Claire Grison

Vincent Escande

Eddy Petit

Laetitia Garoux

Clotilde Boulanger

Claude Grison

Claire Grison

Claude Grison

Psychotria douarrei and Geissois pruinosa, novel resources for the plant-based catalytic chemistry

Introduction

Because of their immobility, plants grow on environments which they cannot escape. Therefore some plants develop very specific biological mechanisms to withstand abiotic or biotic constraints. The heavy-metal content of soil is one of the most important edaphic factors impacting the vegetation composition. Plants only survive by adapting their physiological processes. Metallophytes have known to survive on metal-rich soils. Wide-ranging identifications of hyperaccumulators of Ni from tropical soils of ultramafic origin were reported. For example, in New Caledonia, the soil is derived from ultramafic rocks, which is naturally enriched in Nickel [1]. Around 40 nickel-tolerant species have adapted to this natural nickel-toxicity [2][START_REF] Baker | Metallophytes: the unique biological resource, its ecology and conservational status in Europe, Central Africa and Latin America[END_REF][START_REF] Jaffré | La végétation des roches ultramafiques ou terrains miniers[END_REF]. Species were classified into four categories of Ni accumulation ability: nonaccumulator (<100 mg Ni/ kg), accumulators (100-1000 mg Ni/kg), hyperaccumulators (1000-10,000 mg Ni/kg), and hypernickelophores (>10,000 mg Ni/kg) [START_REF] Reeves | Metal-hyperaccumulating Plants[END_REF]. Nine are hypernickelophores and belong to Geissois genus (Cunoniacae), Homalium (Silicaceae), Hybanthus (Violaceae), Phyllanthus (Phyllanthaceae), Psychotria (Rubiacaea) and Sebertia (Sapotaceae). Among these metallophytes, Psychotria douarrei and Pycnandra acuminata present exceptional tolerance to the nickel enriched soils. They are the both strongest nickelhyperaccumulators [2,[START_REF] Reeves | Metal-hyperaccumulating Plants[END_REF]. Besides, P. douarrei is characterized by the ability to accumulate very high concentrations of nickel, up to 4.7% of Ni in its shoots. The hypernickelophore Geissois pruinosa had a relatively high Cr content (430 ppm). The potential of G. pruinosa and P. douarrei had been identified for the ecological restoration and phytoremediation [START_REF] Boyd | [END_REF]. Therefore we have recently developed a large scale ecological restoration of the Thio Caledonian mining site [7,8] introducing a large number of Ni metallophytes, especially P. douarrei and G. pruinosa. For the first time, it is possible to propose credible outlets to dispose of nickel-enriched biomass. A fundamental question is addressed: What potential do Ni hyperaccumulating plants offer as a resource base for the Green Chemistry? Taking the advantage of the capacity of these plants to concentrate Ni into shoots, we 50 address the direct use of Ni as Lewis acid catalysts for a modern organic synthesis. The use of nickel-enriched biomass to produce catalysts used in organic chemistry could bring valorization for the development of phytoextraction in New Caledonia. In this article, we investigate the potential of P. douarrei biomass as an 55 alternative source of nickel, which is used in the synthesis of a promising antimitotic compound, but difficult to access, the dihydrothiopyrimidinone, named monastrol.

The present study was conducted with two aims: (a) revisiting the inorganic composition of P. douarrei and G. pruinosa extract 60 and the derived catalyst with specific analytical techniques and (b) identifying their abnormal compositions as an inspiration for plant-based and environmental friendly chemistry.

Results and discussion

65

Leaves of P. douarrei and G. pruinosa were harvested in the South province of New Caledonia. 560 samples were collected for three years, two times a year, from two different sites: on Thio-Plateau Mining Site and Mont Koghis near Noumea. In order to determine the mineral composition of P. douarrei an 70 appropriate treatment of the shoots was necessary. The first step was a thermic treatment of leaves at 400°C to destroy the organic matter. The addition of HCl (1M) led to a complex mixture of metallic species of this plant. XRF was used to determine the composition of the plant catalysts obtained. Detailed results are 75 presented in Table 1.

In Table 1, for 1 g of catalyst, P. douarrei furnished 3.3 mmol of Ni, while G. pruinosa gave 1.6 mmol of Ni. Significant amounts of Mn and Si were also noticed in P. douarrei, while alkaline-earth Ca and Mg were highest in G. pruinosa. Moreover 80 solid derived from P. douarrei led to the richest mixture in transition metals thus the most interesting catalyst in organic synthesis. Finally these XRF analyses confirmed the great ability of P. douarrei to concentrate Ni in its shoots.

The ratio Ni/Cl corresponded to partially hydrated NiCl 2 , but could not explain the complex structure of P. douarrei catalyst. That is why XRD analyses were used to identify the crystallized mineral compounds in the catalyst. In Figure 1, the formation of Ni II Cl 2 (H 2 O) 2 was confirmed. Very surprisingly, but very interestingly Ni 2 O 3 H was observed. This unusual result with two oxidation states of Nickel (+2 and higher: +3) should be noted [10,11,12]. This observation had never been found in a living organism; and the oxidation degree +3 was exceptionally observed in [NiF6] 3-or in drastic conditions [10]. As well, Ni IV complexes had rarely been detected [11].

The same treatment of leaves was performed on P. douarrei and G. Pruinosa. Ni III had not been found in G. Pruinosa. G. pruinosa XRD analyses provided more predictable results as KNiCl 3 .

Furthermore in table 1, the P total level should be noted. It was consistent with the composition of serpentine soil in New-65 Caledonia, derived from Fe-and Mg-rich ultramafic rocks and also deficient in available phosphorus. According to certain authors [13], symbiosis with arbuscular mycorrhizal fungi could help the host to overcome phosphorus lack. However, for Nihyperaccumulators, especially for P. douarrei, a high ratio S / P 70 was remarkable. To better understand the form of sulfur assimilation, a purification of 50 catalysts was carried out by anion-exchange chromatography. The results are presented in Table 2.

In Table 2, the amount of sulfate was much higher than the 75 amount of phosphate, which is rare for vascular plants.

Comparing Tables 1 and2, the high level of sulfur resulted in significant amounts of sulfate, and vice-versa, a poor level of phosphorus could be correlated to small amounts of phosphate. P. douarrei is the most demonstrative example. We wondered how 80 the abnormal high percentage of sulfate could be explained by P. douarrei physiology. The sulfur assimilation pathway had been recently investigated [13]. Sulfate derived from environment uptakes into plants is reduced to sulfide through a serie of enzymatic catalyzed 85 transformations [14,15]. The first step is the formation of APS (5'-adenylylsulfate) by a reaction between sulfate and ATP. The following step is the reduction of the sulfate group into sulfite, then the reduction of sulfite into sulfide which is used for the construction of cysteine. Cysteine serves as the precursor of all 90 cellular compounds containing sulfur: methionine, glutathione and proteins. Therefore it was interesting to examine whether the sulfate derived from P. douarrei was the result of the sulfur amino acid decomposition during the thermic treatment, which could explain the abnormal high level of sulfates. It had been 95 reported that cysteine, methionine, glutathione and proteins are thermally decomposed, at 400°C, in various gases such as H 2 O, H 2 S, CO 2 , NO 2 , SO 2 , and N 2 O 3 [16,17]. That is why we concluded that the excessive amount of sulfate measured was not a consequence of an unusual sulfur amino acid contained in the 100 plant.

Sulfate could originate from chemically activated forms of sulfate as 5'-adenylsulfate, [13] or from other biological structures, where the S atom replaces the P atom. Phosphorus is a part of the chemical backbone of the phospholipids that form 105 every cell membrane. Recently, Okanenko et al. [START_REF] Okanenko | Sulfurcontaining plant lipids[END_REF] had reported that plants could use sulfate instead of phosphate in their cell membranes. They have many sulfatolipids instead of phospholipids. Considering this hypothesis, we had launched experiences aimed at the extraction of membrane lipids. The Folch and Bligh and Dyer methods [START_REF] Bligh | [END_REF]20] were developed as rapid but effective methods for determining the total membrane lipid content in P. douarrei. We analyzed the methanol/water phase system, and many organic phases derived from washings of the crude extract with CHCl 3 , CH 2 Cl 2 -MeOH, H 2 O-MeOH and iPrOH. The first 10 analyses were realized by anion-exchange chromatography. Regardless the treatment, the chromatogram (Table 3) showed that sulfate was the major anion. Isopropanol was the best extraction solvent of the sulfate derivatives. In principle, alkali and alkaline earth sulfate are insoluble in organic mixtures. No traces of nickel sulfate, sulfite or sulfide were detected. These observations suggested that the knowledge of the sulfate entity structure had to be improved.

Subsequent analyses by mass spectrometry (ESI-MS, HR-ESI-MS and HR-MS-MS) were performed of the sample, diluted 20 in methanol. Details can be found in supplementary information. Interestingly, a new compound has been identified as a sulfatolipid, 3,4-dihydroxy-tridecanesulfate (figure 2). 2).

Application in green organic synthesis

The original composition of the P. douarrei catalyst prompted to us to investigate how this mixture could initiate Lewis acid catalyzed reactions. We wish to give an illustrative example, 50 which culminated in a three-component reaction, the Biginelli reaction leading to dihydropyrimidinones. Recently dihydropyrimidinones have been the object of an increased interest, as these molecules exhibit exciting biological features like modulating calcium channels, selectively inhibiting α 1a 55 adrenoreceptor and targeting the mitotic machinery [21,22]. Under optimized conditions, the plant catalyst was dispersed on montmorillonite K10. The amount of Ni in the final solid reached a maximum value of 9.05 wt% and Cl:Ni molar ratio of 1:4. The supported catalyst (25% mol Ni /aldehyde), the substrate and 60 the reagent were mixed thoroughly and stirred at 80°C under solvent-free conditions for 12 h. According to our proposal, the P. douarrei catalyst promoted the reaction between 3hydroxybenzaldehyde, ethyl 3-ketopentanoate and thiourea in a one-pot protocol. After recrystallization, the pure expected 65 heterocycle (ethyl 6-methyl-4-(3-hydroxyphenyl)-2-thioxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (monastrol) [22] was obtained with a high yield (83%) (Scheme 1).

The same reaction was carried out with G. pruinosa catalyst dispersed on montmorillonite K10 and commercial NiCl 2 as 70 catalysts.

