Salvatore Maria Anzalone
email: sanzalone@univ-paris8.fr

Jean Xavier

Sofiane Boucenna

Lucia Billeci

Antonio Narzisi

Filippo Muratori

David Cohen

Mohamed Chetouani

Quantifying patterns of joint attention during human-robot interactions: An application for autism spectrum disorder assessment

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

Introduction

Autism Spectrum Disorder (ASD) is characterized by deficits in social communication and social interaction and by restricted and repetitive patterns of behaviours, interests and activities [START_REF] Xavier | Autism spectrum disorders: An historical synthesis and a multidimensional assessment toward a tailored therapeutic program[END_REF]. Although heterogeneous in term of severity, ASD symptoms can significantly impair normal, dailylike activities, in particular social activities. Major genetic risk factors have been found [START_REF] Betancur | Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting[END_REF]). Yet, in many cases, scientists still do not know the exact cause of the disorder and a combination of genetic and environmental risk factors has been supposed [START_REF] Tordjman | Gene× environment interactions in autism spectrum disorders: Role of epigenetic mechanisms[END_REF]Guinchat et al., 2012b,a). The assessment of ASD is possible since the early childhood, typically during the first two years of life [START_REF] Ouss | Infant's engagement and emotion as predictors of autism or intellectual disability in west syndrome[END_REF]. Early diagnosis is fundamental to limit the disorder's effects, allowing clinicians to deploy early intensive behavioural intervention. These protocols take advantage of the learning potential that the children's brain has, focusing in particular on the learning prerequisites that infants should develop for the acquisition of new skills. There is an ongoing evidence suggesting that Applied Behavioural Analysis (ABA) teaching methods and developmental approach such as the Early Start Denver Model (ESDM) are able to help children on remediating areas of weakness by improving their Intelligent Quotient (IQ), their language abilities and their social interaction skills [START_REF] Narzisi | Nonpharmacological treatments in autism spectrum disorders: an overview on early interventions for pre-schoolers[END_REF][START_REF] Reichow | Comprehensive synthesis of early intensive behavioral interventions for young children with autism based on the ucla young autism project model[END_REF][START_REF] Ospina | Behavioural and developmental interventions for autism spectrum disorder: a clinical systematic review[END_REF]. In particular, the ESDM approach, suitable since the early childhood, tries to integrate into the ABA practices a relationship-focused developmental model in which the toddlers' development is view as an interpersonal process strongly enhanced by the sensitivity and responsivity to children's cues [START_REF] Dawson | Randomized, controlled trial of an intervention for toddlers with autism: the early start denver model[END_REF]. According to this framework, the child preferences, choices and motivations will guide the intervention of the therapists and of the parents. The ESDM approach proposes a set of activities in naturalistic scenarios in which positive reinforcements and affect-based relationship are used to the enhancement of interpersonal skills: the child will be able to develop and reinforce social-emotional skills, cognitive competences, and language by playing with toys in a natural playground, guided by an interdisciplinary team of therapists composed by special education teachers, developmental and clinical psychologists, speech-language pathologists, occupational therapists and behaviour analysts, with the key contribution of the parents.

Together with the difficulties of a correct assessment of Autism Spectrum Disorders (ASD) in toddlers [START_REF] Xavier | Autism spectrum disorders: An historical synthesis and a multidimensional assessment toward a tailored therapeutic program[END_REF], recent studies have shown that several factors can limit the effectiveness of therapeutic interventions [START_REF] Narzisi | Nonpharmacological treatments in autism spectrum disorders: an overview on early interventions for pre-schoolers[END_REF][START_REF] Reichow | Comprehensive synthesis of early intensive behavioral interventions for young children with autism based on the ucla young autism project model[END_REF][START_REF] Ospina | Behavioural and developmental interventions for autism spectrum disorder: a clinical systematic review[END_REF]. One of such weaknesses is the artificiality of the context in which the therapeutic activities are usually performed, a hospital or other "laboratory setup". Activities accomplished in such contexts could entail a lack of generalisation. At the same time, activities performed at the hospital are provided just few hours during the week, drawing another important weakness in the intensity of the therapy. To overcome such limitations researches have focused on the integration of information and communication technologies (ICT) in the "classic" protocols for both assessment and treatment of ASD (Boucenna et al., 2014b). Recently, an increasing number of research teams have focused on the use of social robots in ASD treatment [START_REF] Pennisi | Autism and social robotics: A systematic review[END_REF][START_REF] Scassellati | Robots for use in autism research[END_REF][START_REF] Diehl | The clinical use of robots for individuals with autism spectrum disorders: A critical review[END_REF]. Although most research in this field may be regarded as preliminary by clinicians, those kind of robots emerged as an important tool for children with ASD because of the various benefits their use could bring to the therapy, regarding:

• Complexity: social robots in ASD therapy could simplify the inner complexity of the social interactions. While interacting, people exchange an enormous amount of information in both verbal and non-verbal forms (speech, words, prosody, facial expressions, emotions, proxemics, and so on). As social robots are entirely controlled by robot programmers, the behaviours they can express and the interaction proposed could be very simple and predictable. Clinicians and robotics engineers can take advantage of this, developing new experimental protocols using social robots, focusing just on one or few aspects of the interaction, or just on the building blocks of the sociality, simplifying the cognitive load required to "decode" such interactions.

• Embodiment: social robots can communicate and interact in a multimodal way with children, but, unlikely to serious games, avatars, or other software agents, they have their own "physical presence" in the real world. The embodiment of social robots will permit physical explorations and interactions with the environment [START_REF] Kozima | Interactive robots for communication-care: A case-study in autism therapy, in: Robot and Human Interactive Communication[END_REF] as well as a communication with people based also on gestures and touch, widening the possibility of their employment in therapeutic protocols [START_REF] Boucenna | Robots learn to recognize individuals from imitative encounters with people and avatars[END_REF]. In addition, there is a growing evidence that ASD patients may perceive a humanoid robot as a social partner [START_REF] Chaminade | Fmri study of young adults with autism interacting with a humanoid robot[END_REF].

• Shape: the shapes of social robots used in ASD treatment are different, according to their role in the interaction and to the goal of the interaction itself. Android, human-like, animal-shaped, non-anthropomorphic coloured toy: in any case, the shape of the robot should contribute to the reduction of the stress of the children during the experiment, making them comfortable and at ease [START_REF] Scassellati | Robots for use in autism research[END_REF].

The Michelangelo Project

The Michelangelo Project, funded by the European Commission, proposed several, cost-effective, technology tools to bring the ASD assessment and therapy to the home setting. As part of this project, the Michelangelo Study Group developed a network made up by different sensors [START_REF] Ghidoni | A distributed perception infrastructure for robot assisted living[END_REF], such as cameras, RGB-D sensors, microphones, wearable systems for electroencephalographic (EEG) [START_REF] Cester | Enobio: Wearable, wireless, 4-channel electrophysiology recording system optimized for dry electrodes[END_REF] and electrocardiogram (ECG) [START_REF] Billeci | The role of wearable sensors and wireless technologies for the assessment of heart rate variability in anorexia nervosa[END_REF] signals recording, to capture the fine detail of the behaviour of the children in controlled environments [START_REF] Anzalone | Tracking posture and head movements of impaired people during interactions with robots[END_REF][START_REF] Cruciani | Dante: A video based annotation tool for smart environments[END_REF].

This network of sensors has been used to assess imitation and JA skills in children with ASD. In particular, the current study exploits cameras and RGB-D sensors during an interactive game developed by the Michelangelo Study Group that involves the use of a small humanoid robot (Anzalone et al., 2014b;Boucenna et al., 2014a). Focusing on JA, the game tracks the behaviour of the child while exploring the world. JA is a key element of social cognition. It involves a triadic interaction and can be defined as a process in which two individuals share their gaze over the same focus of attention [START_REF] Emery | The eyes have it: the neuroethology, function and evolution of social gaze[END_REF]. One agent alerts a second one of his attention towards an object by eye gazing, by pointing, or by using other verbal or non-verbal indication. Then, the second agent follows the suggested direction towards the same object. It is important to highlight that this definition slightly differs from the shared attention, which implies a coupling between mutual attention and JA, making both the agents aware of the attention of the other: "I know that you are looking at the object, and you know that I am looking at the object" [START_REF] Tomasello | Joint attention as social cognition[END_REF].

At the same time, the study group developed a Gaming Open Library for Intervention in Autism at Home, GOLIAH [START_REF] Bono | Goliah: A gaming platform for home-based intervention in autism-principles and design[END_REF], a therapeutic game based on the Early Start Denver Model [START_REF] Narzisi | Nonpharmacological treatments in autism spectrum disorders: an overview on early interventions for pre-schoolers[END_REF][START_REF] Reichow | Comprehensive synthesis of early intensive behavioral interventions for young children with autism based on the ucla young autism project model[END_REF][START_REF] Ospina | Behavioural and developmental interventions for autism spectrum disorder: a clinical systematic review[END_REF]. The game presents a set of activities that can be done by the child with the help of a clinician in the hospital or with a parent at home. Each activity focuses on the training of specific abilities, in particular imitation and joint attention (JA). The focus on such skills is justified by their importance as "building blocks" for the development of social cognition [START_REF] Toth | Early predictors of communication development in young children with autism spectrum disorder: Joint attention, imitation, and toy play[END_REF][START_REF] Nadel | Does imitation matter to children with autism[END_REF].

