Trung Hieu Le

Xavier Pic

Jeremy Mateos

Marc Antonini

IMPLICIT NEURAL MULTIPLE DESCRIPTION FOR DNA-BASED DATA STORAGE

Keywords: DNA data storage, Multiple Description Coding (MDC), Implicit Neural Network (INR), Quaternary Shannon Fano Entropy Coder (SFC4)

DNA exhibits remarkable potential as a data storage solution due to its impressive storage density and long-term stability, stemming from its inherent biomolecular structure. However, developing this novel medium comes with its own set of challenges, particularly in addressing errors arising from storage and biological manipulations. These challenges are further conditioned by the structural constraints of DNA sequences and cost considerations. In response to these limitations, we have pioneered a novel compression scheme and a cutting-edge Multiple Description Coding (MDC) technique utilizing neural networks for DNA data storage. Our MDC method introduces an innovative approach to encoding data into DNA, specifically designed to withstand errors effectively. Notably, our new compression scheme overperforms classic image compression methods for DNA-data storage. Furthermore, our approach exhibits superiority over conventional MDC methods reliant on auto-encoders. Its distinctive strengths lie in its ability to bypass the need for extensive model training and its enhanced adaptability for fine-tuning redundancy levels. Experimental results demonstrate that our solution competes favorably with the latest DNA data storage methods in the field, offering superior compression rates and robust noise resilience.

INTRODUCTION

The memory of humanity hinges on our capacity to effectively handle ever-expanding volumes of data, spanning timeframes ranging from mere years to several centuries. As our current storage media struggle to keep pace, there is an urgent need to explore groundbreaking solutions that can be swiftly put into practical use. In the development of alternative data storage methods, synthetic molecules, particularly synthetic DNA, appear as one of the most promising options. Due to its density, durability, and its low energy consumption, synthetic DNA is an ideal storage support candidate for long-term data storage. The initial phase in the data encoding process involves constructing a sequence of nucleotides A, T, C, and G (referred to as nts). However, it is imperative that the DNA-encoded information stream follows specific biochemical constraints.

These constraints include avoiding homopolymers, maintaining a balanced GC content, and preventing repetitive patterns. Additionally, it is crucial to acknowledge that the biochemical procedures involved in this process can introduce errors that may compromise the integrity of the stored data. Operations such as synthesis, sequencing, storage, and DNA manipulation can introduce errors in the form of substitutions and indels (insertions or deletions of nucleotides). During the last decade, information therorists have developed different schemes for the encoding of digital data into DNA, with some of them targeting the storage of images [START_REF] Hossein Tabatabaei | Portable and error-free dna-based data storage[END_REF][START_REF] Dimopoulou | A jpeg-based image coding solution for data storage on dna[END_REF]. Some compression algorithm and coders have been developed specifically for this paradigm of data storage [START_REF] Church | Nextgeneration digital information storage in dna[END_REF][START_REF] Goldman | Towards practical, high-capacity, low-maintenance information storage in synthesized dna[END_REF][START_REF] Welzel | Dnaaeon provides flexible arithmetic coding for constraint adherence and error correction in dna storage[END_REF][START_REF] Dimopoulou | A biologically constrained encoding solution for long-term storage of images onto synthetic dna[END_REF]. This work introduces a Single Description Coder (SDC) and a Multiple Description Coder (MDC) designed for DNA data storage with the SDC method exhibiting superior compression performance compared to the existing state of the art. MDC for image encoding involves encoding multiple representations of an image. Historically, it was used for transmission, In this work, the concept of MDC was used to robustify the DNA storage biochemical processes that are prone to generating errors. if one is lost or corrupted during transmission, the remaining descriptions can still be used to reconstruct the original image with some quality degradation. Recent research [START_REF] Zhao | Multiple description convolutional neural networks for image compression[END_REF][START_REF] Zhao | LMDC: learning a multiple description codec for deep learning-based image compression[END_REF] show a potential use of neural networks to generate different descriptions, which involve Generative Networks and Compressive Autoencoders. However, the main drawback of this method is its long training process that has a high computational cost. Furthermore, the training process must be performed with very large datasets to converge towards an optimal model. This is even more challenging in the MDC context due to the redundancy adaptation mechanism, which requires retraining the model.

In recent works on image compression using neural networks, the so-called Implicit Neural Representation (INR), learns to represent an image implicitly through the weights of the INR model, a coordinate map, and possibly a latent space [START_REF] Vincent Sitzmann | Implicit neural representations with periodic activation functions[END_REF][START_REF] Strümpler | Implicit neural representations for image compression[END_REF]. More recently, the Coordinate-based Low Complexity Hierarchical Image Codec (COOL-CHIC) framework [START_REF] Ladune | Cool-chic: Coordinatebased low complexity hierarchical image codec[END_REF] has achieved superior performance compared to traditional image compression methods. The first MD scheme using INR (INR-MDSQC) has been proposed in [START_REF] Hieu | Inrmdsqc: Implicit neural representation multiple description scalar quantization for robust image coding[END_REF] with the following advantages: generalized model training is unnecessary, high performance and flexible redundancy tuning. However, INR-MDSQC's drawback is the number of descriptions, which is limited to two. Moreover, those descriptions are not balanced.

The goals of implementing Multiple Description Coding (MDC) in DNA data storage are twofold: minimizing the reading cost (the number of DNA strands, called oligos, to read), and enhancing noise robustness. This is particularly crucial due to the biochemical constraints inherent in the process, which can result in the absence of certain oligos. To our knowledge, this work constitutes the first MDC application for DNA data

SPATIAL FREQUENCY MDC

In this section, we introduce a SF-MDC approach that incorporating an INR. The SF-MDC architecture, as depicted in Fig. 1, comprises three main components:

• f θ : Synthesis Model with θ its parameters • f ψ : Auto-regressive Model with ψ its parameters • Block Splitter/Merger

Synthesis model

The quantization process is defined as follows:

ŝ = Q(s, ∆s) (1
) where s is the element to be quantized, and ∆s is its associated quantization step. The latent spaces corresponding to each description y j ∈ {y 1 ..y N } are hierarchically organized at different levels of resolution. Accordingly, we denote by y k|j the latent space corresponding to resolution level k for description j. In our solution, each description contains a mix of redundancy (low rate, low quality) and principal (high rate, high quality) blocks. At the decoder, when all the descriptions are received correctly, the decoder will merge all the principal blocks to form the central description. Otherwise, the redundancy blocks will be used to replace any corrupted principal blocks. Therefore at the encoding phase, to distribute equal amounts of redundancy across descriptions, block splitter divides each y k|j into M blocks, each of size 8x8. A block b of the latent y k|j is denoted as y b k|j , where b ∈ {0, 1, ..., M -1} is the block index. Each y b k|j is quantized with a unique quantization step ∆y b k|j . A principal block uses a finer step, and a redundancy block uses a coarser step. The central description ŷ0 is merged from the principal blocks as depicted in Fig. 2. Hence, each quantized block ŷb k|j is expressed as:

ŷb k|j = Q(y b k|j , ∆y b k|j ) (2
) Therefore, the quantized latent space ŷk|j is defined as:

ŷk|j = {ŷ b k|j ∈ Z 8×8 , b ∈ {0, 1, ..., M -1}} (3)
1 http://r0k.us/graphics/kodak/ From this, we deduce description j composed by the set of different quantized latent spaces ŷk|j :

