Eddy Caron

Benjamin Depardon

Frédéric Desprez

Modelization for the Deployment of a Hierarchical Middleware on a Heterogeneous Platform

Keywords: Hierarchical middleware, Deployment, Modelization, Grid Intergiciel hiérarchique, Déploiement, Modélisation, Grille

Accessing the power of distributed resources can nowadays easily be done using a middleware based on a client/server approach. Several architectures exist for those middlewares. The most scalable ones rely on a hierarchical design. Determining the best shape for the hierarchy, the one giving the best throughput of services, is not an easy task. We first propose a computation and communication model for such hierarchical middleware. Our model takes into account the deployment of several services in the hierarchy. Then, based on this model, we propose algorithms for automatically constructing a hierarchy on two kind of heterogeneous platforms: communication homogeneous/computation heterogeneous platforms, and fully heterogeneous platforms. The proposed algorithm aim at offering the users the best obtained to requested throughput ratio, while providing fairness on this ratio for the different kind of services, and using as few resources as possible. For each kind of platforms, we compare our model with experimental results on a real middleware called Diet.

Introduction

Using distributed resources to solve large problems ranging from numerical simulations to life science is nowadays a common practice [START_REF] Berman | Grid Computing: Making the Global Infrastructure a Reality[END_REF][START_REF] Foster | The Grid 2: Blueprint for a New Computing Infrastructure[END_REF]. Several approaches exist for porting these applications to a distributed environment; examples include classic message-passing, batch processing, web portals and GridRPC systems [START_REF] Seymour | The end-user and middleware apis for GridRPC[END_REF]. In this last approach, clients submit computation requests to a meta-scheduler (also called agent) that is in charge of finding suitable servers for executing the requests within the distributed resources. Scheduling is applied to balance the work among the servers. A list of available servers is sent back to the client; which is then able to send the data and the request to one of the suggested servers to solve its problem.

There exists several grid middlewares [START_REF] Caniou | Recent Developments in Grid Technology and Applications, chapter High performance GridRPC middleware[END_REF] to tackle the problem of finding services available on distributed resources, choosing a suitable server, then executing the requests, and managing the data. Several environments, called Network Enabled Servers (NES) environments, have been proposed. Most of them share a common characteristic which is that they are built with broadly three main components: clients which are applications that use the NES infrastructure, agents which are in charge of handling the clients' requests (scheduling them) and of finding suitable servers, and finally computational servers which provide computational power to solve the requests. Some of the middlewares only rely on basic hierarchies of elements, a star graph, such as Ninf-G [START_REF] Tanaka | Ninf-g: A reference implementation of RPC-based programming middleware for grid computing[END_REF] and NetSolve [START_REF] Arnold | Users' Guide to NetSolve V1.4.1[END_REF][START_REF] Casanova | Netsolve: a network server for solving computational science problems[END_REF][START_REF] Yarkhan | Gridsolve: The evolution of a network enabled solver[END_REF]. Others, in order to divide the load at the agents level, can have a more complicated hierarchy shape: WebCom-G [START_REF] Morrison | Webcom-G: grid enabled metacomputing[END_REF] and Diet [START_REF] Amar | Tunable scheduling in a GridRPC framework[END_REF][START_REF] Caron | DIET: A scalable toolbox to build network enabled servers on the grid[END_REF]. In this latter case, a problem arises: what is the best shape for the hierarchy?

Modelization of middlewares behavior, and more specifically their needs in terms of computations and communications at the agents and servers levels can be of a great help when deploying the middleware on a computing platform. Indeed, the administrator needs to choose how many nodes must be allocated to the servers, and how many agents have to be present to support the load required by the clients. Using as many nodes as possible, may not be the best solution: firstly it may lead to using more resources than necessary; and secondly this can degrade the overall performances. The literature do not provide much papers on the modelization and evaluation of distributed middleware. In [START_REF] Tanaka | Design, implementation and performance evaluation of gridrpc programming middleware for a large-scale computational grid[END_REF], Tanaka et al. present a performance evaluation of Ninf-G, however, no theoretical model is given. In [START_REF] Caron | Automatic deployment for hierarchical network enabled servers[END_REF][START_REF] Pushpinder Kaur Chouhan | Automatic middleware deployment planning on clusters[END_REF][START_REF] Chouhan | Automatic Deployment for Application Service Provider Environments[END_REF] the authors present a model for hierarchical middlewares, and algorithms to deploy a hierarchy of schedulers on clusters and grid environments. They also compare the model with the Diet middleware. However, a severe limitation in these latter works is that only one kind of service could be deployed in the hierarchy. Such a constraint is of course not desirable, as nowadays many applications rely on workflows of services. Hence, the need to extend the previous models and algorithms to cope with hierarchies supporting several services.

In this paper, we will mainly focus on one particular hierarchical NES: Diet (Distributed Interactive Engineering Toolbox). The Diet component architecture is structured hierarchically as a tree to obtain an improved scalability. Such an architecture is flexible and can be adapted to diverse environments, including arbitrary heterogeneous computing platforms. Diet comprises several components. Clients that use Diet infrastructure to solve problems using a remote procedure call (RPC) approach. SeDs, or server daemons, act as service providers, exporting functionalities via a standardized computational service interface; a single SeD can offer any number of computational services. Finally, agents facilitate the service location and invocation interactions of clients and SeDs. Collectively, a hierarchy of agents provides higher-level services such as scheduling and data management. These services are made scalable by distributing them across a hierarchy of agents composed of a single Master Agent (MA) (the root of the hierarchy) and several Local Agents (LA) (internal nodes).

Deploying applications on a distributed environment is a problem that has already been addressed. We can find in the literature a few deployment software: DeployWare [START_REF] Flissi | A generic deployment framework for grid computing and distributed applications[END_REF], ADAGE [START_REF] Lacour | Generic application description model: Toward automatic deployment of applications on computational grids[END_REF], TUNe [START_REF] Broto | Autonomic management policy specification in tune[END_REF], and GoDiet [START_REF] Caron | GoDiet : A deployment tool for distributed middleware on grid'5000[END_REF]. Their field of action ranges from single deployment to autonomic management of applications. However, none include intelligent deployment mapping algorithms. Either the mapping has to be done by the user, or the proposed algorithm is random or roundrobin. Some algorithms have been proposed in [START_REF] Caron | Automatic deployment for hierarchical network enabled servers[END_REF][START_REF] Pushpinder Kaur Chouhan | Automatic middleware deployment planning on clusters[END_REF] to deploy a hierarchy of schedulers on clusters and grid environments. However, a severe limitation in these works is that only one kind of service could be deployed in the hierarchy. Such a constraint is of course not desirable, as nowadays many applications rely on workflows of services. Hence, the need to extend the previous models and algorithms to cope with hierarchies supporting several services.

The contribution of this paper is twofold. We first present a model for predicting the performance of a hierarchical NES on a heterogeneous platforms. Secondly, we present algorithms for automatically determining the best shape for the hierarchy, i.e., the number of servers for each services, and the shape of the hierarchy supporting these servers.

We first present in Section 2 the hypotheses for our model, then the model itself in Section 3 for both agents and servers. Then, we explain our approach to automatically build a suitable hierarchy on communication homogeneous/computation heterogeneous platforms in Section 4, and present experimental results. We then present, in Section 5 a genetic algorithm for automatically building a hierarchy on totally heterogeneous platforms, and also give some experimental results. Finally, we conclude this paper in Section 6.

Model Assumptions

Request Definition

Clients use a 2-phases process to interact with a deployed hierarchy. They submit a scheduling request to a Master Agent to find a suitable server in the hierarchy (scheduling phase), and then submit a service request (job) directly to the server (service phase). A completed request is one that has completed both the scheduling and service request phases and for which a response has been returned to the client. The throughput of a service is the number of completed requests per time unit the system can offer. We consider that a set R of services have to be available in the hierarchy. And that for each service i ∈ R, the clients aim at attaining a throughput ρ * i of completed requests per second.

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-1. Figure 1 presents the scheduling and service phases. The example shows the following steps:

1. the client sends a request of type i (i = 2 in Figure 1); 2. the request is forwarded down the hierarchy, but only to the sub-hierarchy that knows service i;

3. the SeDs perform performance predictions, and generate their response;

4. responses are forwarded up the hierarchy, and "sorted", i.e., the best choice is selected at each agent level;

5. the scheduling response is sent back to the client (the response contains a reference to the selected server);

6. the client sends a service request directly to the selected server;

7. the server runs the application and generates the results;

8. the results are directly sent back to the client.

Resource Architecture

We consider the problem of deploying a middleware on a fully connected platform G = (V, E, W, B):

V is the set of nodes, E is the set of edges, w j ∈ W is the processing power in M f lops/s of node j, and the link between nodes j and j ′ has a bandwidth B j,j ′ ∈ B in M bit/s. We do not take into account contentions in the network. We also denote by N = |V | the number of nodes.

Remark 2.1 We consider that whatever the node they are running on, services and agents require a constant amount of computation, i.e., we consider the case of uniform machines where the execution time of an application on a processor is equal to a constant only depending on the machine, multiplied by a constant only depending on the application.

Deployment Assumptions

We consider that at the time of deployment we do not know the clients' locations or the characteristics of the clients' resources. Thus, clients are not considered in the deployment process and, in particular, we assume that the set of computational resources used by clients is disjoint from V , and that we will not consider client/servers direct communications in our model (phases 6 and 8 in Figure 1). This corresponds to the case where data required for the services is already present in the platform, and does not need to be sent by the clients. A valid deployment will always include at least the root-level agent and one server per service i ∈ R. Each node v ∈ V can be assigned either as a server for any kind of service i ∈ R, or as an agent, or left idle. Thus with |A| agents, |S| servers, and |V | total resources, |A| + |S| ≤ N .

Objective

We consider that we work in steady-state. The platform is loaded to its maximum. Thus, we do not take into account the phases when only a few clients are submitting requests to the system, but only the phase where the clients submit as many requests as the middleware is able to cope with.

