Leila Amgoud
email: amgoud@irit.fr

Nabil Hameurlain
email: nabil.hameurlain@univ-pau.fr

An argumentation-based approach for dialog move selection

Modeling different types of dialog between autonomous agents is becoming an important research issue. Several proposals exist with a clear definition of the dialog protocol, which is the set of rules governing the high level behavior of the dialog. However, things seem different with the notion of strategy. There is no consensus on the definition of a strategy and on the parameters necessary for its definition. Consequently, there are no methodology and no formal models for strategies. This paper argues that a strategy is a decision problem that consists of: i) selecting the type of act to utter at a given step of a dialog, and ii) selecting the content that will accompany the act. The first kind of decision amounts to select among all the acts allowed by the protocol, the best option which according to some strategic beliefs of the agent will at least satisfy the most important strategic goals of the agent. The second kind of decision consists of selecting among different alternatives (eg. different offers), the best one that, according to some basic beliefs of the agent, will satisfy the functional goals of the agent. The paper proposes then a formal model based on argumentation for computing on the basis of the above kinds of mental states, the best move (act + content) to play at a given step of the dialog. The model is illustrated through an example of auctions.

Introduction

An increasing number of software applications are being conceived, designed, and implemented using the notion of autonomous agents. These applications vary from email filtering [START_REF] Maes | Agents that reduce work and information overload[END_REF], through electronic commerce [START_REF] Rodriguez | A java-based electronic auction house[END_REF][START_REF] Wellman | A market-oriented programming environment and its application to distributed multicommodity flow problems[END_REF], to large industrial applications [START_REF] Jennings | Using archon to develop real-word dai applications part 1[END_REF]. In all of these disparate cases, however, the agents are autonomous in the sense that they have the ability to decide for themselves which goals they should adopt and how these goals should be achieved [START_REF] Wooldridge | Intelligent agents: theory and practice[END_REF]. In most agent applications, the autonomous components need to interact with one another because of the inherent interdependencies which exist between them. They need to communicate in order to resolve differences of opinion and conflicts of interest that result from differences in preferences, work together to find solutions to dilemmas and to construct proofs that they cannot manage alone, or simply to inform each other of pertinent facts. Many of these communication requirements cannot be fulfilled by the exchange of single messages. Instead, the agents concerned need to be able to exchange a sequence of messages which all bear upon the same subject. In other words they need the ability to engage in dialogs. In [START_REF] Walton | Commitment in Dialogue: Basic Concepts of Interpersonal Reasoning[END_REF] different categories of dialogs have been distinguished including persuasion and negotiation. Work in the literature has focused on defining formal models for these dialog types. Generally, a dialog system contains the following three components: the agents involved in the dialog (i.e their mental states), a dialog protocol and a set of strategies. The dialog protocol is the set of rules of encounter governing the high-level behavior of interacting agents. A protocol defines among other things:

the set of permissible acts (eg. asking questions, making offers, presenting arguments, etc.); the legal replies for each act.

A dialog protocol identifies the different possible replies after a given act. However, the exact act to utter at a given step of the dialog is a strategy matter. While the protocol is a public notion, strategy is crucially an individualistic matter. A strategy can be seen as a two steps decision process:

1. among all the possible replies allowed by the protocol, to choose the move to play.

For instance, in a negotiation dialog, the protocol may allow after an offer act the following moves: accepting/rejecting the offer or making a new offer. 2. to choose the content of the move if any. In the above example, if the agent chooses to make a new offer, it may decide among different alternatives the best one to propose.

In most works on modeling dialogs, the definition of a protocol poses no problems. However, the situation is different for dialog strategies. There is no methodology and no formal models for defining them. There is even no consensus on the different ingredients involved when defining a strategy. Regarding persuasion dialogs, there are very few works devoted to the notion of strategy in the literature if we except the work done in [START_REF] Amgoud | Strategical considerations for argumentative agents[END_REF][START_REF] Kakas | Layered strategies and protocols for argumentation based agent interaction[END_REF]. In these works, different criteria have been proposed for the argument selection. As for negotiation dialogs, it has been argued that the game-theoretic approaches characterize correctly optimal strategies [START_REF] Kraus | Strategic negotiation in multi-agent environments[END_REF][START_REF] Rosenschein | Rules of encounter: Designing conventions for automated negotiation among computers[END_REF]. However, another line of research [START_REF] Jennings | Automated negotiation: Prospects, methods and challenges[END_REF][START_REF] Kraus | Reaching agreements through argumentation: a logical model and implementation[END_REF][START_REF] Parsons | Agents that reason and negotiate by arguing[END_REF][START_REF] Sycara | Persuasive argumentation in negotiation[END_REF] has emphasized the limits of game-theoretic approaches for negotiation, and has shown the interest of arguing during a negotiation. Consequently, the optimal strategies given by game theory are no longer valid and not suitable. In [START_REF] Amgoud | On the study of negotiation strategies[END_REF], the authors have studied the problem of choosing the best offer to propose during a dialog and several criteria have been suggested. However, in that framework, the act offer is supposed to be chosen by the agent. Thus, this work has focused only on the second step of the decision process.

This paper argues that the strategy is a decision problem in which an agent tries to choose among different alternatives the best option, which according to its beliefs, will satisfy at least its most important goals. Two kinds of goals (resp. of beliefs) are distinguished: the strategic and the functional goals (resp. the strategic and basic beliefs).

The strategic goals are the meta level goals of the agent. Such goals will help an agent, on the basis of the strategic beliefs, to select the type of act to utter. Regarding functional goals, they will help an agent to select, on the basis of the basic beliefs to select the content of a move.

We propose a formal model for defining strategies. The model takes as input two sets of goals: the strategic and the functional goals together with the strategic and basic beliefs and returns among the possible replies allowed by the protocol after a given act, the next move (act + its content) to play. The model is an extension of the argument-based decision framework proposed in [START_REF] Amgoud | A general argumentation framework for inference and decision making[END_REF]. The basic idea behind this model is to construct for each alternative the different arguments (reasons) supporting it, then to compare pairs of alternatives on the basis of the quality of their supporting arguments.

The paper is organized as follows: Section 2 presents the different classes of goals and beliefs maintained by an agent. Section 3 introduces the logical language which will be used throughout the paper. Section 4 introduces an abstract argumentationbased decision model which forms the backbone of our approach. Section 5 presents an instantiation of that abstract model for computing the best move to play among the different replies allowed by the protocol. Section 6 introduces a second instantiation of the abstract model for computing the content of the move selected by the first instantiation. The whole framework is then illustrated in section 8. Section 9 is devoted to some concluding remarks and some perspectives.

Agents' mental states

During a dialog, an agent makes two decisions: it first selects the type of act to utter, for instance making a new offer, asking a question or arguing. Once the act chosen, the agent should select the content of the act if necessary. We say if necessary because some acts such as "withdrawal" from a dialog does not need a content. However, for an act "offer", it is important to accompany the act with an appropriate content. If the agents are negotiating the "price" of a car, then the act offer should contain a given price. The two above decision problems involve two different kinds of goals:

Strategic goals: For choosing the type of act to utter, an agent refers to what we call strategic goals. By strategic goals we mean the meta-level goals of the agent such as "minimizing the dialog time", "selling at the end of the dialog", etc. Suppose that at a given step of a negotiation dialog, an agent has to choose between making an offer and asking a question. If the agent wants to minimize the dialog time then it would choose to make an offer instead of spending more time in questions. However, if the agent wants to get a maximum of information about the wishes of the other agent, then the agent would decide to ask a question. Strategic goals are generally independent of the subject of the dialog. If the agents are negotiating the place of a next meeting, then those goals are not related to the place. Functional goals: The goals of the agent which are directly related to the subject of the dialog are called functional goals. They represent what an agent wants to achieve or to get regarding the subject of the dialog. Let us take the example of the agent negotiation the place of a meeting. The agent may prefer a place which is not warm and not expensive. The agent may also prefer a place with an international airport. These functional goals are involved when selecting the content of a move. In a negotiation, an agent proposes offers that satisfy such goals.

As for goals, the beliefs involved in the two decision problems are also of different nature:

Strategic beliefs that are the meta-level beliefs of the agent. They may represent the beliefs of the agent about the dialog, and about the other agents involved in the dialog. In negotiation dialogs where agents are trying to find a common agreement, agents may intend to simulate the reasoning of the other agents. Thus it is important for each agent to consider the beliefs that it has on the other agents'goals and beliefs. Indeed, a common agreement can be more easily reached if the agents check that their offers may be consistent with what they believe are the goals of the others. Basic beliefs represent the beliefs of the agent about the environment and the subject of the dialog. Let us consider again the example of the agent negotiating the place of a meeting. Basic beliefs of the agent may include for instance the fact that "London is not warm", "Tunisia is hot", "London is very expensive", etc. This base may also contain some integrity constraints related to the dialog subject such as "the meeting cannot be at the same time in London and in Algeria".

The logical language

Let L be a propositional language, and W f f (L) be the set of well-formed formulas built from L. Each agent has the following bases:

B b = {(k p , ρ p ), p = 1, . . . , s},
where k p ∈ W f f (L), is the basic beliefs base. The beliefs can be less or more certain. They are associated with certainty levels ρ p . A pair (k p , ρ p ) means that k p is at least certain at a degree ρ p . B s = {(l j , δ j ), j = 1, . . . , m}, where l j ∈ W f f (L), is the strategic beliefs base. Each of these beliefs has a certainty level δ j . G s = {(g q , λ q ), q = 1, . . . , t}, where g q ∈ W f f (L), is a base of strategic goals.

The strategic goals can have different priority degrees, represented by λ q . A pair (g q , λ q ) means that the goal g q is important for the agent at least to a degree λ q . G f = {(go r , γ r ), r = 1, . . . , v}, where go r ∈ W f f (L), is the base of the functional goals of the agent. Each functional goal has a degree of importance denoted by γ r .

The different certainty levels and priority degrees are assumed to belong to a unique linearly ordered scale T with maximal element denoted by 1 (corresponding to total certainty and full priority) and a minimal element denoted by 0 corresponding to the complete absence of certainty or priority. We shall denote by B * b , B * s , G * s and G * f the corresponding sets of propositional formulas when weights are ignored.

Let S be the set of speech acts allowed by the protocol. S may contain acts such as "'Offer' for making offers in negotiation dialogs, "Question" for asking questions, "Assert" for asserting information such as "the weather is beautiful", "Argue" for presenting arguments in persuasion dialogs, etc. The protocol precises for each act the possible replies to it. Let us suppose that the function Replies returns for each act, all the legal replies to it.