As expected, the use of plant catalysts was more efficient than the use of commercial NiCl 2 . An efficient catalysis of Biginelli MCR was observed with an over 90% conversion rate, while NiCl 2 showed a low catalytic efficiency (30%). Since 75 experimental conditions and mole ratio Ni/ ArCHO were identical, it could be concluded that the efficiency difference is due to the polymetallic composition of the plant catalysts. More interestingly, yields were also higher with the plant catalysts. It clearly led to lower yields 11% and the purification of monastrol 80 was very difficult because of the strong association between Ni II and sulfur of the dihydrothiopyrimidinone. This issue was not observed with P. douarrei or G. pruinosa. The conversion was total and the heterocycle crystallized easily and gave pure crystals.

85

G. pruinosa catalyst gave good yield although lower than P. douarrei catalyst. As can be seen in Table 4, the Ni / ArCHO mole ratio and the (Al + Mn + Fe) / ArCHO mole ratio used in the Biginelli reaction were similar. The only difference was the presence of Ni III in P. douarrei catalyst. Therefore it can be 90 assumed that Ni III might be a better catalyst than Ni II in this MultiComponent Reaction.

We assumed that in Ni-hyperaccumulating plant catalysts, a small amount of Ni II formed NiCl 2 , while the major amount of Ni II constituted other associations such as KNiCl 3 (see paragraph 95 2). These unique associations allowed a slow release of NiCl 2 , which limited the concurrent association with the sulfur of the dihydrothiopyrimidinone.

To check the applicability of these catalysts, the reaction was extended to other substrates with the best plant catalyst, P. 100 douarrei catalyst (Scheme 2). Results are presented in table 5. To demonstrate the general applicability of P. douarrei catalyst, a range of components had been studied. The yields were high in all cases tested. P. douarrei catalyst promoted Biginelli reaction with β-ketoester and β-dione, thiourea and urea. Additionally, high yields were obtained with aromatic as well as aliphatic aldehydes. In contrast with literature [START_REF] Kappe | The Biginelli dihydropyrimidine synthesis[END_REF], it should be noted that good results were obtained with aliphatic aldehydes. The usual side reaction, such as enolization and aldol reaction of these substrates were limited with P. douarrei catalyst. This catalyst was composed of a small amount of NiCl 2 ; KNiCl 3 was the major 10 form of Ni salt, which is a Lewis acid storage catalytic converter in solution. This result reflected the softness of P. douarrei catalyst. P. douarrei and G. pruinosa based catalysis constituted a rapid and simple synthesis of dihydrothiopyrimidinone and dihydropyrimidinone. The reaction is general and has been extended to various substrates. To our knowledge, this was a cutting-edge example for a greener sustainable chemistry. With these experimental results, we could show the relevance of endemic plants. Their efficiency as catalysts in organic synthesis therefore justifies the development of their culture in phytorestoration. Developing the culture of these rare plants in order to use them in organic synthesis would contribute to their safeguard. Green Chemistry is thus an opportunity to preserve biodiversity, valuing rare species.

Scheme 2. Biginelli reaction catalyzed by P. douarrei catalyst

wavelength-dispersive spectrometer. The quantitative analysis of major and expected elements was performed on beaded samples for overcoming problems of particle size variation as well as mineralogy effects: the powdered sample is mixed with a Li 2 B 4 O 7 flux with a flux / sample ratio equal to 8, heated in a crucible 30 between 900-1200 °C, then cast in a platinum dish to produce a homogeneous glass-like bead. Extractions of lipids had been carried out according to Folch et al. [START_REF] Bligh | [END_REF]20]. The anion exchange chromatography was carried out in the 35 following conditions. The samples were prepared by dissolution of Geissois pruinosa extract (25.7 mg) and of Psychotria douarrei extract (26.4 mg) in ultrapure water (18.2MW) and 50 µL HNO 3 . A complete dissolution was obtained after ultrasonic activation. This solution is completed to 250 mL with ultrapure A mixture of ethyl acetoacetate (781 mg, 6.0 mmol), 3hydrobenzaldehyde (488 mg, 4.0 mmol), thiourea (457 mg, 6.0 mmol) and P. douarrei crude catalyst (295 mg, amount corresponding to 1.0 mmol of nickel following previous dosing), 70 supported on montmorillonite K10 (300 mg) was placed in a 10 mL sealed tube. The tube was heated to 80°C in oil bath, under magnetic stirring for 12 h. The mixture was then extracted with hot ethanol (10 mL, 70°C) and filtered in order to remove the catalyst, which was reactivated by heating (150°C). The solution 75 was poured into crushed ice (20 g) and stirred for 20 min. The solid separated was filtered under suction, washed with cold water (30 mL) and recrystallized from hot ethanol, affording pure product, as colorless crystals (973 mg, 83%). The same procedure was followed with G. pruinosa catalyst and commercial NiCl 2 .

Conclusion

The inorganic composition of the extract and catalyst derived 90 from the best Ni hyperaccumulating plant, P. douarrei, was revisited by specific analytical techniques. XRF analyses confirmed the exceptional ability of P. douarrei to store Ni in its shoots. XRD analyses revealed the presence of a rare Ni oxidation state, Ni III or Ni IV , for the first time in a living 95 organism. XRF analyses followed by an anion-exchange chromatography disclosed a lack of phosphorus linked to a lack of phosphates filled by an excess of sulfur linked to an excess of sulfates in P. douarrei catalysts. The excess of sulfates was found in the membrane lipids of P. douarrei as sulfatolipids. Among the 100 membrane lipid extract, a new sulfatolipid had been discovered, the 3,4-dihydroxy-tridecanesulfate.

From this exceptional composition, P. douarrei was used as a new catalyst in a MultiComponent Reaction of increasing importance in organic and medicinal chemistry, the Biginelli 105 reaction. This plant-based catalyst led to higher yields in greener conditions than commercial NiCl 2 .

Metallophytes can be the starting point of a novel plantinspired metallo-catalytic platform for synthesis of biologically interesting molecules, and finally should contribute to develop a 110 new concept of phytochemistry.

Notes and references

Figure 1 .

 1 Figure 1. XRD pattern of a P. douarrei catalyst (Radiation used: Cu Kα; λ=1.54056 Å).

55

  Therefore Ni III had not been formed by the chemical and thermic 60 treatment. Ni III was naturally present in P. douarrei.

Figure 2 . 1 .

 21 Figure 2. The sulfatolipid structure

  40water. The analysis was performed with 882 Compact IC Metrohm apparatus equipped with a chemical suppressor, CO 2 suppressor and a conductivity detector. Conditions: Metrosep A Supp 5 -250/4.0 column; Elution: Na 2 CO 3 (3.2mM) / NaHCO 3 (1mM), rate of flow: 0.7 ml.min -1 ; 45 calibration: standard solution standard of Alfa Aesar (reference 041693) F -, Cl -, Br -, NO 3-100µg.mL -1 ). Concentrations were calculated from peak areas. Electrospray ionization mass spectrometry (ESI-MS) was performed with a Waters Alliance e2695 Chain coupled to a 50 Quattro Micro mass spectrometer and a PDA 996. High resolution electrospray ionization mass spectrometry (HR-ESI-MS) was acquired in negative ion mode and recorded on a hybrid quadrupole-time of flight instrument Micromass Q-TOF (Waters) by direct infusion of the sample diluted in methanol, 55 with a syringe pump at a flow rate of 1 mL/min. Conditions: capillary voltage 3000 V; dry gas temperature, 120 ºC; dry gas flow, 400 L.h -1 and nitrogen as nebulizer gas. 0.1% phosphoric acid was used as standard for internal calibration. IR spectra were recorded on a PerkinElmer Spectrum 100 FT-IR 60 spectrometer, in ATR mode. NMR spectra were recorded on a BRÜKER Avance 300, at room temperature.Procedure for the synthesis of ethyl 6-methyl-4

Table 2 .

 2 Major anionic species of G. pruinosa and P. douarrei

	25

Table 3 .

 3 Sulfate and Phosphate amount from P. douarrei lipid membrane according to the extraction solvents

		CHCl3	CH2Cl2-MeOH	i-PrOH	H2O-MeOH
	Phosphate (mg.L -1 )	0	0	0	12.3
	Sulfate (mg.L -1)	10.6	13.1	58.7	14

Table 1 .

 1 Mineral

	G. pruinosa	39.8	58.5
	P. douarrei	28.3	70.4

Phosphate (mg.g -1 )

Sulfate (mg.g -1 ) composition of nickel-hyperaccumulating plant catalysts. Data were compared to G. pruinosa, another endemic Caledonian nickel-hyperaccumulating plant which grows in a similar ecological niche, as control.

Table 4 .

 4 Biginelli reaction tested with the plant catalysts and commercial NiCl2 applied to Monastrol model example

	40
	Catalyst	Ratio	Ratio	Conversion % Yield %
		Ni /	(Al + Mn +
		ArCHO	Fe) /
			ArCHO
	NiCl2	0.25	-	30	11
	G. pruinosa catalyst	0.25	0.014	92	72
	P. douarrei catalyst	0.25	0.014	95	83
	.
	Currently, these data constituted unusual observations and
	complementary information about sulfate assimilation with the

45

dihydroxy-tridecanesulfate (figure

Table 5 .

 5 Synthetic Potential of P. douarrei catalyst through Biginelli reaction

	20
	R 1	R 2	Product	% 1 Yield	Ref. a
		EtO	O	1a 87 [23]
			O	NH
			N H	O
		EtO		1b 83 [23]
		EtO		1c	91 [24]
		EtO		1d 75	[25]

  Institut Européen des Membranes, UMR CNRS-UM2-ENSCM 5635, CC 047 Place Eugène Bataillon 34095 Montpellier, France b Centre d'Ecologie Fonctionnelle et Evolutive, UMR CNRS-UM2 5175,1919 route de Mende, 34293 Montpellier cedex 5, France fax : (+) 33 4 67 61 33 16 ; Tel : 33 4 67 61 33 16 E-mail : claude.grison@cefe.cnrs.fr c Agence de l'Environnement et de la Maîtrise de l'Energie, 20 avenue du Grésillé, BP 90406, 49004 Angers cedex 1, France

a

Acknowledgements

The authors thank ADEME, ANR 11 ECOT 011 01, CNRS for financial supports, Dr. Laurent L'Huillier, IAC, and Dr Sylvain Merlot for their help in harvesting Psychotria douarrei and Geissois pruinosa.

