Emeric Scharbarg

Augustin Walter

Laure Lecoin

Thierry Gallopin

Frédéric Lemaître

Manon Guille-Collignon

Nathalie Rouach

Armelle Rancillac
email: armelle.rancillac@college-de-france.fr

Prostaglandin D 2 controls local blood flow and sleep-promoting neurons in the VLPO via astrocyte-derived adenosine

Keywords: Purine enzymatic biosensors, PGD2 receptors (DP1R), Neurovascular coupling, Sleep-promoting neurons, NREM sleep, Neuroglial Interactions, A2A receptors (A2AR)

Prostaglandin D2 increases local blood flow and activates

INTRODUCTION

Prostaglandin D2 (PGD2) is the most potent endogenous sleep-promoting molecule [START_REF] Ueno | Prostaglandin D2 Induces Sleep When Microinjected into the Preoptic Area of Conscious Rats[END_REF][START_REF] Urade | Prostaglandin D2 and Sleep/Wake Regulation[END_REF][START_REF] Scammell | Activation of Ventrolateral Preoptic Neurons by the Somnogen Prostaglandin D2[END_REF][START_REF] Matsumura | Prostaglandin D2-Sensitive, Sleep-Promoting Zone Defined in the Ventral Surface of the Rostral Basal Forebrain[END_REF] . Its concentration in cerebrospinal fluid exhibits a circadian variation synchronized with the sleepwake cycle [START_REF] Pandey | Concentration of Prostaglandin D2 in Cerebrospinal Fluid Exhibits a Circadian Alteration in Conscious Rats[END_REF] . Immunofluorescence labeling and in situ hybridization have shown that PGD2 receptors (DP1R) are almost exclusively expressed in some arachnoid trabecular cells of the basal forebrain [START_REF] Oida | Expression of Messenger RNA for the Prostaglandin D Receptor in the Leptomeninges of the Mouse Brain[END_REF][START_REF] Mizoguchi | Dominant Localization of Prostaglandin D Receptors on Arachnoid Trabecular Cells in Mouse Basal Forebrain and Their Involvement in the Regulation of Non-Rapid Eye Movement Sleep[END_REF] . Activation of DP1R by PGD2 infusion via microdialysis probe was further shown to induce adenosine release in a dose-dependent manner in the subarachnoid space of the basal forebrain and to promote sleep via adenosine A2A receptor (A2AR) activation. Pretreatment with a specific adenosine A2AR antagonist indeed reduces the amount of PGD2-induced sleep [START_REF] Mizoguchi | Dominant Localization of Prostaglandin D Receptors on Arachnoid Trabecular Cells in Mouse Basal Forebrain and Their Involvement in the Regulation of Non-Rapid Eye Movement Sleep[END_REF] . In vivo administration of PGD2 by microdialysis probes was tested in various rat brain regions and identified the ventral surface of the rostral basal forebrain as the most effective area in promoting non-rapid eye movement (NREM) sleep [START_REF] Ueno | Prostaglandin D2 Induces Sleep When Microinjected into the Preoptic Area of Conscious Rats[END_REF][START_REF] Matsumura | Prostaglandin D2-Sensitive, Sleep-Promoting Zone Defined in the Ventral Surface of the Rostral Basal Forebrain[END_REF] , which was reduced by adenosine A2A antagonist [START_REF] Satoh | Promotion of Sleep Mediated by the A2a-Adenosine Receptor and Possible Involvement of This Receptor in the Sleep Induced by Prostaglandin D2 in Rats[END_REF] . However, combining PGD2 infusion with c-Fos immunolabelling revealed c-Fosimmunoreactivity in the ventrolateral preoptic nucleus (VLPO), the major NREM sleep center [START_REF] Economo | Sleep As a Problem of Localization[END_REF] , whereas the basal forebrain remained unlabeled [START_REF] Scammell | Activation of Ventrolateral Preoptic Neurons by the Somnogen Prostaglandin D2[END_REF] . Therefore, although several key aspects regarding PGD2-induced sleep have been elucidated, our understanding of its physiological mechanisms is still incomplete. The VLPO being a very small brain structure, deeply localized, and not anatomically well-defined, complicate its investigation.

This study thus here aims to decipher how locally in the VLPO, PGD2 leads to the activation of sleep-promoting neurons.

RESULTS AND DISCUSSION

NREM sleep induction by PGD2 infusion being completely dependent on the activation of its DP1 receptor [START_REF] Urade | Prostaglandin D2 and Sleep/Wake Regulation[END_REF] , we first investigated the expression of this receptor in the VLPO (Fig. 1). Confirming previous reports from the literature, we also observed strong immunostaining on the leptomeninges at the border of the VLPO. However, we also find an immunolabelling into the VLPO (Fig. 1A).

We used GFAP-GFP-expressing mice, to further reveal whether astrocytes could indeed express the DP1 receptor. This transgenic mouse line has the interesting property to have only some astrocytes expressing GFP, not all of them, allowing better identification of individual cells. For the first time, we revealed that almost all GFP-GFAP-labeled VLPO astrocytes are expressing DP1R, with a dense expression on the somata and primary processes (Fig. 1B,C). Next, to assess whether an increased level of PGD2 is also associated with adenosine released in the VLPO, we performed real-time measurements of extracellular adenosine using purine enzymatic biosensors on brain slices [START_REF] Llaudet | A Three-Enzyme Microelectrode Sensor for Detecting Purine Release from Central Nervous System[END_REF][START_REF] Scharbarg | Astrocyte-Derived Adenosine Is Central to the Hypnogenic Effect of Glucose[END_REF][START_REF] Sims | Activity-Dependent Adenosine Release May Be Linked to Activation of Na +-K+ ATPase: An in Vitro Rat Study[END_REF][START_REF] Sims | Sleep-Wake Sensitive Mechanisms of Adenosine Release in the Basal Forebrain of Rodents: An in Vitro Study[END_REF] (Fig. 2). Measurements with adenosine (ADO) probes represent total purinergic responses and were referred to as ADO' (Fig. 2B). In the VLPO, we measured a basal ADO' level of 2.24 ± 0.22 µM, which significantly increased to 3.13 ± 0.35 µM under PGD2. ADO's level decreased to 2.23 ± 0.44 µM after the wash (Fig. 2C,D). These experiments were then reproduced using INO biosensors to evaluate the proportion of inosine and its metabolites in the ADO' recording. We measured a variation from 1.13 ± 0.042 to 1.53 ± 0.13 µM in response to PGD2, indicating that approximatively 50% of the ADO' signal was specific to adenosine.

