Jill Rocchetti

Elsa Isingrini

Gregory Dal

Sara Sagheby

Aurore Menegaux

François Tronche

Daniel Levesque

Luc Moquin

Tak Alain Gratton

Pan Wong

Marcelo Rubinstein

Bruno Giros
email: bruno.giros@mcgill.ca

Gregory Dal Bo

Tak Pan Wong

Bruno Giros°1

Presynaptic D2 Dopamine Receptors Control LTD Expression and Memory Processes in the Temporal Hippocampus

Keywords:

BACKGROUND: Dysfunctional mesocorticolimbic dopamine (DA) signaling has been linked to alterations in motor and reward-based functions associated with psychiatric disorders. Converging evidence from psychiatric patients and use of antipsychotics suggest that misbalance of DA signaling deeply alters hippocampal functions. However, given the lack of full characterization of a functional mesohippocampal pathway, the precise role of DA transmission in memory deficits associated with these disorders and their dedicated therapies is unknown. In particular, the positive outcome of antipsychotic treatments, commonly antagonizing D2 dopamine receptors (D2R), on cognitive deficits and memory impairments remains questionable. METHODS: Following pharmacological and genetic manipulation of dopamine transmission, we combined anatomical, neurochemical, electrophysiological and behavioral investigations to uncover the role of dopamine D2-like receptors in hippocampal-dependent plasticity and learning. Naïve mice (n=4-21) were used in the different procedures. RESULTS: We report that DA modulated the both long-term potentiation (LTP) and depression (LTD) in the temporal hippocampus as well as spatial and recognition learning and memory in mice through D 2 dopamine receptors (D2R). Whereas genetic deletion or pharmacological blockade of D2R led to the loss of LTP expression, the specific genetic removal of presynaptic D2R specifically impairs LTD as well as the performances on spatial memory tasks. CONCLUSION: The pre-synaptic D2Rs in DA fibers of the temporal hippocampus tightly modulate LTD expression and plays a major role in the regulation of hippocampal learning and memory. This direct role of mesohippocampal DA input uncovered here adds a new dimension to DA involvement in the physiology underlying neuropsychiatric disorders deficits.

Introduction

In addition to its roles in motor and reward systems [START_REF] Beninger | The role of dopamine in locomotor activity and learning[END_REF][START_REF] Wise | Brain dopamine and reward[END_REF], DA has also been acknowledged to be essential in adaptive behaviors, such as attention, learning and memory [START_REF] Cools | Dopaminergic modulation of cognitive function-implications for L-DOPA treatment in Parkinson's disease[END_REF][START_REF] Nieoullon | Dopamine and the regulation of cognition and attention[END_REF][START_REF] Weinberger | Mesocortical dopaminergic function and human cognition[END_REF]. The development of powerful genetic tools has enabled a better understanding of DA's function in the basal ganglia and the cortical structures [START_REF] Tritsch | Dopaminergic modulation of synaptic transmission in cortex and striatum[END_REF] involved in these motor and reward functions. However, the limited innervation by DA terminals and the low levels of DA receptor (DAR) expression in the hippocampus [START_REF] Beaulieu | Beyond cAMP: The Regulation of Akt and GSK3 by Dopamine Receptors[END_REF][START_REF] Gasbarri | The dopaminergic mesencephalic projections to the hippocampal formation in the rat[END_REF][START_REF] Swanson | The projections of the ventral tegmental area and adjacent regions: a combined fluorescent retrograde tracer and immunofluorescence study in the rat[END_REF] have slowed our understanding of DA's potential role in the mesohippocampal pathway [START_REF] Lisman | A thalamo-hippocampal-ventral tegmental area loop may produce the positive feedback that underlies the psychotic break in schizophrenia[END_REF]. Considering the pathological imbalance in hippocampal DA transmission observed in schizophrenic patients [START_REF] Boyer | Hippocampal abnormalities and memory deficits: new evidence of a strong pathophysiological link in schizophrenia[END_REF][START_REF] Folley | Anomalous neural circuit function in schizophrenia during a virtual Morris water task[END_REF][START_REF] Hanlon | Impairment on the hippocampal-dependent virtual Morris water task in schizophrenia[END_REF][START_REF] Ledoux | Decreased fMRI activity in the hippocampus of patients with schizophrenia compared to healthy control participants, tested on a wayfinding task in a virtual town[END_REF] and the lack of perspective on the impact of antipsychotic treatments in memory processes, there is an urgent need to better understand the DA contribution in hippocampal-related processes.

Long-term synaptic plasticity, with its two counterparts, LTP and LTD, is underlying neuronal circuit tuning. It was shown 40 years ago that microelectrophoretical application of DA in the hippocampal CA3 region of cats depressed glutamate-induced cell firing [START_REF] Biscoe | Micro-electrophoretic studies of neurones in the cat hippocampus[END_REF]. However, how DA modulates glutamatergic transmission in the hippocampus is still poorly understood.

Five DARs have been identified in mammals [START_REF] Beaulieu | The physiology, signaling, and pharmacology of dopamine receptors[END_REF] and classified into the D1/D5 -"D1-like" -and D2/D3/D4 -"D2-like" families. Both D1-like and D2-like receptor agonists and antagonists regulate synaptic plasticity [START_REF] Jay | Dopamine: a potential substrate for synaptic plasticity and memory mechanisms[END_REF], but if the role of D1-like receptors in hippocampal synaptic plasticity is quite well understood, little is known about the precise role of the D2R. D1-like receptor blockade inhibits the expression and maintenance of late LTP [START_REF] Frey | The effect of dopaminergic D1 receptor blockade during tetanization on the expression of long-term potentiation in the rat CA1 region in vitro[END_REF][START_REF] Lemon | Dopamine D1/D5 receptors gate the acquisition of novel information through hippocampal long-term potentiation and long-term depression[END_REF][START_REF] Lisman | A neoHebbian framework for episodic memory; role of dopamine-dependent late LTP[END_REF], whereas the D1-like agonist SKF-38393 favored the early and late phases of LTP [START_REF] Huang | D1/D5 receptor agonists induce a protein synthesisdependent late potentiation in the CA1 region of the hippocampus[END_REF][START_REF] Otmakhova | D1/D5 dopamine receptor activation increases the magnitude of early long-term potentiation at CA1 hippocampal synapses[END_REF]. Accordingly, D1KO mice showed an absence of late LTP in vitro [START_REF] Matthies | Dopamine D1-deficient mutant mice do not express the late phase of hippocampal long-term potentiation[END_REF] and exhibited impaired spatial memory [START_REF] El-Ghundi | Spatial learning deficit in dopamine D(1) receptor knockout mice[END_REF]. Conversely, the role of D2-like receptors in hippocampus remains controversial. Activation of D2R would exert a suppressive effect on CA1 LTD in the rat hippocampus [START_REF] Chen | Roles of dopamine receptors in long-term depression: enhancement via D1 receptors and inhibition via D2 receptors[END_REF] with no effect on LTP [START_REF] Huang | D1/D5 receptor agonists induce a protein synthesisdependent late potentiation in the CA1 region of the hippocampus[END_REF], but D2 agonists directly administered in the hippocampus improved memory performances in radial maze tasks [START_REF] Packard | Dissociation of hippocampus and caudate nucleus memory systems by posttraining intracerebral injection of dopamine agonists[END_REF][START_REF] Wilkerson | Ventral hippocampal dopamine D1 and D2 systems and spatial working memory in rats[END_REF]. Rats treated with the D2R antagonist haloperidol displayed spatial learning deficits [START_REF] Terry | Differential effects of chronic haloperidol and olanzapine exposure on brain cholinergic markers and spatial learning in rats[END_REF][START_REF] Terry | Timedependent effects of haloperidol and ziprasidone on nerve growth factor, cholinergic neurons, and spatial learning in rats[END_REF] and impaired recognition memory [START_REF] Schroder | Impairing effects of chronic haloperidol and clozapine treatment on recognition memory: possible relation to oxidative stress[END_REF][START_REF] Ozdemir | Differential effects of antipsychotics on hippocampal presynaptic protein expressions and recognition memory in a schizophrenia model in mice[END_REF]. Systemic injection of the D2/D3 antagonist sulpiride affected learning in the spatial version of the Morris water maze (MWM) [START_REF] Stuchlik | Morris water maze learning in Long-Evans rats is differentially affected by blockade of D1-like and D2-like dopamine receptors[END_REF], but whether it is a specific effect on the hippocampus remains unclear [START_REF] Setlow | Sulpiride infused into the nucleus accumbens posttraining impairs memory of spatial water maze training[END_REF].

Whereas D1-like receptors are only present on dopaminoceptive neurons, D2R are localized both post-synaptically where they activate multiple signalization pathways [START_REF] Bonci | The dopamine D2 receptor: new surprises from an old friend[END_REF] and pre-synaptically where they exert an inhibitory control over DA synthesis and release [START_REF] Anzalone | Dual control of dopamine synthesis and release by presynaptic and postsynaptic dopamine D2 receptors[END_REF][START_REF] Bello | Cocaine supersensitivity and enhanced motivation for reward in mice lacking dopamine D2 autoreceptors[END_REF]. This dual localization of D2R potentially results in multi-modal processes with complex effects.

Here, we showed that D2R post-synaptic expression was restricted to neurons of the dentate gyrus (DG), whereas DA fibers expressing the dopamine transporter (DAT) originating from the VTA and carrying pre-synaptic D2R innervated the temporal CA1 areas. The genetic deletion of D2R severely impairs both NMDAR-dependent LTP and LTD in CA1, matching with remodeling of mesohippocampal DA fibers and decrease in performance on learning and memory tasks. The pharmacological blockade of D2R in naïve mice reproduced these impairments. Finally, the specific genetic deletion of presynaptic D2R resulted in deficits of both LTD expression and spatial memory without impairing LTP levels.

