Maryne Dupin
email: maryne.dupin@inserm.fr

Samuel Garcia

Julie Boulanger-Bertolus

Nathalie Buonviso

Anne-Marie Mouly
email: annemarie.mouly@cnrs.fr

New insights from 22-kHz ultrasonic vocalizations to characterize fear responses: relationship with respiration and brain oscillatory dynamics

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Introduction

Fear behavior has been shown to depend on the interaction between the median prefrontal cortex (mPFC) and the basolateral amygdala (BLA), and to involve synchronized activity in theta (4-12 Hz) and gamma (30-120 Hz) frequency oscillations [START_REF] Seidenbecher | Amygdalar and hippocampal theta rhythm synchronization during fear memory retrieval[END_REF][START_REF] Popa | Coherent amygdalocortical theta promotes fear memory consolidation during paradoxical sleep[END_REF]Headley and Paré, 2013;[START_REF] Herry | Encoding of fear learning and memory in distributed neuronal circuits[END_REF][START_REF] Likhtik | Prefrontal entrainment of amygdala activity signals safety in learned fear and innate anxiety[END_REF][START_REF] Stujenske | Fear and safety engage competing patterns of theta-gamma coupling in the basolateral amygdala[END_REF][START_REF] Bocchio | Synaptic Plasticity, Engrams, and Network Oscillations in Amygdala Circuits for Storage and Retrieval of Emotional Memories[END_REF]). In addition, recent studies have shown that freezing, the most used index of fear response in rodents, temporally coincides with the development of sustained 4-Hz oscillations causally involved in the synchronization of spiking activity between mPFC and amygdala [START_REF] Dejean | Prefrontal neuronal assemblies temporally control fear behaviour[END_REF][START_REF] Karalis | 4-Hz oscillations synchronize prefrontal-amygdala circuits during fear behavior[END_REF]. Importantly, this slow oscillation is distinct from the theta rhythm and predicts the onset and offset of freezing. Interestingly, recent work has shown that freezing-related 4Hz oscillation in the mPFC was correlated with the animal's respiratory rate, and that disruption of olfactory inputs to the mPFC significantly reduces the 4-Hz oscillation in the mPFC [START_REF] Moberly | Olfactory inputs modulate respiration-related rhythmic activity in the prefrontal cortex and freezing behavior[END_REF]. These data bring further support to the growing body of evidence showing that in addition to its impact on olfactory regions (for a review see [START_REF] Buonviso | Respiratory modulation of olfactory neurons in the rodent brain[END_REF], nasal respiration also entrains oscillations in widespread brain regions including those involved in the fear network like the mPFC and amygdala (for a review see Tort et al, 2018). This suggests that the breathing rhythm, akin to slow oscillatory rhythms, could help coordinate neural activity across distant brain regions [START_REF] Jensen | Cross-frequency coupling between neuronal oscillations[END_REF]Heck et al, 2017), and potentially modulate emotional/cognitive processes. Freezing is only one amongst the different components of rodents' response to a threatening event.

In aversive situations, such as exposure to predator or footshock, rats also emit 22-kHz ultrasonic vocalizations (USV) (Schwarting and Wöhr, 2012). They indicate a negative emotional state and are associated with the termination of social behavior and the avoidance of social contacts. Their study provides a powerful tool to assess emotionality and social behavior in animal models of pathologies like autism (Wöhr and Scattoni, 2013). Surprisingly, to our knowledge no study has assessed the impact of USV production on the animal's brain neural dynamics. Yet such information is crucial to understand how the different components of fear response collectively modulate rat's brain neural dynamics. Indeed the emission of USV is considered as reflecting a change in emotional level and 22-kHz USV rates increase with the aversiveness of the situation, as evidenced when foot shock intensity is increased (Wöhr et al. 2005;[START_REF] Hegoburu | The RUB Cage: Respiration-Ultrasonic Vocalizations-Behavior Acquisition Setup for Assessing Emotional Memory in Rats[END_REF]. Importantly, 22-kHz USV emission drastically slows down the animal's respiratory rate [START_REF] Frysztak | The effect of medial frontal cortex lesions on respiration, "freezing," and ultrasonic vocalizations during conditioned emotional responses in rats[END_REF][START_REF] Hegoburu | The RUB Cage: Respiration-Ultrasonic Vocalizations-Behavior Acquisition Setup for Assessing Emotional Memory in Rats[END_REF][START_REF] Sirotin | Rodent ultrasonic vocalizations are bound to active sniffing behavior[END_REF][START_REF] Boulanger-Bertolus | Understanding pup affective state through ethologically significant ultrasonic vocalization frequency[END_REF], potentially disrupting the respiratory-related brain rhythm described above. The present study thus aimed 1) to investigate whether USV emission coincides with specific changes in oscillatory activities in the fear neural network, and 2) to assess to what extent these changes are related to changes in respiratory rate. To do so, rats were trained in an odor fear conditioning paradigm while local field potentials (LFP) in BLA, mPFC and olfactory piriform cortex (PIR) were monitored simultaneously with USV calls, behavior and respiration, during the post-shock period. We report that USV emission temporally coincides with a significant increase in delta and gamma activities while a decrease in theta activity is observed. Some of these changes are related to respiration. Indeed, the coupling observed between respiratory rate and delta oscillation during silent freezing is lost during USV emission, and in the meantime the modulation of fast oscillations amplitude relative to the phase of respiratory cycle is modified. The present data suggest that USV calls could result in a differential gating of information within the fear neural network thus potentially modulating fear memory/expression as suggested by our observation that the amount of USV during conditioning is a good predictor of the learned fear response at retention.

Material and Methods

Animals

Data were obtained from twenty-three male Long Evans rats (250-270g at their arrival, Janvier Labs, France). They were housed individually at 23•C and maintained under a 12 h light-dark cycle (lights on from 8:00 am to 8:00 pm). Food and water were available ad libitum. All experiments and surgical procedures were conducted in strict accordance with the European Community Council Directive of 22nd September 2010 (2010/63/UE) and the ethics committee of University Claude Bernard Lyon 1 (CEEA-55; Approval reference DR2013-40-v2). Care was taken at all stages to minimize stress and discomfort to the animals.

Surgery

Animals were anesthetized with Equithesin, a mixture of chloral hydrate (127 mg/kg, i.p.) and sodium pentobarbital (30 mg/kg, i.p.), and placed in a stereotaxic frame (Narishige, Japan) in a flat skull position. The level of anesthesia was held constant with regular injections of Equithesin throughout the experiment. Monopolar stainless steel recording electrodes (100 µm in diameter) were then implanted unilaterally in the left hemisphere in three brain areas: the PIR (AP: -1.8, L: +5.5mm, DV: -8 mm), the mPFC (AP: +3.0mm; L: +0.8mm; DV: -3.5mm) and the BLA (AP : -2.8mm, L : +4.9mm, DV : -7.5mm). Accurate positioning of recording electrode depth was achieved using electrophysiological characteristics of multiunit activity (BLA, mPFC) or using field potential profiles evoked by stimulation of a bipolar electrode lowered in the ipsilateral olfactory bulb (for the PIR). A reference electrode was screwed in the skull above the right parietal lobe. The three recording electrodes were connected to a telemetry transmitter (rodentPACK system, EMKA Technologies, Paris, France) fixed to the rat's skull surface by dental acrylic cement and anchored with a surgical screw placed in the frontal bone. The animals were allowed to recover for two weeks following surgery.