Experimental

Procedure for chemical analysis X-ray diffraction (XRD) data measurements on the samples dried at 110°C for 2 hours were performed by using a BRUKER 20 diffractometer (D8 advance, with a Cu Kα radiation λ=1.54086 Å) equipped with a Lynxeyes detector. Chemical analysis of the plant catalyst samples after calcinations (1000°C for 3 h) was performed by X-Ray Fluorescence spectrometry (XRF) using a BRUKER AXS S4 Explorer


Samuel Felton

Élisa Fromont

Eric Marchand

Siame-se(3): regression in se(3) for end-to-end visual servoing

In this paper we propose a deep architecture and the associated learning strategy for end-to-end direct visual servoing. The considered approach allows to sequentially predict, in se(3), the velocity of a camera mounted on the robot's end-effector for positioning tasks. Positioning is achieved with high precision despite large initial errors in both cartesian and image spaces. Training is fully done in simulation, alleviating the burden of data collection. We demonstrate the efficiency of our method in experiments in both simulated and real-world environments. We also show that the proposed approach is able to handle multiple scenes.

I. INTRODUCTION

Visual servoing (VS) is the task of controlling the motion of a robot in order to reach a desired goal or a desired pose using only visual information extracted from an image stream [START_REF] Chaumette | Visual servo control, Part I: Basic approaches[END_REF]. The camera can be mounted on the robot's end effector or directly observing the robot. Visual servoing usually requires the extraction and the tracking of visual information (usually geometric features) from the image in order to design the control law. While there has been progress in extracting and tracking relevant features, a new approach called direct visual servoing (DVS) emerged a decade ago [START_REF] Kallem | Kernel-based visual servoing[END_REF], [START_REF] Collewet | Photometric visual servoing[END_REF], [START_REF] Dame | Entropy-based visual servoing[END_REF], [START_REF] Crombez | Photometric Gaussian mixtures based visual servoing[END_REF]. It has been demonstrated that the sole pixel intensities of the images can be used to control the robot's motion and that conventional tracking and matching processes can thus be avoided. Nevertheless, due to strong non-linearities in the cost function to be minimized, such direct approaches feature a small convergence domain compared to classical techniques.

To alleviate these issues, machine learning techniques have recently been investigated to learn the relation between the image error and the camera's motion. As for direct approaches, the goal is to avoid explicit feature extraction while increasing the convergence area. In recent years, the use of Deep Learning (DL) has soared, and is now the stateof-the-art on many robotics vision tasks. Lately, DL has been studied for the visual servoing use case. Works [START_REF] Bateux | Training deep neural networks for visual servoing[END_REF], [START_REF] Yu | Siamese convolutional neural network for sub-millimeter-accurate camera pose estimation and visual servoing[END_REF], [START_REF] Saxena | Exploring convolutional networks for end-to-end visual servoing[END_REF], [START_REF] Lee | Learning visual servoing with deep features and fitted q-iteration[END_REF], [START_REF] Levine | End-to-end training of deep visuomotor policies[END_REF] already demonstrate interesting advances. Most of these works seek to learn, using reinforcement learning approaches, optimal motion for grasping tasks by directly regressing the joint velocities [START_REF] Lee | Learning visual servoing with deep features and fitted q-iteration[END_REF], [START_REF] Levine | End-to-end training of deep visuomotor policies[END_REF], [START_REF] Finn | Deep spatial autoencoders for visuomotor learning[END_REF]. Other approaches, suited for positioning tasks, aim to estimate the pose difference between the current and desired camera poses [START_REF] Bateux | Training deep neural networks for visual servoing[END_REF], [START_REF] Yu | Siamese convolutional neural network for sub-millimeter-accurate camera pose estimation and visual servoing[END_REF], [START_REF] Saxena | Exploring convolutional networks for end-to-end visual servoing[END_REF].

The latter methods [START_REF] Bateux | Training deep neural networks for visual servoing[END_REF], [START_REF] Yu | Siamese convolutional neural network for sub-millimeter-accurate camera pose estimation and visual servoing[END_REF], [START_REF] Saxena | Exploring convolutional networks for end-to-end visual servoing[END_REF] amount to performing position-based visual servoing [START_REF] Wilson | Relative end-effector control using cartesian position-based visual servoing[END_REF], [START_REF] Chaumette | Visual servo control, Part I: Basic approaches[END_REF] task (PBVS), where the error is regressed by a neural network. Indeed, PBVS is Authors are with Univ Rennes, Inria, CNRS IRISA, Rennes, France Email: {samuel.felton, elisa.fromont, eric.marchand}@irisa.fr directly related to the pose estimation issue [START_REF] Marchand | Pose estimation for augmented reality: a hands-on survey[END_REF]. In [START_REF] Kendall | Posenet: A convolutional network for real-time 6-dof camera relocalization[END_REF], a CNN is used to estimate the camera pose with respect to a fixed scene. The network takes as input a single RGB image, and regresses the pose (camera translation and rotation). The authors expand their work in [START_REF] Kendall | Geometric loss functions for camera pose regression with deep learning[END_REF] and devise new losses to achieve better re-localization accuracy by taking into account geometric information and provide task uncertainty estimation. [START_REF] Bateux | Training deep neural networks for visual servoing[END_REF] proposes two networks based on standard CNN architectures. They are trained to regress the pose of the camera (using an approach similar to [START_REF] Kendall | Posenet: A convolutional network for real-time 6-dof camera relocalization[END_REF]). [START_REF] Saxena | Exploring convolutional networks for end-to-end visual servoing[END_REF] is similar to [START_REF] Bateux | Training deep neural networks for visual servoing[END_REF] in that the two images are given together as different channels as input to a single network which regresses the pose difference. In this case, the chosen network is FlowNet [START_REF] Dosovitskiy | Flownet: Learning optical flow with convolutional networks[END_REF], which is trained to perform optical flow estimation. They propose a 3D dataset, containing indoors and outdoors scenes. They show that their scheme is transferable to the real world and can be used to control the motion of a drone. In [START_REF] En | RPNet: an end-to-end network for relative camera pose estimation[END_REF], a deep Siamese network is used to regress the relative pose between two cameras. Siamese networks are used to process multiple inputs in the same way. They have, for example, been successful to perform metric learning [START_REF] Hoffer | Deep metric learning using triplet network[END_REF], [START_REF] Koch | Siamese neural networks for one-shot image recognition[END_REF] and tracking [START_REF] Bertinetto | Fully-convolutional siamese networks for object tracking[END_REF], [START_REF] Gordon | Re 3 : Real-time recurrent regression networks for visual tracking of generic objects[END_REF]. [START_REF] Yu | Siamese convolutional neural network for sub-millimeter-accurate camera pose estimation and visual servoing[END_REF] uses, as us, a siamese network for visual servoing but, they only regress the pose. They perform an early fusion of the two images with a much shallower siamese part and the network has a very high parameter count. They trained and tested their approach with real and very restricted data (related to plugging connectors).

In this paper, we propose a new learning-based framework to achieve visual servoing tasks. It is based on a siamese network that directly regresses the camera velocity without estimating the relative pose. Indeed we argue that relative pose estimation is error-prone mainly due to ill-defined loss functions. The contributions of this paper are:

• rather than estimating the positioning error and achieve a PBVS task, we directly regress the camera velocity and learn in an "end-to-end" manner. We use a loss function that is less sensitive to non-homogeneous scales between the regressed components; • we propose a new network architecture, Siame-se(3), which is based on a siamese network, that applies a unique processing to multiple inputs (current and desired images); • training is fully done in simulation and only requires a single image per scene, from which we generate multiple viewpoints to create the dataset; • our approach is generic and allows us to consider visual servoing in multiple scenes with the same network.

The paper is structured as follows. First, we recall the principle of visual servoing approaches (section II). In Section III, we show how, starting from classical work dedicated to predicting poses, we can, with a relevant loss, directly regress the camera velocity. The architecture of our solution, Siamese(3) is then described in IV. Finally, we demonstrate the validity of our approach with experiments in Section V.

II. VISUAL SERVOING OVERVIEW

A. Positioning task by visual servoing

The aim of a positioning task is to reach a desired pose of the camera r * , starting from an arbitrary initial pose. To achieve this goal, one needs to define a cost function that reflects, in the image space, this error. Most of the time this cost function is an error measure which needs to be minimized. Considering the actual pose of the camera r the problem can therefore be written as an optimization process: r = arg min r e(r).

(

) 1
which consists in minimizing an error e(r) = s(r) -s * between what the camera sees (a set of features s(r)) and what it wants to see (i.e., the desired configuration of the visual features s * ).

The choice of the visual features s(r) is important since it will determine the camera motion and thus the robot behavior. Different approaches to VS which use various features s have been considered [START_REF] Chaumette | Visual servo control, Part I: Basic approaches[END_REF]. In image-based VS (IBVS), 2D primitives such as points or lines are retrieved from the images. In position-based VS (PBVS), the relative pose (position and orientation) between the current and desired camera position has to be estimated. In any case, it requires to extract and track visual information from the image in order to design the control law.

Recent works propose to directly use the information provided by the entire image [START_REF] Collewet | Visual servoing set free from image processing[END_REF], [START_REF] Collewet | Photometric visual servoing[END_REF]: photometric visual servoing (PhVS). In [START_REF] Collewet | Photometric visual servoing[END_REF], a control law was proposed that minimizes the error between the current image and the desired one: the vector of visual features in nothing but the image itself (s(r) = vec(I(r)) where vec(I) denotes the vectorization of the image matrix I) and the error to be regulated is the sum of squared differences (the SSD): e(r) = vec(I(r))vec(I 0 ). This leads to very precise end positioning, but suffers from a very limited convergence area due to a highly non linear cost function e(r).

In all cases, the visual servoing task is achieved by iteratively applying a velocity to the camera. This requires the knowledge of the interaction matrix L s related to s(r) that links the variation of ṡ to the camera velocity v and which is defined as: ṡ(r) = L s v. The control law is then given by [START_REF] Chaumette | Visual servo control, Part I: Basic approaches[END_REF]:

v = -λL + s e(r) (2)
where L + s is the pseudo inverse of L s and λ a positive scalar.