ŷj = {ŷ k|j ∈ Z H k ×W k , k ∈ {0, 1, ..., L -1}} (4)
where

H k = H 2 k
, H and W denote respectively the height and with of the input picture, W k = W 2 k , and L denotes the hierarchical depth of ŷj . As discussed in [START_REF] Hieu | Inrmdsqc: Implicit neural representation multiple description scalar quantization for robust image coding[END_REF], ŷj is sequentially input into the synthesis model f θ with shared parameters, transforming set of latent spaces into a reconstructed image. In the synthesis model f θ , each ŷk|j is first upsampled using bi-cubic interpolation to match the target image shape before being fed into an MLP (Multilayer Perceptron) composed of 2 linear layers. The output of f θ is defined as: xj = f θ (ŷ j ) where j ∈ {0, .., N } (5) The distortion of each xj compared to the target image is denoted by D j and measured using Mean Squared Error (MSE). Given that the latent space is discrete and the quantization process is non-differentiable, uniform noise is introduced to enable differentiable operations, as described in [START_REF] Ballé | End-to-end optimization of nonlinear transform codes for perceptual quality[END_REF]. Thus, the latent space quantization is defined as:

ŷb k|j = y b k|j + u, u ∼ U[-0.5, 0.5] during training Q(y b k|j ) otherwise
where U is the uniform noise and j ∈ {1, ..., N } (6)

Autoregressive model

Assuming that the distribution of each coefficient in the latent space is conditioned by their neighbors, then according to [START_REF] Minnen | Joint autoregressive and hierarchical priors for learned image compression[END_REF] the probability of the coefficients can be determined by a factorized model:

p ψ (ŷ j ) = i,k p ψ (ŷ ik|j |c ik|j ) ( 7
)
where ŷik|j is the latent coefficient at the position i of level k of description j and c ik|j ∈ Z C is the set of the C decoded coefficients neighboring ŷik|j , representing decoding context. The auto-regressive model p ψ , estimated through a MLP, uses the Laplace distribution as described in [START_REF] Ladune | Cool-chic: Coordinatebased low complexity hierarchical image codec[END_REF] to approximate the real conditional probability of the latent space and by using the factorized model defined in [START_REF] Zhao | Multiple description convolutional neural networks for image compression[END_REF]. The rate for each description ŷj can be expressed as:

R(ŷ j ) = -log 2 (p ψj (ŷ j )) = -log 2 i,k p ψj (ŷ ik|j |c ik|j ) = - i,k log 2 p ψj (ŷ ik|j |c ik|j ) (8)

Multiple description optimization

The optimization process is divided into two distinct phases: training and post-training. The objective of the training phase is to update the model parameters θ and ψ, and to adapt the various latent spaces {y 1 , . . . , y N } to the dynamics of the target image. Its cost function is defined as:

J t = D 0 (ŷ 0 ) + α N j=1 D j (ŷ j ) + N j=1 λ j R(ŷ j ) (9)
where α ∈ [0, 1] is the redundancy factor, R(ŷ j ) denotes the rate as defined in equation ( 8

J p =D 0 (ŷ 0 , θ, ψ) + α N j=1 D j (ŷ j , θ, ψ) + N j=1 λ j (R(ŷ j , θ, ψ) + R( θ) + R( ψ)) (10)
Where, R( θ) and R( ψ) represent the estimated rate utilizing a Laplace model.

Description coder: Range Transcoder

In the binary case, the Range coder [START_REF] Bamler | Understanding entropy coding with asymmetric numeral systems (ans): a statistician's perspective[END_REF] has been used to entropy code the latent space in different MDC schemes. Since the Range coder offers high performance at a very low entropy, we decided to adapt it to DNA by designing a transcoder that encodes its output into DNA. The principle of context latent coding is depicted in Fig. 3, then the encoded values from Range coder are then fed to the C 3 coder described in [START_REF] Pic | Mq-coder inspired arithmetic coder for synthetic dna data storage[END_REF]. In

. ARM and Synthesis Models coder: SFC4

In [START_REF] Pic | A constrained shannonfano entropy coder for image storage in synthetic dna[END_REF], we introduced a novel constrained quaternary entropy coder SFC4 adapted to the biochemical constraints of DNA data storage, with increased performance over the state of the art Huffman/Goldman algorithm [START_REF] Goldman | Towards practical, high-capacity, low-maintenance information storage in synthesized dna[END_REF]. In [START_REF] Hieu | Inrmdsqc: Implicit neural representation multiple description scalar quantization for robust image coding[END_REF], the MLP can be modeled by a Laplace distribution, so the code-book is initialized with a frequency table following this Laplace model. After initialization, the SFC4 encoder will be used to encode all the parameters of the ARM and synthesis models, since they are necessary for decoding.

OLIGO STRUCTURE

DNA data storage requires the use of short oligos, of length generally lying between 100 and 300 nts. In this work, we use oligos of length 200 nts. The decodability is ensured only if we manage to decode at least one of the descriptions, the auto regressive model, and the synthesis model. In our design, we separated the different parts of the encoded data into separate oligos. Some oligos will encode the ARM model, some the synthesis, and other oligos will encode separate latent spaces, as presented in Fig. 4.

EXPERIMENTS

In the following subsections, we are going to introduce comparative results from different DNA coding methods. The images used to conduct the test are extracted from the previously mentioned kodak dataset. The number of hierarchy levels used is six (L = 6).

Performance study

The new SDC (equivalent to a MDC with N = 1) shows better performance over the state of the art image coding methods adapted to DNA as shown in Fig. 5(A). With this new method, we were able to show gains between 0.5 and 3 dB in terms of quality of reconstruction in comparison to the best previous method (JPEG DNA SFC4 Transcoder). The results have been computed and averaged on the kodak dataset.

To ensure the validity of SF-MDC, its performance at central reconstruction should neither surpass the upper limit of the SDC nor fall below the SDC at an N × Rate. As shown in Fig. 5 (B), with α = 0.1, the solution approaches the upper bound limit of the single SDC as the rate increases, and never goes under the lower bound limit for different N . Besides, we observed that the compression rate increases with the number of descriptions used. On the other hand, increasing the number of description makes the coder more robust to noise.

Noise robustness

In this section, we simulate the loss for the MDC case N = 2. As each latent space is entropy coded and independently decodable. Therefore, to analyze a typical case scenario, we drop three out of six latent spaces from each description, alternating between different levels of descriptions (Description 1: 77% oligo loss, Description 2: 23% oligo loss, and Central Description: 50% oligo loss). The results have been computed on the image kodim01 of the kodak dataset previously mentioned. As observed in Fig. 6, the MDC demonstrates a high resilience capacity, maintaining a loss of only 5dB when losing a big part of the information contained in the different latent spaces.

6. CONCLUSION This work introduces an innovative DNA-based image codec that achieves substantial improvements in reconstruction quality when compared to existing DNA-based image codecs. On average, these improvements amount to 3 dB, with peak gains of up to 5 dB. These notable enhancements result from the utilization of the ARM, synthesis networks, and the DNAadapted Range coder, which deliver exceptional performance even at low entropy levels. Furthermore, we present a Multiple Description Coder (MDC) capable of generating a variable number of descriptions. This MDC enhances the resilience of oligos to the noise inherent in DNA data storage channels. We also conducted experiments that involved introducing noise into the storage channel. The result shows that we only lost 5dB in the worst scenario.

In future works, we aim at building a noise model for the DNA data storage channel that could further improve the noise robustness of the MDC.

