Clément Robert

Jérémie Guiochet

Hélène Waeselynck

Testing a non-deterministic robot in simulation -How many repeated runs ?

Keywords: testing, autonomous robot, non-determinism, simulation, Software in the Loop (SiL) testing

Testing a non-deterministic robot in simulation -How many repeated runs ?

I. INTRODUCTION

The validation of an autonomous robot involves missionlevel tests, which may be done partly in the real world (field testing) and partly in virtual worlds (simulation-based testing). In both cases, a concern is that the robot may not behave in the same way for repeated runs with similar environmental conditions. It is true for field tests, not only because reproducing identical inputs and environmental conditions is actually impossible, but also because the system itself is non-deterministic. The embedded software typically has concurrent threads and processes executed at the same time, yielding execution non-determinism. This execution nondeterminism is still observed in simulation where identical test environments can be fully reproduced. It may have a strong impact on the Pass/Fail outcome of a test case. Indeed, a single pass observation does not mean that the test case would pass for all possible executions. Now, suppose we adopt a repeated execution scheme where each test case is run n times. On the one hand, the repetition allows us to possibly catch nondeterministic fails of the test case, that did not occur at the first time. But on the other hand, we may end up spending the test budget on the repetition of tests that pass n times, instead of exploring new tests that would fail. This raises the question of how many runs of a test case are to be performed to efficiently uncover faults.

We address this question in the context of simulation-based testing, where at least identical environment conditions can be repeated for several runs. We consider an industrial case study, an agricultural robot for autonomous weeding. The system is tested in a Software-in-the loop (SIL) configuration, that is, the real software is immersed in virtual worlds and challenged with missions. The test platform includes several components that we developed around the robot simulator: a world generation facility, a test harness to automatically run many simulations (400 in this case study) and a test oracle (i.e., the mechanism determining the Pass/Fail verdict) that checks a set of expected properties. We use this platform to empirically study the variability of the test verdicts over repeated runs, and to experiment with various repeated execution schemes.

The structure of the paper is as follows. Section II discusses related work. Section III introduces the case study: the Oz robot. The experimental design is detailed in Section IV. Section V presents the results and Section VI concludes.

II. RELATED WORK

A deterministic program produces identical output sequences when fed with identical input sequences. This is the case for purely sequential program. In autonomous robotics, the embedded software is generally composed of multiple threads to interact with the environment, one for each subsystem. In such systems, inter-process communication is generally done at best effort, resulting in an asynchronous and nondeterministic behavior. For example, ROS [START_REF] Quigley | Ros: an open-source robot operating system[END_REF], a robotic middleware widely use in the research community, provides basic services for the development of robot software. It relies on a concurrent programming paradigm where nodes encapsulate subsystems. Moreover, the decisional functions of autonomous systems commonly use algorithms involving randomness (e.g. heuristics for mission or trajectory planning), which also induces non-determinism [START_REF] Ingrand | Deliberation for autonomous robots: A survey[END_REF].

The problem of non-determinism is particularly relevant in the context of testing cyber physical system in simulation. The use of simulation for testing is increasing, mainly pushed by automotive, with high fidelity simulators (e.g., see [26], [16] or [15]). They provide specific virtual road environments and have proved useful to thoroughly test advanced driver assistance systems in simulation (see e.g., [START_REF] Ben Abdessalem | Testing advanced driver assistance systems using multi-objective search and neural networks[END_REF], [START_REF] Li | Ontologybased test generation for automated and autonomous driving functions[END_REF]). On the other hand, robots are as various as their applications and each simulator must be built specifically. They are developed based on simulation platforms that are more generic (Gazebo [START_REF] Koenig | Design and use paradigms for gazebo, an open-source multi-robot simulator[END_REF], MORSE [START_REF] Echeverria | Modular open robots simulation engine: Morse[END_REF]) or also based on game engines like Unreal [25] and Unity [24]. Such simulators can then be used for testing the robots, like a rough terrain robot tested in [START_REF] Thierry Sotiropoulos | Virtual worlds for testing robot navigation: a study on the difficulty level[END_REF], a rescue robot in [START_REF] Andrews | World model for testing urban search and rescue (usar) robots using petri nets[END_REF] or a drone in [START_REF] Mikael | Metamorphic model-based testing of autonomous systems[END_REF]. Generally speaking, a simulator may be a complex system in its own right, introducing extra sources of non-determinism into the simulation: concurrency of the simulator's components, use of remote services, time (few things are more non-deterministic than a call to the system clock), resource leaks, etc.

With the non-determinism effect -from the tested system and possibly also the simulator -come difficulties for testing. Automatic testing relies on a component that can determine if a test has passed or failed: the oracle. Testing autonomous systems is already hard as there is no ground truth about the decisions to take during a mission. For example, as noted by Tain and al. [START_REF] Tian | Deeptest: Automated testing of deep-neural-networkdriven autonomous cars[END_REF], specifying what an autonomous driving system should do would essentially involve recreating the logic of a human driver. Adding non-determinism makes the task even harder as the same test inputs can yield different oracle verdicts: test failures are seemingly random. The nondeterminism results in two main issues for testing: the confidence in the oracle verdict to draw conclusions, and the ability to reproduce the fail for diagnosis purposes (the same sequence of events that previously led to a failure may not fail again). In this paper, we focus on the first issue and study the variability of the verdict for different runs of the same test case.

To remove or reduce non-determinism inside a system, an idea is to come back to a temporal deterministic approach, using for instance synchronous programming languages like Esterel [START_REF] Berry | The esterel synchronous programming language: Design, semantics, implementation[END_REF], Signal [START_REF] Benveniste | Synchronous programming with events and relations: the signal language and its semantics[END_REF] or Lustre [START_REF] Halbwachs | The synchronous data flow programming language lustre[END_REF]. Examples of application of synchronous languages to robotic architectures can be found in [START_REF] Borrelly | The orccad architecture[END_REF] and [START_REF] Marchand | Specifying and verifying active vision-based robotic systems with the signal environment[END_REF]. Esterel (version 3) translates concurrent program into equivalent sequential automata. It relies on the synchrony hypothesis that states that every process is instantaneous (the system and the environment states do not change during a process execution). However, some systems cannot be split into atomic processes, thus cannot be fully converted to a deterministic version. Autonomous systems typically involve asynchronous communication between subprocesses on several processors. Such is the case for the weeding robot studied in this paper.

From the simulation side, Reymann and al. [START_REF] Reymann | Repeatable decentralized simulations for cyberphysical systems[END_REF] propose a framework to manage non-determinism induced by time in distributed simulations. The approach consists in adding a software layer that synchronizes the system by handling the interprocess communication by batch. To ensure that there is no deadlock, the method assumes that there is an upper bound time value for subprocesses to produce a message. This method is well-suited to make multiple simulators run together deterministically, but does not address the non-determinism inside each simulator or system.

Previous solutions focus on removing or reducing the nondeterminism. Another approach for testing is to take the non-determinism explicitly into account. A classical way is to repeat the same test several times in order to obtain various execution patterns. The authors of [START_REF] Thierry Sotiropoulos | Virtual worlds for testing robot navigation: a study on the difficulty level[END_REF] used repeated runs to evaluate the difficulty level of generated navigation missions for Mana, an outdoor robot developed at LAAS-CNRS. They also studied the impact of non-determinism by comparing the trajectories of the robot for a given mission in the same 3D environment. The differences in the trajectories were significant among the runs, in the order of meters or even tens of meters. The authors of [START_REF] Cu | Evolutionary testing of autonomous software agents[END_REF] used repeated runs for the evolutionary testing of an autonomous cleaner agent. The evolutionary approach requires a fitness function that quantifies the adequacy of the generated test and guides the generation of new ones. The evaluation of the fitness function required multiple executions of the test cases to take the non-determinism into account. A statistical analysis showed that, for that specific case study, 5 executions were enough to represent the overall result and for the fitness function to converge.

Repeating the same test case may be necessary but is time consuming. For a given test budget, it forces us to reduce the number of test cases executed and therefore it reduces the exploration of diverse test cases. In this paper we provide a statistical study of repetition versus exploration to uncover faults.

III. CASE STUDY: THE OZ ROBOT

Oz is an autonomous robot developed by Naïo Technologies [13] and aims at weeding crop fields (Figure 1). The base frame is mounted over 2 by 2 powered wheels for tank like movement. It is considered harmless for humans as it is small (75 cm × 45 cm × 55 cm) and it can only reach a maximum speed of 0.4 m.s -1 . On the other hand, it can still be dangerous: a faulty navigation function could send the robot outside of the crop field (e.g., a road). Moreover, it would be highly undesirable that the robot causes damages to the crop plants. To complete its mission, the robot must navigate inside the field while pulling a specific weeding tool. A field is usually composed of multiple rows of vegetables. Thus, Oz has to make U-turns when it exits a row and needs to enter the next one. When the row interspace is considered too large by the robot, it can decide that it needs a second pass to complete the weeding. Figure 2 shows an example of a mission where the robot performs one pass in the first interrow and two in the second one.

A mission can be divided in 4 main phases that loops until the mission is completed (See the letters on Figure 2): The Approach (entering an interrow), the Cruise (while between two rows), the Exit (exiting an interrow) and the U-turn. To help the robot performing its mission, landmarks (red stakes) must be placed at the extremities of each row. Oz is equipped with several sensors that are used according to the mission phase: a laser sensor (LiDAR 2D) and two cameras at the front. It also has contact sensors around it to trigger an emergency stop in the event of collision. The software version presented in this paper is an R&D version that only uses the laser data to perform the line tracking during the cruise phase. Cameras are used to detect the red stakes, evaluate the robot position in the field and consequently trigger a change in the mission phase. The cameras also allow the use of stereo visual odometry techniques to detect possible skidding of the robot during Uturns.

IV. EXPERIMENTAL DESIGN

Naïo developed a SiL simulation platform for testing the navigation of Oz. Naïo engineers and us have independently tested an R&D version of the Oz controller (respectively by field tests and by virtual tests) to compare the results and evaluate the potential of simulation-based testing. In another paper [START_REF] Robert | The virtual lands of oz: testing an agribot in simulation[END_REF], we showed that many of the software issues found by field tests are also found in simulation. The virtual tests even found a new issue that was missed in the field. This paper uses the same test platform and the same virtual tests as in [START_REF] Robert | The virtual lands of oz: testing an agribot in simulation[END_REF], but for a different purpose: here, our aim is to study the impact of the non-determinism on the test results. We first present the research questions addressed by our study and then the test platform used by our experiments.

