Caroline Ho

Anita Dehoux

Joël Alexis

Loïc Lacroix

Olivier Dalverny

Sébastien Châtel

Bruce Faure

Understanding mechanisms of adhesion of SiO2 thin films evaporated on a polymeric substrate

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

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible Any correspondence concerning this service should be sent to the repository administrator: tech-oatao@listes-diff.inp-toulouse.fr Abstract : A better understanding of mechanisms of adhesion between a 200 nm thick silicon dioxide layer and a 4.5 µm thick polymeric hardcoat is indispensable for an efficient adhesion at the interface. To reach this purpose, focus is placed on two axes: finding an applicable and effective method to quantify adhesion and in parallel, characterizing mechanical properties of materials composing the system. The second axis is needed to obtain data to feed modeling codes, enabling a better analysis of the adhesion experiment. Modulus of modified Si0 2 was found to be roughly 20% higher than reference Si0 2 , by nanoindentation. AFM experiments showed no difference between modified and reference Si0 2 • Currently, an investigation to detect cracks at the interface of interest for micro-tensile test is ongoing. Adhesion tests, such as micro-compression will be performed as well.

Context

Anti-Reflective Stack

(n m)

To improve wearers' experience, ophthalmic lenses made of plastic polymeric substrates are coated with functional treatments, bringing anti-sratch and anti-reflective properties. These treatments are composed of 5 to 15 layers, ranging from micrometers to First micro-tensile experiments suggest that most noticeable cracks are located at the hardcoat/substrate interface. This was determined by EDS on fractured areas, after tensile stress. Modulus of modified Si0 2 was found to be roughly 20% higher than reference Si0 2 by nanoindentation. Considering the high standard deviation of moduli measured by AFM, no significant difference between moduli of modified and reference Si0 2 was observed by AFM. However, an important difference between moduli of Si0 2 on lens and on Si wafer was observed. This exposes unexpected influence of substrate on mechanical measurements using AFM, which has been hypothetically attributed to impact of Peak Force high frequency oscillations on viscoelastic substrates. Currently, an investigation to detect cracks at the interface of interest after micro-tensile test is ongoing. Other adhesion tests, such as micro-compression will be performed as well.

First Results

Micro-tensile test

1

  This is an author's version published in: http://oatao.univ-toulouse.fr/23316 To cite this version: Ho, Caroline and Dehoux, Anita and Alexis, Joël and Lacroix, Loïc and Dalverny, Olivier and Châtel, Sébastien and Faure, Bruce Understanding mechanisms of adhesion of SiO2 thin films evaporated on a polymeric substrate. (2016) In: 43th International Conference on Metallurgical Coatings and Thin Films (ICMCTF), 25 April 2016 -29 April 2016 (San Diego, United States). (Unpublished) Open Archive Toulouse Archive Ouverte Underst nd•ng Mechonis s o Adhesio o , m De os• ed on a Po ymeric Substr C. Ho 1 . 2 , A. Dehoux 2 . J. Alexis 1 • L. Lacroix 1 . O. Dalvern!:J 1 . S. Châtel 2 . B. Faure 2

  at first crackingoccurrenceinMPa D.C. Agrawal, R Raj, "Measurement of the ultimate shear strength of a metal-oeramîc Interface�, Representation ofForceversusTip-Samp!eSeparation Acknowledgement : Mickael Febvre from Bruker for performing the AF M experiments


Ayoub Tahiri

Daniel C Che

David Ladeveze

Pascale Chiron

Bernard Archimede

Flood Routing Model for Water Resources Optimization Models based on a Network Flow

Keywords:

Real-time operations of multireservoir models should contain a flow routing procedure to predict the impacts of the observed and/or predicted inflows hydrographs on the downstream parts of the river system. Solving separately the hydraulic and operational constraints is only possible when the water resources management model is based on an evolutionary algorithm. Conceptual models for channel routing like Muskingum model can be imbedded within the mathematical formulation of the optimization algorithm when it is linear. Nevertheless, no channel routing model has been developed for algorithms based on network flow approach, whereas they are commonly used to manage operations of multi-reservoir. The purpose of this work is to propose a special network structure which allows the consideration of river routing. The routing model is considered as a network flow and the parameters are calibrated using a genetic algorithm. Simplicity and accuracy are the main reasons for this choice, which are relevant characteristics to be adopted in optimization models. The flood routing model is validated on the Wilson event, and the results obtained are compared to the Muskingum's model results. A real example application on a reach of 40 km in the south west of France provided excellent results.

II. Objectives

  a conceptual flood routing model based on a network flow approach. 2. Improving water resources optimization model by taking flood routing into account. 3. Validating the developed model on a real river which would improve its management.

Figure 2 :

 2 Figure 2: Residual Stor age Model

Figure 7 :Figure 8 :Figure 9 :Figure 10 :Figure 3 :Figure 4 :Figure 5 :Figure 6 :

 789103456 Figure 7: Ar r ats' Water shed

Figure 1 :

 1 Figure 1: Flood routing


Muhammad Ali Memon
email: muhammad-ali.memon@enit.fr1agnes.letouzey@enit.fr2bernard.archimede@enit.fr

Agnès Letouzey

Bernard Archimede

Toulouse Archive Ouverte

Food needs are growing rapidly with the growing population especially in case of developing countries where food preservation technology is not that much mature or even not available. It results enormous increase in prices of food commodities and higher growth rate of inflation each year. Additionally population is dispersed in the country in form of very small and large cities. Similarly, food grown in the country is also dispersed in the different areas of the country. This is highly necessary to use effective modes of transport, to collect food from these regions and distribute it with equilibrium in all the human living regions. Additionally to reduce the wastage of food in case of food shortage especially in some part of the African continent region. Food products and ingredients found and grown in one region are transported to another region, making it possible to use and eat them where they are not found. Small farmers or food producers to reach the scattered areas to distribute their products at time and cost effectively could not purchase their own vehicles, ships or planes to transport their goods. These companies contact third party transporter companies to transport their products, same as a courier company. These companies are called Third Party Logistics enterprises (3PL). These 3PL enterprises take the responsibility of performing logistics functions entirely or partially of an organization. They manage their own fleet of vehicles which drive in a certain and region. The diversity of clients due to different kind of product distribution (fresh, frozen etc.), distance from production companies and distribution cities, the size of the transport network make it even very complicated for a 3PL transporter to manage transportation planning and make efficient use of their resources. Moreover a single 3PL transporter also covers a limited region; they must collaborate with other 3PL enterprises which operate in other regions to make the delivery of the products to faraway locations. Complexity of transportation planning increases further in case of multiple 3PLE enterprises collaborating for the fulfilment of a transport order(s). The objective of this paper is to present a collaborative and distributed model for transportation planning activities aimed at better utilize transport resources by grouping several orders of transports for each effective displacement.

