A New combined transient extraction method coupled with WO 3 gas sensors for polluting gases classification

Keywords: Electronic nose, combined features, Gas sensors, ELM classifier, Support Vector Machine SVM

Purpose-this paper dealt with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors. Design/methodology/approach-To improve the classification accuracy and enhance selectivity, some combined features that were configured through the Principal Component Analysis (PCA) were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array. Findings-The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application since this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF. Originality/value-combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine ELM and Support Vector Machine SVM were used to identify the studied gases.

Introduction

Gas sensors based on tungstic oxide (WO3) have attracted a great deal of research interest in many fields such as environment, control and especially the identification and discrimination of polluting gases. These metal oxide semiconductor (MOX) sensors have many outstanding advantages such as high sensitivity, rapid response speed, good reproducibility and simple design. However, this type of gas sensors exhibits an inherent lack of selectivity. For this reason, investigations have focused on the selective detection for gases using many approaches. Among these approaches, the electronic nose plays a constantly growing role as a general-purpose detector of polluting gases [START_REF] Lecce | Air quality control for health care centres. The application of an intelligent distributed system[END_REF][START_REF] Machado | Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath[END_REF]. The term electronic nose refers to a device that emulates human smell by combining sensor response and recognition algorithms to distinguish gas samples [START_REF] Gardner | A brief history of electronic noses[END_REF][START_REF] Martin | Electronic nose based on metal oxide semiconductor sensors and pattern recognition techniques: characterization of vegetable oils[END_REF].

In the same way of role of metal oxide in pollution detection, [START_REF] Danish | A Systematic Review of Metal Oxide Applications for Energy and Environmental Sustainability[END_REF] discusses the structural features and photocatalytic applications of a variety of metal oxide-based materials. The review paper explores the footprint analysis as well as a simple framework to relate metal oxide application possibilities within energy, environmental, and economic sustainability.

In their research, authors highlight how metal oxide are used in applications for energy and environmental sustainability, taking into account also the limit of their practical application on large scale. WO3 has the advantage of being chemically stable in acidic aqueous media, having a high hole diffusion length and a good electron mobility. It was reported that metal oxides especially continue to be explored for the environmental remediation, for solving the energy crisis and for decarbonization.

Focusing on the electronic nose performance improvement, many researchers have continuously studied the related technologies to enhance the e-nose performance. In the gas detection field, investigations have focused on the two most important parts of the e-nose system: hardware and software (intelligent) parts .For the first part, sensitive material selection, sensor array optimization was investigated [START_REF] Zaretskiy | Theory of gas sensitivity of nano-structured MOX layers. Selection rules for gas sensitive materials[END_REF]. Also, various techniques using different measurement strategies were explored [START_REF] Ghasemi | Temperature modulation of electronic nose combined with multi-class support vector machine classification for identifying export caraway cultivars[END_REF].

Frequently, investigations have been focused on the use of oxides or polymers to discriminate sensitivity between gases based on molecules size or surface reactions. In [START_REF] Yumashev | Development of Polymer Film Coatings with High Adhesion to Steel Alloys and High Wear Resistance[END_REF], the controlled incorporation of nanoinclusions into the polymer matrix suggests itself as an obvious solution to the tribological problem. Data from numerous tests were processed using the Kalman filter.

It is an effective recursive filter that estimates the state vector of a dynamic system using a series of incomplete and noisy measurements.

For the intelligent part, a great deal of research focused on both of the enhanced feature extraction method and the pattern recognition method.

Among the techniques used to improve the electronic nose system, the hybrid approach known as data fusion has emerged as a real solution to solve the lack of cross-selectivity problem of individual electronic sensor systems. The aim of data fusion is to combine information from two or more data sources in order to obtain more complete, better quality global information, allowing for a better decision and action than either of the individual data sources.

In addition, data fusion techniques are also rated as low (sensors combination), mid (features fusion) and high (classifiers fusion), depending on the processing stage at which the fusion occurred.

In literature, several applications of hybrid systems involve the use of the low level of fusion [START_REF] Banerjee | Artificial flavor perception of black tea using fusion of electronic nose and tongue response: A Bayesian statistical approach[END_REF][START_REF] Buratti | E-nose, e-tongue and e eye for edible olive oil characterization and shelf life assessment: a powerful data fusion approach[END_REF]Haddi et al., 2014). This data fusion corresponds to the fusion of information directly at the output of the sensors. It is a simple concatenation of the raw variable vectors. Although it performs very well when it comes to a comparable number of sensors used for each device, it is not very suitable when the source contains a large number of variables. In addition, the combination of several data raises the problem of dimensionality (Boilot et al., 2004).

The high-level fusion (HLF) relates to the merger of information corresponding to the formulation of hypotheses from an expert or a system. Besides, many studies in such various fields as image segmentation, sensor networks, and image recognition have been devoted to classifier fusion [START_REF] Amini | Improving gas identification accuracy of a temperature modulated gas sensor using an ensemble of classifiers[END_REF][START_REF] Sun | An optimized multi-classifiers ensemble learning for identification of ginsengs based on electronic nose[END_REF][START_REF] Vergara | Chemical gas sensor drift compensation using classifier ensembles[END_REF]Faleh et al., 2018). Indeed, after each source has determined its own decision or partial identity of the present objects, this approach combines the information to form a global decision [START_REF] Luo | Enhancing electronic nose performance based on a novel QPSO-RBM technique[END_REF] In this work, we focused on the mid-level fusion (features fusion). It is the fusion of data collected directly from the sensors. The mid-level fusion provides the builder with the possibility of displaying the database size to prevent dimensionality and preponderance problems. This approach has increasingly gained attention for e-nose applications [START_REF] Malegoria | A modified mid-level data fusion approach on electronic nose and FT-NIR data for evaluating the effect of different storage conditions on rice germ shelf life[END_REF][START_REF] Huang | A Multi-Feature Fusion Based on Transfer Learning for Chicken Embryo Eggs Classification[END_REF].

In our previous paper [START_REF] Faleh | A transient signal extraction method of WO3 gas sensors array to identify pollutant gases[END_REF], we used the "classifiers fusion" method to improve the classification accuracy and enhance selectivity. In the present paper, however, we used an alternative method that relies on "features fusion" which is based on transient parameters. The idea was to combine transient features in order to improve the classification accuracy. The remaining of this paper was structured as follows: Section 2 provided an overview on the "features fusion" approach. Section 3 detailed the measurement process for the sensor array and data collection. In section 4 we described the data preprocessing procedure before discussing the experimental results in section 5. Finally, we drew the main conclusions we reached in this study.

Features Fusion approach; State of the art

Motivation

For any specific application, the extraction method is a key factor for good gas discrimination [START_REF] Yan | Electronic Nose Feature Extraction Methods: A Review[END_REF][START_REF] Eklöv | Selection of variables for interpreting multivariate gas sensor data[END_REF]. Current features investigated in gas detection can be divided into two main categories: The first is extracted from the steady-state phase whereas the second extracts features related to the transient phase of the sensor response. A recent research identified certain characteristics in the transformation domain, such as Fourier transform [START_REF] Vergara | Quantitative gas mixture analysis using temperature-modulated micro-hotplate gas sensors: Selection and validation of the optimal modulating frequencies[END_REF][START_REF] Ionescu | Low-level detection of ethanol and H2S with temperature-modulated WO3 nanoparticle gas sensors[END_REF] trying to reach a high classification rate in order to discriminate samples of gases. Another kind of extraction technique is the extraction of characteristics based on a particular model [START_REF] Haddad | A feature extraction algorithm for multi-peak signals in electronic noses[END_REF][START_REF] Chambon | A metallic oxide gas sensor array for a selective detection of the CO and NH3 gases[END_REF]. However, other researchers concentrated on extracting piecemeal signal features, such as maximum sensitivity [START_REF] Xiong | Quality control of Lonicera japonica stored for different months by electronic nose[END_REF].