A. Research questions

The first question concerns the variability of test verdicts in repeated runs of the same test case:

RQ1 -Does non-determinism strongly impact the verdict assigned to a given test case?

Note that it could be the case that the detailed behavior is not exactly the same, but the overall Pass/Fail verdict is consistent from one run to the next. To study this question, we consider 5 repeated runs of a sample of 80 test cases, and measure the proportion of test cases that inconsistently fail. Five runs correspond to the number of repetitions considered in the original experiment [START_REF] Robert | The virtual lands of oz: testing an agribot in simulation[END_REF], mainly for pragmatic reasons: each run takes minutes, and it seemed reasonable to keep the total test time (80x5 runs) around 24H. For our study of nondeterminism, we chose not to increase the number of runs. We focus on large variation effects that make the inconsistent verdicts likely, rather than on small effects that could surface only after an unrealistically high number of repetitions.

The second question concerns the trade-off between the exploration of new test cases and the repetition of previous ones.

RQ2 -Repetition vs exploration: For this case study, what would be the most efficient repetition strategy in order to reveal the faults?

Overall, the original test campaign with 5 repeated runs proved effective at revealing faults, but it is not clear what the added value of the repetitions was. From the complete set of runs, we extract different subsets to mimic strategies with a lower number of repetitions per test case. Then, we assess their efficiency by considering both the first detection and the relative frequency distribution of the various failure types. The first detection allows us to determine how repetition speeds

B. Experimental platform

The test platform used for our experiments is described in Figure 3. It is composed of four components: The System Under Test (SUT), the robot simulator, the test case generator, and the analyzer.

1) The system under test. The SUT, i.e., the Oz Controller developed by Naïo Technologies, is written in C and C++ for total of about 151 KLOC. It consists of sensors processing modules, the navigation module and several mission-specific modules. The navigation module needs some information about the crop field and the robot initial position (e.g., the number of rows, the direction of the first U-turn) that are contained in the mission file (a json file). The Oz controller receives simulated sensors data and produces speed commands sent to the simulator.

2) The robot simulator. The simulator is based on Gazebo, a simulator widely used in robotics research [START_REF] Koenig | Design and use paradigms for gazebo, an open-source multi-robot simulator[END_REF]. It has been developed by Naïo.

To instantiate a virtual world, the simulator requires two main files: a heightmap which is a grayscale jpg file that encodes the terrain elevation and a world file (sdf) that describes the initial position of every object in the scene. Figure 2 illustrates a simulated environment. Note that the virtual crop field is quite small compared to the real-world ones. The virtual example only contains 3 crop rows of 15 meters whereas a real world field can contain tens of rows that can be as long as a hundred meters. The difference is due to simulation performance and time constraints. Although the simulation seems simple, it is very demanding regarding resources. Our available computing power resources consists of: a PC with 2 Quad core Intel Xeon E5-2623 v3s CPU at 3.5GHz, and 64GB of RAM. To ensure tractability of the simulation, we choose to restrict the size of the generated crop field. The second issue is the testing time: the robot is pretty slow and there is no way to accelerate the simulation. Hence, each individual run can take minutes.

As an example, a run on the virtual field in Figure 2 takes about five minutes. Even if it was possible in term of resources, running a test campaign on a real-worldsized crop field would obviously be too time consuming. Despite these constraints, the Gazebo-based simulation proved effective to reveal issues in the Oz Controller [START_REF] Robert | The virtual lands of oz: testing an agribot in simulation[END_REF].

3) The test case generator. A test case consists of a mission in a virtual crop field. As already explained, the mission file is read by the SUT, while the crop field description (the virtual world) is fed into the simulator.

To automatically produce a sample of diverse test cases, we implemented techniques derived from the Procedural Content Generation of worlds [START_REF] Togelius | Search-based procedural content generation: A taxonomy and survey[END_REF]. These techniques aim to create randomly generated environments within a structured framework. The basic idea of PCG is to use a set of high-level parameters to control the production of concrete world contents. In our test input generator, the generation parameters are modelled using Object Oriented Modeling. Then, a Python script randomly generates the mission and world files in the appropriate formats. For that particular case study, 15 high level parameters have been chosen to model a test case. The main generation parameters are the number of rows, the type of vegetable, the space between the rows, the space between the vegetables, the magnitude and the granularity of the ground deformation. In the other paper on the same case study [START_REF] Robert | The virtual lands of oz: testing an agribot in simulation[END_REF], we provide a fully detailed explanation on the generation process. While the random generation gives no guarantee on the input domain coverage, we performed a check (a posteriori) of the sample of 80 generated test cases. It shows that every generation parameter is well covered by considering a subdivision of its range in tenth. That is to say, for a parameter p ∈ [0; 1], there is at least one test case with p ∈ [0; 0.1], one with p ∈ [0.1; 0.2], etc. 4) The analyzer. The analyzer is in charge of reading the traces of the run tests, and of checking for unwanted behavior of the robot (test oracle). This component is really important because it produces the failure reports that are being used to study the non-determinism in this paper.

Table I displays the timestamped data collected by the test platform and made available for the implementation of the checks. The robot logs are collected at the SUT interface. They include error or success reports sent by the robot at the end of a run, as well as some debug data logged by the robot during cruise phases (perceived position and yaw). The simulator logs provide the actual -rather than perceived -positions and attitudes of the robot during the run. The Pass or Fail verdict is based on a set of required properties. If at least one property failed, the entire run is considered failed. Initially, there was no specification from which to extract possible failure definitions for Oz. The list of properties had to be specified from scratch in collaboration with Naïo engineers. To do so, we followed guidelines from our previous work [START_REF] Sotiropoulos | Can robot navigation bugs be found in simulation? an exploratory study[END_REF], and structured the discussion of candidate properties according to five broad categories of requirements: II. Further discussion with Naïo engineers led us to consider P2 and P7 as performance-related properties that should not yield a Fail verdict. Moreover, preliminary runs to debug the checks revealed many transient violations of property P4. Looking further into the matter, we could determine that this was an artifact of the simulation. Indeed, the simulation ignores the engine braking force and overestimates velocity on downward slopes arising from terrain irregularities. We decided to de-activate the P4 check to get rid of the spurious violations. The results presented in the next section are thus for test verdicts based on P1, P3, P5, P6 and P8.

V. EXPERIMENTAL RESULTS

In this section, we present the results of the tests in simulation and we particularly focus on the impact of non-

A. Variability of the test verdicts (RQ1)

The generated set of tests consists of 80 different virtual crop fields along with their corresponding weeding mission. we perform 5 runs in each case to study the non-determinism of test executions.

Our generated environments are very stressful for the robot as a Fail verdict occurs in as much as 48% of the runs (192/400). All properties but P1 (U-turn in 5-7 maneuvers) are violated by some runs. Table III provide the number of failing runs per property. Since a run may violate several properties, the counts sum up to a number greater than 192. Figure 4 illustrates a run with multiple failures. Its view uses a test data visualization facility we developed. The robot (shown as a black rectangle) starts at the external side of the field, along a row of leeks represented by green circles. While performing the U-turn at the end of the row, the robot crosses the specified limits of the field (indicated by a dotted line) and later collides with the red stake and three leeks. This run counts for both P6 (outside of the crop field) and P5 (collision). Overall, collision is the most frequent failure type, observed in 35.5% of the test runs.

Table IV gives the proportion of test cases yielding from n = 0 to 5 Fail verdicts over the five repeated runs (A verdict is "fail" when at least one property Px is violated). A test case has an inconsistent outcome when at least one (but not all) of its repeated runs fails, i.e., n ∈ {1, 2, 3, 4}. About 29% of the test cases (23 out of 80) have such an inconsistent verdict from one run to the other. Moreover, consistent Fail verdicts (n = 5) do not mean that the detected misbehavior is the same across all runs. Figure 5 illustrates different failures of the same test case. The upper part of the figure visualizes the run also presented in Figure 4. As previously discussed, the first U-turn yields multiple failures: one of the maneuvers exceeds the limits of the field, and there are collisions upon entering the next crop row. The rest of the mission is correctly performed. The bottom part of Figure 5 visualizes another run of exactly the same test case. This time, a new failure adds up to the others: the U-turn misses the next row and goes back to an already weeded area (P3 violation). At the end of its return trip, the robot detects that something is wrong and stops with an error report. Clearly, those two runs in Figure 5 consistently yield a Fail verdict but have different property violation patterns.

We thus conclude that the non-determinism strongly impacts the verdict assigned to a given test case.

B. Repetition vs exploration (RQ2) ?

Performing repeated runs is a classical way to account for execution non-determinism. Since each run takes minutes, one may wonder whether less than five repetitions would penalize effectiveness. Starting from the complete set of runs X 80,5 (80 test cases x 5 repeated runs) that are already performed and analyzed, we randomly extract subsets X t,r to mimic strategies with t test cases repeated r times, t ∈ {1, 80} and r ∈ {1, 5}.

We first study the test size to observe at least one failure of each type (P 3 , P 5 , P 6 , P 8 ). Suppose we consider r runs per test case. We keep adding new test cases, and select a subset Fig. 5. Two different results with the same test case. On the top: Oz weeds the correct rows but has collisions (P5) and exceeds the field limits (P6). On the bottom: a wrong row failure (P3) adds up to the previous ones. Fig. 6. Total number of runs to observe every failure type (P 3 P 5 P 6 P 8 ) at least once of r runs for each of them, until we observe every property violation. The size of the test, i.e., the total number of runs, is then r * t. We evaluate the test size 10,000 times for statistical analysis. Figure 6 shows the result of this process in a boxplot. With only one run per test case (the first box), the median test size to violate every property is 20 (r=1; t=20). The size is more than twice as much (46) if we choose 5 runs per test case. It thus seems more efficient to favor test case diversity over execution repetition: strategies with r > 1 slow down the detection of new failure types.

