Olivier Kermorgant

A magnetic climbing robot to perform autonomous welding in the shipbuilding industry $

Keywords: Continuous track, welding robot, climbing robot

In this paper we present the mechanical and control design of a magnetic tracked mobile robot. The robot is designed to move on vertical steel ship hulls and to be able to carry 100 kg payload, including its own weight. The mechanical components are presented and the sizing of the magnetic tracks is detailed. All computation is embedded in order to reduce time delays between processes and to keep the robot functional even in case of signal loss with the ground station. The main sensor of the robot is a 2D laser scanner, that gives information on the hull surface and is used for several tasks. We focus on the welding task and expose the control algorithm that allows the robot to follow a straight line for the welding process.

Introduction

In the advanced manufacturing area, one challenge is to extend the use of autonomous or collaborative robots in several industries such as car companies, plane and ship building or renewable energies. Whereas most of the innovations deal with traditional or mobile robot arms in classical manufacturing plants, another source of productivity can be found in more exotic locations: large structures such as an aircraft or a ship indeed have their own constraints when it comes to mobile robotics. In this work we design a mobile robot able to navigate on vertical steel surfaces such as ship hulls. The robot can perform various tasks, either autonomously or in tele-operation depending on the complexity. Currently, hull welding is performed manually by a welder mounted on a boom lift or scaffoldings. This is both dangerous and costly, as the typical size of a ship is some hundred meters length and a few tens of meters high. Besides, due to the thickness of the hull, several passes have to $ c 2018. This manuscript version is the accepted preprint and is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ Email address: olivier.kermorgant@ec-nantes.fr (Olivier Kermorgant) be performed (up to 10). A typical ship thus requires several kilometers of straight line welding. Human welder expertise is far from being fully exploited in this task, and it can benefit from an autonomous process. The stakes for the ship builder are to reduce lifts or scaffoldings use, and to have their welders available for more complex tasks such as corners or welding in small places where adaptability is crucial. This task requires the accurate positioning of an embedded welding torch. The torch is carried by a 2-degrees of freedom arm located at the rear of the robot and benefits from laser scanner feedback for the analysis of the welding joint. The motion law of the torch is not detailed and we focus on the capabilities of the vehicle in terms of alignment and positioning with regards to the welding joint, which drives the position of the welding torch. Absolute localization on the hull, or longitudinal positioning along the welding joint, are not considered in this work. Welding results are presented to show the reliability of the process.

Most of the works on climbing robots focus on mechanical design and adhesion principle. A survey [START_REF] Silva | A survey of technologies for climbing robots adhesion to surfaces[END_REF] indicates that the two most popular approaches for climbing robots are the use of suction force and, when the surface allows it, magnetic force.

A suction-based climbing robot was presented in [START_REF] Savall | Two compact robots for remote inspection of hazardous areas in nuclear power plants[END_REF] for the inspection of radioactive cylindrical tanks. The payload is only 3 kg. Alternatively, in [START_REF] Kang | Design of quadruped walking and climbing robot[END_REF] a quadruped walking robot is presented with suction pads at each of the 4 contact links. The control of such a robot is of course quite tedious.

In [START_REF] Sekhar | A Novel Design Technique to Develop a Low Cost and Highly Stable Wall Climbing Robot[END_REF] a low cost wall climbing robot is proposed. Classical tracks are used with a vacuum pump that increases the grip on the surface. The payload of this robot is only 500 g. Magnetic force-based robot may use electromagnets [START_REF] Shores | Design, kinematic analysis, and quasi-steady control of a morphic rolling disk biped climbing robot[END_REF][START_REF] Armada | Design of mobile robots[END_REF], which are interesting as they can be activated at will and increase the control possibilities of the platform. On the other hand, permanent magnets can be use with magnetic wheels [START_REF] Berns | Climbing robots for commercial applications-a survey[END_REF][START_REF] Park | Design of a mobile robot system for automatic integrity evaluation of large size reservoirs and pipelines in industrial fields[END_REF]. The main advantage is that no energy is spent on the adhesion, but it makes the control more difficult and requires more power in order to cope with the friction between the wheels and the surface. A magnetic tracked robot is presented in [START_REF] Shen | Proposed wall climbing robot with permanent magnetic tracks for inspecting oil tanks[END_REF] with an emphasis on the sizing of the magnetic pads. The goal is to perform oil tanks inspection with manual control of the robot.

Alternative technologies to suction and magnetic forces have been investigated. In [START_REF] Wang | Large-payload climbing in complex vertical environments using thermoplastic adhesive bonds[END_REF],thermoplastic adhesive bonds are used instead of magnets or suction pads. Even if it leads to a very high payload, the number of cycles is limited and this would induce too many maintenance operations for the ship industry. Another technology has been proposed for non-magnetic wall climbing [START_REF] Murphy | Waalbot II: adhesion recovery and improved performance of a climbing robot using fibrillar adhesives[END_REF], but again the payload is only 100 g and the control is not automated.

High-payload wall climbing robot designs are proposed in [START_REF] Wu | The mechanism design of a wheeled climbing welding robot with passing obstacles capability[END_REF][START_REF] Wu | Design and optimal research of a non-contact adjustable magnetic adhesion mechanism for a wallclimbing welding robot[END_REF]. Here the vehicle is equipped with wheels and an adjustable magnet allows the control of the adhesion force, allowing up to 50 kg payload. Although the robot is designed for welding, no performance analysis is done for the arm positioning and the control also seems to be manual. Another manually-driven robot is proposed in [START_REF] Bibuli | MARC: Magnetic autonomous robotic crawler development and exploitation in the MINOAS Project[END_REF], using magnetic tracks for hull inspection.

In our case, safety imposes that the robot should not fall even in the case of power failure. In addition, the robot is designed to work on ship hulls. That is why we chose magnetic tracks, even if as we will see it leads to constraints on the navigation part.

Most of the mentioned robots assume manual control. On the opposite, in [START_REF] Sánchez | Machine Vision Guidance System for a Modular Climbing Robot used in Shipbuilding[END_REF] a magnetic robot is proposed with a laser feedback for autonomous inspection. The design is similar to [START_REF] Wu | Design and optimal research of a non-contact adjustable magnetic adhesion mechanism for a wallclimbing welding robot[END_REF] with wheels and adjustable permanent magnets. The laser feed-back is similar to our case, though we need a higher accuracy due to the welding task. Navigation are usually not studied for tracked vehicles, as their autonomous modes are often used in large areas where GPS feedback is available and where rough accuracy is enough [START_REF] Schempf | Pandora: autonomous urban robotic reconnaissance system[END_REF]. Improving open-loop odometry relies on the study of the track-soil interaction [START_REF] Le | Estimation of track-soil interactions for autonomous tracked vehicles[END_REF] which may not be enough for welding.