As we have multiple services in the hierarchy, our goal cannot be to maximize the global throughput of completed requests regardless of the kind of services, this would lead to favor services requiring only small amount of computing power to schedule and to solve them, and with few communications. Hence, our goal is to obtain, for each service i ∈ R, a throughput ρ i such that all services receive almost the same obtained throughput to requested throughput ratio: ρi ρ * i , of course we try to maximize this ratio, while having as few agents in the hierarchy as possible, so as not to use more resources than necessary.

3 Servers and Agents Models

"Global" Throughput

For each service i ∈ R, we define ρ schedi to be the scheduling throughput for requests of type i offered by the platform, i.e., the rate at which requests of type i are processed by the scheduling phase. We define as well ρ servi to be the service throughput. Lemma 3.1 The completed request throughput ρ i of type i of a deployment is given by the minimum of the scheduling and the service request throughput ρ schedi and ρ servi .

ρ i = min {ρ schedi , ρ servi } Proof:
A completed request has, by definition, completed both the scheduling and the service request phases, whatever the kind of request i ∈ R.

Case 1: ρ schedi ≥ ρ servi . In this case, requests are sent to the servers at least as fast as they can be processed by the servers, so the overall rate is limited by ρ servi .

Case 2: ρ schedi < ρ servi . In this case, the servers process the requests faster than they arrive. The overall throughput is thus limited by ρ schedi .

Lemma 3.2

The service request throughput ρ servi for service i increases as the number of servers included in a deployment and allocated to service i increases.

Hierarchy Elements Model

We now describe the model of each element of the hierarchy. We consider that a request of type i is sent down a branch of the hierarchy, if and only if service i is present in this branch, i.e., if at least a server of type i is present in this branch of the hierarchy. Thus a server of type i will never receive a request of type i ′ = i. Agents won't receive a request i if no server of type i is present in its underlying hierarchy, nor will it receive any reply for such a type of request. This is the model used by Diet.

Server model

We define the following variables for the servers. w prei is the amount of computation in M F lops needed by a server of type i to predict its own performance when it receives a request of type i from its parent. Note that a server of type i will never have to predict its performance for a request of type i ′ = i as it will never receive such a request. w appi is the amount of computation in M F lops needed by a server to execute a service. m reqi is the size in M bit of the messages forwarded down the agent hierarchy for a scheduling request, and m respi the size of the messages replied by the servers and sent back up the hierarchy. Since we assume that only the best server is selected at each level of the hierarchy, the size of the reply messages does not change as they move up the tree. Figure 2 presents the server model.

Server computation model

We suppose that a deployment with a set of servers S i completes N i requests of type i in a given time frame. Then, each server S j ∈ S i will complete N j i such that:

j∈Si

N j i = N i (1)
On average, each server S j has to do a prediction for N i requests, and complete N j i service requests during the time frame. Let T serverj compi be the time taken by the server S j ∈ S i to compute N j i requests and predict N i requests. We have the following equation:

T serverj compi = w prei .N i + w appi .N j i w j (2)
Now let's consider a time step T during which N i requests are completed. On this time step, we have:

∀i ∈ R, ∀j ∈ S i , T = w prei .N i + w appi .N j i w j (3)
From ( 3), we can deduce the number of requests computed by each server S j ∈ S i for all type of requests i ∈ R:

N j i = T.w j -w prei .N i w appi (4)
From equations ( 1) and ( 4), we can rewrite the time taken by the S i servers to process N i requests of type i:

j∈Si N j i = j∈Si T.w j -w prei .N i w appi N i = T × j∈Si w j w appi 1 + j∈Si wpre i wapp i N i = T × j∈Si w j w appi + |S i | .w prei T = N i × w appi + |S i | .w prei j∈Si w j (5)
Hence the average computation time for one request of type i:

T server compi = w appi + |S i | .w prei j∈Si w j (6)
and the service throughput for service i:

ρ comp servi = j∈Si w j w appi + |S i | .w prei (7)
Server communication model A server of type i needs, for each request, to receive the request, and then to reply. Hence Equations ( 8) and ( 9) represent respectively the time to receive one request of type i, and the time to send the reply to its parent. Communications time depends on the shape of the hierarchy, as the bandwidth is not the same over the platform. Thus, we denote by f j i the father of server S j ∈ S i . Server S j ∈ S i receive time for one request of type i:

T serverj recvi = m reqi B j,f j i ( 8
)
Server S j ∈ S i reply time for one request of type i:

T serverj sendi = m respi B j,f j i (9)
Service throughput Concerning the machines model, and their ability to compute and communicate, we consider the following models:

• Send or receive or compute, single port: a node cannot do anything simultaneously.

ρ servi = 1 max j∈Si T serverj recvi + T serverj sendi + T server compi , (10)
• Send or receive, and compute, single port: a node can simultaneously send or receive a message, and compute.

ρ servi = min 1 max j∈Si T serverj recvi + T serverj sendi , 1 T server compi (11)
• Send, receive, and compute, single port: a node can simultaneously send and receive a message, and compute.

ρ servi = min 1 max j∈Si max T serverj recvi , T serverj sendi , 1 T server compi (12)

Agent model

We define the following variables for the agents. w reqi is the amount of computation in M F lops needed by an agent to process an incoming request of type i. Let A be the set of agents. For a given agent A j ∈ A, let Chld j i be the set of children of A j having service i in their underlying hierarchy. Also, let δ j i be a Boolean variable equal to 1 if and only if A j has at least one child which knows service i in its underlying hierarchy. w respi Chld j i is the amount of computation in M F lops needed to merge the replies of type i from its Chld j i children. We suppose that this amount grows linearly with the number of children. Figure 3 presents the server model. Our agent model relies on the underlying servers throughput. Hence, in order to compute the computation and communication times taken by an agent A j , we need to know both the servers throughput ρ servi for each i ∈ R, and the children of A j . Agent computation model. The time for an agent A j to schedule all requests it receives and forwards,when the servers provide a throughput ρ servi for each i ∈ R, is given by Equation [START_REF] Pushpinder Kaur Chouhan | Automatic middleware deployment planning on clusters[END_REF].

T agentj comp = i∈R ρ servi .δ j i .w reqi + i∈R ρ servi .w respi Chld j i w j (13)
Agent communication model. Agent A j needs, for each request of type i, to receive the request and forward it to the relevant children, then to receive the replies and forward the aggregated result back up to its parent. We also need to take into account the variation in the bandwidth between the agent and its children. Let f j be the father of agent A j in the hierarchy. Equations ( 14) and ( 15) present the time to receive and send all messages when the servers provide a throughput ρ servi for each i ∈ R.

Agent A j ∈ A receive time:

T agentj recv = i∈R ρ servi .δ j i .m reqi B j,f j + i∈R k∈Chld j i ρ servi .m respi B j,k (14)
Agent A j ∈ A send time:

T agentj send = i∈R ρ servi .δ j i .m respi B j,f j + i∈R k∈Chld j i ρ servi .m reqi B j,k (15)
We combine ( 13), ( 14), and ( 15) according to the chosen communication / computation model (similarly to Equations ( 10), [START_REF] Casanova | Netsolve: a network server for solving computational science problems[END_REF], and ( 12)).

Lemma 3.3 The highest throughput a hierarchy of agents is able to serve is limited by the throughput an agent having only one child of each kind of service can support.

Proof: The bottleneck of such a hierarchy is clearly its root. Whatever the shape of the hierarchy, at its top, the root will have to support at least one child of each type of service (all messages have to go through the root). As the time required for an agent grows linearly with the number of children (see ( 13), ( 14) and ( 15)), having only one child of each type of service is the configuration that induces the lowest load on an agent.

Planning on Communication Homogeneous / Computation Heterogeneous Platforms

Given the models presented in the previous section, we propose a heuristic for automatic deployment planning on communication homogeneous/computation heterogeneous platforms. As we consider that we have homogeneous communications, we have ∀j, j ′ ∈ V, B j,j ′ = B. The heuristic comprises two phases. The first step consists in dividing N nodes between the services, so as to support the servers. The second step consists in trying to build a hierarchy, with remaining nodes, which is able to support the throughput generated by the servers. In this section, we present our automatic planning algorithm in three parts. In Section 4.1 we present how the servers are allocated nodes, then in Section 4.2 we present a bottom-up approach to build a hierarchy of agents, and present in Section 4.3 the whole algorithm. Finally, we give experimental results in Section 4.4.

Servers repartition

Our goal is to obtain for all services i ∈ R the same ratio

ρserv i ρ * i
. Algorithm 1 presents a simple way of dividing the available nodes to the different services. We iteratively increase the number of assigned nodes per services, starting by giving nodes to the service with the lowest

ρserv i ρ * i ratio.
We need to take into account the nodes' heterogeneity. Hence, we propose two heuristics: min-first which first give the less powerful nodes to the servers, and max-first which first give the more powerful nodes to the servers.

Agents hierarchy

Given the servers repartition, and thus, the services throughput ρ servi , for all i ∈ R, we need to build a hierarchy of agents that is able to support the throughput offered by the servers. Our approach is based on a bottom-up construction: we first distribute some nodes to the servers, then with the remaining nodes we iteratively build levels of agents. Each level of agents has to be able to support the load incurred by the underlying level. The construction stops when only one agent i ← first service in S 7:

Assign one more node to i: n, following the order of L, and compute the new ρ servi 8:

L a ← L + {n} 9:
if ρ servi ≥ ρ * i then 10:

ρ servi ← ρ * i 11:
S ← S -{i}

12:
S ← Sort services by increasing

ρserv i ρ * i 13: until |L a | = N or S = ∅ 14: return L a
is enough to support all the children of the previous level. In order to build each level, we make use of a mixed integer linear program (MILP): (LP 1 ).