We further study the relative frequencies of property violations. Figure 7 displays the histogram for the set X 80,5 . The data is split into 16 bins, corresponding to every potential combination of property violation: Bin 0 gives the percentage of runs violating no property, and Bin 15 is for runs violating all four properties. We use this histogram X 80,5 as a reference to study the impact of varying t and r. Dissimilarity between X 80,5 and a set X t,r is measured by calculating the Manhattan distance of the bin frequencies: where the 0.5 coefficient rescales the distance to [0, 1], 15 is the maximum index of bins, f Ref i is the relative frequency of bin i of X 80,5 (e.g., f Ref 0 = 52% in Figure 7), and f i is the relative frequency of bin i of the considered set X r,t . The objective is to assess for each subset the distance D to the reference X 80,5 in order to identify which X r,t are sufficient to obtain a close distribution with the reference. For each pair (t, r), we randomly select 10,000 sets X r,t and measure their distance to the reference (X 80,5 ). When there are less than 10,000 possibilities, as is the case for small values of t and r, we exhaustively consider all the possible sets. Figure 8 shows the median distances we obtain. Their value is encoded by a tone on a linear grey scale, where white means D = 0 and black means D = 1. As can be seen for all repeated runs schemes, the distributions of failure violation patterns tend to become more similar as the number of test cases grows (the tone becomes lighter). An interesting point is to look at isodistances. For instance, the red lines in Figure 8 delimitates the (t, r) configurations with a distance D = 0.1 to the reference. One can observe that it takes much longer to get within this distance with repeated runs than without: the total test size ranges from 56 runs up to 165 (5 * 33). Intuitively, it suggests that the final frequencies observed for X 80,5 are more determined by the diverse test cases we execute than by the repeated execution of each test case.

D = 0.5 * 15 i=0 |f i -f Ref i |,
We thus come to a surprising conclusion. Although execution non-determinism has a high impact on the test verdict, it seems cost-effective to simply ignore the issue and use the test budget to execute diverse test cases without repetition. Costeffectiveness is of course relative to the specific case study. More generally, an interesting outcome is to demonstrate that repeated runs are not always the best approach to test nondeterministic applications.

VI. CONCLUSION

The validation of autonomous robots strongly depends on the resources available to perform test campaigns. Through simulation, more intensive campaign can be carried out but the result of tests may be not relevant due to the non-determinism. In this paper, we propose an approach to assess if repeated runs are more efficient than diversity of test cases execution, in the context of debugging. To do so, we use a complete testing framework, from the test generation to the oracle analysis. We then consider 400 runs (80 tests cases, executed 5 times), and analyse the results. This analysis relies on two methods: the first one shows the evolution of the number of run needed so that every property violation occurs at least once. The second one focuses on the evolution of the failure distribution regarding the number of repeated runs and the number of different test cases. Surprisingly, our study shows that for our case study it would be more efficient to favor diversity over repeated run. A future direction will be to apply the same method to a new case study on another agricultural robot from Naïo. This time we will use a custom simulator from Naïo with a lower level of fidelity but far more performant. It will allow us to generate and execute more test cases.

Fig. 1 .

 1 Fig. 1. Oz in operation (source: Naïo Technologies)

Fig. 2 .

 2 Fig. 2. Oz in simulation. Top: virtual crop field using Gazebo. Bottom: expected mission.

Fig. 3 .

 3 Fig. 3. Simulation architecture.

  a) Requirements attached to mission phases. The focus is on how to perform a mission. A mission typically consists of a series of phases, with some expectations on what the robot should or should not do in each phase. b) Thresholds related to robot movement. Here, the aim is to detect abnormal values of kinematic or kinetic variables. c) Critical events, like collisions. d) Requirements attached to error reports. A robot has capabilities to monitor its operation and report errors. Requirements can be attached to the handling of these errors. e) Perception requirements, focusing on unacceptable mismatches between the ground truth and its perception by the robot. It resulted in the elaboration of 8 properties listed in Table

Fig. 4 .

 4 Fig. 4. Visualization of a run with multiple failure types (P6, P5)

  Total number of runs (#repeated runs * #test cases)

Fig. 7 .

 7 Fig. 7. Relative frequencies of property violation combinations for the 400 runs (X 80,5 )

Fig. 8 .

 8 Fig. 8. Median distance to the histogram in Figure 7. In red: isodistance lines for D = 0.1

TABLE IV PROPORTION

 IV OF TEST CASES WITH n FAIL VERDICTS OVER THE FIVE REPEATED RUNS.

ACKNOWLEDGMENT

This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644400 (CPSE Labs project). The authors want to acknowledge the help of colleagues at Naïo Technologies during the study: Simon Vernhes, Gaëtan Séverac, Pascal Schmidt, Marc Jambert.


Joost P Schanstra

Trang T D Luong

Manousos Makridakis

Sophie Van Linthout

Vasiliki Lygirou

Agnieszka Latosisnska

Ioana Alesutan

Beate Boehme

Nadeshda Schelski

Dirk Von Lewinski

William Mullen

Stuart Nicklin

Christian Delles

Guylène Feuillet

Colette Denis

Florian Lang

Burkert Pieske

Jean-Loup Bascands

Harald Mischak

Jean-Sebastien Saulnier-Blache
email: sebastien.saulnier-blache@inserm.fr

Jakob Voelkl
email: jakob.voelkl@charite.de

Antonia Vlahou
email: vlahoua@bioacademy.gr

Julie Klein
email: julie.klein@inserm.fr

System biology identifies cytosolic PLA2 as a target in vascular calcification treatment

Although cardiovascular disease (CVD) is the leading cause of morbi-mortality worldwide, promising new drug candidates are lacking. We compared the arterial high-resolution proteome of patients with advanced (n=17) versus early stage (n=15) CVD to predict, from a library of small bioactive molecules, drug candidates able to reverse this disease signature.

Of the ~4000 identified proteins, 100 proteins were up-and 52 were down-regulated in advanced CVD. Arachidonyl trifluoromethyl ketone (AACOCF3), a cytosolic phospholipase A2 (cPLA2) inhibitor was predicted as the top drug able to reverse the advanced CVD signature.

Vascular cPLA2 expression was increased in patients with advanced CVD. Treatment with AACOCF3 significantly reduced vascular calcification in a cholecalciferol overload mouse model and inhibited osteoinductive signaling in vivo and in vitro in human aortic smooth muscle cells. Finally, long term AACOCF3 treatment also slightly reduced the accumulation of lipids in aortic sinus of ApoE -/-mice fed a high cholesterol diet. In conclusion, using a systems biology approach, we have identified a new compound that prevented typical vascular calcification in CVD in vivo. Apart from the clear impact of this approach in CVD, such strategy should also be able to generate novel drug candidates in other complex diseases.

Introduction

Cardiovascular disease (CVD) is the leading causes of morbidity and mortality worldwide accounting for about 32% of all global deaths [START_REF] Roth | Demographic and epidemiologic drivers of global cardiovascular mortality[END_REF][START_REF] Benjamin | Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association[END_REF]. CVD results from multifactorial causative mechanisms and constitutes a high economic burden on the society and health care system.

Typically, CVD including coronary artery disease, stroke, or peripheral artery disease involves endothelial dysfunction resulting from a vascular injury and are characterized by progressive arterial thickening leading ultimately to thrombosis and vascular occlusion. This atherosclerotic process starts with early lipid deposition in the inner layers of the artery walls and progresses to late complications such as fibrosis and calcification. While calcification of arterial intima is associated with plaque rupture and myocardial infarction, calcification of arterial media leads to vessel stiffening, increased pulse-wave velocity, and reduces cardiac perfusion, with the consequence of ventricular hypertrophy and heart failure [START_REF] Albanese | Atherosclerotic Calcification: Wnt Is the Hint[END_REF][START_REF] Durham | Role of smooth muscle cells in vascular calcification: implications in atherosclerosis and arterial stiffness[END_REF]. Although significant advancements have been made in understanding the pathophysiology of CVD [START_REF] Weber | Atherosclerosis: current pathogenesis and therapeutic options[END_REF], there is still progress to be made to unravel the detailed molecular complexity of the disease and to transform this information into new drug candidates. The extreme complexity and multifactorial character of CVD has prompted researchers to shift from investigating already well known candidate pathways or single molecules to unbiased omicsbased studies [START_REF] Rüegg | Omics meets hypothesis-driven research. Partnership for innovative discoveries in vascular biology and angiogenesis[END_REF]. Omics allows studying the entire "system" simultaneously leading to a better representation of the complex molecular mechanism of CVD [START_REF] Dominiczak | Systems biology to battle vascular disease[END_REF], thus ultimately to the major aim of identifying truly novel CVD targets. This has resulted in a number of recent omics studies in the field identifying potential biomarkers of CVD [START_REF] Vernon | Utilizing state-of-the-art "omics" technology and bioinformatics to identify new biological mechanisms and biomarkers for coronary artery disease[END_REF], but not necessarily resulting in novel drugs yet. The latter could be attributed to the fact that potential targets need to be transformed into drugs which often takes a substantial amount of time and effort, and/or, that most studies employed genomic and transcriptomic strategies which not necessarily translate to changes in proteins, the major drug targets [START_REF] Mokou | Proteomics in cardiovascular disease: recent progress and clinical implication and implementation[END_REF].

As a response to the ever-increasing omics data and targeting their optimal use for drug discovery, a number of initiatives have evolved to create a shortcut associating omics molecular signatures to drugs and small compounds. The Connectivity Map (CMap) is the most advanced example of this type of approaches [START_REF] Lamb | The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease[END_REF] where 564 gene-expression profiles from cultured human cells treated with 164 bioactive small molecules were generated and associated via pattern mining software. This resource was later updated to include associated signatures to 1,309 compounds (https://portals.broadinstitute.org/cmap/).

Attempts have been made to use CMap in CVD but were limited to mouse models [START_REF] Xi | Identification of Gene Expression Changes in the Aorta of ApoE Null Mice Fed a High-Fat Diet[END_REF], in vitro studies [START_REF] Huang | Identify potential drugs for cardiovascular diseases caused by stressinduced genes in vascular smooth muscle cells[END_REF] or focused on human stem cells [START_REF] Li | Gene expression profiling of CD133-positive cells in coronary artery disease[END_REF]. Furthermore, none of these studies tested the therapeutic validity of the selected small CMap molecules.

To our knowledge this is the first effort to identify novel drugs in CVD using drug repurposing based on human molecular signatures. We generated a high-resolution human proteome signature of advanced CVD using peripheral artery samples that allowed the identification of a novel drug candidate arachidonyl trifluoromethyl ketone (AACOCF3), a cytosolic phospholipase A2 (cPLA2) inhibitor. In vivo and in vitro investigations showed that AACOCF3 exerts a potent inhibitory action on arterial calcification.

Results

Clinical data of CVD patients used in vessel proteome analysis.

For the analysis of the vessel CVD proteome, common iliac, external iliac and superficial femoral arteries from 15 patients with early stage CVD and 17 patients with advanced stage CVD were employed (Table 1).