Fig. 1 :

 1 Fig. 1: SF-MDC: During the training process, N latent sets are initially divided into blocks of size 8x8. Each block is then quantized independently with added uniform noise. These quantized latent blocks are then fed into the Block Merger module. In this module, each block is categorized as either redundancy or principal. Principal blocks are merged to form the central description, as illustrated in Fig. 2. Both these side descriptions and the central description are then input into the synthesis module, which generates the corresponding reconstruction and computes the related distortion. The latent space is updated using the back-propagation process, which is based on the distortion measured in MSE. Simultaneously, the Auto-regressive model is refined to better estimate the distribution of the quantized latent space. storage. More precisely, we propose a Spatial Frequency Multiple Description based on INR (SF-MDC) generalized to N descriptions, and evaluate its performance on the Kodak Lossless True Color Image Suite dataset. 1 .

Fig. 2 :

 2 Fig. 2: Block Merger Module: In this example, the number of descriptions is N = 4. The Principal and Redundancy blocks are assigned using the principle of round-robin item attribution. The central description is derived from the principal blocks of the 4 descriptions. Each description is then sequentially fed into the Synthesis model.

  ), D j is the side distortion, and D 0 is the central reconstruction distortion. The differences in distortion, represented by D 1 , . . . , D N , between the side reconstructions x1 , . . . , xN and the central reconstruction distortion D 0 , are dependent on the redundancy factor α. The configuration of cost function (9) pushes the Synthesis model to partition the image information into N distinct descriptions and converges towards maintaining the lowest D 0 possible while accommodating different rates. After training the network, the model parameters ψ, θ are represented as 32-bit values, but such precision is not necessary for transmission. Thus, in the post-training phase the model parameters θ and ψ are quantized according to equation (1), transforming them into θ and ψ, respectively. The quantization steps for θ and ψ are optimized as outlined in [12] by minimizing the post-training cost function:

3 .Fig. 3 :

 33 Fig. 3: Context Entropy Coding with C3 DNA coder:In this example, the model uses C=12 coefficients, c ik|j , to yield µ ik|j and σ ik|j , modeling a Laplacian distribution. The symbol probability is calculated, and an entropy coder encode the latent coefficient ŷik|j into a binary stream. The binary stream is then converted to quaternary stream by using the C3 DNA coder.

  this paper, we introduced an arithmetic coder inspired by the JPEG 2000 MQ coder. This coder is based on a fixed-length code C 3 composed of 48 elements. Further inspired by the Run-length Limited (RLL) binary codes, it has been designed to prevent the occurrence of homopolymers, which are repetitions of the same nucleotide too many times consecutively. The arithmetic coder output will be represented in base 48. Its base 48 development will be encoded in DNA with the C 3 code. C 3 = {AAT, AAC, AAG, AT A, AT C, AT G, ACA, ..., GCT, GCG, GGA, GGT, GGC} |C 3 | = 48 3.2

Fig. 4 :

 4 Fig. 4: Design of the differents oligos format. General format: The format remains consistent across all oligos, with the only variation occurring in the payload. "S" is the orientation nt, ID is the encoded file's label, P is a set of 4 parity nucleotides. GIO: General informations for the encoded image such as the image size, the number of descriptions and the coding dynamics. ARMO: Contains the weight and the bias of the ARM model. SynthO: Contains the weight and the bias of the Synthesis model. DO: Contains the coefficients of the latent spaces.

Acknowledgement

We would like to extend our gratitude to Dr. Eva Gil San Antonio for her innovative ideas, suggestions that improved the quality of the paper.


Erwan Bourdonnais

Cédric Le Bris

Thomas Brauge
email: thomas.brauge@anses.fr

Graziella Midelet

Tracking antimicrobial resistance indicator genes in wild flatfish from the English Channel and the North Sea area: a One Health concern

Keywords: Antimicrobial resistance, indicator genes, flatfish, English Channel, North Sea, anthropic pressure Highlights