Material and Methods

Detailed procedures are listed in the supplemental information section.

Animals:

Mice with a constitutive deletion of the D2R were from the Jackson Laboratories (B6.129S2-Drd2 tm1Low /J), D2cre mice were from Gensat (MMRC; B6.FVB(Cg)-Tg(Drd2-cre) ER44Gsat/Mmucd ) and crossed with mice carrying ROSA-tomato reporter from Jackson Laboratories (Gt(ROSA)26Sor tm9(CAG-tdTomato)Hze ), mice with floxed D2R gene [START_REF] Bello | Cocaine supersensitivity and enhanced motivation for reward in mice lacking dopamine D2 autoreceptors[END_REF] and DATcre mice [START_REF] Turiault | Analysis of dopamine transporter gene expression pattern --generation of DAT-iCre transgenic mice[END_REF] were directly obtained.

Anatomical procedures: DAR mRNAs were detected by radioactive and fluorescent in situ hybridization. Immunolabeling was adapted from previous protocol to detect DAT, CaMK2α, TH and GABA on coronal slices from D2KO, D2 DATcre and sulpiride-treated mice. All experiments were performed on naïve mice (n=4-10; Sup Table 1).

Electrophysiological recordings:

Field excitatory postsynaptic potentials (fEPSP) and whole-cell patch-clamp recordings were performed on coronal slices prepared from D2KO, D2 DATcre and C57Bl/6 WT mice in presence of sulpiride or SCH23390. All experiments were done in the presence of 5 µM bicuculline. All experiments were performed on naïve mice (n=6-11; Sup Table 2).

Behavioral tests: Males D2KO, D2 DATcre , sulpiride treated (i.p.: 50mg/kg; intra-HP: 2.5µg) and their respective controls, were exposed to spatial and cued versions of the MWM, Barnes maze, two objects recognition, contextual and cued fear conditioning.

Only naive mice were tested. All experiments were performed on naïve mice (n=9-21; Sup Table 3).

Statistical comparisons:

Results are reported as mean ± SEM. Statistical analysis were made with Student's t test and ANOVA, unless otherwise specified (see Statistical tables Sup 1, 2 and 3).

Results

Mapping of DA receptors and fibers in the hippocampal formation

The distribution of the five DAR was evaluated by in situ hybridization in C57BL/6 mouse brains (Figure 1A-C given their morphology and their projection patterns (Figure 1J).

As most of the tyrosine hydroxylase (TH) labeling found in hippocampus originates from noradrenergic neurons [START_REF] Giros | Hyperlocomotion and indifference to cocaine and amphetamine in mice lacking the dopamine transporter[END_REF], we visualized DAT + fibers to characterize the DA projections. DAT + fibers were restricted to the temporal area of CA1 (Figure 1K-L Constitutive deletion of D2Rs led to a profound remodeling of the VTAhippocampus dopaminergic pathway D2KO mice had three-to seven-fold more DAT + fibers in the hippocampus than WT animals (Figure 2A-C). In the LMol layer of CA1, D2KO mice showed three-fold increase in DAT + fibers (n=4; 337±52) compared to WT (n=4; 111±30). The radiatum layer showed a similar increase (308±50 in D2KO ; 85±12 in D2WT), and a major sprouting was observed in the oriens and pyramidal cell layers, with seven-fold more DAT + fibers (257±42) in D2KO than in D2WT mice (38±9). Interestingly, whereas DAT + fibers were absent in the hilus of the DG in D2WT animals, they were visible in D2KO mice (32.3±12.2). No DAT + fibers were observable in any other subdivisions of the temporal hippocampus or in septal areas.

To determine the origin of DAT + fibers increase, we injected fluorescent retrobeads into the CA1 of D2KO and D2WT mice (Figure 2C-D and Figure sup 2E-E''). The majority of retrogradely labeled neurons were localized in the VTA (87-89%), only a fraction of them were DAergic (16-21%), whereas the majority was GABAergic (79-84%).

The number (n=27) and the proportion (21%) of TH + neurons in D2KO mice were slightly increased, compared to WT (n=22; 16%). Thus, this increase did not account for the massive sprouting observed in D2KO hippocampus. Furthermore, DA levels in the temporal hippocampus were significantly higher in D2KO mice (293.9±30.9 ng/g) compared to D2WT mice (169.9±21.4 ng/g) (Figure 2E; Figure sup 2G). Finally, we assessed by microdialysis whether this increased sprouting of DA terminals in temporal hippocampus would translate into an increase in KCl-induced DA release (Figure 2F; However, KCl-induced paired depolarization led to 2.4 times greater DA release in the D2KO mice (K1=23.0±6.3 ng/ml; K2=31.5±10.4 ng/ml) compared to WT littermates (K1=11.2±2.2; K2=13.05±2.6). The second KCl-induced stimulation (K2) in D2KO mice resulted in a 1.4-fold increase in DA release, compared to the first depolarization (K1), which was significantly higher than that observed in D2WT mice (1.16), implying the absence of inhibitory control from the presynaptic D2Rs upon DA release (Figure 2F).

Constitutive deletion of the D2 receptor impaired synaptic plasticity in the temporal CA1

We examined whether remodeling of the VTA-hippocampus dopaminergic pathway in D2KO mice would impair N-methyl-D-aspartate receptor (NMDAR)-dependent synaptic plasticity in CA1. In the dorsal area of temporal CA1, where DAT + fibers were detected, high-frequency stimulation induced a significant LTP in D2WT animals (n=6; 1.44±0.06) but not in D2KO mice (n=8; 1.15±0.07) (Figure 3A). For LTD expression, paired-pulse low-frequency stimulation induced stable depression in the D2WT (n=9; 0.83±0.04), but not in D2KO slices (n=7; 1.08±0.04) (Figure 3B). This reduction in longterm synaptic plasticity could be due to a change in glutamatergic transmission; in voltage clamp configuration, we recorded miniature excitatory post-synaptic currents (mEPSC) in glutamatergic pyramidal neurons of temporal CA1 in D2KO (n=11) and D2WT (n=11) mice at -80 mV, in the presence of tetrodotoxin (TTX) (Figure 3C).

Neither the frequency (D2WT: 0.25±0.02 Hz; D2KO: 0.28±0.04 Hz) nor the amplitude (D2WT: 7.44±0.42 pA; D2KO: 6.92±0.49 pA) of the mEPSCs were modified, suggesting that neither the glutamate spontaneous release nor post-synaptic glutamate sensitivity was altered in CA3-CA1 synapses (Figure 3D). Paired-pulse facilitation of fEPSPs at CA3-CA1 glutamatergic synapses at increasing inter-pulse intervals was also conserved in D2KO brain slices (Figure 3E). However, fEPSPs recorded in the D2KO mice (n=10) were significantly larger in the medium range than in D2WT (n=8), revealing an increased excitability of CA3-CA1 synapses (Figure 3F). We wondered whether the To assess the acute effects of D2R blockade on synaptic plasticity, we applied sulpiride (10 µM) to C57BL/6J slices during LTP and LTD induction. Sulpiride application produced the same impairment pattern observed in D2KO slices. Both LTP (n=7; 1.55±0.1) and LTD (n=7; 0.78±0.08) were observed in the untreated slices, whereas sulpiride produced a significant decrease in LTP (n=8; 1.30±0.06) (Figure 5D) and abolition of LTD expression (n=6; 1.03±0.05) (Figure 5E).

As in D2KO mice, C57Bl/6J mice treated for 15 days with sulpiride showed a highly

D2R deletion in DAT + neurons was sufficient to impair LTD expression and spatial memory formation in the hippocampus

To specifically assess the contribution of pre-synaptic D2Rs we crossed Drd2 loxP/loxP mice [START_REF] Bello | Cocaine supersensitivity and enhanced motivation for reward in mice lacking dopamine D2 autoreceptors[END_REF] with BAC-DATicre mice [START_REF] Turiault | Analysis of dopamine transporter gene expression pattern --generation of DAT-iCre transgenic mice[END_REF] Remarkably, the input-output relationship in evoked glutamatergic fEPSPs was not different between the D2 DATcre mice and their WT DATcre littermates (Figure 6C). To assess whether this LTD deficit could be a consequence of pre-synaptic D2R absence on DA release and synthesis, we analyzed DAT + fibers density and DA tissue levels in the temporal hippocampus of D2 DATcre mice. We found that D2 DATcre mice exhibited 3.3 and 7.5 fold more DAT + fibers in the radiatum (D2 DATcre : 228.3±55.6; WT DATcre : 68.4±12.5) and in the oriens/pyramidal cell layers (D2 DATcre : 221.8±64.9; WT DATcre : 29.4±15.3), respectively, compared to WT DATcre littermates, with no changes in the LMol layer (D2 DATcre : 81.5±17.3; WT DATcre : 89.2±33.9) (Figure 6E). Finally, we observed a significant 1.3-fold increase in tissue DA levels in the D2 DATcre mice's temporal hippocampus compared to WT DATcre mice (Figure 6F, Figure sup 5E).

The D2 DATcre mice exhibited dramatic memory impairments in the spatial version of the MWM. The final mean escape latency was significantly increased in the D2 DATcre (50.9±11.2s) compared to WT DATcre (21.8±8.8s) and the percentage of time spent in the active quadrant was significantly decreased in D2 DATcre (22.4±6.7%) compared to WT DATcre mice (44.2±3.9%) (Figure 6G). Further linking the LTD role in these memory deficits, while blockade of D1-like receptors with SCH23390 rescued HFS induced LTP expression in D2KO slices, it had no effect neither on LTD deficits nor on memory deficits (Figure 3H and sup 3B).