Throughout the literature, the use of transient features [START_REF] Siadat | Procedia Engineering New transient feature for metal oxide gas sensor response processing[END_REF]Haddi et al., 2011) has been shown to lead to a better classification. Distante et al. [START_REF] Distante | On the study of feature extraction methods for an electronic nose[END_REF] analyzed the effect of both steady-state feature maximum value and transient features to detect gases using five SnO2 sensors. Their results showed that integral and derivative methods have higher recognition rates than accuracy classification obtained from steady state values. In [START_REF] Zhang | A feature extraction method and a sampling system for fast recognition of flammable liquids with a portable E-nose[END_REF], the Linear Discriminant Analysis (LDA) results showed that the higher classification rate was obtained with transient features. In a previous work, [START_REF] Faleh | A transient signal extraction method of WO3 gas sensors array to identify pollutant gases[END_REF] demonstrated that the transient parameters derivate an integral lead to better classification compared to standard features. However, it is well known that a single feature cannot fully represent the characteristics of the sensor signals, thus leading to low classification accuracy. Hence, the features fusion strategy [START_REF] Giungato | Evaluation of Industrial Roasting Degree of Coffee Beans by Using an Electronic Nose and a Stepwise Backward Selection of Predictors[END_REF]) might be a suitable alternative for improving prediction accuracy for gas sensors array. The aim of features fusion is to combine the parameters obtained from multiple sources and tested individually. This fusion could, therefore, potentially achieve a better definition and improve the classification accuracy [START_REF] Zhi | A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment[END_REF].

Methodology

The underlying idea of features combination is to use the extracted features in the same dataset. As shown in figure 1, the mid level of the data fusion corresponds to the mergence of the extracted raw measurements. Unlike the low level of abstraction, the intermediate level requires a preprocessing step of the data before combining them into a single dataset. This can be ensured by either a direct extraction of sensor responses or a selection of variables among those of origin. The mid-level offers the designer the possibility to control the dimension of the database so as to avoid the problems of dimensionality and preponderance.

Figure1.Mid-level abstraction of data fusion

Related works

In recent years, the hybrid approach, which is a mid-level features combination, has emerged as a real solution to overcome the problem of lack of cross-selectivity of individual sensors. In addition, the use of multiple data sources offers a compelling path to better decision making. Indeed, data fusion is applied in a wide variety of fields like food security and image processing. Many researches showed that hybrid systems or features extraction systems can improve the classification accuracy compared to individual features. The combination of heterogeneous features is widely used in the field of food security,. In fact (Haddi et al., 2014) offer a multisensory data fusion approach which combines an e-Nose and an e-Tongue that were investigated in order to generate combined aroma and taste profiles. Researchers in [START_REF] Buratti | E-nose, e-tongue and e eye for edible olive oil characterization and shelf life assessment: a powerful data fusion approach[END_REF] have shown that classification is often more accurate when using combined features rather than an individual ones. They suggested an efficient mid-level data fusion approach for discussing the applicability of an E-nose, E-eye and E-tongue for quality decline assessment and olive oil characterization, demonstrating their ability to identify samples and substantially improve the K-Nearest Neighbors (KNN) classification model. Combining correlations between chemical features from an E-nose and E-tongue were explored for discrimination of red wines [START_REF] Rodriguez-Mendez | Evaluation of Oxygen Exposure Levels and Plyphenolic Content of Red Wines Using an Electronic Panel Formed by an Electronic Nose and an Electronic Tongue[END_REF].Their results showed a significant improvement of classification accuracy. Besides, previous research demonstrated that a weighted fusion framework with a logarithmic form that focused on the contribution of each function can increase the classification rate [START_REF] Dang | A novel classifier ensemble for recognition of multiple indoor air contaminants by an electronic nose[END_REF].In addition, compared to different standard techniques, the combined sensor response can classify tea samples more accurately and is considered as an appropriate tool for tea discrimination [START_REF] Banerjee | Black tea classification employing feature fusion of E-Nose and E-Tongue responses[END_REF]. Also, and in the same field, a qualitative discrimination, E-nose, E-tongue, E-eye were merged by the SVM and Random Forest RF classification model to obtain a better classification accuracy of black tea and improve the performances of identification and prediction models [START_REF] Xu | The qualitative and quantitative assessment of tea quality based on E-nose, Etongue and E-eye combined with chemometrics[END_REF].In the field of gas detection and electronic nose, a great deal of research has focused on data fusion ,especially features combination, to detect eight gas types that were tested and classified using sensors array [START_REF] Choi | Gas Classification Using Combined Features Based On a Discriminant Analysis for an Electronic Nose[END_REF].

Suggested features combination method

As shown in figure 2, the combined method process is organized as follows: Derivate, integral and time corresponding for the pic derivate parameters is extracted individually from the transient response of sensors constructing hence a data base. A first individual evaluation is achieved. A new data base combines the N observations represented by the variables (D, I, TDmax).These N observations are the input parameters for such classification methods as the SVM and the ELM.

Figure2. Construction process flow of the combined features

Materials and methods

Measurement system

As illustrated in figure 3, the main elements of our test bench are the sensors array, which are fitted in the test chamber, the gas dispensing and mixing system, the polarization electronic circuits, the heating sensors, the measuring devices and the data acquisition system.

To measure almost simultaneously the voltage of each sensor, a data acquisition system that consists of a multimeter (Keithley) controlled by a computer was used. The used gases are ozone, ethanol and acetone. Ozone has been investigated for its oxidation effect. As for ethanol and acetone, they were used as reducing gases. Finally, we investigated a mixture of ozone and ethanol as a separate class.

Figure3. Gas measurement experiment set-up for data acquisition

The ozone generation system consists of a flow meter, a humidity filter and a UV lamp. In fact, the dry air passes through a quartz tube, lit by the UV lamp. The concentration of ozone at the outlet of the tube is calculated based on the flow of dry air at the inlet (constant flow equal to 0.5 L / min) and the surface of the tube exposed to UV. A graduated copper tube is used to more or less expose the dry air to the lamp. The graduations on the copper tube correspond to the concentrations 6, 35, 95, 195 and 265 ppb. The ethanol / acetone test bench is mainly composed of a steam generator. It is named VOC generator. It has been always used the same test enclosure and the same acquisition system. The steam / air mixtures can be generated by bubbling air through ethanol / water or acetone / water mixture at 34°C.The alcohol density values are used at 34 ° C , ethanol = 0.77639 g / ml and acetone = 0.7803 g / ml. Thus, for each concentration is corresponding a volume of liquid to introduce into the bubbler.The measured samples were made up of different ozone concentrations 6, 35, 95, 195, 265 ppb, five concentration levels of acetone and ethanol ranging from 10 to 50 ppm and five concentrations of 265 ppm mixture of ozone with a variation of ethanol concentration from 10 to 50 ppm.

The gas sensors are mounted in a box. It is in the shape of a cylindrical cross. The distribution of gas in the enclosure is done by the four channels used for the gas inlets, as well as for making the electrical contacts (heater and electrodes) and for the evacuation of gases. The gas inlets can be used separately in the case where gas is tested or simultaneously to test a mixture of two or more gases. The used chip contains 4 sensors which are tested one by one.

The polarization voltage of the sensors ranges between 0 and 5 V. The sensors are heated at constant temperatures (isothermal mode). Each sensor works at a given temperature as indicated in table 1. For each temperature value corresponds a heating voltage. Indeed, the temperature is not measured each time. The heating furnace is calibrated up to 250°C. It was previously demonstrated that each sensor has an optimal temperature for each gas (Faleh et al.2018).

The sensor heater part is connected in series by a Keithley 2000 multimeter which is used as an a mmeter.

A very low voltage U of the order of 0.02 V feeds the heater and enables the resistance of the latter to be measured for each temperature value imposed inside the oven.

It then allows the temperature of the sensitive layer to be identified as a function of the heating voltage applied to the heating element terminals.

