Rousseau V Irstea
email: bernard.benet@irstea.fr

Development of an artificial vision progressive local method for auto tracking of vine rows

Keywords: Image processing, vines, culture row tracking

For automated maintenance operations for vines, a vision system, with one embedded camera on a mobile robot, was developed in order to detect and locate, in real time, vine trunks and to achieve automatic vehicle guidance close to the crop rows, to perform the task. The image processing algorithm developed consisted of a series of operations. Some of them include variable parameters depending on the research area in the images. The applied operations are classification techniques to separate vine trunks from other elements (ground, grass or leave), a mathematical morphology method, a labeling operation and a computation of geometric parameters on identified objects. To identify vine trunks, geometric models that characterize this element were used. A linear regression was finally applied to determine the vine trunks line. The success rate obtained with this vision method, working in RGB or HIS space, to detect the vines, was 90%, on a bank of 300 images acquired at different vegetation stages, with some perturbations like variable lighting conditions and shadow presence.

Introduction

Autonomous robotic system development, used for maintenance operations such as weeding, hold promise toward the automation of one of agriculture's few remaining non-mechanized tasks, such as hand weed control (Slaughter, D.C., 2007). Robotic technology, with the use of different sensors such as GPS, cameras or lasers to detect and locate weeds could reduce dependency on herbicides, improving sustainability and reducing environmental impact. Various automatic systems have demonstrated the potential of this technology in the field, for various crops. Automated guidance of the machinery not only reduces operator fatigue but also increases both the productivity and safety of the operation. An essential aspect of automatic guidance is the ability to identify the pathway between the crop rows. Research activities concerning automatic guidance of agricultural vehicles have led to various solutions (Keicher & Seufer, 2000). Sensors, including mechanical ones, global navigation satellite systems, machine vision, laser triangulation, ultrasonic, can generate position, attitude and direction-ofmovement information as input to control algorithms. A review of research in agricultural vehicle guidance technologies was presented in Ming et al (2009), with the analysis of guidance system characteristics. GPS and machine vision fused together, and also with others technologies, are the trend for agricultural vehicle guidance systems. In a vision-based automatic guidance system for row-crop applications, finding guidance information such as known crop row spacing is a solution for achieving accurate control of the vehicle and improvement of measurement accuracy (Han et al, 2004). Current production navigation systems for agricultural vehicles rely on GPS as the primary sensor for steering control. However, for different crops, like for example citrus groves, the tree canopy frequently blocks the satellite signals to the GPS receiver. In these conditions, an alternative method using a vision system and a laser radar has been developed to navigate a tractor [START_REF] Subramanian | Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation[END_REF]. As most crops are cultivated in rows, an important step towards this long-term goal is the development of a rowrecognition system, which allows a robot to accurately follow a row of plants. As examples of guidance devices using artificial vision, [START_REF] Bakker | A vision based row detection system for sugar beet[END_REF] and Astrand & Baerveldt (2004), used methods for recognition of plant rows based on the Hough transform, tested them on sugar beets and rape. Bellingsley & Schoenfisch (1995) proposed a guidance device to follow rows of cotton crops. Vision guidance systems using a stereovision device have also been developed (Kise et al, 2005) to get information on a 3D environment. Supervised guidance systems have also been developed for positioning mechanical or thermal tools correctly near crops [START_REF] Peruzzi | An autonomous ground mobile unit for the precision physical weed Control -Presented at CIGR-AgEng 2012[END_REF]. For vision guidance operations, the acquired images are sensitive to vehicle vibrations. Studies have also been undertaken to improve detection of crop rows, by vision, taking into account the perturbations caused by some unwanted movements of tractor with an embedded camera [START_REF] Ruiz-Ruiz | Camera kinetic model applied to crop row tracking and weed detection -Presented at CIGR-AgEng[END_REF] and [START_REF] Sainz-Costa | An empirical study on the viability of the application of SIFT algorithm to video stabilization in crop fields -Presented[END_REF]. This paper proposes a method to detect and locate vine trunks, by vision, after discriminating the elements vine trunk, ground, leaf and grass, on images, to obtain finally the vine row, in real time. From the vision information, a lateral deviation computation between the vehicle and the row is done, and maintenance tasks near trunk vines can be carried out. The work was realized in a relative mode using a camera, instead of working in an absolute mode with a GPS. The aim was to develop a system, without learning path for guidance, which can adapt to various grounds and vegetation stages and which takes into account variable sizes of vine trunks in the images and lighting variations.

Materials and methods

The vehicle used for carrying the vision sensor which performed data acquisition and image processing operations in vines is a mobile robot with an embedded color camera (resolution 1280 x 720) (Figure 1). The images were acquired on vines corresponding to two vegetation stages (with (a) and without (b) leaves). The image processing algorithm developed to detect and locate vine trunks, consisted of a series of operations. Some of them have variable parameters according to the research area in the images. The goal was to locate vine trunks, especially their lowest point on the images. The image spaces tested were RGB (Red Green Blue) and HIS (Hue Intensity Saturation). Figure 2 shows images corresponding to HIS space, with the computation formula between RGB and HIS spaces.

H = acos ( F / V ) F = 0,5 . (R -G) + (R -B ) V= ((R-G) 2 + (R-B).(G-B)) 0,5 I = ( R + G + B ) / 3 S = 1 -( min (R, G, B) ) / I Figure 2. Vine images in HIS space.
Texture parameters such as contrast and energy were also tested to discriminate the vine trunks from the other three classes, with a threshold operation. For each pixel, the texture parameters were computed in a window (3-3) around the pixel.