B. Deep Learning-based Visual Servoing

DL has been studied for the visual servoing use case. Multiple works [START_REF] Bateux | Training deep neural networks for visual servoing[END_REF], [START_REF] Yu | Siamese convolutional neural network for sub-millimeter-accurate camera pose estimation and visual servoing[END_REF], [START_REF] Saxena | Exploring convolutional networks for end-to-end visual servoing[END_REF], [START_REF] Lee | Learning visual servoing with deep features and fitted q-iteration[END_REF], [START_REF] Levine | End-to-end training of deep visuomotor policies[END_REF] already demonstrate satisfying results. These works seek to learn the optimal motion either by estimating the velocity of each of the robot's joints [START_REF] Lee | Learning visual servoing with deep features and fitted q-iteration[END_REF], [START_REF] Levine | End-to-end training of deep visuomotor policies[END_REF], [START_REF] Finn | Deep spatial autoencoders for visuomotor learning[END_REF] or by estimating the pose difference between the two cameras [START_REF] Bateux | Training deep neural networks for visual servoing[END_REF], [START_REF] Yu | Siamese convolutional neural network for sub-millimeter-accurate camera pose estimation and visual servoing[END_REF], [START_REF] Saxena | Exploring convolutional networks for end-to-end visual servoing[END_REF]. These last methods estimate, from the current I(r) and desired I * images, the displacement ∆r that the camera has to achieve 1 . This is done considering a CNN using a PoseNet-like network [START_REF] Kendall | Posenet: A convolutional network for real-time 6-dof camera relocalization[END_REF]. Nevertheless, this estimation is very sensitive to non-homogeneous scales between translation and rotation. Once the displacement ∆r to be achieved is computed from the CNN, it is immediate to compute the camera velocity using a classical PBVS control law [START_REF] Wilson | Relative end-effector control using cartesian position-based visual servoing[END_REF], [START_REF] Chaumette | Visual servo control, Part I: Basic approaches[END_REF]:

v = -λ c * R c c * t c θu (3)
In such approaches, the quality of the positioning task and camera trajectory is then dependent on the quality of the estimation of the relative pose. In the next section, we will see how to learn the camera velocity in an "end-to-end" manner. Instead of estimating ∆r, we will directly regress the camera velocity v.

III. LEARNING ON SE(3) AND se(3)

Supervised training of neural networks requires the formulation of a loss function that is minimized while training. We explain in the following why it is difficult to define such a loss to successfully learn in SE(3), the space of poses, and how to extend it to the space of se(3).

A. Deep learning for pose estimation

Poses (and pose differences) are elements of SE(3) = R 3 × SO(3), with a real vector t representing the 3D pose position and a 3 × 3 rotation matrix R encoding the pose orientation. In machine learning, the loss function encodes the error between the model's prediction and the ground truth. The loss function of a network trained to predict a pose in SE(3) would need to carefully balance the contributions of both components of the pose since they are not homogeneous (meters and radians). Previous works [START_REF] Bateux | Training deep neural networks for visual servoing[END_REF], [START_REF] Saxena | Exploring convolutional networks for end-to-end visual servoing[END_REF], [START_REF] Kendall | Posenet: A convolutional network for real-time 6-dof camera relocalization[END_REF], [START_REF] Yu | Siamese convolutional neural network for sub-millimeter-accurate camera pose estimation and visual servoing[END_REF] use a loss function of the following form:

L( t, q) = t -t 2 + β q -q 2 ( 4
)
where q is a minimal representation of R, such as a quaternion or an axis/angle vector. The β value is an hyperparameter to be chosen before training and acts as the weighting between the translation and the rotation components of the pose. β is a sensitive parameter which can have a strong effect on the final performance of the learning system. Moreover, this parameter is scene-dependent as the impact in the image space of translation and rotation errors will vary with respect to the depth of the scene. In [START_REF] Kendall | Multi-task learning using uncertainty to weigh losses for scene geometry and semantics[END_REF], a new type of loss is introduced for multi-task learning which aims at automatically estimating the weighting of different tasks.

When learning on SE(3), one can view the estimation of the two components of the pose as two different prediction tasks. Following this idea, it is shown in [START_REF] Kendall | Geometric loss functions for camera pose regression with deep learning[END_REF] that superior result compared to training with the loss in ( 4) can be obtained. The multi-task loss works by introducing one weighting parameter σ x for each task x, to be optimised alongside the network via gradient descent. For the case of estimating SE(3) poses, the loss is then given by:

L( t, q) = 1 2σ 2 t t -t 2 + 1 2σ 2 q q -q 2 + log σ t σ q (5)
with σ t (respectively σ q ) the weighting associated to the translation (respectively rotation) error, and where the last term of the loss log σ t σ q is a regularization term. The added computational cost of this loss is negligible and does not impact inference, as the weights are discarded at inference. B. Towards end-to-end servoing: from SE(3) to se(3)

Wrt. [START_REF] Bateux | Training deep neural networks for visual servoing[END_REF], [START_REF] Saxena | Exploring convolutional networks for end-to-end visual servoing[END_REF], [START_REF] Yu | Siamese convolutional neural network for sub-millimeter-accurate camera pose estimation and visual servoing[END_REF] our goal is to directly learn the velocity v = (υ, ω) required to minimize c * T c . An advantage of this method is that, since we directly output v, we are not restricted to a single control law, as is the case in works estimating the pose difference between the two cameras. It is thus possible to learn control laws based on IBVS, PBVS or incorporate more complex behaviour into the training data [START_REF] Chaumette | Visual servo control, Part II: Advanced approaches[END_REF]. Using the multitask learning framework [START_REF] Kendall | Multi-task learning using uncertainty to weigh losses for scene geometry and semantics[END_REF] presented above, the loss becomes:

L(υ, ω) = S=|υxy,υz,ωxy,ωz| y∈S 1 2σ 2 y ŷ -y 2 + log S y∈S σ y (6)
where S is the set of 4 losses to balance: the combined translation on the x and y axes (υ xy ), the translation on the z axis (υ z ), as well as rotation on the x and y axis (ω xy ) and on the z axis (ω z ). We argue that, while the translation (or rotation) is expressed in the same unit on all axes, their associated motion is not the same and as such, combining the σ weights is not optimal, since estimating one type of translation/rotation may be easier than the other. For example, VS methods are traditionally more tolerant to large rotations around the z (depth) axis, while rotation on the x, y are more constrained.

IV. SIAMESE NETWORKS FOR VELOCITY REGRESSION

In this section, we detail the architecture of our network, that is trained in an end-to-end manner with the loss presented in [START_REF] Collewet | Photometric visual servoing[END_REF]. We then describe the way that we use simulation to craft the dataset.

The proposed architecture is composed of two networks trained end-to-end. The first network extracts features from the desired and current images I and I * . The second network takes as input the difference between the features extracted from I and I * and regresses v (given as ground truth during training).

1) Feature extraction network: To extract the features from I and I * , we use a siamese network S. The term siamese is employed because the same processing is applied to multiple inputs (i.e. one body -the shared weights -with multiple heads -the outputs [START_REF] Koch | Siamese neural networks for one-shot image recognition[END_REF]). The siamese network S extracts the features of image I, denoted as S(I) which takes the form of a vector of fixed size that best describes the image for the task at hand. Since it is the processing for both images, the two feature vectors lie in the same latent space and can be "compared". The general method is independent of the extractor backbone, which can be freely chosen to take into account parameters such as inference speed, accuracy or the transfer learning weights available for a specific architecture (see Section V).

2) Velocity regression network: After the feature extraction step, the velocity v can be regressed. Drawing inspiration from the formulation of the error in visual servoing, we give as an input to the regression network S(I) -S(I * ). Using a subtraction is natural here since the embeddings coming from S lie in the same space and subtraction expresses a notion of signed distance between the vectors' components. Concatenating S(I) and S(I * ) is another possible way of fusing information, but experiments showed that results were inferior, with the added drawback that the number of trainable parameters of the regression network is higher.

When considering Equation ( 2), the computation of v is dependent on an interaction matrix L s specific to the control law, the formulation of the features s and their estimated values. Here, we do not know the interaction matrix expressing ∂S(I) ∂r . This is why we regress v with a nonlinear function of the error f (e). This function f is built from multiple Fully Connected (FC) layers (see Fig. 1), with ReLU [START_REF] Nair | Rectified linear units improve restricted boltzmann machines[END_REF] activations to make f non-linear. The last FC layer has no activation function, as the values of v are unbounded. We choose this simple architecture because we do not make any assumption about the shape of the extracted features and their relations.

A. Training

As deep learning requires large amounts of data and collecting such data on a real robot can be expensive, we resort to simulation to train our network. As in [START_REF] Bateux | Training deep neural networks for visual servoing[END_REF], we simulate the projection of a planar scene I * , I for two different poses. We simulates the desired control law from these poses using equation ( 3) and feed the network using the two images and the corresponding computed camera velocity. A potentially infinite number of viewpoints can then be generated to constitute the training dataset. Data are generated on the fly so as to avoid potential overfitting to specific viewpoints.

To help transfering from simulation to real-world, we use lighting and occlusion augmentations. Indeed, it has been shown that networks trained in simulated environments tend to transfer poorly to real world situations [START_REF] Tobin | Domain randomization for transferring deep neural networks from simulation to the real world[END_REF], [START_REF] Peng | Sim-toreal transfer of robotic control with dynamics randomization[END_REF], and that augmentation can bridge the gap between simulation and real world sample distributions. To ensure that the samples are relevant, they are further filtered so that there is a minimal ). These features are then subtracted to form e, from which we regress the velocity v. To compute the loss L during training, we add free weights σ as done in [START_REF] Kendall | Geometric loss functions for camera pose regression with deep learning[END_REF], [START_REF] Kendall | Multi-task learning using uncertainty to weigh losses for scene geometry and semantics[END_REF] so as to balance the optimized losses. amount of overlap between I and I * (i.e. the cameras are looking at the same part of the scene).

We argue that the proposed approach has multiple benefits. First, regressing the velocity directly allows us to have a generic feature representation, with the architecture being control law-independent. Indeed, although our network was trained with a PBVS control law, other control laws (2D, 2 1/2D,...) could have been considered. One needs only change the features used when generating v, as the network architecture does not require any modification. Because the features are learned, the time usually spent engineering the feature extraction is spent training a network. Compared to other learning approaches, we rely on a siamese network to regress a compact image representation that can then be stored.

V. EXPERIMENTS AND RESULTS

This section details the dataset design, the training process and the experiments performed in simulation and on an actual 6-DOF robot. We consider a positioning task, with the robot being moved to a different starting pose, and the goal being to move back to the desired pose.

A. Network training

As a feature extractor siamese network, we choose a recent state of the art neural network architecture, EfficientNets [START_REF] Tan | EfficientNet: Rethinking model scaling for convolutional neural networks[END_REF], and apply transfer learning [START_REF] Sinno | A survey on transfer learning[END_REF] to benefit from the training of EfficientNets on ImageNet [START_REF] Deng | Imagenet: A large-scale hierarchical image database[END_REF] to initialise the weights of our architecture. EfficientNets is chosen because its ratio accuracy/parameters is higher than other architectures and this alleviates the risk of overfitting. Besides, the number of Floating Point Operations Per Seconds is also lower (1.8B for the EfficientNetB3 vs 4.1B for the nowadays very popular ResNet-50 [START_REF] He | Deep residual learning for image recognition[END_REF]), making inference during servoing faster and more efficient. Finally, It has been shown in [START_REF] Tan | EfficientNet: Rethinking model scaling for convolutional neural networks[END_REF] that the transfer learning capabilities of the EfficientNet are better than other standard architectures, albeit on other classification datasets. There are multiple versions of EfficientNet, going from B0 to B7, with an increasing width, depth and resolution scaling. As a tradeoff between accuracy and computation, we settled on the B3 version, which has 10.5M parameters. A ResNet-50 was also evaluated, but the results were inferior.

