Maissa Abdallah

Nadine Abdallah

Maryline Chetto

Ensuring the sustainability of real-time embedded system under both QoS and Energy Constraints

Nowadays, wireless sensor networks (WSNs) are more and more used in applications such as environment monitoring, healthcare monitoring, etc...The challenge in sensor networks is to ensure the sustainability of the system by guaranteeing the required performance level. However, with the limited capacity of finite power sources and the need of guaranteeing a long lifetime of those systems, it is suitable to use energy harvesting which allows to supply low-power electronic systems by converting ambient energy into electric power. Hence, our study is concerned with the problem of soft periodic and aperiodic tasks scheduling in sensor nodes powered by energy harvesters. In this paper, we address this issue by proposing three energy-aware schedulers, namely BG-Green-RTO, BG-Green-BWP and Green-AWP which aim to improve the responsiveness of aperiodic tasks while still guaranteeing the execution of periodic tasks considering their timing and energy constraints. Such algorithms allow to gracefully cope with processing overload and energy starvation. Moreover, a simulation study permits to show their performance.

1Introduction

Wireless sensor networks have taken more and more place in diverse application domains. For example, in agriculture, it could be possible to deploy a wireless embedded network to detect various measures across a field such as temperature, light levels, soil moisture, ... This example implies that measures should be collected at a specific time and should be treated on time to react correctly to a given situation. Majority of wireless sensor networks represent real-time embedded systems. A realtime system is a system which must guarantee a response within a specified time (often referred to as "deadline"). In other sense, it is a system in which the correctness depends not only on the logical result but also on the time in which it was delivered. So that, if the time constraints are not respected it may lead to a system failure. According to their time constraints, real-time systems are classified into three categories: hard, soft and firm [START_REF] Liu | Real-Time Systems[END_REF]. In a hard real-time system, all deadlines must be met imperatively. In a soft real-time system, missing deadlines will only cause a performance degradation without leading to a system failure. A firm real-time system is a specific case of a soft real-time system so that it must meet its deadlines but with a degree of flexibility. Batteries are the source of power for most embedded system applications. However a battery has a finite lifespan, limited energy density and capacity so when all its energy is consumed, the sensor must be retrieved to replace the battery. Nevertheless, It is not as simple to replace it, especially when the sensor is deployed in an inaccessible place where any human intervention may be either costly or impractical. Thus, such systems should be designed so as to ensure a continuous functioning without having a periodical maintenance due to replacing or recharging batteries. Renewable energy sources are available in an unlimited quantity and there is a variety of available techniques for energy harvesting (i.e. the process of extracting energy from the surrounding environment) such as solar, piezoelectricity, or thermoelectricity, etc... Therefore, it is interesting to target research towards environmental energy harvesting which could be considered as a promising approach to provide power for long term applications. Now, the challenge is to utilize the information about future harvested energy to maximize the overall Quality of Service (QoS) and still guaranteeing the sustainability of the system.

In this paper, we consider a firm real-time embedded system which receives energy from the environment. The energy received is stored in an energy reservoir formed by either a battery or a super-capacitor. This energy reservoir is required to ensure the power requirements of the system even if no energy can be drawn from the environment. Our objective is to ensure the efficiency of the system and enhance the responsiveness of aperiodic task while still guaranteeing the Quality of Service (QoS) of the system according to the available energy level in the battery. The remainder of the paper is organized as follows. Section 2 gives the background material. Section 3 describes the model of the system. In Section 4 we describe our contribution with illustrative example. Finally, Section 5 concludes the paper.

Background material 2.1 Scheduling Periodic Tasks under QoS constraints

The Skip-Over model [START_REF] Koren | Skip-over algorithms and complexity for overloaded systems that allow skips[END_REF] deals with the problem of scheduling firm periodic tasks which allow occasional deadline violations (i.e. skippable periodic tasks), on a uniprocessor system. A task τi is characterized by a worst-case computation time Ci, a period Ti, a relative deadline equal to its period Di = Ti, and a skip parameter si. si gives the tolerance of this task to miss deadlines so that the distance between two consecutive skips must be at least si periods. We consider that a job is the occurrence of a task. In this model, a task is characterized by two kinds of jobs, red ones and blue ones. A red job must complete its execution before its deadline while a blue job can be aborted at any time. For each task τi, (si -1) consecutive jobs are red and the next one is blue. The more the parameter si is large, the less losses are allowed. Consequently, if si equals to infinity, no skips are allowed and the task τi is considered as a hard periodic task. In [START_REF] Caccamo | Exploiting Skips In Periodic Tasks For Enhancing Aperiodic Responsiveness[END_REF], the authors define the equivalent utilization processor 𝑈 𝑝 * given by:

U p * = max L>0 {∑ D(i,[0,L]) L n i=1 } (1) Where, D(i, [0, L]) = (⌊ L T i ⌋ -⌊ L s i T i ⌋)C i (2)

Handling Aperiodic Tasks

In many applications, there are aperiodic and periodic tasks. An aperiodic task is typically activated by the arrival of external events (notified by interrupts). According to the application, it may have hard, soft or non real-time requirements. In our work, we assume that our system contains non critical aperiodic task (i.e. without a specific deadline). Therefore, a real-time system which executes periodic and non-critical aperiodic tasks have to guarantee that:  all periodic jobs meet their deadlines, even in the worst-case.



Aperiodic jobs have a good response time. For a classical uniprocessor system, a lot of efficient server algorithms have been developed. In our work, we were interested in the Background server, proposed in [START_REF] Lehozcky | Enhanced aperiodic responsivess in hard real-time environments[END_REF], which are reasonable in memory requirement, runtime overhead. It consists of scheduling aperiodic tasks in background (i.e., during idle times left by periodic tasks) then, they do not interfere with any periodic task.

