Stéphanie Caharel

Bruno Rossion
email: bruno.rossion@univ-lorraine.fr

The N170 is sensitive to long-term (personal) familiarity of a face identity

Keywords: Event-related potential, N170, Face familiarity, Face identity, EEG

The N170 is a large deflection of the human electroencephalogram (EEG), peaking at about 170 milliseconds over the occipito-temporal cortex after the sudden onset of a face stimulus.

The N170 reflects perceptual awareness of a face and its onset corresponds to the emergence of reliable face-selectivity in the human brain. However, whether sensitivity to the long-term familiarity of a face identity emerges already at this early time-point remains debated. Here we provide a brief survey of the 45 published studies comparing the N170 response to unfamiliar and familiar (famous, experimentally familiarized, personally familiar and own) faces. Even though effects of familiarity on the N170 are relatively small and inconsistent across studies, this overview indicates that face familiarity significantly increases the N170 amplitude. This effect is especially present for personally familiar faces, learned in natural conditions. In the human brain, effects linked to familiarity with specific facial identities therefore appear to emerge between 150-200 ms in occipito-temporal brain regions, i.e. shortly after the onset of face-selectivity but at the same time as the earliest high-level effects of immediate unfamiliar face identity repetition. This observation challenges standard neurocognitive models with a clear-cut distinction between perceptual and memory stages in human face recognition.

Introduction

The N170 event-related potential (ERP) component is a relatively large deflection of the human electroencephalogram (EEG) peaking on average at about 170 milliseconds (ms) after the sudden onset of a face stimulus (with an interindividual variability range between 130 and 200 ms). Initially described by [START_REF] Srebro | Localization of cortical activity associated with visual recognition in humans[END_REF] in an elegant study linking this ERP to face perception, i.e. the subjective experience of seeing a stimulus as a face, it was defined later on by Bentin and colleagues [START_REF] Bentin | Electrophysiological studies of face perception in humans[END_REF], who established the larger N170 amplitude to faces than other familiar visual shapes (Fig. 1; see [START_REF] Rossion | Does physical interstimulus variance account for early electrophysiological face sensitive responses in the human brain? Ten lessons on the N170[END_REF] for a review of the face-selectivity of the N170). The N170 is maximal in amplitude on recording electrodes positioned over the occipito-temporal cortex, with generally a larger response in the right hemisphere, and emerges concurrently to a positive potential recorded on central electrode sites, the vertex positive potential (VPP; [START_REF] Bötzel | Electric brain potentials-evoked by pictures of faces and nonfaces -A search for face-specific eeg-potentials[END_REF][START_REF] Jeffreys | A face-responsive potential recorded from the human scalp[END_REF]see Jeffreys, 1996 for review). The N170 and VPP typically show similar response properties [START_REF] Joyce | The face-sensitive N170 and VPP components manifest the same brain processes: The effect of reference electrode site[END_REF]. One of the most salient, category-selective, response properties of the N170 is its larger amplitude and delayed latency following pictureplane inversion of a face stimulus (e.g., [START_REF] Rossion | The N170 occipito-temporal component is delayed and enhanced to inverted faces but not to inverted objects: An electrophysiological account of face-specific processes in the human brain[END_REF][START_REF] Itier | Inversion and contrast polarity reversal affect both encoding and recognition processes of unfamiliar faces: a repetition study using ERPs[END_REF]. Over the last four decades, the N170 component has been described in hundreds, perhaps more than a thousand, of studies [START_REF] Eimer | The face-sensitive N170 component of the event-related brain potential[END_REF]Rossion and Jacques, 2011 for reviews). In recent years, the N170 has been used as an index of deficient face recognition abilities in clinical populations, with moderate limited success (e.g., in Autism Spectrum Disorders (ASD): [START_REF] Batty | Early processing of emotional faces in children with autism: An event-related potential study[END_REF]in schizophrenia: Obayashi et al., 2009; for reviews, see [START_REF] Mccleery | Meta-Analysis of Face Processing Event-Related Potentials in Schizophrenia[END_REF][START_REF] Bortolon | Face recognition in schizophrenia disorder: A comprehensive review of behavioral, neuroimaging and neurophysiological studies[END_REF]Alzheimer's Disease: Saavedra et al., 2012; for a review of neurological and psychiatric disorders: [START_REF] Feuerriegel | The N170 and face perception in psychiatric and neurological disorders: A systematic review[END_REF].

Despite, or perhaps due to, this flurry of research, whether the N170 is sensitive to the long-term familiarity of a face identity, i.e. does it differ significantly and reliably for pictures of unfamiliar vs. familiar faces (e.g., celebrities, experimentally familiarized, personally familiar, or even one's own face) remains an outstanding issue. While some ERP studies have reported long-term face familiarity effects (i.e., often amplitude increases) on the N170 (e.g., [START_REF] Caharel | Familiarity and emotional expression influence an early stage of face processing: an electrophysiological study[END_REF][START_REF] Barragan-Jason | The neural speed of familiar face recognition[END_REF], others have not reported such effects (e.g., [START_REF] Tanaka | Activation of Preexisting and Acquired Face Representations: The N250 Event-related Potential as an Index of Face Familiarity[END_REF][START_REF] Gosling | An event-related brain potential study of explicit face recognition[END_REF]. Since these familiarity effects have been more consistently observed on subsequent ERP components (e.g., the N250; Schweinberger, et al., 2002a;Schweinberger, et al., 2002b;[START_REF] Tanaka | Activation of Preexisting and Acquired Face Representations: The N250 Event-related Potential as an Index of Face Familiarity[END_REF]Wiese et al., 2019a;Wiese et al., 2019b), the standard view in the scientific literature is that the N170 is an electrophysiological marker of a "perceptual stage" in face recognition (e.g., a "structural encoding stage" according to [START_REF] Bruce | Understanding face recognition[END_REF]'s influential model), which is followed by ERP components marking the access to representations of facial identities stored in memory (see [START_REF] Bentin | Electrophysiological studies of face perception in humans[END_REF][START_REF] Bentin | Structural encoding and identification in face processing: ERP evidence for separate mechanisms[END_REF]Eimer, 2000a;Eimer, 2000b;Eimer;[START_REF] Eimer | The face-sensitive N170 component of the event-related brain potential[END_REF][START_REF] Schweinberger | Repetition effects in human ERPs to faces[END_REF]. From the point of view of fundamental research, clarifying this issue is therefore important because it can provide constraints on our understanding of the neurofunctional organization of human face recognition, i.e. how early in time is this function shaped by specific memories from our lifetime experience. Moreover, at least with a standard (i.e., slow and jittered) mode of visual stimulation to elicit ERPs, the N170 indexes the only reliable category-selective time-window (Fig. 1; [START_REF] Rossion | Does physical interstimulus variance account for early electrophysiological face sensitive responses in the human brain? Ten lessons on the N170[END_REF][START_REF] Rousselet | Time course and robustness of ERP object and face differences[END_REF][START_REF] Ganis | The N170, not the P1, indexes the earliest time for categorical perception of faces, regardless of interstimulus variance[END_REF], so that longterm familiarity effects observed during this window may be related to the contribution of face-selective populations of neurons. From the point of view of applied research, since the N170 is a very clear ERP, it could potentially serve as an implicit index to determine whether one knows a specific face identity or not. [START_REF] Bentin | Electrophysiological studies of face perception in humans[END_REF], (B) [START_REF] Rossion | Does physical interstimulus variance account for early electrophysiological face sensitive responses in the human brain? Ten lessons on the N170[END_REF], and (C) [START_REF] Rossion | ERP evidence for the speed of face categorization in the human brain: Disentangling the contribution of low-level visual cues from face perception[END_REF]. The N170 is systematically larger in response to pictures of faces than to other categories and is practically absent for meaningless stimuli. This face-selectivity occurs well before the peak of the N170, at around 120-130 ms following stimulus onset, and is often limited to this time window (i.e., until 200ms). (D) When pictures of the paintings of G. Arcimboldo are presented upright and usually lead to the perception of a face stimulus, a larger N170 is observed than when the exact same pictures are presented inverted and do not lead to the perception of a face (Figure adapted from [START_REF] Caharel | Early holistic facelike processing of Arcimboldo paintings in the right occipito-temporal cortex: Evidence from the N170 ERP component[END_REF]. (E) The face-selectivity of the N170 is often clearly observed on ERP data of individual subjects (Figure adapted from [START_REF] Rousselet | Time course and robustness of ERP object and face differences[END_REF].

