Abir Ismaili-Alaoui
email: abir.ismaili-alaoui@loria.fr

Ouafae Kasmi
email: kasmi@inpt.ac.ma

Amine Baïna
email: baina@inpt.ac.ma

Karim Baïna
email: karim.baina@um5.ac.ma

Khalid Benali
email: khalid.benali@loria.fr

Mostafa Bellafkih
email: bellafkih@inpt.ac.ma

Priority-based Event Management using Fuzzy Logic for an IoT-BPM Architecture

Keywords: Business Process Management, Fuzzy Logic, Internet of things, Event management, Priority, Criticality. I

Internet of things (IoT) world is growing at a breathtaking pace. This new paradigm shift affects all the enterprise architecture layers from infrastructure to business. Organizations are nowadays faced with new challenges to keep their quality of service and competitive advantage over other rival organizations. Business Process Management (BPM) is a field among others that will be affected by this new technology. Both IoT and BPM communicate through events, and effective and efficient management of those events ensures a better communication channel between the IoT physical layer and the Business layer. However, the huge amount of those IoT generated events and sometimes the subtle difference between their criticality level, generate uncertainty regarding their priority level determination. In this paper, we propose a fuzzy logic-based event management approach to estimate the criticality level of the incoming IoT events using two fuzzy inference systems (FIS) and to manage the priority of business process instances triggered by those events. A case study is presented and the obtained results from our simulations demonstrate the benefit of our approach and allowed us to confirm the efficiency of our assumptions.

INTRODUCTION

Managing business processes successfully allows organizations to evolve their performance and achieve their business goals, as it allows them to have a clear vision of their objectives. In fact, the performance level of any organization is indirectly linked to the efficiency of its processes and the quality of their models. Business process management is seen as the perfect solution that helps organizations adapt to the strategic, organizational and technical evolution. When an organization adopts a business process management approach, it is in order to have more visibility and control over its activities and interactions between these processes, to be able to model, manage, improve and optimize these processes continuously, and therefore gain in terms of agility, flexibility, and performance. Several researches have been done in this area and aimed at improving business processes, by focusing on the optimization of business processes issues at build time and at run-time from different perspectives: Control-flow perspective, data and event data perspective, scheduling (time and resources) and event management perspective. Business process instances scheduling and event management are considered as a crucial step in the journey of business process performance improvement. Several research contributions in the literature focus on scheduling business process instances, and optimizing resource allocation based on different methodologies and different algorithms such as [START_REF] Bessai | Business process scheduling strategies in cloud environments with fairness metrics[END_REF] [2] [3] [4] [START_REF] Bessai | Business process tasks-assignment and resource allocation in crowdsourcing context[END_REF] just to name a few. However, the new digitized era and the rise of several new technologies especially Internet of Things (IoT) implies new business process scheduling problems and challenges linked basically to limited resources or the need to use those resources (especially in case of human resources) in an optimal and flexible way by achieving priority-based scheduling. According to [START_REF] Ismaili-Alaoui | Towards smart incident management under human resource constraints for an iotbpm hybrid architecture[END_REF] to overcome those challenges a communication channel must be created between Business Process Management (BPM) domain and IoT domain in order to bridge the gap between the business layer and the IoT physical layer. In real-time IT systems, this communication channel is based on events. An event represents everything that happens or is considered to be happening [START_REF] Luckham | Epts event processing glossary v2. 0[END_REF], for example, events generated by IoT devices or sensors. Both IoT devices and business processes communicate with their environment through events. In fact, IoT devices collect data and generate new events by sensing their environment, after that, those events trigger specific business process instances or activities according to the specificities of the detected situation. That is why, efficient exploitation and management of these IoT generated events facilitate more this communication and integration between BPM and IoT for any organization and especially ones that deal with incident management processes in supervision and monitoring systems. In our previous work [START_REF] Ismaili-Alaoui | Towards smart incident management under human resource constraints for an iotbpm hybrid architecture[END_REF], we proposed a combination approach which is based on unsupervised machine learning algorithm that we apply on a set of event sources so that we can classify those sources on different clusters using a score that we calculate for each event source based on the frequency of previously generated events, in order to generate clusters of priorities, used to estimate incoming events priority. However, there is always an uncertainty level regarding the criticality/priority level of the event generated from sources that belong to the same cluster. In fact, the huge amount of these IoT generated events and sometimes the subtle difference between their criticality level generate uncertainty regarding their priority level determination, especially in critical use cases such as health care. This issue can be addressed with the use of Fuzzy Logic, to achieve priority-based event management and then ensure effective IoT-BPM communication. In this paper, we propose a Fuzzy Logic approach for priority-based management of IoT generated events that trigger business process instances, using two Fuzzy Inference Systems (FIS). The integration of Fuzzy Logic in the IoT-BPM architecture leads to better benefits from monitoring perspective, as it helps to handle the uncertainties regarding the criticality level of those events and than the priority level of the instances triggered by those events. The remainder of the paper is organized as follows. The next Section presents a background about Business Process Management and Fuzzy Logic. In Section 3 we present our context of work and motivation scenario. In Section 4 we present an overview of related work for the problem of IoT and BPM integration and communication. Section 5 outlines our proposed approach and methodology. Section 6 is devoted to the presentation of our experimental results and discussions. We conclude the paper in Section 7 and we give an outlook on future work.

II. CONCEPTUAL BACKGROUND

In this Section, we discuss the different concepts related to Business Process Management and Fuzzy Logic.

A. Business Process Management Business processes represent a set of activities and tasks that exploit the different resources (human and/or machine) of the organization to achieve one or more objectives previously defined, in order to satisfy an internal or external customer. BPM is defined by M. Dumas, et al. as "the art and science of overseeing how work is performed in an organization to ensure consistent outcomes and to take advantage of improvement opportunities" [START_REF] Dumas | Fundamentals of business process management[END_REF]. C. Moller et al. [START_REF] Moller | What is business process management: A two stage literature review of an emerging field[END_REF] focus on all sides of BPM, by saying that "BPM is a holistic management discipline that uses technology to control and operate the entire business through rules that clearly define business process. BPM is about continuous improvement and optimizing processes to ensure high performance and by achieving agility and flexibility as a tool to gain competitive advantages". This definition shows that BPM is an open and flexible discipline that can interact with several technologies in order to achieve the goals predefined by each organization, and ensure effective and proper functioning of its activities and to meet the requirements of the market [START_REF] Ismaili-Alaoui | Towards smart incident management under human resource constraints for an iotbpm hybrid architecture[END_REF].

B. Fuzzy Logic

Fuzzy Logic is a mathematical discipline used to handle nonlinear uncertainties that exist in physical systems to model human experience and human decision making behavior, it has been introduced by L. A. Zadeh in 1965 [START_REF] Bouchon-Meunier | La logique floue et ses applications[END_REF]. This concept is characterized by fuzzy sets, membership functions, linguistic variable, fuzzy operations, fuzzy If-Then rules, and Fuzzy Inference Systems [11] [12] [25] [START_REF] Kasmi | Multi level integrity management in critical infrastructure[END_REF].

Definitions related to Fuzzy Logic:

 Fuzzy Sets: is defined by its membership function. A point in the universe U, x, belongs to a fuzzy set, A with a membership degree μA(x) ∈ [0, 1].  Memberships function: is a function, which determines the degree to which, a given input or output belongs to a specific set.

 Linguistic variables: are the input or output variables of the system that allow the representation of numerical values with fuzzy sets.  Fuzzy operations: logical connectives, which are union, intersection, containment, and complement.  Antecedent: is the condition in a fuzzy rule (or "If").  Consequent: is the result in a fuzzy rule (or "Then").  Fuzzy Inference Systems (FIS): is the regrouping of fuzzy rules, the linguistic variables of membership functions and fuzzy reasoning. The FIS is designed for constructing complex and nonlinear relationships between the input and output. In this paper, we use Mamdani system [START_REF] Kamboj | Comparison of constant sugeno-type and mamdani-type fuzzy inference system for load sensor[END_REF] as FIS.