Introduction

In developing countries, qualities of roads do not let vehicles travel with low friction or cover the distance with the estimated standard time for that distance, causing delay in the transport. Additionally poor maintenance of transporting vehicles also decreases vehicles performance and increases CO 2 emission. Transporters providing the transport to small and medium enterprises are very rare and hence cannot cover each area in the country. Delay in transport product delivery generates risk of degradation and contamination of food products. Other factors of degradation involve not following sanitary requirements as per standards set by International organizations like ISO. Thus resulting wastage of food and loosing profit earning opportunities. Transportation costs are increasing due to the steady increase in oil prices. These costs have become a major concern for the small and medium food generators, because it increases the cost of the raw materials that they procure as well as the distribution of products to consumers. The impact of transportation costs on products don't permit usually those companies which want to reach faraway clients and manage their own resources (vehicles, ships, aircraft, etc..) to transport their goods. The effort to reduce the cost of transportation has encouraged the emergence of new thirdparty enterprises specializing in logistics and transport, commonly known as 3PLE (Third Part Logistics Enterprise [START_REF] Marasco | Third-party logistics: A literature review[END_REF]. These 3PLEs mutualize the exploitation, warehousing and transportation resources between several companies. They take charge of whole process of transportation from loading products since suppliers' warehouses to the distribution of goods to customers. The increase in number of transportation orders, diversity of clients due to different kind of business activities, distance from production companies and distribution sites, the size of the transport network make it even very complicated for 3PLE to manage transportation planning. To fulfil customers' demands and improve the performance of supply chains, it must manage its own resources and often collaborate with other companies 2PLE (carriers) and 3PLE.

In this paper we propose a framework for transportation planning for a 3PL enterprise in a distributed manner. This framework can help improve transportation for emerging countries by utilizing the resources at maximum. This framework will also reduce the transportation cost for small farmers or food products manufacturers by grouping multiple transport orders together which is the major concern for developing countries. The objective is to evaluate the ability of POVES (Path Finder, Order, Vehicle, Environment, and Supervisor) framework for transportation planning process. After a state of the art on the latest research on transportation planning, we describe POVES model and illustrate its application with a case study. Finally, we conclude with future work.

Related Works

Several approaches have been proposed to solve transportation planning problem. J. Sauer et al [START_REF] Sauer | Integrating transportation in a multi-site scheduling environment[END_REF] proposed a centralized approach with a global scheduler, which schedules transportation planning activities. They model the problem by a 5-tuple(R, P, O, HC, SC), where R denotes the set of required resources, P the set of products, O the set of actual orders, and HC and SC the sets of hard and soft constraints, respectively. They use a rule-based approach and heuristics to produce several scheduling strategies. This approach is centralized and is limited to the planning of transportation activities of a single enterprise. Today, the enormous size of the transport networks requires, to realize the plan with collaboration of many transportation enterprises. These enterprises most often, wish to keep certain confidentiality of their organization, their models, their methods and their data. The need for confidentiality limits the scope of centralized approaches.

A.Baykasoglu et al [START_REF] Baykasoglu | A multi-agent approach to load consolidation in transportation[END_REF] proposed a multi-agent approach to address collaborative transportation problem. This approach is based on cooperation between transport-order agents and truck agents, which propose grouping multiple orders together in a vehicle. Agents communicate with each other in order to choose the best economical way to transport the order. In this approach, transport order agent is bound to accept the proposition from one truck agent, which provide a nonstop delivery from origin to destination. However, in reality considering enormous size of the transport network, a truck rarely alone transports a transport order. A transport order requires, most often, several trucks. R.Sprenger et al [START_REF] Sprenger | icomas: an agentbased system for cooperative transportation planning in the food industry[END_REF] proposed a multi-agent system for cooperative transportation planning. This system decomposes overall transportation problem into sub problems and solve those sub problems on autonomous basis with Ant colony Optimization approach. This work neither explains suitably decomposition methodology and nor decomposition's effect on overall transportation problem. Additionally it does not consider privacy issues between manufacturers and they don't take into account multiple orders together while sharing vehicles.

S. Zegordi et a l [START_REF] Zegordi | A multi-population genetic algorithm for transportation scheduling[END_REF] study integrating transportation and production scheduling by considering multiple sourcing in a two-stage supply chain scheduling problem. In which the first stage is composed of multiple suppliers distributed over various geographic zones. In the second stage, vehicles transport jobs from suppliers to a manufacturing company. This transportation scheduling is based on genetic algorithm. Manufacturing company splits the order between multiple suppliers according to quota. Then, each supplier transports its quota of raw material to the single manufacturing company, which achieves an assembling of all the received raw materials. Suppliers located in the same geographical zone could transport their raw material to the manufacturing company by sharing vehicles. This approach is a centralized approach which will face the confidentiality issue. Additionally, due to critical economic conditions, this is not acceptable that vehicles return empty to suppliers that are resulted from this method.

Tchapnga et al [START_REF] Takoudjou | A Hybrid multistart heuristic for the pickup and delivery problem with and without transshipment 9th International Conference on Modeling[END_REF] propose a multi-agent heuristic to address the transport problem with transhipment. Their methodology is decomposed in four steps. In first step they calculate PDP (pickup and delivery solution) without transhipment/ Cross Docking solution for all random requests. In second step they try to optimize the PDP solution with VND (Variable Neighbourhood Descent method) using Path Relinking. VND consists of three operators; SWR (swap requests between routes), RNR (remove and insert a request) and ADR (Advance or delay request). Path relinking is to transform new iteration with existing solution (I did not understand it properly). In the third step they calculate PDPT (PDP with transhipment) solution and compare it with PDP solution and keep the best one. This whole procedure is repeated to the number of iterations. They do the grouping, but vehicles number is not fixed, they calculate PDPT solution for each PDP solution whether it may be unnecessary in some situations. This work makes an assumption that number of vehicles is not fixed and if no vehicle can satisfy a request because of the noncompliance with the constraints (vehicle capacity, time windows etc.), a new route is created with the new vehicle to welcome the considered request. However in our work we consider that number of resources (vehicles) is fixed, they are not infinite, so if a vehicle cannot satisfy the request, that request has to wait until its return or some other vehicle can satisfy the request that follows the same route of the request entirely or partly. Secondly, this method first calculates PDP solution without transhipment and then improves it, by optimizing it and then it destroys the PDP solution to obtain PDPT solution with transhipment. In our method we try to find PDP solution if we cannot find PDP solution then we find PDPT solution. It is unnecessary to calculate PDPT solution for a request, weather if it can be transported without transhipment. It is possible that results of both methods may generate similar results. But this method is much lengthier than ours.

In further studies, a simulation framework is presented in [START_REF] Sprenger | A simulation framework for assessing the performance of cooperative transportation planning algorithms[END_REF] for assessing the performance of cooperative transportation planning and isolated transportation planning. Groupage systems [START_REF] H¨ulsmann | Collaborative transportation planning in complex adaptive logistics systems: A complexity sciencebased analysis of decisionmaking problems of groupage systems[END_REF] are introduced, which are defined as logistics an inter-organization system that interchange required information and manages capacity balances between different independent transporters. M.Mes et al [START_REF] Mes | Interaction between intelligent agent strategies for realtime transportation planning[END_REF] study the interaction between intelligent agent strategies for realtime transportation planning. A multi-agent theoretical approach on dynamic transportation planning is given in [START_REF] Kozlak | Multi-agent environment for dynamic transport planning and scheduling[END_REF]. D.Yazan et al [START_REF] Yazan | Analyzing the environmental impact of transportation in reengineered supply chains: A case study of a leather upholstery company[END_REF] examines the environmental impact of transportation and how it can be altered by showing current and reengineered supply chain through a case study.