Experimental apparatus

The apparatus has been described in detail in a previous study [START_REF] Hegoburu | The RUB Cage: Respiration-Ultrasonic Vocalizations-Behavior Acquisition Setup for Assessing Emotional Memory in Rats[END_REF]. It consisted of a whole body customized plethysmograph (diameter 20 cm, height 30 cm, Emka Technologies, France) placed in a sound-attenuating cage (L 60 cm, W 60 cm, H 70 cm, 56dB background noise). The plethysmograph was used to measure respiratory parameters in behaving animals. The ceiling of the plethysmograph was equipped with a tower allowing the introduction of a condenser ultrasound microphone (Avisoft-Bioacoustics CM16/CMPA, Berlin, Germany) to monitor USV emitted by the rats. The bottom of the animal chamber was equipped with a shock floor connected to a programmable Coulbourn shocker (Bilaney Consultants GmbH, Düsseldorf, Germany). Three Tygon tubing connected to a programmable custom olfactometer were inserted in the tower on the top of the plethysmograph to deliver air and odorants. Deodorized air flowed constantly through the cage (2 L/min). When programmed, an odor (McCormick Pure Peppermint; 2 L/min; 1:10 peppermint vapor to air) was introduced smoothly in the air stream through the switching of a solenoid valve (Fluid automation systems, CH-1290 Versoix, Switzerland) thus minimizing its effect on change in pressure. The bottom of the animal chamber had a port connected to a ventilation pump which could draw air out of the plethysmograph (at a rate of up to 2 L/min) thus maintaining a constant airflow that did not interact with the animal's breathing pattern. Animal's behavior was monitored with two video cameras on the walls of the sound-attenuating cage.

Fear conditioning paradigm and data acquisition

After the recovery period, the animals were handled individually and placed in the experimental apparatus for 30 min each day during 3 to 4 days before the beginning of the experiments in order to familiarize them with being manipulated and connected to the telemetry transmitter. For the conditioning session, the telemetry transmitter was plugged on the animal's head and the rat was allowed free exploration during the first 4 min, then an odor was introduced into the cage for 20 or 30s sec, the last second of which overlapped with the delivery of a 0.4mA foot-shock. The animal received 10 odor-shock trials, with an intertrial interval of 4 min. After the last pairing, the transmitter was unplugged and the animal returned to its home cage.

Retention test

The conditioned fear response was assessed during a retention test carried out 48h after conditioning. For the retention test, the rat was placed in the experimental cage (equipped with new visual cues and with a plastic floor to avoid contextual fear expression) and allowed a 4-min odor-free period. The CS odor was then presented five times for 20s with a 4-min intertrial interval. The animal's freezing behavior in response to the odor was quantified. Freezing behavior was defined as a crouching posture and an absence of any visible movement except that due to breathing [START_REF] Blanchard | Crouching as an index of fear[END_REF].

Data acquisition and preprocessing

For USV recording, the ultrasound microphone was connected to a recording interface (UltraSoundGate 116 Hb, Avisoft-Bioacoustics) with the following settings: sampling rate = 214285 Hz; format = 16 bit [START_REF] Wohr | Overt behavior and ultrasonic vocalization in a fear conditioning paradigm: a dose-response study in the rat[END_REF]. Recordings were transferred to Avisoft SASLab Pro (version 4.2, Avisoft Bioacoustics, Berlin, Germany) and a Fast Fourier Transform (FFT) was conducted. Spectrograms were generated with an FFT-length of 512 points and a time window overlap of 87.5% (100% Frame, FlatTop window). These parameters produced a spectrogram at a frequency resolution of 419 Hz and a time resolution of 0.29 ms. The acoustic signal detection was provided by an automatic whistle tracking algorithm with a threshold of -20 dB, a minimum dura on of 0.01 s and a hold me of 0.02 s. However, the accuracy of detection was verified trial by trial by an experienced user. The main parameters used in the present study were the duration as well as the peak amplitude and peak frequency of USV calls. No band pass filter has been applied during USV recording. Although a few 50-kHz USV were observed following shock delivery, in the present study we focused on 22-kHz USV. The respiratory signal collected from the plethysmograph was amplified and sent to an acquisition card (MC-1608FS, Measurement Computing, USA; Sampling rate = 1000 Hz) for storage and offline analysis. The detection of the respiratory cycles was achieved using an algorithm described in a previous study (Roux et al., 2006). This algorithm performs two main operations: signal smoothing for noise reduction, and detection of zero-crossing points to define accurately the inspiration and expiration phase starting points. Momentary respiratory frequency was determined as the inverse of the respiratory cycle (inspiration plus expiration) duration.

The video signal collected through the two video cameras was acquired with homemade acquisition software. Offline, the animal's freezing behavior was automatically detected using a LabView homemade software and further verified by an experimenter. Local field potentials (LFP) were collected by telemetry via a three-channel wireless miniature transmitter (<5.2g, RodentPack EMKA Technology). LFP signals were amplified (x1000), filtered (between 0.1 and 100 Hz), digitized (sampling frequency: 1000 Hz) and stored on a computer for offline analysis.

Data analysis

Data selection and experimental categories

Since the aim of the study was to assess the relationship between USV emission, respiration and brain oscillatory activity, we focused our analysis on the 1min-period following shock delivery during which USV were numerous and loud. During this period, the animal's behavior was of two types: Freezing or Escape attempt. No other type of behavior (like grooming, exploration, quiet immobility…) was observed. Freezing was characterized as the absence of any visible movement except respiration. Escape attempt was scored when the animal showed wall climbing, running, saccadic head movements. We first noticed that while the majority of USV were emitted during freezing, a substantial amount of USV also occurred during escape behavior. This led us to distinguish four types of experimental categories: Silent Freezing, USV Freezing, Silent Escape and USV Escape (Figure 1). For each category, only segments longer than 1s were considered for further analysis.

Spectral analysis

The different data (respiration, USV, behavior, LFP signals) were synchronized offline via a TTL synchronization signal generated at the beginning of each experimental session. Once synchronized, the data were analyzed using custom-written scripts under Python.

The LFP signals were first individually inspected in order to eliminate artefacts due to signal saturation or transient signal loss. Because the average duration of individual USV calls was too short to allow proper oscillatory activity analysis (notably for the slow oscillations), we carried out the analysis on blocks of USV corresponding to successive USV with less than 1s between each other (Figure 1). As soon as the interval between two USV exceeded 1s, then a new block was defined. The periods between USV blocks are considered as Silent periods.

To extract the spectral components of the LFP signals, we used the continuous Morlet wavelet transform [START_REF] Kronland-Martinet | Analysis of sound patterns through wavelet transforms[END_REF] instead of the classical windowed Fourier transform. Indeed the continuous wavelet transform is less susceptible to non-stationary events and offers a better timefrequency resolution. This method allowed us to segment the obtained time frequency map in periods of interest with variable durations (corresponding to the four above defined experimental categories) and average them. Four frequency bands were identified for the subsequent analyses: delta (0-5Hz), theta (5-15Hz), beta (15-40Hz) and gamma (40-80Hz). The power spectral density (PSD) of the signal was then computed, and the mean power in the different frequency bands was calculated. The values obtained for each recording site were averaged across animals.

Covariation of LFP slow (delta and theta) oscillatory frequency and respiratory frequency

To study frequency-frequency coupling between LFP signals and respiration, we did not use classical coherence analysis because the respiratory signal is not always sinusoidal (especially during USV calls, see Figure 5A). We therefore designed a homemade method allowing to track instantaneous frequency synchrony. To do so, for each detected respiratory cycle, the frequency was estimated as 1/cycle duration and the time course of the instantaneous respiratory frequency was extracted. In parallel, the continuous Morlet scalogram for the LFP signal was computed in our frequency band of interest (0-15Hz). At each time bin (4ms), the local maximum in the instantaneous power spectrum was extracted together with the corresponding instantaneous frequency of the LFP signal. The time course of the predominant instantaneous frequency curve of the LFP in the 0-15Hz band was then extracted. From the two times series obtained (instantaneous respiration frequency and predominant instantaneous LFP frequency), a 2D matrix histogram was built, with the respiratory frequency represented on the X-axis and the LFP frequency on Y-axis. This 2D histogram was normalized so that the total sum is 1, and point density was represented on a color scale ranging from blue to yellow as the point density increases. The existence of a coupling between respiration frequency and LFP frequency can be assumed when a high point density (ie yellow color) is observed along the diagonal of the 2D histogram (see Figure 6 for an illustration). Conversely, in the absence of coupling a noncorrelated gaussian shape is observed. The two possibilities can co-occur on the same 2D histogram.

