Meena Sriti Murmu
email: meena.murmu@cea.fr

Jeremy Hanoune

Abraham Choi

Valentin Bureau

Michel Renou

Matthieu Dacher
email: matthieu.dacher@upmc.fr

Nina Deisig
email: ndeisig@uni-koeln.de

Modulatory effects of pheromone on olfactory learning and memory in moths

Keywords: moths, olfaction, plasticity, learning and memory, olfactory conditioning, pheromone, proboscis extension response (PER)

Pheromones are chemical communication signals known to elicit stereotyped behaviours and/or physiological processes in individuals of the same species, generally in relation to a specific function (e.g. mate finding in moths). However, recent research suggests that pheromones can modulate behaviours, which are not directly related to their usual function and thus potentially affect behavioural plasticity. To test this hypothesis, we studied the possible modulatory effects of pheromones on olfactory learning and memory in Agrotis ipsilon moths, which are well-established models to study sex-pheromones. To achieve this, sexually mature male moths were trained to associate an odour with either a reward (appetitive learning) or punishment (aversive learning) and olfactory memory was tested at medium-and long-term (1h or 1.5h, and 24h). Our results show that male moths can learn to associate an odour with a sucrose reward, as well as a mild electric shock, and that olfactory memory persists over medium-and long-term range. Pheromones facilitated both appetitive and aversive olfactory learning: exposure to the conspecific sex-pheromone before conditioning enhanced appetitive but not aversive learning, while exposure to a sex-pheromone component of a heterospecific species (repellent) facilitated aversive but not appetitive learning. However, this effect was short-term, as medium-and long-term memory were not improved. Thus, in moths, pheromones can modulate olfactory learning and memory, indicating that they contribute to behavioural plasticity allowing optimization of the animal's behaviour under natural conditions. This might occur through an alteration of sensitization.

INTRODUCTION

Pheromones are chemical substances acting as communication signals. They elicit stereotypical behavioural responses or physiological processes in the receiving individual(s) of the same species (Karlson and Lüscher, 1959). They are therefore crucial cues in animal communication and mediate a wide range of responses in a large spectrum of behavioural and ecological contexts.

Pheromone-elicited behaviours are generally innate and do not require learning [START_REF] Wyatt | Pheromones and animal behavior: chemical signals and signatures[END_REF]. For instance, new-born rabbits display a stereotyped suckling behaviour when exposed to the motheremitted mammary pheromone [START_REF] Coureaud | Immediate postnatal sucking in the rabbit: its influence on pup survival and growth[END_REF]. Similarly, male moths show a stereotyped upwind flight-behaviour towards females when exposed to their sex pheromone [START_REF] Schneider | 100 years of pheromone research[END_REF][START_REF] Hansson | Olfaction in Lepidoptera[END_REF]Allison and Cardé, 2016). In the honey bee, the main component of the sting alarm pheromone (isopentyl acetate) causes the receiver bees to sting, attack and stop foraging [START_REF] Boch | Identification of isoamyl acetate as an active component in the sting pheromone of the honey bee[END_REF].

Furthermore, alarm pheromone released from a stressed rat can serve as a unconditioned stimulus and produce associative learning in a receiver rat [START_REF] Carew | Pheromone-induced odor associative fear learning in rats[END_REF]. Pheromone processing in the brain is usually specialized and dedicated structures often exist, underlying the specific and stereotyped behaviours they elicit; pheromone coding pathways are generally separated from other olfactory, nonpheromonal pathways [START_REF] Christensen | Pheromonal and host-odor processing in the insect antennal lobe: how different?[END_REF]. The components of female moth pheromone blends are detected by specialized olfactory receptor neurons housed in long trichoid hairs on the male antennae [START_REF] Deisig | Responses to Pheromones in a Complex Odor World: Sensory Processing and Behavior[END_REF], and processed in a macroglomerular complex, a sexuallydimorphic region of the antennal lobe constituted of hypertrophic glomeruli dedicated to respond to sex pheromone components [START_REF] Hansson | Function and morphology of the antennal lobe: new developments[END_REF][START_REF] Rospars | Sexually dimorphic and isomorphic glomeruli in the antennal lobes of the sphinx moth Manduca sexta[END_REF][START_REF] Arnold | Comparative study of the antennal lobes and their afferent pathway in the worker bee and the drone (Apis mellifera)[END_REF]. Insects employ a very rich repertoire of pheromones in a wide range of behavioural contexts [START_REF] Sandoz | Understanding the logics of pheromone processing in the honey bee brain: from labeled-lines to across-fiber patterns[END_REF] including recruitment of foragers [START_REF] Frisch | The Dance Language and Orientation of Bees[END_REF], for marking pathways to resources and indicating the resource richness [START_REF] Thom | The scent of the waggle dance[END_REF], and colony defence [START_REF] Hölldobler | The Ants[END_REF].Thus, pheromones have well-documented functions as communication signals in specific contexts (e.g. mate finding, foraging, aggregation, alarm).

A series of results also points to a role of pheromones in modulating behavioural plasticity, contrasting with their classical description as elicitor of stereotyped behaviours. In the European rabbit, the mammary pheromone of females promotes learning of neutral odorants paired with the pheromone in new-born pup: after only a single paired trial, an originally neutral odour elicits a typical nipple searching and grasping response in the pups [START_REF] Coureaud | A pheromone that rapidly promotes learning in the newborn[END_REF]. Furthermore, this pheromone-induced olfactory learning is adaptive because it rapidly extends the range of odours that predict milk reward, thus improving the ability of the pups to find a nipple [START_REF] Coureaud | A pheromone that rapidly promotes learning in the newborn[END_REF].

During fear conditioning, in which a tone is paired with an electric shock, exposure to a recently fearconditioned familiar mouse impairs acquisition of conditioned fear. This effect is mediated by a putative stress-related anxiogenic pheromone emitted by the sender and perceived by the receiver mouse to be conditioned [START_REF] Bredy | Social modulation of associative fear learning by pheromone communication[END_REF]. Similarly, in the honey bee, pheromone could modulate the response to appetitive and aversive stimuli [START_REF] Baracchi | Pheromones modulate reward responsiveness and non-associative learning in honey bees[END_REF][START_REF] Rossi | Pheromones modulate responsiveness to a noxious stimulus in honey bees[END_REF][START_REF] Urlacher | An alarm pheromone modulates appetitive olfactory learning in the honeybee (apis mellifera)[END_REF]. Moreover, exposure of young workers to the queen pheromone abolished aversive learning while the ability to learn appetitive associations (stimulus-reward association) remained intact [START_REF] Vergoz | Queen pheromone blocks aversive learning in young worker bees[END_REF]. Thus, pheromones can modulate behaviours, which are not the primary target of their action by affecting their intensity, their success or probability of occurrence and by promoting plasticity. Some insects are excellent model systems to study the modulatory effects of pheromones on olfactory learning since they possess a rich pheromone repertoire, well-established learning capacities, and an easy access to the neural circuits of pheromone-and odour associative-learning processing [START_REF] Baracchi | Pheromones modulate reward responsiveness and non-associative learning in honey bees[END_REF][START_REF] Rossi | Pheromones modulate responsiveness to a noxious stimulus in honey bees[END_REF][START_REF] Urlacher | An alarm pheromone modulates appetitive olfactory learning in the honeybee (apis mellifera)[END_REF][START_REF] Vergoz | Queen pheromone blocks aversive learning in young worker bees[END_REF].

Insects use olfactory learning to locate food, find mates or avoid potential danger. The ability to associate odours with positive (i.e. reward) or negative (i.e. punishment) events could facilitate insects' capability to locate and find food-sources or potential mating partners, or to avoid potential noxious situations. Olfactory learning and memory have been extensively studied in many insect species, the pioneer being the honey bee which is an excellent learner when it comes to associating odours with a sucrose reward (appetitive conditioning) or a mild electric shock (aversive conditioning). Appetitive conditioning of the proboscis extension response (PER; [START_REF] Takeda | Classical conditioned response in the honey bee[END_REF][START_REF] Bitterman | Classical conditioning of proboscis extension in honeybees (Apis mellifera)[END_REF][START_REF] Giurfa | Invertebrate learning and memory: fifty years of olfactory conditioning of the proboscis extension response in honey bees[END_REF] leads to the formation of an association between an odour and a sucrose reward while in aversive conditioning of the sting-extension response [START_REF] Vergoz | Queen pheromone blocks aversive learning in young worker bees[END_REF][START_REF] Giurfa | Olfactory conditioning of the sting extension reflex in honey bees: memory dependence on trial number, interstimulus interval, intertrial interval, and protein synthesis[END_REF], bees learn to associate an odour with a mild electric shock. Ants can be trained to form an appetitive odour-reward association via conditioning of the maxilla-labium response (Guerrieri andd'Ettorre, 2010, Guerrieri et al., 2011). Aversive conditioning protocols in harnessed ants implicate quantification of the mandible open response (MOR), an indicator of aggression [START_REF] Guerrieri | The mandible opening response: quantifying aggression elicited by chemical cues in ants[END_REF]. Appetitive olfactory learning has also been shown for moths [START_REF] Daly | Olfactory-based discrimination learning in the moth, Manduca sexta[END_REF][START_REF] Daly | Associative olfactory learning in the moth Manduca sexta[END_REF][START_REF] Cunningham | Learning, odour preference and flower foraging in moths[END_REF][START_REF] Riffell | Neural basis of a pollinator's buffet: olfactory specialization and learning in Manduca sexta[END_REF]. As honey bees, moths can be conditioned to release a PER to an odour previously associated with a sucrose reward [START_REF] Hartlieb | Appetitive learning of odours with different behavioural meaning in moths[END_REF][START_REF] Hartlieb | Olfactory Conditioning in the moth Heliothis virescens[END_REF][START_REF] Fan | Olfactory discrimination conditioning in the moth Spodoptera littoralis[END_REF][START_REF] Fan | Behavioural analysis of olfactory conditioning in the moth Spodoptera littoralis (Boisd.) (Lepidoptera: noctuidae)[END_REF][START_REF] Skiri | Associative learning of plant odorants activating the same or different receptor neurones in the moth Heliothis virescens[END_REF].

