C Engrand
email: cecile.engrand@csnsm.in2p3.fr

E Charon

H Leroux

C Le Guillou

J Duprat

E Dartois

S Bernard

B Guérin

L Delauche

M Godard

INNER SOLAR SYSTEM IRRADIATION HISTORY OF MINERALS IN AN ULTRACARBONACEOUS ANTARCTIC MICROMETEORITE (UCAMM)

Introduction: Ultracarbonaceous Antarctic Micrometeorites (UCAMMs) are interplanetary dust particles containing large amounts of N-rich polyaromatic organic matter exhibiting large D enrichments, with heterogeneous distributions of both D/H and 15 N/ 14 N isotopic ratios [1][2][3][4][5][6][7][8]. UCAMMs most probably originate from the outer regions of the protoplanetary disk, i.e. the cometary reservoir [2,4,5]. At least three kinds of organic matter have been identified in UCAMMs, with different nitrogen abundances and highly variable concentrations of small (typically 30-500 nanometer) minerals embedded in the organic matter [8][9][10][11]. The formation process(es) of the different kinds of organic matter found in UCAMMs and the origin of their mixing with minerals are still a matter of debate. We focus here on a mineral assemblage embedded in one UCAMM (DC06-04-43) in which we have found evidence of irradiation.

Results and discussion: A 100 nm-thick FIB section was made at IEMN Lille from a fragment of UCAMM DC06-04-43. This section was examined by STXM-XANES followed by transmission electron microscopy (TEM) [11]. The mineralogy of this UCAMM is dominated by a crystalline assemblage consisting of µm-sized Mg-rich pyroxenes, Fe sulfides, Mg-rich olivines, and Fe-Ni metal. GEMS are embedded in the organic matter, close to the crystalline assemblage. We identified irradiation features (rims and tracks) in some crystalline pyroxenes. The rims' thicknesses range from 20 to 100 nm (average 60 ± 20 nm, 1σ) and irradiation track lengths from 15 nm to ~ 1 µm (average 175 ± 110 nm, 1σ). The average track density is 1.72 x 10 10 cm -2 (772 tracks over 4.2 x 10 6 nm 2 ). SRIM calculations [12] indicate that the observed average rim thickness corresponds to irradiation of the pyroxene by ions with energies ranging from 1 to 10 keV/nucleon, whereas the track lengths are compatible with an irradiation by Solar energetic particles (SEP). The irradiation damages of these minerals are compatible with irradiation in the inner solar system (solar wind and SEP). The average track density corresponds to a few hundred thousand years exposure in space, using the current track production rate at 1 AU determined by Berger & Keller [13]. This exposure time may have been shorter considering early irradiation by the more active young Sun. The observation of irradiation rims in minerals embedded in the organic matter implies an irradiation of these minerals before transport to the outer regions of the protoplanetary disk and later incorporation in the UCAMM parent body. In the scenario where the N-rich organic matter is formed by irradiation of ices by Galactic cosmic rays [4, 14], these minerals would be shielded from irradiation (e.g. buried in ice) in the parent body. The fact that crystalline minerals are usually found in association with low-N organic matter suggests that the low-N and N-rich organic matters in UCAMMs formed in different locations, and/or over different timescales.

Acknowledgments: This work was supported by IPEV-PNRA in Antarctica. Funding from CNRS, PNP, LabEx P2IO, IN2P3 and ANR (COMETOR) are acknowledged.

References: [1] Nakamura T., et al. (2005)

Figure

  Figure: a) TEM micrograph of a pyroxene showing both irradiation rim and tracks. The tracks are highlighted in the bottomright inset. b) irradiation rim observed around another pyroxene mineral, including on the side in contact with the organic matter. a & b: the smooth grey layer in contact with the platinum (in black) is a carbon coating applied to the sample for initial investigation by scanning electron microscopy before FIB-sectioning.

  . MAPS 40S: #5046. [2] Duprat J., et al. (2010). Science 328: 742-745. [3] Dobrică E., et al. (2011). MAPS. 46: 1363-1375. [4] Dartois E., et al. (2013). Icarus 224: 243-252. [5] Dartois E., et al. (2018). A&A 609: A65. [6] Duprat J., et al. (2014). MAPS. 49S #5341. [7] Bardin N., et al. (2015). MAPS 50S #5275. [8] Yabuta H., et al. (2017). GCA 214: 172-190. [9] Dobrică E., et al. (2012). GCA 76: 68-82. [10] Engrand C., et al. (2015) LPSC 46: #1902. [11] Charon E., et al. (2017). LPSC 48: #2085. [12] Ziegler J.F. (2013) SRIM -http://srim.org/. [13] Berger E.L. and Keller L.P. (2015) LPSC 46: 1543. [14] Augé B., et al. (2016). A&A. 592: A99.


Zohaib Amjad Khan
email: zohaibamjad.khan@univ-paris13.fr

Azeddine Beghdadi
email: azeddine.beghdadi@univ-paris13.fr

Mounir Kaaniche
email: mounir.kaaniche@univ-paris13.fr

Alaya Faouzi

Cheikh

RESIDUAL NETWORKS BASED DISTORTION CLASSIFICATION AND RANKING FOR LAPAROSCOPIC IMAGE QUALITY ASSESSMENT

Keywords: Laparoscopic video, image quality assessment, multi-label classification, distortion classification, deep learning

Laparoscopic images and videos are often affected by different types of distortion like noise, smoke, blur and nonuniform illumination. Automatic detection of these distortions, followed generally by application of appropriate image quality enhancement methods, is critical to avoid errors during surgery. In this context, a crucial step involves an objective assessment of the image quality, which is a two-fold problem requiring both the classification of the distortion type affecting the image and the estimation of the severity level of that distortion. Unlike existing image quality measures which focus mainly on estimating a quality score, we propose in this paper to formulate the image quality assessment task as a multi-label classification problem taking into account both the type as well as the severity level (or rank) of distortions. Here, this problem is then solved by resorting to a deep neural networks based approach. The obtained results on a laparoscopic image dataset show the efficiency of the proposed approach.

INTRODUCTION

Image quality assessment (IQA) is a critical task [START_REF] Wang | Why is image quality assessment so difficult?[END_REF] in different vision-based systems like autonomous navigation, medical imaging, video-based surveillance etc [START_REF] Wang | Applications of objective image quality assessment methods [applications corner[END_REF]. In the medical field, there are various imaging modalities like CT, MRI, Ultrasound and laparoscopy where image quality assessment is very important both for analysis and diagnosis purposes. For instance, laparoscopic videos are often affected by different types of distortion like smoke, blur, noise and uneven illumination [START_REF] Khan | Towards a video quality assessment based framework for enhancement of laparoscopic videos[END_REF]. In this case, the knowledge of the kind of distortion affecting an image/video is necessary so that the appropriate quality enhancement method can be applied [START_REF] Sdiri | Efficient enhancement of stereo endoscopic images based on joint wavelet decomposition and binocular combination[END_REF][START_REF] Wang | Multiscale deep desmoking for laparoscopic surgery[END_REF]. This effectively makes IQA a two step process [START_REF] Moorthy | A two-step framework for constructing blind image quality indices[END_REF]. In the first step, it is needed to detect the type of distortion affecting the image whereas in the second step the severity level of distortion needs to be quantified.

On the one hand, distortion classification is a challenging task and only few works have been developed to address it specifically. Indeed, many existing IQA methods like BIQI [START_REF] Moorthy | A two-step framework for constructing blind image quality indices[END_REF], DIIVINE [START_REF] Moorthy | Blind image quality assessment: From natural scene statistics to perceptual quality[END_REF] and BRISQUE [START_REF] Mittal | Noreference image quality assessment in the spatial domain[END_REF] employ a distortion classification stage only to get the class probabilities for an image, which are then used as weights for the results (i.e scores) of the regression step. Although their classification output could be used separately, yet the classification in all these methods is based on non-generic and hand-crafted features. Hence, there is a big gap when it comes to more generic distortion classification for quality monitoring tasks. In this paper, one of the aims is to address this challenge in the case of laparoscopic videos.

On the other hand, the problem of evaluating the severity of distortions in an image is often addressed in the form of an absolute quality score. Most of the state-of-the-art methods are based on extracting specific features from the images followed by supervised learning using subjective scores to get a quality score [START_REF] Shahkolaei | Blind quality assessment metric and degradation classification for degraded document images[END_REF]. Such methods have two disadvantages. First, they strongly depend on the specific distortion categories and the type of distribution used for coefficients modeling to extract features. For this reason, they may not be well adapted to other distortions like those seen in laparoscopic images. Secondly, the subjective scores used for learning are usually inconsistent and may be biased by image content [START_REF] Gao | Learning to rank for blind image quality assessment[END_REF]. Additionally, using methods dependent on subjective scores is not usually possible with medical data, like laparoscopic images, due to the limited image data and the lack of public quality datasets. Moreover, while non-medical quality evaluations are primarily aimed at recording perceptual or aesthetic quality, medical images need diagnostic quality evaluations by medical experts, which is a non-trivial task.

To overcome the problems of lack of labeled data and inconsistencies in subjective scoring, learning based ranking approaches for quality assessment have recently been developed [START_REF] Liu | Rankiqa: Learning from rankings for no-reference image quality assessment[END_REF]. These approaches predict the distortion severity level instead of the absolute quality score and hence do not re-quire subjective scores. The data for these methods can also be easily generated since the ranks are assigned during the distorted data generation process. A similar ranking-based approach has also been used for contrast enhancement evaluation [START_REF] Chetouani | A novel ranking algorithm of enhanced images using a convolutional neural network and a saliency-based patch selection scheme[END_REF]. Hence, in this paper, we have also chosen the ranking method for estimation of severity level during IQA stage for laparoscopic images.

In light of the above studies, we propose here a new methodology for IQA of laparoscopic images in the context of quality monitoring applications, whereby our focus is on the two important problems of distortion classification and quality ranking. More precisely, we propose to solve these two problems using a single deep neural network based approach. In order to achieve this, we have combined these two sub-problems to formulate them as a single multi-label classification problem and then applied residual networks to perform distortion classification and ranking simultaneously.

The remainder of this paper is organized as follows. Section II gives an overview of the related works. This is followed by Section III which presents our methodology to solve the multi-label classification problem. In Section IV, the experimental results are provided and discussed. Finally, some conclusions and future work are given in Section V.

RELATED WORKS

The use of hand-crafted features for IQA has the limitation of being application specific and non-generic. Convolutional neural networks (CNN) allow to overcome this issue by achieving an automated feature learning approach that can be extended to all kinds of distortions. CNNs have been shown to be very effective in several image processing tasks including object classification, enhancement, segmentation and retrieval. For this reason, the use of different deep neural networks (DNN) based on CNN have also been proposed for image quality assessment.