The measured data were collected with a laptop via an HP-VEE program. All experiments were performed using four WO3 gas sensors mounted on the same package (Figure 4). The array consists of a matrix of four sensors in order to have different signatures. In fact, a micro-heater is used in metal oxide gas (MOX) sensors as a hotplate. The gas exposure time (30 s) is long enough to reach the equilibrium state, i.e. to obtain a bearing. These structures may be used as a base component of an electronic nose, in order to selectively detect a number of target gases. The box contains four micro-sensors.

Figure4. (a)Four-element micro gas sensors array mounted on a standard package (b) Presentation of a micro gas sensor 3.2. Sensor characterization Figure 5 (a) shows the response of the WO3 gas sensor (sensor1) for ozone (O3) injections at different concentrations. The gas exposure time is 15 s and the time for return to the baseline is approximately 5 minutes. For the other sensors, similar responses are obtained, except for the resistance ratio value Rs/R0 that was changed, where Rs is the resistance in the presence of a gas is and R0 is the reference resistance. The same figure shows that the sensor responds proportionally to the gas concentration, and that it was stable enough during the test. It should be noted that the WO3 sensor is so sensitive for this gas that it can be a promising oxide for ozone detection even at low concentrations and that the baselines are also stable during the test period. Figure5 (b) shows the response of a sensor based on WO3 for ethanol and acetone injections for different concentration levels from 10 to 50 ppm, using the same temperature. The resistance value was decreasing when the ethanol and acetone concentrations were increased. It is noteworthy that resistance increases and exceeds the initial value at the return time to the baseline. This might be Sensor4 explained by the training of new radicals and the complexity of the chemical reactions during adsorption taking place on the WO3 surface.

Figure5. Sensor WO3 S1 response to (a) oxidizing gas (O3), (b) reducing gases ethanol and acetone

In figure 5.b, it is quite noticeable that upon returning to the baseline, the resistance increases and exceeds the value of the baseline. This can be explained by the fact that the new radicals and by the complexity of chemical reactions during adsorption that take place at the surface of WO3 .By increasing the concentration, this rise disappears. For the extracted parameters, this inconsistence in baseline does not affect any parameter example TDmax is extracted from derivate not from sensors raw data.

Signature representation

As mentioned above, our sensors array consists of four WO3 gas sensors in order to detect different gases simultaneously. The response of the four sensors exposed to a given gas plays an important role in the concept of electronic noses. Figure 6 show that we get a very specific signature for each gas. For the oxidizing gas, we used the ratio Rs/R0 opposite to the reducing gases for which R0/Rs ratio was used as a response. The notation S1 to S4 is the same as WO3 S1 to WO3 S4.These sensors are different in terms of temperature. Gas sensor data such as resistance is raw data that does not show any trend if viewed separately. This is why the normalized parameter Rs/R0 has been calculated. In our experience, the ozone concentration is set at 265 ppb and the ethanol concentration is varied from 10 to 50 ppm. we find that by increasing the concentration of ethanol, the resistance value decreases. This is explained by the presence of the reducing effect.

Signal preprocessing

The pre-processing stage is achieved to pick a set of parameters that identify the sensor array going from feature extraction to dimensionality reduction.

Extracted features

The optimization process consists in using a satisfactory identification rate while minimizing the number of the initially extracted variables [START_REF] Boilot | Electronic noses inter comparison, data fusion and sensor selection in discrimination of standard fruit solutions[END_REF]. The aim of this paper was to reach the best accuracy rate through a good exploration of the information of each experiment. To achieve this purpose, three combined transient features in the same dataset were obtained after analyzing each feature independently. For each sensor, three features were extracted (figure 7) whose expressions are shown in table 2. Before the classification phase, the data of each sensor using mid-level fusion approach were measured. The significant parameters matrix would be a merge of an individual evaluated parameter: derivate, integral and the time corresponding to peak derivate. The N observations represented by the variables (D, I, TDmax), collected on the artificial system, would make a database. The merged matrix composed of the derivate, integral and time corresponding for each pic derivate is presented in the following form:

𝑇 = [ 𝐷 𝑡,1 1 … 𝐷 𝑡,𝑗 1 𝐷 𝑡,4 1 𝐼 𝑡,1 1 … 𝐼 𝑡,𝑗 1 𝐼 𝑡,4 1 𝑇 𝐷𝑚𝑎𝑥 𝑡,1 1 … 𝑇 𝐷𝑚𝑎𝑥 𝑡,𝑗 1 𝑇 𝐷𝑚𝑎𝑥 𝑡,4 1 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 𝐷 𝑡,1 𝑐 … 𝐷 𝑡,𝑗 𝑐 𝐷 𝑡,4 𝑐 𝐼 𝑡,1 𝑐 … 𝐼 𝑡,𝑗 𝑐 𝐼 𝑡,4 𝑐 𝑇 𝐷𝑚𝑎𝑥 𝑡,1 𝑐 … 𝑇 𝐷𝑚𝑎𝑥 𝑡,𝑗 𝑐 𝑇 𝐷𝑚𝑎𝑥 𝑡,4 𝑐 ⋮ ⋱ ⋮ ⋮ ⋮ ⋱ ⋮ ⋮ ⋮ ⋱ ⋮ ⋮ 𝐷 𝑡,
 = t a ds s R t I ) ( ) (

Classification

The multivariate analysis methods serve to construct a data set based on observations of the functioning of the system through learning. Once this step is accomplished, the system is ready to recognize unknown objects that it will receive. There are two main distinct classification approaches applied in sensor array systems; supervised (SVM, KNN, LDA, ANN) and unsupervised (PCA…).

5.1.Support Vector Machine

The Support Vector machine is known to have reached a high classification rate in gas identification (Haddi et al.,2011;Güney and Atasoy,2012). It is a kernel learning method inspired from Vladimir Vapnik's theory introduced in 1979. It was primarily designed for a binary prediction, i.e. discrimination of two classes. However, multi-class versions have been developed in order to consider more complex cases consisting of more than two classes. The SVM Classification can be easily adapted to tackle multiclass problems. The two common strategies are:

One Versus Another method (OVA): This is the simplest and oldest decomposition method where the number of classifiers is selected to be equal to the number of classes.

One Versus One method (OVO): if the class number is k, the classifier number is also designed as k (k-1)/2. Each classifier separates the data into two classes. There are different methods of combining the obtained classifiers and the most common is a simple voting scheme.

In this work, we resorted to the SVM classifier especially the One Versus One method (OVO), as it proved to be a good classification method of the three gases.

5.2.Extreme Learning Machine ELM

The ELM is designed to train the Single Layer Feed-forward Networks (SLFNs) [START_REF] Qiu | Classification and regression of ELM, LVQ and SVM for E-nose data of strawberry juice[END_REF][START_REF] Huang | Extreme learning machine: Theory and applications[END_REF][START_REF] Huang | Extreme learning machines: A survey[END_REF]. This method was proven to be extremely simple and efficient and to enjoy a high speed. The weights of the hidden layer can be initialized randomly and analytically determine the output weights of the SLFNs thus urging only the optimization of the weights of the input layer and the biases of the output layer.

After the input weights and the hidden layer biases are chosen arbitrarily, the SLFNs can be simply considered as a linear system and the output weights (linking the hidden layer to the output layer) of the SLFNs can be analytically determined through a simple generalized inverse operation of the hidden layer output matrices.

Assuming that there are N training samples (xi, ti), where xi= [xi1, xi2… xin]T € Rn denotes one sample point in the n-dimensional space, ti=[ti1,ti2,..,tin]T€ Rm is the sample class label and the number of hidden nodes is M.

The SLFN output and the activation function are defined as:

𝑜 𝑖 = ∑ 𝛽 𝑗 𝑔 𝑗 𝑀 𝑗=1 (𝑥 𝑖 ) = ∑ 𝛽 𝑗 𝑀 𝑗=1 𝑔(𝑤 𝑗 𝑥 𝑖 + 𝑏 𝑗 ) , (1)
𝑤ℎ𝑒𝑟𝑒 𝑊 𝑗 = [𝑤 𝑗1 , 𝑤 𝑗2 , . 𝑤 𝑗𝑛 ] 𝑇 , 𝛽 𝑖 = [𝛽 𝑗1 , 𝛽 𝑗2 , . . 𝛽 𝑗𝑚 ] 𝑇 , j=1,…, N
with xi is the i-th sample, M is the number of hidden nodes; wj and bj denote the input weights of the hidden layer and the output weight linking the j-th hidden node to the output layer, respectively. Meanwhile, bj is a bias of the j-th hidden node and oi is the output vector of the input sample xi.

