A H Pfeffer

B Kaviraj

O Coupiac

F Lefloch

SQUIDs based set-up for probing current noise and correlations in three-terminal devices

Keywords: numbers: 07.20.Mc, 05.40.-a, 72.70.+m I

We have implemented a new experimental set-up for precise measurements of current fluctuations in three-terminal devices. The system operates at very low temperatures (30 mK) and is equipped with three SQUIDs as low noise current amplifiers. A SQUID input coil is connected to each terminal of a sample allowing the acquisition of time-dependent current everywhere in the circuit. From these traces, we can measure the current mean value, the noise and cross-correlations between different branches of a device. In this paper we present calibration results of noise and cross-correlations obtained using low impedance macroscopic resistors. From these results we can extract the noise level of the set-up and show that there are no intrinsic correlations due to the measurement scheme. We also studied noise and correlations as a function of a DC current and estimated the electronic temperature of various macroscopic resistors.

INTRODUCTION

In disordered electrical macroscopic conductors, the amplitude of current flowing in response to an applied voltage is governed by various microscopic mechanisms such as elastic scattering on impurities and inelastic collisions within electrons or between electrons and phonons. The conductance, given by the ratio between the mean value of the current and the applied voltage, averages out all the contributions and is usually well described by the Drude formula. At finite temperature and zero applied voltage, the current fluctuates in time around its mean value because of thermal fluctuations of the electronic state occupation number. The amplitude of fluctuations is given by the fluctuation-dissipation theorem and corresponds to the second moment of current distribution. Through that relation, the spectral density of current-noise is proportional to both the temperature and mean value of conductance. In that sense, thermal noise measurement does not provide any additional information than the conductance. At finite applied voltage, scattering events randomize the flow of electrons and induce, together with the granularity of charge, an additional noise contribution known as 'shot noise'. However, this contribution vanishes in macroscopic disordered conductors when the events are uncorrelated. It is only when the size of the conductor becomes of the order of phase coherence length of the electronic wave function, that shot noise becomes sizable [START_REF] Ya | Shot noise in mesoscopic conductors[END_REF]. This cross-over corresponds to entering the mesoscopic world where quantum effects become important. Since past twenty years, shot noise measurements have contributed to the understanding of electronic transport properties at nanoscale. One of the most emblematic results obtained was the experimental demonstration of existence of fractional charges in two-dimensional electrons gas in fractional quantum Hall regime [START_REF] De Picciotto | Direct observation of fractional charge[END_REF][START_REF] Saminadayar | Observation of the e/3 fractionally charge laughlin quasiparticle[END_REF]. Another very interesting situation was obtained in hybrid superconducting (S) / normal metal (N) nanostructures where the shot noise was found to be doubled (SN junctions) or very much enhanced (SNS junctions) due to conversion of Cooper pairs into normal electrons at the S/N interface [START_REF] Hoffmann | Mesoscopic transition in the shot noise of diffusive superconductornormal-metalsuperconductor junctions[END_REF][START_REF] Jehl | Detection of doubled shot noise in short normalmetal/superconductor junctions[END_REF][START_REF] Lefloch | Doubled full shot noise in quantum coherent superconductor-semiconductor junctions[END_REF][START_REF] Lhotel | Divergence at low bias and down-mixing of the current noise in a diffusive superconductornormal-metal -superconductor junction[END_REF]. But the interest of fluctuation measurements is not limited to noise. For example, the third moment of current distribution reveals its (non-)Gaussian character whereas noise correlations can probe statistics (fermionic or bosonic) of current carriers like in a Hanbury-Brown Twiss type of experiment [START_REF] Hanbury-Brown | Correlation between photons in two coherent beams of light[END_REF]. Cross-correlations can also be investigated to reveal splitting of Copper pairs in hybrid normal-superconducting nanostructures [START_REF] Anantram | Current fluctuations in mesoscopic systems with andreev scattering[END_REF][START_REF] Martin | Wave packet approach to noise in n-s junctions[END_REF]. However, in order to address experimentally these quantities, new instruments need to be developed. The pioneer work on the third moment of a simple tunnel junction has revealed a major importance of the environment showing that these quantities need to be investigated with great care [START_REF] Beenakker | Temperature-dependent third cumulant of tunneling noise[END_REF][START_REF] Reulet | Phase sensitive shot noise in an Andreev interferometer[END_REF]. The same is true for cross-correlations where the signal is much smaller than the noise and where all spurious contributions need to be clearly identified [START_REF] Das | Entangling electrons by splitting Cooper pairs: Two-particle conductance resonance and time coincidence measurements[END_REF][START_REF] Wei | Positive noise crosscorrelation in hybrid superconducting and normal-metal three-terminal devices[END_REF].

II. INSTRUMENTAL SET-UP

In our set-up, the mean value and fluctuations of currents in each branch of a sample are directly measured using three SQUIDs as current amplifiers (see figure 1). The measurements are done at very low temperatures, using a dilution fridge with a base temperature of 30 mK. The SQUID electronics operate in the standard flux lock loop mode which reduces almost completely the spurious feed-back of read-out towards the sample. As current fluctuations are measured, the sample needs to be voltage-biased. This is achieved by placing, in parallel to the sample, a macroscopic "surface mount resistor" R ref whose resistance is much lower than the device resistance and that is mounted at low temperature to reduce its noise contribution (see Fig. 1). Then, a low noise current source I DC1 is used to bias the system. Two additional resistors r 1 and r 2 are placed in series with two other branches of the three-terminal device. With a second low noise current source I DC2 , it is then possible to control the two voltage drops V 1 and V 2 independently. Additionally, each individual voltage drop can be measured using room temperature low noise differential voltage amplifiers. If needed, the differential resistance can also be measured by sending a small ac-current together with the DC-current. As current noise is generally inversely proportional to the sample resistance, our set-up is well adapted to low impedance samples, typically around 1 Ω. This statement is exact for thermal noise and can be somewhat generalized to shot noise for hybrid superconducting nanostructures. Indeed, when superconductivity is involved, it exists a typical voltage scale given by the superconducting gap and the current needed to reach this gap is inversely proportional to the sub-gap impedance of the device. In practice, the spectral density of noise can be measured with a set-up noise of 2 ∼ 3 10 -24 A 2 /Hz = 2 ∼ 3 pA 2 /Hz . This noise floor does not strictly correspond to current fluctuations at the input coil of the SQUIDs but mostly come from the noise of the room temperature electronics and translates into these values using the overall gain of the amplification/conversion chain. The bandwidth is given by the resistance of the overall device (sample in parallel with the reference resistor, R ref ) coupled to the input coil inductance. Using a 0.1 Ω reference resistor and given the SQUIDs input coil inductances (L ∼ 1 µH), we obtain a cut-off frequency f = R ref /(2πL) ∼ 15 kHz that limits the bandwidth. Another important advantage of using SQUIDs is the very low 1/f noise that is sizable only below 1 Hz. With these values and considering a reasonable acquisition time of few minutes, we obtain a sensitivity of order of 0.1 pA 2 /Hz. Figure 1 shows the schematic of the experimental set-up together with the microscope image of a typical threeterminal device [START_REF] Kaviraj | Noise correlations in three-terminal diffusive superconductornormal-metalsuperconductor nanostructures[END_REF]. For this sample, a central superconducting electrode emits Cooper pairs towards two spatially separated superconducting collectors through two normal metal nano-bridges of 1.5µm length. The sample can be modeled by two non-linear resistors R 1 and R 2 . All the wires connecting different parts of the set-up are superconducting and therefore do not add any additional resistance in the circuit. The three SQUIDs sensors are located at the 4.2 K flange of the dilution refrigerator in the helium liquid bath. All other elements are thermalized with the mixing chamber temperature.

Any resistive part of the circuit is associated with a current source δI in parallel, in accordance with Nyquist representation. In this model, the current flowing in each SQUID is given by :

δI i sq = R i R i + R jk δI i - R jk R i + R jk δI j + δI k (1)
where R i is the sum of the resistances in branch i, δI i the weighted current contribution of all the elements in branch i and R jk the equivalent resistance of R j in parallel with R k . For instance,

R 1 = R 1 + r 1 R 3 = R ref δI 1 = R 1 R 1 δI 1 + r 1 R 1 δI r1 δI 3 = δI ref . (2)
From the three fluctuating SQUID currents δI i sq , we can perform three auto-correlations AC i ≡ δI i sq δI i sq and three cross-correlations XC ij ≡ δI i sq δI j sq nonindependent measurements given by:

AC i =< F F T * (δI i sq )F F T (δI i sq ) > XC ij =< F F T * (δI i sq )F F T (δI j sq ) > (3)
where F F T stands for the F ast F ourier T ransf orm, F F T * its complex conjugate and < ... > the rms average at the spectrum analyzer. At this stage, the importance of voltage biasing can be seen. Indeed, in the case of a current biasing scheme, the resistance value of the reference resistor would be much larger than all the other resistances. Applying this condition to the set of equations, it becomes obvious that the correlations are always negative as the result of current conservation applied to current fluctuations : (δI 1 sq + δI 2 sq ) 2 = 0.