We first need to define a few more variables. Let k be the current level: k = 0 corresponds to the server level. For i ∈ R let n i (k) be the number of elements (servers or agents) obtained at step k, which know service i. For k ≥ 1, we recursively define new sets of agents. We define by M k the number of available resources at step k:

M k = M 1 -i∈R k-1
l=2 n i (l). For 1 ≤ j ≤ M k we define a j (k) ∈ {0, 1} to be a boolean variable stating whether or not node j is an agent in step k. a j (k) = 1 if and only if node j is an agent in step k. For 1 ≤ j ≤ M k , ∀i ∈ R, δ j i (k) ∈ {0, 1} defines whether of not node j has service i in its underlying hierarchy in step k. For the servers, k = 0, 1 ≤ j ≤ M 0 , ∀i ∈ R, δ j i (0) = 1 if and only if server j is of type i, otherwise δ j i (0) = 0. Hence, we have the following relation:

∀i ∈ R, n i (k) = M k j=1 δ j i (k). For 1 ≤ j ≤ M k , ∀i ∈ R, Chld j i (k)
∈ N is as previously the number of children of node j that know service i. Finally, for 1

≤ j ≤ M k , 1 ≤ l ≤ M k-1 let c j l (k) ∈ {0
, 1} be a boolean variable stating that node l in step k -1 is a child of node j in step k. c j l (k) = 1 if and only if node l in step k -1 is a child of node j in step k. Using linear program (LP 1 ), we can recursively define the hierarchy of agents, starting from the bottom of the hierarchy.

Let's have a closer look at (LP 1 ). Lines (1), ( 2) and (3) only define the variables. Line (4) states that any element in level k -1 has to have exactly 1 parent in level k. Line (5) counts, for each element at level k, its number of children that know service i. Line [START_REF] Caniou | Recent Developments in Grid Technology and Applications, chapter High performance GridRPC middleware[END_REF] states that the number of children of j of type i cannot be greater than the number of elements in level k -1 that know service i, and has to be 0 if δ j i (k) = 0. The following two lines, ( 7) and ( 8), enforce the state of node j: if a node has at least a child, then it has to be an agent (line (7) enforces a j (k) = 1 in this case), and conversely, if it has no children, then it has to be unused (line (8) enforces a j (k) = 0 in this case). Line [START_REF] Caron | Modelization for the Deployment of a Hierarchical Middleware on a Homogeneous Platform[END_REF] states that at least one agent has to be present in the hierarchy. Line [START_REF] Caron | DIET: A scalable toolbox to build network enabled servers on the grid[END_REF] is the transposition of the agent model in the send or receive or compute, single port model. Note that the other models can easily replace this model in MILP (LP 1 ). This line states that the time required to deal with all requests going through an agent has to be lower than or equal to one second.

Finally, our objective function is the minimization of the number of agents: the equal share of obtained throughput to requested throughput ratio has already been cared of when allocating the nodes to the servers, hence our second objective that is the minimization of the number of agents in the hierarchy has to be taken into account.

Experiments

We ran experiments on Diet hierarchies containing two services: dgemm 100 and Fibonacci 30. We used two clusters on the Lyon site of Grid'5000: Sagittaire and Capricorne. Their respective computing power is w sagittaire = 3249M F lops and w capricorne = 2922M F lops. We used two strategies to sort the nodes that are divided between the services: either we sorted them by increasing w j (heuristic min-first) or by decreasing w j (heuristic max-first).

Theoretical model / experimental results comparison

We validate our model against real executions with the Diet middleware. Figures 4 and5 present the comparison between theoretical and experimental throughput for respectively experiments with min-first and max-first heuristics. Table 1 presents the relative error for those experiments. As can be seen, the min-first heuristic is the most interesting one when dealing with large platforms. This can easily be explained: using less powerful nodes for servers leaves more powerful nodes for the agents, hence the maximum attainable throughput due to agents limitation is higher. Whatever the heuristic, we remain within 18.3% of the theoretical model.

Comparison with star graphs

On the tested platforms, our heuristics create hierarchies having up to three levels of agents. Is this really necessary, or would have a star graph, having the same SeD mapping, given the same or better results? Figure 6 presents the comparison between min-first and a star graphs having the same SeD distribution, and figure 7 present the comparison between max-first and star graphs having the same SeD distribution. As can be seen our approach clearly surpasses the simple star graph approach.

The fact that on Figure 7 the star graph throughput increases with 25-25 nodes, is easily explained by the number of SeDs this star graph has. As can be seen on Figure 11 (25-25 nodes) less SeDs are present than in Figure 10 (20-20 nodes). Thus, the MA is less loaded on the star graph obtained on 25-25 nodes, than on the star graph obtained on 20-20 nodes.

Hierarchy shape

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Fully Heterogeneous Platforms

In this section we finally deal with a fully heterogeneous platform: each node j has a computing power of its own w j , and the communication links between any two nodes j, j ′ are possibly all different: B j,j ′ . Hence, the agents and servers models follow the general model presented in Section 3.

The problem when dealing with totally heterogeneous platforms is that we need information about both the location of the parent, and location of the children. Hence, the bottom-up approach we used so far won't be applicable, nor would be a top-down approach: we won't be able to build a level if we do not know the position of the parent in a bottom-up approach, and conversely we cannot build a level without knowing the position of the children in a top-down approach.

A Genetic Algorithm Approach

As we cannot use an iterative approach to build a hierarchy without risking to have to test all possible solutions, we took a totally different approach: we rely on a genetic algorithm to generate a set of hierarchies, then evolve them, and finally select the best one among them.

In order to define our genetic algorithm, we need to describe a few notions: the objective function, crossover and mutations, and finally the evaluation strategy.

Objective function

The objective function has to encode all the goals we aim at optimizing in a hierarchy. It also needs to be subject to an order relation. Many genetic algorithm frameworks require that the objective function is encoded on an integer or floating point variable.

Our goal is the same as previously: we aim at maximizing the minimum ρ i /ρ * , while minimizing the number of agents constituting the hierarchy. Hence our goal can be summarized with the following point of decreasing importance: (i) maximize min i∈R {ρ i /ρ * }, (ii) then maximize the second ratio, the third. . . , (iii) minimize the number of agents to support the maximum throughput (i.e., maximize N minus the number of agents). In order to encode all these points into a single value, we use the following encoding (presented Figure 12): if we are given a precision ǫ on each ρ i /ρ * i ratio, then we can encode them on 1/ǫ digits; moreover, as the maximum number of agents is N -1, we only need ⌈log 10 (N )⌉ digits to encode the number of agents. Hence, on the whole we need R × 1/ǫ + ⌈log 10 (N )⌉ digits. Actually, in order to guide a bit more the genetic algorithm towards convergence, we added two more metrics that we wish to minimize at the end of the fitness value: the number of agents that do not have any children (in order to remove really useless elements) and the depth of the hierarchy (this shouldn't affect the throughput, but this impacts the response time of the hierarchy, and limits the formation of chains of agents).

Genotype

The genotype needs to encode the whole hierarchy: the parent/children relationship, and the type of each node (agent, server or unused). It can easily be encoded on two arrays of size N : one for encoding the type of the nodes, and one for encoding the parent/children relationship. The alleles, i.e., each value in the arrays, can have the following values. The type of a node can either be 0 if the node is unused, 1 if it is an agent, or i ∈ {2 . . . 2 + R} if the node is a server of type i -2. The parent of a node i can either be itself if the node is unused (i.e., type[i] = 0) or the MA, or the node number corresponding to the parent of i in the hierarchy. Genotypes are randomly generated when creating the first generation of individuals: nodes' types and relationship are randomly chosen in such a way that a valid hierarchy is created.

Crossover

We define a crossover between two hierarchies as follows. Crossovers are only made on the parent array. We randomly select two nodes (one on each hierarchy) and exchange the parent of both selected nodes. Figure 13 presents an example of crossover. Why not define a crossover which replaces a whole part of a hierarchy into another one? This approach works well for a small number of nodes, but has a far too big impact on the hierarchy shape on a large number of nodes. As an example, consider hierarchies H 1 and H 2 in Figure 13, suppose a crossover between node 6 in H 1 and node 1 in H 2 . Transferring node 6 into H 2 in place of node 1 would remove seven nodes, as node 6 is a server. Conversely, we cannot transfer node 1 in place of node 6 into H 1 , as node 1 is the root of the hierarchy, and thus a transfer would lead to a loop in the hierarchy.

Mutation

Mutations on a hierarchy can occur at different levels in the hierarchy. We define the following mutations, also presented in Figure 14:

• Hierarchy modification:

1. we randomly select a node to change its type. If the mutation changes the type from agent to unused or SeD, or from SeD to unused, then we remove the underlying hierarchy and modify the type of the node. If the type changes from unused to agent or SeD, we randomly choose a parent among the available agents.

2. we randomly select a node that will choose a new parent among the available agents. We can end up with two hierarchies (if the new parent is the node itself), in this case we randomly select one of the two hierarchies, and delete the other.

• Pruning: a node is randomly selected, then its whole underlying hierarchy is deleted.

Hierarchy evaluation

In order to evaluate a hierarchy generated by our genetic algorithm, we first compute for all i ∈ R the throughput supported by the servers, we denote by ρ max i this throughput. Then, for each

Quality of the Approach

Genetic algorithms mainly depend on stochastic processes, thus we need to assess the quality of the results. As we do not have any other algorithm for fully heterogeneous platforms, we compare our genetic algorithm with the heuristics proposed in Section 4 for computation heterogeneous platforms.

Comparison with computation heterogeneous, communication homogeneous algorithm

We compare our genetic algorithm (GA) with min-first and max-first. GA was run on 1000 generations, on a population having at least 50 individuals (or 2 × |V | if this is higher than 50). We used the deterministic tournament method of selection, and we ran 100 executions (100 seeds). Figure 15a and 15b respectively present the results for dgemm and Fibonacci services. As can be seen, even if on the mean GA do not obtain results as good as min-first or max-first, the best GA results closely follows the min-first heuristic. Table 2 presents the relative gain/loss obtained with the best solution of the genetic algorithm, compared to the min-first and max-first heuristics. As one can see, the loss is no bigger than 15%, and it gives better results than max-first for the larger platform. This confirms that our approach can be effective: even if one run of GA is not sufficient to obtain the best result, taking the best hierarchy over a few runs of GA can give us good results. Note that this is often the case with genetic algorithms, as it is an exploratory method. We are quite confident that the performance loss obtained with the GA solutions can be reduced by fine tuning the GA parameters.

Initialization difference

We also compared the results of the genetic algorithm for different initialization strategies. We compared four different strategies:

• Random: we first randomly choose the number of nodes for each service, they are mapped on random nodes. Then, we create a random number of agents, and finally we just connect everything randomly, but in a way that creates a valid hierarchy. This is the initialization method used in the previous section.