In this work, we focus on the laser feedback from [START_REF] Hascoet | Shiphull Welding: Trajectory Generation Strategies Using a Retrofit Welding Robot[END_REF], that proposes several trajectories for the welding torch that can be computed from the welding joint 2D profile. This approach is classically used for non-mobile welding robots [START_REF] Landry | Collision-Free Path Planning of Welding Robots[END_REF][START_REF] Shah | A Review Paper on Vision Based Identification, Detection and Tracking of Weld Seams Path in Welding Robot Environment[END_REF] or robot that switch between motion and welding [START_REF] Ku | Design of controller for mobile robot in welding process of shipbuilding engineering[END_REF].

Compared to previous works, we propose a whole mechanical and control design for autonomous hull welding. The prototype has been tested on real ship hulls, which has revealed that the key feature is the autonomous line following in order to ensure accurate positioning of the welding torch.

We exploit the laser feedback needed for the welding, to perform joint tracking. For cost reasons only one laser scanner is considered on the robot. The contribution lies in the design, control law and state estimation of the mobile base. In Section 2 the general design and components of the robot are detailed. We also focus on the main mechanical challenge, that is the magnetic caterpillar. The control architecture is then presented in Section 3, with the design of an estimator and control law to perform welding joint tracking. Experiments on joint tracking and welding results are then presented in Section 4.

Overall design

In this section we detail the mechanical design and the components of the robot. The starting point for the sizing of the platform was the weight and payload that directly impact the number of magnets, hence the general size of the robot. The current form factor is 80 cm length and 50 cm width, with 100 kg total weight including the weight of the external cables (power supply and welding torch cable). The robot structure is composed of aluminum. A 600 W external power supply delivers 3x48 V. We first list the robot components before detailing the sizing of the magnetic track.

Components

The prototype is shown in Fig. 1. The laser scanner is hidden behind a protective layer. On the rear panel (bottom), the embedded controller is visible with its modules wired to the other pieces of hardware. At the center of the robot is the 2-degrees of freedom arm equipped with a functioning welding torch.

On-board computer

The controller is a National Instruments Com-pactRio. It features a dual-core 1.33 GHz Intel Atom processor running a Linux real-time kernel. The CompactRio also features a Xilinx FPGA. All of the code is developed in Labview. The real-time kernel hosts most of the programs, except for the arm low-level controllers that run on the FPGA and the user interface that runs on a remote computer.

Continuous tracks and motors

The continuous tracks and their magnetic pads have been sized to carry the robot and its payload. The sizing is detailed in Section 2.2. The track has 24 pads in contact with the surface with 25 kg grip per pad. This induces a smooth motion without discontinuities, hence no disturbances on the welding process. The pads are composed with neodymium iron boron magnets protected by aluminum boxes, and are linked with a stainless-steel chain.

Each track is associated with a Motor Power Company brushless servomotor with an input voltage of 48 V. A planetary gear with 1:195 ratio then delivers up to 42 Nm torque at velocities up to 6 m/min. The motor speed drives have been tuned with the final payload in working condition (robot on a vertical hull) and allow a fast response to the velocity setpoint of the two tracks.

Although some works address the issue of servomotor cooling in a harsh welding environment [START_REF] Lee | Development of modularized airtight controller for mobile welding robot working in harsh environments[END_REF], this was not considered in the current prototype.

2-degrees of freedom arm

At the center of the robot a 2-degrees of freedom arm is mounted in order to control the position of the welding torch. Each axis is driven with a 24 V DC motor. The motors are driven through Com-pactRio modules allowing real-time position control and feedback. A first calibration step was needed to register the axis with regards to the laser measurement.

Laser scanner

The embedded laser scanner is a MEL scanner, with a measuring range of 84 to 204 mm and a max-imum width of 80 mm, which corresponds to an angle opening of ±15 o . The resolution if 0.06 mm in range and 0.14 mm in X-direction. The laser connects to the CompactRio with Ethernet and sends the X-Z position of 290 points at 100 Hz. During the welding process, some outliers appear in the laser scan due to the welding light. They can easily be filtered out.

Proximity sensors

As the robot may be placed on unfinished hulls, it could fall by simply moving out of its own contact surface. Four Sharp Infrared proximity sensors have thus been mounted at each corner. The voltage output is acquired by an analog IO module on the CompactRio. Again, during the welding some irregular measures happen but they can be eliminated through a hysteresis filter.

Welding process

In the current state of the robot, the welding torch is directly connected to the welding ground station through a cable that provides shielding gas and wire. On longer distances, it will be necessary to mount a wire subfeeder on the robot.

Magnetic track sizing

The main challenge of the mechanical design is the sizing of the magnetic tread. Indeed, safety protocols in the considered industry force a magnetic device to have a grip equal to at least five times its weight. This is due to the possibility of slippery surface in case of humidity or even rain. The robot also has to be able not to fall even in one caterpillar is broken. The considered robot and payload weighting about 100 kg, this means that each caterpillar has to have an attractive force of at least 500 kg. This is where the magnetic track strongly differs from a classical tracked vehicle. Indeed, the attractive force caused by the magnets is far greater that the sole gravity in the case of a vehicle moving on a horizontal plane. Rotational motions, which are induced by having the two tracks running at different velocities, imply that some of the pads are actually slipping on the contact surface. In our case, first experiments have shown that rotational motions can be perfectly performed on the ground but as soon as the robot is on a magnetic surface the attractive force is such that the chain is subject to very high efforts. This induces deformations in the chain and can also make it jump off the gear. In order to cope with this phenomenon, a minimal radius of curvature is imposed during the motion. As we will see in Section 3.2, the use of magnetic tracks also induces uncertainties during autonomous control. This aspect will be detailed in Section 3.3.

Finally, the presented sizing induces no slippery motion on wet hulls. In practice, the welding torch is protected from the rain, and the welding process heats the hull and dries out its surface around the robot position. We now present the software and control architecture of the robot.

Control design

In this section the general control architecture is presented. We then focus on laser-guided line tracking and present an Extended Kalman Filter used to estimate the angle between the robot and the welding joint.