Sex-pheromones have a well-established and important role in moth biology. Moths are essential actors in the study of pheromones [START_REF] Schneider | 100 years of pheromone research[END_REF][START_REF] Hansson | Olfaction in Lepidoptera[END_REF]Allison and Cardé, 2016). Our hypothesis is that sex-pheromone could be modulating moth's learning abilities. Thus, in the present study, we first studied the performance of male Agrotis ipsilon moths in appetitive as well as in aversive olfactory learning assignments, both protocols being new in this species; then, we investigated whether these learnings could be modulated by sex-pheromones.

MATERIALS & METHODS

Animals

Adult male A. ipsilon (Hufnagel 1766, Lepidoptera: Noctuidae) were used at five days old, when they were sexually mature. Moths always had ad libidum access to water, but were deprived of sucrose solution, which is commonly used to feed moths under laboratory conditions. Animals were kept in plastic boxes in an air-conditioned room at 23-24°C, 60-70% humidity and a 16:8h light: dark photoperiod (light at 18h). Since the photoperiod was inverted, all experiments were performed under red light (invisible for the moths). Furthermore, males were never exposed to the female pheromone or tested odours before experiments. For sucrose responsiveness and appetitive conditioning assays, individual moths were restrained in 1 ml pipette tips cut at the top before onset of the scotophase (Figure 1a). Restrained moths were then allowed to adapt to the experimental room for 2h before the experiment.

Sugar responsiveness

Before appetitive olfactory conditioning, we tested sucrose responsiveness in restrained male moths by establishing a dose-response curve in order to determine the optimal sucrose concentration for training, following the protocol of Hostachy et al (2019a). In moths, taste receptors are present on antennae as well as on their feet (Hostachy et al 2019a). For testing sugar responsiveness, both antennae of restrained male A. ipsilon were briefly touched with sucrose solutions of increasing concentrations: 0%, 0.1%, 0.3%, 1%, 3%, 10%, 30% and 60% (weight/weight). The antenna to be stimulated was randomly chosen. This sucrose stimulation on the antenna elicits PER if the concentration is high enough. The interval between successive sucrose presentations (inter-trialinterval, ITI) was five minutes.

Shock responsiveness

To conduct aversive olfactory conditioning, we developed a novel experimental set-up and tested for optimal parameters of shock application to be used later for aversive olfactory conditioning.

The setup consisted of a rectangular Plexiglas arena (Figure 1b, Length: 18 cm, Width: 15 cm, Height: 1 cm) with an electric circuit covering the floor (kindly provided by CRCA Toulouse, France). The height of the arena was chosen to prevent moths climbing on the side walls and walking upside down, thus escaping the electric circuit. The top cover was detachable to allow placement and removal of the moths. A shock-responsiveness curve was established without odour to determine optimal shock intensity. Single moths were subjected to increasing shock intensities: 0.4 mV, 0.6 mV, 0.8 mV, 4 mV, 6 mV, 8 mV and 10 mV. The interval between successive shock intensities (ISI) was ten minutes. The shock was delivered using a Velleman® DC Power Supply (LABPS1503) and the behaviour of moths during shock application was observed and quantified.

Odour Stimulus Delivery

For olfactory conditioning experiments, geraniol (98%, Sigma-Aldrich, St. Quentin Fallavier, France) was delivered via a SYNTECH stimulus controller CS-55 (Kirchzarten, Germany). Geraniol is classically used for insect appetitive olfactory conditioning as it is a component of flower odours.

One µl of pure geraniol was applied to a piece of filter paper (0.5 cm x 0.5 cm, Fisherbrand, Fisher Bioblock, Illkirch, France). Pasteur pipettes connected to two dedicated channels were inserted into a Y-shaped glass hose connector. Air passed either through a Pasteur pipette containing a filter paper without odour (control airflow), or through a Pasteur pipette containing a filter paper with geraniol (odour loaded flow in case of olfactory stimulation). Odour-containing pipette was freshly prepared every experimental day. A permanent humidified airflow (0.5 ml/s) was delivered during the entire experiment to prevent mechanical stimulation by the air puff during stimulation. For olfactory stimulation during conditioning, the airflow was thus switched from one channel (no odour) to the other (odour). An exhaust system was placed behind the animal (appetitive conditioning, see below) or at the opposite side of the arena (aversive conditioning, see below) to remove odour from inside the arena.

Appetitive Conditioning and Memory Tests

During appetitive conditioning, restrained moths were trained to associate an odour (conditioned stimulus, CS) with a 60% sucrose reward (unconditioned stimulus, US). One conditioning trial consisted of a total of 30 seconds (Figure 2a); a moth was positioned in front of the odour-stimulation device, allowed to habituate for approximately 15 seconds before the odour was presented for five seconds. During the last two seconds of CS presentation, the US (sucrose solution) was presented to both antennae, which in general elicits the extension of the proboscis (PER) and moth was allowed to feed for five seconds. The CS-US overlapped for two seconds. The moth was left undisturbed in the device for 5 seconds before it was removed and the next moth was placed in front of the device. In an anticipation to obtain better learning curves, we tested different floral odours (geraniol and linalool) as conditioning stimuli. Similarly, the unconditioning stimulus (i.e. sucrose) was used at two different concentrations (60% and 67%). Geraniol produced better results, and therefore, the experiments described in figure 1-3 are done using geraniol odour as CS. The results obtained by 60% sucrose were better, thus, the experiments described in figure 1-3 are carried out using 60% sucrose as US.

Each moth received ten conditioning trials with an inter-trial interval (ITI) of 5 minutes.

Comparing ITIs of 5 and 10 minutes showed better acquisition for 5 minutes, allowing to train moths by groups of 10. Odour response was evaluated by quantifying the PER (0 = no response, 1 = response); no moth responded to the odour during the first learning trial. Memory was tested by stimulating the antennae with the conditioned odour for five seconds at 1,5h (mid-term memory, MTM) and 24 hours (long-term memory, LTM) post-conditioning.

Aversive Conditioning and Memory Tests

For aversive conditioning experiments, moths were placed individually in the shockconditioning arena. To present geraniol, the arena was connected to the stimulus controller on one side while the other side was connected to an exhaust-system to eliminate the odour. The conditioning protocol followed the same time delays as the appetitive conditioning (Figure 2a): the animal was allowed to adapt for 15 s in the arena in a constant clean airflow, geraniol (CS) was then applied during five seconds; during the last two seconds of CS presentation, 8 mV electric shock (US) was delivered via the electrical grid. The CS-US overlap lasted for two seconds. The total duration of the shock was 7 seconds. The ITI was 10 minutes, so that moths were trained by groups of 10. Response to odour and/or shock was evaluated by quantifying the motor activity of moths (e.g. movement of antennae, legs, wings, body etc.): absence of motor activity was coded as 0 and motor activity as 1.

Mid and long-term memory were tested 1h and 24 hours post-training. For this, moths previously conditioned aversively were transferred to a four-arm olfactometer (Laucoin s.a., Thoiry, France, 50 x 50 cm, height: 3 cm, Figure 2d, [START_REF] Saïd | Adaptation of a four-arm olfactometer for behavioural bioassays of large beetles[END_REF], containing four sectors. Incoming airflow (generated by a membrane pump, Cole Parmer, U.S.A.) to each sector was controlled by a flowmeter (Brooks, U.S.A.) and adjusted to 500 ml/min. The design of the olfactometer allowed to have four independent sectors (plus a neutral sector in the middle, in which the air was extracted from below), which could contain either clean air or an odour, depending on the filter paper inserted at the entry of each sector. In our test protocol, the previously aversively conditioned odour geraniol was present in one sector, while the three other sectors contained clean control air. One µl of geraniol was applied to a piece of filter paper (1 cm x 1 cm) and placed inside a glass reservoir at the entry of the respective sector. The position of the odour-containing arm was rotated between mid and long-term memory testing periods to avoid bias due to possible spatial cues. For each test, an individual moth was released at the centre of the olfactometer, allowed to habituate for 20 Sec and its trajectory was then recorded by the experimenter during a five-minute test period using a custom-written software (EVEN v. 1.0;INRA,France). Data obtained using the software allowed to quantify time spent in each of the four sectors of the olfactometer during the test period. For some experiments (i.e. testing aversive memory after pheromone exposure, figure 3), results were not taken into account if the moths spent more than 20% of the total time in the middle sector. For another experiment (i.e. figure2), we included all moths that moved out from the central zone because most of the moths remained immobile, and we had to take into account all moths which showed some activity.