Advanced CVD patients had clear CVD phenotypes (vascular disease, left ventricular hypertrophy, angina pectoris, intermittent claudication, myocardial infraction). These cardiovascular phenotypes were not found in early CVD patients (Table 1). Hematoxylin and eosin-staining (Figure 1 A,C) and von Kossa-staining (Figure 1 C,D) confirmed intima media thickening and calcification in arteries from the advanced CVD group compared to early stage patients. Advanced stage CVD patients were older than early stage patients. The female/male ratio was also different between the groups with almost exclusively females in the early CVD group. The advanced CVD group had a higher proportion of patients with hypertension, diabetes, and nicotine use. The medication of the two groups was very diverse with no clear distinguishable features between the groups (Table S3).

Arterial protein signature of advanced CVD. Proteome analysis of the arteries led to the detection of about 4000 proteins in total (2500-3500 proteins per patient, detected in at least 60% of samples). Samples were analyzed in two batches and only proteins that were significantly differentially expressed in one batch and displayed the same expression direction in the other batch were selected. This led to the selection of 152 proteins with 100 and 52 that were up and downregulated in advanced CVD, respectively (Table S4). The change in abundance of selected proteins present in the CVD signature (SHMT1, EFEMP1, KDM5D: increase; GUCY1B2: decrease) was confirmed by Western blot analyses of the arteries (Figure S1), supporting in general the validity of the CVD vessel proteome analysis.

Pathway analysis with Reactome database (https://reactome.org/) revealed that these proteins mainly participated in: Signal transduction (37%); Transcription (26%); Metabolism (22%); Immune system (22%); Metabolism of proteins (21%) and Developmental biology (18%). Among the 29 proteins involved in Metabolism, 14 were related to lipid metabolism including Sterol regulatory element-binding protein 1 (SREBF1), ATP-binding cassette subfamily A member 2 (ABCA2) and PTB domain-containing engulfment adapter protein 1 (GULP1) involved in cholesterol synthesis and transport. The CVD signature also contains the Protein AMBP involved in calcium crystallization and EGF-containing fibulin-like extracellular matrix protein 1 (EFEMP1) involved in chondrogenesis.

AACOCF3 as a potential drug-candidate for CVD treatment.

We next used this 152 protein signature to identify potential new drug candidates for CVD treatment. Using CMap [START_REF] Lamb | The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease[END_REF] we screened 1,309 small molecules able to reverse in silico this CVD signature. Among the molecules of the CMap database, 12 were predicted to significantly reverse the CVD signature (Table 2). Among these 12 molecules, arachidonyltrifluoromethane, also called arachidonic acid trifluoromethyl ketone (AACOCF3), an inhibitor of the cytosolic phospholipase A2 (cPLA2), showed the highest negative enrichment score (Table 2).

Immunohistochemistry showed that cPLA2 was present in all vascular areas in early and late CVD (Figure S2A). Semiquantitative analysis showed that cPLA2 was significantly more abundant in the intima of advanced compared to early CVD arteries, while no significant difference was observed in the tunica media and the tunica externa (Figure S2B). Western Blot analysis confirmed that cPLA2 expression was significantly higher (1.4-fold) in arteries from advanced CVD patients compared to early CVD patients (Figure 2).

AACOCF3 protects against vascular calcification.

Arterial calcifications are observed during medial calcification and late stage atherosclerosis [START_REF] Durham | Role of smooth muscle cells in vascular calcification: implications in atherosclerosis and arterial stiffness[END_REF][START_REF] Lang | Therapeutic Interference With Vascular Calcification-Lessons From Klotho-Hypomorphic Mice and Beyond[END_REF]. Vascular smooth muscle cells are important mediators for these calcific processes PMID 29514202. Due to important clinical implications, we have focused our interventional approach on the effects of AACOCF3 in vascular smooth muscle cells.

To evaluate the potential protective effects of AACOCF3 on CVD, experiments were performed in the cholecalciferol (vitamin D 3 )-overload mouse vascular calcification model without or with additional treatment with AACOCF3. Cholecalciferol overload significantly increased serum calcium, CaxPi product and FGF23 C-term concentrations and reduced serum phosphorus levels to similar levels in both, control or AACOCF3 treated mice (Table 3).

Cholecalciferol overload triggered aortic calcification in mice, an effect significantly attenuated by additional treatment with AACOCF3 (Figure 3A). In control mice, AACOCF3 did not modify aortic calcium deposition. The mRNA level of the gene encoding the most prevalent cPLA2 subtype, cPLA2α (Pla2g4a) (15) was significantly higher in aortic tissue of cholecalciferol-treated mice than of control mice (Figure 3B). AACOCF3 treatment alone did not significantly affect aortic Pla2g4a mRNA expression. Western blot analysis showed that cPLA2 protein was clearly present in the aortic tissue, but no significant change was observed after vitamin D or AACOCF3 treatments (Figure S3). These effects were paralleled by inhibition of aortic osteo-/chondrogenic signaling by AACOCF3. As shown in Figure 4, cholecalciferol overload significantly up-regulated the mRNA expression of osteogenic transcription factors Msx2 and Cbfa1 and of the osteogenic enzyme tissue-nonspecific alkaline phosphatase (Alpl), as markers of increased osteo-/chondrogenic transdifferentiation in the aortic tissue, effects again significantly ameliorated by AACOCF3 treatment. Thus, AACOCF3 inhibited aortic osteoinductive signaling and vascular calcification in the cholecalciferol-overload mouse model.

To investigate possible mechanisms of the protective effects of AACOCF3 on vascular calcification, further experiments were performed in primary human aortic smooth muscle cells (HAoSMCs) during high phosphate conditions in the presence or absence of AACOCF3.

As shown by alizarin Red staining and quantification of calcium deposition, treatment with calcification medium increased calcification of HAoSMCs that was significantly blunted by treatment with AACOCF3 (Figure 5A,B). Similarly, AACOCF3 significantly suppressed phosphate-induced MSX2, CBFA1 and ALPL mRNA expression (Figure 5C-E) as well as ALPL activity (Figure 5F) in HAoSMCs. In control conditions, AACOCF3 did not significantly affect mineralization, but tended to reduce CBFA1 mRNA levels (p=0.202) and significantly downregulated MSX2 and ALPL mRNA expression in HAoSMCs (Figure 5A-F). Thus, AACOCF3 suppressed phosphate-induced osteo-/chondrogenic transdifferentiation and calcification of HAoSMCs in vitro.

Moreover, phosphate triggered the release of arachidonic acid (AA) in the cell culture medium of HAoSMCs (Figure 5G). Treatment with AACOCF3 significantly inhibited both basal and Pi-induced release of arachidonic acid (AA), the product of PLA2 activity.

To confirm the involvement of cPLA2 in vascular calcification, the endogenous expression of cPLA2 in HAoSMCs was suppressed by silencing of the PLA2G4A gene using siRNA. As a result, transfection with cPLA2 siRNA significantly suppressed PLA2G4A mRNA levels in HAoSMCs as compared to negative control silenced HAoSMCs (Figure 6A). Phosphate treatment up-regulated PLA2G4A mRNA expression in negative control silenced HAoSMCs (Figure 6A). Furthermore, phosphate significantly increased MSX2, CBFA1 and ALPL mRNA expression in negative control siRNA transfected HAoSMCs, effects significantly blunted in cPLA2 siRNA silenced HAoSMCs (Figure 6B,C,D). Taken together, the protective effects of AACOCF3 on vascular calcification may involve, at least in part, inhibition of cPLA2-mediated osteoinductive signaling.

AACOCF3 inhibits arterial lipid deposition. AACOCF3 was tested in another preclinical model of CVD, the ApoE-/-mice known to develop atherosclerotic lesions. In our conditions ApoE-/mice fed a high cholesterol diet displayed dyslipidemia (Table 4, vehicle) and lipid deposition (Figure 7A) and increased type III collagen expression (Figure 7B) in the aortic sinus, in agreement with previous reports [START_REF] Getz | Animal models of atherosclerosis[END_REF]. Ten weeks treatment with AACOCF3 did not significantly modify plasma lipids and glucose levels (Table 4). In contrast, analysis of the aortic root showed that AACOCF3 had no influence on the total plaque area but it significantly inhibited lipid deposition in the plaques (-35%: Figure 7A) compared to vehicle treated mice. In parallel, AACOCF3 significantly reduced type III collagen plaque expression (-16%: Figure 7B) but had no significant influence on total collagen accumulation as assessed by Sirius red staining (Figure S4). In parallel, AACOCF3 did not significantly changed SMA (myofibroblast marker) and slightly increased MOMA2 (macrophage marker) staining (Figure S4). Finally, AACOCF3 did not significantly change the expression of calcification gene markers (Alpl and Cbfa1) aortic tissue (Figure S5). Collectively, the experiments using the ApoE-/-mouse model show that AACOCF3-treatment reduces lipid accumulation and collagen accumulation but without reducing inflammation.

Discussion

In the present study, we explored the possibility of omics-based disease signature correction by small bioactive compounds to identify and validate in vivo novel CVD drugs. Applying this approach to the high resolution human arterial protein signature of advanced CVD, we identified among >1000 compounds the cytosolic phospholipase A2 (cPLA2) inhibitor AACOCF3 that reversed this CVD signature. AACOCF3 is a cell-permeant trifluoromethyl ketone analogue of arachidonic acid [START_REF] Trimble | NMR structural studies of the tight complex between a trifluoromethyl ketone inhibitor and the 85-kDa human phospholipase A2[END_REF] that behaves as a slow-and tight-binding inhibitor of cPLA2 [START_REF] Riendeau | Arachidonyl trifluoromethyl ketone, a potent inhibitor of 85-kDa phospholipase A2, blocks production of arachidonate and 12-hydroxyeicosatetraenoic acid by calcium ionophore-challenged platelets[END_REF][START_REF] Bartoli | Tight binding inhibitors of 85-kDa phospholipase A2 but not 14-kDa phospholipase A2 inhibit release of free arachidonate in thrombin-stimulated human platelets[END_REF][START_REF] Gelb | Inhibition of phospholipase A2[END_REF][START_REF] Street | Slow-and tight-binding inhibitors of the 85-kDa human phospholipase A2[END_REF]. A literature screen showed that cPLA2 inhibition has not been used in CVD management yet, suggesting AACOCF3 as a possible novel drug candidate for CVD treatment. Our preclinical analysis of the cholecalciferol-overload mouse model demonstrates that AACOCF3 was efficient in vivo at attenuating vascular calcification, one of the major features of CVD. To our knowledge, our work demonstrates for the first time the protective action of AACOCF3 against vascular injury.

