Oscar Franchi
email: oscar.franchi.m@mail.pucv.cl

Léa Cabrol

Rolando Chamy

Francisca Rosenkranz

Correlations between microbial population dynamics, bamA gene abundance and performance of anaerobic sequencing batch reactor (ASBR) treating increasing concentrations of phenol

Keywords:

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

Introduction

Phenol is a widespread compound present in many industrial effluents such as coal conversion processes, petroleum refineries, herbicide manufacturing and petrochemicals. The concentration of phenolic compounds in this kind of effluents varies from 10 to 17,000 mg L -1 contributing to 40-80 % of its total chemical oxygen demand (COD) [START_REF] Veeresh | Treatment of phenol and cresols in upflow anaerobic sludge blanket (UASB) process: a review[END_REF].

To treat effluents containing phenol, several physicochemical and biological treatments are available. Among biological processes, different anaerobic technologies that work with biomass retention such as upflow anaerobic sludge blanket reactor (UASB), expanded granular sludge bed reactor (EGSB) and anaerobic sequencing batch reactor (ASBR) have been implemented to cope with the presence of phenol. The advantages that these anaerobic systems offer compared to other biological technologies are: high organic loading rates capacity; methane production; low sludge generation and the capability of retaining microbes with special functions (e.g. phenolic compounds degradation) [START_REF] Almendariz | Phenolic refinery wastewater biodegradation by an expanded granular sludge bed reactor[END_REF][START_REF] Veeresh | Treatment of phenol and cresols in upflow anaerobic sludge blanket (UASB) process: a review[END_REF][START_REF] Rosenkranz | Relationship between phenol degradation efficiency and microbial community structure in an anaerobic SBR[END_REF].

Due to the antimicrobial properties of phenol, this compound is toxic and inhibitory for most microorganisms (Chen et al., 2008a). In anaerobic sludge, half-maximal effective concentrations (EC 50 ) are reported to be between 120 and 225 mg gVS -1 [START_REF] Hernandez | Inhibition of biogas production and biodegradability by substituted phenolic compounds in anaerobic sludge[END_REF][START_REF] Wirth | Anaerobic degradation of increased phenol concentrations in batch assays[END_REF]. Either in a phenol-acclimated and non-acclimated sludge, increasing concentrations of this compound affects the anaerobic digestion performance, deteriorating both the removal efficiency and methane production rate [START_REF] Fang | Anaerobic degradation of phenol in wastewater at ambient temperature[END_REF][START_REF] Chapleur | Increasing concentrations of phenol progressively affect anaerobic digestion of cellulose and associated microbial communities[END_REF]. According to this, the identification of the microbial populations resistant to high phenol concentrations is relevant to design and establish operational strategies capable to maintain such microbial members in order to improve the system´s stability. In line with this, one of the final goals of the microbial community studies in anaerobic digestion systems is to find the relationship between specific microorganims dynamics and system´s performance in order to determine if T this relationship could be useful to establish performance biomarkers that serves as early warning indicators of process failure or performance enhancement [START_REF] Carballa | Microbial management of anaerobic digestion: exploiting the microbiome-functionality nexus[END_REF].

Molecular approaches such as DNA fingerprinting and sequencing methods have been used to unravel relationships between microbial community dynamics and performance changes under variable phenol concentrations [START_REF] Fang | Anaerobic degradation of phenol in wastewater at ambient temperature[END_REF][START_REF] Rosenkranz | Relationship between phenol degradation efficiency and microbial community structure in an anaerobic SBR[END_REF][START_REF] Wirth | Anaerobic degradation of increased phenol concentrations in batch assays[END_REF][START_REF] Chapleur | Increasing concentrations of phenol progressively affect anaerobic digestion of cellulose and associated microbial communities[END_REF][START_REF] Madigou | Acclimation strategy to increase phenol tolerance of an anaerobic microbiota[END_REF]. In all these studies, the results show that Clostridiales is a highly abundant order during anaerobic digestion of this compound, together with -in some cases-Anaerolinaceae and Syntrophaceae [START_REF] Rosenkranz | Relationship between phenol degradation efficiency and microbial community structure in an anaerobic SBR[END_REF][START_REF] Wirth | Anaerobic degradation of increased phenol concentrations in batch assays[END_REF], suggesting that they play an important role in the process. However, the identification of high abundant microorganisms in the process not necessarily implies high activity from them [START_REF] De Vrieze | Presence does not imply activity: DNA and RNA patterns differ in response to salt perturbation in anaerobic digestion[END_REF] and low abundance populations could even be more active than the most abundant ones, as demonstrated by metatranscriptomic and metaproteomic methods [START_REF] Zakrzewski | Profiling of the metabolically active community from a production-scale biogas plant by means of highthroughput metatranscriptome sequencing[END_REF][START_REF] Hanreich | Metagenome and metaproteome analyses of 610 microbial communities in mesophilic biogas-producing anaerobic batch fermentations 611 indicate concerted plant carbohydrate degradation[END_REF].

For this reason, a robust statistical analysis of the microbial community abundance dynamics must be done to establish which microorganisms are significantly correlated with a particular performance parameter of the process. Pearson correlation analysis tests have been used to identify key microbial players driving the performance/efficiency of anaerobic digesters [START_REF] Regueiro | Key microbial communities steering the functioning of anaerobic digesters during hydraulic and organic overloading shocks[END_REF][START_REF] Shin | Correlations between bacterial populations and process parameters in four full-scale anaerobic digesters treating sewage sludge[END_REF]. Regarding anaerobic digestion process of phenol, a correlation analysis between changes in the abundance of certain microorganisms and the performance of the reactor is still lacking.

Even though the sequencing analysis of microbial communities by 16S rRNA gene display more accurate results than fingerprinting methods (for example, DGGE band sequencing) [START_REF] Pylro | Misguided phylogenetic comparisons using DGGE excised bands may contaminate public sequence databases[END_REF] this analysis also has some limitations due that the identity of microorganisms depends on reported sequences within databases. Because of that, it is recomendable to complement this information by studying functional genes [START_REF] Oka | Dual biomarkers of anaerobic hydrocarbon degradation in historically contaminated groundwater[END_REF].

In the anaerobic degradation pathway of phenol this compound is first oxidized to benzoate, which is then activated and converted to benzoyl-coA by benzoyl-CoA ligase. This intermediate compound is dearomatized by the benzoyl-CoA reductase to form cyclohex-1,5diene-1-carbonyl-CoA. The oxidation of this product follows a kind of beta-oxidation, which includes a hydrolytic opening of the alicyclic ring which is mediated by the 6-oxocyclohex-1-ene-1-carbonyl-CoA hydrolase. The product of this cleavage follows β-oxidation reactions to produce acetate [START_REF] Levén | Conversion of phenols during anaerobic digestion of organic solid waste-a review of important microorganisms and impact of temperature[END_REF][START_REF] Fuchs | Anaerobic metabolism of aromatic compounds[END_REF]. The bamA gene for example codifies the hydrolase which catalyse the ring cleavage step of 6-oxocylcohex-1-ene-1-carbonyl-CoA during the anaerobic degradation of phenol via the 4-hydroxybenzoate to benzoyl-CoA pathway [START_REF] Kuntze | 6-Oxocyclohex-1-ene-1-carbonyl-coenzyme A hydrolases from obligatory anaerobic bacteria: characterization and identification of its gene as a functional marker for aromatic compounds degrading anaerobes[END_REF][START_REF] Kuntze | Combined application of PCR-based functional assays for the detection of aromatic-compound-degrading anaerobes[END_REF][START_REF] Laempe | 6-Hydroxycyclohex-1-ene-1-carbonyl-CoA dehydrogenase and 6-oxocyclohex-1-ene-1-carbonyl-CoA hydrolase, enzymes of the benzoyl-CoA pathway of anaerobic aromatic metabolism in the denitrifying bacterium Thauera aromatica[END_REF]. This gene has been used as a biomarker of aromatic-degrading anaerobes and has been correlated positively with the amount of degraded mono aromatic compounds [START_REF] Sun | Presence, diversity and enumeration of functional genes (bssA and bamA) relating to toluene degradation across a range of redox conditions and inoculum sources[END_REF]. Therefore, the study of the bamA gene dynamics in the anaerobic digestion process of phenol is an interesting approach to estimate the abundance of anaerobes effectively involved in the degradation of this compound.

In this context, the present work aims at studying the microbial community dynamics of an anaerobic sequencing batch reactor (ASBR) treating increasing phenol concentrations, and to assess the potential correlations between reactor performance parameters and abundances of specific phylotypes within the sludge community in order to find potential performance biomarkers within the process. The microbial community was analyzed both at the total diversity level by a taxonomic approach (through next-generation sequencing of 16S rRNA gene by Illumina MiSeq) and at the functional population level by targeting anaerobic phenol-degraders (through bamA gene quantification).

Material and methods

Experimental set-up

A 5 L laboratory-scale ASBR was operated using a granular sludge inoculum from a tobacco waste water treatment plant at 12 g of volatile suspended solids per liter (VSS L -1 ), as previously described in [START_REF] Rosenkranz | Relationship between phenol degradation efficiency and microbial community structure in an anaerobic SBR[END_REF]. Biomass concentration was kept approximately constant by doing sludge purges, every two weeks. The reactor was operated at 37 °C and fed with synthetic wastewater containing phenol (Sigma-Aldrich, 99%) as the sole carbon source and 21.4 mg L -1 of KH 2 PO 4 , 4.3 mg L -1 of NH 4 Cl and NaHCO 3 to maintain alkalinity in a range of 2.5-3.0 g CaCO 3 L -1 . Each cycle of ASBR operation is constituted of 4 steps: feeding (20 min), reaction (the time needed to degrade at least 90% of phenol), biomass settling (1 h) and effluent discharge (20 min). The volume exchange ratio was 60%. After an initial acclimation to phenol at 120-240 mg L -1 that lasted 80 days (22 cycles), the inlet phenol concentration was progressively increased from 120 to 1200 mg L -1 during five operational stages (I to V), as described in Table 1.

Analytical methods

Volatile suspended solids were measured according to Standard Methods [START_REF] Apha | Methods for Examination of Water and Wastewater, nineteen th ed[END_REF]. Phenol was detected and quantified by a gas chromatograph GC-8A (Shimadzu, Kyoto, Japan), equipped with a 30″x4 mm ID packed column GP 60/80 Carbopack C/0.3% Carbowax 20 M/ 0.1% H 3 PO 4 (Sigma Aldrich, St Louis, MO, US). The analysis was carried out at 120 °C, using nitrogen as carrier gas (50 mL/min) and a flame ionization detector at 200 °C.

