Yseult Hejja Brichard

Michel Raymond

Innes C Cuthill

Tamra C Mendelson

Julien P Renoult

From natural to sexual selection: Revealing a hidden preference for camouflage patterns

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

Introduction

Sexual ornamentation in animals is often pulled in opposite evolutionary directions. Sexual selection driven by mate preferences is generally thought to lead to conspicuousness or detectability, to draw the attention of potential mates [START_REF] Andersson | Sexual Selection[END_REF], whereas natural selection should lead to the opposite, favouring camouflage over detectability to avoid attracting predators [START_REF] Endler | Evolutionary ecology of neotropical freshwater fishes: Proceedings of the 1st international symposium on systematics and evolutionary ecology of neotropical freshwater fishes, held at DeKalb[END_REF]. Evidence for such a conflict has been shown in guppies, Poecilia reticulata, in which female preferences are biased towards detectable orange colouration [START_REF] Breden | Male predation risk determines female preference in the Trinidad guppy[END_REF][START_REF] Endler | Geographic Variation in Female Preferences for Male Traits in Poecilia Reticulata[END_REF][START_REF] Schwartz | A test for the parallel co-evolution of male colour and female preference in Trinidadian guppies (Poecilia reticulata)[END_REF] and males develop more intense orange colouration when predation is relaxed [START_REF] Endler | Natural Selection on Color Patterns in Poecilia Reticulata[END_REF][START_REF] Endler | Natural and sexual selection on color patterns in poeciliid fishes[END_REF]. Similarly, in agamid lizards, species with colour-based sexual signals on exposed body parts tend to occupy closed habitats, allowing increased protection from predators, as opposed to species with more concealed ornaments, which tend to be found in more open habitats and thus exposed to predators [START_REF] Stuart-Fox | Sexual selection, natural selection and the evolution of dimorphic coloration and ornamentation in agamid lizards[END_REF]. While conflicts between natural and sexual selection are generally thought to play an important role in shaping sexual signal diversity [START_REF] Gomez | The role of nocturnal vision in mate choice: females prefer conspicuous males in the European tree frog (Hyla arborea)[END_REF][START_REF] Ryan | Perceptual Biases and Mate Choice[END_REF][START_REF] Waitt | Evidence from rhesus macaques suggests that male coloration plays a role in female primate mate choice[END_REF], many of the best examples are based on ornament colour [START_REF] Delhey | Evolutionary predictors of the specific colors of birds[END_REF]. However, sexual signals also include patterns, which for visual images describe the spatial organisation of features, including colours and also shades of grey. Although visual patterns are receiving increased attention in research on sexual signals [START_REF] Endler | Animal Visual Systems and the Evolution of Color Patterns: Sensory Processing Illuminates Signal Evolution[END_REF][START_REF] Stoddard | Animal Coloration Patterns: Linking Spatial Vision to Quantitative Analysis[END_REF], the question of whether and to what extent they are shaped by conflicts between natural and sexual selection has so far remained almost unexplored.

One problem is that conflicts between natural and sexual selection in the evolution of communication signals are primarily framed as a problem of signal detection, perhaps because of a historic focus on signal colour. Detection, however, is only one aspect of communication. The cost of processing a signal should also influence the evolution of signal design. Neural processing is metabolically expensive, and brains evolve to acquire fitness-relevant information efficiently, i.e., at low cost. For visual signals, efficiency is achieved by matching signal design with the sensitivity (i.e., tuning) of visual neurons [START_REF] Barlow | Possible Principles Underlying the Transformations of Sensory Messages[END_REF], at the level of the retina and beyond, making the signal less costly to process. Thus, because visual systems are tuned to efficiently process the statistics of natural scenes [START_REF] Olshausen | Sparse coding with an overcomplete basis set: A strategy employed by V1?[END_REF][START_REF] Simoncelli | Natural Image Statistics and Neural Representation[END_REF], any stimulus that mimics natural visual statistics is also efficiently processed.

The efficient processing of natural visual statistics means that, for patterns, natural and sexual selection need not be in conflict. Indeed, several studies demonstrate that humans and other animals exhibit a general preference for efficiently processed stimuli [START_REF] Fantz | Form preferences in newly hatched chicks[END_REF][START_REF] Munar | Common Visual Preference for Curved Contours in Humans and Great Apes[END_REF][START_REF] Reber | Processing Fluency and Aesthetic Pleasure: Is Beauty in the Perceiver's Processing Experience?[END_REF][START_REF] Renoult | Beauty is in the efficient coding of the beholder[END_REF], termed "processing bias" [START_REF] Renoult | Processing bias: extending sensory drive to include efficacy and efficiency in information processing[END_REF]. A preference for efficiently processed stimuli likely explains why people also tend to prefer images whose statistical properties match those of natural visual scenes (e.g. ( 23)). In nature, background-matching is a type of camouflage whereby the colour and pattern of an animal's phenotype mimic those of its surroundings (see e.g. (24). Given that background-matching relies on matching the stimulus visual statistics with those of the habitat, the processing bias hypothesis predicts that, once detected, camouflage patterns should also be attractive to the observer. Small changes that increase the detectability of background-matching patterns to conspecifics could therefore increase the attractiveness of the signaler, thus aligning natural and sexual selection. This important but as yet untested prediction suggests that naturally selected camouflage patterns could serve as evolutionary precursors of sexual signals.

Here, using humans as an animal model, we test the hypothesis that camouflage patterns are intrinsically visually attractive, and thus that natural and sexual selection can have similar effects on signalling patterns. In a first experiment, we used a detection task to assess the effectiveness of camouflage of different patterned stimuli that varied in their level of background matching. In a second and third experiment, we evaluated the attractiveness of these patterns, using the same patterned stimuli as for the detection task but removing the detectability constraint. In the second experiment, stimuli were presented against a uniform grey background to assess the baseline attractiveness of the target stimuli, independent of the background. In the third experiment, stimuli were presented against the same patterned backgrounds as for the detection task, so that the degree of background matching varied across stimuli. The order of experiment presentation was randomised, and preferences were assessed using a two-alternative-forced-choice design. We predicted that the most effectively camouflaged stimuli according to the detection task would be the most attractive stimuli when rendered detectable.

Results and Discussion

Generating variation in camouflage effectiveness

To evaluate the attractiveness of camouflage patterns, we first needed to generate patterned visual stimuli that differ in their camouflage effectiveness. To do so, we modified the Fourier slope of white noise images to manipulate the relative distribution of their spatial frequencies (from low to high spatial frequencies, corresponding to coarse-grained and fine-grained patterns, respectively). We created circular images ("targets") and large square ("background") images and manipulated both the slope of the targets and the slope of their corresponding background in order to create different levels of background matching (examples in Figure 1A). We designed the detection task with targets randomly positioned onto background images.

We postulated that targets with a slope closer to the slope of the background would be better camouflaged, that is, they would be less frequently detected and require more time to detect. Our results confirmed this prediction: targets with a slope value closer to the background slope were the hardest to find (lower detection rates and higher detection times, Spearman correlation between detection rates and detection times, r² = -0.98 , N = 18) . Targets presented against the steepest background slope (BKG -3) were easier to detect compared to other background conditions (BKG -1 and BKG -2) (Figure 1B) . The effectiveness of camouflage in nature is mostly determined by two visual aspects: background matching and disruptive patterning (i.e., breaking up of the animal outline). The latter likely explains the limited variation in detectability we observed against the steepest background: with such a coarse-grained background, any effect of background matching is overruled by a high pattern disruption.

Overall, results of the first experiment corroborate previous studies showing a link between Fourier slope and camouflage effectiveness. For example, both octopuses and cuttlefish in camouflage mode have been shown to modify their body pattern such that the Fourier slope of their body matches that of their visual environment [START_REF] Josef | Camouflaging in a Complex Environment-Octopuses Use Specific Features of Their Surroundings for Background Matching[END_REF][START_REF] Zylinski | To Be Seen or to Hide: Visual Characteristics of Body Patterns for Camouflage and Communication in the Australian Giant Cuttlefish Sepia apama[END_REF]. In damselfish, matching the spatial frequencies of the background reduces the likelihood of being attacked by predators [START_REF] Phillips | Disruptive colouration in reef fish: does matching the background reduce predation risk?[END_REF]. Our results complement these correlative studies, showing that the Fourier slope can be manipulated to generate variation in the camouflage effectiveness of background-matching stimuli.

Preference for natural statistics in abstract visual patterns

Our second experiment tested whether the patterns we generated for the targets in the first experiment also varied in their attractiveness. In a two-alternative forced-choice task (2-AFC), we assessed participants' preferences for the targets used in the detection task. The targets in this experiment were presented in pairs against a uniform grey background (examples in Figure 2A). We calculated a preference score for each target using the Élő rating method, an iterative algorithm that performs pairwise comparisons and is commonly used to measure preference (see e.g. [START_REF] Clark | Why rate when you could compare? Using the "EloChoice" package to assess pairwise comparisons of perceived physical strength[END_REF][START_REF] Tribot | Confronting species aesthetics with ecological functions in coral reef fish[END_REF]).

Targets with differing Fourier slopes elicited markedly different preferences (Figure 2). The resulting data can be fitted with a simple quadratic model (ranking~slope + slope²), which predicts a preference peak for a target slope of -2.36 (fit adjusted R²: 0.9136). This preference peak matches the preferred peak value reported in previous studies (see e.g. [START_REF] Spehar | Beauty and the beholder: the role of visual sensitivity in visual preference[END_REF]). The most widely accepted explanation for this value is that it matches the Fourier slope that is dominant in the natural visual environment (between -1.81 and -2.4 depending on the study, as reported by [START_REF] Pouli | Image Statistics in Visual Computing[END_REF].