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

Introduction

Antimicrobial resistance (AMR) is a global issue that clearly symbolizes the One Health concept, affecting humans, animals and the environment. Related to bacterial AMR are, more specifically, antimicrobial resistance genes (ARGs), which can be carried by both pathogenic and non-pathogenic bacteria, associated with bacteriophage DNA or in the form of free DNA. This constitutes an indirect risk for human health, as these resistances will constitute a genetic pool from which bacteria will be able to pick up these resistances and potentially transfer them to pathogenic bacteria. The transfer of AMR to human commensal bacteria can increase the reservoir of resistance constituted by the microbiota. These resistances can then spread to the environment. Indeed, AMR has become a major cause of death worldwide leading to 1.27 million deaths in 2019 [START_REF] Murray | Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis[END_REF]. According to the European Centre for Disease Prevention and Control (ECDC) and the World Health Organization (WHO), more than 670,000 infections are attributable to bacteria resistant to antimicrobials and around 33,000 people die from direct consequences each year in Europe [START_REF] Who | Antimicrobial resistance surveillance in Europe 2022-2020 data[END_REF]. If no action is taken to combat AMR, this number could rise over the next few years. In parallel, aquatic products (fisheries and aquaculture) are increasingly consumed worldwide, bringing health benefits to consumers. Consumption of aquatic products was 20.5 kg/capita worldwide in 2018, and 24.0 kg/capita in the European Union (EU) in 2019 (FAO, 2020[START_REF] Eumofa | European Market Observatory for Fisheries and Aquaculture Products -The UE fish market[END_REF]. But this consumption can also be a source of collective foodborne. According to the Centers for Disease Control and Prevention (CDC), nearly 48 million foodborne illnesses occur worldwide each year, resulting in 128,000 hospitalizations and 3,000 deaths. The presence of bacterial AMR and ARGs associated with aquatic products represents two main risks to consumer health. In the event of foodborne infection, treatment with antibiotics may prove less effective, or even lead to therapeutic impasse. What's more, these antimicrobial resistances can be transferred to the consumer's resident or transient flora, forming a gene pool that can spread in the environment. Contamination of aquatic products by bacterial AMR and ARGs can occur during handling or processing, but also directly in the marine environment. ARGs associated with different marine species can accumulate through surface contact with seawater or sediments, respiration or feeding via food webs. It has been suggested that the transmission of ARGs from animals to humans takes place via the food chain. Indeed, marine animals can be naturally colonized by bacteria in their skin, gastrointestinal tract, gills, exoskeleton and even shell in the case of bivalve molluscs. This colonization will depend on several factors, such as the position of the animals in the water column. The bacterial flora of benthic species such as flatfish and molluscs mainly reflects sediment-associated bacteria, while that of pelagic species reflects free bacteria in the water column. The concept of having AMR indicator genes available for monitoring and surveillance of AMR in the environment is crucial, given that only 1% of the bacterial flora in the environment is cultivable [START_REF] Bodor | Challenges of unculturable bacteria: environmental perspectives[END_REF]. In fact, ARGs associated with marine food web and in particular wild marine species are poorly described in the litterature. [START_REF] Muziasari | The resistome of farmed fish feces contributes to the enrichment of antibiotic resistance genes in sediments below Baltic Sea fish farms[END_REF] described the sul1, tet32, tetM, tetO, tetW, aadA1 and aadA2 genes in the fish gut samples of rainbow trout (Oncorhynchus mykiss) and lavaret (Coregonus lavaretus) reared in open cage systems in the northern Baltic Sea and the presence of emrB gene was highest in skin and gill samples, an efflux pump and implicated in multiresistance to antibiotics. The marine environment is particularly pointed as the final receiving compartment supplied by anthropogenic inputs from land and sea activities such as river and marine traffic effluents. In particular, the English Channel and the North Sea, two seas located in the Northwest of Europe, are subject to effluents from the many countries bordering them such as France, Belgium, the United Kingdom, the Netherlands, Germany, Norway and Denmark. These areas are also characterized by significant marine traffic due to fishing activities, shipping and the presence of several offshore platforms, which can also cause direct contamination by the various effluents discharged into the sea. These anthropogenic activities may lead to the contamination of the marine environment by AMR thus representing a risk to human health through the consumption of seafood products. However, unlike the role of farm animals and humans, the role of the marine environment and marine wild animals in the spread of AMR is understudied. Due to the diversity of ARGs in different environmental compartments such as soil and water, monitoring individual genes is time-consuming and costly. The multiplicity of methods for detecting and quantifying ARGs, coupled with different sampling and sample processing techniques, makes it difficult to compare results obtained in different studies. Harmonization of ARG analysis methods and the development of indicators of antimicrobial resistance contamination are therefore necessary if results are to be complementary. The need for such genetic indicators for monitoring and surveillance of antimicrobial resistance is a well-known issue. By definition, an indicator gene should be relatively simple to monitor, provide information on global resistance dynamics, indicate anthropogenic pressure and can be coupled with the search for ARGs to study transfer dynamics (ANSES 2020). Tracking indicator genes rather than indicator bacteria helps to compensate for the rapid loss of cultivability of bacteria in the environment, and to monitor the largescale spatio-temporal fate of antimicrobial resistance. Among clinically relevant ARGs often associated with mobile genetic elements, the tet(A), blaTEM, sul1 and intI1 genes have been proposed as potential indicators for environmental AMR contamination [START_REF] Berendonk | Tackling antibiotic resistance: the environmental framework[END_REF] and have already been identified in coastal and offshore seawaters [START_REF] Zhang | Hospital Wastewater as a Reservoir for Antibiotic Resistance Genes: A Meta-Analysis[END_REF]Bourdonnais et al. 2022a;[START_REF] Han | Occurrence and driving mechanism of antibiotic resistance genes in marine recreational water around Qinhuangdao, China[END_REF], but also associated with various biotic surfaces such as fish in the Mediterranean Sea [START_REF] Brahmi | High Prevalence of Extended-Spectrum β-Lactamase-Producing Enterobacteriaceae in Wild Fish from the Mediterranean Sea in Algeria[END_REF], bivalve mollusks collected near the Norwegian coast [START_REF] Grevskott | Marine Bivalve Mollusks As Possible Indicators of Multidrug-Resistant Escherichia Coli and Other Species of the Enterobacteriaceae Family[END_REF]) and even plankton collected from a river in China [START_REF] Xue | Characteristics of Phytoplankton-Zooplankton Communities and the Roles in the Transmission of Antibiotic Resistance Genes under the Pressure of River Contamination[END_REF]. Given this prevalence in the marine environment and in various marine organisms, these four genes could be suitable indicators for assessing AMR contamination of the marine environment. In the present study, we assessed the prevalence and frequency of the tet(A), blaTEM, sul1 and intI1 AMR indicator genes associated with flatfish samples (skin, gills, guts) in the English Channel and the North Sea to determine whether these marine species are vectors for the dissemination of AMR in the marine environment, and to identify the potential sources of contamination in this environment. This work has been positioned within of our entire research project on the AMR in the marine environment. We determined the occurrence of the four genes tet(A), blaTEM, sul1 and intI1, proposed as AMR indicator genes, in a benthic food web in the English Channel and North Sea. The sampling plan was carried out considering the position of marine species in the water column and in relation to their consumption of lower trophic level marine species. Due to the complexity of food webs, we decided to simplify the membership of the marine species analyzed in this study to a defined trophic level based on the study by [START_REF] Giraldo | Depth gradient in the resource use of a fish community from a semi-enclosed sea[END_REF] carried out in the English Channel. The results of analyses of near-surface samples of open sea water were presented in the work of Bourdonnais et al. (2022a) and constituted level 0 of the food web. Phytoplankton belonged to level 1 and consisted of plant organisms and particulate organic matter. Zooplankton, mainly copepods, represented level 2 of the food web (Bourdonnais et al. 2022b). Level 3 consisted of Aequipecten opercularis (white scallop) (Bourdonnais et al. 2022b) and, two species of benthic flatfish: Limanda limanda (dab) and Pleuronectes platessa (plaice). Both flounder and plaice feed on unicellular organisms and zooplankton such as copepods in the larval and juvenile stages, and on crustaceans and bivalve molluscs as adults. Given the diversity in size and stage of development of the species caught, we have chosen to consider them as belonging to trophic level 3. Plaice and flounders were not differentiated in this study due to their morphological, behavioral and dietary similarities, and also to have a more significant number of samples. We have therefore designated them as "flatfish".

Material and methods

Sampling and animal ethics

The flatfish samples (n = 350) were collected at 35 sampling stations (10 samples per station) in the English Channel and the North Sea during the International Bottom Trawl Survey (IBTS) oceanographic campaign in January 2020. Plaice (Pleuronectes platessa) and common dab (Limanda limanda) individuals were sampled with a 36/47 Grande Ouverture Verticale (GOV) bottom trawl towed during daylight for 30 minutes and at a constant speed of 4 knots according to the standard IBTS protocol (ICES 2015). Following trawling, the samples were identified, placed in airtight bags and stored in a cold room at -20 °C on board the ship, before their transfer to the laboratory at -20 °C. Protocols of all surveys are currently being evaluated by the French research institute for exploitation of the sea (Ifremer) and validated by the ICES IBTS International Group (ICES 2015). Moreover, survey's PIs received training about animal well-being and ethics.

Sample preparation

In the laboratory, flatfish individuals were thawed at room temperature. For this study, we focused on the skin, gills and guts of the fish, which host an important and diversified microbiota due to significant mucus secretion [START_REF] Merrifield | The fish microbiome and its interactions with mucosal tissues[END_REF]. A dry sponge (3M, Saint Paul, USA) was soaked with 10 mL of sterile physiological water and then swabbed on the blind and ocular sides of the fish to collect the mucus present on the fish skin. Solutions were recovered from 10 individuals from the same sampling stations, pooled together and diluted by half with sterile physiological water. Similarly, the gills and guts samples collected from the same 10 individuals were pooled together, diluted by half with sterile physiological water and homogenized for 1 min with a Stomacher grinder (Biomérieux, Marcy-l'Etoile, France). All these samples were supplemented with 20 % of glycerol and stored at -20 °C until total DNA extraction.

DNA extraction

Total DNA was extracted from 1 mL of each sample preparation with the DNeasy® PowerBiofilm® kit (Qiagen, Hilden, Germany) following the manufacturer's instructions with minor modifications. Briefly, the samples were centrifuged for 5 min at 10,000 × g and the pellets were suspended in 400 µL of MBL lysis buffer and 100 µL of FB buffer. The solutions were transferred to Beadtubes and then we followed the manufacturer's protocol until the DNA elution step. For this step, DNA was eluted by adding 50 µL of EB buffer on the membrane twice. The DNA concentration was analyzed with a DS-11 spectrophotometer (Denovix, Wilmington, USA) and diluted 10-fold with nuclease-free water before performing gene quantification by quantitative PCR (qPCR).

Quantification of the AMR indicator genes by qPCR

In this study, the tet(A) (tetracycline resistance), blaTEM (β-lactam resistance), sul1 (sulfonamide resistance) and intI1 (class 1 integron-integrase) AMR indicator genes were quantified by qPCR. Supplementary Table 1 contains the primers and the amplification conditions used. The qPCR reactions were performed as previously described in our previous study [START_REF] Bourdonnais | Occurrence of Indicator Genes of Antimicrobial Resistance Contamination in the English Channel and North Sea Sectors and Interactions With Environmental Variables[END_REF]).