Contrast: C = Energy: E = (1) p(i,j) is the pixel value in I space at position (i,j) on the image Firstly, in the vision algorithm, an object classification method, like SVM (Support Vector Machine) or ANN (Artificial Neural Network), was applied to discriminate the vine trunk class from the three classes, grass, ground and leaf in the vine environment. For the SVM method [START_REF] Hyeran | Applications of Support Vector Machines for Pattern Recognition:A Survey[END_REF], the principle is to separate two classes (Figure 3).

The distance between two hyperplanes is

The objective is to minimize w The following constraint, is added for each pixel i:

y i = +1, w . x i -b ≥ 1 if x i ∈ first class y i = -1, w . x i -b ≤ -1 if x i ∈ second class
This can be rewritten as:

y i (w . x i -b) ≥ 1 for 1 ≤ i ≤ n
Figure 3. SVM method to discriminate two objects in an image.

The principle of the neural network method [START_REF] Krenker | Introduction to the Artificial Neural Networks -Faculty of Electrical Engineering[END_REF], with the different equations, is presented in Figure 4. First, a learning operation was carried out considering color samples of the four objects, minimizing an error calculation and computing the weights of the different layers, to create the network. The input data were RGB or HIS components for each pixel; the output values were one index value (1, 2, 3 and 4) to identify each of the four clases (vine trunk, grass, ground, leaf). Different hidden layer numbers were defined, to obtain the best network as possible.

Then, the test operation consisted of determining the class of all pixels in the vine images.

Results

Classification method for vine environment For classification, SVM and ANN methods, in RGB and HIS color spaces, gave correct results to separate the vines from others elements (grass, ground, leaf) considering both vegetation stages (Figure 6). Discrimination was not reliable in the texture parameters space tested. In the application, it was noticed that the SVM method was faster than the ANN method. However, there is always a classification result, with the ANN method, even if there is not an important difference between pixel bases, whereas the SVM method can sometimes diverge, if the difference between pixel classes is not very significant.

Figure 6. Pixel bases in RGB space used to discriminate the four classes.

Detection of vines row taking into account the research area of each vine individually

A result example of the different image processing operations is presented in Figure 7 (a -d), for one global image. The geometric data of detected objects (vine trunks and noise (other elements)) are shown in Table 1. In the developed local detection method, the classification operation was undertaken once but the morphology, labelisation and identification operations were carried out at each step, with different parameter sets depending on the research area (Table 2). These geometric parameters are the following: Opening M (Mathematical Morphology), Surface A (pixel number for each object), Elongation E (ratio between major axis length and minor axis length Table 1. Geometric data of detected objects (vine trunks and other elements).

- This study was conducted in order to work in variable lighting conditions in the agricultural environment, for detecting vines automatically, with the application of an automatic classification for the four pixel classes, if necessary. Working with fixed permanent bases, some vine trunks could not be detected, either in RGB or HIS space, for high-lighting variations. To remedy this light problem, another (and faster) solution would be to perform several manual classifications corresponding to different light levels, and then choose, in real time, the best one, according to the light level of the environment to detect the vines. To improve the accuracy on vine line detection results, further research will consist of using a lidar sensor, complementary to the camera, to locate, in a fusion mode, vine trunks. It will be also interesting to modify the acquisition conditions (zooming, centering trunks on images), using for example a pan-tilt-zoom camera, to detect some small vine trunks and increase vine trunk number in images. This vine line detection application will be tested on others cultures, to test its robustness.

------------------------------------------------------------------------------------------------
3.3 1.3 3.3 3.1 ------------------------------------------------------------------------------------------------- Table 2.

Conclusion

The vision method developed consisted of searching vine trunks one after the other, automatically modifying at each step, some parameters in the algorithm, in order to detect the most as possible vine trunks in images. If there was a significant lighting variation between a reference image and one captured image, then, before applying the vision algorithm, the database points for the four classes (vine trunk, grass, leaf and ground) were modified to achieve an automatic classification phase and the pixels in the image were also modified, in HIS space. The success rate was 90%, working on a bank of 300 images. The errors obtained in the detection are due to two main reasons: the shadow problems near the vines and large color variation between different vine trunks but also within the same element. From the vine row information, referenced-vision guidance operations are applied to control a vehicle which follows the vine lines and carries out maintenance operations. For classification, SVM and ANN methods, in RGB and HIS color spaces, separated with accuracy the vine trunks from others elements (grass, ground and leaf).

  Mobile robot with a color camera and vines.

Figure 4 .

 4 Figure 4. Neural network building principle to separate some classes in images.

Figure 5 .

 5 Figure 5. Image processing algorithm to detect vine trunks and obtain vine row.

Figure 7 .

 7 Figure 7. Image processing operations to identify vine trunks.

  Figure 8. Vine trunk detection and determination of the vine line.


Laura Gabriela Nisembaum

Laurence Besseau

Charles-Hubert Paulin

Alice Charpantier

Patrick Martin

Elodie Magnanou

Michael Fuentès

Maria-Jesus Jesus Delgado

Jack Falcon
email: falcon@obs-banyuls.fr.

UMR7232 Jack Falcón

In the Heat of the Night: Thermo-TRPV Channels in the Salmonid Pineal Photoreceptors and Modulation of Melatonin Secretion

Keywords: TRPV modulation of melatonin secretion Fish, Pineal, Photoreceptor, Melatonin, TRPV1, TRPV4, Temperature