Performance evaluation

All the following performance indicators excepting the elimination capacity (EC) were calculated during the last cycle of operation at each stage, once the degradation time got stabilized thus indicating the adaptation of the process to each concentration step. The EC was calculated as the total degraded phenol amount divided by the total degradation time over the entire stage, relative to the biomass content (mg phenol g -1 VSS -1 L -1 D -1 ). The initial phenol degradation rate (IDR) was calculated as the slope of the first linear region of the degradation profiles and degradation time (DT) was defined as the time required to degrade at least 90 % of inlet phenol during the last cycle of the stage. -80 °C for the 16S rRNA gene sequencing analysis and quantitative PCR assay (for this study). Briefly, in each stage of operation, sludge samples were collected at the end of the last cycle of operation, once phenol degradation efficiency reached at least 90%. Sludge samples were centrifuged at 10,000 g for 10 min and 0.5 g of pellet obtained were used for DNA extraction using the Powersoil DNA Isolation Kit (MO BIO Laboratories) following the manufacturer´s instructions. The integrity of DNA extracts was checked by 2% agarose gel electrophoresis. DNA concentration and purity were tested by UV spectrophotometric analysis at 260, 280 and 230 nm using a Nanodrop (Shimadzu Bio Spec-nano).

Quantitative PCR (qPCR)

The quantification assays were conducted in an AriaMX real-time PCR cycler (Agilent). Total bacteria and archaea abundances were assayed using the protocol described by [START_REF] Yu | Use of real-time PCR for group-specific quantification of aceticlastic methanogens in anaerobic processes: population dynamics and community structures[END_REF]. The bamA gene quantification was performed using the primer set Bam-sp9 and Bam-asp1 targeting a 300 bp fragment [START_REF] Kuntze | 6-Oxocyclohex-1-ene-1-carbonyl-coenzyme A hydrolases from obligatory anaerobic bacteria: characterization and identification of its gene as a functional marker for aromatic compounds degrading anaerobes[END_REF]. Each 20 u L PCR reaction contained 10 u L of Takyon Rox SYBR MasterMix dTTP Blue (Eurogentec, Köln, Germany), 0.9 u M of each primer and 2 u L of ten fold diluted DNA template. The thermal program consisted of an initial denaturation (95 °C, 3 min) and 44 amplification cycles (95 °C for 3 s; 60 °C for 40 s). Melting curves were constructed from 65 °C to 95 °C, read every 0.5 °C for 5 s. Calibration curves of bamA gene (10°-10 6 gene copies/uL) were prepared using genomic DNA from the anaerobic phenol-degrader Thauera aromatica (DSM 6984) assuming a genome size of 4.6 Mb [START_REF] Kazy | Assessing the correlation between anaerobic toluene degradation activity and bssA concentrations in hydrocarbon-contaminated aquifer material[END_REF] and one copy number of this gene per genome. Quantification was carried out on triplicate samples. The qPCR efficiencies for all reactions were between 90-100 %. The results were expressed in gene copy number per gram of wet weight of sludge.

16S rRNA amplicon gene sequencing

DNA samples were sequenced targeting the V4-V5 hyper variable region of the 16S rRNA gene with primers 515 F and 909R targeting both bacterial and archaeal communities [START_REF] Wang | Conservative fragments in bacterial 16S rRNA genes and primer design for 16S ribosomal DNA amplicons in metagenomic studies[END_REF]. Sequencing was performed at MR DNA laboratory (Shallowater, TX, USA) on a MiSeq Illumina sequencer following the manufacturer's guidelines. Sequence data were processed using a proprietary analysis pipeline (MR DNA, Shallowater, TX, USA). In summary, sequences were joined, depleted of barcodes then sequences < 150 bp removed, sequences with ambiguous base calls removed. Sequences were denoised, OTUs generated and chimeras removed. Operational taxonomic units (OTUs) were defined by clustering at 3% divergence (97% similarity). Final OTUs (Online Resource 1) were taxonomically classified using BLASTn against a curated GreenGenes database [START_REF] Desantis | Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB[END_REF].

Statistical analysis of microbial community

Microbial community structure and correlation analysis between microbial populations and performance parameters at different stages of the reactor were computed with XLSTAT version 2014 (Addin soft). Beta diversity analysis of microbial community at different operational stages of reactor was carry out using the agglomerative hierarchical clustering (AHC) method, based on the relative abundances profiles of microorganisms at genus level [START_REF] Eichorst | Community dynamics of cellulose-adapted thermophilic bacterial consortia[END_REF]. A dendrogram resulting from the AHC analysis was constructed using the mean link agglomeration method and the Bray-Curtis dissimilarity distance which quantifies how dissimilar a pair of samples is, based on specimen counts [START_REF] Wong | Expanding the UniFrac toolbox[END_REF]. Heat-map of genus dynamics over the different operational stages was performed within the Heat-map tool. Principal component analysis (PCA) was performed using the data analysis tool using the option Pearson correlation type and was constructed in order to visualize community structure distribution under different phenol concentrations and its effects on 3 performance parameters: initial degradation rate (IDR), elimination capacity (EC) and degradation time (DT). BamA gene abundance was also included as a biological variable in the PCA plot. Significant correlations between genus abundances, environmental and performance parameters together with bamA gene abundance were determined by Pearson correlation test analysis. The results from a Pearson correlation test having a p-value ≤ 0.05 were considered as significant.

Results and discussion

Effect of increasing phenol concentrations on process performance

After an acclimation period of 80 days, phenol concentration feeding in the ASBR was increased stepwise from 120 ± 60 up to 1200 ± 43 mg L -1 in a total process that was divided in five operational stages (Table 1). The detailed analysis of process performance in response to phenol increase was described previously [START_REF] Rosenkranz | Relationship between phenol degradation efficiency and microbial community structure in an anaerobic SBR[END_REF] and the most relevant indicators of the process were used in this study to enlighten relationships with the microbial community structure and composition revealed by the 16S rRNA gene sequencing analysis. Briefly, the total degradation time (DT) necessary to degrade 90 % of the compound increased continuously as a result of higher substrate load. DT increase was linear during the first three stages (Table 1), got rather stable during Stage IV, and drastically increased during stage V (more than four times compared to the previous stage). As a result, the Elimination Capacity (EC) remained relatively constant (around 26 mg phenol . VSS -1 . d -1 ) from Stage I to IV, and dropped down to 11 mg phenol •VSS -1 • d -1 in Stage V. Within each degradation profile, the Initial Degradation Rate (IDR) is indicative of the capacity of the community to withstand a new charge load. It increased progressively during the first four stages, and then decreased in the last stage. All together, these indicators show a maximal degradation efficiency at 800 mg L -1 of phenol (Stage IV) and suggest the existence of a threshold concentration between 800 and 1200 mg L -1 after which the reaction rate slowdowned, evidencing a deterioration of the reactor performance and possible partial inhibition.

Other studies performed in batch assays under methanogenic conditions reported a complete inhibition of phenol degradation at 2000 mg L -1 [START_REF] Poirier | Community shifts within anaerobic digestion microbiota facing phenol inhibition: Towards early warning microbial indicators?[END_REF][START_REF] Chapleur | Increasing concentrations of phenol progressively affect anaerobic digestion of cellulose and associated microbial communities[END_REF]. In our case, the maximal concentration applied (1200 mg L -1 ) was below the complete inhibition limit: the degradation rate of the reactor is impaired but the adapted anaerobic biomass due to reactor configuration (ASBR) could tolerate and metabolize this load.

Total bacterial and archaeal abundances

As can be seen in Fig. 1A, the Bacteria and Archaea in this system represent on average 70% and 30%, respectively of the total community during the whole period, which is consistent with the operating conditions favoring favouring methanogenic activity. Despite reactor performance was negatively affected at the highest phenol concentration there was only a small decrease in total bacteria and archaea abundances only at Stage II (which was rapidly recovered at the subsequent stage), total microbial abundance stayed rather constant along the operation, independently of the phenol concentration, suggesting that the toxic phenol threshold implying mortality was not reached and that performance impairement was due to a change in microbial structure configuration rather than biomass losses.

DNA extraction

The DNA extracts for this research were obtained from a previous study from our lab [START_REF] Rosenkranz | Relationship between phenol degradation efficiency and microbial community structure in an anaerobic SBR[END_REF]. At that moment the extracts were aliquoted in sterile DNAse free microtubes and stored at

Evolution of bamA gene abundance at different ASBR stages

During the ASBR process the bamA gene levels had an incremental trend as phenol concentration increase (Fig. 1A), suggesting that microorganisms with the capacity to degrade phenol accumulated throughout the process. In our work, phenol is removed through the process and bamA levels increased in a significant positive linear correlation (p < 0.01) with phenol concentration (Fig. 1B). Inlet phenol concentration is therefore a strong and direct selective pressure/driving force stimulating the growth of phenol-degraders.

The bamA gene copy number was 2-4 orders of magnitude lower than the total bacterial and archaeal 16SrRNA gene copy number, confirming that the functional community effectively involved in phenol degradation via the benzoyl-CoA pathway represents only a minor fraction (0.3% maximum) of the total community. Variations in 16S rRNA gene copy number per genome between microorganisms also could originate an underestimation of the relative abundance of bamA gene harbouring populations. This result demonstrates the importance to consider the rare biosphere in anaerobic digestion ecosystems, where subdominant species can act as keystones and impact the overall function despite their low abundance, as highlighted in fermentative ecosystems [START_REF] Rafrafi | Sub-dominant bacteria as keystone species in microbial communities producing bio-hydrogen[END_REF].

There is only one study that employed bamA gene quantification to enumerate anaerobic aromatic degraders under methanogenic conditionns and that suggest a correlation between the increase of bamA gene levels and the removal percentage of an aromatic compound (toluene) [START_REF] Sun | Presence, diversity and enumeration of functional genes (bssA and bamA) relating to toluene degradation across a range of redox conditions and inoculum sources[END_REF]. Based on this, our result confirms that quantification of bamA gene could be useful as a monitoring variable capable of describe the progression of degradation process of mono aromatic compounds.