As in Fig. 1, children have been followed for 6 months of training sessions using GOLIAH, at the hospital but also at home. At the beginning and at the end of the treatment, ses-

Hypothesis

In a previous pilot study (Anzalone et al., 2014b), we already explored the behaviour of children with ASD during a JA elicitation task involving a Nao robot, the Softbank Robotics's small humanoid robot. The proposed protocol take advantage of a RGB-D sensor to capture the movements of the child, while the robot is employed as powerful tool to induce JA. The robot is able to engage TD children by exchanging simple, multimodal, social signals, taking advantage of its simplified but communicative shape, able to reduce the complexity of the interpersonal interactions. In this study we show that, while the robot was able to engage TD children, the response to the JA induction was lower in ASD children than in Typical Development (TD) children, despite we selected and matched them on developmental age and on their ability to perform JA with a human partner.

In this paper, we expand the group of children with ASD and, we introduce new behavioural metrics [START_REF] Anzalone | Evaluating the engagement with social robots[END_REF] exploiting the RGB-D sensor data, based on the analysis of the child's displacement, his gazing and of his kinematic energy. In the following, we illustrate the details of the JA elicitation experimental protocol using a small humanoid robot and of the features employed to describe the children's behavioural response. Then, we propose a statistical analysis of the data obtained in several experiments involving children in typical development (N=42) and children with ASD (N=16). Finally, we assess through the same metrics a subgroup of children with ASD (N=14) that has been trained using GOLIAH. In particular, we compare their behaviours at baseline and after 6 month of intensive training, four time a week at home and one time a week at hospital.

Given the results of our previous pilot study (Anzalone et al., 2014b;Boucenna et al., 2014a), we hypothesized that:

1. the response to the robot's JA elicitation task is lower in ASD children; 2. the postural stability induced by the engagement to the joint activity with the robot, expressed in terms of:

• displacement in the space,

• kinematic energy, is lower in ASD children;

3. the children showing improvement after GOLIAH training, improve also their performance in the JA elicitation task with Nao.

Methods

The JA induction experiment we proposed involves the interaction of a child with a Nao robot used to elicit JA behaviours in a laboratory setup. Data collected from a RGB-D sensor during the experiment are elaborated offline to extract a set of descriptors able to represent behavioural information of each child.

Participants

Table 1 summarizes participants' socio-demographics and clinical characteristics. Children were recruited by two specialized clinics for autism in the Pitié-Salpétrière hospital (Paris, France) and the Stella Maris Foundation (Pisa, Italy). Both teams agreed on participants' inclusion and exclusion criteria. ASD children selected suffered from various social impairments, including language disabilities and poor communicative skills. Assessment of ASD symptoms has been performed using the Autism Diagnostic Interview-Revised [START_REF] Lord | Autism diagnostic interviewrevised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders[END_REF]. The psychiatric assessments and parental interviews were conducted by three clinicians who specialized in autism (AN, JX, DC). The developmental age was assessed using a cognitive assessment. Depending on the children abilities and ages, we used either the Wechsler Intelligence scales [START_REF] Wechsler | Wechsler intelligence scale for children[END_REF], the Kaufman-ABC [START_REF] Kaufman | Kaufman assessment battery for children[END_REF] or the Psychoeducational Profile Revised (PEP-3) [START_REF] Schopler | Psychoeducational profile[END_REF].

TD participants were recruited from several schools in the Paris area. TD participants met the following inclusion criteria: no verbal communication impairment, no intellectual disability (ID), and no motor, sensory or neurological disorders. TD participants were matched to the children with ASD with respect to their developmental ages and genders. For the TD group, the developmental and chronological ages were considered to be the same.

Experimental Setup

The Michelangelo Room is where the therapy and the experiments with the robot take place. It is composed by an experimental area and a hidden control room. The experimental area has been customized for the experiment of joint attention induction. A small, thin, squared carpet has been placed in the position that should be occupied by the child. In front of it, a robot has been placed at around 1mt of distance, as in Fig. 2. The robot used is a Nao robot, from Aldebaran Robotics, a small humanoid able to communicate through simple body movements and verbal language. On its feet, a RGB-D sensor, a Microsoft Kinect is conveniently placed in order to capture the full body of the child and in particular his face. The robot and the RGB-D sensor are placed over a small table to keep its head at the same height of the child. On the two sides, two Focus of Attention (FoA) are placed: an image of a dog and an image of a cat. According to the protocol, the robot will try to induce JA over them. Several cameras allow a researcher hidden in the

Protocol

The design of the protocol conceived for this experiment focuses on the induction of children spontaneous reaction by the small humanoid robot Nao. Each child is introduced in the Michelangelo room by a clinician. The child is guided to establish a first contact with the robot. This stage gives to the child some time for getting used to the robot as a new toy. A second researcher is hidden in a separate control room, following the interaction, able to intervene in case of unexpected behaviors of the child that may either endanger the robot or the child himself. Then, the child is invited to stay over a small, thin carpet, in front of the robot, and then the experiment starts. In the joint attention experiment proposed, the robot tries to induce attention over the two focuses placed on the side of the room. The robot increases in three times the amount of information passed to the child by using more modalities to communicate its attention, as in Fig. 3: just by gazing, using head movements; by gazing and by pointing with the hands; by gazing, pointing and vocalizing, saying "look at the cat", "look at the dog". The behaviours of the robot are offline scripted, being designed offline by mimicking the interpersonal interplay between humans. Through the implementation of simple, recognizable, stereotyped behaviors the dynamics of the interaction becomes simplifyed and its complexity reduced. The child is free to decide to stay in place or go away, to move in place, to respond to the induction of the robot by gazing or by hand gestures. Behavioural data from each child is recorded and analysed offline.

The protocol presented in this paper was approved by the Pitié-Salpêtrière hospital ethics committee for French participants and by the Stella Maris hospital ethics committee for Italian participants. All of the parents received information on the experiment and gave written consent before the participation of their child.

Children Behaviour Metrics

The behaviours of each child, coupled with the robot behaviour, are analysed in order to extract a set of descriptors able to measure in a detailed way the response of each child to the JA elicitation. Such descriptors, introduced in the following, will be able to depict the JA response in terms of gazing behaviour, body movements and the kinetic energy.

Data Acquisition Pipeline

The information of children's movements are acquired through the elaboration of the data perceived by a RGB-D sensor (Anzalone et al., 2014b;[START_REF] Anzalone | Tracking posture and head movements of impaired people during interactions with robots[END_REF][START_REF] Anzalone | Evaluating the engagement with social robots[END_REF]Anzalone et al., , 2014a)). As shown in Fig. 4, the body of the participant is firstly distinguished from the background through a subtraction algorithm, then the skeleton in world coordinate is calculated [START_REF] Shotton | Real-time human pose recognition in parts from single depth images[END_REF][START_REF] Han | Enhanced computer vision with microsoft kinect sensor: A review[END_REF]. At the same time, the RGB image is cropped around the position in which, according to the skeleton information, the head should appear, then a face pose tracking algorithm is applied [START_REF] Xiong | Supervised descent method and its applications to face alignment[END_REF]. High level descriptors, detailed in the following, are finally extracted from such data to characterize gazing, body movements, and the kinetic energies from the body and from the head movements.

Response Events

During the experiment, the robot tries to induce joint attention towards the two focuses placed on the left and right sides of the room. The child can reply or not by gazing towards them or by hand gestures. It is possible to retrieve the presence of those responses by verifying whether or not the child moves his head or his arms. The detection of such movements can be performed through a short time spectrum analysis of the energy of the head movements and of the arms movements of the child [START_REF] Godfrey | Direct measurement of human movement by accelerometry[END_REF]. In such spectra, each movement appears as a linear combination of components at different frequency. Slow movements will be characterized by peaks in low frequencies, while fast movements will produce peaks in high frequencies.

After an empirical analysis of the videos of children movements, slow, significant movements, are isolated and selected by thresholding the components at 1Hz. Such slices are, then, fused together using the DBScan algorithm [START_REF] Ester | A density-based algorithm for discovering clusters in large spatial databases with noise[END_REF], obtaining clusters of temporal slices. When they exist, the center of the cluster closest temporally to the induction event of the robot is considered as the response event. The number of the response events that took place during the experiment can be considered as a measure of the effectiveness of the JA elicitation. A higher response of TD children is expected.