The network is initialized with the pretrained ImageNet weights, which are not frozen during training to allow potentially large readaptation of the network for our regression task. While the siamese backbone is an EfficientNetB3, which works on images of dimensions 300×300, we directly feed images of size 224 × 224.

The network is trained on a single RTX 2080Ti for 50 epochs with 100k samples per epoch and batch size 25, for a total of 5M image pairs seen during training. We use the Adam optimizer [START_REF] Kingma | A method for stochastic optimization[END_REF] with a learning rate of 1e-4 for the model weights and 5e-4 for the loss weights. We initialise these to σ 2 υxy = σ 2 υz = σ 2 ωxy = σ 2 ωz = e 0 = 1.0. While these values are initialized higher than the starting errors, we note that this acts as a learning rate warm-up and helps the convergence.

At inference, since I * is captured at the start, the feature extraction to get S(I * ) is only ran once and S(I * ) is stored, heavily reducing the computational cost. On a RTX 2080Ti, the network runs in ≈20ms per iteration. Common neural network tricks, such as folding batch normalization into convolutional layers or using mixed precision, may be used to deploy Siame-se(3) onto embedded devices. The network is trained with PyTorch [START_REF] Paszke | Pytorch: An imperative style, high-performance deep learning library[END_REF] and the data is generated with ViSP [START_REF] Marchand | ViSP for visual servoing: a generic software platform with a wide class of robot control skills[END_REF]. The training takes approximately 18 hours on a single GPU, making it easy to deploy rapidly. The storage requirements are weak, with the model taking around 50MB of memory vs 550Mb for the VGG.

B. Simulation experiments

We first compare the impact of different settings on the servoing results by running experiments in simulation. The approach is also compared with photometric VS [START_REF] Collewet | Visual servoing set free from image processing[END_REF] and a another recent direct VS approach where the images are transformed in the frequency domain [START_REF] Marchand | Direct visual servoing in the frequency domain[END_REF]. The considered scene is the one used in the experiments of Fig. 4. We run 1000 trajectories, with the mean and standard deviations of the starting translation and rotation errors of 12.5cm ± 7.5cm/17.5 the servoing results of methods [START_REF] Collewet | Visual servoing set free from image processing[END_REF], [START_REF] Marchand | Direct visual servoing in the frequency domain[END_REF], as well as a reimplementation of the network trained in [START_REF] Bateux | Training deep neural networks for visual servoing[END_REF] and various versions of our siamese network. The IBVS method is based on ORB [START_REF] Rublee | ORB: an efficient alternative to SIFT or SURF[END_REF] keypoints. The network denoted as Siame-SE(3) has the same design as Siame-se(3), but is trained to regress the pose difference ∆r. "Siame-se(3) with two task weights" is the velocity regressing Siame-se(3), but only two weights are used during training: one for translation and one for rotation instead of the 4 used in Eq. 6. Direct servoing methods such as DVS and DCT-VS suffer from low convergence rates, but have the advantage of having a very precise end positioning. Compared to our Siamese networks, the VGG from [START_REF] Bateux | Training deep neural networks for visual servoing[END_REF] diverges more often and tends to remain far from the desired position. For our siamese architecture, the results show that regressing the velocity v works well and achieves similar positioning accuracy to when it is trained to output ∆r with the advantages discussed before (genericity and independence to the control law). It can also be seen that our siamese network trained to imitate PBVS succeeds and provides a trajectory that is closer to the ground truth (straight line in the Cartesian space) than with other methods, which either do not have this objective or, in the case of the VGG, realize it poorly, as attested by the trajectory statistics in Table I. While photometric methods will often diverge, our method converges in almost all cases with a small remaining error that is fairly constant. Running photometric VS [START_REF] Collewet | Visual servoing set free from image processing[END_REF] after any of the network-based methods results in a positioning error below the millimetre/tenth of a degree on all samples, illustrating the complementarity of the methods. Finally, Fig. 2 examines the convergence behaviour of different methods as a function of the overlap between the starting and desired images. While traditional methods benefit from higher overlap, Siame-se(3) manages to reliably converge even when the overlap is low. This suggests that in the monoscene case, Siame-se(3) learns a global representation akin to a pose.

C. Multiscene experiments

To further test our method, we train on multiple scenes at the same time: this time the reference scene is drawn randomly. To build the dataset, we select multiple images from the ImageNet dataset. We restrain ourselves to a single class of ImageNet [START_REF] Deng | Imagenet: A large-scale hierarchical image database[END_REF]: the car class. By doing this, we should have common information between the scenes, which is often the case when applying VS to the real world. Because of the added difficulty, we train for 75 epochs. We train several networks, each on different numbers of scenes and with or without color, to demonstrate the scalability (to multiple scenes) of the method, as well as the potential benefit of incorporating chrominance information. We notice that as the number of scene grows, training becomes harder (the training loss does not converge to zero). To remedy this, we use curriculum learning [START_REF] Bengio | Curriculum learning[END_REF]: we progressively add ramp up augmentation over the first 10 epochs. Fig. 3 highlights the obtained results, and reports the results of servoing on the scenes the networks were trained on. It can be seen that adding color information is indeed helpful. As the number of scene grows, the convergence rate decreases, especially for the grayscale version of the network. The end positioning error is also higher, but remains small enough that other servoing methods can be used afterwards to correct it.

D. Experiments on a 6DOF robot

Fig. 4 demonstrates a real-world servoing example. We place the desired pose (see Fig. 4(b)) 80cm above the center of scene and move to another pose to get Fig. 4(a). The initial displacement is very large, with ∆r(0) = (45cm, 19cm, 9cm, 34 • , -20 • , -47 • ), and I(0) contains large specularities. We run 500 iterations, with λ = 0.1. The final error is ∆r = (0.5cm, 0.2cm, 0.06cm, 0.3 • , -0.33 • , 0.12 • ) and the end image difference is displayed in Fig. 4(c). While the end positioning is not perfect, it can be trivially corrected by applying photometric VS [START_REF] Collewet | Visual servoing set free from image processing[END_REF] in the last few iterations, since we are close to the desired pose and the image error is low. Fig. 4(d) shows the values of v regressed by the network, In our second experiment, we test our multiscene network on (printed) A3-sized scenes. During training, we set the average distance from the poster to 30cm. As in our simulation setting, we train on 100 different images of cars. For this experiment, we picked one scene in the training set, and ran the servoing with the 6DOF robot on this printed scene. To illustrate the complementarity of our method and DVS, we first run Siame-se(3) for 500 iterations, then correct the remaining error with DVS. The starting and desired image are presented in 5(a) and (b). There is very low overlap between the two. Indeed, the initial error is ∆r(0) = (-17.9cm, 1cm, 0.5cm, 4.3 • , 18.4 • , 44.6 • ). After our method is over, the remaining error is ∆r(500) = (-1.6cm, -0.4cm, 0.3cm, -0.89 • , 3.16 • , -0.76 • ). The remaining error in image space is displayed in 5(c). In general, the remaining error after VS with Siame-se( 3) is present because of compensations between the translations and rotations on the x and y axes, as the diminution of the error in the image is mostly the same between the two types of motion. From there, DVS is applied and the final distance from the desired pose is 0.06cm/0.13 • . Our method accomplishes most of the motion, providing a fairly straight trajectory and is able to bring the end effector close enough to the desired pose so that it is in the convergence cone of DVS.

VI. CONCLUSION

We have proposed a new siamese deep architecture for end-to-end visual servoing. Our network directly regresses, in real time, the velocity of a camera in se(3). Our method features an accurate positioning, a broad convergence cone and combines well with photometric VS. While we have focused on learning a PBVS control law, this method can be applied to other control laws with minimal effort. Using multi-task learning to regress the different components of the velocity reduces the time spent training and tweaking networks, helping deploy VS solutions faster. Our learning protocol effectively trains our network on simulated data and can be applied to real scenes. Simulated and real experiments demonstrated the efficiency of the proposed approach.

Fig. 1 :

 1 Fig. 1: Diagram of the proposed Siame-se(3) architecture. The images are first processed by a siamese feature extractor to compute S(I), S(I *). These features are then subtracted to form e, from which we regress the velocity v. To compute the loss L during training, we add free weights σ as done in[START_REF] Kendall | Geometric loss functions for camera pose regression with deep learning[END_REF],[START_REF] Kendall | Multi-task learning using uncertainty to weigh losses for scene geometry and semantics[END_REF] so as to balance the optimized losses.

Fig. 2 :

 2 Fig.2: Convergence rate vs overlap between starting and desired images. The first batch has very low overlap (0-10%), while the last one has a higher one (90-99%).

Fig. 3 :Fig. 4 :

 34 Fig.3: Multiscene results: for different number of scenes, the convergence rate of our method is reported. We also report the convergence rate of photometric VS, a non learning-based approach. For each point we also report the end positioning error for samples that converged in the format cm/ • .

Fig. 5 :

 5 Fig. 5: (a) Starting image I. (b) Desired image I * . (c) Image difference before using DVS. (d) Final image difference. (e) Pose difference ∆r. (f) 3d trajectories. Green: ground truth trajectory. Blue: Siame-se(3). Red: DVS.

  • ± 12 • . Results reported in Table I show

	Method	Translation error in cm	Rotation error in degrees	Convergence, %	Trajectory absolute position error in cm	Trajectory absolute rotation error in degrees
	DVS [7]	0.2 ± 1.5	0.1 ± 0.9	34.8	2.19 ± 3.77	1.97 ± 3.47
	DCT-VS, k = 20 [25]	0.01 ± 0.001	0.004 ± 0.003	38.6	1.41 ± 3.5	1.12 ± 3.07
	Point-based IBVS, ORB features [32]	2.3 ± 4.6	2.7 ± 7.1	43.4	2.49 ± 4.51	2.8 ± 7
	[1]: VGG backbone, learned loss [19], regressing ∆r, Eq (5)	5.4 ± 2.0	3.43 ± 1.65	76.9	5.08 ± 1.91	3.28 ± 1.44
	Siame-SE(3), Eq (5)	1.12 ± 0.43	0.79 ± 0.3	98	1.05 ± 0.33	0.74 ± 0.23
	Siame-se(3), Eq (6) with two task weights	1.27 ± 0.44	0.94 ± 0.3	97.8	1.15 ± 0.36	0.85 ± 0.25
	Siame-se(3), Eq (6)	1.0 ± 0.48	0.75 ± 0.3	98.9	1.08 ± 0.48	0.77 ± 0.3

TABLE I :

 I Results of different methods on a set of 1000 simulated trajectories in a single scene setting. The end positioning errors of the servoing, as well as the mean trajectory errors with respect to a PBVS control law are reported in centimetres and degrees for the cases that converge.