Replies: S -→ 2 S Some acts may have a content. For instance, an act "Offer" should be accompanied with a content such as a price, a town, etc. However, the act "Withdraw" does not need any content. Such acts will then have an empty content, denoted by the symbol "?". In what follows, the function Content returns for a given act, the set of its possible contents. Formally:

Content: S -→ 2 W f f (L)∪{?} For instance, Content(W ithdraw) = {?}, Content(Of f er) = {London, Algeria}.
During a dialog, agents exchange moves which are pairs: a speech act and its content. Formally: Definition 1 (Moves). A move is a pair (a, x), where a ∈ S and x ∈ Content(a).

The strategy problem is formalized as follows:

Definition 2 (The strategy problem). Let (a, x) be the current move in a dialog. What is the next move (a , x ) to utter such that a ∈ Replies(a) ?

To answer this question, one should find both a and x . Indeed, a is the "best" element in Replies(a) that satisfies G * s according to B * s . This will be denoted by: B * s , a → G * s . Here by "best" we mean the act that satisfies as much important goals as possible.

Concerning x , this is also the "best" element among X ⊆ W f f (L) that satisfies G * f according to B * b . This will be denoted by: B * b , x → G * f . Here the set X is exactly the set of different alternatives concerning the content of a move. This set may contain different offers (eg. different town) if we have to choose the content of the act "offer", it may contain a set of formulas if we have to choose the content of the act "Assert", it may also contain a set of arguments if one has to select the content of the move "Argue", etc.

The solution to the strategy problem is the pair

(a , x ) such that (B * s , a → G * s ) ∧ (B * b , x → G * f ).

The abstract argumentation-based decision model

Recently, Amgoud [START_REF] Amgoud | A general argumentation framework for inference and decision making[END_REF] has proposed a formal framework for making decisions under uncertainty on the basis of arguments that can be built in favor of and against a possible choice. Such an approach has two obvious merits. First, decisions can be more easily explained. Moreover, argumentation-based decision is maybe closer to the way humans make decisions than approaches requiring explicit utility functions and uncertainty distributions.

Solving a decision problem amounts to defining a pre-ordering, usually a complete one, on a set X of possible choices (or decisions), on the basis of the different consequences of each decision. In our case, the set X may be either the set Replies(a) of the possible replies to a move, or the set Content(a). The basic idea behind an argumentation-based model is to construct arguments in favor of and against each decision, to evaluate such arguments, and finally to apply some principle for comparing the decisions on the basis of the arguments and their quality or strengths. Thus, an argumentation-based decision process can be decomposed into the following steps:

1. Constructing arguments in favor of /against each decision in X . 2. Evaluating the strength of each argument. 3. Comparing decisions on the basis of their arguments. 4. Defining a pre-ordering on X .

Definition 3 (Argumentation-based decision framework

). An argumentation-based decision framework is a tuple <X , A, , P rinc > where:

-X is a set of all possible decisions. -A is a set of arguments.

is a (partial or complete) pre-ordering on A. -P rinc (for principle for comparing decisions), defines a (partial or complete) preordering on X , defined on the basis of arguments.

The output of the framework is a (complete or partial) pre-ordering P rinc , on X . x 1 P rinc x 2 means that the decision x 1 is at least as preferred as the decision x 2 w.r.t. the principle P rinc.

Notation: Let A, B be two arguments of A. If is a pre-order, then A B means that A is at least as 'strong' as B.

and ≈ will denote respectively the strict ordering and the relation of equivalence associated with the preference between arguments. Hence, A B means that A is strictly preferred to B. A ≈ B means that A is preferred to B and B is preferred to A.

Different definitions of or different definitions of P rinc may lead to different decision frameworks which may not return the same results.

In what follows, Arg(x) denotes the set of arguments in A which are in favor of x. At the core of our framework is the use of a principle that allows for an argument-based comparison of decisions. Indeed, these principles capture different profiles of agents regarding decision making. Below we present one intuitive principle P rinc, i.e agent profile. This principle, called promotion focus principle (Prom), prefers a choice that has at least one supporting argument which is preferred to (or stronger than) any supporting argument of the other choice. Formally: Definition 4 (Promotion focus). Let <X , A, , P rom > be an argumentation-based decision framework, and Let x 1 , x 2 ∈ X .

x 1 P rom x 2 w.r.t P rom iff ∃ A ∈ Arg(x 1 ) such that ∀ B ∈ Arg(x 2 ), A B.
Obviously, this is a sample of the many principles that we may consider. Human deciders may actually use more complicated principles. This section presents an instantiation of the above model in order to select the next move to utter. Let us recall the strategy problem. Let (a, x) be the current move in a dialog. What is the next move (a , x ) to utter such that a ∈ Replies(a) and x ∈ Content(a)? The strategic decision model will select among Replies(a) the best act to utter, say a . Thus, the set Replies(a) will play the role of X . Let us now define the arguments in favor of each d ∈ Replies(a). Those arguments are built from the strategic beliefs base B s of the agent and its strategic goals base G s .

The idea is that a decision is justified and supported if it leads to the satisfaction of at least the most important goals of the agent, taking into account the most certain part of knowledge. Formally: Definition 5 (Argument). An argument in favor of a choice d is a triple A = <S, g, d> such that:

-d ∈ Replies(a) -S ⊆ B * s and g ∈ G * s -S ∪ {d} is consistent -S ∪ {d} g
-S is minimal (for set inclusion) among the sets satisfying the above conditions.

S is the support of the argument, g is the goal which is reached by the choice d, and d is the conclusion of the argument. The set A s gathers all the arguments which can be constructed from <B s , G s , Replies(a)>.

Since the bases B s and G s are weighted, arguments in favor of a decision are more or less strong.

Definition 6 (Strength of an Argument). Let A = <S, g, d> be an argument in A s . The strength of A is a pair <Level s (A), W eight s (A)> such that:

-The certainty level of the argument is Level

s (A) = min{ρ i | k i ∈ S and (k i , ρ i ) ∈ B s }. If S = ∅ then Level s (A) = 1. -The degree of satisfaction of the argument is W eight s (A) = λ with (g, λ) ∈ G s .
Then, strengths of arguments make it possible to compare pairs of arguments as follows:

Definition 7. Let A and B be two arguments in A s . A is preferred to B, denoted A s B, iff min(Level s (A), W eight s (A)) ≥ min(Level s (B), W eight s (B)).
Property 1. The relation s is a complete preorder ( s is reflexive and transitive). Now that the arguments defined, we are able to present the strategic decision model which will be used to return the best reply a at each step of a dialog.

Definition 8 (Strategic decision model).

A strategic decision model is a tuple <Replies(a), A s , s , P rinc >.

According to the agent profile, a principle P rinc will be chosen to compare decisions. If for instance, an agent is pessimistic then it will select the Prom priciple and thus the decisions are compared as follows: Definition 9. Let a 1 , a 2 ∈ Replies(a). a 1 P rom a 2 w.r.t P rom iff ∃ A ∈ Arg(a 1 ) such that ∀ B ∈ Arg(a 2 ), A s B. Property 2. The relation P rom is a complete preorder.

Since the above relation is a complete preorder, it may be the case that several choices will be equally preferred. The most preferred ones will be returned by the function Best.

Definition 10 (Best decisions). The set of best decisions is Best(Replies(a)) = {a i ∈ Replies(a), s.t.∀ a j ∈ Replies(a), a i P rom a j }.

Property 3. If A s = ∅, then Best(Replies(a)) = ∅.
Note that when the set of arguments is empty, then the set of best decisions is also empty. This means that all the decisions are equally preferred, and there is no way to choose between them. In such a situation, the decision maker chooses one randomly.

Definition 11 (Best move). The best move to play (or the next reply in a dialog) is a ∈ Best(Replies(a)).

The functional decision model

Once the speech act to utter selected by the previous strategic decision model, say a ∈ Best(Replies(a)), one should select its content if necessary among the elements of Content(a ). Here Content(a ) depends on the nature of the selected speech act. For instance, if the selected speech act is an "Offer", then Content(a ) will contain different objects such as prices if the agents are negotiating a price of a product, different towns if they are negotiating a place of the next holidays. Now, if the selected speech act is "Argue" which allows the exchange of arguments, then the content of this act should be an argument, thus Content(a ) will contain the possible arguments. In any case, we suppose that Content(a ) contains a set of propositional formulas. Even in the case of a set of arguments, every argument will be refered to it by a propositional formula. Arguments in favor of each element in Content(a ) are built from the basic beliefs base and the functional goals base. The strength of these arguments is defined exactly as in the previous section by replacing the corresponding bases.

Definition 13 (Strength of an Argument). Let A = <S, g, d> be an argument in A f . The strength of A is a pair <Level f (A), W eight f (A)> such that:

-The certainty level of the argument is Level

f (A) = min{ρ i | k i ∈ S and (k i , ρ i ) ∈ B b }. If S = ∅ then Level f (A) = 1. -The degree of satisfaction of the argument is W eight f (A) = λ with (g, λ) ∈ G f .
Then, strengths of arguments make it possible to compare pairs of arguments as follows:

Definition 14. Let A and B be two arguments in A f . A is preferred to B, denoted A f B, iff min(Level f (A), W eight f (A)) ≥ min(Level f (B), W eight f (B)).
The arguments against decisions in X are defined in the same way as in the previous section. We have just to replace the base B s by B b , G s by G f and Replies(a) by X . The functional model which computes the best content of a move is defined as follows:

Definition 15 (Functional decision model). A functional decision model is a tuple <Content(a ), A f , f , P rinc >.

Again according to the agent profile, a principle P rinc will be chosen to compare decisions. If for instance, an agent is pessimistic then it will select the Prom principle and thus the decisions are compared as follows:

Definition 16. Let x 1 , x 2 ∈ X . x 1 P rom x 2 w.r.t P rom iff ∃ A ∈ Arg(x 1 ) such that ∀ B ∈ Arg P (x 2 ), A f B.
Here again, the above relation is a complete preorder, and consequently several options may be equally preferred.

Definition 17 (Best decisions). The set of best decisions is

Best(Content(a )) = {x i ∈ Content(a ), s.t.∀ x j ∈ Content(a ), x i P rom x j }.
The content x to utter is an element of Best(Content(a )) chosen randomly. Formally:

Definition 18 (Best move). The best content is x such that x ∈ Best(Content(a )).

Computing the next move in a dialogue

In the previous section, we have presented a formal framework for explaining, oredering and making decisions. In what follows, we will show how that framework can be used for move selection. Let (a, x) be the current move of the dialogue, and an agent has to choose the next one, say (a , x ). The act a is returned as a best option by the framework <Replies(a), A s , s , P rom > (i.e a ∈ Best(Replies(a))), whereas the content x is among the best options returned by the framework <Content(a ), A f , f , P rom >, i.e. x ∈ Best(Content(a )).

The basic idea is to look for the best replies for an act a. In case there is no solution, the answer will be (?, ?) meaning that there is no rational solution. This in fact corresponds either to the situation the set of strategic goals is empty, or the case where no alternative among the allowed replies satisfies the strategic goals of the agent.