In principle, the 6 measurements are related to the noise and cross-correlations of each element of the circuit through a 6x15 matrix ! This can be very much simplified by considering that any cross-correlated noise involving at least one macroscopic resistor is zero. Therefore, the system of equations reduces to :

       AC 1 AC 2 AC 3 XC 12 XC 13 XC 23        = M tot   S 1 S 2 S 12   + N tot   S r1 S r2 S ref   (4)
where S ref , S r1 and S r2 are the thermal noise of the three macroscopic resistors R ref , r 1 and r 2 . The three quantities of interest are the noise in each branch of the device i.e S 1 ≡ δI 1 δI 1 and S 2 ≡ δI 2 δI 2 and the cross-correlated noise S 12 ≡ δI 1 δI 2 . Therefore only three measurements are necessary and we usually choose AC 1 , AC 2 and XC 12 that are related to the physical quantities in the following way:

  AC 1 AC 2 XC 12   = M   S 1 S 2 S 12   + N   S r1 S r2 S ref   (5)
Following equation 1, the matrix elements are just given by the values of various resistances. Note that in the case of a sample with non-linear response, R 1 and R 2 are differential resistances. As an example, the two 3x3 matrices M and N , for From this example, we see that the AC 1 (AC 2 ) is mostly due to the noise S 1 (S 2 ). Focusing on XC 12 , it reads : [START_REF] Jehl | Detection of doubled shot noise in short normalmetal/superconductor junctions[END_REF] where | M ij | refers to the absolute value of the matrix element M ij . This notation shows that the sign of XC 12 is not necessary that of the crossed correlated noise S 12 and that the contribution S ref is always positive. There are different ways to extract S 12 from XC 12 . First, if all the contributions other than S 12 are known, the crossed correlated noise of a sample is known simply by removing those contributions from XC 12 and divide by M 33 . Usually, S 1 and S 2 are not known. In that case, the matrix M in equation 5 can be inverted and both the spectral densities of noise S 1 and S 2 , and the cross-correlations S 12 can be obtained from measurements. The "SUM" accounts for the current conservation law and the solid lines are adjustments of the data using equations 4 and the appropriate offsets (see text).

R 1 = R 2 = 1.0 Ω and R ref = r 1 = r 2 = 0.
XC 12 =-| M 31 | S 1 -| M 32 | S 2 + | M 33 | S 12 -| N 31 | S r1 -| N 32 | S r2 + | N 33 | S ref

III. CALIBRATION

In order to test the performance of our instrument, we have measured noise and correlations of a test sample made from two macroscopic resistors of 1 Ω resistance at room temperature. For this test, these two macroscopic resistors are placed on the sample holder whereas the biasing resistors R ref , r 1 and r 2 are anchored to the mixing chamber. We then expect the current noise in each branch to be given by the thermal noise contribution of each resistive element of the circuit assuming no intrinsic cross-correlated noise between them as they are all macroscopic. The overall results of the six measurements are shown in figure 2 as a function of the temperature. In this plot, the "SUM" is ΣδI i sq 2 = ΣAC i + 2ΣXC ij accounting for the current conservation law. The precise measurement of the resistances at low temperature gives:

R 1 = R 2 = 0.88 Ω and r 1 = r 2 = R ref = 0.088 Ω.
The solid line in figure 2 show the noise and correlations estimates given by equation 4 considering only thermal noise and setting S 12 = 0. The only adjusting parameter is then the set-up noise contribution that can be read extrapolating the data at zero temperature. We obtain a set-up noise level of the order of 2-3 pA 2 /Hz for the three auto-correlation AC i measurements. The set-up noise level correspond to a flux noise of few µΦ 0 / √ Hz in a SQUID, which is close to the best noise level given by the manufacturer considering the overall experimental set-up and its connections to room temperature instruments. Note that these measurements have been performed with one DC-current source connected to the circuit but with zero current at its output. Therefore, our results include any additional noise coming from this external source. Regarding the cross-correlation between the SQUID currents XC ij , there are no fitting parameters as we have fixed the intrinsic correlations S ij to be zero. The agreement with the data is very good so we conclude that there is no measurable intrinsic correlations between the three SQUID output signals.

In a second test, we have measured the noise and correlations as a function of a DC-current at fixed temperature. We used I DC1 for this measurement and ramped the current up to 1.5 mA at fixed temperatures of 100 mK, 300 mK and 500 mK. The overall measurements are shown in figure 3 for a base temperature of 100 mK. As all the resistive elements in the circuits are macroscopic resistors with temperature independent resistances, the shot noise is zero and the noise response should stay constant and given by the thermal noise. However, we clearly see an increase of the noise and correlation responses. This increase corresponds to an elevation of the electronic temperature of the resistors due to Joule heating whereas the temperature of the mixing chamber was kept regulated at 100 mK. From these results, we can estimate the effective temperature of the resistors. However, at a given value of the DC current, the effective temperatures of the various resistors are not equal. There are two reasons for that. First, because the biasing resistors r 1 , r 2 and R ref are anchored to the mixing chamber, their coupling to the cold bath is better than for the R 1 and R 2 resistors that are placed and connected similarly to what a real sample will be i.e on a sample holder. Second, for a given DC current I DC1 , the current flowing through R ref is different from the current though the other resistors. Thanks to the symmetry of the circuitry, we can consider that T R1 = T R2 ≡ T R and T r1 = T r2 ≡ T r , both sets being different from T ref where T R is the effective temperature of a resistor R.

In order to estimate the effective temperatures in the simplest way, we shall be using only the AC 1 and AC 3 measurements (equation 4). Considering only thermal noise, the two equations become :

AC 1 = α 1 T R + α 2 T r + α 3 T ref + AC 0 1 AC 3 = β 1 T R + β 2 T r + β 3 T ref + AC 0 3 (8)
where

α 1 = 4k B × (M tot11 /R 1 + M tot12 /R 2 )
for instance and AC 0 1 and AC 0 3 the set-up noise of the SQUIDs 1 and 3 obtained from the measurements as a function of the temperature. We can simplify these equations by neglecting the noise contribution of the resistances r 1 and r 2 . This assumption is justified, first because α 2 (β 2 ) is ten times smaller than α 1 (β 1 ) and second because the current through r 1 and r 2 is much smaller than though R ref [16]. The effective temperatures T R and T ref are then extracted from equations 8. The results are shown in figure 4 for various base temperatures. We do see that the temperature increase is weaker as the base temperature is raised and is almost absent above 500mK. This behavior is due to the strengthening of the electron-phonon coupling as the temperature is raised. The temperature increase is clearly more pronounced at the reference resistor than for the sample resistors and reaches ∆T ref ∼ 250 mK and ∆T R ∼ 100 mK at the lowest temperature investigated (i.e 100 mK). In the inset, we have plotted the effective temperatures as a function of the power R I 2 that is dissipated in each resistor. The different traces for T R and T ref reflects the fact that the coupling to the mixing chamber of the biasing resistors or the sample resistors are different. The increase of effective temperature in the biasing resistors translates directly into an increase of their noise contribution in the measured quantities (see equation [START_REF] Hanbury-Brown | Correlation between photons in two coherent beams of light[END_REF] and may affect the estimate of the quantities of interest. However, because the corresponding matrix elements are small, this additional contribution stays very moderate but needs to be considered in real samples especially for the XC 12 measurement.

IV. CONCLUSION

In conclusion, we have described the performances of an experimental instrument implemented to measure current noise and correlations of three terminal nanostructures. This set-up is particularly well adapted to low impedance samples and uses three SQUIDs as current amplifiers. We have calibrated the overall response of the system using macroscopic resistors in place of a real sample. We have shown that despite the complexity of the instrument, we can reach very low intrinsic noise floor and that there is no significant intrinsic noise correlations between the SQUIDs outputs. Finally, the increase of the effective temperature of the various resistive elements of the circuit has been measured as a function of a DC-current.

V.

FIG. 1 .

 1 FIG. 1. Schematic description of the experimental set-up.The schematic shows a typical three-terminal nanodevice to be studied with this new instrument.The red circuit's elements are at 4.2 K and the blue ones are thermalized with the mixing chamber of the dilution fridge. For calibration measurements, the sample has been replaced by two macroscopic resistors of ∼ 1 Ω resistance whereas the others macroscopic resistors in the circuit have a resistance of ∼ 0.1 Ω. All the wiring is made from superconducting leads.

