domain: spi phys

text:
Christophe Inguimbert
email: christophe.inguimbert@onera.fr

Thierry Nuns
email: thierry.nuns@onera.fr

Kevin Lemiere

Dominique Hervé
email: dominique.herve@sodern.fr

Aurélien Vriet
email: aurelien.vriet@sodern.fr

Juan Barbero

Juan Moreno

Alexandru Nedelcu
email: alexandru.nedelcu@lynred.com

Samuel Ducret
email: samuel.ducret@lynred.com

Op- Toelectronic

Optoelectronic sensors degradation induced by the radiations of the space environment

Keywords: Radiation effects, image sensors, photodiodes, space

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

INTODUCTION

Space system undergo particularly hard natural radiation environment, but can also potentially be subject to the radiations injected in low earth orbit by the explosion of nuclear weapons. Those radiations can cause errors in electronic systems that creates special design challenges. Preventing and reducing the susceptibility to radiation damage is thus an important issue for all space and military applications. Electronic devices are particularly sensitive to radiation effects and in order to ensure the proper operation of such systems, it is indispensable to develop a better knowledge and modeling of the radiative impacts on embedded electronic devices.

Optoelectronic components find more and more applications in space systems (imaging, attitude control, optical links for secure data transfer or electric isolation…) and involve ever new materials and alloys that have a potential interests in term of technical characteristics but may have critical degradations under a radiation constraint. The sensitivity of the new materials is not well understood and need specific studies.

ONERA in collaboration with some other partners (SODERN, ALTER tech., SOFRADIR) has addressed this issue in the framework of a contract with the European Defense Agency (EDA) and this paper presents its main outcomes.

The study focusses on the effect of the atomic displacement produced after the high energy space and neutron particles irradiation. It concerns both the mean degradation that can be observed in single devices like photodiodes and LEDs, and the dispersion of the degradation that appears on detector arrays due to statistical effects. It proposes both experimental evaluation of detectors and LED in different III-V materials, the evaluation of imagers for star tracking applications and the modeling of the dark signal non uniformity (DSNU). The irradiation of the samples under high energy protons, neutrons, gamma rays and electrons were performed on different European facilities.

For the study of the mean degradation, some photodiodes and LED for application in the infrared, visible and UV domains have been irradiated and tested. Different materials used for each application are tested. The measurements concern the dark current increase for the detectors and the optical output power for the emitters. Some silicon image sensors operating in the visible domain have also been tested. The distribution of dark current is compared to simulation results obtained with a Monte Carlo method that have been developed.

RADIATION ISSUE

Energetic particles of the space environment interact with the atoms and the electrons of the irradiated material, inducing different degradation mechanisms.

Due to their functioning mode, optoelectronic devices are particularly sensitive to the defects enhancement induced by the impinging particles. The atoms of the semiconductor lattice can be displaced thanks to nuclear interactions (coulombian, nuclear inelastic) [START_REF] Srour | Review of displacement damage effects in silicon devices[END_REF], [START_REF] Srour | A framework for understanding displacement damage mechanisms in irradiated silicon devices[END_REF]. These atomic displacements (interstitial/vacancy pairs) can become electrically active defects. These defects change the electrical properties of the semiconductor material (mobility of the carriers, recombination rate,…) and affect the functioning of the devices [START_REF] Srour | Review of displacement damage effects in silicon devices[END_REF] [START_REF] Srour | A framework for understanding displacement damage mechanisms in irradiated silicon devices[END_REF]. Optoelectronic components, due to their specific functioning mode, are particularly sensitive to such kind of degradation mechanism [2] [3]. The defects produced consecutively to an irradiation can contribute to enhance the carriers' thermal generation rate of the depleted regions of a photodiode. The consequence is an increase of leakage current (dark current) of the p/n junctions. These phenomena contribute to increase the dark current of image sensors (CCD: Charge Coupled Device, CIS: CMOS Image Sensor) It can bring some devices, used in systems requiring a good signal-tonoise ratio, out of the specifications. Star tracking systems is a typical example. For such kind of devices evaluating the number of highly degraded pixels, hot pixels presenting a high dark current value, is of particular interest. This work addresses this issue thanks to the development of a dedicated code capable to predict the dark current distribution of irradiated imagers. The current radiation qualification procedure, in order to limit the irradiation tests, often relies on the use of such type of predicting codes. It also relies on the use of simple theoretical damage factors that allow the estimation of the degradation thanks to extrapolation procedures. Theroretical damage factors allow to predict the degradation for a given particle at a given energy when the measurements have been performed with different conditions. It allows predictions for a mission by combining the complex environment involving different species on a large energy range. In this area, the Displacement Damage Dose (DDD) [START_REF] Inguimbert | Equivalent Displacement Damage Dose for On-Orbit Space Applications[END_REF] is used to define the radiation level in the risk assessment procedure. The DDD is a function proportional to the incident fluence of radiations (φ), and to the capability of the incident particles to produce defects in the target material (NIEL: Non Ionizing Energy Loss) [START_REF] Messenger | Non ionizing energy loss (NIEL) for heavy ions[END_REF] [START_REF] Boschini | Nuclear and nonionizing energy-loss for Coulomb scattered particle from low energy up to relativistic regime in space radiation environment[END_REF]. The NIEL is a very useful metric, commonly employed within the radiative risk assessment procedures. It is a theoretical parameter that provides the number of atomic displacements that may be produced by unit of path length of a single incident particle. The International radiation test standards (ASTM [START_REF]Standard Practice for Characterizing Neutron Spectra in Terms of an Equivalent Monoenergetic Neutron Fluence for Radiation-Hardness Testing of Electronics[END_REF], ECSS [START_REF]European cooperation for space standardization[END_REF]) are based on the use of the NIEL parameter. The test procedures assume the proportionality between the number of atomic displacements, the number of defects and thus the measured degradation level consecutively to an irradiation. This approach usually works. Most of the measured degradations are demonstrated to scale linearly with the NIEL [START_REF] Srour | Review of displacement damage effects in silicon devices[END_REF] [3] [START_REF] Arnolda | NIEL scaling: comparison with measured defect introduction rate in silicon[END_REF], but some deviations to the so called "NIEL scaling approach" are observed from time to time [START_REF] Arnolda | NIEL scaling: comparison with measured defect introduction rate in silicon[END_REF] [13] [START_REF] Messenger | NIEL and damage correlations for high-energy protons in gallium arsenide devices[END_REF].These deviations limit the accuracy of the degradation prediction methods. This is a concern for the space applications and some improvements are required in this field. The work presented here addresses this issue. The goal here is to investigate both the sensitivity to radiations of various semiconductor materials operating indifferent wavelength domain and the reliability of the so called NIEL scaling approach. This study relies mostly on an important set of experimental measurements. Some specific damage calculations have also been performed for comparison.

EXPERIMENTAL SETUP

Different photodiodes and LEDs fabricated by different manufacturers, based on different materials, thus operating in different wavelength domains have been irradiated with particles representative of the radiative space environment. Electrons from 0.5 MeV up to 20 MeV, protons of 60 MeV, 100 MeV and 170 MeV and an atmospheric-like neutrons spectrum have been employed. Depending on the type of incident particles and energy, various deposited damage dose have been deposited (~5×10 +06 MeV/g up to 5×10

+09 MeV/g). Different damage factors have been extracted from those measurements and compared to the corresponding NIELs.

Three different silicon based image sensors have also been irradiated with protons of 60 MeV, 100 MeV and 170 MeV and an atmospheric-like neutrons spectrum.