Selection of ERP studies comparing familiar and unfamiliar faces

In this brief review, we provide a list of all the published studies -to the best of our knowledge -that have directly contrasted the N170 elicited by pictures of unfamiliar and familiar faces in human adults. We also consider studies using magnetoencephalography (MEG), focusing on the M170 component (e.g., [START_REF] Kloth | Effects of personal familiarity on early neuromagnetic correlates of face perception[END_REF]. Importantly, we do not consider studies that have compared the immediate repetition of a given face identity compared with the immediate presentation of different faces (i.e., face A followed immediately by face A or by face B) without directly comparing familiar to unfamiliar faces.

For instance, the earliest ERP study testing immediate repetition of face identity reported an increase for different over repeated faces for familiar faces on a posterior ERP component corresponding to the N170 [START_REF] Barrett | Event-related potentials and the matching of familiar and unfamiliar faces[END_REF]. While this effect was found for familiar but not unfamiliar faces tested in the same repetition conditions, indicating a genuine effect of familiarity at that latency, the authors did not directly compare familiar to unfamiliar faces. Moreover, we do not extend our review to face familiarity effects reported at different (i.e., typically later) time-windows in the EEG than the N170 (i.e., genuine post 200 ms effects, e.g., Wiese et al., 2019a;Wiese et al., 2019b; or very early effects likely due to low-level physical differences between images, e.g., [START_REF] Seeck | Evidence for rapid face recognition from human scalp and intracranial electrodes[END_REF][START_REF] Dobs | How face perception unfolds over time[END_REF]. Finally, for the sake of fair comparison, our review focuses on studies reporting ERPs to adult faces only (e.g., excluding studies with children faces as seen by their own mothers; [START_REF] Grasso | ERP correlates of attention allocation in mothers processing faces of their children[END_REF]. However, our brief survey includes all studies contrasting responses to unfamiliar with familiar faces, such as those of famous and personally familiar persons, the participants' own faces, as well as experimentally familiarized faces, and this regardless of the task used. It also considers studies using not only photographic faces but also pictures of faces manipulated experimentally (Mooney, caricatures, ...). Only data from healthy adult participants with no sign of mental or neurological disorder are taken into account. Finally, only papers written in an English language are included.

Studies were selected from searches on the Pubmed and Google Scholar databases. The terms used for this search included ("N170" or "M170" "ERP" or "MEG") and ("familiarity" or "familiar"). From the relevant articles from this research, a review of cited references was also carried out in order to find other articles. Overall, we identified 45 EEG or MEG studies that directly contrasted the N170 in response to familiar vs. unfamiliar faces.

The following information were extracted from each of these studies and reported in tables (from 1 to 3) below: the number and mean age of participants, the task performed, the type of familiar faces, the number of faces and their repetition, and the reference electrode used for analyses.

The key contribution of personally familiar faces on the N170 familiarity effect Among all the studies included in the survey, twenty-one (46,7%) reported a significantly larger N170 amplitude to pictures of familiar faces (Table 1; Fig. 2) and twentyone (46,7%) failed to report any significant difference (Table 2). Remarkably, only very few studies (3; 6,7%) found a significant difference in the other direction, i.e. a smaller N170 for familiar faces (Table 3). (2015) and (E) Wiese et al. (2019b). The N170 amplitude is larger for familiar than for unfamiliar faces and this difference occurs at its peak (i.e., at around 170 ms), that is to say after the face-selectivity effect emerging at the onset of the N170 (i.e., 120-130 ms). What conclusions can be drawn from this brief survey?

First, although a familiarity effect on the N170 appears too small and inconsistent across studies to be of any use as an electrophysiological marker of face familiarity in applied research, too many studies, from various research groups, report a significant effect to still keep claiming that sensitivity to long-term familiarity does not emerge during the timewindow of the N170 but only at later time points (see Fig. 2). A key element supporting this conclusion is the direction of the effect: if the modulation of the N170 by face familiarity was only due to EEG noise fluctuations, there would be no reason to expect a consistent direction of this amplitude difference across studies, i.e. in the form of larger amplitude for familiar faces (i.e., familiar > unfamiliar: 21/24 studies; 87,5%; considering only the studies showing a significant difference: 46.7% vs. 6.7%, X 2 = 18.19, p < .001). Importantly, since ERP studies typically focus on multiple components, there is also no reason to think that only those with positive face familiarity N170 effects are published, artificially increasing the proportion of positive results identified in the present review. If anything, as discussed below, the opposite bias might be present because researchers typically expect effects of face familiarity to emerge on later ERP components, so that effects of face familiarity on the N170 are not always well reported and evaluated (e.g., [START_REF] Herzmann | What's special about personally familiar faces? A multimodal approach[END_REF]Wiese et al., 2009a).

Second, given the wide variety of paradigms and the lack of methodological information reported in some studies, it is, unfortunately, almost impossible to identify clear factors (e.g., expectations/task, number of images, image repetition, time-windows selected for analysis, etc.) explaining the presence or absence of a familiarity effect on the N170 (Table 1). This is also a major reason why a systematic meta-analysis on this issue is currently impossible. For instance, the nature of the task (explicit vs. implicit familiarity judgment) does not seem to explain the divergence of results across the studies. Indeed, an explicit task was used in 13 of the 21 studies that reported a N170 face familiarity effect (62%, Table 1) and in 12 of 21 studies (57%, Table 2) which failed to report this effect (X 2 = .106, p = .74). Also, about the same proportion of studies that showed (15/21; 71%) or did not show (12/21 = 57%) an effect, used the recommended (Joyce and Rossion, 2005) common average reference (X 2 = .87, p = .35), suggesting that this factor is not directly relevant.

Moreover, the same number of studies that showed (7/21; 33%) or did not show (7/21, 33%) a familiarity effect used a number of repetitions of each face identity greater than 40. Yet, interestingly, the level of familiarity, in particular the personal familiarity with the faces used as stimuli, seems to play an important role. First, the majority (14/21; 67%) of studies using pictures of personally familiar faces report a larger N170 to familiar than unfamiliar faces (e.g., [START_REF] Caharel | ERPs associated with familiarity and degree of familiarity during face recognition[END_REF][START_REF] Herzmann | What's special about personally familiar faces? A multimodal approach[END_REF][START_REF] Caharel | Familiarity and emotional expression influence an early stage of face processing: an electrophysiological study[END_REF][START_REF] Mnatsakanian | Familiar and nonfamiliar face-specific ERP components[END_REF][START_REF] Caharel | The effects of inversion and eye displacements of familiar and unknown faces on early and late-stage ERPs[END_REF][START_REF] Kloth | Effects of personal familiarity on early neuromagnetic correlates of face perception[END_REF][START_REF] Caharel | The effects of familiarity and emotional expression on face processing examined by ERPs in patients with schizophrenia[END_REF][START_REF] Zeman | Independent component analysis and clustering improve signal-to-noise ratio for statistical analysis of eventrelated potentials[END_REF][START_REF] Wild-Wall | Interaction of facial expressions and familiarity: ERP evidence[END_REF][START_REF] Keyes | Brain and Cognition My face or yours? Eventrelated potential correlates of self-face processing[END_REF][START_REF] Caharel | Early electrophysiological correlates of adaptation to personally familiar and unfamiliar faces across viewpoint changes[END_REF][START_REF] Geng | Dissociations of subliminal and supraliminal selfface from other-face processing: Behavioral and ERP evidence[END_REF][START_REF] Caharel | Face Familiarity Decisions Take 200 msec in the Human Brain: Electrophysiological Evidence from a Go / No-go Speeded Task[END_REF]Wiese et al., 2019b; Table 1), with only 6 studies (29%) that did not report such effect (67% vs. 29%, X 2 = 5.93, p =. 015). Moreover, all of the six ERP studies that did not report any N170 modulation for personally familiar faces used a single face identity (often participants' own face) repeated dozens of times [START_REF] Sui | Self-face recognition in attended and unattended conditions: an event-related brain potential study[END_REF][START_REF] Tanaka | Activation of Preexisting and Acquired Face Representations: The N250 Event-related Potential as an Index of Face Familiarity[END_REF][START_REF] Miyakoshi | ERP study of viewpointindependence in familiar-face recognition[END_REF][START_REF] Pierce | The N250 brain potential to personally familiar and newly learned faces and objects[END_REF][START_REF] Alzueta | Is your own face more than a highly familiar face?[END_REF]Wiese et al., 2019a) (Table 2). Most recently, Wiese et al. (2019a) contrasted pictures of two personally (highly) familiar face identities to two unfamiliar face identities, but with variable pictures for both of them. In the main (#1) experiment of that study, the N170 was larger in amplitude for personally familiar faces but the difference did not reach significance (p = .07; supplemental material of Wiese et al., 2019a). However, a careful look at the results of this study strongly suggests in fact an effect of familiarity on the N170 (bottom waveforms on Fig. 2a of Wiese et al., 2019a). Interestingly, while the interaction with the factor block -for stimulus repetitionwas not reported by the authors, the waveforms of the last two blocks at least clearly show a larger N170 amplitude for familiar faces (see also Fig. 2a of Wiese et al., 2019a). This is consistent with a subsequent study of these authors reporting significant face familiarity effect on the N170 (Wiese et al., 2019b; see Fig. 2), and suggesting overall that the N170 is genuinely larger in amplitude for pictures of personally familiar faces than unfamiliar faces, even if this effect is not reported or emphasized in the published studies.