FIG. 2 .

 2 FIG. 2. NoiseACi and correlations XCij of the three SQUIDs in response to the circuit depicted in figure1as a function of the mixing chamber temperature with zero DC current. The "SUM" accounts for the current conservation law and the solid lines are adjustments of the data using equations 4 and the appropriate offsets (see text).

FIG. 3 .

 3 FIG.3. Noise ACi and correlations XCij of the three SQUIDs as a function of the total DC current IDC1 applied on the reference resistor side at 100mK.

1 FIG. 4 .

 14 FIG. 4. Electronic temperature of the reference resistor R ref and of the sample resistors R1 and R2 as a function of the dc current flowing through each of them for three different regulated temperatures. The inset shows the effective temperatures at a base temperature of 100 mK as a function of the dissipated power.

  1 Ω, are :

	M = N =   -0.000589 -0.000589 0.0059   0.7072 0.0049 -0.1179 0.0049 0.7072 -0.1179 -0.0589 -0.0589 0.7105   0.007072 0.000049 0.0059 0.000049 0.007072 0.0059  	(6)

ACKNOWLEDGMENT

This work has been partially financed by the French Research National Agency (ANR) through two main contracts (ACI-JC 2003 and ANR-PNANO 2008). We acknowledge the RTRA Nanoscience foundation for its support to the PhD grant of A. H. Pfeffer.


Claude Le Milbeau
email: claude.le-milbeau@univ-orleans.fr

Marlène Lavrieux

Jérémy Jacob

Jean-Gabriel Bréheret

Renata Zocatelli

Jean-Robert Disnar

Methoxy-serratenes in a soil under conifers and their potential use as biomarkers of Pinaceae

Keywords: pentacyclic triterpenes, serratane, soils, biomarkers, Pinaceae

In the frame of a study aiming at defining new soil molecular biomarkers that could attest to former land use and that could also be screened in lake sediments for paleoenvironmental reconstruction, we analysed the 2-4 cm depth layer of a soil developed under a conifer forest in the Lake Aydat catchment (French Massif Central). The lipid fraction of this sample contained 12 serratenoids bearing at least a methoxyl (OMe) group at C-3 or C-21 and various additional functional groups (alcohol, ketone or acetate). A survey of the literature provides indubitable evidence that these compounds are typical for Pinaceae species, in agreement with the surrounding vegetation at the sampling site. Due to the economic and ecological importance of these taxa, the presence of these highly specific compounds in soil, sediments or peat would thus help in unravelling the timing of forestry activity.

Introduction

Reconstructing the evolution of continental landscapes through time in relation to the development of human societies and climate change constitutes a challenge for both of potential economic importance [START_REF] Huang | Significance of long chain iso andanteiso monomethyl alkanes in the Lamiaceae (mint family)[END_REF]. However, from an environmental point of view, all these compounds attest to the development of open vegetation, either under the influence of natural conditions or due to human activity. In contrast, very few molecular biomarkers have been reported as specific for trees, allowing, for example, distinguishing between angiosperm and conifer trees. For example, the contribution of gymnosperms to the organic matter (OM) of soils or sediments is often estimated through the quantitation of lignin monomers, a low syringyl/vanillyl phenols ratio (S/V) being typical for these plants (e.g. [START_REF] Hedges | The characterization of plant tissues by their lignin oxidation products[END_REF]. More specific biomarkers of gymnosperm input are diterpenes and sesquiterpenes that are largely used to identify a gymnosperm contribution to OM in amber and resin, soil, sediments, rocks (coals included) and even aerosols (e.g. [START_REF] Simoneit | Cyclic terpenoids of contemporary resinous plant detritus and fossil woods, ambers and coals[END_REF][START_REF] Simoneit | Lignin pyrolysis products, lignans, and resin acids as specific tracers of plant classes in emissions from biomass combustion[END_REF][START_REF] Killops | Chemostratigraphic evidence of higher-plant evolution in the Taranaki Basin, New-Zealand[END_REF][START_REF] Otto | Biomarkers of Holocene buried conifer logs from Bella Coola and north Vancouver, British Columbia, Canada[END_REF]Otto et al., 2005;[START_REF] Bechtel | Influence of floral assemblage, facies and diagenesis on petrography and organic geochemistry of the Eocene Bourgas coal and the Miocene Maritza-East lignite (Bulgaria)[END_REF][START_REF] Otto | Degradation and preservation of vascular plant derived biomarkers in grassland and forest soils from Western Canada[END_REF]. Nevertheless, few of these terpenoids are specific at the level of the genus or at least of the family (e.g. [START_REF] Aplin | The taxonomic distribution of some diterpene hydrocarbons[END_REF][START_REF] Otto | Sesqui-, di-, and triterpenoids as chemosystematic markers in extant conifers-A review[END_REF]).

Here we report on a series of oxygenated methoxy-triterpenoids having a basic serratane structure and a wide diversity of functional groups [methoxyl(s) as well as ketone, acetate and alcohol] and configurations that were detected in the lipid extracts of a soil developed under a conifer forest. After describing their mass spectral characteristics and assignment, we discuss their known sources and potential as chemotaxonomical markers and as potential tracers of plants designed for palaeoenvironmental studies.

Material and methods

Setting

As in many other parts of the world, French landscapes have been intensively transformed over the two last centuries. After centuries of extensive clearing and overexploitation the forest surface progressively redeveloped to be doubled until ca. 1830, mainly thanks to the combined effect of rural exodus, increased agricultural productivity and industrial revolution [START_REF] Bazin | Systèmes Agraires et Pratiques Paysannes dans les Monts Dômes[END_REF][START_REF] Cinotti | Evolution des surfaces boisées en France : proposition de reconstitution depuis le début du XIXe siècle[END_REF]. Representative of this situation is the Massif Central, a basement massif in the centre of France. Its mainly artificial refforestation dates back to the middle of the 19 th century, to further national programs aiming at fighting against erosion and floods, and at developing industrial management of the forests in a context of a worldwide increase in demand for wood [START_REF] Gadant | Le reboisement en Auvergne[END_REF][START_REF] Cinotti | Evolution des surfaces boisées en France : proposition de reconstitution depuis le début du XIXe siècle[END_REF]. Rapid growth, hardiness and quality of the wood made resiniferous trees the preferred species: Picea Abies (Norway/common spruce; 50%), Pseudotsuga menzeiesii (Douglas fir; 15%), Pinus sylvestris (Scots pine; 15%), Abies alba (silver fir; 10%) and Abies grandis (grand fir; 10%), insu-00759832, version 1 -3 Dec 2012 plus Picea sitchensis (Sitka spruce), Pinus nigra (European black pine) and its subsp. laricio var. corsicana (Corsican pine) and Pinus strobus (Eastern white pine) were planted in the region [START_REF] Gadant | Le reboisement en Auvergne[END_REF].

Located on the northern part of the Massif Central, on the northeastern side of the volcanic Sancy Massif (ca. 25 km SW of Clermont-Ferrand), Pessade wood is a midmountainous forest in the Lake Aydat catchment. Ranging from 1175 to 1300 m above sea level (m,a.s.l.), this artificial forest is mainly made up of ca. 60 yrs-old trees (Pinus sylvestris, Abies alba, Picea abies and, to a lesser extent, Pseudotsuga menzeisii). The management of the forest is shared between wood exploitation (that started at the beginning of the 20 th century) and a ski resort.

Sampling, lipid extraction and separation

Several soil samples representatives of the different contexts encountered in Lake Aydat catchment were sampled in course of the ERODE project (EC2CO/INSU/CNRS) and depicted in previous papers [START_REF] Lavrieux | Biomarqueurs moléculaires d'occupation des sols, du sol au sédiment: exemple du bassin-versant et du lac d'Aydat[END_REF][START_REF] Zocatelli | Free fatty acids composition of soils developed under distinct vegetation cover in the catchment of Lake Aydat (Massif central, France)[END_REF][START_REF] Lavrieux | Preservation of an ancient grassland biomarker signature in a forest soil from the French Massif Central[END_REF].