In order to confirm that the slope with the peak preference score in our second experiment corresponds with the dominant slope of natural scenes, we used our code to estimate the slope of 4,319 images of the Landscape Pictures dataset [START_REF] Rougetet | Landscape Pictures[END_REF] which contains images of different types of landscapes (e.g. mountain, beach, desert). Methods of Fourier slope calculation vary across the literature, thus any comparison of slope values across datasets must be based on the same calculation. We found an average slope of -2.39 for the images in the Landscape Pictures dataset. This is very similar to the preference peak inferred from our experiment on a grey background (-2.36), further corroborating the general finding that people prefer the Fourier slope of natural scenes. In comparison, human faces have an average slope of around -3.35 (+/-0.08) as tested on the Chicago (CFD and CFD-MR, [START_REF] Ma | The Chicago face database: A free stimulus set of faces and norming data[END_REF] and the FACES (34) datasets and close to what is reported in the literature [START_REF] Keil | Does face image statistics predict a preferred spatial frequency for human face processing?[END_REF][START_REF] Redies | Artists portray human faces with the Fourier statistics of complex natural scenes[END_REF] and human-made objects such as furniture also have steeper slopes, around -3.89 (+/-0.27), as tested on the Bonn dataset (37) and also similar to what was previously reported for this kind of objects [START_REF] Torralba | Statistics of natural image categories[END_REF]. This result, combined with those from the detection task, validate the use of the Fourier slope to manipulate both the camouflage effectiveness and the attractiveness of stimuli.

For display reasons, the background colour here is lighter than that of the stimuli used in the experiment (see Methods). (B) Élő rankings for the 2-AFC task with a grey background. Each condition (i.e. target slope values) is represented by five closed circles (made more visible using jittering) corresponding to five different stimuli versions. Dots indicate the mean ranking values for each of the five stimuli versions of each condition (indicated on the x-axis). Thin box plots give the variability of preference scores across those versions. The dotted line indicates a quadratic fit of the data.

A preference in humans for the Fourier slope of natural scenes has been reported in numerous studies in experimental psychology. In a particularly compelling study, [START_REF] Spehar | Beauty and the beholder: the role of visual sensitivity in visual preference[END_REF] showed that stimuli to which people have the highest sensitivity (i.e. absolute detection, discrimination, and contrast sensitivities) are also the most attractive. Their result emphasises the influence of efficient processing on visual preferences: patterns whose properties coincide with visual sensitivity are processed more efficiently by the visual system and are generally preferred over patterns that are further away from visual sensitivity peaks. These and our own results are therefore consistent with the processing bias hypothesis of [START_REF] Renoult | Processing bias: extending sensory drive to include efficacy and efficiency in information processing[END_REF], which posits that the spatial statistics of natural scenes are attractive because they are the easiest to process by the brain. They also help explain a large body of results in empirical aesthetics, which show that visual artwork imitates natural spatial statistics [START_REF] Graham | Statistical regularities of art images and natural scenes: Spectra, sparseness and nonlinearities[END_REF][START_REF] Graham | Statistical regularities in art: Relations with visual coding and perception[END_REF]. For example, faces painted by portrait artists across time and cultures tend to have a slope value matching that of natural landscapes even though real faces typically do not, suggesting that artists increase the attractiveness of their work by unconsciously mimicking the spatial statistics of natural scenes [START_REF] Redies | Fractal-like image statistics in visual art: similarity to natural scenes[END_REF].

A preference for the Fourier slope of natural scenes is not only limited to humans. [START_REF] Hardenbicker | Peacock spiders prefer image statistics of average natural scenes over those of male ornamentation[END_REF] recently discovered that peacock jumping spiders prefer images with the Fourier slope of natural scenes over two other options. In addition, in several fish species, [START_REF] Hulse | Sexual signaling pattern correlates with habitat pattern in visually ornamented fishes[END_REF] found a correlation between the Fourier slope of species-typical habitats and the sexual signalling patterns of males (but not females). That study did not measure preferences, so whether fish are attracted to natural Fourier slopes remains to be tested, but a correlation between the spatial statistics of male sexual signals and their habitat is reminiscent of studies that demonstrate a correlation between the Fourier slope of human artwork and natural scenes.

Hidden preference for camouflage patterns

In our third experiment, we assessed the attractiveness of patterns that varied in their degree of background matching. We used the same stimuli as for the detection task (i.e., targets were displayed in front of a patterned background, rather than a grey background as in the previous experiment), but we removed the detectability constraint by circling and centering the targets. All targets therefore were assumed to be equally conspicuous. We assessed participants' preferences for different levels of background-matching patterns with a 2-AFC task and computed a preference score for each combination of background matching using the same Élő-ranking method as in the previous experiment.

Overall, participants preferred targets with a slope similar to the baseline preference, defined as the peak preference estimated from targets against the grey background (-2.36, as determined in the second experiment), as expected (Figure 3). However, we found that this preference was modulated by the level of background matching. Using linear regression modelling with preference scores as a response variable and the difference between target slope and background slope, target slope, and a quadratic term for background slope as explanatory variables, we found that the most preferred target stimuli were those with the lowest slope difference between target and background, i.e., with the highest background matching (estimate: -120.292 ± 12.015; t = -10.012, df = 89.413, p = 0.000, see Table S2). Similar significant effects were also obtained when data for each background were analysed separately (Table S3). This, the main result of this study, indicates that stimuli that were more effectively camouflaged in the detection experiment were also more attractive when rendered detectable.

Figure 3. Preference task on patterned background. (A) Exemplars of presented stimuli with target values indicated below each image. (B) Élő rankings for the 2-AFC task with a patterned background. Each condition (i.e. one combination of background slope and target slope) is represented by five closed circles (made more visible using jittering) corresponding to five different stimuli versions. Thin box plots give the variability of preference scores across those 5 stimuli versions. Background slope values are colour-coded: blue for -1, orange for -2, and grey for -3. Dashed lines represent a quadratic fit to the data for each background separately (-1, -2, and -3) and arrows indicate the corresponding background value. C) Heuristic representation of the observed shift of preference of visual patterns away from the general baseline value of -2.36 (pink curve, a peak value obtained from Experiment 2) as a result of the modulation of the background slope.

Last, we fitted a simple quadratic model (ranking~slope + slope²) to find the target slope that predicts the highest attractiveness ranking for each background (based on the data shown in Figure 3). For a background slope of -1, a target slope of -1.92 predicts the highest ranking (77.67, fit adjusted R²: 0.9993). For a background slope of -2, a target slope of -2.14 predicts the highest ranking (44.06, fit adjusted R²: 0.9418). For a background slope of -3, a target slope of -2.51 predicts the highest ranking (80.32, fit adjusted R²: 0.9943). These results indicate that preferences for visual patterns can be shifted away from a general baseline value (for the Fourier slope: -2.36) and toward the value of the background, as predicted by a preference for background-matching stimuli and the processing bias hypothesis.

Notably, a similar modulation of visual attractiveness by background statistics was shown for human faces. [START_REF] Menzel | Fourier Power Spectrum Characteristics of Face Photographs: Attractiveness Perception Depends on Low-Level Image Properties[END_REF] varied the Fourier slope of the background against which faces were presented and asked participants to rate their attractiveness. They found that participants rated faces as more attractive when those faces were presented against a background that was closer to the Fourier slope of both natural scenes (-2) and faces (-3) compared to other background slopes (-1 and -4).

These results have important implications for animal sexual signalling. First, on a short time scale, they suggest that animals should track the pattern of the visual background against which they are displaying. If able, they should respond behaviourally or physiologically to match the statistical features of their patterns with those of the background. Similarly, if the sexual signal changes, as observed in some fish that change sex during development, individuals should move to another displaying site. Previous studies have shown that animals choose their display site in relation to their own sexual signals [START_REF] Endler | Interacting Effects of Lek Placement, Display Behavior, Ambient Light, and Color Patterns in Three Neotropical Forest-Dwelling Birds[END_REF][START_REF] Fleishman | Can Sensory Drive Explain the Evolution of Visual Signal Diversity in Terrestrial Species? A Test with Anolis Lizards[END_REF]. Those studies, however, focused on coloration, showing that animals gain attractiveness by maximising the contrast with the background and the ambient light. Our results extend these findings to signal patterns, but predict that, for this signal component, animals would benefit from mimicking the visual environment.

On an evolutionary scale, our results suggest that signal patterns will be shaped by the spatial statistics of the environment. Because the visual system adapts to efficiently process the spatial statistics of natural environments, patterns that mimic those statistics will also be efficiently processed and therefore attractive [START_REF] Renoult | Processing bias: extending sensory drive to include efficacy and efficiency in information processing[END_REF]. This prediction of the processing bias hypothesis is an extension of "sensory drive," which proposes a similar mechanism for detectability, with animal signals evolving to be more easily detected [START_REF] Endler | Sensory ecology, receiver biases and sexual selection[END_REF]. The African cichlid fish Pundamilia nyererei, for example, lives in an environment with red-shifted light. Females exhibit an increased expression of red sensitive photoreceptors that render red stimuli more detectable, and males have evolved reddish coloration [START_REF] Seehausen | Speciation through sensory drive in cichlid fish[END_REF]. The sensory drive hypothesis is supported mainly by studies on colouration; our results suggest that expanding sensory drive beyond the framework of signal detection will allow us to predict the evolution of signal patterns, in addition to colouration.