In case there is at least one preferred solution, one should look for a possible content. If there is no possible content, then the chosen act is removed and the same process is repeated with the remaining acts. Note that the case of the existence of a preferred act but no its content is explained by the fact that the strategic goals of the agent are not compatible with its functional goals. Moreover, two forms of incompatibilities are distinguished: strong incompatibility in which there is no act which can be accompanied with a content, and a weak incompatibility in which only some acts can be associated with contents. The above idea of computing the next move is skeched in the following algorithm:

Function 1 Computing the best move

Parameters: a current move (a, x), a theory X , Bs, B f , Gs, G f 1: X ← Replies(a); 2: failure ← ⊥; 3: while X = ∅ and ¬ failure do 4:

if Best(X ) = ∅ then 5: failure ← ; return (?, ?); 6: 7:

else a ∈ Best(Replies(a)) of the argumentation system Replies(a), As, s, P rom (a' is chosen randomly); 8:

if Content(a ) =? then 9:

failure ← ; return (a , ?); 10: 11: else 12:

if Best(Content(a )) = ∅ (best decisions of the argumentation system Content(a ), A f , f , P rom ); then 13:

X ← X -{a }; 14: else 15:

failure ← ; 16:

return (a , x ) with x ∈ Content(a );

The following properties can be shown:

Property 4. If G s = ∅, or B s = ∅, then the next move is (?, ?).
To illustrate the formal model, we will present an example of auction protocols, the Dutch auction, which is used in the implementation of the fish market interaction protocol [START_REF] Noriega | Towards a test-bed for trading agents in electronic auction markets[END_REF].

The idea here is that seller S wants to sell an item using an auction. A number of potential buyers B 1 , . . . , B n , called also bidders, participate in rounds of auctions. There is at least one round for each item during, which the auctioneer counts down the price for the item and buyers simply send a signal to say if they want to bid at the current price or not.

In the context of fish market, the protocol is indeed, organized in terms of rounds. At each round, the seller proposes a price for the item. If there is no bidder then the price is lowered by a set amount until a bid is received. However, if the item reaches its reserve price the seller declares the item withdrawn and closes the round. If there is more than one bid, the item is not sold to any buyer, and the seller restarts the round at a higher price. Otherwise, if there is only one bid submitted at the current price, the seller atributes the item to that buyer. In this protocol, the set of allowed moves is then:

S = {Of f er, Accept, P ass, Attribute, W ithdraw}
The first move allows the seller to propose prices, the second move allows buyers to bid i.e to accept current price, the move P ass allows also the buyers to pass their turn by saying nothing, the move Attribute allows the seller to attibute the item to the selected buyer, and the last move W ithdraw allows the seller to withdraw the item from the auction. The following possible replies are also given by the protocol:

Replies(Of f er) ⊆ {Accept, P ass} Replies(Accept) ⊆ {Of f er, Attribute} Replies(P ass) ⊆ {Of f er, W ithdraw} The dialog starts always by a move Of f er uttered by the seller.

The seller has a strategic goal which consists of minimizing the auction time. This goal is stored in the strategic goal base of the agent. The seller has also some functional goals. The first one consist of maximizing its gain max -gain. Moreover, a seller has a starting price and also a reserve price which represents the minimum amount that it will accept for the item. Thus a functional goal of this agent would be to have a price at least equal to the reserve price, good -price.

G S f = {(good price, 1)}

The functional beliefs of the seller are given in its beliefs base:

B S f = {(current price > reserve price ∧ Of f er(current price) → good price, 1), (current price > reserve price ∧ Attribute(current price) → good price, 1), (current price < reserve price ∧ Of f er(current price) → ¬good price, 1)}

Regarding the buyers, the aim of B 1 is to get the item for the lowest possible price cheap at most at bound price, and the aim of B 2 is to get the item for the lowest possible price max prof it at most at bound price/2, that is the agent B 2 bid for the current price only when he could make at least 100% profit on the item. These last are functional goals of the buyers since it concerns the subject of the negotiation. For the sake of simplicity, these agents do not have strategic beliefs and goals. S : Of f er(current price) . In this case, the only possible move to the agent is Of f er. Indeed, this is required by the protocol. An agent should select the content of that move. Here again, the agent has a starting price so it will present it. At this stage, the agent does not need its decision model in order to select the move. B 1 andB 2 : Accept(current price) . In this case, the current price is lower than bound price/2 for the agents. The agents have an argument in favor of Accept.

G B1 f ={(cheap,
In this case, they will choose Accept. S : Of f er(current price) . In this case, the item is not sold to any buyer since there is more than one bid. The seller restarts the round at a higher price. Indeed, this is required by the protocol. The only possible move to the agent is Of f er. An agent should select the content of that move. Here again, the agent has a higher price so it will present it as the cuurent price. At this stage, the agent does not need its decision model in order to select the move. Let'us suppose that the bound price/2 < current price < bound price.

B 1 : Accept(current price) . In this case, the current price current price is lower than the price bound of the agent. In this case the agent has an argument in favor of Accept because this will support its important goal cheap. In this case, the agent will choose Accept. B 2 : P ass . In this case, the current price current price is higher than bound price/2, and then the agent could not make 100% profit on the item. In this case the agent has a counter argument again Accept because this will violate its important goal max prof it, and no arguments in favor of it. However, it has an argument in favor of P ass since it will not violate the important goal. In this case, the agent will choose P ass. S : Attribute(current price) . The only possible move of the agent is Attribute.

Inded this is required by the protocol since there is only one bidder submitted at the current price. Moreover, the current price is higher than the reserve price. In this case the seller has an argument in favor of the content current price since this will support its important goal good price. The seller decides then to attribute the item to the bidder B 1 and closes the round.

Conclusion

A considerable amount of work has been devoted to the study of dialogs between autonomous agents and to development of formal models of dialog. In most works, the definition of a protocol poses no problems and several dialog protocols have been defined even for particular applications. However, the situation is different for dialog strategies. There are very few attemps for modeling strategies. Indeed, there is no methodology and no formal models for defining them. There is even no consensus on the different parameters involved when defining a strategy. This paper claims that during a dialog, a strategy is used only for defining the next move to play at each step of the dialog. This amounts to define the speech act to utter and its content if necessary. The strategy is then regarded as a two steps decision process: among all the replies allowed by the protocol, an agent should select the best speech act to play, then it should select the best content for that speech act.

The idea behind a decision problem is to define an ordering on a set of choices on the basis of the beliefs and the goals of the agent. We have argued in this paper that selecting a speech act and selecting a content of a speech act involve two different kinds of goals and two different kinds of beliefs. Indeed, an agent may have strategic goals which represent the meta-level goals of the agents about the whole dialog. An agent may have also functional goals which are directly related to the subject of the dialog. Similarly, an agent may have strategic beliefs which are meta-level beliefs about the dialog, the other agents, etc. It may also have some basic beliefs about the subject of the dialog. We have shown that the choice of the next speech is based on the strategic beliefs and the strategic goals, whereas the choice of the content is based on the basic beliefs and the functional goals.

We have then proposed a formal framework for defining strategies. This framework can be regarded as two separate systems: one of them take as input the possible replies allowed by a protocol, a set of strategic beliefs and a set of strategic goals and returns the best speech act, and the second system takes as input a set of alternatives, a set of basic beliefs and a set of functional goals and returns the best content of a speech act. The two systems are grounded on argumentation theory. The basic idea behind each system is to construct the arguments in favour and against each choice, to compute the strength of each argument and finally to compare pairs of choices on the basis of the quality of their supporting arguments. We have shown also the agents profiles play a key role in defining principles for comparing decisions. In this paper we have presented two examples: pessimistic agents which represent very cautious agents and optimistic agents which are adventurous ones.

An extension of this work would be to study more deeply the links between the strategic and the functional goals of an agent. In this paper, we suppose implicitly that there are coherent. However, in reality it may be the case that an agent has a strategic goal which is incompatible with a functional one. Let us take the example of an agent negotiating the price of a car. This agent may have as a strategic goal to sell at the end of the dialog. It may have also the goal of selling his car with highest price. These two goals are not compatible since if the agent wants really to sell at the end its car, it should reduce the price.

Definition 12 (

 12 Argument). An argument in favor of a choice d is a triple A = <S, g, d> such that:d ∈ Content(a ) -S ⊆ B * b and g ∈ G * f -S ∪ {d} is consistent -S ∪ {d} g -S isminimal (for set inclusion) among the sets satisfying the above conditions. S = Support(A) is the support of the argument, C = Consequences(A) its consequences (the goals which are reached by the decision d) and d = Conclusion(A) is the conclusion of the argument. The set A f gathers all the arguments which can be constructed from <B b , G f , X >.

  G S s = {(min time, 0.8)} This agent has some strategic beliefs such as: if the time spent in the round is higher than a certain bound time bound then it should stop the auction. B S s = {(time spent > time bound ∧ W ithdraw → min time, 1), (time spent < time bound ∧ Of f er → min time, 1), (time spent < time bound ∧ Attribute → min time, 1)(time spent > time bound ∧ Of f er → ¬min time, 1)}

  {(current price < bound price ∧ Accept(current price) → cheap, 1), (current price < bound price ∧ Accept(current price) → buy, 1), (current price > bound price ∧ Accept(current price) → ¬buy, 1), (current price > bound price ∧ Accept(current price) → ¬cheap, 1), (current price > bound price ∧ P ass → ¬buy, 1)}

	0.8), (buy, 0.7)}
	G B2 f ={(max prof it, 0.8), (buy, 0.7)}
	The buyers are supposed to have the following beliefs.
	B B1 f = B B2 f = {(current price < bound price/2 ∧ Accept(current price) →
	max prof it, 1), (current price < bound price/2 ∧ Accept(current price) →
	buy, 1), (current price > bound price/2 ∧ Accept(current price) →
	¬buy, 1), (current price > bound price/2 ∧ Accept(current price) →

¬max prof it, 1), (current price > bound price/2 ∧ P ass → ¬buy, 1)} Let us now consider the following dialog between the seller S and the two buyers B 1 and B 2 :


Johannes Schetelig
email: johannes.schetelig@uniklinikum-dresden.de

Liesbeth C De Wreede

Michel Van Gelder

Linda Koster

Jurgen Finke

Dietger Niederwieser

Dietrich Beelen

Uwe Ghulam J Mufti

Jürgen Finke

Uwe Platzbecker

Arnold Ganser

Silke Heidenreich

Johan Maertens

Gerard Socié

Arne Brecht

Matthias Stelljes

Guido Kobbe

Liisa Volin

Arnon Nagler

Antonin Vitek

Thomas Luft

• Per Ljungman

Ibrahim Yakoub-Agha

Marie Robin

Nicolaus Kröger

Late treatment-related mortality versus competing causes of death after allogeneic transplantation for myelodysplastic syndromes and secondary acute myeloid leukemia

Late treatment-related mortality versus competing causes of death after allogeneic transplantation for myelodysplastic syndromes and secondary acute myeloid leukemia.