Discussion

The organization of the dopamine circuitry in the hippocampus suggests a tonic functional mode of transmission

We localized DAR expressing cells using sensitive in situ hybridization techniques; D5R mRNA expression in the entire granular cell layer of the DG and the pyramidal cell layer of CA1 and CA3, and D1R expression restricted to the granular cells of the DG confirm previous reports in rats and primates [START_REF] Bergson | Regional, cellular, and subcellular variations in the distribution of D1 and D5 dopamine receptors in primate brain[END_REF][START_REF] Ciliax | Dopamine D(5) receptor immunolocalization in rat and monkey brain[END_REF][START_REF] Fremeau | Localization of D1 dopamine receptor mRNA in brain supports a role in cognitive, affective, and neuroendocrine aspects of dopaminergic neurotransmission[END_REF][START_REF] Levey | Localization of D1 and D2 dopamine receptors in brain with subtype-specific antibodies[END_REF][START_REF] Tiberi | Cloning, molecular characterization, and chromosomal assignment of a gene encoding a second D1 dopamine receptor subtype: differential expression pattern in rat brain compared with the D1A receptor[END_REF]. We could not detect D3R or D4R transcript expression in the hippocampus: D3 mRNA was observed only in the amygdalohippocampal area [START_REF] Kato | Dendritic aberrations in the hippocampal granular layer and the amygdalohippocampal area following kindled-seizures[END_REF], but the absence of D4 mRNA detection contrasts with its previously described participation in CA1 synapses depotentiation through CA3 GABAergic interneurons [START_REF] Andersson | Neuregulin and dopamine modulation of hippocampal gamma oscillations is dependent on dopamine D4 receptors[END_REF][START_REF] Kwon | Neuregulin-1 regulates LTP at CA1 hippocampal synapses through activation of dopamine D4 receptors[END_REF]. mRNA localization and binding experiments showed the restricted expression of D2R in the hilus, as well as in the DG, flanking the granular cell layer. Furthermore, as D2Rs binding was detectable in the hippocampus of D2 DATcre mice (Figure sup 5D), it clearly indicated that the labeling in the DG mostly originated from the hilar D2-positive cells. The demonstration that D2mRNA co-localized with CaMKIIα in the hilus confirmed the recent report of putative D2R expression in mossy cells using Drd2-EGFP transgenic mice [START_REF] Gangarossa | Characterization of dopamine D1 and D2 receptor-expressing neurons in the mouse hippocampus[END_REF]. Morphological characteristics of these cells confirmed that these were glutamate mossy interneurons [START_REF] Amaral | The dentate gyrus: fundamental neuroanatomical organization (dentate gyrus for dummies)[END_REF][START_REF] Jinde | Hilar mossy cell degeneration causes transient dentate granule cell hyperexcitability and impaired pattern separation[END_REF][START_REF] Scharfman | Hilar mossy cells of the dentate gyrus: a historical perspective[END_REF]. Dopaminergic innervation, assessed by DAT expression, was restricted to the temporal area of CA1. The organization of the DA system, with an absence of DAT + fibers in the polymorphic layers of the DG, is typical of a volume transmission modus operandi [START_REF] Descarries | Dual character, asynaptic and synaptic, of the dopamine innervation in adult rat neostriatum: a quantitative autoradiographic and immunocytochemical analysis[END_REF][START_REF] Rice | Dopamine spillover after quantal release: rethinking dopamine transmission in the nigrostriatal pathway[END_REF][START_REF] Zoli | Volume transmission in the CNS and its relevance for neuropsychopharmacology[END_REF]. Retrograde labeling with fluorobeads injected into the temporal hippocampus confirmed results obtained in rats [START_REF] Gasbarri | Mesolimbic dopaminergic neurons innervating the hippocampal formation in the rat: a combined retrograde tracing and immunohistochemical study[END_REF], we showed about 20% of the efferent VTA neurons are TH + neurons and identified all other projecting neurons as GABAergic. Using in vivo microdialysis, we showed that the functionally releasable pool of DA is under the control of pre-synaptic D2 activation, as described in the striatum [START_REF] Anzalone | Dual control of dopamine synthesis and release by presynaptic and postsynaptic dopamine D2 receptors[END_REF][START_REF] Bello | Cocaine supersensitivity and enhanced motivation for reward in mice lacking dopamine D2 autoreceptors[END_REF]. The presynaptic D2Rs conferred to DA neurons a high structural plasticity, as shown by the massive sprouting of these DAT-positive fibers in the D2KO, D2 DATcre mice and following a chronic treatment with sulpiride. This observation is in accordance with previous studies reporting that D2R activation exerted an inhibitory effect on DA sprouting in the striatum [START_REF] Parish | Effects of longterm treatment with dopamine receptor agonists and antagonists on terminal arbor size[END_REF][START_REF] Tripanichkul | D2 Dopamine receptor blockade results in sprouting of DA axons in the intact animal but prevents sprouting following nigral lesions[END_REF]. Therefore, the upregulation of DAT fibers, DA tissue and DA release levels suggest that chronic use of antipsychotics could produce drastic anatomical changes in the hippocampus.

Dopamine D2 receptors control the plasticity of the temporal hippocampus

The potential role played by D2Rs in the modulation of long-term hippocampal synaptic plasticity was poorly documented [START_REF] Huang | D1/D5 receptor agonists induce a protein synthesisdependent late potentiation in the CA1 region of the hippocampus[END_REF][START_REF] Chen | Roles of dopamine receptors in long-term depression: enhancement via D1 receptors and inhibition via D2 receptors[END_REF]. Our anatomical description emphasized a critical role for pre-synaptic D2R in DAT fiber architecture in the temporal region. Therefore, we focused on this region to investigate the role of D2R activation in synaptic plasticity.

In D2KO mice, we observed a suppression of both LTP and LTD after HFS or paired-pulse LFS of the Schaffer collaterals in temporal CA1. As it was previously reported regarding cortico-striatal plasticity [START_REF] Calabresi | Abnormal synaptic plasticity in the striatum of mice lacking dopamine D2 receptors[END_REF], no critical changes in basal glutamatergic transmission were detected. Because we could not detect post-synaptic D2R expression in CA1, we assumed that D5R overactivation at glutamatergic cells was potentially involved. Interestingly, blocking D1-like receptors with SCH23390 in D2KO slices reestablished LTP levels but did not restore LTD. Functional response of D1Rs activation has been shown to follow an "inverted U-shape" curve in the prefrontal cortex [START_REF] Vijayraghavan | Inverted-U dopamine D1 receptor actions on prefrontal neurons engaged in working memory[END_REF]. Our results suggested that this model is relevant regarding LTP expression at glutamatergic synapses of CA1 but that LTD expression responds to different mechanisms.

The impairments in LTP and LTD were almost entirely replicated by application in C57BL/6J mice slices of sulpiride that has high specificity for D2/D3 receptors [START_REF] Caley | Sulpiride: an antipsychotic with selective dopaminergic antagonist properties[END_REF]. In particular, LTD was fully blocked as reported in D2KO mice. These results showed that D2Rs played a pivotal role in the expression of CA3-CA1 plasticity.

Dopamine D2 receptors control spatial learning in the temporal hippocampus

Given the drastic changes in synaptic plasticity and the dopaminergic circuit remodeling observed in D2KO and sulpiride-treated mice, we anticipated downstream functional impairments in hippocampus-dependent learning and memory processes. Systemic administration of D2-like antagonists in rats had been shown to trigger learning and memory deficits [START_REF] Terry | Differential effects of chronic haloperidol and olanzapine exposure on brain cholinergic markers and spatial learning in rats[END_REF][START_REF] Schroder | Impairing effects of chronic haloperidol and clozapine treatment on recognition memory: possible relation to oxidative stress[END_REF][START_REF] Stuchlik | Morris water maze learning in Long-Evans rats is differentially affected by blockade of D1-like and D2-like dopamine receptors[END_REF][START_REF] Ertugrul | The influence of Ndesmethylclozapine and clozapine on recognition memory and BDNF expression in hippocampus[END_REF][START_REF] Ploeger | Effects of haloperidol on the acquisition of a spatial learning task[END_REF][START_REF] Prokopova | Synergistic effects of dopamine D2-like receptor antagonist sulpiride and beta-blocker propranolol on learning in the carousel maze, a dry-land spatial navigation task[END_REF]. Accordingly, the profound deficits in both LTP and LTD observed in D2KO mice were associated with dramatic impairments in three hippocampus-dependent learning and memory paradigms: the spatial MWM, the Barnes maze and the NORT. The absence of deficits in the cued version of the MWM, known to involve mainly striatal DA regulation [START_REF] Morris | Selective impairment of learning and blockade of long-term potentiation by an N-methyl-D-aspartate receptor antagonist[END_REF][START_REF] Teather | Differential induction of c-Jun and Fos-like proteins in rat hippocampus and dorsal striatum after training in two water maze tasks[END_REF] implied an absence of sensorimotor deficits in the D2KO strain.

The experiments using various treatment times with Sulpiride provided greater insight into the rapid dynamics of changes that occur after D2R blockade and revealed a reversible adaptive response controlled by D2R activity. This observation is consistent with the rapid onset of the beneficial effects of antipsychotics on positive symptoms of schizophrenia in human patients [START_REF] Agid | Delayed-onset hypothesis of antipsychotic action: a hypothesis tested and rejected[END_REF].

Finally, stereotaxic administration of sulpiride into the temporal hippocampi of C57BL/6J animals fully replicated the learning deficit observed in D2KO mice. This unambiguously demonstrated the specific participation of D2Rs localized in the temporal hippocampus in the MWM performance.

Specific deletion of pre-synaptic D2Rs is correlated with LTD absence in the temporal hippocampus and learning deficits

In D2 DATcre mice, with pre-synaptic D2R deletion, a dichotomy between LTP and LTD expression was observed, LTP expression remaining intact whereas LTD was abolished.

