed effects, while μ was considered as a random effect. The term ηijklm is known as the linear predictor. This term is obtained by applying the variable pijklm to the link function of the model. The choice of the link function depends on the pijklm distribution law. The identity link function is suitable for values provided by colorimetric measurements, i.e. continuous data, either positive or negative [START_REF] Wolfinger | Generalized linear mixed models a pseudo-likelihood approach[END_REF]. GLMM parameters were estimated using the penalized quasi-likelihood (PQL) technique (MASS package; glmmPQL function; [START_REF] Wolfinger | Generalized linear mixed models a pseudo-likelihood approach[END_REF]. A Wald chi-square test was performed to determine the significance degree of each explanatory variable in the GLMM (car package; Anova function; [START_REF] Fox | Applied regression analysis and generalized linear models[END_REF]. The GLMM was completed with a Tukey's post-hoc test in order to check the significance of differences between means (lsmeans package; lsmeans function; [START_REF] Bretz | Multiple comparisons using R[END_REF].

Climatic monitoring

The temperatures recorded in the white and pink areas of stone and in the air were compared through a one-way analysis of variance (ANOVA; stats package; aov function; [START_REF] Fisher | Statistical methods for research workers[END_REF] after checking the homogeneity of distribution variances with a Bartlett test (stats package; bartlett.test function; [START_REF] Bartlett | Properties of sufficiency and statistical tests[END_REF]. This statistical test checks if the means of several samples are equal, and is useful to compare three or more means. The ANOVA test was completed by post-hoc analysis to rank the different means. The significance of differences between means was assessed using Tukey's range test (multcomp package; glht function; [START_REF] Faraway | Practical regression and ANOVA using R[END_REF].

The relative air humidity values were used to calculate dew points to check for potential condensation on the surface of the stone (weathermetrics package; function humidity.to.dewpoint).

Alpha and beta diversity of microbial communities

Alpha diversity, i.e. the diversity of microbial species in each sample, and beta diversity, i.e. the difference in microbial communities between samples, were identified using QIIME 2 2018.11. After generating a phylogenetic tree (phylogeny plugin; align-to-tree-mafft-fasttree function), the Shannon's diversity index and the Pielou's evenness index were calculated to characterize alpha diversity (diversity plugin; core-metrics-phylogenetic function; [START_REF] Hill | Diversity and evenness: a unifying notation and its consequences[END_REF], while beta diversity was assessed through principle coordinates analysis (PCoA) based on the determination of the Bray-Curtis distance (diversity plugin; core-metrics-phylogenetic function; [START_REF] Legendre | Ecologically meaningful transformations for ordination of species data[END_REF]. The significance of the groups obtained was then assessed with a Kruskal-Wallis non-parametric test for alpha diversity indexes (diversity plugin; alpha-group-significance function; [START_REF] Hollander | Nonparametric statistical methods[END_REF], and with PERMANOVA for beta diversity (999 permutations; diversity plugin; beta-groupsignificance function; [START_REF] Anderson | A new method for non-parametric multivariate analysis of variance[END_REF].

Results

Colorimetric monitoring

Scratching the surface of the stone sufficed to completely remove any visible evidence of pink coloration. The colors in both the white and pink areas showed no sign of change over the three and a half years of monitoring: the scratched sections did not become pink again, and no changes were observed in the colors of the white and pink areas on each of the two steps.

The results of the general linear mixed models designs were the same for the three dimensions of the L*a*b* color space (Table 1): Color area, Scratching and Measurement date and interactions between these factors all had a significant effect on the model, whereas Covering did not. The effect of factors was more pronounced for L* and b* variables than for a* variable. The measurements recorded in the unscratched pink area and the first measurement made in September 2014 (i.e. before scratching) in the scratched pink area showed that the pink area was characterized by a lower L* value and higher a* and b* values than those recorded in the white area (Fig. 3 and supplementary fig. 2). The difference was statistically supported for L* and b*, but not for a*. The difference recorded between sections that had not been scratched remained the same throughout the study. Measurements recorded after scratching in September 2014 showed that the scratching led to an increase in the L* value and a decrease in a* and b* values in the pink and white areas. This difference in the evolution of scratched areas was statistically supported by L* and b* values for the pink area and only by the L* value for the white area. Finally, these differences remained statistically stable in terms of L* and a* values for all studied modalities from after scratching in September 2014 until the end of the colorimetric study in January 2018. The differences remained statistically stable in terms of b* value from the second date of assessment, May 2015, until the end of the study. In other words, the significant effect of the Date factor was almost entirely supported by the differences observed at the first date of assessment before and after scratching and the color did not evolve until the end of the study, confirming visual observations.

Raman spectroscopy

After baseline correction and removal of stone-related noise, Raman spectroscopy revealed the presence of a pigment belonging to the family of carotenoids in the pink powder scratched from the step (Supplementary fig. 3). Carotenoids are usually characterized by three peaks corresponding to C-CH bending, C-C stretching and C=C stretching. C-C and C=C stretching were clearly identifiable at 1151 and 1506 cm -1 , and C=CH3 bending was also present at 1003 cm -1 .

Climatic monitoring

The room temperature in the staircase ranged from 3.22 to 27.33 °C during the study (Table 2). Climatic monitoring showed that the room temperature was linked to the outside temperature (Supplementary fig. 4a): the seasonal variations in temperature had a clear impact on room air temperature, with low temperatures recorded during winter and warm temperatures during summer. Daily room temperature variations due to day/night alternation were less marked than those observed outdoors. Moreover, the room temperature was warmer during winter and cooler during summer than the outside temperatures. The relative humidity of room air ranged from 30.01 -89.2 %. As observed for room temperature, variations in relative humidity of room air followed those recorded for outside relative humidity, although these values are lower than those recorded for outdoor relative humidity. Indoor relative humidity remained between 45 and 65 % for most of the year and rarely exceeded 80 % (Supplementary table 1).

The surface temperature in the white area and in the pink area followed room temperature variations (Supplementary fig. 4b), but the temperature was significantly warmer in the pink area than in both the white area and the room air (Supplementary table 2). The ANOVA study also showed that the temperature in the white area was significantly lower than the room temperature. However, this result should be considered with caution as the interval value was lower than the accuracy of the ambient probe. Finally, the calculation of dew points from ambient relative humidity and surface temperatures showed that no condensation could have been formed at the surface of the step during the year of climatic monitoring.

Alpha and beta diversity of microbial communities

The only perceptible alpha diversity difference between the white and the pink areas was related to bacterial communities (Supplementary table 3). Bacterial richness (Shannon index) and equitability (Pielou's index) were higher in the pink area than in the white area, and this difference is almost significantly supported (Supplementary table 4). No alpha diversity difference was observed between sampling areas for fungal and eukaryote communities.

The study of beta diversity among bacterial communities clearly separated samples into two groups on the first PCoA axis (Supplementary fig. 5): communities sampled in the pink area shift to positive values on this axis as compared to communities sampled in the white area.