However, the use of deep learning approaches for IQA is not a straightforward task and there are many variations for its application to IQA. For instance, in [START_REF] Varga | Deeprn: A content preserving deep architecture for blind image quality assessment[END_REF], a blind IQA is proposed based on DNN to predict distribution of quality rating instead of the scores. They have used ResNet-101 with Huber's loss function using full size images as input. Similarly, in [START_REF] Talebi | Nima: Neural image assessment[END_REF], the authors have explored VGG16, Inception-v2 and MobileNet to also predict distribution of aesthetic and technical quality ratings using Earth Mover's Distance loss function. Moreover, Ma et al. [START_REF] Ma | End-to-end blind image quality assessment using deep neural networks[END_REF] have tackled IQA using a multi-task DNN using two sub-networks with shared initial layers. They have proposed to use one of the sub-networks for distortion identification and the other one for quality prediction. The output of the first sub-network is a class probability vector which is then combined with the output of the second subnetwork in a weighted summation. Their approach is close to the proposed one which presents the advantage of resorting to one single opinion-unaware network for both distortion classification and ranking.

Besides these methods, some CNN-based methods for IQA also take image patches as inputs [START_REF] Kang | Convolutional neural networks for no-reference image quality assessment[END_REF][START_REF] Bosse | A deep neural network for image quality assessment[END_REF][START_REF] Chetouani | A blind image quality metric using a selection of relevant patches based on convolutional neural network[END_REF]. Since the subjective score exists for the entire image, these methods either assume total score as an average of all patches or as a weighted average, based on saliency or output of another subnetwork. Some recent works have also proposed techniques for quality based image ranking. For instance, Xu et al. [START_REF] Xu | Multi-task rank learning for image quality assessment[END_REF] use machine learning based approach to predict ranking for images before evaluating the absolute score. More recently, in [START_REF] Liu | Rankiqa: Learning from rankings for no-reference image quality assessment[END_REF], Siamese neural network has been employed to learn ranks of images. However, unlike our proposed approach here, they are only focused on the aspect of quality ranking and do not address the distortion classification problem.

PROPOSED MULTI-LABEL DISTORTION CLASSIFICATION AND RANKING WITH RESNET

In this section, we describe the proposed methodology for laparoscopic image quality assessment. As mentioned earlier, the distortion classification and quality ranking tasks are jointly performed using a single multi-label classification problem. Indeed, a multi-label classification problem can be transformed into a single-label multiclass classification task using one of the Problem Transformation methods [START_REF] Tsoumakas | Multi-label classification: An overview[END_REF]. Among these methods, Label Powerset approach is simpler and works well for a smaller number of total labels. In this approach, each combination of labels in the dataset is considered as a separate class resulting in C classes in total. This task can then be solved using an appropriate multiclass classification method.

Multi-label classification formulation

More precisely, given a set of N possible distortions, D = {D 1 , D 2 , ..., D N } and another set of M possible discernible distortion levels, L = {L 1 , L 2 , ..., L M }, each image can be labeled with one label D i L j , where 1 ≤ i ≤ N and 1 ≤ j ≤ M . For our laparoscopic data X, we have chosen five different distortion classes that often affect laparoscopic images. These are defocus blur (DB), motion blur (MB), additive white Gaussian noise (AWGN), smoke (SM) and uneven illumination (UI). For distortion severity, we have considered four levels, namely hardly visible (HV), just noticable (JN), visible and annoying (VA) and extremely annoying (EA). Hence, with these labels we have the following two sets:

D X = {DB, M B, W N, SM, U I} (1) L X = {HV, JN, V A, EA} (2)
We have opted for discrete semantic severity levels instead of continuous in light of their ease of use and understanding in clinical context. For notations concision, the labels indicating the distortion level will be ranked from 1 to 4 while 4 being the worst (EA). After the multiclass reformulation step, we get the single following set of C = 20 classes:

C X = {(DB j ) 1≤j≤4 , (M B j ) 1≤j≤4 , (W N j ) 1≤j≤4 , (SM j ) 1≤j≤4 , (U I j ) 1≤j≤4 } (3)
Before solving this multi-label classification problem, let us first describe the proposed approaches used to simulate the 5 typical distortions considered above.

Distortions generation

For reference images, we have extracted different frames from the videos in Cholec80 dataset [START_REF] Twinanda | Endonet: a deep architecture for recognition tasks on laparoscopic videos[END_REF]. The latter contains 80 different videos of cholecystectomy surgeries. For our training and test dataset, we have tried to select images with an attempt to include maximum possible variations of the scene content. Examples of these variations are images containing content like organs (liver and gall-bladder), vessels, blood, single and multiple instruments, water, staples, cut and stretched tissue and organ extraction bag.

To generate synthetic distortions in the selected reference images, we have used MATLAB to simulate all the distortions with different levels for our database. For defocus blur, a symmetric low-pass Gaussian filter was applied to each image. The filter size and the standard deviation were changed to generate different levels of this distortion. For directional and uniform motion blur, due to camera or/and object motion during the image acquisition, a linear filter was used with various values of the kernel motion parameter to generate different blurring levels . Similarly, for noise distortion an additive white Gaussian noise was generated. In this case, variance of the Gaussian distribution was adjusted to generate multiple levels of noisy images. For uneven illumination, a special gray-scale mask was generated having a circular bright region of high intensity values. The areas surrounding the circular region were generated with decreasing intensities as a function of distance from the center of the bright region. The multiplication of this mask with the original image gave us the unevenly illuminated distorted image. By changing the two parameters of the center location of the bright region and its area, we generated four different levels for uneven illumination. Finally, in order to generate smoke, we have used screen blending model [START_REF] Khan | Towards a video quality assessment based framework for enhancement of laparoscopic videos[END_REF]. In this technique, real smoke image having a black background is combined with the reference image in such a way that black areas produce no change to the original image while the brighter areas overlay the original ones. Using different opacity levels for the smoke frame we have generated four different levels of severity for smoke. An illustration of the generated distorted images at levels 1 and 4 is provided in Fig. 1 for different samples of our laparoscopic dataset. Once the distortion generation process as well as the multi-label classification problem are described, the latter will be solved by using a deep learning approach based on the residual network architecture.

(f) U I 4 (g) SM 1 (h) SM 4 (i) W N 1 (j) W N 4
Fig. 1: Generated distorted images at levels 1 and 4 for different samples of our laparoscopic dataset.

Residual network architecture

Deeper convolutional neural networks, which simply stacked many layers that exceed a certain threshold may result in gradient vanishing problem and lead to an accuracy drop [START_REF] He | Deep residual learning for image recognition[END_REF].

To alleviate this problem, residual network, which replaces direct stacked layers by skip connections that sum-up the output of a block of layers to its input, has been developed [START_REF] He | Deep residual learning for image recognition[END_REF].

Residual network, referred to as ResNet, is considered as a class of deep neural networks which is commonly applied to image recognition as well as classification [START_REF] He | Deep residual learning for image recognition[END_REF]. For instance, ResNet-50 is a deep network, with 50 layers and over 23 million trainable parameters, which has excellent generalization performance. For these reasons, we have retained the ResNet architecture, with different numbers of layers, to achieve our distortion classification and ranking tasks. To this end, for a given ResNet architecture with a fixed number of layers, only the output fully-connected layer is replaced with a layer of size 20 corresponding to the number of classes in the dataset. Then, the training of the model is done on our laparoscopic dataset. This is performed by using the cross-entropy as a loss function, which is often considered in classification problems. This function is defined as follows:

L = - C c=1 y o,c log(p o,c ) (4)
where C = 20 is the number of classes in our distorted laparoscopic dataset, y o,c is a binary indicator with value 1 if the class label c is the correct classification for the observation o, and 0 if not, and p o,c is the network predicted probability that the observation o is of class c. The minimization of the above loss function is achieved using the Adam optimizer.

EXPERIMENTAL RESULTS

To evaluate the performance of the proposed methodology, we have used 1000 images of size 512 × 288 extracted from 1).

To the best of our knowledge, our methodology is the first one which aims to perform distortion classification as well as ranking simultaneously. For this reason, we will propose to compare our approach separately to classification methods and ranking methods. Indeed, the results of quality ranking from our method are firstly compared to a recent deep learning based method which employs a Siamese network for ranking before fine-tuning its single trained branch to obtain quality scores [START_REF] Liu | Rankiqa: Learning from rankings for no-reference image quality assessment[END_REF]. The latter is designated by RankIQA. For our comparison, we have only used the ranking part of their network with Hinge loss (see Table 1). In order to evaluate these ranking methods, we have used Spearman Rank-Order Correlation Coefficient (SROCC). Table 2 provides the SROCC values for the RankIQA method as well as the proposed one using ResNet-18. The obtained results show that our approach yields better ranking performance compared to the recent deep learning-based RankIQA method.

Moreover, the performance of the proposed methodology is evaluated in terms of classification accuracy. To this end, and in addition to ResNet-18, two other deeper variants with more layers have been considered. Table 3 shows the mean classification accuracy, for ResNet-18, ResNet-34 and ResNet-50, after 10 repetitions. Thus, it can be observed that the used ResNet-18 leads to good accuracy results (around Methods SROCC Proposed one (with ResNet-18) 0.69 RankIQA [START_REF] Liu | Rankiqa: Learning from rankings for no-reference image quality assessment[END_REF] 0.57 It is worth pointing out that classical machine learning approaches, like BRISQUE and BIQI, as well as deep learning approaches have not been considered in this classification accuracy comparison since the latter perform generally classification based only on the distortion type (i.e. 5 classes corresponding to the five kinds of distortion) whereas our methodology uses 20 classes as it jointly performs distortion classification and its severity ranking. Therefore, the obtained results show the benefits of the proposed approach for efficient quality ranking of the distortion severity level while yielding good classification accuracy.

CONCLUSION AND PERSPECTIVES

In this paper, we have proposed a novel strategy of image quality assessment for quality monitoring tasks especially in the context of laparoscopic images. More precisely, a single neural network (ResNet) is used to solve the two most important IQA sub-tasks, namely distortion classification and quality ranking, thanks to a multi-label classification formulation. Then, we have tested our method on a laparoscopic dataset and obtained promising results. In future, we plan to consider other kinds of distortions including distortion mixtures and an additional step of quality score prediction using a labeled laparoscopic image dataset with human subjective scores.

ACKNOWLEDGMENT

This research work is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 722068.

  (a) M B 1 (b) M B 4 (c) DB 1 (d) DB 4 (e) U I 1

Table 1 :

 1 Hyperparameters for the two networks used for comparison different videos of Cholec80 laparoscopic dataset[START_REF] Twinanda | Endonet: a deep architecture for recognition tasks on laparoscopic videos[END_REF]. As mentioned in Section 3, we selected images with various contents like organs (liver and gall-bladder), vessels, blood, single and multiple instruments, etc. Moreover, five distortions at four different severity levels were then generated for all the images resulting in a total of 20000 images (1000 images per class). Since the images were distorted synthetically, we already had the labels for all images corresponding to distortion type and severity level and hence did not need the input of clinical experts. Eighty percent of image data from each class (800 images) was used for training of ResNet while the remaining twenty percent of images were reserved for testing. The training and testing for ResNet model used to solve our multi-label classification problem was carried out by using PyTorch framework on an NVIDIA GeForce GTX 1080, 64 Gb GPU. The learning rate used to train our model was set to 0.001, the mini-batches of size was set to 10, and the number of epochs was set to 300 (see Table

	Hyperparameters RankIQA Proposed
	Learning rate	1e-5	1e-3
	Epochs	100	300
	Mini-batch size	5	10
	Loss function	Hinge loss Cross-entropy
	Optimizer	SGD	Adam

Table 2 :

 2 SROCC of ranking methods with laparoscopic dataset

	Proposed method with Accuracy
	ResNet-18	83.3%
	ResNet-34	84.7%
	ResNet-50	87.3%

Table 3 :

 3 Classification accuracy of the proposed method with different ResNet models for our laparoscopic dataset. 83%). Moreover, by using deeper ResNet architectures, the accuracy is improved and reaches 87.3% with ResNet-50.