Here we only report results from a soil sample collected between 2 and 4 cm depth from a core (22 cm long x 8 cm diameter) drilled in the Pessade Forest that exhibited the largest diversity of methoxy-serratenes. After drying in an oven (40 °C, 48 h), samples were crushed in a mortar and sieved at 2 mm. Free lipids were extracted from ca. 2 g dried and milled soil samples by using an automated solvent extractor (Dionex ASE 200®) with dichloromethane DCM:MeOH (9:1 v/v). After solvent evaporation under N 2 , the total extract was separated into neutral, acidic and polar compounds using solid phase extraction with aminopropyl bonded silica. Neutral compounds were eluted with DCM:isopropanol (2:1 v/v) and acidic compounds with Et 2 O after acidification of the medium with Et 2 O:HCO 2 H (9:1 v/v). Neutral lipids were fractionated by way of flash chromatography on silica activated (48h) at 110 °C and then deactivated with 5% water. After successive elution of aliphatic hydrocarbons, aromatics and ethers, the ketone fraction (F4-5) was eluted with hexane:EtOAc (19:1 and then 9:1). Two alcohol fractions F6 and F7 were then recovered with hexane:EtOAc (17:3 and then 4:1). The alcohol fractions were silylated using BSTFA and pyridine, and an internal standard [5(H)-cholestane] was added in all fractions.

Quantification and identification

The ketone and silylated alcohol fractions were analysed using gas chromatographymass spectrometry (GC-MS) with a TRACE-PolarisGCQ instrument equipped with an AS insu-00759832, version 1 -3 Dec 2012 0.25 mm i.d., 0.25 μm film thickness) with 5 m of guard column. The GC operating conditions were: 40 °C (1 min) to 100 °C at 10 °C min -1 , then to 300 °C (held 30 min) at 4 °C min -1 . The sample in toluene (2µl) was injected splitless, at 280°C. The carrier gas was He and at 1 ml min -1 . The mass spectrometer was operated in the electron ionisation (EI) mode at 70eV and scanned from m/z 50 to 600. Assignment was performed using two authentic standards [3-methoxy-serrat-14-en-21-ol (PJ1) and 3-methoxy-serrat-14-en-21-ol (PJ2), provided by R. Tanaka, Osaka University of Pharmaceutical Sciences, Japan], and their silylated and acetylated counterparts, by comparison of mass spectra and retention times (RTs) with published data (Kutney et al., 1969a;[START_REF] Tanaka | 3-oxo-serratene triterpenoids from the stem bark of Picea jezoensis Carr. hondoensis[END_REF][START_REF] Tanaka | [END_REF] and interpretation of mass spectra. Quantitation was performed by comparing the area of the peak of 5α(H)cholestane with that of the compound of interest on the total ion chromatogram (TIC). In cases of coelution, the area was measured on specific ion chromatograms and then converted to the calculated TIC peak area by applying a correction factor.

Results

Twelve compounds were assigned as methoxy-serratenes: compounds 1 to 7 were present in the ketone fraction (F4-5) between 58 and 71 min and compounds 8 to 12 were in the alcohol fractions (F6 and F7) between 57 and 73 min RT (Fig. 1). Concentration ranged from ca. 0.01 to 20 (avg. 2) μg g -1 soil.

Common MS features

The mass spectra and structures of the compounds detected in the soil sample are illustrated in Fig. 3, their main characteristics and the proposed fragmentation mechanisms are reported in Table 1 and in Fig. 4, respectively. Carbon numbering, ring labelling and location of functional groups are displayed in Fig. 2. Only structures bearing two or three oxygenated functional groups at C3 (R1), at C21 (R2) and C15 (R3) were detected but other compounds discussed in Section 4.2 and cited in Table 2 bear functional groups R4 to R8 (Fig. 2).

Rearranged serratane structures such as piceanane and jezanane (Fig. 2) are only briefly discussed in Section 4.2.1.

Serratane triterpenes contain seven tertiary methyls (instead of eight in common pentacyclic triterpenes), a central seven membered C ring and a double bond between C-14 and C-15 ( 14 ) or C-13 and C-14 ( 13 ; Fig. 1). Such a double bond location implies a fragmentation pattern quite similar to that of taraxerane-type compounds (Budzikiewicz et al., insu-00759832, version 1 -3 Dec 20121963;Kutney et al., 1969a), i.e. retro Diels-Alder (RDA) rearrangements leading to the fragments f, f', g and h, and ring C cleavage leading to the fragments i, j, k, k' and l (Fig. 4).

All 12 compounds bear at least two oxygenated functional groups at C-3 (R1) and C-21 (R2), respectively (Fig. 2). Compound 12 supposedly bears an additional oxygenated group at C-15 (R3). The loss of a methoxy from C-3 or C-21 is evidenced by the mass spectra of all compounds via the presence of pairs of ions differing by 32 mass units (i.e. fragments ab, a'-b', a'-c', c-d, f-g, f'-h, i-j and k'-l,Fig. 4). Considering these general characteristics, the assignment of individual methoxy-serratenes is now discussed as follows on the basis of different oxygenated functional groups, and their location and isomerism.

Dimethoxy-serratenes

The mass spectra of compounds 2 and 3 (Fig. 3), found in the ketone fraction, exhibit molecular ion M +. (a; Fig. 4) at m/z 470, consistent with a C 32 H 54 O 2 formula (Table 1). The loss of a first methoxyl group (OMe) from the molecular ion is evidenced by ion [M -32] +.

(m/z 438; fragments b and c) and is followed by elimination of another OMe as attested by a significant fragment [M -64] +. (m/z 406; d). This implies that compounds 2 and 3 are dimethoxylated and is further supported by the loss of OMe groups on both rings AB and DE after ring C opening (fragments i-j at m/z 221-189 and fragments k'-l at m/z 219-187, respectively). RDA rearrangements that affect ring D (due to the presence of  14 bond) lead to ring AB fragments pairs f-g and f'-h (m/z 316-284 and m/z 301-269, respectively).

Accordingly, compounds 2 and 3 are assigned as 3,21-dimethoxy-serrat-14-ene and 3,21-dimethoxy-serrat-14-ene, respectively, in agreement with Kutney et al. (1969a). The attribution of 3,21and 3,21 configuration is based on the elution order of the standards PJ1 and PJ2, the 21 alcohol analogues of compounds 2 and 3 (Section 3.5).

Methoxy-acetoxy-serratene

The mass spectrum of compound 7 (Fig. 3) displays a M +. ion at m/z 498 consistent with a C 33 H 54 O 3 formula. The presence of an OMe on rings A-B is evidenced by fragments f, f', g and h at m/z 316, 301, 284 and 269 (Fig, 4) that arise from RDA rearrangements from the Accordingly, compound 7 is interpreted as a 3-methoxy-serrat-14-en-21-yl acetate.

insu-00759832, version 1 -3 Dec 2012

Comparison of the RT of 7 with those of the acetylated counterparts of 21alcohols PJ1 and PJ2 allowed us to attribute a 21 configuration, with no further information on the configuration of the methoxy at C-3. Therefore, compound 7 is tentatively assigned as 3methoxy-serrat-14-en-21-yl acetate.

Methoxy-keto-serratenes

The mass spectra of compounds 1, 4, 5 and 6 (Fig. 3) are characterised by a M +. (a; In the spectra of compounds 4 and 6, fragments f (m/z 300) and f' (m/z 285) attest to RDA rearrangement from the  14 bond. For 5, the rearrangement is evidenced by a weak fragment g at m/z 284, the corresponding f fragment at m/z 316 being very low in abundance.

Compound 1 does not show any fragment that could be attributed to a RDA rearrangement and is therefore interpreted as a  13 structure, in agreement with observations by Kutney et al. (1969a) on such compounds. Therefore, compounds 2, 5 and 6 are serrat-14-enoids whereas compound 1 is a serrat-13-enoid.

In the spectra of 4 and 6 (Fig. 3) the loss of an OMe from m/z 219 (k') is evidenced by an ion at m/z 187 (l). This ion further indicates that the OMe is located at C-21. In addition, fragments f (m/z 300) and f' (m/z 285) resulting from the RDA rearrangement in these  14 structures attest to the presence of a keto group at C-3. Hence, 4 and 6 are interpreted as C-3 keto, C-21 methoxy-serratenoids. The spectra of 1 and 5 do not show any of these fragments.

They are thus interpreted as C-3 methoxy, C-21 keto-serratenes, consistent with the observation of [START_REF] Tanaka | 3-oxo-serratene triterpenoids from the stem bark of Picea jezoensis Carr. hondoensis[END_REF] who noticed that fragments m/z 454, 439, 422 and 407 are more abundant in C-3 methoxy, C-21 keto-serratenoids than in their C-3 methoxy, C-21 keto counterparts. Accordingly, 1 is interpreted as being a 3-methoxy-serrat-13-en-21-one.