The community assessed in the outgroup sample was separated from the two other groups by shifting to positive values on the second PCoA axis. The PERMANOVA test associated to the study of beta diversity confirmed that the sampling area had a significant effect on bacterial community composition, even if the post-hoc test failed to separate the white and pink areas (Supplementary table 5). Conversely, the PCoA expressed no difference between sampling areas in fungal and eukaryotes communities, with the communities of some samples located closer to the outgroup sample than to other samples in the same area.

The assignment of OTUs revealed that the majority of the bacteria found in the white and in the pink area were Actinobacteria, while the outgroup sample was dominated by Proteobacteria. Among Actinobacteria, the communities of white area samples were largely dominated by the genus Rubrobacter, while the communities of pink area samples were largely dominated by the genus Nitriliruptor (Fig. 4). Low proportions of Rubrobacter were also detected in pink area and low proportions of Nitriliruptor were also detected in white area. All other bacterial taxa detected in white and pink areas were in low proportions.

The phylogenetic analysis of fungal and eukaryotes communities did not produce significant differences between samples, especially regarding pink-pigmented yeasts or algae.

Discussion

Bacterial origin of rosy discoloration

The most important result of this study is the confirmation of the biological origin of the rosy discoloration affecting the stone staircase in Sully-sur-Loire castle. Raman spectroscopy assessment of the pink pigment produced on the stone revealed the characteristic bands of C=C and C-C stretching and C-CH bending of carotenoids [START_REF] De Oliveira | Carotenes and carotenoids in natural biological samples: a Raman spectroscopic analysis[END_REF]. Nextgeneration sequencing did not reveal any differences between fungal communities and more generally between eukaryote communities on pink and white areas. Conversely, the composition of bacterial communities was strongly related to the type of sampling area. This result is in accordance with past studies that assigned such discoloration to bacterial biofilms (Piñar et al., 2014a). Actinobacteria was the most abundant phylum identified in both pink and white areas in our study. This phylum has already been widely reported in studies that focused on the deterioration of stone in general [START_REF] Gorbushina | Life on the rocks[END_REF][START_REF] Mcnamara | Microbial deterioration of historic stone[END_REF][START_REF] Zanardini | Diversity of archaeal and bacterial communities on exfoliated sandstone from Portchester Castle (UK)[END_REF], and on rosy discoloration in particular [START_REF] Ettenauer | Halophilic microorganisms are responsible for the rosy discolouration of saline environments in three historical buildings with mural paintings[END_REF][START_REF] Laiz | Isolation of five Rubrobacter strains from biodeteriorated monuments[END_REF]Tescari et al., 2018b). Nitriliruptor was the main bacterial genus identified in the pink area, while Rubrobacter was the main genus identified in the white area. This result differs from those obtained in past studies in which Rubrobacter was generally the main genus associated with rosy discoloration [START_REF] Jurado | Rubrobacter bracarensis sp. nov., a novel member of the genus Rubrobacter isolated from a biodeteriorated monument[END_REF][START_REF] Laiz | Isolation of five Rubrobacter strains from biodeteriorated monuments[END_REF][START_REF] Nugari | Biodeterioration of mural paintings in a rocky habitat: The Crypt of the Original Sin (Matera, Italy)[END_REF]. The unusual nature of this result led us to duplicate the study with new samples, but the second batch of samples gave the same result (data not shown). Nitriliruptor is an haloalkaliphilic actinobacterium first identified in soda lakes [START_REF] Sorokin | Nitriliruptor alkaliphilus gen. nov., sp. nov., a deep-lineage haloalkaliphilic actinobacterium from soda lakes capable of growth on aliphatic nitriles, and proposal of Nitriliruptoraceae fam. nov. and Nitriliruptorales ord[END_REF]. This single species genus has also been reported in saline-alkali soil [START_REF] Peng | The effect of land use on bacterial communities in salinealkali soil[END_REF] and in arid soil [START_REF] Neilson | Life at the hyperarid margin: novel bacterial diversity in arid soils of the Atacama Desert, Chile[END_REF]. The only reference of Nitriliruptor in the field of cultural heritage was in mineral crystals found on the skin of an 18 th century mummy [START_REF] Kraková | The microbiomes of a XVIII century mummy from the castle of Krásna Hôrka (Slovakia) and its surrounding environment[END_REF].

Consequently, this first evidence of the role played by Nitriliruptor in stone deterioration is of interest. This bacterial genus has been poorly studied to date, but numerous highly similar environmental clones of this genus have been identified, suggesting that this organism is quite abundant in nature despite being rare in culture collections (Goodfellow et al., 2012a).

Possible replacement of the main agent of rosy discoloration by Nitriliruptor sp.

The main data about Nitriliruptor in relation with this study is that this species is not known to produce pink pigment, with available strains described as colorless (Goodfellow et al., 2012b). Moreover, the colorimetric assessment of the white and the pink areas showed no evolution of the color in each area during the period of monitoring from the day the stone was scratched in September 2014 and January 2018. The only significant change in color was recorded at the beginning of the experiment between the measurements before scratching and the measurements after scratching. This suggests that the process leading to the formation of the rosy discoloration of stones was already finished at the beginning of the experiment and that the presence of Nitriliruptor may indicate a subsequent evolution of this process.

Bacterioruberin-related carotenoids are frequently associated to Rubrobacter sp. and to the rosy discoloration phenomenon on stones [START_REF] Imperi | The bacterial aetiology of rosy discoloration of ancient wall paintings[END_REF]. In our study, the position of the bands in the Raman spectrum obtained from the pink powder corresponds to that expected for bacterioruberin-related carotenoids [START_REF] Jehlička | Bacterioruberin and salinixanthin carotenoids of extremely halophilic Archaea and Bacteria: A Raman spectroscopic study[END_REF][START_REF] Marshall | Carotenoid analysis of halophilic archaea by resonance Raman spectroscopy[END_REF]. A plausible hypothesis is that the rosy discoloration may have been produced under the action of Rubrobacter strains, which may have then been replaced by Nitriliruptor strains; the pigments usually remain very stable on the materials, even if the causative microorganisms are already dead (Piñar et al., 2014a). The development of pink biofilms on stone has often been linked to water infiltrations and the formation of salt crystals (Piñar et al., 2014a). Carotenoids are known to protect the cells against photo-oxidative damage and to act as membrane stabilizers against salt stress [START_REF] Köcher | The nature and function of carotenoids in the moderately halophilic bacterium Halobacillus halophilus[END_REF]. The development of Rubrobacter would therefore be favoured when desiccation occurs through temperature increase and salt formation [START_REF] Imperi | The bacterial aetiology of rosy discoloration of ancient wall paintings[END_REF][START_REF] Laiz | Isolation of five Rubrobacter strains from biodeteriorated monuments[END_REF][START_REF] Schabereiter-Gurtner | Rubrobacterrelated bacteria associated with rosy discolouration of masonry and lime wall paintings[END_REF].