Modulation of LFP beta and gamma power by respiratory cycle phase

To investigate whether LFP beta and gamma amplitudes were modulated by respiration phase, we computed a so called "cycle-frequency scalogram" of the LFP signal (adapted from [START_REF] Roux | A wavelet-based method for local phase extraction from a multi-frequency oscillatory signal[END_REF]. In analogy to a classical time-frequency map that computes the energy of LFP signal over time and frequency, we computed the energy of LFP signal over respiratory cycle duration and frequency. Briefly this method consists in three steps 1) Compute the continuous Morlet scalogram (time-frequency map) 2) Use detected respiratory cycle to segment this scalogram in 2 phases (inhalation, exhalation) 3) Stretch by linear interpolation each segment so that all the segments fit the same normalized template (range from 0 to 1). The result of this analysis is very similar to the classical time frequency scalogram except that it presents small time distortions locally that do not affect the instantaneous power. Since all the cycles were normalized to the same size, they were averaged together. The typical template of an individual respiratory cycle is defined as follows: inhalation from 0 to 0.4 and exhalation from 0.4 to 1. This ratio corresponds to the average value calculated over all cycles and animals. On the scalogram, LFP signal power for the different frequency bands is represented using a color scale ranging from blue to yellow as the power increases (see Figure 7B for an illustration). The maximum power in the beta and gamma bands was also extracted and represented on a curve throughout the respiratory cycle.

Statistical analysis

All analyses were performed with Systat 13.0® software. For each test, the significance level was set at p<0.05. USV parameters (Figure 2; Duration, peak amplitude, peak frequency) were compared between USV Freezing and USV Escape using paired t-tests. Average LFP oscillatory activity parameters (Figure 3, 4; power, peak amplitude, frequency) are calculated in the different frequency bands: delta (0-5 Hz), theta (5-15 Hz), beta (15-40 Hz) and gamma (40-80 Hz), and expressed as means ± SEM across animals. To test for differences between the four experimental categories defined above, we used two-way ANOVA for repeated measures (Factors USV and behavior) followed by paired t-tests. The time course of beta and gamma activity power throughout the respiratory cycle (Figure 8) was compared between silent state and USV state using a two-way ANOVA for repeated measures (Factors USV and respiratory phase).

Histology

At the end of the experiment, the animals were sacrificed with a lethal dose of pentobarbital, their brains were removed, postfixed, cryoprotected in sucrose (20%). The brains were then sectioned (40µm coronal slices) for verification of electrodes tips by light microscopy.

Results

22-kHz USV are observed during both passive and active defense responses

The 1min-period following shock delivery was analyzed for behavior, USV emission and brain oscillatory activity. During this period, the animal's behavior was of two types: Freezing or Escape. While the majority of USV were emitted during freezing, a non-negligible amount of USV also occurred during escape attempts. This led to the distinction of four experimental categories: Silent Freezing, USV Freezing, Silent Escape and USV Escape. Figure 2A illustrates the repartition of the different categories throughout the 1min post-shock period. The animals spent 71.6% of the time in Freezing vs 28.4% in Escape. While the animals spent equivalent amounts of time in silent freezing (32.3%) compared to USV Freezing (39.3%), they spent more time in silent Escape (22%) than in USV Escape (6.4%). We then compared the characteristics of the USV emitted during Freezing versus Escape (see Figure 2B for individual examples of the two subtypes of USV). Paired t-test comparisons revealed that call duration (Figure 2C) was significantly lower for USV emitted during escape than during Freezing (t21 = 2.198, p-value = 0.039), while call peak amplitude (Figure 2D) and peak frequency (Figure 2E) was significantly higher during escape than during Freezing (peak amplitude: t21 = -3.957, p-value = 0.001; peak frequency: t21 = -3.928, p-value =0.001).

Finally we assessed whether the amount of USV emitted during conditioning could predict the animal's performance during the retention test carried out 24h later. For this, we plotted for each animal the number of USV (USV Freezing or USV Escape) emitted during the 1-min post-shock period at training against the amount of freezing exhibited in response to the learned odor during retention (Figure 2F). We showed that the number of USV Freezing was positively correlated with the amount of freezing at retention (Pearson correlation coefficient: R22=0.47, p<0.02) while the number of USV Escape was not (R22=0.15). In summary, 22-kHz USV are emitted during both passive (Freezing) and active (Escape) defense responses. USV emitted during escape are shorter and louder than those emitted during freezing, and exhibit a higher peak frequency. In addition, the amount of USV Freezing during training was a good predictor of the animal's learned fear response at retention.

USV emission is associated with changes in oscillatory activity power

The local field potential signals collected in mPFC, BLA and PIR during the 1min post-shock period were analyzed separately for each recording site in the four categories described above. The main objective was to assess whether USV emission is associated with changes in oscillatory activity power compared to the silent behavioral state, and if these changes are similar across the three recording sites. Because USV are emitted during two different defense responses, we also compared oscillatory activity power between these two behavioral states (freezing versus escape).

Delta (0-5Hz) and theta (5-15Hz) bands mean power spectral density

For each recording site, LFP mean power spectral density was calculated per animal and averaged across animals (Figure 3, left part). Visual inspection of the spectra suggests that in each recording site, the signal power in the delta and theta bands depends on both the animal's behavioral state (freezing vs escape) and, for a given behavioral state, the emission of USV (USV vs silent). Averaged mean power was calculated for each frequency band, in the four categories. A two-way ANOVA for repeated factors (USV and Behavior) was used to assess significant differences. Delta band mean power (Figure 3, middle column) In the three recording sites, the ANOVA revealed a significant effect of factors USV and Behavior, and no significant USVxBehavior interaction except for the mPFC (see Table 1 for all the statistical results). Paired t-test comparisons first showed that in the three structures, delta mean power was higher during silent freezing than during silent escape. In addition, in both behavioral states (except for mPFC for which the effect of USV was only significant during escape), the emission of USV was associated with a significant enhancement in delta mean power. Theta band mean power (Figure 3, right column) In BLA and mPFC, the ANOVA revealed a significant effect of factors USV and Behavior, and no significant USVxBehavior interaction (see Table 1). In PIR, no significant effect of factor USV was observed. Paired t-test comparisons (see Table 1) showed that in the three structures, theta mean power was lower during silent freezing than during silent escape. Moreover, in BLA and mPFC, USV emission was associated with a decrease in theta mean power.

In summary, when compared to silent escape, silent freezing is characterized by a higher power of oscillatory activity in the delta band while a lower power was observed in the theta band. Importantly, in both freezing and escape states, the emission of USV coincides globally with an increase in power in the delta band in all three regions and a decrease in the theta band in BLA and mPFC.

Beta (15-40Hz) and gamma (40-80Hz) bands mean power spectral density

Mean power spectral density of local field potentials is represented for each recording site on Figure 4, left part. Visual inspection of the spectra suggests that in each recording site, the signal power in the gamma band depends on both the animal's behavioral state (freezing vs escape) and, for a given behavioral state, the emission of USV (USV vs silent). Furthermore, in the PIR, the signal power in the beta band also seems to be affected during the emission of USV during escape. Averaged mean power was calculated for each frequency band, in the four categories. Beta band mean power (Table 2): No significant changes were observed in beta activity in mPFC and BLA, therefore the data are not presented in the table. In the PIR, the two-way ANOVA revealed a significant effect of USV and Behavior, and a significant USVxBehavior interaction. Paired t-tests showed that USV emission during escape induced an increase in beta mean power. Gamma band mean power (Figure 4, right column):

In the three recording sites, the two-way ANOVA revealed a significant effect of Behavior, and a significant USVxBehavior interaction (Table 3). Paired t-test comparisons showed that gamma mean power was lower during silent freezing than during silent escape. In addition, USV emission during escape temporally coincided with an increase in gamma mean power. Noteworthy, USV emission during both freezing and escape was associated with a narrowing of the activity towards the lower range of the band and an increase in gamma peak power (as can be seen on the power spectra of Figure 4, left column).