Microbial community analysis

3.4.1. Effect of phenol concentration at overall microbial community structure in ASBR Within Bacteria, Miseq amplicon sequencing analysis shows that Anaerolineae, Bacteroidia, Clostridia and Deltaproteobacteria were the dominant bacterial classes in almost all operational stages, except for Stage I (Fig. 2), where Alphaproteobacteria and Bacilli were dominant at the expense of Deltaproteobacteria abundance. The Bacteroidia and Clostridia classes have been reported as dominant in anaerobic batch reactors treating phenol as the sole carbon source at different concentrations [START_REF] Wirth | Anaerobic degradation of increased phenol concentrations in batch assays[END_REF]. The Deltaproteobacteria class has been found dominant in mesophilic methanogenic phenol degrading enrichments at 500 mg L -1 (Chen et al., 2008b). Anaerolineae, on the other hand, is regarded as an important class from the Chloroflexi phylum, which is part of the "core group" of organisms always found in anaerobic digesters independently of substrate nature [START_REF] Cabezas | How to use molecular biology tools for the study of the anaerobic digestion process?[END_REF].

Within the archaeal domain, Methanobacteria was the dominant class in all operational stages followed by Methanomicrobia. According to these results, it is likely that anaerobic digestion of phenol in this system was carried out mainly via the hydrogenotrophic methanogenesis pathway.

Structural changes at genus level during ASBR operation

Fig. 3B shows the AHC analysis of microbial community profiles at the genus level (only those with abundances equal or over 0.5% in at least one sample) and the relative abundances of each genus at different operational stages in ASBR. The dendrogram (Fig. 3A) shows the strongest differentiation between community from stage I (corresponding to the lowest phenol concentration at 120 mg L -1 ) and the other stages, highlighting the sludge acclimatation process to new environmental conditions imposed by phenol feeding. This differentiation was caused mainly by the highest relative abundance of Mycoplana which dominated the bacterial community (33% of total bacteria), as well as the presence of Clostridium (4%) and Sporosarcina (4%), while they were all almost negligible in the following stages (Fig. 4B). As far as we know, this is the first report of Mycoplana presence in an anaerobic digester treating phenol. The capacity of Mycoplana to degrade aromatic compounds has been reported previously in pure culture experiments [START_REF] Lakshmi | Optimization of minimal salt medium for efficient phenanthrene biodegradation by Mycoplana sp. MVMB2 isolated from petroleum contaminated soil using factorial design experiments[END_REF] and was found at low abundance levels in a microcosm treating nonylphenol at 100 ppm under nitrate reducing conditions [START_REF] Wang | Anaerobic biodegradation of nonylphenol in river sediment under nitrate-or sulfate-reducing conditions and associated bacterial community[END_REF]. Thus, Mycoplana was presumably the main phenol degrader at this stage, followed by Clostridium and Sporosarcina, which have been reported at high abundance in methanogenic reactors treating phenolic compounds [START_REF] Tartakovsky | Enhanced selection of an anaerobic pentachlorophenol-degrading consortium[END_REF][START_REF] Hernandez | Anaerobic biodegradation of 13C6-phenol: analysis of bacteria population involved in two different degradation kinetics[END_REF]. The drastical decrease of Mycoplana at the following stages suggest that this genus is not adapted to growth efficiently at higher phenol concentrations under methanogenic conditions.

Regarding dominant archaeal genera, Methanobacterium (an hydrogenotrophic methanogen) was the most abundant followed by Methanosaeta (acetotrophic) and Methanolinea (hydrogenotrophic). Methanobacterium dominance, in anaerobic digestion of phenol, has been reported previously [START_REF] Madigou | Acclimation strategy to increase phenol tolerance of an anaerobic microbiota[END_REF], but its high abundance is not a necessary requirement to carry out the process efficiently, since other studies involving phenol degradation reported that Methanosaeta, Methanosarcina and Methanoculleus can be dominant too [START_REF] Poirier | Community shifts within anaerobic digestion microbiota facing phenol inhibition: Towards early warning microbial indicators?[END_REF][START_REF] Zhang | Microbial characteristics of a methanogenic phenol-degrading sludge[END_REF].

After Stage I, the community structure followed a clear progressive evolution along the phenol concentration gradient applied (between 240 and 1200 mg L -1 ), as evidenced in the dendrogram representation, where the successive stages cluster together. Within this group, the medium-concentration samples (between 240 and 800 mg L -1 ) clustered together (at ∼70% of similarity), clearly apart from stage V sample, in which the highest concentration of phenol was applied. This last stage is characterized by the highest abundance of Melioribacter (18%) (which dominated the community), and the presence of Spirochaeta, Methyloversatilis and Methanobrevibacter, in contrast with the previous stages where they were almost negligible, suggesting that these genera were able to tolerate phenol concentration as high as 1200 mg L -1 . Within these genera, Methyloversatilis is the only microorganism capable of metabolizing phenol [START_REF] Smalley | Functional and genomic diversity of methylotrophic Rhodocyclaceae: description of the new species Methyloversatilis discipulorum sp[END_REF]. Melioribacter and Spirochaeta on the other hand, were reported to be present in methanogenic systems treating aromatic compounds but their effective role in phenol degradation is not clear [START_REF] Li | Performance and microbial community profiles in an anaerobic reactor treating with simulated PTA wastewater: the ASBR acidification. Notably, two paralel metabolic pathways may be co-occuring during the stage with highest efficiency, which suggest a positive synergic contribution of these archaeal members to the process. These results agree with our previous findings regarding the positive interactions (cooperation) between Syntrophorhabdus, Methanosaeta and hydrogenotrophic archaeas in a context of anaerobic digestion of phenolic compounds[END_REF][START_REF] Xiong | Crude oil degradation by bacterial consortia under four different redox and temperature conditions[END_REF]. Within all archaeal members, Methanobrevibacter was the only genus that had its maximum abundance at this stage indicating that this hydrogenotrophic methanogen can growth better than other methanogens under this environmental condition.

At stage IV, in which performance of ASBR was better, Methanosaeta and Methanolinea were the methanogens that showed their highest abundance (19 and 4%, respectively) in comparison to other stages as can been seen in the heat-map (Fig. 5). Methanosaeta is a well-known microorganism that produces methane via the acetotrophic pathway [START_REF] Smith | Methanosaeta, the forgotten methanogen?[END_REF]. This genus probably uses the acetate generated from phenol conversion by direct phenol or benzoate degraders. Regarding bacterial members, the genera that had their highest abundance value at this stage (compared to the other stages) were Smithella, Syntrophorhabbdus and Chloroflexus (0.9, 2.7 and 4.6%, respectively) as can be seen in the heat-map (Fig. 5). From those, Syntrophorhabdus is the only known phenol degrader that acts in syntrophy with a hydrogen scavenger microorganism to grow [START_REF] Qiu | Syntrophorhabdus aromaticivorans gen. nov., sp. nov., the first cultured anaerobe capable of degrading phenol to acetate in obligate syntrophic associations with a hydrogenotrophic methanogen[END_REF]. Based on this, the highest abundance of the syntrophic phenol degrader Syntrophorhabdus coupled to a greater abundance of the hydrogenotrophic methanogen Methanolinea could be the reason of the increase in phenol degradation performance at this stage. An increase in phenol degradation must lead to an increment on acetate production rate, which would explain the greater abundance of Methanosaeta.

Identification and dynamics of core community

The analysis of the microbial populations also allowed us to establish a group of genera that maintained relative abundances of at least 2% in all operational stages, which implies that they can be of great importance to carry out the process successfully. This group was formed Fig. 3. Microbial community structure at the genus level in ASBR operating at different feeding phenol concentrations (I = 120, II = 240, III = 500, IV = 800 and V = 1200 mg L-1). In A, community clustering analysis based on Bray-Curtis dissimilarity between samples. In B, relative abundance of genera at each stage. Only genera having abundance ≥0.5% in at least one sample are represented otherwise were considered as "others".

by the genera Bellilinea (3.7-7.4%), Longilinea (2.5-14.3%), Anaerophaga (2.1-22.7%) and Methanobacterium (16-29%). Bellilinea and Longilinea are filamentous fermenters that produce volatile fatty acids. They lack the ability to use phenol as carbon source [START_REF] Yamada | Bellilinea Caldifistulae gen. nov[END_REF] and it is suggested that they may be relevant to maintain the structure of granules in the anaerobic sludge [START_REF] Yamada | Diversity, localization, and physiological properties of filamentous microbes belonging to Chloroflexi subphylum I in mesophilic and thermophilic methanogenic sludge granules[END_REF], an essential feature needed to work properly in SBR type reactors. On the other hand Anaerophaga is a strict anaerobe fermenter and its presence has been reported previously in microcosms enriched with benzoate [START_REF] Herrmann | Enrichment of anaerobic benzene-degrading microorganisms by in situ microcosms[END_REF] an intermediate compound produced in the anaerobic phenol degradation [START_REF] Ju | Novel microbial populations in ambient and mesophilic biogasproducing and phenol-degrading consortia unravelled by high-throughput sequencing[END_REF], thus Anaerophaga could be using (at least) part of the benzoate generated as carbon source during the phenol degradation process.

As can be seen in Fig. 4, the increase of phenol concentration negatively affected the abundances of Dethiobacter, Longilinea and Methanobacterium. All these three genera reducted their abundances to the minimum at the highest phenol concentration. A drop in members belonging to the core group could be one of the causes for performance impairment at this operational stage. As can be noted in the heat-map, the abatement in Methanobacterium abundance was compensated by a raise of Methanolinea and Methanobrevibacter, both hydrogenotrophic methanogens that share the same ecological function as Methanobacterium (i.e. hydrogenotrophic methanogenesis) and have been found in the same ecological niches [START_REF] Yu | Methane-rich biogas production from waste-activated sludge with the addition of ferric chloride under a thermophilic anaerobic digestion system[END_REF][START_REF] Vincent | Characterization and dynamic shift of microbial communities during start-up, overloading and steady-state in an anaerobic membrane bioreactor[END_REF][START_REF] Prajapat | Microbial diversity and dynamics in hydrocarbon Resource environments[END_REF]. Abundance changes of co-ocurring archaeal members sharing the same function is caused by an environmental condition that favors the growth of one archaeal member over another. In this case, phenol concentration increase fauvored the growth of Methanolinea and Methanobrevibacter over Methanobacterium. [START_REF] Vincent | Characterization and dynamic shift of microbial communities during start-up, overloading and steady-state in an anaerobic membrane bioreactor[END_REF] reported that during an increase in organic loading in a methanogenic digester Methanobrevibacter increased its relative abundance over Methanobacterium. The latter suggests that the increase in organic load caused by a phenol concentration increment in the ASBR promoted the growth of Methanobrevibacter as well as Methanolinea.