Interestingly, the ecological niche of Nitriliruptor seems to be very close to that of Rubrobacter; we have already mentioned that Nitriliruptor has been found in environments with high salinity. The studies that highlighted the presence of Nitriliruptor strains in a habitat also detected Rubrobacter strains [START_REF] Ma | A meta-analysis of the publicly available bacterial and archaeal sequence diversity in saline soils[END_REF][START_REF] Neilson | Life at the hyperarid margin: novel bacterial diversity in arid soils of the Atacama Desert, Chile[END_REF]. The Nitriliruptor development on the staircase of Sully-sur-Loire could therefore be a natural progression of the Rubrobacter development under such environmental conditions. Further studies about physicochemical properties of the stone should be performed to determine whether particular conditions had favoured the development of Nitriliruptor. This is particularly important in the light of this bacterial genus' ability to use short-chain organic acids, amides and aliphatic nitriles as energy and carbon sources (Goodfellow et al., 2012b).

On the other hand, Rubrobacter was also detected on stones that did not suffer from rosy discoloration [START_REF] Chimienti | Profile of microbial communities on carbonate stones of the medieval church of San Leonardo di Siponto (Italy) by Illumina-based deep sequencing[END_REF][START_REF] Li | Distribution and diversity of bacteria and fungi colonization in stone monuments analyzed by high-throughput sequencing[END_REF][START_REF] Zanardini | Nutrient cycling potential within microbial communities on culturally important stoneworks[END_REF]. It is not therefore impossible that Rubrobacter DNA was detected in the white area of the staircase, especially since the production of carotenoids by microorganism can vary largely according to environmental conditions [START_REF] Calegari-Santos | Carotenoid production by halophilic archaea under different culture conditions[END_REF][START_REF] Fong | Carotenoid accumulation in the psychrotrophic bacterium Arthrobacter agilis in response to thermal and salt stress[END_REF].

The genus Nitriliruptor so far includes only one species [START_REF] Sorokin | Nitriliruptor alkaliphilus gen. nov., sp. nov., a deep-lineage haloalkaliphilic actinobacterium from soda lakes capable of growth on aliphatic nitriles, and proposal of Nitriliruptoraceae fam. nov. and Nitriliruptorales ord[END_REF]. Considering that the study of V3-V4 16s rRNA region is not accurate enough to provide species definition [START_REF] Bukin | The effect of 16S rRNA region choice on bacterial community metabarcoding results[END_REF], and that Actinobacteria are frequently carotenoid producers [START_REF] Klassen | Phylogenetic and evolutionary patterns in microbial carotenoid biosynthesis are revealed by comparative genomics[END_REF], the possibility that Nitriliruptor sp. detected on the staircase is a new undescribed carotenoid producer species cannot either be dismissed. The production of carotenoid by this species could also vary largely according to environmental conditions. A shotgun metagenomics approach would help to answer this question in future studies by reconstructing the partial genome of the most prevalent species in the samples thus testing the presence of carotenoid synthesis genes.

Environmental factors involved in the development of rosy discoloration

Previous studies have noted that the development of pink biofilms has often been linked to problems of water infiltration and salt crystallization, and in some cases could be associated with the combined effect of an increase in temperature and the sudden decrease in water availability when the infiltration problem is solved [START_REF] Imperi | The bacterial aetiology of rosy discoloration of ancient wall paintings[END_REF][START_REF] Laiz | Isolation of five Rubrobacter strains from biodeteriorated monuments[END_REF]. The climatic monitoring of the staircase showed that no condensation theoretically occurred at the surface of the stone during the year of study: indeed, the temperature recorded at this surface stayed above the dew point. Moreover, the relative humidity in the staircase air appeared relatively adequate in terms of conservation conditions. It released between 45 and 65 % of humidity more than 60 % of the time, which corresponds to the relative humidity values generally advised for conservation [START_REF] D'agostino | Computational fluid dynamics (CFD) modeling of microclimate for salts crystallization control and artworks conservation[END_REF][START_REF] Merello | Microclimate monitoring of Ariadne's house (Pompeii, Italy) for preventive conservation of fresco paintings[END_REF].

Nevertheless, this humidity range could be problematic if nitrates are present: sodium nitrate and magnesium nitrate crystallize when relative humidity drops below 60 % [START_REF] Arnold | Monitoring wall paintings affected by soluble salts[END_REF]. However, no efflorescence had been noted in the staircase during the observations and the presence of soluble salts was not assessed during this study. The origin of the water and of possible salt crystallization is most likely the result of external rainfall seeping through the external wall of the staircase. The location of the biofilm at the surface of the staircase steps supports this hypothesis: only the external part of the steps is affected, and the upper part of the staircase seemed to be more affected than the lower part. The administrators of the castle confirmed that external infiltration occurred through the roof of the stairwell, and that the problem had since been resolved. This reinforces the hypothesis that the rosy discoloration observed at the stone surface of the staircase corresponds to a past phenomenon which no longer occurs. Finally, the climatic monitoring showed that the temperature at the stone surface in the pink area was slightly higher than the temperature in the white area. This could have emphasized the desiccation phenomenon and favoured the development of Rubrobacter in the pink area [START_REF] Imperi | The bacterial aetiology of rosy discoloration of ancient wall paintings[END_REF][START_REF] Schabereiter-Gurtner | Rubrobacterrelated bacteria associated with rosy discolouration of masonry and lime wall paintings[END_REF].

These observations may explain the distribution of the pink area on the staircase surface and could indicate the action of several factors: the infiltration of external water through the wall, the subsequent formation of salts through crystallization and dissolution process, and the microclimatic differences of temperature occurring at the surface of the staircase.

Conclusions

The study confirms that the rosy discoloration occurring at the surface of the spiral staircase was due to bacterial growth. Bacteria belonging to Rubrobacter genus may have been the main producer of pink pigment, before being replaced by bacteria from the Nitriliruptor genus. Further studies about the physicochemical properties of the stone should be performed to determine if specific conditions favored the development of Nitriliruptor. The most abundant genera are labelled in pale blue, while the less abundant genera are labelled in dark blue. The dendrograms show the proximity between the different samples and the proximity between identified bacterial genera.

Captions for Figures

  Four qualitative variables were used to design the GLMM, namely the Color area (two modalities corresponding to the health status of the sampling area: White for the healthy part of stones and Pink for the pink part of stones); Covering (two modalities: Yes for samples that had been covered during the monitoring and No for all others); Scratching (two modalities:Yes for samples which had been scratched prior to monitoring and No for all others); and Measurement date (six modalities corresponding to the six measurement dates, i.e. Sept. 2014 before scratching,Sept. 2014 after scratching, May 2015, Jan. 2016, July 2017, and Jan.

Figure 1 :

 1 Figure 1: Location of the study area in Sully-sur-Loire castle.The study focused on the stones of the "escalier d'honneur" of the donjon (a). The crosssection of the "escalier d'honneur" shows the location of the pink discoloration (b).3D model realized with SketchUp 2016 from plans, video recordings and pictures.