• min-first or max-first: we use the random initialization for all but one hierarchy. We initialize this latter with the hierarchy found by min-first or max-first (this of course is only possible for a communication homogeneous platform).

• star-graph: we randomly choose the type of each nodes, and ensure that only one can be an agent. Then elements are connected as a star graph under the agent.

Figures 16a and16b present the results. The star-graph gives the worst results, as new levels of hierarchy can be created only through mutations, and they do not occur often. Without much surprise, the min-first and max-first initialization give the best results as they are guided by the result of the bottom-up algorithm. Finally, the random initialization give results that are in between min-first and max-first on the mean, but which best result is almost as good as minfirst method. These results also assess the effectiveness of our approach, as starting from totally random hierarchies, we obtain results as good as with the min-first heuristic.

Experiments

We ran experiments on Diet hierarchies, with the same services as before: dgemm 100 and Fibonacci 30. We used three clusters present on three different sites of Grid'5000: Sagittaire in Lyon, Chti in Lille and Paradent in Rennes. Their respective computing power is w sagittaire = 3249M F lops, w Chti = 3784M F lops and w P aradent = 4378M F lops. Figure 17 present the comparison between theoretical and experimental throughput, and Table 3 presents the relative error. As can be seen, the experiments follow the model. Even though the error can be quite big for small platforms (less than 10 nodes), the performance prediction becomes more accurate for larger platforms, as the relative error remains lower than 16%. The higher errors obtained for small platforms can be explained by the fact that the platform benchmarks have been done by stressing the network and machines. This stress is more easily attained when many nodes are involved in the experiments. Figure 18 presents the shape of the hierarchy obtained for a 17-17-16 platform. As can be seen, not all the available nodes are used: as previously, adding more nodes does not necessarily provide better performances as it tends to overload the agents. Blue nodes are on the Chti cluster, red ones on Paradent, and the white ones on Sagittaire.

Conclusion

In this paper we presented a computation and communication model for hierarchical middleware, when several services are available in the middleware. We proposed algorithms to find a hierarchy that gives the best obtained throughput to requested throughput ratio for all services on two different kinds of platforms: communication homogeneous/computation heterogeneous platforms, and fully heterogeneous platforms. The algorithm for communication homogeneous platforms uses a bottom-up approach, and is based on a linear program to successively determine levels of the hierarchy. The algorithm for totally heterogeneous platforms relies on a genetic algorithm.

Our experiments on a real middleware, Diet, show that the obtained throughput closely follows what our model predicts and that both approaches (bottom-up algorithm, and genetic algorithm) provide excellent performances. We clearly showed that they add new levels of agents whenever required, and that it outperforms the classical approach of deploying the middleware as a balanced star graph.

  $"#%&'(#&%"#$)*"&+ !"#$"#%&'(#&%"#$)*"&,

Figure 1 :

 1 Figure 1: Scheduling and service phases.

Figure 2 :

 2 Figure 2: Server model parameters.

Figure 3 :

 3 Figure 3: Agent model parameters.

Algorithm 1

 1 Servers repartition Require: L: list of available nodes Ensure: L a : list of nodes allocated to the servers 1: S ← list of services in R 2: L a ← ∅ 3: N ← |L| 4: L ← L sorted according to min-first or max-first heuristic 5: repeat 6:

Figure 4 :

 4 Figure 4: dgemm 100, Fibonacci 30 theoretical and experimental throughput, with min-first heuristic.

Figure 5 :

 5 Figure 5: dgemm 100, Fibonacci 30 theoretical and experimental throughput, with max-first heuristic.

Figures 8 , 9 ,

 89 Figures 8, 9, 10 and 11 give an example of the shape of the hierarchy generated by respectively min-first on a 20-20 and 25-25 platform, and max-first on a 20-20 and 25-25 nodes platform. Red nodes are on the Sagittaire cluster, white nodes on the Capricorne cluster. "D" stands for dgemm, and "F" stands for Fibonacci.

Figure 6 :

 6 Figure 6: Comparison min-first with a star graph with the same SeD distribution.

  /s) Fibonacci throughput (requests/s) Number of nodes (Sagittaire-Capricorne) dgemm 100 Fibonacci 30 dgemm, max-first Fibonacci, max-first dgemm, star graph Fibonacci, star graph

Figure 7 :

 7 Figure 7: Comparison max-first with a star graph with the same SeD distribution.

Figure 8 :

 8 Figure 8: min-first : 20-20.

Figure 9 :Figure 10 :Figure 11 :

 91011 Figure 9: min-first : 25-25.

Figure 12 :

 12 Figure 12: Objective value encoding.

Figure 13 :

 13 Figure 13: Crossover. Colored nodes are the one selected for crossover, within hierarchy elements represent the nodes' number.

Figure 15 :

 15 Figure 15: Comparison genetic algorithm results with min-first and max-first results.

Figure 16 :

 16 Figure 16: Different methods of initialization.

Figure 17 :

 17 Figure 17: dgemm 100, Fibonacci 30 theoretical and experimental throughput, with genetic algorithm approach.

Figure 18 :

 18 Figure 18: Hierarchy shape for a 17-17-16 platform.

Table 1 :

 1

	min-first	dgemm Fibonacci 4.1% 0.3%	5.2% 5.1% 10.2% 6.1%	3.2% 9.7%	3.9% 10.0%	2.9% 10.6%	4.20% 11.0%
	max-first	dgemm Fibonacci 4.6% 10.8% 12.7% 2.1% 0.1% 7.0%	7.7% 12.8%	8.9% 18.3%	2.3% 4.5%	3.8% 3.0%

Relative error, using min-first and max-first heuristics.

Table 2 :

 2 Genetic algorithm gains/loss compared to the min-first and max-first heuristics. A positive number denotes a gain, whereas a negative one denotes a loss.

  13-14-13 17-17-16

	dgemm	5.42%	2.29% 14.26% 0.33%	4.44%	11.73%	3.12%
	Fibonacci 98.63% 35.70%	8.16% 4.96%	4.91%	10.53%	15.95%

Table 3 :

 3 Relative Error, using genetic algorithm.

X X X X X X X X X X X X X X X X X X ρ i /ρ i * x 1/ε sorted by increasing values N -NbAgents ...Objective value to maximize

Acknowledgment

Experiments presented in this paper were carried out using the Grid'5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RE-NATER and several Universities as well as other funding bodies (see https://www.grid5000.fr).

Minimize

Building the whole hierarchy

We do not detail the whole algorithm for building the hierarchy, as it is the same as the one presented in [START_REF] Caron | Modelization for the Deployment of a Hierarchical Middleware on a Homogeneous Platform[END_REF] for homogeneous platforms. So, we refer the reader to this research report. agent in the hierarchy, we compute the maximum throughput ρ i for each service supported by the agent, we use (LP 2 ) to compute ρ i .

Maximize

Implementation and parameters

Genetic algorithms rely on quite a lot of different parameters. Each one of them can influence the quality of the result. Among them are the following:

• Selection method : when comparing x individuals, we need to choose which one should "survive." Different approaches exist: deterministic tournament, stochastic tournament, roulette or ranking. Our tests showed that the deterministic tournament was the one giving on average better results. Hence, we use this selection method in our experiments. When we force that the best parent replaces the weakest child if the child has a lower fitness, we talk about weak elitism. Weak elitism can possibly provide better solutions as it forces the algorithm to converge towards a locally good solution. However, it also reduces the population diversity, and thus, can lead to a local optimum. Our simulations tended to provide worst solutions when weak elitism was used, thus, in the following studies, we do not use it.

• Size of the population: with n machines, we varied the size of the population between n/4 and 2 × n. The worst results were always obtained for a size of n/4. The best results were obtained for populations having 5/4 × n and 2 × n. On average, better results were obtained for populations of sizes between 5/4 × n and 2 × n.

• Probability of crossover and mutation: crossover rate should generally be high, around 80%-90%, and mutation rate quite low, around 0.5%-1%. A very small mutation rate may lead to genetic drift, whereas a high one may lead to loss of good solutions. We chose set the mutation and crossover rates respectively to 1% and 80%.

We used the ParadisEO [START_REF] Cahon | Paradiseo: A framework for the reusable design of parallel and distributed metaheuristics[END_REF] framework to implement our genetic algorithm.


THE BEAM-GAS METHOD FOR LUMINOSITY MEASUREMENT AT LHCb P. HOPCHEV Laboratoire d'Annecy-le-vieux de Physique des Particules, France

The high resolution of the LHCb vertex detector makes it possible to perform precise measurements of the vertex positions of beam-gas and beam-beam interactions. With these measurements beam parameters such as width and position can be measured. A novel method for determining the absolute luminosity at the LHC using the directly measured beam parameters is presented. The data taken in 2009 is used to illustrate the procedure.

Plans for luminosity measurement in LHCb

Luminosity is a fundamental accelerator characteristic, related to the amount of collisions produced. The rate of a certain process can be expressed as the product of the the cross-section for that process and the instantaneous luminosity. LHCb is a forward spectrometer at the LHC optimised for precise studies of heavy flavour decays 1 . In addition, the unique pseudo-rapidity coverage (2 < y < 5) of the detector will allow interesting measurements of production crosssections in a hitherto unexplored kinematic region. For these measurements, good knowledge of the luminosity is essential. In LHCb several methods for measuring the luminosity are under investigation.

1. Beam imaging with gas 2 , which is described in this paper 2. van der Meer scan, which consists of moving the two beams agains each other, while measuring the interaction rate 3. Several indirect methods using processes with well known cross-sections, e.g. Z-boson and elastic diphoton dimuon production 2 Beam-gas luminosity method

In a circular collider the luminosity for 2 colliding bunches of particles can be expressed in the following way 3 :

L = f N 1 N 2 2c cos 2 (φ/2) ρ 1 (x, t)ρ 2 (x, t) d 3 xdt (1)
where f is the revolution frequency of the two counter-rotating bunches travelling with the speed of light c, N 1,2 are the bunch intensities, φ is the beams crossing angle and ρ 1,2 are the time-and space-dependent bunch densities. The integral in Eq. 1 is known as the beam overlap integral.