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

I N T R O D U C T I O N

The pineal photoreceptor cells of ectothermic vertebrates transduce light and temperature information into a nervous and a neurohormonal output. The former, aspartate and/or glutamate, is released in the dark at the synaptic contacts with the pinealofugal neurons; the neurons then convey rapid (msec) electric information to the brain [START_REF] Ekström | The pineal organ of teleost fishes[END_REF]2). The latter, melatonin, is produced at night and this 24h rhythm is an essential component of the fish time keeping system [START_REF] Falcón | Molecular and cellular regulation of pineal organ responses[END_REF][START_REF] Falcón | Current knowledge on the melatonin system in teleost fish[END_REF]. The duration of the melatonin surge is dictated by photoperiod while temperature controls the amplitude [START_REF] Falcón | Molecular and cellular regulation of pineal organ responses[END_REF][START_REF] Falcón | Cellular circadian clocks in the pineal[END_REF]. This melatonin rhythm contributes to synchronizing metabolism, physiology and behavior to the daily and annual variations of the environment (4). While the impact of photoperiod on the pineal outputs is relatively well understood, virtually nothing is known on how temperature operates. More generally, little progress has been made in the last decades concerning temperature regulation and transduction of the temperature information in ectothermic vertebrates [START_REF] Bicego | Physiology of temperature regulation: Comparative aspects[END_REF][START_REF] Boulant | Temperature receptors in the central nervous system[END_REF][START_REF] Crawshaw | Temperature regulation in vertebrates[END_REF]. Temperature sensitive neurons have been identified in the lateral line [START_REF] Rubin | Thermal reception in fishes[END_REF], spinal cord [START_REF] Iriki | Effects of thermal stimulation to spinal-cord on heartrate in cyprinid fishes[END_REF][START_REF] Nagai | Body color changes induced by spinal thermal stimulation of crucian carp (Carassius carassius)[END_REF], and in the epithalamus and preoptic area of the brain [START_REF] Greer | Temperature sensitive neurons in the brain of brook trout[END_REF][START_REF] Greer | Characterization of responses from temperature-sensitive units in trout brain[END_REF]; the latter also receives thermal input from other unidentified photosensitive structures.

Interestingly, the pineal organ of fish has been involved in behavioral thermoregulation mediated by both a rapid (nervous) and a slow (hormonal) mechanism [START_REF] Kavaliers | Effects of pineal shielding on the thermoregulatory behavior of the white sucker Catostomus commersoni[END_REF][START_REF] Kavaliers | Pineal mediation of the thermoregulatory and behavioral activating effects of endorphin[END_REF][START_REF] Kavaliers | Pineal involvement in the control of behavioral thermoregulation of the white sucker, Catostomus commersoni[END_REF]. And, it has been established that both the nervous [START_REF] Yañez | Pineal projections in the zebrafish (Danio Rerio): Overlap with retinal and cerebellar projections[END_REF] and neurohormonal [START_REF] Herrera-Perez | Melatonin receptors in the brain of the European sea bass: An in situ hybridization and autoradiographic study[END_REF] information released by the organ reach the preoptic area, a major neuroendocrine center. Altogether, although clear direct evidence is still missing, there is strong support that the fish pineal organ is a photo-thermo-sensor. The questions then rise to know how and where is the temperature information captured and transduced within the organ?

A number of observations suggested to us that the pineal cone-like photoreceptor is also a thermoreceptor. (i) The nocturnal rise in melatonin production is a calcium-and cAMP-dependent process [START_REF] Falcón | Molecular and cellular regulation of pineal organ responses[END_REF][START_REF] Bégay | Calcium and melatonin production in dissociated trout pineal photoreceptor cells in culture[END_REF][START_REF] Bégay | Calciproteins regulate cyclic AMP content and melatonin secretion in trout pineal photoreceptors[END_REF][START_REF] Meissl | Regulation of melatonin production and intracellular calcium concentrations in the trout pineal organ[END_REF]: photoreceptor depolarization in the dark induces the opening of voltage-gated Ca 2+ channels (VGCC); the subsequent increase in [Ca 2+ ] i activates both the synthesis of cAMP and the activity of the arylalkylamine N-acetyltransferase (AANAT; EC: 2.3.1.87), the first step in the conversion of serotonin into melatonin [START_REF] Klein | The melatonin rhythmgenerating enzyme: molecular regulation of serotonin N-acetyltransferase in the pineal gland[END_REF]. This dose-dependent process is reversed upon illumination [START_REF] Falcón | Molecular and cellular regulation of pineal organ responses[END_REF]. (ii) A similar involvement of Ca 2+ and VGCC in the release of the neurotransmitter is also suspected, as is the case in the retinal cone [START_REF] Schmitz | Dependence of photoreceptor glutamate release on a dihydropyridinesensitive calcium channel[END_REF]. (iii) In cultured pineal organs, both cAMP production and AANAT activity display the same pattern in response to temperature; this is a speciesdependent process [START_REF] Falcón | Cellular circadian clocks in the pineal[END_REF]. It is also a cellular-mediated process because the response of AANAT activity to temperature variation differs if measured on the native (i.e., in situ) or recombinant enzymes [START_REF] Cazaméa-Catalan | Unique arylalkylamine N-acetyltransferase-2 polymorphism in salmonids and profound variations in thermal stability and catalytic efficiency conferred by two residues[END_REF][START_REF] Cazaméa-Catalan | Functional diversity of Teleost arylalkylamine N-acetyltransferase-2: is the timezyme evolution driven by habitat temperature?[END_REF]. (iv) In mammals, temperature sensing is mediated by members of the Transient Receptor Potential (TRP) channels family (TRPA1, TRPV1-4 and TRPM8), which are permeable to Ca 2+ [START_REF] Kauer | Hot flash: TRPV channels in the brain[END_REF][START_REF] Patapoutian | Thermo TRP channels and beyond: Mechanisms of temperature sensation[END_REF], and activate at different ranges of temperature [START_REF] Patapoutian | Transient receptor potential channels: targeting pain at the source[END_REF].