Because only a 3 epimer has been reported, 1 is likely to be 3-methoxy-serrat-13-en-21one. Compound 5 is interpreted as a 3-methoxy-serrat-14-en-21-one with no additional information on the configuration at C-3. Finally, 4 and 6 are assigned as 21-methoxy-serrat-14-en-3-ones. Tentative assignment of the C-21 OMe configuration can be based on the following considerations: (i) PJ1 (3 elutes earlier than PJ2 (3, consistent with the statement that 3 epimers of methoxy-triterpenes elute earlier than their 3 counterparts [START_REF] Jacob | Pentacyclic triterpene methyl ethers in recent lacustrine sediments (Lagoa do Caçó, Brazil)[END_REF]; (ii) serratenes can be viewed as symmetrical structures (except for the insu-00759832, version 1 -3 Dec 2012 double bond location; Fig. 2). One can argue that the elution order would be the reverse for the  and  epimers at the C-21 (i.e. 21 elutes earlier than 21), considering the presence of an identical group at C-3. Compounds 4 and 6 are thus tentatively attributed a 21 and a 21 configuration, respectively.

Methoxy-hydroxy-serratenes

The spectra of 8, 9, 10 and 11 (Fig. 3) display M +. at m/z 528, consistent with silylated serratene structures bearing both a OMe and an OH group with a C 31 H 52 O 2 formula.

Compounds 9 and 10 are assigned as 3-methoxy-serrat-14-en-21-ol (PJ1) and 3-methoxyserrat-14-en-21-ol (PJ2), respectively, by comparison with authentic standards.

As observed for compound 1 in the ketone fraction, compound 8 elutes earlier than any other compound in the alcohol fraction. Because of a lack of any fragment attributable to a RDA rearrangement (fragment f at m/z 316 and g at m/z 284) in its spectrum, the double bond of 8 must be a  13 one. Compound 8 could be 3-methoxy-serrat-13-en-21-ol, the sole methoxyserrat-13-en-ol reported in natural samples (Table 2). Conversely, significant fragments at m/z 316 (f) and 284 (g) in the spectrum of compound 11 which result from a RDA rearrangement, attest to a  14 structure with an OMe at C-3. The two 21 epimers being already assigned as 9 (PJ1), 10 (PJ2) and 11 must be a 21 epimer. No additional information on the configuration of the OMe group at C-3 can be provided.

Methoxy-diketo-serratene

The spectrum of 12 is very similar to that of 15-keto-21-methoxy-serrat-13-en-3-one reported by [START_REF] Tanaka | New methoxytriterpene dione from the cuticle of Picea jezoensis carr. jezoensis[END_REF], with the tentative fragmentation scheme depicted in Fig. 4.

Discussion

Serratane-type compounds constitute an original group of naturally occurring pentacyclic triterpenes with an unusual 7 carbon C ring. These 14-homo-27norgammaceranes are biogenetically related to -onocerin, i.e. synthesized through the cyclisation of bis-epoxy-squalene and not from epoxy-squalene, the precursor of more common pentacyclic triterpenes (oleanane, ursane, lupane…; [START_REF] Xu | On the origins of triterpenoid skeletal diversity[END_REF].

Known occurrences of serratenoids in plants and in geological archives

insu-00759832, version 1 -3 Dec 2012

To our knowledge, the only descriptions of serratane derivatives in geological archives was reported by [START_REF] Tieguan | Geochemical characteristics of crude oils and source beds in different continental facies of four oil-bearing basins, China[END_REF] who reported on the occurrence of serratanes in oils and by [START_REF] Métayer | 4,4'-Dimethyldinaphtho[a,d]cycloheptane, a naturally-occurring polyaromatic derivative related to triterpenoids of the serratane series[END_REF] who found a related aromatic hydrocarbon in Oligocene sediments from the Rhine valley (France). The latter compound was interpreted as resulting from the microbial aromatisation of a triterpene related to serratenediol but with no clear relationship with a potential source.

More than 100 different serratenoids (serratenes, serratenones, serratene acids, serratene polyols and their acetates…) have been reported, most having been identified in club mosses such as Lycopodiaceae and Huperziaceae, in Polypodiaceae, in Conifers, and in few Angiosperms (Primulaceae and Leguminosae; see Supplementary Material).

Known occurrences of methoxy-serratenes in plants

As opposed to other serratenoids, methoxy-serratenes appear constrained to conifers, and more specifically to Pinaceae (Table 2). Four exceptions should be noted. [START_REF] Beneš | A triterpenoid of the serratane type from the liverwort Nardia scalaris[END_REF] reported the occurrence of 2l-methoxy-serrat-14-en-3-one ( 6) in the liverwort Nardia scalaris but indicated that the plant studied was sampled under common spruce and was probably contaminated by the latter species. Similarly, 3-methoxy-serrat-14-en-21-ol (9 -PJ1), 3-methoxy-serrat-14-en-21-ol (10 -PJ2) and 3-methoxy-serrat-14-en-21-one [START_REF] Tanaka | [END_REF] that were in Homalia trichomanoides (Bryophytes, Neckeraceae; [START_REF] Wang | Complete NMR chemical shift assignments for three serratane triterpenoids isolated from moss Homalia trichomanoides[END_REF] might have originated from Pinaceae close to the sampling location. For the same reason, the detection of six methoxy-serratenes in the tree moss Pseudevernia furfuracea (Ascomycetes) that grows on Pinus sylvestris could be attributed to a contamination by the host tree [START_REF] Joulain | Lichen extracts as raw materials in perfumery. Part 2: treemoss[END_REF]. Finally, Homogynes alpina (Asteraceae) was cited as a potential source for 2l-methoxy-serrat-14-en-3-one (6), allowing [START_REF] Beneš | A triterpenoid of the serratane type from the liverwort Nardia scalaris[END_REF] to argue for a larger than expected distribution of methoxy-serratenes in plants. Due to the lack of precise information on this report, this citation must be taken with caution.

Except from these peculiar cases, most of the 40 methoxy-serratenes reported up to now (Table 2) were isolated from Pinaceae: Picea and Pinus genera and, to a lesser extent, plants of the Abies and Cathaya genera. It is worthwhile noting that these methoxy-serratenes appear tissue-specific since they were only reported in bark (Norin, 1972). The following discussion completes and updates a previous compilation by [START_REF] Otto | Sesqui-, di-, and triterpenoids as chemosystematic markers in extant conifers-A review[END_REF].

Picea insu-00759832, version 1 -3 Dec 2012

With 23 structures reported, Picea jezoensis (Jezo or Yeddo spruce, Picea ajanensis) displays the greatest diversity of methoxy-serratenes of all plants studied [START_REF] Chernenko | Extractive substances of the bark of Picea ajanensis[END_REF]1992a;[START_REF] Tanaka | 3-oxo-serratene triterpenoids from the stem bark of Picea jezoensis Carr. hondoensis[END_REF][START_REF] Tanaka | 21α-hydroxy-3βmethoxy-serrat-14-en-30-al and other triterpenoids from the cuticle of Picea jezoensis[END_REF][START_REF] Tanaka | Two new epoxyserratanes from the cuticle of Picea jezoensis[END_REF][START_REF] Tanaka | New methoxytriterpene dione from the cuticle of Picea jezoensis carr. jezoensis[END_REF][START_REF] Tanaka | Two serratane triterpenes from the stem bark of Picea jezoensis var. hondoensis[END_REF]Tanaka et al., , 1999a, b;, b;2001[START_REF] Tanaka | [END_REF][START_REF] Doi | Chemopreventive effects of 13,14-epoxy-3b-methoxy-serratan-21-ol insu-00759832, version 1 -3 Dec 2012 (PJJ-34), a serratane-type triterpenoids, in a rat multi-organ carcinogenesis bioassay[END_REF]. This species was reported to produce a large compound spectrum, from methoxyserratenones [START_REF] Norin | [END_REF][START_REF] Tanaka | [END_REF][START_REF] Tanaka | [END_REF], their 29-nor equivalents and methoxy-serratenedione [START_REF] Tanaka | [END_REF], methoxyserratenols [START_REF] Tanaka | [END_REF][START_REF] Tanaka | [END_REF] and methoxy-serratenediols, methoxy-hydroxy-serratenone, methoxyhydroxy-serratenals, methoxy-epoxy-serratenones and methoxy-epoxy-serratenols. In addition, P. jezonensis is the sole known source of rearranged serratene structures such as piceanane and jezanane (Fig. 2; Tanaka et al., 1999a;[START_REF] Tanaka | [END_REF].