To further investigate whether adenosine is released by astrocytes in response to PGD2, we performed real-time ADO measurements in pre-treated slices and continuously perfused with the glial toxin fluoroacetate (FAC; 5 mM). As shown in figure 2D, FAC treatment completely abolished the adenosinergic tone below the detection limit of the probe, as well as the ADO response to PGD2 application (0.1 ± 0.1 µM; Fig. 2D). This result points to the role of glial cells as essential effectors of the PGD2-dependent increase in extracellular ADO levels. Adenosine is a potent vasodilator acting on vascular adenosine A2A receptors (A2AR) [START_REF] Ngai | Receptor Subtypes Mediating Adenosine-Induced Dilation of Cerebral Arterioles[END_REF][START_REF] Ralevic | Purinergic Transmission in Blood Vessels[END_REF] . We thus investigated the putative release of adenosine in response to PGD2 application by recording blood vessel diameter changes in the VLPO (Fig. 3). We found that PGD2 reversibly dilated intraparenchymal arterioles within the VLPO, by 12.17 ± 3.93% (n = 7; P ≤ 0.001; Wilcoxon test, Fig. 3). This response was blocked in the presence of a selective A2AR antagonist (ZM241385; 10 µM, n = 5; P = 0.125, Wilcoxon test, Fig. 3B,C), and was also significantly impaired compared to control condition (P < 0.01; Mann-Whitney U-test, Fig. 3C), indicating that PGD2 induces adenosine-mediated signaling. Several reports have suggested that increased adenosine levels can modulate the activity of adenosine A2AR-expressing neurons in the VLPO [START_REF] Urade | Prostaglandin D2 and Sleep/Wake Regulation[END_REF][START_REF] Scharbarg | Astrocyte-Derived Adenosine Is Central to the Hypnogenic Effect of Glucose[END_REF][START_REF] Huang | Prostaglandins and Adenosine in the Regulation of Sleep and Wakefulness[END_REF][START_REF] Gallopin | The Endogenous Somnogen Adenosine Excites a Subset of Sleep-Promoting Neurons via A2A Receptors in the Ventrolateral Preoptic Nucleus[END_REF][START_REF] Kumar | Adenosine A2A Receptors Regulate the Activity of Sleep Regulatory GABAergic Neurons in the Preoptic Hypothalamus[END_REF] . To further investigate whether PGD2 can induce neuronal depolarization within the VLPO, we performed patch-clamp recordings of sleeppromoting neurons in response to the bath application of PGD2 (Fig. 4A,B). To identify sleep-promoting neurons in the VLPO, we used a transgenic mouse selectively expressing the green fluorescent protein (GFP) in galanin-expressing neurons (Gal-GFP mice). Galanin is indeed a reliable chemical marker of sleep-promoting neurons in the VLPO and allows clear identification of these neurons in an acute slice preparation [START_REF] Gaus | Ventrolateral Preoptic Nucleus Contains Sleep-Active, Galaninergic Neurons in Multiple Mammalian Species[END_REF][START_REF] Chauveau | Neuropeptide S Promotes Wakefulness through the Inhibition of Sleep-Promoting Ventrolateral Preoptic Nucleus Neurons[END_REF][START_REF] Kroeger | Galanin Neurons in the Ventrolateral Preoptic Area Promote Sleep and Heat Loss in Mice[END_REF] . Patch-clamp recordings of Gal-GFP neurons showed that PGD2 application induces a significant membrane depolarization of 5.79 ± 0.98 mV (n = 6; P < 0.05, Wilcoxon test; Fig. 4A,B).

Additionally, their input resistance was reduced by 8.77% (from 977.68 ± 66.89 to 891.95 ± 54.85 MΩ) in response to PGD2 application. The depolarizing effect of PGD2 persisted in the presence of TTX and was not statistically reduced compared to the one induced in the absence of TTX (3.04 ± 0.92 mV, n = 5; P = 0.515, ANOVA and Kruskal-Wallis multicomparisons test; Fig. 4B). Moreover, PGD2 application in the presence of TTX still reduces neuronal input resistances by 8.86% (from 1183.19 ± 168.05 to 1078.39 ± 148.51 MΩ) indicating a direct effect of PGD2 on sleep-promoting neurons. However, in the presence of an adenosine A2AR antagonist, ZM241385, the depolarization induced by PGD2 was blocked (-0.08 ± 0.85 mV; n = 4; P < 0.01, ANOVA and Kruskal-Wallis multi-comparisons test; Fig. 4B) and the reduction of the input resistance significantly less reduced, by 4.37% (from 9886.78 ± 92.32 to 943.67 ± 88.50 MΩ; ANOVA and Kruskal-Wallis multi-comparisons test; Fig. 4B). These data further show that in the VLPO, PGD2 induces adenosine release which could then activate A2AR on sleep-promoting neurons.

The presence of A2AR on sleep-promoting neurons was previously suggested either pharmacologically by electrophysiological recordings [START_REF] Scharbarg | Astrocyte-Derived Adenosine Is Central to the Hypnogenic Effect of Glucose[END_REF][START_REF] Gallopin | The Endogenous Somnogen Adenosine Excites a Subset of Sleep-Promoting Neurons via A2A Receptors in the Ventrolateral Preoptic Nucleus[END_REF] , or molecularly using A2A receptordeficient mice [START_REF] Urade | Sleep Regulation in Adenosine A2A Receptor-Deficient Mice[END_REF] or using sleep-wake behavior analyses associated with c-Fos-IR quantification in the VLPO [START_REF] Kumar | Adenosine A2A Receptors Regulate the Activity of Sleep Regulatory GABAergic Neurons in the Preoptic Hypothalamus[END_REF] . To firmly demonstrate that these receptors are indeed expressed by sleep-promoting neurons, we performed the immunostaining of A2AR and found that nearly all Gal-GFP neurons expressed this receptor, and ~ 50% of A2AR expressing cells were Gal-GFP (n = 5, Fig. 4C,D). This result indicates that sleep-promoting neurons of the VLPO also express A2AR. Taken together, the results of this study clearly define the cellular and molecular signaling pathway of PGD2 in VLPO.

Here, we demonstrate and quantify for the first-time adenosine release in response to PGD2 application into the VLPO. In addition, we established that astrocytes are crucial elements involved in increased levels of adenosine. These cells express DP1R to sense PGD2 accumulation during wakefulness and their alteration using FAC abolishes adenosine release. Finally, we show that in response to PGD2 application, adenosine release simultaneously induces A2AR-mediated vasodilation and depolarization of sleep-promoting neurons. Altogether, our results provide new evidence regarding PGD2 regulation of neuronal activity in the VLPO.