Correlations between microorganisms, bamA gene and performance parameters

A principal component analysis was performed in order to visualize how changes in feeding phenol concentration affects the microbial community structure variability during the different operational stages in ASBR (Fig. 5). In the PCA plot each sample point has an specific position based on its particular genera abundance profile. This position was represented as circles whose size was proportional to the inlet phenol concentration applied to each operational stage. As can be noted, PC1 (X axis) and PC2 (Y axis) explains a great part of the samples variability (47 % and 33 %, respectively). In addition, performance and environmental variables were represented as arrows, whose direction and length indicate to which sample the increase of this variable is explaining the microbial abundance patterns and the magnitude of that variable in explaining the microbial profiles variation, respectively. Based on the aforementioned, it is possible to observe that phenol concentration (PC) is related with the X axis principal component which means that microbial community structure was shaped at a great extent by the change of phenol feeding concentration. Genera correlated significantly with a specific variable is presented in Table 2 and were also plotted in the PCA. Significant positive correlations between the abundance of bamA gene with abundances of Cytophaga and Smithella were observed. These could mean that (i) they are directly involved in phenol degradation (either through bamA pathway, or another gene not targeted in our assay) or (ii) they are indirectly related for example through consumption of by-products of phenol degradation. Althought there is no information reported regarding the metabolic capacity of Smithella to degrade aromatic compounds, this genus is capable of syntrophically oxidize butyrate and propionate to acetate [START_REF] Zhang | Metagenomic insight into the microbial networks and metabolic mechanism in anaerobic digesters for food waste by incorporating activated carbon[END_REF] supporting the hypothesis (ii) of indirect relationship with phenol degraders. On the other hand, Cytophaga have been found in anaerobic reactors treating trichlorophenol [START_REF] Collins | Anaerobic treatment of 2, 4, 6trichlorophenol in an expanded granular sludge bed-anaerobic filter (EGSB-AF) bioreactor at 15 C[END_REF] and clones belonging to this genus have been reported to be involved in polyaromatic hydrocarbon degradation under methanogenic conditions [START_REF] Braun | Similar PAH fate in anaerobic digesters inoculated with three microbial communities accumulating either volatile fatty acids or methane[END_REF]. Based on this and knowing that Cytophaga members harbour the bamA gene (by consulting Kyoto Encyclopedia of Genes and Genome) [START_REF] Kanehisa | KEGG: kyoto encyclopedia of genes and genomes[END_REF]), we infer that Cytophaga might be a direct phenol degrader.

Analysis between reactor parameters and microorganisms reveals that there are six genera that correlated positively with phenol concentration and ten with degradation time (Table 2). Of these, Flavobacterium, Ignavibacterium, Spirochaeta and Methanobrevibacter had positive correlations with both parameters. Ignavibacterium was found to be a dominant aniline-degrader in methanogenic microcosm [START_REF] Sun | Identification of anaerobic aniline-degrading bacteria at a contaminated industrial site[END_REF]. Accordingly, this genus has the metabolic machinery to degrade monoaromatic compounds and, based on these results, the capability to tolerate high phenol concentrations. No aromatic degrading capacity was reported for the other genera. However, spirochaetal organisms were found in granular sludge of an EGSB-AF reactor treating 50 mg L -1 of trichlorophenol and were capable to tolerate the presence of this highly toxic compound [START_REF] Collins | Anaerobic treatment of 2, 4, 6trichlorophenol in an expanded granular sludge bed-anaerobic filter (EGSB-AF) bioreactor at 15 C[END_REF] thus, it is expected that members of Spirochaeta genus are also able to cope with high phenol concentrations. On the other hand, higher degradation time in ASBR observed in stage V indicate that microbial members positively correlated with these variables were able to maintain the anaerobic digestion process stable (due to their capacity to tolerate high phenol concentration) but less efficiently either because a partial inhibition phenomenon or lower total degradation activity of the microorganisms which leads to display a higher degradation time. Correlation tests also showed that the genera Syntrophorhabdus, Chloroflexus and Smithella (maximum abundances at stage IV) have a significant positive correlation with the initial degradation rate. Syntrophorhabdus was widely reported in methanogenic systems treating phenolic compounds like phenol and p-cresol [START_REF] Chen | Characterization of active microbes in a full-scale anaerobic fluidized bed reactor treating phenolic wastewater[END_REF][START_REF] Levén | Molecular characterisation of two anaerobic phenol-degrading enrichment cultures[END_REF][START_REF] Ju | Novel microbial populations in ambient and mesophilic biogasproducing and phenol-degrading consortia unravelled by high-throughput sequencing[END_REF][START_REF] Na | Microbial com[END_REF]Franchi et al., 2018a). [START_REF] Ju | Novel microbial populations in ambient and mesophilic biogasproducing and phenol-degrading consortia unravelled by high-throughput sequencing[END_REF] reported the presence of Syntrophorhabdus in two methanogenic batch reactors treating phenol concentrations between 400 and 1000 mg L -1 displaying different degradation rates and suggested that a higher proportion of this microorganism could explain the higher rate of one reactor. At archaeal level, Methanobacterium showed a significant negative correlation with phenol concentration while Methanobrevibacter had a positive correlation with that parameter. Therefore, the concentration of this compound has a negative linear effect on the growth of Methanobacterium and, conversely, promotes in a linear manner the abundance of Methanobrevibacter. One possible explanation of this result, could be the presence of a competition relationship between these archaeal members, a phenomenon which offen occurs in closely related OTUs (both genus belongs to Methanobacteriaceae family) requiring similar resources for growth (i.e. carbon dioxide and hydrogen) [START_REF] Wu | Long-term successional dynamics of microbial association networks in anaerobic digestion processes[END_REF]. According to this, the phenol concentration increment, could be a relevant environmental variable in promoting the competition strength of Methanobrevibacter to the detriment of Methanobacterium. On the other hand, although Methanolinea and Methanosaeta had positives correlations with initial degradation rate of ASBR the p-values of these correlations were just above the significance threshold (0.052 and 0.054, respectively). Despite this, our results show that the abundance increase of these archaeal members tends to improve the phenol degradation performance in a linear manner. The theorical reason of this trend could be explained first by an increase of the hydrogen scavenging capacity of the system due to a higher abundance of Methanolinea thus favouring Syntrophorhabdus degrading activity and secondly by an increase in the acetate consumer Methanosaeta which would be contributing to prevent Fig. 5. Principal component analysis of the microbial structure distribution at genus level and correlations with bamA gene abundance, phenol concentration (PC) and the performance parameters; Elimination capacity (EC), Initial degradation rate (IDR) and degradation time (DT). For each operational stage, the circle size is proportional to feeding phenol concentration. Genera that correlated significantly with one or more variables were presented in the plot together with their specific positions denoted by diamonds.

Table 2

Pearson correlation coefficients (r) between significant correlated genera and Initial degradation rate (IDR), degradation time (DT) (performance variables), phenol concentration (PC) (environmental variable) and bamA gene (biological variable). The p-value is indicated: * < 0.05, ** < 0.01.

Conclusions

The increase of phenol concentration at more than 800 mg L -1 in ASBR system triggered a performance impairment on degradation process that was associated with an abundance reduction of a core group of microorganisms. At 800 mg L -1 of phenol, the degradation performance of ASBR reached its maximum in correspondence with highest abundances of Syntrophorhabdus, Chloroflexus, Smithella, Methanolinea and Methanosaeta which in turn were positively correlated with the initial degradation rate, indicating that the synergistic growth of these microorganisms promoted a better performance of the anaerobic degradation of phenol in the system. On the other hand, bamA gene dynamics showed that aromatic degraders targeted by the qPCR assay were accumulating through the process and correlated possitively with inlet feeding phenol concentration, suggesting that bamA gene quantification could be a useful monitoring variable capable to describe the growth progression of phenol degrading bacteria.

Author contributions

Author 1: Oscar Franchi Collected the data Performed the analysis Wrote the paper Author 2: Rolando Chamy Conceived and designed the analysis

Fig. 1 .

 1 Fig. 1. BamA gene dynamics in ASBR. In A, the abundance of bamA gene, total bacteria and total archaea at different operational stages in ASBR. In B, correlation between phenol concentration applied to inlet feeding in ASBR and bamA gene abundance. The dotted line correspond to the linear model describing the significant correlation between variables. Error bars represents one standard deviation arround the mean.

Fig. 2 .

 2 Fig. 2. Relative abundance at class level of microbial communities in the ASBR at the last cycle of each operational stage. Only classes having abundance ≥ 1% in at least one sample are represented otherwise were considered as "others".

Fig. 4 .

 4 Fig. 4. Heat map of abundance dynamics for each genera at different ASBR operational. stages. Only genera having a relative abundance ≥ 0.5% in at least one operational stage are represented.

Table 1

 1 Operational parameters and performance indicators of the phenol-fed ASBR reactor under the five operational stages.

	Stage	I	II	III	IV	V
	Inlet phenol concentration [mg L -1 ]	120 ± 60	240 ± 4	500 ± 50	800 ± 7	1200 ± 43
	Initial degradation rate (mg phenol • VSS -1 • d -1 )	1 0	1 1	1 3	1 5	1 3
	Elimination capacity (mg phenol • VSS -1 •d -1 )	2 6	2 8	1 9	3 1	1 1
	Degradation time of last cycle (h)	13	20	45	50	215
	Operational time (d) /number of cycles	39/22	18/7	21/6	33/7	85/6

Acknowledgements

This research was funded by CONICYT Project 781302010 and FONDECYT Project 1151161 from CONICYT-CHILE.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Javiera Toledo-Alarc On
email: javiera.toledo@pucv.cl

Lea Cabrol

David Jeison

Eric Trably

Jean-Philippe Steyer

Estela Tapia-Venegas

Impact of the microbial inoculum source on pretreatment efficiency for fermentative H 2 production from glycerol

Keywords:

Microbial community in inocula has a great impact on pretreatments efficiency. In aerobic sludge no pre-treatment is required to increase hydrogen yield. Biokinetic control has a strong influence on the Clostridiaceae family selection. Low/unstable hydrogen production is associated with the Enterobacteriaceae family.

Introduction

The growing environmental pollution of cities has motivated the search for new sources of clean and renewable energy. In this context hydrogen (H 2 ) appears as a great environment friendly alternative for transportation. Indeed, its combustion produces only water vapor instead of greenhouse gases, with a combustion efficiency 2.75 (122 kJ/g) times higher than traditional fuels and can also be easily converted into electricity in electric vehicle fuel cells [START_REF] Patel | Nanoparticles in biological hydrogen production: an overview[END_REF][START_REF] Cabrol | Microbial ecology of fermentative hydrogen producing bioprocesses: useful insights for driving the ecosystem function[END_REF]. Green H 2 is considered a renewable energy since it is produced from renewable resources, such as organic matter by dark fermentation. This latter technology has been widely studied because of a high simplicity and the low operating and maintenance costs when compared to other biological H 2 production systems, such as photofermentation and biophotolysis. In addition, a wide variety of substrates and inocula can be used allowing the production of energy while treating waste [1,3e6]. Different types of waste and organic substrates have already been studied including simple sugars such as glucose and more complex organic matter such as organic industrial wastes [START_REF] Toledo-Alarc On | Basics of bio-hydrogen production by dark fermentation[END_REF]. A special interest has been focusing on crude glycerol, the main byproduct of the biodiesel industry, as a low-cost feedstock [8e10].