Pheromone Exposure

Immediately prior to conditioning (appetitive or aversive, respectively), moths were exposed to one of the following compounds (Figure 3a): i) the sex pheromone blend emitted by A. ipsilon females (composed of three components: Z7-12:Ac, Z9-14:Ac and Z11-16:Ac in a ratio of 4:1:4, [START_REF] Picimbon | Sex Pheromone of the French Black Cutworm Moth, Agrotis ipsilon (Lepidoptera: Noctuidae): Identification and Regulation of a Multicomponent Blend[END_REF][START_REF] Gemeno | Chemical and Behavioral Evidence for a Third Pheromone Component in a North American Population of the Black Cutworm Moth, Agrotis ipsilon[END_REF]Haynes, 1998, Hoffmann et al., 2020), ii) one component of the sexual pheromone emitted by the heterospecific species A. segetum (i.e. Z5-10:Ac, which replaces Z11-16:Ac in the A. ipsilon pheromone blend, [START_REF] Renou | Electrophysiological investigations of pheromonesensitive sensilla in the hybrids between two moth species[END_REF] or iii) the pheromone solvent hexane (as control) for 15 minutes. For exposure, 5 µl of solution containing either compound at the appropriate dilution (10 ng/µl) were deposited on a piece of 1 cm x 1 cm filter paper placed inside a 1 ml Eppendorf tube. Pheromone or hexane exposure was carried out by placing five moths inside a small, dark plastic box containing one of the three compounds. Pheromones were purchased from Pherobank (http://www.pherobank.com, Wijk bij Duurstede, Netherland). N-Hexane (pure) was purchased from Carlo Erba Reagents S.A.S. (Val de Reuil, France).

Data and Statistical Analysis

For statistical analysis of binary data (appetitive and aversive training, retrieval test in appetitive memory), a binomial generalized linear mixed model (GLMM) was used. For statistical comparison of non-binary data in aversive memory retrieval, linear mixed model (LMM) was used.

All statistical analysis was performed using the program R 3.4 and with an α risk of 5%.

RESULTS

Sucrose and Shock Responsiveness in male A. ipsilon

In a first series of experiments, the responsiveness of five-day old naïve male A. ipsilon was studied by establishing stimulus intensity-response curves. Behavioural responses were quantified in response to increasing concentrations of sucrose (%) or intensities of electric shock (mV).

PER rate increased linearly with the log of sucrose concentration (Figure 1c, N = 22); pure water did not elicit any PER. At the lowest sucrose concentration (0.1%) 32% of moths already showed PER. At concentrations above 3% more than 70% of moths showed PER, and the maximal PER rate was observed in the highest sucrose concentration.

In the absence of shock (control), moths were mostly immobile and did not show any movement (Figure 1d, N = 22). However, the application of increasing shock intensities generated three distinct types of motor behaviours: i) twitching ii) jumping, and iii) running. These behaviours were phase-locked with the shock-onset and could be interpreted as 'shock avoidance behaviour'. The probability of moths showing avoidance behaviours increased with increasing shock intensities with more than 40% of moth responding to the electric shock at 8 mV.

These results on sucrose and shock sensitivity allowed us to determine optimal intensities for the US in appetitive and aversive learning experiments: i.e. 60% (w/w) for the sucrose solution and 8 mV for the mild electric shock.

Appetitive Olfactory Learning and Memory in Male Moths

When trained along 10 conditioning trials, some male A. ipsilon successfully learned to associate geraniol with a 60% sucrose reward (Figure 2b, GLMM binomial family: p < 0.0001, N = 87). Correct responses increased along training and maximum learning was found in the 9 th conditioning trial where approximately 40% of moths showed conditioned PER to the odor. During training, even non-responding moths always released a PER in response to sucrose solution (US). A few moths formed an appetitive memory for geraniol, which lasted for 1h30 (mid-term-memory) and stayed stable up to 24 h (long-term memory, Figure 2c).

Further studies confirmed that our training parameters were optimal. Performance during appetitive learning was significantly higher for 5-min ITI group than for 10-min ITI group (GLMM binomial family; significant effect of interval (p = 0.0001) or trial (p = 0.0001, Figure S1a). In addition, moths trained to associate geraniol with 60% sucrose reward showed higher percentage of PER than the moths trained to associate geraniol with 67% sucrose reward (GLMM binomial family: significant effect of trial, p = 0.0001, and concentration, Figure S1b). Furthermore, moths trained to associate 1 µl pure geraniol (0.8 mg) with 60% sucrose reward displayed higher percentage of PER than the moths trained to associate sucrose with diluted geraniol or pure linalool (GLMM binomial family: p = 0.012 for lower concentration geraniol, p < 0.001 for linalool, Figure S1c). Finally, we found that the moths that were trained using the usual conditioning protocol displayed significantly higher percentage of PER than those trained using the trace conditioning protocol, i.e. a protocol during which CS and US do not overlap (GLMM binomial family: significant effect of protocol, p = 0.06) or trial effect (p = 0.0001, Figure S1d).

Aversive Olfactory Learning and Memory in Male Moths

When trained along 10 conditioning trials, male A. ipsilon did not respond to geraniol in the first trial, however, a single pairing with US (electric shock) resulted in an increased locomotor activity (avoidance-like behaviour) to the odour in the second trial, which then decreased during the successive trials. Due to this response pattern, the learning curve was overall not significantly different from 0 (Figure 2e, GLMM binomial family: p = 0.080, N = 30; a generalized additive model was used to take into account the non-linear performance).

The moths did not form an aversive memory (neither MTM, nor LTM) for geraniol since they spent the same amount of time in all four sectors of the olfactometer (Figure 2f). These data suggest that unlike appetitive memory, medium-or long-term aversive memory was not formed as the moths failed to avoid the previously shock-associated odour. It is likely that moths have a hard time learning to associate flower odour compound (geraniol) to an artificial stimulus (electric shock).

Pheromone Exposure and its Effect on Appetitive Olfactory Learning and Memory

Exposure to a conspecific female sex pheromone blend (Z7-Z9-Z11) for 15 minutes prior to conditioning significantly increased learning of geraniol compared to the solvent-exposed group (Figure 3b, red vs. blue curve, Binomial GLMM, effect of treatment: p = 0.0001). Exposure of males to A. segetum pheromone component Z5 did not modulate learning compared to the control group (Figure 3b, yellow vs. blue curve, Binomial GLMM, effect of treatment: p = 0.15). Interestingly, performance in the control group (i.e. solvent exposed) was lower in this experiment than in the previous controls (i.e. controls not exposed to any compound, Figure 2b and Figure S1).

MTM and LTM for geraniol did not differ in moths exposed prior to conditioning to the conspecific sex pheromone or the heterospecific pheromone component Z5. However, appetitive LTM for geraniol was significantly increased in moth exposed to solvent prior to conditioning compared to MTM (Figure 3c, blue bars, Binomial GLMM, effect of memory, p = 0.030), suggesting LTM performance is not correlated to previous performance (see e.g. [START_REF] Dacher | Involvement of NO-synthase and nicotinic receptors in learning in the honey bee[END_REF]; however, this is not consistent with results shown in Figure 2c. A similar effect was not observed for animals exposed to sex-pheromone or Z5, suggesting LTM formation is impaired in these groups.

This suggests sex-pheromone improves performance during training, but prevents LTM formation; only performance during training is affected.

The facilitation in appetitive learning performance was not due to changes in appetitive motivation. Moths exposed to solvent or pheromone showed similar sucrose responsiveness during appetitive conditioning. Likewise, appetitive pheromone does not modulate sugar responsiveness in our sugar responsiveness test (Figure S2a), which is consistent with the study by Hostachy et al. (2019b). Thus, we assume that the facilitation of appetitive learning induced by a conspecific pheromone is not due to changes in reward perception.

Pheromone Exposure and its Effect on Aversive Olfactory Learning and Memory

Exposure of males to heterospecific pheromone component Z5 for 15 min before training increased aversive learning of geraniol (Figure 3d, yellow vs. blue lines, Binomial GLMM, interaction trial*treatment: p = 0.003). By contrast, conspecific sex-pheromone did not improve aversive learning (Figure 3d, red vs. blue lines, Binomial GLMM: p = 0.177). The performance of the control groups is similar in Figure 2e and3d, and as observed previously is not significant (Figure 3d, blue line, Binomial GLMM: p = 0.508).

During MTM testing, control animals did not avoid the sector with the shock associated odour (Figure 3e). By contrast, males exposed to the conspecific pheromone blend or to the heterospecific pheromone component Z5 seemed to avoid this sector at 1h but not 24h after training (Figure 3e-f); however, the difference is not significant, (except for the Z5 group, which spent more time in arm 3, GLMM, p = 0.021). There was no difference between the group during LTM testing. Time in e-f is in minutes.

DISCUSSION

Moth Feeding and Avoidance Behaviours

In Lepidoptera, sugars (e.g. sucrose, fructose, and glucose) stimulate feeding behaviour, and constitute the most important food source [START_REF] Boggs | Ecology of nectar and pollen feeding in Lepidoptera[END_REF][START_REF] Gilbert | Butterfly ecology[END_REF]. It is thus vital for these insects to detect them. It is equally important for insects to detect warning or aversive signals as they could indicate the presence of a predator, pathogen or harmful substances. In laboratory conditions, feeding behaviour in moths has extensively been studied using the proboscis extension reflex (PER) assay (Hostachy et al., 2019a). In contrast to the PER, the behavioural response elicited by aversive signals is studied to a lower extent; nevertheless, there is data to suggest that moths can detect and avoid aversive signals [START_REF] Salloum | Appetitive and aversive learning in Spodoptera littoralis larvae[END_REF].