A Al

E Lauga

Low Complexity Architecture of N-Path Mixers for Low Power Application

Keywords: N-Patch mixer, mixer-first, receiver, softwaredefined radio, harmonic rejection, blocker

The complexity of harmonic rejection N-path mixer based receivers is studied in this paper. A solution to improve harmonic rejection without increasing the complexity is presented and compared with conventional LNA-first receivers. This solution is based on π-delayed driving signals that enable a N-path mixer to present the same harmonic rejection than its 2N-path counterparts while reducing by 2 the frequency of operation and showing a reduced number of differential gain stages and switches. A 5-path mixer is thus proposed. It allows rejecting up to the 8 th harmonic with only 3 differential gain stages whereas only the 6 th harmonic would be rejected with conventional topologies having the same number of amplifier stages.

I. INTRODUCTION

With the development of Software Defined Radios (SDR) and the multi-standards dedicated to 5G, there is today a growing interest for wideband receivers. Receivers based on N-Path Mixers (NPMs) are good candidates since they are inherently wideband, tuneable and frequency selective. In addition, they are very attractive for low power and low cost systems. A major drawback of these structures is their sensitivity to blockers due to harmonics mixing. To reach large bandwidth receivers, harmonic rejection structures (HR-NPMs) have been proposed in [START_REF] Xu | A Blocker-Tolerant RF Front End With Harmonic-Rejecting N-Path Filter[END_REF], [START_REF] Ru | Digitally Enhanced Software-Defined Radio Receiver Robust to Out-of-Band Interference[END_REF] and [START_REF] Andrews | A Passive Mixer-First Receiver With Digitally Controlled and Widely Tunable RF Interface[END_REF], leading to more complex systems. [START_REF] Xu | A Blocker-Tolerant RF Front End With Harmonic-Rejecting N-Path Filter[END_REF] and [START_REF] Ru | Digitally Enhanced Software-Defined Radio Receiver Robust to Out-of-Band Interference[END_REF] present LNA-first receivers whereas [START_REF] Andrews | A Passive Mixer-First Receiver With Digitally Controlled and Widely Tunable RF Interface[END_REF] and [START_REF] Andrews | Implications of Passive Mixer Transparency for Impedance Matching and Noise Figure in Passive Mixer-First Receivers[END_REF] gives solutions for mixer-first receivers. While the latter achieves good linearity, LNA-first receivers provide good sensitivity. Whatever the considered structures, reducing the complexity of HR-NPMs is a major issue, specifically when wideband harmonic rejection is required.

In this context, this paper describes a low complexity and high harmonic rejection receiver, based on N-path mixers. The principle of harmonic rejection in NPMs is first detailed and a generic architecture representation for LNA-first receivers is given with a special focus on their level of intricacy. Then, their harmonic rejection performances are studied on the basis of the Effective Local Oscillator (EFLO) signal properties. In a second part, a structure based on πdelayed driving signals is presented and compared to other ones in terms of harmonic rejection and specific Figures-of-Merit related to complexity.

II. HARMONIC REJECTION N-PATH MIXERS (HR-NPMS)

A. Principle

A simple NPM with N=4 (4PM), fed with an input signal vs(t), is depicted in Fig. 1, on the basis of [START_REF] Andrews | Implications of Passive Mixer Transparency for Impedance Matching and Noise Figure in Passive Mixer-First Receivers[END_REF]. vs(t) is distributed over N paths that consist each in a switch and a capacitor. The switch is driven by a delayed version of the LO: Sh(t), a periodic square signal with a duty cycle equal to 1/N. The output of each path (outn(t)) is a sampled version of the input signal and is expressed as follows:

( ) ( ). ( )

n s out t v t eflo t = (1)
where eflo(t) is the Effective Local Oscillator (EFLO) signal that transposes the RF input. This structure achieves a selective RF filtering that can be tuned accordingly to the LO frequency. Selectivity is proportional to N as explained in [START_REF] Klumperink | N-Path Filters and Mixer-First Receivers: A review[END_REF]. However, it suffers from a main issue that limits its applicability: the presence of harmonics in eflo(t) generates harmonic mixing [START_REF] Andrews | Effects of LO Harmonics and Overlap Shunting on N-Phase Passive Mixer Based Receivers[END_REF]. By using an RF differential signal as depicted on Fig. 1b, the even harmonics can be removed from the frequency spectrum of eflo(t).

B. Generic Architecture of HR-NPMs

A generic topology is proposed in Fig. 2. This representation is well suited to LNA-first architectures. However strong similarities exist with mixer-first receivers. It consists of N paths and H branches. Along each path, H switches, driven by Sh(t), sample the input RF signal through H gain stages (Gh). The computation of the gain coefficients has been studied in previous works with several Funded by the European Community through ECSEL program and OCEAN12 Project methodologies [START_REF] Klumperink | N-Path Filters and Mixer-First Receivers: A review[END_REF]. In this work, we consider that eflo(t) is based on a sine wave sampled with a sampling frequency Fs=H.FLO, which maximizes the number of null coefficients. Hence the complexity of the design is reduced as a null gain stage, Gh=0, does not need to be implemented. These coefficients are given by the following series defined for 0 ≤ h ≤ H-1: Since eflo(t) is a sampled and hold sine wave, it can be expressed by means of Gh as follows:

sin 2 2 1 G N h h H N π =   +           (2)
( ) ( ) ( ) . 1 ( ) s h s h N eflo t ol t g t G t h T g t H δ ∞ =-∞     = * = -+ *          (3)
where ols(t) is the sampled version of the sine wave with sampling frequency Fs=H.FLO and g(t) is the square function of width Τs=TLO/N.

Fig. 3 shows eflo(t) for the conventional NPMs of Fig. 1 obtained for H=1 and H=2 with N=4, respectively, whereas eflo(t) for the HR-8PMs, that will be further illustrated on Fig. 5, is obtained for H=8 and N=8. Increasing H leads to a better fit of eflo(t) with a pure sine wave. The output signal (outn(t)) spectrum at each path is given by ( 4):

( ) ( ) ( ) * n S OUT f V f EFLO f = ( 4
)
with:

( ) ( ) ( ) 0 . . sinc . 2 LO s s LO LO n A EFLO f H F T T f f nH F F π δ +∞ = = - ±  ( 5
)
where A is the amplitude of g(t). As represented on Fig. 4, harmonics occur at Fhn=n.Fs ± FLO that will be in the meantime the folded harmonics. Hence, the highest rejected harmonic is Hr=(H-2) which is equal to 6 for the HR-8PMs of [START_REF] Xu | A Blocker-Tolerant RF Front End With Harmonic-Rejecting N-Path Filter[END_REF] and [START_REF] Ru | Digitally Enhanced Software-Defined Radio Receiver Robust to Out-of-Band Interference[END_REF] where H = N = 8. In fact, many HR-NPMs have been proposed in the literature, for example in [START_REF] Xu | A Blocker-Tolerant RF Front End With Harmonic-Rejecting N-Path Filter[END_REF]- [START_REF] Andrews | Implications of Passive Mixer Transparency for Impedance Matching and Noise Figure in Passive Mixer-First Receivers[END_REF] but most of them are differential structures with eight paths, i.e. N = 8, while H, the number of samples per period, equals N, i.e. H = N = 8 as displayed on Fig. 5. Fig. 5. Structure of a HR-8PM LNA-first receiver with a differential structure, as in [START_REF] Xu | A Blocker-Tolerant RF Front End With Harmonic-Rejecting N-Path Filter[END_REF] or [START_REF] Ru | Digitally Enhanced Software-Defined Radio Receiver Robust to Out-of-Band Interference[END_REF]. Eight paths and eight samples. Samples corresponding to a null value do not need to be implemented theoretically.

The synthesis for HR-8PMs with 8 samples consists in splitting each path into 3 differential branches with specific gain ratios [1;√2;1], on the basis that 1/√2 corresponds to the sine of 45°, i.e. 1/8 of a sine period. Depending on the receiver architecture, the gains come from LNA in LNA-first receiver or from baseband amplifier in mixer-first topology. This allows the rejection of the 3 rd and 5 th harmonics during mixing. Hence the first harmonic that arises in the frequency spectrum of eflo(t) is 7FLO, the last one to be rejected is 6FLO.

C. Complexity Analysis

The performance in terms of harmonic rejection and selectivity are respectively related to H and N whereas the complexity is related to the number of gain stages, Hg, and the number of switches, Sw. Hence, ratios η1=Hr/Hg and η2=Hr/Sw are interesting Figures-of-Merit (FoM) in terms of harmonic rejection relatively to complexity.

As previously stated, in most cases, coefficient Gh is achieved through a gain stage. Conventionally, to reach the highest harmonic rejection as possible, H is equal to N, which results in a high number of gain stages. In other words, the system complexity increases with the targeted harmonic rejection. Fortunately, the effective number of branches, Hef, corresponding to the number of branches to be physically implemented, is reduced when some coefficients Gh of eflo(t) are null as it is the case on Fig. 5 where Hef = 6 while H = 8. This means that the number of gain stages, Hg, is equal to Hef. In the case of RF differential input, the number of differential gain stages, Hg, is reduced to Hef/2. In both cases, the total number of switches is Sw = Hef.N.

For a given H (i.e. a given harmonic rejection Hr = (H-2)), the normalized coefficients Gh are described in Table 1, in which the number of coloured cells corresponds to Hef while the number of dark coloured cells corresponds to the number of differential gain stages, Hg. The maximum rejected harmonic, Hr, the number of gain stages, Hg, and the number of switches, Sw, are reported in the column entitled (H = N) for conventional structures as previously reported. It appears that (i) even structures are favoured to odd ones, (ii) for some even numbers of paths, the sampling coefficients appear twice in half of a period, with a perfect symmetry. This corresponds to an integer number of non-null samples in a quarter of period, that is to say, the following cases: H = 2(2i+1) = 6, 10, 14, 18, … Those cases are particularly interesting in terms coefficients symmetry. This symmetry can be leveraged to propose a very performing architecture based on π-delayed driving signals.