Altogether, it is not unreasonable to hypothesize that the fish cone-like photoreceptor cell is also a thermo-receptor utilizing both VGCC and TRPs to modulate [Ca 2+ ] i and, consequently, to control the nocturnal production of melatonin and the secretion of the excitatory neurotransmitter; in other words, one intracellular second messenger for two external cues and two outputs. In a first attempt to tackle this question we report here the cloning of two TRP channels (TRPV1 and TRPV4) in rainbow trout. We describe their tissue distribution and localization and bring the first evidence that TRPV are expressed in the retinal and pineal photoreceptors and that they contribute to controlling pineal melatonin secretion at night. Pharmacological experiments suggest that at least TRPV1 channels are involved in thermo-sensing.

M A T E R I A L S A N D M E T H O D S

Animals

Two years-old rainbow trout, Oncorhynchus mykiss, were provided by "Les Viviers Cathares" (Chalabre, France). Animals were sacrificed at the hatchery by electrocution and the organs of interest were dissected out and placed either in ice-cold fixative, RNA later® or culture medium. All experiments were performed according to the European Union regulations (European directive 91/492/CCE).

Pineal organs culture

The organs were cultured as indicated in the Supplemental Materials and Methods section under conditions of photoperiod and temperature similar to those at which the animals were acclimated before sacrifice; medium was replaced once after 24 h of culture. The impact of TRP agonists and antagonists was investigated after 48 h of culture. For this purpose, the glands were placed in the dark for 6 h (starting at 09:00 of the LD cycle) in the absence or presence of the compounds tested (concentrations are given in the Results section and figures). Controls included organs cultured in the presence of vehicle and placed either in the dark or maintained under light. At the end of the experiments, glands and media were then collected separately and stored at -80 °C.

Melatonin quantification

Melatonin in the culture medium was quantified using High Performance Liquid Chromatography (HPLC) or Enzyme-Linked Immuno-Sorbent Assay (ELISA) as indicated in the Supplemental Materials and Methods section.

RNA extraction, cDNA synthesis and sequences analysis

See Supplemental Materials and Methods.

Reverse transcription (RT)-PCR, cloning of TRPV1 and TRPV4, real time quantitative (q)PCR

The TRPV1 and TRPV4 sequences were obtained by PCR using degenerated primers (Supplemental Table 1). The PCR and qPCR conditions are detailed in Supplemental Materials and Methods.

In situ hybridization

Tissues were fixed in 4% paraformaldehyde in 0.2 M Sorensen buffer, prepared and processed as detailed [START_REF] Besseau | Melatonin pathway: breaking the 'high-at-night' rule in trout retina[END_REF]. The TRPV1 and TRPV4 riboprobes were generated from pituitary extracts and primers as indicated in Supplemental table 1.

Statistics and drawings

Statistical analyses and plotting were performed using Prism.v6 (GraphPad TM Software Inc., San Diego, CA). The pharmacological and qPCR data were analyzed using the one-way or two-ways ANOVA followed by a post-hoc Tukey's comparison of means, or by the unpaired Students t-test.

Compounds and chemicals

See Supplemental Materials and Methods.

R E S U L T S

Cloning of TRPV1 and TRPV4

O. mykiss TRPV1 and TRPV4 were cloned from pineal extracts. The TRPV1 mRNA sequence was 2807 bp long (GenBank Acc # KJ135121); the corresponding coding region was 2415 bp long and encoded a 805 aa sequence that displayed high identity with the TRPV1 sequences from S. salar (96%), and the predicted TRPV1 sequence from Esox lucius (80%) and lower identity with those from other fish (~60% with O. niloticus and D. rerio) and tetrapods (<53%) (Supplemental Fig. 1). The TRPV4 mRNA sequence was 3035 bp long (GenBank Acc # KJ135122); the corresponding ORF (2637 bp) encoded a 879 aa sequence which shared 98% identity with S. salar TRPV4, 89% with D. labrax TRPV4, 79% with D. rerio and O. mossambicus TRPV4 and 70-75% with tetrapods TRPV4 (Supplemental Fig. 1). The InterProScan search for conserved domains confirmed that trout TRPV1 and TRPV4 belong to the superfamily of TRPV channels, possessing the typical ankyrin repeats, the calcium moiety and the 6 transmembrane domains (Supplemental Fig. 1). In addition, aa residues known to be important for the function of the rat TRPV1 were also identified in the fish sequences, as discussed later and Supplemental Fig. 1. Finally, the phylogenetic reconstruction indicated TRPV1 and TRPV4 grouped with their respective orthologues (not shown).

Tissue distribution and relative expression of TRPV1 and TRPV4

TRPV1 and TRPV4 distribution was ubiquitous but the levels of expression varied from an organ to another (Fig. 1). TRPV1 expression in intestine and kidney was a two-fold higher than in the retina, brain, pineal organ, spleen, heart and blood cells. The pituitary gland, skin and liver had the lowest levels of expression. The highest expression of TRPV4 was found in extracts from the pineal organ, kidney, intestine and heart, while in extracts from all other nervous and peripheral tissues investigated the levels were a four-fold lower (Fig. 1).

Cellular localization of TRPV1 and TRPV4 in the pineal organ and retina

In the pineal organ, both TRPV1 and TRPV4 mRNA were specifically localized in the photoreceptor cells, identified by their typical shape and localization in the pineal epithelium (Fig. 2).

Similarly, expression was also detected in the outer nuclear layer (ONL) of the retina (Fig. 3a,c). Differences were however observed in other layers. TRPV1 expression was also seen in the basal part of the inner nuclear layer (INL) as well as in the ganglion cell layer (GCL) and the corresponding axons that make the optic nerve (Fig. 3b). TRPV4 expression was seen in the central and basal parts of the INL; it was detected neither in the GCL nor in the optic nerve (Fig. 3c).