Introduction

The key motivation toward alloHCT for patients with myelodysplastic syndromes (MDS) is the expectation of long-term disease control or even cure. Data on long-term outcomes after alloHCT for MDS is still scarce however. Disease-specific long-term follow-up data have been published for patients with chronic myeloid leukemia, chronic lymphocytic leukemia, and in mixed cohorts dominated by patients with acute leukemias [START_REF] Bhatia | Late mortality after allogeneic hematopoietic cell transplantation and functional status of long-term survivors: report from the Bone Marrow Transplant Survivor Study[END_REF][START_REF] Goldman | Relapse and late mortality in 5-year survivors of myeloablative allogeneic hematopoietic cell transplantation for chronic myeloid leukemia in first chronic phase[END_REF][START_REF] Martin | Life expectancy in patients surviving more than 5 years after hematopoietic cell transplantation[END_REF][START_REF] Wingard | Long-term survival and late deaths after allogeneic hematopoietic cell transplantation[END_REF][START_REF] Van Gelder | Long-term survival of patients with CLL after allogeneic transplantation: a report from the European Society for Blood and Marrow Transplantation[END_REF]. The absolute number and percentage of patients with MDS in these cohorts was relatively small; children were included and thus the median age of patients with MDS in these studies was below 35 years [START_REF] Martin | Life expectancy in patients surviving more than 5 years after hematopoietic cell transplantation[END_REF][START_REF] Wingard | Long-term survival and late deaths after allogeneic hematopoietic cell transplantation[END_REF][START_REF] Socie | Long-term survival and late deaths after allogeneic bone marrow transplantation. Late Effects Working Committee of the International Bone Marrow Transplant Registry[END_REF]. In consequence, these data might not reflect the contemporary population of adult patients with MDS who are referred for alloHCT [START_REF] Lim | Allogeneic hematopoietic stem-cell transplantation for patients 50 years or older with myelodysplastic syndromes or secondary acute myeloid leukemia[END_REF][START_REF] Kroger | Allogeneic stem cell transplantation for elderly patients with myelodysplastic syndrome[END_REF][START_REF] Heidenreich | Allogeneic stem cell transplantation for patients age >= 70 years with myelodysplastic syndrome: a retrospective study of the MDS subcommittee of the Chronic Malignancies Working Party of the EBMT[END_REF][START_REF] Getta | Allogeneic Hematopoietic stem cell transplantation is underutilized in older patients with myelodysplastic syndromes[END_REF][START_REF] Yucel | Cytogenetics and comorbidity predict outcomes in older myelodysplastic syndrome patients after allogenic stem cell transplantation using reduced intensity conditioning[END_REF].

In our study, we present a large cohort of adult patients with MDS who were registered with EBMT. We focus on two issues: first, we present 10-year outcomes after alloHCT from a very large group of patients, representing current practice in Europe. Second, we analyzed three types of mortality in one comprehensive model: general population mortality, relapse-related mortality, and treatmentrelated mortality (TRM). On the basis of this model we can show that for patients transplanted at an age ≥65 years, a significant proportion of total nonrelapse mortality (NRM) can be explained by general population mortality. For elderly patients we propose an estimate for TRM which is more accurate than assuming that TRM equals total NRM.

Methods

The study population included all MDS patients who received a first alloHCT between January 2000 and December 2012, and who were recorded in the European Society for Blood and Marrow Transplantation (EBMT) registry. Patients were excluded if they fulfilled any of the following criteria: (a) had received cord blood as a stem cell source; (b) had a mismatched related or syngeneic donor; (c) were below the age of 18 years at alloHCT, or (d) where essential data were missing. Patients were also excluded if they originated from countries which contributed less than 25 patients to the dataset, or for which no population data were available in the Human Mortality Database. The dataset was closed in December 2016.

The endpoints of interest were overall survival (OS), event-free survival (EFS), relapse/progression, and NRM. OS was defined as the time from either alloHCT or landmark (LM) until death, with surviving patients censored at the moment of last follow-up. LM times were fixed at 2 and 5 years after alloHCT. At each LM, the analysis population consisted of either all patients still in follow-up (OS population) or all patients in follow-up without previous relapse/progression (EF population) (see Figure S1). EFS was defined as time to death or relapse/progression (whichever occurred first), with surviving patients censored at the last time-point they were reported disease-free. Its components, the cumulative incidences of relapse/progression (CIR) and NRM, were analyzed together by a competing risks model [START_REF] Iacobelli | Suggestions on the use of statistical methodologies in studies of the European Group for Blood and Marrow Transplantation[END_REF]. A multivariable Cox cause-specific hazards model was fitted to assess the dependence of the risk of NRM on year of alloHCT.

Probabilities of NRM and death after relapse of MDS were estimated on the basis of the data. We investigated which proportion of these probabilities could be considered as population mortality. Population mortality is defined as death due to causes acting in the general population from which the patient originates. This proportion could be estimated by assuming that the hazard (risk) for population mortality is the same for the patient population as for the general population. The hazard was calculated by the common method in relative survival, in which patients are matched by age, sex, and country in the year of alloHCT to a synthetic cohort from the general population, for whom survival information is available in the Human Mortality Database population tables (www.mortality.org) [START_REF] Perme | Analysing population-based cancer survival-settling the controversies[END_REF][START_REF] Pohar | Relative survival analysis in R[END_REF]. Population NRM was subtracted from total NRM to obtain excess NRM. We used this as an approximation of TRM, defined as mortality which is related to either alloHCT (e.g., from graft-versus-host disease (GVHD)) or previous MDS treatment (e.g., resulting from iron overload). Due to the very poor prognosis associated with relapse/progression, the contribution of population mortality to death after relapse/ progression was negligible. For this reason, death after relapse due to population reasons is not shown as a separate outcome while death after relapse and relapse-related mortality can be considered equivalent. All associated probabilities were estimated using a multistate model (see Supplementary material) [START_REF] De Wreede | The mstate package for estimation and prediction in non-and semi-parametric multi-state and competing risks models[END_REF].

We then fitted Cox proportional-hazards models for the excess hazard for death, defined as the difference between the observed hazard of the patient cohort and the hazard of the matched general population. Risk factors investigated were age, sex, year of alloHCT, MDS subclassification, conditioning, and donor. For the OS LM populations, previous relapse was added as a risk factor.

All analyses were performed in SPSS Version 23 and R 3.3.0 (https://cran.r-project.org/), packages "survival", "cmprsk", "prodlim", "relsurv", and "mstate".

Results

Patient characteristics

Data of 6434 patients from 21 countries were analyzed. The number of transplants increased from 185 in 2000 to 862 in 2012. Median age at alloHCT increased from 49 years (range: 18-70 years) in 2000 to 58 years (18-76 years) in 2012 (Wilcoxon rank sum test, p < 0.001). During the same period, the number of patients who were 65 years or older at the time of alloHCT increased from nine (5% of all patients transplanted in that year) to 150 (17%). Totally, 21% of patients received alloHCT for MDS without excess blasts (EB), 42% for MDS with EB, and 37% for secondary acute myeloid leukemia (sAML) with a history of MDS. Patients with higher risk MDS subtypes were older at the time of alloHCT. For example, 29% of patients with MDS without EB were below 45-year old compared to 20% of patients with sAML, but only 8% of patients with MDS without EB were over 65-year old compared to 16% of patients with sAML (chi-square test, p < .001). The percentage of patients with unrelated donors increased from 35% in 2000 to 69% in 2012. Details on baseline patient characteristics are in Table 1.

Completeness of follow-up

The median follow-up of survivors after alloHCT was 4.4 years. To assess the reporting quality during follow-up, we calculated the completeness of follow-up index (briefly, C-index), which gives the ratio of the total observed person-time of follow-up and the potential time of follow-up [START_REF] Clark | Quantification of the completeness of follow-up[END_REF]. Overall, the reporting quality was good. The proportion of patients with complete data was 0.71. The median C-index for patients lost-to-follow-up was 0.56. The median C-index per center was 87% with an interquartile range from 53 to 99%. OS was not significantly different across centers with different reporting quality grouped by the C-index (log-rank test, p = 0.15).

As sensitivity analyses, the main analyses have been redone in the subgroup of patients transplanted in the period 2000-2008 (see Tables S1 andS2) to investigate if the inclusion of more recent patients biased the estimates of interest. For this earlier period, the median follow-up of patients alive at last follow-up was 7.6 years, the proportion of patients with complete data was 0.75 and the median Cindex for patients with incomplete data was 0.58. Tables S1 andS2 show that the results were only marginally different from those performed on the whole cohort and would not lead to differences in interpretation. Therefore, results shown in this manuscript are based on the whole cohort.

We also investigated the impact of the risk factors included in our models, extended with center of transplantation, on censoring time and status, and found that only center, year of alloHCT and conditioning had a significant impact. For the last two variables, this is fully explained by the fact that patients transplanted more recently per definition have a shorter follow-up, and that reduced intensity conditioning was given more frequently in recent years. We investigated the impact of center on OS by means of a frailty model and found no significant impact. For these

Outcomes from transplantation and of LM populations

Outcome from alloHCT

For the whole cohort of patients, the probability of OS decreased from 53% (95% CI: 52-55%) at 2 years to 43% (95% CI: 42-45%) at 5 years and to 35% (95% CI: 34-37%) at 10 years after alloHCT. The probability of EFS at 2 years was 47% (95% CI: 46-49%) and decreased to 32% (95% CI: 30-33%) at 10 years. NRM and Relapse were causes of treatment-failure of the same magnitude. The NRM probability was 26% (95% CI: 25-27%) at 2 years and 34% (95% CI: 33-35%) at 10 years after alloHCT. Death after relapse was closely associated with the moment of relapse: the median survival after relapse was only 4.6 months (95% CI: 4.2-5.0 months). Two-year survival after relapse was 19% (95% CI: 17-21%). Plots for OS, EFS, CIR, and NRM since alloHCT by type of MDS are shown in Fig. 1.

The risk of NRM has decreased in recent years. In a multivariable model for the cause-specific hazard of NRM, a more recent year of transplantation was significantly associated with improved outcome (hazard ratio (HR) for 5 years difference 0.9, 95% CI: 0.8-1.0, p value = 0.002). Cumulative incidence of relapse was 27% (95% CI: 26-28%) at 2 years and 34% (95% CI: 33-36%) at 10 years after alloHCT.