Scheduling soft Periodic Tasks under Energy Sustainability constraints

The problem of real-time task scheduling on one processor without considering energy constraint has been an active area of research for more than thirty years [START_REF] Liu | Real-Time Systems[END_REF]. Since the last decade, researchers have become more and more interested to address power and scheduling issues. Most of the works in this area focus on either minimizing the energy consumption [START_REF] Yao | A scheduling model for reduced CPU energy[END_REF][START_REF] Min-Allah | Towards dynamic voltage scaling in real-time systems -a survey[END_REF] or ensuring a long-term sustainability of the energy system [START_REF] Allavena | Scheduling of Frame-based Embedded Systems with Rechargeable Batteries[END_REF][START_REF] Aydin | Determining optimal processor speeds for periodic real-time tasks with different power characteristics[END_REF][START_REF] Moser | Real-time scheduling with regenerative energy[END_REF][START_REF] Chetto | Real-time scheduling of periodic tasks in a monoprocessor system with rechargeable energy storage[END_REF]. In our work, we want to deal with Real-Time Scheduling for Energy Sustainable Systems so that we consider both rechargeability of the batteries and real-time constraints. In [START_REF] Chetto | Real-time scheduling of periodic tasks in a monoprocessor system with rechargeable energy storage[END_REF], The authors proposed a real-time scheduling algorithm called Earliest Deadline with energy guarantee (EDeg). It consists of scheduling a hard periodic task set (i.e., all jobs must meet their deadline) on a uniprocessor system which uses the energy stored in a rechargeable battery and the energy harvested from the environment. Every task is characterized by the energy consumption Ei in addition to the traditional timing parameters (i.e. Ci,Di, Ti). All jobs are executed according to the EDF algorithm as soon as possible if the energy level in the battery is sufficient to provide energy for current and future occurring tasks without involving deadline violations. The system starts executing a job only if the so-called slack energy is positive and the battery is not empty. The slack energy represents the energy surplus that can be consumed by a job while still satisfying energy constraints of highest priority jobs. It is used to quantify the energy consumed by future jobs so that it prevents from violating deadlines in case of energy shortage. If energy is not sufficient to execute current or future occurring jobs, the system stops its activity as long as the slack time is positive and the battery is not fully replenished.

Model and terminology

We consider a uniprocessor system composed of n firm periodic tasks. This system is powered by a rechargeable battery. The battery is recharged by a renewable energy source. Pr(t) is the instantaneous charging rate that incorporates all losses caused by power conversion and charging process. The nature and dynamics of the energy source are not specified, making our approach more easily implemented in real systems. Our system uses an ideal energy storage unit which could be a supercapacitor or a battery. It has a nominal capacity, namely C, expressed in Joules or Watts-hour. The energy level has to remain between two boundaries Emin and Emax with C = Emax -Emin. We assume that firm periodic tasks comply with the Skip-Over model so each task is characterized by computation, energy and QoS requirements. We assume the following notations in this paper. Let Τ be a firm periodic task set defined as follows: T = τi( Ci, Di, Ti, si, Ei); i = 1 to n. Ei is the Worst Case Energy Consumption (WCEC). Tasks are independent, preemptable and deeply-red (i.e. synchronous activation at time t=0). Tasks have an implicit deadline (Di = Ti). A task generates a sequence of jobs periodically. The k th job of the task τi is denoted by τi,k, and it has an absolute deadline denoted by di,K equal to the released time of the next job, (i.e. di,k = ri,k + Ti). In [START_REF] Abdallah | Energy-aware schedulers for real-time energy harvesting systems with quality of service requirements[END_REF], we define the equivalent energy factor U e * as follow:

U e * = max L>0 {∑ g * (i,[0,L]) E(0) + E r (0,L) n i=1 } (3)
Where, In this paper, we assume that the power received by the environment is constant during a hyperperiod P (i.e. P = LCM(T1s1, .., Tisi, ..,Tnsn)). Then as Pr(t) = Pr, ∀t, Er(0,L) = Pr.L where L represents the red job end points during the interval [0,P[.

g * (i, [0, L]) = (⌊ L T i ⌋ -⌊ L s i T i ⌋)E i (4
In [], we define and prove the following feasible tests:

Theorem 1: A skippable periodic task set T is schedulable under energy and timing constraints only

∀L| ∑ g * (i, [0, L]) n i=1
≤ E(0) + Er(0;L) Theorem 2: A set of skippable periodic tasks T is schedulable under energy and timing constraints only if :𝑈 𝑝 * ≤ 1 𝑎𝑛𝑑 𝑈 𝑒 * ≤ 1

In [START_REF] Abdallah | Energy-aware schedulers for real-time energy harvesting systems with quality of service requirements[END_REF], we proposed the Green-RTO and the Green-BWP schedulers which are based on the skipOver [START_REF] Koren | Skip-over algorithms and complexity for overloaded systems that allow skips[END_REF] model and EDeg algorithm [START_REF] Chetto | Real-time scheduling of periodic tasks in a monoprocessor system with rechargeable energy storage[END_REF]. Green-RTO runs as follows:  When the battery is not empty, the processor is active if there is a pending red job and the system has enough slack energy. Ready red jobs are processed according to the EDF algorithm.

 The processor is inactive if the slack time is not null or there is no red jobs ready to be executed. The slack time of the system at time t represents the maximum time available from t to postpone red jobs while still satisfying all timing constraints. Hence, it permits to put the processor to an idle state and recharge the battery. The Slack time is computed by using the Earliest Deadline as Late as possible (EDL) algorithm [START_REF] Chetto | Some results of the earliest deadline scheduling algorithm[END_REF]. The slack energy of the system at time t represents the energy surplus that can be consumed by a red job which has the highest priority at time t while still satisfying energy constraints of future red jobs that will have a highest priority than the current job. It is used to avoid violating deadlines of red jobs in case of energy shortage.

Contribution

In this paper, we propose real-time scheduling techniques for reducing the response time of soft aperiodic tasks scheduled with real-time periodic tasks on an uniprocessor system under QoS and energy constraints. We consider that a soft aperiodic task is defined by 𝜑 𝑖 (𝑟 𝑖 𝑎 ,𝐶 𝑖 𝑎 ,𝐸 𝑖 𝑎 ) where rai is its arrival time , Cai is its worst case execution time, and Eai is its worst case energy time. The response time of 𝜑 𝑖 is expressed by 𝑇𝑅 𝑖 𝑎 = 𝑓 𝑖 𝑎 -𝑟 𝑖 𝑎 where 𝑓 𝑖 𝑎 is its completion time. Our work consists of a mixture of the BG server with Green-RTO or Green-BWP.