Fig:1 POVES Model

POVES MULTI-AGENT MODEL

Description of model

The POVES multi-agent model (Figure . 1) is developed for collaborative transportation planning activities. It is inherited from SCEP multi-agent model [START_REF] Xu | A distributed multi-agent framework for shared resources scheduling[END_REF][START_REF] Archimede | Reactive scheduling using a multi-agent model: the SCEP framework[END_REF]. Limitations restricted SCEP for transportation planning are given in [START_REF] Memon | Towards a distributed framework for transportation planning: A food supply chain case study[END_REF], due to that POVES emerged after overcoming these limitations. POVES introduces an indirect cooperation between two communities of agents, Order agents called (O) and vehicle agents called (V), leading to a high level of co-operation. Each order agent manages one transport order from first party logistics (1PL). Each vehicle agent manages one vehicle of the organization. A supporting agent "Path Finder" elaborates for a managed transport order the traveling route between pickup and delivery locations. The cooperation between order agents and vehicle agents is performed synchronically through the background environment agent E. The model functioning is controlled by the supervisor agent S. The detail working procedures and dynamic of the model is introduced in next section.

Dynamic of model

In this model transport activities are associated to elementary travelings achieved by vehicles. A transport activity (TA) is a nonstop travel from the loading location to the unloading location. However, transportation planning needs the definition of all sequential TAs necessary between origin and destination for each transport order. This set of sequential TAs is important for each order agent for evaluating the predicted route to go from origin to destination. On the other hand, vehicles require most often the grouping of multiple orders according to their maximum loading capacity. Before starting the scheduling process, all order agents are invited by the supervisor agent to contact the Path Finder agent in order to obtain their possible traveling routes from their own pickup to delivery locations. Based on transportation network information graph and 3PL enterprise vehicle, TAs between cities stored in its database, Path Finder agent elaborates for the managed transport order the traveling route. This route is a sub-graph of the overall transportation network graph, where each arc corresponds to a TA achieved by 3PL vehicle. Order agents send to the environment tasks which represents the different requests for performing activities by vehicle. A task is an execution of TA. Each task in the environment is associated with one TA, which may be performed by several vehicle agents. The set of tasks related to the routing followed by a transport agent constitute its intervention domain. Two orders may have tasks that require same TA and these two tasks can be and may be grouped to perform by the vehicle at the same time, if these two tasks lie in close time interval and sum of their capacity is less than or equal to the capacity that the corresponding vehicle can accommodate. In perfect correlation with the model definition, each task only concerns one order agent. But some TAs can belong to the intervention domains of several vehicle agents, because multiple vehicles may achieve the same TA. The position format of task A is [(S, F), N], where (S, F) represents a continuous temporal interval between a starting date S and a final date F, and N represents the name of vehicle executing task A. Each task has four positions, wished position (WP), effective position (EP), potential position (PP), and confirmed position (CP). The WP is the position requested by the order agent. The EP results from the scheduling of all the tasks associated with the propositions collected from the environment. The PP results from the scheduling of one task associated with a proposition collected from the environment. The CP is the final position after all the scheduling process. The supervisor agent provides functions of creating the agent society, generating the inside tasks and initializing the environment. Then, the supervisor agent triggers the cycle of cooperation process by activating the order agents and telling the vehicle agents to wait. The order agents firstly ask for EP and PP of the associated tasks from the environment. The environment sends the results back, of course the result is null in the first cycle. The order agents schedule the operations which have not been validated, and influence the associated tasks by alternative WP. If the WP of one task is the same as the EP and PP, order agents will make the confirmation. At last, the order agents send CP and WP of the associated tasks to the environment. Each order agent performs its actions simultaneously but remains independently from others. It will inform the supervisor agent once its actions are finished. Once the end of the action from the last order agent has been recorded by the environment, the supervisor agent activates the vehicle agents and sends the wait signal to the order agents. The vehicle agents firstly ask for the CP and WP of the tasks belonging to its intervention domain from the environment. The environment sends the results back; the vehicle agents record the CP and schedule the tasks which are not definitely positioned. They influence these tasks by alternative EP and PP to the environment. Each vehicle agent performs its actions independently and informs the supervisor agent as soon as its actions finished. When the end of the action from the last vehicle agent is recorded, the supervisor agent finishes the first cycle of the cooperation and starts the next cycle immediately. In each cycle (except the first one), at least one task should be confirmed to avoid the deadlock problem (Figure . 2).

Fig:2 POVES sequence model

At each cycle, the choice of the best path in the sub-graph is achieved by order agents regarding criteria which may be shortest distance, earliest delivery or minimum cost to reach the delivery location. At each cycle the vehicle agents group the transportation tasks regarding their pickup time, associated quantity and achieve a plan based on Vehicle Routing Algorithm. Nevertheless these algorithms may introduce several empty travels. The alternation cycle between the activation of order agents and vehicle agents will be repeated until the CP of all the environmental tasks is fixed. When all tasks are confirmed, there is no WP from order agents anymore. The alternative (opt) area will be executed and the supervisor agent will terminate the environment, order, and Path finder and vehicle agents. The whole scheduling process is finished.

POVES MULTI-AGENT MODEL

Case study description

We consider a simple case study of transporting dairy products from manufacturers to large cities. We name this case study "Food Supply Case Study" (FSCS). A 3PL enterprise is responsible to transport those dairy products. 3PL transportation network is located in the central region of Cameron. The network comprises of seven sites Mokolo (MO), Waza(WA), Maroua(MA), Yagoua(YA), Kaélé (KZ), Garoua(GA) and Kousseri(KO)(Figure . 3). From which two are Supplier sites (KO, MO), three are Intermediate Distribution Centers(WA, MA, KA) and all of the eight sites are customers. Supplier at MO produces two products (P1:Yogurt and P2:Cheese). Supplier at KO produces two products (P3: Milk and P4: Cream).

All of these four products need to be delivered at each customer site. 3PL uses these Intermediate distribution centres (IDCs) for transit purpose, to provide the facility to store the products on temporary basis. They are equipped with refrigeration facility and stock the products until they are picked by vehicles for delivery to another IDC or to city markets. 3PL manages its own fleet of 7 transportation vehicles (V1, V2, V3, V4, V5, V6), which are assigned default Start Locations (Loc), Avail (Availability Time at Loc) and MWT (maximum waiting time for an order when it is taken into account by the vehicle) by its management. The activities represent a direct nonstop travel from origin to destination. For example, a direct travel from Mokolo to Maroua is an activity "MokoloMaroua", which is performed by V1 as shown in Table 1. A return travel from Maroua to Mokolo is another activity "MarouaMokolo" that is also performed by the same resource V1. Each vehicle follows the rule "FIFO-CUMUL". FIFO represents traditional "First in First Out" data structure, means vehicle will serve transport orders on FIFO basis. CUMUL represents cumulative, means vehicle can group more than one orders for delivery depending on its maximum capacity. Vehicles are also equipped with refrigeration facility. They charge different price for transportation, which is pre-negotiated between suppliers and 3PL enterprise management. Each vehicle has different load capacity. In FSCS we consider a standardized box for packaging, for which we undertake following assumptions.