Figure 2 :

 2 Figure 2: Half-pink step chosen for colorimetric and climatic monitoring.The picture shows the distribution of the rosy discoloration on the outer part of the stairs.

Figure 3 :

 3 Figure 3: 3D representation of the colorimetric monitoring of the stone step in the L*a*b* colour space.Values are depicted for the pink and in the white areas, scratched (crosses) or not (circles), between September 2014 and January 2018.

Figure 4 :

 4 Figure 4: Heatmap of bacterial communities according to each sample at the genus level. W: white sample; P: pink sample; O: outgroup sample.

Table 1 :

 1 Effect of the qualitative variables on L*a*b* color space values, assessed with a Wald X 2 test associated with GLMMs.

		L		a		b
	Source of variation	Χ 2 -value	p-value	Χ 2 -value	p-value	Χ 2 -value	p-value
	Color area (Co)	802	<0.001***	71	<0.001***	366	<0.001***
	Covering (C)	1.2	0.26	2.1	0.15	0.2	0.65
	Scratching (S)	1052	<0.001***	44	<0.001***	768	<0.001***
	Date (D)	1062	<0.001***	57	<0.001***	1163	<0.001***
	Co:S	255	<0.001***	19	<0.001***	291	<0.001***
	Co:D	27	<0.001***	22	<0.001***	78	<0.001***
	S:D	423	<0.001***	21	<0.001***	264	<0.001***
	Co:S:D	74	<0.001***	13	0.02*	74	<0.001***
	* p < 0.05, ** p < 0.01, *** p < 0.001

Table 2 :

 2 Summary of climatic data assessed on the stone steps between September 2014 and January 2018.

			Temperature (°C)			Relative humidity (%)
	Color area	Minimum	Mean	Maximum	Minimum	Mean	Maximum
	White	3.22	14.35	25.78	(-)	(-)	(-)
	Pink	4.44	15.39	26.67	(-)	(-)	(-)
	Room air	3.22	14.66	27.33	30.01	58.95	89.2
	Outdoor	-3	12.53	35	43	80.65	98
	(-): parameter not assessed

Acknowledgment

We thank Joanna Lignot for English language editing.


Joël Greffier
email: joel.greffier@chu-nimes.fr

Nicolas Grussenmeyer-Mary

Ahmed Larbi

Jean Goupil

Guillaume Cayla

Bertrand Ledermann

Jean Paul Beregi

Julien Frandon

Experimental evaluation of a radiation dose management system-integrated 3D skin dose map by comparison with XR-RV3 Gafchromic® films

Keywords: Radiation physics, Fluoroscopy, Experimental, Dosimetry, Physics, Radiation safety, Dosimetric comparison Peak skin dose RDMS: Radiation dose management system SDM: Skin dose map RDSR: Radiation dose structured reports

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Interventional radiology and cardiology procedures are of great diagnostic and therapeutic benefit [1; 2]. However, during long and complex procedures, the skin doses delivered to the patient can be high and exceed the thresholds for deterministic effects (skin dose > 2Gy) [START_REF] Balter | Fluoroscopically guided interventional procedures: a review of radiation effects on patients' skin and hair[END_REF][START_REF] Koenig | Skin injuries from fluoroscopically guided procedures: part 1, characteristics of radiation injury[END_REF][START_REF] Miller | Radiation doses in interventional radiology procedures: the RAD-IR study: part II: skin dose[END_REF][6][START_REF] Vano | Skin radiation injuries in patients following repeated coronary angioplasty procedures[END_REF]. In these cases, it may be necessary to perform a Peak Skin Dose (PSD) assessment in order to locate the maximum skin dose delivered to the patient and improve their therapeutic follow-up.

PSD assessment is usually complex and time-consuming. Several direct measurement systems of PSD exist, including radiochromic films [START_REF] Greffier | Assessment of peak skin dose in interventional cardiology: A comparison between Gafchromic film and dosimetric software em.dose[END_REF][START_REF] Greffier | Assessment of Patient's Peak Skin Dose Using Gafchromic Films During Interventional Cardiology Procedures: Routine Experience Feedback[END_REF][START_REF] Ying | Patient skin dose measurements during coronary interventional procedures using Gafchromic film[END_REF][START_REF] Jones | How accurately can the peak skin dose in fluoroscopy be determined using indirect dose metrics?[END_REF][START_REF] Jones | Calculating the peak skin dose resulting from fluoroscopically guided interventions. Part I: Methods[END_REF][13]. These large-size films provide a dose map at the end of the procedure where film darkening is proportional to the dose delivered to the patient's skin. However, these films are expensive and have many constraints to their routine use (such as long and tedious calibration, correct positioning of the film, and specific storage conditions) [8; 13].

Commercial systems are available to evaluate PSD during the procedure (Dose Tracking System (Canon Medical systems, Tokyo, Japan) [START_REF] Rana | Updates in the real-time Dose Tracking System (DTS) to improve the accuracy in calculating the radiation dose to the patients skin during fluoroscopic procedures[END_REF], Dose Map (GE Healthcare, Milwaukee, USA) [15 ; 16] or a posteriori (em.dose (esprimed)) [8; 17]. These systems calculate the PSD using the dosimetric and technical information recorded during the procedure. Recently, skin dose estimation algorithms have been integrated into radiation dose management system (RDMS) such as Radiation Dose Monitor (Medsquare, Paris, France), Radimetrics TM (Bayer Healthcare, Whippany, USA) and DOSE (Qaelum, Leuven, Belgique). The aim of these tools is to provide a retrospective assessment of PSD value and a dose map, independent of the make and model of the imaging system. A common requirement is that the DICOM radiation dose structured report (RDSR) is sent to the RDMS, containing dose indices, acquisition information, and beam and table geometry.

There is little data in the literature concerning the validation of PSD and skin dose map distributions for these software applications. The few studies available have some limitations in the comparative assessment as defined by Habib Geryes et al. [START_REF] Habib Geryes | Benchmarking the DACS-integrated Radiation Dose Monitor(R) skin dose mapping software using XR-RV3 Gafchromic(R) films[END_REF]. In their study, the authors compared the PSDs and the 2D dose distributions calculated by the Radiation Dose Monitor tool (Medsquare, Paris, France) and measured with Gafchromic ® XR-RV3 films. This precise and complete phantom study was performed on two interventional radiology systems for 17 different configurations (simple and complex).

The purpose of this study is to compare the PSD and skin dose map distribution as measured with XR-RV3 Gafchromic ® films and estimated with the interactive Skin Dose Map ® tool (SDM Tool ) integrated the RDMS DoseWatch ® (GE Healthcare, Milwaukee, USA).

This phantom study was performed on an interventional radiology system for 13 different exposure configurations.