Consistent with previous findings in various moth species including Agrotis ipsilon [START_REF] Hartlieb | Olfactory Conditioning in the moth Heliothis virescens[END_REF][START_REF] Fan | Behavioural analysis of olfactory conditioning in the moth Spodoptera littoralis (Boisd.) (Lepidoptera: noctuidae)[END_REF][START_REF] Daly | Learning modulates the ensemble representations for odours in primary olfactory networks[END_REF][START_REF] Skiri | Associative learning of plant odorants activating the same or different receptor neurones in the moth Heliothis virescens[END_REF]Hostachy et al., 2019a), touching of moth's antennae with sucrose resulted in the moth extending its proboscis, which was consistent and highly reproducible among individuals and from trial to trial. Furthermore, PER was concentrationdependent: increasing the sucrose concentration increased the probability of the moth displaying a PER. These results indicate that sucrose induce robust PER and could be reliably used to characterize feeding behaviours in Agrotis ipsilon moths, as reported previously by Hostachy et al (2019a).

Presentation of an aversive stimulus (i.e. electric shock) produced a wide range of avoidance behaviours, which was phased-locked with the shock onset. Increasing the shock intensity increased the probability of avoidance-like behaviours. Taken together, our shock sensitivity assay set-up not only allowed us to accurately monitor behavioural responses to aversive signal in Agrotis ipsilon adults, but also allowed us to discover voltages that evoke the best avoidance response.

Moths Learn and Memorize Odour Cues Associated with Reward and Warning Signals

Moths use olfaction not only to locate food, find mates or oviposition sites, but also to detect potentially noxious stimuli. Learning to associate an olfactory cue with reward or warning signal is thus important for food-searching, reproduction and defensive behaviours. It is well known in honey bees that olfactory learning in laboratory conditions is relevant, as they can be transferred to freeflying foraging, and vice-versa [START_REF] Gerber | Honey bees transfer olfactory memories established during flower visits to a proboscis extension paradigm in the laboratory[END_REF][START_REF] Sandoz | Olfactory information transfer in the honeybee: compared efficiency of classical conditioning and early exposure[END_REF][START_REF] Chaffiol | Prior classical olfactory conditioning improves odour-cued flight orientation of honey bees in a wind tunnel[END_REF][START_REF] Gil | Apis mellifera bees acquire long-term olfactory memories within the colony[END_REF]. Our conditioning results show that tethered Agrotis ipsilon male moths learn to associate a floral odour with sucrose, suggesting that like bees, they could use such information during foraging [START_REF] Riffell | Neural basis of a pollinator's buffet: olfactory specialization and learning in Manduca sexta[END_REF].

In appetitive conditioning, CS alone did not initially induce a conditioned PER; however, after a single CS-US pairing (e.g. during the second trial), 11% of moths displayed a conditioned response (i.e. PER) to the odorant. Unlike honey bees, moths are known to require a higher number of trials (e.g. at least 8 trials to reach a maximal CS-US association, [START_REF] Skiri | Associative learning of plant odorants activating the same or different receptor neurones in the moth Heliothis virescens[END_REF]. This phenomenon was also observed in our appetitive conditioning experiments as the conditioned PER was found to be highest during the 9 th trial. In our study, the highest proportion of moths that learned CS-US association is around 40%, which is not unusual: similar acquisition curve has been reported in other moth species [START_REF] Skiri | Associative learning of plant odorants activating the same or different receptor neurones in the moth Heliothis virescens[END_REF][START_REF] Hartlieb | Olfactory Conditioning in the moth Heliothis virescens[END_REF]. Lower learning performance could not be explained by the lack of motivation in moths since they systematically responded to sugar during appetitive conditioning. It is entirely possible that the moths possess inherent lower learning capabilities than social insects given their shorter life span and solitary living habits.

The attributes of the CS are critical in the associative olfactory learning process. The identity, concentration and frequency of CS regulate the animal's ability to learn CS-US association. Certain odorants result in a better learning curve than others [START_REF] Skiri | Associative learning of plant odorants activating the same or different receptor neurones in the moth Heliothis virescens[END_REF]. Similarly, the ability to learn CS-US association is sensitive to the concentration, and better learning performance are observed when odour concentration is optimal [START_REF] Skiri | Associative learning of plant odorants activating the same or different receptor neurones in the moth Heliothis virescens[END_REF][START_REF] Daly | Olfactory-based discrimination learning in the moth, Manduca sexta[END_REF]. Consistently with these findings, we found that the moth's ability to learn an odour-sucrose association was regulated by the identity and intensity of the CS (Figure S1c). These results suggest that the characteristics of the CS are critical in determining whether the moths can learn an olfactory cue associated with a reward signal, how fast the moths learn an odour-reward association as well as the overall learning success rates.

Similar to the CS, the features of the US also affected the abilities of moths to learn odoursucrose association. We found that as compared to 67% sucrose reward, a pairing of an odour (CS) with 60% sucrose (US) resulted in a higher proportion of moths (up to 32% increase) showing a conditioned PER to the odour. We also observed that trace conditioning (Conditioning protocol in which the CS and US do not overlap but are presented separately with an interval of time in between) resulted in fewer moths (-87% decrease) showing a conditioned PER to the odorant. Furthermore, we found that the CS-US association was optimal at 5-min ITI relative to 10-min ITI. These results provide important insights into associative olfactory learning mechanisms in moths.

In retrieval tests, only some of the moths were able to remember CS-US association. Representation of geraniol at 1h30 resulted in approximately 34% of moths showing a conditioned PER to the odour. They did not forget information related to the rewarding stimulus 24h post-conditioning.

These results imply that moths are capable of building long-term olfactory memories. In moths, learning of plant odours primarily serves self-consumption and oviposition. It is likely that the appetitive olfactory memory observed in our study is adequate to mediate foraging and oviposition purposes in moths. However, not all moths were able to learn; moths that did learn might be advantaged in nature.

In moths, aversive learning has been studied to a lower extent because of some obvious difficulties. Absence of a robust physical demonstration of defensive behaviour (such as extension of sting in honey bees) makes aversive learning in moths challenging; nevertheless, in some species of moths, aversive learning has been studied by quantifying the suppression of PER [START_REF] Jorgensen | Effects of two bitter substances on olfactory conditioning in the moth Heliothis virescens[END_REF]. We developed a new set-up to study the moth's ability to learn an odour-shock association and form aversive memories. In aversive conditioning, CS alone did not induce an avoidance response; however, after a single CS-US pairing (e.g. during the second trial), 30% of moths displayed a conditioned avoidance response to the odour. In an anticipation of punishment, the moths tried to avoid an odour previously associated with an aversive signal. In the consecutive trials, there was a significant reduction in the proportion of moths showing CS-US association. We did not investigate how attributes of the aversive stimulus, for instance, the intensity, and duration regulate the moth's ability to form odour-shock association. Future studies will address whether varying the intensity, duration or an inter-trial interval improve the overall aversive learning curves in moths.

Avoidance of an odour was not retained at mid-or long-term memory testing periods indicating that the moths seem to forget information about the aversive signal rather quickly.

Alternatively, their responses during training might be supported by a non-associative learning, i.e. sensitization to the electric shock: they would initially respond to the odour because they are excited by the shock rather than because they recognize the odour. In that case, they would not learn at all. By contrast, in some moth species, associative aversive long-term memory is retained for at least 48h post conditioning [START_REF] Jorgensen | Effects of two bitter substances on olfactory conditioning in the moth Heliothis virescens[END_REF], but unlike our study, the aversive stimulus used was the bitter compound found in nectars [START_REF] Barlow | Distasteful Nectar Deters Floral Robbery[END_REF]. It is possible that we did not use the most suitable memory-testing set-up, as electric shocks are not natural stimuli. Moreover, to assess MTM and LTM, moths were placed in a four-arm olfactometer where avoidance to an odour-containing arm was measured. This was carried out in anticipation to obtain better retention scores given that the aversive acquisition curve was relatively low. It is possible that changing the set-up (and thus the context) prevented the animals to fully recall the learned CS-US association [START_REF] Rosas | Context change and associative learning[END_REF].

Future experiments should address whether the aversive retention scores could be improved using the same set-up that was used for the aversive conditioning experiments.

We found that the few moths learning odour-shock associations did so at a much faster rate than for odour-sucrose association. After a single CS-US pairing, 30% of moths showed a conditioned avoidance response, whereas only 10% of moths showed a conditioned PER to an odour. Aversive signals represent danger and are associated with the noxious stimulus. It seems logical that the moths learn olfactory cues associated with the aversive signal faster to respond quickly and generate appropriate defensive behaviour; this is also consistent with the hypothesis that the initial response is actually sensitization rather than associative learning. Olfactory cues associated with a reward signal probably does not require an immediate response. Interestingly, although the moths learn odour-shock association at a much faster rate, the conditioned avoidance response decreased significantly during the consecutive trials. By contrast, moths learn odour-sucrose association at a much slower rate, but the conditioned PER increased during the consecutive trials.