Conclusion

Because most approaches to understanding the evolution of signal design are based on signal detection, natural and sexual selection are traditionally pitted in conflict along an axis of detectability. Our results suggest instead that these mechanisms can act synergistically. Natural selection generally favours camouflaged signals, such as background-matching patterns. Due to their low detectability, those camouflaged patterns are not deemed particularly attractive. However, if a mutation occurs that makes camouflaged patterns more easily detectable, for example by adding colour or a sharp outline, their attractiveness should increase because of their processing efficiency. If predation is a strong and present danger, those conspicuous additions may not increase in frequency. However, if a change in ecology occurs that decreases the intensity of natural selection by predation, hidden preferences for those efficiently processed patterns are predicted to lead to an increase in the attractive phenotypes. In this way, camouflage patterns could be evolutionarily co-opted by sexual selection (Figure 4). Here, we provide the first evidence that background matching patterns are attractive, and although demonstrated here only in humans, the visual mechanisms underlying this result are expected to generalise to other species, given similarities in the early visual systems of humans and those of macaques [START_REF] Fize | Humans and monkeys share visual representations[END_REF], cats (50), mice (51) and birds [START_REF] Clark | Neurons in the pigeon visual network discriminate between faces, scrambled faces, and sine grating images[END_REF].

Figure 4. Evolution from camouflage to sexual signalling through the exploitation of processing bias. 1. Natural selection due to predation generally results in inconspicuous phenotypes. Phenotypes with a pattern that matches that of the environment ('inconspicuous pattern') are well-camouflaged and thus gradually increase in frequency in the population. Such phenotypes typically do not elicit strong preferences among conspecifics because of their low detectability. 2. Any new mutation increasing conspicuousness should be negatively selected by predation (low survival)

. However, because they are highly detectable by potential mates and thus reduce their cost of mating, these phenotypes are potentially attractive. Among conspicuous phenotypes, camouflage patterns that match the visual structure of the habitat ('conspicuous patterned') should be preferred (ΔP) over those that do not ('conspicuous plain') because they are even more efficiently processed. In this study, we have provided the first empirical evidence supporting this prediction. 3. These 'sleeping beauties' would remain negatively selected by natural selection until a change in ecological factors (e.g., colonisation of habitats or regions with fewer predators, the appearance of new, alternative prey, predators' focus on new sensory cues) makes sexual selection strong enough to counteract the effects of predation on phenotype evolution 4. Thus, conspicuous phenotypes that have evolved from camouflage patterns can be evolutionarily co-opted by sexual selection.

To fully test the hypothesis that camouflage patterns are evolutionary precursors of sexual signals will require additional evolutionary and behavioural studies. For example, ancestral state reconstruction can evaluate the rate of transitions from camouflage to sexual signals (for a similar approach estimating transitions from camouflage to aposematism, see [START_REF] Loeffler-Henry | Evolutionary transitions from camouflage to aposematism: Hidden signals play a pivotal role[END_REF]. Behaviourally, the choice of display sites in relation to local background patterning could suggest whether processing bias operates on a short time scale. By providing evidence of a processing bias, the key mechanism by which camouflage patterns can be co-opted to function as sexual signals, this study highlights the importance of considering the "psychological landscape" (54) of signal receivers, and of interfacing research in evolutionary biology with the vast literature in human empirical psychology.

Methods

Stimulus and experimental design

Stimulus design: Sets of stimuli for the three experiments were generated with the Python programming language (Python Software Foundation, https://www.python.org/). The procedure to create each image for the different experimental conditions for the first experiment was the following: two randomly generated white noise images (650 x 650 pixels), a target image and a background image, were first created and then Fourier transformed in order to change the slope of the relationship between the log-amplitude spectrum and the log-frequency corresponding to our different conditions (background image: slope = -1, -2, -3; target image: slope = background slope ± 0.25; ± 0.5; ±0.75). A circular selection with a radius of 80 pixels was selected from a random location of the target image and superimposed onto the background image at the same random coordinates. Targets could appear anywhere on the background, as long as they did not get cropped out of the frame. To eliminate pixelated edges, a very slight amount of blur was applied to the target edges. More specifically, a Gaussian filter (with a standard deviation of 0.5) was applied around the edge of each target before superimposing it onto the background image. Similarly, Gaussian filtering was applied to the background image at the target's location to smooth the contours around the target. This procedure had only a marginal effect on the slope of the amplitude spectrum of the entire targets/backgrounds. The final shape and dimensions of those stimuli were a square of 650 x 650 pixels. Those dimensions are fixed and do not vary with the monitor size of participants.

Using the same procedure, for the second and third experiments we created a second set of stimuli of larger dimensions (650 x 1300 pixels) to contain two targets, one centred in each half of the stimulus. To make the targets conspicuous (i.e. easily detectable), we outlined them with a dark grey contour (linewidth: 1.5 and pixel intensity of the grey colour: 64). The set of stimuli for the second experiment was created with a uniform, grey background (pixel intensity of the grey colour: 128) in contrast to the patterned background of experiment 3.

For each of the three experiments, we created 5 versions of stimulus images per condition to introduce variability in the pattern design. Thus, in total, experiment 1 had 18 different conditions (3 levels for the background variable: -1; -2; -3 and 6 levels for the difference between the background and the target slope: ± 0.25; ± 0.5; ± 0.75), each with 5 versions of the stimulus images, representing a total of 90 unique images. Experiment 2 with a patterned background had a total of 90 conditions (3 background slope values: -1; -2; -3, 2 targets with 6 slope differences each: ±0.25; ±0.5; ±0.75, and 2 sides: left or right), each with 5 stimulus versions, resulting in 450 unique images. Experiment 3 had a total of 78 conditions (1 background slope value: "grey", 2 targets with 6 slope differences each: ±0.25; ±0.5; ±0.75, and 2 sides: left or right), each with 5 stimulus versions resulting in 390 unique images. Note that paired targets were never identical.

Experimental design: Each participant engaged in two experiments: the detection task and one of the two forced-choice tasks (grey or patterned background), which was randomly assigned. The presentation order of the two experiments was counterbalanced between participants.

Detection task (Experiment 1):

Each stimulus was displayed at the centre of the webpage, one at a time in a randomised order for a maximum duration of 8000 ms. The intertrial interval was picked from a uniform distribution between 500 and 2000 ms (in steps of 1 ms). The task contained 21 trials. Each stimulus was randomly picked from the pool of the 90 created images and presented only once, with the exception of three of the ten first trials that were repeated at the very end to check for within-participant consistency. At the end of each trial (i.e. after the participant clicked on the stimulus or when the response time limit had been reached), participants received feedback in the shape of a yellow circle, highlighting the location of the target. Prior to the experimental trials, three practice trials were presented to make sure the participant understood the task. Participants were instructed to "Click on the target as fast as possible". Forced-choice task (Experiments 2 or 3): Stimuli were presented in a randomised order for a maximum duration of 12000 ms. The intertrial interval was picked from a uniform distribution between 500 and 2000 ms (in steps of 1 ms). The task contained 21 trials. Each pair of stimuli randomly picked from the pool of the 390 (grey background, Experiment 2) or 450 (patterned background, Experiment 3) created images was presented only once with the exception of three of the ten first trials that were repeated at the very end to check for within-participant consistency. Pairs of stimuli varied on two treatment dimensions: the background slope values (-1; -2; -3 or a grey background) and the position (left-side or right-side of the image) of the target. Importantly, both targets always differed in their slope value. Participants were asked the following question, displayed above the stimuli: "Compare the patterns in the two circles, which one do you prefer?". Questionnaires: The participants were asked 5 questions before and 2 questions after they performed both tasks. Questions were demographic in nature and related to education level and frequency of exposure to visual art (Table S1).

Online experiment and participant recruitment:

The experiment took place online on a website hosted by the Montpellier Bioinformatics Biodiversity platform (University of Montpellier, France) and is still available for demonstration purposes: http://isemsurvey.mbb.univ-montp2.fr/pattern/. The experiment was programmed using a custom HTML script (including JavaScript, CSS, and PHP) and jsPsych, a JavaScript library for creating online experiments [START_REF] De Leeuw | jsPsych: A JavaScript library for creating behavioral experiments in a Web browser[END_REF], and is available to play on all standard internet browsers. The experiment must be conducted with a desktop or laptop computer, excluding smartphones and tablets. Participants were recruited through advertisements on dedicated mailing lists and on social networks. Participants were not paid, and they had to be at least 16 years old. Approval from the French (deemed exempt, CNIL number 2-21044)) and American (deemed exempt, UMBC IRB #522) ethics committees was obtained prior to the start of the experiment. Prior to launching the online experiment, the study's objectives and planned analyses were pre-registered following open science practices (available on https://osf.io/nujh7/).

Measure of pattern preference

Preference scores for a target with a specific slope value are expressed as a ranking, computed via the Élő score (using the EloChoice package in R, v0.29.4; (56)), an iterative algorithm traditionally used in chess games and that performs pairwise comparisons. It is now frequently used as a measure of aesthetic value and is well suited for unbalanced design as the case of our study (see [START_REF] Tribot | Confronting species aesthetics with ecological functions in coral reef fish[END_REF][START_REF] Clark | Why rate when you could compare? Using the "EloChoice" package to assess pairwise comparisons of perceived physical strength[END_REF] for similar use; Goodspeed, 2017 for a comparison of different pairwise ranking systems). As in [START_REF] Clark | Why rate when you could compare? Using the "EloChoice" package to assess pairwise comparisons of perceived physical strength[END_REF], we used the elochoice function with initial Élő scores set to 0, and k set to 100. We ran 1000 simulations to make sure our ratings were stable. We ran the analysis image-wise, that is by considering each of five versions of a stimulus as an independent condition to account for potential variability between stimulus versions. Note that running the same code but by considering all five versions of a stimulus as the same condition did not yield different results.We estimated the minimum number of participants required to achieve stability in ratings using the raterprog function of the EloChoice package with the ratershuffle argument set to 10 to avoid a participant order effect. We found that using the data from 378 participants for images with a grey background (reliability index = 0.57, Figure S1) and from 263 participants was sufficient to reach stable ratings (reliability index = 0.59, Figure S2) for images with a patterned background.