Using amperometric biosensors on mouse VLPO slices, we directly demonstrated that extracellular adenosine is increased in response to PGD2 application. Previous microdialysis analysis already reported extracellular adenosine release in response to PGD2 infusion, but only in the subarachnoid space of the basal forebrain [START_REF] Mizoguchi | Dominant Localization of Prostaglandin D Receptors on Arachnoid Trabecular Cells in Mouse Basal Forebrain and Their Involvement in the Regulation of Non-Rapid Eye Movement Sleep[END_REF] . Indeed, the small size of the VLPO does not facilitate its study. However, PGD2 is a fat-soluble molecule. This paracrine signaling agent can diffuse into the brain tissue. The accumulation of PGD2 during the day will then activate astrocytes via DP1Rs and induce adenosine release. It has indeed already been shown that PGD2-induced increase in extracellular adenosine is absent in mice lacking the DP1R, indicating that activation of these receptors is required for this increase of adenosine [START_REF] Mizoguchi | Dominant Localization of Prostaglandin D Receptors on Arachnoid Trabecular Cells in Mouse Basal Forebrain and Their Involvement in the Regulation of Non-Rapid Eye Movement Sleep[END_REF] .

In this study, we found that approximately 50% of the ADO' signal was specific to adenosine, which is similar to previous studies. However, the basal adenosine levels we measured in this study seem higher than those recorded in the basal forebrain [START_REF] Sims | Sleep-Wake Sensitive Mechanisms of Adenosine Release in the Basal Forebrain of Rodents: An in Vitro Study[END_REF] , which probably results from different experimental protocols. Quantification of extracellular adenosine levels in the presence of FAC, a molecule known to block cell metabolism, further revealed an astrocytic source of adenosine. As FAC is preferentially taken up by astrocytes relative to neurons [START_REF] Keyser | Synaptic Transmission in the Hippocampus: Critical Role for Glial Cells[END_REF] , the abolition of adenosinergic tone in the VLPO by FAC suggests an astrocyte-derived adenosine release in response to PGD2 application. However, we cannot exclude that the release of a paracrine mediator by the leptomeninges may also participate in astrocyte activation.

Here, we did not investigate how extracellular adenosine levels were increased. Many pathways can indeed regulate extracellular adenosine levels, including adenosine exocytosis [START_REF] Pascual | Astrocytic Purinergic Signaling Coordinates Synaptic Networks[END_REF] , concentrative nucleoside transporters (CNTs), or transporters such as equilibrative nucleoside transporters (ENTs) [START_REF] Paes-De-Carvalho | Adenosine as a Signaling Molecule in the Retina: Biochemical and Developmental Aspects[END_REF] . Alternatively, ATP can also be released by astrocytes, via P2X7 receptors or hemichannels and converted into adenosine following a degradative enzymatic activity from ectonucleotidases. This latest mechanism was recently proposed in the VLPO following the photostimulation of astrocytic channelrhodopsin-2 [START_REF] Choi | Astrocyte-Derived Adenosine Excites Sleep-Promoting Neurons in the Ventrolateral Preoptic Nucleus: Astrocyte-Neuron Interactions in the Regulation of Sleep[END_REF] . Future work would be required to elucidate the molecular pathways underlying extracellular adenosine release in the VLPO in response to PGD2.

In the VLPO, adenosine then acts on both A1 and A2AR. In particular, A1R is widely expressed, by local GABAergic interneurons, whose activation is proposed to indirectly promote sleep via disinhibition of sleep-promoting neurons [START_REF] Gallopin | The Endogenous Somnogen Adenosine Excites a Subset of Sleep-Promoting Neurons via A2A Receptors in the Ventrolateral Preoptic Nucleus[END_REF][START_REF] Choi | Astrocyte-Derived Adenosine Excites Sleep-Promoting Neurons in the Ventrolateral Preoptic Nucleus: Astrocyte-Neuron Interactions in the Regulation of Sleep[END_REF][START_REF] Morairty | Disinhibition of Ventrolateral Preoptic Area Sleep-Active Neurons by Adenosine: A New Mechanism for Sleep Promotion[END_REF][START_REF] Chamberlin | Effects of Adenosine on GABAergic Synaptic Inputs to Identified Ventrolateral Preoptic Neurons[END_REF] . Regarding A2AR, their expression on blood vessels is well established [START_REF] Ralevic | Purinergic Transmission in Blood Vessels[END_REF] . However, their expression by sleep-promoting neurons was only indirectly suggested by c-Fos protein immunoreactivity, pharmacological blockade or activation, electrophysiological recordings, as well as by the use of KO mice [START_REF] Scharbarg | Astrocyte-Derived Adenosine Is Central to the Hypnogenic Effect of Glucose[END_REF][START_REF] Gallopin | The Endogenous Somnogen Adenosine Excites a Subset of Sleep-Promoting Neurons via A2A Receptors in the Ventrolateral Preoptic Nucleus[END_REF][START_REF] Kumar | Adenosine A2A Receptors Regulate the Activity of Sleep Regulatory GABAergic Neurons in the Preoptic Hypothalamus[END_REF][START_REF] Scammell | An Adenosine A2a Agonist Increases Sleep and Induces Fos in Ventrolateral Preoptic Neurons[END_REF][START_REF] Huang | Adenosine A2A, but Not A1, Receptors Mediate the Arousal Effect of Caffeine[END_REF] . Here, we demonstrate for the first time by immunolabelling that A2AR is indeed expressed by sleep-promoting neurons in the VLPO. Moreover, our study also highlights a key role for PGD2 in the neurovascular coupling (NVC), linking via A2AR activation, vasodilation, and increased neuronal activity of sleep-promoting neurons. The tight link between blood flow regulation and neuronal activity is essential for normal brain function. This result strengthens the role of prostaglandins in the NVC.

Prostaglandin E2 was indeed also reported to be in the cortex, a key vasoactive messenger of NVC responses to sensory stimulation [START_REF] Lacroix | COX-2-Derived Prostaglandin E2 Produced by Pyramidal Neurons Contributes to Neurovascular Coupling in the Rodent Cerebral Cortex[END_REF] .