This SLFN can approximate those N samples with zero error, which means that:

∑ ‖𝑜 𝑖 -𝑡 𝑖 ‖ 𝑀 𝑖=0 = 0 , (2)
Where ti is the sample class label vector of the input sample xi. Based on this approximation, the parameters vectors βj, w and bj can be written as follows:

∑ 𝛽 𝑗 𝑀 𝑗=1 𝑔(𝑤 𝑗 𝑥 𝑖 + 𝑏 𝑗 ) = 𝑡 𝑖 (3)
This can be written as:

( 𝑔(𝑤 1 𝑥 1 + 𝑏 1 ) ⋯ 𝑔(𝑤 𝑀 𝑥 1 + 𝑏 𝑀 ) ⋮ ⋱ ⋮ 𝑔(𝑤 1 𝑥 𝑁 + 𝑏 1 ) ⋯ 𝑔(𝑤 𝑀 𝑥 𝑁 + 𝑏 𝑀 ) ) ( 𝛽 1 𝑇 ⋮ ⋮ 𝛽 𝑀 𝑇 ) 𝑀,𝑚 = ( 𝑡 1 𝑇 ⋮ ⋮ 𝑡 𝑀 𝑇 ) 𝑁,𝑛 (4)
Equation ( 4) may then also be written in a matrix form:

𝐻𝛽 = 𝑇 (5)
Where H is the hidden layer output matrix and it is written as:

𝐻 = [ ℎ(𝑥 1 ) ℎ(𝑥 𝑁 ) ] = ( 𝑔(𝑤 1 𝑥 1 + 𝑏 1 ) ⋯ 𝑔(𝑤 𝑀 𝑥 1 + 𝑏 𝑀 ) ⋮ ⋱ ⋮ 𝑔(𝑤 1 𝑥 𝑁 + 𝑏 1 ) ⋯ 𝑔(𝑤 𝑀 𝑥 𝑁 + 𝑏 𝑀 ) ) (6)
To train the SLFN is equivalent to find a least squares solution of the linear system. The leastsquare solution takes the following form:

β ′ = H + T, (7) where H+ is the Moore-Penrose generalized inverse of the matrix. To calculate the output weights, Huang et al. suggested adding a positive value 1/C (C is a regularization coefficient). According to the ridge regression theory, it is calculated as follows:

β = H T ( 1 C + HH T ) -1 T (8)
The SLFN output function is:

f (xi) = h(xi) β, (9)
where h(xi) is the output of the hidden nodes and actually maps the data from input space to the hidden layer feature space H.

Therefore, substituting Equation (8) for Equation (9) will describe the output function as follo ws:

f(x i ) = [ h(x i ) h(x 1 ) T h(x i ) h(x N ) T ] ( 1 C HH T ) -1 T (10)
While the discrimination power of each feature was tested using PCA, the ELM and SVM classifiers were applied to complete the classification process. The Matlab software was used for the data analysis.

Results and discussion

The PCA was used for data preprocessing and dimensionality reduction. The e-nose dataset was projected in a low dimensional space. This was achieved by computing the orthogonal and uncorrelated variables of the covariance matrix from the features of the sensors' response. The two first principal components were used based on their greatest amount of variance.

From each sensor response curve, three different transient features were extracted for each sample. The first feature extraction is the peak derivate from the transient part. The second feature extraction presents the difference accumulative total reaction in the presence of sample gas, i.e., I1-I0. The last one is the time corresponding of the peak derivate.

Each data set extracted from each proposed feature in the form of X20×4 was introduced into the PCA process. A mean-centering pre-processing technique was applied to the datasets. The PCA results of the data sets were extracted by the proposed method.

The peak derivate, integral, and time corresponding to the peak derivate and the combination of the three features are shown in Figure8 a-d, respectively. The PCA results show that the feature extraction based on the peak derivate and integral cannot classify and discriminate between ethanol and acetone. The Time corresponding to the peak derivate seems to give a better result than derivate and integral. However, the linearity of the samples classification does not seem to lead to a better classification rate since it is not enough to guarantee the best classification accuracy. [START_REF] Faleh | A transient signal extraction method of WO3 gas sensors array to identify pollutant gases[END_REF] proved that combining derivate and integral leads to a better classification rate compared to standard features. For the feature extraction using the integral, peak derivate and the time corresponding to the peak derivate, the PCA results show a perfect classification between the four classes and the problem of discrimination between the two reducing gases ethanol and acetone has been overcome. The best classification scenario can be observed from the plot and each gas is widely separated from the others.

The classification and validation of the combined transient features method PCA were performed to explore the pollutant gases ozone, ethanol, acetone and a mixture of ozone and ethanol. However, this qualitative method cannot be used as an identification method. In this way, the ELM and SVM models were used to evaluate the effectiveness of the transient extracted features. Leave-one-out cross validation method was employed to evaluate the performances of the used methods in this experiment for the data set.

The main idea underlying the ELM is that the network hidden layer parameters were randomly assigned. The only parameter is the number of hidden nodes in the SLFN hidden layer, which is normally obtained by a trial. While the input weights are within (-1, 1), those of the hidden layer biases are within (0, 1). According to the number of hidden nodes in the hidden layer, 20 experiments were carried out Figure 9 shows only the classification results of one of the 20 repeated experiments to display the change process with the number of hidden nodes in the hidden layer varying from 1 to 20. It can be clearly seen that the classification rate gradually improved with the number of hidden nodes from 4 to 6 and from 17 to 18, while the classification rate gradually declined with the number of hidden nodes from 7 to 16. Moreover, the best classification accuracy the ELM can achieve is 85% when the number of hidden nodes is 4, 6. .

Figure9.

Performance of ELM using combined features according to the number of hidden nodes from 1 to 20. Table 3 shows that both different features and classification models impact the classification accuracy of the four gas classes. It also proves that the combined features method outperforms the classification accuracy of the studied classes. This very same table displays that regardless of the used classification method, the combined features present the highest accuracy compared to individual transient features.

Using the peak derivate, integral and time of peak derivate, the SVM classification accuracy was better than that achieved when using the polynomial kernel. However, using the combined features, the best accuracy rate was obtained using the RBF Kernel method. For the ELM classification, the best classification depends on the hidden nodes number that is 20 in our case. When combining transient features, the ELM and SVM classifiers provided very satisfactory results for the gas classification.

The following remarks can be therefore forwarded: the first is that using combined transient features yields a better classification rate than individual features. The second is that the SVM classifier implementation with a selection of a kernel function leads to a better discrimination capability than the ELM classifier. In this context, we are thinking that optimizing this classifier would be an interesting future perspective.

Conclusion

The aim of this work was to improve the performance of the existing electronic nose by exploiting three new transient features combined in the same matrix. Initially, samples from the data set were introduced in the PCA algorithm to analyze the ability of the sensors array to discriminate the three gases and a mixture of two gases. This does illustrate that the system was able to discriminate the samples. To improve the classification accuracy, we resorted to another classification algorithm namely the SVM classifier and the ELM algorithm. As a future perspective, we are planning to improve the ELM algorithm by using the kernel ELM (K-ELM) and PSO optimization.

Figure 6 .

 6 Figure 6. Normalized response of four sensors for (a) 265ppb of ozone (b) 40ppm of ethanol, (c) 40 ppm of acetone, (d) 265ppb of ozone and 40ppm of ethanol.

Figure7. a )

 a Figure7.a) Characteristic response of a chemical gas sensor showing the transient part b) derivate integral signals and TDmax measured for each gas sensors

Figure 8 .