According to [START_REF] Valášková | The role of fuzzy logic in decision making process[END_REF], the fuzzy processing procedure is realized in four important steps, including Fuzzification, Fuzzy If-Then rule, Aggregation, and Defuzzification. This process is represented as follows:

 Fuzzification: is the first step for fuzzy modeling. It represents the process of transforming a crisp or real value into fuzzy sets by using membership functions. In this step, the crisp input values are transformed into linguistic variables.  Fuzzy If-Then rule: Fuzzy rules consist of three parts, antecedent, fuzzy operation, and consequence. For example: if x 1 is A and/or x 2 is B then x 3 =C. where, A, B, C are linguistic values, and x 1 , x 2 , x 3 are linguistic variables.  Aggregation is the combination of the output of every rule into one fuzzy set before the defuzzification step.  Defuzzification: is the process of converting a fuzzy output of a FIS into a crisp output.

In the next Section, we present our context of work and a motivation example.

III. CONTEXT OF WORK AND MOTIVATION SCENARIO

The case study of our research work belongs to Silver Economy domain which is a new industrial sector officially launched in 2013 in France, in order to create personalized services and new technologies that are expected to improve disability free life expectancy or to help dependent elderly people and their caregivers on a day-to-day basis. Supervision and incident management business processes in health-care are considered as event-driven business processes.

The instances of these processes are, in some cases, triggered by IoT generated events, especially in the current research fields related to health-care. Let us consider a video surveillance company that edits an automatic fall detection system for elderly people and offers a 24/7 automatic alert solution and a quick rescue without the intervention of the person in danger. The incident management process used in this case study is based on real-time analysis of alerts received from 24/7 streaming cameras for detecting falls of elderly people. The events that launch our business process instances in this case study are generated by different cameras placed in each patient room in order to detect possible falls. During the launched instance of this business process, the incident (that triggers this instance) is qualified by human agents and classified during the qualification step into four categories (see Figure. false alerts (empty place -level 0), false alerts (active person -level 1), true alerts with average risk level (seated personlevel 2), and true alerts with high risk level (lying down person -level 3). The human resource determines whether an assistance action is necessary or not, that is why each received event require an in time and vigilant qualification in order to prevent delayed intervention or incorrect qualification. Because falls can have an adverse physical and sometimes psychological impact on elderly people. Once the incident has been confirmed, a handling step will take place to take care of the patient that triggers this instance. After that, the whole activities go through quality assessment step before closing the event. Different instances of the same process may be simultaneously launched depending on the events generated by the IoT devices, and those instances are allocated to the available resources in chronological order. In such a critical case study, managing efficiently the uncertainty of events priority, is the key success to ensure a priority based execution of business process instances triggered by IoT generated events. In the next Section, we present an overview of related work to the problem of IoT-BPM communication and integration.

IV. RELATED WORK

IoT world is growing at a breathtaking pace, from 2 billion objects in 2006 to a projected 200 billion by 2020, and with 26 smart objects for every human being on Earth. With more than 76 million of smart meters, 30 billion RFID tags, 420 million wearable health monitors. IoT infrastructures can vary from instrumented connected devices providing data externally to smart, and autonomous systems. With this recent increase in IoT device usage organizations are faced with new challenges to keep better customer service and competitive advantages over rival organizations. Business Process Management is a field, among others, that will be affected by this new technology. Several researches are underway to ensure a smooth integration of IoT layer within a global and smart enterprise architecture, and to provide effective communication between the IoT layer and the other layers and especially the business one. As a matter of fact, this IoT integration directly affects business processes at both levels: build-time and run-time. In [START_REF] Chang | Mobile cloud business process management system for the internet of things: a survey[END_REF] authors present the challenges that need to be addressed in Business Process Management Systems (BPMS) to achieve an efficient integration of IoT such as the absence of direct interaction between the business layer and the edge network or the problem of complex and inflexible business process models due to a lack of standardization when modeling IoT elements and components in BPM. In [START_REF] Cherrier | From bpm to iot[END_REF] authors propose an approach to achieve an interaction between BPM world and IoT world via a conceptual middle-ware to link between IoT services and BPM applications. This gateway transfers events from IoT network to business processes. Those IoT generated events can trigger business process instances. For authors in [START_REF] Song | Towards improving context interpretation in the iot paradigm: a solution to integrate context information in process models[END_REF] the combination between IoT and BPM passes through context interpretation to achieve IoT-awareness in BPM, by integrating data generated by IoT sensors as context information into process models. An end-to-end integration architecture of IoT devices (front-end) and business processes applications (back-end) is proposed in [START_REF] Dar | A resource oriented integration architecture for the internet of things: A business process perspective[END_REF], authors in this article use a resource oriented approach inspired by the REST communication paradigm to ensure the integration of IoT components and data into business processes and ensure also an event management through an IoT-aware business process. In the same perspective of event management and IoT-BPM integration, authors in [START_REF] Ismaili-Alaoui | Towards smart incident management under human resource constraints for an iotbpm hybrid architecture[END_REF] propose an approach inspired by Haze Architecture and Cascading Analytics incarnated by a DIKW (Data Information Knowledge Wisdom) discovery pattern crossing the architecture from device then Fog/Edge to the cloud, and a learning feedback loop that feeds forward insight to adjust either Fog/Edge or device algorithms. This incident/event management approach ensures also a beneficial IoT and BPM communication for achieving a smart IoT event management and also a flexible business process instances scheduling under human resource constraints. Even though there are several proposed approaches in the literature to ensure this IoT-BPM communication at different levels within an organization, there is still a lot of work to do to take full advantage of this collaboration between the IoT domain and the BPM one. We propose in this paper an approach for IoT event management that ensures a beneficial IoT and BPM communication using Fuzzy Logic to determine the criticality level of the IoT generated event and the priority level of the business process instances triggered by those events. In the next Section, we provide the details of our Fuzzy Logic-based event management approach.

V. PROPOSED APPROACH

The global business process of this case study is simple but it represents several hard functional constraints such as: real-time data analysis and the obligation to maintain limited resources for the viability of the business. An instance of this process is handled by one agent (from end-to-end). That is why, in this paper, we focus on estimating the priority of the whole instance instead of the different tasks of this business process. This Section provides details for our proposed approach. Our goal is to efficiently manage and ensure the communication between events received from IoT devices and business process instances using Fuzzy Logic, which provides a process for formulating the mapping from inputs to output through a Fuzzy Logic system.

Our IoT-FIS-BPM architecture is composed of three main layers: (i) The Edge/Fog based IoT Layer which ensures incident data acquisition and filtering. We use a message broker to facilitate the communication and data exchange between the IoT layer (source) and the Fuzzy Inference System (target). Message broker is responsible for routing, storing, retrieving and transforming the information. (ii) The control layer is decomposed into a series of two FIS, where the output of the first FIS is one of the inputs of the second FIS. These two FIS are used here as a decision-making technique to ensure the processing of the IoT generated events, based on several criteria, in order to determine the criticality level of each event source (FIS 1) and then the priority level of those generated events (FIS 2) and their triggered business process instances. (iii) The BPM layer used to coordinate the execution of business process instances and the resource allocation, taking into consideration the priority levels previously determined by our FIS layer. The communication between the control layer and BPM layer is ensured through an application programming interface (API) (see Figure . 2). In this article, we focus only on the implementation of the control layer, as it represents the core phase of our architecture.

A. Fuzzy Inference System Modeling

Our fuzzy system is divided into two Fuzzy Logic Systems, the first one is for the Patient Criticality Level determination and the second one is for the Event Priority Level determination. Each system consists of four important steps including Fuzzification, If-Then rules, Aggregation, and Defuzzification. Figure 3 describes different blocks diagram of FIS for Patient Criticality Level and Event Priority Level determination respectively.