Strikingly, performance in the spatial MWM was equally impaired in D2 DATcre and in D2KO mice. LTP and LTD operate by different molecular mechanisms, with potentially distinct roles [START_REF] Holscher | Synaptic plasticity and learning and memory: LTP and beyond[END_REF][START_REF] Kemp | Hippocampal long-term depression and long-term potentiation encode different aspects of novelty acquisition[END_REF]. In particular, LTD has been shown to be essential for spatial memory consolidation [START_REF] Ge | Hippocampal long-term depression is required for the consolidation of spatial memory[END_REF], novel spatial learning [START_REF] Goh | Spatial Object Recognition Enables Endogenous LTD that Curtails LTP in the Mouse Hippocampus[END_REF] and behavioral flexibility [START_REF] Nicholls | Transgenic mice lacking NMDAR-dependent LTD exhibit deficits in behavioral flexibility[END_REF]. NMDAR-LTD in the hippocampus relies on phosphatase activity [START_REF] Luscher | NMDA receptor-dependent long-term potentiation and longterm depression (LTP/LTD)[END_REF], whereas the expression of NMDAR-LTP is dependent upon the postsynaptic activation of calcium/calmodulin-dependent kinase II (CaMKII) and trafficking of GluA1 containing AMPARs [START_REF] Selcher | Glutamate receptor subunit GluA1 is necessary for long-term potentiation and synapse unsilencing, but not long-term depression in mouse hippocampus[END_REF]. Notably, the activity of the G protein-independent glycogen synthase kinase-3beta (GSK3β)/Akt pathway might be required in NMDAR-dependent LTD expression in the hippocampus [START_REF] Peineau | A systematic investigation of the protein kinases involved in NMDA receptor-dependent LTD: evidence for a role of GSK-3 but not other serine/threonine kinases[END_REF][START_REF] Peineau | LTP inhibits LTD in the hippocampus via regulation of GSK3beta[END_REF]. In the mouse striatum, D2R activation is essential for Akt inhibition and GSK3β activation [START_REF] Beaulieu | Beyond cAMP: The Regulation of Akt and GSK3 by Dopamine Receptors[END_REF][START_REF] Beaulieu | Regulation of Akt signaling by D2 and D3 dopamine receptors in vivo[END_REF], therefore the recruitment of these pathways in D2-dependent plasticity in the hippocampus remains to be investigated.

Another interesting question is the function of D2Rs expressed in the mossy cells of the DG. Whereas it is unlikely that these postsynaptic D2Rs would be implicated in LTD and spatial memory deficits, because they are intact in D2 DATcre mice, they might participate in the deficit of LTP expression of D2KO mice through a modulation of CA3 to CA1 excitability.

Overall, our data uncover that LTD expression in the temporal hippocampus plays a determinant role in the regulation of learning and memory and that pre-synaptically expressed D2Rs in DA fibers tightly modulate this aspect of hippocampus function. On the one hand, D2Rs control synaptic and extrasynaptic DA levels, DA innervation, LTD expression thresholds and learning and memory performance through their presynaptic location in temporal CA1. On the other hand, they seem to be implicated in LTP modulation through postsynaptic D1R overactivation. Even if a clarification of the mechanisms involved in these processes is still necessary, these contributions were largely underestimated until now. The functional role of VTA dopaminergic input in the hippocampus potentially shapes a complex circuitry when added to the loop from the hippocampus to the nucleus accumbens and then to the VTA, which has been shown to control the firing of DA neurons [START_REF] Lisman | A thalamo-hippocampal-ventral tegmental area loop may produce the positive feedback that underlies the psychotic break in schizophrenia[END_REF][START_REF] Lisman | The hippocampal-VTA loop: controlling the entry of information into long-term memory[END_REF]. The existence of a direct mesohippocampal functional DA pathway and the role of pre-synaptic D2R are therefore key elements to consider in the etiology of psychotic symptoms and the long-term effects of antipsychotic treatments, including their potential impact on hippocampal volume in schizophrenic patients [START_REF] Ho | Long-term antipsychotic treatment and brain volumes: a longitudinal study of first-episode schizophrenia[END_REF] and the role of DA in aberrant salience and the etiology of delusions [START_REF] Kapur | Psychosis as a state of aberrant salience: a framework linking biology, phenomenology, and pharmacology in schizophrenia[END_REF].

,

  Figure sup 1). D3R and D4R mRNA expression in the hippocampus (Figure sup 1B-C) fell below our detection limit (Figure sup 1B). D1R mRNA expression was restricted to the granular cell layer of the DG (Figure sup 1A), and D2R mRNA expression was observed only in the polymorphic layer (hilus) of the DG (Figure 1A-B). D5R mRNA was expressed in granular and pyramidal cell layers (Figure sup 1D). Radiolabeling with [I 125 ]-iodosulspride the presence of D2R in the granular cell layer, in addition to the polymorphic layer of the DG (Figure 1D-E). The specificity of D2R expression was shown using D2KO mice (Figure 1C-1F). Combined fluorescent D2R in situ hybridization and immunolabeling against CaMKIIα showed the D2R-expressing neurons in the hilus to be glutamatergic (Figure 1G-I, Figure sup 1E-H). Using reporter tdTomato D2cre mice, we identify these neurons as mossy neurons,

  ) and totally absent in the septal part of the hippocampus (Figure sup 2A-B). The strongest labeling was observed in the dorsal part of the stratum radiatum and the ventral part of the lacunosum moleculare (LMol) of CA1 (Figure sup 2C-D). The specificity of DAT labeling was confirmed using DATKO mice (38) (Figure sup 2E).

Figure

  Figure sup 2F). Basal levels of extracellular DA were similar in D2WT and D2KO mice.

  increased DA released in D2KO mice would overactivate postsynaptic D5Rs expressed at CA1 glutamatergic cells and produce deficits in synaptic plasticity. The D1/D5 antagonist SCH23390 (1 µM, 30 min) reinstated HFS-induced LTP on D2KO slices to a level comparable to D2WT slices (n=9; 1.37±0.06) (Figure 3G), whereas it had no effect on the LTP induced in D2WT slices (n=6; 1.40±0.09) (Figure sup 3A). Nonetheless, SCH23390 application (1 µM, 40 min) did not affect the level of LTD in D2KO slices (n=6; 1.04±0.05) (Figure 3H). Constitutive deletion of the D2 receptor caused spatial and recognitionmemory deficits In the spatial version of the MWM, D2KO mice were unable to learn the platform location. The mean latency during the last training session was significantly longer in D2KO mice (72.4±7.4 s) compared to D2WT (38.5±8.9 s). During the challenge session performed 90 min after the final training, D2KO mice (23±4.7%) spent significantly less time in the active quadrant than WT (43.8±2.2%), suggesting impairments in short-term spatial memory (Figure 4A). This deficit contrasted with the normal performance of D2KO mice in the cued version of the MWM (Figure 4B), where no differences in escape latencies were observed between the D2KO (day 5: 25.4±5.4) and D2WT (day 5: 22.5±5.4). In the spatial Barnes maze, D2KO acquired properly the escape hole location during training but did not significantly visit the active quadrant during the challenge session performed 24 hrs after the last training (D2WT: 35.5±4; D2KOs: 21.6±4.5), suggesting long-term spatial memory impairments (Figure 4C). Recognition memory was also impaired in the two-object recognition task, D2KO mice exhibited significantly decreased percentages of time spent exploring the new object compared to WT (D2KOs: 53.1±4%; D2WT: 65.2±3.8%) (Figure 4D). Finally, D2KO mice exhibited no impairment in the fear-conditioning paradigm (Figure sup 4). Pharmacological blockade of the D2Rs produced a loss of spatial memory and of synaptic plasticity in the hippocampus Given the possible compensatory mechanisms that could occurred during D2KO mouse development, we tested pharmacological blockade of D2Rs in naïve C57BL/6J animals. Daily systemic administration of the D2/D3R antagonist sulpiride (50 mg/kg) 30 minutes before the first of two training sessions in the spatial version of the MWM revealed a slow-onset effect. During the first 4 training days, the escape latency in the sulpiride group (58.3±11 s) and the NaCl group (50.3±5.4 s) were identical. However, within subsequent trainings, sulpiride-treated mice became slower to find the platform location (sulpiride: 70.2±10.6 s; control: 39.6±7.3 s), and spent less time in the active quadrant (sulpiride: 21.1±10.4%; control: 42.6±4.1%) during the challenge (Figure 5A). When the sulpiride treatment started 1 week before the MWM training and was maintained during the procedure, the mice were unable to learn the platform location (sulpiride: 71.1±9.5 s; control: 44.1±10.1 s) and spent significantly less time in the active quadrant during the challenge (sulpiride: 17.5±3.7%; control: 42.6±4.1%) (Figure 5B). Interestingly, if the sulpiride pre-treatment was stopped prior to the MWM training, the deficit during the first 3 days of training (sulpiride: 80.4±5.9 s; control: 60±4.6 s) disappeared during the last days (sulpiride: 44.1±10.1 s; control: 39.6±7.3 s) and the challenge (sulpiride: 36±4.9%; control: 42.6±4.1%) (Figure 5C).

  significant sprouting (1.3-1.7 fold more) of DAT + fibers in LMol (Sulpiride: 132.3±22.5 ; control: 99.1±10.6), Radiatum (Sulpiride: 126.7±5.9; control: 76.2±5.9) and Pyramidal/oriens (Sulpiride: 51.1±6.4; control: 32.7±2.2) layers (Figure5F and sup 2H-H'). Dopamine levels were also increased in treated mice (Sulpiride: 204.4±20.1 ng/g; vs control:169.8±12.4 ng/g), attesting the fast remodelling of DA system in hippocampus (Figure5Gand sup 2I).To undoubtedly associate the effects of D2R blockade with hippocampal-dependent processes, we implanted bilateral cannulas into the temporal part of the hippocampi of C57BL/6J mice (Figuresup 2J). Direct sulpiride infusion (5 µg/µl, 0.1 µl/side), initiated 1 week before the MWM procedure and maintained during training, reproduced the learning deficit observed in D2KO mice (Figure5H). The final escape latency was significantly increased in sulpiride-treated mice (77.3±6.6 s) compared to control mice (29±7.5 s), and the time spent in the active quadrant was significantly decreased (19.6±3.9%) compared to controls (33.2±4.1%).

  to remove D2 autoreceptors in DAT + neurons (Figure sup 5A-C). Pre-synaptic knockout of the D2R gene (D2 DATcre mice) had no effect on the expression of LTP in CA1 (n=8; 1.40±0.05) in comparison with WT DATcre animals (n=6; 1.44±0.06) (Figure 6A). Conversely, D2 DATcre mice totally loss their LTD expression (n=6; 1.06±0.05) compared to WT DATcre mice (n=9; 0.83±0.04), (Figure 6B).