The devices have been irradiated with protons, electrons, neutrons and gammas thanks to five different facilities. Two electron energies (0.5 and 1.5 MeV) were performed with the Van de Graaff accelerator GEODUR of ONERA (Toulouse, France) [START_REF] Geodur | Chambre d'irradiation électrons multi applications (matériaux, composants[END_REF]. Three higher electron energies (6 MeV, 12 MeV and 20 MeV) were performed at the RADEF facility (LINAC of the University of Jyväskylä, Finland) [START_REF]Jyväskylän Yliopisto[END_REF]. Some devices have also been irradiated with protons of 60 MeV, 100 MeV, and 170 MeV, as well as with the atmospheric-like neutron spectrum of The Svedberg Laboratory (TSL, Uppsala, Sweden) facility [START_REF]The ANITA neutron beam facility[END_REF]. The PAULA facility (Proton fAcility in UppsaLA). Provides a Quasi-Monoenergetic proton beam while the the ANITA facility (Atmospheric-like Neutrons from thIck Target) provides a continuous spectrum ranging from thermal energies up to ~ 180 MeV. The devices have also been irradiated at the CAN-RADLAB laboratory (National Centre for Accelerators [18]), using a Cobalt-60 source placed into a Gammabeam® X200 irradiator. Those gamma photon irradiations have demonstrated that all these devices have a low sensitivity to ionizing dose effects. All the measured degradations are dominated by the DDD allowing us to interpret confidentially the data in term of displacement damage effects.

LEDs

Both the change in the optical power and a drift on the I(V) forward characteristics of LEDs have been studied. The emission spectrum has also been recorded. The I(V) characteristics of most of the LEDs do not depict any significant change, neither the emission spectrum. But the output power is demonstrated to decrease after the irradiations. The relative decrease depends on the material. The Figure 1Figure 2, Figure 3Figure 4Figure 5 shows the relative output power as a function of the displacement damage dose for the 5 LEDs after protons and electron irradiations. The InGaAsP LEDs are the most sensitive to DDD, and one can see that the output power is very low after irradiation (less than 20% after a DDD of 8 MeV/g). The less sensitive are the AlGaN devices (20% of power loss after almost a DDD of 10 MeV/g). The results do not seem to be bias dependent. The only material type that may exhibit a bias effect is the InGaAlP. Finally, some discrepancies may appear when comparing the different types of irradiation (for example the different proton energies). This may be due a lack of accuracy of the NIEL evaluation. Large gap materials photodiodes (InGaN, GaP) are demonstrated to be less sensitive to radiation than small gap materials (InGaAs-1.1 eV) due to the fact that, at a given temperature, the defects in a larger gap are less effective to produce a thermal current. The analysis has been focused on InGaAs photodiodes that present the most important susceptibility to radiation damage. Lynred [START_REF]Lynred[END_REF] provided specific test vehicles of the SNAKE device which is an array of InGaAs PIN photodiodes operating in the 0.9 µm to 1.7 µm wavelength domain. The test vehicles consist in a set of circular single photodiodes of different dimensions (diameter in the range [4 µm, 300 µm]) and 10x10 sub-arrays of pixels with various pitches (in the range [10 µm, 30 µm]) where all pixels are addressed in parallel. A maximum of six different elements, selected within the available topologies of the test vehicles, have been measured during this study. Two other InGaAs devices have been tested. The PIN photodiode C30618 from Excelitas [START_REF]EXCELITAS: Excelitas technologies[END_REF] and the FCIQ1000 from OSI [21]. Excelitas device is designed for use in OEM fiberoptics communications systems and high-speed receiver applications. It operates between 1000 nm and 1600 nm and has a diameter of 350 µm and a maximum intrinsic dark current of typical ~1 nA with an applied voltage of -10 V (the bias at which the current measurements were made). The OSI photodiode is segmented into four quadrants with a full active area of 1000 µm diameter and presents a 0.5 nA dark current at -5 V (bias for the current measurements). The Q1000 photodiode is optimized for a good responsivity from 1100 nm to 1620 nm.

The increase in dark current of photodiodes is proportional to the number of defects N t :

Eq. 1 ( ) Φ ∆ ⋅ ⋅ ⋅ = ∆ t th i Dark N v n J σ
Where φ is the incident fluence, σ, the capture cross section of the defect, n i the density of intrinsic carrier and v th the thermal velocity of the carriers. The amount of produced defects after irradiation is equal to ∆N t . It is proportional to the NIEL and to the incident fluence (DDD).

Figure Comparison of experimental InGaAs damage factors measured in OSI, Excelitas, and

Lynred devices with theoretical NIEL.

The voltage applied on the photodiodes have been chosen in order to be in a full depleted regime dominated by this generation current. No significant differences have been observed between the devices that have been biased and shorted during the irradiations. The electrical measurements and the irradiations have been performed at ambient First of all, one can notice the overall good agreement between the NIEL and the experimental data. The so called "NIEL scaling" approach is validated at first order for InGaAs material. For protons the energy dependence of the experimental damage factors is consistent with the NIEL. For electrons the dependence with the NIEL is not linear but "quadratic". i. e., the damage factor is not an affine function of the NIEL, but a power of two function. This behavior is identical for all the tested references. It is in line with previous observations made for Si and GaAs [START_REF] Srour | Review of displacement damage effects in silicon devices[END_REF] [14] [START_REF] El Allam | Gamma and Electron NIEL Dependence of Irradiated GaAs[END_REF]. The discrepancy observed between experimental damage factors for electrons does not necessarily deal with electrons. It is the consequence to the normalization that has been performed relatively to proton data. At this stage, a discrepancy can be noticed, but it is not possible to attribute this effect to a particle type or another. The underestimation of the degradation by the neutron NIEL could be attributed to the uncertainty in the definition of the equivalent damage fluence of the ANITA neutron spectrum. The equivalent damage energy of 2.07 MeV, calculated for InGaAs is subject to the accuracy of both the definition of the ANITA spectrum, and of the neutron InGaAs NIEL.

Image sensors

The radiation damage processes are stochastics and after studying the average degradation levels on isolated photodiodes, it is interesting to study the effect of inhomogeneities on the large set of photodiodes that represents a CMOS imager. Indeed, the damage level produced by an irradiation is not homogeneous through the array, but on the contrary, fluctuates from a pixel to another. The induced global damage distribution over the pixels array (DCNU) is the combination of the damage produced by individual interactions, that change from an interaction to another, "weighted" by the probability distributions (Poisson, normal) governing the statistic of the number of interactions per pixel. Some defective pixels that significantly deviate from the average behaviour of their neighbours can be troublesome for some applications such as star trackers. The presence of highly degraded pixels, presenting high dark current values reduce the performances of the attitude control systems where Image sensors can be used. These bright pixels, known as "hot" pixels, can be mixed with real stars, and slow down the stabilization of the satellite attitude. The image sensors are nowadays becoming more popular thanks to their low-power, low-cost and high-integration capabilities. They are currently used in machine vision, security and surveillance, scientific imaging and of course in aerospace and defense applications where imaging sensors can face harsh radiative environments. Protons are particularly troublesome because, unlike electrons, they initiate nuclear reactions that produce high energy recoil nuclei (>1 MeV), able to generate high degradation levels of pixel properties.

DCNU calculation

Hence, the dispersion of the degradation from a pixel to another cannot be determined thanks to the NIEL which provides only an average degradation. Actually, a different number of interactions is produced in each pixel, and each interaction produces a different number of defects, resulting in a wide range of damage from a pixel to another. This random process which is governed by a Poissonian law can be modelled thanks to a Monte Carlo method. This issue has been addressed thanks to the development of a Monte Carlo code based on the GEANT4 library [START_REF] Agostinelli | Geant 4 a simulation toolkit[END_REF]. GEANT 4 is a C++ toolkit assembled by an international collaboration, for describing radiation interaction with matter [START_REF] Agostinelli | Geant 4 a simulation toolkit[END_REF]. It allows the transport of radiation in complex 3D geometries and handling physical parameters such as ionizing and non-ionizing deposited energy. An application, based on that library, has been developed in order to calculate the damage produced in the depleted regions of a defined array of pixels, where the thermal generation should occur. The method is based on the following algorithm. The principle of the calculation is as follow. For each type of interactions the contribution to the DCNU is evaluated according to a similar process. For a given pixel and a given type of interaction:

1-The number of interaction is randomly selected in a Poison's law, 2-The damage produced by each interaction is calculated, 3-The total damage is the sum of the degradation induced by individual interactions, 4-The corresponding dark current is calculated assuming that it is proportional to the number of atomic displacements.