Schedulers

BG-Green-RTO

BG-Green-RTO consists of executing a ready aperiodic task if there is no ready red jobs. Hence at the time t, if there is no ready red jobs and there is at least one ready aperiodic task, an aperiodic task is chosen according to the FIFO algorithm (i.e. First In First Out). The aperiodic task chosen will be executed if it will not jeopardize the execution of future periodic tasks. Then the aperiodic task will be partially or entirely executed according to the surplus of energy available in the battery Eav. This surplus of energy available is computed to ensure that the battery will be fully charged at the next release of a future red job. The surplus of energy available at the time t is computed as follows: Eav(t) = E(t) + (rmin-t)Pr-(Emax) where, rmin represents the next release of a future red job and Emax corresponds to the maximal level of energy in the battery.

4.1.2BG-Green-BWP

BG-Green-BWP (i.e. Blue When Possible) relies on the same principle of BG-Green-RTO, but it executes blue jobs when there is no ready red jobs. Then an aperiodic task is executed if there is no ready periodic jobs (red or blue). Red and blue jobs are executed according to Green-BWP algorithm. If there is no ready periodic jobs, an aperiodic task is chosen from the ready aperiodic task list according to FIFO algorithm. Then Eav is computed so that the aperiodic task is executed only if the battery will be fully charged when the closest red job will be released.

BG-Green-AWP

Green-AWP (i.e. Aperiodic When Possible) has the same principle of BG-Green-BWP but it executes blue jobs when there is neither ready red job nor ready aperiodic task . Under Green-AWP, blue jobs have the smallest priority and an aperiodic task is executed if there is no ready red jobs. Red and blue jobs are executed according to Green-BWP algorithm. If there is no ready red jobs, an aperiodic task is chosen from the ready aperiodic task list according to FIFO algorithm. Then Eav is computed so that the aperiodic task is executed while Eav is positive. It means that the battery must be fully charged when the closest red job is released.

Illustrative examples

In this section, we consider the task set T which consists of 2 firm periodic tasks given by T T = τi( Ci, Di, Ti, si, Ei)= {τ1 [START_REF] Yao | A scheduling model for reduced CPU energy[END_REF][START_REF] Abdallah | Energy-aware schedulers for real-time energy harvesting systems with quality of service requirements[END_REF][START_REF] Abdallah | Energy-aware schedulers for real-time energy harvesting systems with quality of service requirements[END_REF][START_REF] Chetto | Some results of the earliest deadline scheduling algorithm[END_REF][START_REF] Allavena | Scheduling of Frame-based Embedded Systems with Rechargeable Batteries[END_REF], τ2 [START_REF] Yao | A scheduling model for reduced CPU energy[END_REF]15,15,[START_REF] Chetto | Some results of the earliest deadline scheduling algorithm[END_REF][START_REF] Aydin | Determining optimal processor speeds for periodic real-time tasks with different power characteristics[END_REF].

We assume that Pr = 2. The battery is initially fully charged, E(0) = C = H.Pr=20 = 6. Up = 0.45 and Ue = 0.54 so that the system is not overloaded in energy and timing point of view. Moreover, 𝑈 𝑝 * = 0.4 and 𝑈 𝑒 * = 0.45

We assume that 3 aperiodic tasks arrive during the interval [0,60]: 𝜑 = {𝜑 𝑖 (𝑟 𝑖 𝑎 ,𝐶 𝑖 𝑎 ,𝐸 𝑖 𝑎 ) ; 𝜑 1 (0, 9, 15); 𝜑 2 (0, 3, 8); 𝜑 3 (20, 7, 15).

Figure 1 shows the scheduling of the task set T under BG-Green-RTO during a hyperperiod P = PPCM(T1, T2) = 60 (the equivalent hyperperiod is equal to P * = PPCM(T1s1, T2s2) =120).

The aperiodic tasks 𝜑1and 𝜑2 arrive at time t=0. Butτ1 and τ2 are ready red jobs, so according to Green, RTO τ1 is executed first. Then τ2 is executed. At time t=6, there is no ready red job so that an aperiodic task could be executed if there is sufficient energy and if the battery will be full at the next red job release. The amount of energy available until the next release of a red job (at time t=rmin = 24) is computed: Eav(6) = E( 6) + (rmin-6)Pr C = 3 + 36 -6 = 33.Eav( 6) is positive then 𝜑1 begins its execution at time t=6 until either a red job is released or the energy available in the battery becomes null. 𝜑1 complete its execution at time t=15. As 𝜑2 is waiting and there is no ready red job, the available energy is computed. Eav(15) is positive then 𝜑2 begins its execution at time t=15. It completes its execution at time t=18. At time t=20, 𝜑3 arrives. There is no ready red jobs then the available energy is computed (Eav(20) = 8). As Eav(20) is positive, 𝜑3 begins its execution at time t = 20. However the available energy becomes null at time t=23, then 𝜑3 stops its execution. The processor is idle for one unit of time to recharge the battery, etc.. We notice that in this example, 𝜑1, 𝜑2 and 𝜑3 have respectively the response time equal to 15, 18 and 15 time units. This shows that even if the system isn't overloaded in terms of time and energy, the response time of aperiodic tasks is still too long. Moreover, as blue job is always rejected, the QoS of the system depends on the loss parameter (si) of tasks. In this example, the QoS obtained is 50%.

The Figure 2 shows the scheduling of the task set T under BG-Green-BWP during P = 60. We notice that in this example, 𝜑1, 𝜑2 and 𝜑3 have respectively the response time equal to 21, 24 and 24 time units. In comparison with BG-Green-RTO, BG-Green-BWP gives a better QoS which is equal to 100% but the response time of aperiodic task is longer.