FIGURE LEGENDS Figure 1 :

 LEGENDS1 FIGURE LEGENDSFigure 1: DA D2 receptors and DA fibers distribution in the hippocampus. A-C) D2R mRNA expression detected by antisens [ 35 S]-oligonucleotides in hippocampus of WT mice (A,B) was restricted to the polymorphic layer of dentate gyrus along anteroposterior axis. No signal was detected on coronal slices from D2KO mice (C). D-F) Autoradiographic detection of D2R using [ 125 I]-iodosulpride labeling in the granular layer of dentate gyrus and in the proximal pyramidal cells of CA3 in WT mice (D,E) with no signal in the D2KO slices (F). G-I) Fluorescent in situ hybridization for D2R mRNA (G, red) coupled to immunolabeling for CaMK2α (H, green) in mossy cells of dentate gyrus (I) with Hoechst staining (blue).Scale bar=25µm. J) Morphological confirmation of mossy cells expressing D2R in D2-tomato reporter mice. Scale bar=25µm. K) Schematic 3D representation of mice hippocampus with representation of coronal sections within temporal hippocampus (anterior to bregma, -3.2 mm). L) Immunolabeling for DAT (red) positive fibers restricted in CA1 division of mice hippocampus. Slices were counterstained with Hoechst staining (blue). Scale bar=50µm. LMol: Laconosum Molecular layer; Rad: Stratum radiatum; Pyr: Pyramidal layer.

Figure 2 :

 2 Figure 2: Anatomical and physiological characterizations of hyperdopaminergia in D2KO mice. A) Immunolabeling of DAT positive fibers (red) in the stratum radiatum layer of the hippocampus of D2WT and D2KO mice. Coronal slices containing the hippocampus were counterstained with Hoescht (blue). Scale bars: 50 µm. B) Quantification of DAT+ fibers in CA1 layers (LMol: Laconosum Molecular layer; Rad: Stratum radiatum; Pyr: Pyramidal layer; Ori: Oriens) of the right hippocampus in D2WT and D2KO mice (n=4 for each group; compared to D2WT: **p<0.01). C) Schematic representations of retrolabeled neurons distribution in ventral mesencephalon of D2WT (left) and D2KO (right) mice. D) Table compiling the numbers and the proportion of DAergic or GABAergic neurons retrogradelly labeled in VTA or SNc in D2WT (n=10) and D2KO (n=10) mice. E) HPLC analysis of tissue catecholamine (DA and DOPAC) levels in the hippocampus of D2WT (n=10) and D2KO (n=9) mice (compared to D2WT: **p<0.01). F) Left, Microdialysis analysis of extracellular DA in the right temporal hippocampus of D2WT and D2KO mice (n=6 per group) at baseline and during two KCl stimulations (K1, K2). Center, Area under the curve during the two KCl-evoked DA release (K1, K2) in D2WT and D2KO mice. Right, DA concentration measured by the ratio K2/K1 in D2WT (1.05) and D2KO (1.38) mice (Compared to D2WT: *p<0.05).

Figure 3 :

 3 Figure 3: Synaptic plasticity of the temporal hippocampus in D2KO mice A) Left, High frequency induced LTP in D2WT (n=6) and D2KO (n=8) mice hippocampus slices. Right, Mean final slope in each genotype (Compared to baseline: ### p<0.001; Compared to D2WT: **p<0.01). B) Left, Paired-pulse low frequency induced LTD in D2WT (n=9) and D2KO (n=7) mice hippocampus slices. Right, Mean final slope

Figure 4 :

 4 Figure 4: Hippocampal-dependent memory performance in the D2KO mice. A) Left, Mean escape latency in the spatial hidden-platform version of the MWM in D2WT (n=17) and D2KO (n=18) (Compared to D2WT: *p<0.05; ***p<0.001). Right, Percentage of time spent in the active quadrant during the probe test of the MWM in each genotype (compared to chance performance of 25%: ## p<0.01; compared to D2WT: *p<0.05). B) Mean escape latency in the Cued version of the MWM for the D2WT (n=16) and the D2KO (n=15) groups. C) Left, Escape latency in the spatial Barnes maze in D2WT (n=9) and D2KO (n=10) groups. Right, Percentage of time spent in the active quadrant during the probe test of the Barnes maze in each genotype (compared to chance performance of 25%: # p<0.05; compared to D2WT: *p<0.05). D) Percentage of time exploring the new object in the novel object recognition task in D2WT (n=9) and D2KO (n=10) (compared to chance performance of 50%: ## p<0.01; compared to D2WT: *p<0.05).

Figure 5 :

 5 Figure 5: Spatial memory performance and synaptic plasticity after pharmacological blockage of D2R by sulpiride A) Left, Mean escape latency in the spatial hidden-platform version of the MWM in Control (NaCl 0.9%, n=21) and Sulpiride (i.p., 50 mg/kg, n=9) treated mice during the 7 days of MWM (compared to Control: *p<0.05; ** p<0.01; ***p<0.001). Right, Percentage of time spent in the active quadrant during the probe test of the MWM in each condition (compared to chance performance of 25%: ### p<0.001; compared to Control: *p<0.05). B) Left, Mean escape latency in the spatial hidden-platform version of the MWM in Control (NaCl 0.9%, n=21) and Sulpiride (i.p., 50 mg/kg, n=11) treated mice with treatment started 7 days before the MWM and carried on during the 7 days of experiment (compared to Control: *p<0.05; ** p<0.01; ***p<0.001). Right, Percentage of time spent in the active quadrant during the probe test of the MWM in each condition (compared to chance performance of 25%: ### p<0.001; compared to Control: ***p<0.001). C) Left, Mean escape latency in the spatial hidden-platform version of the MWM in Control (NaCl 0.9%, n=21) and Sulpiride (i.p., 50 mg/kg, n=11) treated mice with treatment started 7 days before the MWM but stopped during the 7 days of experiment (compared to day 1: *p<0.05; ***p<0.001). Right, Percentage of time spent in the active quadrant during the probe test of the MWM in each condition (compared to chance performance of 25%: # p<0.05; ### p<0.001). D) Left, High frequency induced LTP in C57BL/6J mice hippocampus slices after Sulpiride (10 µM, 30 min) application (n=9) compared to untreated slices (n=7). Right, Mean of the final slope in each condition (compared to baseline: ## p<0.01; compared to untreated slices: *p<0.05). E) Left, Paired-pulse low frequency induced LTD in C57BL/6J mice hippocampus slices after Sulpiride (10 µM, 40 min) application (n=6) compared to untreated slices (n=8). Right, Mean of the final slope in each condition (compared to baseline # p<0.05; compared to untreated slices: *p<0.05). F) Quantification of DAT+ fibers immunostaining in CA1 layers (LMol: Laconosum Molecular layer; Rad: Stratum radiatum; Pyr: Pyramidal layer; Ori: Oriens) of the right hippocampus in control and Sulpiride-treated mice (n=4 for each group, compared to WT: *p<0.05). G) HPLC analysis of tissue catecholamine (DA and DOPAC) levels in the hippocampus of control (n=7) and Sulpiride-treated (n=7) mice (compared to control: *p<0.05). H) Left, Mean escape latency in the spatial hiddenplatform version of the MWM for Control (NaCl 0.9%; n= 10) and Sulpiride (2.5 µg each side; n=11) injected mice (compared to Control: *p<0.05, ***p<0.005). Right, Percentage of time spent in the active quadrant during the probe test of the MWM in each condition (compared to Control: *p<0.05).

Figure 6 :

 6 Figure 6: Synaptic plasticity, anatomical characterization and spatial memory in presynaptic D2R knock-out mice A) Left, High frequency induced LTP in WT DATCre (n=6) and D2 DATCre (n=9) mice hippocampus slices. Right, Mean of the final slope in each genotype (compared to baseline: ### p<0.001). B) Left, Paired-pulse low frequency induced LTD in WT DATCre

Fig. 1 Rocchetti

 1 Fig. 1

Fig. 4 Rocchetti 30 Fig. 6

 4306 Fig. 4

Acknowledgements: BG is a Canadian Research Chair in the Neurobiology of Mental Disorders. GDB and EI are supported by a postdoc grant from the FRQS. This work was supported by the Canada Research Chairs program (BG), the Canadian Foundation for Innovation and the Graham Boeckh Foundation for Schizophrenia Research (BG). We thank Ms Erika Vigneault and Marie-Eve Desaulnier for excellent care and maintenance of all mice colonies. Financial disclosure: All authors report no biomedical financial interests or potential conflicts of interest.


Frédéric Guidec

Hervé Roussain

Asynchronous Document Dissemination in Dynamic Ad Hoc Networks

This paper presents a document-oriented model for information dissemination in dynamic ad hoc networks, such as those composed of highly mobile and volatile communicating devices (e.g. laptops and PDAs). This model relies on an asynchronous, peer-to-peer propagation scheme where documents can be cached on intermediate devices, and be later sent again -either spontaneously or on demand-in the network.