A2AR immunolabelling here reveals that all sleep-promoting neurons express this receptor in mice. This result contrasts with previous reports in rats, where the depolarizing effect of A2AR was only observed in a subset of sleep-promoting neurons [START_REF] Gallopin | The Endogenous Somnogen Adenosine Excites a Subset of Sleep-Promoting Neurons via A2A Receptors in the Ventrolateral Preoptic Nucleus[END_REF] . However, this is consistent with our previous study in mice, showing that adenosine as well as an A2A agonist were able to depolarize all recorded sleep-promoting neurons [START_REF] Scharbarg | Astrocyte-Derived Adenosine Is Central to the Hypnogenic Effect of Glucose[END_REF] . The expression of A2AR on sleep-promoting neurons was further confirmed in mice, by the depolarizing effect of PGD2 on sleep-promoting neurons, which is prevented in the presence of the A2AR antagonist. However, we cannot exclude the possibility that sleep-promoting neurons could also express A1R. Indeed, adenosine release is well known to decrease the inhibitory tonus exert by local A1R-expressing interneurons on sleep-promoting neurons, thereby favoring their depolarization [START_REF] Morairty | Disinhibition of Ventrolateral Preoptic Area Sleep-Active Neurons by Adenosine: A New Mechanism for Sleep Promotion[END_REF] . But simultaneously, direct activation of A1R on sleep-promoting neurons would counterbalance the indirect disinhibition by the interneurons. As in the presence of ZM241385, PGD2 does not affect sleep-promoting neurons, this supposes that A1R is also expressed, as observed in rats [START_REF] Gallopin | The Endogenous Somnogen Adenosine Excites a Subset of Sleep-Promoting Neurons via A2A Receptors in the Ventrolateral Preoptic Nucleus[END_REF] . Consequently, in the presence of TTX, which blocks directly inhibited by A1R activation, while they are depolarized by A2AR. Thus, the global response to PGD2 tends to decrease compared to the control condition.

In summary, we have here demonstrated that astrocytes express DP1R in the VLPO, and regulate adenosine levels in response to PGD2 application. Then, adenosine increases the blood flow and depolarizes sleep-promoting neurons, via A2AR. Activation of the VLPO would then lead to the inhibition of the wake-promoting structures, resulting in sleep.

METHODS AND MATERIALS

Animals. Male-only C57BL/6 mice (Charles River, France) of P40-60 days old, were used to analyze the vascular effect of PGD2 and perform ADO measurements. FVB Gal-GFP mice (Mutant Mouse Regional Resource Center (MMRRC), USA) of P40-60 days old, were used for patch-clamp recordings. FVB Gal-GFP [START_REF] Gong | A Gene Expression Atlas of the Central Nervous System Based on Bacterial Artificial Chromosomes[END_REF] and GFAP-GFP [START_REF] Nolte | GFAP Promoter-Controlled EGFP-Expressing Transgenic Mice: A Tool to Visualize Astrocytes and Astrogliosis in Living Brain Tissue[END_REF] mice, P40-60 days old, male-only, were used for immunostainings.

The Gal-GFP mouse strain used for this research project, STOCK Tg(Gal-EGFP)HX109Gsat/Mmucd, identification number 016342-UCD, was obtained from the MMRRC, an NIH-funded strain repository, and was donated to the MMRRC by the NINDS funded GENSAT BAC transgenic project. All animals were housed in a temperature-controlled (20-22°C) room under a 12-hour light-dark cycle with lights on at 08:00 a.m. All animals had ad libitum access to food and water. All animal procedures were conducted in strict compliance with our institutional

Immunohistochemistry and Confocal Microscopy. Mice were deeply anesthetized with

Euthasol Vet (Pentobarbital, DECHRA) and perfused intracardially with paraformaldehyde (PFA 2%), between Zeitgeber Time (ZT) -1 and 3, so between 1 and 3 hours after the light was switched on in the animal house. The brains were carefully dissected and post-fixed overnight with PFA 2% at 4 °C. The next day, PFA was removed and the brains were cryoprotected overnight in PBS containing 30% sucrose. The brains were then cut into 40 µm sections with a freezing microtome (HM 450, Thermo Fisher Scientific). Slices were stored in PBS at 4 °C until use. For immunohistochemistry, slices were washed 3 times 15 min in PBS 1X at room temperature (RT) and antigen retrieval was performed by incubating slices in 50 mM sodium citrate (147 mg trisodiumcitrate di-hydrate in 10 mL water, pH 8.5-9.0) preheated at 80 °C for 30 min. Slices were washed 3 times 15 min at RT with PBS 1X and incubated 24 h at RT with primary antibodies (A2AR: AAR-002, polyclonal rabbit, Alamone Labs at 1:500; GFP: Abcam, Chicken, ab13970 at 1:500; DP1R: ADI-905-800-100, polyclonal rabbit, Enzo at 1:500; GFP: AVES lab, Chicken at 1:1000 in PBS 1X / Triton X100 0.5% / 0.02% NaN3) and then 4 nights at 4°C. Anti-adenosine A2A receptor is a highly specific antibody directed against the (C)RQLKQMESQPLPGER epitope of the mouse protein, corresponding to amino acid residues 201-2015 of the 3 rd intracellular loop of mouse adenosine A2A receptor. PGD2 receptor (DP1) polyclonal antibody the immunogen sequence used was NESYRCQTSTWV, corresponding to the N-terminus of DP1, amino acids 2-23 [START_REF] Frenguelli | High-Resolution Real-Time Recording with Microelectrode Biosensors Reveals Novel Aspects of Adenosine Release during 28 Hypoxia in Rat Hippocampal Slices[END_REF] .