Assumption 1: A box of same volume, dimension and size is used for all kinds of products. After packaging, box has the same weight for all products. Assumption 2: For all boxes, number of products is constant. However quantity that box can contain for each product depend on the kind of product and, not on the box. Assumption 3: The number of boxes in a vehicle is always an integer constant.

Usually, in a 3PL enterprise, vehicles visit several sites. If the number of sites is superior to 2, the number of activities depends on the network organization. In our example, we assume that the number of sites visited by each vehicle is equal to 2. It means that each vehicle is reserved to perform two activities. Let us notice A and B the two sites and t AB and t BA the two activities. When a vehicle is at site A, t AB is the next activity and t BA is the return activity. When a vehicle is at site B, t BA is the next activity and t AB is the return activity.

For the Transportation network estimated distance and time between two sites are specified in the Table 2. Transportation time between two sites may variate depending on several conditions. If t AB represents transportation time from site A to site B and t BA from site B to site A, then t AB != t BA .

1. If vehicle is loaded when going from A to B and is empty when coming back from B to A or vice versa. Loaded vehicle will take more time to travel than when it is empty.

2.

If vehicle is fully loaded when going from A to B and it is partially loaded when coming back from B to A or vice versa. Fully Loaded vehicle will move slower than when it is partially loaded.

3.

If vehicle drive through a route which is inclined, so going upwards to the route is slower than coming downwards from the same route.

4.

If there might be disturbances of traffic jam or vehicle broken or bad weather in either of the direction of going to A from B or coming back to B from A.

Table 2. Estimated distance and time between sites of Transportation network

Sites KO GA WA MA MO KA YA KO - - 192km (2h30) - - - - GA - - - 200km (2h) - - - WA 140km (2h30) - - 130km (2h30) - - - MA - 200km (2h) 130km (2h30) - 120km (1h30) 104km (1h30) - MO - - - 120km (1h30) - - - KA - - - 104km (1h30) - - 17km (1h) YA - - - - - 17km (1h) -
In the context of this paper, we don't consider above time variation conditions. We use the same estimated time for going form site A to B and vice versa. In order to keep this case study simple, we consider that loading and unloading time of products to IDCs and supermarkets is included in traveling duration.

Illustrative Example

In our case study, we take a wallet of 4 Transportation Orders (TO) as shown in Path Finder Agent receives input PL and DL of a TO and for it elaborates elementary traveling activities (sub-graph). Table . 4 describe the precise routing used by TO proposed by Path Finder Agent. For each step of the travel, task number, associated activity, colour and best estimated duration (taken from Table . 2 are given. Different colour is assigned to each activity for illustration purpose to distinguish similar activities. Same colour represents that two orders have two similar tasks that belong to the same activity, which can be grouped and performed by the vehicle(s) together probably at the same time. For example first task of TO1 and TO2 belongs to same activity "MokoloMaroua". These two tasks can be performed probably at the same time by the same resource. Order agents realize an infinite capacity planning for all tasks of their transport orders (TO1, TO2, TO3 and TO4), and generate for them the wished position as shown in Figure . 4(see section 3 for detail). After, the wished positions are sent to the environment. Taking into account their published basic activities, each vehicle agent perceives the different tasks to schedule. Then, vehicle agents realize simultaneously a finite capacity planning for all the perceived tasks considering their capacity and maximum waiting time duration for a transport order. For this, firstly vehicle agent sorts the perceived tasks according to the corresponding activity. Tasks associated to the same activity are arranged in FIFO order taking into account the ascending pickup date. Secondly, vehicle agent groups the different tasks of an activity according to the maximum waiting time duration and the capacity of its associated vehicle. If associated vehicle's current standing position is at the beginning of the next activity then vehicle executes the activity for this group of tasks. If associated vehicle is not present at the next activity but it is at the beginning of another return activity, then vehicle agent first executes displacement from return activity and then generates the transportation plan for that next activity. Preventing the empty displacement from return activity, it first regroups the tasks at the return activity for delivery. Tasks that could not become part of the group in first place due to vehicle's finite capacity limitation are planned later on its return to the same activity. Tasks which are planned later may arrive late at their delivery location.

We can see in Figure . 5 the gant chart resulting from the complete planning process computed by "POVES" for all four TOs. As we stated earlier that the choice of the best path in the sub-graph is achieved by order agents regarding criteria, which in our case is objective attribute that is part of TO 9-tuple. Keeping the other attributes constant, if we set the value of objective attribute (TO1=Early, TO2=Early, TO3=Less costly, TO4=Less Costly), then we get the planning as shown in

Conclusion

In this paper we discussed a collaborative transportation planning problem and to solve that, proposed a distributed multi-agent framework called "POVES" which functions on the cooperation between two communities of agents (order agents and vehicle agents). In POVES, firstly Path Finder Agent elaborates, when solicited for each order the traveling routes between pickup and delivery locations. Secondly Order agents offer transport jobs through sequential auctions and vehicle agents compete with each other to serve those jobs. Vehicle agents propose grouping these jobs together to execute them simultaneously.

There are several directions for future research. Instead of one 3PL, multiple 3PL transporters can collaborate with each other to deliver the orders and how these 3PL enterprises will collaborate with each other needs to be investigated. Other parameters like size, type and weight of the transported products have to be considered and how much they effect on over all planning process. Agility is a very important factor in such kind of dynamic systems that need to be addressed effectively. Issues like traffic delays, vehicle breakdown and penalties have to be researched. To keep the case study simple and to facilitate the method understanding, we restricted the number of sites visited by a vehicle to two. The taking into account of more than two sites is also one of our future studies.

  Figure.2 illustrates the sequence diagram of POVES model.

Figure 3 .

 3 Figure 3. Food Supply Case Study (FSCS)

Figure 4 .

 4 Figure 4. Wished Position for all transport orders

Figure 5 .

 5 Figure 5. Grouped planification when Objective attribute for all TO1=TO2=TO3=TO4= Less Costly

Figure. 6 .

 6 If we change TO1 to Less Costly from Early, then we get the planning as shown in Figure. 7. If we set TO1 back to Early but change TO2 to Less Costly from Early, then we get the planning as shown in Figure.8. Change in objective attribute of TO3 and TO4 does not affect their planning, due to 3PL vehicle's availability in our case study.

Figure 6 .Figure 7 .Figure 8 .

 678 Figure 6. Grouped planification when Objective attribute for all TO1=TO2=Early & TO3=TO4= Less Costly

Table 1 . 3PL Vehicles and activities Resource Loc Capacité Avail MWT Règle Activité

 1

	V1	Mokolo	200	7h	1h	FIFO-CUMUL	MokoloMaroua MarouaMokolo
	V2	Maroua	100	9h30	1h	FIFO-CUMUL	MarouaKaélé KaéléMaroua
	V3	Kaéle	100	12h	1h	FIFO-CUMUL	KaéleYagoua YagouaKaéle
	V4	Kousseri	200	7h	1h	FIFO-CUMUL	KousseriWaza WazaKousseri
	V5	Maroua	150	9h30	1h	FIFO-CUMUL	MarouaWaza WazaM
	V6	Maroua	50	9h30	1h	FIFO-CUMUL	MarouaGaroua GarouaMaroua

Table 3 .