Figure 3 shows the scheduling of the task set T under Green-AWP during P = 60.We notice that in this example, 𝜑1, 𝜑2 and 𝜑3 have respectively the response time equal to 15, 18 and 16 time units. With Green-AWP, the response times obtained are better than the one obtained with BG-Green-BWP but the QoS obtained is worse : QoS=77.5%.In Comparison with BG-Green-RTO, the QoS obtained with Green-AWP is better (77.5% vs 50%) and the response times of aperiodic tasks are the same.

)

  𝑔 * (𝑖, [0, 𝐿]) represents the energy consumed by red jobs of task τi in the interval [0;L]. E(0) represents the initial energy level in the battery, 𝐸 𝑟 (0, 𝐿) represents the energy received by the battery during the interval [0,L].

CONCLUSION AND FUTURE WORKS

In this paper, we have considered a firm real-time system that consists of firm periodic tasks and non critical aperiodic task. Our goal was to exploit losses due to the non execution of blue jobs to minimize the response time of aperiodic tasks. Based on the background server and the two energy aware schedulers Green-RTO and Green-BWP, we presented three techniques to execute aperiodic tasks in conjunction with periodic tasks namely BG-Green-RTO, BG-Green-BWP and Green-AWP. BG-Green-RTO gives the worst QoS among the three techniques but as the blue jobs are systematically rejected it will improve the response time of aperiodic tasks because there will be more energy available for them. BG-Green-BWP gives the best QoS among the three techniques by trying to execute blue jobs in priority but the response time of aperiodic tasks will be longer than the two other techniques. Finally, Green-AWP comes in the middle position in the sense that it improves the response time comparing to the one obtained with BG-Green-BWP, and it offers an acceptable QoS better than the one obtained with BG-Green-RTO. We conclude that according to the kind of the application, it will be better to choose one among the three techniques. If the event that occurs suddenly should be treated as soon as possible, it will be better to use BG-Green-RTO or Green-AWP. In future work, we are interested in minimizing the response time of aperiodic tasks by proposing new techniques to execute aperiodic tasks as soon as possible while still ensure acceptable QoS of the system.


Venkata Suresh Vajrala

Fadhila Sekli Belaidi

Gabriel Lemercier

Dodzi Zigah

Michel Rigoulet

Anne Devin

Neso Sojic

Pierre Temple-Boyer

Jérôme Launay

Dr Stéphane Arbault
email: stephane.arbault@u-bordeaux.fr

Microwell Array integrating Nanoelectrodes for Coupled Opto-electrochemical Monitorings of Single Mitochondria

Keywords: Microdevice, Microwells, Nanoelectrode, Cyclic voltammetry, Fluorescence microscopy, Mitochondria, Oxygen, Bioenergetics

Chips composed of microwell arrays integrating nanoelectrodes (OptoElecWell) were developed to achieve dual optical and electrochemical detections on isolated biological entities. Each array consists in 10 6 microwells of 6 µm diameter × 5.2 µm height each, with a transparent bottom surface for optical observations, a platinum nano-ring electrode at its halfheight for in situ electrochemistry, and a top open surface to inject solutions. Then, populations of individual mitochondria isolated from yeasts (Saccharomyces cerevisiae) were let to sediment on the array and be trapped within microwells. The trapping efficiency reached 20 % but owing to the large number of microwells on the platform, hundreds of them could be filled simultaneously by single mitochondria. This allowed to follow up their individual energetic status based on fluorescence microscopy of their endogenous NADH. Simultaneously, the array of interconnected Pt nanoelectrodes in the microwells was used to monitor in situ variations of dioxygen consumed by all mitochondria captured in the device. Mitochondrial bioenergetics were modulated sequentially using respiratory chain-ATP synthase substrates (ethanol and ADP) and inhibitor (antimycin A). Overall, we show how two complementary analytical approaches, fluorescence and electrochemical detections, can be coupled for a multi-parametric monitoring of mitochondrial activities, with a resolution ranging from a small population (whole device) to the single mitochondrion level (unique well).

Introduction

Mitochondria are the power factories of aerobic cells since they comprise the most prominent source of ATP, they are also involved in multiple anabolic and catabolic pathways. Due to genetic flaws and/or function alterations mitochondria play a central part in cellular ageing and in numerous diseases, including myopathies, cardiovascular disorders and cancers (Alston et al. 2017;Chinnery et al. 2012;Galluzzi et al. 2012;Gonzalez-Freire et al. 2015;Hauser and Hastings 2013;Wallace 2012;Yin et al. 2014). Mitochondria within a cell usually constitute a network with genetic and metabolic individual discrepancies and evolutions which increase along cell life (Chinnery et al. 2012;Dowling 2014;Kembro et al. 2013;Reiner et al. 2010;Youle and van der Bliek 2012). In other words, mitochondria are not all equivalent in terms of bioenergetics efficiency and status. This was until recently poorly taken into consideration for mitochondrial studies which are usually based on large populations of isolated entities or cells and diverse detection methods. Indeed, the most widely used techniques including fluorescence spectroscopy, chemiluminescence and electrochemistry (e.g. the Clark electrode) provide mean responses of mitochondria populations following bioenergetics activation/inhibition protocols (Allen et al. 2009;Dikalov and Harrison 2014;Hall et al. 2013;Quarato et al. 2011;Vasdekis and Stephanopoulos 2015;Zand et al. 2013). As a consequence, in order to decipher on mitochondrial heterogeneity and exact metabolic responses, studies at single organelle level have drawn huge interest (Appelhans and Busch 2017;Chang and Marshall 2017;Follain et al. 2017;Norregaard et al. 2017;Suraniti et al. 2013;Vajrala et al. 2014;[START_REF] Vajrala | [END_REF]). Among all available techniques, spectrophotometry and electrochemistry are the ones providing higher detection sensitivities while using limited amounts of mitochondria. Electrochemical methods are simple, sensitive, and provide real-time detection. Particularly, ultramicroelectrodes (UME) have led to numerous biomedical applications, since these micrometric-size electrodes sense concentration gradients of redox species within environments of a few tens of micrometers (pL volumes) (Amatore et al. 2008a;Amatore et al. 2008b;Ino et al. 2017;Youle and van der Bliek 2012;Zoski 2002). In addition, they offer advantageous features such as a true steady-state diffusion-limited currents, small ohmic drop (I × R) from solution resistance and short response times (low capacitive currents). They have thus been used to monitor responses from single cells (adrenal cells, neurons, fibroblasts, macrophages…) (Amatore et al. 2006;Amatore et al. 2008b;Amatore et al. 2010;Lin et al. 2018;Schulte and Schuhmann 2007). We and others have reported the real-time quantification of dioxygen consumption and hydrogen peroxide release by isolated mitochondria using amperometry with platinized UMEs (Ben-Amor et al. 2013;Ben-Amor et al. 2014;Marcu et al. 2012;Suraniti et al. 2014). However, in order to scale it down and target the single organelles, we have envisioned the use of nanoelectrodes (Clausmeyer and Schuhmann 2016;Fan et al. 2016;Karimian et al. 2017;Rassaei et al. 2014;Wang et al. 2012). The diffusion volume probed by a nanoelectrode would indeed be adequate for sensing the environment of a single mitochondrion, which characteristic size is between 300 nm to 1200 nm.