Pheromone Modulation of Appetitive or Aversive Learning

Learning and memory is not a static process. Wide ranges of intrinsic (e.g. age, motivational state) and extrinsic (e.g. environmental, stressors) factors modulate cognitive process [START_REF] Mallon | Immune response inhibits associative learning in insects[END_REF][START_REF] Iqbal | Virus infection causes specific learning deficits in honeybee foragers[END_REF][START_REF] Farooqui | Iron-induced oxidative stress modulates olfactory learning and memory in honeybees[END_REF][START_REF] Amdam | Honeybee associative learning performance and metabolic stress resilience are positively associated[END_REF]. Among the extrinsic factors, pheromones have recently been reported to modulate the probability of performing certain behaviours including learning in several model organisms including vertebrates and insects. Pheromones act as modulators of cognitive tasks and affect the capacity of animal to learn and memorize specific information about its environment. Recent studies indicate that olfactory appetitive or aversive learning can be enhanced or impaired by pheromones depending on the valence of the task [START_REF] Urlacher | An alarm pheromone modulates appetitive olfactory learning in the honeybee (apis mellifera)[END_REF][START_REF] Vergoz | Queen pheromone blocks aversive learning in young worker bees[END_REF][START_REF] Free | Pheromones of Social Bees[END_REF][START_REF] Hunt | Flight and fight: a comparative view of the neurophysiology and genetics of honey bee defensive behaviour[END_REF][START_REF] Nieh | A negative feedback signal that is triggered by peril curbs honey bee recruitment[END_REF]. Our results suggest that moths exposed to a conspecific (Z7-Z9-Z11) but not heterospecific (Z5) sex-pheromone showed appetitive learning performances that were significantly higher than moths exposed to the solvent, hexane; they had the opposite effect on aversive learning. Pheromone exposure prior to conditioning leads to better learning by affecting the success of learning. We observed that in the appetitive conditioning assay, conspecific-pheromone exposed male moths not only learn faster as compared to the solvent or heterospecific-pheromone exposed group but exhibit higher olfactory appetitive learning than the latter two. It is interesting that the pheromones emitted by the conspecific females enhance olfactory appetitive but not olfactory aversive learning while pheromones emitted by the heterospecific males (i.e. Z5) enhance aversive but not appetitive learning. These results suggest that pheromones provide contextual information rendering subsequent learning more or less relevant. Our hypothesis is that exposure to sex-pheromones prior to conditioning makes the moth more vigilant or alert allowing them to respond better to relevant stimuli while at the same time preventing them from being distracted by stimuli of secondary importance. Foraging, a collective behaviour is largely ruled by olfaction. In some insects, sex pheromones play a crucial role in foraging [START_REF] Bordereau | Pheromones and Chemical Ecology of Dispersal and Foraging in Termites[END_REF] and promote food-searching behaviours [START_REF] Poivet | The use of the sex pheromone as an evolutionary solution to food source selection in caterpillars[END_REF]. In the context of olfactory appetitive learning, it could be argued that the male moths might perceive exposure to conspecific sex pheromones as a signal to aggregate at a common site that could favour enhance food intake, and thus show enhanced olfactory appetitive learning. On the other hand, pheromone emitted by a heterospecific males does not signal resource richness and therefore, do not improve appetitive olfactory learning. In the context of aversive olfactory learning, pheromone emitted by heterospecific males, but not conspecific females may act as a relevant cue for unfavourable conditions (e.g. resource scarcity) resulting in higher olfactory aversive learning. A modulation of the non-associative component of learning [START_REF] Mcsweeney | Sensitization and habituation regulate reinforcer effectiveness[END_REF] could explain our result: conspecific sexpheromone would facilitate sensitization to sucrose, and heterospecific pheromone would facilitate sensitization to electric shock. As sensitization and habituation are opposing phenomena (Peeke and Petrinovich, 1984;[START_REF] Eisenstein | The Evolutionary Significance of Habituation and Sensitization Across Phylogeny: A Behavioral Homeostasis Model[END_REF][START_REF] Eisenstein | A Behavioral Homeostasis Theory of Habituation and Sensitization: II. Further Developments and Predictions[END_REF][START_REF] Blumstein | Habituation and sensitization: new thoughts about old ideas[END_REF], this explanation is consistent with the impairing effect of sex-pheromones on sucrose habituation that we previously observed (Hostachy et al., 2019b; see also Baracchi et al., 2018 for honey bees).

Conclusion

To conclude, our study provides important insights into pheromonal action. Moths are agricultural pests of various plants of economic importance (e.g. tomatoes, corn). The findings that pheromones can modulate the learning capabilities of moths have a functional significance in the fields of pest control. How pheromones influence insect learning and memory is valuable for the conception and development of novel methods of pest control. A comprehensive knowledge on the olfactory modulation of moth's learning capacities by attractant (e.g. Z7-Z9-Z11) or deterrent (e.g. Z5) pheromones is useful, and could be used to develop specific traps to attract or lure moths or to deter them from specific cultures.

Figure 1 .

 1 Figure 1. Testing responsiveness of Agrotis ipsilon males to increasing concentrations of sugar or

Figure 2 .

 2 Figure 2. Agrotis Ipsilon Males Perform Appetitive and Aversive Olfactory Tasks. a) Moth is

Figure 3 .

 3 Figure 3. Pheromone Modulation of Appetitive and Aversive Learning/Memory Formation. a)

ACKNOWLEDGEMENTS

We thank our colleagues Lucie Conchou and Camille Meslin for their comments on this article and technicians from iEES Paris for rearing the animals used in the experiments. This work was supported by Agence Nationale de la Recherche (project PheroMod, ANR grant number ANR-14-CE18-0003).

AUTHOR CONTRIBUTIONS

MSM, MR, MD and ND designed the experiments, which were conducted by MSM, JH, AC and VB. MSM wrote the first draft of the manuscript, and all authors contributed to manuscript revision, read and approved the submitted version.

DECLARATION OF COMPETING INTEREST

The authors declare no competing financial interests.

SUPPLEMENTARY DATA

DECLARATION OF COMPETING INTEREST

The authors declare no competing financial interests.


Stephane Nzetchou
email: stephane.nzetchou@utc.fr

Alexandre Durupt
email: alexandre.durupt@utc.fr

Sébastien Remy

Benoit Eynard
email: benoit.eynard@utc.fr

Sebastien Remy
email: sebastien.remy@utt.fr

Review of CAD Visualization Standards in PLM

Keywords: PLM, 3D modelling, File formats, Data exchange, Application Interoperability

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

The way companies communicate, has changed. Companies send most of information digitally instead of paper and it is considered as a great forward in saving time and money. One of the ways to save money is the business mindset of Product Lifecycle Management (PLM).

Inside the PLM system, the data from which the information is extracted, are heterogeneous by their type (photo, video, text file, CAD file, etc.); the CAD data can be heterogeneous by their formats: legacy formats (CATIA, CREO, NX, etc.), neutral formats (STEP, JT, IGES, STL, etc.), but also from the expertise that created the data or which it is intended for. Thus, the links and the dependences, which appear between heterogeneous CAD data, are becoming more complex during the daily activities of users. When products are developed in 3D for the engineering domain, the data are initially stored in the legacy file format of the used CAD software. If this 3D CAD data is to be made available to people who do not have this software, neutral 3D formats are needed. Some file formats are: 3D PDF from Adobe, X3D, U3d from Web3D consortium, JT and STEP [START_REF] Katzenbach | JT format (ISO 14306) and AP 242 (ISO 10303): The step to the next generation collaborative product creation[END_REF]. The proprietary nature of CAD vendor specific data limit the possibility of open-ended analysis of the 3D data and creates restricted universality of such tools since they work only for the format or API of the particular application [START_REF] Solihin | Multiple representation approach to achieve highperformance spatial queries of 3D BIM data using a relational database[END_REF]. The access to the information becomes a laborious task due to the non-structuring of the data and the lack of sharing information. "How can we overcome dependence on legacy formats in order to access geometric and topological information? How can we allow any user to query and retrieve geometric and topological information to a desired granularity?" This raises the need to analyze 3D neutral formats to see their distinctive features view and PMI (product & manufacturing information).

Product Lifecycle Management

PLM is made to be composed of digital product definition and delivery. The tasks are based on computer models processed with computer assistance. According to Terzi [START_REF] Terzi | Product lifecycle management: from its history to its new role[END_REF], most of the product data are generated at beginning of life (BOL). The software owned by a company, particularly 3D CAD systems, have to be able to communicate with each other. This type of tool is developed to help product designers and to provide a traceability support of the evolution of an artefact from the requirements to the product use and even recycling. Digital product definition forms the origin and core of PLM. Product definition evolved from engineering drawings to computer-aided X (CAx) as said in [START_REF] Zhu | Implementations of Model Based Definition and Product Lifecycle Management Technologies: a Case Study in Chinese Aeronautical Industry[END_REF]. Most of CAD systems enable exchange of the shape models with CAx (CAE, CAPP, CAM etc.) systems. Tools are being developed to optimize the design subject to conflicting requirements in one pass. This enables designers to release physical testing and simulation to digital simulation and prototyping for much of the design.