Introduction

Today most laptops and personal digital assistants (PDAs) feature wireless interfaces, many of which are capable of ad hoc communication. Our work aims at fostering the design, the implementation, and the deployment of application services capable of running specifically on devices participating in a dynamic ad hoc network, that is, a network in which nodes are highly mobile and volatile. Node mobility in a dynamic network is the consequence of the fact that devices are carried by users, which are themselves mobile. Node volatility results from the fact that, since mobile devices have a low power-budget, they are frequently switched off and on by their owners. An additional problem with dynamic ad hoc networks is that in many realistic scenarios such networks present themselves as disconnected networks. As a consequence, direct transmissions between any pair of devices is not always feasible, as such transmissions require that both devices are active simultaneously in the network, and that a connected-path can be established between these devices at transmission time.

The problem of delivering messages in disconnected ad hoc networks has been approached several times and following different lines in the past few years. For example, a new network architecture relying on the general principle of message switching in store-and-forward mode has been proposed in [START_REF] Fall | A Delay-Tolerant Architecture for Challenged Internets[END_REF]. With this approach pieces of information are transported as so-called bundles between bundle forwarders, which are capable of storing messages (or bundles) before they can be sent again in the network.

With Epidemic Routing [START_REF] Vahdat | Epidemic Routing for Partially-Connected Ad Hoc Networks[END_REF][START_REF] Lindgren | Probabilistic Routing in Intermittently Connected Networks[END_REF][START_REF] Li | Sending Messages to Mobile Users in Disconnected Ad Hoc Wireless Networks[END_REF], messages are buffered in mobile hosts, and random pairwise exchanges of messages among these hosts are expected to allow eventual message delivery in partially-connected networks.

The service we present in this paper compares with the models proposed in the abovementioned papers. However it can be observed that these papers mostly address the problem of message delivery in disconnected networks from a theoretical viewpoint: they propose new algorithms and heuristics for delivering messages in such networks, and they report the results of simulations that are meant to demonstrate how these algorithms should perform in realistic conditions. In contrast, our approach is more practical, since it consists in actually implementing a service for document dissemination in ad hoc networks, and then using this service as a building block with which applicationlevel services can be developed and tested in realistic experimental conditions.

Service overview

The general architecture of the service we propose is shown in Figure 1. This service is not meant to be used directly by end-users. Instead it is meant to serve as one of the basic building blocks with which higher-level services can later be developed. Moreover this service is document-oriented. Basically, we propose that any document sent in the network be maintained as long as possible in a local cache by as many devices as possible, so it can remain available for those devices that could not receive it at the time it was sent originally. The underlying idea is that the dissemination of multiple copies of the same document may help do with the volatility of devices, while the mobility of these devices can itself help transport information between islands in a fragmented network. Besides providing a caching system where documents can be maintained in mobile devices, our service also provides facilities for document advertisement, document discovery, and document transport between neighboring devices. For example, a device can sporadically or periodically notify its neighbors about all or part of the documents stored in its cache. It can also look for specific documents in its neighborhood, and either push documents toward -or pull documents from-its neighbors.

Structure of documents.

In the model our service relies on, each document can be associated a document descriptor, which provides information about its type, author, keywords, content, etc. A document may encapsulate its own descriptor, but the descriptor can also be handled separately (which means it can for example be transmitted, edited, stored, and displayed separately). When an application-level document must be sent in the network, it must itself be encapsulated in a transfer document, whose descriptor specifies transmission parameters for this document, such as its type, origin, and destination, as well as indications about how long and how far this document should propagate in the network. Examples of document descriptors can be found in [START_REF] Roussain | A Peer-to-Peer Approach to Asynchronous Data Dissemination in Ad Hoc Networks[END_REF]. Caching documents. Once a document has been received by a device, it is expected that this document be stored for some time on this device, and possibly sent again later in the network. Each device thus maintains a cache, whose capacity can of course be adjusted depending on the resources available locally. Local strategies can additionally be defined on each device in order to specify caching modalities for documents. Possible criteria for defining such strategies are document size, type, origin, destination, lifetime, etc. The caching service is not itself responsible for deciding how it should behave with respect to documents. Instead, it provides interfaces (not detailed in this paper) with which higher-level services can specify strategies regarding how one or another category of document should be managed locally. Moreover, attributes found in a document's descriptor can help determine how this document should be managed by the caching service. For example, attributes may indicate how long a document should be considered as being valid in the network, and how often the availability of this document should be announced in the network. Document producers and consumers. Each device that participates in the dissemination of documents can play several distinct roles with respect to these documents. A device is considered as the provider of a document if this document is stored locally (in its cache), and if it can send this document in the network. Symmetrically, a device is considered as being a potential consumer for a document if it can receive this document from the network, and either use this document immediately or store this document in its cache (or both). Depending on circumstances, a device may play only one of these two roles, or both roles simultaneously. The behavior of a device may actually not be the same for all types of documents. For example the caching service may be configured so as to accept and receive only a certain category of documents from the network. Moreover these documents may be received and stored in the cache only for the benefit of other local application services. The device would thus behave as a consumer for certain documents, while refusing to disseminate these documents further. Conversely a device may be configured so as to provide its neighbors with documents produced locally, while refusing to consume similar documents received from the network. Besides playing the role of a producer or consumer for a category of documents, a device can behave either proactively or reactively (or both) with respect to each of these roles. A device that plays the role of a document provider can behave proactively by sending spontaneously this document in the network. It may also behave reactively by sending a document in the network after this document has been explicitly requested. It can of course show a mixed behavior, sending for example one document periodically (with a rather long period so as not to load the network too much), and replying immediately to explicit requests for this document.

Similarly, a device that plays the role of a document consumer can behave either proactively or reactively, or show both kinds of behavior simultaneously. A document consumer can behave proactively by sending requests for this document in the network (thus soliciting a reactive behavior from devices that possess a copy of this document). It can also behave reactively by receiving a document from the network, and consuming this document even if it has not been explicitly requested before.

Advertisement and request documents. Specific kinds of documents have been defined in order to allow the advertisement, discovery, and transmission of documents between neighboring devices. For example, an "advertisement document" can be sent by a device to announce that it owns one or several documents in its cache, and that it can provide any of these documents on demand. An advertisement document is thus a special kind of transfer document whose payload is composed of one or several document descriptors, corresponding to the descriptors of the documents whose availability is being announced.

Another special kind of document, called a "request document", can likewise be sent by a device to ask for the transmission of a document, or that of several documents. A request can be addressed specifically to a given device (for example after an advertisement has been received from this device), or it can be sent to all or part of the devices in the neighborhood. The payload of a request document is composed of one or several descriptor patterns. The structure of a descriptor pattern compares with that of a descriptor, but for all or part of the attributes that can appear in a document descriptor, it specifies a regular expression to be applied to the corresponding attribute. A device receiving a descriptor pattern can thus use this pattern to examine the descriptors of the documents it maintains in its cache, and to decide which of these descriptors match the pattern. Selected documents can then be sent in the network.

Implementation details and ongoing work

The service for asynchronous document dissemination presented in the former section has been implemented in Java. Documents and document descriptors are also reified as standard Java objects. They can be transported in the network either as serialized Java objects, or as XML-formatted documents (examples can be found in [START_REF] Roussain | A Peer-to-Peer Approach to Asynchronous Data Dissemination in Ad Hoc Networks[END_REF]). It is worth mentioning that the code we developed can be deployed equally on a singlehop network, or on a multi-hop network relying on algorithms for dynamic routing and flooding.

The development of several application-level services is also under way in our laboratory. These services all rely on the facilities offered by the document dissemination service, but each of them defines its own strategy regarding what documents must be disseminated, and in what conditions. Among these application-level services are a peer-to-peer messaging service, a presence announcement service, and a service for the distribution and the deployment of software packages. Details about the latter service can be found in [START_REF] Le | JASON: an Open Platform for Discovering, Delivering and Hosting Applications in Mobile Ad Hoc Networks[END_REF].

The service presented in this paper permits the asynchronous dissemination of documents in dynamic ad hoc networks, such as those composed of highly mobile and volatile communicating devices. It proposes an asynchronous, peer-to-peer, documentoriented propagation model, where each document received by a device can be maintained in a local cache in this device, so it can later be sent again in the network, either spontaneously, or after a request for this document has been received from another device. This approach is expected to help do with the volatility of devices, since it permits that documents reach devices that are only active sporadically in the network. It is also expected to permit information dissemination in a fragmented network, taking advantage of the mobility of devices which can serve as carriers between disconnected parts of the network.

Acknowledgements. This work is supported by the French "Conseil Régional de Bretagne" under contract B/1042/2002/012/MASC.


Yves Mahéo

Frédéric Guidec

Luc Courtrai

Middleware Support for the Deployment of Resource-Aware Parallel Java Components on Heterogeneous Distributed Platforms

This paper reports the development of the Concerto platform, which is dedicated to supporting the deployment of resource-aware parallel Java components on heterogeneous distributed platforms, such as pools of workstations in labs or offices. Our work aims at proposing a basic model of a parallel Java component, together with mechanisms and tools for managing the deployment of such a component on a distributed platform. Moreover, we strive to provide components with means to perceive their runtime environment, so they can for example dynamically adapt themselves to changes occurring in this environment. The Concerto platform was designed in order to allow the deployment of parallel components on a distributed platform. It additionally defines and implements an open and extensible framework for distributed resource discovery and monitoring in such an execution environment.

Introduction

There is a growing demand for distributed applications that can be ported on a wide variety of platforms, including the distributed platforms obtained by assembling regular workstations in labs and company offices. However, developing distributed applications from scratch is known to be a difficult and tedious task, requiring much knowledge and expertise. The component-oriented approach can help significantly with this respect. This approach promotes the development of so-called "software components", that can later serve as deployment units, and that can further be assembled while developing more complex components, or full-featured applications. Although the advantages of this approach are now widely admitted, little effort has been invested so far on the development of components specifically designed for distributed platforms.