The 5-year survival probability was lower with increasing age from 53% (95% CI: 50-56%) for patients <45 years of age at alloHCT, 46% (95% CI: 43-48%) for patients aged 45-55 years, 40% (95% CI: 38-42%) at ages 55-65 years, to 35% (95% CI: 32-39%) for patients above 65 years. Conversely, 5-year nonrelapse mortality was higher with increasing age: 25% (95% CI: 22-27%), 30% (95% CI: 28-32%), 32% (95% CI: 30-34%), and 36% (95% CI: 32-39%) for the respective age groups. Notably, the oldest patient alive at last follow-up (83 years) was 67-year old at transplant. The highest age reached in this cohort came from a patient who was 75 years old at transplantation and died aged 84 years. Survival estimates for patients with MDS with or without EB or secondary AML are provided in Table S3.

LM populations

Characteristics of patients still alive and in follow-up are in Table S4. Outcomes of LM populations were evaluated to focus on the long-term perspectives of patients who had survived the most risky, early period after alloHCT.

Overall, the 1-year and 5-year risks of mortality for the 2-year OS LM population were 9% (95% CI: 8-10%) and 26% (95% CI: 24-28%). Separated by information on relapse between alloHCT and the 2-year LM, 2-year survivors who had experienced previous relapse had a 1-year mortality of 34% (95% CI: 29-39%), whereas 2-year eventfree survivors had a 1-year mortality of 6% (95% CI: 5-7%). Information on outcomes of patients who passed the 2-year LM event-free is in Table 2.

We evaluated the causes of death of all 676 patients whose death occurred more than 2-year post-alloHCT. Causes of death for patients who were deceased between 2 and 5 years after alloHCT are given in Table S5. Between 5 and 10 years, 208 deaths were reported, 104 of which were after relapse. Notably, even three patients (3%) of this subgroup died subsequent to the diagnosis of a second malignancy unrelated to MDS. In these three patients the secondary malignancy was adjudicated as primary cause of death by the local transplant physician.

NRM was also reported for 104 patients. Information on the main and contributing causes of death was available for 91 patients (87.5%). The main cause of death was directly related to alloHCT in 33 patients (36%). In these cases, GVHD was mentioned for 21 patients and infection for 14 patients as contributory cause of death. Secondary malignancies as the main cause of death were reported for 33 patients (36%). Among other main causes of death which were adjudicated to be independent from alloHCT for 21 patients, cardiovascular events (23%) were mentioned most frequently followed by neurologic diseases and chronic lung failure.

Treatment-related mortality and risk factors for excess mortality

Overall, population mortality explained only a small part of early mortality after alloHCT. When we calculated different components of mortality for patients who were alive without previous relapse at the 2-year LM, we saw a different picture (see Fig. 2): for patients <45 years at alloHCT the estimated 5-year population mortality was 0.5% (95% CI: 0.4-0.6%) compared to 8% (95% CI: 7-8%) for patients who were ≥65 years at transplantation. The estimated 5-year TRM of these LM populations were 6% (95% CI: 4-8%) and 17% (95% CI: 10-25%) for these age groups, respectively. Combining this information shows that population mortality accounted for 8% of 5-year post-LM NRM for patients transplanted at an age <45 years, compared to 31% of NRM for patients transplanted at an age ≥65 years.

Finally, we performed multivariable analyses of risk factors for excess mortality. We confirmed the adverse impact of higher age on excess mortality (Table 3 andS6). For later LM populations (HR = 1.2, 95% CI: 1.1-1.3 and HR = 1.3, 95% CI: 1.1-1.6, for the 2-and 5-year LM OS population, respectively), this impact was higher than at HCT (HR = 1.1, 95% CI: 1.1-1.2). Female patients had a significantly better prognosis than male in the 5-year LM population (HR = 0.7, 95% CI: 0.5-1.0). Later calendar year of HCT was associated with better outcome in the first 2 years after HCT. We found a slightly worse outcome after the 2-year LM in more recent calendar years, possibly indicating that in recent years, less early mortality came at the expense of a somewhat increased later mortality, suggesting that for frail patients, cure had not been achieved. The more advanced stages of MDS kept their adverse prognostic impact over time, with even the highest HRs for the patients event-free at the 5-year LM (MDS with EB: 3.1, 95% CI: 1.4-7.0, p value = 0.005, sAML: 2.8, 95% CI: 1.2-6.3, p value = 0.01).

Discussion

The 10-year event-free survival in this population of patients with MDS was 32% (95% CI: 30-33%). The observed rate of late relapse leveled off: in only 2% of patients with MDS and without EB and 3% of patients with sAML, relapse occurred between 5 and 10 years after alloHCT. These very low incidences of late relapses argue in favor of sustained immunologic disease control or complete eradication of the malignant founding clone after alloHCT.

Outcome after alloHCT improved during the 13-year period of treatment. We observed a risk reduction of 10% per 5-year period for excess mortality (HR = 0.9, p < 0.001). In line with data reported from one large center that compared past and recent outcomes during a 14-year period, this improvement was mainly due to a reduction of NRM [START_REF] Gooley | Reduced mortality after allogeneic hematopoieticcell transplantation[END_REF].

Patients aged below 45 years at the time of alloHCT who passed the 2-year LM event-free have an excellent prognosis. Their excess mortality compared to the matched general population is relatively small (see Fig. 2a). The main strength of this study is, however, to show results of elderly patients, transplanted at a wide variety of centers. Altogether, 848 patients in this study were transplanted at an age above 65 years. Compared to existing publications on long-term survival, the median age of this cohort is more than 20 years higher [START_REF] Bhatia | Late mortality after allogeneic hematopoietic cell transplantation and functional status of long-term survivors: report from the Bone Marrow Transplant Survivor Study[END_REF][START_REF] Martin | Life expectancy in patients surviving more than 5 years after hematopoietic cell transplantation[END_REF][START_REF] Wingard | Long-term survival and late deaths after allogeneic hematopoietic cell transplantation[END_REF]. The percentage of patients who were transplanted at an age ≥65 years increased from 4% in 2002 to 17% in 2012. Taking into account these rapid changes in patients referred for alloHCT, this cohort provides robust data on long-term survival of contemporary patients [START_REF] Heidenreich | Allogeneic stem cell transplantation for patients age >= 70 years with myelodysplastic syndrome: a retrospective study of the MDS subcommittee of the Chronic Malignancies Working Party of the EBMT[END_REF]. The importance of having such data is underscored by the notion that alloHCT is probably underutilized in patients ≥65 years [START_REF] Getta | Allogeneic Hematopoietic stem cell transplantation is underutilized in older patients with myelodysplastic syndromes[END_REF].

Current recommendations for alloHCT for patients with MDS state that fit patients without comorbidities should be considered for alloHCT regardless of their age [START_REF] De Witte | Allogeneic hematopoietic stem cell transplantation for MDS and CMML: recommendations from an international expert panel[END_REF]. Indeed, there is some controversy over the impact of age on transplant outcomes. McClune et al. [START_REF] Mcclune | Effect of age on outcome of reducedintensity hematopoietic cell transplantation for older patients with acute myeloid leukemia in first complete remission or with myelodysplastic syndrome[END_REF] reported that age was no predictor for 2-year survival rates after alloHCT. However, it has been demonstrated more recently that even after adjustment for comorbidities and the performance status, age retains an impact on survival after alloHCT [START_REF] Yoshizato | Genetic abnormalities in myelodysplasia and secondary acute myeloid leukemia: impact on outcome of stem cell transplantation[END_REF][START_REF] Sorror | Comorbidity-age index: a clinical measure of biologic age before allogeneic hematopoietic cell transplantation[END_REF]. We also found an impact of age on survival rates in our cohort of patients with MDS. However, in line with the publication from McClune et al. the impact of age on excess mortality was relatively small (HR = 1.1, 95% CI: 1.1-1.2) in the first 2 years after alloHCT. Notably, the impact of age on excess mortality increased for the 2-year (HR = 1.2, 95% CI: 1.1-1.3) and 5-year (HR = 1.3, 95% CI: 1.1-1.6) LM populations. The numbers given indicate average effects over all ages included in the study. For the oldest age group (>70 years at transplantation), not enough data were available to give a precise estimate of the incremental impact of higher age. Outside the setting of alloHCT, Della Porta et al. [START_REF] Porta | Validation of WHO classification-based Prognostic Scoring System (WPSS) for myelodysplastic syndromes and comparison with the revised International Prognostic Scoring System (IPSS-R). A study of the International Working Group for Prognosis in Myelodysplasia (IWG-PM)[END_REF] integrated age into their age-adjusted WPSS and showed that higher age may lead to a shift to a higher risk category. Current data thus suggest that age should be taken into account when assessing the risk profile, but not used to preclude elderly patients from alloHCT [START_REF] Brand | Comparison of allogeneic stem cell transplantation and non-transplant approaches in elderly patients with advanced myelodysplastic syndrome: optimal statistical approaches and a critical appraisal of clinical results using non-randomized data[END_REF][START_REF] Muffly | Geriatric assessment to predict survival in older allogeneic hematopoietic cell transplantation recipients[END_REF][START_REF] Muffly | Increasing use of allogeneic hematopoietic cell transplantation in patients age 70 years and older in the United States[END_REF].