Eight serratene derivatives were extracted from the bark of Picea sitchensis, six being methoxy-serratenes (5, 10, 3,21-dimethoxy-serrat-14-ene, 3-methoxy-serrat-14-en-21one, 3-methoxy-serrat-13-en-21-ol and 3-methoxy-serrat-14-en-21-ol) the two others being 21-episerratenediols [START_REF] Kutney | Novel triterpenes from Sitka spruce (Picea sitchensis [Bong.] Carr)[END_REF][START_REF] Kutney | [END_REF][START_REF] Rogers | Neutral terpenes from bark of Sitka spruce (Picea sitchensis (Bong) Carr.)[END_REF]. The bark of Picea abies was shown to contain 3-methoxy-serrat-14-en-21-ol ( 9), 21-methoxy-serraten-14-one ( 6) and 3-methoxy-serrat-14-en-21-ol (10;Norin and Winell, 1972a). PJ1 ( 9) and PJ2 [START_REF] Tanaka | [END_REF] were extracted from Picea glehni (Sakhalin spruce; [START_REF] Tanaka | Bioactive triterpenoids from the stem bark of Picea glehni[END_REF] and Picea obovata (Siberian spruce; [START_REF] Chernenko | [END_REF]. They were accompanied by 3-methoxy-serrat-14-en-21-yl formate and piceanonol A in the former and by compounds 1 and 5 (both isomers) in the latter.

Pinus

Eleven Pinus species have been reported to contain, in total, a series of 16 methoxyserratenes. These species comprise Pinus armandii (Chinese white pine; [START_REF] Fang | Serratene triterpenes from Pinus armandii[END_REF][START_REF] Fang | Chemical constituents of some Endemic conifers in Taiwan[END_REF], Pinus banksiana (Jack Pine; Rowe, 1964, Rowe, 1965;[START_REF] Rowe | Triterpenes of Pine barks: naturally occurring derivatives of serratenediol[END_REF][START_REF] Rowe | Terpenoids of lodgepole pine bark[END_REF], Pinus contorta [START_REF] Rowe | Terpenoids of lodgepole pine bark[END_REF], Pinus lambertiana (Sugar Pine; Rowe, 1964;[START_REF] Rowe | Triterpenes of Pine barks: naturally occurring derivatives of serratenediol[END_REF], Pinus luchuensis [START_REF] Cheng | The neutral part of the bark of Pinus luchuensis mayer[END_REF][START_REF] Wada | Triterpene constituents from the stem bark of Pinus luchuensis and their DNA topoisomerase II inhibitory effect[END_REF], Pinus palustris (Longleaf Pine; Rowe 1964), Pinus strobus [START_REF] Zinkel | Terpenoids of Pinus strobus[END_REF], Pinus taeda (Loblolly Pine; Rowe 1964), Pinus radiata [START_REF] Weston | Neutral extractives from Pinus radiate bark[END_REF] and Pinus taiwanensis [START_REF] Cheng | The neutral part of the bark of Pinus taiwanensis Hayata[END_REF]. Of importance is the detection of compounds 3, 5, 10, 11, 3,21-dimethoxy-serrat-14-ene, 21-methoxy-serrat-14-en-3-ol, 3-methoxyserrat-14-ene-21,29-diol, 3-methoxy-serrat-14-en-21,29-diol, 3-methoxy-serrat-14-ene-21,30-diol and 29-nor-3-methoxy-serrat-14-en-21-one in Pinus monticola Doug. (Pinus albicaulis, Western White Pine or Douglas Pine; [START_REF] Rowe | Terpenoids of lodgepole pine bark[END_REF][START_REF] Conner | Terpenoid and other extractives of western white pine bark[END_REF][START_REF] Conner | 30-Nor-3-methoxy-serrat-14-en-21one: First reported natural occurrence of a norserratene triterpene[END_REF] insu-00759832, version 1 -3 Dec 2012 1984) whereas 2, 3 and 5 were found in the bark of Pinus sylvestris (Norin and Winnel, 1972b).

Abies, Cathaya and Cedrus genera

Except for the Pinus and Picea genera, reports on the occurrence of methoxyserratenes in other Pinaceae are very restricted. Recently, Ou-Yang et al. ( 2011) found 3methoxy-serrat-14-en-21-one, 21-methoxy-serrat-14-en-3-one and 3-methoxy-serrat-14en-21-ol in Abies nephrolepis. [START_REF] He | On serratene components in relation tothe systematic position of Cathaya (Pinaceae)[END_REF] and Ma et al. (1982) detected compound 5 in Cathaya argyrophylla. These are the only reports of methoxy-serratenes in these two genera.

Cedrus, the last genus of the Pinaceae family, is not reported to contain methoxy-serratenes.

As a matter of fact, [START_REF] Joulain | Lichen extracts as raw materials in perfumery. Part 2: treemoss[END_REF] did not report any methoxy-serratene in lichens grown on a Cedrus sp., whereas they found some in lichens grown on pines (see above).

From the above evidences, and in agreement with a previous statement by [START_REF] Otto | Sesqui-, di-, and triterpenoids as chemosystematic markers in extant conifers-A review[END_REF], it is clear that methoxy-serratenes are almost exclusive to Pinaceae bark, and more specifically to the Pinus and Picea genera.

Potential of methoxy-serratenes as chemotaxonomical markers of Pinaceae

The local conifer vegetation developed in the Pessade forest, mainly dedicated to forestry, is made up of Picea abies, Pinus sylvestris, Abies alba and Pseudotsuga menziesii.

No Pseudotsuga species is known to produce methoxy-serratenes. Abies alba is not reported to produce these compounds but the occurrence of 3-methoxy-serrat-14-en-21-one ( 5), 21methoxy-serrat-14-en-3-one ( 6) and 3-methoxy-serrat-14-en-21-ol [START_REF] Tanaka | [END_REF] in Abies nephrolepis could preclude the discovery of such compounds in Abies alba. The bark of Picea abies was shown to contain compounds 6, 9 and 10 (Norin et al., 1972a) and that of Pinus sylvestris is the source of 2, 3 and 5. Current knowledge on the distribution of methoxyserratenes in Pinaceae thus only allows explaining the presence of compounds 2, 3, 5 (both isomers), and 6, 9 and 10. Among the compounds in the extract of the Pessade wood soil sample, only compound 7 (3-methoxy-serrat-14-en-21-yl acetate) has never been reported in living plants. Compounds 1, 4, 8, 11 and 12 are known in plants other than those presently growing in the Pessade wood.

As stated earlier (e.g. [START_REF] Jacob | Pentacyclic triterpene methyl ethers in recent lacustrine sediments (Lagoa do Caçó, Brazil)[END_REF], the detection of peculiar biochemicals in soils and sediments allows anticipating phytochemical discoveries because they integrate the insu-00759832, version 1 -3 Dec 2012 chemical diversity of the local vegetation. Accordingly, the discrepancy between the diversity of methoxy-serratenes in our soil sample and that expected in the local vegetation on the basis of literature data could result from our incomplete phytochemical knowledge of the distribution of methoxy-serratenes in the plant kingdom.

Very few studies on methoxy-serratenes address the question of their variability in a single species depending on environmental conditions or simply to intra-specific variability.

When compared to extracts directly obtained from P. sylvestris (Norin and Winell, 1972b), the greater diversity of methoxy-serratenes in lipid extracts of Pseudevernia furfuracea grown on this latter species [START_REF] Joulain | Lichen extracts as raw materials in perfumery. Part 2: treemoss[END_REF] potentially illustrates this intra-specific variability.

New structures could also arise from transformation of existing methoxy-serratenes or other compounds during early diagenesis. Several studies exemplify the ability of early diagenetic transformations to increase chemodiversity through the transformation of biochemicals to geochemicals unknown in living organisms (see for example [START_REF] Jacob | Pentacyclic triterpene methyl ethers in recent lacustrine sediments (Lagoa do Caçó, Brazil)[END_REF]. Despite the presence of serratenediol or serratenetriol in numerous Pinaceae and especially Picea (Roger and Rozon, 1970) and Pinus (Rowe, 1970) species, no such compound was found in our soil sample. It can thus be hypothesized that some of our methoxy-serratenes could be derived from the transformation of precursor serratenediols or serratenetriols. Double bond migration or epimerisation of functional groups, known to occur in other pentacyclic triterpenes [START_REF] Ageta | Acid-induced rearrangement of triterpenoids hydrocarbons belonging to the hopane and migrated hopane series[END_REF][START_REF] Rullkötter | Early diagenesis of terrigenous triterpenoids and its implications for petroleum geochemistry[END_REF][START_REF] Jacob | Pentacyclic triterpene methyl ethers in recent lacustrine sediments (Lagoa do Caçó, Brazil)[END_REF], can also lead to compounds alien to the local vegetation.

From these evidences, methoxy-serratenes can be considered as biomarkers of Pinaceae but their use as biomarkers of more restricted taxa, or even at the species scale, must rely on additional data on their distribution in plants and on potential structural alterations they could suffer in soils.