For PLM being successful, data formats of digital product must be able to convey design intent, be machine interpretable, and lose as little information as possible in the translation process. From McHenry [START_REF] Mchenry | An overview of 3D data content, file formats and viewers[END_REF], some problems arisen within companies having different file formats and due to format conversions and companies outsourcing are: Missing, collapsed, inverted faces, models that do not form closed solids (surfaces and edges do not connect). The major at hand is the interoperability between 3D CAD systems. Companies can have an issue even in-house if multiple 3D CAD systems are used.

3

Research background of some interoperable 3D file format

Studies of interoperable files

Research Triangle Institute for the National Institute of Standards and Technology has performed a research to determine the differences of STEP and lightweight file formats in 1999. This study analyzed the transfer of models from one CAD package to another by way of STEP and an outdated standard, IGES [START_REF] Tassey | Interoperability Cost Analysis of the U . S . Automotive Supply Chain[END_REF]. It was very simple and maybe effective for 1999, but technology, the standard, and other variables have improved and changed since then. More recent studies performed on lightweight file formats like lightweight Formats for Product Model Data Exchange and Preservation [START_REF] Ball | Lightweight Formats for Product Model Data Exchange and Preservation[END_REF]. However, this study only covers the basics of model fidelity, metadata support, security, features, file size, software support, and it is important since it compares basis differences between many lightweight formats. Academia has performed studies to try and to evaluate the difference between STEP, JT, 3DXML, and U3d. This study was performed to demonstrate all the difference regarding the functionality of part registered inside these formats. [START_REF] Hartman | A Comparison of Functionality between STEP AP 203 E2, 3DXML, JT, and U3D Technologies[END_REF]. ProSTEP iViP Association is an international association that helps drive the development of vendor-neutral file standards and validates the quality of software solutions for interoperability. ProSTEP performs research on benchmarking STEP and JT. ProSTEP published 3D Formats in the Field of Engineering, a Comparison in 2010, in which JT, STEP, and 3D PDF were analyzed using multiple CAD systems and several attributes. The CAD systems used were CATIA V5, CREO, and Siemens NX [START_REF] Prostep | 3D Formats in the Field of Engineeringa Comparison[END_REF].

It is clear that an in-depth multi-level comparison should be done with the STEP standard and other new coming file formats as JT because the design in context is one of the most processes which could be based on JT and structure information (STEP AP242XML[10] -figure 2) coming from various 3D CAD systems. The latter two file formats include the annotations and attributes associated with the edges and faces of CAD model in order to detail product geometry and specifications in a manufacturing perspective. Also, non-geometric data such as surface texture specifications finish requirements, process notes, material specifications and welding symbols.

Features of 3D file formats

Different CAD systems have different ways of representing a CAD model, which is determined by the system's modelling kernel. Some CAD systems use the same modelling kernel as other systems. For example, ACIS by Spatial Technology Corporation and Parasolid by Siemens. Other companies use completely proprietary modelling kernels that are not shared. The modelling kernel determines how the CAD model is mathematically represented, semantically represented, and the internal accuracy of the geometric definitions. [START_REF] Gerbino | Tools for the interoperability among CAD systems[END_REF] The issue of legacy information have brought about interoperable and neutral (lightweight) file formats into existence. The problems between those formats are mainly perceived when we look at the features (the shape, the appearance and the scene) of the in each neutral file format and also the PMI.

 The shape of a model is often stored as a set of 3D points or vertices. The surfaces are stored as a series of polygons or faces that are constructed by indexing these vertices. This kind of format is known as tessellated.. If truly smooth surfaces are required, at any scale, then a convenient option is the use of Non-Uniform Rational B-Spline patches (or NURBS). These parametric surfaces are made up of a relatively small number of weighted control points and a set of parameters known as knots. B-Rep provides explicit representations for the geometrical elements such as vertexes, edges, faces, etc. Designing a shape can be done along the lines of constructive solid geometry (CSG) that uses Boolean operations on simple shape primitives such as cubes, cylinders, spheres, etc.  The appearance, in its most common form materials entails applying an image or texture to the surface of the model. A model that does this must store these texture coordinates within the 3D data file. Most 3D file format support texture mapping.

There would be three dimensions for three-color components corresponding to color spaces, such as RGB. Many file formats are supported storing material properties. However, an application that loads material properties usually ignores many of these properties when a user is manipulating the object.  The scene describes the layout of the 3D model in terms of cameras, light sources, and other nearby 3D models. The camera is defined by some parameters as magnification and principal point, location, the direction the camera is facing and an arrow indicating which direction is up. Different formats for sharing the model.

Case study

Due to the ephemeral nature of CAD file formats and the applications that work with them, the migration of CAD information into lightweight formats could be a solution for preserving, exchanging and recovering information in the future. So, the need to correctly visualize the model, access the geometric and topologic information and geometric definitions and tolerances, entails the analysis and choice of some standard formats, but also the answers to: Which format are we going to choose? Which free viewers are available? Have the formats been recognized by some standard organization? What about the data exchange and the visualization?

Based on the formats supported by the community, those used by most of the companies and the CAD packages and third party software available to our use, we selected STEP, JT, 3D PDF, and STL in order to notice their discrepancies with respect to some criteria chosen.

The chosen criteria are:

-The size: Since we are dealing with the so-called lightweight file format, the size is an important criteria for checking which format less size than other. CATIA from Dassault Systèmes and CREO from PTC were used in order to perform the test. CrossManager (dkt) software from DATAKIT was used to convert legacy CAD file format to STEP, STL, JT and 3D PDF. CAD Assistant from Open Cascade was used to analyze the completeness of the interoperable file formats and the integrity of all the CAD models. It could not read file formats as STL, 3DPDF at the time of testing. So, the tested file formats were STEP and JT. The results were the measures of the volume, surface. For visualizing 3D PDF, we used adobe reader and STL the "window 3D viewer."

4.2

Survey data Analysis Fig. 2. Set of models used.

Figure 2 shows the set of models used. The approach was, by taking one model designed in CATIA and CREO with the same characteristics, same features ( everything the same) and converted to STEP, STL, 3D PDF and JT formats through the means of CrossManager, what could be the difference in terms of the chosen criteria? Are the file format more influenced by the translation process or by the legacy CAD systems?

NIST_ctc_02_asme1_ap242 for observing the discrepancies with respect to the same model translated to other formats.

Figures 3 shows the synopsis of the translations process. AXE_COUV represents an assembly model. Surface is a surface part. The same models are represented in CATIA and CREO in order to be analyzed and to observe the differences.

Cat_dkt_stp and Prt_dkt_stp are the STEP file format got using directly the translation of the legacy file format through Crossmanager.

Cat_dkt_JT and Prt_dkt_JT are the JT file format got using directly the translation of the legacy file format through Crossmanager.

NIST_ctc_02asme1_ ap242_JT is the original file format from NIST translated in JT.

Cat_dkt_PDF and Prt_dkt_PDF are the PDF file format got using directly the translation of the legacy file format through Crossmanager.

Cat_dkt_STL and Prt_dkt_STL are the STL file format got using directly the translation of the legacy file format through Crossmanager.

Fig. 3. Direct translation from legacy files format to another through Crossmanager

The file formats were analyzed and compared. The data analysis was based on the preservation of information. The results were compared to each other in various ways to help determine possible trends. If changes occurred in models between translation processes, then those changes were determined using percent change where applicable. Percent change was determined by the relative change between the attribute of the newly translated model and the original attribute of the model.

Regarding the area and the volume, the comparison was just performed using STEP and JT thanks to the information obtained in CAD assistant. Therefore, we have compared the area of legacy file format with respect to JT and STEP by doing: Delta_Area = (Area of the translated file format / Area of the legacy file format) *100

(1) Delta_Volume = (volume of the translated file format / volume of the legacy file format) *100

During our analysis, we have observed an increase of the model's areas from CREO during the translation process to other formats, while those from CATIA decrease their areas in comparison to the original ones. The volumes of the STEP files are almost equal to the legacy formats while those from JT are a little bit different. The difference regarding the volume and the area is around 4% on average. Consequently, we have a slight difference between regions and volume of objects in the two files. 1 and cover many computable representations for several types of 3D model data, including geometric definition and tolerances. The STEP and JT characteristics allow 3D PMI module to represent product information that is machine-readable. The module uses XML and EXPRESS schema languages to define product data model. CAD assistant shows the material converted in STEP and JT. In addition, the color of the part is visible. STL is dedicated to printing format, which has been used to transfer information from CAD software to printing hardware. The STEP and JT file keep the characteristics of precise geometry, tessellated geometry and tolerances as we can see in figures 4-a and figures 4-b.

Additional features of lightweight formats not part of STEP specification and 3D PDF, as advanced material lighting properties, level of detail mechanism, some level of compression or many STEP features not available in the lightweight formats as from features, construction history drafting capabilities. Some possibly features, not available by default need to be extended manually in the GT2Go. Consequently, JT and STEP are suited for this task.

SIZE

Regarding the file size of the different models, the test results were found using the file size of the legacy CAD. STEP and STL are the ones much sized and JT file sizes are the lowest in most cases. By doing some calculations with respect to our samples, JT files are almost 60% lighter than STEP files and is the best one in terms of size. The size of the data is determined by the data content instead of the format itself. The fact that JT is less size that another format could make JT the ideal candidate suited in a PDM system because its opening could be faster than the others.

DOCUMENTATION

Because of its document-oriented structures, 3D PDF is an excellent solution for this purpose and any device that allows adobe reader can read it. STEP and JT can also be suited for this since they have been standardized ISO, so they provide a very good container for 3D models.