The beam-gas luminosity method is based on the detection of beam-gas vertices. The position of the beam-gas interactions can be used to measure the beam angles, profiles and relative positions. At a first approximation we can neglect possible phase shifts and do not consider effects from the longitudinal shape of the bunches. Then, having the transverse shapes of the bunches, we can calculate the overlap integral. The second important ingredient are the bunch intensities, knowledge of which comes from instrumentation installed and operated by the accelerator team.

Once the absolute luminosity is measured with satisfactory precision we will calibrate a reference cross-section and dedicated "lumi counters" in order to propagate the knowledge of the absolute scale. Currently the beam-gas luminosity method is being applied for a first time.

VELO vertex reconstruction

The LHCb vertex detector, the VELO, is located around the LHC interaction point 8 and can be used to reconstruct interaction vertices arising from beam-beam collisions and collisions between the beam protons and atoms of the residual gas in the beam-pipe. The VELO consists of 21 stations of radial and azimuthal silicon-strip sensors, see Fig. 1. In addition two backward stations, the so called 'pile-up' system, are used for providing a signal at the earliest level of the trigger system. Only the products of the beam1-gas interactions fly into the acceptance of the LHCb spectrometer.

In November 2009 LHC delivered its first proton-proton collisions at center of mass energy equal to 900GeV . In the following weeks several million collision events were recorded by LHCb. The events were triggered by activity in the calorimeter system or from significant activity in the pile-up system in the backward region of the VELO.

The vertex resolution for beam-beam interactions has been estimated with the data collected in 2009. Preliminary results are shown in Fig. 2. In 2009 the VELO was not fully closed and each of the two halves was retracted by 15 mm in the horizontal direction (along the x-axis). This leads to a worse resolution in x. For beam-gas interactions outside the luminous region we also take into account the dependence of the vertex resolution on the position of the vertex along the beam direction.

Measured beam properties

The VELO is positioned very close to the beam-axis which determines its very good acceptance for beam-gas events along a wide range in z, where z is the coordinate measured parallel to the beam axis, see Fig. 3(a). The x-z and y-z projections of the positions of the reconstructed beam-gas vertices can be used to determine the beams slopes and widths. Fig. 3 The measured beam and luminous-region sizes in the y-direction are shown in Fig. 4. One of the two colliding bunch pairs has been used to demonstrate the measurement. The overlap of the the two beams in the interaction point is important for optimizing the luminosity. Using the beams directions and sizes one can also make a prediction about the collision region and learn more about the systematic effects which have impact on its position and shape, Fig. 5.

Summary and prospects

The presented preliminary studies show the feasibility of the beam-gas luminosity method. For 2009 data the expected precision on the absolute luminosity is about 20%, decomposed roughly into 10% from the measurement of the beam overlap and 15% from the measurement of the beam intensities a . It has been shown that the vertex resolution plays a small, but not a The final analysis of the 2009 luminosity using this method in fact achieved a relative precision of 15% 4 negligible role in the determination the sizes of the beams. In 2010 more extensive studies will be possible allowing better estimate of the systematics and the higher amount of data will result in a decreased uncertainty. Considerable effort has been put into providing more precise beam intensity measurements. Therefore for 2010 we expect very competitive results on the precision of the determined luminosity.

Figure 1 :

 1 Figure 1: A simplified sketch of the VELO, including the 2 pile-up stations on the left. The colour arrows indicate the direction of the two LHC beams and example trajectories of the collision products from beam-gas interactions.Only the products of the beam1-gas interactions fly into the acceptance of the LHCb spectrometer.

  (b) shows the measured beam slopes during one of the 2009 runs.

Figure 2 :Figure 3 :

 23 Figure 2: Preliminary estimate of the VELO vertex resolution in the 2009 runs. The resolution in the transverse directions (x and y) for beam-beam interactions are shown as function of the number of tracks per reconstructed vertex.

Figure 4 :

 4 Figure 4: Measured bunch and luminous-region profiles in the vertices (y-) direction. From left to right the plots correspond to bunch1, bunch2 and their luminous region. The green line represents the vertex resolution, the red shows the observed size and the yellow -the size after deconvolving the vertex resolution.

Figure 5 :

 5 Figure 5: Observed beam positions and sizes at z = 0 before and after beam adjustments (mini scan). The colors have the following meaning: blue -beam1, red -beam2, light grey -predicted luminous region, purple -measured luminous region.


Károly Erdélyi
email: kerdelyi@gmail.com

László Dencső

Róbert Lehoczki

Miklós Heltai

Krisztina Sonkoly

Sándor Csányi

Norbert Solymosi

Endemic papillomavirus infection of roe deer (Capreolus capreolus)

Keywords: papillomavirus, epidemiology, roe deer

Roe deer papillomavirus (CcPV1) infection has been identified as an endemic disease in roe deer populations of the Carpathian basin in Central Europe (Hungary, Austria and Croatia).

The disease is characterized by easily recognizable skin tumours similar to Deer Papillomavirus infection of North American deer species. In 2006, a questionnaire study was conducted among all Hungarian game management units (GMUs) in order to assess the distribution of the disease and its major epidemiological features. Categorical information was collected about disease occurrence, trend and frequency of detection, on primarily affected age classes in both sexes, and association of lesions with mortality. Replies were received from 539 GMUs representing 50,9% of total GMU territory and disease presence was reported by 295 (54.7%) GMUs. Older age classes of both sexes were found to be more affected. Association of various environmental factors with disease occurrence was evaluated and data were collected on the occurrence of similar skin lesions in other European countries.

Pathological features of CcPV1 infection were described and the localisation of both CcPV1 antigen and DNA was characterised by immunohistochemistry and in-situ DNA hybridisation in skin lesions. Virus presence was also demonstrated by PCR and PCR product sequencing.

Introduction

Papillomaviruses (PVs) cause neoplastic lesions in a wide range of mammalian and some bird species. PVs are epitheliotropic, non-enveloped, icosahedral viruses, 50-55nm in diameter, containing a circular, double-stranded DNA genome, ranging from 7.4 to 8.6 kb. Tumours induced by PVs are especially common in ruminants and humans. Papillomas, fibropapillomas or fibromas associated with PV infection have been reported in a number of cervid species e.g. white-tailed deer [START_REF] Shope | An infectious cutaneous fibroma of the Virginia whitetailed deer (Odocoileus virginianus)[END_REF][START_REF] Lancaster | Characterization of papillomaviruses isolated from cutaneous fibromas of white-tailed deer and mule deer[END_REF], mule deer A c c e p t e d M a n u s c r i p t 3 [START_REF] Lancaster | Characterization of papillomaviruses isolated from cutaneous fibromas of white-tailed deer and mule deer[END_REF], European elk [START_REF] Moreno-Lopez | Characterization of a papilloma virus from the European elk (EEPV)[END_REF], reindeer [START_REF] Moreno-Lopez | Reindeer papillomavirus transforming properties correlate with a highly conserved E5 region[END_REF], red deer [START_REF] Moar | A cutaneous fibropapilloma from a red deer (Cervus elaphus) associated with a papillomavirus[END_REF][START_REF] Sundberg | Papillomavirus infections in animals[END_REF]Erdélyi et al., in press), roe deer [START_REF] Takács | Occurrence of cutaneous fibromatosis in roe deer (Capreolus capreolus) populations on the Great Plain of Hungary[END_REF][START_REF] Erdélyi | Characterisation of the first complete genome sequence of the roe deer (Capreolus capreolus) papillomavirus[END_REF]. The complete genomes of six cervid papillomaviruses have been published so far [START_REF] Groff | Molecular cloning and nucleotide sequence of deer papillomavirus[END_REF][START_REF] Ahola | Organization and expression of the transforming region from the European elk papillomavirus (EEPV)[END_REF][START_REF] Stenlund | European elk papillomavirus: characterization of the genome, induction of tumors in animals, and transformation in vitro[END_REF][START_REF] Moreno-Lopez | Reindeer papillomavirus transforming properties correlate with a highly conserved E5 region[END_REF][START_REF] Gottschling | Novel papillomavirus isolates from Erinaceus europaeus (Erinaceidae, Insectivora) and the Cervidae (Artiodactyla), Cervus timorensis and Pudu puda, and phylogenetic analysis of partial sequence data[END_REF][START_REF] Erdélyi | Characterisation of the first complete genome sequence of the roe deer (Capreolus capreolus) papillomavirus[END_REF]. The best known and most widely distributed cervid PV causing skin and lung fibromatosis is the deer papillomavirus (DPV), infecting primarily White-tailed deer (Odocoileus virginianus) in North America [START_REF] Shope | An infectious cutaneous fibroma of the Virginia whitetailed deer (Odocoileus virginianus)[END_REF][START_REF] Sundberg | Deer papillomaviruses[END_REF].

Presumably, the same virus also infects other closely related deer species i.e. Mule deer and Black-tailed deer (Odocoileus hemionus) inhabiting the same areas [START_REF] Sundberg | Deer papillomaviruses[END_REF]. Another cervid PV with a wide distribution is the European elk papillomavirus (EEPV), inducing skin tumours [START_REF] Borg | Viltsjukdomar[END_REF] and pulmonary fibromatosis (Moreno-Lopez et al., 1986) of European elk (Alces alces) in Scandinavia. Epidemiological studies of DPV infections suggest that PVs spread in wild cervid populations by direct contact and bloodfeeding insect vectors [START_REF] Sundberg | Deer fibroma: a review[END_REF]. The disease is assumed to be self limiting and DPV mainly affects younger animals. In contrast however, fibropapillomas are most commonly seen in older specimens of European elk and moose [START_REF] Sundberg | Papillomavirus infections in animals[END_REF].

Roe deer fibropapillomatosis [START_REF] Kocsner | Skin fibromatosis of roe deer[END_REF][START_REF] Takács | Occurrence of cutaneous fibromatosis in roe deer (Capreolus capreolus) populations on the Great Plain of Hungary[END_REF]) is an endemic viral disease producing typical skin tumours, similar to DPV infection of North American deer species and endemic fibromatosis of European elk in Sweden. The causative agent is a novel Roe-deer papillomavirus (CcPV1) belonging to the ruminant-specific Deltapapillomavirus genus [START_REF] Erdélyi | Characterisation of the first complete genome sequence of the roe deer (Capreolus capreolus) papillomavirus[END_REF], affecting roe-deer populations of the Carpathian basin (Hungary, eastern parts of Austria (Theodora Steineck personal communication) and northern parts of Croatia [START_REF] Salajpal | Cutaneous fibroma of roe deer (Capreolus capreolus) -still a "novel" disease in Croatia[END_REF]).