Conclusions

The analysis of the lipid content of a soil from a conifer forest revealed a series of methoxy-serratenes bearing a central seven membered C ring and a wide diversity of functional groups. According to a literature survey, such compounds appeared to be mainly produced by Pinaceae. They thus constitute a new family of biomarkers specific for these tree species. When considering their low production in plants, the accumulation of these compounds in soils underlines their potential high preservation capacity that might favour their efficient transfer and archiving in sediments. Even if their occurrence in the sediments insu-00759832, version 1 -3 Dec 2012 remains to be verified, their detection in soils and other natural archives could attest to the evolution of forested surfaces with time, under natural or anthropogenic constraints. Filling a gap in specific biomarkers of arborescent vegetation, and in addition to diterpenoids, they potentially constitute a new powerful tool for palaeoenvironmental reconstruction. However, detailed studies of the distribution and variability of methoxy-serratenes in Pinaceae and on their behaviour during diagenesis are still necessary to fully exploit the possibilities offered by these new biomarkers.

Table captions

Table 1 Main GC-MS characteristics of methoxy-serratenes detected in a soil under conifer forest in the catchment of Lake Aydat [compounds in bold identified with authentic standards (R.

Figure captions

Fig. 1: Chromatograms of serratane-type triterpenes in the ketone (a) and alcohol (b and c) fractions. Numbers correspond to structures in Table 1, to mass spectra in Fig. 3 and to some compounds for which possible sources are listed in Table 2.

Fig. 2: Structure of serratane-and rearranged serratane-type compounds with carbon numbering, functional groups and double bond location. The correspondence between functional groups and structure names is indicated in Table 1 for compounds detected in the soil sample. The name of fragments corresponds to fragmentation patterns reported in Table 1 and found in the spectra in Fig. 3. insu-00759832, version 1 -3 Dec 2012



  14 bond. Pairs of ions differing by 60 amu (a-b at m/z 498-438, a'-b' at m/z 483-423 and c-d at m/z 466-406; Fig.4) reveal the presence of an acetate group. This group is located on rings D-E as attested by the 60 amu difference between ions k' (m/z 247) and l (m/z 187).

Fig. 4 )

 4 Fig. 4) at m/z 454 and a fragment at m/z 422 [M +. -32] (b or c; Fig. 4) that attest to the loss of an OMe. These fragments are accompanied by their demethylated counterparts at m/z 439 (a') and 407 (b' or c'; Fig. 4).

Fig. 3 :Fig. 4 :

 34 Fig. 3: Mass spectra and structures of methoxy-serratenes detected in the ketone fraction of lipids extracted from soil developed in the Aydat catchment. Mass spectra of 9 and 10 were recovered from the corresponding standards (PJ1 and PJ2). The structures of compounds tentatively identified are indicated with "?".

  Figure 1

  Tanaka, Osaka University, Japan].

	N°a	Assignment	RT b	  R1 d	R3 d	R2 d	Formula	a	a'	b	b'	c	c'	d	e	f	m/z e f'	g	h	i	j	k	k'	l	m	n
	1	3-methoxy-serrat-13-en-21-one	58,38 13 OMe H	O	C31H50O2	454 439 422 407 -		-

---269 221 189 -

Acknowledgments

This study was realised as part of the ERODE (funded by the EC2CO program, CNRS/INSU) and OTARIE (funded by the Région Centre) projects. M.L. benefited from a PhD grant provided by the Région Centre. The authors would like to thank R. Tanaka (Osaka University of Pharmaceutical Sciences, Japan) for providing the PJ1 and PJ2 standards.

a Refers to compound numbers in Figs. 1 and3; b retention times (min); c location of double bond; d functional groups depicted in Fig. 2; e fragmentation patterns leading to fragments a to n are illustrated in Fig. 4. Compounds indicated in bold were identified with authentic standards (R. Tanaka, Osaka University, Japan).

insu-00759832, version 1 -3 Dec 2012

Table 2 Inventory of methoxy-serratenes occurrences in plants (numbers in bold correspond to compounds detected in the soil developed under conifer forest in the catchment of Lake Aydat. 1 2 3 4 6 7 8 9 10 12 Order Family Genus Species abies X X X [START_REF] Chemenko | [END_REF]; [START_REF] Tanaka | [END_REF]; [START_REF] Tanaka | [END_REF]; [START_REF] Tanaka | [END_REF]; [START_REF] Tanaka | [END_REF]; [START_REF] Tanaka | [END_REF]; [START_REF] Tanaka | [END_REF]; [START_REF] Tanaka | [END_REF]; [START_REF] Tanaka | [END_REF]; [START_REF] Tanaka | [END_REF]; [START_REF] Doi | [END_REF] [START_REF] Fang | [END_REF]; [START_REF] Fang | [END_REF] banksiana X [START_REF][END_REF]; [START_REF][END_REF]; [START_REF] Rowe | [END_REF]


Jérôme Chopard
email: jerome.chopard@uwa.edu.au

Christophe Pradal

Daniel Barbeau

Thomas Cokelaer

Christophe Godin

Scientific workflow for reusing plant/FSPM models

Keywords: Dataflow, Python programming language, functional structural plant modelling

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

INTRODUCTION

Models are increasingly being used in the plant research community to understand plant growth and physiology. Most of these rely on the same components to describe plant functioning. However, few of them can be reused directly either by integrating or combining them with other plant function models to answer scientific questions. Platforms have been proposed in different fields (e.g. biochemistry with the Vision project [START_REF] Sanner | A component-based software environment for visualizing large macromolecular assemblies[END_REF], visual data mining with the Orange project [START_REF] Demsar | Orange: from experimental machine learning to interactive data mining[END_REF] ) with these goals in mind: i) ease the creation of new models by reusing elements of old models and ii) simplify the combination of these elements. In the plant modelling community, two large projects, L-studio [START_REF] Federl | Virtual laboratory: an interactive software environment for computer graphics[END_REF] and GroImp [START_REF] Hemmerling | The rule-based language XL and the modeling environment GroIMP illustrated with simulated tree competition[END_REF], focusing on computer graphics, offer an integrated environment to develop new models. These approaches force the user to develop new components in their specific language in order to plug them into the simulation kernel.

The OpenAlea platform [START_REF] Cokelaer | Plant modeling with Python components in OpenAlea[END_REF], Pradal et al. 2010, Cokelaer et al. 2010[START_REF] Fournier | Building modular FSPM under OpenAlea: concepts and applications[END_REF] was created to allow users to interact easily with models developed by the community. OpenAlea uses the well-known Python language to link heterogeneous components together and provide a solution to solve the four main problems that usually prevent people using the models from other teams:

-i) Applications are a combination of components designed by modellers to be used by different users. Each component can be rearranged by users to suit the required degree of complexity of a particular problem. Components can be implemented in different languages (e.g. C, C++, Java, R) and reused in the platform by providing a Python interface.

-ii) Common resources are easily shared in the platform and multiple scientific libraries are already available to read, write and plot data in 2D and 3D or to perform statistical analyses.

-iii) Documentation is strongly encouraged among the community. This documentation is part of the code of components and can be read directly or translated into webpages to provide user and developer documentations.

-iv) From the developer point of view, tools to deploy applications on multi-platforms are available. From the user point of view, the complexity of implementation is hidden: a common graphical user interface, VisuAlea, allows components to be combined together in a workflow, which describe the different steps of evaluation, without a single line of code.

In this article we intend to demonstrate how a single component designed for a particular task can be reused directly as part of two types of models: a simple decision model and a functional-structural plant model.

PLATFORM DESCRIPTION

Components definition

An application is a combination of components designed by modellers to be used by users. Each component can be rearranged by users to suit the required degree of complexity of a particular problem. Components can be implemented in different languages (e.g. C, C++, Java, R) and reused in the platform thanks to a Python interface.

Each component is fully characterized by the parameters it takes in inputs and the values it returns on completion of its task (outputs). The actual complexity of the task performed by a component is not bounded and can vary from single mathematical operations (e.g., a "+" node takes two parameters and returns a single value or "soil potential" node in figure 1) to more integrated/computationally intensive/complex models (an FSPM node may take a single "age" parameter and return the structure of the plant). It does not have to be deterministic and the same inputs can produce a different output each time the component is evaluated.

While Python does not require typed arguments, interfaces have been proposed to characterize inputs and outputs to simplify the connection between heterogeneous components. The most common (number, date, colour ...) are already registered to increase the standardization between models but the user can easily declare their own interface that they want to expose to the community(e.g. "tree" to represent the topological structure of a plant or "grid" to represent a voxelisation of space). with Figure 1. Simple "soil water potential" component as defined in [START_REF] Smettem | The relation between soil water retention and particle size distribution parameters for some predominantly sandy Western Australian soils[END_REF].