The following notations are used: ∆r = ( c * tc, θu), where t describes the translation part of the homogeneous matrix c * Tc related to the transformation from the current Fc to the desired frame F c * , while its rotation part c * Rc is expressed under the form θu, where u represents the unit rotation-axis vector and θ the rotation angle around this axis.


Yann Thillier

Guillaume Losfeld

Vincent Escande

Christelle Dupouy

Jean-Jacques Vasseur

Françoise Debart
email: debart@univ-montp2.fr

Claude Grison
email: claude.grison@cefe.cnrs.fr

Jean- Jacques Vasseur

Metallophyte wastes and polymetallic catalysis: a promising combination in green chemistry. The illustrative synthesis of 59-capped RNA3

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

Introduction

The concept of green chemistry holds great promise for reducing the negative environmental impacts of organic synthesis. The use of chemical processes designed to reduce waste products, to facilitate efficient reuse and recycling, to optimize high atom-economy, and to use reagents that are less hazardous to human health and the environment have contributed to significant advances in the development of clean processing pathways. The search for starting materials derived from renewable resources and abundant bio-based materials are among the relevant strategies under development in chemistry today. However, green chemistry implies contributing to solving or mitigating environmental problems as well as improving industrial efficiency. It can play a key role in this area for example through the development of innovative solutions.

The European Environmental Agency estimates that 3 million sites in European Union countries are polluted. The most common harmful contaminants are heavy metals. The discovery of metal hyperaccumulator plants able to extract and concentrate heavy metals from contaminated soils in their leaves and shoots provides an attractive opportunity to remove them from polluted soils and ecosystems. The use of such plants has led to the development of various phytoextraction techniques. [1][START_REF] Berti | Phytoremediation of toxic metals: using plants to clean up the environment[END_REF][START_REF] Jaffre | Mines et environnement en Nouvelle-Cale ´donie: la reve ´ge ´talisation des terrains miniers[END_REF][START_REF] Robinson | [END_REF][5] Today, using vascular plants to 'clean up' the environment may potentially be used in large-scale costeffective projects, and have a high degree of public approval. [START_REF] Chaney | Phytoremediation of Contaminated Soil and Water[END_REF][START_REF] Glass | Phytoremediation of toxic metals: using plants to clean up the environment[END_REF][START_REF] Li | [END_REF][9] Benefits for the environment and for human health are well quantified. This has resulted in a clear European-wide call for greater efforts to be made in ecological remediation and restoration of polluted sites. However, the financial aspect of phytoextraction remains a difficulty, because existing techniques of phytotechnology lack real economic outlets, and thus the costs outweigh the economic benefits. In other words, there is no clear business case for industrial stakeholders and public funds are not currently made available for this purpose.

Our previous research has demonstrated that phytoextraction of heavy metals can constitute the starting point of an original and attractive approach for 'green' or 'environmentally-friendly' heterogeneous catalysis. [10][11][12] Notably, the synthetic potential of these new systems was illustrated through model reactions of industrial interest, Friedel-Crafts and Lucas reactions. In the present contribution, we demonstrate that these novel, 'bio-inspired' metallic catalysts promote the synthesis of high added-value molecules characterized by structural complexity. A key example is the chemical functionalization of RNA with cap structures at their 59-end (Fig. 1). The cap moiety consists of a N 7 -methylguanosine nucleoside ( 7m GpppN) linked to the 59-terminal nucleoside of the pre-mRNA via a 59-59 triphosphate bond. 13,14 This modification is critical for efficient translation, 15 for limiting RNA degradation by cellular 59 exonucleases and for avoiding recognition of mRNA as ''nonself'' by the innate immunity machinery. 16 Convenient access and availability of large quantities of capped RNA ( 7m GpppRNA) are of great interest for biologists for structural and mechanistic studies of their complexes with RNA capping enzymes. In vitro enzymatic methods cannot produce sufficient amounts of 7m GpppRNA of defined sequences. Although a few chemical methods had been described, most of them produce a very limited amount of capped RNA. 17,18 Therefore the need of an universal and efficient method to obtain these synthetic RNA in significant quantities and high purity was critical. To overcome this bottleneck, we have recently developed an efficient strategy for the production of capped RNA with high yields and without any limitation concerning the nucleotide present at the 59-end of the RNA substrate. 19 This consists in the combination of chemical solid-phase synthesis of GpppRNA followed by enzymatic N 7 -methylation. In this method, the functionalization at 59-end of RNA with the cap structure involves the use of zinc chloride for the coupling reaction of guanosine diphosphate (GDP) with a preactivated 59-monophosphate oligonucleotide to form GpppRNA. Here we report on the capping reaction using innovative and various metal chlorides, which are derived from different metallophyte species to functionalize RNA at their 59-end with the cap structures Gppp or 7m Gppp (Fig. 1). Furthermore for the first time we describe a straightforward chemical synthesis to obtain 7m GpppRNA. The compatibility of bio-based catalysts was studied for the chemical synthesis of various lengths and sequences of natural and chemical modified DNA and RNA.

Results and discussion

Green polymetallic catalysts derived from various metallophytes species: preparation and analysis

The selection of hyperaccumulator plants was undertaken in the context of our ongoing phytoremediation programs (Fig. 2). Zn hyperaccumulating leaves were derived from Noccaea caerulescens and Anthyllis vulneraria. They were collected from plants growing on the Les Avinie `res mine site, at Saint-Laurent-Le-Minier (Gard) in the Mediterranean climate region of southern France. Ni hyperaccumulating leaves were derived from Psychotria douarrei, Geissois pruinosa and Pycnandra accuminata. They were collected from plants growing in the Southern Province of the subtropical Pacific island of New Caledonia.

Leaves were harvested before flowering, air-dried and crushed. The obtained solid was calcined at 400 uC for 5 h and the resulting powder was added to a 5 M HCl solution. After heating the mixture at 60 uC for 2 h, then filtration on celite, the resulting solutions, composed of different metal chlorides, were then concentrated under vacuum. Dry residues were either used crude or partially purified in order to decrease the concentration of alkali and alkaline earth metals in the catalytic solid. When Ni hyperaccumulating metallophytes (P. douarrei and G. pruinosa) were used, a chelating resin allowed Ni concentration and partial elimination of undesired metals ions. Thus, elution of alkali and alkaline earth metals was performed with HCl at pH 2.5 whereas transition metals were eluted with 12 M HCl. With Zn hyperaccumulating metallophytes (N. caerulescens and A. vulneraria), IRA 400 ion exchange resin was considered and its use resulted in adsorption of Zn II on the resin, and elution of alkali and alkaline earth cations.

Treatment with 0.5 M HCl eliminated a part of Fe III fixed on the resin before the elution of heavy metals (Zn II , Cd II , Pb II ) with 0.005 M HCl. Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the composition of the various plant extracts obtained. Detailed results are presented in Table 1. According to Pearson's HSAB (hard and soft (Lewis) acids and bases) principle, the catalytic solids generated from metallophytes led to modulation of the hard/soft ratio. The obtained catalytic solids could be distinguished according to three types of Lewis acid level: the purified N. caerulescens extract, called fraction 1 (Table 1, entry 2), led to a Pearson's ''Hard Lewis Acid'' mixture, because the Mg 2+ , Ca 2+ , Al 3+ , Fe 3+ contribution represented 99.6% of the cationic mixture.

Purified N. caerulescens extract, called fraction 3 (Table 1, entry 3), purified P. douarrei (Table 1, entry 6) and G. pruinosa (Table 1, entry 8) extracts led to a Pearson's ''Borderline Lewis Acid'' composition, with a mixture of borderline and soft Lewis acids (Mn 2+ + Ni 2+ + Cu 2+ + Zn 2+ + Cd 2+ + Pb 2+ = 62, 64, 66%). Crude extracts were constituted by miscellaneous cations composition with respect to Pearson's classification system.

Chemical synthesis of hexathymidylate with 59-cap structure (GpppT 6 ) (Table 2, entries 1-12)

Before the capping reaction on RNA sequences of biological interest, we tested various catalytic metals derived from metallophyte species with a DNA homosequence as a model. Therefore we initiated the study with the synthesis of 59-guanosyl triphosphate hexa-29-deoxythymidylate (GpppT 6 ) 5, which was prepared at 1 mmol scale on solid support following the same route (Scheme 1) as recently described. 19 The solid-supported T 6 1 was assembled by the standard automated phosphoramidite method using a controlled pore glass support (CPG). After elongation, T 6 1 was converted into its 59-H-phosphonate derivative 2 which was activated as its phosphoroimidazolidate 3 by amidative oxidation with quantitative yield.

The key step is the coupling between the commercial guanosine diphosphate (GDP) and hexathymidinyl 59-phosphoroimidazolidate 3 linked to the solid support. The general mechanism is based on a nucleophilic attack of the GDP phosphoryl moiety on the 59-phosphoramidate 3 displacing the imidazolide group. The ideal conditions to obtain the triphosphate bond were satisfied if:

-unprotected and poorly soluble GDP sodium salt in organic solvent was converted into GDP bis(tri-n-butylammonium) to pass into solution by association with a divalent metal chloride;

-the metal salt activated phosphoramidate 3 to enhance the coupling reaction with GDP;

-the activation conditions should be compatible with fragility of the GDP and the formed triphosphate bridge.

Among several divalent metal salts, ZnCl 2 in anhydrous DMF has been found to be the most efficient. This result illustrated the interest of Pearson's ''Borderline Lewis Acids'' in this reaction. However, the reaction remains quite a delicate problem. This could be accomplished by careful and delicate reaction condition control and moderate yields. In the context of our investigations, we aimed to improve the reaction efficiency with our 'eco-friendly' and non-conventional catalysts. From a statistical point of view, it appeared reasonable to expect that a mixture of different metal halides could better interact with three distinct coordination sites than a single metal cation; consequently, a polymetallic catalyst could strongly promote the reaction sequence.