Dark fermentation H 2 production performances from glycerol are mostly dependent to the microbial physiological capacities. As microbial inoculum, strains of known H 2 -producing bacteria could be used in pure cultures, including facultative anaerobes as Klebsiella sp. and Enterobacter sp. of the Enterobacteriaceae family, as well as the strict anaerobes Clostridium sp. of the Clostridiaceae family [11e14]. Mixed cultures coming from natural and engineered ecosystems such as soil, compost, anaerobic sludge and other anaerobic environments [15e18] have also been used as inocula, with the advantage of providing better adaptation capacity in response to environmental stresses including substrate limitation and abrupt changes in pH and temperature [START_REF] Toledo-Alarc On | Basics of bio-hydrogen production by dark fermentation[END_REF][START_REF] Wong | A review of sustainable hydrogen production using seed sludge via dark fermentation[END_REF][START_REF] Wang | Factors influencing fermentative hydrogen production: a review[END_REF]. The higher robustness of mixed cultures has been attributed to the diversity of the microbial community, enabling positive interspecies interactions such as syntrophy [START_REF] Cabrol | Microbial ecology of fermentative hydrogen producing bioprocesses: useful insights for driving the ecosystem function[END_REF][START_REF] Kouzuma | Microbial interspecies interactions: recent findings in syntrophic consortia[END_REF]. Some mixed community members can also generate adverse effects on the system performance through negative interactions [START_REF] Cabrol | Microbial ecology of fermentative hydrogen producing bioprocesses: useful insights for driving the ecosystem function[END_REF]. The origin of the inoculum, its pre-treatment and the operating strategy of the reactors including biokinetic control, i.e. selection pressure on the microbial community imposed by low pH and short HRT, are of crucial importance to ensure H 2producer enrichment and achieve high and stable H 2 -production performance [21e27].

Inocula pre-treatments seek to eliminate H 2 econsumers such as hydrogenotrophic methanogenic archaea and enrich the community with H 2 -producers [START_REF] Saady | Homoacetogenesis during hydrogen production by mixed cultures dark fermentation: unresolved challenge[END_REF][START_REF] Viana | Fermentative H2 production from residual glycerol: a review[END_REF]. Heat shock pre-treatment is the most used at lab scale for its efficiency in batch systems. Pre-treatment conditions are generally arbitrary and range from 50 C to 125 C and from 20 to 30 min [29e32]. In this case, the microbial community is enriched with spore-forming species such as the H 2 -producer Clostridium sp., resisting to high temperature [START_REF] Toledo-Alarc On | Basics of bio-hydrogen production by dark fermentation[END_REF][START_REF] Bundhoo | Effects of pretreatment technologies on dark fermentative biohydrogen production: a review[END_REF]. However, other non-spore-forming H 2 -producing species are also depleted such as Klebsiella sp., and Enterobacter sp. [START_REF] Wong | A review of sustainable hydrogen production using seed sludge via dark fermentation[END_REF][START_REF] Kumar | Impact of pH control and heat pre-treatment of seed inoculum in dark H2 fermentation: a feasibility report using mixed microalgae biomass as feedstock[END_REF]. Moreover, heat shock pre-treatment requires additional energy consumption, which is questionable in terms of economic and technical feasibility for a potential industrial application [START_REF] Kothari | A critical review on factors influencing fermentative hydrogen production[END_REF][START_REF] Baghchehsaraee | The effect of heat pretreatment temperature on fermentative hydrogen production using mixed cultures[END_REF]. Another less common pre-treatment is aeration, which enriches the inoculum with aerobic and facultative anaerobic H 2 -producers such as Klebsiella sp., but also eliminates other oxygen-intolerant H 2 -producing bacteria such as some Clostridium sp [START_REF] Wang | Principle and application of different pretreatment methods for enriching hydrogen-producing bacteria from mixed cultures[END_REF]. Unlike heat shock pretreatment, aeration could be performed in-situ as an industrially viable alternative to the common instability problems of continuous systems during H 2 production [START_REF] Castell O | Possible causes for the instability in the H2production from cheese whey in a CSTR[END_REF][START_REF] Bakonyi | Review on the start-up experiences of continuous fermentative hydrogen producing bioreactors[END_REF].

This paper aims to study the combined effects of inoculum source and pre-treatment on continuous H 2 production efficiency from glycerol, with special focus on the dynamics of microbial communities. For this, two inocula (aerobic and anaerobic sludge) and two pre-treatments (aeration and heat shock pre-treatment) were compared.

Materials and methods

Inocula source

Two mixed cultures were used as inoculum: (i) anaerobic sludge (13.1 gVSS.l À1 ) from a sludge stabilizing anaerobic reactor and (ii) aerobic sludge (15.8 gVSS.l À1 ) from an activated sludge reactor. Both were collected from the sewage treatment plant La Farfana located in Santiago, Chile.

Pre-treatments of inocula

Inocula were either used without pre-treatment (in control conditions), or prepared using two different pre-treatments prior to reactors inoculation (Table 1). A heat treatment (HT)

Microbial community analysis

For molecular biology analysis, 2 mL biomass samples were collected from original inocula, after pre-treatments and at the end of the continuous operation. Biomass samples were centrifuged, and the pellet was stored in 9% NaCl at À20 C. Total genomic DNA was extracted with the Power Soil DNA isolation kit (MoBio Laboratories, Carlsbad, CA, USA). The V3eV4 region of the bacterial 16s rRNA gene was PCRamplified according Carmona-Martinez et al. (2015) [START_REF] Carmona-Martı ´nez | Long-term continuous production of H2 in a microbial electrolysis cell (MEC) treating saline wastewater[END_REF]. The community composition was evaluated by sequencing using the MiSeq v3 chemistry (Illumina) with 2 Â 300 bp paired-end reads at the GenoToul platform (http://www.genotoul.fr). Sequences were retrieved after demultiplexing, cleaning, clustering (97%) and affiliating sequences using Mothur [START_REF] Schloss | Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities[END_REF]. A total of 3216 operational taxonomic units (OTU) were found and then used for statistical analysis. Sequences have been submitted to GenBank with accession No. KX632952-KX636081.

Data analysis

Averages and standard deviations (±SD) of biomass production, H 2 yields and metabolites concentrations were calculated from daily measurements during continuous operation (for at least 16 HRT). H 2 yields was expressed in moles of H 2 produced by moles of glycerol consumed. Chemical oxygen demand (COD) mass balance was performed and the metabolites concentrations were expressed in %COD i.e. COD of metabolites produced by COD of glycerol consumed.

A one-way ANOVA analysis was performed, after checking normal data distribution, to evaluate significant differences in H 2 yields and biomass production between conditions. For H 2 yield, Mann-Whitney as post-hoc test was performed to find out which sample pairs were statistically different. Simpson diversity index was calculated to compare microbial diversity at the beginning and end of each condition. Principal component analysis was performed from (i) initial and final microbial community and, (ii) final microbial community and metabolic patterns. For the PCA were used the microbial community data with a relative abundance >5.0% in at least one sample. All statistical analyses were carried out with PAST 3.24 software (http://folk.uio.no/ohammer/past/).

Results & discussion

Microbial communities in original and pre-treated inocula

The initial microbial community was analyzed in the original inocula (AI and AnI) and in the pre-treated inocula (AI-HTi, AnI-HTi, AI-ATi and AnI-ATi). Whereas the Simpson Diversity Index quantifies microbial diversity, where 1 represents infinite diversity and 0 represents no diversity. The original inocula have a high diversity with a Simpson Diversity Index of 0.98 and 0.92 for aerobic and anaerobic sludge, respectively. After any pre-treatments the Simpson Diversity Index showed no great changes compared to the original inocula (Table 2). When comparing all communities, the highest similarity is observed between both original inocula AI and AnI, as shown on the PCA (Fig. 1).

At the phylum level, aerobic and anaerobic inocula were dominates by Bacteroidetes and Spirochaetae phyla, representing between 34.8%e40.4% and 20.3%e26.4% of bacterial community respectively. In particular, the most abundant families in aerobic sludge (AI) were Spirochaetaceae (12.4%) and Rikenellaceae (10.3%), with OTU11 (8.2%) dominating. OTU11 had 91% of 16S rRNA sequence similarity with Rectinema cohabitans. In anaerobic sludge (AnI) the most abundant families were Rikenellaceae (18.8%) and WCHB1-69 (11.5%), with OTU5 (18.4%) and OTU6 (16.2%) dominating (Fig. 2). These two OTUs were related to Mucinivorans hirudinis (87% 16S rRNA sequence similarity with OTU5) and Eubacterium minutum (78% 16S rRNA sequence similarity with OTU6). Although these families have been reported as dominant in other initial microbial communities of H 2 -producing reactors [START_REF] Tapia-Venegas | Bio-hydrogen production during acidogenic fermentation in a multistage stirred tank reactor[END_REF][START_REF] Marshall | Electrosynthesis of commodity chemicals by an autotrophic microbial community[END_REF], none of these dominant species in AI or AnI have been reported as H 2producing.

Heat shock pre-treatment has a rather limited impact on total microbial community, as shown on the PCA in Fig. 1. After heat shock, the total abundance of the initially dominant Bacteroidetes and Spirochaetae phyla decreased in both inocula. By contrast, the relative abundance of Firmicutes and Proteobacteria increased, representing between 9.4 e 18.5% and 29.2e29.5% of the bacterial community respectively (Table 2). Proteobacteria became dominant in both inocula. However, at the family level, the same Rikenellaceae family as before the heat shock was maintained dominant in the anaerobic inoculum (AnI-HTi) (17.3%) and became dominant in the aerobic inoculum (AI-HTi) (8.2%), even if its relative abundance slightly decreased with respect to the original inocula. In anaerobic inoculum the same OTU5 (17.1%) and OTU6 (14.5%) remained dominant, while in aerobic inoculum OTU25 (5.3%) became dominant. The OTU25 had 95% of 16S rRNA sequence similarity with Desulfonatronobacter acetoxydans. As expected, the aerobic inoculum community was more evenly distributed than the anaerobic one, even after heat-treatment. Besides, heat treatment not only favored families with known sporeforming species such as Peptostreptococcaceae, but also families with non-spore forming species such as Desulfobacteraceae and Christensenellaceae [START_REF] Morotomi | Description of Christensenella minuta gen. nov., sp. nov., isolated from human faeces, which forms a distinct branch in the order Clostridiales, and proposal of Christensenellaceae fam. nov[END_REF][START_REF] Rosenberg | The prokaryotes: prokaryotic physiology and biochemistry[END_REF]. However, this finding is not unusual since other studies have reported that nonspore forming species can survive drastic treatments such as heat shock [START_REF] Cabrol | Microbial ecology of fermentative hydrogen producing bioprocesses: useful insights for driving the ecosystem function[END_REF]. Surprisingly, the heat treatment resulted in a very limited enrichment of the community with members of well-known H 2 -producing families such as Clostridiaceae or Enterobacteriaceae.