For the 3 types of interactions able to produce damage (Coulombian nuclear elastic and nuclear inelastic), the damage produced by individual interactions is calculated with different methods. The coulombian contribution is evaluated thanks to known analytical expressions [START_REF] Inguimbert | NEMO: A code to compute NIEL of protons, neutrons, electrons, and heavy ions[END_REF]. On a contrary, a dedicated GEANT4 application toolkit has been developed for both nuclear elastic and inelastic interactions. The nuclear elastic and inelastic interactions are able to produce recoil nuclei of several MeV that can travel several tens of micrometers in silicon. These recoil nuclei can come from neighbours' pixels or surrounding dead zones and degrade the studied depleted region. The GEANT4 application allows taking into account these border effects. A database of damaged pixels is fulfilled which then is used to compute the dark current distribution during a second phase. The final DCNU is calculated during this second phase by combining the degradation induced by several different interactions randomly selected in the databank or calculated analytically for Coulombian interactions. A full description of this method can be found in refs. [24][26].

The dark current distributions have been calculated for the different tested devices.

Tested devices

Three

Results

Some comparisons of simulated DCNU with experimental measurements are shown in the following figures.

The Figure 8 shows simulation results of the DCNU of Python device for various applied fluences. We can see the evolution of the DCNU. The number of highly degraded pixels increases at the expense of the number of weakly degraded pixels. The maximum of the distribution is shifted towards higher values with increasing fluences. These observations are similar for the two different energies (58.5 MeV, and 169.5 MeV).

On Figure 9Figure 10 the comparisons with experimental measurements are quite satisfactory for protons of 58.5 MeV and 169.5 MeV. The agreement between simulations and experiment is clearly worse for neutrons. As can be seen in Figure 11 Figure 12 for HAS2 and CMV2000 devices the simulations underestimate significantly the degradation. In addition the experimental DCNU shows a significantly smoother behavior. The simulations predict a significant number of not degraded pixels while the experimental data show a continuous number of degraded pixels down to very low damage levels. The damage level induced by neutrons is quite low and is still strongly driven by the initial dark current level before irradiations. The simulations do not take this into account and only show a dark current increase DCNU. Within the simulations, the pixels are taken blanked of defects, that is clearly not the case in the reality. To improve our prediction the initial dark current distribution shall be included in the calculation.

CONCLUSION

The study has highlighted different phenomenon. First of all, the NIEL scaling approach commonly used in the space community has been validated for InGaAs material. Contrary to other materials operating in UV and visible domain, this low gap material operating in the infrared domain is shown to present a quite strong sensitivity to radiations. Similar deviations observed for Si and GaAs are observed for incident electrons.

dependence of measured degradation level to NIEL seems to be quadratic.

The comparisons of DCNU Monte Carlo simulations with experimental measurements performed on three different image sensors irradiated with various conditions including protons and neutrons are variabler. The agreement is quite good for protons and some significant deviations are observed for neutrons that are attributed to the fact that initial dark current distributions are neglected in the simulations. To improve our DCNU different mechanisms shall be added:

-Electric field enhancement of generation centers, -Recombination effects, -Taking into account the nature of produced defects.

Finally the Monte Carlo simulations have allowed us to interpret some deviations of the NIEL scaling method as a statistical artefact induced by the stochastic nature of the deposition of the displacement damage dose [START_REF] Inguimbert | About the scatter of displacement damage and its consequence on the NIEL scaling approach[END_REF]. This new understanding of the damaging processes is an important outcome of the combined analysis of the DCNU and the III-V semiconductor degradation proposed in this work. However, the full validation of this new interpretation of some data shall be performed thanks to specific irradiation tests, complementary to, that have been performed during this study.

Figure 1 :

 1 Figure 1: Relative output power loss of the SETI LED after irradiation as a function of the DDD.

Figure 2 :

 2 Figure 2: Relative output power loss of the Roithner 370 LED after irradiation as a function of the DDD.

Figure 3 :

 3 Figure 3: Relative output power loss of the Kyosemi 370 LED after irradiation as a function of the DDD.

Figure 4 :

 4 Figure 4: Relative output power loss of the Vishay 370 LED after irradiation as a function of the DDD.

Figure 5 :

 5 Figure 5: Relative output power loss of the Roithner1550 LED after irradiation as a function of the DDD.

  at 10 mA (V) DDD (MeV/g) LED 6 ROITHNER-LED1550-03 e-6 MeV OFF e-20 MeV OFF p+ 60 MeV ON p+ 60 MeV OFF p+ 100 MeV ON p+ 100 MeV OFF p+ 170 MeV ON p+ 170 MeV OFF temperature (~24°C). The measurements have been performed at different annealing times. Two months storage at room temperature shows negligible impact of the annealing processes (~10% recovery). All tested photodiodes have depicted a linear dark current variation as a function of the incident fluence allowing a reliable extraction of damage factors. These damage factors have been plotted as a function of the energy of the incident particles and compared to the theoretical NIEL.

Figure 7 :

 7 Figure 7: Algorithm of the Monte Carlo appraoch.

Figure 8 :

 8 Figure 8: DCNU calculated with the Monte Carlo method for the various test conditions of the Python device (54.9 MeV and 169.5 MeV protons with various fluences).

Figure 9 :

 9 Figure 9: Comparison of calculated DCNU with the measurements for the Python device irradiated with 169.5 and 58.5 MeV protons at a fluence of 4.96 × 10 +11 p/cm².

Figure 10 :

 10 Figure 10: Comparison of calculated DCNU with the measurements for the Python device irradiated with 58.5 MeV protons at a fluence of 10 +11 p/cm².

Figure 11 :

 11 Figure 11: Comparison of calculated DCNU with the measurements for the HAS2 device irradiated with Anita spectrum and 14 MeV neutrons.

Figure 12 :

 12 Figure 12: Comparison of calculated DCNU with the measurements for the HAS2 device irradiated with Anita spectrum and 14 MeV neutrons.

Table 1 :

 1 List of tested devices (LEDs and photodiodes).

	Manufacturer	Reference	material	Lambda (nm)
		LEDs
	SETI	UVTOP255-FWTO18	AlGaN	255
	Vishay	VLMK33R1S2	AlInGaP	617
	Roithner	LED1550-35 UVled370-110e	InGaAsP AlGaN	1550 375
	Kyosemi	kede 1552	InGaAsP	1550
		Photodiodes
	Roithner Kyosemi	GUVV-T10GD KPDU37H1	InGaN	400
	Jenoptik Hamamatsu	EPD-525 G1963	GaP	200-550
	Excelitas	C30618
	OSI	FCI Q1000	InGaAs	1000-1700
	Sofradir	Snake

Table 2 :

 2 List of tested image sensors.

	Manufacturer	reference	Pixel pitch
	CMV2000	CMOSIS	5.5 µm
	HAS2 Python	On Semi.	18 µm 4.8 µm
	4. RESULTS

Table 3 :

 3 Image sensors test conditions (in grey) with the different applied fluences in particles/cm²

			Neutron	Proton	Proton
		14 MeV	Anita	60 MeV	170 MeV
	HAS2	2.4 10 +10	5.23 10 +09
	Python			1. 10 +11 3. 10 +10	4.96 10 +11 1.66 10 +11
				1. 10 +10	4.96 10 +10
					1.66 10 +10
	CMV2k 2.4 10 +10	5.23 10 +10		4.96 10 +11 1.66 10 +11
					4.96 10 +10 1.66 10 +10


text:
Quentin Berthomé
email: q.berthome@teemphotonics.com

Arnaud Grisard

Basile Faure

Grégoire Souhaité

Eric Lallier

Antoine Godard

Vadim Smirnov

Ruslan Vasilyeu

Actively Q-switched Tunable Narrowband 2 µm Tm:YAP Laser Using a Transversally Chirped Volume Bragg Grating

A pulsed, narrow-linewidth, wavelength-tunable Tm:YAP laser was realized. 1 kHz stable operation with 200 µJ, 50 ns pulses is reported. Spectrum was narrowed to 0.2 nm and tuned from 1940 to 1960 nm with a transversally chirped volume Bragg grating.