NRM is usually regarded as an indicator of the risk of a transplant procedure. While the causality of alloHCT and transplant-specific complications such as GVHD can be established unequivocally, the definition of NRM by itself does not constitute a relationship between the treatment and the complication leading to death. At the individual level it is often impossible to assess whether a fatality was transplantation-related, pretreatment-related or independent. For example, GVHD may cause or aggravate endothelial damage and thus may increase the risk of cardiovascular disease, which is one of the most common causes of death in the general population. The same is true for secondary cancers diagnosed after alloHCT. The risk for certain cancers is increased after HCT, compared with the general population [START_REF] Inamoto | Secondary solid cancer screening following hematopoietic cell transplantation[END_REF]. Yet, at the level of an individual patient, it is usually impossible to distinguish between a treatmentinduced and a spontaneous solid cancer. The same holds true for cardiovascular disease and some infections for which treatment-induced and idiopathic events cannot be differentiated at patient level [START_REF] Bhatia | How I monitor long-term and late effects after blood or marrow transplantation[END_REF][START_REF] Norkin | Characteristics of late fatal infections after allogeneic hematopoietic cell transplantation[END_REF]. To avoid subjective adjudication of causes of death, we chose a populationbased approach. We assumed that patients with MDS had the same background risk of death from causes unrelated to the MDS and its treatment as the general population and that the excess risk of death was caused either directly or indirectly by the MDS and its treatment. Based on this assumption, we could split NRM into population mortality and excess NRM, where we interpreted the latter as TRM. We show that for patients transplanted at an age <45 years, total NRM is near-identical to TRM. However, for patients who are transplanted at an age ≥65 years, a substantial part of total NRM is population mortality, i.e., mortality which patients of that age inevitably face. Recent data suggest that patients with MDS are at greater risk of death from cardiovascular disease [START_REF] Brunner | Risk and timing of cardiovascular death among patients with myelodysplastic syndromes[END_REF]. The percentage of cancer survivors may also be higher among patients with MDS than in the general population [START_REF] Murphy | Prevalence of prior cancer among persons newly diagnosed with cancer: an initial report from the Surveillance, Epidemiology, and End Results Program[END_REF]. This implies that part of the excess NRM might not be TRM, but death due to morbidity associated with-but not caused by-MDS, and that true TRM might be somewhat smaller. Huge efforts are being made to gain deeper insights into late complications after alloHCT and to improve long-term survivorship [START_REF] Battiwalla | National Institutes of Health Hematopoietic Cell Transplantation Late Effects Initiative: developing recommendations to improve survivorship and long-term outcomes[END_REF][START_REF] Shaw | National Institutes of Health Hematopoietic Cell Transplantation Late Effects Initiative: The Research Methodology and Study Design Working Group Report[END_REF]. Nevertheless, Rubinstein et al. [START_REF] Rubinstein | Cancer Survivorship Care in Advanced Primary Care Practices: a qualitative study of challenges and opportunities[END_REF] show that cancer survivorship services are not well integrated in primary care. A more precise approximation of treatment-related mortality by correcting for population Fig. 2 Mortality of the 2-year Landmark population by age groups. The plots show modelbased probabilities of mortality by age groups due to different causes. These probabilities apply to patients still alive without relapse/progression at 2 years after alloHCT. The lower curve is the EFS curve; the curve above is the OS curve. The difference (gray area) between these two curves indicates the probability to be alive after relapse/progression. The observed non-relapse mortality has been split in two parts, based on mortality data for the general population: population mortality (blue area) and treatment-related mortality (red area). Death due to relapse is represented by the orange area. The model also incorporated the possibility of population mortality after relapse. Since its contribution is almost zero, it is not visible in the figures. Curves are stacked, meaning that the probabilities of the different outcomes are indicated by the distances between the lines mortality may help to define more realistic targets for cancer-survivor programs and to communicate more clearly with patients. It may already make a difference for a cancersurvivor to know that a substantial part of the risk of mortality is shared by an age-and sex-matched population of that country. In particular, for the growing number of elderly patients who are referred for alloHCT, the distinction between population mortality and true treatment-related mortality becomes increasingly important.

In conclusion, we propose that the consideration of population mortality should become standard, especially when long-term follow-up data after alloHCT are reported for elderly patients.

Fig. 1

 1 Fig. 1 Long-term outcomes after alloHCT by subtype of MDS. a-c show the Kaplan-Meier plots for OS of three subtypes of MDS since alloHCT, with their 95% confidence intervals. The dashed lines show the survival probability of the general population matched by age, sex, country, and calendar year. a MDS without EB; b MDS with

Table 1

 1 Patient characteristics at baseline

			Table 1 (continued)
	Parameter	Total number, N = 6434 [%]	Parameter	Total number, N = 6434 [%]
	Patient sex		Other donor	59
	Male	59	Patient-donor sex constellation
	Female	41	Male-male	40
	Age at alloHCT		Male-female	19
	Median age (range) [y]	56 (18-76)	Female-male	23
	≤45 years [%]	21	Female-female	17
	45-55 years	25	Conditioning
	55-65 years	41	Myeloablative	44
	>65 years	13	Reduced Intensity	56
	Year of alloHCT		Source of graft
	≤2002	11	Bone marrow	14
	2003-2004	10	Peripheral Blood	86
	2005-2006	14
	2007-2008	18
	2009-2010	21
	2011-2012	27
	Patient Nationality
	Belgium	4
	France	13
	Germany	31
	Great Britain	13
	Italy	9
	The Netherlands	5
	Spain	6
	Remaining Countries*	19
	MDS subtype
	MDS w/o excess blasts	21
	MDS with excess blasts	42
	Secondary AML	37
	Secondary origin (N = 5016, 78%)
	Preceding malignancy/autoimmune	19
	disease
	Interval diagnosis MDS-alloHCT
	Median time (range) [y]	1 (0-43)
	Remission status at alloHCT
	Complete remission	34
	No complete remission	35
	No remission attempt	31
	Karnofsky Status (N = 4323, 67%)
	90-100%	72
	≤80%	28
	Previous auto HCT
	No	99
	Yes	1
	Donor match
	HLA-identical sibling	41

alloHCT allogeneic hematopoietic stem cell transplantation, y year, N number, MDS myelodysplastic syndrome, w/o without, AML acute myeloid leukemia Numbers and percentages behind the variable name indicate number of patients with data for this variable, if less than 95% of data available; *countries contributing less than 3% patients to the study reasons, we conclude that the data allow to estimate reliable long-term outcomes.

Table 2

 2 Outcomes of patients who passed the 2-year landmark event-free: overall and event-free survival, cumulative incidences of relapse/ progression and non-relapse mortality by MDS subtype and age groups at 5 years since landmark CI confidence interval, PD progressive disease, MDS myelodysplastic syndrome, EB excess blasts, w/o without, sAML secondary acute myeloid leukemia

	Classification		Overall survival	Event-free survival	Incidence of relapse/PD	Non-relapse mortality
			Point estimates at 5 years since landmark in % (95% CI)
	MDS subtype	MDS w/o EB	87 (84-90)	85 (82-88)	5 (3-8)	10 (7-12)
		MDS with EB	76 (73-80)	70 (67-74)	16 (13-19)	14 (11-17)
		sAML	74 (70-77)	70 (66-74)	16 (13-19)	14 (11-17)
	Age group	<45 years	88 (86-91)	83 (80-87)	10 (7-13)	7 (4-9)
		45-55 years	79 (75-82)	75 (71-79)	14 (11-17)	12 (9-15)
		55-65 years	74 (71-78)	71 (67-74)	14 (12-17)	15 (12-18)
		≥65 years	63 (55-71)	56 (49-65)	19 (13-25)	25 (18-32)

Acknowledgements The authors thank Maja Pohar Perme (Institute for Biostatistics and Medical Informatics, University of Ljubljana) and Hein Putter (Department of Medical Statistics and Bioinformatics, Leiden University Medical Center) for their comments on statistical methodology.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/. Cox models for excess mortality in defined time periods for patients alive at different landmarks.

LM landmark, HR hazard ratio, CI confidence interval, NA not applicable. Patients with missing information for conditioning were kept in the analysis in a separate category (not shown)

Affiliations

Johannes Schetelig 1,2


D Chlala

A Griboval-Constant

N Nuns

J.-M Giraudon
email: jean-marc.giraudon@univ-lille1.fr

M Labaki

J.-F Lamonier

Effect of Mn loading onto hydroxyapatite supported Mn catalysts for toluene removal: Contribution of PCA assisted ToF-SIMS

Keywords: Chemometrics, PCA, ToF-SIMS, catalysis, toluene, total oxidation

A series of hydroxyapatite (Hap; of ideal formula: Ca10(PO4)6(OH)2) supported MnOx materials (MnxHap ; x = Mn wt.%: 2.5, 5.0, 10, 20, and 30 based on MnO2) has been tested in toluene total oxidation (800 ppmv in dry air) at 220°C for 40h. It was shown that the fresh catalysts can be ranked as follow by decreasing activity: Mn5Hap > Mn2.5Hap > Mn10Hap > Mn20Hap > Mn30Hap and that Mn10Hap exhibited the best resistance to deactivation. Investigation of the surface state of the fresh and used catalysts has been performed using X-ray photoelectron spectroscopy (XPS) and principal component analysis (PCA) assisted static time-of-flight secondary-ion mass spectrometry (ToF-SIMS). Based on the XPS results a Mn speciation scheme can be proposed: initial incorporation of Mn 2+ into Hap (2.5 wt% Mn) followed by additional well dispersed oxidized manganese species on Hap (5-10 wt% Mn) to end up with the formation Mn2O3/-MnO2 particles detected by XRD (20-30 wt% Mn). Principal components analysis (PCA) has been applied to the (+) ToF-SIMS spectra taking into account of all the detected significant manganese containing secondary ions. PC1 (75% of the variance) discriminates well the fresh and used samples in a similar way and was related to the surface manganese content. The resulting volcano plot obtained by plotting PC2 (12% of the variance) as a function of the resistance to deactivation discriminates the catalysts in the same way as depicted in the PC2 score plot. Consequently, also taking into account of the PC2 loading plot it was suggested that a redispersion of Mn n+ /Ca 2+ takes place with time on stream preferentially for MnxHap (x = 5-10 wt%) improving their catalyst stability.

Introduction

Hydroxyapatite (Hap) based materials have attracted much interest for use in a variety of applications since they are safe, non-toxic, inexpensive and readily available in the natural environment [1]. Furthermore, these materials present a high chemical and thermal stability and a weak solubility in water. It is also of importance in many industrial applications, such as catalysis, ion exchange, and metal removal [2][3][4][5]. However, few studies deal with the dispersion of an active phase such as transition metal oxides on hydroxyapatite [6,7]. Supported MnOx catalyst may provide a viable prospect for total oxidation of volatile organic compounds (VOCs) as MnOx catalysts exhibit an easy redox cycling ability, have long-lifetime, low cost and have biocompatibility properties. With that respect Hap supported oxidized Mn (Mn: 10 wt%) catalysts have been recently investigated for total oxidation of toluene and the effect of the counter-anion of the Mn(II) precursor has been particularly studied [8]. The best catalytic performances have been associated with the nitrate Mn(II) precursor. The Mn oxidized species well dispersed on Ca 2+ enriched apatite were responsible for a good toluene conversion. By opposition the calcination performed with the acetate Mn precursor resulted in a lower Mn dispersion responsible for a lower activity [8]. Another important question to address in the follow-up to this previous work is the effect of Mn loading by the highlight of possible surface-reactivity relationship. Efficient complementary tools for surface investigation can be X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary-ion mass spectrometry (ToF-SIMS). Indeed, XPS which has a depth of detection of about 10 nm can give an average information considering the average state of Mn while ToF-SIMS due to its high mass resolution and sensitivity can provide molecular information in the outermost layers of the material (1-3nm) [9]. Such ToF-SIMS/ XPS combination has been successfully implemented to elucidate mechanism of catalyst deactivation in oxidation of chlorinated VOCs as well as the beneficial role of water in the performances of a post-plasma catalytic process [10][11]. However, the great number of secondary ions in the ToF-SIMS spectra makes direct data interpretation impossible. The application of a Multivariate analysis (MVA) is, therefore, necessary. Among them the principal component analysis (PCA) [9,[12][13][14][15] is of considerable interest due to its successfully application in the processing of ToF-SIMS data of organic and biological materials [16][17][18][19][20][21]. In the field of heterogeneous catalysis a few studies have been carried out using chemometrics to better interpret the catalytic performances in terms of chemical and physical properties of the catalysts [22][23][24][25][26][27][28]. PCA has been previously applied to evaluate the influence of different parameters such as temperature, pressure, Gas Hourly Space Velocity (GHSV), H2/CO ratio on the catalytic performances of rhodium promoted catalyst supported on silica for carbon monoxide hydrogenation [26]. J.E. Hensley et al. have shown that PCA is an effective tool for structure-reactivity correlation. Based on XPS data, PCA allows to clarify the role of surface carbide species, coke and oxidation for the deactivation of potassium-promoted cobalt molybdenum sulfide for the production of mixed alcohols synthesis [24]. PCA carried out to study the relationships between catalytic and physicochemical properties of Co3O4 supported on alumina with different loadings for methane combustion by Q. Wang et al. highlights the significance of Co species and the redox cycle [25]. In the present work, a series of hydroxyapatite supported MnOx catalysts (Mn wt. %: 2.5, 5.0, 10, 20, and 30) has been prepared by wet impregnation using manganese(II) nitrate as precursor in order to investigate the effect of Mn loading on the catalytic performances in the total oxidation of toluene. For this purpose, the fresh and used catalysts have been studied by PCA assisted ToF-SIMS and XPS in order to find possible surface-reactivity relationships.