The disease had been first described in certain parts of Hungary during the early nineteensixties [START_REF] Kocsner | Occurrence of roe deer fibromatosis in Hungary[END_REF] but the first attempts to characterise the contemporary disease situation are fairly recent. [START_REF] Kocsner | Skin fibromatosis of roe deer[END_REF] reported the presence of fibropapillomas in three Takács and Nagy-Bozsoki (1998) identified a fourth location where they detected the disease in 1996. Histopathological and immunohistochemical studies of roe deer fibropapillomas demonstrated the presence of papillomavirus antigen [START_REF] Kocsner | Skin fibromatosis of roe deer[END_REF]) and a complete genome of CcPV1 has also been identified in these same lesions [START_REF] Erdélyi | Characterisation of the first complete genome sequence of the roe deer (Capreolus capreolus) papillomavirus[END_REF].

The aim of this study was to assess the epidemiological and pathological features of roe deer fibropapillomatosis, its distribution area within Hungary and its status in a wider European context.

Material and methods

Questionnaire survey

A mailed questionnaire survey was conducted among all Hungarian game management units (GMUs) i.e. local Hunting Associations in order to assess the distribution and ecology of the disease. Questionnaires were mailed at the end of March 2006 to all 1201 GMUs then in existence, covering the whole territory of Hungary. The one page multiple choice questionnaire was accompanied by a single page covering letter explaining the purpose of the study and containing photographic illustrations of the lesions typical for the disease. Replies were signed by presidents of hunting associations thus authorizing data use, but the actual disease information was most often provided by professional hunters managing the hunting areas. A reminder was sent out to non-responding GMUs two weeks after the response deadline. Knowing that comprehensive statistics or records of disease outbreaks, mortality data or diagnoses are not available at the vast majority of GMUs the questionnaire was designed to collect categorical information about disease occurrence, trend and frequency of detection, on primarily affected age classes in both sexes, and the association of lesions with roe deer mortality.

Disease occurrence was categorized as: 1. Not detected; 2. Intermittent; and 3. Permanent. A question on how many cases were detected over the past five years could be answered by one A c c e p t e d M a n u s c r i p t 5 of three categories: 1. An odd case over several years; 2, 1-2 cases per year; and 3. Several cases per year. These categories were coded into "Disease Frequency Indicator" values (DFI 1-3) which were later used to characterise the spatial distribution and the intensity of the infection.

The trend of disease occurrence could be characterized as 1. Declining; 2. Stagnating; or 3.

Increasing. Identification of primarily affected age classes of both sexes (juveniles, subadults and adults) was also requested. The adult male category was further divided into middle aged and old bucks, as these age classes can be safely distinguished by visual observation methods.

The question whether the described lesions were seen on animals found dead at the GMU was to be answered by Yes or No.

Additional data were collected on the occurrence of roe deer fibropapillomas throughout Europe, from institutions performing wildlife disease diagnostics or maintaining wildlife disease databases. Available databases were searched retrospectively for diagnoses of skin tumours in general and specifically papillomas, fibropapillomas or fibromas found in roe deer and for the total number of roe deer samples submitted for examination during the same time period. Institutions kindly providing these data were: AFSSA-LERRPAS (SAGIR database)

France, Institute for Zoo and Wildlife Research (IZW) Berlin, Germany, National Veterinary Institute (SVA) Uppsala Sweden, Veterinary Laboratories Agency Diseases of Wildlife Scheme (VLADoWS), United Kingdom.

Data and analysis

Roe deer population data (yearly population size estimates and hunting bag sizes for bucks, does and fawns) were obtained from the Hungarian National Game Management Database (OVA). Averages of the previous six years' data (2001-2006) were calculated for each GMU separately and used in the study. Retrospective roe deer population data were obtained for the whole of France from Réseau Ongulés Sauvages -ONCFS-FNC-FDC. Spatial analysis and visualisation of data was performed in ArcGIS 9.0 (Environmental Systems Research Using the GMUs with known disease status and the proportion of CLC2000 classes within these GMUs a classifier was created based on random forest supervised machine learning algorithm [START_REF] Breiman | Random Forests[END_REF]. This algorithm was used to predict the disease status of nonresponding GMUs and estimate the wider distribution area of the disease based on population density and the CLC2000 environmental data. Logistic regression and Pearson's product moment correlation coefficient was used to quantify the association of disease occurrence, environmental and host population data.

Sample collection

All 42 roe deer carcasses submitted for post mortem examination to the Central Veterinary Institute in the period between 2004 and 2006 from different parts of Hungary were examined for the presence of skin lesions. Tissue samples were collected in 10% neutral buffered formalin for histopathology and frozen at -20°C for subsequent DNA extraction. Formalin fixed tissues were embedded in paraffin and cut into 4 μm thick sections. Three sections from each block were mounted on 3-aminopropyltriethoxy-silane (Sigma Aldrich Co., St Louis, MO, USA) treated glass slides and submitted for Hematoxilin-Eosin (HE) staining, immunohistochemistry (IHC) and in-situ hybridisation (ISH).

Virus identification and visualisation

PCR assays were performed to demonstrate the presence of CcPV1 DNA in fibropapillomas of roe deer originating from distinct locations in Hungary as described earlier by [START_REF] Erdélyi | Characterisation of the first complete genome sequence of the roe deer (Capreolus capreolus) papillomavirus[END_REF]. Amplified PCR products were directly sequenced from both directions with the Viral antigen was visualised in tumour tissues by the immunohistochemistry method described by Erdelyi et al. (in press). Briefly, after antigen retrieval (0.1% protease XIV solution (Sigma Aldrich Co., St Louis, MO, USA) at 37 °C for 10 min) sections were first incubated in 3% H2O2 solution for 10 min and then in a 2% solution of skimmed milk powder for 20 min. Polyclonal rabbit anti BPV1 antibodies (B0580, DAKO, Glostrup, Denmark) targeting the BPV-1 L1 capsid protein were added to the sections at a 40000 fold dilution and incubated at 4 °C overnight. Antibody binding was detected by horseradish peroxidase-labelled polymer (EnVisionTM+ Kit; Dako, Glostrup, Denmark).

In-situ DNA hybridisation was performed with a 392 bp long DIG labelled DNA probe synthesised by PCR from the L1 ORF region of the CcPV1 genome (nt 6407-6798; EF680235), using the PCR DIG Probe Synthesis Kit (Roche Diagnostics GmbH, Mannheim, Germany) according to the manufacturer's instructions. The original Pap2 fragment used for sequencing of the CcPV1 genome [START_REF] Erdélyi | Characterisation of the first complete genome sequence of the roe deer (Capreolus capreolus) papillomavirus[END_REF] served as a template for probe sythesis. The UPV1 (5'-HATWSARGAYGGDGANATG-3') and UPV2 (5'-CCRTTRTTHHDDCCYTG-3') primer pair utilised in this reaction was originally designed for the detection of all Delta PVs with PRIMER2 (Scientific and Educational Software, Stateline, PA, USA) and PrimerSelect from the Lasergene software package (DNASTAR Inc., Madison, WI, USA). The hybridisation reaction was performed according to the "In-situ hybridisation to DNA in cells and tissue sections" protocol by [START_REF] Schwarzacher | Practical in situ Hybridization[END_REF]. Briefly, the mounted sections were dewaxed and rehydrated according to routine procedures and treated with proteinase K (20 μg/ml) at 37°C for 30 minutes followed by a 4% paraformaldehyde fixation at 4°C for 20 minutes. All slides were incubated with 200 μl aliquots of hybridisation buffer (50% formamide; 2x standard saline citrate buffer (SSC); 1 mM Tris HCl, pH 7; 0.1 mM EDTA; 30 ng/μl ssDNA; 10% dextrane sulphate) for 2 hours at according to the manufacturer's instructions and counterstained with methyl green.

Results

Pathology and virus demonstration

From the 42 roe deer carcasses examined between 2004 and 2006, 14 showed macroscopic lesions consistent with skin fibropapillomatosis. Fibropapillomas of roe deer were found to be firm, round, multiple skin tumours of varying size. On average the lesions were 3-5 cm in diameter (Figure 1.) but sometimes they reached 11-15 cm. Although the number of fibropapillomas on one animal was usually less than 10, individuals carrying more than 150 tumours were also found. Fibropapillomas were typically localised on the head, neck, belly and legs of infected animals. Some tumour surfaces exhibited moderate and transient papillary structure but the typical appearance was of a firm, hairless mass covered with smooth or verrucous epidermis. The epidermis was often pigmented and the surface of larger tumours was frequently eroded and ulcerated. The main tumour mass was located in the dermis and microscopically it consisted of groups of proliferating fibroblasts embedded in a compact mesh of collagen fibres, usually covered by a layer of loosely arranged collagen fibres and fibroblasts of the upper dermis. The epidermis showed signs of acanthosis and hyperkeratosis (Figure 2.). Occasional groups of keratinocytes containing moderate amounts of dark brown, coarse melanin granula were seen in the stratum spinosum and variable quantities of basophilic, keratohyalin granula were present in cells of the stratum granulosum.

A c c e p t e d M a n u s c r i p t 9 PV DNA was successfully amplified by PCR from samples of all eight selected cases exhibiting typical skin lesions. All obtained nucleotide sequences were found to be identical.

Both PV L1 antigen and DNA were visualized only in the epidermis covering the tumour stroma. PV DNA homologous to the L1 ORF sequence was present in a few keratinocyte nuclei of the stratum basale but the stratum spinosum and stratum granulosum had much larger numbers of keratinocytes containing PV L1 DNA in both their nuclei and cytoplasm (Figure 3a). PV major capsid antigen was predominantly localised in the cytoplasm and nuclei of keratinocytes of the stratum granulosum (Figure 3b), but antigen aggregations were often observed between the keratin layers of the stratum corneum as well.