Context

Tunable long wave infrared coherent sources emitting between 8 and 12 µm can be used for versatile remote gas sensing thanks to atmospheric transmission at these wavelengths. In ref [START_REF] Clément | Longwave infrared, single-frequency, tunable, pulsed optical parametric oscillator based on orientation-patterned GaAs for gas sensing[END_REF] we implemented a parametric oscillator, pumped by a 2 µm microlaser, in which wavelength tunability was achieved by temperature tuning of the crystal indices of refraction. It is well known that a similar wavelength range can be addressed by tuning the pump wavelength rather than the crystal temperature [2]. In addition, pump tuning can be significantly faster than temperature tuning. In this context, to control the spectral output of the optical parametric oscillator (OPO), which is based on Vernier spectral filtering in doubly resonant cavities [START_REF] Clément | Longwave infrared, single-frequency, tunable, pulsed optical parametric oscillator based on orientation-patterned GaAs for gas sensing[END_REF], we also need a single longitudinal mode (SLM) pumping laser. Pulsed, tunable 2 µm sources laser have been developed [START_REF] Jin | Wideband tunable graphene-based passively Q-switched Tm:YAP laser[END_REF][START_REF] Jelínková | Diode pumped Tm:YAP laser for eye microsurgery[END_REF] but they require complex alignment or do not have narrow line-width emission and they exhibit relatively long pulses. For this purpose we fisrt built a CW 2 µm Tm:YAP laser with a transversally chirped volume Bragg grating (TC-VBG) to narrow the spectrum and perform wavelength tuning [5]. To pump the OPO, ~250 µJ pulses of 10-100 ns are targeted. We first implemented passive Q-switching laser with a Cr:ZnSe saturable absorber [START_REF] Grisard | High energy single frequency passively Q-switched 2-micron microlaser in thuliumdoped yttrium aluminium perovskite[END_REF]. In this work, we investigate active Q-switching with an acousto-optic modulator (AOM) to have a better control of the repetition rate and to increase it to a few kilohertz while preserving the single longitudinal mode behaviour.

Experimental set-up

We designed a Tm:YAP laser with a TC-VBG as output coupler and an AOM to perform active Q-switching of the laser with repetition rates of a few kilohertz. The cavity, shown in Fig. 1, is composed of a highly reflective dielectric concave mirror around 2 µm with a radius of curvature of 50 mm, a 4 mm long 4 %at. thulium-doped acut YAP rod, an AOM working at 2 µm, and a TC-VBG provided by OptiGrate with 1940 nm to 1960 nm tuning range, 85 % reflectivity and 0.8 nm bandwidth (FWHM). The AOM is used to switch the cavity losses and achieve pulsed operation. The cavity is about 50 mm long and is pumped with a continuous-wave 9 W laser diode emitting near 790 nm, which results in 7 W incident on the laser crystal due to Fresnel losses of VBG facets at 790 nm. The pumping laser is focused in the laser medium through the TC-VBG with a lens doublet to get a beam radius of 100 µm (1/e 2 ). A dichroic mirror is used to separate the 2 µm laser output from the input pump beam.

Results

With this cavity, we obtained 28 % slope efficiency with a 4 W pump threshold in continuous wave operation, see Fig 2 . In pulsed operation the threshold was around 3.5 W and the slope efficiency was 31.6 % at a pulse repetition rate of 1 kHz. Repetition rates of 0.5, 1 and 2 kHz have been tested. Configurations with up to 200 µJ with 50 ns duration have been found. No stable operation with pulse energy higher than 200 µJ has been found.

Fig. 2. Average output power at different repetition rates

The laser has a 20 nm tuning range between 1.94 µm and 1.96 µm when translating the volume Bragg grating, see Fig. 3. (a). The TC-VBG narrows the spectrum down to 0.2 nm with a continuous output power of 1 W obtained with 7 W incident pump power on the laser rod, Fig. 3.(b). The spectrum is slightly larger when the laser is pulsed but does not exceed 0.25 nm. Spectra shown in Fig. 3 were obtained with a slightly different but equivalent cavity.

Comments

Stable pulsed operation is obtained close from the desired levels of 250 µJ and higher pulse energies were reached although they remained instable. As no damages were observed on the optics of the cavity and the laser stability recovered when the pumping power was lowered, thermal lensing effects are suspected to be the source of the instabilities at high energy operation. As expected, temperature rising was mainly observed in the laser rod. To reduce the impact of the thermal lens on the laser, the length of the cavity will be reduced to its minimum. In addition to this, better thermal management of the Tm:YAP rod will be implemented to make its thermal behaviour more repeatable and to help reduce high energy operation instabilities. Efforts will also be made to reduce residual wavelength drift of the pumping diode. Pulse duration and average output power are satisfying.

Although the output spectrum exhibits a good spectral purity, it still represents a few longitudinal modes of the cavity which is not a favourable to control the output spectral purity of the OPO. In order to reduce the output spectrum to a single longitudinal mode, filtering elements could be inserted in the cavity.

Conclusion

We demonstrated, for the first time to our knowledge, efficient actively Q-switched diode pumped Tm:YAP laser tunable from 1.94 µm to 1.96 µm with narrow spectrum, using a transversally chirped volume Bragg grating as output coupler and an acousto-optic modulator. The tunability on a 20 nm range obtained on the 2 µm laser translates to a 500 nm range tunability near 10 µm after down conversion which is enough to record some significant absorption spectra for gas sensing [START_REF] Clément | Longwave infrared, single-frequency, tunable, pulsed optical parametric oscillator based on orientation-patterned GaAs for gas sensing[END_REF]. Moreover, using different TC-VBG it is possible to access any wavelength in the gain band of the Tm:YAP. Efforts will be made to stabilize its behaviour at high pulse energy and a Fabry-Perot etalon will be added to the cavity to filter the remaining modes and achieve SLM operation.

Fig. 1 .

 1 Fig. 1. Laser cavity

Fig. 3 .

 3 Fig. 3. (a) Output spectra for several longitudinal position of the TC-VBG (b) Output spectrum at maximum continuous output power for one longitudinal position of the TC-VBG


Onur Tasar
email: onur.tasar@inria.fr

S L Happy

Yuliya Tarabalka

Pierre Alliez

SEMI2I: SEMANTICALLY CONSISTENT IMAGE-TO-IMAGE TRANSLATION FOR DOMAIN ADAPTATION OF REMOTE SENSING DATA

Keywords: Domain adaptation, data augmentation, semantic segmentation, dense labeling, image-to-image translation, generative adversarial networks, GANs

Although convolutional neural networks have been proven to be an effective tool to generate high quality maps from remote sensing images, their performance significantly deteriorates when there exists a large domain shift between training and test data. To address this issue, we propose a new data augmentation approach that transfers the style of test data to training data using generative adversarial networks. Our semantic segmentation framework consists in first training a Unet from the real training data and then fine-tuning it on the test stylized fake training data generated by the proposed approach. Our experimental results prove that our framework outperforms the existing domain adaptation methods.

INTRODUCTION

Over the years, U-net [START_REF] Ronneberger | U-net: Convolutional networks for biomedical image segmentation[END_REF] and its variants [START_REF] Khalel | Multi-task deep learning for satellite image pansharpening and segmentation[END_REF] have become one of the most commonly used network architecture for semantic segmentation problem because of their continuous success in various benchmarks. However, the difference between the data distributions of training and test images caused by atmospheric effects, position of the sun, intra-class variability, etc. makes the U-net likely to fail to generate good maps. To overcome this issue, the traditional data augmentation methods such as gamma correction, random contrast change [START_REF] Tasar | Incremental learning for semantic segmentation of large-scale remote sensing data[END_REF][START_REF] Tasar | Continual learning for dense labeling of satellite images[END_REF], histogram matching [START_REF] Gonzalez | [END_REF], color constancy algorithms [START_REF] Buchsbaum | A spatial processor model for object colour perception[END_REF] are widely used. However, adopting such augmentation techniques is usually insufficient to generalize the model to unseen data, especially when the domain shift between training and test data is highly large.