 8 Figure 8.PCA discrimination (a) using derivate parameter (b) integral parameter(c) time corresponding to the derivate pic (d) using the three combined parameters

Table 3 .

 3 Classification results

	Features	Method	Parameters	Classification rate
	Peak derivate	ELM	Activation function: RBF	76,5%
			Hidden nodes=20
		SVM	Polynomial Kernel, p=2,	85,83
			C=1
	Integral	ELM	Activation function: RBF	75%
			Hidden nodes=20
		SVM	RBF kernel, σ =0,2	90,41
	Time of peak derivate	ELM	Activation function: RBF	80%
			Hidden nodes=20
		SVM	Polynomial Kernel, p=2,	92,97%
			C=1
	Combined features	ELM	Activation function: RBF	90%
			Hidden nodes=20
		SVM	RBF kernel, σ =0,2	93,03%


Their antiplasmodial potencies are thus dramatically decreased, when compared to 3f. On the other hand, the derivatives with electro-releasing groups like -NH 2 (3o) or -CH 3 (3p) presented increased pK a values and revealed good antiplasmodial potencies. Since their antimalarial activities are strongly linked to their basicity, the reverse-benzamidine compounds seem to act in a similar way as the alkylamidine choline analogs described by Calas et al. 6 Indeed, they observed the same correlation between the antiplasmodial activities and the pK a values of the molecules, suggesting that the pK a values of alkylamidines reflect their ability to form strong bonds with the target. This correlation indicates that the mechanism of action of reverse-benzamidine compounds would be rather linked to the capacity for these bis-cations to mimic the choline structure and to inhibit the phospholipidic metabolism, than associated to the ability of antiparasitic dibenzamidines to bind DNA as pentamidine or furamidine. 19,20 The in vivo antimalarial activities of our compounds were investigated against the Plasmodium vinckei petteri strain (279BY) in female Swiss mice. 21 The in vitro antiplasmodial activity of 3e, 3g, and 3h being too low, they were not evaluated in vivo. The mice were infected on day 0 and treated with compounds either intraperitonally (ip) or orally (po) once daily for four consecutive days (days 1-4 post infection, n = 3 per dose). The parasitemia levels were monitored in mice at day 5. All the reversed N-alkylamidines exhibited potent antiplasmodial activities, except 3a (Table 1). But the modulations performed in N-alkylamidine series did not improve M64 activity. The reverse-benzamidine 3i did not reveal any antimalarial activity, while a slight antimalarial activity could be detected with 3f, 3j, 3k, and 3l (ip administration of 5 mg/kg of 3l decreased parasitemia of 40% as compared to control). After ip administration, the other reverse-benzamidines (3m, 3n, 3o, and 3p) exhibited as potent in vivo antimalarial activities (ED 50 ip <10 mg/kg) as the best N-alkylamidines, while having lower in vitro antimalarial activities.

Oral administration of 180 mg/kg of N-alkylamidines or of the reverse-benzamidines 3f, 3j, 3k, 3l, and 3p did not reveal any antimalarial effect. On the other side, significant activities were observed with the other compounds 3m, 3n, and 3o, but the parasitemia clearance was not achieved and no ED 50 po could be calculated. Thereby, after administration of 180 mg/kg of 3m, 3n, and 3o, respectively, 42%, 20%, and 58% of decrease of parasitemia could be observed as compared to control. These compounds appeared more efficient by oral administration at 180 mg/kg than the N-alkylamidines, which revealed no significant effects at that concentration. However, further studies to improve the bioavailability, as well as pharmacokinetics experiments of the best compounds are needed to obtain compounds suitable for drug development. For example, specific prodrug strategies might be applied to the most potent compounds in this new reverse-benzamidine series.

In conclusion, the reverse-benzamidines have been designed as a new series of antimalarials. The introduction of a phenyl aromatic ring within the polar head can lead to molecules with improved in vivo antimalarial activity. Indeed, four reversebenzamidine compounds exhibited potent antimalarial activities (ED 50 ip <10 mg/kg) and three of them (3m, 3n, and 3o) led to a decrease of parasitemia was detected after oral administration (180 mg/kg). Furthermore we have shown that the antimalarial potency can be strongly modulated by introducing aromatic substituents that modify the basicity of the reverse-benzamidines.

Acknowledgments

These studies were supported by the European Commission (Antimal integrated project LSHP-CT-2005-018834). We are grate-ful to Christophe Tran Van Ba for his assistance in testing the compounds.

Supplementary data

Supplementary data ( 1 H and 13 C NMR, MS (FAB or ESI), FTIR data of new compounds and biological protocol) associated with this article can be found, in the online version, at doi:10.1016/ j.bmcl.2010.07.124.


Melissa Degardin

Sharon Wein

Thierry Durand

R Escale

Henri Vial

Yen Vo-Hoang
email: yen.vo-hoang@univ-montp1.fr

N-substituted bis-C-alkyloxadiazolones

published or not. The documents may come   L'archive ouverte pluridisciplinaire

A convenient route to N-substituted bis-C-alkylamidines possessing antiplasmodial activity and their oxadiazolone and amidoxime prodrug candidates, is described. These three families of compounds were available after a key N-alkylation step of the parent oxadiazolone 1a. Testing of the three compound classes in vitro and in vivo is also presented.

Widespread strains of Plasmodium falciparum are becoming resistant to most available antimalarial drugs. 1 Faced with this problem, bis-alkylamidines have been developed as a potential new chemotherapy (Fig. 1). 2,3 Bis-alkylamidines share the same novel mechanism of action as bis-thiazolium salts (T3). 4-6 By mimicking choline structure, these compounds target the parasitic de novo phosphatidylcholine biosynthesis. 6-8 Thus, the mechanism of action of bis-alkylamidine drugs differs from benzamidines which also possess anti parasitic activities including antimalarial activities. [9][10][11] In contrast, benzamidines are supposed to exert their activity as DNA minor groove binder, by means of their planar and rigid structure. 12 Bis-alkylamidines, where M34 is the lead compound, possess potent in vitro and in vivo antimalarial activities. However, they are not active after oral administration due to their cationic charges and/or their very polar heads.

Strategies to improve oral bioavailability of amidines have focused on the design of prodrugs, attempting to temporarily mask the positive charges. Clement's group originally applied the prodrug principle on pentamidine, an anti-parasitic drug with a benzamidine moiety. 13 This group introduced an oxygen atom to mask the cationic charge of benzamidines. 14 The resulting amidoximes were therefore less basic and not protonated under physio-logical conditions. These amidoxime neutral prodrugs possess improved bioavailability. Clement's group showed that these benzamidoximes are readily reduced by cytochrome P450 enzymatic system to the active aromatic amidines. 15 We previously described the bis-C-alkyloxadiazolone 1a and the bis-C-alkylamidoxime 2a. 16 (Fig. 2) They were able to tempo-

H 2 N N H 2 NH 2 + + NH 2 Cl - Cl - M34 N + S S N + OH HO T3
Br - Br - ( ) 10 IC 50 = 0.3 nM ED 50 i.p. > 10 mg/kg ED 50 p.o. >100 mg/kg IC 50 = 2.2 nM ED 50 i.p. = 0.2 mg/kg ED 50 p.o. = 13 mg/kg ( ) 10

N

  -substituted bis-C-alkyloxadiazolones as dual effectors: Efficient intermediates to amidoximes or amidines and prodrug candidates of potent antimalarials Mélissa Degardin a , Sharon Wein b , Thierry Durand a , Roger Escale a , Henry Vial b , Yen Vo-Hoang a, * a Institut des Biomolécules Max Mousseron, UMR 5247, CNRS-Université Montpellier I, Université Montpellier II, 34093 Montpellier, France b Dynamique Moléculaire des Interactions Membranaires, UMR 5235, CNRS-Université de Montpellier II, CP 107, 34095 Montpellier, France Keywords: Bis-C-alkylamidine Total clearance of parasitemia Design of prodrug candidates Convenient route N-substituted alkyloxadiazolone Alkylamidoxime Oral antimalarial agent a b s t r a c t

Figure 1 .Figure 2 .