General control architecture

As in multi-process software, the Labview design paradigm allows the definition of several parallel loops that interact through shared variables. The designed software is composed of the following loops:

1. The state machine is the core of the control architecture. Depending on the state of the system (manual control, welding process...) it sends different setpoints to the track motors and the arm. The state machine also includes the security checks based on the proximity sensors in order to cancel any motion making the robot fall off the hull. 2. Two user-interface loops at 50 Hz: one for the inputs and one for the display. • Manual : both the mobile base and the arm are manually controlled. This is used to perform the approach of the welding joint and to calibrate the arm.

• Alignment: here the vehicle automatically aligns with the joint at a given forward velocity. No welding is performed, the goal is to be sure the vehicle is aligned.

• Welding: in this state the vehicle has a forward velocity coming from the welding control and the angular velocity is controlled to stay parallel to the joint. The arm follows a YZ trajectory that is defined for the welding process.

• Return: when a welding pass is over the robot goes backward to perform another pass. The arm is motionless while the vehicle follows the welding joint in reverse.

The sequence between the main steps is described in Fig. 2. A manual control is typically required to approach the joint and to move to the next one. Before actual welding, an automated alignment is performed.

We now detail the laser-guided joint tracking that is used during the Alignment, Welding and Return states.

Raw profile Edges Barycenter

Laser-guided navigation

Laser-guided navigation is used during the welding process in order to ensure the robot stays parallel to the welding joint. Only the angular velocity ω is controlled, as the linear velocity v is imposed by the welding process.

Other strategies for line tracking include vision [START_REF] Ismail | Visionbased system for line following mobile robot[END_REF] and trajectory tracking [START_REF] Gu | Receding horizon tracking control of wheeled mobile robots[END_REF]. In the considered environment, vision is however unreliable due to light conditions and poor contrast between the hull and the joint. Trajectory tracking requires a global frame where the robot and the trajectory are defined. Here the only exteroceptive sensor is a laser and cannot reconstruct the absolute pose.

Assuming the robot lies on the welding joint, the embedded laser provides the joint profile that can be processed to extract feature points. A typical profile is represented on Fig. 3 with a seam of approximately 3 cm width × 2 cm depth. The raw profile is first smoothed with a Savitzky-Golay filter [START_REF] Savitzky | Smoothing and Differentiation of Data by Simplified Least Squares Procedures[END_REF]. For joint tracking, we are interested in extracting the two edges of the hull parts that are to be welded (red diamonds on the figure). These edge points are found by an iterative robust line fitting algorithm [START_REF] Thiel | A rank-invariant method of linear and polynomial regression analysis, Part 3[END_REF] on the hull. Line fitting is performed on an increasing number of (leftmost or rightmost) points. When the last considered points are systematically outliers for several iterations then the edge point is assumed to be the last inlier. Other methods have been proposed to perform weld joint detection and tracking [START_REF] Dinham | Detection of fillet weld joints using an adaptive line growing algorithm for robotic arc welding[END_REF][START_REF] Shah | Butt welding joints recognition and location identification by using local thresholding[END_REF] Once the edges have been found, the barycenter of the joint is computed according to previous works [START_REF] Hascoet | Shiphull Welding: Trajectory Generation Strategies Using a Retrofit Welding Robot[END_REF]. Green's theorem is used to compute the position of the barycenter (green in Fig. 3). The barycenter is robust to small variations of the edges detection and is assumed to draw a straight line through the joint. Other features points, used to control the position of the welding torch, are also extracted but not displayed.

The general configuration of the robot with regards to the welding joint is shown in Fig. 4. The origin O denotes the instantaneous center of rotation. Unlike classical unicycle robots [START_REF] Consolini | Stabilization of a hierarchical formation of unicycle robots with velocity and curvature constraints[END_REF], the position of O is not known for tracked vehicles as angular motions imply that some pads are slipping while others are not. y is the distance between O and the joint, l is the distance between O and the laser beam and θ is the orientation. These values are linked through the equation:

d = y/ cos θ + l tan θ (1)
As ẏ = v sin θ, the derivative of ( 1) is:

ḋ = -yω sin θ/ cos 2 θ + v tan θ + l tan θ + lω(1+tan 2 θ) (2)
We now detail the continuous transfer function between the angular velocity ω and the distance d.

In the desired work configuration, the robot state is centered and aligned with the joint, and we can assume that for a particular configuration the force repartition on the tracks is stable and l does not vary. ( 2) is thus linearized around the position (y = 0, l = 0, θ 1):

ḋ = vθ + lw (3)
The Laplace transform of (3) yields:

sD(s) = v s Ω(s) + lΩ(s) (4)
where s is the Laplace variable. The corresponding transfer function H(s) = D(s)/Ω(s) yields:

H(s) = D(s) Ω(s) = v + sl s 2 (5)
The system thus has two very different behaviors, depending on the moving direction v.

Going forward

If vl ≥ 0 then the system is minimum-phase. This is the case during Welding state, as the laser scanner is located at the front of the robot (l > 0). A simple PID from d to ω is used to control the angular velocity, inducing high stability and precision as represented on Fig. 5. Even if the system has a double integrator, another Integrator term is needed in the controller. Indeed, the radii of the tracks may be subject to small changes because of surface defects or accumulated iron filings on the magnets.

Going backward

If vl < 0 then it is non minimum-phase. This is the case when the robot has finished a welding pass and is going back to the starting point. The previous controller cannot be used since regulating d to 0 does not induce staying parallel to the joint, as represented on Fig. 6. However, in this state we only aim at going back to the starting position while tracking coarsely the joint. Small variations on d are thus acceptable and we regulate an error composed of the distance d and an estimation of the angle θ:

e = d + αθ ( 6
)
where α is a tuning parameter. The corresponding transfer function yields:

H e (s) = E(s) Ω(s) = v + (l + α)s s 2 (7)
We see that even if v < 0, the zero of H e is at -v/(l+α) and can thus be made negative by choosing α < -l. Actually, if we simulate a virtual laser located at l in the rear direction then the output d of this laser would be:

d = d -(l + l ) tan θ (8)
This is equivalent to ( 6) for small values of θ. Ensuring α < -l in ( 6) amounts to having l > 0 in [START_REF] Park | Design of a mobile robot system for automatic integrity evaluation of large size reservoirs and pipelines in industrial fields[END_REF], which means the virtual laser is indeed at the rear of the robot. The forward control law can thus be used from this virtual laser, using a PID from e to ω.

In order to use this controller an estimation of θ is required. We now present the Extended Kalman Filter that is used to do so.