Impact of temperature on melatonin secretion

Before investigating the effects of TRPV analogs in vitro, it was necessary to know what the best conditions for culturing the pineal organs are. For this purpose, pineal glands were cultured at different times and different temperatures in the dark as indicated in figure 4. The highest levels of secretion were found after incubation times of 6 and 12 h with peak values at temperatures between 15 and 20°C (Fig. 4A). It is noteworthy that at the long incubation times (6 and 12 h) melatonin secretion increased from 0 to 5°C, then marked a plateau between 5 and 10°C, and rose again from 10 to 15°C.

Figure 4B shows the release of melatonin as a function of time and temperature expressed as a percent of the value found after 1 h of incubation. The different profiles obtained indicate that melatonin secretion increased continuously up to 12 h of culture at 0°C, while between 5 and 20°C it increased up to 6 h and then initiated a decrease. Conversely at temperatures above 20/25°C melatonin secretion decreased continuously, either progressively (25°C) or dramatically (30 and 35°C).

Impact of TRPV agonists and antagonists on pineal melatonin

In view of the above results we decided to investigate the effects of the TRPV analogs at the temperature at which the animals were adapted in their natural habitat at the time the experiments were done (i.e., between 10 and 20°C) and for a 6 h incubation. Under these conditions, capsaicin, a TRPV1 agonist, induced a dose-dependent effect on melatonin release, stimulatory at low, and inhibitory at high, concentrations; both effects were neutralized in the presence of capsazepine, a TRPV1 antagonist, (1 µM; Fig. 5a). No effect was detected with the agonist 4--phorbol-12,13-didecanoate (4PDD) alone (Fig. 5b). This suggested to us that the TRPV4 channel expressed in the photoreceptor cells (Fig. 2), did not respond to the agonist challenge or were already maximally active. The later explanation remains possible because in the presence of ruthenium red (RuR; 10 µM), a TRPV4 blocker, 4PDD induced inhibition at low concentrations and stimulation at high concentrations (Fig. 5b).

The later experiment and the role devoted to TRPV channels in mammals (see introduction) suggested to us that TRPV1 and TRPV4 channels might be under different states of activity depending on the ambient temperature. To investigate this point, pineal glands were cultured at different temperatures and in the absence or presence of either 1 µM for capsazepine, the TRPV1 antagonist, or 10 µM gadolinium, the TRPV4 antagonist. Figure 6 shows the data obtained from two independent experiments performed in spring. We found that in the presence of the TRPV1 antagonist melatonin secretion was ~40 % lower at 16°C and ~20% lower at 20°C, compared to controls. The results were however significant only at 16°C. The TRPV4 antagonist gadolinium was found to inhibit melatonin secretion at 8°C by ~30% (Fig. 6); the effect was however not statistically significant.

Impact of temperature and TRPV agonists on the pineal expression of TRPV channels in vitro

We took advantage of the previous experimental set up to investigate whether temperature and the TRPV antagonists used had an impact on the TRPV1 and TRPV4 gene expression. We found that neither the temperature of incubation nor the pharmacological treatments affected significantly the amount of TRPV1 and TRPV4 mRNA (not shown). However, we found that TRPV1 and TRPV4 mRNA expression varied with the month at which the experiments were performed, i.e., March and June (in which water temperature was 8°C and 15°C, respectively; Fig. 7).

D I S C U S S I O N

The present study adds to the very short list of TRPV sequences available in fish. Our analyses of the cloned sequences confirmed that they belong to the TRPV1 and TRPV4 families. Both displayed a ubiquitous distribution, as described for mammals [START_REF] Venkatachalam | TRP channels[END_REF], zebrafish and sea bass [START_REF] Bossus | Transient receptor potential vanilloid 4 in the European sea bass Dicentrarchus labrax: A candidate protein for osmosensing[END_REF][START_REF] Gau | The zebrafish ortholog of TRPV1 isrequired for heat-induced locomotion[END_REF].

Whether the TRP channels have the same function in all the tissues where they are expressed remains an open question. One main function of TRPV1 and TRPV4 is to transduce thermal stimuli [START_REF] Patapoutian | Transient receptor potential channels: targeting pain at the source[END_REF][START_REF] Baez | Gating of thermally activated channels[END_REF]; but they may also be sensitive to chemical, mechanic and ionic stimuli [START_REF] Bossus | Transient receptor potential vanilloid 4 in the European sea bass Dicentrarchus labrax: A candidate protein for osmosensing[END_REF][START_REF] Fowler | Drosophila TRP channels and animal behavior[END_REF][START_REF] Seebacher | Responses to temperature variation: integration of thermoregulation and metabolism in vertebrates[END_REF]. It is thus possible that the quantitative differences observed between the organs and tissues where they are expressed reflect specific requirements to either one or several of these stimuli (see [START_REF] Bossus | Transient receptor potential vanilloid 4 in the European sea bass Dicentrarchus labrax: A candidate protein for osmosensing[END_REF][START_REF] Amato | TRPV4 in the sensory organs of adult zebrafish[END_REF] for extensive discussion). It is interesting that the kidney and intestine exhibited high TRPV mRNA levels. A previous investigation in the sea bass also identified a TRPV4-like compound by immunocytochemistry in the kidney and intestine [START_REF] Bossus | Transient receptor potential vanilloid 4 in the European sea bass Dicentrarchus labrax: A candidate protein for osmosensing[END_REF]. The immunocytochemical labeling in the intestine was interpreted as non-specific; the present demonstration of the presence of mRNA encoding both TRPV1 and TRPV4 would indicate the channels are indeed present in this tissue.