 12 Figure 1. Bis-cationic antimalarial drugs.


Bertille Burgunter-Delamare
email: bertille.burgunter-delamare@sb-roscoff.fr

Gwenn Tanguy

Erwan Legeay

Catherine Boyen

Simon M Dittami
email: simon.dittami@sb-roscoff.fr

Sampling methods for kelp microbiomes 1/18

Keywords: Silica gel, swab, liquid nitrogen, brown algae, microbiome, metabarcoding, holobiont. _ Sampling methods for kelp microbiomes 3/18

Brown macroalgae, including the kelp Saccharina latissima, are of both ecological and increasing economic interest. Together with their microbiota, these organisms form a singular entity, the holobiont. Sampling campaigns are required to study the microbiome of algae in natural populations, but freezing samples in liquid nitrogen is complex in the field, particularly at remote locations. Here we tested two simple alternative methods for sampling the microbial diversity associated with the kelp S. latissima: silica gel conservation of tissue and swab samples preserved in DNA/RNA shield solution.

We used these techniques to compare apex and meristem samples from Roscoff (Brittany, France) and evaluated their impact on the results of 16S rDNA metabarcoding experiments. Both methods were able to separate apex and meristem microbiomes, and the results were concordant with results obtained for flash-frozen samples. However, differences were observed for several rare genera and ASVs, and the detection of contaminant sequences in the silica gel-preserved samples underline the

Introduction

Brown macroalgae, particularly kelps (Laminariales), play essential ecosystem engineering roles in coastal temperate marine environments. They contribute to primary productivity and are habitat engineers providing food and shelter to the local biodiversity [START_REF] Schiel | Gradients of disturbance to an algal canopy and the modification of an intertidal community[END_REF][START_REF] Schiel | The population biology of large brown seaweeds: ecological consequences of multiphase life histories in dynamic coastal environments[END_REF]. In addition, kelp species are important in many industries to produce alginates [START_REF] Peteiro | Alginate production from marine macroalgae, with emphasis on kelp farming[END_REF], human food, medicine [START_REF] Smit | Medicinal and pharmaceutical uses of seaweed natural products: A review[END_REF], or food for abalone aquaculture [START_REF] Roussel | Impact of nine macroalgal diets on growth and initial reproductive investment in juvenile abalone Haliotis tuberculata[END_REF].

Macroalgal functioning has to be seen as the result of the interactions between the algal host and its associated microbiota, constituting a complex system termed the algal holobiont [START_REF] Egan | The seaweed holobiont: understanding seaweed-bacteria interactions[END_REF].

It has been shown that macroalgal health, fitness, pathogen resistance [START_REF] Wiese | Diversity of antibiotic-active bacteria associated with the brown alga Laminaria saccharina from the Baltic Sea[END_REF], acclimation to a changing environment [START_REF] Dittami | Hostmicrobe interactions as a driver of acclimation to salinity gradients in brown algal cultures[END_REF], and metabolism (Burgunter-Delamare et al., 2020) are regulated and supported by bacterial partners [START_REF] Goecke | Chemical interactions between marine macroalgae and bacteria[END_REF]. Considering the biofilm composition and deciphering the interactions within the holobiont is thus essential to fully understand the biology of algae.

To study the microbiota of natural populations, especially in remote regions, we need simple sampling protocols and storage methods. Methods available involve flash-freezing in liquid nitrogen [START_REF] Tourneroche | The bacterial and fungal microbiota of Saccharina latissima (Laminariales, Phaeophyceae)[END_REF][START_REF] Van Der Meer | Cryopreservation of Gracilaria tikvahiae (Rhodophyta) and other macrophytic marine algae[END_REF], ethanol [START_REF] Hammer | Evidence-based recommendations on storing and handling specimens for analyses of insect microbiota[END_REF][START_REF] Song | Preservation methods differ in fecal microbiome stability, affecting suitability for field studies[END_REF], various preserving reagents [START_REF] Hammer | Evidence-based recommendations on storing and handling specimens for analyses of insect microbiota[END_REF][START_REF] Song | Preservation methods differ in fecal microbiome stability, affecting suitability for field studies[END_REF], and silica gel [START_REF] Esteban | Alternative methods for sampling and preservation of photosynthetic pigments and tocopherols in plant material from remote locations[END_REF][START_REF] Hoarau | A fast and inexpensive DNA extraction/purification protocol for brown macroalgae[END_REF][START_REF] Phillips | An effective DNA extraction protocol for brown algae[END_REF][START_REF] Toishi | Humidity control in a closed package[END_REF]. These methods can be applied to both algal tissue and surface swabs [START_REF] Lachnit | Epibacterial community patterns on marine macroalgae are host-specific but temporally variable[END_REF][START_REF] Parrot | Mapping the surface microbiome and metabolome of brown seaweed Fucus vesiculosus by amplicon sequencing, integrated metabolomics and imaging techniques[END_REF][START_REF] Qiu | Future climate change is predicted to affect the microbiome and condition of habitat-forming kelp[END_REF]. A few comparative studies of conservation methods in insect-, soil-, and human microbiota have established that differences introduced by storage techniques, while perceptible, did not outweigh differences classically found in the bacterial communities between species, individuals, or sample types [START_REF] Hammer | Evidence-based recommendations on storing and handling specimens for analyses of insect microbiota[END_REF][START_REF] Lauber | Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples: Influence of short-term storage conditions on microbiota[END_REF][START_REF] Song | Preservation methods differ in fecal microbiome stability, affecting suitability for field studies[END_REF]. Furthermore, a study on the red alga Porphyra umbilicals has shown that silica gel was as effective as flash-freezing/lyophilisation [START_REF] Quigley | A common garden experiment with Porphyra umbilicalis (Rhodophyta) evaluates methods to study spatial differences in the macroalgal microbiome[END_REF] to preserve the core microbiome. However, as red and brown algae belong to different lineages, _ Sampling methods for kelp microbiomes 4/18 display different metabolic pathways, and are at least partially in interaction with different bacteria [START_REF] Hollants | What we can learn from sushi: a review on seaweed-bacterial associations[END_REF], caution needs to be taken when transferring results from one group of algae to another. Here we examine if these results were transferable also to the kelp S. latissima.

The sugar kelp or sea belt Saccharina latissima (L.) (Phaeophyceae, Laminariales) is one of the dominant kelp-forming species of brown macroalgae in Europe and is becoming a research model for holobiont studies and others [START_REF] Staufenberger | Phylogenetic analysis of bacteria associated with Laminaria saccharina[END_REF][START_REF] Tourneroche | The bacterial and fungal microbiota of Saccharina latissima (Laminariales, Phaeophyceae)[END_REF][START_REF] Wiese | Diversity of antibiotic-active bacteria associated with the brown alga Laminaria saccharina from the Baltic Sea[END_REF].

We compared the impact of flash-freezing of tissue in liquid nitrogen, desiccation of tissue in silica gel, and swab sampling followed by preservation in DNA/RNA shield solution on DNA metabarcoding results of algal apex and meristem samples. Our data show that all three methods yield similar results

for the vast majority of genera and that both swabs and silica gel are viable alternatives to flashfreezing of tissues in the field.

Material & Methods

Biological material

Saccharina latissima (Phaeophyceae) samples were collected by hand at low tide on 22 March 2019, at Perharidy (48°43'47.0 "N 4°00'17.1 "W), Roscoff (France). Among young individuals (<1 m length), ten algae were randomly selected. The algal material was immediately placed in sterile plastic bags and rapidly transported to the laboratory in an icebox at ca. 4°C.