Statistical analyses

We removed participants who did not finish the experiment and participants who took part in the experiment more than once (IP address verification procedure). In total, we kept the data of 1757 participants out of the 2567 participation events that were recorded (68.6%). For details on sociodemographic data, see supplementary material.

For the detection task, we removed trials where responses were faster than neurophysiological limits (i.e. below 150ms, see e.g. [START_REF] Thorpe | Speed of processing in the human visual system[END_REF]. For the two alternative forced-choice tasks, we found no lateral bias in participants' responses: the left target was chosen in 50.5 to 51% of the trials.

We fitted our data with linear mixed-effects models (lme4 package, [START_REF] Bates | Fitting Linear Mixed-Effects Models Using lme4[END_REF]) with REML set to false. We averaged the participants' Élő scores to obtain one score per image and used it as our dependent variable. Our independent variables (fixed effects) included the value of the difference between the background slope and the target slope (0.25, 0.50, 0.75) and the sign of the difference (positive or negative, coded as 1 and -1, respectively), the interaction between the slope value of the background and of the target, and the quadratic of the background slope. We added the value and the sign of the difference between the background slope and the target slope and the stimulus version (5 versions per stimulus type) as random effects. In addition to our main model, we ran three similar models for each background separately, removing the background variables (the interaction with the target slope and the quadratic of the background slope).

Data and code availability

An OSF repository contains the code to reproduce the stimuli and the images used for the online experiment in high resolution, the preprocessed data and the code to reproduce the main analyses and figures, and the pre-registration file [https://osf.io/nujh7/].

Participants recruitment and experiment questionnaire

Recruitment: Participants were recruited through advertisements on dedicated mailing lists and on social networks. Participants were not paid, and they had to be at least 16 years old, corresponding to the legal digital majority in the EU. Prior to the experiment, participants were asked to report their month and year of birth to enforce that rule. For American citizens aged between 16 and 18 years, participants were asked to obtain their parents' verbal approval before they participated in the experiment. Participants were asked to take part in the study only once. On top of informing them of that condition prior to the experiment, The IP address of the participant was only used to enforce that rule, and was not associated with personal information, following a protocol approved by the CNIL . Collected data were stored on-site.

All research was performed in accordance with relevant guidelines/regulations. In accordance with the French legislation, the protocols for this study have been submitted and approved by the French National Commission on Informatics and Liberty (CNIL number 2-21044). The CNIL, an independent public authority affiliated to the European Data Protection Board, is responsible for ensuring that information technology ethically serves the citizen and that it does not infringe on human identity, human rights, privacy or individual or public liberties. The present research is an online experiment with volunteers interacting remotely and anonymously, thus no ethical approval was required-nor possible-from an academic or scientific comity under French legislation, as their recourse is only possible if it is compulsory. The European GDPR (General Data Protection Regulation) was fully applied, and all participants were informed of the subject of the study.

Procedure: At the beginning of an experiment, participants were offered to choose the instruction language between French, English, and Spanish. After agreeing to the study details (consent form) and answering some questions about participants' demographic details (Table S1), the experiment started. In addition to the slope difference, our statistical model further revealed that the slope of the background against which targets are presented also significantly influenced attractiveness in the regression model run with all background conditions [estimate: 270.011 ± 27.951; t = 9.66, df = 79.829, p = 0.000] (see Table S2). Analysing results for each background separately confirmed the link between the preference for a target and the level of background matching across backgrounds. Those models (see Suppl Table S3) further showed that preference is influenced by the target slope for both background slopes of -1 [estimate: -122.748 ± 12.811; t = -9.581, df = 26, p = 0.000] and -3 [estimate: 126.47 ± 22.67; t = 5.578, df = 26, p = 0.000], though in opposite directions, but it is not with background slope of -2. This result indicates a preference for targets with slope values closer to -2 than to any other background slope value against which the target was presented, but only when presented against a background different from -2.

Figure 1 :

 1 Figure 1: Detection task. (A) Exemplars of presented stimuli with target values indicated below each image. (B) Detection rate (%) for the different conditions. Each condition (i.e. one combination of background slope and target slope) is represented by five closed circles (made more visible using jittering) corresponding to five different stimuli versions (see Methods). Thin box plots indicate the amount of within-condition variation in detection rates between the stimuli versions. Background slope values are colour-coded: blue for -1, orange for -2, and grey for -3. Dashed lines represent a quadratic fit to the data for each background separately (-1, -2, and -3). Arrows indicate the corresponding background value.

Figure 2 :

 2 Figure 2: Preference task on grey background.(A) Exemplar of a presented stimulus. Target slopes are -1.25 for the left target and -0.25 for the right one.For display reasons, the background colour here is lighter than that of the stimuli used in the experiment (see Methods). (B) Élő rankings for the 2-AFC task with a grey background. Each condition (i.e. target slope values) is represented by five closed circles (made more visible using jittering) corresponding to five different stimuli versions. Dots indicate the mean ranking values for each of the five stimuli versions of each condition (indicated on the x-axis). Thin box plots give the variability of preference scores across those versions. The dotted line indicates a quadratic fit of the data.

Table S1 : Experiment questionnaire and response options

 S1 Analysis of DevianceTable (Type II Wald chisquare tests)

	Stimulus version (Intercept) 3.885e-31	6.233e-16
	Background slope (Intercept) 8.417e-32	2.901e-16
	Residual	4.467e+02	2.113e+01
	Number of obs: 90, groups: target slope, 12; stimulus version, 5; background
	slope, 3
	Fixed effects
		Estimate	Std. Error df	t value	Pr(>|t|)
	(Intercept)	69.334	20.105	85.947	3.449	0.000875
	Slope diff. (value) -120.292	12.015	89.413	-10.012	2.89e-16
	Slope diff. (sign)	4.868	9.028	26.492	0.539	0.594279
	Background slope	270.011	27.951	79.829	9.660	4.55e-15
	Target slope	-263.859	22.341	27.701	-11.811	2.51e-12
	(background slope)² 134.334	7.368	88.940	18.232	< 2e-16
	Background slope	-133.711	7.052	34.594	-18.960	< 2e-16
	:target slope
	Response: ranking
		Chisq		Df	Pr(>Chisq)
	Slope diff. (value)	100.2344	1	< 2.2e-16
	Slope diff. (sign)	0.2907		1	0.5898
	Background slope	24.6102		1	7.018e-07
	Target slope	0.0423		1	0.8371
	(background slope)²	332.4018	1	< 2.2e-16
	Background slope	359.4748	1	< 2.2e-16
	:target slope
	Questions				Options
	Please indicate what best describes your		Female (cis/trans), Male (cis/trans), Non
	gender				binary, I prefer not to say, Other / I prefer
					to self-describe
	Please indicate your birth date			Year and Month
	Do you have uncorrected colour-blindness ?	Yes, No
	Education: select the option that			Graduate Degree, Associate Degree or
	corresponds to your highest qualification		Bachelors Degree, High School,
					Vocational training school certificate, CGE
					SSCE, PSC no diploma

Table S3 : Model for each background separately Background slope: -1

 S3

	Groups name		Variance	Std.Dev.
	Stimulus version (Intercept) 1.123e-31	3.352e-16
	Residual		2.051e+02	1.432e+01
	Number of obs: 30, groups: stimulus version, 5
	Fixed effects
		Estimate	Std. Error df	t value	Pr(>|t|)
	(Intercept)	-92.505	14.560	26.000	-6.353	9.96e-07
	Slope diff. (value) -60.486	12.811	26.000	-4.721	7.01e-05
	Slope diff. (sign) -1.270	6.919	26.000	-0.184	0.856
	Target slope	-122.748	12.811	26.000	-9.581	5.12e-10
	Background slope: -2
	Random effects
	Groups name		Variance	Std.Dev.
	Stimulus version (Intercept) 1.586e-31	3.983e-16
	Residual		3.391e+02	1.841e+01
	Number of obs: 30, groups: stimulus version, 5
	Fixed effects
		Estimate	Std. Error df	t value	Pr(>|t|)
	(Intercept)	46.936	34.119	26.000	1.376	0.1807
	Slope diff. (value) -143.801	16.469	26.000	-8.731	3.3e-09
	Slope diff. (sign) 15.397	8.895	26.000	1.731	0.0953
	Target slope	-12.482	16.469	26.000	-0.758	0.4553
	Random effects

Background slope: -3

	Random effects
	Groups name		Variance	Std.Dev.
	Stimulus version (Intercept) 8.913e-32	2.985e-16
	Residual		6.427e+02	2.535e+01
	Number of obs: 30, groups: stimulus version, 5
	Fixed effects
		Estimate	Std. Error df	t value	Pr(>|t|)
	(Intercept)	450.92	69.12	26.00	6.524	6.46e-07
	Slope diff. (value) -143.00	22.67	26.00	-6.307	1.12e-06
	Slope diff. (sign) -12.90	12.25	26.00	-1.054	0.302
	Target slope	126.47	22.67	26.00	5.578	7.37e-06

Acknowledgements

We would like to thank Claudia Ximena Restrepo-Ortiz for her help with the Spanish translation of the online experiment. This work was supported by the National Science Foundation grant NSF IOS 2026334.