The main focus of this study was to investigate the possible involvement of thermo-TRPs in the temperature-dependent control of melatonin production in trout. Two studies in the zebrafish indicated TRPV1 and TRPV4 are involved in temperature sensing [START_REF] Gau | The zebrafish ortholog of TRPV1 isrequired for heat-induced locomotion[END_REF][START_REF] Hunt | A novel zebrafish model of hyperthermiainduced seizures reveals a role for TRPV4 channels and NMDA-type glutamate receptors[END_REF]. It was therefore of interest that both thermo-TRPs were expressed in the pineal gland and retina, the two main sites where melatonin is produced. As emphasized in the introduction, the pineal organ deserves special attention because of its capacity to sense both light and temperature [START_REF] Falcón | Cellular circadian clocks in the pineal[END_REF], and because it is involved in behavioral thermoregulation [START_REF] Kavaliers | Pineal mediation of the thermoregulatory and behavioral activating effects of endorphin[END_REF][START_REF] Kavaliers | Pineal involvement in the control of behavioral thermoregulation of the white sucker, Catostomus commersoni[END_REF]. We bring here the first and unequivocal evidence that TRPV1 and TRPV4 are co-expressed exclusively in the melatonin producing cells of the pineal organ, i.e., the cone photoreceptors (5). This contrasts with previous studies in the zebrafish where pineal expression was reported neither for TRPV1 nor for TRPV4, although both were expressed in all other sensory organs [START_REF] Gau | The zebrafish ortholog of TRPV1 isrequired for heat-induced locomotion[END_REF][START_REF] Amato | TRPV4 in the sensory organs of adult zebrafish[END_REF]. It is noteworthy, however, that a TRPV1-like protein was immuno-detected in the rat pinealocyte [START_REF] Reuss | TRPing' synaptic ribbon function in the rat pineal gland: Neuroendocrine regulation involves the capsaicin receptor TRPV1[END_REF]. This cell type is a homologue of the fish pineal photoreceptor; it produces melatonin but has lost its direct photosensitive properties [START_REF] Falcón | Current knowledge on the melatonin system in teleost fish[END_REF]. The presence of TRPV1 in the melatonin producing cells of the fish and mammalian pineal glands appears thus as an ancestral character, shared by these homologous cells. In the mammalian pinealocyte the TRPV1-immuno-detected protein was associated with the synaptic ribbons, which function is enigmatic. Indeed, in contrast with the fish pineal photoreceptor the mammalian pinealocyte does not establish synaptic transmission sensu stricto; the gland has lost the second-order neurons, which in fish convey the light information to the brain [START_REF] Falcón | Current knowledge on the melatonin system in teleost fish[END_REF][START_REF] O'brien | Pineal and retinal relationships[END_REF].

Interestingly however, TRPV1 has also been observed in the synaptic ribbons of the retinal photoreceptors of the zebrafish and goldfish [START_REF] Zimov | Localization of vanilloid receptor 1 (TRPV1/VR1)-like immunoreactivity in goldfish and zebrafish retinas: Restriction to photoreceptor synaptic ribbons[END_REF]. It is well established that the fish pineal photoreceptors display structural and functional analogies with the vertebrates' retinal cones; both derive from a common diencephalic origin [START_REF] O'brien | Pineal and retinal relationships[END_REF]. For this reason we felt interesting to explore TRPV1 and TRPV4 sites of expression also in the retina. Our study confirms the presence of TRPV1 in the retinal photoreceptors and thus extends the already high number of analogies between the retinal and pineal photoreceptors. We also extend these analogies to TRPV4. The identification of TRPV4 in trout, contrasts with previous observations in the zebrafish retina [START_REF] Amato | TRPV4 in the sensory organs of adult zebrafish[END_REF][START_REF] Sanchez-Ramos | Expression of TRPV4 in the zebrafish retina during development[END_REF]. Perhaps these discrepancies result from the use of immunocytochemistry vs. in situ hybridization. The former is probably less specific particularly when it comes to detect a membrane bound protein, and with an antibody generated against TRP from other more distant species. The presence of TRPVs in trout pineal and retinal photoreceptors is consistent with the idea that TRPVs modulate the Ca 2+ -dependent release of the excitatory neurotransmitter at the ribbons synapses, as is the case in cerebral neurons [START_REF] Kauer | Hot flash: TRPV channels in the brain[END_REF][START_REF] Hunt | A novel zebrafish model of hyperthermiainduced seizures reveals a role for TRPV4 channels and NMDA-type glutamate receptors[END_REF]. We may speculate that the TRPVs expressed in the synaptic pedicles of the pineal photoreceptors are involved in the regulation of the described effects of temperature on the nervous discharges of the trout pineal neurons (2). We also found that TRPV1 and TRPV4 were expressed in the INL of the trout retina, as is the case in zebrafish [START_REF] Amato | TRPV4 in the sensory organs of adult zebrafish[END_REF][START_REF] Zimov | Vanilloid receptor 1 (TRPV1/VR1) co-localizes with fatty acid amide hydrolase (FAAH) in retinal amacrine cells[END_REF]. In contrast the GCL of trout expressed TRPV1 only, while the opposite was found in the zebrafish. It is beyond the scope of this work to speculate on the discrepancies and roles played by TRPV in the different layers of the retina that may be due to species-dependent characteristics, or to methodological and technical issues.