FIS 1 Modeling

a-inputs and output of FIS 1

For modeling the control system, the first FIS (FIS -Device Reliability Level presents all features of the device: its identification, location, date of the first use, a total of false alerts ranged from 2015 to 2018 and whether the device has been changed or not since its first use. This reliability level is determined based on the state of each device. In fact, The state of the device deteriorates with time, so the device can either generate a lot of false alerts or not detect the real ones. The FIS 1 is characterized also by one output parameter, which is the chance value of Patient Criticality Level. The Patient Criticality Level output refers to the criticality level of each patient that generates an event at a time slot t. The numerical values of these inputs are converted into linguistic variables using membership functions during Fuzzification step.

b. Membership Functions

The linguistic values for the Patient Status input are Low, Average, Serious and Very Serious. The second fuzzy input parameter is the Device Reliability Level. The linguistic values for this input are Low, Medium, High and Very High. The output of the first FIS is Patient Criticality Level. This output is divided into four linguistic values as follows: Low, Average, Serious and Very Serious. The membership functions of all inputs and output are represented by triangular membership functions because this type of functions is the most frequently used and gives better results [START_REF] Kasmi | Multi level integrity management in LTE/LTE-A networks[END_REF].

c. Fuzzy If-Then rules

The chance values (Patient Criticality Level) is accomplished by using predefined fuzzy If-Then rules to handle the uncertainty. We have two inputs, each divided into four linguistic variables, thus we obtain 2 4 =16 possible chance values. The defuzzification step uses these values to obtain crisp output values. For the defuzzification process, the method of the Center of Area (CoA) is used [START_REF] Korol | A fuzzy logic model for forecasting exchange rates[END_REF], called also the Center of Gravity (CoG) method. The concept of this method is that the fuzzy controller determines the range of the output variables according to the area under the scaled membership functions.

FIS 2 Modeling

The FIS 2 modeling follows the same steps of FIS 1 modeling as discussed above: Fuzzification, Fuzzy rules, and Defuzzification.

a-inputs and output of FIS 2

The second FIS has two fuzzy input parameters: Patient Criticality Level, which is the output of the first FIS and Event Type, and one output which is the Event Priority Level.  Event Type input represents the type of the last event generated by the device in question. These events could either be false alerts or true alerts.  Event Priority Level output is used to choose between two or more events that will trigger two or more business process instances. Those instances may need the same resources at the same time (in case of limited (human) resources). The event with the lowest priority must wait for the resource occupied by executing the event with the highest priority.

b. Membership Functions

The

c. Fuzzy If-Then rules

According to the number of inputs and the number of linguistic variables for each input (2x4) 8 rules are determined. The chance values (Event Priority Level) are accomplished by using predefined fuzzy If-Then rules to handle the uncertainty.

VI. RESULTS AND DISCUSSION

In the following, we present a summary of the results obtained from our experiments, and a comparison with Machine learning-based approach [START_REF] Ismaili-Alaoui | Towards smart incident management under human resource constraints for an iotbpm hybrid architecture[END_REF]. All our experiments were conducted on an Intel(R) Core(TM) i5-540 M 2.53 GHz.

A. Simulation settings

The performance of our proposed approach is evaluated using MATLAB. The Fuzzy Logic toolbox allows users to create Fuzzy inference for estimating conclusions problems. The simulation of our system is built through the Graphical User Interface (GUI) Tools using five GUI tools that are being executed to have simulations for the input and output: building, editing FIS, Membership function, Rules, and reviewing Rule and surface. In order to achieve our goal which is the estimation of business process instances priority through the estimation of the criticality level of the events that trigger those instances, we used two Fuzzy Inference Systems. The first FIS is dedicated to determining the Patient Criticality Level, and the second FIS is dedicated to determining the event priority level based on the result of the first FIS. We worked with two datasets as input for the Fuzzy Inference System: the first dataset is an event log from our business process, the second dataset contains IoTdevice characteristics.

B. Simulation Results using Fuzzy Logic 1) Simulation Scenario Description

In this section, we describe the results obtained after applying the steps of each FIS, explained in the previous section. To evaluate our proposed approach, we analyzed our historical data (this dataset contains 238228 observations generated by 81 patients) of each event source (IoT device in each patient room) and discuss several cases to estimate the priority level of each received event. To this end, we choose eleven different patients with different IoT devices in eleven different rooms (using 11 patients here only for demonstration purposes). We report four cases of simulation to observe the Event Priority Level in different situations (Low, Average, Serious and Very Serious):

2) Analysis and discussion

Case 1: If (Patient Status is Low) and (Device Reliability Level is Low) then the Patient Criticality Level is Low. This rule expresses that if the linguistic value of Patient Status is Low and Device Reliability Level is also Low (the device generates a lot of false alerts), then the output will be Low. As we can see in Figure 5, the input vector contains two linguistic variables (the numerical values corresponding to these linguistic values are 26.6 and 441 respectively) used to determine the Patient Criticality Level as an output parameter. In this case, the patient is considered as a noncritical patient. Case 3: If (Patient Criticality Level is Low) and (Event Type is False) then the Event Priority Level is Low. This rule expresses that if the linguistic value of the Patient Criticality Level is Low, and the Event Type is False, then the output will be Low. As we can see in Figure 7, the input vector contains two linguistic variables (29.8 and 0.123), used to determine the Event Priority Level output. In this case, the Patient is non-critical and she/he has a lower priority level. As we can see in Figure 9, the eleven patients that we have randomly chosen for this simulation (represented in the Figure by the ID of the events generated by their devices) have almost the same score value (2.5), except the patient with ID event (313647) which is considered as a non-critical patient. However, those ten patients do not have the same criticality level. So, it is important to efficiently manage the criticality levels of each patient even when the Device Reliability Level is Low (until this device is changed or repaired). As we can see, the criticality level values fall into the range of [65, 500]. There are 3 important points to consider in Figure 9: the first point is that the score all alone is not enough to determine the criticality/priority level of an event. For example, the Id event (313671) has the same patient score compared to other events, but it has the lower level of criticality and lower event priority. The second point is that the events generated by patients with the highest criticality level, have the highest priority level, as we can see for the Id event (313679). The third point is that the events generated by patients with close values of criticality level have finally different priority level. If we take, for example, the events with Id (264779) and (173771) they are both generated from two different patients with approximately the same criticality level (64,81) and (64,69) respectively. However, they have different priority levels (162, 45) and (93,81) respectively.

C. Simulation Results using Machine Learning

In our previous work [START_REF] Ismaili-Alaoui | Towards smart incident management under human resource constraints for an iotbpm hybrid architecture[END_REF] the criticality level was estimated using the score value to create clusters of priority (with K-Means algorithm for clustering). We obtain four clusters with the K-means algorithm based on the score of each patient calculated using the total number of his/her falls, taking into consideration level 2 and 3 only. So the patients in the same cluster have the same profile and then the same criticality/priority level. The Event Priority Level is estimated based on the criticality level of the patient (source) that generates this event. With the clustering-based approach, there is a bijection between criticality levels and priority levels. Two scenarios are encountered when applying this approach: (i) the patients belong to different clusters. (ii) both patients belong to the same cluster. The subtle difference between the criticality level in the second scenario generates uncertainty regarding their priority level determination (See Table 1). As we can see in this Table, the four patients belong to the same cluster (Cluster 3) in spite of their different scores, and in this case their generated events will have eventually the same priority level.

D. Comparison: Fuzzy Logic Vs Machine Learning -Methods Comparison

In the following, we provide a brief comparison between Fuzzy Logic and K-Means algorithm. As we can see in Table 2, when using Fuzzy Logic, the decision-making process for determining criticality/priority level is based on multicriteria (Patient Status, Events Type, etc), which are represented by linguistic variables, and it is based also on the uncertainty management problem in the case of the events that have the same characteristics (score). The whole decision-making process in Fuzzy Logic is inspired by human reasoning. While the k-Means method is based on the partition of n observations into k cluster for decision-making. This algorithm works by choosing k initial cluster centers and then assigns every data point to the nearest cluster (based on distance) based on the provided features that define the resulting clusters (using feature similarity).