Slices were rinsed 3 times 15 min in PBS 1X at RT and incubated with secondary antibodies (A2AR: A21429, goat-Alexa Fluor 555, Life Technologies at 1:1000; GFP: A11032, goat-Alexa Fluor 488, Invitrogen, 1:1000; DP1: goat anti-chicken 488 at 1:500; goat anti-rabbit 647 at 1:500 in PBS 1X / Triton X100 0.5% / 0.02 % NaN3) for 48 h at 4°C. Slices were then washed 3 times 15 min with PBS 1X at RT, incubated with DAPI (1:1000) 20 min at RT, and washed again 10 min at RT before mounting with Fluoromount-G (00-4958-02, Invitrogen). For each immunostaining, a minimum of 4 fields per animal were acquired at the 25X and 63X objective (spinning disk W1, Zeiss Axioobserver Z1 with motorized XYZ stage; CSUW11 (Yokogawa) spinning-disk scan head). GFP + , A2A + and DP1 + cells were counted manually in at least 1 region of interest (ROI) per slice per animal and at least in 3 mice. Then, a Z-stack of 30 µm thick with a 2 µm step was made on each field. Acquisitions parameters were strictly conserved throughout different animals. Each biosensor included a platinum wire 50 µm in diameter and 0.5 mm in length, coated with a permselective layer and an enzymatic matrix surrounding the platinum electrode [START_REF] Llaudet | A Three-Enzyme Microelectrode Sensor for Detecting Purine Release from Central Nervous System[END_REF][START_REF] Dale | Direct Measurement of Adenosine Release during Hypoxia in the CA1 Region of the Rat Hippocampal Slice[END_REF] . For the ADO biosensors, the enzymatic layer contained nucleoside phosphorylase, xanthine oxidase, and adenosine deaminase, whereas the INO biosensors lacked adenosine deaminase and were sensitive to adenosine. Amperometric measurements started when hydrogen peroxide oxidized the platinum wire to create an electrical signal proportional to the purine concentration. Measurements with adenosine biosensors gave a total purinergic concentration of adenosine and its metabolites referred to as ADO [START_REF] Dale | Direct Measurement of Adenosine Release during Hypoxia in the CA1 Region of the Rat Hippocampal Slice[END_REF]36 . In contrast, INO sensors were used to measure the response to inosine and its metabolites in a separate set of experiments, due to the small size of the VLPO. We thus Vascular Reactivity. Blood vessels in the VLPO within the focal plane that exhibited a welldefined luminal diameter (10-20 µm) were selected for vascular reactivity. Images of blood vessels were acquired every 15 s with the Media Cybernetics software. The eventual drift of the images during the recording time was corrected either online for the z-drift or offline for the x and y axes using Image Pro Plus 7.0. Manual replacement of images to minimize the differences between two consecutive frames was performed using a subtraction tool from Image Pro Plus. To determine the location with the most movement, luminal diameters were quantified at different locations along the blood vessel using custom-written routines running within IgorPro (Wavemetrics). Control baselines were determined for 5 min at the start of the recording, with temperature maintained at 32 ± 1°C. Unresponsive blood vessels or vessels with unstable baselines were discarded from the analysis. Vessels were considered unstable when their diameter moved more than 5% during the control baseline test. Only one vessel per slice was recorded.

Vasomotor responses were expressed as percentages of the mean baseline diameter, 5 min before the switch with the solution containing PGD2. Pharmacological effects were then analyzed by performing statistical comparisons between the 1 min period before PGD2 application and the mean blood vessel diameter change at 9-10 min after PGD2 application onset. Electrophysiological Recordings. Electrophysiological experiments were performed at 32 ± 1°C using a MultiClamp700B (Axon Instruments) amplifier connected to an acquisition board (Digidata 1440; Axon Instruments), filtered at 5 kHz, digitized at 50 kHz, and recorded to a computer running pCLAMP9.2 software (Axon Instruments). Data were analyzed offline using the Clampfit software (Molecular Devices).

Patch-clamp recordings in whole-cell configuration.

Current clamp recordings were made at resting membrane potential and without series resistance compensation. Patch-clamp pipettes (3-6 MΩ) were pulled from a borosilicate glass tube (1.5 mm o.d., 0.86 mm i.d.; Harvard Apparatus, France) on a horizontal puller (Model P-100; Sutter Instrument, Novato, USA), and were filled with the following (in mM): 144 K-gluconate; 1 MgCl2; 0.5 EGTA; 10 HEPES at pH 7.2; 285-295 mOsm. A short hyperpolarizing current step was applied at the beginning of each sweep, every 60 s, to monitor changes in input resistance throughout the recordings. This protocol frequently triggered low-threshold spikes (LTS). The liquid junction potential of the patch pipette and the perfused extracellular solution was 11 mV and was not accounted for in the data. The pipette was slowly brought to the selected neuron to be recorded in whole-cell configuration using infrared videomicroscopy guidance. Data were rejected if the access resistance changed by more than 25% during the recording of the control period and data reduction was performed to reduce data handling. Membrane potentials were measured between 4 and 6 min after the onset of the drug application and over a 2-min period for baseline before PGD2 application. Input resistances were measured by the application of short steps of hyperpolarization each minute.

Drugs. The selective A2A receptor antagonist ZM 241385 (4-(-2-[7-amino-2-{2-furyl}{1,2,4}triazolo{2,3-a} {1,3,5}triazin-5-yl-amino]ethyl)phenol; 100 µM; Merck) was used to investigate the molecular pathway of the PGD2-induce neuronal depolarization. To block neuronal transmission, PGD2 was applied in the presence of tetrodotoxin (TTX; 1 µM; Latoxan) as well as 10 min after TTX application onset. Glial metabolism was inhibited using fluoroacetate (FAC; 5 mM and 50 min of preincubation; Sigma). PGD2, (5Z,9α,13E,15S)-9,15-Dihydroxy-11oxoprosta-5,13-dien-1-oic-acid (1 µM, Sigma).

Statistical Analyses. Data are expressed as mean ± standard error of the mean (SEM) and represented as a box plot, where the median is plotted as the central bar of the box, the upper and lower quartiles are the edges of the box, and the whiskers show the extremes of the distribution.

The statistical significance of the data was assessed using either parametric tests (paired t-test and Student's t-test) or non-parametric tests (Wilcoxon test and Mann-Whitney U-test), depending on whether the values from the considered data set followed a normal distribution or not.

Figure 1 .

 1 Figure 1. Astrocytes in the VLPO express DP 1 R. A, Confocal images showing immunostaining of DP1R (in red) in the leptomeninges and the VLPO, as highlighted by a white dashed triangle. B and C, Confocal images reveal that astrocytes from GFAP-GFP mice (in green) are also expressing DP1R (in red). White arrows indicate regions of strong co-labeling.

Figure 2 .

 2 Figure 2. In situ measurements of astrocytic-derived adenosine in response to PGD 2 application in the VLPO. (A) Sagittal schematic representation of the mouse brain (top) and a VLPO slice at Bregma 0.14 mm (bottom). Photomicrograph of an ADO biosensor within the VLPO. Scale bar: 180 µm. (B) Enzymatic cascades used to measure adenosine and inosine. Adenosine biosensor measurements give a total purinergic concentration of adenosine and its metabolites called ADO'. The signal of INO is thereafter subtracted to obtain the ADO signal. (C) Representative recording of real-time ADO' signals within the VLPO. ADO biosensors were lowered into the VLPO area ('Placed'; black arrowheads) and raised ('Removed' white arrowheads) successively in control, 1 µM of PGD2, and after 10 minutes wash back again into the tissue to prevent the probe desensitization. Scale bare: 1 min, 0.05 nA. (D) Left: box plots of the ADO' measurements in the control condition, under PGD2 (1 µM) and in the presence of fluoroacetate (FAC; 5 mM), a glial toxin. The median is plotted as the central gray bar of the box, the upper and lower quartiles are the edges of the box and the whiskers show the extremes of the distribution. Sample sizes are indicated in parentheses. Right: example of a calibration curve from the adenosine biosensor showing a linear response from 1 to 10 μM adenosine (top), with ratios of ADO (n = 6) and INO (n = 3) detected in the VLPO in the control condition (bottom). *P < 0.05, Wilcoxon matched-pairs signed rank test.