Kalman filter for angle estimation

While the laser scanner provides very accurate measurement of the joint profile, it lacks the orientation of the robot which is needed during backward motion. An Extended Kalman Filter (EKF) [START_REF] Julier | A new extension of the Kalman filter to nonlinear systems[END_REF] is thus used to estimate the missing value. The state of the robot is chosen as (v, ω, y, θ, l), and we assume no time variation for (v, ω, l) in the filter definition. The continuous formulation of the state transition model yields:

           v = 0 ω = 0 ẏ = v sin θ θ = ω l = 0 (9)
The available measurements are the position d of the joint barycenter in the laser beam and the encoders of the tracks which are considered in a differential drive model [START_REF] Consolini | Stabilization of a hierarchical formation of unicycle robots with velocity and curvature constraints[END_REF]. The observation model thus yields:

   ω l = 1 r v + b 2r ω ω r = -1 r v + b 2r ω d = y/ cos θ + l tan θ (10)
where (ω l , ω r ) are the angular velocities of the left and right motors, r is the track radius and b is the distance between the tracks. The last equation is the same as [START_REF] Silva | A survey of technologies for climbing robots adhesion to surfaces[END_REF]. A classical Extended Kalman Filter is then used with the models ( 9) and ( 10) in order to estimate the orientation that is needed during the backward motion.

Experiments

In this section we present some results on autonomous line tracking. We first describe the experimental setup before analyzing the results. Finally, the actual welding is briefly exposed.

Experimental setup

In order to have the ground truth for the angle α and the robot state, a second laser scanner is located at the end of the robot. It is not used in the control law or in the state estimation. As described in Fig. 2, the robot is first manually driven above the welding joint. The robot then goes through the following steps:

1. Alignment for 30 s 2. Return for 30 s 3. Welding for 60 s 4. Return for 60 s In the graphs, the state is indicated as FWD (Alignment / Welding) or BWD (Return). During the Alignment and Welding steps, the linear velocity is set to +0.02m/s and the steering is regulated through the PID described in Section 3.2.1. During the Return steps, the linear velocity is set to -0.04m/s and the steering is regulated through the PID described in Section 3.2.2. We are interested in the regulation of d and in the torch position error with regards to the joint. In practice, additional motion of the Y-Z axis is used to perform the welding but its performances mainly depend on the torch position error caused by an undesired angle between the robot and the joint.

Two robot designs are compared in order to detail the compromise that arise between the robot control and the torch positioning. The first one is shown in Fig. 7 and corresponds to the initial design of the prototype. The laser is at the center while the welding torch is at the rear of the mobile base. The idea was to reduce interactions between the welding process and the components of the robot (especially the laser and the computer). We will see that in this case the joint tracking is less satisfactory as a small angle induces a large lateral error on the welding torch. Besides, the welding process itself is not protected from external disturbances such as wind or a small rain. The final design hence corresponds to Fig. 1, with a laser at the front and a centered welding torch. It is the current design of the prototype and we will show it leads to a smaller position error for the torch.

Initial design: centered laser, torch at the rear

In the initial design, the laser scanner is located near the center (l = 0.15m) while the welding torch is at the rear of the robot.

Fig. 8a shows the estimation error of the distance to the welding joint y (blue), the robot orientation θ (green) and the distance l (red). The EKF presented in Section 3.3 allows a fine estimation of y and θ, but due to the large initialization error on l (0.05 m instead of 0.15) this value is not correctly estimated. As the value of l is quite small, it has few impact on the measurement model [START_REF] Wang | Large-payload climbing in complex vertical environments using thermoplastic adhesive bonds[END_REF] which makes it difficult to estimate.

This does not prevent the robot from following the joint: Fig. 8b shows that d (joint center in laser scan) quickly reaches 0 in Forward motion. Indeed we have shown in Section 3.2.1 that the steering is stable when going forward. The welding torch being at the rear of the robot, its distance to the joint is highly dependent on the robot angle. Hence, high oscillations can be observed during the first Forward motion.

After 30 s, the robot is in Return state. It is visible on Fig. 8b that the distance d is not regulated to 0 according to [START_REF] Berns | Climbing robots for commercial applications-a survey[END_REF]. The vehicle still roughly follows the line, despite the error on the estimation of l. We can see that the error on d increases while the torch position decreases. The vehicle thus roughly follows the joint and is able to get back to the starting position. After 60 s, the robot is in Welding state. As for the initial Alignment, because of the required steering to center the joint in the laser scan, the torch position error oscillates between -4 and +2 mm at the beginning of the welding. This would typically lead to a bad welding quality for the first centimeters. The position error then converges to 0 and the robot stays aligned during the end of the Welding and the tracking Return state. We now expose the results from the final design, that leads to a better torch position.

4.3.

Final design: centered welding torch, laser at the front In the final design, the laser scanner is located at the front of the robot (l = 0.50m) while the welding torch is near to the robot center.

Fig. 9a shows the estimation of the distance to the welding joint y (blue), the robot orientation θ (green) and the distance l (red). The larger value of l makes it easier to estimate, and the estimation error reduces over time. As with the previous design, the estimation of y is not perfect but this does not prevent the robot from tracking the joint. As shown in Section 3.2, having the laser at the front improves the gain of the system when going forward. Hence both d and the torch quickly align during the first Alignment step as seen in Fig. 9b. Again, during the Return step between 30 and 60 s, the regulated parameter is a compromise between d and the angle, hence a small offset appears while the torch stays at the same distance to the joint. After 60 s, the Welding step begins and d is very quickly regulated to 0. The torch starts with a position error of 0.6 mm that converges to 0 without oscillations. This is within the acceptable error of 1 mm for the first pass on ship hulls. Once the robot is aligned, the torch position error is almost null which results in a very accurate welding process. This continues when going backward after 120 s: both the laser and the torch stay centered above the joint. In this analysis only 60 s forward and backward are demonstrated as the robot always stays aligned with the joint after this typical time period.

Comparing the performances of two designs reveals that from an automation point of view, and as shown in Section 3.2, the initial design induces lower performances both in terms of state estimation (larger estimation error on l) and in process quality (non desired oscillations of the torch before stabilization). The necessity to be able to stay aligned during the Return phases is caused by the small torch offset that may happen at the beginning of a new welding pass. From a process point of view, having the torch at the center of the robot actually protects the weld from external disturbances. The main drawback of the final design is that the welding is performed near to several robot parts (magnetic pads, tread wheels). As far as experiments revealed, no negative impact on the robot struc- ture was observed after having welded several tens of meters. We now present some welding results.