III. N-PATH MIXERS BASED ON Π-DELAYED DRIVING SIGNALS

A. Principle

With conventional HR-NPMs, for which H = N, the large number of branches required to reach high rejection leads to high VCO frequency, related to the sampling frequency Fs = H.FLO. As H = N, this also leads to numerous switches and to numerous amplifiers which just goes in disfavour of simplicity and power consumption. To increase harmonic rejection while keeping a low VCO frequency and without pushing up intricacy, a solution consists in choosing H = 2N where N = (2i+1) is an odd number of paths. The resulting HR-NPM presents only N paths and a smart combination of switches together with their driving signals enable to synthesize an effective local oscillator signal with H = 2N samples. Practically, two versions of driving signals are used to control the switches: one for the positive branches, Sh + , which generate a positive effective LO, eflo + (t), and one for the negative branches, Sh -, which generate a negative effective LO, eflo -(t), as shown in Fig. 6. Sh -is simply a πdelayed version of Sh + . Combined with the appropriate gains Gh, the EFLO signal at each path is similar to the standard EFLO of a conventional HR-NPM where H = N = 2(2i+1). It has to be mentioned that the VCO frequency needed for this π-delayed architecture is not equal to 2N.FLO but only to N.FLO which is a great advantage for clock generation and distribution in the IC. Finally, the reduced number of paths, N, leads to lower complexity. As illustrated on Fig. 6, or in Table 1, with this solution, the number of differential gain stages is equal to half the number of non-null coefficients reduced by one, Hg = (Hef/2 -1) The total number of switches is only Sw = (Hef/2 -1).N in that case where Hef = H-2.

B. Simulation results

Frequency response of the presented N-path π-delayed structure has been carried out theoretically with Matlab software in order to validate the harmonic rejection in the case of H = 10 (and so N = 5) and to provide a comparison with conventional LNA-first topologies based on nondelayed structures [START_REF] Xu | A Blocker-Tolerant RF Front End With Harmonic-Rejecting N-Path Filter[END_REF]. Simulation results are illustrated Fig. 7. The first harmonic occurs at 3.6 GHz for an input frequency signal of 400 MHz which corresponds to the 9 th harmonic. Simulations also confirm that the same harmonic rejection is reached with H = N = 10.

For N = 5 and H = 10, the presented solution needs to implement Hg = 3 differential LNAs and Sw = 30 switches whereas the equivalent H = N = 10 standard structure requires Hg = 4 LNAs and Sw = 80 switches. In addition, the frequency response of the π-delayed structure is compared to a standard HR-NPM which has quite a similar complexity (Hg = 3, Sw = 48) corresponding to H = N = 8. As expected the 7 th harmonics occurs at 2.8 GHz reducing the system bandwidth as compared to the presented solution. Table 1 shows the Figures-of-Merit η1=Hr/Hg and η2=Hr/Sw. They strongly go in favor of the π-delayed architecture.

G 0 G 1 G 2 G 3 G 4 G 5 G 6 G 7 G 8 G 9 G 10 G 11 G 12 G 13 H r H g Sw η 1 η 2 5
In terms of theoretical sensitivity, Fig. 8 shows the effects of the gain ratio error and the phase error between the output LNAs signals, of respective gains 1 and 1.62, on the fundamental response of the effective LO, eflo(t), over its harmonic response. This ratio, HR-Fun/har, is proportional to the HRR of the total mixer. It is observed from the curves that the system performance decreases when increasing the mismatching between the output LNA signals. The slope of HR-Fun/har with respect to the gain error is faster than the slope with respect to the phase error, which concludes that our system is more sensitive to LNA gain error than phase error. LNA errors can be potentially overcome thanks to biasing adjustment. There cannot be observable HR-Fun/har due to LNA on the 5 th harmonic since the clock duty cycle is equal to 1/5.

In comparison to the state of the art, our theoretical study of the proposed structure shows an improvement on the 5th harmonic rejection with respect to the gain and phase LNA outputs mismatching. Furthermore, it can be outlined using equations of harmonic attenuation (HA) [START_REF] Xu | A Blocker-Tolerant RF Front End With Harmonic-Rejection N-Path Filter[END_REF], that the 3rd HA of our proposed architecture shows similar behavior with the conventional HR8PM. This results depends on the gain and phase errors between the output LNA signals.

IV. CONCLUSION

A theoretical, general, study has been proposed that can be useful for the design of conventional N-path mixers based on LNA-first receivers. This approach highlights some particular cases of odd numbers of paths presenting interesting symmetrical properties in terms of sampling gain coefficients. This symmetry has been utilized in the framework of a π-delayed driving signals architecture that enables high harmonic rejection while using a lower VCO frequency and presenting a lower complexity, in terms of the required number of differential amplifiers and switches, than its conventional counterparts. The solution is illustrated by a 5-path mixer that achieves a high harmonic rejection up to the 8 th harmonic with only 3 differential amplifiers and 30 switches.

Fig. 1 .

 1 Fig. 1. The structure of a 4PM with (a) single, (b) differential modes.

Fig. 2 .

 2 Fig. 2. Generic topology of a HR-NMP

Fig. 3 .

 3 Fig. 3. Sampling coefficients of a sine wave for various H and N

Fig. 4 .

 4 Fig. 4. Frequency response of the effective LO, eflo(t).

Fig. 6 .

 6 Fig. 6. π-delayed architecture with H = 10 and N = 5.

Fig. 7 .

 7 Fig. 7. Matlab simulations of the presented solution based on π-delayed driving signals for H = 10 and N = 5 (solid line). Comparison with conventional structures with H = N = 8 (light dashed line) and H = N = 10 (dark dashed line).

  Fig. 8. Matlab simulations showing the effect of (a) the gain ratio error on the HR-Fun/har of the eflo(t) of the proposed architecture with zero phase error and (b) the phase error between output LNA signals on the HRR of the eflo(t) of the proposed architecture with zero gain error. No impact is observed on HR-Fun/har for 5 th harmonic.

TABLE I .

 I NORMALIZED COEFFICIENTS FOR A SINE PERIOD FOR VARIOUS SAMPLING OF THE SINE WAVE OF FREQUENCY FLO.

	H	Hr Η g	Sw	η 1	η 2

Authorized licensed use limited to: CEA GRENOBLE. Downloaded on September 26,2023 at 16:09:28 UTC from IEEE Xplore. Restrictions apply.

ACKNOWLEDGMENT

This work has been granted by the European program ECSEL through the projects OCEAN12.


Takeshi Takeuchi
email: t.takeuchi@oist.jp

Laurent Plasseraud

Isabelle Ziegler-Devin

Nicolas Brosse

Chuya Shinzato

Noriyuki Satoh

Frédéric Marin

Biochemical characterization of the skeletal matrix of the massive coral, Porites australiensis -The saccharide moieties and their localization

Keywords: Porites australiensis, biomineralization, coral, skeletal organic matrix, saccharide acid-insoluble matrix ASM: acid-soluble matrix DSL: Datura stramonium lectin ELLA: enzyme-linked lectin assay FT-IR: Fourier transform infrared spectroscopy SOM: skeletal organic matrix

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

Introduction

Scleractinian corals are marine animals known for their capacity to elaborate calcium carbonate exoskeletons of complex shapes, many of which form reefs of great size [START_REF] Spalding | World atlas of coral reefs Univ of California Press[END_REF]. Among metazoans, scleractinians are the main producers of biogenic aragonite in marine ecosystems [START_REF] Milliman | Production and accumulation of calcium carbonate in the ocean: budget of a nonsteady state[END_REF]. From a geochemical viewpoint, scleractinian biomineralization can be considered as the uptake of inorganic ions from the environment and conversion of these ions into a spatially structured network of calcium carbonate biocrystals that contain trace elements [START_REF] Beck | Sea-Surface Temperature from Coral Skeletal Strontium/Calcium Ratios[END_REF][START_REF] Tambutté | Coral biomineralization: From the gene to the environment[END_REF]. Because this process literally 'freezes' environmental information into skeletal tissues that fuel steadily sedimentary archives, scleractinian skeletons are often used to reconstruct sequences of paleoenvironmental conditions, in particular, past seawater temperatures via measures of Sr/Ca, Li/Ca, and δ 18 O/ δ 16 O ratios [START_REF] Beck | Sea-Surface Temperature from Coral Skeletal Strontium/Calcium Ratios[END_REF][START_REF] Druffel | Geochemistry of corals: Proxies of past ocean chemistry, ocean circulation, and climate[END_REF][START_REF] Hathorne Ed | Lithium in the aragonite skeletons of massive Porites corals: A new tool to reconstruct tropical sea surface temperatures[END_REF][START_REF] Simkiss | Biomineralization Elsevier[END_REF] or past ocean pH via boron isotopic composition (δ 11 B) [START_REF] Rollion-Bard | Boron isotopes as pH proxy: A new look at boron speciation in deep-sea corals using 11B MAS NMR and EELS[END_REF]. However, in spite of the increasing use of these geochemical proxies in corals, the molecular basis of scleractinian skeleton formation is far from understood.

From a cellular viewpoint, scleractinian skeletal biomineralization is typically an epitheliumdriven process, i.e., all precursor components of the exoskeleton are secreted by the calicoblastic epithelium (also called the aboral ectoderm), a subset of the ectoderm [START_REF] Allemand | Biomineralisation in reef-building corals: from molecular mechanisms to environmental control[END_REF][START_REF] Constantz | Acidic macromolecules associated with the mineral phase of scleractinian coral skeletons[END_REF][START_REF] Tambutté | Coral biomineralization: From the gene to the environment[END_REF], in contact with the nascent mineral layer. These components comprise inorganic ions -calcium, bicarbonate and minor elements, i.e., strontium and magnesium -that are extruded via membrane channels and pumps (transcellular pathway) or via intercellular space (paracellular pathway), and are combined into CaCO3 minerals in the sub-calicoblastic space [START_REF] Tambutté | Coral biomineralization: From the gene to the environment[END_REF]. They also include a large set of organic macromolecules that constitute the skeletal organic matrix (SOM), which is allegedly released via a classical secretory pathway. This latter is however poorly documented for cnidarians in general.

From a biochemical viewpoint, in many coral species examined to date, the SOM contains proteins, glycoproteins, lipids, and polysaccharides that are included in the skeleton during the calcification process [START_REF] Constantz | Acidic macromolecules associated with the mineral phase of scleractinian coral skeletons[END_REF]Cuif et al., 1999b;[START_REF] Dauphin | Comparative studies of skeletal soluble matrices from some Scleractinian corals and Molluscs[END_REF][START_REF] Farre | Occurrence and diversity of lipids in modern coral skeletons[END_REF][START_REF] Puverel | Soluble organic matrix of two Scleractinian corals: Partial and comparative analysis[END_REF]. The SOM is distributed heterogeneously in the coral skeleton, being concentrated in "centres of crystallization" [START_REF] Bryan | Spherulitic crystallization as a mechanism of skeletal growth in the hexacorals[END_REF][START_REF] Ogilvie | Microscopic and systematic study of madreporarian types of corals[END_REF] or in "early mineralization zones" [START_REF] Cuif | XANES mapping of organic sulfate in three scleractinian coral skeletons[END_REF], from which fiber-like crystals distribute radially.