Better data augmentation strategy would be to generate semantically consistent and test stylized fake training data. To accomplish this task, especially in the field of computer vision, various image-to-image translation (I2I) approaches have already been proposed [START_REF] Zhu | Unpaired image-to-image translation using cycle-consistent adversarial networks[END_REF][START_REF] Liu | Unsupervised image-to-image translation networks[END_REF][START_REF] Huang | Multimodal unsupervised image-to-image translation[END_REF][START_REF] Lee | Diverse image-to-image translation via disentangled representations[END_REF]. The biggest challenge for the style transfer problem is to generate test stylized fake training image that semantically matches the real training image, so that one can use the fake training image and the ground-truth for the real training image to train or to fine-tune a model. The recent I2I approaches [START_REF] Zhu | Unpaired image-to-image translation using cycle-consistent adversarial networks[END_REF][START_REF] Liu | Unsupervised image-to-image translation networks[END_REF][START_REF] Huang | Multimodal unsupervised image-to-image translation[END_REF][START_REF] Lee | Diverse image-to-image translation via disentangled representations[END_REF] fail to keep the real and the fake training images semantically the same. For instance, some of them replace trees by buildings, buildings by roads, etc., whereas some of them create totally artificial objects in the fake training image, which are not available in the real training data. Thus, the fake training images generated by these approaches and the ground-truth for the real training data do not correspond. Hence, training a model on such image and ground-truth pair causes the model to yield a poor performance. Another approach is to adapt the model to test data [START_REF] Tsai | Learning to adapt structured output space for semantic segmentation[END_REF], which is a more difficult task, rather than modifying the data.

To overcome the limitations described above, Tasar et al. have recently proposed ColorMapGAN [START_REF] Tasar | Col-orMapGAN: Unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks[END_REF], which learns to change the color distribution of training data without doing any structural change. Although the method works very well, it has some limitations. Firstly, it does not support style transfer when the images in both domains have different number of channels. Secondly, it learns to map the colors of the source domain linearly, which may not be strong enough in some cases. Finally, since it transforms each color separately, its output is usually slightly noisy. In this work, we propose semantically consistent image-to-image translation (SemI2I) method, which overcomes all the limitations of Col-orMapGAN. By following the same segmentation strategy explained in [START_REF] Tasar | Col-orMapGAN: Unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks[END_REF], we use the fake training images and the original ground-truth to fine-tune the U-net trained on real data.

METHODOLOGY

Let us assume that both domains are denoted by A and B, respectively. The essential goal of SemI2I is to generate fake A with the style of B, and fake B with the style of A. The design of SemI2I is illustrated in Fig. 1. The main components and the stages of SemI2I are as follows:

Style transfer. The crucial components for the style transfer are adaptive instance normalization (adaIN [START_REF] Huang | Arbitrary style transfer in real-time with adaptive instance normalization[END_REF]) and adversarial losses. To generate B stylized fake A and A stylized fake B, we first encode both A and B. In the encoded embeddings, we compute mean µ and standard deviation σ for each feature channel. The embedding itself has the content information, whereas µ and σ carry the style information. We combine the content of A and the style of B, and the content of B and the style of A by using adaIN. After this operation, since we switch the styles of A and B, we obtain fake A by the decoder B and fake B by the decoder A. Moreover, to make B and fake A, and A and fake B visually as similar as possible, we minimize the adversarial losses between them. The combination of an encoder and a decoder forms a generator. For the discriminator, we use the same architecture as in CycleGAN [START_REF] Zhu | Unpaired image-to-image translation using cycle-consistent adversarial networks[END_REF]. As can be verified from Fig. 1, there are 6 generators and 2 discriminators in SemI2I.

Semantic consistency. By following the same strategy described above, we switch the styles of fake A and fake B to obtain A and B (cross reconstruction). In an ideal transformation A and A , and B and B have to be exactly the same. In addition, when we encode A with encoder A and decode the embedding with decoder A, we must reconstruct itself. We call this operation self reconstruction. The self reconstruction also applies to B. Besides, we compute the image gradients A and fake A by sobel filter. We force the difference between the gradients calculated from A and fake A to be as small as possible to help them to be semantically consistent. Again, the same rule applies to B. Finally, we have visually verified that the filters in the first convolution layer of each encoder learns the low level features such as edges. We re-size and concatenate these low level features from each encoder with the input to each deconvolution layer in the corresponding decoder (see gray arrows in Fig. 1). Such concatenation allows the decoder to have a footprint of the objects in the real data; therefore, it guides the decoder to place the right objects in the correct locations.

Training. We now formulate the losses to train SemI2I.

We compute the cross reconstruction loss L cross , which is the sum of L1 norm between A and A and L1 norm between B and B . Similarly, the self reconstruction loss L self is computed by summing L1 norms between A and A , and B and B . By adding L1 norms between the image gradients calculated from A and fake A, and the gradients from B and fake B, we compute the gradient loss L grad . Finally, we use the loss functions in LSGAN [START_REF] Mao | Least squares generative adversarial networks[END_REF] to compute the adversarial losses.

The adversarial loss for the generators L adv G is the sum of the LSGAN generator losses between A and fake B, and B and fake A. Similarly, the adversarial loss for the discriminators L adv D is computed by adding the LSGAN discriminator losses between A and fake B, and B and fake A. The overall generator loss L G that is minimized in the training stage is computed as:

L G = λ 1 L cross + λ 2 L self + λ 3 L grad + λ 4 L adv G , (1)
where λ 1 , λ 2 , λ 3 , and λ 4 are used to adjust the relative importance of each loss. The discriminator loss is defined as :

L D = λ 4 L adv D . (2)
To train SemI2I, we simultaneously minimize L G and L D .

Test. In the test stage, to generate B stylized fake A, we need the encoder A and the decoder B (see the top left generator in Fig. 1 that generates fake A). However, as can be seen in Fig. 1, before feeding the embedding encoded by the encoder A from real A to the decoder B, it needs to be normalized by adaIN using µ B and σ B calculated from the embedding of B. When generating fake A, one may think of randomly sampling a patch from B, encoding it by the encoder B, computing its µ and σ values, and using them to normalize the embedding of A. However, this is not a good idea, because depending on which patch is sampled from B, we may end up generating fake A having a different style in each run. We have exactly the same problem when generating fake B. We want SemI2I to generate exactly the same fake data every time when we test it. To do this, we estimate the global µ and σ values for the embeddings of both domains via Alg. 1 during the training. In Alg. 1, d rate is a parameter ranging between 0 and 1. Note that this parameter needs to be set to a value that is close to 1 (e.g., 0.95), so that the current patches would not change the global mean and standard deviation values too much. When generating fake A, we use µ gB and σ gB . Similarly, we use µ gA and σ gA while generating fake B. The images contain RGB color channels, and their resolution is 1 m. The annotations for building, road, and tree classes have been manually prepared. We perform city-to-city domain adaptation. In the first experiment, we use Bad Ischl as training and Villach as test data. In the second experiment, we switch the training and the test data.

We split the cities into 256×256 patches with an overlap of 32 pixels. In each training iteration, we randomly sample only 1 patch from each domain. We train SemI2I for 25 epochs, where the number of iterations in each epoch is the minimum of the number of patches from each domain. We optimize SemI2I with Adam optimizer. We set the learning rate to 0.001 in the first 15 epochs, and compute it in the rest of the epochs as:

LR = 0.001 × num epochs -epoch no num epochs -decay epoch , (3)
where LR, num epochs, epoch no are the current learning rate, the total number of epochs, and current epoch no. decay epoch stands for the epoch, which is set to 15 in our experiments, where the learning rate is started to be reduced. We set λ 1 , λ 2 , λ 3 and λ 4 in Eqs. 1 and 2 to 10, 10, 10, and 1, respectively. We have found these values empirically. To generate maps, we first train a U-net for 8,000 iterations on the real training data. We then fine-tune it using the fake image patches generated by SemI2I and the original ground-truth for 2,500 iterations. In each training iteration of both the initial training and the fine-tuning steps, we sample a mini-batch of 32 patches. We optimize U-net in both steps via Adam optimizer with the learning rate of 0.0001. Among the I2I approaches, we compare SemI2I with Cy-cleGAN [START_REF] Zhu | Unpaired image-to-image translation using cycle-consistent adversarial networks[END_REF], UNIT [START_REF] Liu | Unsupervised image-to-image translation networks[END_REF], MUNIT [START_REF] Huang | Multimodal unsupervised image-to-image translation[END_REF], DRIT [START_REF] Lee | Diverse image-to-image translation via disentangled representations[END_REF], histogram matching [START_REF] Gonzalez | [END_REF], and gray world algorithm [START_REF] Buchsbaum | A spatial processor model for object colour perception[END_REF]. To make a fair comparison, we replace the fake images used in the finetuning step by the fake images generated by these methods. We also provide the results for the traditional U-net [START_REF] Ronneberger | U-net: Convolutional networks for biomedical image segmentation[END_REF] without doing domain adaptation and AdaptSegNet [START_REF] Tsai | Learning to adapt structured output space for semantic segmentation[END_REF], which aims at adapting the model instead of modifying the data.