Materials and methods

Skin Dose Map (SDM) calculation formalism

The RDMS DoseWatch ® includes the estimation of skin dose as part of its core dose management capabilities. The interactive Skin Dose Map ® tool (SDM Tool ) is integrated in the RDMS DoseWatch ® . At the end of the procedure, each interventional imaging system transfers the RDSR data to DoseWatch ® RDMS. RDSR data include dosimetric (such as kVp, additional filtration, Air Kerma, Kerma Area Product) and geometric (such as primary and secondary angulation, table position) information for each event of fluoroscopy and fluorography. SDM Tool uses RDSR data to calculate the PSD per event following recommendations defined by K. Jones et al. [START_REF] Jones | Calculating the peak skin dose resulting from fluoroscopically guided interventions. Part I: Methods[END_REF]:

𝑃𝑒𝑎𝑘 𝑆𝑘𝑖𝑛 𝐷𝑜𝑠𝑒 = 𝑅𝑃𝐴𝐾 × 𝑆𝑅𝑃𝐷² 𝑑 𝑆𝑘𝑖𝑛 2 × 𝐶𝐹 × 𝐵𝐹 × 𝐴𝐹 𝑆𝑘𝑖𝑛 × 𝐴𝐹 𝑇𝑎𝑏𝑙𝑒 (1)
The Reference Point Air Kerma (RPAK) provided by the equipment for each event is the cumulative air kerma at the interventional reference point (IRP) located 15 cm back from isocenter towards the focal spot of an interventional imaging system. The Source to Reference Point Distance (SRPD) is defined by the manufacturer for each system and corresponds to the distance between the source and the IRP.

Correction factors (CF) are used to correct the difference between the RPAK provided by the equipment and RPAK measured in-house by a qualified physicist. Usually, the medical physicist defines CF per equipment and for fluoroscopy and fluorography events from a reproducible protocol. The tolerance accepted for CF by the AAPM Imaging Physics Committee Task Group 190 is ± 35% [START_REF] Lin | Accuracy and calibration of integrated radiation output indicators in diagnostic radiology: A report of the AAPM Imaging Physics Committee Task Group 190[END_REF].

The distance from source to patient skin (d Skin ) is computed by SDM Tool in two steps based on RDSR data. First, the SDM Tool uses information from DICOM tags (standard or private): "Table Height Position", "Height of System", " ): "Positioner Primary Angle" and "Positioner Secondary Angle" correspond to the primary angulation (Left Anterior Oblique and Right Anterior Oblique) and the secondary angulation (Cranial-Caudal) for each event. "Height of system" and "Source Object Distance (SOD)" are fixed for each system.

𝑑 𝑇𝑎𝑏𝑙𝑒 = 𝑇𝑎𝑏𝑙𝑒 ℎ𝑒𝑖𝑔ℎ𝑡 𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 + 𝑆𝑂𝐷 -𝐻𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑠𝑦𝑠𝑡𝑒𝑚 (2) "Table Height Position", "
Second, an average compressed mattress thickness is added to d Table to model the mattress placed between the table and the patient's back. This value was defined especially for this study by measuring the average thickness of the compressed mattress on 20 patients of different morphologies.

For each event, SDM Tool defines a Half-Value Layer (HVL) based on kVp and filtrations (inherent and additional) used. The HVL values are calculated beforehand with the Spekcalc software (McGill University, Montreal, Canada) by taking into account the inherent and additional filtrations and kVp available in the system used. Once the beam quality is determined based on the HVLs, the backscatter factor (BF), the skin absorption factor (AF Skin ) and the mattress and table attenuation factor (AF Table ) are determined based on lookup tables in the software.

The backscatter factor (BF) is computed using available data in Table 1 of the ICRU report 74 [20] as function of kVp, HVL and equivalent-square field sizes. When present in the DICOM file, the collimated field area on the detector is used. Otherwise, and when present in the DICOM file, private tags such as Philips Shutters positions are used to compute the collimated field area at a known distance from the source. Otherwise, the collimated field area at the reference point is approximated by dividing the KAP by the RPAK. Then, field size at the patient entrance position (d Skin ) is computed by applying inverse square law.

The skin absorption factor (AF Skin ), corresponding to the skin-to-air mass energy absorption coefficients, is defined following the composition of soft tissues for children or adults available in Table 13 A ray-tracing algorithm [16; 23; 24] computes the table and mattress thicknesses for each event using the primary angulation, the secondary angulation and the d Skin reported in the RSDR. Since the composition of the table and/or the mattress tend to be proprietary by manufacturers, the µ [START_REF] Hubbell | Tables of X-Ray Mass Attenuation Coefficients and Mass Energy-Absorption Coefficients 1 keV to 20 MeV for Elements Z = 1 to 92 and 48 Additional Substances of Dosimetric Interest[END_REF] based on calculated HVL for each incidence.

The different parameters used for the calculation of PSD by the SDM Tool are presented in Table 1.

Skin Dose Map (SDM) patient representation and dose reporting

DoseWatch® uses two types of phantom to estimate the dose: flat (2D) and the adult male or female ICRP (3D).

The flat phantom represents a simplified geometry not intended for real patient events. This flat phantom was designed to perform dosimetry validation studies, typically using Gafchromic ® films, in an easier and more accurate way. It is modeled as a flat surface of 0.055 cm² triangles laying on top of the mattress and spanning the patient table. The PSD was computed in a ROI of 1 cm² to mimic the reading methodology of Gafchromic ® films.

The ICRP reference male and reference female phantoms are based on the voxelated phantom representations provided by ICRP 110 [START_REF] Menzel | ICRP Publication 110. Realistic reference phantoms: an ICRP/ICRU joint effort. A report of adult reference computational phantoms[END_REF]. Both phantoms are available in DoseWatch ® and the same methodology using a mesh of triangles is used to generate their 3D shapes. The average area per triangle is 0.055 cm².

DoseWatch ® extracts all irradiation event information from the DICOM Radiation Dose Structured Report object (RDSR). It automatically choses the appropriate 3D phantom based on the patient's sex. The table and mattress are modeled as a flat surface. The table position is determined using the information available in the RDSR (table height, table longitudinal and lateral position, table head tilt, table tilt). The beam is represented as a pyramid, the focal spot being the summit, the surface on the detector being the base. However, the RDSR does not provide, in a standard way, any information regarding the shape of the beam, which is generally assumed to be square. For some devices though, the information is available in vendor-specific private tags that DoseWatch ® extracts from the RDSR. For the Philips system used during this work, the information is provided through the position of the shutters DICOM Tag "99PHI-IXR-XPER/005". The beam position is determined using various data fields in the RDSR (left/right and cranial/caudal angulations, C-arm position). The phantom is positioned on top of the table. The software rotates the phantom, if needed, to consider the patient position provided in the RDSR. The patient position relative to the table top has to be manually adjusted as this information is not present in the RDSR; the default setting is 10 cm between the table top edge and the patient's head. The dose information is cumulated on all the irradiation events to provide a comprehensive skin dose map distribution for the whole exam. DoseWatch ® presents the skin dose map distribution as a 3D interactive view that the user can rotate and zoom in or out to focus on relevant body regions (Figure 1). This provides visualization of complex dose distributions potentially resulting from bi-plane systems. The PSD is extracted from the skin dose map distribution, as the maximum dose cumulated on the meshed triangle surface. To better assess the contribution of a single event, a timeline is provided, and the various views of the software updated to display a partial SDM and PSD up to the selected event.