The aeration pre-treatment resulted in more drastic composition changes than the heat shock, and more divergent communities depending on the inoculum source, as shown on PCA in Fig. 1. Especially, aeration decreased the abundance of the initially dominant Bacteroidetes and Spirochaetae phyla, strongly increasing the relative abundance of Firmicutes and Proteobacteria representing between 32.2 e 41.7% and 32.9e41.7% of the bacterial community respectively (Table 2). After aeration, Clostridiaceae became the most abundant family in both aerobic (AI-ATi) and anaerobic Table 2 e Simpson diversity index and microbial community composition at the family level, expressed as percentage of total community. DNA samples were collected from original inocula, after pre-treatments and after continuous operation. Only families with a relative abundance ≥5.0% in at least one sample are shown. AI, AnI, HT, AT and C represent aerobic inoculum, anaerobic inoculum, heat shock pre-treatment, aeration pre-treatment and control, respectively. The "i" at the end of the sample names refers to samples taken after pre-treatments.

Family

Original Our results show that aerobic pre-treatment allows the selection of species with aerobic and facultative anaerobic metabolisms belonging to families such as Pseudomonadaceae and Moraxellaceae (Fig. 2). Especially, the increase of the Enterobacteriaceae family known for its facultative anaerobic H 2 producing members was observed [START_REF] Sinha | Role of formate hydrogen lyase complex in hydrogen production in facultative anaerobes[END_REF]. Surprisingly, the important presence of the Clostridiaceae family was also observed, whose members managed to remain and multiply despite theoretically lethal aeration conditions. This could show positive interactions, where non oxygen tolerant microorganisms could be protected by others through oxygen consumption during stressful conditions such as aeration.

Performance indicators during continuous H 2 production

Biomass production for all experiments did not differ significantly (See ANOVA in Supplementary Material), with average growth yields reaching 6.0 ± 2.1 g VSS .mol gly-consumed À1 (Table 3). All experiments produced H 2 with yields ranging from 0.29 ± 0.10 to 0.55 ± 0.08 mol H2 .mol gly-consumed

À1

, except for the anaerobic sludge after aeration treatment (AnI-AT), which produced H 2 unsteadily (Table 3). In general, the H 2 yields obtained in this study are within the ranges reported in literature (0.05e0.58 mol H2 mol gly-consumed

À1

) for dark fermentation from glycerol using mixed cultures in continuous systems [15,27,48e50]. Soluble metabolites produced concomitantly with H 2 are detailed in Table 3. Butyrate was the main metabolite in all experiments, reaching between 22.0 ± 8.8% COD consumed and 39.7 ± 21.5% COD consumed . Succinate production represented between 12.0 ± 4.5% COD consumed and 16.1 ± 5.6% COD consumed in experiments that used heat-treated sludge (AI-HT and AnI-HT) and aeration-treated aerobic sludge (AI-AT), but was in less amount 2.5 ± 1.2% COD consumed in the control experiments (AI-C and AnI-C) and in the experiment with unstable H 2 production (AnI-AT). Ethanol production represented less than 12.4 ± 5.4% COD consumed in all experiments except in AI-AT reaching 27.3 ± 10.1% COD consumed . Acetate and propionate were also detected in all experiments but at low concentrations (<5.7 ± 2.0% COD consumed ) except in AnI-AT where acetate accumulated 13.2 ± 9.4% COD consumed . Formate was also produced at very low concentrations (<2.9 ± 4.4% COD consumed ), only in aerobic sludge experiments (AI-C, AI-HT and AI-AT). Overall, glycerol removal was between 74 ± 22% COD consumed and 90 ± 15% COD consumed .

Comparing the two-original sludge (i.e., not pre-treated) in the control experiences (AI-C and AnI-C), a 72% higher H 2 yield was obtained along with 31.2% more butyrate and 47.2% less ethanol using aerobic sludge than using anaerobic sludge. This is consistent with literature where higher H 2 production is often associated with higher butyrate production [START_REF] Cabrol | Microbial ecology of fermentative hydrogen producing bioprocesses: useful insights for driving the ecosystem function[END_REF][START_REF] Lee | Thermodynamic evaluation on H2 production in glucose fermentation[END_REF][START_REF] Ghimire | A review on dark fermentative biohydrogen production from organic biomass: process parameters and use of byproducts[END_REF]. This demonstrates a better adaptability for H 2 production of untreated aerobic sludge compared to anaerobic sludge in a continuous system using glycerol as substrate. As already reported, untreated anaerobic sludge may require more time to adapt to glycerol [START_REF] Tapia-Venegas | Adaptation of acidogenic sludge to increasing glycerol concentrations for biohydrogen production[END_REF]. Besides, although no methane production was observed in any reactor, a part of H 2 could have been consumed by other H 2consuming microorganisms present in the untreated anaerobic sludge such as homoacetogens or hydrogenotrophic methanogenic archaea.

When inoculum pre-treatments were performed, different effects were observed depending on the inoculum sources. Within aerobic sludge experiments (AI-C, AI-HT, and AI-AT), the compared pre-treatments did not have any significant effect on H 2 yield (See ANOVA and test of Mann-Whitney pairwise in Supplementary Material). On the contrary, within anaerobic sludge experiments, heat treatment (AnI-HT) increased H 2 -yields by 45% compared to control (AnI-C), as already reported by other authors [START_REF] Yang | Microbial community diversity during fermentative hydrogen production inoculating various pretreated cultures[END_REF][START_REF] Kumar | Seed inocula for biohydrogen production from biodiesel solid residues[END_REF]. In addition, the heat pre-treatment resulted in two similar H 2 production systems (AnI-HT and AI-HT) with slightly different metabolite production but statistically equal H 2 yields (See ANOVA and test of Mann-Whitney pairwise in Supplementary Material), despite the inocula came from different sources. This shows the reproducibility and effectiveness of heat treatments, leaving evidence why has been widely reported in the literature to prepare different inocula for the H 2 production by dark fermentation [33,52,55e58].

Unlike heat treatment, aerobic treatment on anaerobic sludge (AnI-AT) generated a negative effect respect to the control (AnI-C), causing unstable H 2 production during all operation days. Consequently, when comparing the behavior of both sludge when exposed to aerobic treatment, again aerobic sludge showed a better adaptability to H 2 production compared to anaerobic sludge.

In conclusion, and depending on the inoculum source, three effects of pre-treatment on H 2 production can be observed respect to the control: Positive effect (i.e. heat pretreatment on anaerobic sludge), negative effect (i.e. aerobic pre-treatment on anaerobic sludge) and neutral effect (i.e. heat pre-treatment and aerobic pre-treatment on aerobic sludge). Consistently, the inoculum source importance on the efficiency of the pre-treatment was already evidenced but in a study performed in batch mode operation using glucose, and comparing two pre-treatments: heat treatment and acidification [START_REF] Hino | Effect of inoculum conditioning on hydrogen fermentation and pH effect on bacterial community relevant to hydrogen production[END_REF].

Link between final microbial community and metabolic patterns during continuous H 2 production

DNA samples were collected at the end of the continuous operation to assess changes in the microbial community. As shown in Table 2, the Clostridiaceae family was most abundant in all conditions, with a relative abundance between 35.3% and 56.4% and was mainly represented by OTU1 and OTU3 (Fig. 2). OTU1 was dominant in AnI-C (44.7%), AI-HT (46.2%), AnI-HT (49.5%) and AI-AT (39.2%), while OTU3 in AI-C (28.5%) and AnI-AT (34.9%). The Prevotellaceae family was the second most abundant in AnI-C (25.6%), AI-HT (35.9%), AnI-HT (34.1%) and AI-AT (32.2%) reactors and was represented by OTU2 (Fig. 2). The second and third most abundant family in AI-C were Enterococaceae (27.1%) and Porphyromonadaceae (20.8%) and were mainly represented by OTU7 (18.0%) and OTU8 (20.8%), respectively (Fig. 2). These two OTUs were related to Enterococcus gallinarum (99% 16S rRNA sequence similarity with OTU7) and Dysgonomonas mossii (100% 16S rRNA sequence similarity with OTU8). In AnI-AT, the second and third most abundant family were Enterobacteriaceae (25.4%) and Prevotellaceae (24.9%) and were mainly represented by OTU4 (22.4%) and OTU10 (17.5%), respectively. These two OTUs were related to Klebsiella aerogenes (99% 16S rRNA sequence similarity with OTU4) and Prevotella dentalis (90% 16S rRNA sequence similarity with OTU10). In AnI-C the Enterobacteriaceae (16.7%) family was the third most abundant and was mainly represented by OTU16 (16.4%). The OTU16 had 100% of 16S rRNA sequence similarity with Raoultella ornithinolytica.

Illustratively Fig. 3 shows a principal component analysis (PCA) performed from final microbial community at family level and metabolic patterns to observe the relations between them according to each experiment. The PCA shows that the control experiences (AI-C and AnI-C) are negatively related, probably due to the great impact of the inoculum origin on both the final microbial communities and reactor behavior. Particularly, AI-C is related to butyrate production and with Enterococaceae and Porphyromonadaceae families. While, AnI-C is slightly related to acetate and ethanol production. The heat-treated reactors, independently of the inoculum (AI-HT and AnI-HT), were characterized by higher abundance of the Clostridiaceae and Prevotellaceae families along with the production of ethanol, acetate and butyrate, as is observed in Fig. 3. This is consistent with the literature, since some species of the Clostridiaceae family could present an acidogenic or solventogenic metabolism, associated to a higher H 2 production along with acetate-butyrate pathway and a lower H 2 production along with the production of alcohols such as ethanol, respectively [START_REF] Sarma | Microbial hydrogen production by bioconversion of crude glycerol: a review[END_REF]. Unlike heat pre-treatment, aerobic pre-treatment generated two slightly different microbial communities. Fig. 3 shows how AnI-AT is related to the Enterobacteriaceae family, while AI-AT is related to the succinate production.