Fig. 2 depicts the close-ups from the city pair used in the experiments, test stylized fake training images generated by SemI2I, the ground-truth, and the predictions by U-net and our framework. As can be seen, our method performs the style transfer perfectly, and real and the fake images are semantically consistent. The figure shows that U-net performs extremely poorly, when there is a large shift between the data distributions of training and test images. The improvement of our framework against U-net can clearly be observed. The quantitative results for our segmentation framework and for the other methods are reported in Table 1, which demonstrates the superior performance of our methodology.

In this work, we presented our novel SemI2I approach, which is a new data augmentation method. We compared our segmentation framework with no less than 10 different methods and showed that it exhibits a much better performance than the existing domain adaptation approaches. In the future, we plan to tackle multiple cities-to-multiple cities adaptation problem instead of performing city-to-city adaptation.

Fig. 1 .

 1 Fig. 1. SemI2I. ks and s stand for kernel size and strides. µ and σ are the mean and standard deviation values for each feature channel of the embeddings. Since SemI2I performs I2I, one can consider A as training and B as test data, or vise versa.

Algorithm 1 :

 1 Estimation of the global µ and σ values for the embeddings of both domains. * corresponds to a domain (either A or B). output: global mean µ g * and std. σ g * for the embed. µ g * ← 0, σ g * ← 0 d rate ← 0.95 ; // decay rate foreach training iteration do sample a patch, compute µ * and σ * of its emb. µ g * ← d rate × µ g * + (1d rate) × µ * σ g * ← d rate × σ g * + (1d rate) × σ * end 3. EXPERIMENTS AND CONCLUSION We use Pléiades images collected from Bad Ischl and Villach in Austria, covering 27.71 km 2 and 43.59 km 2 , respectively.

(a)Fig. 2 .

 2 Fig. 2. Training and test data used in both experiments, their ground-truth, test stylized fake training images generated by SemI2I, and the predictions generated by U-net and our framework. Red, white, and green colors represent building, road, and tree classes, respectively.

Table 1 .

 1 IoU scores.

			Method	Training: Bad Ischl, Test: Villach building road tree Overall building road Training: Villach, Test: Bad Ischl tree Overall
			U-net	23.61	0.91 40.53	21.68	5.84	0.24	0.50	2.19
		AdaptSegNet Single[11]	6.01	4.37 10.43	6.94	3.06	2.71 10.23	5.33
		AdaptSegNet Multi[11]	24.59	9.02 56.08	29.86	14.26	4.46 24.66	14.46
	Our proposed	framework with	CycleGAN[7] UNIT[8] MUNIT[9] DRIT[10] Gray world[6] Histogram matching[5]	43.03 30.86 0.02 0.01 25.19 24.95	28.96 68.86 15.84 63.00 1.38 47.23 3.96 8.72 26.43 56.15 29.34 61.59	46.95 36.57 16.21 4.23 35.92 38.63	43.62 19.29 6.20 0.00 29.55 6.45	38.69 71.68 36.83 35.57 0.13 0.05 10.19 0.01 24.80 46.41 0.92 1.28	51.33 30.56 2.13 3.40 33.58 2.88
			ColorMapGAN[12]	48.47	37.82 58.92	48.40	49.16	41.75 59.84	50.25
			SemI2I (ours)	47.79	38.22 68.76	51.59	53.38	47.59 79.17	60.05


Didier Pujalte

Sylvie # Claeysen

Samuel Dietz

Jeannie Chapal

Dominique Hillaire-Buys

Pierre Petit

Inhibition of glucose-induced insulin secretion by a peripheral-type benzodiazepine receptor ligand (PK 11195)

Keywords: Peripheral benzodiazepine receptors, Insulin secretion, PK 11195, Rat isolated pancreas

We have recently shown that benzodiazepines with high affinity for peripheral-type receptors such as 4'-chlordiazepam inhibit insulin secretion in vitro. PK 11195 [1-(2-chlorophenyl)-Nmethyl-N-(1-methylpropyl)-3-isoquinoline-carboxamide], a potent and selective ligand for peripheral benzodiazepine binding sites, was also shown to inhibit insulin release from rat pancreatic islets. Both substances have been reported to interact with mitochondrial binding sites. Hence, the present study was performed to investigate the effects of PK 11195 on insulin secretion induced by either a metabolic or a non-metabolic stimulus. In the rat isolated pancreas perfused at a constant pressure with a Krebs-bicarbonate buffer containing a slightly stimulating glucose concentration (8.3 mM), PK 11195 (10 -7 -10 -5 M) induced a progressive and concentration-dependent decrease in insulin secretion. Simultaneously, we recorded the effects on the pancreatic flow rate; in contrast to 4'-chlordiazepam, previously shown to induce vasodilation in the same preparation, PK11195 was ineffective. The differential effects of these two substances on vascular resistance and insulin secretion may suggest the existence of different subtypes of peripheral benzodiazepine receptors on pancreatic b-cells and vessels. A metabolic stimulation of insulin secretion was induced by a glucose increment from 4.2 mM to 8.4 mM or by 2 mM a-ketoisocaproic acid (KIC), which is directly metabolized in the mitochondria; these stimulations could be reduced by 10 -5 M PK 11195 (P<0.05). In contrast, the drug was ineffective on the insulin secretion induced by 5 mM or 10 mM KCl in the presence of a nonstimulating glucose concentration (4.2 mM). These results suggest that PK

Introduction

In addition to interacting with central-type benzodiazepine receptors that mediate anxiolytic, anticonvulsant and sedative effects, benzodiazepines bind to peripheraltype receptors that are distributed both in the brain and in a variety of peripheral tissues (Anholt et al. 1985;[START_REF] Verma | Peripheral type benzodiazepine receptors[END_REF]. The physiological role of the peripheral benzodiazepine receptors (PBRs) is not yet clearly elucidated [START_REF] Verma | Peripheral type benzodiazepine receptors[END_REF]. These receptors, often associated with mitochondrial membranes [START_REF] Anholt | The peripheral-type benzodiazepine receptor: localization to the mitochondrial outer membrane[END_REF][START_REF] Snyder | Molecular mechanisms of peripheral benzodiazepine receptors[END_REF], have been identified in glandular tissues including the pancreas (De [START_REF] Souza | Peripheral-type benzodiazepine receptors in endocrine organs: autoradiographic localization in rat pituitary, adrenal, and testis[END_REF]. More recently, a biochemical characterization of PBRs has been performed in porcine pancreatic islets [START_REF] Cosimi | Benzodiazepine receptors in porcine pancreatic islets[END_REF][START_REF] Marchetti | Characterization of peripheral benzodiazepine receptors in purified large mammal pancreatic islets[END_REF] and in isolated human pancreatic islets [START_REF] Giusti | Peripheral benzodiazepine receptors in isolated human pancreatic islets[END_REF].

Experiments in vitro have shown that 4'-chlordiazepam, which displays high affinity for peripheral receptors [START_REF] Schoemaker | Specific high-affinity binding sites for [3H]Ro-5-4864 in rat brain and kidney[END_REF], inhibited in a concentration-dependent manner the glucose-induced insulin secretion whereas clonazepam, which binds very weakly to peripheral sites [START_REF] Braestrup | Specific benzodiazepine receptors in rat brain characterized by high-affinity [3H]diazepam binding[END_REF], was ineffective [START_REF] Petit | Effects of a peripheral-type benzodiazepine on glucose-induced insulin secretion[END_REF]). The signaling mechanism of the inhibiting effect of 4'-chlordiazepam was shown to be independent of the adenylyl cyclase-cAMP pathway [START_REF] Petit | Recherche du mécanisme de l'inhibition de la sécrétion d'insuline induite par une benzodiazépine périphérique, le 4'-chlordiazépam[END_REF]. In vivo, diazepam was reported to impair glucose tolerance in the rat [START_REF] Seyer-Hansen | The effects of diazepam on glucose tolerance in rats[END_REF]. More recently, [START_REF] Najim | Effect of some drugs acting at the peripheraltype benzodiazepine receptors on blood glucose in mice[END_REF] reported that 4'-chlordiazepam increased glycaemia after intraperitoneal administration in mice.