Data analysis

We validated the qPCR results according to the NF T90-471:2015-06 standard (amplification efficiency between 75 and 125 %, coefficient of determination R² > 0.99 and no quantification of the negative control). The number of gene copies was calculated by Cq values and standard curves. The total indicator genes frequency was calculated by normalizing the sum of the number of gene copies with the amount of extracted DNA, expressed in log copies/ng of DNA [START_REF] Paul | Dissemination of Antibiotic Resistance Genes Associated with the Sporobiota in Sediments Impacted by Wastewater[END_REF]. The map representing the total indicator genes frequencies in the English Channel and the North Sea was designed in part with RStudio Software version 1.4.1717 (RStudio, Inc., Boston, United States) using the ggmap package.

Results and discussion

Occurrence of the AMR indicator genes in the flatfish

The four tet(A), blaTEM, sul1 and intI1 indicator genes were detected in each sample type (skin, gills and guts). The overall prevalence of the indicator genes, defined as the number of pooled fish samples for which at least one gene was quantified, was 71.4 %. The sul1 and intI1 genes had both a higher prevalence in skin samples (31.4 and 45.7 %, respectively) compared to gills (17.1 and 5.7 %) and guts (8.6 and 5.7 %) samples (Fig. 1A). We observed less than 17.1 % prevalence of the tet(A) and blaTEM genes in all fish samples. Among all sample types analyzed, the prevalence and frequency for all four AMR indicator genes were lowest in the fish gut samples (Fig. 1A and1B). The frequency of the indicator genes in the flatfish skin, gill and gut samples ranged from 1.8 to 5.5 log copies/ng of DNA (Fig. 1B). In the skin and gill samples, the sul1 and intI1 genes were in the same order of frequencies and mainly ranged from 2.0 to 3.5 log copies/ng of DNA and were above 3 log copies/ng of DNA for the tet(A) and blaTEM genes. To our knowledge, these four AMR indicator genes have not been targeted in the marine environment. However, other genes encoding the same AMR have been identified in aquatic organisms. Some research had revealed the occurrence of the blaDHA, tetM, mphA and vgaB genes in the skin and fillets of farmed rainbow trout (Oncorhynchus mykiss) individuals and were quantified up to 2 × 10 -1 gene copies/16S rDNA copy [START_REF] Helsens | Antibiotic Resistance Genes and Bacterial Communities of Farmed Rainbow Trout Fillets (Oncorhynchus Mykiss)[END_REF]). Indeed, fish skin is characterized by mucus secretion that constitutes an immune barrier colonized by different commensal or opportunistic bacterial species belonging mainly to the Proteobacteria, Firmicutes and Acidobacteria phyla which may carry ARGs [START_REF] Minniti | The Skin-Mucus Microbial Community of Farmed Atlantic Salmon (Salmo Salar)[END_REF]. The intestinal cells of fish also secrete mucus to protect them from aggression and fish gut is thus defined by a high bacterial diversity that may be associated with ARGs. In fact, high abundances of aminoglycoside, βlactam and tetracycline resistance genes have been observed in the intestine of wild soles (Cynoglossus semilaevis) from Bohai Bay in northeast China [START_REF] Jia | Distribution and Transfer of Antibiotic Resistance Genes in Coastal Aquatic Ecosystems of Bohai Bay[END_REF]. Similarly to our results, ARGs such as sul1 and blaTEM were quantified from intestinal mucus samples of carp (Cyprinus carpio), barbel (Luciobarbus graellsii) and trout (Salmo trutta) in Spain [START_REF] Marti | Abundance of Antibiotic Resistance Genes and Bacterial Community Composition in Wild Freshwater Fish Species[END_REF]). In addition, the fish gills are a key organ for respiration and exchange with the environment and are subject to the accumulation of environmental contaminants such as ARGs through the filtration of large volumes of water. In the Mediterranean Sea, several strains of Enterobacteriaceae have been isolated from gills and intestines of sardines (Sardina pilchardus), salema (Sardina pilchardus) and horse mackerel (Trachurus trachurus) with a 23 % prevalence of the blaTEM gene and 17 % of the sul1 gene among the 64 isolates [START_REF] Brahmi | High Prevalence of Extended-Spectrum β-Lactamase-Producing Enterobacteriaceae in Wild Fish from the Mediterranean Sea in Algeria[END_REF]) which was consistent with our results on flatfish samples. In our entire research project on the AMR in the marine environment, the analysis of enriched and non-enriched seawater (Bourdonnais et al. 2022a) and benthic food web samples (phytoplankton and zooplankton communities (Bourdonnais et al. 2022b), Aequipecten opercularis (scallop) (Bourdonnais et al. 2022b),and fatfish) revealed a total prevalence of 81.4% of the four antimicrobial resistance indicator genes in the English Channel and North Sea, with a prevalence of 39.5% for the tet(A) gene, 36.7% for the blaTEM gene, 57.1% for the sul1 gene and 61.0% for the intI1 gene. The sul1/intI1 genes were the most quantified indicators in seawater, phytoplankton, zooplankton, bivalve mollusc and flatfish samples. The tet(A)/blaTEM genes were poorly quantified in the samples, but had a high prevalence in sample enrichments, notably gills and flatfish viscera. Seabed fauna such as flatfish is particularly exposed to skin AMR contamination because of direct contact with sediments that constitute a major reservoir of ARGs [START_REF] Chen | Metagenomic Profiles of Antibiotic Resistance Genes (ARGs) between Human Impacted Estuary and Deep Ocean Sediments[END_REF]. ARGs can also accumulate in the gills as fish filter large volumes of seawater containing ARGs as we have shown in a previous study (Bourdonnais et al. 2022a), and also into their gut by consuming contaminated lower trophic level marine species such as plankton and bivalve mollusks (Bourdonnais et al. 2022b), also potential carriers of AMR [START_REF] Xue | Characteristics of Phytoplankton-Zooplankton Communities and the Roles in the Transmission of Antibiotic Resistance Genes under the Pressure of River Contamination[END_REF][START_REF] Silva | Antimicrobial resistance and potential virulence of Vibrio parahaemolyticus isolated from water and bivalve mollusks from Bahia, Brazil[END_REF]. Due to the migratory flows of flatfish and their movement favored by marine currents, these marine species can contribute to the dissemination of AMR in the marine environment, even over long distances. As they live on the seabed, flatfish contribute to the spread of AMR by releasing excrement that may contain bacteria and ARGs, enriching marine sediments with AMR. As a result of their resuspension by seabed movements, there is a risk of contamination of other living benthic or even pelagic organisms. The presence of AMR associated with wild fish can affect marine biodiversity, notably by promoting the proliferation of resistant bacteria to the detriment of other non-resistant organisms, representing a risk to environmental health. This also represents a human health risk if these fish are consumed raw or undercooked, with the risk of transmission of pathogenic antimicrobial-resistant bacteria to humans via the food chain. Indeed, bacteria or ARGs occurring on fish skin, in their gills or intestines can interact with fish flesh during handling, potentially posing a human health problem through consumption [START_REF] Ziarati | Zoonotic Diseases of Fish and Their Prevention and Control[END_REF]. The presence of ARGs in non-pathogenic bacteria also poses a risk, as these genes can be transferred to human commensal flora. This can increase the reservoir of resistance constituted by the microbiota, which will then diffuse into the marine environment, the final receptor of effluents contaminated by man, thus forming a cycle of AMR between the environment, humans and animals, as referred to the One Health concept.