The nodular lung fibrosis occasionally associated with DPV infection was not detected in roe deer with skin fibropapillomatosis. However, the presence of a moderate, diffuse lung fibrosis was seen in two completely processed cases, but neither L1 antigen nor L1 DNA could be visualised in these lesions by IHC and ISH respectively.

Epidemiology

Replies to the questionnaire were received from 539 GMUs. The overall questionnaire retrieval rate was 45,3% representing 50,9% of the total territory of all Hungarian GMUs i.e. effectively half of the country. The occurrence of the disease was reported by 293 GMUs (54,4% of responders), characterised as intermittent occurrence by 200 (37,1%) and permanent presence by 93 (17,4%) GMUs. Regarding frequency of disease detection, an odd case over several years (DFI=1) was reported by 126 GMUs (43% of positive territories), 1-2 cases per year (DFI=2) by 109 (37,2%) and more than 2 cases per year (DFI=3) by 58 (19,8%). Association plots of disease presence versus DFI categories (Figure 4.) showed an overlapping, positive and significant association (p<0.001) between permanent disease presence and DFI categories 2 and 3, and between intermittent disease presence and DFI categories 1 and 2. The trend of disease occurrence was characterized as declining in 60 (21,1%), stagnating in 187 (65,6%) and increasing in 38 (13,3%) GMUs.

A c c e p t e d M a n u s c r i p t 10 Bucks were found to be only slightly more affected (51,2%) by the disease than does (48,4%) but this difference was not statistically significant (McNemar's Chi-squared test with continuity correction, p=0.7657). The distribution of primarily affected age classes within each sex (bucks/does) was 2,3%/2,8% juveniles, 14,9%/35,8% subadults, 56,6% middle aged adult bucks, 26,2% old adult bucks and 61,4% adult does (Figure 5.). Differences detected in the age distribution of the disease within each sex were found to be significant by Pearson's Chi-squared test (p<0.001). Fibropapillomas were seen on animals found dead in 34 GMUs, i.e. 11% of the 309 units answering this question.

Observed (questionnaire data) and predicted (for non-responding GMUs) spatial distribution of the disease is presented on Figure 6 As the preliminary visual assessment of disease distribution and random forest variable importance values suggested a potential effect of lowland areas and major waterways on PV distribution, we tested the association of disease occurrence with water courses, forested areas and roe deer population density data. Results of the logistic regression analysis showed that both roe deer density (OR: 1.284, 95% CI: 1.151-1.433, p<0.001) and the presence of waterways (OR: 1.318, 95% CI: 1.105-1.572, p=0.002) increased, while broad-leaved forest cover decreased (OR: 0.974, 95% CI: 0.965-0.984, p<0.001) the likelihood of infection by CcPV1 (Figure 7.). Pearson's product moment correlation coefficient analysis showed no association (R: 0.07, p=0.12) between water courses and roe deer density, a weak negative (R:

-0.244, p<0.001) association between broad-leaved forest cover and roe deer density, and a moderate, positive association between non-irrigated arable land cover and roe deer density (R: 0.303, p<0.001).

An extensive literature search and the results of a survey of European wildlife disease databases showed that the endemic presence of roe deer fibropapillomatosis has not been [START_REF] Borg | Rådjur, Dödsorsaker, miljöpåverkan och rättsmedicin. Naturvårdsverket[END_REF]. However, a photograph of a fibroma-like solitary tumour on the head of a roe deer is presented in an earlier review [START_REF] Borg | On mortality and reproduction of roe deer in Sweden during the period 1948-1969[END_REF]. Additionally, a single case of a solitary skin fibroma and a single fibrosarcoma had been diagnosed in the 2417 roe

Discussion

Pathomorphological and epidemiological features of CcPV1 infection closely resemble DPV infections of white-tailed deer and other indigenous deer species in North America, except for the absence of nodular lung fibromatosis. The possible implication of CcPV1 in the mild fibrotic lung lesions of roe deer could be examined further by in-situ demonstration of the activity of early PV genes and proteins. However, the finding that the disease predominantly affects older age classes of both sexes is similar to the epidemiology of fibropapillomatosis in European elk and Moose in contrast to DPV induced lesions which are more common in young white-tailed deer. This feature also suggests a cumulative disease tendency and a slow regression of lesions, but our data are not sufficient to estimate the duration of disease.

Results of the questionnaire survey confirmed the presence of roe deer fibropapillomatosis in areas identified by all our earlier case records and with the data presented by [START_REF] Kocsner | Occurrence of roe deer fibromatosis in Hungary[END_REF]2001) and [START_REF] Takács | Occurrence of cutaneous fibromatosis in roe deer (Capreolus capreolus) populations on the Great Plain of Hungary[END_REF]. Many infected areas were identified for the first A c c e p t e d M a n u s c r i p t 12 time and were confirmed by additional samples submitted after the survey. The positive statistical correlation of disease distribution with both host density and waterways, and the fact that there was no correlation between these two variables suggests that waterways may have an independent effect on the distribution of roe deer fibropapillomatosis. One of the possible explanations, which should be experimentally tested, is that the habitats in question may be especially suitable for arthropod vectors which might play a significant role in the ecology of CcPV1. However, the potential effect of an increased, seasonal roe deer aggregation induced by these habitats in an agricultural landscape should not be excluded either.

The endemic, geographically confined distribution of roe deer fibropapillomatosis established by our study lends further support to the hypothesis that host related genetic factors may have an influence on the susceptibility of roe deer populations to CcPV1 infection [START_REF] Erdélyi | Characterisation of the first complete genome sequence of the roe deer (Capreolus capreolus) papillomavirus[END_REF]. The emergence of an introduced CcPV1 infection would have been expected in other susceptible roe deer populations, especially considering the high population densities throughout Europe (e.g. France: 9,503 km -2 , Hungary: 3,404 km -2 ), the universal presence of other environmental stress factors and the multiple translocation events from endemic areas in the past [START_REF] Vernesi | The genetic structure of natural and reintroduced roe deer (Capreolus capreolus) populations in the Alps and central Italy, with reference to the mitochondrial DNA phylogeography of Europe[END_REF].

Despite its practicality, the drawback of our survey approach is that classical epidemiological indicators (e.g. disease prevalence or incidence) cannot be calculated from the categorical data provided, and that certain bias may be inherent in the estimates provided by interviewees.

This may be most pronounced in the answers provided by those GMUs where disease occurrence is scarce.

Nevertheless, results of this study indicate that there is a certain niche for questionnaire surveys in the study of wildlife disease ecology. Although skin tumours are special because of their more memorable and easily recognizable physical aspect, we believe that territorially organised game management and nature conservation systems may be used to obtain baseline data on the occurrence, distribution and the estimated or perceived impact and importance of

  a 1,1% prevalence of the infection in one closely studied roe deer population.

  Data of individual environmental categories were extracted from the Corine Land Cover 2000 database (CLC2000) (European Environment Agency) for every GMU territory and used for subsequent analyses. R-environment was used for statistical analysis and result visualisation (R Development Core Team, 2008).

  A c c e p t e d M a n u s c r i p t 7 primers used for amplification (Pap1f and Pap1r) on an ABI 373A automated DNA sequencer (Applied Biosystems, Foster City, CA, USA).

  42°C and thereafter replaced by 60 μl of hybridisation mix containing 50 ng/μl of DIG A c c e p t e d M a n u s c r i p t 8 labelled DNA probe diluted in hybridisation buffer. Target DNA was denatured at 95°C for 10 minutes, then gradually cooled down to 42°C and allowed to hybridise overnight (20 hours) in a moist chamber. Sections were rinsed in 2x SSC solution and then gently shaked in a posthybridisation stringent wash solution (0.1x SSC; 2 mM MgCl 2 ; 0.1% Tween 20) for 45 minutes at 42°C. Slides were finally washed twice in 1x SSC for 10 minutes at room temperature and once in 1x PBS for 5 minutes. The hybridisation reaction was visualized using the DIG Nucleic Acid Detection Kit (Roche Diagnostics GmbH, Mannheim, Germany)

  , characterised by DFI values. Both random forest algorithms produced for disease occurrence and frequency data utilized the following variables: Non-irrigated arable land (CLC-code 211), Pastures (CLC-code 231), Broad-leaved forest (CLC-code 311), Water courses (CLC-code 511) and Roe deer density.

  specialist laboratories in France, Sweden, Germany or United Kingdom. A total of 10585 roe deer carcasses were examined in France during the 1986-2007 period with only a single case of a skin tumour, a sarcoid-like lesion detected on the eyelid of a roe deer doe found dead in 2005. In a review of 4168 roe deer cases processed at SVA in the 1947-1982 period, there is no mention of roe deer fibropapillomas amongst the diagnosed neoplastic diseases

  deer samples submitted to SVA between 1986 and May 2008. No skin tumours were detected on any of the 231 roe deer examined between 1972 and 2008 at the pathology unit of the IZW, Berlin, Germany. According to the VLA Diseases of Wildlife Scheme (VLADoWS) database no skin tumours were found on the 70 roe deer carcasses examined between 2000 and 2008 in the United Kingdom.

FiguresFigure 1 .

 1 Figures

Figure 2 .

 2 Figure 1. Typical appearance of fibropapillomas on the head and hind legs of a roe deer buck.Figure 2. Histological picture of a tumour surface exhibiting typical morphological features of a fibropapilloma with pronounced rete ridges formed by proliferating fibroblasts and collagen fibres, covered by the upper dermis and an epidermis showing signs of acanthosis and hyperkeratosis. Hematoxilin-Eosin, Bar = 50 μm.Figure 3. A) Immunohistochemistry visualizing aggregations of CcPV1 major capsid antigen in the cytoplasm and nuclei of keratinocytes of the stratum granulosum and between the keratin layers of the stratum corneum. B) In-situ hybridization showing the presence of Papillomavirus L1 ORF DNA in stratum spinosum and stratum granulosum keratinocyte nuclei and cytoplasm and fewer positive keratinocyte nuclei in the stratum basale. CcPV1 L1 ISH and anti BPV1 IHC. Bar = 100 μm.Figure 4. Association plot showing Pearson residuals of disease presence against disease frequency (DFI categories) revealing an overlapping, positive and significant association (p<0.001) between permanent disease presence and higher disease frequency (DFI categories 2 and 3) as well as between intermittent disease presence and low disease frequency (DFI categories 1 and 2).Figure 5. Diagram of the distribution of primarily affected age classes within each sex (bucks/does). Figure 6. Spatial distribution of roe deer fibropapillomatosis occurrence frequency among Game Management Units (GMUs) represented by DFI values (1-3) obtained from questionnaire data (Observed) and a Random forest based prediction for nonresponding GMUs (Predicted). DFI values: 0 -No disease; 1 -An odd case over several years; 2 -One to two cases per year; 3 -More than 2 cases per year. Empty polygons -No response. Figure 7. Results of the logistic regression analysis showing that both roe deer density (A) (OR: 1.284, 95% CI: 1.151-1.433, p<0.001) and the presence of waterways (B) (OR: 1.318, 95% CI: 1.105-1.572, p=0.002) increase, while broad-leaved forest cover decrease (C) (OR: 0.974, 95% CI: 0.965-0.984, p<0.001) the odds of infection by CcPV1.