Why is the N170 familiarity effect found more often for pictures of personally familiar faces than famous or familiarized faces? On the one hand, personally familiar faces are encountered much more regularly and are learned in more natural and varied visual conditions than famous faces. They may be automatically associated with more, and more diverse, semantic and affective traits, making their neural representations more "robust", facilitating detection, recognition of identity and social cues, and activation of person knowledge [START_REF] Ramon | Familiarity matters: A review on prioritized processing of personally familiar faces[END_REF]. On the other hand, it is also likely that participants of a given study diverge greatly in their knowledge of celebrities, and that their level of familiarity for various famous faces also varies substantially, potentially blurring any N170 effect of long-term familiarity for famous faces. Another factor could be that participants are familiar with the specific pictures of the personally familiar faces used in an experiment but not with those of famous faces, potentially boosting the effects observed for the former.

However, this may be an issue for famous faces too, which are often presented under wellknown 'iconic' views [START_REF] Carbon | Famous faces as icons. The illusion of being an expert in the recognitionoffamous faces[END_REF].

Third, the N170 is a relatively early response originating from high-level visual areas of the human brain, and is therefore certainly more sensitive than later components (e.g., N250) to physical aspects of stimuli. Hence, any imbalance between the physical aspects of image sets compared in a given study is likely to affect more a difference at the level of the N170 than at following components, potentially blurring an early effect of face familiarity.

Unfortunately, physical aspects of familiar and unfamiliar face sets are rarely if ever described and controlled in the published studies. However, across studies, consistent effects of long-term personal face familiarity on the N170 are unlikely to reflect image-based effects because, contrary to sets of famous faces used, personally familiar faces differ for every participant of a given study.

Fourth, while face-selectivity effects (i.e., differences between faces and other visual stimuli) emerge generally at the onset of the N170 time window (i.e., 120-130 ms, see [START_REF] Rossion | Does physical interstimulus variance account for early electrophysiological face sensitive responses in the human brain? Ten lessons on the N170[END_REF], positive face familiarity effects are rather observed on the peak of the component (at around 170 ms) (Fig. 2), i.e. indicating a delay of at least 40-50 ms between the two types of responses. This indicates that recognizing a stimulus as a face temporally precedes the recognition of its familiarity as an individual, but that the two functions nevertheless largely overlap in time, within the N170 time window. To our knowledge, a relationship between these two functions, in terms of EEG signal, across individuals, has not been established and could be further investigated in future studies.

Moreover, interestingly, effects of familiarity appear to emerge at the same time as the (immediate) face identity repetition effect for unfamiliar faces (i.e., also at the peak of the N170), this latter effect being observed across substantial changes of size and head orientation but not stimulus inversion (i.e., ruling out an effect merely due to low-level visual cues; Jacques et al., 2007;Caharel et al., 2009;Caharel et al., 2015).

Fifth, a familiarity effect on the N170 does not mean that all that the process of attributing familiarity to a face stimulus is completed at this level, but only that enough evidence has been accumulated by the visual system to elicit an early detectable (i.e., significant) response on scalp EEG. In contrast, a much larger and consistent prolonged face familiarity recognition response from 200 ms onset has been described in numerous studies (i.e., on a N250 component; e.g., Schweinberger et al., 2002a;Schweinberger et al., 2002b;[START_REF] Herzmann | What's special about personally familiar faces? A multimodal approach[END_REF][START_REF] Tanaka | Activation of Preexisting and Acquired Face Representations: The N250 Event-related Potential as an Index of Face Familiarity[END_REF][START_REF] Pierce | The N250 brain potential to personally familiar and newly learned faces and objects[END_REF]Wiese et al., 2019a;Wiese et al., 2019b) and may better serve this purpose. In the same vein, a face familiarity effect does not mean that access to the unique familiar identity of the face has been completed at this level (after all, how often do we recognize that a face is familiar without being able to identify it, let alone recall its name?). However, recognizing (i.e., providing a selective reproducible response to) a face as being familiar implies that it is at least differentiated at the individual level from unfamiliar faces. That is, the distinction between a familiar and an unfamiliar face requires access to representations of specific individuals derived from past experience.

Theoretical implications and future directions

Following this brief synthesis, a number of outstanding questions or implications can also be raised for future studies. For instance, since it is well established that increased N170 amplitude depends on the subjective experience of a face, i.e. perceptual awareness [START_REF] Srebro | Localization of cortical activity associated with visual recognition in humans[END_REF][START_REF] Caharel | Early holistic facelike processing of Arcimboldo paintings in the right occipito-temporal cortex: Evidence from the N170 ERP component[END_REF][START_REF] Navajas | Uncovering the mechanisms of conscious face perception: a single-trial study of the N170 responses[END_REF][START_REF] Churches | Emoticons in mind: An event-related potential study[END_REF]see Rossion, 2014 for review), does this also apply to the familiarity effect observed as early as this component? In other words, is the familiarity effect (typically an amplitude increase) of the N170 associated with awareness of perceiving a familiar face or could it be elicited in the absence of conscious recollection (i.e., a form of covert familiar face recognition; [START_REF] Bruyer | A case of prosopagnosia with some preserved covert remembrance of familiar faces[END_REF][START_REF] De Haan | Covert and overt recognition in prosopagnosia[END_REF]?

Most importantly, a genuine effect of familiarity on the N170, i.e. starting at an earlier level than expected, appears to challenge standard (hierarchical) functional models of human face recognition [START_REF] Bruce | Understanding face recognition[END_REF][START_REF] Calder | Understanding the recognition of facial identity and facial expression[END_REF][START_REF] Young | Understanding person perception[END_REF]. Indeed, according to this standard model, the N170 has been associated with a "structural encoding stage" (e.g., [START_REF] Bentin | Electrophysiological studies of face perception in humans[END_REF][START_REF] Bentin | Structural encoding and identification in face processing: ERP evidence for separate mechanisms[END_REF]Eimer, 2000a;Eimer, 2000b;[START_REF] Eimer | The face-sensitive N170 component of the event-related brain potential[END_REF]. That is, a visuo-perceptual processing stage at which the idiosyncratic characteristics of the face are exacted, thought to occur before and independently of the activation of representations of faces stored in memory. Later ERP components, such as the N250 or the N400, would then reflect the association of this perceptual representation to representations of faces stored in memory and access to specific semantic information (i.e., familiar faces) (Bentin and Deouel, 2000;Eimer, 2000a;Eimer, 2000b;Schweinberger et al., 2002a;Schweinberger et al., 2002b;[START_REF] Tanaka | Activation of Preexisting and Acquired Face Representations: The N250 Event-related Potential as an Index of Face Familiarity[END_REF][START_REF] Pierce | The N250 brain potential to personally familiar and newly learned faces and objects[END_REF][START_REF] Saavedra | Event-related potentials elicited by face identity processing in elderly adults with cognitive impairment[END_REF][START_REF] Towler | The face-sensitive N170 component in developmental prosopagnosia[END_REF][START_REF] Olivares | Source Reconstruction of Brain Potentials Using Bayesian Model Averaging to Analyze Face Intra-Domain vs. Face-Occupation Cross-Domain Processing[END_REF].