The SOM is also detected on the top surface and growing steps in crystals [START_REF] Cuif | The two-step mode of growth in the scleractinian coral skeletons from the micrometre to the overall scale[END_REF]. The complex topographical relationship between the organic and mineral phases suggests that the SOM plays an active role in regulating the initiation and growth of the calcium carbonate crystals. In particular, the SOM contains acidic macromolecules, usually considered as key-components for interacting with calcium carbonate crystals: in different in vitro functional assays, they have been shown to bind calcium ions [START_REF] Isa | Some observations on the Ca 2+ -binding phospholipids from scleractinian coral skeletons[END_REF] and modify the shapes of calcium carbonate crystal [START_REF] Naggi | Structure and function of stony coral intraskeletal polysaccharides[END_REF][START_REF] Ramos-Silva | The Skeleton of the Staghorn Coral Acropora millepora: Molecular and Structural Characterization[END_REF] similarly to what has been observed with mollusc SOM [START_REF] Addadi | Interactions between acidic proteins and crystals: stereochemical requirements in biomineralization[END_REF][START_REF] Weiner | Soluble protein of the organic matrix of mollusk shells: a potential template for shell formation[END_REF]. Amino acid analyses of SOMs of various coral species reported high contents of aspartic and glutamic acids [START_REF] Constantz | Acidic macromolecules associated with the mineral phase of scleractinian coral skeletons[END_REF]Cuif et al., 1999a;[START_REF] Gautret | Composition of soluble mineralizing matrices in zooxanthellate and non-zooxanthellate scleractinian corals: Biochemical assessment of photosynthetic metabolism through the study of a skeletal feature[END_REF][START_REF] Mitterer | Amino-acid composition and metal-binding capability of skeletal protein of corals[END_REF][START_REF] Puverel | Soluble organic matrix of two Scleractinian corals: Partial and comparative analysis[END_REF]. More recently, aspartic acid-rich proteins in coral skeletons were predicted by whole genome sequencing of Acropora digitifera [START_REF] Shinzato | Using the Acropora digitifera genome to understand coral responses to environmental change[END_REF], and subsequently confirmed by transcriptomic and proteomic studies [START_REF] Drake | Proteomic analysis of skeletal organic matrix from the stony coral Stylophora pistillata[END_REF]Ramos-Silva et al., 2013b;[START_REF] Takeuchi | Stepwise evolution of 25 coral biomineralization revealed with genome-wide proteomics and transcriptomics[END_REF].

Contrary to these recent comprehensive surveys of skeletal proteomes, sugar moieties in coral SOM have not been well characterized, even though they are always detected in coral skeletons and are believed to play important, although undetermined functions in biomineralization [START_REF] Albeck | Polysaccharides of Intracrystalline Glycoproteins Modulate Calcite Crystal Growth In Vitro[END_REF]. The sugars in coral SOM include common monosaccharides, such as neutral (fucose, rhamnose, galactose, glucose, mannose, xylose), aminated (galactosamine and glucosamine) and acidic (glucuronic/galacturonic acids) hexoses. Previous studies showed that their proportions vary greatly among species (Cuif et al., 1999b;[START_REF] Naggi | Structure and function of stony coral intraskeletal polysaccharides[END_REF][START_REF] Ramos-Silva | The Skeleton of the Staghorn Coral Acropora millepora: Molecular and Structural Characterization[END_REF], such that some present remarkable monosaccharide signatures. For example, in the Acropora millepora SOM, arabinose represents more than 60% of all monosaccharides, a finding correlated with its abundance in mucus of that species [START_REF] Ramos-Silva | The Skeleton of the Staghorn Coral Acropora millepora: Molecular and Structural Characterization[END_REF][START_REF] Wild | Influence of coral mucus on nutrient fluxes in carbonate sands[END_REF]. In addition, sulphated acidic sugars were detected in SOMs of several scleractinian coral species [START_REF] Dauphin | Soluble organic matrices of aragonitic skeletons of Merulinidae (Cnidaria, Anthozoa)[END_REF][START_REF] Puverel | Soluble organic matrix of two Scleractinian corals: Partial and comparative analysis[END_REF]. However, polymeric structure and localization of saccharides in coral skeletons have not been studied.

In this context, we have undertaken the overall biochemical characterization of the SOM of the massive colonial coral, Porites australiensis. By virtue of their longevity and ability to accumulate massive calcium carbonate skeletons, the genus Porites represents one of the most important corals as a recorder of environmental conditions [START_REF] Beck | Sea-Surface Temperature from Coral Skeletal Strontium/Calcium Ratios[END_REF][START_REF] Cobb | El Nino/Southern Oscillation and tropical Pacific climate during the last millennium[END_REF][START_REF] Linsley | Decadal Sea Surface Temperature Variability in the Subtropical South Pacific from 1726 to 1997[END_REF][START_REF] Watanabe | Permanent El Nino during the Pliocene warm period not supported by coral evidence[END_REF]. We characterized the Porites SOM by using various molecular analyses, including SDS-PAGE, FT-IR, CHNS analysis, and in vitro calcium carbonate crystallization experiment. We confirmed the presence of sulphate, which contributes to the acidic nature of the Porites SOM. Furthermore, monosaccharides were quantified, and for the first time in corals, enzyme-linked lectin assay (ELLA) was performed.

In situ localization of polysaccharide in coral skeleton was achieved using a biotinylated lectin (Datura stramonium Lectin -DSL) associated with anti-biotin antibody conjugated to nanogold. Our results showed that a DSL-reactive saccharide fraction was evenly dispersed on the surfaces of calcium carbonate crystals, suggesting that it may shape crystal growth during coral skeleton formation.

Materials and methods

Sample collection and cleaning

A living coral colony of Porites australiensis (approx. weight 50 grams) was collected at the Sesoko Marine Station, University of Ryukyus, Okinawa, Japan, under Okinawa prefecture permit (Number: 20-69). The organism was immersed overnight in 3L of 10x diluted household bleach solution and then extensively rinsed with water. This process, which was continued until the complete removal of animal tissue and other organisms on the surface, comprised the initial bleaching. The coral skeleton was rinsed, air-dried, and crushed into ~2mm fragments with a Jaw-crusher (Retsch BB200). Fragments were immersed in 0.1x sodium hypochlorite 10-15% (SIGMA ref. number 71696) for 50h (second bleaching). Then, the fragments were washed with twice-distilled water, dried, and powdered using a mortar grinder (Frisch Pulverisette 2). The powder (48 grams) was sieved (pore size <200 µm) and separated into two batches. The first was subsequently decalcified (see below), while the second was bleached overnight in NaOCl solution (third bleaching), thoroughly washed and air-dried at 37°C before decalcification.

Extraction of skeletal organic matrices

The cleaned powder samples (second or third bleaching) were suspended in cold water and decalcified overnight at 4°C by progressively adding (100 µL every 5 sec.) cold dilute acetic acid (10% vol/vol) with an electronic burette (Titronic Universal, Schott, Mainz, Germany).

After 15 hours (final pH 4), the clear solution was centrifuged (3900 G, 30 min.), in order to separate the pellet containing the acid-insoluble matrix or AIM from the supernatant, i.e., the acid-soluble matrix (ASM). The AIM pellet was resuspended in Milli-Q water, centrifuged, and the supernatant discarded. After three cycles, the pellet was freeze-dried. The ASM solution was filtered (5 µm) on a Nalgene filtration apparatus and concentrated by ultrafiltration (Amicon stirred cell 400 mL) on a 10kDa cutoff membrane (Millipore, ref.

PLGC07610). The concentrated solution (15 mL) was dialyzed 4 days against 1 L MilliQ water with several water changes, and freeze-dried.

SDS-PAGE

ASM lyophilisates were suspended in Milli-Q and an aliquot was added to an equal volume of 2x Laemmli sample buffer containing ß-mercaptoethanol. AIM pellets were gently fragmented with a scalpel and a part was directly resuspended in 1x Laemmli sample buffer.

All preparations were denatured for 5 min at 99˚C, cooled on ice and briefly centrifuged.

While ASM was totally dissolved by denaturation, a part of AIM remained insoluble.

Consequently, only the Laemmli-soluble fraction of AIM (referred to as LS-AIM) was further analyzed on gels. Proteins were run on precast 10%-20% gradient mini-gels (Bio-Rad) in mini-Protean III system. Gels were stained with silver nitrate [START_REF] Morrissey | Silver stain for proteins in polyacrylamide gels: A modified procedure with enhanced uniform sensitivity[END_REF], Stains-all [START_REF] Campbell | Staining of the Ca2+-binding proteins, calsequestrin, calmodulin, troponin C, and S-100, with the cationic carbocyanine dye" Stains-all[END_REF][START_REF] Marin | Caspartin and Calprismin, two proteins of the shell calcitic prisms of the Mediterranean fan mussel Pinna nobilis[END_REF] and Alcian blue [START_REF] Thornton | Identification of glycoproteins on nitrocellulose membranes and gels[END_REF] at pH 1.0 in order to detect sulphate groups.

FT-IR spectroscopy

FT-IR spectroscopy was used to check the overall chemical properties of the extracted matrices (ASM and AIM) after two or three bleaching steps. In each case, minute chips of lyophilized samples were analyzed with a Bruker Vector 22 instrument (Bruker Optics Sarl, Marne la Vallée, France) fitted with a Specac Golden Gate Attenuated Total reflectance (ATR) device (Specac Ltd, Orpington, UK) in the 4000-500 cm -1 wavenumber range (twelve scans at a spectral resolution of 4 cm -1 ). The choice for ATR mode was dictated by its reliability and reproducibility, as recently shown [START_REF] Beasley | Comparison of transmission FTIR, ATR, and DRIFT spectra: implications for assessment of bone bioapatite diagenesis[END_REF]. The qualitative assignment of absorption bands was performed by comparison with previously described spectra, determined by us or available in the literature [START_REF] Dauphin | Comparative studies of skeletal soluble matrices from some Scleractinian corals and Molluscs[END_REF][START_REF] Dauphin | Soluble organic matrices of aragonitic skeletons of Merulinidae (Cnidaria, Anthozoa)[END_REF][START_REF] Kanold | Spine and test skeletal matrices of the Mediterranean sea urchin Arbacia lixula -a comparative characterization of their sugar signature[END_REF].

Elemental analysis

Elemental analyses were performed at the ''Plateforme d'Analyse Chimique et de Synthèse Moléculaire de l'Université de Bourgogne (PACSMUB)'' on a Fisons EA 1108 CHNS-O apparatus (M. Soustelle). As the technique requires 5 mg of material, only one AIM extract (AIM 3bl) was tested, and the measurement was performed twice.