Supplementary material

Supplementary Materials

Materials and Methods Supplementary Text Figs. S1 and S2 Tables S1 to S3 References What is your country of current residency ? Does your job or any of your hobbies involve visual artistic activity or visual creation? How often do you go to art exhibitions (museums, galleries)?

List of countries

Not at all, Sometimes, Often, Daily Once a week, Once a month, Once a year, Never

Sociodemographic data

Participants whose data were included in the analysis were 61% women (n=1069), 36% men (n=629), and 3% in other categories (non-binary: 38, prefer to self-describe: 5, prefer not to say: 16). A majority of participants had graduate degrees (66%, n=1162). 35 participants were colour-blind, representing less than 2% of the participants. Most participants were born after 1980, with a peak in the early 2000s, corresponding to undergraduate students who took part in the study as a way to get extra credit. When asked about their hobbies, most participants (82%) reported having hobbies that don't involve visual creativity or only rarely. Most participants go to art exhibitions once a year or less (82%). Participants were mostly located in France (46%) and in the US (29%) with an additional good representation of Western Europe and Canada. Our participants are therefore WEIRD (from Western, Educated, Industrialised, Rich and Democratic countries as described in Henrich et al., 2010), which was expected given our advertising methods (mostly academic or professional mailing lists in Western countries).

It is possible that certain demographic traits, as well as higher exposure to visual art or creativity, might influence stimulus preferences. Therefore, we looked for potential relationships between demographic variables (age, gender, education level) and the ranking scores, and between visual exposure to art (presence of visual arts activity in jobs or hobbies, and frequency of art exhibition visits) and the ranking scores. For each demographic variable of interest, we computed the ranking score for patterned and grey images for a subset of the data, corresponding to the targeted participants. We then randomly sampled from the whole participant dataset a subset of the same size and computed the ranking for those participants as well (for both patterned and grey images). After making sure the rankings of both subsets (or groups of participants) were normally distributed, we compared them using a t-test.. Overall, no difference was found for any of the tested sociodemographic variables.

Linear models


Yseult Héjja-Brichard

Kara Million

Julien P Renoult

Tamra C Mendelson

Using generative artificial intelligence to test hypotheses about animal signal evolution: A case study in an ornamented fish

The sensory drive hypothesis of animal signal evolution suggests that animal communication signals evolve in response to environmental pressures. While classical approaches to testing this hypothesis focus on one aspect of the signal, deep learning techniques like generative models can create and manipulate stimuli without targeting a specific feature. Here, we used a technique called style transfer to experimentally test preferences for colour patterns in a fish. We manipulated how similar or dissimilar male body patterns were to their habitats using the Neural Style Transfer (NST) algorithm. We predicted that males whose body patterns are similar to their habitats are easier to process and thus preferred by conspecifics. Our findings suggest that both males and females tend to be sensitive to habitat congruence in their preferences, but to different extents, requiring additional investigation. Nonetheless, this study demonstrates the potential of deep learning techniques in testing hypotheses about animal communication signals.

Introduction

Animals are characterized by a vast array of colours and patterns used for signalling. In the context of mating in particular, understanding the drivers of such diversity in sexual signals has been a central focus of evolutionary biology. Several hypotheses have attempted to explain the evolution of those sexual signals, one of the most influential being the sensory drive hypothesis [START_REF] Endler | Signals, Signal Conditions, and the Direction of Evolution[END_REF][START_REF] Endler | Sensory ecology, receiver biases and sexual selection[END_REF][START_REF] Seehausen | Speciation through sensory drive in cichlid fish[END_REF]. Based on signal detection theory, sensory drive suggests that the exploitation of some sensory biases, driven by the environment, leads to the development of different signals. Evidence supporting this hypothesis has been reported in many studies and species. A recent example is the case of the colourful dewlap of Anolis lizards. [START_REF] Fleishman | Can Sensory Drive Explain the Evolution of Visual Signal Diversity in Terrestrial Species? A Test with Anolis Lizards[END_REF] analysed the dewlap colours of 17 Anolis species and examined the relationship between colour and habitat light intensity of the different species. They found that red dewlaps are more visible in bright environments and white dewlaps in darker environments. This result complements previous studies [START_REF] Fitch | The Anolis Dewlap: Interspecific Variability and Morphological Associations with Habitat[END_REF][START_REF] Fleishman | The Influence of the Sensory System and the Environment on Motion Patterns in the Visual Displays of Anoline Lizards and Other Vertebrates[END_REF] showing that Anolis species with red or orange dewlaps are more often found in bright habitats, while species with white or yellow dewlaps tend to live in darker-lit habitats. This is a strong case in favour of sensory drive, suggesting an adaptation of the dewlap colours to the light environment to enhance the detectability of a body part used in courtship.

Expanding research on sensory drive from simple features like colour to the study of patterns, Hulse and colleagues (2020) assessed the link between the body patterns of colourful fish species ("darters") and the spatial statistics of their respective habitats. Authors here were testing a "processing bias" hypothesis, which posits that the spatial statistics of natural scenes are attractive because they are the easiest to process by the brain [START_REF] Renoult | Processing bias: Extending sensory drive to include efficacy and efficiency in information processing[END_REF]. Thus animal breeding patterns that mimic the spatial statistics of their habitats should be selected by potential mates and therefore evolutionarily favoured. Using the Fourier slope, a measure of the distribution of spatial frequencies (ie luminance distribution) in images, they found differences between males and females: the Fourier slope of male body patterns correlated with the slope of the habitat, whereas female patterns did not. Sexual selection could have favoured males whose body patterning imitates the statistics of their environment as those patterns might be preferred by females.

While these results, showing a correlation between colour or pattern and species habitat, are compelling examples of sensory drive, the approaches are significantly limited in that they focus on one aspect of the signal only, the colour or the spatial frequencies. This is a common pitfall in classical approaches to the study of communication signals, where researchers usually select one feature to investigate, in a more or less arbitrary manner and independently of other features, such as the geometry or symmetry of patterns, colours, or the interaction between multiple features.

Limiting the study of signals to one dimension only fails to reveal the importance of complex patterns or multimodal signalling in animal communication and sexual selection. For instance, Wilson and colleagues (2013) theoretically demonstrated how relying on multimodal signaling is less prone to interpretation errors by the receiver. Similarly, in their review, Pérez-Rodríguez and collaborators (2017) highlighted the need to consider animal color patterns as a whole in complement to individual color features to account better for the multi-component aspect of color patterns.

To address this limitation, tools from artificial intelligence and in particular from deep learning represent a valuable complementary approach. Generative models, such as variational autoencoders (VAEs; [START_REF] Kingma | An Introduction to Variational Autoencoders[END_REF], generative adversarial networks (GANs, [START_REF] Goodfellow | Generative Adversarial Networks[END_REF]2020) or (latent) diffusion models [START_REF] Sohl-Dickstein | Deep Unsupervised Learning using Nonequilibrium Thermodynamics[END_REF][START_REF] Ho | Denoising Diffusion Probabilistic Models[END_REF][START_REF] Song | Generative Modeling by Estimating Gradients of the Data Distribution[END_REF], can be used to create or manipulate stimuli without the need to target a specific feature. For instance, CycleGAN [START_REF] Zhu | Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks[END_REF]) is a GAN-based algorithm that can be trained to learn the mapping between two sets of images belonging to different classes (e.g. two types of habitats, or a habitat and the organism that lives there) and be used to transform one image into a different category. Diffusion models can take a sentence as input and turn it into an image (see e.g. [START_REF] Ramesh | Zero-Shot Text-to-Image Generation[END_REF][START_REF] Nichol | GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models[END_REF] and were made famous with the release of the DALL-E application by OpenAI. These models can thus be used to synthesize rich and controlled images to test specific hypotheses about signal preferences. Although they have interesting properties and can generate high-quality images, such models require a large number of images to be trained, extensive computer power, and they have a somewhat complex architecture. The use of APIs such as DALL-E can be used as an alternative but offers little control to the user.

One tool of artificial intelligence that allows a high level of control over stimulus generation is the Neural Style Transfer (NST) algorithm developed by Gatys andcolleagues (2015, 2017). Using the architecture of a convolutional neural network (CNN), the NST algorithm extracts the content (global arrangement of an image, such as the objects that constitutes it) and the style (i.e. texture information, local variations of luminance) of two images, respectively. The algorithm creates a third image that has both the content of one image and the style of the other one (figure 1). Importantly, the user can determine how much weight to put on the style or on the content to modulate the output image as desired. Moreover, the algorithm only requires two input images and no extra training from the user end, and there are several accurate implementations that are ready to use and with tutorials.

Here we used NST to study mate preferences in Etheostoma caeruleum, a freshwater fish species native to eastern North America. Following previous results showing a correlation between male body patterns and the species' habitat [START_REF] Hulse | Sexual signaling pattern correlates with habitat pattern in visually ornamented fishes[END_REF], we asked whether this link could be the result of sexual selection. The processing bias hypothesis stipulates that patterns that are easier to process will be preferred by conspecifics [START_REF] Renoult | Processing bias: Extending sensory drive to include efficacy and efficiency in information processing[END_REF]. We thus predicted that males whose body patterns are the closest to their habitat's statistics, thus being easier to process, will be preferred over males that are less similar to their habitats. We manipulated male colour patterns to be either more or less similar to species-specific habitats, using the style transfer technique. We tested the preference of male and female darters for images of conspecifics that do and do not exhibit the "style" of their own habitat.