In summary, during silent freezing, gamma band mean power is lower than during silent escape. In addition, the emission of USV coincides with an increase in gamma band activity (mainly during escape) added to a narrowing of the activity towards the lower range of the band where the peak power is increased. Finally, in the PIR (but not in BLA or mPFC), the emission of USV during escape is associated with an increase in beta activity power.

USV emission strongly affects instantaneous respiratory rate

As previously reported in the literature, we found that the emission of USV drastically changes the shape and frequency of the respiratory signal (see individual examples on Figure 5A). As illustrated on Figure 5B, the PDF of respiration in our four experimental categories shows that for both freezing and escape states, the emission of USV shifts the dominant respiratory frequency toward lower values, going from 6.3Hz to 1.4Hz for Escape and from 2.8Hz to 1Hz for Freezing (Figure 5B,insert). The next step of our study was then to assess to what extent the frequency of oscillatory activity in the delta and theta range followed respiratory frequency, and consequently whether USV emission has an impact on this relationship.

Covariation between delta and theta oscillatory frequencies and respiratory frequency

Figure 6 (upper part) represents on the same graph the power spectral density of LFP and the PDF of respiratory signal for each experimental category. This representation highlights the fact that the respiratory frequency range delimits the range of LFP oscillatory frequency for which co-variation between the two signals frequency could be assessed.

In each experimental category, we carried out covariation matrices (Figure 6, lower part) depicting pairwise similarities between respiratory frequency and oscillatory frequency in the delta and theta bands. During Silent Escape and USV Escape, theta activity is preferentially expressed and shows no obvious coupling with respiration: whatever the respiratory frequency, theta activity is mostly observed with a fixed frequency around 6-7Hz. During Silent Freezing, both theta and delta activities are expressed. While a clear-cut covariation is observed between delta frequency and respiratory frequency, no covariation is seen for theta frequency. During USV Freezing, both theta and delta activities are expressed with no coupling with respiratory frequency. In summary, the only experimental category leading to a clear-cut frequency-frequency coupling between respiration and LFP signal is Silent Freezing and concerns the delta band. During USV emission, no covariation is observed between respiratory rate and delta or theta oscillatory activities.

Modulation of beta and gamma power with the phase of the respiratory cycle

It was shown that respiration can modulate not only slow neuronal oscillations, but also beta and gamma band oscillations, which amplitude is modulated in phase with respiration [START_REF] Cenier | Respiration-gated formation of gamma and beta neural assemblies in the mammalian olfactory bulb[END_REF][START_REF] Ito | Whisker barrel cortex delta oscillations and gamma power in the awake mouse are linked to respiration[END_REF]. We therefore investigated whether activity in the beta and gamma bands was modulated by the phase of the respiratory cycle in our different experimental categories, and whether USV emission has an impact on this modulation. Figure 7A illustrates an individual example of LFP signal collected in the PIR, with the corresponding respiratory signal and USV calls. A time frequency analysis carried out on the LFP signal at the level of the respiratory cycle, clearly shows that respiration modulates beta and gamma activity power, with higher beta activity power at the beginning of expiration and higher gamma activity throughout expiration. This modulation is further evidenced by the analysis illustrated on Figure 7B which represents the respiration phase-frequency map of LFP signal. We carried out this analysis in the four experimental categories for each recording site. For this, in each experimental category, individual respiratory cycles were plotted on a relative duration scale (between 0 and 1), averaged per animal and further averaged across animals. The transition between inspiration and expiration was fixed at 0.4 (this value corresponds to the mean ratio between inspiration and expiration over the four experimental categories). The data illustrated on Figure 8 represent the time course of gamma and beta activity maximal power throughout the respiratory cycle. For each recording site, a two-way ANOVA for repeated factors (USV and Respiratory cycle time) was carried out separately for Freezing and Escape to compare the time course of LFP oscillatory activity power throughout the respiratory cycle, either with or without USV. In the Gamma band, during freezing (Figure 8, right part), the ANOVA revealed a significant effect of Respiratory cycle time and a significant interaction for USVxRespiratory cycle time (see Table 4). During Silent Freezing, the maximum power is observed during inspiration, while during USV Freezing, two maxima are observed, one during inspiration, and the other during the late part of expiration. Concerning escape, (Figure 8, left part), the ANOVA revealed a significant effect of Respiratory cycle time and USV (except for BLA), and a significant interaction for USVxRespiratory cycle (see Table 4). During Silent Escape, the maximum power is observed at the transition between inspiration and expiration, while during USV Escape, the maximum is shifted toward inspiration. In the Beta band in the PIR, during Freezing (Figure 8, right part), the ANOVA revealed a significant effect of Respiratory cycle time and a significant interaction for USVxRespiratory cycle time (see Table 4). During Silent Freezing, the maximum power is observed during inspiration, while during USV Freezing, the maximum is shifted toward the early part of expiration. Concerning escape (Figure 8, right part), the ANOVA revealed a significant effect of Respiratory cycle time but no effect of USV or USVxRespiratory cycle interaction (see Table 4). In summary, gamma power in the three recording sites is strongly modulated by the phase of the respiratory cycle during both freezing and escape, although presenting slightly different patterns. The emission of USV is associated with drastic changes in the time course of this modulation. Beta power in the PIR is also modulated by the phase of the respiratory cycle during freezing and the pattern of this modulation is changed during USV emission.

Discussion

The present study assessed for the first time the impact of 22-kHz USV production on brain dynamics in the network involved in fear expression, including the mPFC and the BLA. We report that USV emission modulates oscillatory activities differentially depending on their frequency band. Specifically, it temporally coincides with an increase in delta and gamma power, and a decrease in theta power. In addition, in the PIR, an increase in beta activity is observed. Some of these changes co-occur with USVinduced respiration changes. Indeed, during USV calls, the coupling observed between respiratory frequency and delta oscillatory frequency during silent freezing is lost, and the time course of gamma and beta power within the respiratory cycle is modified. The present data suggest that USV calls could result in a specific gating of information within the fear network, potentially modulating fear memory, as suggested by our observation that the amount of USV emitted during conditioning is a good predictor of the learned fear response at retention.

22-kHz USV are observed during both passive and active fear responses

22-kHz USV in rats are emitted in aversive situations such as foot-shock delivery and are considered as reflecting a negative affective state [START_REF] Litvin | Rat 22kHz ultrasonic vocalizations as alarm cries[END_REF]Schwarting and Wöhr, 2012;[START_REF] Brudzynski | Ethotransmission: communication of emotional states through ultrasonic vocalization in rats[END_REF]. We found that USV are predominantly produced during freezing, which is consistent with the literature (Brudzynski and Ocepia, 1992;Wöhr et al, 2005;[START_REF] Hegoburu | The RUB Cage: Respiration-Ultrasonic Vocalizations-Behavior Acquisition Setup for Assessing Emotional Memory in Rats[END_REF][START_REF] Boulanger-Bertolus | Understanding pup affective state through ethologically significant ultrasonic vocalization frequency[END_REF]. However, we also found that some USV are emitted during escape. Although some examples of 22-kHz USV during locomotion have been reported [START_REF] Laplagne | Rats Synchronize Locomotion with Ultrasonic Vocalizations at the Subsecond Time Scale[END_REF][START_REF] Boulanger-Bertolus | Understanding pup affective state through ethologically significant ultrasonic vocalization frequency[END_REF], the characteristics of these USV remain poorly investigated. Here we show that 22-kHz USV emitted during escape are shorter and louder than those emitted during freezing and exhibit a higher peak frequency. We also show that although these two types of USV have globally similar effects on brain oscillations, they present different relationships with performance at 48h-retention. Indeed while there is a positive correlation between the number of USV Freezing during conditioning and the amount of freezing at retention, this correlation is not found for USV Escape. This suggests that the two subtypes of 22-KHz USV reflect different aspects of fear response. For instance, USV Escape might be more related to the unconditioned response to shock, while USV Freezing are generally considered as part of the conditioned fear response (Wöhr and Schwarting, 2008).