-Complexity analysis

The computational complexity of the Fuzzy Logic method is presented as follows: O (Nrule Ndim), where Nrule is the number of fuzzy rules and Ndim is the number of dimensions of the input [START_REF] Balázs | Comparison of fuzzy rulebased learning and inference systems[END_REF]. For K-Means method, the computational complexity is expressed as O (n 2 ), where n is the input data size. So its performance is directly proportional to the square of the size of the dataset used as input. Generally, K-Means performance deteriorates when using very large datasets. With this computational complexity, K-Means can be seen as a greedy algorithm [START_REF] Malay | A linear time-complexity k-means algorithm using cluster shifting[END_REF]. Improving the computational complexity of the K-Means algorithms is basically linked to the initial inputs (selecting of better initial centroids, choosing an effective number of clusters, ...), and to the execution conditions (using parallel and distributed execution environment, reducing the number of scans over the dataset, especially for very large datasets,...). As we can see, Fuzzy Logic method offers a lower computational complexity in our case compared to the K-Means algorithm.

The simulation results prove that the integration of the Fuzzy Logic in the IoT-BPM architecture has been advantageous for the priority-based event management. From the results above, we can consider that the performance of the proposed architecture, at this step, has been achieved. The highest priority level is assigned to the highest criticality level (i.e a critical patient). Thus, it has a positive impact on managing those events and on reducing the waiting time of some critical cases, especially when the organization has a limited number of human resources. Both methods have advantages and disadvantages. In fact, Kmeans is considered as an easy to implement algorithm that gives easy to interpret results. However, it can be difficult to predict the K value, besides the initial inputs (k, features, the order of the data, dataset quality and tidiness, ...) have a huge impact on the final clustering results. Same thing for Fuzzy Logic. In fact, it is characterized by its simplicity and flexibility. However, in a high complex system, using Fuzzy Logic become an obstacle to verify the system reliability.

VII. CONCLUSION

In this paper, we presented an IoT-FIS-BPM architecture to ensure effective communication between IoT layer and BPM layer through event management. This approach is based on the integration of a Fuzzy Inference System in order to determine the criticality level of the IoT generated events on the one hand (FIS 1), and the priority level of the business process instances triggered by those events on the other hand (FIS 2). The main advantage of this approach is that the Fuzzy logic allows us to handle the degree of uncertainty regarding the criticality/priority level of the events generated by some sources that may have the same characteristics. This proposed approach can be applied for (IoT) generated events triggering any other entities not only business processes, for example, telecommunication application for management services, or any system that deals with concurrent access to shared resources (human and/or machine) where priority/criticality level determination is important for the viability of the business. The simulation results prove that the events generated by different patients are not treated and managed in the same way using Fuzzy Logic, even if the patients have similar profiles, unlike the K-Means approach. To avoid the limitations encountered in this paper and to enhance event management in this IoT-BPM architecture, we focus in our future work on how the advantages of both methods can be merged in order to develop a hybrid algorithm based on these technologies. And we evaluate the end-to-end architecture with more experiments.

  1):

Figure 1 :

 1 Figure 1: Process model of incident qualification and assessment

Figure 2 :

 2 Figure 2: IoT-FIS-BPM Architecture for priority-based event management

Figure 3 :

 3 Figure 3: Fuzzy Inference System (FIS) for Patient Criticality Level and Event Priority Level determination

  linguistic values of Patient Criticality Level input are Low, Average, Serious and Very Serious. The second fuzzy input parameter is the Event Type. The linguistic values for this input are True and False. The output of the second FIS is Event Priority Level. This output is divided into eight linguistic values as follows: Very Low, Low, Little Average, High Average, Serious, High Serious and Very Serious.

Case 1 :Case 2 :

 12 Low Patient Status (i.e. a patient considered as a non-critical case) and Low Device Reliability Level. Very Serious Patient Status (i.e. a patient considered as a very critical case) and Very High Device Reliability Level. Case 3: Low Patient Criticality Level and False Event Type. Case 4: Very Serious Patient Criticality Level and True Event Type.

Figure 5 :Case 2 :

 52 Figure 5: Rule viewer for Case 1 Case 2: If (Patient Status is Very Serious) and (Device Reliability Level is Very High) then Patient Criticality Level is Very Serious. This rule expresses that if the linguistic value of Patient Status is Very Serious and DeviceReliability Level is Very High (the device has a low rate of false alerts), then the output will be Very Serious. Figure6describes the input vector, which contains two linguistic variables (233 and 4000 respectively) used to determine the Patient Criticality Level (230). In this case, the patient is critical.

  Figure 6 describes the input vector, which contains two linguistic variables (233 and 4000 respectively) used to determine the Patient Criticality Level (230). In this case, the patient is critical.

Figure 6 :

 6 Figure 6: Rule viewer for Case 2

Figure 7 :Case 4 :

 74 Figure 7: Rule viewer for Case 3Case 4: If (Patient Criticality Level is Very Serious) and (Event Type is True) then Event Priority Level is Very Serious. This rule expresses that if the linguistic value of the Patient Criticality Level is Very Serious and the Event Type is True, then the output will be Very Serious. Figure8describes the input vector, which contains two linguistic variables (456 and 0.959 respectively), used to determine the priority level (423). In this case, the patient is critical and has the highest priority level.

  Figure 8 describes the input vector, which contains two linguistic variables (456 and 0.959 respectively), used to determine the priority level (423). In this case, the patient is critical and has the highest priority level.

Figure 8 :

 8 Figure 8: Rule viewer for Case 4

Figure 9 :

 9 Figure 9: Score patient vs Event priority level vs patient criticality level.

TABLE I :

 I Id Event and Clusters of patient with the same profile based on their score value

	Id Event	Score	Id Patient	Id Cluster
	313671	2.4875	70	Cluster 3
	313679	2.4979	49	Cluster 3
	264779	2.4995	30	Cluster 3
	173771	2.4989	22	Cluster 3

TABLE II :

 II Comparison: Fuzzy Logic Vs K-Means

	Fuzzy Logic	K-Means
	Uncertainty-based decisions	Clustering-based decisions
	Human reasoning:	Based on distance (Euclidean
	inspired by the processes of	distance or Manhattan distance):
	human	to identify the set of objects with
	perception and cognition	similar characteristics
	Linguistic Variables	Features
	Building decisions using	Building decisions through
	multicriteria	partitioning n observations into k
		clusters

* This work is equally based on respective PhD thesis work

ACKNOWLEDGMENT

The authors would like to thank the French Embassy in Morocco for their financial support and Angel Assistance for providing us with the necessary data to accomplish our work. For privacy management, all data has been anonymized. Prof. Karim Baïna Acknowledgement must go to The Ministry of National Education, Higher Education, Staff Training, and Scientific Research, Morocco for accepting and supporting his sabbatical leave to do research, and return to ENSIAS refreshed. He also acknowledges his colleagues at ENSIAS maintaining the superb teaching and learning culture in the school in his absence."