In light of the present review, this standard functional model may still hold, but the structural encoding stage for faces may already be affected by previous experience with specific facial identities and thus not be purely based on physical features of the stimulus (e.g., in a "face-space" representation; [START_REF] Valentine | Face space: A unifying concept in face recognition research[END_REF]. This would be compatible with the well-documented observations that familiar faces are matched much better according to their identity across different viewing conditions than unfamiliar faces (e.g., [START_REF] Bruce | Changing faces: visual and non visual coding processes in face recognition[END_REF][START_REF] Jenkins | Variability in photos of the same face[END_REF], although this effect could also well be due to the rich associations of familiar faces with semantic, affective and even verbal information [START_REF] Bruce | Changing faces: visual and non visual coding processes in face recognition[END_REF][START_REF] Schwartz | The roles of perceptual and conceptual information in face recognition[END_REF][START_REF] Rossion | Humans are visual experts at unfamiliar face recognition[END_REF]. In any case, an adjustment/revision of the two-stage (perceptual/mnesic) standard functional model of human face recognition and this association with successive ERP components would be required.

Alternatively, this distinction between two independent processing stages, as well as their respective association with consecutive ERP components, may have to be more fundamentally revised. According to an alternative theoretical framework, there would not be "perceptual stage" preceding recognition, perception being rather defined as the subjective experience that arises from recognition (Rossion and Retter, 2020). Recognition of faces occurs when (low-level, topographically organized, unconscious) sensory inputs match representations of faces in memory (i.e., populations of neurons in the human ventraloccipito-temporal cortex that have been selected to respond to specific inputs across different viewing conditions). According to this latter view, rather than reflecting two subsequent stages with two levels of facial representations, the successive effects observed in EEG would merely reflect accumulation of sensory evidence within the same neurofunctional system. That is, within the same selective populations of neurons in the ventraloccipito-temporal cortex, there is sufficient accumulation of evidence to significantly distinguish between faces and nonface categories (i.e., generic face recognition) precociously (i.e., at the N170 onset, at about 120-130 ms, [START_REF] Rossion | Does physical interstimulus variance account for early electrophysiological face sensitive responses in the human brain? Ten lessons on the N170[END_REF], with additional evidence required for the finer-grained distinction between familiar and unfamiliar face identities (i.e., face identity recognition and familiarity effects beginning at the N170 peak, at around 160-170 ms). The familiarity effect starting at about the N170 peak latency would simply reflect the onset of a prolonged single continuous process of (longterm) face familiarity, which would be expressed prominently during the N250 time-window, i.e. until about 400 ms. The richer the representation of familiar faces, as for instance in the case of personally familiar faces, the earlier and larger the differential neural response signaling familiarity.

This alternative theoretical account is certainly more parsimonious because it does not require two copies/representations of face identities (i.e., a perceptual one, derived from the input, and a representation stored in memory) (Rossion and Retter, 2020). It also agrees with the lack of clear-cut division between apperceptive and associative forms of prosopagnosia (Farah, 1990(Farah, /2004)), and the lack of reliable differences between neural sources of the N170 and N250, both thought to originate mainly from the (right) ventral occipito-temporal cortex (Schweinberger et al., 2002b;[START_REF] Kaufmann | N250 ERP Correlates of the Acquisition of Face Representations across Different Images[END_REF][START_REF] Rossion | The N170: Understanding the time-course of face perception in the human brain[END_REF][START_REF] Olivares | Source Reconstruction of Brain Potentials Using Bayesian Model Averaging to Analyze Face Intra-Domain vs. Face-Occupation Cross-Domain Processing[END_REF]. Finally, it also incorporates the well documented effects of short-lagged immediate unfamiliar face repetition also starting on the N170 peak (Rossion and Jacques, 2011 for review) and extending until about the 200-400 ms timewindow, into an integrated theoretical framework.

Evaluating this proposal with human electrophysiological recordings certainly requires going beyond description of ERP components as stages of processing and focus on differential waveforms between conditions (with confidence intervals), avoiding the problem of a subjective and often suboptimal selection of time-windows of interest (e.g., 140-180 ms)

for data analysis. In recent years, human EEG studies with face stimuli have been increasingly performed using a fast periodic mode of visual stimulation, generating high signal-to-noise ratio (SNR) responses in the frequency-domain exactly at the stimulation frequency rate ("steady-state visual evoked potentials" or "frequency-tagging", see Norcia et al., 2015 for review). This type of approach holds significant advantages in terms of sensitivity and objectivity, and the signal can be compared for familiar and unfamiliar faces presented during different fast periodic trains of images [START_REF] Collins | Distinct neural processes for the perception of familiar versus unfamiliar faces along the visual hierarchy revealed by EEG[END_REF]. Most interestingly, pictures of familiar faces can be periodically (e.g., 1.2 Hz) inserted in rapid ( 6Hz) periodic trains of unfamiliar faces [START_REF] Zimmermann | An objective, sensitive and ecologically valid neural measure of rapid human individual face recognition[END_REF]. The common response to familiar and unfamiliar faces can therefore be selectively projected to the general stimulation frequency (6 Hz), allowing to focus on their differential response expressed at the slower familiar face stimulation frequency [START_REF] Zimmermann | An objective, sensitive and ecologically valid neural measure of rapid human individual face recognition[END_REF][START_REF] Campbell | Identity-specific neural responses to three categories of face familiarity (own, friend, stranger) using fast periodic visual stimulation[END_REF]Yan et al., 2020). These studies have generally concentrated on frequency-domain EEG responses, reporting robust differences between familiar and unfamiliar face identities over the occipito-temporal cortex in individual participants [START_REF] Zimmermann | An objective, sensitive and ecologically valid neural measure of rapid human individual face recognition[END_REF][START_REF] Campbell | Identity-specific neural responses to three categories of face familiarity (own, friend, stranger) using fast periodic visual stimulation[END_REF]Yan et al., 2020). Yet, since time-domain analyses of such effects can also be performed, providing that adequate stimulation parameters are used (see [START_REF] Retter | Uncovering the neural magnitude and spatio-temporal dynamics of natural image categorization in a fast visual stream[END_REF]Rossion et al., 2020), they should be able to reveal robust differential waveforms between familiar and unfamiliar faces emerging at around 170 ms as sustained effects rather than separate components reflecting different functional "stages" (see Yan and Rossion, 2020).

Finally, while recordings of face-selective responses inside the human brain have been described for decades with intracranial recordings [START_REF] Allison | Face recognition in human extrastriate cortex[END_REF][START_REF] Halgren | Spatio-temporal stages in face and word processing. I. Depth-recorded potentials in the human occipital, temporal and parietal lobes[END_REF], there has been a surge of such studies in recent years, with recordings performed inside brain regions typically defined as face-selective in fMRI such as the lateral section of the (right) middle fusiform gyrus [START_REF] Jacques | Corresponding ECoG and fMRI category-selective signals in human ventral temporal cortex[END_REF][START_REF] Jonas | A face-selective ventral occipito-temporal map of the human brain with intracerebral potentials[END_REF]. The present overview suggests that effects of long-term familiarity of the faces -especially for personally familiar faces -should be observed at the level of intracranial responses, most likely within face-selective occipito-temporal regions, emerging significantly before 200 ms.

In conclusion, our brief survey of two decades of ERP studies in which pictures of long-term familiar and unfamiliar faces were compared indicates that a significant effect linked to familiarity with specific facial identities emerges earlier in the human brain than generally thought, i.e. between 150-200 ms in occipito-temporal brain regions. This effect briefly follows the emergence of reliable face-selectivity at the N170 onset, but occurs at the same time as the earliest high-level effects of immediate unfamiliar face identity repetition.

These observations challenge standard neurocognitive models with a clear-cut distinction between perceptual and memory stages in human face recognition.

Fig. 1 .