Substitution of the imidazole by GDP (0.14 mmol) was successfully accomplished at 30 uC for 18 h in dry DMF in the presence of a divalent metal salt (0.2 mmol) to give the capped GpppT 6 4 still anchored to the solid support (1 mmol). After removal of the capping solution from the synthesis reactor using a mix of water and EDTA followed by several washes with CH 3 CN, the capped GpppT 6 were deprotected and released from solid support. First, cyanoethyl groups were eliminated from the phosphates with 1 M 1,8-diazabicyclo[5.4.0]undec-7ene (DBU) solution in dry CH 3 CN for 3 min. Then, treatment with 30% aqueous ammonia at room temperature cleaved GpppT 6 5 from the CPG support. The conversion yields of T 6 into the desired capped GpppT 6 were calculated by integration of the major peaks corresponding to the capped T 6 in the ionexchange HPLC chromatograms of the crude materials (Fig. 3).

The capping yield could reach up to 66% (Table 2, entry 6). Further characterization was completed by MALDI-TOF mass spectrometry (Table 2).

A global analysis shows the interest of these new systems to facilitate catalysis during the coupling reaction. All crude extracts allowed the complete dissolution of GDP and With the purified fraction issued of P. douarrei extract (Table 2, entry 9), the coupling yield with GDP was increased twice as much as compared with NiCl 2 (56% instead of 26%) and reached the 55% yield obtained with ZnCl 2 (Table 2, Fig. 3). It may be concluded that a synergetic effect exists between the present Lewis acids which improves catalytic performance, when a mixture of borderline and soft Lewis acids was made (purified extracts). These observations agree with Mikkola and et al., who found that a combination of two metal halides leads to more active systems than individual components in the 59-cap nucleotides synthesis. 20 As discussed above, their impact on catalysis is threefold. Thus, understanding the mechanisms underlying polymetallic catalysis is difficult. In order to learn more about these results, we have compared the ratio of expected and observed sideproducts for each of the catalytic compositions. Obtained results suggest that Ni metallophytes favoured GppT 6 formation instead of the expected GpppT 6 (Table 3). So, it seems that Ni catalysts promote partial hydrolysis of GDP into GMP before coupling reaction. However the association of Ni and other metals, more efficient than sole NiCl 2 , can be explained by the binding of two metal ions to two adjacent Lewis base centers f The catalyst-GDP solution was not centrifuged, but the final mixture was eluted on Sephadex. g The acidity of the catalyst was neutralized by refluxing the catalytic extract in dioxane to prevent depurination.

of phosphoroimidazolidate 3 (Fig. 4). In view of the excellent affinity of the imidazole ring for Ni 2+ , we can suppose an interaction between the heterocycle and NiCl 2 , which strengthens the leaving group capacity of imidazole. This effect is added to the coordination between other metals and the phosphate group that increases electrophilicity of the activated phosphoryl ligand of 3. This double effect promotes the coupling reaction. N. caerulescens + A. vulneraria crude extract (Table 2, entry 7) led to poor yield (6%). This result can be correlated with the amount of AlCl 3 (Table 1, entry 4). Finally, N. caerulescens purified extract (Table 2, fraction 1, entry 4) didn't promote the coupling reaction, since it is known that Mg 2+ and Ca 2+ have high affinity for the phosphate oxygen (Table 1, entry 2). Hard Lewis Acid mixture cannot activate the P-N bond. It can be concluded that essential cations for plant growth (Na I , K I , Ca II , Mg II , Cu II ) are not useful in the aimed reaction, whereas Zn II , Ni II , Cd II and Pb II which result of heavy-metal hyperaccumulation promote the coupling reaction. Overall, these results show that the metallophyte species led to the preparation of an interesting polymetallic composition, which allowed coordination not only with the phosphate oxygen but also with the cyclic imidazole of the substrate.

Chemical synthesis of RNA with 59-cap structure (GpppRNA or 7m GpppRNA) (Table 2, entries 13-21) Encouraged by the efficiency of these catalysts, the reaction was extended to the synthesis of Gppp6-mers (Table 2, entries 14, 17, 20), Gppp19-mers (Table 2, entries 13, 16, 19) and 7m Gppp6-mers (Table 2, entries 15, 18, 21) RNA heteropolymers with the two best catalytic systems: N. caerulescens and P. douarrei extracts.

Regarding the synthesis of 59-capped RNA on solid support, the instability of N 7 -methylguanosine under acidic and basic conditions and RNA sequence fragility are additional difficulties of the second part of this work. Indeed, because of the positive charge on the N 7 -methylguanosine (Fig. 1), the nucleoside is hydrolytically less stable than standard purine nucleosides. Under basic conditions used for standard RNA deprotection, the opening of the imidazole ring of the 7-methylguanine would occur. For this reason, the synthesis of 7m GpppRNA completely achieved on solid support excludes the ammonia treatment to deprotect and to release 7m GpppRNA. Therefore the strategy for RNA assembly to provide 7m GpppRNA was different from the pivaloyloxymethyl (PivOM) technology used for RNA synthesis to get GpppRNA. 21 The major feature of this technology developed by our group Scheme 1 Solid-phase synthesis of 59-GpppT 6 , 59-GpppRNA or 59-7m GpppRNA.

for RNA synthesis on solid support, is to use base-labile protecting groups exclusively removed under basic conditions without RNA damage. Thus, for GpppRNA synthesis (Table 2, entries 13, 14, 16, 17, 19, 20) RNA sequences were assembled on an automated synthesizer by the phosphoramidite solidphase method involving the base-labile 29-O-PivOM groups. [START_REF] Lavergne | Current Protocols in Nucleic Acid Chemistry[END_REF] As for T 6 sequences, after RNA elongation, the 59-OH was converted into its phosphoroimidazolide 3 ready to react with GDP (Scheme 1) in the presence of purified catalysts derived from N. caerulescens and P. douarrei extracts. As shown for GpppT 6 synthesis, the use of a clear solution resulting from centrifugation of catalytic extracts and GDP in DMF conditioned the success of the capping reaction. The purities of Gppp18-mers (Table 2, entry 16, 43%) (Fig. 5) and Gppp6-mers (Table 2, entry 17, 32%) obtained with N. caerulescens were similar to the one with conventional catalyst ZnCl 2 (entries 13 and 14, 46% and 45%). With P. douarrei extract, the coupling yields of the same RNA sequences (Table 2, entries 19 and 20) with GDP were reasonably lower: 34% and 23%, respectively.

Finally, we synthesized 7m GpppAUA UUA (Table 2, entries 18 and 21) where as mentioned above the major difficulty is the fragility of N 7 -methylguanine under basic or acidic conditions. RNA sequences were assembled using 29-O-propyloxymethyl (PrOM) ribonucleosides amidites. [START_REF] Martin | [END_REF] This 29-O-protecting group structurally closed to PivOM could be removed upon nucleophilic attack in dry organic solvent without using ammonia conditions which would lead to complete destruction of the N 7 -methylguanine structure. After elongation on synthesizer, both RNA 6-mers were functionalized at their 59-end with a phosphoroimidazolide to react with 7m GDP bis(tri-n-butylam-Fig. 3 Anion-exchange HPLC profiles of the crude mixture of 59-GpppT 6 obtained with ZnCl 2 (Table 2, entry 1), N. caerulescens purified extract (fraction 3) (Table 2, entry 6), NiCl 2 (Table 2, entry 2) and with P. douarrei purified extract (Table 2, entry 9). monium) in the presence of ZnCl 2 (Table 2, entry 15), purified N. caerulescens (Table 2, entry 18) and P. douarrei extracts (Table 2, entry 21) (Scheme 1). First attempts in the same conditions previously used for GpppRNA were not successful and depurination of N 7 -methylguanosine was observed which was confirmed by the disappearance of the peak in the HPLC profile monitored at 300 nm (characteristic wavelength of UV absorption of 7m G). 20 This major problem was certainly due to the presence of acidic traces in the catalytic extracts. When the acidity of catalyst derived from N. caerulescens (Table 2, entry 18) was neutralized by refluxing the catalytic extract in dioxane, the purity of 7m GpppAUAUUA was acceptable (33%) and near to ZnCl 2 (38%) (Fig. 6). It should be underlined that this is the first efficient all-chemical synthesis on solid support of an RNA with a 7m cap structure and free 29-OH. Indeed only short 29-OMe RNA bearing a 7m cap were reported. 18,24 Finally, with P. douarrei extract, the capping reaction was not as efficient as with N. caerulescens since the desired compound was present at 7% only in the reaction mixture.

Experimental

Preparation of catalytic extracts from metallophytes species

Leaves were harvested before flowering, air-dried and crushed. The obtained solid (150 g) was calcined at 400 uC for 5 h and the resulting powder (148 g) was added to 1 L of a solution of 5 M HCl solution. The solution was heated at 60 uC and stirred for 2 h. The reaction mixture was filtered on celite. The resulting solutions, composed of different metal chlorides, were then concentrated under vacuum. Dry residues were either used crude or partially purified in order to decrease the concentration of alkali and alkaline earth metals in the catalytic solid. When Ni hyperaccumulating metallophytes (P. douarrei and G. pruinosa and P. accuminata) were used, a chelating resin such Dowex M4195, allowed Ni concentration and partial elimination on undesired metals ions. The catalytic solution was introduced on the uppermost surface of the Dowex M4195 (about 60 g of resin per gram of solid). Operating purification conditions were as follows: elution of alkali and alkaline earth metals with HCl at pH 2.5 (3 mL min 21 ); transition metals elution was performed with 12 M HCl. With Zn hyperaccumulating metallophytes (N. caerulescens and A. vulneraria), Amberlite IRA 400 ion exchange resin was used for adsorption of Zn II on the resin, and elution of alkali and alkaline earth cations. Treatment with 0.5 M HCl eliminated a part of Fe III fixed on the resin before the elution of heavy metals Zn II , Cd II , Pb II with 0.005 M HCl. ICP-MS was used to determine the composition of the various plant extracts obtained. ICP-MS analyses were performed using the Metal Analysis of total dissolved solutes in water. The samples were acidified with nitric acid 2.5% and stirred for 30 min. The digestates were diluted to 0.005 g L 21 . Three blanks are recorded for each step of the digestion and dilution procedure on a HR-ICP-MS Thermo Scientific Element XR.