Displacement

The protocol proposed in this work requests the children to spend some time in front of the robot. A measure of displacement around each one's average position could be informative, together with gaze direction, about the engagement towards the current activity. Children focused on the activity will move in the space less than inattentive children. A two-dimensional histogram of the displacement is a convenient way to depict the participant's behaviour (Fig. 5). The magnitude of the displacement and the displacement along two preferential directions, left-right and forward-backward axis, have been selected as descriptors able to resume the displacement information. A higher displacement in the space of ASD children is expected.

Gazing

The robot elicit JA behaviours that is mainly expressed by children through a gazing response. In the considered scenario the position of the focus of attentions are chosen with the explicit goal of forcing head movements towards them. In these terms, such movements can be seen as an approximation of the gazing. Those movements are captured in terms of pitch and yaw, as shown in Figure 6, then modelled in different ways. A two-dimensional histogram of the head movement can easily illustrate how children explore the environment. In particular, the displacement of the gaze from each one's average position has been considered in terms of: (i) the magnitude of the head displacement, to depict the movement of the head without a particular prevalent direction; (ii) the head displacement along the pitch axis and yaw axis.

Head movement data from children in typical development has been employed to compute a model of their gazing through machine learning. This model has been employed, then, to evaluate the ASD gazing behaviour. Here we employed k-Means to find three clusters (K=3) corresponding to the three focus of attention: looking towards the robot, looking towards the left and the right focus of attention. Data from each participant has been categorized according to such model. Then, the numbers of samples belonging to each cluster, representing how much the child spent looking towards the cluster's direction, and their displacement measures, both in terms of magnitude and along the pitch and the yaw axis, can be calculated for each cluster. Differences in clusters dimensions and in the amplitude of the head movements are expected in TD children, highlighting the effectiveness of joint attention induction.

Energy

To describe the quantity of the movement of each participant, the energy of the upper body as well as the energy of the head have been calculated. This has been expressed in terms of total kinetic energy, the sum of the translational and of the angular energy, as in Eq. 1:

E(t) = k=limbs 1 2 M k v 2 k (t) + 1 2 I k ω 2 k (t) (1)
where k is the identifier of a limb, M k is its mass, I k is its inertia, v k (t) is its translational speed and ω(t) is its angular speed. [START_REF] De Leva | Adjustments to zatsiorsky-seluyanov's segment inertia parameters[END_REF].

For the upper body, only the translational energy has been calculated, while the angular energy has been used just for the head movements. As a measure of the quantity of movement of the body limbs, the contribution of the locomotion of the body on the ground has been subtracted, calculating the energy from the displacement of the body around the torso.

Masses of each limb are calculated using the Zatsiorsky's model adjusted by de Leva [START_REF] De Leva | Adjustments to zatsiorsky-seluyanov's segment inertia parameters[END_REF], shown in Figure 8, considering an average total body mass of 25kg as the average weight of children in the considered population [START_REF] Kuczmarski | cdc growth charts for the united states: methods and development. Vital and health statistics[END_REF]. Inertia of the head has been calculated by modelling it as an solid sphere of 8.35cm of radius, as 52.5cm head circumference [START_REF] Rollins | United states head circumference growth reference charts: birth to 21 years[END_REF], (I = 2 5 MR 2 ). The energy is described in terms of its median. According to previous results (Anzalone et al., 2014b), higher energies are expected in movement of children with ASD.

Results

Joint attention elicitation effects at baseline

Statistical analysis were conducted to compare the behaviour of the TD population with the behaviour of the ASD population. Because of technical issues during recording, full data were available for 37 participants (ASD: N=25 vs. TD: N=12). We used the Mann-Whitney-Wilcoxon test for independent samples to compare the two groups according to the modality employed by the robot to elicit behaviours (gazing, pointing, vocalizing). Table 2 summarizes the main extracted features. The comparison between the behaviour of TD children and children with ASD revealed several statistically significant differences. As expected, the analysis shows that children with TD respond more than children with ASD to the JA induction performed by the robot, in terms of head movements responses to JA induction.

By focusing on the gazing movement clustering, it is possible to observe a significant difference on how much time children spend on gazing towards the two focus of attention: children with ASD spend less time gazing towards the focus of attention than TD children, while they spend more time looking in front. This confirms the first hypothesis.

From the analysis of the displacement of children from their average position, it emerges a significant difference in the behaviour of the two studied groups: the amplitude of the displacement on the ground is higher for children with ASD than for TD children. Moreover, the analysis shows a preferential direction along the left-right axis. In addition, the analysis shows also an important difference on the kinematic energy of the body: ASD children employ a higher amount of energy than that of TD children. energy shows a similar behaviour: in the case of ASD group, the energy employed for head's movement is higher than in TD children Those results can be interpreted as a measure of the stability of children's body, confirming the second hypothesis of this work: children with ASD are less stable in terms of displacement in the environment, as well as in terms of their body movements.

6-month follow-up assessment of children with ASD exposed to GOLIAH

Among the 14 individuals with ASD trained with GOLIAH, we only had 8 individuals with full data available at both baseline and 6-month follow up. Due to the low number of GO-LIAH users, no statistical test was conducted. However, a qualitative description of the results is given in Table 3. We only present the features that were significantly different at baseline between ASD and TD children (see Table 3). As shown in the middle column, it seems that features extracted during the JA task at 6 months from children with ASD tend to change in a direction that is closer to TD value.

In particular, the response to JA increases in ASD children after 6 months of GOLIAH training, showing an incremented reaction of the children to the behaviours elicited by the robot. Gazing movements in terms of standard deviation tend towards the results obtained in TD group. The same tendency is confirmed by a detailed analysis of the gaze, in terms of focus of attention clusters: here, the time spent looking frontally, towards the robot, decrements after 6 months of GOLIAH training, turning towards the TD children behaviour; at the same time, the time spent looking the focus of attention on the two side of the room increments. Despite of this, children body displacement and kinematic energy of this experimental group still remains significant and higher compared to the TD group. While those results seem paradoxical, they could evidence the existence of impairments in postural control described in children with ASD, with increased sways and meaningful variability in posture associated to a micro-instability in their movement's trajectories (for a review, see [START_REF] Lim | Standing postural control in individuals with autism spectrum disorder: Systematic review and meta-analysis[END_REF]), named micromovements by [START_REF] Torres | Autism: the micro-movement perspective[END_REF]. Such micro-instability should emerge in the features describing the child's motor control and, in particular, in the energies. At the baseline, such children do not use to respond to the JA induction; despite this inactivity, elevated energies reveal such behavioural microinstability. After six months from the baseline, children start to respond to the behaviour induction: in this case, the higher energies are explainable as the mutual contribution of JA behaviour and micro-instability.

In the following section, we detail a single case with a rather positive evolution. The behaviour of a child at the beginning of GOLIAH training is compared with the behaviour of the same child after 6-months. The behaviour of a TD child is employed as basis for comparison. The gaze of the ASD child at baseline reveals an effective response to the JA induction elicited by the robot just on the vocalization stage (Figure 9 Variation between the behaviour of the sample ASD child at the beginning of the therapy (Figure 12.b) and after 6 months (Figure 12.c) are also highlighted by the kinematic energy of his body: the child responds more to JA in terms of his body movements after 6 months of training. Moreover, the energy employed by the ASD child is always higher than the one employed by the TD child, highlighting how the joint activity with the robot induces a lower postural stability in the ASD child than in the TD child. More in detail, the energy employed by the ASD child during the proposed task is lower than the one measured after 6 months, supporting the hypothesis of the contribution of micro-instability to the JA behaviour.

Conclusions

This paper presents an experimental protocol focusing on the use of a small humanoid robot as useful and natural tool to elicit behaviours in a JA taks. A set of metrics based on the analysis of body and head movements, gazing magnitude, gazing directions (left vs. front vs. right) and kinetic energies, able to describe children's behaviour during the task with the robot has been introduced. Results from experiments with TD children and children with ASD show the usefulness and the benefits of the presented protocol as well as of the informativeness of the metrics introduced. Such metrics show potential to measure JA characteristics during natural interaction since we were able (i) to distinguish TD children and children with ASD; (ii) to show improvements of children towards the behaviour of TD group after 6-month training of JA using GOLIAH. The proposed measures, however, do not have the ambition of becoming a standard that can be used in different institutions and hos-pitals: they should be seen as dependents to the particular scenario in which they are employed. A standardization of such metrics would involve their validation across different scenarios, employing bigger populations than the one that participated to our experiment. On the contrary, it is possible to generalize the proposed methodology in similar scenarios to evaluate JA. In this case, measures should be adapted to the specific context and to the specific protocol.