Gafchromic ® Film

Reflective XR-RV3 Gafchromic ® films (Ashland, Advanced Materials, USA) were used in this study (batch number: 09241802). Films were calibrated free-in-air in an interventional imaging system: Allura Xper FD 20 (Philips Medical Systems, Best, Netherlands) according to the same methodology previously described [27; 28]. Films were placed on a 6 cm-thick expanded polystyrene support providing negligible backscatter and irradiated at 37 cm from the focal spot. A flat ionization chamber 10x6-60 (Radcal, Monrovia, USA) is positioned 1.5 cm above the films at 38.5 cm from the focal spot (Figure 2.a). The inverse square law is applied to correct air Kerma (AK) measurements. In order to have the totality of the ionization chamber in the exposure field, a diagonal of 48 cm without electronic zoom was used. The ionization chamber and its converter are calibrated in a dosimetry laboratory for three different beam qualities: RQR3, RQR5 and RQR9 beams [29]. Films were cut to 7 cm x 8 cm, and placed white side facing source as recommended by the film manufacturer. Each film was exposed individually in the same position of the X-ray field. Films were exposed at seven different dose levels (0, 0.2, 0.5, 1.0, 1.5, 2.0 and 3.0 Gy). Calibration was performed for three different beam qualities (70, 90, 120 kVp and additional filtration of 0.1 mmCu + 1 mmAl) to best represent clinical exposures. The half layer value for each beam quality was equal to 4.29 mm for 70 kVp, 5.37 mm for 90 kVp and 7.03 mm for 120 kVp.

The exposed films were stored in a support in stable conditions of temperature and humidity after the procedure [30; 31]. Films were scanned, orange-side down in the scanner, at 1 week ±1 h post exposure, with an Epson 10,000 Expression XL as per manufacturer recommendations. Each exposed film (7 cm x 8 cm) is scanned individually in the center of the scanner to overcome the non-uniformity of the scanner [27; 32]. The software Film Qa-XR (Ashland, Advanced Materials, USA) was used to analyze film images. A square region of interest (ROI), approximately 2 cm x 2 cm, was placed in the center of each film image.

According to the manufacturer recommendations, the red color channel was used [27; 28].

The mean measured reflective density was associated to the measured AK and a calibration curve was obtained.

An uncertainty assessment was performed for the skin dose measurements with XR-RV3 Gafchromic films (Table 2). The relative combined standard uncertainty was estimated at 13.2% (k = 1) and relative expanded uncertainty was 26.4% (k = 2). These estimated uncertainties are in the same range as those observed in previous studies [17 ; 18; 27; 32; 33].

Comparison of PSD obtained with Gafchromic ® film and SDM tool

Dose maps and PSD's obtained with Gafchromic ® films were compared against those with DoseWatch ® SDM Tool for flat and ICRP phantoms. A PMMA phantom of 30x30 cm² and 20 cm thick was positioned on the table and on the mattress (Figure 2.b). XR-RV3 Gafchromic ® films were placed between the mattress and the phantom. Thirteen configurations were evaluated in an interventional imaging system available in our institution.

Eleven configurations were performed to compare the independent impact of simple projection, multiple projections, detector to tube distance, patient to tube distance, FOV, collimation (field size), the use of wedge filter and table displacements (in all directions) between fluorography events and during fluoroscopy events. Two configurations were performed to compare the combined impact of previous parameters on skin dose map calculation. The description of the different configurations was presented in Table 3.

The 13 exposed films were read 1 week ±1 h after the exposure, respecting the same process described previously for the film calibration. Based on the beam quality selected by the X-ray system when exposing the PMMA phantom, the 70 kVp calibration curve most closely matched the beam quality for all film exposures. A square ROI of 1 cm² was manually positioned by the medical physicist in the maximum dose region with the software Film Qa-XR ® . The average value of this ROI represented the PSD Film . SDM Tool calculates the skin dose for each event from the previously defined calculation formalism. At the end of the calculation, SDM Tool provides the PSD SDM and dose distribution obtained on the flat phantom and the ICRP phantom. For the flat phantom, PSD SDM was computed from a square ROI of 1 cm² to be similar to the measurement of PSD Film .

Statistical Analysis

Results

Comparison of measured and computed PSD

Table 4 and Figure 3 show the results of the PSD Film and PSD SDM for the flat and ICRP phantoms for the 13 configurations. PSD Film values ranged from 459 to 1027 mGy and PSD SDM values ranged from 471 to 1033 mGy for flat phantom and from 472 to 1028 mGy for ICRP phantom. The average difference between PSD Film and PSD SDM was 6%±6% (range from -3% to 22%) for flat phantom and 5%±7% (range from -3% to 25%) for ICRP phantom.

The concordance between PSD Film and PSD SDM is good (>0.9) according to Lin's concordance correlation coefficient (Figure 4). The Lin's coefficient estimation and 95% CI are 0.979 [0.875; 0.984] for flat phantom and 0.977 [0.877; 0.985] for ICRP phantom.

Comparison of dose maps

The dose maps obtained with SDM Tool are similar to those recorded with the software Film Qa-XR after film assessment. Sizes, shapes and positions of the different exposure fields on the dose maps with SDM Tool are relatively the same to those present on the Gafchromic ® films, especially for simple configurations. Figure 5 shows five dose maps obtained with the two softwares for simple configurations (C4, C5, C7, C8, C9) and Figure 6 shows the dose maps obtained for the two combined configurations (C12 and C13).

Variations in dose map representation are observed for two configurations: C10 and C11. Figure 7.a shows that software does not model one-sided and two-sided wedge filters.

The PSD being located in the center of the field, this omission does not involve a significant variation of the PSD obtained with the film and the software (10% for both phantoms). Figure 7.b depicts the impact of table displacement during fluoroscopic events. Indeed, the DICOM RSDR gives the geometric positions of the tube and the table only for the last fluoroscopy image and therefore does not take into account any table or tube displacements during a fluoroscopic event. This has the effect of overestimating the PSD calculated with both phantoms (22% for flat and 25% for ICRP).