On the other hand, fluorescence monitoring of single mitochondria evolved recently as a strategy for highly sensitive analyses of mitochondrial metabolic markers (Quarato et al. 2011;Suraniti et al. 2013;Vajrala et al. 2014;[START_REF] Vajrala | [END_REF]Zand et al. 2013). Markers include the intrinsic autofluorescent NADH and FAD metabolites, as well as extrinsic dyes (rhodamine derivatives) for the mitochondrial membrane potential (ΔΨ), reactive oxygen species and other parameters [START_REF] Kilgore | Current protocols in cytometry[END_REF]Li et al. 2016;Zhu et al. 2016). The microscopic platforms for single mitochondria monitoring are generally dependent on nanofluidics, nanohole arrays for trapping and surface functionalization for immobilization (Lim et al. 2012;Zand et al. 2013).

Nevertheless, with such systems are certain restrictions such as an efficient delivery of oxygen and nutrients, a size dependent immobilization of mitochondria and the low number of entities measured. Lately, our work has been focused on microwell array strategy, which rely on random mitochondrial immobilization and do not require specific surface modifications.

Platforms were obtained from etched optical fiber bundles and PDMS microwells, which both offer large scale studies of single mitochondria under bioenergetic activation and inhibition conditions (Suraniti et al. 2013;Vajrala et al. 2014;[START_REF] Vajrala | Methods in molecular biology[END_REF][START_REF] Vajrala | [END_REF].

Both above methodological families equally provide important information about mitochondrial metabolism, from either an external (electrochemistry) or internal (fluorescence) side. The goal of the present work was to develop platforms allowing these two techniques to run simultaneously on a same mitochondrial sample. A well-ordered microwell array (Belaidi et al. 2016) with integrated nanoelectrodes (OptoElecWell) was built over a transparent glass support. Based on silicium microtechnologies in conjunction with reactive ion etchings, we obtained opened microwells with ring nanoelectrodes inside each well. (Belaidi et al. 2016). The present work describes (i) the optimization of these OptoElecWell microdevices (ii) their efficient entrapment of single mitochondria and (iii) simultaneous electroanalysis of their oxygen consumption and fluorescence microscopy of individual NADH variations. The sensitivity and reliability of measurements was shown by the use of mitochondrial respiratory chain effectors.

Materials and Methods

Preparation of mitochondria

This work was based on mitochondria extracted from yeasts (Saccharomyces cerevisiae, Yeast Foam strain). Yeast culture, mitochondria isolation and conservation in frozen state (beads at -80°C) were achieved as described previously (Suraniti et al. 2013;[START_REF] Vajrala | Methods in molecular biology[END_REF].Mitochondria were analyzed in a specific medium, namely the Mitochondrial Buffer (MB) containing 0.63 M deionized mannitol, 0.36 mM EGTA, 10 mM Tris-maleate, 5 mM Tris-phosphate, adjusted at pH 6.8.

OptoElecWell microdevice fabrication

Microdevice development was based on silicon microtechnologies. (Belaidi et al. 2016) Briefly, on a glass wafer (B33 type, 500 µm thickness) were deposited 2.5 μm thickness silicon oxide by plasma-enhanced chemical vapour deposition (PECVD), a 200 nm platinum layer in sandwitch between two 20 nm-thick titanium adhesion layers, and another 2.5 μm SiO2 layer to form the SiO2/Ti/Pt/Ti/SiO2 stacking. Through a patterned photoresist mask, inductively coupled plasma reactive ion etching (ICP-RIE) with three different gaseous mixtures (CF4/Ar, SF6 and Cl2) was used to etch the whole stack and fabricate microwells (diameter: 6 μm, depth : 5.2 μm) with integrated ring nanoelectrodes. At last, a CF4/Ar etching step was performed to contact electrically the nanoelectrode array.

Electrochemical measurements

The electrochemical measurements were carried out using a potentiostat (BioLogic Inc. VSP-300, EC-Lab software) equipped with an ultralow current module. A freshly chlorinated silver wire and a platinum wire were used as the reference RE and counter CE electrodes, respectively, and placed within the solution on top of the microchip (nanoelectrode array as the working electrode WE).

Fluorescence microscopy of mitochondria

All experiments were performed at 25°C on the stage of an inverted microscope (Leica DMI 6000, Germany). Imaging was achieved with a 40× (N.A. 0.9) oil immersion objective, recorded by a camera (ORCA-Flash4.0 V2 SCMOS type, Hamamatsu, Japan) using a 200 ms exposure time without additional gain. Mitochondrial NADH was monitored by epifluorescence (Leica EL6000 source) with a DAPI-type filter (excitation and emission wavelengths are BP 340-380 nm and LP 425 nm, respectively). Fluorescence intensities were measured (Metamorph, Molecular Devices, USA) by drawing regions of interest (line scan of 4-8 µm length) centered on each single mitochondrion spot. All intensities refer to the difference between maximum (mitochondrial fluorescence) and minimum (microwell background) values, meaning they were individually background-subtracted. Further statistical analyses of data were performed (Origin 8.0, OriginLab, USA).