In addition, Fig. 3 shows that microbial diversity is positively related to AnI-AT and negatively related to AI-HT and AnI-HT. The literature is not clear on how microbial diversity could affect H 2 production. Contradictorily, it has been reported that greater diversity may increase the possibilities of selecting H 2 -producing bacteria, but it may also increase competition among members of the microbial community, leading to a decrease in the H 2 production [35,61e63]. Our results show that the microbial community was considerably simplified, and that the Simpson diversity index decreased by 15.8e33.0% compared to the initial inocula. In particular, heat shock pre-treatment reduced microbial diversity by 32.7 ± 0.5%, while aeration decreased by 19.5 ± 1.0% (Table 2). However, greater microbial diversity could be linked to lower H 2 production efficiency.

Combined effect of inoculum source and pre-treatments on microbial community Fig. 1 shows a PCA performed from samples taken before inoculating the reactors and at the end of continuous operation. Three main groups are observed, in which the change of the microbial community from the original sludge, after pretreatment and after continuous operation is clearly evidenced. In the first group (Fig. 1, on the right) the original sludge (AI and AnI) is associated with the sludge after heat pre-treatment (AI-HTi and AnI-HTi). In turn, this group is associated with the most important families of their microbial community, i.e., Rikenellaceae, Spirochaetaceae and WCHB1-69. The second group (Fig. 1, top) includes sludge after aeration pre-treatment (AI-ATi and AnI-ATi) and are related to families that increased their relative abundance in at least one of these samples such as Sphingomonadaceae, Flavobacteriaceae and Pseudomonadaceae. While the third group (Fig. 1, left down) is composed of all the samples taken at the end of the continuous operation and are related to the families that dominated the final microbial communities in each case, i.e. Porphyromonadaceae, Enterococcaceae, Clostridiaceae, Prevotellaceae and Enterobacteriaceae. In all cases, there is more similarity between reactor communities inoculated with different sludge exposed to same pre-treatment, suggesting that the pretreatment has more impact than the inoculum source on the total community structure. Despite the pre-treatments performed and the inoculum origin, the selection pressure imposed by biokinetic control appears to be crucial in determining the dominant families of the H 2 -producing microbial community, particularly in the selection of Clostridiaceae family members. This is consistent with the literature, as members of this family are often selected during continuous H 2 production operated at low pH (values between 5.0 and 6.0) and short HRT (<12 h) [START_REF] Palomo-Briones | Darkfermentative biohydrogen pathways and microbial networks in continuous stirred tank reactors: novel insights on their control[END_REF][START_REF] Silva-Illanes | Impact of hydraulic retention time (HRT) and pH on dark fermentative hydrogen production from glycerol[END_REF][START_REF] Rafrafi | Sub-dominant bacteria as keystone species in microbial communities producing bio-hydrogen[END_REF][START_REF] Palomo-Briones | Hydrogen metabolic patterns driven by Clostridium-Streptococcus community shifts in a continuous stirred tank reactor[END_REF].

When considering the experiments that used untreated inoculum, it is observed that despite the impact of biokinetic control (as discussed above) on selection of Clostridiaceae family members, AI-C had a 72% higher H 2 yield than in AnI-C (Table 3). Among the dominant species of the AI-C microbial community is Dysgonomonas mossii (Fig. 2), a fermentative but not H 2 -producing bacteria [66e68]. AI-C reach the maximum H 2 yield of this study, suggesting a positive interaction of Dysgonomonas mossii with the microbial community and especially with the known H 2 -producing bacteria. Unlike AI-C, all dominant families in the AnI-C microbial community (i.e., Clostridiaceae, Prevotellaceae, and Enterobacteriaceae) have known H 2 -producing members, but the low H 2 yield obtained in this experiment suggests the predominance of negative interactions in the microbial community. Therefore, the inoculum source plays a key role in determining the final microbial community when no pre-treatment is performed.

Comparing the metabolic patterns when a heat-treated inoculum (AI-HT and AnI-HT) was used, and especially the H 2 yields, no statistically significant differences are observed, although the inocula come from different sources. Surprisingly, the final microbial community of both is very similar, with Clostridium and Prevotella as dominant genus. The relative abundance of Clostridium at the end of these experiments was more than 50%, which was expected since the heat treatment objective is to enrich the microbial community with sporeforming species such as Clostridium. Contrary to our results, Baghchehsaraee et al. ( 2008) obtained lower H 2 yield when using heat-treated aerobic sludge, attributed to a decrease in microbial diversity due to pre-treatment [START_REF] Baghchehsaraee | The effect of heat pretreatment temperature on fermentative hydrogen production using mixed cultures[END_REF]. However, they worked with glucose as substrate in batch mode operation and heat pre-treatment conditions were 65 , 80 or 95 for 30 min. Consequently, they are all important parameters affecting the microbial community composition.

Aeration as pre-treatment generated important differences in microbial communities and H 2 yields depending on the inoculum source (AI-AT and AnI-AT). The main difference was the relative abundance of Klebsiella aerogenes in AnI-AT, a known H 2 producing bacteria. However our results show that it is negatively related to H 2 production suggesting a negative interaction with other known H 2 producers in the community, which are in the ratio 1.1:1.6:1.0 for Prevotella:Clostridium:Klebsiella, respectively [START_REF] Cabrol | Microbial ecology of fermentative hydrogen producing bioprocesses: useful insights for driving the ecosystem function[END_REF]. In contrast to our results Silva-Illanes et al. (2017) evidenced the existence of positive interactions between all H 2 -producing bacteria present in the microbial community, which are in the ratio 1.2:5.4:1.0 for Prevotella:Clostridium:Klebsiella, respectively [START_REF] Silva-Illanes | Impact of hydraulic retention time (HRT) and pH on dark fermentative hydrogen production from glycerol[END_REF]. Consequently, the difference in the results is the relative abundance of H 2 producers and their ratio, while in our results the genera are in a ratio around 1.0, in Silva-Illanes et al. (2017) Clostridium is the most important being 5.4 times more abundant.

In conclusion it was shown that the inoculum source played a key role for H 2 production in continuous reactors. The inoculum source determines not only the metabolic patterns when using untreated sludge, but also affects the efficiency of the pre-treatments performed. A combined effect between pre-treatments and inoculum sources was evidenced by probably affecting the microbial interactions and final selection of the microbial community.

Conclusion

Inoculum source has a strong impact on the reactor behaviors when non-pretreated sludge is used, but also on pretreatment efficiency. Heat pre-treatment of anaerobic sludge increased H 2 yield, while aeration resulted in unstable H 2 production. Whereas when aerobic sludge is used no pretreatment is necessary, as there are no statistically significant differences in H 2 yields when comparing all experiments, including control. In addition, biokinetic control was key in the Clostridium sp. selection as dominant in the microbial community of all assays. While, lower or intermittent H 2 production were associated with higher relative abundance of Enterobacteriaceae family members. Our results allow a better understanding of H 2 production in continuous systems, providing key information for an efficient selection of operating for future industrial applications.

Fig. 1 eFig. 2 e

 12 Fig. 1 e Principal component analysis (PCA) based on initial and final microbial population distribution. PCA was performed from correlation matrix. Triangle and circle shapes represent the anaerobic (AnI) and aerobic (AI) sludge, respectively. Filled and empty symbols represent the initial and final samples, respectively. Purple, blue and yellow symbols represent the samples with heat shock pre-treatment (HT), aeration pre-treatment (AT) and control (C), respectively. The "i" at the end of the sample names refers to samples taken prior to reactor inoculation i.e. after pre-treatments. Dotted lines represent Euclidean distances between PCA axes and taxonomic families. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3 e

 3 Fig. 3 e Principal component analysis (PCA) based on metabolic patterns and final microbial population distribution. PCA was performed from variance-covariance matrix.Triangle and circle shapes represent the anaerobic (AnI) and aerobic (AI) inoculum, respectively. Purple, blue and yellow symbols represent the samples with heat treatment (HT), aeration (AT) and control (C), respectively. Plain lines and dotted lines represent Euclidean distances between PCA axes and taxonomic families and metabolic yields, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Table 1 e

 1 Summary of experimental design.

	Assay	Inoculum	Pretreatment	Name of assay

Table 3 e

 3 Performance indicators during continuous operation of H 2 producing reactors, including biomass yield, H 2 yield, and soluble metabolites production. Average values and standard deviations (±SD) were calculated from daily measurements during continuous operation.

	Parameter	Unit	AI-C	AnI-C	AI-HT	AnI-HT	AI-AT	AnI-AT
	Biomass yield	g VSS mol gly-consumed À1	5.8 (±2.1)	6.3 (±1.5)	7.0 (±3.1)	5.8 (±2.4)	5.3 (±2.0)	6.7 (±2.7)
	H 2 yield	mol H2 mol gly-consumed À1	0.50 (±0.19)	0.29 (±0.10)	0.47 (±0.17)	0.42 (±0.08)	0.55 (±0.08)	a
	Ethanol	%COD	6.5 (±2.6)	12.3 (±5.1)	12.4 (±5.4)	3.0 (±1.3)	27.3 (±10.1)	9.5 (±6.4)
	Acetate	%COD	3.7 (±1.9)	5.7 (±2.0)	4.8 (±1.5)	4.1 (±1.6)	3.4 (±1.2)	13.2 (±9.4)
	Propionate	%COD	1.4 (±0.7)	3.2 (±1.5)	2.7 (±1.0)	1.4 (±0.6)	1.6 (±0.4)	5.2 (±3.5)
	Butyrate	%COD	32.8 (±11.1)	25.0 (±8.7)	22.0 (±8.8)	30.7 (±13.8)	23.4 (±8.2)	39.7 (±21.5)
	Succinate	%COD	1.9 (±0.8)	2.5 (±1.2)	12.0 (±4.5)	16.1 (±5.6)	15.5 (±5.9)	1.8 (±3.9)
	Formate	%COD	1.1 (±0.5)	e	1.8 (±0.7)	e	1.6 (±0.4)	2.9 (±4.4)
	Glycerol removal efficiency	%	90 (±15)	79 (±19)	78 (±20)	80 (±31)	87 (±16)	74 (±22)

Metabolite distribution based on COD mass balance. %COD were calculated based on total glycerol consumed. a During AnI-AT the H 2 production was unstable.