Welding results

Welding is performed by defining a simultaneous control of the 2-degrees of freedom arm and the robot forward motion, in order to have a 3D trajectory of the welding torch. The trajectory generation depends on the welding configuration (vertical or horizontal) and uses feedback from the joint profile measured by the laser scanner. The actual trajectories of the welding torch are adapted from [START_REF] Hascoet | Shiphull Welding: Trajectory Generation Strategies Using a Retrofit Welding Robot[END_REF], they are not detailed due to confidentiality reasons. Depending on the configuration being vertical or horizontal, the trajectories may correspond to a constant forward velocity of the mobile base, or some motionless time periods. Similarly, the arm motion may be constantly oscillating between two values that are extracted from the joint profile, or stay motionless for some duration at particular positions. In order to ensure an optimal welding process, the vertical position control of the torch is regulated from the welding current feedback that varies positively with the distance between the torch and the weld point. In practice the vertical position update is computed through a PID provided by the torch manufacturer.

The resulting process for vertical configuration is shown in Fig. 10. An interesting feature of the robot is that the joint profile is entirely scanned at each pass. Therefore, the complete evolution of the profile can be analyzed for process quality and optimization. Fig. 10a shows the complete 3D raw profiles for 10 welding passes of 1 m length. As discussed in Section 2.1.4, laser scan aberrations are visible and are due to the welding light. They are only a few peaks and can be filtered out. The highlevel welding strategy is visible on Fig. 10b. Here 10 consecutive passes are represented using the mean profile along the joint. We can see that depending on the current profile, the welding strategy is to fill only a part of the joint. The final pass is wider and covers the whole joint, even if more material is dropped on the right part of the joint. This strategy is based on numerous exchanges and discussions with welders and previous work in [START_REF] Hascoet | Shiphull Welding: Trajectory Generation Strategies Using a Retrofit Welding Robot[END_REF]. The visual aspect of the final pass can be seen in Fig. 11. As expected, the weld is very regular along the joint, which helps the quality assessment of the process.

Conclusion

We have presented a new robot designed to perform varying tasks on ship hulls. The mechanical design was exposed and is still being adapted to the latest experimental results. Compared to previous works on wall-climbing robots, one objective of this project is also to prove that autonomous welding can be performed on long distances. To do so, a control law based on the laser feedback was designed and makes the robot autonomously weld several passes. As shown in Section 3.2, the performances in forward motion are accurate enough to track a reference trajectory. Another solution would be to use additional sensors but cameras are difficult to use during welding, and another laser scanner is not possible within the targeted price of the system. Future works on this project now focus on improving welding quality through better laser-based welding torch trajectories. To reduce the twist effort on the chain, using two chains per track is also investigated. Inspection of welding quality typically has to be done several hours after the welding [START_REF] Weman | Welding processes handbook[END_REF], hence another mobile base may be equipped with inspection sensors while the welding robot works on another part of the hull. Finally, a strong limit of the prototype is of course that it is suited only for straight joints. However, ship builders currently spend most of their welding time on these joints. The proposed approach hence allows to automate this task while human welders cope with much more complex configurations.

Figure 1 :

 1 Figure 1: Overview of the mobile robot in vertical position on a steel surface. Front support carry proximity sensors. The laser scanner is protected from external light. Embedded electronics are on the rear panel and dispatched around the CompactRio. The two external cables are the power supply (grey) and Ethernet connexion (blue). Welding is being performed at the center of the robot.

Figure 2 :

 2 Figure 2: Graph of a typical process. Manual control is required before and after the welding.

Figure 3 :

 3 Figure 3: Typical laser profile with the welding joint (Y and Z scales have been changed). Edges (red) are extracted, then the barycenter (green) is computed. Units and angles are intentionally not displayed.

Figure 4 :

 4 Figure 4: Schematic of the robot above the welding joint. The laser (red beam) measures the distance d. The angle θ and distance y between the origin O and the joint are unknown. The distance l between the laser and the origin is also unknown.

Figure 5 :

 5 Figure 5: Going forward. If d is regulated at 0 (joint in the middle of the laser beam) then the robot naturally aligns with the welding joint.

Figure 6 :

 6 Figure 6: Going backward. If is regulated at 0 (joint in the middle of the laser beam) then the robot naturally moves away from the joint.

Figure 7 :

 7 Figure7: Initial robot design. The laser is at the center, the welding torch is at the rear. Electronics integration was not done yet on this design.

y

  error [mm] θ error [rad] l error [mm](a) EKF error on y (blue), θ (green) and l (red).

  ] torch position error [mm](b) Position (in mm) of the welding joint in the laser beam (blue) and distance of the welding torch (green).

Figure 8 :

 8 Figure 8: Initial design. (a) Estimation error on (y, θ, l). (b) Joint center in the laser scan and torch position error.

  mm] θ error [rad] l error [mm] (a) EKF error on y (blue), θ (green) and l (red).

  ] torch position error [mm] (b) Position (in mm) of the welding joint in the laser beam (blue) and distance of the welding torch (green).

Figure 9 :

 9 Figure 9: Final design. (a) Estimation error on (y, θ, l). (b) Joint center in the laser scan and torch position error.

  (a) 3D profile of 1 m welding. Laser scan outliers are visible as peaks. The consecutive passes are shown in different colors. (b) History of 10 performed welding passes for a 2 m welding.The mean value of the profile is used, hence some registration aberrations. The last pass is about 3 cm wide.

Figure 10 :

 10 Figure 10: History of the joint profiles. Units and angles are intentionally not displayed due to confidentiality reasons. (a) 3D profiles. (b) Cross-section of average joint profiles for 10 consecutive passes.

Figure 11 :

 11 Figure 11: Visual result after the final pass.

Acknowledgements

This work was supported by the CHARMAN project managed by IRT Jules Verne (French Institute in Research and Technology in Advanced 10 Manufacturing Technologies for Composite, Metallic and Hybrid Structures). The authors wish to associate the industrial and academic partners of this project, respectively, IRT Jules Verne, STX France, DCNS, Servisoud, LS2N and Bureau Veritas.