Anaïs Kirsch

Hélène Dubois-Pot-Schneider

Hami Musaoglu

Aurélie Remy

Caroline Fontana

Hervé Schohn

Laurent Gaté

Yves Guichard

Biomarkers involved in cell transformation induced by nano-silica materials in the murine Bhas 42 model

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

Synthetic amorphous silica nanomaterials (SAS) are used in many industrial sectors. However, we have previously found that SAS (namely, NM-200, NM-202 and NM-203) led to cell transformation in the cell transforming assay developped by Sasaki et al (2015). Among them, NM-203 was the most active in triggering Bhas 42 cell transformation (Fontana et al., 2015) (Seidel et al., 2017). In order to gain insight into the molecular mechanisms involved in the early stages of the SAS-induced cell transformation, we carried out a global and unsupervised transcriptomic analysis after a 48h-treatment with NM-203, Min-U-Sil 5 (a carcinogenic crystalline silica microparticle), DE (Diatomeous Earth, an amorphous silica microparticle) and TPA (12-O-Tetradecanoylphorbol-13-acetate, a tumor promoter) (Table 1). Genes regulated by NM-203, Min-U-Sil 5, and TPA-induced cell transformation were validated by RT-qPCR. Finally, we analyzed whether similar changes were also found after cell exposure to NM-200 and NM-202.

The Bhas 42 cell transformation assay was carried out as followed: Cells were seeded at high density and treated for 48 h with NM-203 at 1 and 2 µg/cm² (non cell transforming doses) and at 5 μg/cm² (a cell transforming dose), DE at 5 µg/cm², Min-U-Sil 5 at 20 µg/cm² and TPA at 0.05 µg/ml (Figure 1). Characteristics of tested chemicals are presented in Table 1. *anais.kirsch@inrs.fr et al., 2015). Bhas 42 cells are then considered to be "initiated" cells. This cell line was used to study the effect of diverse SAS.

Hierarchical clustering showed that non cell transforming treatments (NM-203 at 1 or 2 µg/cm², DE and controls) are closed and interestingly cell transforming treatments (NM-203 at 5 µg/cm², Min-U-Sil 5 and TPA) induced similar changes in gene expression (Figure 2). Several genes were differentially expressed after treatment with NM-203, Min-U-Sil 5 and TPA, while we did not find any differentially expressed genes with DE. Furthermore, IPA analysis showed that the main pathways common to NM-203, Min-U-Sil 5 and TPA treatments were involved in cell proliferation. Finally, twenty genes are common to all three conditions and among them, twelve relevant genes were selected (Figure 3). The expression of these genes was validated by RT-qPCR. In addition, deregulation of selected genes were also found with NM-200 and NM-202 as shown for two selected genes POSTN (Periostin) and AUNIP (Aurora kinase A and ninein interacting protein) (Figure 4).

Relative mRNA level

Relative mRNA level

RESULTS

  cell transformation induced by nano-silica materials in the murine Bhas 42 model Anaïs Kirsch* ,1,2 , Hélène Dubois-Pot-Schneider 2 , Hami Musaoglu 1 , Aurélie Remy 1 , Caroline Fontana 1 , Hervé Schohn 2 , Laurent Gaté 1 & Yves Guichard 1 For the first time, 20 common genes potentially involved in cell transformation were identified by a transcriptomic analysis after a 48h-treatment of Bhas 42 cells with NM-203, Min-U-Sil 5 and TPA.  Deregulation of common genes has been found for two other SAS (NM-200 and NM-202).  A set of 12 genes could be used as potential biomarkers of the early stages of Bhas 42 cell transformation for other SAS or other transforming nanomaterials.

  1. Institut National de Recherche et de Sécurité (INRS), CS 60027 Vandoeuvre-lès-Nancy -Cedex) -2. Université de Lorraine, CNRS, CRAN, F-54000 Nancy, France

Figure 2 :

 2 Figure 2: Microarray data analysis : Non supervised hierarchical clustering was obtained after filtration of microarray data (clustering was performed using a Gene Cluster 3 software and visualized with Treeview). Red circle show similar signature of three compounds NM-203, Min-U-Sil 5 and TPA.

Figure 3 :

 3 Figure 3: Heatmap of 20 common genes regulated by NM-203, Min-U-Sil 5 and TPA. p < 0.005; FC > 4

Figure 1 :

 1 Figure 1: Bhas 42 cell transformation assay and experimental strategy: The Bhas 42 cell line is a fibroblastic murine cell line derived from Balbc / 3T3 cells transfected with the viral oncogene v-Ha-ras (Sasakiet al., 2015). Bhas 42 cells are then considered to be "initiated" cells. This cell line was used to study the effect of diverse SAS.

  al, J Environ Sci Health C Environ Carcinog Ecotoxicol Rev, 2015. Fontana C et al, Mutat Res, 2017. Seidel C et al, Nanotoxicology, 2017. AUNIP POSTN

Table 1 :

 1 Physical characteristics of silica particles used in this study and cell transforming effect of compounds tested

DLS Silica/ Promoter agent Bhas 42 assay Main phase Primary particle size Impurities (wt%) BET SSA (m 2 /g) Z-average in Water (nm)

								Polydispersity
								index
	NM-200	+	Amorphous	18.3 ± 4.5 nm	Al (0.87); Na (0.88); S (0.46)	189	ND	ND
	NM-202	+	Amorphous	17.7 ± 2.5 nm	Al (0.45); Ca (0.18)	204	232.8 ± 7.3	0.441 ± 0.012
	NM-203	+	Amorphous	24.7 ± 17.7 nm	Al (0.43); S (0.04)	204	318.7 ± 4.9	0.423 ± 0.026
	Min-U-Sil 5


Moustapha Koala

Drissa Sérémé

Florence Vignols

Eric Lacombe

Martine Bantgratz

James Bouma

Christophe Neya

Nicolas Brugidou

Oumar Barro

Traoré

Bouma James Neya

Christophe Brugidou

Nicolas Barro

Oumar Traoré

Molecular Variability and Genetic Structure of IYMV in Burkina Faso

Keywords: Imperata yellow mottle virus (IYMV), Coat Protein (CP), Genetic Variability, Phylogenetic Analysis

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

Introduction

The perennial grass Imperata cylindrica (L.) P. Beauv., a common and persistent weed in many food crops such as like cassava, maize, sorghum and rice is consi-M. Koala dered as traditional and important medicinal plant in several African country such as Uganda, Ghana and Cameroun, where the roots of I. cylindrica have been described as snakebite treatment in Uganda [START_REF] Ntume | Ethnopharmacological Survey of Medicinal Plants Used in the Treatment of Snakebites in Central Uganda[END_REF]. At Ghana, the properties of management of hypertension have been identified to I. cylindrica leaf extract while in Cameroon the properties of management of typhoid fever have been identified to I. cylindrica [START_REF] Tsobou | Medicinal Plants Used against Typhoid Fever in Bamboutos Division, Western Cameroon[END_REF] [START_REF] Mak-Mensah | American Journal of Plant Sciences Ethanolic Extract of Imperata Cylindrica Leaves in Animal Models[END_REF].

Recently, only one viral disease has been reported that affects I. cylindrica, and was defined as the Imperata yellow mottle virus (IYMV) because of the typical mottled yellowing appearing at the I. cylindrica leaf surface. IYMV was first characterized in I. cylindrica in West Africa in 2008 [START_REF] Sérémé | Biological and Molecular Characterization of a Putative New Sobemovirus Infecting Imperata Cylindrica and Maize in Africa[END_REF] and classified as a new member of the sobemovirus genus. Like all sobemoviruses, IYMV is readily transmitted mechanically. Up to now, natural infection with IYMV has been observed and demonstrated conclusively in Zea mays and I. cylindrica [START_REF] Sérémé | Biological and Molecular Characterization of a Putative New Sobemovirus Infecting Imperata Cylindrica and Maize in Africa[END_REF]. Experimentally, the virus has a crop host range including two cereals (Sorghum bicolor, Pennisetum glaucum) [START_REF] Koala | Imperata Yellow Mottle Virus an Emerging Threat to Maize, Sorghum and Pearl Millet in Burkina Faso[END_REF] and three wild grasses (Rottboellia exaltata Setaria verticillata, Brachiaria xantholeuca [START_REF] Sérémé | Biological and Molecular Characterization of a Putative New Sobemovirus Infecting Imperata Cylindrica and Maize in Africa[END_REF] [START_REF] Koala | Imperata Yellow Mottle Virus an Emerging Threat to Maize, Sorghum and Pearl Millet in Burkina Faso[END_REF]. Contrary to other sobemoviruses, it remains unknown whether insects such as beetles or even the I. cylindrica seeds themselves can serve as vector for IYMV infection.