Chopard et al., Scientific workflow for reusing plant/FSPM models

Dataflow descriptionconnection between components

In OpenAlea, complex models are built as an assemblage of multiple components performing atomic tasks. This workflow explicit the steps needed to compute the final result from the evaluation of each component. It uses a dataflow to keep track of the connections between each component. When one output of a component A is connected to an input of another component B, the value computed by A is used as an input by B. Hence, the dataflow is an acyclic graph where components play the part of nodes and each edge (line between two nodes) represents a transfer of information between an output of one component and the input of another. Different cases occur depending on how nodes are connected (see figure 2):

 Nodes whose entries are not connected to anything must take default values for these inputs;  Multiple inputs of nodes N 1 , ..., N n connected from the same output of node A share a reference on the object actually produced by A. This can lead to unwanted side effects since evaluation order is not guaranteed between nodes connected to the same input (i.e. if node N 1 modifies the object, the object seen by node N i is not the one produced by A but the one already modified).  If multiple outputs from nodes N 1 , …, N n are connected to the same input of node A, the actual value input in A is the list of the n values ordered according to the spatial position of the nodes (V i will be before V j in the list if node N i is horizontally on the left of node N j ).

From an evaluation point of view, a dataflow is also a component. Its inputs correspond to the list of nonconnected inputs and its output corresponds to the list of non-connected outputs. Hence, an assemblage of simple components to perform a task can be reused as a component in a more complex model. This multiscale approach simplifies the design of complex models by grouping components together and reducing the total number of components at each scale (e.g., a complex model of plant growth can be decomposed into three high-levels components soil/plant/atmosphere. Each high-level component is also an assemblage of smaller scale components. E.g. the "plant" growth component can incorporate photosynthesis/water transport/carbon storage … and so forth).

The inside of a component is opaque and the actual code is not required for evaluation (see below). That way, a component can easily be replaced by another component that exposes the same inputs/outputs. For instance in the previous example, a first model may be made with the three components soil/plant/atmosphere where each component is a single equation/function. Later on, the user can choose to replace the plant component by a more complex dataflow that exposes the same inputs/outputs but provides a more detailed modeling.

Evaluation of a dataflow

A dataflow represents the structure of a model. It makes explicit the connections between the different components of the model. However, the actual evaluation of the dataflow to produce the result of the model is not constrained. Its evaluation strongly depends on the semantics associated with components. It is important to disaggregate models into components with the appropriate granularity. That way, components may be reused in other projects to answer different scientific questions.

The evaluation offered by the platform is a two pass process. In the first pass, the dataflow is walked from the bottom leaves (nodes without outputs or whose outputs are not connected) to the top nodes (nodes without inputs or whose inputs are not connected). The first pass orders the evaluation of all the components to ensure that a component is evaluated only when all the components connected to its inputs have been evaluated. The second pass actually performs the evaluation of each node. For each node, the values of its inputs are retrieved from the outputs of the nodes connected to it. Then, the component performs its task and the result is stored in the output of the node.

This simple raw evaluation can be improved by introducing the concept of laziness in the network. With this approach, in the second pass of the algorithm, a node is evaluated only if it is tagged as non-lazy or if the values of its inputs have changed since the last evaluation. This approach saves resources by computing only the parts of the dataflow that need to be recomputed between two evaluations and provide a framework to perform temporal simulations through scheduling. If a node A implements a task and is tagged non-lazy once every two evaluations of the dataflow whereas a node B implements a task permanently non-lazy, continuous evaluations of the dataflow will result in task B being performed twice as much as task A (see figure 3).

VisuAlea

VisuAlea provides a visual programming environment allowing users to dynamically combine existing and independent pieces of software into a customizable dataflow. This environment offers several interesting points for managing scientific workflows. First, users have access to the program state at run-time and thus can introspect, modify, document and debug their workflows interactively. Second, users can build graphically complex workflows by interactively assembling OpenAlea components. Finally, the visualisation of the workflow"s structure eases the communication between different users.

The definition of types for inputs and outputs allows the system to automatically propose a graphical user interface for each component. Alternatively, the developer that creates a new component can also design a more suitable GUI and attach it to the node, overriding the default behaviour.

EXAMPLE OF USE (AND REUSE)

In this section we will make use of a single component presented above (figure 1) that embeds a very simple mathematical function relating the potential of water in the soil to its water content as defined in [START_REF] Smettem | The relation between soil water retention and particle size distribution parameters for some predominantly sandy Western Australian soils[END_REF]. Hence, the five inputs of the component are:

 a number (theta) that describes volumetric water content in the soil;  four numbers (theta_s, theta_r, n and alpha) that describe the structure of soil pores.

The last four parameters that describe the structure of the soil are not expected to vary through time. The unique output is a number that represents the water potential for this specific soil and water content.

Simple straight evaluation

Evaluating an atomic dataflow with only this node will make use of the default parameters stored in the node to estimate the value of water potential. This value is readily accessible by connecting a "print" node to the output of the node.

A more interesting behavior is obtained by connecting a "range" node to the first input of our component.

Since this node produces a new value in the given range each evaluation, we can readily compute the set of values taken by the water potential in the soil over the range of expected conditions. These values, stacked together to produce an array, are easily displayed using one of the standard graphical libraries.

This node can also be part of a more complex decision model that determines a water management decision.

A first model provides the soil water content throughout time whereas the decision model is a simple switch that decides to water the plants as soon as the soil water potential drops below a given threshold. Hence, our small component is used as an interface to match the output of the earth model with the input of the decision model.

Figure 5. This workflow evaluates the soil water potential (soil pot node) over a range of linearly spaced parameters values (linspace node), stacks the values together (map node) and plots the resulting curve (PyLabPlot node). Inputs and outputs are color coded according to the type of interface they declare. Hence, most of the time an input and an output connected together will have the same color.

Figure 6. "Soil pot" node is used as an interface to adapt the output of the earth model to the input of the decision model

Reuse in a dynamic FSPM

The scheduling evaluation of the dataflow is another great opportunity to reuse this simple component in a more complex FSPM of plant water use. Traditionally that kind of simulation implies a loop, where the state of the plant at time t is used to compute the state of the plant at time t+1. Water content in the soil and the atmospheric conditions drive water uptake by the plant which in turn modifies water content in the soil. However, since a dataflow is acyclic, we cannot connect the output of a node to the input of another node which is "above" it (see figure 2). One solution is to identify the state variable of the problem and store them independently from the dataflow in a datapool. Hence, a node that modifies the value of a state variable can store it back and this new value will be used for the next evaluation of the dataflow (see the time part of the example below for a small such loop, figure 7).

Our example plant model depends on two state variables:

 The actual time is provided by a node that increases the current time by a fixed delay at each evaluation of the dataflow;  The water content in the soil is modified by the soil node internally. Other components of the models use these state variables to compute physiological processes.

CONCLUSIONS

Throughout this article we have described the method for developing new models in a modular way by assembling components together. The advantages of this approach are:  It reduces the development time by reusing components developed by others;  It offers an incremental approach in developing the complexity of the model: simple components can be replaced by more complex ones;  It explicit the structure of the model. Each task is a separate component that can be developed and tested individually. When assembled together afterward, connections between tasks explicit visually their dependency; The OpenAlea platform has been created with these goals in mind and offers a scientific environment to exchange pieces of models. This exchange is facilitated by the different levels of accessibility to the models:

 developers can develop new components in their favorite language;  advanced users can use the Python language to write scripts to interact with models in new ways;  final users can use VisuAlea to connect components through a graphical user interface without writing a single line of code. The platform addresses the traditional difficulties associated with computer programming (compilation, installation...) on multiple environments. The users can then focus on implementing their models and share them with the community.

Figure 2 .

 2 Figure 2. Dataflow examples: a) simple linear dataflow, b) multiple inputs on one output, c) multiple outputs in one input and d) circular connections (not a proper dataflow)

Figure 3 .

 3 Figure 3. Scheduling, node A is tagged non-lazy every 2 evaluations whereas node B is tagged non lazy every evaluation. The result of continuous evaluation of the dataflow is displayed on the right.

Figure 4 .

 4 Figure 4. Screenshot of the main VisuAlea user interface used to connect components together and evaluate the scientific workflow. The package manager lists all the components available. The datapool keeps track of state variables. Widgets are attached to the dataflow to display results or query user inputs. A Python interpreter provides a way to introspect the current workflow.

Figure 7 .

 7 Figure 7. Dynamic model of water transfer in soil, plant and atmosphere. Green nodes stand for the two state variables of the simulation: time and soil water content. Pink nodes in the center are the three main processes.