In vitro crystallization of CaCO3 with ASM

In vitro crystal growth experiments in the presence of ASM was tested as described in a previous paper [START_REF] Kanold | Spine and test skeletal matrices of the Mediterranean sea urchin Arbacia lixula -a comparative characterization of their sugar signature[END_REF]. In brief, 200 µL of 10 mM CaCl2 containing ASM at increasing concentrations (1-32 µg/ml) was applied to a 16-well culture slide (Lab-Tek, Nunc/Thermo Scientific, Rochester, NY, USA). The glass slide was closed with a plastic cover in which 1-mm holes were pierced above each well to allow exposure of the solution to ammonium bicarbonate vapor, and sealed with Parafilm. The slide was placed in a desiccator, together with ammonium bicarbonate crystals, under vacuum and incubated at 4 ˚C for 3 days. Subsequently, the solution was carefully removed by suction with a blunt needle connected to a vacuum pump and CaCO3 crystals generated on the glass bottom were dried for 4h at 37˚C. These were directly observed using a tabletop scanning electron microscope (TM 1000, Hitachi) without carbon sputtering.

Monosaccharide analysis

Monosaccharide quantification of ASMs and AIMs after two or three bleaching steps was performed according to the HPAE-PAD technology (High Pressure Anion-Exchange -Pulsed Amperometric Detection) on an ICS-3000, Dionex system equipped with a Dionex CarboPac TM PA-20 (3x150 mm) analytical column. In short, lyophilized samples were hydrolyzed in 2M trifluoroacetic acid at 105 °C for 4 h (100 µg/100 µL), and the solution was neutralized with sodium hydroxide. Hydrolytic conditions deacetylate N-acetyl-glucosamine and N-acetyl-galactosamine, which are subsequently analyzed as glucosamine and galactosamine, respectively. Filtered samples (20 µL) were eluted at 0.4 mL/min (35°C) using the following sodium hydroxide gradient: pure water 99.2% /250 mM NaOH 0.8% : 0à20 min; pure water 75%/250 mM NaOH 20% /NaOAc (1M)-NaOH (20 mM) 5%: 20à37min ; pure water 40% /250 mM NaOH 20%/NaOAc (1M)-NaOH (20 mM) 40%: 37à41min. Each elution was followed by a wash and subsequent equilibration time. External sugar and uronic acids standards were used for calibration (7 points per curve): fucose, glucose, xylose, galactose, mannose, rhamnose, arabinose, glucosamine, galactosamine, galacturonic acid and glucuronic acid (all provided by Sigma-Aldrich).

Enzyme-Linked Lectin Assay (ELLA)

Enzyme Linked Lectin Assay (ELLA) was conducted as described previously [START_REF] Kanold | Spine and test skeletal matrices of the Mediterranean sea urchin Arbacia lixula -a comparative characterization of their sugar signature[END_REF] on ASM fractions. This test, performed in solution, cannot be applied to AIM. Briefly, 96-well plates (MaxiSorp, Nunc/Thermo Scientific, Nunc A/S, Roskilde, Denmark) were coated with ASM (50 ng/well) and incubated for 90 min at 37˚C. They were washed three times with a solution of TBS/Tween-20 (0.5 mL Tween 20 per L) spread using a manual microplate 8-channel washer (Nunc Immuno Wash), and subsequently blocked with Carbofree blocking solution (Vector Laboratories, ref. for 60 min at 37˚C. Three sets of 7 biotinylated lectins were tested (Vector Laboratories, Peterborough, UK, ref. BK-1000, BK-2000, BK-3000). They were applied to the wells (dilution to 10 µg/mL) and incubated for 90 min at 37˚C. Unbound lectins were removed by washing five times with TBS/Tween-20.

Then, a solution containing alkaline phosphatase-conjugated avidin (Avidin-AP, Sigma A7294, St Louis, MO, USA) diluted 70,000 times was added (100 µL per well) and incubated for 90 min at 37 ˚C. Microplates were washed as before, and incubated with ELISA substrate solution (10% vol/vol diethanolamine in Milli-Q water, pH 9.8) containing phosphatase substrate (0.5 mg/mL, 4-nitrophenyl phosphate disodium salt hexahydrate (pNPP) tablet, Sigma, ref. UN3500-A) at 37 °C. They were incubated at 37°C and read every 15 minutes at 405 nm using a Bio-Rad Model 680 micro-plate reader. Results were normalized and converted to percentage of reactivity by subtracting the background (negative control comprising ASM without lectin but with Avidin-AP) from all values and by considering the highest response as 100%. The test was repeated at least three times. For detailed information on the binding preference of each lectin, see [START_REF] Immel | The shell of the invasive bivalve species Dreissena polymorpha: biochemical, elemental and textural investigations[END_REF].

In situ localization: lectin-gold assay.

Sodium hypochlorite-bleached fragments of P. australiensis skeleton were cut with a diamond saw (Dremel) and one surface was flattened with fine sand paper. After rinsing with sonication, the surface was finely polished with an alumine suspension (0.05 µm) on a rotary polisher until a mirror polish was obtained. Samples were thoroughly cleaned by sonication and submitted to bleach (NaOCl, 0.26 % active chlorine) to remove contaminants that may have spread over the surface during polishing. They were rinsed twice with Milli-Q water, and gently dried on Whatman paper. The flat surface was then etched 5 minutes with a solution of 1% EDTA (wt/vol) in the sonication bath to expose the skeletal matrix, then rinsed with Milli-Q water (5 min.).

All incubation steps described after were performed at room temperature. In brief, coral samples were incubated one hour in Carbo-free blocking solution alone ((Vector Laboratories, ref. , then overnight in the same solution containing the biotinylated DSL (Datura stramonium lectin, diluted 100 times), and in the presence of the bactericidal agent, sodium azide (0.005% wt/vol). Samples were gently rinsed several times with TBS/Tween20, then incubated at least 90 minutes in a Carbo-free solution containing diluted (1/100) goat antibiotin antibody conjugated to ultra-small gold particles (0.8 nm) (GABio, Ultra Small, ref.

800.088; Aurion, Wareningen, The Netherlands). Samples were rinsed several times with TBS/tween20 and gently dried on filter paper. Enhancement of the gold signal was achieved by incubating samples in silver enhancement solution (British Biocell International, ref.

SEKL15) for about 15-20 minutes. Samples were rinsed once in Milli-Q water, then dried at 37°C.

Negative controls were performed in parallel under identical conditions, by omitting one or more incubation steps: no incubation step with DSL; incubation with gold-coupled anti-biotin, followed by silver enhancement; no incubation with DSL nor with biotin, but with silver enhancement. All samples were carbon-sputtered and observed either with a JEOL JSM 760 F field emission scanning electron microscope (JEOL, Tokyo, Japan) or with an ultra-high resolution, cold-field emission Hitachi SU8230 scanning electron microscope. Quick checks were performed with a tabletop SEM Hitachi TM1000. The experiment was repeated five times.

Results

Cleaning of coral skeleton and total matrix quantification

Because the Porites skeleton is porous, having a mesh-like structure, thorough cleaning of the skeleton was required to remove soft tissues of the coral and microorganisms present in the pores. We repeatedly cleaned pieces of coral skeleton by immersing them completely in bleach solution and rinsing exhaustively with ultrapure water until tissue and epibionts on the skeletal surface were completely removed (first bleaching). However, even though the skeleton looked superficially clean after this initial treatment, a careful check using SEM indicated that fibrous organic material was still present in the pores (Figure 1A,C). After a second bleach treatment, these contaminants were removed (Figure 1B,D). Finally, to ensure the complete removal of contaminants, a third bleaching was performed on sieved skeletal powder (grain size < 200 µm). The total organic matrices were quantified after 2 nd and 3 rd bleaching as shown in Table 1. Interestingly, the amount of organic matrix -both soluble (ASM) and insoluble (AIM) -extracted after the 3 rd bleaching was significantly lower (reduced by half) than that obtained after the 2 nd one. The organic matrix represents 0.14% and 0.07% of the total skeleton by weight, after the 2 nd and the 3 rd bleaching, respectively.

SDS-PAGE

The ASM and the AIM (or, at least, its Laemmli-soluble moieties, referred to as LS-AIM) extracted from the 2 nd and 3 rd bleachings were analyzed by poly-acrylamide gel electrophoresis (SDS-PAGE) after staining with silver nitrate, Stains-all, and Alcian blue (Figure 2). Overall, broad and smeary staining was observed in all staining methods. Weak, blurred bands of high molecular weight (>170 kDa) and in the range of 50-70 kDa were detected in the silver stained gel, as well as negatively stained zones at the bottom of the lanes (<10 kDa for lane 2 nd bleaching, and <17 kDa for the three other lanes) (Figure 2A). Stains-all staining showed a strong purple color in ASMs, while AIMs appeared more pinkish. (Figure 2B). Such purple staining -between metachromatic blue and red -has already been observed in a previous study for ASM of other skeletal matrices, suggesting their ability to bind calcium [START_REF] Marin | Caspartin and Calprismin, two proteins of the shell calcitic prisms of the Mediterranean fan mussel Pinna nobilis[END_REF]. Alcian blue staining at low-pH (pH 1.0) stained both ASM and AIM (Figure 2C), suggesting the presence of sulphate groups in all four samples [START_REF] Thornton | Identification of glycoproteins on nitrocellulose membranes and gels[END_REF]. Low-molecular weight ASMs stained more intensely, while for AIMS, highmolecular weight compounds stained more strongly.

FT-IR and CHNS analyses

Fourier transform infrared (FT-IR) spectra were analyzed for SOMs (Figure 3). First, the absence of the typical carbonate peaks in the ASM and in the AIM demonstrated that both extracts did not contain any contaminating salts resulting from the decalcification. In all fractions, bands attributed to protein or sugar backbones were detected at around 3380 cm -1 , 1650 cm -1 , and 1540 cm -1 , which correspond to amide A (νN-H), amide I (νC=O), and amide II (νC-N) bands, respectively. The signal of amide I band was stronger than that of amide II in ASM samples, while they were equivalent in AIM fractions. Weak signals at 2921-2923 cm -1 in AIM and 2927-2937 cm -1 in ASM, representing νC-H stretching vibrations, were also detected and may correspond to lipids. Signals around 1450 and 1410 cm -1 may be related to carboxylic groups. An absorption band specific to carbohydrate was observed near 1060-1070 cm -1 in all samples. In AIM fractions, this signal was significantly weaker after the third bleaching AIM with respect to the adjacent signal at 1150 cm -1 , which was sharply detected in both AIM samples. The band at 1150 cm -1 , together with signals at 961, 636 and 554 cm -1 , corresponds to phosphate group [START_REF] Panda | FTIR, XRD, SEM and solid state NMR investigations of carbonate-containing hydroxyapatite nano-particles synthesized by hydroxide-gel technique[END_REF][START_REF] Rivera-Muñoz | Hydroxyapatite-Based Materials: Synthesis and Characterization[END_REF]. These bands were hardly visible in ASM (shoulders at 1150 cm -1 and 961 cm -1 ); presumably because of their weak amplitudes, they were masked by other broad signals. The signal at 1230-1250 cm -1 is related to νS=O stretching vibration [START_REF] Cabassi | Infrared absorption and raman scattering of sulfate groups of heparin and related glycosaminoglycans in aqueous solution[END_REF][START_REF] Cael | Polarized infrared spectra of crystalline glycosaminoglycans[END_REF][START_REF] Longas | Sulfate composition of glycosaminoglycans determined by infrared spectroscopy[END_REF]. The presence of sulfur in the third bleaching AIM was also supported by CHNS analysis (Table 2).