In our work we experiment with the development of "parallel components", that is, components that are meant to be deployed on a distributed platform, and that each encapsulate a parallel code to be run on this kind of platform. Moreover, since these components we consider are liable to be deployed and executed on possibly unstable and heterogeneous platforms, we strive to provide these components with means to perceive their execution environment, so they can adapt dynamically to changes observed in this environment. By doing so, we try to foster the development of adaptive parallel components, that is, components whose behavior is not defined statically, but components that can instead adjust their behavior dynamically in order to react to environmental variations.

Although component adaptability is our prime objective, this paper does not focus on this topic per se. Instead it presents a Java-based middleware platform we have designed in order to provide support for such components. The platform Concerto implements a number of facilities for deploying, launching, and controlling the execution of parallel components on a distributed platform. To achieve these goals it uses facilities we have implemented in D-RAJE (Distributed Resource-Aware Java Environment), an open and extensible framework that makes it possible to monitor the state of the many resources offered in a distributed platform. Figure 1 shows the overall organization of the Concerto platform.

The remaining of this paper is organized as follows. Section 2 gives an overview of D-RAJE. Section 3 presents the basic model of a parallel component we propose and implement in Concerto, and it shows how this model can be used to implement a demonstrator parallel component that encapsulates an adaptive, distributed genetic algorithm. This section also describes the facilities we implemented for managing the deployment and the execution of parallel components on a distributed platform. Section 4 discusses related projects on distributed software components. Section 5 concludes the paper.

Support for resource modeling and monitoring

One of the main objectives of project Concerto is to provide parallel software components with means to perceive their runtime environment, so that they can adapt to variations observed in its characteristics. Environment modeling and monitoring in distributed systems has already been addressed in numerous projects pertaining for example to Grid computing or to network computing (such as [START_REF] Czajkowski | Grid Information Services for Distributed Resource Sharing[END_REF][START_REF] Krauter | A Taxonomy and Survey of Grid Resource Management Systems for Dis-tributed Computing[END_REF][START_REF] Kon | 2K: A Distributed Operating System for Dynamic Heterogeneous Environments[END_REF][START_REF] Sacerdoti | Wide Area Cluster Monitoring with Ganglia[END_REF]). In many of these projects, though, resource modeling is performed at a rather coarse grain (e.g., computing node, global free memory, network link), and information about resources is often limited to system resources, and collected using ad hoc methods. An important part of Concerto is made of a software framework called D-RAJE. This platform was designed so as to offer an extensible model in which any kind of resource in a distributed environment can be reified as a Java object. It also provides various facilities for monitoring these resources in a homogeneous way. A brief description of D-RAJE is given below. A more detailed presentation can be found in [START_REF] Guidec | A Java Middleware Platform for Resource-Aware Distributed Applications[END_REF].

Resource modeling

With D-RAJE a distributed system can be modeled and monitored using Java objects that reify the various resources offered in this system. As a general rule, we qualify as "resource" any hardware or software entity a software component is liable to use during its execution. To date the types of resources considered in D-RAJE -and thus in the Concerto platform-include system resources (CPU, system memory, swap, network interfaces, etc.) that chiefly characterize the underlying hardware platform, as well as so-called "con- ceptual" resources (sockets, processes, threads, directories, files, RMI servers, etc.) that rather pertain to the applicative environment in which a component is running. An extensible hierarchy of classes models these resources. Any resource object (that is, any instance of one of these classes) is capable of producing on demand an observation report, which provides a summary of its state.

From the viewpoint of an application programmer, resource objects can be created explicitly, or one can rely on a resource discovery monitor that is able to automatically instantiate (and destroy) resource objects pertaining to some classes of resources.

At creation time, references to resource objects are added to a local resource register. A resource manager is distributed on the distributed platform so as to play the role of a global register. This resource manager makes it possible to access information about distributed resources in a homogeneous way, regardless of where these resources are actually located in the system. Figure 2 shows the general architecture of the distributed resource manager. Local instances of this manager are created on each host, and these instances interact with one another thanks to the Java RMI mechanism. Moreover, each instance maintains a weak reference to any resource object created locally, so it can observe the state of any local resource at any time, and provide remote instances of the manager with similar information whenever required. Weak references are used -instead of standard references-so as not to prevent application components from deleting resource objects when these objects are not needed anymore at application level. D-RAJE somehow compares with the Common Information Model [START_REF][END_REF] as far as resource modeling is concerned. However, D-RAJE goes beyond pure modeling, as it additionally implements facilities for collecting and exploiting information about the state of any system or conceptual resource (please see [START_REF] Guidec | A Java Middleware Platform for Resource-Aware Distributed Applications[END_REF] for details on this topic).

Resource selection and observation

The resource manager can be consulted when looking for a specific kind of resource in the distributed system, or in order to request information about the status of any resource in this system. Since the population of resource objects can be quite large, a selection can be performed based on their location (e.g. on a specific host, in the whole distributed system, or in neighboring hosts), their type, or any other criterion. Resource filtering is obtained by using socalled resource patterns. Any programmer can define new patterns, although many patterns are already available as predefined patterns in D-RAJE. With these patterns, one can for example identify all objects modeling host CPUs, or network interfaces, or TCP sockets. These objects can additionally be searched for on a specific host, or in the whole distributed platform. One can even perform a more selective search, and look for example for TCP or UDP sockets that have been bound to a local port whose number is in a specific range (say, values between 0 than 1024), and that maintain connections with remote hosts whose IP addresses conform to a specific pattern (say, all addresses in domain 195.83.160/22). Resource identification can thus be achieved at quite a fine grain if needed.

Once a resource has been identified, information on its status can be obtained through any one of two observation mechanisms. The first of these mechanisms allows direct observation of the resource considered: the resource manager can be asked to collect and return an observation report concerning a specific resource, wherever this resource is located in the system. The second observation scheme relies on an event-based model: local or distant monitors are automatically created when needed. These monitors can observe the resource considered periodically, and notify the user when user-specified events occur. A thorough description of these topics can be found in [START_REF] Guidec | A Java Middleware Platform for Resource-Aware Distributed Applications[END_REF].

Parallel Components

The Concerto platform is dedicated to supporting adaptive parallel components. Our aim is to propose a basic component model suited for parallel and distributed applications, and to provide an infrastructure that enforces the adaptability of components. We propose below a definition of what we mean by parallel component in Concerto.

A programmer who wishes to develop a parallel component for Concerto must design his component as a set of cooperating threads. He must also define the business part of the component (name, interface, implementation). The platform offers facilities for managing non-functional aspects of the component. • Business interface: No constraint is put on the type of the business interface. The programmer of a component may for example propose an interface based on Java RMI. The component is then an object implementing the Remote interface, whose methods are to be called remotely by the component's clients (Section 3.2 describes a demonstrator component that has been designed along this line). The component may also behave as a server listening to a specific TCP or UDP port, with which a client program can communicate. Alternatively, one may design a distributed interface (that is to say, an interface associated with several objects that each implement a part of the interface), so the component can be connected in parallel with another parallel component.

Component model

• Life-cycle interface: Through this interface, the different steps of the life of the component can be controlled.

To date, this covers deploying the component on the distributed platform, launching all the activities (i.e., starting the threads) of the components, and stopping these activities. In the future we may include persistence services in this interface.

• Resource interface: The component exhibits a resource interface through which the user can access information related to the resources used by the component. Actually, the component itself is considered as a resource. Hence any component in Concerto must implement a method observe() that returns an observation report. By default, the observation report generated by a component simply aggregates the observation reports of all the resources it itself uses. For security reasons, the component's programmer may find it desirable to change this default implementation, for example by restricting the amount of information disclosed to the component's client.

Internal structure of a component

When building a parallel component, the programmer develops a set of Java threads (actually, a set of classes implementing the Runnable interface). These threads cooperate to perform functions specified in the component's business interface. Threads are grouped in placement (or distribution) entities called fragments. A fragment is a set of threads that belong to a single component, and that must run within the same virtual machine on the same distributed platform host. The threads of a fragment can thus share a common object space. Communication and synchronization between such threads can be performed just like in any other multithreaded Java program. On the other hand, threads that belong to different fragments must rely on external communication and synchronization mechanisms, such as sockets and RMI.

Information on the component's structure is available through facilities implemented in D-RAJE, since threads, fragments and the component itself are modeled as resources in D-RAJE. As already mentioned, some of this information is also available to other components through each component's resource interface.

Component deployment

The deployment of an application on a distributed platform is known to be a tedious and error-prone task. In order to alleviate the deployment of parallel components on such platforms, we have developed an XML dialect that permits a component programmer to describe the component's structure, placement directives for its fragments, and constraints imposed for the deployment to be feasible (availability of a specific version of the JVM, of a RMI registry, etc.)

The Concerto platform includes an administration tool with which a distributed platform can be managed, using either a command line or a graphical user interface. Figure 3 shows an example of what can be displayed by the graphical interface. With this administration tool, hosts can be added to or removed from the distributed platform at any time. A parallel component can be loaded in the platform by providing a .jar file that aggregates Java code for the component's threads, an XML deployment descriptor, and possibly additional documentation and data associated with the component. During this loading operation, a new Component resource object is created in D-RAJE, and automatically registered with the resource manager. Once a component has been loaded, it can be considered as a new resource in the distributed platform. Another component can therefore trigger its deployment and activation on the platform. Alternatively, these operations can be triggered by any administrator -or user-of the platform thanks to the above-mentioned administration tool.

Example

This section illustrates the utilization of the Concerto platform for building a genetic algorithm (GA) component that can be deployed on a heterogeneous distributed platform. In this particular example the GA component aims at finding an arithmetic expression that approximates a function that can be numerically evaluated. Of course our GA component deals with a population of individuals that are submitted to genetic operators such as mutation and crossover. A selection mechanism makes it possible to identify the best individuals in the population, so that they can participate in the production of new individuals.