Nevertheless, the protocol proposed arouses several questions. In particular, the proposed protocol is implemented in a laboratory setup, a strongly controlled environment. The degrees of freedom of the proposed interaction with the robot are still very limited. Thus, the behaviour of the child is in some way very constrained by such factors. Those constraints come mainly from the limitations of the technology employed. The sensor employed to monitor the child activity is a single RGB-D sensor that has a limited field of view and is not able to track people in the entire environment [START_REF] Anzalone | Audio-video people recognition system for an intelligent environment[END_REF]. The implementation of the protocol in a space equipped with a network of synchronised RGB-D sensors will permit to the researcher to follow and analyse a wider range of movements of the child, as well as objects in the environment and other people, as clinicians [START_REF] Ghidoni | A distributed perception infrastructure for robot assisted living[END_REF][START_REF] Anzalone | Tracking posture and head movements of impaired people during interactions with robots[END_REF]. In such situation, the robot would be able not only to induce more complex behaviours from the children, but would also actively participate to free interactions with the child. In an ideal scenario, the robot would be involved as a real partner to the ESDM activities with the child and the clinician.

The robot employed in the presented protocol is a small humanoid robot Nao. It has been chosen for his simplified aspect as well as for his ability of establishing a natural, emphatic communication with children [START_REF] Ivaldi | Robot initiative in a team learning task increases the rhythm of interaction but not the perceived engagement[END_REF]. However, a similar protocol could be proposed using other kind of robots, humanoids, androids, animal-like, or with more abstract shapes. Each robot will be able to accomplish the protocol in different way and induce a different degree of behaviour elicitation.

Presented results also support the existence of postural control impairments and atypicalities in the kinematics aspects of movements in ASD. Their exploration, requiring further studies, poses significant pragmatic challenges for researchers and clinicians alike. In this regard, computational modeling involving human-machine interaction may be promising.

Fig. 1 :

 1 Fig. 1: Michelangelo Project's ASD treatment protocol.

  control room to have a complete knowledge about what is going on in the experimental area while managing and controlling the robot. For security reasons, he can always take the complete control of the robot. The control room has been equipped with two computers, one to control the robot and acquire the raw data from the RGB-D sensor, one as Michelangelo base station that centralizes all the other cameras displaced on the environment. Through this second computer, the hidden operator can monitor and record video data and events from the experimental room. As the experiments were conduced in the two clinics involved in the project, in Pisa and in Paris, identical Michelangelo Rooms have been set up in the two cities.

Fig. 2 :

 2 Fig. 2: The experimental area of the Michelangelo Room.

Fig. 3 :

 3 Fig.3: The joint attention elicitation protocol followed by the robot.

Fig. 4 :

 4 Fig. 4: The RGB-D data processing pipeline able to estimate people gaze, body position and posture.

Fig. 5 :

 5 Fig. 5: Histogram of the displacement of the child in the environment represented as heatmap. Colors indicate the areas around their own average in which the child moved.

Fig. 6 :

 6 Fig. 6: Histogram of the gazing movement of the child represented as heatmap. Colors indicate the areas towards the child looked at.

  (a) The child interacting. (b) The head movements clusters (c) The child's head trajectory.

Fig. 7 :

 7 Fig. 7: The robot elicits JA behaviours to the child in front of him (a); gaze clusters generated using k-means to locate the three main focuses of attention of the Michelangelo Room (b); child head movements in terms of yaw (x-axis) and pitch (y-axis) retrieved through the RGB-D data processing, while in red the beginning and the end of each JA induction event performed by the robot (by gazing, by gazing and pointing, by gazing, pointing and vocalizing) (c).

  .b). After 6 months (Figure 9.c), the child respond to almost all the elicitation behaviours. Gazing trace clearly shows this change: the behaviour at 6 months (Figure 9.c) tends to the behaviour of TD children (Figure 9.a).

Fig. 9 :

 9 Fig. 9: Gazing movement trajectory for a sample TD child and for a sample ASD child, at baseline and after 6 months.

  Fig. 10: Gazing heatmaps for a sample TD child and for a sample ASD child, at baseline and after 6 months.

  Fig. 11: Displacement heatmap for a sample TD child and for a sample ASD child, at baseline and after 6 months.

  Fig. 12: Body movement energy for a sample TD child and for a sample ASD child, at baseline and after 6 months.

Table 1 :

 1 Socio-demographic and clinical characteristics of the participants.

  Fig. 8: Body skeleton and percentage masses for each upper body part

		Middle Trunk Upper Trunk	Head
	Trunk	14.65	15.45	6.68
	Arm	Upper Arm	Forearm	Hand
	Left	2.55	1.38	0.5
	Right	2.55	1.38	0.5

Table 2 :

 2 The analysis of children head's movements Features comparison results for the JA experiment between TD (N=12) and ASD children (N=25) and their statistical significance.

	Feature	T D	σ(T D)	AS D	σ(AS D)	Variation
	Head movement response to JA induction	75%	29%	42%	28%	*
	Gazing std magnitude	10.2 •	3.9 •	2.9 •	7.3 •
	Gazing std yaw	14.9 •	6.0 •	9.7 •	3.8 •	*
	Gazing std pitch	4.4 •	1.5 •	4.7 •	2.2 •
	Gazing frequency towards the front	73%	14%	88%	8%	*
	Gazing frequency towards the left FoA	13%	8%	7%	4%	*
	Gazing frequency towards the right FoA	14%	7%	5%	5%	*
	Displacement std magnitude	0.9 cm	0.4 cm	2.8 cm	3.8 cm
	Displacement std left-right	1.4 cm	1.2 cm	3.9 cm	4.9 cm
	Displacement std front-back	1.2 cm	0.4 cm	2.3 cm	2.0 cm
	Body energy median	0.162 mJ 0.095 mJ	0.775 mJ	0.925 mJ	*
	Head energy median	0.01 mJ	0.003 mJ	0.022 mJ	0.025 mJ
	= ASD>TD, = ASD<TD, while p ≤ 0.05; * if p ≤ 0.01.

  ). At 6 months from the

	Feature	ASD Baseline	σ	ASD after 6 months	σ	TD baseline	σ
	Head movement response to JA induction	37%	2%	58%	3%	75%	29%
	Gazing std magnitude	8.2 •	2.4 •	9.2 •	3.6 •	10.2 •	3.9 •
	Gazing std yaw	10.5 •	3.0 •	13 •	5.5 •	14.9 •	6.0 •
	Gazing std pitch	4.8 •	2.7 •	4.5 •	1.3 •	4.4 •	1.5 •
	Gazing frequency towards the front	87%	7%	80%	13%	73%	14%
	Gazing frequency towards the left FoA	8%	4%	10%	6%	13%	8%
	Gazing frequency towards the right FoA	5%	4%	10%	7%	14%	7%
	Displacement std magnitude	1.6 cm	1.59 cm	1.7 cm	0.7 cm	0.9 cm	0.4 cm
	Displacement std left-right	2.2 cm	2.5 cm	2.4 cm	1.5 cm	1.4 cm	1.2 cm
	Displacement std front-back	1.6 cm	1.0 cm	1.9 cm	0.9 cm	1.2 cm	0.4 cm
	Body energy median	0.473 mJ	0.527 mJ	0.689 mJ	0.54 mJ	0.162 mJ	0.095 mJ
	Head energy median	0.023 mJ	0.02 mJ	0.039 mJ	0.039 mJ	0.01 mJ	0.003 mJ

Table 3 :

 3 Features extracted during the JA induction task at base line and 6 month follow-up individuals with ASD trained with GOLIAH (N=8) and at baseline for TD children (N=12).

Acknowledgements

Authors would like to thank all the MICHELANGELO Study group: Silvio Bonfiglio (FIMI, Italy), Fabio Apicella, Federico Sicca, Chiara Lucentini, Francesca Fulceri (Fondazione Stella Maris, Italy), Giovanni Pioggia, Alessandro Tonacci (CNR, Italy), Federico Cruciani, Cristiano Paggetti (I+, Italy), Angele Giuliano, Maryrose Francisa (Accros Limit, Malta), Koushik Maharatna, Valentina Bono, Wasifa Jamal (University of Southampton, UK), Leo Galway, Mark Donnelly (University of Ulster, UK), Anne-Lise Jouen, Elodie Tilmont (ISIR-UPMC). The authors would also like to thank all the patients and families who participated in the current study. They are also very grateful for the support from the hospitals staff.

Authors would give many thanks to A. Arrigo, R. Grassia and G. Varni for their kind support and collaboration. The current study was supported by a grant from the European Commission (FP7: Michelangelo under grant agreement No. 288241), the fund "Entreprendre pour aider", the Laboratory of Excellence SMART (ANR-11-LABX-65) supported by French State funds managed by the ANR within the Investissements d'Avenir programme under reference ANR-11-IDEX-0004-02. The funding agencies and the University were not involved in the study design, collection, analysis and interpretation of the data, the writing of the paper, or the decision to submit it for publication.


Giuliano Giacalone

Nonlinear coupling of flow harmonics in heavy-ion collisions

Keywords: nucleus-nucleus collisions, LHC, anisotropic flow, hydrodynamic simulations 2 + .