Discussion

Accurate PSD assessment is notoriously complex and so the use of Gafchromic ® films remains the reference method. As RDMS become more established, integrated skin dose calculation tools not only facilitate skin dose assessment but also make it more accessible to users (such as medical physicists or physicians). To our knowledge, these angioplasty for one or two vessels disease or complex coronary angioplasty of chronic total occlusion [START_REF] Greffier | Assessment of peak skin dose in interventional cardiology: A comparison between Gafchromic film and dosimetric software em.dose[END_REF]. The variation between these two studies is due to the different calculation methodologies. Magnier et al. [17] used the fluoroscopy and fluorography information available in the RSDR, whereas in the study by Greffier et al. [START_REF] Greffier | Assessment of peak skin dose in interventional cardiology: A comparison between Gafchromic film and dosimetric software em.dose[END_REF], the PSD computation was carried out based on the DICOM information available on the PACS for the fluorography events. To factor for the contribution of the fluoroscopy on PSD, two methods were used: either equal spread over all digital cine event series (method A) or cumulated at the position of the most exposing cine event (method B). Regarding Dose Tracking System ® (Canon Medical systems, Tokyo, Japan), Rana et al. found a <4% difference for the SK150 head phantom [START_REF] Rana | Updates in the real-time Dose Tracking System (DTS) to improve the accuracy in calculating the radiation dose to the patients skin during fluoroscopic procedures[END_REF]. However, in this study, the Gafchromic ® films were not calibrated free-in-air but in contact with a PMMA phantom accounting for backscattered radiation. Finally, using the Radiation Dose Monitor tool, Habib Geryes et al. [START_REF] Habib Geryes | Benchmarking the DACS-integrated Radiation Dose Monitor(R) skin dose mapping software using XR-RV3 Gafchromic(R) films[END_REF] found an average difference of 10%±7% for a Siemens Artis Zee and 9%±7% for GE Innova IGS interventional systems.

This robust and comprehensive study carried out on phantoms for 17 configurations is the most appropriate for comparison. Indeed, the methodology used in our study was modeled on that described by Habib Geryes et al. [START_REF] Habib Geryes | Benchmarking the DACS-integrated Radiation Dose Monitor(R) skin dose mapping software using XR-RV3 Gafchromic(R) films[END_REF]. However, our study is carried out with 13 configurations on the same system. It should also be noted that in the present study, the PSD is calculated on both 2D flat phantom and 3D ICRP phantom whereas in the Habib Geryes study [START_REF] Habib Geryes | Benchmarking the DACS-integrated Radiation Dose Monitor(R) skin dose mapping software using XR-RV3 Gafchromic(R) films[END_REF] it is only calculated on a flat 2D phantom with the Radiation Dose Monitor software.

The results of this study also show that the obtained dose maps with SDM Tool are relatively similar to those obtained from the Gafchromic ® films. Indeed, the shapes, sizes and positions of the beams are concordant. However, variations in dose maps are observed for two configurations (C-10 and C-11). For the C-10 configuration, the software doesn't model the wedge filters used during fluoroscopy and fluorography events. This is due to lack of information in the RDSR. The software assigns the same skin dose value to the entire exposed field because no information on the use of wedge filters is available in the RDSR. This has no impact on the PSD calculated in this phantom study because there is no change of tube angulation or table movement for this configuration. However, in patients this could lead to overlapped areas of field not present on the film, increasing the calculated PSD by the software. In clinical practice, these wedge filters are very often used in interventional cardiology procedures to correct the attenuation difference between the lung and the heart.

For the configuration C-11, for each fluoroscopy event, the software will place the exposure field based on the last position of the table and tube. Indeed, the software is limited by the geometrical information present in the RSDR, which does not contain data on manipulation of the table and the tube throughout the fluoroscopy events. In our study, this leads to the addition of two overexposure zones not present on the film, resulting in a 25% increase in the value of PSD on the ICRP phantom. On a patient, this could cause variations of the PSD calculated in either direction according to the variations of positioning of the tube and the table between the events of fluoroscopy and fluorography. In addition, this will especially impact procedures where the proportion of fluoroscopy is high compared to fluorography (such as complex coronary angioplasty of chronic total occlusion).

As with any software, SDM Tool has differentiators and limitations. First, the results of this study show that this tool can accurately calculate the PSD for simple and combined configurations. PSD can be calculated for all patients tracked in the system. PSD values are computed automatically without additional manual action, are easily accessible from the RDMS, but are not provided with calculation uncertainties. Limitations of the SDM Tool are not due to algorithm gaps but from data gaps in the RDSR. Upon connection of a new system to the RDMS, the reliability of RDSR data generated by the system needs to be assessed. As with Radiation Dose Monitor, the PSD is obtained retrospectively but much faster than with Gafchromic ® films or software like em.dose. In addition, the PSD is not obtained in real time as with Dose Tracking System ® or Dose Map ® during the procedure.

Second, this tool calculates the PSD on an anthropomorphic 3D ICRP phantom, which makes it possible to improve the distribution of the dose according to the anatomical zone, the incidence and the positioning of the table. However, only two phantoms are currently available, and it is necessary to increase the phantom library to account for differences in patient morphology (impacting the dose distribution). To our knowledge, with the exception of Radimetrics ® using 15 phantoms, all other available systems for calculating the PSD offline use a single 2D phantom. Third, the exposure fields are positioned on the 3D phantom based on the table information (up / down, right / left) available in the RDSR and this positioning does not always reflect the actual patient's position, resulting in the dose being attributed to the wrong skin location. This point is very important, especially for cumulative PSD evaluation during iterative procedures on the same anatomical area. To better reflect the reality and cover the right anatomical region, it is possible to modify case by case in the SDM Tool the "distance between the top of the table and the head of the patient". However, the lateral position of the patient on the table cannot be adapted; the tool assumes that the patient is centered. Using a positioning camera existing in some CT scan might further improve the accuracy of cumulative PSD and skin dose maps. It should be noted that this problem is common to all solutions.

Fourth, the SDM Tool is able to reproduce rectangular fields through the use of private fields available in the RDSR. This is not currently available via Radiation Dose Monitor. This improves the precision of the dose map representations and the calculated PSD values.

Finally, like Radiation Dose Monitor, the SDM Tool is an interactive tool able to replay each fluoroscopic and fluorographic event by projecting them onto the phantom. This is an educational tool making the operator (interventional radiologist or cardiologist) aware of the impact of operator-dependent parameters (such as collimation, electronic zoom, frame rate, variations in the incidence of tube-patient and patient-detector distances) on the PSD and the dose map.

This study presents some limitations. First, Gafchromic ® films are used as the gold standard in this study, however their precision is close to ± 20% [17; 18; 27; 32; 33].

Second, this study is done for a single manufacturer available in our institution and without rotational events. A similar study should be performed to assess the reproducibility of the results on the other systems and with rotational events. Third, this experimental study is performed on a PMMA phantom with uniform density, which is relatively far from the patient density. A study on patients should be performed to assess the reproducibility of the results for interventional cardiology and other fluoroscopically guided procedures. Finally, as defined by Habib Geryes et al. [START_REF] Habib Geryes | Benchmarking the DACS-integrated Radiation Dose Monitor(R) skin dose mapping software using XR-RV3 Gafchromic(R) films[END_REF], a gamma index analysis between the obtained dose maps with the films and with SDM Tool could not be performed because of the lack of suitable software.