Coupling of solid-supported T 6 or RNA phosphoroimidazolidate with GDP Guanosine-59-diphosphate sodium salt was converted before coupling into its tri-n-butylammonium salt as previously described. 19 In a dry 2 mL microcentrifuge tube (Costar), bis(tri-n-butylammonium) GDP (103 mg, 0.14 mmol) and the freshly dehydrated catalytic extracts required for the coupling (0.2 mmol) whose amount was estimated from the concentration of the metal (Zn or Ni) determined by ICP-MS (Table 1) were mixed in anhydrous DMF (0.5 mL). The tube was closed and the mixture was vortexed for 5 min on a Top-Mix 1118 (Fischerbrand) and centrifuged in a tabletop centrifuge (Sigma 1-13) at 6000 min 21 for 30 s. This operation was repeated twice. The supernatant was taken using a glass syringe filled with 3 beads of 4 Å molecular sieves. Using another syringe, the solution was applied to the column containing the solidsupported 59-phosphoroimidazolidate oligonucleotide 3 (1 mmol), and left to react for 18 h at 30 uC. The solution was removed and the support was washed with water (2 6 2 mL), then with a 0.1 M aqueous solution of EDTA (pH 7, 2 6 2 mL), and dry CH 3 CN (4 6 2 mL). Finally the column was dried by blowing argon through it during 1 min. The same procedure was applied for coupling with 7m GDP except a two-fold decrease in the quantities of reagents used.

Conclusion

This study clearly shows the interest of the metallophyte-based polymetallic catalysts for the synthesis of RNA 59-cap structures. They differ notably from classical systems which employ chemical uniformity and monometallic composition. Partial purification of the plant extracts was sufficient for catalytic performance. The polymetallic composition of our bio-based catalysts is particularly adapted to multi-step transformations, which are founded on different mechanisms and so required different catalytic effects. The coupling of the cap structure on DNA or RNA is illustrative for numerous issues in the development of a new concept for the ''ecological'' concept. Interestingly in the development of new ''environmental'' chemical processes it is noteworthy that this pyrophosphate bond formation was also the key point of an atom-economic methodology recently developed with the use of a ball-mill. 25 Future developments of these plant-based catalysts are exciting, and can inspire the creativity of synthetic chemists. They constitute an important incentive for the development of phytoextraction and the subsequent polymetallic catalysis.

Fig. 2

 2 Fig. 2 Metallophytes species for polymetallic catalysis. Fig. 1 mRNA cap structure 7m Gppp.

Fig. 1

 1 Fig. 2 Metallophytes species for polymetallic catalysis. Fig. 1 mRNA cap structure 7m Gppp.

Fig. 4

 4 Fig. 4 Hypothetic mechanism for polymetallic participation in the coupling reaction.

Fig. 5

 5 Fig. 5 Anion-exchange HPLC profiles of the crude mixture of 59-GpppAGUUGUUAGUCUUACUGG obtained with ZnCl 2 (Table2, entry 13) and 59-GpppA OMe GUUGUUAGUCUUACUGGA obtained with N. caerulescens purified extract (fraction 3) (Table2, entry 16) and with P. douarrei purified extract (Table2, entry 19).

Table 1

 1 Detailed composition of catalytic solids derived from metallophyte species

			Concentration (ppm)
	Entry	Catalysts	Mg	Al	Ca	Mn	Fe	Ni	Cu	Zn	Cd	Pb
		N. caerulescens extract
	1	-crude	34 769	10 294	330 362	594	11 448	584	409	89 157	7119	21 050
	2	-purified (fraction 1)	49 513	209	358 327	190	1002	506	58	524	140	131
	3	-purified (fraction 3)	30 130	8530	180 873	3598	30 740	3598	655	405 477	6400	36 078
		N. caerulescens +
		A. vulneraria extract
	4	-crude	28 202	14 878	134 925	936	27 275	2985	523	91 232	4845	29 249
		P. douarrei extract
	5	-crude	20 768	5419	96 731	1402	5570	80 153	299	7902	80	703
	6	-purified	22 320	2250	91 212	1596	2808	184 600	401	6863	131	942
		G. pruinosa extract
	7	-crude	76 265	5302	119 756	1066	7882	73 863	551	12 524	215	2808
	8	-purified	31 969	4764	87 667	3155	4589	150 395	406	9597	153	2083
		P. accuminata extract
	9	-crude	22 519	4002	40 659	1399	5791	115 163	115	2344	224	1393

Table 2 ,

 2 entry 4), all purified fractions (Table2, entries 5, 6, 9, 11) led to comparable results to conventional catalysts ZnCl 2 and NiCl 2 (Table2, entries 1 and 2). It is noteworthy that the purified fraction 3 derived from N. caerulescens (Table2, entry 6) was better than commercial ZnCl 2 (66% and 55%, respectively) (Fig.3, Table2, entries 1 and 3). Catalysts derived from Ni hyperaccumulating plants (Table 2, entries 8-12) should also be noted since G. pruinosa and P. douarrei led to expected products with satisfactory yields ranging from 42% (Table 2, entry 11) to 56% (Table 2, entry 9) whereas it is well known that NiCl 2 is a poor acid-catalyst. Besides to date, no such capping reaction catalyzed by NiCl 2 has ever been described. Crude P. douarrei extract (Table 2, entry 8) had a similar catalytic activity to NiCl 2 (Table 2, entry 2) with 26% coupling yield. Interestingly, despite of its weaker interest in our phytoremediation program for Ni-rich metallophytes, P. accuminata crude extract gave the best coupling yield (33%). The P. douarrei and G. pruinosa purified extracts (Table 2, entries 9 and 11) were more active than commercial NiCl 2 .

Table 2

 2 Data for synthesized 59-GpppT 6 , 59-GpppRNAs and 59-7m GpppRNAs Crude yield c Isolated yield d Molecular formula Calc. m/z e Found m/z e

	Entry Catalysts a Yield (%) b 1 59-sequence-39 ZnCl 2 GpppT 6 55	498	nd	C 70 H 93 N 17 O 57 P 8	2267.35	2267.29
	2	NiCl 2	GpppT 6	26	421	nd	C 70 H 93 N 17 O 57 P 8	2267.35	2267.61
	N. caerulescens extract (Zn-enriched)
	3	crude	GpppT 6	31	430	nd	C 70 H 93 N 17 O 57 P 8	2267.35	2267.34
	4 5	purified (f1) GpppT 6 purified (f3) f GpppT 6	0 55	351 487	nd nd	C 70 H 93 N 17 O 57 P 8 C 70 H 93 N 17 O 57 P 8	2267.35 2267.35	-2267.25
	6	purified (f3) GpppT 6	66	512	nd	C 70 H 93 N 17 O 57 P 8	2267.35	2267.65
	N. caerulescens + A. vulneraria extract
	7	crude	GpppT 6	6	402	nd	C 70 H 93 N 17 O 57 P 8	2267.35	2267.57
	P. douarrei extract (Ni-enriched)
	8 9	crude purified f	GpppT 6 GpppT 6	26 56	437 378	nd nd	C 70 H 93 N 17 O 57 P 8 C 70 H 93 N 17 O 57 P 8	2267.35 2267.35	2267.36 2267.80
	G. pruinosa extract (Ni-enriched)
	10	crude	GpppT 6	18	423	nd	C 70 H 93 N 17 O 57 P 8	2267.35	2267.34
	11	purified	GpppT 6	42	461	nd	C 70 H 93 N 17 O 57 P 8	2267.35	2267.29
	P. accuminata extract (Ni-enriched)
	12	crude	GpppT 6	33	456	nd	C 70 H 93 N 17 O 57 P 8	2267.35	2267.19
	13	ZnCl 2	GpppAGUUGUUAGUCUUACUGG	46	331	73	C 151 H 225 N 70 O 141 P 20 6254.57	6254.86
	14 15	ZnCl 2 ZnCl 2	GpppAUAUUA 7m GpppAUAUUA	45 38	408 303	138 89	C 67 H 83 N 26 O 53 P 8 C 68 H 85 N 26 O 53 P 8	2348.31 2362.34	2348.32 2362.67
	N. caerulescens extract (Zn-enriched)
	16	purified (f3) GpppA OMe GUUGUUAGUCUUACUGGA 43	491	92	C 192 H 239 N 75 O 145 P 21 6597.80	6597.45
	17 18	purified (f3) GpppAUAUUA purified (f3) g 7m GpppAUAUUA	32 33	372 312	102 54	C 67 H 83 N 26 O 53 P 8 C 68 H 85 N 26 O 53 P 8	2348.31 2362.34	2348.15 2362.59
	P. douarrei extract (Ni-enriched)
	19	purified	GpppA OMe GUUGUUAGUCUUACUGGA 34	422	81	C 192 H 239 N 75 O 145 P 21 6597.80	6597.03
	20 21	purified purified	GpppAUAUUA 7m GpppAUAUUA	23 7	344 302	73 nd	C 67 H 83 N 26 O 53 P 8 C 68 H 85 N 26 O 53 P 8	2348.31 2362.34	2348.32 2362.36

a Final concentration in the reaction mixture of [ZnCl 2 ] = [NiCl 2 ] = 0.4 M in DMF. b Percentage yield of oligonucleotide in the crude as calculated from the integration of the IEX chromatogram. c nmol crude total material. d nmol pure product GpppRNA obtained after HPLC purification. nd: not determined. Oligonucleotides (entries

1-12, 20)

were not HPLC purified. e MALDI-TOF characterization in negative mode.

Table 3

 3 Comparison of the ratio of expected GpppT 6 and observed sideproducts GppT 6 depending on Ni composition of catalysts derived from Ni hyperaccumulating plants Percentage yield of oligonucleotide in the crude as calculated from the integration of the IEX chromatogram.

	Catalytic extracts	GpppT 6 (%) a	GppT 6 (%) a	Ni (ppm)
	NiCl 2	25	9	473 684
	P. douarrei extract
	-crude	26	3	80 153
	-purified	56	12	184 600
	G. pruinosa extract
	-crude	18	3	73 863
	-purified	42	15	150 395
	P. accuminata extract
	-crude	33	13	115 163

a

Table 2

 2

, entry 13) and 59-GpppA OMe GUUGUUAGUCUUACUGGA obtained with N. caerulescens purified extract (fraction 3) (Table

2

, entry 16) and with P. douarrei purified extract (Table

2

, entry 19). Fig.

6

Anion-exchange HPLC profiles of the crude mixture of 59-7m GpppAUAUUA obtained with ZnCl 2 (Table

2

, entry 14), with N. caerulescens purified extract (fraction 3) (Table

2

, entry 17) and with P. douarrei purified extract (Table

2

, entry 20).

Acknowledgements Y. Thillier thanks the Ministe `re National de la Recherche et de la Technologie for the award of a research studentship. The authors gratefully acknowledge financial support from the Agence Nationale de la Recherche (Opportunite ´(E) 4 program), FEDER program and two Grants from the Ecole Polytechnique, Palaiseau and ADEME. The authors thank the Institut Agronomique Cale ´donien for the biomass harvests of Ni hyperaccumulating metallophytes.