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 5 ( 2 0 2 0 ) 1 5 9 7 e1 6 0 7

Acknowledgements

We thank Pontificia Universidad Cat olica de Valparaı ´so (PUCV) for providing postdoctoral funding for J.T-A. This study was funded by GRAIL 613667 (KBBE-7PM) and ECOS-CONICYT program project N C12E06. This work is dedicated to the memory of our beloved colleague, Prof. Gonzalo Ruiz-Filippi.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijhydene.2019.11.113. r e f e r e n c e s


Titre :

Image Registration for Biomedical Images on Irregular Grids

Auteurs :

Nhung Pham, David Helbert, Pascal Bourdon, Philippe Carré Résumé (100-200 mots) :

Microendoscope with spiral scanning produces images on irregular grids [5]. We propose a novel image registration strategy on irregular grids using Graph Wavelet (GW) constraint under the Log-Demons framework. The GW transformation is flexible to apply to different types of data regardless of the data point densities and how complex the data structure is. We also show how the Log-Demons framework can be adapted to optimize the objective function defined for images on irregular grids. Our results compare the performance of our method on irregular grid with the existing methods on a regular grid. To the best of our knowledge, this research is the first study on image registration on an irregular grid.

Mots-clefs (3 à 5 max.) :

Irregular grid, Graph Wavelets, microendoscopic images, Log-Demons

Contexte et état de l'art :

Our work focuses on registering two images acquired on irregular grids. Such an image has floating point coordinates and different point densities. The images are taken by a multiphoton microendoscopic whose optical fiber scans along a spiral path, starting from the center to the periphery of the area [5]. Such scanning produces a list of raw pixels on an irregular grid with floating coordinates. Motivated by the fact that the system requires a robust image mosaics, it is important to have a sufficient non-rigid registration between the overlapping regions of two frames to compensate for the non-rigid deformations. These overlapping regions are at different positions of the two images, thus, have different point densities. Apart from the difficulties of the image grid characteristic, such images come up with much noise, illumination changes and non-rigid deformations.

The Log-Demons registration method [1] is known as a popular method for medical image registration, which is simple to implement and fast to compute. However, the optimization with a Gradient based update is easy to fall into local minima and lacks geometric invariance. It was shown in [3] that the graph spectrum is geometric invariant can be successfully embedded in a direct feature matching method under the Log-Demons framework to capture large and complex deformations. However, the registration using graph spectrum is sensitive to noise.

Recent research of Hammond et al [2] proposed a new computation of Spectral Graph Wavelets defined in the graph spectral domain. It is a flexible model constructed on an irregular weighted graph, thus, allows us to utilize the benefits of the graph representation of image on an irregular grid. More importantly, the Spectral Graph Wavelets not only represent images at different frequencies but also are isometric invariant under deformations of shape. Our previous work [4] proves the advantages of registration on regular grid using Spectral Graph Wavelets (SGW) [2] over the benchmark method that uses the graph spectra, especially when noise exists. In this paper, we exploit the previous research on image registration on regular grid that uses SGW under the Log-Demons framework to formalize a novel image registration method for images on irregular grids.

Travail proposé (Décrire l'objectif du travail et indiquer clairement le problème/défi technique étudié) : The Laplacian matrix of a graph 𝐺 is given by 𝐿 = 𝐷 -𝐴, where 𝐷 is a diagonal degree matrix and each value 𝐷 𝑖𝑖 on the diagonal is the sum of the weights of all the edges incident to vertex ν i .

Graph Fourier Transform: Given a graph signal 𝑓 ∈ ℝ 𝑁 where 𝑁 is the number of vertices, the eigendecomposition of the Laplacian matrix is is written as v ℓ = 𝜆 ℓ v ℓ , where the set of eigenvalues and eigenvectors {𝜆 ℓ , v ℓ } ℓ=1,…,𝑁 form the eigensystem of the Laplacian matrix. The graph Fourier transform is given by:

𝑓 ̂(ℓ) = ∑ v ℓ * (𝑛)𝑓(𝑛), 𝑁 𝑛=1
Spectral Graph Wavelet Transform: Once the graph Fourier transform is defined, the Spectral Graph Wavelets are modulated by a generating kernel 𝑔. The Wavelet coefficients of graph signal 𝑓 at scale 𝑡 is given by:

𝑊 𝑓 (𝑡, 𝑛) = ∑ 𝑔(𝑡𝜆 ℓ ) 𝑁 ℓ=1 𝑓 ̂(ℓ)v ℓ (𝑛)
In order to encode the low frequency of signal 𝑓, a second class of waveforms ℎ is defined similarly to the low pass filter in classical wavelet transform, such that: ℎ(0) > 0 and lim 𝑛→∞ ℎ(𝑥) = 0. The scaling function coefficient 𝑆 𝑓 (𝑛) is given by:

𝑆 𝑓 (𝑛) = (ℎ(𝐿)𝑓)(𝑛) = ∑ ℎ(𝜆 ℓ ) 𝑁 ℓ=1 𝑓 ̂(ℓ)v ℓ (𝑛)
Registration Method: 𝐼 𝑆 is first interpolated to the grid of 𝐼 𝑇 with scatter interpolation. Then, compute the SGWs of 𝐼 𝑇 and 𝐼 𝑆 on an irregular grid at t scales, resulting 𝑡 + 1 column vectors for each image. We choose the first 𝑘 vectors at lower bands for registration, given by 𝑊 𝑇 ∈ ℝ 𝑀×𝑘 for the target image and 𝑊 𝑆 ∈ ℝ 𝑁×𝑘 for the source image. The optimization is an iterative approach under the Log-Demons framework [1].

Résultats : Conclusion et perspectives :

Our method shows that the Log-Demons framework can be adapted to the registration of images with floating point coordinates. The experimental results show that this application on the raw coordinates without any projection on Cartesian coordinate system performs better than the classical one on a regular grid. In all tested cases, the registration based on Graph Wavelets is more robust than the Graph Spectral based method. We believe that, our work will motivate other researches on image processing on irregular grids. Our future work will focus on studying the impact of graph construction and to create a complete panorama from the image sequence.

Références (5 max.):

[1] Vercauteren, T., Pennec, X., Perchant, A., Ayache, N. We benchmark our method against the two methods on regular grid (Spectral Demons, SGW Demons) and Spectral Demons on irregular grid. We experiment two random pairs of frames in the sequence. The target and source images are cropped from the chosen frames. The system allows us to know the global shift between every two frames. For visualization, the images are projected on 100x100 Cartesian grid, and so for the tests on regular grid. In Fig. 2a, each row represents the registered images of one pair of images with different methods. It can be clearly seen that the registration on a regular grid suffers from the smoothing effect. Fig. 2b shows the Signal to Noise Ratio (SNR) values w.r.t the number of iterations. In both cases, the registration on regular grid is inconsistent. On irregular grids, the SGW Demons improves approximately 0.6 dB in the first pair and 0.5 dB in the second pair. Fig. 3 allows us to see more clearly the result of our method applied on the first pair of images on irregular grids. In the representation of the difference, the target image is in the red channel, the source and registered images are in the green channel. The region covered by red line shows that the differences between the registered image and the target image have an improvement compared to the initial difference.

Figure 1 :

 1 Figure 1: Graph topology of two different images on irregular grid (Blue: first image, Red: second image) Problem: Find transformation 𝑠 from target image 𝐼 𝑇 and source image 𝐼 𝑆 such that 𝐼 𝑇 = 𝐼 𝑆∘𝑠 with 𝐼 𝑇 ∈ ℝ 𝑀 , 𝐼 𝑆 ∈ ℝ 𝑁 with floating point coordinates: 𝑋 𝑇 = (𝑉 𝑥𝑡 , 𝑉 𝑦𝑡 ), 𝑋 𝑆 = (𝑉 𝑥𝑠 , 𝑉 𝑦𝑠 ), resulting in different graph topologies (Fig. 1). Background: Graph definition: A graph 𝐺 = (𝒱, ℰ) is composed of a finite set of vertices 𝒱 = {ν 1 , ν 2 , … , ν N } and a set of edges ℰ that represents the connection between edges, e.g. (𝑖, 𝑗) ∈ ℰ if ν i is connected to ν j . In this research, each sample point is represented by a vertex and edges represent neighboring structure of points. Such graph can be represented on an adjacency matrix 𝐴 where 𝐴 𝑖𝑗 undirected edge weight between vertices ν i and ν j .The Laplacian matrix of a graph 𝐺 is given by 𝐿 = 𝐷 -𝐴, where 𝐷 is a diagonal degree matrix and each value 𝐷 𝑖𝑖 on the diagonal is the sum of the weights of all the edges incident to vertex ν i .Graph Fourier Transform: Given a graph signal 𝑓 ∈ ℝ 𝑁 where 𝑁 is the number of vertices, the eigendecomposition of the Laplacian matrix is is written as v ℓ = 𝜆 ℓ v ℓ , where the set of eigenvalues and eigenvectors {𝜆 ℓ , v ℓ } ℓ=1,…,𝑁 form the eigensystem of the Laplacian matrix. The graph Fourier transform is given by:

Figure 2 :Figure 3 :

 23 Figure 2: Comparison between the registered images and SNR values of our method and the existing methods for two pairs of images (𝛼 𝑔 = 1, 𝛼 𝑠 = 5, 𝛼 𝑖 = 20 for registration on irregular grid)

  The registration, thus, can be cast into a feature matching problem that matches 𝐓 = (𝛼 𝑖 𝐼 𝑇 , 𝛼 𝑠 𝑋 𝑇 , 𝛼 𝑔 𝑊 𝑇 ) and 𝐒 = (𝛼 𝑖 𝐼 𝑇 , 𝛼 𝑠 𝑋 𝑇 , 𝛼 𝑔 𝑊 𝑇 ), where 𝛼 𝑖 , 𝛼 𝑠 , 𝛼 𝑔 account for the intensity, spatial and geometric weights. The objective function is defined as: 𝐸(𝑇, 𝑆, 𝑠) = 𝛼 𝑖 ||𝐼 𝑇 -𝐼 𝑆∘𝑠 || 2 + 𝛼 𝑠 ||𝑋 𝑇 -𝑋 𝑆∘𝑠 || 2 + 𝛼 𝑔 ||𝑊 𝑇 -𝑊 𝑆∘𝑠 || 2

  Lombaert, Herve, et al. "Spectral Demons-image registration via global spectral correspondence." Computer Vision-ECCV 2012. Springer, Berlin, Heidelberg, 2012. 30-44. [4] Pham, Nhung, et al. "Spectral Graph Wavelet based Nonrigid Image Registration." 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. [5] Ducourthial, Guillaume, et al. "Development of a real-time flexible multiphoton microendoscope for labelfree imaging in a live animal." Scientific reports 5 (2015): 18303.

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