Razan A Elkhatib

Marine Paci

Guy Longepied

Jacqueline Saias-Magnan

Blandine Courbiere

Marie-Roberte Guichaoua

Nicolas Levy

Catherine Metzler-Guillemain

Michael J Mitchell
email: michael.mitchell@univ-amu.fr

Homozygous deletion of SUN5 in three men with decapitated spermatozoa

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

Introduction

It has been estimated that 14% of couples fail to conceive a child naturally after one year, and compromised male fertility is identified in 60% of these couples [START_REF] Thonneau | Incidence and main causes of infertility in a resident population (1,850,000) of three French regions (1988-1989)[END_REF]. Assisted reproduction technology (ART) will provide a therapeutic solution for half of these couples, and today accounts for 1-3% of live births in developed countries [START_REF]European Society of Human Reproduction and Embryology[END_REF]. Despite this the genetic causes of male infertility and their pathophysiological consequences for gamete quality are known in few cases. The identification of genetic causes will be imperative for the development of personalised treatments, the extension of treatment to more infertile couples, and informed risk assessment for individuals conceived by ART and their descendants. Presently, the most solid progress has been made in uncovering the genetic basis of teratozoospermia.

Monomorphic teratozoospermia is a group of rare conditions in which almost all the spermatozoa share a specific malformation that renders them non-functional. Over the last decade, homozygous loss-of-function (LOF) mutations have been identified in four genes for three teratozoospermia phenotypes: AURKC (MIM 603495) in large-headed multiflagellar polyploid spermatozoa (MIM 243060) (3), DPY19L2 (MIM 613893) [START_REF] Koscinski | DPY19L2 deletion as a major cause of globozoospermia[END_REF][START_REF] Harbuz | A recurrent deletion of DPY19L2 causes infertility in man by blocking sperm head elongation and acrosome formation[END_REF] in globozoospermia (MIM 613958), SPATA16 (MIM 609856) [START_REF] Dam | Homozygous mutation in SPATA16 is associated with male infertility in human globozoospermia[END_REF] in globozoospermia (MIM 102530), and DNAH1 (MIM 603332) in MMAF (multiple morphological abnormalities of the flagellum) [START_REF] Ben Khelifa | Mutations in DNAH1, which encodes an inner arm heavy chain dynein, lead to male infertility from multiple morphological abnormalities of the sperm flagella[END_REF].

In a recent study of seventeen Chinese men with acephalic spermatozoa syndrome (MIM 617187), rare coding variants in the spermatid-specific SUN5 gene (MIM 613942) were found homozygous or compound heterozygous in eight cases [START_REF] Zhu | Biallelic SUN5 Mutations Cause Autosomal-Recessive Acephalic Spermatozoa Syndrome[END_REF]. In this syndrome the sperm head frequently separates from the flagellum, because the sperm head-tail junction is absent [START_REF] Perotti | Ultrastructural study of the decapitated sperm defect in an infertile man[END_REF][START_REF] Chemes | Acephalic spermatozoa and abnormal development of the head-neck attachment: a human syndrome of genetic origin[END_REF]. SUN5 is an attractive candidate for a gene involved in the attachment of the flagellum to the sperm head because the protein localises to the vicinity of the head-tail [START_REF] Zhu | Biallelic SUN5 Mutations Cause Autosomal-Recessive Acephalic Spermatozoa Syndrome[END_REF][START_REF] Yassine | Dynamics of Sun5 localization during spermatogenesis in wild type and Dpy19l2 knock-out mice indicates that Sun5 is not involved in acrosome attachment to the nuclear envelope[END_REF], and SUN family proteins are known to be part of links between the inner nuclear membrane and the cytoskeleton [START_REF] Tapley | Connecting the nucleus to the cytoskeleton by SUN-KASH bridges across the nuclear envelope[END_REF].

Six of the ten SUN5 variants reported are missense with an unknown effect on the protein. Although one variant, p.Gly114Arg (c.340G>A), was predicted to affect splicing, this was not confirmed experimentally. The other four variants were certainly LOF variants: intronic splice site (c.425+1G>A), frameshift (p.Val128Serfs*7) and two nonsense mutations (p.Trp72* and p.Ser284*). However, only one man was homozygous for a certain LOF variant, p.Ser284*, and the variants identified in SUN5 were designated as causal based on the high frequency of men carrying two rare SUN5 alleles in the study group. In three cases with a missense variant, the SUN5 protein was not detected at the head-tail junction, although it was present in spermatozoa from two control patients with acephalic spermatozoa syndrome but without a variant in SUN5 [START_REF] Zhu | Biallelic SUN5 Mutations Cause Autosomal-Recessive Acephalic Spermatozoa Syndrome[END_REF]. This is consistent with these missense variants being LOF, but the possibility that SUN5 absence is secondary to junction loss cannot be completely excluded, because the pathological mechanism in the acephalic controls is unknown, and could differ from that in the SUN5 variant cases. Furthermore six of the SUN5 variants are present at a low frequency in the gnomAD database [START_REF] Lek | Analysis of protein-coding genetic variation in 60,706 humans[END_REF], and four predominate in, or are exclusive to, the East Asian group, including the most frequent, the p.Val128Serfs*7 LOF variant, which is present in 1:140 individuals. Indeed overall in gnomAD approximately 1:100 individuals from the East Asian group are heterozygous for one of the rare variants designated as causal of acephalic spermatozoa syndrome. If all these variants were LOF, this sperm phenotype would be expected to have a high incidence among infertile men in East Asia, but this has never been reported, raising a doubt about the pathogenicity of some SUN5 missense variants.

Thus although the case is persuasive that the loss of SUN5 function causes a failure of the sperm head-tail junction, definitive proof requires a second case where two certain LOF

Results

Homozygous deletion of exon 8 of SUN5

Two brothers (13-1662 and 13-2016) and their cousin (13-4335) were diagnosed with acephalic spermatozoa syndrome following consultation for primary couple infertility at our clinic. They are members of a consanguineous family from Algeria (Fig. 1). We have previously described the phenotype of the two brothers [START_REF] Porcu | Pregnancies after ICSI using sperm with abnormal head-tail junction from two brothers: case report[END_REF], and clinical details for all three men are summarized in Table 1. Based on the recent report of the association of SUN5 variants with acephalic spermatozoa (8), we sequenced the coding region of SUN5 in two of the affected individuals (brother 13-1662 and cousin 13-4335) to determine if mutation in SUN5 was responsible for their infertility. We found no single nucleotide variants, but we failed to amplify exon 8. An additional marker tested in the middle of intron 8 was also negative in the three men, confirming the existence of a homozygous deletion in the three affected men (Fig. 2A). To further define the deletion, we amplified from upstream of exon 7 to downstream of exon 9 (7 kb) and upstream of exon 8 to downstream of exon 9 (6.2 kb) and obtained shorter than expected bands of, respectively, 2 kb (not shown) and 1.2 kb (Fig 2A). The 1.2 kb fragment was sequenced across the deletion junction revealing the deleted segment to cover 5090 bp that include the entirety of exon 8 (Fig. 2 and Fig. S1). There is an insertion of four bases, TGGT, at the junction of the breakpoint. There are no blocks of homology at the breakpoints, showing that the deletion was not mobilised by unequal crossing-over and is unlikely to be a recurrent event. The deletion of exon 8 is predicted to inactivate SUN5 as it will induce a frameshift, p.(Leu143Serfs*30), making the transcript lacking exon 8 a substrate for NMD (nonsense mediated decay). Using a PCR test that amplifies the deletion allele specifically, we did not detect the SUN5 deletion in 150 infertile men without the acephalic sperm phenotype recruited from our clinic, showing it to be a rare variant.