With respect to PLM context, 3D PDF is the most suited because it allows 3D information to be represented together with other information. All the data are contained in a single document or file.

DATA EXCHANGE

Because of the numerous applications, STEP seems best suited for that since it matured long time ago. JT is also suited to exchange information, but compared to STEP, it needs to mature. Regarding 3D PDF or STL, because of the lack of exact representation and the lack of some information they are not suitable. It provides implementation methods for data exchange VISUALIZATION With the translation of files through Crossmanager, we can obtain either a faceted or a tessellated representation. In addition, the capacities of the available viewers make JT (figures 4-b) and 3D PDF (figures 4-c) ideal for visualization. In fact, with JT and 3D PDF, we can notice in the tree view, the parts forming the assembly of the legacy file translated, while with STEP, it is not possible.

In terms of metadata, all elements constituent the assembly structure in these formats are displayed at the level of the tree view regardless the viewer. So, the metadata are visible. The name of the shares (parts, sub-parts, assembly etc.) is as described in the native CAD system. Consequently, JT is suited for the visualization case thanks to the access to the geometry, topology and PMI

CONCLUSION

Due to the examination methodology selected for this study, we used CAD systems CATIA and CREO and third party software CAD assistant and JT2Go. After analyzing the different 3D neutral formats, it is clear that depending on the specific use case, one file format could have an advantage over another. However, given the interest which our study has shown in focusing on the geometric and topologic information and reading of PMI (color, material, tolerances, etc.), it could be said that the JT file is very promising. Because, despite the translations from one file format to JT file format, it is always possible to access the PMI correctly and visualize the geometric and topologic information. The tree view of JT neutral file format is done unambiguously, with a clear representation of the components.

[ 11 ]Fig. 1 .

 111 Fig.1. ISO Roadmap for JT and STEP AP242.[START_REF] Katzenbach | JT format (ISO 14306) and AP 242 (ISO 10303): The step to the next generation collaborative product creation[END_REF]

Fig. 4 .

 4 Fig. 4. NIST model in a) STP; b) JT; c) 3D PDF; d) STL

Table 1 .

 1 Translation path material property preservation

			material name Colour	Density
	From Cat or Prt to STP yes	yes	No
	From Cat or Prt to JT	Yes	yes	No
	From STP to JT	yes	yes	No
	From JT to STP	Yes	yes	No
	4.3	Results assessment
	PMI
	STEP AP242 and JT integrate table


Tianyu Li

Mary Comer

Josiane Zerubia

AN UNSUPERVISED RETINAL VESSEL EXTRACTION AND SEGMENTATION METHOD BASED ON A TUBE MARKED POINT PROCESS MODEL

Keywords: Retinal vessel segmentation, vessel network extraction, connected tube model, marked point process

Retinal vessel extraction and segmentation is essential for supporting diagnosis of eye-related diseases. In recent years, deep learning has been applied to vessel segmentation and achieved excellent performance. However, these supervised methods require accurate hand-labeled training data, which may not be available. In this paper, we propose an unsupervised segmentation method based on our previous connected tube marked point process (MPP) model. The vessel network is extracted by the connected-tube MPP model first. Then a new tube-based segmentation method is applied to the extracted tubes. We test this method on STARE and DRIVE databases and the results show that not only do we extract the retina vessel network accurately, but we also achieve high Gmeans score for vessel segmentation, without using labeled training data.

INTRODUCTION

Vessel extraction and segmentation in retina fundus images are essential for aiding analysis of eye-related pathology, such as hypertension, arteriosclerosis and diabetic retinopathy [START_REF] Fraz | Blood vessel segmentation methodologies in retinal images -a survey[END_REF]. Many segmentation methods have been proposed over the past decades. In general, these methods can be classified as supervised or unsupervised methods, based on whether training data is needed or not.

Unsupervised methods are mostly rule-based, taking advantage of the contrast information, vessel structure and other features. Hoover et al. [START_REF] Hoover | Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response[END_REF] segment the vessels by applying a 2D filter to the retinal images. Yin et al. [START_REF] Yin | A probabilistic based method for tracking vessels in retinal images[END_REF] apply a probabilistic tracking method for the segmentation. Neto et al. [START_REF] Neto | An unsupervised coarseto-fine algorithm for blood vessel segmentation in fundus images[END_REF] propose a coarse-to-fine algorithm with morphological operations and curvature analysis.

Most supervised methods take vessel segmentation as a binary classification problem. Traditional machine learning methods, such as support vector machines (SVMs) [START_REF] Tuba | Retinal blood vessel segmentation by support vector machine classification[END_REF] and the Radius-Based Clustering algorithm(RACAL) [START_REF] Salem | Segmentation of retinal blood vessels based on analysis of the hessian matrix and clustering algorithm[END_REF], have been applied to vessel segmentation, and achieve good performance. In recent years, with the development of deep learning and with the use of GPUs, convolutional neural networks (CNNs) have been widely applied to image segmentation, including retinal vessel segmentation. Liskowski, Oliveira et al. [START_REF] Oliveira | Retinal vessel segmentation based on fully convolutional neural networks[END_REF] segment the vessels by a fully convolutional neural network with a stationary wavelet transform. Li et al. [START_REF] Li | Residual u-net for retinal vessel segmentation[END_REF] realize the segmentation by a U-Net [START_REF] Ronneberger | U-net: Convolutional networks for biomedical image segmentation[END_REF] combined with residual blocks. The results from deep neural networks are promising. However, the performance depends largely on accurate hand-labeled data, which may not always be available. For example, when equipment is upgraded or an image is captured with a different acquisition method, the data collected could be very different. Also the deep neural network tends to learn the behavior of the expert who labels the data, but for different experts, the labeling could be different. Thus, it is still meaningful to do research on unsupervised methods, even though the results may not be as competitive as deep learning methods with training data.

In this paper, we propose an unsupervised vessel segmentation method based on our previous connected-tube MPP model [START_REF] Li | A Connected-Tube MPP Model for Object Detection with Application to Materials and Remotely-Sensed Images[END_REF]. The framework of this method is presented in Fig 1 . The image is pre-processed with morphological operations first. Then the vessel network is extracted by our connected-tube marked point process (MPP) model. Finally, a tube-based binary segmentation algorithm is applied to the detected tube objects. The remainder of this paper is organized as follows: In Section 2, we present the pre-processing method and review the connected-tube MPP model. In Section 3, we introduce our tube-based segmentation algorithm. In Section 4, qualitative and quantitative results are given and discussed. Finally, conclusions are drawn in Section 5.

VESSEL NETWORK EXTRACTION

Preprocessing

Color retinal images usually exhibit some artifacts such as poor focus and camera misalignment [START_REF] Neto | An unsupervised coarseto-fine algorithm for blood vessel segmentation in fundus images[END_REF]. Therefore, it is beneficial to enhance the image before processing. Since the green channel of the image provides the best contrast between the background tissue and vessel pixels [START_REF] Soares | Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification[END_REF], we process the image in this channel. We use a similar preprocessing method as in [START_REF] Neto | An unsupervised coarseto-fine algorithm for blood vessel segmentation in fundus images[END_REF]. The main operations include: contrast limited adaptive histogram equalization (CLAHE) [START_REF] Vostatek | Performance comparison of publicly available retinal blood vessel segmentation methods[END_REF], Gaussian smoothing, and a top-hat operator. The preprocessing pipeline is given in Fig 2 . We enhance the contrast of the green channel image GImg by CLAHE first to get EImg. Then a Gaussian filter with size of 3 × 3 is applied to the inverted EImg (that is 255 -EImg) to get SImg. A morphological top-operator with isotropic disk of radius 6 is applied to SImg to generate T Img6. We enhance T Img6 by using a gray-level intensity mapping such that 1% of the data is saturated at the high and low intensity values to get EImg6.

Connected tube-MPP model

The connected-tube MPP model can be used for curvilinear structure extraction in both 2D and 3D images. In this section we briefly review the connected-tube MPP model and show its performance on retinal vessel extraction. For the 2D image, let Y denote the observed image, S the image is the inner area, the red region is the outer area, which has width of 2 pixels, the blue region and yellow region are the front connection area and back connection area, respectively. A tube object is defined as a vector w i = (S i , M i ) ∈ W , where W ⊆ S × M . Let Ω W be the configuration space, which denotes the space of all possible realizations in W . Then w = (w 1 , w 2 , ..., w n ) ∈ Ω W is a possible object configuration, where n is the number of objects in this configuration.

The density of the marked point process is given by

f (w|y) = 1 Z exp{-V d (y|w) -V p (w)} ( 1
)
where Z is the normalizing constant, V p (w) is the prior energy introducing the prior knowledge on the object configuration. V d (y|w) is the data energy, which describes how well the objects fit the observed image. As in [START_REF] Li | A Connected-Tube MPP Model for Object Detection with Application to Materials and Remotely-Sensed Images[END_REF] we include the overlap prior, tube length prior and connection prior. The optimization of the model is realized by the Birth and Death kernel and Perturbation kernel. More detail about the model is stated in [START_REF] Li | A Connected-Tube MPP Model for Object Detection with Application to Materials and Remotely-Sensed Images[END_REF].