Business interface

In order to develop a Concerto component based on this genetic algorithm, the programmer must first specify the business part of the component. In this particular case, the business interface is the interface of a RMI server. As illustrated in Figure 4, this interface offers methods for initializing the population, for launching the reproduction process, for obtaining the current generation, and for halting the reproduction process.

Parallel implementation

The master-slave paradigm is used for implementing a parallel version of the algorithm. Notice that in this particular case our aim is not to produce the best possible parallel genetic algorithm, but simply to illustrate the utilization of Concerto. Therefore, no in-depth optimization has been applied to the original parallel algorithm, nor to its parallel implementation. The population in the GA component is to be distributed on a set of distributed hosts, which will be considered as slave hosts. Each slave will deal with its own sub-population, and will make it evolve through crossovers, mutations, and selections. A particular host, called the master, will periodically gather all the sub-populations, perform a selection phase on the whole population, and scatter it again over the slave hosts.

The resulting GA component is thus composed of a set of slave threads, conducted by a unique master thread. Each of these threads is placed in a distinct fragment. Communication between the slaves and the master is performed through RMI calls. In order to simplify the architecture, the master thread also behaves as an RMI server, implementing the business interface of the GA component.

Deployment

Deployment directives are specified in a Concerto component descriptor that comes in the form of an XML file (see Figure 5). When the deployment of the genetic algorithm component is triggered -for example via the Concerto administration tool-, a succession of operations is performed that consists in analyzing the XML descriptor, checking the requirements specified (in this case, verifying the presence of an RMI registry), loading classes ThMaster and ThSlave on the different JVMs, and creating all the fragments and threads (that will consequently be registered as resources in D-RAJE). These threads will actually be started when the component is activated.

Adaptation

A first, straightforward adaptation of the algorithm can be performed by having the master take the actual number of slave threads into account. This number is determined during the deployment phase, based on the actual number of hosts in the distributed platform at deployment time. In the future we plan to insert a new placement directive (ALL-ALWAYS) in our XML dialect, so that replicated fragments can be automatically created and started when hosts are inserted in the distributed platform while a parallel component is running.

Figure 6 shows how the resource manager can be asked to identify the slave fragments. In this example a resource pattern called FragmentPattern is used in order to look for resources that are actually component fragments. Moreover a parameter is passed to this pattern in order to select only those fragments named "GA-Slave".

The real adaptation of the algorithm is performed by allowing for the heterogeneity of the hosts on which the slave threads are running, as well as for the variability of the CPU workload on these hosts. Indeed, we cannot assume that the CPU type is identical on all hosts in the distributed platform, and that these hosts are all dedicated to running only the GA component. The time a slave thread can spend applying genetic operators on its sub-population may thus vary a lot. As <?xml version="1.0" encoding="ISO-8859-1" ?> <component> <description>Genetic Algorithm Component</description> <name> <concerto-name>GenAlgoRes</concerto-name> <rmi-name>GeneticAlgorithmServer</rmi-name> </name> <requirements> <java kaffe="1.0.7" d-raje="1.0"/> <concerto-vers="1.0"/> <rmiregistry/> </requirements> <file>GenAlgorithm.jar</file> <fragment-list> <fragment name="GA-Master"> <node>ANY</node> <thread-list> <runnable-class="ThMaster"/> </thread-list> </fragment> <fragment name="GA-Slave"> <node>ALL</node> <thread-list> <runnable-class="ThSlave"/> </thread-list> </fragment> </fragment-list> </component> a consequence, the master should be able to tune the size of the sub-population it sends to a particular slave based on the computation power that can be expected from this slave. The CPU power available on a given host can be deduced from the CPU frequency of this host, and from the amount of time the CPU remains idle during an observation period (a period of one second was chosen for this particular ap- plication). Information about each CPU (including its frequency) can be obtained by collecting reports from CPU objects in D-RAJE. Likewise, D-RAJE makes it possible to implement and to deploy monitors that observe the state of a given resource periodically. With such monitors, calculating how much computing power is used on a host of the distributed platform is straightforward.

Measurements

We have conducted a set of preliminary measurements in order to evaluate the impact of the utilization of Concerto -and that of the adaptation facilities it offers-on the overall performance of our GA parallel component. In this experiment the population consisted of 1024 individuals. At each iteration step, each slave computed 10 generations before sending back its sub-population to the master. The overall process iterated 100 times. Table 1 shows the execution times obtained for a varying number of hosts. During this experiment we used evenly loaded 2.4 GHz Pentium 4 PC hosts running Linux 2.4.20. The speedup remains acceptable for the distributed platform sizes considered. The impact of adaptation is showed in Table 2. In this case a slower host (400 MHz Pentium PC) was included voluntarily in the distributed platform, and a random load was put on every CPU by running dummy processes. In the case of one host, the table shows the execution times observed depending on whether the slow host was used, or one of the fast hosts. Executions involving more than one host systematically implied the slow host, plus one or several additional fast hosts. Experiments were performed with and without component adaptation. Results show that a significant gain is obtained when adaptation is performed.

Related Work

The originality of the Concerto approach lies in the fact that it combines a parallel component model together with some support for distributed resource observation. Component [START_REF] Omg | CORBA Components[END_REF]. However these technologies were not specifically designed to support parallel components, that is, components that inherently involve parallel activities. Few works have been carried out on models or platforms that target parallel and distributed components. One of them is the Common Component Architecture [START_REF] Amstrong | Towards a Common Component Architecture for High-Performance Scientific Computing[END_REF], which defines a component model suited for parallel scientific applications. In this architecture, emphasis is put on the definition of a scientific interface definition language (SIDL), and the specification of ports that permit component interoperabilty. The main objective in CCA is to allow the efficient interoperability of pre-existing scientific codes. Like in Concerto, few constraints are put on the way the functional part of a parallel component is implemented. However, no support is provided in CCA for component adaptation. Project Padico [START_REF] Denis | Padico: A Component-Based Software Infrastructure for Grid Computing[END_REF] bears similarities with CCA as it is dedicated to coupling scientific codes. It extends the Corba Component Model, and it relies on a specific communication toolkit in order to provide efficient communication between several SPMD parallel codes encapsulated in components. Some form of adaptation is provided in the communication layer, which allows for several commonly used communication libraries. The work described in [2] also focuses on parallel and distributed components: an implementation of the Fractal [START_REF] Bruneton | Recursive and dynamic software composition with sharing[END_REF] hierarchical component model is proposed in order to provide so-called Grid Components. This implementation is based on the Pro-Active library [START_REF] Caromel | Towards Seamless Computing and Metacomputing in Java[END_REF]. It provides facilities for asynchronous communication, migration of activities, deployment and debugging. Like in Concerto, a parallel component may be implemented according to the MIMD model. Introspection on both the components structure and placement is possible, though both kinds of introspection rely on distinct mechanisms.

None of the above-mentioned works proposes a homogeneous way to get precise information about the environment of a component (including the system), and about its sub-components.

In

Conclusion

In this paper we have presented the Concerto platform, which aims at allowing the deployment and the support of parallel components on distributed platforms. Ongoing work focuses on proposing a basic parallel component model, as well as tools for the deployment of such components. In a first stage, our objective is to adopt as simple and flexible a model as possible.

The distributed platforms targeted by the Concerto platform are primarily those composed by enlisting non-dedicated, heterogeneous, and possibly unstable workstations in labs or offices. The runtime environment offered to parallel components is therefore liable to exhibit much heterogeneity and dynamicity. For this reason, the Concerto platform implements facilities for discovering how resources are distributed in the distributed platform, and for monitoring these resources at runtime.

The development of the Concerto platform is still in progress. Moreover a number of parallel components are also being developed. In the near future we plan to use the platform for deploying instances of these components on different kinds of distributed platform. A series of experiments will then be run, in order to evaluate the benefits of designing parallel components that can monitor their environment, and that can adjust their behavior to variations observed in this environment.

Figure 1 .Figure 2 .

 12 Figure 1. Overview of the Concerto platform (on one host)

3. 1 . 1 .

 11 Component interface Any parallel component hosted by the Concerto platform must define the following interfaces.

Figure 4 .

 4 Figure 4. Business interface of a Concerto component.

Figure 3 .

 3 Figure 3. Snapshot of the Concerto graphical administration tool

Figure 5 .

 5 Figure 5. Example of an XML deployment descriptor.

Figure 6 .

 6 Figure 6. Illustration of how the resource manager can be used to look for specific resources in the whole distributed platform. In this example specific component fragments are sought.

Table 1 . Execution times of the GA on a ho- mogeneous distributed platform

 1 -based application development is already possible through the use of "standard" component technolo-

	# of slaves Execution time
	1	582 sec.
	2	381 sec.
	3	297 sec.
	4	221 sec.
	gies, such as Microsoft COM [12] or Sun's Enterprise Java Beans. The OMG (Object Management Group) also developed its own solution with the CORBA Component Model

Table 2 . Impact of adaptation on a heterogeneous distributed platform resource

 2 Concerto, we strove to associate Java facilities for observation with the implementation of an open component model suited for adaptive application development. In this respect, we share a similar goal with the framework for adaptive components described in[START_REF] David | Towards a framework for Self-Adaptive Component-Based Applications[END_REF]. This framework is based on Fractal and it defines context-awareness services. Well-designed adaptation mechanisms are built in this framework, but no specific support for distribution aspects is provided.

	# of slaves	Execution time with adaptation Execution time without adaptation
	1 (fast host)	400 sec.	407 sec.
	1 (slow host)	2112 sec.	2150 sec.
	2	344 sec.	1344 sec.
	3	234 sec.	974 sec.
	4	203 sec.	806 sec.

Acknowledgments

This work is supported by the French Ministry of Research in the framework of the ACI GRID programme.