SUN5 exonic variant c.340G>A (p.Gly114Arg) inhibits splicing of exon 5

To further strengthen the evidence that loss of SUN5 function is a cause of acephalic spermatozoa syndrome, we investigated the effect of the c.340G>A (p.Gly114Arg) variant on splicing. This rare variant affects the last base of exon 5 and was previously reported in the homozygous state in a man with decapitated spermatozoa [START_REF] Zhu | Biallelic SUN5 Mutations Cause Autosomal-Recessive Acephalic Spermatozoa Syndrome[END_REF]. We made two expression constructs, one with the variant (SUN5-G114R) and the other without (SUN5-WT), in which the genomic region from exon 4 to exon 6 of SUN5 is under the transcriptional control of the CMV promoter. We transfected SUN5-G114R and SUN5-WT into HeLa cells and assessed the splicing of exons 4, 5 and 6 by RT-PCR with primers in exon 4 and in exon 6. Our results show that both constructs produce transcripts lacking exon 5, but that only SUN5-WT produces transcripts that include exon 5 (Fig 3). The transcripts without exon 5 from the SUN5-WT construct are certainly artefacts of our cellular assay system, since the same transcript was not amplified from human testis cDNA (Fig. 3). The absence of transcripts that include exon 5 from the SUN5-G114R construct shows that p.Gly114Arg greatly diminishes splicing efficiency, and will lead to exclusion of exon 5 (62 bases) from the mature SUN5 transcript. This will cause a frameshift preventing expression of the SUN5 protein.

Discussion

Here we present the second homozygous LOF mutation in SUN5, in three related men of North African origin who produce decapitated spermatozoa. We also provide functional evidence that the previously published variant p.Gly114Arg strongly affects splicing and will result in a reduction of SUN5 protein levels. Taken together with the recent report of biallelic The two brothers in our study have had treatment of their infertility by ICSI ( 14), using spermatozoa with the head and tail still joined. Despite the severity of the sperm phenotype in these men in whom the proximal centriole is not anchored to the nucleus ( 14), ICSI resulted in the development of good quality embryos in both cases. Following embryo transfer, pregnancy was obtained for each brother, resulting in the birth of a healthy girl and triplets, including identical twins. Thus for infertile men who produce decapitated spermatozoa consequent to the loss of SUN5 function, ICSI can be envisaged as a therapeutic solution. However a full evaluation of the efficiency of ICSI with SUN5-deficient spermatozoa, and the associated risks, will require additional cases and the long-term followup of the children conceived.

Several knockout mouse models share key features with acephalic spermatozoa syndrome in human: isolated male infertility, the presence of epididymal spermatozoa and an extreme fragility of the head-tail junction. The mouse genes inactivated in these models are Hook1 (15), Oaz3 (16), Odf1 (17) and Spata6 (18). Like SUN5, all are expressed specifically in the testis, and represent good candidates for genes that could carry causal mutations in unresolved cases of human male infertility with decapitated spermatozoa.

In conclusion, our results establish that SUN5 plays a key role in the attachment of the flagellum to the sperm head and demonstrate the prognostic value of testing for SUN5 mutations in men with decapitated spermatozoa seeking to father a child.

Materials and methods

F

Patients

Patient 13-4335 and his wife were 34 and 31 years old, respectively, and had been trying to have a child for three years without success. Both had a normal karyotype, the patient was 46,XY,21ps+ and his wife's was 46, XX. Both were healthy with no history of significant illness. His wife had regular menses, and her hysterosalpingography and hormone assessment was normal. Two semen samples from patient 13-4335, collected after three days of abstinence, were analysed and revealed severe oligozoospermia and complete teratozoospermia (Table 1). While there were only 2.3 x 10 6 intact spermatozoa/ml in each sample, there were, respectively, 41 x 10 6 and 64 x 10 6 headless flagella/ml. These characteristics are similar to those of his cousins (13-1662 and 13-2016) and are typical of acephalic spermatozoa syndrome.

The clinical details of the two brothers, 13-1662 et 13-2016, have been described elsewhere [START_REF] Porcu | Pregnancies after ICSI using sperm with abnormal head-tail junction from two brothers: case report[END_REF], but are summarised for comparison with those of 13-4335 in Table 1. The three men gave their informed consent for their samples to be used in the search for the genetic cause of their infertility.

SUN5 sequencing and deletion interval characterisation

The SUN5 gene was sequenced with the BigDye v1.1 terminator cycle sequencing kit (Applied Biosystems) following amplification of the coding exons with specific flanking primers (Table S1). PCR products for sequencing SUN5 and deletion mapping were amplified with Q5 Taq polymerase (New England Biolabs). The allele-specific PCR assay used to screen controls was performed as a duplex PCR with primers o5226 and o5340 that flank the deleted segment around exon 8 (deletion allele = 490 base pairs) and o5216 and o5217 that flank exons 2 and 3 of SUN5 (positive control = 802 bp). The segment from exon 4 to exon 6 of the SUN5 gene was amplified from genomic DNA from a fertile man using primers o5336 (exon 4) and o5337 (exon 6) with, respectively NheI and XhoI sites added at their 5 ́ end. To obtain the SUN5-WT construct the genomic fragment was cloned into the NheI and XhoI sites of pcDNA3.1-cGFP (pcDNA3.1+ -Life Technologies -with a C-terminal GFP cassette cloned between XhoI and ApaI sites). For the SUN5-G114R construct, a SUN5-G114R insert was cloned into pcDNA3.1-cGFP, following its amplification from SUN5-WT using two-step PCR-fusion with flanking primers o5336 and o5337 and central primers o5338 and o5339 specifying the p.Gly114Arg variant. The sequence of both constructs was determined and showed that neither contained any errors. All PCR steps used the Q5 DNA polymerase (New England Biolabs).