Freezing and Escape differentially modulate brain oscillatory activities

We first investigated whether in the absence of USV emission, the way fear is expressed is associated with different changes in brain oscillatory activities. Freezing is a passive defense response while Escape is an active response. Recent studies pinpointed that active and passive fear responses involve distinct and mutually inhibitory neurons in the central amygdala [START_REF] Gozzi | A neural switch for active and passive fear[END_REF][START_REF] Fadok | A competitive inhibitory circuit for selection of active and passive fear responses[END_REF]. Here we show that these two response modes also differentially modulate oscillatory activity in the fear circuit. Indeed, compared to escape, freezing is characterized by a higher delta power and a lower theta and gamma power. Our data in the delta band are in line with the literature as several recent studies showed that freezing temporally coincides with the development of 4-Hz oscillations in prefrontal-amygdala circuits [START_REF] Dejean | Prefrontal neuronal assemblies temporally control fear behaviour[END_REF][START_REF] Karalis | 4-Hz oscillations synchronize prefrontal-amygdala circuits during fear behavior[END_REF][START_REF] Moberly | Olfactory inputs modulate respiration-related rhythmic activity in the prefrontal cortex and freezing behavior[END_REF]. In awake animals, theta activity is known to occur preferentially during voluntary locomotor activities [START_REF] Vanderwolf | Hippocampal electrical activity and voluntary movement in the rat[END_REF]Buszaki, 2002), thus explaining the increase in theta power observed here during escape. Finally, the increase in gamma power observed during escape might reflect an increased emotional level compared to freezing. Indeed, previous studies both in humans and animals have shown that gamma oscillations are enhanced during emotional situations (reviewed in Headley and Paré, 2013;[START_REF] Stujenske | Fear and safety engage competing patterns of theta-gamma coupling in the basolateral amygdala[END_REF][START_REF] Concina | Coherent Activity between the Prelimbic and Auditory Cortex in the Slow-Gamma Band Underlies Fear Discrimination[END_REF].

22-kHz USV emission alters respiration and is associated with changes in oscillatory activities

While several studies have investigated the neural circuit involved in USV production (reviewed in Schwarting and Wöhr, 2012), and the correlates of USV perception in the brain of conspecifics receivers [START_REF] Sadananda | Playback of 22-kHz and 50-kHz ultrasonic vocalizations induces differential c-fos expression in rat brain[END_REF][START_REF] Parsana | Positive and negative ultrasonic social signals elicit opposing firing patterns in rat amygdala[END_REF][START_REF] Roberts | Responses to Social Vocalizations in the Dorsal Cochlear Nucleus of Mice[END_REF], to our knowledge no study has assessed the effect of USV production on the sender animal's brain oscillatory activities. We showed that USV emission coincides with an increase in delta power and a decrease in theta power. In addition, an increase in gamma power is observed. The effects are globally similar for both types of USV, and in the three recording sites although small differences exist. Furthermore, an increase in beta power is specifically observed in the PIR. Importantly, some of the changes observed during USV emission co-occurred with changes in respiratory rhythm. USV are produced during expiration upon constriction of the vocal folds resulting in an increase in subglottal pressure and a reduction of airflow through the nose [START_REF] Riede | Subglottal pressure, tracheal airflow, and intrinsic laryngeal muscle activity during rat ultrasound vocalization[END_REF][START_REF] Sirotin | Rodent ultrasonic vocalizations are bound to active sniffing behavior[END_REF]. Consequently, 22-kHz USV emission induces drastic changes in the shape and frequency of the respiratory signal [START_REF] Frysztak | The effect of medial frontal cortex lesions on respiration, "freezing," and ultrasonic vocalizations during conditioned emotional responses in rats[END_REF][START_REF] Hegoburu | The RUB Cage: Respiration-Ultrasonic Vocalizations-Behavior Acquisition Setup for Assessing Emotional Memory in Rats[END_REF][START_REF] Boulanger-Bertolus | Understanding pup affective state through ethologically significant ultrasonic vocalization frequency[END_REF]. It is known from a long time that respiration drives oscillations time-locked to breathing cycles in the olfactory pathways [START_REF] Adrian | Olfactory reactions in the brain of the hedgehog[END_REF][START_REF] Fontanini | Variable coupling between olfactory system activity and respiration in ketamine/xylazine anesthetized rats[END_REF][START_REF] Buonviso | Respiratory modulation of olfactory neurons in the rodent brain[END_REF][START_REF] Kay | Olfactory oscillations: the what, how and what for[END_REF][START_REF] Courtiol | Individual and synergistic effects of sniffing frequency and flow rate on olfactory bulb activity[END_REF][START_REF] Esclassan | Faster, deeper, better: the impact of sniffing modulation on bulbar olfactory processing[END_REF][START_REF] Zelano | Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function[END_REF] and modulates the amplitude of local beta and gamma oscillations in the olfactory bulb [START_REF] Buonviso | Rhythm sequence through the olfactory bulb layers during the time window of a respiratory cycle[END_REF][START_REF] Cenier | Respiration-gated formation of gamma and beta neural assemblies in the mammalian olfactory bulb[END_REF][START_REF] Rosero | Sniffing shapes the dynamics of olfactory bulb gamma oscillations in awake behaving rats[END_REF] and in the olfactory cortex [START_REF] Fontanini | Variable coupling between olfactory system activity and respiration in ketamine/xylazine anesthetized rats[END_REF][START_REF] Zelano | Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function[END_REF]. Several recent papers have highlighted that outside its impact on olfactory regions, nasal respiration also entrains oscillations in widespread brain regions in awake rodents [START_REF] Ito | Whisker barrel cortex delta oscillations and gamma power in the awake mouse are linked to respiration[END_REF][START_REF] Nguyen Chi | Hippocampal Respiration-Driven Rhythm Distinct from Theta Oscillations in Awake Mice[END_REF]Biskamp et al, 2017;[START_REF] Zhong | Selective entrainment of gamma subbands by different slow network oscillations[END_REF]Rojas-Líbano et al, 2018;Tort et al, 2018) and modulates the amplitude of fast oscillations [START_REF] Ito | Whisker barrel cortex delta oscillations and gamma power in the awake mouse are linked to respiration[END_REF]Biskamp et al, 2017;[START_REF] Zhong | Selective entrainment of gamma subbands by different slow network oscillations[END_REF]. Importantly a recent work has specifically investigated the link between respiration and freezing-related 4Hz oscillation in the mPFC [START_REF] Moberly | Olfactory inputs modulate respiration-related rhythmic activity in the prefrontal cortex and freezing behavior[END_REF], and reported that during freezing, mice respiratory frequency is correlated with the 4-Hz oscillation in the mPFC, and that disruption of olfactory inputs to the mPFC significantly reduces the 4-Hz oscillation.

Here we show that during silent freezing, dominant frequency in the delta band covaries with respiratory frequency. In addition, we report that the activity in the gamma band for the three recording sites, and in the beta band for PIR, are modulated in phase with respiration, with greater beta and gamma power during inspiration than expiration (see summary on Figure 9). Importantly we also show that USV emission, particularly during freezing, coincides with important changes in the relationship between respiration and oscillatory activity. First, USV emission induces a deep slow-down of respiratory frequency. In parallel, the coupling between delta band dominant frequency and respiratory frequency is lost. In addition, a reorganization of beta and gamma activity power during the respiratory cycle occurs, with increased beta power in PIR during the first half of expiration phase, and increased gamma power in the three recording sites during the second half of expiration.