Identification of anthropogenic pressures in the English Channel and the North Sea

We postulated that the frequency of the AMR indicator genes, observed in fish samples, could reflect the presence of anthropogenic inputs, i.e. sources of contamination of the marine environment. The total frequency of the indicator genes, corresponding to the number of copies of the four tet(A), blaTEM, sul1 and intI1 genes per ng of DNA, was plotted on a map of the study area (the English Channel and the North Sea) for each flatfish sample type to better identify the potential link with anthropogenic sources of contamination (Fig. 2). A more significant contamination of fish with AMR, as reflected by the quantification of the total indicator genes in all three sample types (skin, gills, guts), was observed in the English Channel and the North Sea near European ports, namely Le Havre (France), Dunkirk (France) and Rotterdam (Netherlands). In addition to these port activities, two of the three areas are subject to the effluents from large rivers such as the Seine in Le Havre port and the Rhine in Rotterdam port. The contamination of these rivers by AMR has been reported in previous studies such as the presence of multi-resistant E. coli strains associated with a high prevalence of the intI1 gene in the Seine and high abundances of the intI1, sul1, ermB and tetM genes in the Rhine [START_REF] Laroche | Occurrence of Antibiotic Resistance and Class 1, 2 and 3 Integrons in Escherichia Coli Isolated from a Densely Populated Estuary (Seine, France)[END_REF][START_REF] Paulus | International Tempo-Spatial Study of Antibiotic Resistance Genes across the Rhine River Using Newly Developed Multiplex qPCR Assays[END_REF]. We also observed high contamination of flatfish near the East Coast of England, an area characterized by a cluster of wind farms. Indeed, offshore wind farms can act as artificial reefs attracting marine species such as fish and crustaceans [START_REF] Degraer | Offshore Wind Farm Artificial Reefs Affect Ecosystem Structure and Functioning: A Synthesis[END_REF]). These species attract higher trophic level marine species that consume them, such as mammals and birds, which may also be a vector of contamination by AMR in the marine environment. A study conducted in the North Sea highlighted the attraction of some bird species such as gulls to these wind platforms [START_REF] Vanermen | Seabird avoidance and attraction at an offshore wind farm in the Belgian part of the North Sea[END_REF] while the feces of marine birds were found to be an important vector of contaminations with ARGs such as bla, sul and tet genes [START_REF] Poeta | Seagulls of the Berlengas Natural Reserve of Portugal as Carriers of Fecal Escherichia Coli Harboring CTX-M and TEM Extended-Spectrum Beta-Lactamases[END_REF]. Finally, we noticed a scattered contamination of flatfish in the North Sea, near the coasts, but also in the open sea. This observation may reflect an important dissemination of AMR in marine environment favored by the migration of flatfish over long distances and facilitated by deep ocean currents.

There are two possible pathways of wild flatfish contamination with ARGs. The first is characterized by direct contact with ARGs or ARG-carrying bacteria in free form in the water column and originating from effluents from land-based sources. The second is based on a transfer of ARGs from the marine sediments, which are known to be a reservoir of AMR, resulting from the sedimentation of the ARGs following the discharges into the marine environment [START_REF] Chen | Metagenomic Profiles of Antibiotic Resistance Genes (ARGs) between Human Impacted Estuary and Deep Ocean Sediments[END_REF]. ARGs could therefore be associated with the skin of flatfish through close contact with contaminated sediment or seawater, but also the gills as fish filter large volumes of seawater through brachial respiration, seawater being also a reservoir of ARGs. Indeed, we showed in a previous study that surface waters of the English Channel and the North Sea were an important carrier of the sul1 and intI1 AMR indicator genes, detected in approximately half the seawater samples collected, with abundances of up to 3.6 gene copies/ml seawater (Bourdonnais et al. 2022a). However, we observed a higher level of AMR contamination in flatfish collected near the ports while seawater contamination was higher off the West Coast of the Netherlands. These results indicate that ARGs disseminate in seawater at the surface but also in the seabed associated with flatfish. ARGs released into the marine environment can also accumulate in lower trophic level marine species such as plankton that flatfish feed on and whose role in AMR transport is still understudied compared to freshwater environments [START_REF] Xue | Characteristics of Phytoplankton-Zooplankton Communities and the Roles in the Transmission of Antibiotic Resistance Genes under the Pressure of River Contamination[END_REF]. This accumulation of ARGs in the digestive system of wild fish thus represents a risk of dissemination in the environment with release via feces and transmission between individuals through close contact. Furthermore, all these results lead us to believe that the sul1 and intI1 genes would be suitable indicators for assessing AMR contamination of the marine ecosystem, their quantification allowing us to identify sources of contamination while assessing the potential for AMR transfer via integrons. Indeed, the class 1 integrase is associated with integrons, genetic structures that can transport and exchange ARGs between different bacteria, and is therefore an important component in the dissemination of AMR in bacterial populations and the environment. Previous studies have demonstrated a correlation between the intI1 gene and various measures of human impact in aquatic environments, making this gene an ideal candidate to be an indicator gene for AMR for the marine environment [START_REF] Gillings | Using the Class 1 Integron-Integrase Gene as a Proxy for Anthropogenic Pollution[END_REF][START_REF] Pruden | Correlation between Upstream Human Activities and Riverine Antibiotic Resistance Genes[END_REF]. The sul1 gene encoding sulfonamide resistance is very often associated with these class 1 integrons, thus easily transferable within bacterial communities and potentially leading to the emergence of multidrug resistant bacterial strains. It is important to pursue studies and to monitor AMR not only in seawater, but also in various wild marine species, which correspond to two essential dimensions of the One Health perspective, in order to improve our understanding of the risk caused by AMR to human health, the third component of the One Health framework.

Conclusion

In summary, we found that the four tet(A), blaTEM, sul1 and intI1 AMR indicator genes were present in the skin, gills and gut of wild flatfish with an overall prevalence of 71.4 %. The sul1 and intI1 genes were more prevalent in skin samples than in gills and guts, and the frequency of all four genes were lowest in the fish gut samples. In our entire project on the research of the four tet(A), blaTEM, sul1 and intI1 AMR indicator genes in the marine environment, we described 81.4% of the four antimicrobial resistance indicator genes in the English Channel and North Sea, with a prevalence of 39.5% for the tet(A) gene, 36.7% for the blaTEM gene, 57.1% for the sul1 gene and 61.0% for the intI1 gene. The sul1/intI1 genes were the most quantified indicators. The prevalence and abundance of these genes varied not only according to the nature of the sample, but also according to the sampling area, due to the different anthropogenic impacts associated with high gene dissemination. Greater contamination of samples was observed in the Eastern Channel, an area considered to be a veritable freeway of the sea due to intense maritime traffic and involving various discharges into the marine environment, as well as at the mouth of the Thames, which is subject to river effluents. Seawater samples were more contaminated with antimicrobial resistance indicator genes near the West Coast of the Netherlands, an area characterized by the presence of offshore wind and oil platforms. The sul1 and intI1 genes are good indicators of marine environmental contamination. Marine fish and environment, two major components of the One Health framework, are important vectors of AMR. They pose a risk of transmission to humans, the third component of the One Health framework. It is important to improve and standardize the monitoring of AMR in the marine environment to better characterize its role in the spread and transmission of resistance to humans through the consumption of fishery products.

Figure 1 :

 1 Figure 1 : Prevalence (A) and frequency (B) of the tet(A), blaTEM, sul1 and intI1 AMR indicator genes in the different samples (skin, gills, guts) from pooled flatfish.