Figure 3 .

 3 Figure 1. Typical appearance of fibropapillomas on the head and hind legs of a roe deer buck.Figure 2. Histological picture of a tumour surface exhibiting typical morphological features of a fibropapilloma with pronounced rete ridges formed by proliferating fibroblasts and collagen fibres, covered by the upper dermis and an epidermis showing signs of acanthosis and hyperkeratosis. Hematoxilin-Eosin, Bar = 50 μm.Figure 3. A) Immunohistochemistry visualizing aggregations of CcPV1 major capsid antigen in the cytoplasm and nuclei of keratinocytes of the stratum granulosum and between the keratin layers of the stratum corneum. B) In-situ hybridization showing the presence of Papillomavirus L1 ORF DNA in stratum spinosum and stratum granulosum keratinocyte nuclei and cytoplasm and fewer positive keratinocyte nuclei in the stratum basale. CcPV1 L1 ISH and anti BPV1 IHC. Bar = 100 μm.Figure 4. Association plot showing Pearson residuals of disease presence against disease frequency (DFI categories) revealing an overlapping, positive and significant association (p<0.001) between permanent disease presence and higher disease frequency (DFI categories 2 and 3) as well as between intermittent disease presence and low disease frequency (DFI categories 1 and 2).Figure 5. Diagram of the distribution of primarily affected age classes within each sex (bucks/does). Figure 6. Spatial distribution of roe deer fibropapillomatosis occurrence frequency among Game Management Units (GMUs) represented by DFI values (1-3) obtained from questionnaire data (Observed) and a Random forest based prediction for nonresponding GMUs (Predicted). DFI values: 0 -No disease; 1 -An odd case over several years; 2 -One to two cases per year; 3 -More than 2 cases per year. Empty polygons -No response. Figure 7. Results of the logistic regression analysis showing that both roe deer density (A) (OR: 1.284, 95% CI: 1.151-1.433, p<0.001) and the presence of waterways (B) (OR: 1.318, 95% CI: 1.105-1.572, p=0.002) increase, while broad-leaved forest cover decrease (C) (OR: 0.974, 95% CI: 0.965-0.984, p<0.001) the odds of infection by CcPV1.

Figure 4 .

 4 Figure 1. Typical appearance of fibropapillomas on the head and hind legs of a roe deer buck.Figure 2. Histological picture of a tumour surface exhibiting typical morphological features of a fibropapilloma with pronounced rete ridges formed by proliferating fibroblasts and collagen fibres, covered by the upper dermis and an epidermis showing signs of acanthosis and hyperkeratosis. Hematoxilin-Eosin, Bar = 50 μm.Figure 3. A) Immunohistochemistry visualizing aggregations of CcPV1 major capsid antigen in the cytoplasm and nuclei of keratinocytes of the stratum granulosum and between the keratin layers of the stratum corneum. B) In-situ hybridization showing the presence of Papillomavirus L1 ORF DNA in stratum spinosum and stratum granulosum keratinocyte nuclei and cytoplasm and fewer positive keratinocyte nuclei in the stratum basale. CcPV1 L1 ISH and anti BPV1 IHC. Bar = 100 μm.Figure 4. Association plot showing Pearson residuals of disease presence against disease frequency (DFI categories) revealing an overlapping, positive and significant association (p<0.001) between permanent disease presence and higher disease frequency (DFI categories 2 and 3) as well as between intermittent disease presence and low disease frequency (DFI categories 1 and 2).Figure 5. Diagram of the distribution of primarily affected age classes within each sex (bucks/does). Figure 6. Spatial distribution of roe deer fibropapillomatosis occurrence frequency among Game Management Units (GMUs) represented by DFI values (1-3) obtained from questionnaire data (Observed) and a Random forest based prediction for nonresponding GMUs (Predicted). DFI values: 0 -No disease; 1 -An odd case over several years; 2 -One to two cases per year; 3 -More than 2 cases per year. Empty polygons -No response. Figure 7. Results of the logistic regression analysis showing that both roe deer density (A) (OR: 1.284, 95% CI: 1.151-1.433, p<0.001) and the presence of waterways (B) (OR: 1.318, 95% CI: 1.105-1.572, p=0.002) increase, while broad-leaved forest cover decrease (C) (OR: 0.974, 95% CI: 0.965-0.984, p<0.001) the odds of infection by CcPV1.

Figure 5 .

 5 Figure 1. Typical appearance of fibropapillomas on the head and hind legs of a roe deer buck.Figure 2. Histological picture of a tumour surface exhibiting typical morphological features of a fibropapilloma with pronounced rete ridges formed by proliferating fibroblasts and collagen fibres, covered by the upper dermis and an epidermis showing signs of acanthosis and hyperkeratosis. Hematoxilin-Eosin, Bar = 50 μm.Figure 3. A) Immunohistochemistry visualizing aggregations of CcPV1 major capsid antigen in the cytoplasm and nuclei of keratinocytes of the stratum granulosum and between the keratin layers of the stratum corneum. B) In-situ hybridization showing the presence of Papillomavirus L1 ORF DNA in stratum spinosum and stratum granulosum keratinocyte nuclei and cytoplasm and fewer positive keratinocyte nuclei in the stratum basale. CcPV1 L1 ISH and anti BPV1 IHC. Bar = 100 μm.Figure 4. Association plot showing Pearson residuals of disease presence against disease frequency (DFI categories) revealing an overlapping, positive and significant association (p<0.001) between permanent disease presence and higher disease frequency (DFI categories 2 and 3) as well as between intermittent disease presence and low disease frequency (DFI categories 1 and 2).Figure 5. Diagram of the distribution of primarily affected age classes within each sex (bucks/does). Figure 6. Spatial distribution of roe deer fibropapillomatosis occurrence frequency among Game Management Units (GMUs) represented by DFI values (1-3) obtained from questionnaire data (Observed) and a Random forest based prediction for nonresponding GMUs (Predicted). DFI values: 0 -No disease; 1 -An odd case over several years; 2 -One to two cases per year; 3 -More than 2 cases per year. Empty polygons -No response. Figure 7. Results of the logistic regression analysis showing that both roe deer density (A) (OR: 1.284, 95% CI: 1.151-1.433, p<0.001) and the presence of waterways (B) (OR: 1.318, 95% CI: 1.105-1.572, p=0.002) increase, while broad-leaved forest cover decrease (C) (OR: 0.974, 95% CI: 0.965-0.984, p<0.001) the odds of infection by CcPV1.

Figure 6 .

 6 Figure 1. Typical appearance of fibropapillomas on the head and hind legs of a roe deer buck.Figure 2. Histological picture of a tumour surface exhibiting typical morphological features of a fibropapilloma with pronounced rete ridges formed by proliferating fibroblasts and collagen fibres, covered by the upper dermis and an epidermis showing signs of acanthosis and hyperkeratosis. Hematoxilin-Eosin, Bar = 50 μm.Figure 3. A) Immunohistochemistry visualizing aggregations of CcPV1 major capsid antigen in the cytoplasm and nuclei of keratinocytes of the stratum granulosum and between the keratin layers of the stratum corneum. B) In-situ hybridization showing the presence of Papillomavirus L1 ORF DNA in stratum spinosum and stratum granulosum keratinocyte nuclei and cytoplasm and fewer positive keratinocyte nuclei in the stratum basale. CcPV1 L1 ISH and anti BPV1 IHC. Bar = 100 μm.Figure 4. Association plot showing Pearson residuals of disease presence against disease frequency (DFI categories) revealing an overlapping, positive and significant association (p<0.001) between permanent disease presence and higher disease frequency (DFI categories 2 and 3) as well as between intermittent disease presence and low disease frequency (DFI categories 1 and 2).Figure 5. Diagram of the distribution of primarily affected age classes within each sex (bucks/does). Figure 6. Spatial distribution of roe deer fibropapillomatosis occurrence frequency among Game Management Units (GMUs) represented by DFI values (1-3) obtained from questionnaire data (Observed) and a Random forest based prediction for nonresponding GMUs (Predicted). DFI values: 0 -No disease; 1 -An odd case over several years; 2 -One to two cases per year; 3 -More than 2 cases per year. Empty polygons -No response. Figure 7. Results of the logistic regression analysis showing that both roe deer density (A) (OR: 1.284, 95% CI: 1.151-1.433, p<0.001) and the presence of waterways (B) (OR: 1.318, 95% CI: 1.105-1.572, p=0.002) increase, while broad-leaved forest cover decrease (C) (OR: 0.974, 95% CI: 0.965-0.984, p<0.001) the odds of infection by CcPV1.

  Figure 3

  Figure 5

  Figure 6

Acknowledgements

This research was partly funded by the Game Management Fund grant No 222/2006/PO of the Ministry of Agriculture and Rural Development and the National Scientific Research Foundation grant OTKA T049157. We would like to thank Marie-Eve Terrier (AFFSA, SAGIR database, France), Olivier Alabaric (Ecole Nationale Vétérinaire de Nantes, France), Erik Agren (SVA, Uppsala, Sweden), Gudrun Wibbelt (IZW, Berlin Germany), Paul Duff (VLADoWS, United Kingdom) for contributing the search results of their databases and Christine Saint-Andrieux (ONCFS, France) for providing French roe deer population data.