On the other hand, a trend to increased glycaemia without significant modification of insulinemia was shown after a single dose administration of diazepam in healthy volunteers [START_REF] Syvälahti | Serum growth hormone, serum immunoreactive insulin and blood glucose response to oral and intravenous diazepam in man[END_REF]. An aggravation of hyperglycaemia has been reported during benzodiazepine treatment in diabetic patients [START_REF] Zumoff | Aggravation of diabetic hyperglycemia by chlordiazepoxide[END_REF].

A pilot investigation in patients treated with the imidazopyridine compound alpidem, which displays high affinity for PBRs, was reported to alter glucose tolerance [START_REF] Bottaï | An imidazopyridine anxiolytic alters glucose tolerance in patients: a pilot investigation[END_REF].

These experimental and clinical results suggest that PBR ligands may interfere with the control of insulin secretion and the glucose homeostasis. The progressive inhibitory effect of the PBR agonists [START_REF] Petit | Effects of a peripheral-type benzodiazepine on glucose-induced insulin secretion[END_REF]) together with the mitochondrial localization of these receptors suggest that they may act intracellularly.

The aim of the present study was to further investigate the mechanism whereby PBR agonists decrease glucoseinduced insulin secretion, using a potent ligand of these receptors: 1-

(2-chlorophenyl)-N -methyl-N -(1-methylpropyl)-3-isoquinoline-carboxamide (PK 11195).
This non-benzodiazepine compound was initially proposed as a putative antagonist (Le Fur et al. 1983aFur et al. , 1983b)). However, in rat isolated islets, it did not counteract the inhibitory effect of 4'-chlordiazepam on insulin secretion but elicited by itself a potent inhibitory response [START_REF] Petit | Effects of a peripheral-type benzodiazepine on glucose-induced insulin secretion[END_REF]). In the present work, the action of PK 11195 was studied on isolated and perfused rat pancreas with monitoring of insulin and vascular responses. We investigated comparatively the effects of PK 11195 on either a metabolic [glucose increment or a -ketoisocaproic acid (a -KIC) infusion] or a nonmetabolic (KCl infusion) stimulus of the insulin-secreting b-cell.

Materials and methods

Isolated perfused pancreas

Experiments were performed on male Wistar rats weighing 320-370 g and fed ad libitum. The pancreas was completely isolated from neighboring tissues according to a technique previously described [START_REF] Loubatières | Etude expérimentale d'un nouveau sulfamide hypoglycémiant particulièrement actif, le HB 419 ou glibenclamide. I. Action bêtacytrope et insulinosécrétrice[END_REF]) and perfused through its own arterial system with a Krebs-Ringer bicarbonate buffer containing 8.3 mM or 4.2 mM glucose and 2 g/l bovine serum albumin. A mixture of O2 (95%) and CO2 (5%) was bubbled through this medium at atmospheric pressure. The pH of the solution was 7.4. The preparation was maintained at 37.5°C. Each organ was perfused at a constant pressure (40-50 cmH2O) selected so as to produce a flow rate of 2.5 ml/min at the start of the experiment; under these conditions, any change in the flow rate reflects a change in vascular resistance.

Experimental protocol

In all experiments a 30-min adaptation period was allowed before the first sample was taken for insulin assay. Two sets of experiments were performed. In a first set, the pancreas was continuously perfused with the physiological solution containing a slightly stimulating glucose concentration (8.3 mM) and from time 45 min, PK 11195 was infused for 30 min at different concentrations: 10 -7 -3x10 -5 M. In the second set of experiments, the pancreas was perfused from the beginning by a physiological medium containing a non-stimulating glucose concentration (4.2 mM). PK 11195 was added for 60 min from time 30 min; the stimulating agent of insulin secretion was added for 30 min or 20 min from time 60 min. Each effluent fraction was collected for 1 min in a graduated tube; the flow rate was measured and a fraction of the sample was immediately frozen for insulin immunoassay.

Insulin assay

Insulin was assayed by the radioimmunological method of [START_REF] Herbert | Coated charcoal immunoassay of insulin[END_REF] using [ 125 I]insulin (C.I.S. International, Gif-Sur-Yvette, France), purified rat insulin as standard (Novo, Copenhagen, Denmark), the biological activity of which was 14.2 µU/ng, and antiinsulin serum (ICN Biochemicals, Miles, Puteaux, France). The sensitivity of the assay was 0.1 ng/ml. Insulin output from perfused pancreas is expressed as ng/min and was determined by multiplying the hormone concentration in the effluent fraction by the flow rate.

Expression of results and statistical analysis

For the kinetics of insulin output, the results are expressed as changes in relation to the value at time 45 min taken as 100% (experiments with 8.3 mM glucose), or in absolute values (ng/min) in the experiments with 4.2 mM glucose. The mean insulin output rate was calculated as follows: the area under the curve for the PK 11195 infusion period divided by the number of minutes (AUC/30). The data are expressed as means ± standard error of the mean (SEM). They were submitted to Student's t-test or to analysis of variance and Newman-Keuls multiple comparison test [START_REF] Zar | Multiple comparisons[END_REF].

Medium and chemicals

The medium used was a Krebs-bicarbonate buffer (composition in mM: NaCl 118, KCl 4.7, MgSO4 1.2, KH2PO4 1.2, NaHCO3 25, CaCl2 2.5) equilibrated with 95% O2-5% CO2, pH 7.4. PK 11195 was dissolved in dimethylsulfoxide (DMSO), and the appropriate concentration of drugs was added to the solution so that the final concentration of DMSO never exceeded 0.1%. Although DMSO was ineffective alone at the concentration used, it was added to the control media. PK 11195, bovine serum albumin (Fraction V) and a-ketoisocaproic acid were from Sigma Chemical (St. Louis, Mo., USA); DMSO was from Merck (Darmstadt, Germany).

Results

Effects of PK 11195 on insulin secretion and vascular flow rate from the rat isolated pancreas perfused with a slightly stimulating glucose concentration

In the presence of 8.3 mM glucose, PK 11195 (10 -6 M and 10 -5 M) induced a progressive and sustained inhibition of insulin secretion (Fig. 1). This inhibition is statistically significant from time 65 min with the lower concentration, and from time 49 min with the higher concentration. In pancreas perfused with DMSO alone (controls) the rate of insulin secretion is not significantly modified throughout experiments and averaged 15.6±1.9 ng/min at time 45 min (reference value). The mean insulin output rate during the 30 min of PK 11195 infusion (AUC/30) is significantly decreased to 79.1±2.7% (P<0.01) and 65.9±6.7% (P<0.01) for 10 -6 M and 10 -5 M PK 11195, respectively, as compared with 98.0±6.0% for control experiments.

After stopping PK 11195 infusion, the insulin output progressively returned to control values.

Furthermore, the effect of PK 11195 was concentration-dependent in the range of 10 -7 -10 -5 M, the maximal effect being obtained at this latter concentration (Fig. 2).

In addition, the infusion of PK 11195 at the concentrations of 10 -6 M and 10 -5 M, which are effective in reducing insulin secretion, does not alter the pancreatic vascular flow rate recorded in the same experiments (Table 1).

Effects of PK 11195 on insulin secretion inducedby a metabolic stimulation of insulin secretion

Glucose increment

The increase in glucose from a non-stimulating concentration (4.2 mM) to a slightly stimulating concentration (8.4 mM) induces a biphasic insulin secretion (Fig. 3). The infusion of PK 11195 (10 -5 M) does not modify the insulin secretion in the presence of 4.2 mM glucose, but it significantly decreases the biphasic insulin response to the glucose increment: it reduces the peak of the first phase as well as the amplitude of the second phase (Fig. 3). The mean of areas under the curves (AUC) in the presence of PK 11195 is significantly different from the control AUC during the first and second phases. The insulin output during the first 5 min (first phase) and the following 25 min (second phase) of 4.2 mM glucose addition was 13.6±2.1 ng and 83.7±14.3 ng for control experiments, respectively; the values fall to 9.0±1.4 ng (P <0.04) and 57.6±6.4 ng (P <0.05), respectively, for the treated pancreas.

a-ketoisocaproic acid (KIC) infusion

In the presence of glucose 4.2 mM, a-KIC 2 mM induces a biphasic insulin secretion (Fig. 4). After a peak which culminates at the second min and lasts 5 min, a second phase of insulin secretion increase is observed throughout the a-KIC infusion. A PK 11195 (10 -5 M) infusion 30 min before the a-KIC infusion partially inhibits the biphasic insulin response (Fig. 4). The means of AUC in the presence of PK 11195 are significantly different from those of control AUC during the first and the second phase. The insulin output during the first 5 min (first phase) and the following 25 min (second phase) of a-KIC infusion are, for control experiments, 23.9±3.5 ng and 100.1±12.6 ng, respectively; for the PK 11195-treated pancreas, they are only 15.5±2.7 ng (P <0.04) and 64.7±10.7 ng (P <0.02), respectively.