 1 Fig. 1. Grand average ERP waveforms elicited by different categories of stimuli at occipito-temporal electrode sites. Figures adapted from the studies of (A)[START_REF] Bentin | Electrophysiological studies of face perception in humans[END_REF], (B)[START_REF] Rossion | Does physical interstimulus variance account for early electrophysiological face sensitive responses in the human brain? Ten lessons on the N170[END_REF], and (C)[START_REF] Rossion | ERP evidence for the speed of face categorization in the human brain: Disentangling the contribution of low-level visual cues from face perception[END_REF]. The N170 is systematically larger in response to pictures of faces than to other categories and is practically absent for meaningless stimuli. This face-selectivity occurs well before the peak of the N170, at around 120-130 ms following stimulus onset, and is often limited to this time window (i.e., until 200ms). (D) When pictures of the paintings of G. Arcimboldo are presented upright and usually lead to the perception of a face stimulus, a larger N170 is observed than when the exact same pictures are presented inverted and do not lead to the perception of a face (Figure adapted from[START_REF] Caharel | Early holistic facelike processing of Arcimboldo paintings in the right occipito-temporal cortex: Evidence from the N170 ERP component[END_REF]. (E) The face-selectivity of the N170 is often clearly observed on ERP data of individual subjects (Figure adapted from[START_REF] Rousselet | Time course and robustness of ERP object and face differences[END_REF].

Fig. 2 .

 2 Fig. 2. Grand average ERP waveforms elicited by familiar (famous, personally familiar, and own faces) and unfamiliar faces at right occipito-temporal electrodes. Figures adapted from the studies of A) Caharel et al. (2006), B) Keyes et al., (2010), C) Caharel et al., (2011), (D) Barragan-Jason et al.(2015) and (E)Wiese et al. (2019b). The N170 amplitude is larger for familiar than for unfamiliar faces and this difference occurs at its peak (i.e., at around 170 ms), that is to say after the face-selectivity effect emerging at the onset of the N170 (i.e., 120-130 ms).

Table 1 .

 1 List of studies that reported a larger N170 amplitude for familiar than unfamiliar faces.

	Authors	Year	N (controls) (mean age)	Task	Familiar face types	Number of faces per familiarity level	Number of repetitions for each face	Reference electrode used for analyses
	Caharel et al.		11 (24.5)	Passive view	Own face/ Famous	1	100	Common average
	Herzmann et al.		16 (20.9)	Familiarity judgment (priming)	Personally familiar (lecturing staff of the University)/ Famous	15 Personally Unfamiliar familiar/ 15 Famous /30	6	Common average
						1 Own and
	Caharel et al.		14 (23)	Task 1: Familiarity judgment/ Task 2: Expression judgment	Own face/ Personally Familiar (mother)/ Famous`	Personally familiar/ 20 Famous and	100/ 5	Common average
						Unfamiliar
	Mnatsakanian and Tarkka		13 (nr)	Face and Pattern (dots) tasks	Personally familiar	nr	nr	Common average
	Caharel et al.		20 (24)	Familiarity judgment	Personally familiar (family members and friends)	10	10	Common average
	Kloth et al. -MEG		12 (24.8)	Familiarity judgment	Personally familiar (students and lecturers)/ Famous	66 Personally Unfamiliar Familiar/ 66 Famous /132	2	_
	Caharel et al.		18 (36.3)	Familiarity judgment	Own face, Personally familiar (University professors)	1 Own and Unfamiliar Personally familiar/ 2	50/ 25	Common average
	Zeman et al.		12 (31)	Target (Joe face) decision	Own face/	1 Own/ 1	70 Own/ 70	Linked-

Table 2 .

 2 List of studies that failed to report a N170 difference in amplitude between familiar and unfamiliar faces.

	Authors	Year	N (controls) (mean age)	Task	Familiar face types	Number of faces per familiarity level	Number of repetitions for each face	Reference electrode used for analyses
	Rossion et al.	1999	9 (19-26)	Task 1: Gender discrimination/ Task 2: Familiarity judgment	Experimentally learned	20 Learned/ 40 Unfamiliar	6 Learned/ 3 Unfamiliar	Common average
	Bentin and Deouell	2000	Task 1: 14 (nr)/ Task 2: 24 (nr)	Task 1: to count butterflies/ Task 2: to count famous politicians	Famous	60	0	Tip of the nose
				Task 1: target (hands)
	Eimer	2000a	15 (24.9)	detection/ Task 2: target (digit) detection or	Famous	50	4	Tip of the nose
				repetition detection
				Task 1: target (hands)
	Eimer	2000b	24 (27)	detection/ Task 2: target (digits) detection or	Famous	50	6	Tip of the nose
				repetition detection
	Schweinberger et al.	2002a	12 (21.2)	Familiarity judgment (priming)	Famous	90	0	Common average

Table 3 .

 3 List of studies that reported a smaller N170 amplitude for familiar than unfamiliar faces.

	Authors	Year	N (controls) (mean age)	Task	Familiar face types	Number of faces per familiarity level	Number of repetitions for each face	Reference electrode used for analyses
	Jemel et al.	2003	13 (25)	Familiarity judgment (priming)	Famous (Mooney faces)	150	0	Common average
	Marzi and Viggiano	2007	9 (29.75)	Familiarity judgment	Famous	90	0	Mastoids
	Huang et al.	2017	15 (18-26)	Familiarity judgment	Famous	16	18	Common average

Acknowledgments

The authors would like to thank John Towler, Holger Wiese and an anonymous reviewer for their helpful comments on a previous version of this manuscript.


Clarisse Gosset-Erard

Minjie Zhao

Sonia Lordel-Madeleine

Saïd Ennahar
email: ennahar@unistra.fr

Identification of punicalagin as the bioactive compound behind the antimicrobial activity of pomegranate (Punica granatum L.) peels

Keywords: Pomegranate, Punica granatum, punicalagin, antimicrobial activity, peel

Extracts from 'Zhéri' and 'Hicaznar' varieties of pomegranate, Punica granatum L., were obtained by subjecting powdered peels to extraction using water, water/ethanol (1:1; v/v), ethanol, acetone and heptane. Using the agar diffusion assay, extracts with water and/or ethanol were shown to display significant antimicrobial activity with diameters of inhibition zones up to 20 mm. Ethanolic extracts, which were the most active, were fractionated using SPE, HPLC and UHPLC, and the active compounds they contain were identified by mass spectrometry. Punicalagin, under its α and β anomeric forms, was identified as the antibacterial compound in pomegranate peel extracts. Both forms were active with MIC values between 0.3 and 1.2 µg.ml -1 , and they easily converted from one to the other with an α/β equilibrium ratio of 3/7. Their spectrum of activity targeted 10 out of 13 Gram positive and two out of three Gram negative bacteria as well as a yeast strain.

Introduction

Punica granatum L., commonly known as pomegranate, is native to the Mediterranean region and is cultivated and consumed worldwide today as either fresh fruit, extracted juice, jam, infusion or ingredient in various recipes. It has been used in folk medicine since ancient times and is known for a range of health promoting effects including antioxidant, anti-inflammatory, anti-cancer, antidiabetic, and antimicrobial properties [START_REF] Andrade | Pomegranate and grape by-products and their active compounds: Are they a valuable source for food applications[END_REF]. The most commonly found bioactive molecules are anthocyanins and hydrolysable tannins, namely ellagitannins, gallotannins, flavonoids, lignans, triterpenoids, phytosterols, fatty acids, organic acids and phenolic acids.

Pomegranate peels (PgP), the main waste component of pomegranate juice production, has been reported to contain 249.4 mg/g phenolic compounds (tannic acid equivalents), while the arils contain 24.4 mg/g [START_REF] Li | Evaluation of antioxidant properties of pomegranate peel extract in comparison with pomegranate pulp extract[END_REF]. It is particularly rich of hydrolysable tannins, specifically punicalagins, which are multiple esters of gallic acid and glucose known for their antioxidant, antimicrobial and anti-inflammatory properties [START_REF] Foss | Antifungal activity of pomegranate peel extract and isolated compound punicalagin against dermatophytes[END_REF][START_REF] Jurenka | Therapeutic applications of pomegranate (Punica granatum L.): A review[END_REF][START_REF] Kharchoufi | Antimicrobial and antioxidant features of 'Gabsi' pomegranate peel extracts[END_REF][START_REF] Pagliarulo | Inhibitory effect of pomegranate ( Punica granatum L.) polyphenol extracts on the bacterial growth and survival of clinical isolates of pathogenic Staphylococcus aureus and Escherichia coli[END_REF].