Functional interpretation

How can we integrate the present data to the existing literature? Although additional experiments are needed to understand the functional role of USV-related changes in oscillatory activities in the fear network, tentative proposals can be made. Respiration-locked oscillations in non-olfactory regions were shown to depend on nasal airflow [START_REF] Ito | Whisker barrel cortex delta oscillations and gamma power in the awake mouse are linked to respiration[END_REF][START_REF] Yanovsky | Slow oscillations in the mouse hippocampus entrained by nasal respiration[END_REF]. Indeed olfactory sensory neurons have mechanosensitive properties [START_REF] Grosmaitre | Dual functions of mammalian olfactory sensory neurons as odor detectors and mechanical sensors[END_REF] and the signals elicited by rhythmic airflow in the nose are transmitted to the olfactory bulb and the PIR [START_REF] Fontanini | Ketamine-xylazine-induced slow (< 1.5 Hz) oscillations in the rat piriform (olfactory) cortex are functionally correlated with respiration[END_REF][START_REF] Wu | Activity Patterns Elicited by Airflow in the Olfactory Bulb and Their Possible Functions[END_REF]. The PIR has direct connections with the PFC [START_REF] Clugnet | Olfactory input to the prefrontal cortex in the rat[END_REF] and the olfactory information has unique direct access to the amygdala [START_REF] Mcdonald | Cortical pathways to the mammalian amygdala[END_REF]. We propose that the deep slow-down of respiratory rate added to the reduction of airflow through the nose during USV calls [START_REF] Riede | Subglottal pressure, tracheal airflow, and intrinsic laryngeal muscle activity during rat ultrasound vocalization[END_REF][START_REF] Sirotin | Rodent ultrasonic vocalizations are bound to active sniffing behavior[END_REF] is responsible for the loss of coupling between nasal rhythm and delta oscillation. During USV calls, brain delta oscillations become independent of nasal respiration and their power increases. In parallel, beta and gamma activity power increases during expiration. Since we observed that the amount of USV emitted during freezing at training is correlated with the learned freezing response at retention, we suggest that the window of a USV call and its particular respiratory pattern added to its specific combination of brain oscillatory activity, might enhance plasticity at given sites of the network and ultimately strengthen long-term fear memory. A better knowledge of the impact of USV production on brain neural dynamics is not only important for understanding the respective weight of the different components of fear response, but is also particularly relevant for rodent models of human neuropsychiatric disorders, for which socio-affective communication is severely impaired (Wöhr and Scattoni, 2013).

Acknowledgements

This work was performed within the framework of LABEX CORTEX (ANR-11-LABX-0042) of Université de Lyon, within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). The authors gratefully acknowledge Emmanuelle Courtiol and Rémi Gervais for very valuable discussions of the data and careful reading of the manuscript, and Ounsa Ben-Hellal for taking care of the animals. Julie Boulanger-Bertolus present address:

Center for Consciousness Science, Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States. The authors declare no competing financial interests.


Evelyn Paiz-Reyes

Nadile Nunes-De-Lima
email: nadilend@stud.ntnu.nosule.yildirim@ntnu.no

Sule Yildirim-Yayilgan

GIF Image Retrieval in Cloud Computing Environment

Keywords: GIF image format, content-based image retrieval, hashing, cloud computing

GIF images have been used in the last years, especially on social media. Here it is explored a content-based image retrieval system to work specifically with GIF file format. Its implementation is extended to a cloud computing environment. Given the Tumblr GIF dataset, it is created a "search by example" image retrieval system. To describe the images, low-level features are used: (1) color, (2) texture and (3) shape. The system performs the search using just GIF images as query images.

To obtain faster results on the retrieval process, a hashing indexing approach is used. The system showed a complexity of O(n 2 ) for indexing and O(log(n)) for retrieval. Additionally, better results were obtained (in relation to precision and recall) for simple images, instead of images with a lot of movements.

Introduction

The importance of images in people's daily life has increased along with the advances in digital technology. The emergence of imaging devices, such as smartphones, cameras, and computers, has led the world to witness the growth in quantity, availability, and value of images [START_REF] Xia | A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing[END_REF]. The need to access this type of data anytime and anywhere has increased its importance. Content-based image retrieval (CBIR) has been widely studied [START_REF] Zouaki | Indexing and content-based image retrieval[END_REF] [3] [START_REF] Kaur | A novel technique for content based image retrieval using color, texture and edge features[END_REF]. A CBIR system retrieves the relevant images from a database to a search query based on visual characteristics, where each image in the database is mapped to a feature vector including visual, structural and conceptual characteristics [START_REF] Datta | Image retrieval: Ideas, influences, and trends of the new age[END_REF]. On the other hand, a CBIR maintenance is considered to be a typical example of cloud computing. It offers a great opportunity for on-demand access to a wide computation and storage resources [START_REF] Xia | A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing[END_REF].

Among the many image formats that can be applied on a CBIR system, one that is interesting to mention is the GIF (Graphics Interchange Format) file format. Because of its high compression ratio and less disk space needs, this image format has been spread quickly and supported by a wide variety of applications [START_REF] Zhang | The studies and implementation for conversion of image file format[END_REF]. It was designed "to allow the easy interchange and viewing of image data stored on local or remote computer systems" [START_REF] Tiwari | Evaluation of various lsb based methods of image steganography on gif file format[END_REF].

GIF has become well known and it is the second most common image format used on the World Wide Web after JPEG. Up until now, there have been few attempts to explore the design and development of a CBIR system for this file format. The objective of the present work is first to design and develop a CBIR system for indexing and retrieval of GIF file formats using the Tumblr GIF dataset [START_REF] Li | TGIF: A New Dataset and Benchmark on Animated GIF Description[END_REF]. Secondly to extend the CBIR to a cloud computing environment.

Related works

Content-based image retrieval system

No matter what type of CBIR system is intended to be built, the following steps are the ones needed to complete it [1] [9] [10]:

Image descriptors An image feature can be described as anything that is localized, meaningful and detectable from an image [START_REF] Kaur | A novel technique for content based image retrieval using color, texture and edge features[END_REF]. In practice, the use of multiple features is needed to be able to achieve a good description of the image. The features/signatures intended for this work are color [START_REF] Kaur | A novel technique for content based image retrieval using color, texture and edge features[END_REF], texture [START_REF] Kaur | A novel technique for content based image retrieval using color, texture and edge features[END_REF] [10] and shape [START_REF] Kaur | A novel technique for content based image retrieval using color, texture and edge features[END_REF].

Dataset indexing Indexing can be described as the process of quantifying the dataset by utilizing an image descriptor to extract features from each image.

Definition of similarity metric Images can be compared using a distance metric or similarity function. This function takes the feature vectors as inputs and its output is a number representing how "similar" the feature vectors are. The choice is highly dependent on (1) the dataset and (2) the types of features that were extracted.

Search The search process is the following: (1) A user submits a query image to the system, (2) its features are extracted and (3) the similarity function is applied to compare the query features with the features already indexed. From there, (4) the results are sorted by relevancy and then presented to the user.

CBIR and Graphics Interchange Format (GIF)

GIF is a standard image representation and has the possibility to create animations, which made it popular on social media. There is no work related to a CBIR system specific for indexing and retrieving GIF files. The GIF format was developed in 1987 by Steve Wilhite at CompuServe Network [START_REF] Zhang | The studies and implementation for conversion of image file format[END_REF]. Its purpose is to store multiple bitmap images in a single file for exchange between platforms and images. It is stream based and is made up of a series of data packets called blocks and protocol information. In GIF, lossless file compression method is applied [START_REF] Tiwari | Evaluation of various lsb based methods of image steganography on gif file format[END_REF].

GIF file format comprises a number of frames that are displayed in succession. Each frame is displayed with a time delay to wait until the frame is drawn [START_REF] Zhang | The studies and implementation for conversion of image file format[END_REF]. Then, why not call a GIF a video? Because a video is a collection of both inter and intra coded frames. In a video, the inter-coded frames take advantages of the other frames, so compression is far more efficient. On the other hand, a GIF is just a collection of intra frames and each frame is coded on its own [START_REF] Hu | A survey on visual contentbased video indexing and retrieval[END_REF].

Methodology

The goal is a GIF-based CBIR system. A GIF is an image file and not a video file [START_REF] Hu | A survey on visual contentbased video indexing and retrieval[END_REF], but it has the characteristic of frames as video files do. Therefore, this is considered in the design of the system (Figure 1).