IYMV is a positive single stranded RNA virus with the particle of 32 nm in diameter. Its genome is 4.447 nucleotide long and comprises five ORFs [START_REF] Ling | An Essential Fifth Coding ORF in the Sobemoviruses[END_REF].

ORF1 (45 -686 nt), which is located at the 5' end of the genome, encodes a P1-like protein. P1 is involved in the cell-to-cell and systemic movement of the virus [START_REF] Bonneau | Expression of the Rice Yellow Mottle Virus P1 Protein in Vitro and in Vivo and Its Involvement in Virus Spread[END_REF]. ORF2, has two overlapping ORFs, encodes the putative central polyproteins. ORF2a (713 -2509 nt) encodes a serine protease and a viral genome-linked protein (VPg), and ORF2b (2176 -3768 nt), encodes a RNA dependant RNA polymerase (RdRp). ORF4 (3560 -4381 nt) is translated from the subgenomic RNA at the 3' end of the genome and encodes the coat protein. Recently, the presence of a fifth ORF (ORFx), conserved was reported in all sobemovirus [START_REF] Ling | An Essential Fifth Coding ORF in the Sobemoviruses[END_REF]. Such putative fifth ORF is also present in the IYMV genome, and overlaps the 5' end of the ORF2a in the +2 reading frame (Figure 1).

Until now, only one complete IYMV genomic sequence from western region of Burkina Faso (West Africa) had been published [START_REF] Sérémé | Biological and Molecular Characterization of a Putative New Sobemovirus Infecting Imperata Cylindrica and Maize in Africa[END_REF]. The molecular diversity is therefore not documented and several important factors of epidemiology of IYMV are still poorly understood, such as alternative hosts in fields. Nevertheless, the knowledge of IYMV genetic diversity is essential for a better description of its aetiology, pathogenicity, and ecology develop appropriate strategies to counteract the IYMV spread and disease. The most common molecular markers for investigation of genetic diversity of the genus sobemovirus and other plants virus is a coat protein [8] [9]. In addition, on the basis of coat protein genes sequences, various viruses have been grouped [START_REF] Neeraj | Coat Protein Sequence Shows That Cucumber Mosaic Virus Isolate from Geraniums (Pelargonium spp.) Belongs to Subgroup II[END_REF]. The aim of this study was therefore to investigate the genetic variability based on molecular analyses of CP gene sequences originated from IYMV isolates obtained from different locations of Burkina Faso. DOI: 10.4236/ajps.2018.92025 313 American Journal of Plant Sciences

Materials and Methods

Survey and Sample Collection

Imperata cylindrica leaves showing viral symptoms of Imperata yellow mottle virus infection were collected in 10 different locations belonging to the high bas- sins region (Bama, Banzon, N'Dorola, Koloko, Tondogosso, Karangasso Sambla)

and cascades regions (Banfora, Karfiguela, Lomouroudougou and Niangoloko)

of Burkina Faso (West Africa), as indicated in Figure 2. One virus sample isolated from an individual I. cylindrica plant was considered as one isolate. Infected I. cylindrica plants were either used for extraction of total RNA or stored at 80˚C for future use.

RNA Extraction, RT-PCR Amplifications and Sequencing

Total RNA was extracted from frozen infected Imperata cylindrica leaves using the RNeasy Plant Mini Kit (Qiagen), according to manufacturer's instructions. Slight modifications were made on the protocol to optimize the quality and quantity of the total RNA. The quality of RNA extraction was compared by measurement of RNA concentration. Reverse-transcription (RT) was performed using the primers IYMV-R4438-4454 while Polymerase Chain Reaction (PCR) was performed using IYMV-F3483-3502 and IYMV-R4385-4394 described by Koala et al., 2017. All steps and conditions, including, RT and PCR followed the protocol of koala 2017 [START_REF] Koala | Imperata Yellow Mottle Virus an Emerging Threat to Maize, Sorghum and Pearl Millet in Burkina Faso[END_REF]. All PCR products of the correct size were purified from 1% agarose gels using GENECLEAN turbo Protocols columns before being sent to Genewiz (Essex, UK) for sequencing.

Recombination and Genetic Diversity Analysis

The sequences contigs obtained in this study were assembled using the Seqman II program in the DNASTAR 10.0 (DNAStar Inc., Madison, USA). The 38 sequences were then compared and analyzed with the available GenBank accession NC-011536 sequence (Table 1). Multiple nucleotide sequence alignments were performed by using CLUSTAL W with default parameter [START_REF] Thompson | CLUSTAL W: Improving the Sensitivity of Progressive Multiple Sequence Alignment through Sequence Weighting, Position-Specific Gap Penalties and Weight Matrix Choice[END_REF].

Alignments were also adjusted manually to guarantee correct reading frames. Noncoding sequences were removed before alignment.

As frequent recombination can provide a false positive signal for positive selection in codon specific analytical methods this paragraph is necessary. So, you need to identify and remove recombinant sequences before implemented selection pressure acting on CP genes. Interestingly, this analysis could provide important results which can improve the paper quality. [12]- [START_REF] Gibbs | Sister-Scanning: A Monte Carlo Procedure for Assessing Signals in Recombinant Sequences[END_REF]. The default detection thresholds were used. Only events supported by three kinds of methods were retained.

Pairwise genetic distances among nucleotide and amino acid sequences were calculated using the Kimura's two parameters [START_REF] Kimura | A Simple Method for Estimating Evolutionary Rates of Base Substitutions through Comparative Studies of Nucleotide Sequences[END_REF] and using the Jones Taylor Thornton (JTT) model implemented in MEGA v.6.0 [START_REF] Koichiro | MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0[END_REF]. To evaluate variation in selection pressure, during CP evolution, the direction and degree of selective constraints operating in a coding region were assessed by the ratio between nucleotide diversities at nonsynonymous and synonymous positions (dNS/dS).

The extent of IYMV variation among these sequences was evaluated using the index π by DnaSp version 5.0. With a sliding window of 100 nt and a step size of 25 nt. The parameter π is the mean number of nucleotide differences per site between two sequences to measure the nucleotide diversity.The value assigned to the nucleotide was that of the window midpoint.

Construction of Phylogenetic Trees

Phylogenetic relationships between isolates were inferred by maximum-likelihood (ML) methods. The best fitting nucleotide substitution model with the lowest BIC score was determined using MEGA v.6.0 [START_REF] Koichiro | MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0[END_REF]

Results

Imperata cylindrica Harvest Campaigns Identify Up to 38 IYMV Isolates in South Western Burkina Faso

Within the frame of a 3-year harvest campaign in distinct areas of South-Western Burkina Faso (Figure 2), a total of 38 samples of I. cylindrica leaves were analyzed for Imperata yellow mottle virus detection.

As expected, RT-PCR on total RNAs from infected plant materials resulted in the amplification of DNA fragments of about 1000 bp for all sample listed in Table 1. PCR amplifications representative of different plants are shown (Figure 3).

Recombination Analysis

In total, four Potential Recombinant Events (PREs) named PRE_iymv34-BF, PRE_NC-011536, PRE_iymv2-BF and PRE_iymv9-BF were detected by at least one of the models (Figure 4). These four potential recombinants were detected by one or two methods of RDP program with a low degree of confidence. In addition, one of the parental isolates was often unknown. Based on the criteria of recombination selection, these Potential Recombination events were not accepted. No evidence for potential recombination events was found among the other isolates using RPD4.