Pomegranate is symbolic of fruits that are rich in phenolic compounds which have gained increased popularity over the past three decades. Due to pomegranate's reported health properties, the global demand for its processed products has spiked in recent years triggering the expansion of pomegranate processing industry worldwide [START_REF] Hasnaoui | Valorization of pomegranate peel from 12 cultivars: Dietary fibre composition, antioxidant capacity and functional properties[END_REF]. For the most part, the fruit is today industrially used for juice extraction, a process that generates large amounts of waste, mainly PgP: 50% of the three million tons of pomegranate processed annually worldwide [START_REF] Qu | Integrated Extraction and Anaerobic Digestion Process for Recovery of Nutraceuticals and Biogas from Pomegranate Marc[END_REF][START_REF] Teixeira Da Silva | Pomegranate Biology And Biotechnology: A Review[END_REF]. Due to its high organic matter content, PgP is subjected to the EU Directive 2008/98/EC (2008), which prohibits land disposal and landfilling, as this poses an environmental risk of uncontrolled greenhouse gas production as well as a threat to watercourses [START_REF] Qu | Integrated Extraction and Anaerobic Digestion Process for Recovery of Nutraceuticals and Biogas from Pomegranate Marc[END_REF]. Food industries have therefore to rethink their waste management systems, mainly based on massive landfilling, and turn more sustainable strategies aimed at reducing the amount of waste to be disposed. A great way of achieving this goal is by turning fruit and vegetable wastes into added-value resources through the recovery and use of the beneficial components they contain in agricultural, food, cosmetic and/or pharmaceutical industries. It is also a circular economy approach in waste management, one that supports sustainable food production and consumption and that participates in reducing the socio-economic and environmental impacts of food wastes [START_REF] Sagar | Fruit and Vegetable Waste: Bioactive Compounds, Their Extraction, and Possible Utilization[END_REF][START_REF] Vilariño | Food loss and Waste Reduction as an Integral Part of a Circular Economy[END_REF].

While the antioxidant activity of pomegranate has been the subject of numerous investigations, little is known regarding its antimicrobial activity. It has been reported however that PgP extracts have higher antimicrobial activity than extracts from juice, seeds or [START_REF] Dahham | Studies on Antibacterial and Antifungal Activity of Pomegranate (Punica granatum L.)[END_REF], and that the antimicrobial spectra obtained are typically broad including Grampositive and Gram-negative bacteria as well as yeasts [START_REF] Bharani | Pomegranate (Punica granatum L.) Peel extract-A study on potential source of pharmacological activities[END_REF][START_REF] Ferrazzano | In Vitro Antibacterial Activity of Pomegranate Juice and Peel Extracts on Cariogenic Bacteria[END_REF][START_REF] Pagliarulo | Inhibitory effect of pomegranate ( Punica granatum L.) polyphenol extracts on the bacterial growth and survival of clinical isolates of pathogenic Staphylococcus aureus and Escherichia coli[END_REF]. The growing concern among consumers and food processors for the possible health hazards of synthetic food additives has led to the search for more natural antimicrobials as alternatives to chemical additives such as butylated hydroxyanisole, butylated hydroxytoluene and propyl gallate which have been linked to health issues [START_REF] Echegaray | Chestnuts and by-products as source of natural antioxidants in meat and meat products: A review[END_REF][START_REF] Estévez | Dietary protein oxidation: A silent threat to human health?[END_REF]. A similar search for new antimicrobials is also going on in the pharmaceutical sector sparked by the alarming increase in antimicrobial resistance among pathogens [START_REF] Fair | Antibiotics and bacterial resistance in the 21st century[END_REF]. Being able to use PgP as a source of antimicrobial compounds that can be applied, not only to food products, but also to cosmetic and pharmaceutical formulations is therefore an attractive prospect.

Yet, the compounds behind the antimicrobial activity of pomegranate have yet to be clearly identified. To date, no two studies have confirmed the presence of the same antimicrobial compound in pomegranate, despite the fact that the identified compounds are among the most commonly found in pomegranate [START_REF] Read | Identification of potentially cytotoxic phenolics present in pomegranates (Punica granatum L.)[END_REF]. Discrepancies may be due to differences in the methods used, but they may also be linked to different phytochemical compositions between plants of different varieties and/or geographical regions [START_REF] Mekni | Comparative Study between Extracts of Different Pomegranate Parts Issued from Five Tunisian Cultivars (Punica granatum L.): Phytochemical Content, Volatile Composition and Biological Activity[END_REF]).

It appears therefore that there is a need for an analytical investigation in order to clarify this issue.

The present study sets out to investigate and identify the antimicrobial molecules present in PgP extracts of two varieties of pomegranate (Zheri and Hicaznar) originating from two geographical regions (Morocco and Turkey).

Materials and methods

Chemicals and media

All solvents used in this study were high purity solvents. Methanol was purchased from Honeywell (Levallois Perret, France), acetonitrile and formic acid were purchased from Sigma-Aldrich (Lyon, France), acetone and heptane were purchased from Carlo Ebra (Val-de-Reuil, France) and ethanol was purchased from Thermo Fisher Scientific (Illkirch-Graffenstaden, France). Ultra-pure water was produced by a Milli-Q system (Synergy UV; Millipore SAS, Molsheim, France). Punicalagin standard (mixture of α and β anomers) was acquired from Sigma-Aldrich. Mueller-Hinton agar medium was purchased from VWR International (Radnor, PA, USA), de Man Rogosa and Sharpe (MRS) broth was purchased from Biokar Diagnostics (Beauvais, France) and Brain Heart Infusion agar was purchased from Bio-Rad (Marnes-La-Coquette, France).

Microbial strains

The microbial strains used in the present study are listed in Table 3. All cultures were maintained as frozen stocks held at -80°C in Cryobank cryogenic beads (Bio-Rad, Hercules, CA, USA). For short term storage, strains were subcultured on brain heart infusion agar and kept at 4°C. Before experimental use, the strains were cultivated twice at 37°C for 18 to 24 h in MRS broth. Culturing was done without aeration.

Samples

Peels of fresh Punica granatum L. 'Zhéri' (a Moroccan variety with a sweet taste) and Punica granatum L. 'Hicaznar' (a Turkish variety with a sour-sweet taste) were manually separated from the rest of the fruit, freeze-dried, crushed into a fine powder using a freezer mill and stored at -80°C for further use.

Solid-liquid extraction

To determine the solvent that would extract high levels of antimicrobial compounds, solid-liquid extraction was performed using solvents over a wide polarity range. Tested solvents consisted of water, water/ethanol (1:1; v/v), ethanol, acetone and heptane. Two grams of sample were mixed with 10 ml of solvent, sonicated at ambient temperature for 20 minutes and centrifuged at 5000 rpm for 10 min. The supernatant was then filtered through a 0.45 µm pore-size polyester membrane.

Solid phase extraction

Solid phase extraction was used as a fractioning step. First, pomegranate peel extracts were diluted 10 fold in ultra-pure water. Then extraction was carried out on a 1-g SPE cartridge made of a divinylbenzene and vinyl pyrrolidone co-polymer (Oasis HLB; Waters, Dublin, Ireland). The sorbent was conditioned with 10 ml of methanol and equilibrated with 10 ml of 10% methanol in ultra-pure water. Fifty ml of the diluted extract were loaded on the cartridge, and the sorbent was washed with 10 ml of 10% methanol in ultra-pure water. Successive elutions were then performed with 10 ml of 20 %, 30 %, 40 %, 50 %, 60 % 70 %, 80 %, 90 % and 100 % methanol in ultra-pure water. All the fractions were collected separately and tested for antimicrobial activity (see Section 2.7). Active fractions were pooled and used for composition determination and purification of antimicrobial compounds.

Bioassay-guided purification

Step 1. Pooled active fractions from SPE were subjected to reverse-phase HPLC using a 2695 series Alliance HPLC system (Waters) equipped with a C18 PFP column (250 mm x 4.6 mm i.d., 5 µm; ACE, Aberdeen, Scotland) and controlled with the Empower software (Waters). The sample injection volume was set at 100 µl. A binary mobile phase gradient with 0.1% formic acid in ultrapure water (A) and 0.1% formic acid in acetonitrile (B) was used at a flow rate of 1 ml.min -1 . The following elution gradient was used: 0 to 5 min, 0 to 10% B; 5 to 15 min, 10 to 15% B; and 15 to 50 min, 15 to 90% B. The detection was achieved on a Photodiode Array Detector by scanning wavelengths from 200 to 400 nm and 3D UV spectra were recorded. Fractions (0.5 ml or 30 s) were collected with a Roucaire Cygnet collector and concentrated by vacuum evaporation using a SPD12P SpeedVac Savant concentrator (Thermo Fisher Scientific; Waltham, MA, USA) before being tested for antimicrobial activity (see Section 2.7).