Conclusion

This experimental evaluation demonstrates that this RDMS-integrated 3D Skin Dose Map is a suitable alternative to the use of Gafchromic ® film, which can be complex, time

  tools are available and marketed in several RDMS: Radimetrics (Bayer) Radiation Dose Monitor (Medsquare), DOSE (Qaelum) and DoseWatch (GE Healthcare). For the first time, this phantom study compares experimentally the PSD estimated with the SDM Tool integrated to the DoseWatch ® RDMS to the ones measured with Gafchromic ® films for 13 configurations on a Philips interventional imaging system. The outcomes of this study show a good correlation between the PSD values measured with the Gafchromic ® films and those estimated with SDM Tool for the for both phantom selections. The obtained results are in the same range as those obtained with other software and available in the literature [8; 14-18; 34]. Using Dose Map, Bordier et al. found a difference of 25.0% on an anthropomorphic phantom and 14.3% on one patient for a complex fenestrated aortic endovascular repair [15; 16]. Two teams compared em.dose: Magnier et al. found an average difference of 4.7% on anthropomorphic phantom and a median difference of 8.5% over 59 thoracic or abdominopelvic endovascular interventions[17]; and Greffier et al. found a difference of 3.4% ± 21.1% (method A) and 17.3% ± 23.9% (method B) on 40 patients who received coronary angiography and coronary

  consuming, and expensive to use, especially when the film technique fails due to positioning, or processing errors. Differences between dose maps and PSDs measured with the film technique and those calculated with SDM Tool are acceptable considering the uncertainties associated with the use of Gafchromic ® films. A study on patients should be performed to assess the reproducibility of the results found in this study for interventional cardiology and vascular procedures. Delle Canne S, Carosi A, Bufacchi A et al (2006) Use of GAFCHROMIC XR type R films for skin-dose measurements in interventional radiology: Validation of a dosimetric procedure on a sample of patients undergone interventional cardiology. Phys Med 22:105-110 32 Farah J, Trianni A, Ciraj-Bjelac O et al (2015) Characterization of XR-RV3 GafChromic((R)) films in standard laboratory and in clinical conditions and means to evaluate uncertainties and reduce errors. Med Phys 42:4211-4226 33 Farah J, Trianni A, Carinou E et al (2015) Measurement of maximum skin dose in interventional radiology and cardiology and challenges in the set-up of European alert thresholds. Radiat Prot Dosimetry 164:138-142 34 Bednarek DR, Barbarits J, Rana VK, Nagaraja SP, Josan MS, Rudin S (2011) Verification of the performance accuracy of a real-time skin-dose tracking system for interventional fluoroscopic procedures. Proc SPIE Int Soc Opt Eng 7961

Figure 1 .

 1 Figure 1. Screen shot of DoseWatch ® Skin Dose Map tool.

Figure 2 .Figure 3 .

 23 Figure 2. a. Free-in-air calibration of XR-RV3 Gafchromic films; b. Exposure conditions of

Figure 4 .

 4 Figure 4. Lin's concordance correlation between measured and calculated PSD.

Figure 5 .

 5 Figure 5. FilmQA-XR dose maps versus SDM Tool maps obtained with flat and ICRP phantoms for simple configurations: C-4 (a), C-5 (b), C-7 (c), C-8 (d) and C-9 (e).

Figure 6 .

 6 Figure 6. FilmQA-XR dose maps versus SDM Tool maps obtained with flat and ICRP phantoms for combined configurations: C-12 (a) and C-13 (b).

Figure 7 .

 7 Figure 7. FilmQA-XR maps versus SDM Tool maps obtained with flat and ICRP phantoms for configurations: C-10 (a) and C-11 (b).

  Table Longitudinal Position", "Table Lateral Position", "Positioner Primary Angle", "Positioner Secondary Angle" and (SOD) to compute the distance from source to table (d Table

  Table Longitudinal Position" and "Table Lateral Position" correspond respectively to the vertical, longitudinal and lateral table position for each event.

  .2 of ICRP report 89[21] combined with the elemental values of National Institute of Standards and Technology (NIST)[START_REF] Hubbell | Tables of X-Ray Mass Attenuation Coefficients and Mass Energy-Absorption Coefficients 1 keV to 20 MeV for Elements Z = 1 to 92 and 48 Additional Substances of Dosimetric Interest[END_REF]. For each HVL, mass energy absorption coefficients for air (µ en /ρ) air and skin (µ en /ρ) Skin are computed. AF Skin corresponds toThe table attenuation factor (AF Table)includes table and mattress attenuations as follows: 𝐴𝐹 𝑇𝑎𝑏𝑙𝑒 = 𝑒 -µ 𝑇𝑎𝑏𝑙𝑒 𝑎𝑛𝑑 𝑀𝑎𝑡𝑟𝑒𝑠𝑠 ×𝑑 𝑇𝑎𝑏𝑙𝑒 𝑎𝑛𝑑 𝑀𝑎𝑡𝑡𝑟𝑒𝑠𝑠 .

	the ratio [	µ𝑒𝑛 𝜌 ]	𝑎𝑖𝑟 𝑆𝑘𝑖𝑛	for a given HVL.

  Table and Mattress cannot be computed directly. Based on the published works from DeLorenzo et al.[25], a nominal equivalent table and mattress thickness in aluminum of 1.4mm was used. The equivalent thickness of table and mattress (d Table and Mattress ) in aluminum, traversed by the x-ray beam, is then calculated for each incidence. The aluminum attenuation coefficient of table and mattress (µ Table and Mattress ) is computed from the elemental values of NIST

Table 1 .

 1 Parameters available used for the calculation of the PSD by the SDM Tool on the system assessed.

	Model	Allura Xper FD10
	SRPD (cm)	61.5
	SOD (cm)	76.5
	Thickness of the uncompressed mattress (cm)	7
	Average compressed mattress thickness (cm)	4
	CF fluorography	0.986
	CF fluoroscopy	1.020
	Inherent filtration	3 mmAl
	Additional filtration	0.1 mmCu + 1 mmAl
		0.4 mmCu + 1 mmAl
		0.9 mmCu + 1 mmAl
	kVp range	From 50 to 125

Table 4 .

 4 Peak Skin Dose measured with XR-RV3 Gafchromic ® films and computed by SDM Tool for each configuration.

			Peak Skin Dose (mGy)
	Configuration
		Film	SDM Tool -Flat	SDM Tool -ICRP
	C-1	825	803	800
	C-2	744	781	774
	C-3	653	672	672
	C-4	681	683	674
	C-5	674	689	687
	C-6	1027	1033	1028
	C-7	963	986	981
	C-8	602	654	643
	C-9	688	762	749
	C-10	622	686	683
	C-11	459	562	575
	C-12	463	471	472
	C-13	846	910	909

Acknowledgements

We thank C. Steinville, D. Miller and S. Kabani for their help in editing the manuscript. We thank P.A. Daviau and C. Demattei for their support in this study.

Disclosure of Conflicts of Interest

N. Grussenmeyer-Mary works for the GE Healthcare company. All the other authors have no relevant conflicts of interest to disclose.

Statistical analysis was performed using our in-house developed Matlab routine (MathWorks, Natick, USA). The relationship between PSD Film and PSD SDM was evaluated with Lin's concordance correlation coefficient and classified as nonexistent (0.00-0.59), poor (0.60-0.79), acceptable (0.80-0.89), or good (0.90 -1.00).