Functional analysis of the proteins present in the human CVD vessel signature highlighted enrichment of proteins associated with cholesterol and lipid metabolism. Moreover, we also detected an increase in EFEMP1, also called Fibulin-3, a protein involved in the regulation of osteo-/chondrogenic transdifferentiation of vascular smooth muscle cells (VSMCs) [START_REF] Luong | Fibulin-3 Attenuates Phosphate-Induced Vascular Smooth Muscle Cell Calcification by Inhibition of Oxidative Stress[END_REF].

Because vascular calcification is preferentially associated with advanced stage CVD, we focused on the mechanisms of AACOCF3 in this process. Vascular calcification is mediated, mainly, by osteo-/chondrogenic reprogramming of VSMCs in response to various triggers, especially hyperphosphatemia [START_REF] Lang | Therapeutic Interference With Vascular Calcification-Lessons From Klotho-Hypomorphic Mice and Beyond[END_REF][START_REF] Voelkl | Zinc Inhibits Phosphate-Induced Vascular Calcification through TNFAIP3-Mediated Suppression of NF-κB[END_REF]. In liver tissue, excessive phosphate intake in rats is associated with increased arachidonic acid concentrations [START_REF] Chun | A High Phosphorus Diet Affects Lipid Metabolism in Rat Liver: A DNA Microarray Analysis[END_REF]. The osteo-/chondrogenic transdifferentiated VSMCs are characterized by increased expression and activity of osteogenic and chondrogenic transcription factors as well as of osteogenic enzymes such as tissue non-specific alkaline phosphatase (ALPL), leading to active mineralization of the vascular tissue [START_REF] Steitz | Smooth muscle cell phenotypic transition associated with calcification: upregulation of Cbfa1 and downregulation of smooth muscle lineage markers[END_REF][START_REF] Lanzer | Medial vascular calcification revisited: review and perspectives[END_REF][START_REF] Voelkl | SGK1 induces vascular smooth muscle cell calcification through NF-κB signaling[END_REF][START_REF] Luong | Fibulin-3 Attenuates Phosphate-Induced Vascular Smooth Muscle Cell Calcification by Inhibition of Oxidative Stress[END_REF] . We show here that AACOCF3 treatment attenuates vascular osteo-/chondrogenic transdifferentiation and calcification in vitro and in the cholecalciferoloverload mouse model in vivo. Moreover, vascular cPLA2 expression is increased during calcification conditions, both in vitro and in vivo. Accordingly, osteo-/chondrogenic transdifferentiation of VSMCs during elevated phosphate levels in vitro is blunted by silencing of cPLA2 and, thus, cPLA2 knockdown mimics the protective effects of AACOCF3.

These observations strongly suggest that the in vivo protective effect of AACOCF3 may result, at least partially, from its direct action on VSMCs through inhibition of cPLA2dependent osteoinductive signaling.

There exist multiple PLA2 isoforms classified as secreted (sPLA2) and cytosolic (cPLA2) that have different structures and various physiological functions. PLA2G4A, i.e. cPLA2, is a calcium-dependent cytosolic PLA2. Some secreted isoforms such as the lipoproteinassociated PLA2 (Lp-PLA2) or sPLA2-X or sPLA2-V (28) were shown causative in atherosclerosis and some of their inhibitors were in clinical trials but did not reach the market [START_REF] Kokotou | Inhibitors of phospholipase A2 and their therapeutic potential: an update on patents (2012-2016)[END_REF]. Expression of cPLA2 has been previously found in arteries from atherosclerotic patients, mainly localized in the intimal regions with an inflammatory infiltrate consisting of macrophages and smooth muscle cells [START_REF] Elinder | Expression of phospholipase A2 isoforms in human normal and atherosclerotic arterial wall[END_REF]. Moreover, a PLA2G4A gene variant was associated with a decreased risk of coronary artery disease, although the functional consequence of the mutation remains still elusive [START_REF] Hartiala | Genetic contribution of the leukotriene pathway to coronary artery disease[END_REF]. Our results are in agreement with those observations since we found that cPLA2 is indeed present in arteries and its expression is increased in advanced CVD. So far, the use of a cPLA2 inhibitor as a possible drug candidate for CVD treatment has never been proposed.

A first limitation of our study is that, for our study, we have deliberately compared patients with advanced vascular disease and early vascular disease. Whilst these features were confirmed by histology, the initial screen was based on presence or absence of risk factors and hence differences in age, blood pressure and other cardiovascular risk factors between the group are present. We cannot rule out that elements of the proteomic data are driven by these risk factors rather than specifically by the vascular phenotype. But by selecting of patients with multiple risk factors and advanced disease we believe that our findings represent a molecular signature of advanced arteriosclerosis, rather than a single risk factor.

A further limitation might be that the CVD signature was established from peripheral arteries (iliac and femoral) and cannot necessarily be extrapolated to a CVD signature in other arterial locations. However, our results show that AACOCF3 treatment was efficient in aorta of the animal models suggesting that its protective action is not limited to peripheral arteries and that apparently the molecular mechanisms overlap between these different vessel compartments/segments. We are also aware that the specificity of AACOCF3 for cPLA2 is likely not exclusive. It has indeed been reported that AACOCF3 was able to inhibit cyclooxygenases 1 and 2 [START_REF] Leis | Inhibition of cyclooxygenases 1 and 2 by the phospholipaseblocker, arachidonyl trifluoromethyl ketone[END_REF]. However, our results show that the effect of AACOCF3 could be reproduced by cPLA2 knockdown in vitro.

In conclusion, using a systems biology approach to optimally describe the molecular mechanisms of human advanced CVD, we identified a new compound that reversed typical vascular lesions in CVD in vivo. Apart from the clear impact of this approach in CVD, such strategy should be able to generate novel targets in other complex diseases.

Methods

Arterial sample acquisition and CVD grading method. 18 common iliac arteries, 15

superficial femoral arteries and 2 external iliac arteries were collected from 15 early and 17 advanced CVD patients with the following specific distribution: early CVD (11 common iliac arteries, 2 external iliac arteries, 4 superficial femoral arteries), advanced CVD (7 common iliac arteries, 11 superficial femoral arteries). All advanced CVD patients had clear CVD phenotypes (vascular disease, left ventricular hypertrophy, angina pectoris, claudication intermittens, myocardial infraction). These cardiovascular phenotypes were not found early CVD patients exhibits (Table 1). Following harvesting, the vascular tissues were snap frozen in liquid nitrogen and stored at -80C until preparation for proteomics analysis. Blood and urine samples were also collected from the same patients for biochemical analysis. The study has been conducted according to the principles expressed in the Declaration of Helsinki. Written informed consent has been given by each patient or representative. The study was reviewed and approved by the local ethics committee (approval number: 26-355 ex 13/14).

Sample preparation and LC-MS/MS analysis.

Approximately 10-20 mg (net weight) of tissue specimens were homogenized in lysis buffer consisting of 0.1 M Tris-HCl pH 7.6 supplemented with 4% SDS and 0.1 M DTE. Homogenization was performed with the bullet blender homogenizer (Next Advance) [START_REF] Makridakis | GeLC-MS: A Sample Preparation Method for Proteomics Analysis of Minimal Amount of Tissue[END_REF]. Ten micrograms of each protein sample were analyzed in SDS-PAGE (5% stacking, 12% separating). Electrophoresis was stopped when samples just entered the separating gel. Gels were fixed with 30% methanol, 10% acetic acid for 30 min followed by 3 washes with water (5 min each) and stained with Coomassie Colloidal Blue overnight. Each band was excised from the gel and further sliced into small pieces (1-2 mm). Gel pieces were destained with 40% Acetonitrile, 50 mM NH 4 HCO 3 and then reduced with 10 mM DTE in 100 mM NH 4 HCO 3 for 20 min RT. After reduction, samples were alkylated with 54 mM Iodoacetamide in 100 mM NH 4 HCO 3 for 20 min at room temperature in the dark. Samples were then washed with 100 mM NH 4 HCO 3 for 20 min at room temperature, followed by another wash with 40% Acetonitrile, 50 mM NH 4 HCO 3 for 20 min at room temperature and a final wash with ultrapure water under the same conditions.

Gel pieces were dried in a centrifugal vacuum concentrator and trypsinized overnight in the dark at room temperature, by adding 600 ng of trypsin per sample (trypsin stock solution: 10 ng / μL in 10 mM NH 4 HCO 3 , pH 8.5). Peptides were extracted after incubation with the following buffers: 50 mM NH 4 HCO 3 for 15 min at room temperature followed by two incubations with 10% Formic Acid, Acetonitrile (1:1) for 15 min at room temperature.

Peptides were eluted in a final volume of 600 μL and filtered with PVDF filters (Merck Millipore) before dried in a centrifugal vacuum concentrator. Dried peptides were reconstituted in mobile phase A buffer (0.1% formic acid, pH 3.5) and processed with LC-MS/MS analysis.

2.5 μg of protein digest were injected from each sample into a Dionex Ultimate 3000 RSLS nano flow system configured with a Dionex 0.1 × 20 mm 5 μm C18 nano trap column. Mobile phase was 2% ACN : 0.1% FA with a flow rate of 5 µL / min. The analytical column was an Acclaim PepMap C18 nano column 75 μm × 50 cm, 2 μm 100 Å at a flow rate of 300 nL / min. The trap and nano-flow column were maintained at 35°C. The samples were eluted with a gradient starting at 1% B for 5 min rising to 5% B at 10 min then to 25% B at 360 min and 65% B at 480 min. Mobile phase A was 0.1% Formic acid while mobile phase B was 80% ACN:0.1% Formic acid. The column was washed and re-equilibrated prior to each sample injection. The eluent was ionized using a Proxeon nano spray ESI source operating in positive ion mode. For mass spectrometry analysis, an Orbitrap LTQ Velos (Thermo Finnigan) was operated in MS/MS mode, scanning from 380 to 2,000 m / z. Ionization voltage was 2.6 kV and the capillary temperature was 275°C. The resolution of ions in MS1 was 60,000 and 7,500 for higher-energy collisional dissociation (HCD) MS2. The top 20 multiply charged ions were selected from each scan for MS/MS analysis using HCD at 35% collision energy.