Figure 3 .

 3 Figure 3. PGD 2 induces A 2A R-mediated vasodilation in the VLPO. (A) Photograph of a vascular response induced by bath application of PGD2 (1 µM; 2 min). The dotted white lines highlight the inner diameter of the blood vessel. (B) Mean ± SEM vascular responses induced by 2 min application of PGD2 alone (black diamond; n = 7) or in the presence of ZM241385, an A2A receptor antagonist (open circle; n = 5). (C) Box plots of the vascular response measured at 9-10 min after PGD2 application onset. The median is plotted as the central grey bar of the box, the upper and lower quartiles are the edges of the box and the whiskers show the extremes of the distribution. Sample sizes are indicated in parentheses. **P < 0.01; Mann-Whitney U-test.

Figure 4 .

 4 Figure 4. PGD 2 depolarizes Gal-GFP neurons via A 2A R activation. (A) Left: epifluorescence and infrared images of the recorded Gal-GFP neuron. Right: mean (black) ± SEM (orange) electrophysiological response in current-clamp of a Gal-GFP neuron to PGD2 application (1 µM; 2 min, n = 6). The black dashed bar indicated the membrane potential of -58 mV. (B) Box plots of the depolarizations induced by PGD2 (top) and of the reduction of the input resistance (bottom).

  protocols and were approved by the European Community Council Directive of 22 September 2010 (010/63/UE) and the local ethics committee (Comité d'éthique en matière d'expérimentation animale number 59, C2EA-59, 'Paris Centre et Sud'). Accordingly, the number of animals in our study was kept to the necessary minimum.

Fluorescence

  cells were then counted on a maximal-intensity Z-projection image. The presented data corresponds to the average of all ROIs fluorescence intensities per condition. Image analyses were made using Fiji and ImageJ softwares (National Institute of Health, United States). Hypothalamic Slices. Mice were rapidly decapitated between Zeitgeber Time (ZT)-1 and 3 and brains were quickly removed and placed into cold (~ 4°C) artificial cerebrospinal fluid (aCSF) containing (in mM): 130 NaCl; 5 KCl; 2.4 CaCl2; 20 NaHCO3; 1.25 KH2PO4; 1.3 MgSO4; 5 Dglucose; and 15 sucrose (pH = 7.35). Brains were constantly oxygenated with 95% O2 -5% CO2. During slicing, 1 mM kynurenate was added to the aCSF. Coronal brain slices (300 µm thick) containing the VLPO were cut with a vibratome (VT1200°S; Leica) and transferred to a constantly oxygenated (95% O2 -5% CO2) holding chamber containing aCSF. Next, individual slices were placed in a submerged recording chamber maintained at 32°C and perfused (1.5 ml/min) with oxygenated kynurenate-free aCSF. Blood vessels were visualized using infrared videomicroscopy, with Dodt gradient contrast optics (Luigs and Neumann) mounted on an upright microscope (Zeiss) equipped with a CDD camera (Hamamatsu). Purine Biosensor Recording. Purine biosensors (Sarissa Biomedical, Coventry, United Kingdom) were used to detect extracellular levels of either adenosine (ADO) or inosine (INO).

  obtained a specific ADO signal by subtracting the signal of INO sensors from ADO sensors; approximately 51% of the signal from the ADO/INO biosensor was specific to adenosine in our conditions. Before use, all biosensors were rehydrated in a buffer containing (in mM): 100 NaCl, 50 Tris, 20 glycerol, 1 MgCl2, and 1 CaCl2. All biosensor sets were calibrated at the beginning and end of each experiment with 10 µM adenosine or inosine. The ADO and INO recordings were performed after a 30 min stabilization period. The sensors were inserted sequentially during a 3 min period in the VLPO in aCSF: without PGD2 (control condition), followed by PGD2 (1 µM), and again after 10 min wash without PGD2. The biosensors were placed in contact with the surface of the slice by micromanipulators, which was visually confirmed as a slight deformation on its surface, indicating that the electrode was properly embedded. During the experiment, microelectrode biosensors were polarized at +500 mV versus Ag/AgCl. Purine concentrations were assessed by subtracting the electrical signal of the sensor when inserted into the VLPO from the mean of the electrical signals measured 30 s before and after the insertion. The biosensor signals were acquired with an amperometric detector (AMU 110; Radiometer Analytical, Villeurbanne, France) using an acquisition board (Digidata 1322A; Molecular Devices) attached to a computer running the pCLAMP 9.2 software package (Molecular Devices). Analyses were performed using Clampfit (Molecular Devices), SigmaPlot (Systat), and Python software programs.

Acknowledgments. This work was supported by the Centre National de la Recherche Scientifique (CNRS), the French Institute of Health and Medical Research (Inserm), ESPCI ParisTech, Bettencourt Schueller Foundation and the Collège de France. We thank all members of the animal house facility from the Collège de France.

PSL, 75005 Paris, France. orcid.org/0000-0002-1818-3971 Augustin Walter -Neuroglial Interactions in Cerebral Physiology and Pathologies, Center for Interdisciplinary Research in Biology, Collège de France, CNRS, Inserm, Labex Memolife, Université PSL, 75005 Paris, France. orcid.org/0000-0002-7426-2674 22 Laure Lecoin -Neuroglial Interactions in Cerebral Physiology and Pathologies, Center for Interdisciplinary Research in Biology, Collège de France, CNRS, Inserm, Labex Memolife, Université PSL, 75005 Paris, France. orcid.org/0000-0001-5259-4339 Thierry Gallopin -Brain Plasticity Unit, CNRS, ESPCI-ParisTech, Labex Memolife, Université PSL, 75005 Paris, France. orcid.org/0000-0002-9741-3738 Frédéric Lemaître -PASTEUR, Département de Chimie, Ecole Normale Supérieure, PSL University PSL, Sorbonne Université, CNRS, 75005 Paris, France. orcid.org/0000-0003-4424-0981 Manon Guille-Collignon -PASTEUR, Département de Chimie, Ecole Normale Supérieure, PSL University PSL, Sorbonne Université, CNRS, 75005 Paris, France. orcid.org/0000-0003-2510-5549 Nathalie Rouach -Neuroglial Interactions in Cerebral Physiology and Pathologies, Center for Interdisciplinary Research in Biology, Collège de France, CNRS, Inserm, Labex Memolife, Université PSL, 75005 Paris, France. orcid.org/0000-0002-5574-888

AUTHOR INFORMATION

Note

The authors declare no competing financial interest.