Besides manipulating the fish patterns, we also alternated the video background of the fish animations to determine whether individuals have a preference for the fish modified with the style of the species-specific habitat, irrespective of the habitat in which it is found (i.e. the background video). We compared the amount of time fish spent with the animated fish exhibiting the species-specific habitat (and with a non-species-specific habitat) when shown in a species-specific habitat vs in a non-species-specific habitat. Finally, we explored the influence of style transfer by comparing preferences for modified vs non-modified animated fish. Our results shed light on the hypothesis that sexual signal design is driven by pattern preferences shaped by the spatial statistics of a species' environment.

Methods

Fish collection and maintenance

We collected males and females of E. caeruleum from Israel Creek (39°29'35.1492' N, 77°19'28.0488'' W) and Tuscarora Creek (39°15'6.948'' N, 77°28'48.288'' W) in Frederick County, Maryland between March 15th and April 21st 2022. Fish were transported to the University of Maryland Baltimore County in aerated coolers and individually housed in a recirculating aquarium system (Aquaneering, Inc.). They were provided natural lighting matching their photoperiod with a constant temperature of 19°C and fed a diet of frozen bloodworms every other day. Fish were euthanized humanely at the end of the study using tricaine methanesulfonate (MS-222).

Permission to collect fish was granted by the Maryland Department of Natural Resources Fishing and Boating Services Scientific Collection (permit number SCP202246A). Housing and experimental procedures were approved by the Institutional Animal Care and Use Committee of the University of Maryland, Baltimore County (protocol 623).

All tested individuals were sexually receptive based on the presence of nuptial colouration in males and visible gravidity in females.

Stimuli creation

We used Neural Style Transfer (NST), a CNN-based algorithm developed by Gatys and colleagues (2017), to alter the spatial statistics of photographs of male E. caeruleum. The algorithm requires two input images, one for content and one for style, and aims to render the content image in the style of the style image. The style information is extracted via a Gram matrix while the content information corresponds to the fourth feature layer of a 5-feature layer CNN (figure 1).

For our study purposes, a picture of an original fish and a picture of the habitat (either gravel or sand) were fed to the NST algorithm as content and style images, respectively. Content and style have adaptable weights to improve the final result. We kept them constant across our stimuli, using the recommended values (content weight = 1e0; style weight = 1e3). For the style, we only considered the two first layers (out of 5) as they tend to represent the local statistics of the given image.

The original colours of the fish were put back on the final image, using the colour control option of the NST, to make it look more naturalistic. An example is shown in figure 2. Stylized fish were then animated with the Blender software (Blender Online [START_REF] Community | Blender -a 3D modelling and rendering package[END_REF] and displayed in videos depicting either sand or gravel habitats.

The

PyTorch [START_REF] Paszke | PyTorch: An Imperative Style, High-Performance Deep Learning Library[END_REF] implementation of the algorithm (https://pytorch.org/tutorials/advanced/neural_style_tutorial.html) with our modifications as well as the detailed procedure used to create our stimuli are available at: https://github.com/yseulthb/NST_fish.

To control for potential biases, we used three images per habitat category (gravel or sand) and styled five different male individuals. Animated fish were presented against a background video of either gravel (species-specific habitat) or sand (foreign habitat), taken using an Ikelite 200DL underwater housing for a Canon EOS 5D Mark IV digital camera equipped with a Sigma 24 mm f/1.4 lens (see [START_REF] Hulse | Sexual signaling pattern correlates with habitat pattern in visually ornamented fishes[END_REF] for details). We selected three different background videos per habitat. For each pair of video animations presented to test individuals, we matched their luminance values (both in terms of background and animated fish). Pairs of videos also presented different animated individuals. Beyond that constraint, all possible combinations were randomized across trials and across tested individuals.

In short, we applied the style of a species-specific habitat to the body of a male fish in order to present live fish with a choice between animated fish styled in different habitats. We used gravel as the preferred habitat and sand as the foreign habitat as E. caeruleum is not typically found on that type of substrate. Conveniently, sand habitats are also the most different from gravel habitats when comparing the spatial frequencies of freshwater habitat types [START_REF] Hulse | Sexual signaling pattern correlates with habitat pattern in visually ornamented fishes[END_REF].

Dichotomous choice experiments

We ran a series of six experiments to determine whether E. caeruleum showed a preference for individuals modified to exhibit their habitat statistics. Focal fish were presented with a pair of stimuli with different animated fish but the same video background. The different experimental conditions, their characteristics and sample sizes are summarised in Table 1. In addition to estimating preference for differently styled fish, we estimated in a seventh experiment their preference for their own habitat irrespective of the animated fish (ie with the same animated, unstyled fish shown on both sides).

Trials took place in a 37.9L tank flanked by two monitors along the short sides showing the video animations. Water was systematically changed between trials and tanks were filled up to 75% of their capacity. Trials were video-recorded using an overhead video camera (Logitech). A 12cm wide zone in the middle of the tank was delineated with transparent removable dividers and served as an acclimation area. Association zones were defined as the 5cm-wide area in front of the video animations (see Figure 3). This distance corresponds to the usual courting distance between males and females (darters are pair spawners) and is in the range of an estimated visual acuity for that species [START_REF] Caves | Visual acuity in ray-finned fishes correlates with eye size and habitat[END_REF].

To control for experimental side bias, each trial alternated on which side of the tank the stimulus conditions (eg gravel-styled or sand-styled) were shown. The tank's lateral (long) sides were blocked with black matte plexiglass to isolate the fish from its surroundings and prevent fish reflection to avoid any distractions. Most fish took part in all seven trials and the experiment order was randomised between individuals to control for potential order effects. Furthermore, the use of 5 different individuals as animated fish combined with the different modification styles limits any recognition bias that could influence our results.

For the first five minutes of a trial, the video animations were blocked with opaque dividers. Those opaque dividers were then removed while the fish was still contained in the acclimation area for an additional five minutes. This gave the focal fish time to visually explore both stimulus sides. Finally, transparent dividers were removed and the focal fish could freely move in the tank for the next 15 minutes. We defined a fish's preferred stimulus by comparing the amount of time spent in the two association zones during the first 12 min after the transparent dividers were removed. Time spent with a stimulus in a dichotomous mate choice trial has been shown to be a reliable proxy for preference in freshwater fishes [START_REF] Aspbury | Repeatable female preferences, mating order and mating success in the poeciliid fish, Heterandria formosa[END_REF][START_REF] Brooks | Female Guppies Agree to Differ: Phenotypic and Genetic Variation in Mate-Choice Behavior and the Consequences for Sexual Selection[END_REF][START_REF] Lehtonen | Repeatability of mating preferences in the sand goby[END_REF]. Trial videos were scored using the BORIS software [START_REF] Friard | BORIS: A free, versatile open-source event-logging software for video/audio coding and live observations[END_REF] by four persons that were blind to experimental conditions. Interobserver reliability was tested on 6 trials and estimated to be 90% before equally sharing trials across scorers.

Statistical analyses

Statistical analyses were performed with the software R (R Core Team, 2021). For all seven experiments, we first checked for a potential side bias by comparing times spent in the left vs right association zones irrespective of the stimuli shown. We ran either t-tests or Wilcoxon tests depending on the data distribution.

To estimate habitat or fish preference, we compared times spent in the association zone for each condition as well as times spent in the central area. This latter measure informs us of the fish's interest in the stimuli. To compare times spent in those three areas, we ran one-way ANOVAs. If ANOVA results were significant, we ran posthoc tests (Tukey HSD tests) to identify which pairs of stimuli were significantly different. We also report the number of entries in each association zone, a proxy for the fish's mobility. Finally, we counted the number of times fish entered the association zones and used chi-square tests to identify any difference.

All analyses are reported for males and females separately.

Results

Fish preference

We analysed the data of 13 to 17 females and 16 to 20 males (see Table 1) for fish preference. We tested a minimum of 15 individuals per experiment. However, individuals that either froze or stayed in the same location without further moving for the entire duration of the experiment were discarded. We found no side bias for any of the six experiments testing fish preference, neither for females nor males. Detailed test results are provided in the supplementary material.

Experiment 1 tested the preference for fish modified with the statistics of species-typical habitat versus foreign habitat with a background video of species-typical habitat. ANOVA results were significant for females only (F(2,48) = 4.75 p = 0.013 for females; F(2,57) = 1.89 p = 0.16 for males). Tukey's HSD Test for multiple comparisons found that the amount of time females spent in the association zones of the fish with species-typical statistics was significantly greater than the amount of time spent in the central zone (t = -3.03, p = 0.011).

Figure 4: Time (ms) spent in the association zones of the fish with foreign habitat statistics or of the fish modified with species-typical statistics and in the central zones for females (left) and males (right). The video background depicts the species-typical habitat (gravel).

On average, males entered the association zone of the fish modified with species-typical statistics 6.5 times and of the fish with foreign habitat statistics 7.8 times. For females, they entered 6.35 times and 5.53 times, respectively. No significant difference was found for either group (X² = 0.05708, df = 1, p = 0.811 for females; X² = 0.1182, df = 1, p = 0.731 for males).

Experiment 2 tested a preference for fish modified to reflect the statistics of species-typical habitat versus foreign habitat with a background video of a foreign habitat. Again, ANOVA results were significant for females only (F(2,45) = 10.3 p < 0.001 for females; F(2,45) = 10.3 p < 0.001 for males). Tukey's HSD Test for multiple comparisons revealed statistical differences between the time females spent in the association zone of the fish with the statistics of their own habitat (t = -4.54, p < 0.001) and of the fish modified to reflect a foreign habitat (t = 2.57, p = 0.035) compared to the central zone.