Dynamic exclusion was enabled with a repeat count of 1, exclusion duration of 30 s.

MS data processing and quantification.

Raw files were analyzed with Proteome Discoverer 1.4 software package (Thermo Finnigan), using the Sequest search engine and the Uniprot human (Homo sapiens) reviewed database, downloaded on May 30, 2016. The search was performed using carbamidomethylation of cysteine as static and oxidation of methionine as dynamic modifications. Two missed cleavage sites, a precursor mass tolerance of 10 ppm and fragment mass tolerance of 0.05 Da were allowed. False discovery rate (FDR) validation was based on q value: target FDR (strict): 0.01, target FDR (relaxed): 0.05. Quantification analysis was performed at the peptide level with the clustering approach as previously described [START_REF] Lygirou | Plasma proteomic analysis reveals altered protein abundances in cardiovascular disease[END_REF] with some minor modifications. Peptides were clustered using "window" of mass (±5ppm) and retention time (±15 min). Only peptides being present in 60% of the samples in at least one group were further processed for quantification and statistical analysis (Mann-Whitney). The differentially expressed proteins selected for further analysis, were considered those with a fold change of ≥1.2 or ≤0.83 in both batches and a p-value of ≤0.05 in at least one batch.

CMap analysis.

Each up-and down-protein ID from the CVD signature was converted into gene ID and were analyzed with the bioinformatic tool Connectivity Map (CMap) (CMap, build 02). CMap is a collection of genome-wide transcriptional expression signatures (6,100 treatment vs. vehicle control pairs) from cultured human cells (MCF7, PC3, and HL60) treated with 1,309 bioactive small chemical molecules at varying concentrations, and analyzed by simple pattern-matching algorithms [START_REF] Lamb | The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease[END_REF].

Preparation of AACOCF3 solutions. AACOCF3 (Arachidonyl trifluoromethyl ketone) was

supplied by Cayman Chemical company (ref 62120). For in vivo treatments, the initial solution of AACOCF3 (50 mg/5 ml ethanol) was concentrated to 250 mg/ml and 20 µl was diluted in 4 ml of a 0.5% methylcellulose (Fisher Scientific, ref 10246190) in water. That solution was used to treat the animals. For in vitro treatment, AACOCF3 (Cayman Chemical) 10 mM stock solution in ethanol, was diluted to a final concentration of 10 µM in cell culture medium. Equal amounts of vehicle were used as control.

Mouse model of vascular calcification.

Female C57Bl/6 mice were treated with AACOCF3 (10 mg/kg) or its vehicle (methylcellulose 0.5% containing 0.5%M ethanol) via gavage and were fed with calcium reduced diet (C1031, Altromin). Subsequently, mice were injected subcutaneously with 400 000 IU/kg BW of cholecalciferol (Vitamine D 3 ) (Sigma-Aldrich) or vehicle for 3 days [START_REF] Han | Functional cooperation between vitamin D receptor and Runx2 in vitamin D-induced vascular calcification[END_REF][START_REF] Alesutan | Augmentation of phosphate-induced osteo-/chondrogenic transformation of vascular smooth muscle cells by homoarginine[END_REF]. After six days of treatment, blood was collected by retroorbital puncture under isoflurane inhalative anaesthesia, and the mice were immediately sacrificed by cervical dislocation and aortic tissues were collected and snap-frozen for further experiments. All animal experiments were conducted according to the recommendations of the Guide for Care and Use of Laboratory Animals of the National Institutes of Health as well as the German law for the welfare of animals and were approved by local authorities.

Mouse model of atherosclerosis.

ApoE-/-mice (37, 38) (6 weeks old males) (Charles River) were fed a high cholesterol diet (1.25% of cholesterol, 6% fat and 2% sucrose (Ssniff, Soest, Germany diet: TD.96335 mod. -1.25 % cholesterol) and simultaneously treated (gavage with 200 µl; 5 days a week) with AACOCF3 (10 mg/kg) or its vehicle (methylcellulose 0.5% containing 0.5%M ethanol) for 10 weeks. The mice were sacrificed by pentobarbital injection and heart aortic sinus was collected and snap-frozen in liquid nitrogen for further analysis.

Blood was collected 1 week before sacrifice by intra-caudal puncture in heparinized tubes.

Ice-cold blood was centrifuged for 5 min at 1500 g and upper plasma phase was collected and immediately frozen at -80°C. The project was approved by the local (Inserm/UPS US006 CREFRE) and national ethics committees under the number 02604.02.

Biochemical analysis.

The plasma concentrations of phosphorus and calcium were measured by QuantiChrom Phosphate assay kit and QuantiChrom Calcium assay kit, respectively (BioAssay Systems). An ELISA kit was used to determine plasma concentrations of FGF23 Cterm (Immutopics), according to manufacturer's instructions. Blood glucose concentration was measured using a glucometer. Plasma concentrations of cholesterol and triglyceride were measured by autoanalyzer.

Immuno-histology.

For human, histologic paraffin sections of iliac arteries samples were cut at 2 μm, mounted on charged slides, and stained with hematoxylin-eosin (H&E) and von Kossa, for pathological evaluation. cPLA2 staining was performed on 1-2 µm paraffin sections of A. ilia communis samples of histopathologically classified early (n=15) and advanced (n=24) patients, using a 1:50 dilution of the rabbit anti-human PLA2G4A antibody (ProSci Incorporated, Poway, CA, USA). Heat-induced epitope retrieval was performed at pH8. For detection, the EnVision+ System-HRP Labelled Polymer Anti-Rabbit kit (Dako, Hamburg, Germany) was used. Nuclei were counterstained with hematoxylin and sections, cover-slipped with gelatin. The expression levels of cPLA2 were evaluated per tissue section (vessel cross section), distinguishing between the different vessel layers (Tunica intima, Tunica media, and Tunica externa). The abundance of cPLA2 was scored as follows: 0.5 for no expression, 1 for weak expression, 2 for moderate expression and 3 for strong expression. Images were acquired using the AxioImager Z1 microscope (Carl Zeiss MicroImaging, Inc.). All evaluations were performed in a blinded manner.

For aortic sinus of ApoE-/-mice, lipid accumulation and collagen expression were analyzed on serial cross-cryosections of the heart as previously described [START_REF] Lesnik | Decreased atherosclerosis in CX3CR1-/-mice reveals a role for fractalkine in atherogenesis[END_REF]. For lipid analysis, cryosections were post-fixed with formaldehyde and stained with oil red O and counterstained with hematoxylin. For collagen, cryo-sections were post-fixed with methanol/acetone and exposed to a type III collagen primary antibody (Acris BP8014) and revealed with an antirabbit HRP secondary antibody (DAKO 4010). Samples were imaged with a Nikon Y-FL microscope equipped with a computer-based imaging system. Staining was expressed as the percentage of total sinus area. Reported intensities used for further statistical analysis correspond to the mean value of 5 sections per animal.

Western Blot. Arteria iliaca communis samples of histopathologically classified control (n=15) and disease (n=24) patients were homogenized in lysis buffer (Invitrogen, Darmstadt, Germany) supplemented with proteinase inhibitors (Roche, Munich, Germany). Next, 30 µg of protein was loaded on 10% SDS-polyacrylamide gels. EFEMP1, GUCY1B2, KDM5D, cPLA2, and SHMT1 were detected with a rabbit anti-human EFEMP1 antibody (1:1000, Abcam, Cambridge, UK), rabbit anti-human GUCY1B2 antibody (1:500, Thermo Fisher Scientific, Walthamn MA, USA), rabbit anti-human KDM5D (Jarid 1D, 1:5000, Novus Biologicals, Centennial, Colorado, USA), rabbit anti-human PLA2G4A antibody (1:500, ProSci Incorporated, Poway, CA, USA), and rabbit anti-human SHMT1 (1 250, Sigma Aldrich, St.

Louis, MO, USA), respectively, followed by a goat anti-rabbit IR dye 680 secondary antibody

Cell culture of primary human aortic smooth muscle cells. Primary human aortic smooth

muscle cells (HAoSMCs, Thermo Fisher Scientific) were cultured in Waymouth's MB 752/1 medium and Ham's F-12 nutrient mixture in a 1:1 ratio (Thermo Fisher Scientific) containing 10% FBS (Thermo Fisher Scientific), 100 U/ml penicillin and 100 μg/ml streptomycin (Thermo Fisher Scientific) [START_REF] Alesutan | Inhibition of Phosphate-Induced Vascular Smooth Muscle Cell Osteo-/Chondrogenic Signaling and Calcification by Bafilomycin A1 and Methylamine[END_REF][START_REF] Alesutan | Involvement Of Vascular Aldosterone Synthase In Phosphate-Induced Osteogenic Transformation Of Vascular Smooth Muscle Cells[END_REF]. HAoSMCs were used in all experiments from passages 4 to 11 and each experiment was performed in at least two different batches of HAoSMCs at different passages, depending on the availability of the cells. HAoSMCs were treated for 24 hours (qRT-PCR, AA release), 7 days (ALPL assay) or 11 days (calcification) with 2 mM βglycerophosphate (Sigma Aldrich) [START_REF] Steitz | Smooth muscle cell phenotypic transition associated with calcification: upregulation of Cbfa1 and downregulation of smooth muscle lineage markers[END_REF][START_REF] Liao | MiR-133a modulates osteogenic differentiation of vascular smooth muscle cells[END_REF][START_REF] Qiao | MicroRNA-205 regulates the calcification and osteoblastic differentiation of vascular smooth muscle cells[END_REF][START_REF] Alesutan | Inhibition of Phosphate-Induced Vascular Smooth Muscle Cell Osteo-/Chondrogenic Signaling and Calcification by Bafilomycin A1 and Methylamine[END_REF][START_REF] Alesutan | Augmentation of phosphate-induced osteo-/chondrogenic transformation of vascular smooth muscle cells by homoarginine[END_REF] or with 10 µM AACOCF3. Equal amounts of vehicle were used as control. For the mineralization analysis and Alizarin Red staining, treatment with 10 mM β-glycerophosphate and 1.5 mM CaCl2 (Sigma-Aldrich) for 11 days was used as calcification media (Calc.) [START_REF] Villa-Bellosta | Role of calcium-phosphate deposition in vascular smooth muscle cell calcification[END_REF]. Fresh media with agents were added every 2-3 days. HAoSMCs were transfected with 10 nM cPLA2 siRNA (ID no. s10592, Thermo Fisher Scientific) or with 10 nM negative control siRNA (ID no. 4390843, Thermo Fisher Scientific) using siPORT amine transfection agent (Thermo Fisher Scientific) according to the manufacturer's protocol. The cells were used 48 hours (qRT-PCR) after silencing and silencing efficiency was determined by quantitative RT-PCR.