Step 2. Active fractions were further purified by UHPLC on a UPLC Acquity system (Waters) equipped with an Acquity Phenyl BEH column (100 mm x 2.1 mm i.d., 1.7 µm; Waters) and controlled by the Compass 1.3 software for micrOTOF (Bruker; Billerica, MA, USA). The sample injection volume was set at 10 µl. The following mobile phase gradient was used at a flow rate of 1 ml.min -1 : 0 to 15 min, 2 to 20 %B ; 15 to 18 min, 20 to 30 %B; and 18 to 20 min, 30 to 95 %B.

Detection and fraction (0.06 ml or 12 s) collection were achieved as described in Step 1.

Antimicrobial activity assay

The determination of antimicrobial activity was done by the agar diffusion assay as described by [START_REF] Izquierdo | Production of enterocins L50A, L50B, and IT, a new enterocin, by Enterococcus faecium IT62, a strain isolated from Italian ryegrass in Japan[END_REF] with slight modifications. This test is based on the diffusion of antimicrobial agents in an agar culture medium resulting in clear zones of inhibition of the cultured microorganism growth. Five ml of Mueller-Hinton agar melted and inoculated with 50 µl of a bacterial strain (indicator strain) were poured in a Petri dish. After solidification and drying for 15 min, 10 µl of the pomegranate peel extracts or purified compounds were dropped on the agar. Extraction solvents were tested as blanks. After an overnight incubation at 37°C, Petri dishes are examined for the presence of inhibition zones. For a quick assessment of the antimicrobial activity, the diameter of the observed clear zones was measured. Alternatively, titration of the activity was performed by testing the activity of 2-fold serial dilutions of an initial solution. A minimal inhibitory concentration (MIC) was then determined as the smallest concentration showing activity against the indicator strain. Strains with the highest sensitivity to pomegranate extracts were selected as indicator strains.

Mass spectrometry

Active fractions from SPE were analyzed by mass spectrometry (MS) using a micrOTOF-QII (Bruker, Billerica, MA, USA) mass spectrometer consisting of an electrospray ionization source (ESI), a quadrupole (Q) and a time of flight analyzer, and hyphenated with a UHPLC system. ESI was operated in positive mode and the spectra were acquired in "full scan" mode in the range of m/z 100 -2000. The electrospray conditions were: nebulizer pressure of 40.2 psi; drying gas flow of 8.5 L/min at 200 °C; capillary voltage of 4000 V. The chromatographic system and conditions were as described in section 3.5, Step 2.

Results and discussion

Extraction solvent and target bacterial strain

Solvents with different polarity indices were tested for solid-liquid extraction of antimicrobials from PgP and the obtained extracts were tested using the agar diffusion assay against strains of Pseudomonas aeruginosa ATCC 9027 and Staphylococcus epidermidis ATCC 12228. The diameters of the obtained clear zones were indicative of the extent of the antimicrobial activity in the extracts. An illustration is shown in Figure 1 with S. epidermidis ATCC 12228 as a target. The results showed that with both pomegranate varieties, Zhéri and Hicaznar, the highest antimicrobial activity was obtained in ethanol extracts with diameters of inhibition zones up to 20 mm, followed by a water/ethanol mixture then water (Table 1). This indicated that the antimicrobial compounds in PgP are of a polar nature, which allows their extraction using a simple, green and economic method based on ethanol and/or water. This is highly important when it comes to large-scale human or industrial applications.

It is worth noting that the used solvents did not show any activity against the used target strains in the agar diffusion assay conditions (Table 1). The observed activity was therefore due solely to the active compounds contained in PgP. Based on these results, ethanol was chosen as the extraction solvent in this study.

As far as the target bacteria are concerned, activity was observed against P. aeruginosa ATCC 9027 (a Gram-negative species) and S. epidermidis ATCC 12228 (a Gram-positive species) which were both sensitive to the three active extracts. S. epidermidis ATCC 12228 was chosen as the target strain for further experiments as it gave clear and easily measurable inhibition zones. P. aeruginosa ATCC 9027 on the other hand gave zones with a blurred limit, which is generally linked to the growth of bacterial cells developing some level of resistance near the edge where the antimicrobial's concentrations are low.

Composition of the extracts

Ethanol extracts of sweet and sour-sweet PgPs were subjected to co-polymer-based SPE with a multi-step elution consisting of increasing amounts of methanol in water. Only the fractions collected between 40 and 80% of methanol showed antimicrobial activity. They were pooled and analyzed by UHPLC-ESI-MS-TOF for composition determination. The chromatograms obtained with extracts from sweet and sour-sweet PgPs are given Figure 2. Compounds were identified based on UV and MS spectra, relative retention times and data collected from the literature. The identified compounds in both extracts are listed in Table 2. The results showed that PgP extracts were mainly made of phenolic compounds such as pedunculagins I and II, ellagic acid, ellagic acid-O-pentoside, and punicalagins α and β, which is in line with previous findings [START_REF] Fischer | Identification and quantification of phenolic compounds from pomegranate (Punica granatum L.) peel, mesocarp, aril and differently produced juices by HPLC-DAD-ESI/MSn[END_REF][START_REF] Hernández-Corroto | Extraction and identification by high resolution mass spectrometry of bioactive substances in different extracts obtained from pomegranate peel[END_REF][START_REF] Kharchoufi | Antimicrobial and antioxidant features of 'Gabsi' pomegranate peel extracts[END_REF][START_REF] Lu | Determination of Punicalagin Isomers in Pomegranate Husk[END_REF][START_REF] Pagliarulo | Inhibitory effect of pomegranate ( Punica granatum L.) polyphenol extracts on the bacterial growth and survival of clinical isolates of pathogenic Staphylococcus aureus and Escherichia coli[END_REF].

Among the identified compounds in the ethanolic extract of sweet PgP, the major components are the hydrolysable tannins punicalagin α and punicalagin β. Identification of punicalagin was based on its MS spectrum which showed an exact monoisotopic mass is 1084.066 amu, and a 1067.0625 m/z peak corresponding to its molecular ion [M+H-H2O] + (mass precision of 1.3 ppm) (Figure 3B).

In-source-fragmentation showed a 765.0545 m/z peak corresponding to the punicalin [M+H-H2O] + ion and a 303.0158 m/z peak corresponding to the ellagic acid [M+H] + ion.

Our results also showed that the overall compositions of the ethanolic extracts from PgPs of the sweet and sour-sweet varieties were very similar despite taxonomic and geographic differences.

Only the relative proportions of the identified compounds did vary between the two samples.

Similar data has been obtained regarding the phenolic content of pomegranate juices of different varieties [START_REF] Labbé | Characterization of 16 chemical compositions and bioactive compounds in juices from pomegranates ('Wonderful', 'Chaca' and 'Codpa') at different maturity stages[END_REF][START_REF] Mekni | Comparative Study between Extracts of Different Pomegranate Parts Issued from Five Tunisian Cultivars (Punica granatum L.): Phytochemical Content, Volatile Composition and Biological Activity[END_REF].

Activity-guided purification and identification of the antimicrobial compounds

Reverse-phase HPLC separation of sweet and sour-sweet PgPs extracts followed by fraction collection and activity testing yielded very similar chromatograms with two fractions independently active against S. epidermidis ATCC 1222. Figure 3A shows an example with the sweet PgP extract.

Mass analysis of the two fractions showed the same mass spectrum corresponding to punicalagin (Figure 3B). The two fractions corresponded in fact to α and β anomers of punicalagin. Although higher peak intensities were obtained with the sour-sweet Hicaznar variety than with the sweet Zhéri variety, no conclusion could be drawn as to a difference in punicalagin content between the two varieties since extraction and analysis steps in this study were not quantitative. Following HPLC separation of PgP extracts, active fractions were pooled and further separated by UHPLC.

As with HPLC separation, only two independently active fractions, correspondeding to punicalagin's α and β anomers, were obtained.