Anisotropic flow coefficients beyond triangular flow receive important contributions from lower-order harmonics through nonlinear coupling. We present a theoretical framework which allows one to quantify the contribution induced by such nonlinear coupling to any flow harmonic of any order. We show the effectiveness of this formalism through an application to hexagonal flow, V 6 . We study, in particular, the coupling of V 6 to triangular flow, V 3 , in Pb+Pb collisions at √ s = 2.76 TeV, using both Large Hadron Collider data and event-by-event hydrodynamic calculations.

Introduction

Particles detected in the final states of relativistic nucleus-nucleus collisions present a spectacular degree of azimuthal anisotropy, in the sense that if we perform a harmonic analysis of their azimuthal directions via Fourier decomposition:

dN p t d p t dφ = dN p t d p t 1 2π ∞ n=-∞ V n e inφ , V -n = V * n , (1)
the coefficients V n turn out to be nonzero. This observation has a natural explanation in a hydrodynamic framework [1], according to which particles are emitted to the final state after the hydrodynamic expansion of a fluidlike medium created in the collision zone. In this framework, Fourier anisotropy is of geometric origin [2], as the two most prominent coefficients of the spectrum, V 2 and V 3 , emerge as a response of the fluid to the elliptic and triangular anisotropy, respectively, of its initial geometry [3]. The origin of harmonics with n > 3 is instead more complicated, as shown by the following argument [4]. Anisotropic flow in the medium yields a transverse field of velocity amenable to a an expansion in harmonics:

u t (φ u ) = u t,0 + u t,2 cos(2φ u ) + u t,4 cos(4φ u ) + . . . + odd terms. ( 2
)
At freeze-out, since the momentum density of emitted particles is close to an equilibrium distribution, dubbing φ p the azimuthal orientation of transverse momentum, we can use Eq. ( 2) to write:

dN p t d p t dφ p ∝ exp - u t • p t T exp - p t T u t,0 cos(φ u -φ p ) 1 + p t T u t,2 cos(2φ u ) + 1 2 p t T u t,2 cos(2φ u )
Now, using the saddle-point approximation explained in Ref. [5], we set φ u = φ p inside the square brackets on the right hand side of Eq. ( 3), and we obtain that the angular anisotropy of the velocity field contributes to all orders in the harmonic spectrum of the final-state momenta. For instance, the term involving cos(2φ u ) 2 on the rhs generates a V 2 2 in the spectrum of the lhs, which eventually contributes to the overall fourth harmonic, V 4 . This feature is crucial in the context of heavy-ion collisions. Since the measured spectrum is strongly ordered, i.e., V 2 > V 3 > V 4 > . . . , one naturally expects of order n > 3 to originate almost entirely from their coupling to V 2 and V 3 . In the following, we present a theoretical formalism that allows one to compute the contributions to a given flow coefficient, V n , induced by harmonics of lower order.

A framework of nonlinear coupling of flow harmonics

The formalism was introduced in Ref. [6]. For a given Fourier harmonic, V, in a given class of impact parameter (or centrality), we perform a decomposition of the kind:

V = p k=1 χ k W k + U, (4)
where W k are combinations of lower-order harmonics that contribute to V, and U is a complex quantity which we define by requiring

W * k U = 0, ∀k, (5)
where . . . is an average over events. The quantities we are eventually interested in obtaining are the coefficients, χ, the so-called nonlinear response coefficients, that allow us to quantify the coupling between V and the lower-order harmonics, W. Using Eq. ( 5), we can write down the following identity

W * j V = p k=1 χ k W * j W k , (6)
which corresponds to a linear system of p equations for p constants. Now, defining the following p × p hermitian matrix

Σ jk ≡ W * j W k , (7)
the previous system, Eq (6), can be rewritten in matrix form:

M = ΣX, (8)
where M is a p-vector with components W * j V , and X a vector containing the p coupling constants. Therefore, if Σ and M are known, the coupling coefficients come from straightforward matrix inversion:

X = Σ -1 M. (9)
Equation ( 9) is our main result. In the remainder of this paper, we perform an application of this formalism to the extraction of the coupling coefficients characterizing hexagonal flow, V = V 6 .

Application to hexagonal flow

The nonlinear mode expansion of hexagonal flow reads 1 [7].

V 6 = χ 62 V 3 2 + χ 63 V 2 3 + χ 624 V 2 U 4 + U 6 , (10)
1 We always neglect contributions involving the first harmonic, V 1 , which is subleading.

with the notation v n = |V n | and u n = |U n |. We neglect effects of parity violation, and we take Σ (6) to be real. We extract both Σ (6) and M from experimental data on Pb+Pb collisions at √ s = 2.76 GeV. Almost all the required moments can be obtained from ALICE data [8,9], though one has to resort to ATLAS data [10] on event-plane correlations for two-and three-plane correlators. We refer to Ref. [6] for a thorough description of the extraction procedure. It is instructive to look at the centrality dependence of the elements of Σ (6) , displayed in Fig. 1(a). The matrix is to a good approximation diagonal, with only a tiny off-diagonal term provided by the three-plane correlator V 2 3 U * 4 V * 2 . Application of Eq. ( 9) yields, eventually, the coupling coefficients of V 6 as function of centrality percentile. The resulting coefficients of V 6 are reported in Ref. [6].

For reasons that will appear clear in a moment, here we focus solely on the coupling between V 6 and V 3 , χ 63 , which is displayed as full symbols in Fig. 2. Results are excellent, in the sense that the coefficient has a mild dependence on the centrality percentile, as one expects from generic arguments [11]. Now, as pointed out in Ref. [6], χ 63 provides a clear example of the effectiveness of the matrix formalism outlined above. Previous analyses of nonlinear flow modes [7,9,11,12,13] have been carried out neglecting the mutual correlations between nonlinear terms in Eq. (10), or in our language, neglecting all off-diagonal terms of Σ (6) :

With this approximation, experimental data on χ 63 are given by the white symbols in Fig. 2, and turn out to present a much stronger centrality dependence, which indicates a worse determination of the true value of the coupling coefficient. Therefore, the nonzero off-diagonal element of Σ (6) , though very tiny, leaves a visible signature in χ 63 . Such effect is fully taken into account in the matrix formalism, where no approximation is made concerning the correlations that contribute to V 6 .

We go a little beyond the purely data-driven analysis of Ref. [6], and check whether the influence of a nonzero V 2 3 U * 4 V * 2 on the coefficient χ 63 is an actual prediction of hydrodynamics. To this aim, we run event-by-event (ebye) viscous hydrodynamic simulations of Pb+Pb collisions at √ s = 2.76 TeV, using the code v-USPHYDRO [14,15], with the same parameter setup as in Ref. [16]. We first compute Σ (6) , whose elements are shown in Fig. 1(b). Hydrodynamics captures the generic feature displayed by the experimental data: The matrix is essentially diagonal, with one tiny off-diagonal element given by

Computing M, and using Eq. ( 9), we eventually extract the coefficient χ 63 . Results are shown in Fig. 2, and are in good agreement with the data. We compute the coefficient both including the full matrix Σ (6) (dashed line), and neglecting off-diagonal terms (solid line). We note that the centrality dependence of χ 63 becomes much weaker if the full matrix is included, as observed for the experimental data. We conclude that hydrodynamics correctly captures the effect of a nonzero off-diagonal element of Σ (6) on the determination of χ 63 .

Conclusive remarks and outlook

We expect high-statistics LHC2 data, combined with our theoretical formalism, to lead to a rich phenomenology of nonlinear harmonic coupling in the near future. The coefficients χ appear to depend mildly on centrality, initial conditions, and in general on medium properties during the hydrodynamic phase. They probe, on the other hand, some generic feature of the late-time dynamics of the quark-gluon plasma. What we are missing is precisely a solid understanding of what such physics is. Clarifying this issue in view of upcoming measurements is an exciting prospect for future theoretical studies.

We thank Jacquelyn Noronha-Hostler for providing us with the results of v-USPHYDRO calculations.


Tsing-Lee Tang-Huau

Elodie Segura
email: elodie.segura@curie.fr

Human in vivo-differentiated monocyte-derived dendritic cells

Keywords: monocyte, dendritic cell, inflammation, auto-immune disease, cancer

When entering tissues, monocytes can differentiate into cells that share morphological and functional features with either dendritic cells (DC) or macrophages. Monocyte-derived DC have been observed in humans at mucosal tissues and in inflammatory settings, where they are usually referred to as « inflammatory DC ». In this chapter, we review recent studies on the characterization of these cells in humans. We also discuss nomenclature and examine the criteria defining in vivo-differentiated human mo-DC.