Analysis of calcification.

To quantify vascular calcification, abdominal aortic tissues were incubated overnight at 37°C in 0.6 M HCl. HAoSMCs were incubated in 0.6 M HCl for 24 hours at 4°C. Calcium content in the supernatant was determined by using QuantiChrom Calcium assay kit (BioAssay Systems) according to the manufacturer's protocol. Tissues and HAoSMCs were lysed with 0.1 M NaOH/ 0.1% SDS and protein concentration was determined by the Bradford assay (Bio-Rad Laboratories). The results are shown normalized to total protein concentration [START_REF] Alesutan | Inhibition of osteo/chondrogenic transformation of vascular smooth muscle cells by MgCl2 via calcium-sensing receptor[END_REF]. To visualize calcification, HAoSMCs were fixed with 4% paraformaldehyde and stained with 2% Alizarin Red (pH 4.5). The calcified areas are shown as red staining [START_REF] Alesutan | Augmentation of phosphate-induced osteo-/chondrogenic transformation of vascular smooth muscle cells by homoarginine[END_REF].

Alkaline phosphatase (ALPL) activity assay. ALPL activity in HAoSMCs was determined by using the ALP colorimetric assay kit (Abcam) according to the manufacturer's protocol [START_REF] Luong | Fibulin-3 Attenuates Phosphate-Induced Vascular Smooth Muscle Cell Calcification by Inhibition of Oxidative Stress[END_REF][START_REF] Alesutan | Involvement Of Vascular Aldosterone Synthase In Phosphate-Induced Osteogenic Transformation Of Vascular Smooth Muscle Cells[END_REF]. The results are shown normalized to total protein concentration measured by the Bradford assay (Bio-Rad Laboratories).

Arachidonic acid (AA) release. Medium AA levels were determined by using an arachidonic acid (AA) ELISA kit (Elabscience) according to the manufacturer's instructions. The results are shown normalized to total protein concentration measured by the Bradford assay (Bio-Rad Laboratories).

RNA isolation and quantitative RT-PCR. Total RNA was isolated from aortic tissue and HAoSMCs by using Trizol Reagent (Thermo Fisher Scientific) according to the manufacturer's instructions [START_REF] Voelkl | Spironolactone ameliorates PIT1-dependent vascular osteoinduction in klotho-hypomorphic mice[END_REF][START_REF] Voelkl | Up-regulation of hepatic alpha-2-HS-glycoprotein transcription by testosterone via androgen receptor activation[END_REF][START_REF] Voelkl | AMP-activated protein kinase α1-sensitive activation of AP-1 in cardiomyocytes[END_REF]. Reverse transcription of total RNA was performed using oligo(dT)12-18 primers (Thermo Fisher Scientific) and SuperScript III Reverse Transcriptase (Thermo Fisher Scientific). Quantitative RT-PCR was performed with the iCycler iQ TM Real-Time PCR Detection System (Bio-Rad Laboratories) and iQ TM Sybr Green Supermix (Bio-Rad Laboratories) according to the manufacturer's instructions. The mouse and human primer sequences are given in Table S1 and Table S2, respectively. The specificity of the PCR products was confirmed by analysis of the melting curves. All PCRs were performed in duplicate and relative mRNA expression was calculated by the 2 -ΔΔCt method using GAPDH as housekeeping gene normalized to the control group.

Statistics. Proteomics statistical analysis was based on the Mann-Whitney test performed using R package. For both batches, protein changes between early stage and advanced stage CVD with p-value ≤0.05 were considered as statistically significant. Other statistical analysis: Data are shown as scatterdot plots and arithmetic means ± SEM. N indicates the number of independent experiments performed at different passages of the cells or the number of mice examined, respectively. Normality was tested with Shapiro-Wilk test. Non-normal datasets were transformed (log, reciprocal or sqrt) prior to statistical testing to provide normality according to Shapiro-Wilk test. Statistical testing was performed by one-way ANOVA followed by Tukey-test for homoscedastic data or Games-Howell test for heteroscedastic data. Non-normal data were tested by the Steel-Dwass method. Two groups were compared by unpaired two-tailed t-test or Mann-Whitney test. P<0.05 was considered statistically significant [START_REF] Luong | Fibulin-3 Attenuates Phosphate-Induced Vascular Smooth Muscle Cell Calcification by Inhibition of Oxidative Stress[END_REF]. * Abbreviations: "Mean", is the arithmetic mean of the connectivity scores for those signatures; "n" is the number of signatures of a given compound available in the Cmap database; "enrichment" indicates the degree of matching between a query signature (here the CVD signature) and the reversed signature of a given compound; "p" (permutation p) estimates the likelihood that the enrichment would be observed by chance; "specificity" provides a measure of the uniqueness of the matching between a compound and the CVD signature; "percent non-nul" measures the support for the connection between a set of compound signature and the query signature based upon the behavior of the individual signature in that set. More details can be found here: https://portals.broadinstitute.org/cmap/help_topics_frames.jsp

( 1 :

 1 7500, LI-COR Biosciences, Lincoln, NE, USA). All samples were normalized towards GAPDH (1:2000, BPS Bioscience, San Diego, CA, USA) as internal control. Visualization and quantitative analysis were performed on Odyssey (LI-COR Biosciences) via Odyssey V3.0 software (LI-COR Biosciences).

Figure legends Figure 1 .Figure 2 .Figure 3 .

 legends123 Figure legends

Figure 4 .

 4 Figure 4. AACOCF3 ameliorated aortic osteo-/chondrogenic signaling during cholecalciferol

Figure 5 .Figure 6 .

 56 Figure 5. AACOCF3 suppressed phosphate-induced calcification and osteo-/chondrogenic signaling in HAoSMCs. Representative images showing Alizarin red staining (A, n=3) and scatterdot plots and arithmetic means ± SEM of calcium content (B, n=4, µg/mg protein) in HAoSMCs following treatment with control (CTR) or with calcification medium (Calc.) without or with additional treatment with 10 µM AACOCF3(AAC). The calcified areas are shown as red staining. C-G. Scatterdot plots and arithmetic means ± SEM of MSX2 (C), CBFA1 (D) and ALPL (E) relative mRNA expression (n=6; arbitrary units, a.u.) and of ALPL activity (F, n=4, U/mg protein) in HAoSMCs as well as of arachidonic acid (AA) levels (G, n=4; ng/mg protein) in the cell culture medium of HAoSMCs following treatment with control (CTR) or with β-glycerophosphate (Pi) without or with additional treatment with 10 µM AACOCF3 (AAC). *(p<0.05), **(p<0.01), ***(p<0.001) statistically significant vs. control treated HAoSMCs; †(p<0.05), † †(p<0.01), † † †(p<0.001) statistically significant vs. HAoSMCs treated with Calc./Pi alone (One-way ANOVA with Games-Howell post-hoc test for B,C,F or with Tukey-HSD post-hoc test for D,E,G).

Figure 7 .

 7 Figure 7. AACOCF3 reduced atherosclerosis in vivo. Representative pictures and scatterdot

Figure S2 .Figure S5 .

 S2S5 Figure S2. cPLA2 protein expression is increased in the intima of A. iliaca communis of disease versus control patients. A. Representative pictures of A. iliaca communis of a early (left) and adavnced (right) CVD patient at 100x magnifcation, scale bar 100 µm. B. Dot plots representing the mean±SEM of cPLA2 expression in the Tunica intima, media and externa of early (n=15) and advanced (n=24) CVD patients. Expression is depicted semiquantitatively with the following classification: 0.5 = very weak expression, 1 = weak expression, 2 = moderate expression, 3 = strong expression.

Table 1 . Clinical parameters of the early and advanced CVD patients for whom high resolution vessel proteome analysis was performed.

 1

	Tables
		Early CVD					Advanced CVD
		mean	SD	min	max	N* yes/no mean SD	min	max	N* yes/no T-test
	gender	14m/1f				15	9m/8f				17
	age [years]	45,4	18,2	21	74	15	66,1	9,2	50	78	17	0,0018
	weight [kg]	80,9	9,6	62	95	15	74,5	11,0	60	100	17	0,0609
	height [cm]	1,77	0,09	1,60	1,90	15	1,69	0,08	1,58	1,82	17	0,0208
	BMI	25,9	2,3	22	31	15	26,1	3,5	21	35	17	0,9699
	Nicotin					1/7					5/9
	Hypertension**					5/10					17/0
	familiy history of CVD***					0/7					2/8
	diabetes					0/15					5/12
	vascular disease					1/13					17/0
	left ventricular hypertrophy					0/14					6/11
	angina pectoris					0/15					2/15
	claudicatio intermittens					0/14					9/8
	myocardial infarction					0/14					2/15
	death from a CV event					1/13					8/9
		Routine analysis
	creatine kinase [U/l]	518	731	39	2790 13	138	141	11	529	14	0,0886
	creatine kinase isoform MB [U/l]	21,5	10,3	10	40	13	18,2	9,7	5	39	14	0,4090
	lactate dehydrogenase [U/l]	296	111	164	556	13	204	87	128	471	14	0,0283
	troponin T [pg/ml]	112	181	3	597	13	62	106	3	354	15	0,4100
	NT-proBNP [pg/ml]	117	88	5	283	10	74	91	4	233	8	0,4140
	creatinine [mg/dl]	1,0	0,3	1	2	14	0,9	0,3	1	2	17	0,1726
	Na [mmol/l]	150	7	137	161	13	143	8	133	163	17	0,0074
	K [mmol/l]	4,2	0,4	4	5	14	4,1	0,4	4	5	17	0,5596
	Ca [mmol/l]	2,1	0,3	1	2	14	2,3	0,1	2	2	16	0,0114
	leukocytes [g/l]	11,3	3,2	7	17	14	10,5	4,3	5	21	17	0,5380

Table 2 . Top molecules predicted to reverse human arterial protein signature of advanced CVD following CMap analysis.