We had reasons to believe that TRPV1 and TRPV4 could be involved in the control of melatonin secretion in the pineal photoreceptor as detailed in the introduction. Also, the study in the mammalian pinealocyte indicated that the TRPV1 agonist capsaicin can stimulate melatonin release by cultured glands in vitro [START_REF] Reuss | TRPing' synaptic ribbon function in the rat pineal gland: Neuroendocrine regulation involves the capsaicin receptor TRPV1[END_REF]. We thus decided to investigate the impact of TRPV1 and TRPV4 agonists and antagonists on melatonin secretion by trout pineal glands in culture. As a first step we examined which where the best in vitro conditions to perform these experiments. We found that at temperatures above 20°C melatonin secretion decreased with the duration of the incubation, progressively at 25°C and dramatically at 30° and 35°C. Considering the aerobic scope of the trout, we believe that the survival of the organs was challenged at temperatures above 20°C. At 20° and below, melatonin secretion increased with time up to 6 h of incubation. After 6 h a decrease was observed except at 4°C; this probably reflects a feed-back inhibition of melatonin production by the increasing concentrations of melatonin released in the media as previously shown [START_REF] Bégay | Pineal photoreceptor cells in culture: Photoperiodic control of melatonin production after cell dissociation and culture[END_REF][START_REF] Yañez | Secretion of methoxyindoles from trout pineal organs in vitro: indication for a paracrine melatonin feedback[END_REF]. We concluded that a 6 h incubation in the dark, at temperatures ranging from 15 to 20°C, were the best conditions to investigate the effects of the TRPV analogs were. Under these conditions, it appeared unambiguously that the TRPV1 agonist capsaicin induced a bimodal effect on melatonin release that was suppressed in the presence of the TRPV1 antagonist capsazepine. In the rat the stimulation needed higher concentrations of capsaicin while no inhibition was seen [START_REF] Reuss | TRPing' synaptic ribbon function in the rat pineal gland: Neuroendocrine regulation involves the capsaicin receptor TRPV1[END_REF]. The increase in melatonin secretion observed at low capsaicin concentrations is consistent with the fact that Ca 2+ entry into the photoreceptor stimulates synthesis. The inhibition observed at higher concentrations of capsaicin might result from a feed-back inhibition by high melatonin levels, as commented above [START_REF] Bégay | Pineal photoreceptor cells in culture: Photoperiodic control of melatonin production after cell dissociation and culture[END_REF][START_REF] Yañez | Secretion of methoxyindoles from trout pineal organs in vitro: indication for a paracrine melatonin feedback[END_REF].

Alternatively, it might reflect an effect of the agonist at other sites; indeed, the pharmacological classification of TRP agonists and antagonists is based on studies in mammals only, with no warrantee that this classification applies to fish. The effect of capsaicin is however consistent with the observation that the trout TRPV1 sequence possesses a conserved aa residue that mediates the effects of capsaicin in the TRPV1 sequence of the rat [START_REF]TRP channels and pain[END_REF][START_REF] Jordt | Molecular basis for species-specific sensitivity to "hot" chili peppers[END_REF]. It is to note that the zebrafish TRPV1 sequence displays a mutated aa residue at the similar position, and the channel does not respond to capsaicin [START_REF] Gau | The zebrafish ortholog of TRPV1 isrequired for heat-induced locomotion[END_REF]. In contrast to TRPV1, the involvement of TRPV4 in the control of melatonin secretion was less clear because the TRPV4 agonist 4PDD had no visible effect at the concentrations tested. However, a dose-dependent inhibition of melatonin secretion was observed in the presence of the antagonist ruthenium red. Several possibilities non-mutually exclusive might explain these observations: (i) all or part of the channels were already activated stimulating melatonin secretion, and the addition of the antagonist counteracted these effects; (ii) 4PDD is not a TRPV4 agonist in trout while ruthenium red acts as an agonist (this situation is not unusual as drugs characterized in mammals may not have the same pharmacological profile in fish); (iii) the antagonist ruthenium red was acting at other TRP channels than TRPV4 as already observed [START_REF] Vriens | Pharmacology of vanilloid transient receptor potential cation channels[END_REF].

If the hypothesis is valid that the fish TRPV1 and TRPV4 channels are thermo-sensors, the use of antagonists of these channels alone should induce visible effects at some, but not all, temperatures. Indeed we found that the TRPV1 antagonist capsazepine had a significant impact on melatonin secretion at 16°C. This would suggest that the increase in melatonin secretion observed at 16°C was due in part to the activation of TRPV1 channels, which became inactive in the presence of capsazepine. A further support to the idea that trout TRPV1 is a temperature receptor comes from the analysis of the rat and trout sequences. Both possess conserved aa sequences in the pore and Cterminal regions of the channel [START_REF] Baez | Gating of thermally activated channels[END_REF][START_REF]TRP channels and pain[END_REF][START_REF] Kim | Single residues in the outer pore of TRPV1 and TRPV3 have temperature-dependent conformations[END_REF]; Supplemental Fig. 1). These include a tyrosine residue Y637, located in the pore region, which corresponding residue Y652 in rat is specifically required for temperature activation; this tyrosine is located close to other residues required for temperature and chemical activation [START_REF] Kim | Single residues in the outer pore of TRPV1 and TRPV3 have temperature-dependent conformations[END_REF]. It is also found in the zebrafish sequence (Y659). Interestingly, one study in the zebrafish indicated that TRPV1, which is required for normal heat-induced locomotion, activates at a temperature ≥ 25°C [START_REF] Gau | The zebrafish ortholog of TRPV1 isrequired for heat-induced locomotion[END_REF]. This is above the suspected range of temperature sensitivity found here in the trout, but below the activation threshold of the rat TRPV1 (42°C). It has been suspected that differences in the C-terminal tail length of TVPV1 are responsible for the observed differences in threshold activation of TRPV1 in vertebrates [START_REF] Brauchi | A hot-sensing cold receptor: Cterminal domain determines thermosensation in transient receptor potential channels[END_REF]. They may reflect specific adaption to the environmental requirements of the species considered.

Experiments aiming at investigating the impact of the TRPV4 antagonist gadolinium at different temperatures did not bring clear-cut information. Although not statistically significant, our data suggest a temperature activation at the low range of temperatures (± 8°C). The unequivocal demonstration that trout TRPV4 is activated in the cold range of temperatures needs further experimental support by investigating a narrower temperature interval and/or different times of the year. Indeed, the data presented in figure 6 are the mean of two independent experiments performed in March of two successive years (with an identical adaptation to an environmental temperature of 8°C).