Sequence Analysis

The average of genetic diversity among the 39 listed in Table 1 was 4.6% for nt, with the peak (7.6%) of nucleotide substitutions per site between sequences present at the 5' half N-terminal protein coding region (Figure 5). The average number of nucleotide substitutions per synonymous sites was high (π s = 0.164), yet 18 times higher than the number of nonsynonymous diversity (π a = 0.009),

i.e. a ω ratio (π a /π s ) of 0.07. The maximum of the nonsynonymous and synonymous diversity between two any sequences was 2.1% and 25%, respectively.

As ω < 1, this suggests that the CP sequences are under high purifying selective constraints. The p-value of the Z test was highly significant (P < 0.001) and confirmed that, diversification in the CP gene of the BF isolates was found under a strong purifying selection. Using Fisher's codon based exact test included in MEGA v.6.0 there was no evidence for positive selection (data not shown) [START_REF] Koichiro | MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0[END_REF].

Total number of nucleotide sites of the 39 IYMV sequences was 822 nt encoding 273 amino acids. The 273 aa residues were dominated by hydrophobic amino acids.

Analyses of the polymorphic sites among sequences of the Burkina Faso isolates revealed 136 variable sites for nucleotide and 24 for amino acid sequences. Indeed, 13% and 10% of amino acids changes resulted of mutations at 1st and 2nd nt positions of codons, respectively. We also noted that conserved amino acid sequence of CP of IYMV exhibit several common features of sobemoviruses. The N-terminal region is rich in basic amino acids and contains an arginine

Phylogenetic Analysis

A total 39 CP gene sequence were analyzed. The phylogenetic relationships among the sequences were constructed using maximum-likehood methods (Figure 6). The 39 CP nt sequences revealed segregation of the isolates under study into six clades.

Clade I was composed of ( 5) isolates from Tondogosso, (1) from Bama, [START_REF] Ntume | Ethnopharmacological Survey of Medicinal Plants Used in the Treatment of Snakebites in Central Uganda[END_REF] from Banfora and (1) from Banzon. Clade II included of (

Discussion

To assess the genetic diversity of IYMV, we compared CP sequences of 39 isolates from different areas in Burkina Faso. Analysis of CP nucleotide sequences revealed that the global genetic diversity (4.6%) was low according the low CP sequence diversity (3% -10%) of other RNA viruses [21] [22]. In spite of low genetic diversity of IYMV populations, phylogenetic analysis showed that the isolates of Burkina Faso diverged into six Clades (Clade I to Clade VI). However, the clade I and clade II are poorly supported (33% and 26% bootstrap values respectively, Figure 6). Analysis of distribution of IYMV isolates according to the DOI: 10.4236/ajps.2018.92025 320 American Journal of Plant Sciences geographic origin indicates that isolates collected in the same locality belong to two different clades, whereas isolates from distant areas clustered in the same clade. Similarly, at amino acids level, two isolates collected from distant areas belong in the same clade. These results suggest lack of correlation between genetic diversity and geographic distribution of IYMV isolates. Similar results were also reported for Tobacco mild green mosaic virus (TMGMV) and Citrus tristeza virus (CTV) that infected perennial crops such as Nicotiama glauna and citrus species respectively [START_REF] Vigne | Population Structure and Genetic Variability within Isolates of Grapevine Fanleaf Virus from a Naturally Infected Vineyard in France: Evidence for Mixed Infection and Recombination[END_REF] [23] [START_REF] Fraile | Genetic Diversity in Tobacco Mild Green Mosaic Tobamovirus Infecting the Wild Plant Nicotiana glauca[END_REF]. The fact that Imperata cylindrica is a perennial grass, a nonfood and designated as a noxious weed in agricultural and nonagricultural fields the West Africa prevent the exchange of Imperata cylindrica propagation material. The spread via rhizomes is main mechanism of spread of Imperata cylindrica although some research indicated a spread by seed dispersal [START_REF] Yager | Woody Shrubs as a Barrier to Invasion by Cogongrass (Imperata cylindrica)[END_REF]. Therefore, Imperata cylindrica cannot spread to very long distance. The heterogeneous sequences between isolates of IYMV could be explained by the great potential of genetic variation in Imperata cylindrica reported recently [START_REF] Chiang | Characterization of 21 Microsatellite Markers from Cogongrass, Imperata cylindrica (Poaceae), a Weed Species Distributed Worldwide[END_REF].

Indeed, perennial grass survive a long time in nature, adapting to different environmental conditions and consequently sometimes involves the development of new ecotypes. This is also true for viruses that infect perennial plants to maintain themselves and adapt to new environmental conditions [START_REF] Chiang | Characterization of 21 Microsatellite Markers from Cogongrass, Imperata cylindrica (Poaceae), a Weed Species Distributed Worldwide[END_REF]. Indeed, during the adaptation to the new conditions the multiplication of viruses is accompanied by various mutations due to the lack of repair process associated with their RNA dependent RNA polymerase [START_REF] Garcia-Arenal | Variability and Genetic Structure of Plant Virus Populations[END_REF]. In addition, it have been reported that the purifying selection often results in amino acid changes with functional or structural modifications such as genome protection, cell-to-cell movement, transmission between plants, interactions with the host and/or vector, etc. [START_REF] Varanda | Genetic Diversity of the Coat Protein of Olive Latent Virus 1 Isolates[END_REF].

IYMV CPs sequences are under high purifying selective constraints, the structuration of phylogenetic clades revealed in our analysis that the structure in six clades were associated with amino acid changes, particularly the R-domain region of coat protein (1 -66) (Table 2). These amino acid substitutions are consequent as shown by the strong changes of amino acid physicochemical properties: (P) 13 (T), (P) 28 (L) (P is hydrophilic, T and L are hydrophobic but T is a polar); (G) 65 (A) (G and A are hydrophobic but G is polar uncharged); Several authors have shown that the bipartite targeting sequence plays an essential role in addressing the CP protein to the nucleus [START_REF] Dingwall | Nuclear Targeting Sequences-A Consensus[END_REF]. However, although the basic residues R and K have similar properties we cannot say whether all substitutions at different positions in the bipartite targeting sequence have structural consequences on RNA encapsidation, stability of viral particles or other unknown properties of CP during the biological cycle of IYMV. It remains to be determined if these amino acids substitutions involve biologically distinct strains.

Cons K R K T A V Q L G R P S V G S I C M R A A S V

Conclusion

This is the first study of the genetic diversity of IYMV in Burkina Faso and we think that will allow contributing to a better understanding of IYMV evolution and epidemiology in Burkina Faso. In addition, the diagnosis using the specific primers will make of useful tool for population structure studies of IYMV in Burkina Faso. Although, its results are a prerequisite for further management of imperata yellow mottle disease, it would be interesting to study the genetic diversity in the neighboring countries such as in Mali and Benin (Data not shown)

where the presence of IYMV has been suspected. As the global diversity of IYMV is low, it would be interesting to obtain the complete sequence of other proteins in a number of other viral protein from the different isolates representative of the 6 clades. In this context, will be particularly interesting to sequence the P1 protein as it has been demonstrated for Rice yellow mottle virus [START_REF] Sérémé | Sites under Positive Selection Modulate the RNA Silencing Suppressor Activity of Rice Yellow Mottle Virus Movement Protein P1[END_REF] that P1 displayed the highest diversity in the RYMV genome, and the VPg protein has is the major determinant for resistance breaking in RYMV [START_REF] Traoré | The Adaptation of Rice Yellow Mottle Virus to the eIF (iso) 4G-Mediated Rice Resistance[END_REF].

Figure 1 .

 1 Figure 1. A schematic representation of the genome organization of IYMV; see text for details.

DOI: 10 .

 10 4236/ajps.2018.92025 314 American Journal of Plant Sciences

Figure 2 .

 2 Figure 2. Geographical location of IYMV sample collection sites and symptoms on infected Imperata cylindrica in its natural habitat. (a) A Map of Burkina Faso showing the south-Western (in yellow) where sampling was done; (b) Precise rural provinces within the two South-Western regions where IYMV was detected and collected; (c) Typical mottle yellowing of Imperata cylindrica leaves guiding plant harvests.