We input the preprocessed image DImg into the connectedtube model to extract the vessel networks. Fig 4 presents the extracted vessels from STARE-im0163. By drawing all the tubes (including the inner and outer area) as a binary image, we get 0.8819 recall and 0.8035 accuracy on average for the STARE dataset. Since the tube model does not segment the image pixel-wise, the accuracy is low at this object level. Though the accuracy is not high, it gives us the position of most vessels and their approximate width, which provide useful information and are helpful for fine segmentation of vessels.

(a) Detected tube network.

(b) Skeleton of vessels.

VESSEL SEGMENTATION

With the vessel network extracted using the connected-tube MPP model, the tubes with bad connection or bad contrast are removed and then we segment the vessels from the expanded tube object area. To ensure the vessels are contained in the object area, we expand the width of the detected tubes by 4 pixels. And each step, we take three connected tubes for segmentation, as shown in Fig 5 . The three green tubes are connected, and the segmentation area includes both the tube object area (inner shaded area) and the expanded area (orange shaded area). We deal with the segmentation task as a clustering problem. Given the pixels in the segmentation area, we partition these pixels into two clusters, cluster C 1 for vessel pixels (labeled as 1) and cluster C 0 for background pixels (labeled as 0). The clustering process is realized by a K-means [START_REF] Hartigan | Algorithm as 136: A k-means clustering algorithm[END_REF] framework with a distance measure defined hereafter.

Due to the distortions in the vessel images, such as central vessel reflex and noisy vessel boundaries, it is ineffective to use gray values F g as the only feature for clustering. To deal with the distortions, we introduce two other features to describe each observation. One is location-based feature F d , which is defined as the Euclidean distance between the pixel and the nearest center line of the tube objects. The other one F r is the ratio of pixels belonging to C 1 among a pixel's 8 neighbor pixels. F d encourages the pixels that are far away from the center line to be labeled as 0. F r guarantees the consistency between close pixels.

With F g , F d , F r , we define the distance between a pixel p and cluster C 0 by Eq. ( 2) and the distance between p and C 1 using Eq. ( 3).

D 0 (p) = w g DistG 0 (p) + w d (1 -Rad(p)) + w r F r (p) (2) D 1 (p) = w g DistG 1 (p) + w d Rad(p) + w r (1 -F r (p)) (3)
where w g , w d , w r are weights, DistG 0 (p) and DistG 1 (p) measure the gray distance of p to the two clusters:

DistG 0 (p) = 1 -exp -(F g (p) -u 0 ) 2 w g δ 2 0 + 1 (4) DistG 1 (p) = 1 -exp -(F g (p) -u 1 ) 2 w g δ 2 1 + 1 (5)
Rad(p), defined in Eq. 6, measures the location distance between p and cluster C 1 . The location distance between p and cluster C 0 is given as 1 -Rad(p).

Rad(p) = 1 -exp -F d (p) 2 w c R(p) 2 -0.5 (6)
where w g and w c are weights; R(p) is the half width b of the tube that is close to p; u 1 is the mean gray value of the pixels labeled as 1, which we take as the center of C 1 ; u 2 is the mean value of the pixels labeled as 0, which is the center of C 0 .

To initialize u 1 and u 0 , we label pixels in the expanded area as 0, and the pixels on the center line of tubes as 1. Others pixels are considered to be unknown. The center line of tubes can be taken as the vessel skeleton, as in Fig 4 (b), for example. We update the label of each pixel in each iteration according to its distance to C 1 and C 0 respectively. If DistG 1 (p) < DistG 0 (p), then pixel p is labeled as 1, otherwise it is labeled as 0. Also u 1 , u 0 , C 1 and C 0 are updated at each iteration. The clustering process stops when maximum iteration 20 is reached or both u 1 and u 0 are not changed between previous iteration and current iteration.

EXPERIMENTS

To demonstrate the performance of our method, we test it on two publicly available databases: STARE [START_REF] Hoover | Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response[END_REF] and DRIVE [START_REF] Staal | Ridge-based vessel segmentation in color images of the retina[END_REF]. STARE database contains 20 color retinal images (700 × 605) in which 10 of them are from individuals with medical conditions affecting the eye. The DRIVE database contains 40 color retinal images (565 × 584). The DRIVE images are divided into training and test sets, each including 20 images. For each database, there are two manual segmentations made by different experts. To compare our method with others' methods, we take the first observer's segmentation as ground truth; only pixels inside the field of view (FOV) are used. The comparison of our method with state-of-the-art algorithms (supervised methods: Fraz et al. [START_REF] Fraz | An ensemble classification-based approach applied to retinal blood vessel segmentation[END_REF], Orlando et al. [START_REF] Orlando | A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images[END_REF], Oliveira et al. [START_REF] Oliveira | Retinal vessel segmentation based on fully convolutional neural networks[END_REF], Li et al. [START_REF] Li | Residual u-net for retinal vessel segmentation[END_REF], and unsupervised methods: Zhang et al. [START_REF] Zhang | Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores[END_REF], Neto et al. [START_REF] Neto | An unsupervised coarseto-fine algorithm for blood vessel segmentation in fundus images[END_REF]) on STARE and DRIVE datasets are given in Table 1 and Table 2. It can be seen that we get the best Se among all these methods and the best G score among the unsupervised methods. From our analysis, there are mainly two reasons for the relatively lower Sp and Acc scores. One is that the edge pixels tend to be classified as vessel pixels due to the top-hat operation in preprocessing; the other one is the noise caused by bad tissues. We will address these problems in future work.

CONCLUSIONS

In this paper, an unsupervised segmentation algorithm for retina vessel segmentation is proposed. By applying connectedtube MPP model to the enhanced retina image, the vessel network can be extracted accurately. Then a tube-based segmentation algorithm is applied to the expanded tube object area for vessel segmentation. Experiments on STARE and DRIVE databases show the performance of the proposed method is better than other unsupervised segmentation algorithms in G score. We get high sensitivity but relatively lower accuracy and specificity. In future work, we expect to improve accuracy and specificity by analyzing the structure of the extracted tube networks and the contrast quality for each tube.

Fig. 1 .

 1 Fig. 1. The framework of the proposed method.

Fig. 2 .

 2 Fig. 2. The pipeline of preprocessing.

  Finally we generate 2 images as DImg = CLAHE(EImg -0.22EImg6) and DImg6 = CLAHE(EImg -EImg6). DImg is fed to the connected-tube MPP model for vessel network extraction. DImg6 is used for segmentation of vessels, as the pixels of a vessel tend to be darker than the background. One example (the im0163 image in STARE) of the preprocessing result can be seen in Fig 3: (a), (b) and (c).

  (a) Color image. (b) DImg6. (c) DImg. (d) Shape model of a tube.

Fig. 3 .

 3 Fig. 3. Example of image preprocessing for im0163 in STARE and the shape tube model.

Fig. 4 .

 4 Fig. 4. Example of vessel extraction.

Fig. 5 .

 5 Fig. 5. Illustration of a segmentation area.

  The sensitivity(Se = T P T P +F N ), specificity(Sp = T N T N +F P ), accuracy(Acc = T P +T N N ), and G-mean(G = √ Se × Sp), are used for performance evaluation and comparison. N is the total pixels inside the FOV, TP is true positive, TN is true negative, FN is false negative and FP is false positive. The parameters for the connected-tube MPP model are set as a min = 3, a max = 16, b min = 1, b max = 6. For the segmentation algorithm, we set w g = 0.58, w d = 0.27, w r = 0.15, w g = 1.75, w c = 1.63 by trial and error. Fig 6 shows the qualitative results for one retinal image im0077 from STARE. We can see that the vessel networks are extracted accurately by our connected-tube MPP model. For this image, the segmentation scores are Se = 0.9220, Sp = 0.9549, Acc = 0.9514, G = 0.9383.

  (a) Original image. (b) Extracted tube network. (c) Ground truth. (d) Segmentation result.

Fig. 6 .

 6 Fig. 6. Qualitative result of im0077 in STARE.

Table 1 .

 1 Results on STARE dataset (SV is supervised, USV

	is unsupervised)
	Method	Se	Sp	Acc	G
	2nd observer	0.8956 0.9381 0.9346 0.9166
	S Fraz et al.(2012)	0.7548 0.9763 0.9534 0.8584
	V Orlando et al.(2017) 0.7680 0.9738 0.9515 0.8648
	Oliveira et al.(2018) 0.8315 0.9858 0.9694 0.9053
	Li et al.(2019)	0.8101 0.9795 -	0.8905
	U Zhang et al.(2016)	0.7791 0.9758 0.9554 0.8720
	S Neto et al.(2017)	0.8344 0.9443 -	0.8876
	V Proposed method	0.8394 0.9536 0.9422 0.8932

Table 2 .

 2 Results on DRIVE dataset (SV is supervised, USV

	is unsupervised)
	Method	Se	Sp	Acc	G
	2nd observer	0.7760 0.9725 0.9473 0.8687
	S Fraz et al.(2012)	0.7406 0.9807 0.9480 0.8522
	V Orlando et al.(2017) 0.7897 0.9684 0.9454 0.8745
	Oliveira et al.(2018) 0.8039 0.9804 0.9576 0.8878
	Li et al.(2019)	0.7969 0.9799 -	0.8837
	U Zhang et al.(2016)	0.7743 0.9725 0.9476 0.8678
	S Neto et al.(2017)	0.7806 0.9629 -	0.8670
	V Proposed method	0.8063 0.9529 0.9339 0.8761