Definition of dataset

A collection of 2k randomly selected GIF images from the Tumblr GIF dataset (containing 100k animated GIFs in total) is used. The dataset consists of various GIFs images from Tumblr taken from random posts between May and June of 2015 [START_REF] Li | TGIF: A New Dataset and Benchmark on Animated GIF Description[END_REF].

Because the dataset was not previously divided into classes, the collection was manually classified into four categories where the images belonging to each category were considered similar (subjectively by the experimenter). It is important to mention that it was not possible to categorize all images. The dataset was composed of categorized plus uncategorized images. The final classes were the following:

1. Grayscale with couples. 2. Grayscale focused on faces and/or mainly just one person throughout the image. 3. Sports on a field (golf, soccer, baseball, etc.) 4. Animals.

Definition of image descriptor

The most basic image descriptors (color, texture, and shape) are employed. For each descriptor, a specific feature vector is created and then the three merged into a single feature vector. This means that each image in the dataset is represented and quantified using only a list of floating point numbers. Figure 2 shows this process. Additionally, the ad-hoc values selected were the ones that gave better result concerning the visual perception of the image and processing time. Color-based descriptor The color-based image descriptor is a 3D color histogram in the HSV color space. It is used 8 bins for the hue, 12 for the saturation, and 3 for the value, yielding a feature vector of dimension 8 × 12 × 3 = 288 [START_REF] Rosebrock | The Complete Guide to Building an Image Search Engine with Python and OpenCV[END_REF].

Texture-based descriptor The method of local binary patterns (LBP) [START_REF] Ojala | Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[END_REF] is applied. First, the image is converted to grayscale and, for each pixel, a circular neighborhood of p = 24 points and radius r = 8, surrounding the center pixel is selected. An LBP value is calculated for each center pixel and stored in an output 2D array [START_REF] Rosebrock | Local Binary Patterns with Python & OpenCV[END_REF]. Its computed histogram is the texture-based image descriptor [START_REF] Ojala | Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[END_REF]. A total feature vector of dimension 24 + 2 = 26 is obtained.

Shape-based descriptor Zernike polynomials are orthogonal to each other and there is no redundancy of information between moments [START_REF] Chaumette | Image moments: a general and useful set of features for visual servoing[END_REF] [START_REF] Kim | A region-based shape descriptor using zernike moments[END_REF]. First, the image is converted to grayscale and segmented. Next, the Zernike moments are applied (with r = 21, where r is the radius of the polynomial) to characterize the shape of the object. The result is a vector with the first 25 Zernike moments from the segmented image [START_REF] Rosebrock | Building a Pokedex in Python: Indexing our Sprites using Shape Descriptors (Step 3 of 6)[END_REF].

System architecture

The system is divided into three basic modules: (1) averaging and description of the images, (2) indexing of the data and (3) retrieval from a query. Figure 1 shows a graphical representation of the final architecture.

Averaging and description This module takes as an input a GIF image.

A GIF image has the property of containing various images (frames) inside, and instead of working with all these data, an average image is obtained (see Algorithm 1). Next, it is computed the three feature vectors described in section 3.2. The output is the combination of these vectors in a single feature vector. Indexing The basic process of indexing is done as in a normal CBIR (features of each image are extracted and stored), but with the inclusion of averaging. For each image of the dataset (section 3.1), the average is calculated and features are extracted. The resulting feature vectors are saved in a hash table in the form of a dictionary, where information of the image (name), a feature vector and hashed value of the feature vector are stored. The method used for indexing is the locality sensitive hashing (LSH) [START_REF] Datar | Locality-sensitive hashing scheme based on p-stable distributions[END_REF], aiming to maximize the probability of a "collision" for similar items.

Retrieval The module works in the following way: (1) The input is a query GIF image; (2) the image is averaged and features are extracted to a single vector;

(3) The query feature vector is compared with the stored feature vectors using Euclidian distance and a set of similar results is obtained; and (4) The top 5 results are retrieved and displayed to the user.

Indexing and retrieval time

In steps of 200 images, both indexing and retrieval time were tested (Figure 3). The indexing time of each one of the image descriptors (individually) was also measured. A PC (Intel Core i5; 2.5 GHz) with macOS High Sierra and Python 2.7 was used in the experiment. The result data (from the measured time) was fitted on a curve to obtain the complexity of both indexing and retrieval. For indexing, all image descriptors (individually and combined) showed a polynomial behavior. In retrieval, the result was a logarithmic structure instead. The final outcome of the experiment revealed a complexity of O(n 2 ) for indexing and O(log(n 2 )) for retrieval.

Precision and recall of the system

On the second experiment, the precision and recall of the system were measured (Figure 3). A total of 4 images from each category were selected randomly as query and the values of precision and recall were measured for each one of the queries (retrieving 1 to 10 images as a result). The average results were computed for each category. A final average of all the categories was the resulting precision and recall curve. Finally, a ROC curve was also computed. The same measurements of the precision-recall curve were used. Instead of estimating precision and recall, it was computed sensitivity and specificity. Figure ?? shows the resulting curves.

User happiness

The final experiment was a user happiness evaluation. The study was performed with 6 adult volunteers from the Norwegian University of Science and Technology (NTNU). For each category (same as section 3.1), a set of four images was shown. The user selected one each time and for the retrieved GIFs, the participant indicated what was considered relevant and irrelevant to him/her.

It was mentioned from their point of view that the time response for the retrieval was acceptable (83%). Also, users considered the categories 1 and 2 as similar. Finally, during the classification of what was relevant and irrelevant for the user, the semantics of the files had a strong influence. The participant tended to consider a file relevant when it contained the same actions or emotions as the query (i.e. if the query contains a person crying, the relevant results will be GIFs with people sad or crying as well).

Discussion and conclusion

The complexity of the system was divided in two: indexing and retrieval. When the feature extraction is evaluated separately, it is clear that the texture description is the process that takes the majority of the time. This is because on the LBP method every single pixel in the image is computed, increasing the time needed to extract the features. Therefore, for future improvement of the system, a new method that is able to reduce the complexity of describing texture is advise.

The relevance/irrelevance of the files retrieved was judged by users and similarities between the answers were found. However, GIFs nowadays are also part of the social interaction, being used to transmit emotions and opinions towards some subject online. Therefore, only low-level features might not be enough to find the relation between the content of the file and what is the meaning the user is looking for.

The best results of the system were related to queries that did not have many motion through the images inside the file. The improvement of this condition can be a valid future work for this project. The insertion of semantics and classification would be also recommended, but first, a precise and detailed description must be done for the dataset (classifying every image). Hence, this classification would allow the use of machine learning to improve the retrieval process.

Fig. 1 .

 1 Fig. 1. Flowchart representing the architecture of the designed system.

Fig. 2 .

 2 Fig. 2. Flowchart representing an example of the feature extraction.

Algorithm 1 :

 1 Proposed averaging GIF image algorithm. Data: List of frames/images in the GIF file 1 Set sum image to an empty image; 2 for each frame in the GIF image do 3 add sum image with the frame image pixel by pixel; 4 end 5 average image = (sum image / total number of frames); 6 Return average image;

( a )

 a Indexing time. (b) Retrieval time. (c) Precision and recall curve. (d) ROC curve.

Fig. 3 .

 3 Fig. 3. Measuring curves of the system from 0 to 2000 images.


Jean-Pierre Levraud
email: jlevraud@pasteur.fr

Philippe Herbomel
email: herbomel@pasteur.fr

Resident macrophage lookalikes of unexpected origin

) identify in zebrafish skin macrophagelike cells that sample the environment through trans-epithelial protrusions and import antigen from the water for traditional tissue-resident macrophages. Remarkably, these "metaphocytes" originate from the epidermis, challenging current assumptions about the lineage of tissue-resident macrophages.