Opto-electrochemical measurements on mitochondria

A PDMS (Poly-dimethylsiloxane, RTV615 kit, Momentive Performance Materials, USA) barricade (20 mm diameter x 3 mm height) was glued (CAF4 paster, Bluestar Silicon, USA) on top of the chip and let to dry. Before use, each OptoElecWell system was cleaned properly with water and ethanol, then treated by an oxygen plasma (Harrick Plasma cleaner) for 10 minutes.

Meanwhile mitochondrial sample was defrozen and stored in an ice bath. The microdevice tank was filled with 500 µL of buffer, then followed by addition of mitochondria solution (1 mg.mL - 1 ). The whole solution was homogenized very gently and let to rest in order for mitochondria to sediment progressively in the microwell array. Further, washings were performed by removing and adding at least four times 2 mL of buffer in the PDMS tank. All electrodes were connected to the potentiostat and the microscopy focus was adjusted to monitor mitochondria in the microwells. Ethanol (analytical grade), Adenosine diphosphate (ADP) and antimycin A (AMA) solutions were prepared in the mitochondrial buffer at following final concentrations: Ethanol 1% v/v, 2 mM ADP and AMA 1.8 µM. For each bioenergetic stage, 100 µL of stimulation-inhibition solution was introduced in the device tank.

Results and Discussion

Design and optimization of OptoElecWell microdevices

We have first designed microsystems allowing to isolate and observe single mitochondria while achieving simultaneously the electrochemical detection in situ and fluorescence spectrophotometry. Our past experience in single mitochondria monitoring by fluorescence microscopy on glass slides and PDMS surfaces, led us to design microwells organized in an array of at least hundreds of units, in order to gain a statistically significant number of mitochondria under analysis. The diameter and depth of microwells had to be sufficient to let mitochondria deposit and sediment within wells while enabling fast diffusion of solutions (activators/inhibitors). The distance between wells was also another key parameter since it should prevent the mixing of contents between adjacent microwells. In addition, even if a recessed feature favors confinement, ring nanoelectrodes have to be sufficiently separated one from the other to avoid any crosstalk between their diffusion layers (Godino et al. 2009;Huang et al. 2009;Zoski 2002).

Consequently, we have developed protocols to obtain a microwell array, on a transparent glass substrate, with individual diameters D of 6 µm and of equivalent depth (H = 5.2 µm) (roughly a unitary aspect ratio), and an inter-distance between them of 5 × radius (R) to 20×R (Figure 1).

Each microwell must contain a working platinum ring nanoelectrode (RNE, 200 nm width) to detect local variations of mitochondrial metabolites (O2 or H2O2). We have recently reported the protocols for the building of such microdevice (Belaidi et al. 2016). The microfabrication process is based on layer by layer deposits of: SiO2 (2.5 µm mean thickness) / Ti (20 nm thickness as an adhesion layer) / Pt (200 nm thickness conductive layer) / Ti (20 nm) / SiO2 (2.5 µm) over a transparent glass substrate (500 µm thickness). The microwells (6 ± 0.2 µm D × 5.2 ± 0.2 µm H) were obtained by a multi-step reactive ion etching protocol providing SiO2 surfaces and flanks with low roughness and discovering efficiently the platinum surfaces (see images in Figure 1) (Li et al. 2003;Matsutani et al. 2012;Park et al. 2005).

Glass

Because of material-dependent etching kinetics, platinum surfaces appeared as regular rings (200 nm thickness) with an exposed top border (300 nm width) within all wells. Multi-physic simulations were conducted to study the behavior of the recessed nanoelectrodes as a function of all geometric parameters. (Belaidi et al. 2016) The highest collection efficiency (nearly 90%) is obtained for an aspect ratio H/D ≥ 1, and for nanoelectrodes located at the mid-height of wells. Concerning the distance between wells, this parameter is critical for both the electrochemical detection and the entrapment of mitochondria. Indeed, while wells should be very close from each other (dense packing) in order to maximize their filling success rate, they should be distant enough (loose packing) so as to let nanoelectrodes work independently.

Simulations and experiments have shown that quasi steady-state voltammograms were obtained (ferrocene-methanol and ferrocyanide redox probes) for a separation distance of 5×R between wells whereas pure steady-state voltammograms were obtained for 20×R value (Belaidi et al. 2016). As shown experimentally below, the dioxygen electrochemical detection in microwells will be achieved by fast cyclic voltammetry (scanning rate ≥ 1 V.s -1 ) in transient mode, which response was not dependent on device geometry. Consequently, when considering all constraints, OptoElecWell devices constituted of 10 6 wells of 6 µm (D) × 5.2 µm (H), distant of 15 µm (5×R), integrating the nanoelectrodes and external counter (millimetric Platinum wire) and reference (Ag/AgCl) electrodes in a PDMS reservoir on top, have been used for in vitro characterizations and further mitochondrial studies.

Electrochemical responses of the OptoElecWell microdevices

Platinum was chosen as the material for nanoelectrodes because of its wide electroactive window in water and since this is the most usual and efficient material for electrochemical oxygen sensing (generating water over a 4e -/4H + reaction), which is a major goal of this work.