Figure 2 :

 2 Figure 2 : Map indicating the total AMR indicator genes frequency in the flatfish samples from the English Channel and the North Sea associated with potential sources of pollution.

Acknowledgements

We thank the Ifremer institute for allowing us to be part of the IBTS oceanographic campaign to collect the flatfish samples analyzed in this study. The graphical abstract was carried out on Biorender.com under the agreement number OW25BKRVVG.

Funding

This study was supported by a doctoral fellowship from Région Hauts-de-France and Pôle Métropolitain de la Côte d'Opale (PMCO).

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.

Supplementary Table 1 : qPCR primers and probes used for quantification of the four AMR indicator genes.

Genes

Primer and probe sequences (5' -3') References Amplification conditions


Fatima Bekkar

Carlos Arrambide

Claudine El Khoueiry

Rachid Meghabar

Guilhem Arrachart

Stéphane Pellet-Rostaing

Diglycolamide phenolic foams as sorbent for rare earth elements

Keywords: Phenolic foam, Sorbents materials, Diglycolamide, Rare-earth elements, Liquid-solid extraction

In this present study, we developed a new series of sorbents based on phenolic foam resin for the extraction of rare earth elements. Chelating phenolic foams were prepared using appropriate formulation of blowing agent, surfactant and curing acid agent and a prepolymer resulting from an alkaline polymerisation reaction of phenolic diglycolamide derivative with admixture of phenol, catechol or resorcinol in the presence of formaldehyde as crosslinker agent. The foaming copolymer resins were completely described and engaged in sorption studies with three types of lanthanide (La, Eu, Yb), which represent light, medium and heavy REEs, respectively.

INTRODUCTION

Rare-earth elements (REEs) play a major role in the development of new current technologies such as optical, electronics and renewable energy [START_REF] Balaram | Rare earth elements: A review of applications, occurrence, exploration, analysis, recycling, and environmental impact[END_REF]. Whatever the sources of the REEs (mining deposit, exploitation of secondary sources or recycling), these strategic metals are recovered via hydrometallurgical processes. Solid liquid extraction (SLE) of metal ions is increasingly developed due to its various advantages over other hydrometallurgical methods, since it is free from the use of toxic or volatile organic solvents, which are involved in conventional liquid-liquid extraction processes. As a result, this has led to the research for insoluble polymers such as ion-exchange or chelating resins with good stability and high capacity for metal ions. Among them, formo-phenolic resins, which result from the alkaline polycondensation of formaldehyde with a phenolic compound, have demonstrated sorption properties from their phenolic OH groups and/or from active chelating groups for various metal ions [START_REF] Bhatt | Sorption studies of heavy metal ions by salicylic acid-formaldehyde-catechol terpolymeric resin: Isotherm, kinetic and thermodynamics[END_REF][START_REF] Arrachart | Pellet-Rostaing, Design and Evaluation of Chelating Resins through EDTA-and DTPA-Modified Ligands[END_REF][START_REF] Cruz | Selective extraction and separation of germanium by catechol based resins[END_REF] including REEs [START_REF] Al-Rimawi | Chelation Properties of Some Phenolic-Formaldehyde Polymers Toward Some Trivalent Lanthanide Ions[END_REF][START_REF] Draye | Selective Separation of Lanthanides with Phenolic Resins: Extraction Behavior and Thermal Stability[END_REF][START_REF] Oye Auke | Terephthalaldehyde-phenolic resins as a solid-phase extraction system for the recovery of rare-earth elements[END_REF]. Recently, the use of resol-type resins incorporating diglycolamide derivative for the extraction of REEs showed promising results [START_REF] Arrambide | Extraction and recovery of rare earths by chelating phenolic copolymers bearing diglycolamic acid or diglycolamide moieties[END_REF]. In most of these applications, the resins are studied in the form of powders but the advantage of the phenolic resin is that they can be obtained in different forms in particular in the form of foams [START_REF] Mougel | Phenolic foams: A review of mechanical properties, fire resistance and new trends in phenol substitution[END_REF]. To the best of our knowledge, no study has yet been reported on the phenolic foams (PFs) with active chelating groups dedicated to the extraction of REEs. The present study aims for the synthesis of a new class of foaming chelating copolymers based on phenolic resin that was synthetised by alkaline polycondensation of formaldehyde and phenolic compound with diglycoamide derivative. The different copolymers obtained were expanded or foamed by using a blowing agent, a surfactant and a curing acid agent (Figure 1). Then the materials were engaged for the extraction of La, Eu, Yb, which represent light, medium and heavy rare-earth elements, respectively.

MATERIALS AND METHODS

Synthesis

The diglycolamide (DGA) phenolic precursor was synthetised according to the procedure previously described in the literature [START_REF] Arrambide | Extraction and recovery of rare earths by chelating phenolic copolymers bearing diglycolamic acid or diglycolamide moieties[END_REF]. The experimental procedure for the preparation of prepolymer was adapted from previous studies [START_REF] Arrambide | Extraction and recovery of rare earths by chelating phenolic copolymers bearing diglycolamic acid or diglycolamide moieties[END_REF][START_REF] Samanta | Studies on Cesium Uptake by Phenolic Resins[END_REF][START_REF] Favre-Réguillon | Synthesis and Evaluation of Resorcinol-Based Ion-Exchange Resins for the Selective Removal of Cesium[END_REF]. In a first part, a mixture of DGA/formaldehyde/solution NaOH (6 M in aqueous solution)/H2O in a 1/2.5/1.5/30 molar ratios was prepared. Then, a mixture of phenolic compound (phenol (P), catechol (C) or resorcinol (R))/formaldehyde/NaOH/H2O in a molar ratio 1/2.5/1.5/ 50 was added to the initial mixture. The reaction mixture with phenol was stirred 24 h at 50°C, then at 95°C for 2 h while with catechol or resorcinol the mixture was stirred for 24 h at room temperature and 1 h at 60°C. The resulting pre-polymers were then engaged in the preparation of the foam. Foaming resin was prepared from the pre-polymer mixed thoroughly at room temperature with 10 wt.% of n-pentane as blowing agent, 3 wt.% of Tween 80 as surfactant and then 12 wt.% sulphuric acid (50 wt.%) as curing agent. The mixture was heated for 1 h at 80°C for expansion and then the temperature was increased gradually from 100°C to 130°C for 24 h. After the curing, the foaming phenolic resins were submitted to a washing step with 1 M HCl/H2O/1 M NaOH/H2O cycles for removing the unreacted compounds and the oligomers that have not been crosslinked. Finally, for the sorption experiments the resins were converted in their Na+ form by washing step with 1 M NaOH and then water until neutralisation, followed by drying in air oven at 80°C for 24 h. In the present study, DGA-polymeric foams with a 50% of functional group and 50% of phenolic precursor were studied (Table S1).

Materials

1 H and 13 C liquid NMR spectra and 13 C CP MAS NMR spectra were recorded on a Brucker advance 400 ultra-shield VS spectrometer. Chemical shifts are reported in ppm using D2O as an internal reference and using a MAS probe using 3.2 mm outer diameter rotors with a spinning rate of 12 KHz (typical contact: 2-3 ms, typical recycle delay: 5 s). FT-IR spectra were recorded with a Perkin Elmer 100 spectrometer on a range 4000-380 cm -1 using ATR crystal with 4 cm -1 resolution. Thermal gravimetric analysis (TGA) measurements were performed on a Mettler Toledo instrument (heating rate of 10°C/min from 30°C to 950°C under N2 atmosphere). Microstructure and morphology of phenolic foams were characterised by environmental scanning electron microscopy (model FEI QUANTA 200 ESEM FEG) with X-EDS analyses using a Bruker AXS X-Flash 5010. Quantification of cation concentration in solution (before and after batch experiments) was conducted by inductively coupled plasma/atomic emission spectroscopy (ICP/AES) using a SPECTRO ACROS spectrometer.