Tissue-resident macrophages / dendritic cells (DCs) play key roles in local homeostasis, detection of foreign material, and repair. For historical reasons, these cells bear different names according to their tissue of residence: for example, microglia in the brain, Küpffer cells in the liver, and Langerhans cells in the epidermis. Since 1968 (van Furth and[START_REF] Van Furth | The origin and kinetics of mononuclear phagocytes[END_REF]) and over the next 40 years, the prevailing view had been that all of these cells arise from mesoderm-derived hematopoietic progenitors, and more precisely from hematopoietic stem cell (HSC)-derived monocytes continually produced in the bone marrow. Then, over the last decade, following the work of a few pioneers around 1990, the second part of this model was deeply revised, with the demonstration that most tissue-resident macrophages in adult mice do not derive from HSC/monocytes, but from hematopoietic progenitors born in the yolk sac, that colonized tissues during embryogenesis and then self-renewed there throughout life [START_REF] Ginhoux | Tissue-Resident Macrophage Ontogeny and Homeostasis[END_REF][START_REF] Perdiguero | The development and maintenance of resident macrophages[END_REF]. Yet even in this revised, current model, all tissue macrophages were still mesoderm-derived. This view may have to be reconsidered in the light of the findings reported in this issue of Developmental Cell from [START_REF] Lin | An Ectoderm-Derived Myeloid-Like Cell Population Functions as Antigen Transporters for Langerhans Cells in Zebrafish Epidermis[END_REF].

Alongside mice, zebrafish has been an important vertebrate model to pinpoint the different hematopoietic waves and their progeny [START_REF] Robertson | Understanding the regulation of vertebrate hematopoiesis and blood disorders -big lessons from a small fish[END_REF]. However, emphasis has largely been put on the transparent embryonic and larval stages, and the macrophages and DC populations of the adult zebrafish are still poorly characterized. DCs -functionally defined by their ability to present antigen to and prime naive T cells, requiring, among others, expression of MHC class II genes -have been shown to exist in zebrafish [START_REF] Lugo-Villarino | Identification of dendritic antigen-presenting cells in the zebrafish[END_REF]. Moreover, with the help of fluorescent reporter zebrafish lines driven by various promoters, cells with dendritic morphology and high MHC class II expression have been observed in several organs [START_REF] Martins | Resident Immunity in Tissue Repair and Maintenance: The Zebrafish Model Coming of Age[END_REF], notably in the skin, which remains easy to image in adult zebrafish, as pigment cells are located underneath the epidermis. The mpeg1 promoter [START_REF] Ellett | Mpeg1 Promoter Transgenes Direct Macrophage-Lineage Expression in Zebrafish[END_REF] is currently the most commonly used promoter to fluorescently highlight macrophages in larval and adult zebrafish.

In an earlier work, the authors had already addressed the origin of epidermal resident myeloid cells (Langerhans cells) of the zebrafish [START_REF] He | Adult zebrafish Langerhans cells arise from hematopoietic stem/progenitor cells[END_REF]. Their fate-mapping approach was based on temporally and locally controlled (via tamoxifen-dependence and targeted laser heating) activity of Cre recombinase, inducing red to green switch of a mpeg1driven fluorescent reporter. This approach revealed that most skin mpeg1-expressing cells descended from definitive HSCs arising from the ventral floor of the dorsal aorta (VDA), but not from embryonic macrophage progenitors in the yolk sac or posterior blood island. These HSC-derived mpeg1+ cells were distributed evenly in the entire epidermis, and present in other organs and in the blood. Surprisingly however, in a substantial subset of zebrafish subjected to VDA-targeted laserheating, Cre-switched cells were present in only some skin patches, and absent from the blood. This observation, which may have been disregarded as an experimental glitch, was instead investigated in more detail in a set of elegant experiments that led to the outstanding discovery reported in this issue of Developmental Cell. [START_REF] Lin | An Ectoderm-Derived Myeloid-Like Cell Population Functions as Antigen Transporters for Langerhans Cells in Zebrafish Epidermis[END_REF] hypothesized that these spatially restricted mpeg1+ cells could arise from collaterally laser-irradiated cells while targeting HSC precursors in the VDA. They indeed found that these cells also appeared after targeting dorsal regions devoid of any hematopoietic cell at this stage. Furthermore, the distribution of these cells in the adult skin matched the region irradiated in the embryo. Mid-blastula cell transplantation assays then showed that presence of donor-derived spatially restricted mpeg1+ skin cells (but not of universally distributed ones) required the presence of donor epidermal cells in the same region. Finally, by using epidermis-or mesoderm-driven CreERT2 transgenes, they confirmed that the population of skin mpeg1+ cells could be subdivided in two subsets: 70% were mesoderm-derived conventional Langerhans Cells (cLCs), with circulating counterparts, while the remaining 30%, designated myeloid-like cells (MLCs), were ectoderm-derived and found exclusively in the skin. [START_REF] Lin | An Ectoderm-Derived Myeloid-Like Cell Population Functions as Antigen Transporters for Langerhans Cells in Zebrafish Epidermis[END_REF] then compared the properties of the two skin cell subsets. Morphologically they looked identical. Transcriptionally, they were very similar -much closer to each other than to T cells or keratinocytes -but distinguishable with proper markers. The authors observed a strong difference in cell mobility by time-lapse imaging in scale explant assays: whereas MLCs were spontaneously more mobile than cLCs, only the latter were attracted to a wound or a bacterial infection. Remarkably, upon adding labelled ovalbumin to the fish water and monitoring its internalization in the skin, MLCs were the first to acquire OVA, in just a few hours. However, over a few days of chase, the signal disappeared from MLCs and appeared in cLCs, with evidence suggesting that cLCs engulfed apoptotic antigen-laden MLCs. High-resolution 3D imaging of skin indicated that MLCs sent protruding dendrites between the superficial keratinocytes, reaching the external mucus layer -much like CX3CR1+ DCs in the mammalian gut [START_REF] Niess | CX3CR1-mediated dendritic cell access to the intestinal lumen and bacterial clearance[END_REF]. This strongly suggests that MLCs sampled antigens from the outside environment, which led the authors to rename them "metaphocytes" referring to their role as antigen carriers.

The findings of [START_REF] Lin | An Ectoderm-Derived Myeloid-Like Cell Population Functions as Antigen Transporters for Langerhans Cells in Zebrafish Epidermis[END_REF] immediately raise a question: in mammals, are there also myeloid-like cell subsets of ectodermal origin? Despite a considerable body of work in rodents, this remains possible, considering the caveats of irradiation and bone-marrow transplantation, and that the more recent retrospective fate mapping studies have focused on known hematopoietic lineages [START_REF] Ginhoux | Tissue-Resident Macrophage Ontogeny and Homeostasis[END_REF][START_REF] Perdiguero | The development and maintenance of resident macrophages[END_REF]. If anything, transcriptome-based cellular "phylogenies" should not be taken as proof of ontogeny, as shown in this study by the similar gene expression of ectoderm-derived metaphocytes and mesoderm-derived Langerhans cells. There are precedents for such situations: among cells of ectodermal origin, neural crest cells are well known to be able to differentiate into cell types primarily derived from mesoderm, such as chondrocytes or smooth muscle cells. Could metaphocytes also derive from neural crest? This seems unlikely, considering the shared localization of donor-derived keratinocytes and metaphocytes following mid-blastula transplantation, but it remains to be more directly examined.

There are also many exciting questions regarding the functions and behavior of metaphocytes for future exploration. Why would these cells express high levels of MHC class II if they only serve to transfer antigen to cLCs? How do they maintain their population; is there a metaphocyte-generating stem cell, and does it produce other epidermal cells? Why are they so spatially restricted if they are so mobile? Considering this mobility, how do they manage to maintain transepithelial dendrites with tight junctions? Their analysis will probably illuminate the workings of cells much harder to visualize in vivo, such as the CX3CR1+ DCs of the mammalian gut.

Figure 1. Macrophages in the zebrafish epidermis

In the zebrafish epidermis, Langerhans cells (green) and the very similar, but epidermisderived, metaphocytes (red) wander amongst basal keratinocytes (yellow), above the pigment cells (orange) that overlay the dermis (not shown). Metaphocytes send protrusions between surface keratinocytes (pink), sample antigens trapped in the mucus layer secreted by goblet cells (blue & light blue) on the skin surface (light pink), and transfer these to Langerhans cells.