Our observation that the relative abundance of TRPV1 and TRPV4 varied from March to June leads us to suspect possible annual variations and/or a possible effect of previous temperature history on their expression levels. Another possibility could be that the TRPV4 activation depends on both the ambient temperature and salinity as previously shown (50).

Conclusions

The present study brings the first demonstration that thermos-TRPV channels are specifically expressed in the cone photoreceptors of a fish pineal gland were they contribute to controlling the nocturnal rise in melatonin secretion. We also bring evidence that TRPV1 activation is temperature dependent. Thus, the well establish effect of temperature on the amplitude of the nocturnal rise in melatonin secretion by the fish pineal gland [START_REF] Falcón | Molecular and cellular regulation of pineal organ responses[END_REF][START_REF] Falcón | Cellular circadian clocks in the pineal[END_REF] is likely to involve TRPV1 channels. These data strengthen the hypothesis that the fish pineal photoreceptor is a photo-thermo-receptor where light and temperature are likely to act through the same intracellular messenger, Ca 2+ . The ubiquitous distribution of thermos-TRP channels support the hypothesis that thermoregulation in fish is achieved through a hierarchically organized temperature-sensing network [START_REF] Crawshaw | Temperature regulation in vertebrates[END_REF]. In view of the importance the pineal gland and its outputs play in behavioral thermoregulation and synchronization of rhythmic metabolic and neuroendocrine processes, we believe it occupies a crucial position in this network. It might also be a sensor of the internal medium properties. Indeed, TRPV can also integrate subtle variations in membrane stretching, pH, ionic and chemical components; and, photoreceptors are in direct contact with the cerebrospinal fluid (CSF) (51). An old hypothesis, never investigated so far, suggested that the pineal gland of fish could sense variations in CSF pressure (52). Finally, the channels might also be part of the yet unidentified bridge that links the pineal glial cells to the photoreceptors; indeed, arachidonic acid a metabolite produced mainly by the glial cells (53), is known to modulate TRPVs activity (54,55).

Fish have adapted for thousands of years to temperature changes occurring in synchrony with the daily and annual variations of photoperiod. This harmony is being challenged now by the current global climate change: temperature is changing while photoperiod remains the same. This may in part explain the recently observed perturbations of timed biological processes in vertebrates. More information is thus urgently needed now, including a functional characterization of all thermo-TRPs expressed in fish, in order to better understand how factors of the external and internal environment affect the production of the time-keeping hormone melatonin. Melatonin was measured after 6 h of culture in the dark and in the absence or presence of the drugs as indicated. a, capsaicin: One-way ANOVA indicated significant effects of capsaicin (one arrow). Two-way ANOVA indicated a significant interaction of both drugs (two arrows). b, 4PDD: The variations were not statistically significant as analyzed by the two-way ANOVA. One-way ANOVA indicated no significant effects of 4PDD alone. In the presence of the antagonist ruthenium red the inhibitory effect of 4PDD was significant (one-way ANOVA; one arrow). The pineal organs were cultured for 6 h in the dark and at different temperatures as indicated in the abscissae, either in the presence of the TRPV1 antagonist capsazepine (1 µM) or of vehicle (methanol 0.01% in the case of capsazepine). The data are the mean from 2 independent experiments performed in March and June (capsazepine), and March from 2 successive years (gadolinium); for each experiment melatonin values were normalized to the values measured at 5°C. Mean  SEM (n = 13-14). Two-way ANOVA indicated significant effects of capsazepine (P<0.05), which interacted significantly with temperature (P<0.005). The multiple comparison of means indicated that the effect was significant at 16°C; this was confirm by using the multiple t test for comparison of means (P<0.0009). In the case of gadolinium, neither the two-way ANOVA nor the multiple t test for comparison of means indicated any statistically significant difference.

L E G E N D O F F I G U R E S

Figure 1 .

 1 Figure 1. Relative expression of TRPV1 and TRPV4 in trout tissues and organs

Figure 2 .

 2 Figure 2. Localization of TRPV1 and TRPV4 mRNA in trout pineal organ.

Figure 3 .

 3 Figure 3. Localization of TRPV1 and TRPV4 mRNA in trout retina.

Figure 4 .

 4 Figure 4. Effects of time and temperature on pineal melatonin secretion in vitro.

Figure 5 .

 5 Figure 5. Impact of capsaicin (a) and 4PDD (b) on pineal melatonin secretion in vitro.

  Mean  SEM (n = 13-14) from two independent experiments performed respectively, in January and May (a), and May and December (b).

Figure 6 .

 6 Figure 6. Effects of TRPV1 and TRPV4 antagonists on pineal melatonin secretion in vitro at

Figure 7 .e l l s s k i n r e t i n a p i t u i t a r y p i n e a l o p t i c t e c t u m t e l e n c e p h a l o n C e r e b e l l u m d i e n c e p h a l o n l i v e r k i d n e y i n t e s t i n s p l e e

 7 Figure 7. Relative expression of TRPV1 and TRPV4 at two times of the year.

Acknowledgements

We thank the following organizations for their financial support: CNRS, UPMC and Agence Nationale de la Recherche (ANR; project SALTEMP-BIOADAPT; ANR-12-ADAP-0021-01). We also thank the platform Bio2Mar at the Observatoire Océanologique de Banyuls sur Mer (France), and the group Neuroendocrinología de Peces at the Universidad Complutense de Madrid (UCM, Spain), for their help in the HPLC quantifications. Our warm thanks also to Jean Marc Pirlot and the personnel of "les Viviers Cathares" (Chalabre, France) for providing the trout and the friendly access to their facilities.

This work was supported by the CNRS, the UPMC Univ Paris 06, and the ANR (grant ANR-12-ADAPT-0021-01), and the aquaculture farm "les Viviers Cathares", Chalabre, France, which provided the trout. L.G.N was supported by the ANR-12-ADAP-0021-01.