Sample preparation technics

All three techniques were carried out under a sterile hood and for each individual. Two areas of the blades were sampled: the basal meristem part and the tip (Figure 1). Two discs (Ø 2 cm) were punched out in immediate proximity for each part of the blade, ensuring that no epiphytes or animal colonizers were in the sampled area. One of the discs was placed in a 15 ml Falcon tube containing 5ml of clean silica gel (2-6 mm; VWR) and stored at 4°C for ca two weeks before use. The other disc was _ Sampling methods for kelp microbiomes 5/18 placed in a 2 ml cryotube, flash-frozen in liquid nitrogen, and stored at -80°C until use. For the swab samples, an area of 2 cm² was swabbed (Swab collection kit, Zymo Research) until a brown colouration was reached (30 s -1 min), and the swab was placed in a collection tube filled with 1 ml of DNA/RNA Shield (Zymo Research) and stored at -20°C until use. S1). As these ASVs corresponded to potential contaminations in the silica gel, they were removed from the dataset prior to further processing.

16S rDNA metabarcoding

The bacterial community composition associated with algal cultures was determined by 16S rDNA metabarcoding. A mock community comprising a mix of DNA from 26 cultivated bacterial strains [START_REF] Thomas | Evaluation of a new primer combination to minimize plastid contamination in 16S rDNA metabarcoding analyses of alga-associated bacterial communities[END_REF] was run parallel to the DNA extracts. For all of these samples, the V3 and V4 regions of the 16S rDNA gene were amplified using the NOCHL primers including Illumina adapters [START_REF] Thomas | Evaluation of a new primer combination to minimize plastid contamination in 16S rDNA metabarcoding analyses of alga-associated bacterial communities[END_REF], to avoid plastid DNA amplification. Then the adapted Illumina protocol for metabarcoding [START_REF] Illumina | 16S Metagenomic sequencing library preparation. Prep. 16S Ribosomal RNA Gene Amplicons Illumina[END_REF] [START_REF] Mcmurdie | phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data[END_REF]. Organellar and eukaryote reads, rare ASVs (<0.01% of total reads) and ASV that were more abundant in the blank samples than in all other samples, as well as samples with less than 7000 total reads were removed, leading to a final number of 3 to 5 replicates per condition. Non-Metric

Multidimensional Scaling analyses (NMDS) were carried out using the Bray-Curtis distances derived from the ASV table using the vegan R package version 2.5-6. The Shannon H diversity index was also calculated based on the ASV table using Past version 4.02 [START_REF] Hammer | PAST: Paleontological statistics software package for education and data analysis[END_REF]. Statistical analysis of differential abundance was carried out at the genus level using ANCOM-BC version 1.4.0 (Lin and Peddada, 2020) with default parameters. A joint analysis was performed with both methods and thallus part as factors to identify ASVs specifically impacted by the storage methods. Venn diagrams were generated using BioVenn [START_REF] Hulsen | BioVenn -a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams[END_REF], and the mean abundance of genera across storage methods was compared using linear regression on log10-transformed data in Past version 4.02. The residuals of the linear regressions were subjected to a Shapiro-Wilk test to confirm that they did not deviate significantly from a normal distribution (p>0.05).

Results

A total of 3,935,663 raw sequences were generated and, after filtering, assembled into 1,743,565 contigs. The taxonomic assignation of mock samples was consistent with the mock composition. A total _ Sampling methods for kelp microbiomes 8/18 of 11,106 ASVs were identified in the dataset corresponding to 572 genera. The final ASV matrix is provided as Supplementary Table S1.

3.1 Comparison of apex and meristem samples with the three storage methods

Regardless of the storage method used, the sequences followed the same general patterns. In all samples, they corresponded predominantly to Alphaproteobacteria (34.8% of reads, on average), followed by Gammaproteobacteria (31.5% of reads) and Bacteroidota (21.1% of reads), although their exact proportion varied slightly (Figure 2). Planctomycetota were significantly more abundant (t-test, p <0.001) in the apex samples (5 to 8% of reads) than in the meristem ones (0.76% to 1.95% of reads).

Actinobacteriota were less detected after desiccation in silica gel (meristem: 0.08% and apex: 0.7% of reads) compared to the samples treated with liquid nitrogen (meristem: 1.85% and apex: 1.93% of reads); and Acidobacteriota were almost exclusively found in the swab samples (meristem: 0.35% and apex: 0.16% of reads; <0.04% in others, t-test p=0.049). Overall, as indicated by the Shannon H index in Figure 3, the alpha diversity was higher in apex samples than meristem samples, although this difference was statistically significant only for the liquid nitrogen and the silica gel samples. Finally, NMDS analyses confirmed a clear separation of apex and meristem samples regardless of the sampling and storage method (Figure 4A-C). This separation was also observed in a combined NMDS plot (Figure 4D). Here, separation according to the storage method was only detected at a smaller scale in the meristem samples.

_

Sampling methods for kelp microbiomes 9/18

Direct comparison of storage methods

Differences were observed primarily regarding the detection of different genera depending on the storage and sampling methods (Figure 5). Among the 572 genera in our dataset 158 were detected in at least one sample with all storage methods. 143 genera were detected in samples from two different storage/sampling methods, with the highest overlap occurring between liquid nitrogen and swab samples. However, most genera (271) were found only with one of the tested methods, and most of these with the swab samples. Please note, however, that as shown in Figure 5, these method-specific genera correspond only to a small percentage of the total reads. 96.7% of all reads were covered by the genera detected with all methods. Moreover, among these shared genera as well the genera present in samples from two sampling methods, total read counts were strongly correlated across methods (Figure 6). This confirms that similar read abundances were observed across the different methods for >96% of the reads. As for the method-specific ASVs, global ANCOM-BC analyses of the abundance of all ASVs across methods and sample types confirmed several significant differences.

_

Sampling methods for kelp microbiomes 11/18

Thirty-one ASVs (corresponding to 1.79% of total reads) were significantly differentially abundant between the liquid nitrogen and silica methods, 52 ASVs (3.48% of total reads) between liquid nitrogen and swabs methods, and 55 ASVs (3.10% of total reads) between the liquid nitrogen methods and the other two methods (Supplementary Table S2). Four ASVs were only detected by the swab method (a Granulosicoccus sp., a Bacillus sp., a Robiginitomaculum sp., and a Thalassotalea sp. ASV; 0.54% of total reads), and two ASVs only by the silica method (an unclassified Saprospiraceae and another Granulosicoccus sp. ASV; 0.162% of total reads). No ASVs were detected specifically with the liquid nitrogen method. However, most of the differentially abundant ASVs were detected at similar levels with two methods but absent in the third. The 55 ASVs undetected in the liquid nitrogen dataset, the 48 ASVs undetected in the swab dataset, and the 29 ASVs undetected in the silica dataset are listed in Supplementary Table S2. They belong mainly to the Gammaproteobacteria and Alphaproteobacteria, but also Bacteroidota, Planctomycetota, and Firmicutes. We did not find any apparent link between the method-specific ASVs and the characteristics methods employed.

Discussion & Conclusion

Simple sampling protocols are required to study bacterial partners in natural populations of macroalgae. In this study, we wanted to test if using silica gel and swab techniques, both of which are more convenient to put into place during field sampling campaigns, would introduce a bias in the results compared to flash-freezing in liquid nitrogen. Unlike similar studies carried out on insect-soiland human microbiota [START_REF] Hammer | Evidence-based recommendations on storing and handling specimens for analyses of insect microbiota[END_REF][START_REF] Lauber | Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples: Influence of short-term storage conditions on microbiota[END_REF][START_REF] Song | Preservation methods differ in fecal microbiome stability, affecting suitability for field studies[END_REF], we use more subtle differences as a benchmark for our comparisons, i.e. different thallus parts of the same alga rather than different species (butterfly vs bee vs grasshopper vs beetle or human vs dog) or different sample types (faeces vs skin vs soil). Furthermore, unlike the former two studies, we examined the results not only at the family or class level but also at the genus and ASV level. These differences render our analyses more sensitive to small biases introduced by the sampling method.