Electrochemical performances of the platinum nanoelectrodes within the microwell array were first characterized by cyclic voltammetry in a 0.1 M H2SO4 solution (see Figure 2a). The voltammograms obtained from such thin layer platinum deposits depicts clear proton reduction waves and oxidation waves related to the formation of platinum oxides. It was manifest that the response of these platinum nanoelectrodes within microwells is very similar to the one of native solid electrodes and also demonstrates the electrochemical/structural quality of our fabrication subroutine to build up this integrated chip. Further characterizations were achieved in aqueous buffer solutions, PBS (pH 7.4) and a specific buffer for mitochondrial experiments (pH 6.8, see experimental section). With a classical chemical redox probe in PBS, namely ruthenium hexaamine, a quasi-reversible reduction wave was observed with a pseudo steady-state behavior at low scan rate (10 mV.s -1 ) and peak-shaped voltammograms at higher scan rates (≥ 100 mV.s -1 ). Pure steady-state voltammograms could not be obtained even at very low scan rates (about 1 mV.s -1 ). This is due to the recessed position of nanoelectrodes within microwells and to their inter-distance, which induces a diffusion field intermediate between planar and hemi-spherical geometries (Guo and Lindner 2009;Ma et al. 2013). Consequently, voltammograms shape as pseudo steady-state responses. The next goal was to evaluate the oxygen sensing ability of the device. Electrochemical reduction of dioxygen was performed in PBS under various concentrations (Figure 2c). The oxygen reduction was well detected with a plateau current reached at about -0.75 V vs Ag/AgCl. The sensitivity of the nanoelectrode array to O2 concentration variations was excellent and fairly comparable to a usual platinum Clark electrode. Nevertheless, when achieving multiple scans, we observed a progressive loss of amplitude of the reduction wave, showing that oxygen is consumed faster within microwells (0.14 pL mean individual volume) than it diffuses from the solution. This meant that the time-scale of measurements should be adapted in order to not consume oxygen significantly and deprive mitochondria from their substrate. In further experiments, cyclic voltammograms were consequently performed at faster scan rates, from 1 to 4 V.s -1 , with a delay of 10 to 20 seconds between scans (see part 3.4).

Entrapment of single mitochondria in microwells

OptoElecWell microdevices are dense microwell arrays, e.g. 2500 microwells / mm², for the high throughput entrapment and metabolic monitoring of single mitochondria. The handling of solutions in the device was achieved by adding a 5 mm-high PDMS tank on the upper side of the chip, while keeping the glass bottom surface open for microscopy (Figure 3a). The tank contained 2 mL of buffer solution above microwells, preventing evaporation and the lack of oxygen for mitochondria. The device was pre-treated by an oxygen plasma to improve hydrophilicity of surfaces (Jacobs et al. 2012;Jokinen et al. 2012). We observed a rapid and homogenous filling of microwells by aqueous solutions. Then, we added a suspension of mitochondria (1 mg.mL -1 ) in the tank and observed their appearance in the microwells by microscopy. Observations could be performed in transmission mode with white light but this mode did not allow to easily differentiate between mitochondria and possible protein or membrane aggregates. Epi-fluorescence observation helped better to define the localization of mitochondria, thanks to their NADH content. First observations depicted strong fluorescence responses in most of microwells (left image of Figure 3b). This was due to the high density of mitochondria into and also on top of the microwells. The OptoElecWell were then subjected to careful washings with buffer in order to eliminate efficiently the excess of mitochondria. Under these conditions, the mean trapping efficiency of individual mitochondria was 18 %, while a few more additionally contained several mitochondria (2-4 entities). When applying less washing steps, one may reach a higher trapping efficiency in microwells (about 50%), but with multiple mitochondria in each (typically 1 to 5 entities). This situation corresponding to the analysis of small populations would lead to higher responses with both analytical methods (see below), but would escape from the single mitochondrion criterion targeted herein. NADH displays a fluorescence emission (peak at 460 nm), while the oxidized form NAD + does not fluoresce. NADH is the substrate of complex I and of internal NADH dehydrogenases in mammalian and yeast mitochondria (Saccharomyces cerevisiae used here), respectively. Its steady-state concentration was estimated as a fraction of a millimolar (Bakker et al. 2001;Talbot et al. 2007). Mitochondrial oxygen consumption has also been recognized traditionally as the key parameter for assessing the respiratory chain efficacy. The most efficient approach for oxygen sensing is the classic Clark electrode based on a platinum working electrode, and we have demonstrated in section 3.2 that this can also be performed with OptoElecWell platforms. The microscope imaging (40× objective) of the array provided the simultaneous observation of about 400 microwells, and the device was not displaced during the whole experiment (20-30 min.) in order not to modify the focus and provoke mechanical movements. Once mitochondria were let to sediment in the microwells, their individual NADH fluorescence were measured every minute by using region of interest patterns (around each microwell containing a single entity), at different bioenergetics states following additions of respiratory substrates and inhibitors. Figure 4 shows the mean variations of 75 individual mitochondrial responses measured over 3 different experiments. Only a few mitochondria (20 % at most) were not responding favorably to activators/inhibitors, as observed in our previous studies (Suraniti et al. 2013;[START_REF] Vajrala | [END_REF]. Meanwhile, electrochemical measurements were conducted at the nanoelectrode array in the microsystem. As mentioned earlier, a sampling detection method is necessary to sense rapidly and at regular times the local oxygen concentration variations next to mitochondria, without modifying substantially this concentration (220 µM at room temperature and normal atmosphere) or possibly modifying also the local pH. We used fast cyclic voltammetry (CV, scan rate of 1 to 4 V.s -1 , with a delay of 10 to 20 seconds between scans) for measuring O2 variations in microwells. CV was preferred to other methods since it constantly informs on the reduction processes involved, at least the O2 reduction wave evolution (Figure 5). Repetitive scans were applied at the nanoelectrodes for at least 20 min. before launching mitochondrial measurements. The CV shape was fairly stable and the amplitude started to decrease slightly when mitochondria were injected and present into microwells. Though the number of mitochondria under observation was only a few tens, the whole microsystem was much larger in dimensions and we estimated that about 50000 mitochondria participated to dioxygen concentration decrease. Measurements were conducted simultaneously to NADH ones reported in Figure 4.

After washings Entrapment of mitochondria

Then, ethanol (1% V/V in buffer) was injected in the buffer solution surrounding the array.