Julie Bourbeillon

Catherine Garbay

Françoise Giroud

Mass data exploration in oncology: an information synthesis approach

Keywords: Information Synthesis, Model, Task-oriented Retrieval, Biomedical Domain, Oncology, Tissue MicroArrays

New technologies and equipment allow for mass treatment of samples and research teams share acquired data on an always larger scale. In this context scientists are facing a major data exploitation problem. More precisely, using these data sets through data mining tools or introducing them in a classical experimental approach require a preliminary understanding of the information space, in order to direct the process. But acquiring this grasp on the data is a complex activity, which is seldom supported by current software tools. The goal of this paper is to introduce a solution to this scientific data grasp problem. Illustrated in the Tissue MicroArrays application domain, the proposal is based on the synthesis notion, which is inspired by Information Retrieval paradigms. The envisioned synthesis model gives a central role to the study the researcher wants to conduct, through the task notion. It allows for the implementation of a task-oriented Information Retrieval prototype system. Cases studies and user studies were used to validate this prototype system. It opens interesting prospects for the extension of the model or extensions towards other application domains.

INTRODUCTION

Experiments in sciences are becoming more and more expensive in matters of time, material, equipment, etc. Two solutions have arisen among scientists in the biomedical field. The first one is the miniaturisation of samples and the automation of processes. A typical example could be the Tissue MicroArrays (TMA) technology, which allows for the mass treatment of hundreds of microsamples on a single histological slide. The second option is to reuse the data acquired by other teams, which are increasingly put on-line in an effort of resources sharing.

These approaches lead to a tremendous increase in the volume of available data. However these large masses of data also pose a real problem of understanding the data sets. This preliminary understanding is a mandatory stage for a more advanced exploitation. For instance, data mining tools have to be directed, which requires a minimal knowledge of the data space. In the same trend, hypothesis validation on an extract of a data set implies checking if the available information is sufficient. In the perspective considered in this paper, this process requires solving a set of complex problems:

search and extraction of interesting data for a particular study, using potentially multiple data sources; -aggregation of interesting items in a single information pool; -organisation of relevant elements into a structure that facilitates interpretation; -display of the relevant elements and of their structural organisation. The complexity of these problems leads to an increasing need for a computerised assistance. The proposed solution is a synthesis notion, which federates the activities underlining the data grasping problem. It brings an original point of view on Information Retrieval (IR) by considering it as a component of the experimental approach.

This paper introduces this concept of synthesis in a particular application domain: the exploitation of data acquired during Tissue MicroArrays experiments. This application domain is presented in Section 2. After a state of the art review in Section 3, we present in Section 4 the model which supports our proposed theoretical framework. Section 5 introduces the resulting system architecture and the current prototype, and Section 6 presents its application in the TMA domain and a first validation of the approach.

THE TISSUE MICROARRAYS APPLICATION DOMAIN

The oncology research context

In situ molecular expression studies conducted as part of oncology research are a typical example of the information explosion phenomenon in the biomedical field. Indeed, oncology research relies, among other approaches, on the discovery of the oncogenesis mechanisms through tissue studies. The acquisition of such anatomopathological data classically relies on the construction, based on archived tissue samples, of histological slides on which the expression of interesting molecules is revealed.

Such processes fit well in the classical frame of the experimental approach, where hypotheses relating to a specific question are tested through experiments conducted on a limited group of individuals. But these processes are long to conduct, costly in reactants and lead to the depletion of non-renewable resources: tissue samples at research disposal.

TMA technology: to go beyond the limits of current approaches

In order to bypass these limitations, the TMA technology [START_REF] Kallioniemi | Tissue microarray technology for high-throughput molecular profiling of cancer[END_REF] collects and aggregates miniaturised biological samples into a gridlike receiver block, to support the high-throughput investigation of some phenomenon at the macroscopic level (e.g. evolution of cancers).

The overall process is presented in Fig. 1. Patients' samples are selected depending on the study to perform and organised in a TMA plan design. Small tissue cores (0.6-2 mm in diameter) are extracted from the corresponding biopsy paraffin blocks (donor block), then inserted into a TMA receiver block according to the plan. Slides are cut out of the TMA block and treated as conventional histological slides. Images of these TMA slides are then acquired and partitioned into individual spot images, where each spot corresponds to the slicing of one tissue core. Spot images are annotated by a pathologist; quantitative descriptors are further computed to characterise e.g. the staining intensity or marker distribution inside the cell compartments. The data sets consisting of images, annotations and quantitative evaluations are then stored in a database for further use. For instance data analyses are performed in the hope of getting answers or leads regarding the original question.

New limits imply new approaches

This technology helps solving issues linked with the ''whole slices" approach but its principle also leads to new limitations. For instance [START_REF] Rubin | Tissue microarray sampling strategy for prostate cancer biomarker analysis[END_REF] proposes a method to solve the sampling issue and obtain a statistical representation of the whole biopsies. However the main problems are linked with the large volume of data. Indeed the high-throughput nature of the approach suggests a massive sharing of data. It also implies that the careful design of TMA blocks to test a narrow hypothesis is seldom conducted.

The large volume of data issue has been addressed through computer-driven data management. Most software focus on storage and data access [START_REF] Berman | The tissue microarray data exchange specification: a community-based, pen source tool for sharing tissue microarray data[END_REF], coupled with data visualisation [START_REF] Sharma-Oates | TmaDB: a repository for tissue microarray data[END_REF] and image analysis or data mining tools [START_REF] Demichelis | On Information organization and information extraction for the study of gene expressions by tissue microarray technique[END_REF]. But the biologist is still faced with a tremendous amount of information that is only loosely coupled with a biological question. Therefore the data grasping problem remains. One solution is the concept of virtual TMA slides [START_REF] Conway | The development and validation of the virtual tissue matrix, a software application that facilitates the review of tissue microarrays on line[END_REF] which displays data from several experiments at once. However the inclusion of relevant spots into the virtual slide is a manual process. Another approach goes towards careful design of TMA blocks according to a question, as presented in [START_REF] Kajdacsy-Balla | Practical aspects of planning, building, and interpreting tissue microarrays: the cooperative prostate cancer tissue resource experience[END_REF], but this is not always practical, e.g. when very few samples relevant to a problem are available.