When each of the two active fractions, containing either anomer of punicalagin, was separately analyzed by UHPLC, we observed the systematic presence of both anomers. This is due to the α/β inter-conversion of the glucose in the punicalagin molecule [START_REF] Kraszni | NMR assignments and the acid-base characterization of the pomegranate ellagitannin punicalagin in the acidic pH-range[END_REF]. This mutarotation reaction is possible when glucose is linearized prior to a new hemiacetalisation which closes the sugar in one anomeric form or the other [START_REF] Silva | A theoretical study of glucose mutarotation in aqueous solution[END_REF]. In our separation conditions, an α/β equilibrium is reached at a 3/7 ratio, which is close to the 1/2 ratio reported by [START_REF] Barbieri | Isolation of punicalagin from Punica granatum rind extract using mass-directed semi-preparative ESI-AP single quadrupole LC-MS[END_REF] in a H2O/MeOH mixture.

Punicalagin was the only extracted compound from sweet and sour-sweet PgP with antibacterial activity against the target strain S. epidermidis ATCC 12228. This finding is supported by reports on the antimicrobial activity of hydrolysable tannins, especially punicalagin [START_REF] Ekambaram | Scope of Hydrolysable Tannins as Possible Antimicrobial Agent[END_REF][START_REF] Xu | Antimicrobial Activity of Punicalagin Against Staphylococcus aureus and Its Effect on Biofilm Formation[END_REF] which is found not only in pomegranate but also in other plants such as in the leaves of Lafoensia pacari, a Brazilian medicinal plant [START_REF] Carneiro | Chemopreventive effect and angiogenic activity of 14 punicalagin isolated from leaves of Lafoensia pacari A[END_REF], and of Indian almond tree Terminalia catappa [START_REF] Lin | Antioxidant and Hepatoprotective Activity of Punicalagin and Punicalin on Carbon Tetrachloride-induced Liver Damage in Rats[END_REF]. Besides its antimicrobial activity, punicalagin has also been reported to possess antioxidant [START_REF] Seeram | In vitro antiproliferative, apoptotic and antioxidant activities of punicalagin, ellagic acid and a total pomegranate tannin extract are enhanced in combination with other polyphenols as found in pomegranate juice[END_REF], antiproliferative [START_REF] Seeram | In vitro antiproliferative, apoptotic and antioxidant activities of punicalagin, ellagic acid and a total pomegranate tannin extract are enhanced in combination with other polyphenols as found in pomegranate juice[END_REF], angiogenic [START_REF] Carneiro | Chemopreventive effect and angiogenic activity of 14 punicalagin isolated from leaves of Lafoensia pacari A[END_REF], and hepatoprotective [START_REF] Lin | Antioxidant and Hepatoprotective Activity of Punicalagin and Punicalin on Carbon Tetrachloride-induced Liver Damage in Rats[END_REF] properties.

As opposed to the data presented here, a recent investigation by [START_REF] Khan | Punica granatum peel extracts: HPLC fractionation and LC MS analysis to quest compounds having activity against multidrug resistant bacteria[END_REF] has identified valoneic acid dilactone, monogalloyl-hexoside, hexahydroxydiphenoyl-hexoside and coumaric acid as the antibacterial compounds in PgP, but surprisingly not punicalagin [START_REF] Khan | Punica granatum peel extracts: HPLC fractionation and LC MS analysis to quest compounds having activity against multidrug resistant bacteria[END_REF]. In our case, no activity was observed in the fractions containing valoneic acid dilactone and monogalloylhexoside, while hexahydroxydiphenoyl-hexoside and coumaric acid were not identified in the extracts studied. It is noteworthy however that in the study of [START_REF] Khan | Punica granatum peel extracts: HPLC fractionation and LC MS analysis to quest compounds having activity against multidrug resistant bacteria[END_REF], active peel extracts have been fractionated and analyzed by MS/MS to identify the compounds they contain, but antimicrobial tests have not been done on the individual compounds to confirm their activity. In this study, no observation was made that could suggest that antimicrobials other than punicalagin may be present, such as for instance a difference in the activity spectrum between crude extracts and pure fractions. Yet, antimicrobial compounds other than punicalagin may be present in PgP but in small amounts, with low activity and/or that target microbial strains other than those tested in this study. The presence of such compounds, which may go undetected, should therefore not be ruled out.

Antimicrobial spectrum of punicalagin

Pure punicalagin in its two anomeric forms was tested against a range of target microbial strains mainly representative of common foodborne pathogens, as well as clinically related enterococcal species and a yeast species. Antimicrobial activity was observed against two out of three Gramnegative and 10 out of 13 Gram-positive bacteria (Table 3). MIC values of punicalagin varied between 0.3 and 1.2 µg.ml -1 , with no apparent differences in sensitivity according to the Gram type.

Activity was also observed against a yeast strain of Candida albicans. No activity however of punicalagin was observed up to 2.4 µg.ml -1 against tested strains of Bacillus cereus, Lactobacillus plantarum, Enterococcus faecium and Salmonella enteritidis. This could be due to a strain-specific action of punicalagin which may be more or less efficient against strains of the same species.

Previous studies have in fact shown activity of pomegranate peel extracts against strains of species which showed no sensitivity in the present study [START_REF] Al-Hassnawi | Evaluation of antibacterial activity of aqueous and methanolic extracts of pomegranate peels (Punica granatum lin.) against some bacteria[END_REF][START_REF] Al-Zoreky | Antimicrobial activity of pomegranate (Punica granatum L.) fruit peels[END_REF]). Yet, the observed spectrum of activity of punicalagin is broad one targeting both Gram-positive and Gramnegative bacteria, as well as a yeast strain.

Conclusions

The use of fruit and vegetable wastes as a renewable bioresource for the production of high-value bioactive compounds whilst also addressing the huge problem of food waste disposal is an appealing prospect. This contributes towards a circular economy way of managing human wastes and is in line with the goals of sustainable development. The present study shows that punicalagin contained in pomegranate peels is at the origin of their antimicrobial activity. These waste peels could therefore be a source of an antimicrobial compound with potential applications in the food and pharmaceutical industries.

Figure 1 .

 1 Figure 1. Agar diffusion antimicrobial assay against S. epidermidis ATCC 12228 with extracts from sweet (A) and sour-sweet (B) PgP.

Figure 2 .

 2 Figure 2. UHPLC-ESI-MS-TOF chromatograms in positive mode obtained with active fractions from SPE of ethanolic extracts from sweet (A) and sour-sweet (B) PgP.

Figure 3 .

 3 Figure 3. HPLC-DAD (λDAD = 260 nm) chromatogram of pooled active fractions from SPE of

  Figure1

Table 1

 1 Diameters (mm) of inhibition zones obtained with sour-sweet and sweet PgP extracts tested on P. aeruginosa ATCC 9027 and S. epidermidis ATCC 12228

			P. aeruginosa ATCC 9027		S. epidermidis ATCC 12228
	Extraction solvent	Blank	Sweet PgP	Sour-sweet PgP	Blank	Sweet PgP	Sour-sweet PgP
	Water	0	14	14	0	13	16
	Water/ethanol (1:1; v/v)	0	16	17	0	14	17
	Ethanol	0	20	19	0	17	17
	Acetone	0	0	0	0	0	0
	Heptane	0	0	0	0	0	0

Table 3

 3 Minimal inhibitory concentrations of punicalagin (α and β anomers) against various strains of yeast, Gram-positive bacteria and Gram-negative bacteria.

	Target	Strains*

* LC, laboratory collection; CIP, Collection de l'Institut Pasteur; ATCC, American Type Culture Collection; CECT, Coleccion Española de Cultivos Tipo ; DSMZ, Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH

f[START_REF] Lu | Determination of Punicalagin Isomers in Pomegranate Husk[END_REF]; g[START_REF] Pagliarulo | Inhibitory effect of pomegranate ( Punica granatum L.) polyphenol extracts on the bacterial growth and survival of clinical isolates of pathogenic Staphylococcus aureus and Escherichia coli[END_REF].

Acknowledgement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. [START_REF] Abid | Antioxidant properties and phenolic profile characterization by LC-MS/MS of selected Tunisian pomegranate peels[END_REF]b (Al-Rawahi, 2014); c [START_REF] Fischer | Identification and quantification of phenolic compounds from pomegranate (Punica granatum L.) peel, mesocarp, aril and differently produced juices by HPLC-DAD-ESI/MSn[END_REF]d (Hernández-Corroto et al., 2019); e [START_REF] Kharchoufi | Antimicrobial and antioxidant features of 'Gabsi' pomegranate peel extracts[END_REF];