Effects of PK 11195 on insulin secretion induced by a non-metabolic stimulation of insulin secretion: KCl infusion

In the presence of a non-stimulating glucose concentration (4.2 mM) KCl 10 mM induces a monophasic insulin response (Fig. 5). The peak of insulin secretion reaches its highest point at the second min of KCl infusion. The prior administration of PK 11195 (10 -5 M) 30 min before KCl infusion does not significantly modify the effect of this latter substance (Fig. 5): the mean AUC/10 from min 60 to min 70 was 2.2±0.5 ng/min in the presence of PK11195 vs. 2.7±0.4 ng/min for controls. Furthermore, the effect of a lower concentration of KCl (5 mM) is still unaffected by PK 11195, although its magnitude is lower: the mean AUC/10 from min 60 to min 70 was 0.9±0.2 ng/min in the presence of PK 11195 vs. 1.1±0.4 ng/min for controls.

Discussion

The present results clearly show that PK 11195, a selective isoquinoline-carboxamide ligand of PBRs, inhibits insulin secretion induced by glucose in vitro in the rat isolated pancreas. This effect is concentration-dependent and of progressive setting-up, which is in accordance with an intracellular site of action. A similar decrease in insulin secretion was previously reported with the benzodiazepine PBRs agonist 4'-chlordiazepam on the same preparation [START_REF] Petit | Effects of a peripheral-type benzodiazepine on glucose-induced insulin secretion[END_REF]). However, when comparing the effective concentrations of the two drugs it can be noted that the inhibitory effect of PK 11195 is about tenfold more potent than that of 4'-chlordiazepam. On the other hand, PK 11195 was completely ineffective on the vascular flow rate of the rat isolated pancreas. In contrast, in the same experimental conditions, 4'-chlordiazepam was previously shown to induce a vasodilator response of the pancreatic vascular bed [START_REF] Petit | Effects of a peripheral-type benzodiazepine on glucose-induced insulin secretion[END_REF]). The differential effects on vascular resistance of these two compounds, which both inhibit insulin secretion, suggest that two different subtypes of PBRs may be recognized in pancreatic b-cells and in pancreatic vascular cells. The reduction of glucose-induced insulin secretion by PBR ligands could explain the increase in blood glucose concentration, which was reported in mice in vivo after an intraperitoneal administration of 4'chlordiazepam (Ro 5-4864) by [START_REF] Najim | Effect of some drugs acting at the peripheraltype benzodiazepine receptors on blood glucose in mice[END_REF]. In that study, when PK11195 was administered before 4'-chlordiazepam no antagonism was observed, but a greater increase in blood glucose, which is in agreement with our results. Thus, PK11195 as well as 4'chlordiazepam are able to impair glucose homeostasis and insulin secretion, and we further investigated at which level in the b-cell a PBR ligand could modify the insulin secretion. It is worth recalling that glucose stimulates insulin secretion by being metabolized first with glycolysis and then in the mitochondrial citric acid cycle. The oxidative events lead to an increased cytoplasmic ATP/ADP ratio, which inhibits the ATP-sensitive K + channel (KATP channels). This results in a membrane depolarization and the opening of voltage-dependent Ca 2+ channels, leading to increased cytoplasmic calcium concentration and the initiation of exocytosis of the insulin-containing secretory granules [START_REF] Rorsman | The pancreatic beta-cell as a fuel sensor: an electrophysiologist's viewpoint[END_REF]. Since the stimulussecretion coupling of glucose in the b-cell involves both metabolic and electrophysiological events, we compared the effects of PK 11195 on insulin secretion induced by metabolic stimulus or membrane depolarization. The stimulation of insulin secretion by potassium depolarization (KCl infusion) was not modified by PK11195. Thus, it may be suggested that this drug does not directly interfere with the membrane potential or the subsequent electrophysiological events such as the opening of voltage-dependent calcium channels.

Moreover, the progressive time course of the effect of PK 11195 on glucose-induced insulin secretion contrasts with the immediate effect of a potassium channel opener such as diazoxide in similar conditions [START_REF] Mariani | Etude de l'action inhibitrice du diazoxide sur l'insulino-sécrétion du pancreas isolé et perfusé du rat: absence d'implication des récepteurs a-adrénergique[END_REF]). Hence, it may also be suggested that PK 11195 does not directly block the KATP channels; it may rather act upstream of that target. The metabolism of glucose involves both glycolysis and oxidative events in the mitochondrial citric acid cycle.

In contrast, the deamination product of the amino acid L -leucine, a-ketoisocaproate (KIC), is a fuel secretagogue which is directly metabolized in the mitochondria [START_REF] Panten | Studies on the mechanism of D-leucine and a-ketoisocaproic acid-induced insulin release from perifused isolated pancreatic islets[END_REF]. A low millimolar concentration of KIC was chosen because it has been reported to be insulinotropic in rodents [START_REF] Ashcroft | Metabolic controls of insulin secretion[END_REF]; furthermore, higher concentrations have been shown to directly inhibit the KATP channel activity [START_REF] Bränström | Direct inhibition of the pancreatic bcell ATP-regulated potassium channel by a-ketoisocaproate[END_REF]) and the low concentration used is insufficient to directly reduce channel activity to a significant extent. Thus, an effect on KIC-induced insulin release may discriminate between actions on the glycolytic vs. the mitochondrial part of glucose signaling. Under our experimental conditions, a glucose increment from 4.2 mM to 8.4 mM or addition of 2 mM KIC to 4.2 mM glucose induced a biphasic stimulation of insulin secretion of similar magnitude. These effects were both reduced by the PBR agonist PK 11195, suggesting that this drug may interfere with the oxidative events implicated in glucose-induced insulin release. This functional result is in agreement with the mitochondrial localization of PBRs. To summarize, the insulin-inhibitory effect of PK 11195 seems to proceed independently of events related to membrane depolarization. In contrast, it seems to be dependent on intracellular metabolism, particularly mitochondrial oxidative events. Therefore, it may be suggested that PBR agonists act via mitochondrial benzodiazepine receptors in the pancreatic b-cell, and decrease insulin release by reducing oxidative metabolism. Furthermore, our results suggest the existence of different subtypes of peripheral benzodiazepine receptors on pancreatic b-cells and vessels.

Figure legends Figure 1

 1 Figure legends

Figure 2

 2 Figure 2 Concentration-response curve for the inhibitory effect of PK 11195 on glucose-induced insulin secretion. Each point is the result of 6-7 individual experiments and represents the mean (±SEM) reduction in the insulin output rate (AUC/30 as a percentage) in comparison with control values.

Figure 3

 3 Figure 3Effects of PK 11195 on the insulin secretion induced by an increment of glucose concentration from 4.2 mM to 8.4 mM ( controls, PK 11195 10 -5 M). Results are expressed as means ± SEM of 10 experiments.

Figure 4

 4 Figure 4Effects of PK 11195 10 -5 M () on the insulin secretion induced by an infusion of KIC 2 mM; ( controls). Results are expressed as means ± SEM. of 11 experiments.

Figure 5

 5 Figure 5Effects of PK 11195 10 -5 M () on the insulin secretion induced by an infusion of KCl 10 mM; () controls). Results are expressed as means ± SEM of 7 experiments.

Figure 1 Figure 2 Figure 3 Figure 4 Figure 5

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Acknowledgements

The authors thank V. Montesinos and N. Linck for technical assistance.