On average, males entered the association zone of the fish modified with species-typical statistics 7.06 times and of the fish modified to reflect a foreign habitat 8.12 times. For females, it was 4.4 times and 4.2 times, respectively. No significant difference was found for either group (X² = 0.004106, df = 1, p = 0.949 for females; X² = 0.07387, df = 1, p = 0.786 for males).

Figure 5: Time (ms) spent in the association zones of the fish with foreign habitat statistics or of the fish modified with species-typical statistics and in the central zones for females (left) and males (right). The video background depicts a foreign habitat (sand).

Experiment 3 tested a preference for fish modified to reflect the statistics of species-typical habitat versus a non-modified fish with a background video of the species' habitat. ANOVA results were significant for both males (F(2,54) = 13.7 p < 0.001) and females (F(2,36) = 7.94, p = 0.0014). The Tukey test for multiple comparisons found that the amount of time females spent in the association zone of the fish modified to reflect the statistics of their own habitat was significantly different from the amount of time spent in the central zone (t = -3.96, p < 0.001).

For males, statistical differences were found between times spent in the association zone of the fish modified to reflect the statistics of their own habitat compared to the central zone (t = -5.17, p <0.001) and between times spent in the association zone of the fish modified to reflect the statistics of their own habitat compared to the non-modified fish (t = -3.27, p = 0.0052). Thus males preferred the fish that was modified to reflect their own habitat over the natural version of the fish.

On average, males entered the association zone of the fish modified with species-typical statistics 6.3 times and of the fish modified to reflect a foreign habitat 5.2 times. For females, it was 4.9 times and 4.4 times, respectively. No significant difference was found for either group (X² = 0.02308, df = 1, p = 0.879 for females; X² = 0.107, df = 1, p = 0.744 for males). Experiment 4 tested a preference for fish modified to reflect the statistics of a foreign habitat versus a non-modified fish with a background video of the species' habitat. ANOVA results were significant for both males (F(2,45) = 3.45 p = 0.04) and females (F(2,42) = 6.9, p =0.0026). The Tukey test for multiple comparisons revealed statistical differences between the time females spent in the association zone of the fish with foreign habitat statistics (t = 3.65, p = 0.0021) and of the non-modified fish (t = 2.44, p = 0.0487) compared to the central zone. For males, statistical differences were found between times spent in the association zones of the non-modified fish compared to the central zone (t = 2.48, p = 0.044).

On average, males entered the association zone of the fish with foreign habitat statistics 6.33 times and of the non-modified fish 6.73 times. For females, it was 6.0 times and 6.06 times, respectively. No significant difference was found for either group (X² = 0.01224, df = 1, p = 0.912 for females; X² = 0.0003238, df = 1, p = 0.986 for males). Experiment 5 tested a preference for fish modified to reflect the statistics of a species' habitat versus a non-modified fish with a background video of a foreign habitat.ANOVA results were significant for both males (F(2,57) = 3.52 p = 0.036) and females (F(2,48) = 10.2, p < 0.001). Tukey test for multiple comparisons revealed statistical differences between the time females spent with the fish modified to reflect the statistics of their own habitat (t = -3.86, p = 0.00099) and of the non-modified fish (t = 3.97, p = 0.00073) compared to the central zone. For males, statistical differences were found between times spent in the association zone of the fish modified to reflect the statistics of their own habitat compared to the central zone (t = -2.43, p = 0.048).

On average, males entered the association zone of the fish modified with species-typical statistics 7.75 times and of the non-modified fish 8.0 times. For females, it was 4.12 times and 3.94 times, respectively. No significant difference was found for either group (X² = 0.003864, df = 1, p = 0.95 for females; X² = 0.003968, df = 1, p = 0.95 for males). Experiment 6 tested a preference for fish modified to reflect the statistics of a foreign habitat versus a non-modified fish with a background video of a foreign habitat. ANOVA results were significant for females only (F(2,45) = 7.24 p = 0.0019 for females; F(2,54) = 3.01, p = 0.058 for males). Tukey's HSD Test for multiple comparisons found that the amount of time females spent in the association zones of the fish modified to reflect the statistics of a foreign habitat was significantly different from the amount of time spent in the central zone (t = 3.81, p = 0.0013).

On average, males entered the association zone of the fish with foreign habitat statistics 8.1 times and of the non-modified fish 8.7 times. For females, it was 6.6 times and 5.9 times, respectively. No significant difference was found for either group (X² = 0.045, df = 1, p = 0.832 for females; X² = 0.02368, df = 1, p = 0.878 for males).

Experiment 7. Habitat preference

A processing bias hypothesis assumes that animals prefer the visual statistics of their own habitat. To estimate preference for species-typical versus foreign habitat, we analysed the data of 13 females (out of 15 tested) and 16 males (out of 20 tested). Discarded individuals either froze or stayed in the same location without further moving for the entire duration of the experiment. T-tests for the remaining males and females showed no difference between times spent on either side of the association zone (females: t = -1.0095, df = 23.992, p = 0.3228; males: t = 0.79333, df = 29.307, p = 0.434); thus we did not exclude any individuals due to side bias.

ANOVA results were significant for both males (F(2,45) = 6.35 p = 0.0037) and females (F(2,36) = 17.3, p < 0.001). Tukey's HSD Test for multiple comparisons found that the amount of time males spent in the association zone of their own habitat was significantly different from the amount of time spent in the central zone (t = -3.54, p = 0.0026). Males spent more time close to the video of their own habitat compared to the central zone.

For females, statistical differences were found between times spent close to their habitat compared to the central zone (t = -5.85, p <0.001) and between times spent close to their habitat compared to another species' habitat (t = -3.46, p = 0.0039). Thus females preferred their own habitat over the foreign habitat.

On average, males entered the association zone of their own habitat 6.8 times and of the foreign habitat 6.5 times. Females entered the association zone of their own habitat 5.4 times and of the foreign habitat 4.0 times. No significant difference was found for either group (X² = 0.2043, df = 1, p = 0.651 for females; X² = 0.007336, df = 1, p = 0.932 for males).

Discussion

Across all experiments, females spent more time in the association zones than in the central zone, with a bias for fish modified to reflect the species-specific habitat, especially when the background video was depicting their own habitat (experiments 1 and 3). On the contrary, males spent more time in the association zones in half of the experiments only (experiments 3, 4, 5). However, when they showed a preference, males preferred animated fish modified to reflect their own habitat over the non-modified animated fish, spending more time in the corresponding association zone than in the two other zones (experiment 3).

As for habitat preferences alone, females preferred the video of their own habitat, spending more time in the corresponding association zone than in the two other zones. Males, on the other hand, only preferred their own habitat over the central zone. For males, no difference was found between times spent alongside their own habitat versus the foreign habitat for males.

Overall, our findings suggest that both males and females tend to be sensitive to individuals modified to reflect habitat-specific statistics in their preferences, providing some support for the sensory drive theory and the processing bias hypothesis. Those biased preferences appeared more consistently for females than for males. Nonetheless, males showed an interesting result, with a preference for the fish modified to reflect the species-specific habitat over a non-modified fish. This latter result may highlight the origin of latent aesthetic preferences, which are preferences for yet-to-exist traits. Latent aesthetic preferences for complex stimuli, such as the preference for artificial crests in Australian finches [START_REF] Burley | A Taste for the Beautiful": Latent Aesthetic Mate Preferences for White Crests in Two Species of Australian Grassfinches[END_REF], are often assumed to emerge incidentally. Our males' preferences suggest that such latent preferences might instead be determined by their visual environment and reflect a sensory bias, thus supporting a sensory drive origin of latent preferences [START_REF] Endler | Signals, Signal Conditions, and the Direction of Evolution[END_REF][START_REF] Endler | Sensory ecology, receiver biases and sexual selection[END_REF][START_REF] Seehausen | Speciation through sensory drive in cichlid fish[END_REF].

In our experiments, we kept the colour of our animated fish constant. One important future direction will be to test the interaction between colours and patterns while modulating spatial statistics of signal designs and ask how this influences female and male preferences. An interaction between colour and pattern could also bear on the interplay between detectability and camouflage. Because of their conspicuous coloration, males are more visible during the mating season, thus more closely matching natural statistics could help to make them less conspicuous to predators. This trade-off between colourful designs and camouflage has been documented in the damselfish (Phillips et al., 2017). One way to investigate the presence of such trade-off in darters would be to test a predator of darters using the same paradigm and ask whether they attack fish that are closer to their own habitat's statistics, potentially better camouflaged, or closer to another habitat. Fish that exhibit body patterns matching the statistics of their habitat will be better camouflaged, thus less likely detected by a predator, decreasing their susceptibility to attacks.

Another future direction will be to focus on specific body parts of the fish rather than the body as a whole to assess whether some parts are more susceptible to sexual selection. For instance, Gumm and Mendelson (2011) found no correlation between body colours and fin colours of several darter species, suggesting this might also be the case for other features. This has important consequences since dorsal fins are often erected for courtship purposes or in male-male fights (see e.g. [START_REF] Johnston | Spawning Behavior of the Goldstripe Darter (Etheostoma parvipinne Gilbert and Swain) (Percidae)[END_REF]. Given that dorsal fins can be concealed, unlike the body, they may be under less predation pressure. Therefore, fin patterns may be evolving due to sexual selection alone and be less constrained by camouflage.