In vitro CaCO3 crystallization with ASM

Results of the in vitro crystallization assay (diffusion test) are shown in Figure 4. Crystals were grown in solution containing ASM, extracted after two (Figure 4A to E) and three (Figure 4G to L) bleach treatments. Addition of 1 µg/mL ASM (Figure 4A, G) resulted in almost no difference with the control experiment (without ASM, Figure 4M), in which typical, rhombohedral calcite crystals were synthesized. The morphology of crystals was altered in the presence of 4 µg/mL ASM (Figure 4B,H). Terraced structure appeared on the edges, and the crystal size was reduced. The increased concentration of ASM (16 µg/mL, 2 nd bleach) resulted in polycrystalline structure (Figure 4C, D), and this effect was more significant at 32 µg/mL of ASM (Figure E, F). In comparison, the alteration of crystal shape was more severe when high concentrations of ASM extracted after 3 bleaching steps were used: at 16 µg/mL, crystal edges were rounded and the surface was rough-textured (Figure 4I, J). At the highest concentration (32 µg/mL), flat crystal faces were completely destroyed, and aggregation of crystals was frequently observed (Figure 4K,L). In summary, higher concentrations of ASMs altered the morphology and size of CaCO3 crystals, and this effect was more accentuated when using ASM extracted after 3 bleaching steps.

Monosaccharide analysis

Results of the monosaccharide analysis after mild hydrolysis (with TFA) of both ASM and AIM, after 2 or 3 bleaching steps, are shown in Figure 5, as relative percentages of the total sugar composition. The data allow a double comparison, between AIM and ASM on the one hand, and between the 2nd and 3rd bleaching on the other hand. First, in both ASMs, the most abundant saccharides are galactose>arabinose>fucose, with the two first representing ≥50% of all sugars. Xylose, glucosamine, glucose and mannose are minor monosaccharides (between 4 and 10%) while rhamnose and galactosamine are quantitatively negligible (below 3%). The sugar profiles of both AIMs are somewhat similar to those of ASMs, except that galactose and glucose are the dominant sugars (~≥ 20% each), with ~15% arabinose. Xylose and fucose (>10%) are next most abundant, followed by glucosamine and mannose (5-10%).

As in ASMs, rhamnose and galactosamine are extremely minor. Galacturonic and glucuronic acids were not detected in any of our samples. Second, the third bleaching treatment had a more pronounced effect on ASMs than on AIMs: while it did not modify the sugar abundances in AIMs, resulting in two superimposable AIM histogram patterns, it induced in ASM a large decrease of arabinose (from 36.2 to 20.7%) and slight increases in galactose (28.6 to 29.2%), fucose (13.2 to 16.2%), glucosamine (4.2 to 8.4%), glucose (5.1 to 7.4%) and xylose (5.6 to 8.6%). However, in spite of these few percentage changes, the 3rd bleaching did not fundamentally modify the monosaccharide composition pattern of ASM.

ELLA

In order to determine the types of glycan structures present in ASM (extracted after two and three bleaching steps), the affinity of these two extracts for biotinylated lectins was examined by enzyme-linked lectin assay (ELLA) [START_REF] Kanold | Spine and test skeletal matrices of the Mediterranean sea urchin Arbacia lixula -a comparative characterization of their sugar signature[END_REF]. The results, expressed as percentages of reactivity with respect to the most reactive lectin, are shown in Figure 6. It is noteworthy that the two ASM extracts reacted almost identically to the 21 different lectins: in both cases, the strongest signal by far was observed with Datura stramonium lectin (DSL), which preferentially binds to (β-1,4) linked GlcNAc oligomers, LacNAc, and poly-LacNAc [START_REF] Sondej | Glycoprofiling of the Human Salivary Proteome[END_REF]. Greatly reduced affinities (20-25% of those obtained with DSL) were recorded with Concanavalin A (ConA), Lycopersicon esculentum lectin (LEL), and Solanum tuberosum lectin (STL). Other lectins including, SBA, DBA, PSA, LCA, RCA120, PHA-E, ECL, UEA I, SJA, PNA, Jacalin, WGA, GSL I, PHA-L, Succinylated WGA, VVA, and GSL II, exhibited very weak reactivity (less than 15% of DSL) for the two ASM extracts.

In situ localization study using gold-conjugated DSL

Since DSL showed the strongest affinity to Porites ASM in ELLA, an in situ assay was developed to localize the target saccharide moiety in the coral skeleton. Figure 7 shows SEM images of the coral skeleton treated with biotinylated DSL, which was subsequently targeted with an anti-biotin antibody coupled with gold nanoparticles. Figure 7A, acquired in secondary electron (SE) mode, shows tips of the fibers that radiate from the early mineralization zone (not shown), where crystallization occurs. Figure 7B presents the same area in back-scattered electron (BSE) mode. It shows tiny bright spots that represent gold particles, and consequently sugar-bound biotinylated lectins. Since an SEM image in SE mode (Figure 7A) does not show any signal, this confirms that the signals detected in BSE mode come from the gold particles. In Figure 7B, there is no particular concentration of gold signals at the apex of the fibers, as one might have expected. Rather, the signals are dispersed along the crystal surfaces.

Figures 7C, D andE (in BSE mode) display successive enlargements of the same area. Here again, one notices that signals are homogeneously distributed on the surfaces of the fibers, irrespective of the growth lines (belt-like structures) that are perpendicular to the direction of crystal growth (Figure 7C andD). At high magnification (Figure 7E), the density of spots reaches about 10-15 per µm 2 . At very high magnification (x100 000), gold particles can be measured (diameter ~ 50 nm) (Figure 7F). In none of our experiments did we detect concentrations of gold spots near the centers of the spherulites, a finding that may suggest that DSL-binding sugar moieties are not involved in crystal nucleation, but that they have

Discussion

In the present study, we have characterized the skeletal organic matrix (SOM) of the massive coral, Porites australiensis, using various biochemical techniques, including FT-IR, CHNS, ELLA, gel electrophoresis, monosaccharide analysis by chromatography, and in vitro crystallization. We have also localized sugar components directly in the coral skeleton, with a technique that we adapted for the first time for corals. Our discussion focuses on three points: the influence of the bleaching, the presence of sulphate and the properties of the sugar moieties.

As discussed in one of our earlier papers (Ramos-Silva et al., 2013a), bleaching coral skeletal tissues with sodium hypochlorite is crucial for obtaining a SOM free of organic contaminants, namely soft tissues originating from the ectoderm, or microorganisms accumulated in skeletal pores and pockets. We chose a cleaning strategy consisting of successive bleaching treatments to eliminate these contaminants, and characterized the extracted SOMs (ASMs, AIMs) after the second and third bleaching steps. Depending on the technique used, we found either that the third bleaching did not produce any modification of the biochemical properties of the SOMs, or that it induced some significant changes.

Among the techniques that did not detect any change, SDS-PAGE, regardless of the staining used, showed smeared patterns in all SOMs, with no differences between the two ASMs and the two AIMs. Similarly, ELLA on the two ASMs showed that both exhibit very similar lectin-binding signatures featuring strong reactivity with DSL, moderate affinity with ConA and LEL, and very low affinity with all 18 other lectins. Last, monosaccharide analyses of the AIMs showed almost no differences in their composition. Taken together, these techniques suggest that the third bleaching did not alter the qualitative biochemical characteristics of the saccharidic moieties of the SOM.

However, other techniques employed in our study evidenced noticeable effects that were induced by the third bleaching step. First, the SOM quantity (ASM and AIM) was reduced by half. Second, FT-IR detected a decrease of the sugar peak around 1000-1050 cm -1 for both ASM and AIM. Third, monosaccharide analysis of the ASMs showed that arabinose was significantly reduced. Finally, the in vitro crystallization assay evidenced a noticeable difference: the ASM extracted after the third bleaching induced stronger effects on crystal morphologies than the ASM extracted after the second bleaching. These results suggest that some sugar-containing macromolecules of the SOM (either polysaccharides or glycoproteins) are peripheral to the mineral grains and destroyed by the third bleaching step, while some others are protected from the harsh treatment and should be considered as 'intracrystalline', to employ the old terminology of Crenshaw [START_REF] Crenshaw | The soluble matrix from Mercenaria mercenaria shell[END_REF]. It is significant that the 'intracrystalline' ASM fraction, which is supposed to have a strong affinity for calcium carbonate, induces stronger effects in vitro than the ASM extracted after two bleaching steps.

To summarize this point, we reemphasize the importance of cleaning skeletal tissues with sodium hypochlorite and think that applying successive bleaching steps to the skeletal powder may be an elegant manner to pre-select SOM macromolecules that have the highest affinity to the mineral phase.

Our second focus is the presence of sulphate in the SOM of Porites australiensis, demonstrated by Alcian blue staining at low pH, FT-IR, and CHNS analysis. First, Alcian blue binds polyanionic groups, whatever they are, in particular those found in mucopolysaccharides. At low pH (=1), Alcian blue specifically binds sulphate-containing mucopolysaccharides [START_REF] Thornton | Identification of glycoproteins on nitrocellulose membranes and gels[END_REF]. In the present case, both ASM and LS-AIM are stained, but not identically: stronger staining occurred in the lower two-thirds of the gel for ASM, and the upper third for LS-AIM, indicating that the distribution of sulphate differs in these two fractions. Bulk analysis by FT-IR spectroscopy confirms the presence of sulphate groups, characterized by a peak in the range 1200-1250 cm -1 . Interestingly, pairwise comparisons of sulphate peak amplitudes from the second and third bleaching steps showed that the additional cleaning treatment did not degrade sulphate. This result is consistent with the CHNS analysis, which unambiguously detected sulfur (>1%) in AIM after three bleaching steps. Our data consequently suggest that the sulphate-containing macromolecules -likely polysaccharides -are truly intracrystalline.

More generally, the presence of sulfur in coral skeletons is a recurrent finding that leaves the question of its role open [START_REF] Dauphin | Soluble organic matrices of aragonitic skeletons of Merulinidae (Cnidaria, Anthozoa)[END_REF]. Previous studies demonstrated that sulfur was present in the SOM of Stylophora, Pavona, Hydnophora, and Merulina [START_REF] Dauphin | Soluble organic matrices of aragonitic skeletons of Merulinidae (Cnidaria, Anthozoa)[END_REF][START_REF] Puverel | Soluble organic matrix of two Scleractinian corals: Partial and comparative analysis[END_REF]. Furthermore, sulphated sugars were found to be predominant over sulfur-containing amino acids (methionine and cysteine) in ASM [START_REF] Dauphin | Soluble organic matrices of aragonitic skeletons of Merulinidae (Cnidaria, Anthozoa)[END_REF]. As a general rule, sulphated groups on sugars (ester sulphate) increase the acidic nature of the SOM. They can bind calcium ions or exert an ionotropic effect by creating a very localized anionic environment to concentrate calcium ions in the vicinity of the nucleation spots.