 3 TO arrives in the system as 9-tuple(O,P, PL, DL, PT, DT, PD, DD, and PQ) of attributes, where O is Objective(Less costly, early delivery), P is Product ID, PL stands for Pickup Location of supplier from where vehicle loads the product packages. DL represents Delivery Location of customer where order should be delivered. PD and PT stands for Pickup Date and Pickup Time. On this date and time, TO is ready for loading at supplier's warehouses. Similarly DD and DT are Delivery date and Delivery time. DD and DT are the latest date and time on which TO should be delivered at customer site. Finally PQ is the Product Quantity (number of boxes).

Table 3 . Transport Orders (TO)

 3

	NO	O	P PL DL PD	PT	DD DT PQ
	1 Moins Cher P1 MO KA 1/1/14 6h 1/1/14 19h 50
	2 Moins Cher P2 MO YA 1/1/14 7h 1/1/14 20h 50
	3 Moins Cher P3 KO MA 1/1/14 6h 1/1/14 19h 50
	4 Moins Cher P4 KO YA 1/1/14 7h30 1/1/14 20h 50

Table 4 . Routing table

 4

	TO Routing Task	Activity	Color Time
	1	1	1 MokolaMaroua 2 MarouaGaroua	1h30 2h
			1 MokolaMaroua	1h30
	2	2	2 MarouaKaélé	1h30
			3 KaéléYagoua	1h
			1 KousseriWaza	2h30
	3	3	2 WazaMaroua	2h30
			3 MarouaGaroua	2h
			1 KousseriWaza	2h30
	4	4	2 WazaMaroua 3 MarouaKaélé	2h30 1h30
			4 KaéléYagoua	1h


Keywords: Iron, metal, transporter, IRT1, regulation, transcription, endocytosis, transceptor

Transporters are at the center of regulatory modules allowing the optimal assimilation, distribution or efflux of substrate molecules. The IRT1 root metal transporter represents a textbook example where detailed regulatory networks have been shown to integrate several endogenous and exogenous cues at various levels to regulate its expression and to fine tune iron uptake. Here, we summarize recent advances in the dissection of the transcriptional and post-translational control of IRT1 by its various metals substrates and discuss the emerging role of IRT1 in the direct sensing of non-iron metals flowing through IRT1 to drive its metaltriggered degradation. We propose that transceptors are likely a common theme in the regulation of nutrient transport by sensing local nutrient concentrations.

The bifunctional transporter-receptor IRT1 at the heart of metal sensing and signaling

Virginia Cointry and Grégory Vert # Laboratoire de Recherche en Sciences Végétales, UMR5546 CNRS/Université Toulouse 3, 24 chemin de Borde Rouge, 31320 Castanet-Tolosan, France.

# corresponding author : Gregory.Vert@lrsv.ups-tlse.fr ; +33534323810 Twitter account : @GregVertLab ORC ID Grégory Vert : 0000-0002-0844-9991 Transcriptional regulatory networks driving IRT1 expression III.

Ubiquitin-mediated endocytosis and degradation of IRT1

IV.

From IRT1 transporter to IRT1 transceptor

V. Conclusions

The plasma membrane represents a selective barrier between the extracellular medium and the cytoplasm for ions, small molecules and macromolecules in all living cells. Membrane transport proteins control fluxes of their substrates across membranes, and as such, their activities underlie physiological processes as diverse as nutrition, detoxification of xenobiotics, hormone transport, electrical activity, etc. The Arabidopsis genome encodes about 1,200 transporters involved in the uptake, the short-and long-distance distribution, the compartmentalization, the remobilization or the excretion of a wide variety of substrates in regulation that are integrated in the root epidermis to fine tune the uptake of essential iron ions [START_REF] Brumbarova | Molecular mechanisms governing Arabidopsis iron uptake[END_REF]. Recent work also uncovered that IRT1 is not only the final target of these regulatory networks, but is also part of the metal sensing machinery [START_REF] Dubeaux | Metal Sensing by the IRT1 Transporter-Receptor Orchestrates Its Own Degradation and Plant Metal Nutrition[END_REF]. Below, we will review and discuss the molecular basis of metal uptake regulation and the intricate mechanisms allowing the IRT1 transporter to act as the major root iron transporter to take up iron from the soil and to directly perceive metal availability.

Transcriptional regulatory networks driving IRT1 expression

The regulation of nutrient transporters is usually part of a homeostatic control to provide enough of its substrate to cells and to avoid an excess that can be deleterious. This is a fine balance between substrate availability and the plant need, the latter being highly influenced by plant physiological state and environmental conditions. As the major root iron transporter responsible for iron uptake from the soil, IRT1 expression is strongly and rapidly upregulated when plants are experiencing iron limitation [START_REF] Eide | A novel iron-regulated metal transporter from plants identified by functional expression in yeast[END_REF][START_REF] Vert | IRT1, an Arabidopsis transporter essential for iron uptake from the soil and for plant growth[END_REF]. The transcriptional regulation of IRT1 by iron uses an elegant dual control that integrates local information about soil iron availability and a systemic signal translating the shoot iron status [START_REF] Vert | Dual regulation of the Arabidopsis high-affinity root iron uptake system by local and long-distance signals[END_REF]. This ensures that IRT1 is expressed not only when plants need iron but also when iron is found in non-negligible amounts in soils. A wealth of upstream regulators required for IRT1 induction by low iron have been identified [START_REF] Brumbarova | Molecular mechanisms governing Arabidopsis iron uptake[END_REF].

These include RING E3 ligases from the BRUTUS (BTS) family and a repertoire of bHLH transcription factors acting in cascade downstream of BTS, culminating in the direct binding of heretodimers between the master regulator FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT) and group Ib bHLHs to IRT1 promoter and promoters of many iron-regulated genes [START_REF] Colangelo | The essential basic helix-loop-helix protein FIT1 is required for the iron deficiency response[END_REF][START_REF] Yuan | FIT interacts with AtbHLH38 and AtbHLH39 in regulating iron uptake gene expression for iron homeostasis in Arabidopsis[END_REF][START_REF] Sivitz | Arabidopsis bHLH100 and bHLH101 control iron homeostasis via a FIT-independent pathway[END_REF][START_REF] Selote | Iron-binding E3 ligase mediates iron response in plants by targeting basic helix-loop-helix transcription factors[END_REF][START_REF] Zhang | The bHLH transcription factor bHLH104 interacts with IAA-LEUCINE RESISTANT3 and modulates iron homeostasis in Arabidopsis[END_REF][START_REF] Li | Two bHLH Transcription Factors, bHLH34 and bHLH104, Regulate Iron Homeostasis in Arabidopsis thaliana[END_REF][START_REF] Liang | bHLH transcription factor bHLH115 regulates iron homeostasis in Arabidopsis thaliana[END_REF]. FIT activity is itself controlled in a complex way in response to iron starvation, involving nuclear translocation, destabilization, homo-and heterodimerization with group Ib bHLHs, part of it being controlled by CALCINEURIN B-LIKE-INTERACTING PROTEIN KINASE-11 (CIPK11)-dependent phosphorylation of FIT [START_REF] Meiser | Posttranslational regulation of the iron deficiency basic helix-loop-helix transcription factor FIT is affected by iron and nitric oxide[END_REF][START_REF] Sivitz | Proteasome-mediated turnover of the transcriptional activator FIT is required for plant iron-deficiency responses[END_REF][START_REF] Gratz | CIPK11-Dependent Phosphorylation Modulates FIT Activity to Promote Arabidopsis Iron Acquisition in Response to Calcium Signaling[END_REF].