Conveniently, the most recent implementation of the NST algorithm [START_REF] Gatys | Controlling Perceptual Factors in Neural Style Transfer[END_REF] has two control options that make it very relevant for addressing such questions: spatial and colour controls. The spatial control lets the user pre-define which part of an image to transfer the style. This is useful for modifying subparts of a body and testing which one affects visual preferences or communication. With the colour control, the model gives the possibility to either keep the original color of the content image to only "transfer" the achromatic information or transfer both chromatic and achromatic information. The algorithm's versatility makes it convenient for additionally testing the influence of color on visual preferences.

Our suggestions for future studies show the many ways that the style transfer algorithm and similar techniques can be used to study the evolution of signal design. For instance, besides style transfer, generative adversarial networks (GANs) can be used creatively to study the evolution of animal signals. Exploiting the architecture of GANs, Talas and colleagues (2020) developed CamoGAN to study the gradual evolution of background camouflage. GANs are made of two networks that compete with each other: a discriminative one and a generative one. The discriminative network learns to make the difference between real data given as input to the model and fake data that the generative network produces. Over time, both networks become better at discriminating between real and fake data, or at generating fake data that looks more and more like real data. This makes CamoGAN a great option for simulating the evolutionary arms race between the camouflage of a synthetic prey and its predator, which is what the authors successfully did.

Conclusion

Our study demonstrates how artificial neural networks can be used to test hypotheses about animal signal evolution, by creating or manipulating stimuli without the need to target a specific feature. To make our case, we used style transfer, an easy-to-implement algorithm that the non-familiar reader will be able to adapt to their own questions. Our experiments revealed preferences for male patterns that were manipulated to be more similar to the statistics of a species-specific habitat. This preference was found in both females and males, although to different extents. Importantly, our results provide some support for the sensory drive hypothesis. As such, we showed that generative models are a powerful tool for addressing questions in visual ecology. Indeed, such models will allow us to move beyond the analysis of single features toward a more holistic perceptual representation of complex signals, paving the way to exciting new questions.

Figure 6 :

 6 Figure 6: Time (ms) spent in the association zones of the non-modified fish or of the fish modified with species-typical statistics and in the central zones for females (left) and males (right). The video background depicts the species-typical habitat (gravel).

Figure 7 :

 7 Figure 7: Time (ms) spent in the association zones of the non-modified fish or of the fish with foreign habitat statistics and in the central zones for females (left) and males (right). The video background depicts the species-typical habitat (gravel).

Figure 8 :

 8 Figure 8: Time (ms) spent in the association zones of the non-modified fish or of the fish modified with species-typical statistics and in the central zones for females (left) and males (right). The video background depicts a foreign habitat (sand).

Figure 9 :

 9 Figure 9: Time (ms) spent in the association zones of the non-modified fish or of the fish with foreign habitat statistics and in the central zones for females (left) and males (right). The video background depicts a foreign habitat (sand).

Figure 10 :

 10 Figure 10: Time (ms) spent in the association zones of a foreign habitat or of their own habitat and in the central zones for females (left) and males (right). The animated fish is a non-modified one.

Table 1 :

 1 Experimental conditions and sample sizes for the fish preference experiments

	Experiment	Background video Styled fish 1	Styled fish 2	Sample size
	Experiment 1 species-typical	species-typical	foreign	M: 20	F: 17
	Experiment 2 foreign	species-typical	foreign	M: 17	F: 16
	Experiment 3 species-typical	species-typical	non-modified	M: 19	F: 13
	Experiment 4 species-typical	foreign	non-modified	M: 16	F: 15
	Experiment 5 foreign	species-typical	non-modified	M: 20	F: 17
	Experiment 6 foreign	foreign	non-modified	M: 19	F: 16

Table 2 :

 2 Summary of the significant results

		Significant results	Shown in
	Experiment 1	species-specific zone > central zone	Females only
	Experiment 2	species-specific zone > central zone	Females only
		foreign zone > central zone
	Experiment 3	species-specific zone > central zone	Females & males
		species-specific zone > non-modified	Males only
	Experiment 4	species-specific zone > central zone	Females only
		non-modified zone > central zone	Females & males
	Experiment 5	species-specific zone > central zone	Females & males
		non-modified zone > central zone	Females only
	Experiment 6	foreign > central zone	Females only
	Experiment 7	species-specific > central zone	Females & males
		species-specific > foreign	Females only

Acknowledgements

We would like to thank the students of the DART lab who helped with trials and video scoring. This work was supported by the National Science Foundation grant NSF IOS 2026334.


Elodie Alessandri-Gradt

Samuel Constant

Jean-Christophe Plantier

Georgios Stroulios

Salvatore Simmini

Caroline Tapparel

Génération

Génération d'organoïdes respiratoires à exposition extérieure apicale à partir de cellules de pools de donneurs

est destinée au dépôt

Introduction

Les infections virales respiratoires représentent la majorité des motifs de consultation en soins primaires 1,2 , générant un problème de santé publique préoccupant. L'étude in vitro des principaux virus à l'origine de ces infections tels que le virus respiratoire syncytial (VRS) et les rhinovirus (RV), doit rendre compte de la complexité de l'environnement respiratoire. Ainsi, les modèles d'étude tissulaire en système Air-liquide et les organoïdes respiratoires remplacent progressivement les cultures cellulaires en lignées 2D. Pour autant, certains de ces systèmes complexes sont difficilement transposables pour des applications à large échelle (screening de drogues, études pharmacotoxicologiques…)

Méthodes

Production des organoïdes:

Des cellules de l'épithelium nasal d'un pool de 14 donneurs produits par la société Epithelix ont été cultivées en T75 (milieu hAEC, Epithelix) 37°C 5% CO 2 . A confluence, 1.2x10 5 cellules ont été déposées dans chaque puit d'une plaque Aggrewell TM 400-24w (Stemcell) en milieu Pneumacult TM apical-out airway organoid (AOAO Stemcell) 3 . Les agrégats obtenus ont été transférés en plaque de culture 24 puits pour maturation avec renouvellement de 50% du milieu tous les 3 jours jusqu'à J15.

Conditions d'infection:

Les organoïdes matures ont été filtrés (reversible strainer 37μm, Stemcell) et infectés par un VRS-A marqué en fluorescence rouge (mcherry) (0.02 MOI) et un RV-C15 (0.2 MOI) en milieu PneumacultTM AOAO 3 , et transférés en plaque 96 puits dans un volume final de 100μl. A J2 et J3, 50μl de surnageant ont été extraits (EasyMag Biomerieux) et le nombre de copies de génome viraux a été quantifié par real-time RT-PCR en présence de standards de références 4,5 .

Résultats

Ces résultats montrent la faisabilité de la production d'organoïdes respiratoires de polarisation inversée et permissifs au VRS-A et RV-C en format utilisable pour des criblages à haut débit. Une standardisation de la reproductibilité et une dose-réponse de la réplication du VRS en présence de doses croissantes de ribavirine sont en cours. Ces données ouvrent la porte à des perspectives innovantes pour l'étude de ces virus d'impact majeur et pour lesquels aucun traitement efficace n'est encore disponible.

Fig1. Cinétique du nombre d'organoïdes moyen comptés par puits (n=3) à J7 (A), J10 (B) et J14 (C) selon la durée d'agregation (jours)

Le nombre moyen d'organoïdes générés par plaque 24 puits était de 3090, avec observation de battements ciliaires dès J11, attestant de la présentation apicale exterieure des cellules épithéliales. L'observation des organoïdes produits à J7, J10 et J14 montrait une production optimale en nombre moyen d'organoïdes/puits (max 261 à J10) et sans phénomène de fusion après 4 jours en Aggrewell TM 400 (Figure 1).

Les organoïdes supportent une infection par le VRS-A et le RV-C avec élévation du titre viral sur 3 jours

Fig2. Evolution de la charge virale du surnageant des organoïdes infectés par VRS-A (A), RV-C15 (B) n=1

A) B)

Après transfert des organoïdes matures en plaque 96 puits, l'infection par le VRS-A mcherry a produit un titre de 4.4 Log RNA cp/μL à 48h post-infection (pi) et stable à 72h (Figure 2.A). Les observations microscopiques montraient également une fluorescence des organoïdes infectés dès J2 pi (Figure 3).

1

  Univ Rouen Normandie, INSERM UMR1311, DYNAMICURE, CHU hôpitaux de Rouen Normandie, 76 000 Rouen, France 2 Epithelix SAS, Genève, Suisse 3 STEMCELL Technologies UK Ltd., Cambridge, Royaume-Uni 4 Département de Microbiologie et Médecine moléculaire, Université de Genève, 1206 Genève, Suisse générés après 2 jours en plaque Aggrewell TM 400 (Microscopie optique x40) B) Organoïdes obtenus après 15 jours de maturation (Microscopie optique X100 Caractéristiques des souches virales utilisées Conclusions

  Pour le RV-C15, le titre viral obtenu à 72h pi était de 5.7 Log RNA cp/μL avec un gain de plus de 2 Log entre le 1er et le 3ème jour pi (Figure2.B).

Fig3.

  Fig3. Observations microscopiques à J2 pi

Objectifs →Générer un modèle d'organoides respiratoires avec exposition extérieure apicale à partir d'un pool de cellules primaires nasales et fixer les conditions optimales d'agrégation →Tester les conditions d'infection par VRS et RV

	(RV-C15, pour lequel les modèles cellulaires sont
	actuellemment limités)
	2550
	3. Stroulios et al. Scientific Report 2022 12:7673
	4. Essaidi-Laziosi et al. J virol method 2016 Sep;235:9-14
	5. Essaidi-Laziosi et al. J Allergy Clin Immunol. 2018
	Jun;141(6):2074-2084

1. Jin et al. eClinicalMedicine 2021 Jul 37:100986 2. Liu et al. Viruses 2022 Nov 18;14(11):