Materials and Methods

Synthesis of the catalysts

The hydroxyapatite supported manganese materials were prepared using the wet impregnation method. Samples loaded with 2.5, 5, 10, 20 and 30 wt.% of manganese (calculated using MnO2 as the basis) were synthesized using Mn(NO3)2.4H2O (Sigma Aldrich, purity: 97%) as the metal precursor. The support material, Hap synthesized according to previous procedure [8], was calcined at 400°C for 4 h prior to impregnation. The sample was subsequently dried at 80°C for 20 h and then cooled and ground to ensure a homogeneous particle distribution. The sample was then heated to 400°C at a heating rate of 2°C/min and was calcined at that temperature for 4h. The materials were labelled MnxHap where x stands for the Mn weight percentage.

Catalytic tests

Catalytic oxidation runs for abatement of toluene were performed in a continuous flow fixed bed Pyrex micro-reactor at atmospheric pressure. The reactive gas mixture was 800 ppmv of toluene diluted in air (100 mL/min) at a fixed GHSV of 30000 mL/(g.h). The flow rate was adjusted and controlled with digital flowmeter (MFC Mass Flow Controller). The micro reactor was placed in an electrical furnace which provides the required temperature for catalytic reaction. The catalysts were pretreated 2 h at 300 C (10 °C/min) in flowing air (75 mL/min) before to be cooled at 220 °C (10°C/min). After temperature stabilization, a test of duration in 800 ppmv of toluene diluted in air (100 mL/min) was carried out for 40h. The catalysts were finally allowed to cool down to 20°C in static reactive atmosphere. The catalysts were labelled MnxHap-ATS where ATS means after test stabilization. The concentrations of the gaseous species in inlet and outlet gas streams were analyzed online by Gas Chromatography (7860A Agilent Gas Chromatograph) equipped with a Thermal Conductivity Detector (TCD) and Flame Ionization Detector (FID) with two columns: Restek Shin Carbon ST/Silco HP NOC 80/100 micropacked, to separate permanent gases (Air, CO and CO2) and a capillary column Cp-Wax 52 CB25 m, Ø = 0.25 mm, to separate hydrocarbons and aromatic compounds. Toluene conversion (Ct), CO yield and rate of toluene conversion r, expressed in mole of reacted toluene per mole of manganese and per hour, were calculated as follows:

100 [toluene] [toluene] - [toluene] (%) C in out in t   Y (%) = [CO] 7[toluene] × 100 Mn t n C F r  
Where [toluene]in and [toluene]out were toluene inlet and outlet concentrations, F was the flow rate (mol/h) of toluene and nMn was the molar amount of manganese.

XPS measurements

Spectroscopy XPS experiments were performed using an AXIS Ultra DLD Kratos spectrometer equipped with a monochromatised aluminum source (Al K = 1486.7 eV) and charge compensation gun. The binding energies were referenced to the C 1s core level at 285 eV. The atomic compositions were estimated from the Ca 2p, P 2p, C 1s, O 1s and Mn 2p core shell levels. The average oxidation state (AOS) of Mn was determined from the energy separation of the 2 peaks present in the Mn 3s core shell level (see below) with an uncertainty of  0.2.

ToF-SIMS

Positive and negative ToF-SIMS measurements were performed with a TOF.SIMS5 spectrometer (ION-TOF GmbH Germany) equipped with a bismuth liquid metal ion gun (LMIG). The tableted samples were bombarded with pulsed Bi3 + primary ion beam (25 keV, 0.25 pA) rastered over a 500500m 2 surface area. With a data acquisition of 100 s, the total fluence does not amount up to 10 12 ions/cm 2 ensuring static conditions. Charge effects were compensated by means of a 20 eV pulsed electron flood gun. In these experiments, the mass resolution (m/m) was about 3200 at m/z = 175 for Ca2PO4 + . The secondary ions were identified by their exact mass, coupled with the appropriate intensities for the expected isotope pattern.

Principal Component Analysis

PCA was performed on a data set consisting of 20 non-saturated containing Mn secondary ions in positive polarity. These secondary ions were identified in bold character in Table 1 which contained all the significant positive secondary ions amounting to 41. For this purpose, a PLS_Toolbox_794 (Eigenvector Research, Manson, Washington, USA) was run under MATLAB version 7.11.0 (R2010b) (The MathWorks, Natick, Massachusetts, USA). A pretreatment procedure was performed before PCA analysis: (i) the peak intensities were first normalized with respect to total intensity of the selected ions. The result of the data normalization was a [10 х 20] matrix where the rows (10) were the fresh and used catalysts and the columns (20) were the variables, namely the normalized intensity of the Mn selected ions; (ii) auto-scaling was then performed in order to give equal significance to each m/z ion in the spectrum. After the pre-treatment procedure, the data were analyzed by PCA.

Results and discussion

Catalytic results

The MnxHap catalysts have been tested in dry air containing 800ppmv of toluene at atmospheric pressure and isothermal conditions (220°C) at a fixed GHSV of 30000mL/(g.h). Fig. 1 shows the evolution in toluene conversion with time-on-stream over the Hap supported Mn based catalysts. The rates of toluene removal expressed per mole of manganese have been reported in Table 2. The fresh catalysts can be ranked by decreasing activity as follows: Mn5Hap > Mn2.5Hap > Mn10Hap > Mn20Hap > Mn30Hap. It is observed a decrease in toluene conversion with time on steam for all catalysts but to different extents. To quantify the resistance against deactivation an activity coefficient a220, was defined as the ratio between the toluene conversion after 40 h reaction to that at initial time [START_REF]Catalyst Deactivation 2001[END_REF]. Plot of a220 against Mn content given in Fig. 2a shows a volcano plot whose position of the maximum corresponds to the most resistant catalyst towards deactivation, namely Mn10Hap. The ranking of the used catalysts by decreasing activity (cf. Table 2) now becomes: Mn5Hap-ATS  Mn10Hap-ATS  Mn2.5Hap-ATS > Mn20Hap-ATS >> Mn30Hap-ATS. Along with CO2, which is the main product of oxidation some amount of CO has also been detected. Fig. 3 displays CO yield as a function of time on stream for the different catalysts. The initial CO yield was the highest for Mn5Hap (3.4%) to decrease with time to reach 1.6% after 40h. Conversely the CO yield increases in the course of the reaction for Mn10Hap from 0.7 to 1.6% while it keeps rather stable with time regarding the remaining catalysts. Additionally, benzene and benzaldehyde were detected as gaseous by-products with concentrations ranging from 1.0-4.0 and 0.5-3.5 ppmv, respectively. It is to be noted that carbon balances are equal to or higher than 97%.

XPS results

XPS analysis has been performed to provide surface information regarding the effect of manganese loading on the Mn AOS and Mn dispersion. Mn AOS has been determined from the energy separation of two peaks which appear in the global envelope of the Mn 3s core level as shown in Fig. 4 and the relevant data as well as XPS compositions of the catalysts are reported in Table 3. The appearance of two peaks is due to the coupling of non-ionized 3s electron with 3d valence-band electrons. Interestingly the magnitude of the peak splitting ΔE is recognized to be a diagnostic of Mn AOS as ΔE decreases with the increase of Mn AOS. Some linear relationships between ΔE and Mn AOS have been reported in the literature and the one reported by Galakhov et al. [30] has been used in that work. Overall, for fresh samples, the Mn AOS increases from 1.7 to 3.7 with increased Mn content. The low Mn AOS of 1.7 in Mn2.5Hap, close to +2, can be explained by the diffusion of Mn as Mn 2+ into the outermost layers of hydroxyapatite support, most likely, by exchange with Ca 2+ surface sites. This is supported by the fact that the outmost layer of the bare Hap is enriched in Ca 2+ as shown by LEIS [31] and that Mn 2+ ion readily substitutes for Ca 2+ in Hap [32]. The diffusion of Mn into the support during calcination therefore seems to be unavoidable despite the dispersion of Mn on Hap by wet impregnation. For medium Mn content (5-10 wt%) additional Mn can interact with the porous Hap surface to give well dispersed Mn oxide clusters with an Mn AOS of about 2.6 in accordance with the low temperature of calcination at 400°C and the oxidizing nature of the NO3 -counter-anion of the Mn precursor. Finally, at higher Mn contents (20-30 wt%) nucleation and growth of manganese oxide particles with higher Mn valence (Mn AOS  3.4-3.7) are expected to occur. This is consistent with the detection by X-ray diffraction (not shown in this study) of -MnO2 and -MnO2/Mn2O3 phases present in the Mn20Hap and Mn30Hap samples, respectively. Fig. 5a shows a logarithmic evolution of the XPS atomic ratio Mn/(Ca + P) as a function of Mn loading both for fresh and spent Mn-Hap samples in agreement with a relative loss of Mn manganese dispersion with increasing Mn loading. Interestingly the higher position of the trace belonging to the used catalysts, due to higher Mn(Ca+P) atomic ratio, might suggest a Mn surface in the depth probed by XPS. It should be noted that the Mn AOS does not differ before and after catalysis for 10-20 wt% Mn loadings but clearly decreases for 5 wt% as shown in Fig. 4 and 30 wt% Mn loadings (Table 3) due to toluene acting as reductant in accordance with a redox mechanism.