 2

	CMap name	Mean* n	enrichment p	specificity percent non-null
	arachidonyltrifluoromethane -0.538 2	-0.918	0.01368 0.0261	100
	atracurium besilate	-0.556 3	-0.915	0.00104 0	100
	Prestwick-691	-0.542 3	-0.891	0.0025	0.0263	100
	vinblastine	-0.551 3	-0.888	0.00268 0.0382	100
	isoflupredone	-0.555 3	-0.871	0.00421 0.1083	100
	ouabain	-0.48	4	-0.854	0.00084 0.0263	100
	viomycin	-0.596 4	-0.849	0.00097 0.0576	100
	STOCK1N-35696	-0.511 2	-0.846	0.04797 0.0616	100
	quinpirole	-0.524 4	-0.827	0.00171 0	100
	ajmaline	-0.559 3	-0.827	0.01024 0.0284	100
	lisuride	-0.529 5	-0.816	0.00054 0.0082	100
	thioperamide	-0.479 5	-0.791	0.00078 0	100

Table 3 .

 3 Effect of AACOCF3 during cholecalciferol overload. Arithmetic means ± SEM of serum calcium, phosphorus, CaxPi product and FGF23 C-term concentrations in mice receiving vehicle (CTR) or high-dosed cholecalciferol (vD) without or with additional treatment with AACOCF3 (AAC). *(p<0.05), **(p<0.01), ***(p<0.001) statistically significant vs. control mice.

		CTR	AAC	vD	vD + AAC	n
	Calcium [mg/dl]	10.29 ± 0.23 9.63 ± 0.23 20.01 ± 0.60***	19.00 ± 0.56***	5-6
	Phosphorus [mg/dl]	7.78 ± 0.25	7.23 ± 0.55 5.53 ± 0.11***	5.81 ± 0.14**	5-6
	CaxPi product	79.9 ± 1.8	69.6 ± 5.3	110.3 ± 2.0***	110.2 ± 3.1***	5-6
	FGF23 C-term [pg/ml] 554 ± 52	525 ± 31	172980 ± 18530* 192196 ± 19727* 5-6

Acknowledgements

This work was supported by grants from: the European Union Seventh Framework Program (FP7/2007-2013-603288-SysVasc) for JPS, VL, SVL, CD, FL, BP, JLP, HM, JSSB, JV, AV, JL; the European Union ERA CVD JTC2017 PROACT (ANR-17-ECVD-0006 for JK, GF, CD, JSSB and JPS; 01KL1805 via the BMBF for HM); the Institut National de Santé et de Recherche Médicale (INSERM) and the "Fondation pour la Recherche Médicale" (grant number DEQ20170336759) for JK, GF, CD, JSSB and JPS; the BHF Centre of Research Excellence Award RE/13/5/30177; the Deutsche Forschungsgemeinschaft (AL2054/1-1, VO2259/2-1) and the Berlin Institute of Health (BIH) and the Else Kröner-Fresenius-Stiftung for T.T.D.L., J.V., I.A., B.B., N.S. Immunopathological analysis of cPLA2 was provided by the iPATH.Berlin -Core Unit Immunopathology for Experimental Models of the Charité -Universitätsmedizin Berlin (Berlin, Germany).

Authors' contributions

JPS, JK, JLB, AV, CD, JV, IA, HM, BP, FL designed research; MM, SVL, VL, AL, TTDL, JV, IA, BB, NS, GF, CD, performed experiments; JPS, JK, JSSB, JLB, AV, CD, JV, IA, HM, BP, FL analyzed and interpreted data; JSSB, JPS, JK, JLB, AV, CD, JV, IA, HM, BP, FL wrote the manuscript. All authors gave final approval of the version to be published.

Disclosure Statement

All authors disclose that they have no potential conflict of interest.


Xiaoning Wang

Chuanhong Zhou

Eynard Benoit
email: cbenoit.eynard@utc.fr

Xuan Zhao

Research on Modeling of the RoHS Compliance System on UML

Keywords: UML, ROHS, Product Lifecycle Management, conformity model, XMI

With the application of the ROHS standard in the products management, the Product Overall Lifecycle Management has been increasingly complex. Based on the UML2.0 modeling technology, this paper discussed the ROHS conformity system modeling process by the modeling tool VP-UML. According to the information model, the core data about Product Life Cycle can be converted to the XMI document, and be used to get the optimization of the model.

Introduction

The goal of EU Green environmental protection instruction RoHS (Restriction of Hazardous Substances) is to eliminate six toxic substances in electronic and electrical products. Lack of basic theory and methods in RoHS conformity information management and product design in China results in that some enterprises can't deal with RoHS directive Implementing intra-organizational RoHS compliance management system, which needs a feasible enterprise management system and integrate it into the PLM system, is a kind of feasible solutions. This paper adopts the mainstream visual modeling language-UML in order to ensure the structure of the system design good and the management system development quickly and efficiently. UML can play a considerable role in the software development with its good inheritance of the development process, life stage and implementation technology. This paper displays how to use UML2.0 for object-oriented analysis and modeling from the ROHS conformity model development process, with emphasis on Model building management process.

System modeling with UML

UML (Unified

Use case diagram

Use case diagram is used to describe system requirements which have user and case as two core elements from the user's point of view. User is the system-use and system-interactive object outside of the system [START_REF] Medvidovic | Modeling software architectures in the unified modeling language[END_REF]. Case is a group of description about action sequence including variable, the system implementing these actions, and generate observed results which transfer the value of specific participants.

As indicated in Fig. 1, the user (actor) is raw materials suppliers, components suppliers and Products business. Three main users complete the related operation of whole system. First, raw material suppliers need to provide the material declaration of conformity, generate material database and modify materials database. And the components business in the middle position needs to complete information interaction and provide product information management model. As the top, products business is mainly responsible for the product information optimization calculation and feed back the information to the downstream. In the final, optimal results and BOM have a integration and generating conformity BOM information.

Activity diagram

As shown in Fig . 2 shows, this activity diagram use three lane to describe the role of business scope, the name of each lane on behalf of the business role, elliptic representative business activity, rectangular representative business entity. The specific business activity is: Material supplier will provide the materials to detection mechanism with professional appraisal qualification. If the test content to comply with relevant standards, a written proof can be deposited in the product database, in order to reference. Then components suppliers will integrate harmful material testing data into product material database, and uploaded to the product business PLM system. Product business then make BOM and material database combination, generate conformity BOM, and finally put it into the corresponding XMI file for later use optimized.

Class diagram

Description and management of the object are conducted with a mechanism of Class. First of all, search object from the problem domain determined by requirements, abstract into class and build relationships .And then analysis the interaction between objects around the cases and reconstruct the relationship among the objects [START_REF]Unified modeling language super structure specification[END_REF]. Class diagram shows the static relationship between class (object), mainly including relevance, polymerization, generalization (inherit), etc.

XML of UML model

In the UML modeling software, it offers a variety of operating mode with interoperability. The following is a meta-model class definition of "Substance": <ClassName="Substace"UserIDLastNumericValue="0"Root="false"QualityScore="92"QualityRe ason="1122"PmLastModified="2012-07-05T11:05:56.012"PmCreateDateTime="2012-05-09T21: 55:38.949"PmAuthor="Administrator"Leaf="false"Id="otxHjOyB0xiUSgmU"Documentation_plai n=""BusinessModel="false"Abstract="false"Visibility="public"ConnectToCodeModel="1"Busines sKeyMutable="true" Active="false"> In the above class diagram XMI document description, the source document is a direct modeling software conversion, so the text includes not only the direct description of class, but also the description of related class diagram. In data processing, this paper adopted the XML data file which is relatively simple for data processing. A system may contain many different models [START_REF] Lee | Computing simple and complex matching's between XML schema for transforming XML documents[END_REF]. Each meta-model class is expressed by XML element: element name corresponding class name, elements definition listing the class attribute, the references of association end related to class and inner classes. The order of the attributes and the relationship does not make the request.

The XML document content is only a XML grammatical form description for class diagram which have limited help data concise effectiveness required for this article, so we need a reconversion to achieve this effect. Below is a XML file derived from content which can be found miscellaneous and low operable.

-<Project Xml_structure="simple" UmlVersion="2.x" TextualAnalysisHighlightOptionCaseSensitive="false" Name="IPC1751" ExporterVersion="8.2.0" ExportedFromDifferentName="false" DocumentationType="html" CommentTableSortColumn="Date Time" CommentTableSortAscending="false"

Author="Administrator"> +<ProjectInfo>----------------------------------------------------------------

XSLT transformation on XMI document

XSLT (eXtensible Stylesheet Language Transformation) is a part of W3C developing standard XSL (eXtensible Stylesheet Language), which is responsible for the conversion of the XML document.

XSLT defines the conversion rules of the source tree and results tree corresponding part, namely template rule, which with the tag < XSL: template >, can indicate that treatment of objects (element/attribute), the means of processing or conversion results. Rules will be instantiated, when meet document element of the root node in XSLT processor. From the DOM tree as shown in Fig. 4

Conclusions

In the production of full life cycle of the product design, integration and optimization about product information is the key technology to reduce the redundancy of the product management and upgrade real-time interactive management of the product information at all levels of manufacturers. Using object-oriented visualization modeling technology to achieve information interaction at all levels of physical layer, simplify the model calculation complexity and achieve the optimization design real-time guidance of all manufacturer production process.

  Modeling Language) is a kind of object technology support visual modeling language. Static modeling mechanism of UML includes use case diagram, class and object diagram, package diagram, construction diagram and layout diagram. Kinematic model of UML includes state diagram, sequence diagram, cooperation diagram and activity diagram [1].

Fig. 2 Fig. 1

 21 Fig. 2 The activity diagram of ROHS compliance system

Fig. 4

 4 Fig. 4 The corresponding DOM tree of UML class diagram

  , the data are included in a table, so table, as anchor (namely positioning poi), can be expressed as the form of XPath expression. The expression "HTML/body/model [n] / class [n]" specifies the path from the root < HTML > to anchor. According to the rules about XPath node information extraction, information will be stored in the selection rules library as XSLT file form. The rules specify how to retrieve the user information from XHTML document converted by the external program. If we need to extract the subsidiary attribute and related text of Class[n] in Model [n], XSLT style-sheet can be written briefly as follow. In the above style-sheets, it's just a simple attribute value extraction. We can use the same method of nested extraction to get add value under text. After determined the style sheet, the software will automatically convert it to the corresponding XML documents.

	< xsl:template match =/>
	< xsl:for-each select =html / body /model[n]/class[n]/modelchildren>
	< attribute >
	< xsl:value-of select =attribute[n]/>
	< / attribute >

Acknowledgement

This work is supported by National Natural Science Foundation of China (No. 61073122).