The nature of the iron sensing machinery at stake in the local and long-distance signaling cascade, and how these pathways are integrated however remain obscure. The fact that BTS gene is transcriptionally induced by low iron, combined to the ability of BTS protein to bind iron and act as an E3 ubiquitin ligase targeting group IVc bHLHs, suggests that BTS could integrate local and long-distance iron signaling pathways [START_REF] Selote | Iron-binding E3 ligase mediates iron response in plants by targeting basic helix-loop-helix transcription factors[END_REF] (Fig. 1).

Another candidate for integrating both pathways is FIT itself, provided that its activation by calcium and CIPK11 would be confined as a local response. Further dissection of the regulatory mechanisms involved in iron-deficiency responses using split-root experiments is required to disentangle the contribution of the local and systemic.

Iron uptake and IRT1 expression are highly connected to other pathways, with several endogenous and exogenous cues (e.g. circadian clock or hormones) impinging on IRT1 transcription [START_REF] Brumbarova | Molecular mechanisms governing Arabidopsis iron uptake[END_REF]. This is achieved mostly through the integration of many signaling pathways converging at the level of IRT1 promoter to provide the plant with an optimized and robust iron uptake machinery. Well-characterized examples include the interaction between FIT and the ethylene signaling transcription factors ETHYLENE INSENSITIVE-3 (EIN3)/EIN3 LIKE-1 (EIL1) or the gibberellin signaling DELLA repressors that both modulate FIT activity and iron-deficiency responses [START_REF] Lingam | Interaction between the bHLH transcription factor FIT and ETHYLENE INSENSITIVE3/ETHYLENE INSENSITIVE3-LIKE1 reveals molecular linkage between the regulation of iron acquisition and ethylene signaling in Arabidopsis[END_REF][START_REF] Wild | Tissue-Specific Regulation of Gibberellin Signaling Fine-Tunes Arabidopsis Iron-Deficiency Responses[END_REF].

Ubiquitin-mediated endocytosis and degradation of IRT1

Several nutrient transporters from model organisms have been shown to be downregulated at the protein level upon substrate excess. Paramount to this downregulation is the role of ubiquitin(Ub)-mediated endocytosis and degradation in the vacuole. In the case of IRT1, iron excess does not trigger IRT1 degradation [START_REF] Barberon | Monoubiquitin-dependent endocytosis of the iron-regulated transporter 1 (IRT1) transporter controls iron uptake in plants[END_REF][START_REF] Dubeaux | Metal Sensing by the IRT1 Transporter-Receptor Orchestrates Its Own Degradation and Plant Metal Nutrition[END_REF].

Rather, IRT1 is post-translationally regulated by non-iron divalent metals (Zn, Mn, Co) that are also substrates of IRT1 due to their similarity with iron and that massively accumulate in plant cells upon iron deficiency [START_REF] Eide | A novel iron-regulated metal transporter from plants identified by functional expression in yeast[END_REF][START_REF] Vert | IRT1, an Arabidopsis transporter essential for iron uptake from the soil and for plant growth[END_REF]. IRT1 is gradually removed from the plasma membrane by differential ubiquitination with exposure to increasing concentrations of these highly reactive non-iron metals. Moderate non-iron metal concentrations trigger the multimonoubiquitination of IRT1 on lysine(K) K154 and K179 by an unknown plasma membrane-localized E3 Ub ligase [START_REF] Barberon | Monoubiquitin-dependent endocytosis of the iron-regulated transporter 1 (IRT1) transporter controls iron uptake in plants[END_REF][START_REF] Barberon | Polarization of IRON-REGULATED TRANSPORTER 1 (IRT1) to the plant-soil interface plays crucial role in metal homeostasis[END_REF][START_REF] Dubeaux | Metal Sensing by the IRT1 Transporter-Receptor Orchestrates Its Own Degradation and Plant Metal Nutrition[END_REF]. This triggers IRT1 internalization towards early endosomes and a decrease in the plasma membrane pool of the transporter. Higher concentrations of non-iron metals lead to the extension of multimonoubiquitination into K63-linked polyUb chains by the RING E3 Ub ligase IRON DEGRADATION FACTOR-1 (IDF1) [START_REF] Dubeaux | Metal Sensing by the IRT1 Transporter-Receptor Orchestrates Its Own Degradation and Plant Metal Nutrition[END_REF], and presumably by the E2 UB-CONJUGATING ENZYMES-35/36 (UBC35/36) that are responsible for K63 polyUb chain formation in Arabidopsis [START_REF] Li | A lysine-63-linked ubiquitin chain-forming conjugase, UBC13, promotes the developmental responses to iron deficiency in Arabidopsis roots[END_REF]. The conversion of multimonoubiquitin decorating IRT1 into K63 polyUb chains occurs somewhere between the cell surface and early endosomes, and is thought to increase the avidity for the recognition by the ENDOSOMAL SORTING COMPLEX REQUIRED FOR TRANSPORT (ESCRT) necessary for vacuolar targeting [START_REF] Dubeaux | Zooming into plant ubiquitin-mediated endocytosis[END_REF]. Besides the conserved subunits from the ESCRT complex found in eukaryotes, a plant-specific protein ESCRT-associated factor named FAB1, YOTB, VAC1, EEA1-1 (FYVE1) has been shown to interact with both IRT1 and Ub to drive proper intracellular sorting of endocytosed IRT1 [START_REF] Barberon | Polarization of IRON-REGULATED TRANSPORTER 1 (IRT1) to the plant-soil interface plays crucial role in metal homeostasis[END_REF][START_REF] Gao | A unique plant ESCRT component, FREE1, regulates multivesicular body protein sorting and plant growth[END_REF]. Another endosomal located protein, SORTING NEXIN-1 (SNX1), also controls the fate of IRT1 by promoting its recycling to the cell surface [START_REF] Ivanov | SORTING NEXIN1 is required for modulating the trafficking and stability of the Arabidopsis IRON-REGULATED TRANSPORTER1[END_REF]. K63 polyubiquitination of IRT1 likely tugs the balance between vacuolar targeting and recycling to push IRT1 towards the lytic vacuole for degradation.