Ethanol (EtOH) is employed as an energetic substrate since it is converted in Saccharomyces cerevisiae mitochondria into acetaldehyde and NADH by alcohol dehydrogenases. NADH induces respiratory chain functioning (non-phosphorylating condition) and consumption of dioxygen, as monitored by OptoElecWell, i.e. a 19 % increase of NADH (figure 4b) and a decrease of 12-15 µM O2.min -1 local oxygen concentration (negative slope of 2.5 nA.min -1 in figure 5b). After 5 min., the microsystem was provided with additional 2 mM adenosinediphosphate (ADP). ADP is the substrate of mitochondrial ATP synthases and induces the coupling of phosphorylation (ATP formation) and respiratory activities (OXPHOS), therefore Resting +Ethanol + ADP + antimycin A time increasing the rate of oxygen consumption (3.5 nA.min -1 slope, corresponding to 17-20 µM O2.min -1 ) by a factor of "1.4" (figure 5b). This value quoted as the mitochondrial respiratory control-ratio, was measured for a minute population of mitochondria (5 × 10 4 ) in microwells, and is in good agreement with conventional studies with the same mitochondria (Beauvoit et al. 1993;Ben-Amor et al. 2013;Suraniti et al. 2014). However, the usual quantities involved for oxygraphy measurements are milligrams of proteins, meaning 10 6 -10 8 mitochondria. On the other hand, the addition of ADP increases the rate of NADH consumption in functioning mitochondria, which caused NADH fluorescence to decrease progressively (Figure 4a). Finally, antimycin A, an inhibitor of complex III of the respiratory chain, was added to lead to the blockade of oxygen consumption and to a decrease of NADH consumption, inducing herein a 35 % increment of mean NADH fluorescence compared to the resting stage (Figure 4a and4b), and a progressive increase of oxygen concentration in microwells (see the positive slope on the graph after 15 minutes in Figure 5b) associated with the balancing of diffusion from the solution.

All these results obtained with OptoElecWell microdevices are in excellent agreement with our previous works with fluorescence and electrochemical measurements performed separately on mitochondria from the same origin (Ben-Amor et al. 2014;Suraniti et al. 2014;Suraniti al. 2013;[START_REF] Vajrala | [END_REF]. 4b), but the sum of increment or decrement can be very different from one mitochondrion to the other (heterogenic nature). This observation is clearly in full agreement with our previous studies (Suraniti et al. 2013;[START_REF] Vajrala | [END_REF], nevertheless the higher interest of OptoElecWells resides in the simultaneous usage of fluorescence imaging and electrochemical sensing which eventually leads to the dynamic monitoring at least of two mitochondrial metabolic parameters.

Conclusion

We designed, developed and characterized a microdevice based on a microwell array integrating an optical window and ring-type Pt nanoelectrodes. We demonstrated how such microwell array could be employed for the entrapment of single mitochondria and provided a proof-of-concept study on simultaneous dual opto-electrochemical monitoring of bioenergetic activities: electroanalysis of oxygen consumption on a small population (~ 5 × 10 4 ) and fluorescence microscopy to follow up endogenous NADH at single organelle level. The current work will open new opportunities in line with sensitive multi-parametric measurements of mitochondrial redox status in diverse physio-pathologic dysfunctions, including the Warburg effect in cancer cells, myopathies or degenerative processes of neurons.

Figure 1 -

 1 Figure 1-(a) left: Scheme of the OptoElecWell array for the monitoring of single mitochondria activities, right: Cross-sectional SEM view of a typical microwell; (b) SEM images of the SiO2based microwell array (mean diameter: 6 μm; mean depth: 5.2 μm) with integrated platinumring nanoelectrodes (thickness: ~ 200 nm) on a glass substrate.

Figure 2 -

 2 Figure 2-Electrochemical characterizations by cyclic voltammetry of the nanoelectrode array in the OptoElecWell microdevice: (a) voltammogram (100 mV.s -1 ) recorded in a 0.1 M H2SO4 solution; (b) voltammogram (10 mV.s -1 ) recorded in a 3 mM Ru(NH3)6Cl3 solution in PBS buffer (pH 7.4, 10 mM); (c) voltammograms (10 mV.s -1 ) recorded in PBS buffer (pH 7.4, 10 mM) containing different oxygen concentrations. An Ag/AgCl reference electrode and a platinum wire used as a counter electrode were both placed in the bulk solution on top of the chip (2 mL volume).

Figure 3 -

 3 Figure 3-(a) Schematic representation of the entrapment of single mitochondria within the OptoElecWell device starting from a suspension of mitochondria in buffer and following several washing steps; (b) Optical imaging of the evolution of mitochondria presence in microwells before and after washings with the buffer solution. Observations are based on mitochondrial NADH fluorescence microscopy (40× objective). Red circles show up some entrapped single mitochondria, displaying different sizes and intensities.

Figure 4 -

 4 Figure 4-(a) Fluorescence monitoring along the bioenergetic modulations of NADH fluorescence in individual mitochondria entrapped within OptoElecWell. Each point is a mean response (n=75 mitochondria from 3 different experiments) ± SEM at each time of measurement. All values were normalized versus their initial one at resting stage (no activation or inhibition). (b) Comparison of the mean values at each stage, i.e. following injections of 1% ethanol, 2 mM ADP and 1.8 µM antimycin A solutions, respectively.

Figure 5 -

 5 Figure 5-(a) Electrochemical monitoring of the oxygen consumption by mitochondria present in the OptoElecWell device under different stages (Scan rate: 4 V.s -1 , delay of 10 sec. between scans). (b) Evolution of the reduction current at -0.8 V vs Ag/AgCl measured on each scan, following additions of bioenergetic substrates (Ethanol, ADP) and an inhibitor (antimycin A).

From a spectrophotometric

  perspective, OptoElecWell platforms allow to evaluate and accurately compare mean mitochondrial NADH intensities, but it is likewise possible trace single mitochondria variations and point out specific mitochondrial populations exhibiting different activities and trends compared to the mean values. Figure S1 presents the histograms of value distributions in the four bioenergetics stages (Stage 1 to 4) and scatter plots describing NADH intensity increments/decrements in energized, phosphorylating and respiratory inhibited stages when compared to the previous stage. From figure S1a, we observe that individual values are showing general trends (higher or lower intensities) in line with the mean values (Figure

Acknowledgments

This project was financially supported by the CNRS ("Centre National de la Recherche Scientifique ": UPR 8001, UMR 5255 and UMR 5095), University of Bordeaux, INP Bordeaux, and the ANR ("Agence Nationale pour la Recherche", project NANOMITO n°ANR2011BSV502501). The technological realisations and associated research works were partly supported by the French RENATECH network. Bertrand Goudeau and Nicole Avéret are acknowledged for help during yeast culture and mitochondria preparations.

Declaration of conflict of interest

The authors declare no conflict of interest.