Transfection of SUN5-WT and SUN5-G114R constructs

Constructs were transfected into HeLa cells using JetPrime and the RNAs harvested after 24 h. RNA was reverse-transcribed to cDNA using the GFP-specific primer o5374 and the PCR performed using primers o5336 and o5337. 16. Tokuhiro, K., Isotani, A., Yokota, S., Yano, Y., Oshio, S., Hirose, M., Wada, M., Fujita, K., Ogawa, Y., Okabe, M., et al. (2009) OAZ-t/OAZ3 is essential for rigid connection of sperm tails to heads in mouse. PLoS Genet., 5, e1000712. 17. Yang, K., Meinhardt, A., Zhang, B., Grzmil, P., Adham, I.M. and Hoyer-Fender, S.

(2012) The small heat shock protein ODF1/HSPB10 is essential for tight linkage of sperm head to tail and male fertility in mice. Mol. Cell. Biol., 32, 216-225.

18. Yuan, S., Stratton, C.J., Bao, J., Zheng, H., Bhetwal, B.P., Yanagimachi, R. and Yan, W.

(2015) Spata6 is required for normal assembly of the sperm connecting piece and tight headtail conjunction. Proc. Natl. Acad. Sci. U. S. A., 112, E430-439.

Figure Titles and Legends

5 SUN5

 5 alleles are found associated with acephalic spermatozoa syndrome. We therefore searched for pathogenic variants in the SUN5 gene in three men with decapitated spermatozoa.

7 SUN5

 7 variants in Chinese men with acephalic sperm syndrome, our findings establish that loss of SUN5 function causes a failure of head-tail junction formation during spermiogenesis, and show that LOF mutation of SUN5 is also a cause of acephalic sperm phenotype in non-Asian populations.

  shape (azh) mutant mouse. Hum. Mol. Genet., 11, 1647-1658.

Figure 1 :

 1 Figure 1: Family pedigree of the three infertile men presenting with acephalic spermatozoa

Figure 2 :

 2 Figure 2: Homozygous deletion of exon 8 from SUN5 in two brothers and a cousin with

Figure 3 :

 3 Figure 3: The SUN5 p.Gly114Arg variant reduces splicing efficiency in HeLa cells.

Figure S1 :

 S1 Figure S1: Schematic representation of the SUN5 deletion with the electropherogram of

1 PCRFigure 1 :Figure 2 :Figure 3 :

 1123 Figure 1: Family pedigree of the three infertile men presenting with acephalic spermatozoa syndrome (MIM: 617187). Infertile men: 1) 13-1662; 2) 13-2016; 3) 13-4335. 109x73mm (300 x 300 DPI)

Table 1 :

 1 Sperm parameters of the three related infertile men carrying a homozygous deletion within the SUN5 gene.

.G114R expression construct and reverse transcription (RT) primers

	Primer Number	Target	Primer sequence
		SUN5 gene sequencing
	o5214	Exon 1f	CACCAGCTCCCAGAGTTCC
	o5215	Exon 1r	GTGCCCAGAGATAGGCATCA
	o5216	Exon 2-3f	GGTAGGAGCAGACAAAGGAAC
	o5217	Exon 2-3r	CCTCTCCAGCCACCAAAGA
	o5218	Exon 4f	GTGCCCACCTGTAGTGACCT
	o5219	Exon 4r	GTGCAAATGTGTGGAGGTGG
	o5220	Exon 5f	CTGCCACTCATGAGCTCCCA
	o5221	Exon 5r	GGAGGGAGGAGATTCCTTTG
	o5222	Exon 6f	CAGCCAGCATGGAGATCATA
	o5223	Exon 6r	TCTGGGTCAAGTCAGGAACTG
	o5224	Exon 7f	GAGATAGGAGGCTAGGTGAA
	o5225	Exon 7r	TGACTTGCCTAAGGTCAGCC
	o5226	Exon 8f	CTGATGAATGGGTCCAGGGATG
	o5227	Exon 8r	CCCAGCCAAACTATCAACCA
	o5228	Exon 9f	GGTCAGGATGGCTTTGTGTC
	o5229	Exon 9r	CCAGCTTGGAACAGAGGATG
	o5230	Exon 10f	ACATGGCTGTAATCACATTTCC
	o5231	Exon 10r	GCCCATGCCCAGTGTAGTC
	o5232	Exon 11f	GAACTGAAGTAAGGCTTACC
	o5233	Exon 11r	GCCTTAACCAAGCTGCATTC
	o5234	Exon 12f	CTCTGACTCCCAGGCCAGTG
	o5235	Exon 12r	GCCACCCACTACGGAGACTCT
	o5236	Exon 13f	GTCTGTCCTTCTGGCCTCAG
	o5237	Exon 13r	ACCCGGAATGGGATCAACC
		SUN5 deletion mapping
	o5326	Intron 8f	GGAGTCCCCAGGGAATATGCAG
	o5327	Intorn 8r	GTGGCCCTGGAGACAGCAG
	o5340	Intron 8r	TCTGCTGCCAGATGCGGATG
	po5336	Exon 4f_NheI	AGCCTGGCTAGCATGGCCTGGTTCACCTGTTTTGCCTG
	o5337	Exon 6r_XhoI	AGGTTCCTCGAGCTGCCAGACTTTCATTTTCGATGGTAAG
	o5338	Exon 5f_G>A to add p.G114R variant	CTGTGCTTTCAGTGTGTCCCAG
	o5339	Exon 5r_C>T to add p.G114R variant	CTGGGACACACTGAAAGCACAG
	o5374	eGFPr_RT	TGGTGCAGATGAACTTCA

Table S1 :

 S1 Primer sequences.

Acknowledgements

We are grateful to the patients who gave their informed consent to the use of their samples for research. We thank C. Metton and M.J. Fays-Bernardin for technical assistance and Germetheque support. This work was supported by grants from the Agence de la biomédecine, AOR "AMP, diagnostic prenatal et diagnostic génétique" 2013, Inserm and Aix-Marseille Université. The Germetheque biobank was supported by grants from the ANR (Agence Nationale de la Recherche), the Agence de la biomedicine, the Centre Hospitalier Universitaire of Toulouse and APHM (Assistance Publique Hôpitaux de Marseille). RAE was funded by the Islamic Development Bank and the Lebanese Association for Scientific Research (LASeR).

Conflict of interest