Introduction

Monocytes have long been used as precursor cells for the in vitro generation of human dendritic cells (DC) [START_REF] Sallusto | Efficient presentation of soluble antigen by cultured human dendritic cells is maintained by granulocyte/macrophage colony-stimulating factor plus interleukin 4 and downregulated by tumor necrosis factor alpha[END_REF]. However, it has become clear that, in vivo, most DC derive from a specific precursor, termed pre-DC, distinct from monocytes [START_REF] Naik | Intrasplenic steady-state dendritic cell precursors that are distinct from monocytes[END_REF][START_REF] Liu | In vivo analysis of dendritic cell development and homeostasis[END_REF][START_REF] See | Mapping the human DC lineage through the integration of high-dimensional techniques[END_REF][START_REF] Villani | Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors[END_REF][START_REF] Lee | Restricted dendritic cell and monocyte progenitors in human cord blood and bone marrow[END_REF][START_REF] Breton | Circulating precursors of human CD1c+ and CD141+ dendritic cells[END_REF][START_REF] Breton | Human dendritic cells (DCs) are derived from distinct circulating precursors that are precommitted to become CD1c+ or CD141+ DCs[END_REF]. The physiological relevance of monocytes as DC precursors was demonstrated in the mouse in a Leishmania infection model in which monocytes could differentiate in vivo into cells that shared phenotypic and functional features with DC [START_REF] Leon | Monocyte-derived dendritic cells formed at the infection site control the induction of protective T helper 1 responses against Leishmania[END_REF]. Numerous subsequent studies in mice have 2 shown that DC can differentiate in vivo from monocytes in various inflammatory settings, including infections and models of auto-immune diseases [START_REF] Segura | Inflammatory dendritic cells in mice and humans[END_REF][START_REF] Mildner | A close encounter of the third kind: monocyte-derived cells[END_REF]. Because they arise during inflammation, these in vivo-differentiated monocyte-derived DC (mo-DC) were initially termed « inflammatory DC ». However, this term can be misleading as it implies that mo-DC are « inflammatory » by essence. Yet, mo-DC can be found in mice in the absence of inflammation at mucosal sites, such as the intestine and the skin [START_REF] Tamoutounour | Origins and functional specialization of macrophages and of conventional and monocyte-derived dendritic cells in mouse skin[END_REF][START_REF] Varol | Intestinal lamina propria dendritic cell subsets have different origin and functions[END_REF][START_REF] Bogunovic | Origin of the lamina propria dendritic cell network[END_REF], and in skeletal muscles [START_REF] Langlet | CD64 expression distinguishes monocyte-derived and conventional dendritic cells and reveals their distinct role during intramuscular immunization[END_REF].

In the classification of DC lineages based on ontogeny [START_REF] Guilliams | Dendritic cells, monocytes and macrophages: a unified nomenclature based on ontogeny[END_REF], mo-DC represent a separate DC subset. Importantly, monocytes can also differentiate into macrophages, which are distinct from "resident" macrophages derived from embryonic precursors [START_REF] Lavin | Regulation of macrophage development and function in peripheral tissues[END_REF] (figure 1).

Whether mo-DC are fundamentally distinct from monocyte-derived macrophages (mo-Mac), and how to distinguish these two populations has been controversial. In this chapter, we will cover in vivo-differentiated human mo-DC, both generated in the steady-state (referred to as « steady-state mo-DC ») and arising during inflammation (referred to as « inflammatory mo-DC »). We will also discuss recent data on the molecular requirements for mo-DC versus mo-Mac differentiation.

Definition of human in vivo-differentiated mo-DC

Identification of human inflammatory mo-DC

« Inflammatory dendritic epidermal cells » (IDEC) were first described in the skin of atopic dermatitis patients [START_REF] Wollenberg | Immunomorphological and ultrastructural characterization of Langerhans cells and a novel, inflammatory dendritic epidermal cell (IDEC) population in lesional skin of atopic eczema[END_REF]. These cells were absent from healthy skin and characterized by a surface phenotype distinct from that of Langerhans cells. Subsequently, IDEC were also identified in the skin of psoriasis patients [START_REF] Wollenberg | Expression and function of the mannose receptor CD206 on epidermal dendritic cells in inflammatory skin diseases[END_REF]. Another study reported in the skin of psoriasis patients the presence of a population of « inflammatory dermal DC », phenotypically distinct from the dermal DC found in healthy skin [START_REF] Zaba | Psoriasis is characterized by accumulation of immunostimulatory and Th1/Th17 cell-polarizing myeloid dendritic cells[END_REF]. Whether these inflammatory DC represented an activated form of classical DC or were related to monocytes was not investigated at the time.

Several lines of evidence suggest that in humans, as in mice, monocytes recruited to inflammatory sites can differentiate into DC. Radio-labelled autologous monocytes are recruited to the gut in inflammatory bowel disease patients [START_REF] Grimm | Direct evidence of monocyte recruitment to inflammatory bowel disease mucosa[END_REF] and monocyte numbers are increased in the intestine of Crohn's disease and ulcerative colitis patients compared to healthy donors [START_REF] Kamada | Unique CD14 intestinal macrophages contribute to the pathogenesis of Crohn disease via IL-23/IFN-gamma axis[END_REF]. Influx of monocytes were also observed in cantharidin-induced skin blisters [START_REF] Jenner | Characterisation of leukocytes in a human skin blister model of acute inflammation and resolution[END_REF], in the peritoneum of patients with dialysis-related peritonitis [START_REF] Liao | Peritoneal macrophage heterogeneity is associated with different peritoneal dialysis outcomes[END_REF] and in the nasal mucosa of subjects with induced allergic rhinitis [START_REF] Eguiluz-Gracia | Rapid recruitment of CD14(+) monocytes in experimentally induced allergic rhinitis in human subjects[END_REF]. In the latter study, monocytes were recruited in the nasal mucosa within hours after allergen challenge while a new population of DC appeared after 3 days, consistent with the potential differentiation of monocytes into mo-DC. Moreover, we have identified in synovial fluid from rheumatoid arthritis patients and in peritoneal ascites from cancer patients a population of « inflammatory DC » with a phenotype different from that of classical blood DC [START_REF] Segura | Human Inflammatory Dendritic Cells Induce Th17 Cell Differentiation[END_REF]. We showed by transcriptomic analysis that these cells represent a distinct DC subset and share gene signatures with in vitro-generated monocyte-derived cells, supporting the idea that these inflammatory DC are more closely related to monocytes than to classical DC [START_REF] Segura | Human Inflammatory Dendritic Cells Induce Th17 Cell Differentiation[END_REF][START_REF] Goudot | Aryl Hydrocarbon Receptor Controls Monocyte Differentiation into Dendritic Cells versus Macrophages[END_REF].

Finally, mo-DC have been evidenced in human lung and colorectal tumors, based on their phenotypic similarity with tumor-associated mo-DC in mice tumor models [START_REF] Laoui | The tumour microenvironment harbours ontogenically distinct dendritic cell populations with opposing effects on tumour immunity[END_REF].

Collectively, these results support the notion that monocytes recruited to inflammatory sites can differentiate in situ into mo-DC in humans.

Defining features of in vivo-differentiated mo-DC

DC have been traditionnally defined based on 1) their distinctive morphology, with the presence of dendrites ; 2) their superior ability to activate naive T cells ; 3) their capacity to migrate to lymph nodes. In addition, distinct DC subtypes can be defined based on their ontogeny and their requirement for distinct transcription factors [START_REF] Guilliams | Dendritic cells, monocytes and macrophages: a unified nomenclature based on ontogeny[END_REF]. However, because these properties can be difficult to investigate in humans, most studies rely on the differential expression of phenotypic markers to distinguish DC from other myeloid cells, and DC subsets from one another.

Human inflammatory mo-DC are HLA-DR + CD11c + cells that express markers found on classical DC such as CD1c, CD1a, CD1b, FcεRI, as well as molecules that are also expressed by macrophages including CD206, CD14 and CD11b [START_REF] Wollenberg | Expression and function of the mannose receptor CD206 on epidermal dendritic cells in inflammatory skin diseases[END_REF][START_REF] Eguiluz-Gracia | Rapid recruitment of CD14(+) monocytes in experimentally induced allergic rhinitis in human subjects[END_REF][START_REF] Segura | Human Inflammatory Dendritic Cells Induce Th17 Cell Differentiation[END_REF][START_REF] Laoui | The tumour microenvironment harbours ontogenically distinct dendritic cell populations with opposing effects on tumour immunity[END_REF][START_REF] Guttman-Yassky | Major differences in inflammatory dendritic cells and their products distinguish atopic dermatitis from psoriasis[END_REF]. By contrast, they lack the macrophage markers CD16 and CD163 [26] [28]. Inflammatory mo-DC can also be distinguished from macrophages by their morphology (figure 2) and their capacity to stimulate T cell proliferation [START_REF] Zaba | Psoriasis is characterized by accumulation of immunostimulatory and Th1/Th17 cell-polarizing myeloid dendritic cells[END_REF][START_REF] Segura | Human Inflammatory Dendritic Cells Induce Th17 Cell Differentiation[END_REF].