Our results demonstrate that, regardless of the sampling and storage method, coherent results were obtained. The global bacterial composition dominated by Alphaproteobacteria, Gammaproteobacteria, and Bacteroidota was obtained regardless of the method employed and is typical for brown algae-associated microbiomes [START_REF] Hollants | What we can learn from sushi: a review on seaweed-bacterial associations[END_REF][START_REF] Kleinjan | Exploring the cultivable Ectocarpus microbiome[END_REF][START_REF] Parrot | Mapping the surface microbiome and metabolome of brown seaweed Fucus vesiculosus by amplicon sequencing, integrated metabolomics and imaging techniques[END_REF][START_REF] Tourneroche | The bacterial and fungal microbiota of Saccharina latissima (Laminariales, Phaeophyceae)[END_REF]. In the same vein, global differences in the community composition _ Sampling methods for kelp microbiomes 13/18

between apex and meristem samples persisted regardless of the sampling methods. These differences were expected, as S. latissima is a perennial species, and growth occurs mainly in the meristem region.

Hence, the younger meristem tissues thus are typically less colonised by bacteria and, as confirmed in our study, exhibit lower bacterial diversity [START_REF] Goecke | Chemical interactions between marine macroalgae and bacteria[END_REF][START_REF] Ihua | Diversity of bacteria populations associated with different thallus regions of the brown alga Laminaria digitata[END_REF][START_REF] Staufenberger | Phylogenetic analysis of bacteria associated with Laminaria saccharina[END_REF]. Planctomycetes, which we detected predominantly in the apex samples, are typical components of algal biofilms [START_REF] Lage | Planctomycetes and macroalgae, a striking association[END_REF] and are, in agreement with our results, more abundant also in apices of the brown alga Fucus vesiculosus [START_REF] Parrot | Mapping the surface microbiome and metabolome of brown seaweed Fucus vesiculosus by amplicon sequencing, integrated metabolomics and imaging techniques[END_REF].

The main differences between the examined sampling methods were observed at the genus and ASV levels, where our analyses show that numerous genera and ASVs were not detected with all sampling methods. This observation may seem disconcerting at first, but these differences were driven by rare genera and ASVs, which were usually detected in one or a few replicates. Our data are thus in line with the results obtained by [START_REF] Quigley | A common garden experiment with Porphyra umbilicalis (Rhodophyta) evaluates methods to study spatial differences in the macroalgal microbiome[END_REF] on the red alga P. umbilicalis, who found silica gel and flash freezing to yield similar patterns for the abundant core taxa that constituted >0.1% of sequences. Furthermore, an important observation was that significant ASVs were absent in one method compared to the other two, even in the liquid nitrogen subset. This suggests that each method has its own biases regarding rare genera, and there is no one method superior to the others.

We can currently only speculate why the phenomenon of method-specific detection of genera was more pronounced in the swab samples, although this was not the case at the ASV level. The swab protocol is different from the two other protocols because of the storage method (DNA/RNA Shield solution, vs silica gel, vs flash-freezing) and the DNA extraction protocol. The exact composition of the DNA/RNA Shield solution has not been published, so it is difficult to evaluate if some microbes may still develop after fixation. Furthermore, it is known that different extraction methods may impact the recovery of microbial reads, and this effect is strongest for rare species [START_REF] Liu | Rare plankton subcommunities are far more affected by DNA extraction kits than abundant plankton[END_REF]. Lastly, swabs sample only detect microbes at the algal surface, unlike the other methods. All of these factors likely contribute to the observed differences. However, the fact that these differences were not also _ Sampling methods for kelp microbiomes 14/18

observed at the ASV level after statistical testing using ANCOM suggests that a fourth factor may be important: variability. Although efforts were made to standardize the swabbing procedure, differences in the applied pressure or the target area's swabbing precision may occur. This constitutes an additional source of variability, and higher variability could explain why more (rare) genera were detected overall, while the number of statistically significant ASVs stayed comparable.

Based on our data, we conclude that caution needs to be taken when interpreting data on rare species or genera, as these may vary according to the sampling method. As suggested by the presence or absence of genera in the swab samples, random processes may enhance these differences if no statistical evaluation is applied. For silica-gel preserved samples, we observed more contaminants sequences in the blank samples emphasising the importance of this latter control. When examining the global patterns, however, only slight biases are introduced by the tested methods, and all three methods, flash-freezing in liquid nitrogen, drying with silica gel followed by tissue grinding, and swabbing followed by preservation in DNA/RNA shield solution, are suitable to assess the microbiome of S. latissima.

_

Sampling methods for kelp microbiomes 15/18

Declarations

Figure 1 - 18 (

 118 Figure 1 -Sampled parts of the thallus of S. latissima. Two discs (Ø 2 cm) were punched out in immediate proximity for each part of the blade, and an area of 2cm² was swabbed.

Figure 2 -

 2 Figure 2 -Distribution of 16S rRNA gene metabarcoding sequences per phylum.

Figure 3 -

 3 Figure 3 -Box plot of alpha-diversity (Shannon H index) across sample types. P-values correspond to the results of a two-sided t-test; n.s. = not significant (p > 0.05).

Figure 4 -

 4 Figure 4 -NMDS analysis of the microbiome composition. Results show a clear separation of the apex and meristem samples for the (A) liquid nitrogen, (B) silica gel, and (C) swab method, as well as (D) all methods combined. The points of the same colours and connected by lines correspond to the same "parent" sample.

Figure 5 -

 5 Figure 5 -Venn diagram illustrating shared genera between the liquid nitrogen (pink), silica gel (blue), and swab (green) datasets. Numbers in parentheses indicate the percentage of total reads represented by the genera in each section.

Figure 6 -

 6 Figure 6 -Correlation of log10-transformed mean sequence abundance across the sampling protocols. Only genera shared by at least two of the protocols were considered. R=Pearson correlation coefficient, red line = linear regression, p < 0.001.

  An amplicon sequence variant (ASV) table was constructed, and chimaeras were removed. The taxonomy of the remaining ASVs was assigned using the Silva_SEED 138 database. The resulting abundance table and taxonomic classification were analysed using Phyloseq version 1.30.0 (McMurdie

	2.5 Analyses
	Sequence analysis was performed using the DADA2 version 1.14.0 package (Callahan et al., 2016)
	on	R	3.6.2.	Following	the	protocol	by	Benjamin	Callahan
	(https://benjjneb.github.io/dada2/tutorial.html), sequences were filtered allowing for a maximum of
	two expected errors and reducing the read length to 291 bp for forward reads and 265 bp for reverse
	reads.
	concentration of 4 nM, diluted to 3 pM, spiked with 10% PhiX (Illumina), and sequenced on an Illumina
	MiSeq sequencer at the Genomer platform (Station Biologique de Roscoff) using a MiSeq v3 kit
	(2x300bp, paired-end).
	_			Sampling methods for kelp microbiomes		7/18

was run using the Q5® High-Fidelity 2X Master mix (New England BioLabs, MA, USA), the AMPure XP for PCR Purification Kit (Beckman Coulter, Brea, CA, USA), and the Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA, USA). Libraries were quantified with a Quantifluor® ds DNA System (Promega, WI, USA), and the mean fragment size was determined using a LabChip® GX Touch™ (Perkin Elmer, MA, USA). An equimolar pool of all samples was generated at a

Funding: This work was funded partially by the ANR project IDEALG (ANR-10-BTBR-04) "Investissements d'Avenir, Biotechnologies-Bioressources" and the CNRS momentum call (2017). BBD was funded by a joint PhD scholarship from the Brittany region (Project HOSALA) and Sorbonne University (ED227).

Data availability: Raw sequence data were deposited at the European Nucleotide Archive under project accession number ENA: PRJEB37561.

Competing interests:

The authors declare that they have no competing interests.

Authors' contributions: Designed study: BBD, SD; Performed experiments: BBD, EL, GT; Analyzed data: BBD, SD; Wrote the manuscript: BBD, SD; Provided valuable input and corrected the manuscript: CB.

Acknowledgements

We thank François Thomas, Maéva Brunet, and Nolwen Le Duff for sharing primers and providing the mock community. This work benefited from access to the Station Biologique de Roscoff, an EMBRC-France and EMBRC-ERIC Site.

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