  PRE_iymv34-BF have been the result of recombination of the major NC-011536 with an iymv38-BF minor parent. PRE_NC-011536 have been the result of recombination of the major parent iymv29-BF with an iymv2-BF minor parent. PRE_iymv2-BF shows the recombination between iymv29-BF as the major parent and Unknown (iymv25-BF) as the minor parent. These PREs (iymv34-BF, NC-011536, iymv2-BF) were detected by MaxChi methods with average P-value 2760 × 10 -5 , 1068 × 10 -2 and 4298 × 10 -2 respectively. PRE_iymv34-BF also have been the result of recombination between iymv37-BF as the major parent and iymv38BF as the minor parent in the Chimaera and SiScan methods with average P-value 1393 × 10 -2 , 1393 × 10 -3 , respectively. Finally, PRE_iymv9-BF show the recombination between iymv38-BF as the major parent and Unknown (NC-011536) as the minor parent. This recombination event was detected by Chimaera method with average P-value > 1.0.

Figure 3 .

 3 Figure 3. RT-PCR mediated molecular diagnostic for IYMV occurrence in I. cylindrica in Burkina Faso. Lane M: 1kb DNA size standard. Lanes 1 to 5: IYMV infected leaves from five individual plants. Lane+: PCR product from the referent accession sample NC-011536 of the Imperata yellow mottle virus (IYMV). Lane-: RT-PCR control performed without plant RNAs.

Figure 4 .

 4 Figure 4. Description of potential recombination events. PRE_iymv34-BF, PRE_NC-011536, PRE_iymv2-BF and PRE_iymv9-BF are the Potential Recombinant Events (PREs). See text for details of major and minor parents.

Figure 5 .

 5 Figure 5. Distribution of IYMV genetic variation estimated by nucleotide diversity (π). The sliding window was 100 sites wide with slide set at 25 site intervals.

Figure 6 .

 6 Figure 6. Phylogenetic analysis of 39 IYMV isolates from Western region of Burkina Faso based on the CP gene. The isolate CP_BF1 of RYMV from Burkina Faso was used as an out Clade. Number below branches are bootstrap percentages Scale bar indicates a genetic distance of 0.1.

(

  A) 66 (S) (A is and S are polar and hydrophobic); (N) 268 (D) (N and D are hydrophilic but N is polar). Among these amino acid substitutions in the R-domain region, we noticed particularly two amino acid changes with threonine instead proline and leucine instead proline at the position 13 and position 18 respectively. It is well established that the exceptional conformational rigidity of proline affects the secondary structure of protein suggesting strong change for the N-terminus coat protein properties[START_REF] Sõmera | Overview on Sobemoviruses and a Proposal for the Creation of the Family Sobemoviridae[END_REF].Interestingly, we noticed that changes occurring particularly in the bipartite sequence (RKSKKMTQAAAVKNQQLAPSRR) of the IYMV CP which allowed to distinguish clade I from other others. M. Koala et al. DOI: 10.4236/ajps.2018.92025 321 American Journal of Plant Sciences

  et al.

DOI: 10.4236/ajps.2018.92025 312 American Journal of Plant Sciences

Table 1 .

 1 IYMV Isolates identified in different sub regions of South-Western Burkina Faso (BF). The unique IYMV sequence identified prior to this analysis[START_REF] Sérémé | Biological and Molecular Characterization of a Putative New Sobemovirus Infecting Imperata Cylindrica and Maize in Africa[END_REF] is given as the referent accession NC-011536.

	Origin	Year	Isolate	GenBank (Acc. No)	Reference
	N'Dorola	2016	IYMV1-BF	MF043148	This study
	N'Dorola	2016	IYMV2-BF	MF043149	This study
	Lomouroudougou	2016	IYMV3-BF	MF043150	This study
	Lomouroudougou	2016	IYMV4-BF	MF043151	This study
	Banfora	2016	IYMV5-BF	MF043152	This study
	Karfiguela	2016	IYMV6-BF	MF043153	This study
	Niangoloko	2016	IYMV7-BF	MF043154	This study
	Lomouroudougou	2016	IYMV8-BF	MF043155	This study
	Banfora	2016	IYMV9-BF	MF043156	This study
	Tondogosso	2016	IYMV10-BF	MF043157	This study
	Koloko	2016	IYMV11-BF	MF043158	This study
	Karfiguela	2016	IYMV12-BF	MF043159	This study
	Lomouroudougou	2016	IYMV13-BF	MF043160	This study
	Tondogosso	2016	IYMV14-BF	MF043161	This study
	Karfiguela	2016	IYMV15-BF	MF043162	This study
	Bama	2016	IYMV16-BF	MF043163	This study
	Banfora	2016	IYMV17-BF	MF043164	This study
	Koloko	2016	IYMV18-BF	MF043165	This study
	Tondogosso	2016	IYMV19-BF	MF043166	This study
	Tondogosso	2016	IYMV20-BF	MF043167	This study
	Tondogosso	2016	IYMV21-BF	MF043168	This study
	Tondogosso	2016	IYMV22-BF	MF043169	This study
	Karfiguela	2016	IYMV23-BF	MF043170	This study
	Banzon	2016	IYMV24-BF	MF043171	This study
	N'Dorola	2016	IYMV25-BF	MF043172	This study
	Tondogosso	2016	IYMV26-BF	MF043173	This study
	Niangoloko	2016	IYMV27-BF	MF043174	This study
	Karangasso Sambla	2016	IYMV28-BF	MF043175	This study
	Banfora	2016	IYMV29-BF	MF043176	This study
	N'Dorola	2015	IYMV30-BF	MF043177	This study
	N'Dorola	2014	IYMV31-BF	MF043178	This study
	Banzon	2015	IYMV32-BF	MF043179	This study
	Banfora	2013	IYMV33-BF	MF043180	This study
	Bama	2014	IYMV34-BF	MF043181	This study
	Banfora	2014	IYMV35-BF	MF043182	This study
	N'Dorola	2014	IYMV36-BF	MF043183	This study
	Bama	2014	IYMV37-BF	MF043184	This study

. ML analyses were performed under the T92 + G + I model. Isolate CP-BF1 (GenBank M. Koala et al. DOI: 10.4236/ajps.2018.92025 315 American Journal of Plant Sciences Banfora DOI: 10.4236/ajps.2018.92025 316 American Journal of Plant Sciences accession number: AJ279901.1) of Rice yellow mottle virus (RYMV) was used as outgroup for phylogenetic analysis. Robustness of phylogenetic relationships was assessed by 1000 bootstrap replications.

Table 2 .

 2 Multiple alignment of CP amino acid sequences of IYMV Burkina Faso isolates from different localities. Consensus sequence obtained with CLUSTAL W algorithm is shown above the alignment as a consensus/majority. The amino acids identical to the consensus are indicated by points within the alignment.

	3	7 11 13 17 18 22 23 30 31 39 40 49 65 66 99 164 187 190 227 254 257 261 268

IYMV38-BF MF043185This study Banzon 2008 NC-011536 NC-011536[START_REF] Sérémé | Biological and Molecular Characterization of a Putative New Sobemovirus Infecting Imperata Cylindrica and Maize in Africa[END_REF]

Acknowledgements

Financial supports for this study were provided in part by the Mixed International Laboratory LMI Patho-Bios (www.pathobios.com), by the PROVEG program (Proveg: a network on plant protection through Program to support network based research in Africa, PARRAF http://proveg.org/accueil) and by the International Foundation for Science (IFS) through fellowship N˚ C/5358-1 to Moustapha KOALA. M.K was also the recipient of a financial support by the project n˚1102004 funded by the International Agropolis Foundation for his education & training in molecular cloning at IRD Institute, France. We thank Nils Poulicard for helpful discussions.