Sulphate is concentrated in early mineralization zones, suggesting that it may also promote crystal formation [START_REF] Cuif | XANES mapping of organic sulfate in three scleractinian coral skeletons[END_REF][START_REF] Lloyd | I. Polysaccharide sulphates[END_REF]. Interestingly, in other calcified tissues, such as nacre of the cephalopod Nautilus pompilius, a chemical mapping of single nacre tablets showed that sulphate-containing macromolecules were localized in a central annular zone in close association with carboxylate-rich macromolecules that are supposed to be the calcium carbonate nucleators [START_REF] Nudelman | Mollusk shell formation: Mapping the distribution of organic matrix components underlying a single aragonitic tablet in nacre[END_REF]. This peculiar localization suggests a cooperative role of both categories of macromolecules. Clearly, future work on coral biomineralization should elucidate the precise function of sulphated sugars, either as Ca 2+ concentrators or as mineral nucleators.

The third focus of our discussion concerns some specific properties of the saccharidic moieties of Porites australiensis SOM, including the lectin-binding signature, the monosaccharide composition and the in situ localization of DSL-binding saccharide. At first, the peculiar lectin-binding signature of the ASM deserves mention. Among all tested lectins, DSL gave by far the strongest signal. DSL, a lectin extracted from the jimson weed, binds chitotriose, chitobiose, and N-acetyl-D-glucosamine, with decreasing affinity. Its carbohydrate-binding site recognizes oligomers of N-acetyl-D-glucosamine; thus, it exhibits strong affinity for chitin. DSL also binds the disaccharide N-acetyllactosamine (formed from the condensation of beta-galactose and N-acetyl-glucosamine) and its oligomers. It is remarkable that other lectins that typically bind chitin / chitin-like motifs, such as WGA, STL, or LEL give very little or no signal with the ASM of P. australiensis. This may suggest that the saccharidic motif predominant in ASM is not N-acetyl-glucosamine, but rather Nacetyllactosamine or its oligomers. This is consistent with the high amount of galactose, by far the most abundant monosaccharide identified in ASM and AIM. On the other hand, none of the galactose-binding lectins, such as jacalin, PNA, SBA, GSL-1, RCA, and SJA give signal with P. australiensis ASM, but their lack of reactivity may reflect individual recognition specificities for each of them, as listed by Immel and coworkers [START_REF] Immel | The shell of the invasive bivalve species Dreissena polymorpha: biochemical, elemental and textural investigations[END_REF]. Beside this, additional useful structural information can be extracted from lectin signature: for instance, jacalin interacts with most O-linked glycoproteins, but is unreactive with ASM, while ConA, a lectin that binds mannose of N-linked glycopeptides, gives the second highest signal (although moderate). This may suggest that most ASM glycoproteins of P. australiensis skeleton are of the N-linked-type. Fucose is the third most abundant monosaccharide in ASM, but UEA-1, which binds strongly to α(1,2)-linked fucose, does not react. This suggests either that fucose residues present in the ASM are predominantly of the α(1,3) or α(1,6)-types, or that they occupy internal positions in the saccharidic chains, or that they form fucose-containing trisaccharides, a motif that inhibits UEA-1.

Finally, the three most abundant sugar residues of P. australiensis ASM, namely galactose, arabinose, and fucose, represent the residues that are predominantly represented in the soluble SOM of two acroporid corals, Montipora caliculata and Astreopora myriophtalma [START_REF] Cuif | The organo-mineral structure of coral skeletons: a potential source of new criteria for Scleractinian taxonomy[END_REF]. Galactose and fucose are also abundant in Heliastrea curta and Fungia rependa while galactose and arabinose predominate in Acropora danae [START_REF] Cuif | The organo-mineral structure of coral skeletons: a potential source of new criteria for Scleractinian taxonomy[END_REF].

Interestingly, the monosaccharide composition of P. australiensis ASM is in agreement with that of zooxanthellate coral ASMs in general, which was statistically distinguished from that of non-zooxanthellate coral ASMs by the presence of arabinose -a monosaccharide exclusive to zooxanthellate corals -the abundance of galactose, and low amounts of galactosamine and glucosamine (Cuif et al., 1999b). This monosaccharide composition contrasts singularly with that recently published by Naggi and coworkers (2018) who identified by other techniques (chromatography and mass spectrometry) glucuronic acid as the main saccharide residue present in the SOM of three stony corals, Astroides calycularis, Balanophyllia europaea, and Stylophora pistillata. We did not detect this acidic sugar in our samples and then reason of the discrepancy between our data and theirs is not known.

Last, using gold-conjugated DSL, we localized sugar components that have a strong affinity to DSL lectin. In all experiments, we obtained a homogeneous distribution of gold particles on the surface of radiating aragonite fibers. We did not observe signals concentrated along growth lines, perpendicular to fiber elongation nor signals at the tips of the fibers. Finally, the centers of spherulites are enriched in organics that are supposed to be the nucleating macromolecules. However, we did not detect any high concentration of gold spots around these centers, i.e., at the bases of the fibers that are the closest to the center. In this latter case, we cannot exclude that the treatment of the polished surface -prior to lectin incubationwhich consists in cleaning with bleach followed by slight etching with EDTA, may have eliminated most of the organics located at the centers of spherulites. In spite of these potential preparation artifacts, our observations suggest that the DSL-binding saccharide fraction is neither involved in crystal nucleation nor in stopping crystal growth (where it would be distributed along the growth lines). Because the signal homogeneously covers the aragonite fibers, and because DSL-reactive ASM exerts a significant in vitro effect on crystal size and shape, we put forward the hypothesis that this fraction modulates size, shape, and/or fiber growth kinetics. In order to better understand the role of these sugar moieties in P. australiensis biomineralization, further experiments, such as in vitro crystallization experiments with purified DSL-binding polysaccharide, should be conducted.

Conclusions

Our study represents the first biochemical characterization of the skeletal organic matrix of the massive coral, Porites australiensis, under different cleaning conditions. We focused on the sugar moieties. Besides obtaining peculiar signatures of monosaccharide composition and of lectin profile, we localized in situ, for the first time, a DSL lectin-binding fraction. Our results re-emphasize the role of saccharides in coral biomineralization and aims at identifying sugar signatures of coral SOMs, by waiting for understanding the structure-function relationships of this class of macromolecules in biomineralization. Further investigation of this organic fraction, together with the ongoing proteomic analysis of Porites SOM, is essential to obtain the most complete picture of macromolecular constituents required for building the skeleton of these major reef-forming corals. Second bleaching ASM: 1µg•mL -1 (A), 4µg•mL -1 (B), 16µg•mL -1 (C,D), 32µg•mL -1 (E,F).

(G-I) Third bleaching ASM: 1µg•mL -1 (G), 4µg•mL -1 (H), 16µg•mL -1 (I,J), 32µg•mL -1 (K,L).

(M) CaCO3 crystals generated in ASM-free solution. Morphology of the generated crystals are affected by the presence of ASM in dose-dependent manner. The effect of third bleaching ASM is more pronounced than that of second bleaching ASM.

  other functions related to crystal growth. Negative controls are shown on Figure 7G (SE mode) and H (BSE mode). Very few signals were obtained when gold-coupled anti-biotin was added to the skeleton in the absence of biotinylated DLS, confirming the low background of the sample and the specificity of sugar labeling.

Figure 1 .

 1 Figure 1. SEM images of Porites australiensis skeleton before and after second bleaching. (A) Surface of a skeleton fragment before the second bleaching. Fibrous structures of microorganisms (red arrowheads) are visible. (B) The same skeleton surface of (A) after the second bleaching. Contaminating fibers were completely removed. (C) Magnified image of (A). (D) Magnified image of (B).

Figure 2 .

 2 Figure 2. SDS-PAGE of skeletal organic matrices. (A) Silver staining, (B) Stains-all staining, (C) Alcian blue staining. Twenty µl of sample solution was loaded in each lane for silver staining and 15 µl for Stains-all staining and Alcian blue staining. ASM: Acid-soluble matrix. LS-AIM: Laemmli-soluble acid-insoluble matrix. 2bl: second bleaching. 3bl: third bleaching. MM: Molecular weight markers.

Figure 3 .

 3 Figure 3. FT-IR spectra of skeletal organic matrices. Major peaks discussed in the main text are colored. These signals indicate the presence of proteins or saccharides (i), lipids (ii), sulphates (iii), phosphates (iv), and saccharides (v). ASM: Acid-soluble matrix. AIM: Acidinsoluble matrix. 2bl: second bleaching. 3bl: third bleaching.

Figure 4 .

 4 Figure 4. SEM images of CaCO3 crystal formed in the presence of Porites ASM. (A-F)

Figure 5 .

 5 Figure 5. Monosaccharide composition of ASM and AIM after two (left) or three (right) bleachings. While the third bleaching did not produce any important change in the relative percentages of monosaccharides in AIMS, it was more effective for ASMs, where it induced a noticeable decrease of arabinose percentage.

Figure 6 .

 6 Figure 6. Enzyme-linked lectin assay (ELLA) on ASMs. ELLA was performed with 21 lectins after the second (left) and third (right) bleaching steps. Absorbance values at 405 nm were normalized to the highest value (DSL), corresponding to 100% reactivity (n = 3, means

Figure 7 .

 7 Figure 7. In situ localization of the DSL-reactive saccharide fraction. Observation with SEM in secondary electron (SE, A and H) and in back-scattered electron (BSE, B to G) modes. A to F: positive control, with Datura stramonium lectin (DSL). G, H: negative control, in the absence of DSL, but with gold-coupled anti-biotin. In (A) and (B), tips of needle-like crystals point to the early mineralization zone (front side of the pictures). The early mineralization zone is indicated in dotted circle in (C). Note that the gold particles, characterized by tiny bright spots in BSE mode, are distributed evenly on the aragonite fibers.

  Figure 1.

  Figure 2.

  Figure 3.

  Figure 5.

Table 1 .

 1 Quantification of AIMs and ASMs extracted from P.

	australiensis skeleton.
				Total organic
	AIM (mg•g -1 ) ASM (mg•g -1 )	matrix (% w/w)
	2nd bleaching	1.31	0.12 ± 0.01	0.143
	3rd bleaching	0.64	0.06 ± 0.01	0.07
	732
	733

Acknowledgements

We thank Dr. Akira Iguchi (Okinawa National College of Technology) for providing coral specimens. We also thank Marcel Soustelle (PACSMUB, Dijon) for elemental analysis and Frédéric Herbst for helping in handling the two SEM. We thank Dr. Steven D. Aird for editing the manuscript. All data were generated during a six-month stay of Dr. T. Takeuchi (Oct. 2015-March 2016) in the laboratory of Dr. F. Marin, UMR CNRS 6282 Biogéosciences in Dijon (UBFC).

Funding

This study was supported by internal funds from the Okinawa Institute of Science and Technology (OIST).