Molecular ontogeny of mo-DC

The relationship of mo-DC to monocytes is suggested by their expression of CCR2, a chemokine receptor essential for monocyte recruitment into peripheral tissues [START_REF] Boring | Impaired monocyte migration and reduced type 1 (Th1) cytokine responses in C-C chemokine receptor 2 knockout mice[END_REF]. However, this is not an absolute marker of monocyte-derived cells, as a CCR2 + DC population from mouse intestine has been shown to derive from pre-DC [START_REF] Scott | CCR2(+)CD103(-) intestinal dendritic cells develop from DC-committed precursors and induce interleukin-17 production by T cells[END_REF].

The ontogeny of human DC is usually inferred from the analysis of gene patterns and specific transcription factors, but can also be analyzed using in vitro differentiation models [START_REF] Lee | Restricted dendritic cell and monocyte progenitors in human cord blood and bone marrow[END_REF][START_REF] Breton | Circulating precursors of human CD1c+ and CD141+ dendritic cells[END_REF][START_REF] Guilliams | Dendritic cells, monocytes and macrophages: a unified nomenclature based on ontogeny[END_REF]. Inflammatory mo-DC from ascites express a unique combination of transcription factors that are either shared with classical DC (IRF4, BATF3, ZBTB46) or with macrophages (EGR1, EGR2), but they do not express MAFB which is restricted to macrophages [START_REF] Segura | Human Inflammatory Dendritic Cells Induce Th17 Cell Differentiation[END_REF]. Using a novel model of human monocyte differentiation, we have recently shown that MAFB controls monocyte differentiation into mo-Mac, while IRF4, BLIMP-1 and Aryl Hydrocarbon Receptor (AHR) are essential for monocyte differentiation into mo-DC [START_REF] Goudot | Aryl Hydrocarbon Receptor Controls Monocyte Differentiation into Dendritic Cells versus Macrophages[END_REF]. Moreover, patients suffering from systemic juvenile idiopathic arthritis underexpress AHR in their monocytes, which are biaised towards mo-Mac differentiation [START_REF] Cepika | A multidimensional blood stimulation assay reveals immune alterations underlying systemic juvenile idiopathic arthritis[END_REF].

Precursors of human classical DC have been shown to be pre-committed to either lineage at an early stage [START_REF] Breton | Human dendritic cells (DCs) are derived from distinct circulating precursors that are precommitted to become CD1c+ or CD141+ DCs[END_REF]. By contrast, single-cell RNA-seq analysis of human CD14 + monocytes has revealed that monocytes do not contain two populations that would be precommitted to differentiate into mo-DC versus mo-Mac [START_REF] Goudot | Aryl Hydrocarbon Receptor Controls Monocyte Differentiation into Dendritic Cells versus Macrophages[END_REF]. Rather, blood CD14 + monocytes are programmed to spontaneously differentiate into blood CD16 + monocytes [START_REF] Patel | The fate and lifespan of human monocyte subsets in steady state and systemic inflammation[END_REF], unless they extravasate into tissues. Monocytes express a partial mo-Mac gene signature, suggesting they may possess in tissues a default differentiation pathway into macrophages unless they encounter specific cues, such as inflammatory cytokines and AHR ligands [START_REF] Goudot | Aryl Hydrocarbon Receptor Controls Monocyte Differentiation into Dendritic Cells versus Macrophages[END_REF]. Because their differentiation is governed by distinct transcription factors, mo-DC and mo-Mac do not represent variations of a highly plastic cell population as previously proposed [START_REF] Guilliams | Dendritic cells, monocytes and macrophages: a unified nomenclature based on ontogeny[END_REF], but rather are bona fide cell lineages derived from monocytes (figure 3).

Slan-DC

CD16 + cells that express a 6-sulfo LacNAc modification of the P selectin glycoprotein ligand 1 (PSGL-1) have been termed « slan-DC » and proposed to represent an inflammatory subtype of DC based on their capacity to produce large amounts of TNF-α upon stimulation [START_REF] Schakel | 6-Sulfo LacNAc, a novel carbohydrate modification of PSGL-1, defines an inflammatory type of human dendritic cells[END_REF]. These cells were initially described in the blood, but can also be found in tissues such as the skin of psoriasis patients [START_REF] Hansel | Human slan (6-sulfo LacNAc) dendritic cells are inflammatory dermal dendritic cells in psoriasis and drive strong TH17/TH1 T-cell responses[END_REF], tumor-draining lymph nodes [START_REF] Vermi | slanDCs selectively accumulate in carcinoma-draining lymph nodes and marginate metastatic cells[END_REF], intestinal lamina propria [START_REF] Ogino | Increased Th17-inducing activity of CD14+ CD163 low myeloid cells in intestinal lamina propria of patients with Crohn's disease[END_REF], mesenteric lymph nodes and intestinal mucosa of Crohn's disease patients [START_REF] Bsat | Differential accumulation and function of proinflammatory 6-sulfo LacNAc dendritic cells in lymph node and colon of Crohn's versus ulcerative colitis patients[END_REF], renal carcinoma [START_REF] Toma | Accumulation of tolerogenic human 6-sulfo LacNAc dendritic cells in renal cell carcinoma is associated with poor prognosis[END_REF], tonsil [START_REF] Vermi | slanDCs selectively accumulate in carcinoma-draining lymph nodes and marginate metastatic cells[END_REF] and brain lesions of patients with multiple sclerosis [START_REF] Thomas | Accumulation and therapeutic modulation of 6-sulfo LacNAc(+) dendritic cells in multiple sclerosis[END_REF].

Comparative transcriptomic analysis identified slan-DC as a subset of CD16 + monocytes [START_REF] Cros | Human CD14dim monocytes patrol and sense nucleic acids and viruses via TLR7 and TLR8 receptors[END_REF][START_REF] Hofer | slan-defined subsets of CD16-positive monocytes: impact of granulomatous inflammation and M-CSF receptor mutation[END_REF] and showed that they are distinct from the DC lineage [START_REF] Van Leeuwen-Kerkhoff | Transcriptional profiling reveals functional dichotomy between human slan+ non-classical monocytes and myeloid dendritic cells[END_REF]. These results are further supported by functional analysis showing that slan-DC are poor antigen-presenting cells compared to classical DC [START_REF] Cros | Human CD14dim monocytes patrol and sense nucleic acids and viruses via TLR7 and TLR8 receptors[END_REF][START_REF] Van Leeuwen-Kerkhoff | Transcriptional profiling reveals functional dichotomy between human slan+ non-classical monocytes and myeloid dendritic cells[END_REF]. In addition, slan-DC do not express CCR7 [START_REF] Toma | Accumulation of tolerogenic human 6-sulfo LacNAc dendritic cells in renal cell carcinoma is associated with poor prognosis[END_REF] and exhibit a macrophage morphology in tissues [START_REF] Ogino | Increased Th17-inducing activity of CD14+ CD163 low myeloid cells in intestinal lamina propria of patients with Crohn's disease[END_REF]. These observations suggest that slan-DC may be a specialized monocyte subset rather than mo-DC.

A practical key to identify human mo-DC from biological samples

Most studies rely on a small number of surface markers to distinguish cell populations.

Because macrophages, monocytes, mo-DC and classical DC subsets display overlapping phenotypes, identifying these different populations in biological samples can be challenging.

Based on the results discussed above, we propose that several categories of criteria should be combined for identifying human in vivo-differentiated mo-DC : 1) Phenotype. Mo-DC are HLA-DR + CD11c + CD14 int CD206 + CD1c + cells. Additional useful markers to distinguish them from other myeloid cells are shown in Table 1.

2) DC Function. Mo-DC possess typical DC functions, including the ability to efficiently stimulate naive T cells and the capacity to express CCR7 (potentially enabling their migration to lymph nodes).

3) DC morphology. Mo-DC display a typical DC morphology : they are of small size, possess dendrites and lack large cytoplasmic vacuoles (as opposed to macrophages) (figure 2). 4) Ontogeny. Mo-DC derive from monocytes, which can be assessed by analyzing gene signatures or the expression of CCR2. Mo-DC can be distinguished from mo-Mac by the expression of specific transcription factors such as IRF4 and AHR, and the absence of MAFB expression.

Distribution of human mo-DC

Human steady-state mo-DC in mucosal tissues

Several studies have analyzed the composition of myeloid cells at mucosal sites, including cells displaying the phenotype of mo-DC. Lungs of healthy donors comprise a population of CD14 + CD206 + CD1c + CD1a + cells that express IRF4 [START_REF] Desch | Flow Cytometric Analysis of Mononuclear Phagocytes in Nondiseased Human Lung and Lung-Draining Lymph Nodes[END_REF].