Overall, the dual substrate-dependent control of IRT1 ensures that plants limit the accumulation of potentially noxious heavy metals. However, this would occur at the expense of the uptake of essential iron without a trick taking advantage of different signaling mechanisms. Plants indeed use a systemic control of IRT1 transcription by low iron via a shoot-borne signal, and a local regulation by non-iron metal excess underlying metal transport rates [START_REF] Vert | Dual regulation of the Arabidopsis high-affinity root iron uptake system by local and long-distance signals[END_REF][START_REF] Dubeaux | Metal Sensing by the IRT1 Transporter-Receptor Orchestrates Its Own Degradation and Plant Metal Nutrition[END_REF]. Consequently, plants experiencing iron limitation will accumulate IRT1 protein in roots only where non-iron metals are low to optimize the uptake of essential iron (Fig. 2).

The metal-dependent destabilization of IRT1 by IDF1 raises important questions about the mechanisms allowing the recruitment of IDF1 to IRT1 upon metal stress, considering that IDF1 expression appears to be rather constitutive. The phosphorylation of target to be ubiquitinated is often a trigger to recruit E3 Ub ligases. IRT1 follows this rule with the recruitment of IDF1 to multimonoubiquitinated IRT1 upon metal excess requiring phosphorylation of IRT1 in serine/threonine residues neighboring Ub sites [START_REF] Dubeaux | Metal Sensing by the IRT1 Transporter-Receptor Orchestrates Its Own Degradation and Plant Metal Nutrition[END_REF]. This is achieved by the CIPK23 kinase, a well-known kinase phosphorylating other plant transport proteins such as ARABIDOPIS K+ TRANSPORTER-1 (AKT1) and HIGH AFFINITY K+ TRANSPORTER-5 (HAK5), CHLORATE RESISTANT-1/NITRATE TRANSPORTER 1.1 (CHL1/NRT1.1) or AMMONIUM TRANSPORTER 1 (AMT1) [START_REF] Xu | A protein kinase, interacting with two calcineurin B-like proteins, regulates K+ transporter AKT1 in Arabidopsis[END_REF][START_REF] Ho | CHL1 functions as a nitrate sensor in plants[END_REF][START_REF] Ragel | The CBL-Interacting Protein Kinase CIPK23 Regulates HAK5-Mediated High-Affinity K+ Uptake in Arabidopsis Roots[END_REF][START_REF] Straub | The Kinase CIPK23 Inhibits Ammonium Transport in Arabidopsis thaliana[END_REF]. Although the reported mechanisms of transporter regulation appear to be radically different for AKT1, CHL1/NRT1.1 and IRT1, CIPK23 is a central regulator in all cases. Interestingly, no crosstalk appears to exist between these CIPK23-dependent processes, suggesting that these responses are likely spatially restricted so that locally activated CIPK23 specifically modifies the correct target. This is in part explained, in the case of IRT1, by the original mechanism of CIPK23 triggered by direct metal binding to IRT1 (see below).

From IRT1 transporter to IRT1 transceptor

A major advance in our understanding of how plants regulate IRT1 and plant metal responses came from the discovery that IRT1 acts as a bifunctional transporter, capable of directly binding and sensing non-iron metal availability [START_REF] Grossoehme | Metal-binding thermodynamics of the histidine-rich sequence from the metal-transport protein IRT1 of Arabidopsis thaliana[END_REF][START_REF] Dubeaux | Metal Sensing by the IRT1 Transporter-Receptor Orchestrates Its Own Degradation and Plant Metal Nutrition[END_REF]. IRT1 perceives intracellular non-iron metal excess using a histidine-rich motif in an intrinsically disordered cytosolic loop [START_REF] Dubeaux | Metal Sensing by the IRT1 Transporter-Receptor Orchestrates Its Own Degradation and Plant Metal Nutrition[END_REF] (Fig. 1). Mutation of the corresponding histidine residues does not impair metal transport but abolishes downstream responses to high non-iron metal levels culminating in the degradation of IRT1 [START_REF] Kerkeb | Ironinduced turnover of the Arabidopsis IRON-REGULATED TRANSPORTER1 metal transporter requires lysine residues[END_REF][START_REF] Dubeaux | Metal Sensing by the IRT1 Transporter-Receptor Orchestrates Its Own Degradation and Plant Metal Nutrition[END_REF]. Mechanistically, the binding of non-iron metals to such histidine residues triggers the recruitment of CIPK23, which phosphorylates IRT1 [START_REF] Dubeaux | Metal Sensing by the IRT1 Transporter-Receptor Orchestrates Its Own Degradation and Plant Metal Nutrition[END_REF]. The sensing loop has been proposed to sit at the exit of the permeation domain, thus perceiving the flux of metals going though IRT1. This suggests that the histidine-rich motif is loaded with non-iron metal upon high metal flux to recruit CIPK23 and trigger IRT1 degradation by IDF1-dependent K63 polyubiquitination. One may speculate that metal binding to the histidine-rich domain in IRT1 may fold this otherwise unstructured loop (Fig. 3), offering an interacting platform to specifically interact with CIPK23. A less likely, but not exclusive hypothesis, may reside in metal-loaded IRT1 serving as Mn donor to directly activate the peculiar Mn-dependent CIPK23 kinase [START_REF] Hashimoto | Phosphorylation of calcineurin B-like (CBL) calcium sensor proteins by their CBL-interacting protein kinases (CIPKs) is required for full activity of CBL-CIPK complexes toward their target proteins[END_REF]. This would however not explain the ability of Zn alone to trigger IRT1 Ub-mediated endocytosis.

Regardless, these two processes would result in high spatial restriction of CIPK23 recruitment and activation, thus providing specificity to the metal response to IRT1. Future structural studies of IRT1, unbound and bound to metals and possibly in complex with CIPK23, will help unravel the precise molecular mechanisms employed by IRT1 to sense and recruit downstream factors required for its own degradation.

IRT1 is the only reported target of its own sensing/signaling activity in the control of plant metal uptake and responses to metal overload. Other transceptors in yeast and plants have however been shown to drive various aspects of nutrient responses [START_REF] Steyfkens | Multiple Transceptors for Macro-and Micro-Nutrients Control Diverse Cellular Properties Through the PKA Pathway in Yeast: A Paradigm for the Rapidly Expanding World of Eukaryotic Nutrient Transceptors Up to Those in Human Cells[END_REF].

For example, the CHL1/NRT1.1 Arabidopsis nitrate transceptor controls : i) the fast induction of early nitrate-responsive genes, ii) the long-term repression of nitrate responses by high nitrogen provision, and iii) the modification of root system architecture [START_REF] Ho | CHL1 functions as a nitrate sensor in plants[END_REF][START_REF] Bouguyon | Multiple mechanisms of nitrate sensing by Arabidopsis nitrate transceptor NRT1.1[END_REF]. Whether IRT1 is also part of other aspects of plant metal responses, such as the genomic reprogramming upon changes in non-iron metal availability, will have to be investigated using plants expressing mutant versions for the histidine-rich motif to uncouple transport and sensing.