Adsorption experiments

The sorption experiment was carried out using feed aqueous solutions consisting in REEs at 500 ppm in nitric acid at pH = 4. The solutions were prepared from La (NO3)3.6H2O), Eu (NO3)3. 6H2O and Yb (NO3)3.6H2O in nitric acid solution in order to get a pH = 4. Batch contacts were performed for 24 h; even then, kinetic studies have shown that the equilibrium was reached in 1-3 h. The mixtures were shaken at 1 rotation/s under rotary agitation. Then, the mixtures were filtered through a 0.2 μm cellulose acetate membrane filter and analysed by ICP-OES to determine the residual metal concentration. The adsorption capacity Q (mg/g) and extraction efficiencies E (%) were calculated, respectively, using the formulae: Q = (Ci-Cf) × V/m and E = (Ci-Cf)/Ci × 100, where Ci denote the initial concentration of metal in solution while Cf denote the residual concentration of metal in solution, V is the volume of solution and m is the mass of resin used.

RESULTS AND DISCUSSION

As already described [START_REF] Arrambide | Extraction and recovery of rare earths by chelating phenolic copolymers bearing diglycolamic acid or diglycolamide moieties[END_REF], the homopolymerisation of chelating ligand in basic conditions with formaldehyde as crosslinker lead generally to water-soluble oligomers. The same behaviour was observed for the foaming resin prepared in similar conditions. In order to use these functionalised resins in a SLE process, a copolymerisation with phenolic precursors (50% molar ratio) was implemented. The copolymerisation lead to uncontrolled crosslinking through a methylene linkage between chelating ligand and phenolic precursors. Foaming chelating phenolic resins were prepared based on previous studies involving: i) Tween 80 a non-ionic surfactant [START_REF] Yang | Pulverized polyurethane foam particles reinforced rigid polyurethane foam and phenolic foam[END_REF] that stabilises the emulsion before the foaming, with an interfacial tension decrease and hindering coalescence phenomenon; ii) Pentane [START_REF] Li | One-step in situ synthesis of a novel α-zirconium phosphate/graphene oxide hybrid and its application in phenolic foam with enhanced mechanical strength, flame retardancy and thermal stability[END_REF], as blowing agent which produces gas bubbles in the phenolic matrix when the temperature increases during the curing step and/or the exothermic reaction between the pre-polymer mixture and the acid catalyst; iii) H2SO4 as catalyst/curing agent, which accelerates the curing reaction [START_REF] Yu | Performance of modified aramid fiber reinforced phenolic foam[END_REF]. After curing, the resins were washed in order to remove the partially soluble oligomeric species. All foaming chelating phenolic resins exhibit the same characteristic FT-IR bands and 13 C CP/MAS NMR peaks corresponding to the functional group and the phenolic moiety (Figure 2). The presence of amide bands indicating that the ligand has been incorporated in the final polymer was observed at 1650 and 1560 cm -1 . This was also confirmed with the band at 1130 cm -1 assigned to the ether group function of the DGA group (-CH2 -O-CH2). The presence of the DGA moiety was confirmed from the signals at 70ppm and at 170ppm in the solid state NMR spectra corresponding, respectively, to the methylene between the central oxygen and the carbonyl C=O. A comparison of the 13 C spectra of the prepolymer in the liquid state and the resulting foam in the solid state is provided in the ESI (Figure S1). In addition, elemental analysis indicates the presence of nitrogen according to the incorporation of the DGA functional group. The process of thermal decomposition observed from the thermogravimetric analysis could be divided into three different stages (Figure 3) [START_REF] Zárate | Thermal degradation of a phenolic resin, vegetable fibers, and derived composites[END_REF]. The first thermal degradation peak was below 300°C attributed to the release of excess phenol, formaldehyde, short oligomers and water [START_REF] Bo | Synthesis of a cardanol-based phosphorus-containing polyurethane prepolymer and its application in phenolic foams[END_REF] as well as the DGA functional group. The second stage in the thermal decomposition was observed in the range of 300-450°C; during this stage, the reaction of pyrolysis and polymerisation reaction of pyrolysis start to occur. The third stage, in the range of 450-900°C, was the main process of thermal mass loss; during this process, the methylene-bridged structure and the long-chain molecules degraded of all phenolic foaming polymer and formation of carbonaceous char.

The SEM morphologies of the foaming polymers are presented in Figure 4. All phenolic foams showed cells which are roughly spherical. High volatile hydrocarbons such as pentane are known to induce the formation of open cells. The cells exhibit some perforation, which may be caused by the presence of water in the foaming polymer. The efficiency of the foam for the REEs extraction was demonstrated from the sorption experiments results, which show that most of the cation were extracted (Figure 5). An interesting point is that the DGA foam exhibits higher efficiencies for Yb>Eu>La which is in agreement with previous studies showing that the DGA functional group exhibits higher affinity for heavier REEs [START_REF] Wehbie | Organization of diglycolamides on resorcinarene cavitand and its effect on the selective extraction and separation of HREEs[END_REF]. Indeed, the extraction increases by increasing the atomic number or by decreasing the ionic radius of the rare earths. This can be attributed to the charge density, which is higher in heavy rare earths due to their smaller ionic radii, which facilitates their complexation by the DGA functional group [START_REF] Xie | A critical review on solvent extraction of rare earths from aqueous solutions[END_REF]. The uptake was also demonstrated by submitting the solid phase loaded with the REEs to an X-EDS analysis (Figure S2).

CONCLUSION

For the first time, diglycolamide phenolic foams have been synthesised by a copolymerisation process with diglycolamide moieties and various phenolic groups using pentane as foaming agent. The expanded phenolic resins were subjected to sorption experiments in order to determine their extraction efficiency for REEs showing a preferential extraction of HREEs by the DGA. Ongoing studies are in progress in order to improve the toughness of the PFs.

Figure 1 .

 1 Figure 1. Illustration of the synthesis of the DGA-phenolic foams.

Figure 2 .

 2 Figure 2. a) FTIR spectra and b) 13 C solid state NMR spectra of chelating PFs I (DGA-Phenol), II (DGA-Catechol) and III (DGA-Resorcinol).

Figure 3 .

 3 Figure 3. Thermal analysis data of chelating foaming phenolic polymers I (DGA-Phenol). a) TG and b) DTG curves at 10°C/ min under N2 atmosphere.

Figure 4 .

 4 Figure 4. SEM images of chelating PF I (DGA-Phenol).

  Extraction efficiency of La 3+ , Eu 3+ and Yb 3+ for chelating PFs I (DGA-Phenol), II (DGA-Catechol) and III (DGA-Resorcinol). Polymer concentration (5 g/L: V/m=200), nitric acid solution (10 -4 mol/L) with 500 ppm of La 3+ , Eu 3+ or Yb 3+ , 24 h of contact at 25°C.

	100	La
		Eu
		Yb
	80
	60
	E(%)
	40
	20
	0
		I	II	III
			Phenolic Foams
	Figure 5.

Acknowledgments

The authors acknowledge B. Baus-Lagarde, F. Giusti and C. Rey for technical support, the Algerian government and the Labex Project CheMISyst (ANR-10-LABX-05-01) for the financial support.

SUPPORTING INFORMATION