CD14 + CD206 + CD1c + CD163 -cells are also present in the broncho-alveolar lavage (BAL) of healthy volunteers and express IRF4 and CCR2 [START_REF] Baharom | Dendritic Cells and Monocytes with Distinct Inflammatory Responses Reside in Lung Mucosa of Healthy Humans[END_REF]. Another study reported the presence of CD14 + CD1c + CD1a + DC in the BAL of healthy volunteers and showed that these cells can upregulate CCR7 [START_REF] Patel | Transcriptional Classification and Functional Characterization of Human Airway Macrophage and Dendritic Cell Subsets[END_REF]. In addition, transcriptomic analysis indicated that this population overexpresses CCR2, CD206 and CD11b [START_REF] Patel | Transcriptional Classification and Functional Characterization of Human Airway Macrophage and Dendritic Cell Subsets[END_REF]. These results suggest that mo-DC are present in human lungs at the steady-state. CD14 low CD206 + CD1c + CD1a + FcεRI + CD11b + cells can also be found in the peritoneum as shown through the analysis of dialysate of patients, with or without dialysis-induced peritonitis [START_REF] Liao | Peritoneal macrophage heterogeneity is associated with different peritoneal dialysis outcomes[END_REF]. These cells express CCR2, IRF4 and upregulate CCR7 upon activation [START_REF] Liao | Peritoneal macrophage heterogeneity is associated with different peritoneal dialysis outcomes[END_REF], suggesting they are mo-DC .

Three main DC populations were identified in the small intestine, including a CD1c + CD11b + population that was increased in patients with hyperemic mucosa (i.e. with increased redness and blood flow, consistent with inflammation) [START_REF] Watchmaker | Comparative transcriptional and functional profiling defines conserved programs of intestinal DC differentiation in humans and mice[END_REF]. The transcriptomic signature of this population was closer to blood monocytes than to classical blood DC, suggesting they are mo-DC.

Finally, whether mo-DC are present in human skin in the steady-state remains unclear.

Dermal CD14 + DC were initially proposed to be mo-DC based on their reconstitution by donor-derived cells after hematopoietic stem cell transplantation and on their transcriptional proximity with blood monocytes [START_REF] Haniffa | Differential rates of replacement of human dermal dendritic cells and macrophages during hematopoietic stem cell transplantation[END_REF][START_REF] Haniffa | Human Tissues Contain CD141(hi) Cross-Presenting Dendritic Cells with Functional Homology to Mouse CD103(+) Nonlymphoid Dendritic Cells[END_REF]. However, this population may be heterogeneous and a large proportion of them have been shown to be actually monocyte-derived macrophages [START_REF] Mcgovern | Human Dermal CD14(+) Cells Are a Transient Population of Monocyte-Derived Macrophages[END_REF].

Human mo-DC in cancer

Several studies in mouse models have shown that circulating monocytes are recruited to tumors and can differentiate at the tumor site [START_REF] Shand | Tracking of intertissue migration reveals the origins of tumor-infiltrating monocytes[END_REF][START_REF] Franklin | The cellular and molecular origin of tumor-associated macrophages[END_REF][START_REF] Qian | CCL2 recruits inflammatory monocytes to facilitate breast-tumour metastasis[END_REF]. It is therefore likely that human tumors contain inflammatory mo-DC within the myeloid infiltrate, but this question has been little studied. Tumor-associated mo-DC have been identified based on their phenotype in lung and colorectal tumors [START_REF] Laoui | The tumour microenvironment harbours ontogenically distinct dendritic cell populations with opposing effects on tumour immunity[END_REF]. CD14 + CD1c + cells, which may be mo-DC, have also been observed by histology in melanoma skin lesions and colon metastasis [START_REF] Bakdash | Expansion of a BDCA1+CD14+ Myeloid Cell Population in Melanoma Patients May Attenuate the Efficacy of Dendritic Cell Vaccines[END_REF]. Finally, a singlecell analysis of lung adenocarcinoma has evidenced that some CD1c + DC express monocyte/macrophage markers (CD14, CCR2, CD206, CD64, CD11b), which could indicate the presence of a population of mo-DC [START_REF] Lavin | Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses[END_REF] .

Human mo-DC in blood

Human mo-DC have been identified in a variety of tissues (Table 2), but whether circulating mo-DC are present in the blood remains unclear. Blood CD14 + CD1c + CD11b + cells have been described, but these cells do not express CD206 nor CD1a [START_REF] Bakdash | Expansion of a BDCA1+CD14+ Myeloid Cell Population in Melanoma Patients May Attenuate the Efficacy of Dendritic Cell Vaccines[END_REF]. This population is elevated in the blood of melanoma patients compared to healthy donors.

Comparative transcriptomic analysis showed that this population is closely related to monocytes, but whether they are a subpopulation of monocytes or bona fide mo-DC requires further investigation.

In addition, single-cell RNA-seq analysis showed that blood CD1c + DC comprise two distinct subgroups, one of which was termed « inflammatory » [START_REF] Villani | Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors[END_REF]. These « inflammatory » CD1c + DC express mRNA for CD14, but not the protein at the cell surface. They can be separated from the other CD1c + DC subgroup by their differential expression of CD163 and CD36. Despite being termed « inflammatory », these cells are transcriptionnally closer to classical DC rather than to monocytes, and are most likely derived from pre-DC. The properties of these two CD1c + DC populations remain to be characterized.

Functional properties of in vivo-differentiated mo-DC

Due to the difficulty of isolating human mo-DC directly from biological samples, there is limited data on their functional properties. Human mo-DC efficiently activate CD4 and CD8 T cells in ex vivo assays [START_REF] Zaba | Psoriasis is characterized by accumulation of immunostimulatory and Th1/Th17 cell-polarizing myeloid dendritic cells[END_REF][START_REF] Liao | Peritoneal macrophage heterogeneity is associated with different peritoneal dialysis outcomes[END_REF][START_REF] Segura | Human Inflammatory Dendritic Cells Induce Th17 Cell Differentiation[END_REF]. Peritoneal mo-DC secrete IL-6, TNF-α, IL1-β and IL-12p70 upon ex vivo restimulation [START_REF] Liao | Peritoneal macrophage heterogeneity is associated with different peritoneal dialysis outcomes[END_REF]. Mo-DC from BAL also secrete TNF-α upon restimulation, but their ability to secrete other cytokines was not reported [START_REF] Baharom | Dendritic Cells and Monocytes with Distinct Inflammatory Responses Reside in Lung Mucosa of Healthy Humans[END_REF]. By contrast, inflammatory mo-DC from tumor ascites secrete IL-6, TNF-α, IL1-β without the need for ex vivo restimulation, suggesting they are already activated in situ by the inflammatory environment [START_REF] Segura | Human Inflammatory Dendritic Cells Induce Th17 Cell Differentiation[END_REF]. Ascites mo-DC, but not macrophages, also secrete IL-12p70 and IL-23 upon ex vivo restimulation [START_REF] Segura | Human Inflammatory Dendritic Cells Induce Th17 Cell Differentiation[END_REF].

Consistent with their ability to produce IL-23, inflammatory mo-DC from tumor ascites and from synovial fluid from rheumatoid arthritis patients are potent inducers of Th17 polarization ex vivo [START_REF] Segura | Human Inflammatory Dendritic Cells Induce Th17 Cell Differentiation[END_REF]. Inflammatory mo-DC from the skin of psoriasis patients also induce Th17 polarization ex vivo [START_REF] Zaba | Psoriasis is characterized by accumulation of immunostimulatory and Th1/Th17 cell-polarizing myeloid dendritic cells[END_REF]. Because Th17 cells play a major role in tissue damage in these diseases, these results suggest that inflammatory mo-DC could be invoved in the pathogenesis by fueling the inflammation. Nevertheless, the properties of inflammatory mo-DC will likely be influenced by their micro-environment, and their T cell-polarizing ability may be different in a Th1-or Th2-driven pathology.

Conclusion and perspectives

The literature discussed in this chapter indicates that mo-DC are present in vivo in humans and represent a bona fide DC subset. Recent technical advances, such as single-cell transcriptomic analysis, may reveal mo-DC populations in additionnal tissues or pathological situations. A major challenge is to better understand their functional properties and potential role in the physiopathology of inflammatory disorders or cancer. This would pave the way for manipulating mo-DC differentiation or function for therapeutic intervention.

Figure 1 .

 1 Figure 1. Ontogeny of macrophages and dendritic cells. Macrophages and dendritic cells

Figure 2 .

 2 Figure 2. Morphology of human DC versus macrophages. DC and macrophages from the

Figure 3 .

 3 Figure 3. Molecular ontegeny of monocyte-derived cells. In response to external signals

Acknowledgements

This work is funded by the program «Investissements d'Avenir» from Agence Nationale de la Recherche (ANR-10-LABX-0043 and ANR-10-IDEX-0001-02 PSL), Fondation Bristol-Myers Squibb pour la Recherche en Immuno-Oncologie and INSERM. T-L Tang-Huau is supported by an industry-funded PhD fellowship at Université Paris Descartes.

[20] Synovial fluid rheumatoid arthritis [START_REF] Segura | Human Inflammatory Dendritic Cells Induce Th17 Cell Differentiation[END_REF]