ToF-SIMS results

Regarding the Hap support, investigation of the (-) mass spectra of the investigated samples reveals the following peaks: O -, OH -, P -, O2 -, PO -, PO2 -, and PO3 -. The fragment ions of the PO4 3-anion such as P -(m/z = 30.97), PO -(m/z = 46.97), PO2 -(m/z = 62.96), and PO3 -(m/z = 78.96) are clearly observed. The characteristic peaks shown in the (+) spectra include (ion, m/z value): Ca + , 39.96; CaO + , 55.96; CaOH + , 56.96 and Ca2O + , 95.92. Some impurities are also detected such as Na + , Mg + , K + , and hydrocarbons. Several fragment ions were found to be derived from the Ca10(PO4)6(OH)2 support (ion, m/z value) such as Ca2PO3 + , 158.88; Ca2PO4 + , 174.88; Ca3P2O3H + , 230.82; Ca4P2O4H + , 286.78; Ca5P2O5H + , 342.74; Ca6P3O4H2 + , 398.69. Subtraction of the hydroxyl ion OH -from Ca5(PO4)3OH affords the ion Ca5(PO4)3 + detected at m/z = 484.67. The ToF-SIMS (-) spectra of the Hap supported Mn samples exhibit as the most characteristic intense Mn ions MnO2H -, MnO3 -and MnO3H -in the m/z 2-150 range. Additionally, the NO -, 30.00; NO2 -, 45.99 and NO3 -, 61.99 ions are clearly observed on the fresh MnxHap samples and attest of the presence of residual nitrate after calcination. All investigated ToF-SIMS (+) spectra evidence 10 secondary ions both containing Ca and Mn ions of general formula CavMnyOzHw + (v = 1,2 ; y = 1-3 ; z = 1-4 ; w = 0, 1; labelled (1) to (10) in Table 1) among the overall Mn containing ions. As an example, Fig. 6 shows characteristic secondary ions in the 182-189 m/z range assigned to the Ca2MnO3 isotopic pattern. It is worth mentioning that the relative contribution of such secondary ions given in Table 4 increases logarithmically as shown in Fig. 7 with Mn content in line with the detection of MnOx particles at high Mn content. By contrast only one Mn-P related ion, namely PMn2O3H + (labelled (14) in Table 1) is observed. Furthermore, four CaMnP related ions are also detected: CaMn2PO4H + , CaMn2PO5 + , Ca2Mn2PO5H + and Ca2Mn2PO6 + (labelled ( 17)-( 20) in Table 1). Hence CavMnyOzHw + (v, y, z  0) ions strongly contribute to the m/z signals of the spectra in comparison with PxMnyOzHw + (x, y, z  0) indicating that manganese preferentially interacts with calcium than phosphorus. These observations are in accordance with previous works showing that the synthesis of hydroxyapatite using a reflux method allows to get an enriched Ca 2+ surface as observed by LEIS [31] which is believed to promote a better Ca-O-Mn interaction. It should also be noted the observance of secondary ions such as CaCO3 (m/z = 99.95) and Ca2HCO3 + (m/z = 140.92) showing interaction of Ca 2+ with carbonates.

Principal Component Analysis (PCA)

It is found that two PC account for about 87% of the data variance as seen in Table 4.

The results are presented in a PC1-PC2 plot given in Fig. 8. PC1 variability accounts for 75 % of total variance of the data set. Interestingly PC1 increases with Mn content up to 20 wt% with scores increasing from -5 to +4. Plot of PC1 score as a function of Mn content displayed in Fig. 5b shows a logarithmically increase in a similar way to that previously noticed when plotting Mn/(Ca+P) ratio against Mn wt%. Hence PC1 provides information regarding manganese amount dispersed on the hydroxyapatite surface. Additionally, PC1 clearly enables to separate most of the used catalysts in a similar way than the fresh ones. As a consequence, similar conclusions hold for the used catalysts. Conversely it is found a clear discrimination among the fresh and used catalysts when considering the score plot of PC2 in Fig. 8. Indeed, the PC2 splits the samples in two groups on both sides of PC2 score equal to 0. The used catalysts have globally a positive score while the fresh catalysts exhibit a negative one. Consequently, PC2 is mainly related to the manner according to which the reactive gaseous mixture affects the outermost layers of the samples. Additionally, it is observed in Fig. 2b a volcano plot when plotting PC2 scores as a function of Mn loading for the used catalysts with the best PC2 score obtained for Mn10Hap. Based on the similarities with the previous volcano plot representing the coefficient factor a220 as a function of Mn loading (Fig. 2a) it follows that PC2 is related to the extent of catalyst deactivation. Consequently, it is strongly suggested that a re-dispersion occurs during the first hours of the test on MnxHap catalysts (x = 5-10 wt%) implying significant changes in Ca-O-Mn interaction consistent with the higher loadings relative to the 2 variables CaOMn + and Ca2MnO2 + m/z signals as shown in Fig. 9.

Discussion

The oxidation of toluene on hydroxyapatite supported manganese oxides is a complex reaction, as among other things, redox, oxygen mobility and acidity can play important roles in the reaction. Additionally, these parameters can be easily tuned by modifying the nature and number of active sites as well as metal-support interactions with change in manganese loading. It has been shown here that at low Mn loading (2.5 wt%) Mn 2+ can be incorporated into the hydroxyapatite by substitution of Ca 2+ . With increased Mn loadings (5-10 wt%) additional Mn can react with the surface to give well dispersed Mn oxide clusters taking advantages of OH groups at the surface of the hydroxyapatite. At higher Mn loadings (20-30 wt%) the detection by XRD of manganese oxide particles consistent with -MnO2/Mn2O3 phases is consistent with a decrease in Mn dispersion. The global increase of the XPS Mn AOS with Mn content is in accordance with these different manganese locations. It is to be noted that the main PC (PC1) is able to discriminate the catalysts as a function of surface Mn dispersion as supported by Fig. 5b. For low Mn loading the weak Mn(II)-O bond involved in the reaction can lead to the formation of more active oxygen species. Conversely assuming a Mars and Van Krevelen mechanism, Mn20Hap and Mn30Hap catalysts with highest Mn AOS would have highest oxidation activities. Soylu et al. [33] and Carabineiro et al. [34] have showed that the acidity of the catalyst has also to be taken account in toluene oxidation reaction. It was shown that toluene adsorption occurred through interaction of methyl and phenyl groups on catalyst surface followed by H and C subtraction [35][36][37]. Toluene has not to be not to be strongly adsorbed (weak acid sites) in order to facilitate the subsequent oxygen attack from the lattice/surface of the catalyst. B.Chen al. reported elsewhere [6] that the acidity of surface Hap increases when adding manganese suggesting that an intermediate acidity is required to better activate the toluene molecule. In this study, the Mn5Hap catalyst appears to be the best catalyst for total oxidation of toluene. It might be composed of some Mn oxide clusters well-dispersed on Mn 2+ incorporated hydroxyapatite taking advantage of a (Mn(II)/Mn(III)) valence pair, and might present a good oxygen mobility and an intermediate acidity. For higher Mn loading, the lower Mn dispersion precludes to get efficient catalysts whereas for lower Mn loading, a lower acidity might explain a lower activity. The extent of deactivation has been shown to depend on the Mn local environment and location. The volcano plots obtained conjointly from the activity coefficient against Mn loading and from the PC2 score as a function of Mn content show that PC2 is clearly related to the deactivation extent of the catalysts. An investigation by ToF-SIMS in the 50-180 m/z range has been performed both in negative and positive polarities in order to track a possible chemical deactivation over the catalysts. The search of increase in intensity of possible organic fragment secondary ions originated from species formed through partial decomposition/oxidation of toluene and inorganic/organic secondary ions consistent with poisoning effects has been carried out. The possible change in intensity of the m/z = 60 signal relative to CO3 2-has also been investigated to provide evidence of strongly bounded carboxylates. All these studies do not lead to significant differences in the intensity of the m/z signals when comparing the fresh and used catalysts discarding a priori such chemical schemes of deactivation. Furthermore it is difficult to ascertain some significant decrease of the mean crystallite size of the Mn2O3/-MnO2 phases detected over MnxHap (x = 20 and 30) enabling to show a possible sintering of the Mn active phase. By opposition the decrease in toluene activity which occurs in the five first hours of the tests of stability could be attributed to a competition of the reactants and water for the specific sites as previously noticed by C. Lahousse et al. on -MnO2 in the total oxidation of hexane [38]. Consistent with the PC2 loadings it is postulated a re-dispersion at the outermost layers of hydroxyapatite affecting the Ca-O-Mn interaction. This re-dispersion which mainly affects Mn5Hap and Mn10Hap samples might lead to a better resistance of the catalyst towards deactivation.

Conclusion

Synthesis of Hap supported MnOx materials (Mn wt%: 2.5, 5, 10, 20 and 30) has been carried out by wet impregnation using manganese (II) nitrate as precursor followed by calcination at 400 °C. It has been shown by XPS that Mn loading significantly affects the nature of manganese species in terms of Mn dispersion and average oxidation state. PCA assisted ToF-SIMS study allows to discriminate the catalysts by their surface manganese content (PC1) and behavior to deactivation (PC2) in relation through changes in Mn-O-Ca interactions.

Fig. 1 .

 1 Fig. 1. Conversion of toluene as a function of time on stream over MnxHap catalysts.

Fig. 2 :

 2 Fig.2: a: Evolution of a220 as a function of Mn (wt%) content ; b: PC2 score as a function of Mn (wt%) content.

Fig. 3 :

 3 Fig. 3: CO yield as a function of time on stream over MnxHap catalysts.

Fig. 4 :

 4 Fig.4: Simulation of the XPS Mn 3s core level spectra of fresh and used Mn5Hap catalysts.

Fig. 5 :

 5 Fig. 5: a: Evolution of the surface atomic ratio Mn/(Ca + P) as a function of Mn loading ; b: PC1 score as a function of Mn loading.

Fig. 6 :

 6 Fig. 6: ToF-SIMS (+) spectrum in the m/z 182-189 range collected from the Mn10Hap sample.

Fig. 7 :

 7 Fig. 7: Relative abundance of CavMnyOzHw + secondary ions as a function of Mn content.

Fig. 8 :

 8 Fig. 8: PC1-PC2 score plot of the selected ToF-SIMS data.

Fig. 9 :

 9 Fig. 9: Loading plot of PC2.

Acknowledgements

Chevreul Institute (FR 2638), Ministère de l'Enseignement Supérieur et de la Recherche, Région Nord -Pas de Calais and FEDER are acknowledged for supporting and funding this work. D. Chlala acknowledges the award of a doctoral fellowship by the Agence Universitaire de la Francophonie (AUF) -Région du Moyen-Orient. M. Labaki is also very grateful to this institution for research fellowship. This work was also supported by a grant from the project PHC CEDRE 2015 N° 32933QE and by a grant from the Lebanese CNRS project N°01-08-15. The authors are grateful to Martine Trentesaux for XPS measurements.