Conclusions

The downstream signaling mechanisms of transporter regulation are rapidly emerging, but the early events of sensing and initial signal transduction remain elusive in most cases. These may use a genuine receptor, as described for nutrient-sensing G Protein-Coupled Receptors (GPCRs) in yeast and mammals, or a more direct sensing/signaling mechanism by transporter themselves [START_REF] Holsbeeks | The eukaryotic plasma membrane as a nutrient-sensing device[END_REF]. Receptors have been proposed to originate from nutrient transporters, due to the similarity between ligand such as neurotransmitters and nutrients [START_REF] Boyd | Chemical neurotransmission: an hypothesis concerning the evolution of neurotransmitter substances[END_REF]. Transceptors would represent an intermediate evolutionary step between transporters and receptors capable of sensing substrates, which have been conserved to directly detect nutrient molecules in the environment or inside the cell. Transceptors represent a perfect option to participate in the detection of substrate availability, intracellular substrate concentration or even substrate flux, thus acting as local sensors.

Transceptors are either actively transporting nutrient permeases as receptor like GENERAL AMINO ACID PERMEASE-1 (GAP1) in yeast [START_REF] Donaton | The Gap1 general amino acid permease acts as an amino acid sensor for activation of protein kinase A targets in the yeast Saccharomyces cerevisiae[END_REF], or proteins homologous to transporters capable of sensing but devoid of transport activity such as the SUCROSE NON-FERMENTING-3 (SNF3) yeast sucrose transceptor [START_REF] Ozcan | Two glucose transporters in Saccharomyces cerevisiae are glucose sensors that generate a signal for induction of gene expression[END_REF]. In plants, no GPCR-type nutrient sensor has been described to date and only a handful of transceptors have been demonstrated or proposed [START_REF] Steyfkens | Multiple Transceptors for Macro-and Micro-Nutrients Control Diverse Cellular Properties Through the PKA Pathway in Yeast: A Paradigm for the Rapidly Expanding World of Eukaryotic Nutrient Transceptors Up to Those in Human Cells[END_REF]. All the reported examples of plant transceptors fall into the actively transporting category. Besides the wellcharacterized CHL1/NRT1.1 and IRT1, other possible transceptors comprise the AMT1 family of ammonium transporters and the SULFATE TRANSPORTER 1;2 (SULTR1;2) [START_REF] Lima | Ammonium triggers lateral root branching in Arabidopsis in an AMMONIUM TRANSPORTER1;3-dependent manner[END_REF][START_REF] Rogato | Characterization of a developmental root response caused by external ammonium supply in Lotus japonicus[END_REF][START_REF] Zhang | Aberrant gene expression in the Arabidopsis SULTR1;2 mutants suggests a possible regulatory role for this sulfate transporter in response to sulfur nutrient status[END_REF].

To be considered as a transceptor, a transporter must bind substrates either in the binding pocket used for entry into the translocation channel or to another location to trigger divergent substrate-dependent conformational changes to initiate signaling [START_REF] Diallinas | Transceptors as a functional link of transporters and receptors[END_REF]. While for IRT1 the identity of the metal binding site used for perception could be anticipated by the ability of histidine to directly bind metals, binding sites for most substrates cannot be predicted. While more transceptors likely exist in plants, their identification will likely emerge from forward genetic screens, through the isolation of mutations uncoupling transport and sensing functions, and from biochemical studies using non-transported substrates analogs able to activate signaling. These approaches, combined to the flourishing number of structural studies on membrane proteins including transporters/transceptors, will certainly shed light on the atomic determinants of substrate binding sites, of substrate transport and selectivity, and finally of early signaling mechanisms.

  Figures to be published in color : all figures

  the plant. Their functional importance in plant growth, development and stress responses is now widely accepted, as revealed by the many reports of severe phenotypes from corresponding loss-of-function mutants. As such, transport proteins are the major determinants of what comes in and out of a cell, when it is safe to transport substrates and in what amount, and thus representing the target of many signaling pathways. The precise control of transport proteins requires sophisticated sensing and signaling mechanisms to perceive specific cues, such as substrate concentration or environmental conditions (e.g. salt, drought, wounding, etc.), and to transduce the information to regulate transporter genes. The study of the Arabidopsis IRON-REGULATED TRANSPORTER-1 (IRT1) root metal transporter has revealed an exquisite network of transcriptional and post-translational

Figure 1 .

 1 Figure 1. Metal sensing and signaling in the root epidermis. When plants are in demand for iron, the IRON-REGULATED TRANSPORTER-1 (IRT1) gene is transcriptionally upregulated in the root epidermis in response to local and long-distance signals underlying iron availability and the shoot iron status, respectively. How these signals are integrated is currently unknown. Downstream of BRUTUS (BTS) are found group IVc bHLH transcription factors (bHLH34, 105, 105, 115) that are positive regulators of iron deficiency responses. bHLH IVc bind to the promoters of bHLH Ib (bHLH38, 39, 100, 101) transcription factors to activate their transcription in iron-starved plants. bHLH Ib heterodimerize with the FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT) bHLH and bind to IRT1 promoter, thus driving IRT1 gene expression in irondeficient conditions. FIT activity is positively regulated by CALCINEURIN B-LIKE-INTERACTING PROTEIN KINASE-11 (CIPK11)-dependent phosphorylation, represented by a phosphate group attached to FIT. Non-iron metals (Zn, Mn, Co) transported by IRT1,

Figure 2 .

 2 Figure 2. Spatial separation of metal-dependent signaling pathways targeting IRT1.IRT1 transcription in response to low iron is dually controlled by local and long-distance signals (red arrows) to allow IRT1 gene expression in roots. When roots encounter a non-iron metal-rich soil patch, IRT1 protein is locally degraded in that area of the soil. IRT1 protein is therefore only found in the zone depleted for non-iron metals to take up traces of essential iron and limit the absorption of highly-reactive non-iron metals.

Figure 3 .

 3 Figure 3. Molecular basis of IRT1 Tranceptor function.When plants need to take up iron and express high levels of IRT1 in a soil that contains noniron metals, the flux of the latter is directly sensed by a histidine-rich stretch in IRT1 sitting in an unstructured cytosolic loop at the exit of IRT1 permeation domain. Metal-bound IRT1 recruits the CIPK23 kinase, which in turn phosphorylates serine and threonine residues in the vicinity of the histidine-rich motif. Phosphorylates IRT1 has the ability to interact with the E3 ubiquitin ligase IRON DEFICIENCY FACTOR-1 (IDF1) to promote lysine(K)-63 polyubiquitin-mediated endocytosis and degradation of IRT1. We propose that non-iron metal binding to histidine residues folds the otherwise unstructured loop (tentatively represented here by a helix) to allow the recruitment of the CIPK23 kinase.

Acknowledgements

Acknowledgements

We apologize to authors whose relevant works have not been cited because of length constraints. We thank Maria Naranjo Arcos for critical reading of the manuscript and helpful suggestions. The support of the French National research Agency (ANR, ANR-13-JSV2-0004-01), CNRS (ATIP and ATIP Plus program), the Fondation pour le Recherche Médicale (FRM, ECO20170637545), and the French Laboratory of Excellence "TULIP" (ANR-10-LABX-41; ANR-11-IDEX-0002-02) is gratefully acknowledged.