Going beyond the limits of current tools when it comes to assisting scientists in a preliminary data exploration implies:

providing assistance in hypothesis formulation to express the needs of a biological study; -constructing a relevant data set on which the hypothesis could be tested; -displaying this data set so that leads regarding the biological problem can be inferred; -taking into account existing knowledge in the TMA domain and quality constraints. The characteristics of this problem suggest it can be considered as a synthesis problem. Therefore we should explore the relevant concepts in the various domains which relate to synthesis.

STATE OF THE ART

Information synthesis: a multi-dimensional notion

In the scientific domain, synthesis appears mainly as an intellectual operation which consists in methodically reuniting the composing items of a whole. It emerges from the increasing need to compact a tremendous amount of information. This is achieved for instance through ''review" papers or bibliographic syntheses, as introduced in the practical guide presented in [START_REF] Goldschmidt | Information synthesis: a practical guide[END_REF]. Adopting this point of view on synthesis, we are faced with a concept with multiple facets such as mining data, retrieving information or representing parts of complex processes.

A data mining dimension

As the construction of a compact view on a large data set, synthesis can be linked with the data mining field [START_REF] Fayyad | From data mining to knowledge discovery in databases[END_REF], which aims at adding value to large data sets by extracting knowledge through mathematical and computerised methods. Several TMA-related software suits include such data mining packages [START_REF] Demichelis | On Information organization and information extraction for the study of gene expressions by tissue microarray technique[END_REF]. However a successful use of these tools implies a background knowledge of both the underlying algorithms and information space to study [START_REF] Friedman | Data mining and statistics: what's the connection?[END_REF]. In this context, tools allowing for the acquisition of a preliminary understanding of the data sets make sense.

The level of complexity involved in large data sets implies focusing on organisational and presentational issues. Information Visualisation [START_REF] Keim | Information visualization and visual data mining[END_REF] exploits the graphical abilities of modern computers to construct global views from data. Several tools providing rather compact and informative representations of data have been successfully used in the biomedical domain. For instance, Chan et al. [START_REF] Chan | Using growing self-organising maps to improve the binning process in environmental whole-genome shotgun sequencing[END_REF] rely on growing self-organising maps to solve the binning problem posed by environmental whole genome shot-gun sequencing. Baehrecke et al. [START_REF] Baehrecke | Visualization and analysis of microarray and gene ontology data with treemaps[END_REF] uses Treemaps to visualise DNA MicroArrays data in relation with Gene Ontology annotations.

However the spacial organisation remains centred on attributes explicitly present in the data. Moreover the tools which allow for a focus on part of the data or a reorganisation generally consist in a posteriori treatments such as filters; synthesis implies the construction of views oriented a priori towards a precise goal. The problem is to select both the relevant information and tools, in order to display the appropriate view, given the objective.

An IR dimension

These selection problems are linked with the IR dimension associated with the synthesis concept.

The implementation of IR theories and algorithms [START_REF] Baeza-Yates | Modern information retrieval[END_REF] initially resulted in computerised systems relying on keywords. Pubmed1 is a typical example in the biomedical field. However, while this model allows for evaluations as part of campaigns like TREC1 ,it may not reflect the complexity underlying information access. Example solutions could be enhancements added to Pubmed2 , such as improved document indexing approaches [START_REF] Neveol | Multiple approaches to fine-grained indexing of the biomedical literature[END_REF] or the inclusion of visualisation techniques to present the results [START_REF] Xuan | An active visual search interface for Medline[END_REF].

The most interesting approaches might be those aiming to satisfy the need of the user, which is the focus of information behaviour research. The user is considered as a thinking entity in a context which can be socio-economic, cultural or affective [START_REF] Kuhlthau | Inside the search process: information seeking from the user's perspective[END_REF]; he is faced with the cognitive problem of an incomplete representation of the world. The IR process therefore aims at extending his knowledge. These studies are mostly theoretical or based on experimental studies of users. For instance, Grefsheim and Rankin [START_REF] Grefsheim | Information needs and information seeking in a biomedical research setting: a study of scientists and science administrators[END_REF] survey information needs and search behaviour of researchers and research administrators of the US National Institute of Health. This type of studies led to models of the interaction mechanisms between a user and information sources [START_REF] Wilson | Models in information behaviour research[END_REF]. Unfortunately they cannot directly lead to IR implementations.

The need for operational systems raises the issue of the use which is made of the retrieved information. This problem is pointed out in the field of bioinformatics in [START_REF] Bartlett | How is Information used? Applying task analysis to understanding information use[END_REF]: IR is integrated as part of an experimental protocol implying progressive hypothesis refinement steps. Introducing the notion of problem solving or task in the IR process appears to be a major issue [START_REF] Järvelin | Information seeking research needs extension toward tasks and technology[END_REF]. This dimension still remains underexploited or implicit, because of the complexity of its understanding, analysis and expression.

Among the approaches in this direction, Choo et al. [START_REF] Choo | Information seeking on the Web -an integrated model of browsing and searching[END_REF] aims at designing taxonomies of tasks, by collecting data regarding the behaviour and motives of user groups. These taxonomies can be used to expand queries with user-selected and task-oriented controlled vocabularies [START_REF] Liu | GOOSE: a goal-oriented search engine with commonsense[END_REF]. But they remain difficult to exploit because they are too general [START_REF] Choo | Information seeking on the Web -an integrated model of browsing and searching[END_REF] or limited to one particular task [START_REF] Bartlett | How is Information used? Applying task analysis to understanding information use[END_REF].

A knowledge representation dimension

These various attempts at enriching the search context have a common feature: they rely on a representation of knowledge regarding the user or the task he performs. A deeper focus on the user is provided by adaptive hypermedia [START_REF] Brusilovsky | From adaptive hypermedia to the adaptive web[END_REF], which constructs a representation aimed at a specific audience. To achieve this goal, a user representation is devised to offer personalised services [START_REF] Kobsa | Personalized hypermedia presentation techniques for improving online customer relationship[END_REF]. Such approaches are used for instance in medical tutoring systems to adapt exercises to the student [START_REF] Soula | An adaptive medical e-learning environment: the MEDIDACTE project[END_REF].

The task notion is central to some Artificial Intelligence paradigms such as Problem Solving Methods and knowledge representations. These can be used to enrich the retrieval context. For example, the medical problems-solving steps in the tutoring system from [START_REF] Crowley | An intelligent tutoring system for visual classification problem solving[END_REF] are represented using the Unified Problem-Solving Method Description Language. Nowadays the objects manipulated through these tasks are represented through ontologies [START_REF] Gruber | Towards principles for the design of ontologies used for knowledge sharing[END_REF]. Numerous examples exist in the medical domain [START_REF] Rubin | Biomedical ontologies: a functional perspective[END_REF], from simple controlled vocabularies such as Gene Ontology1 , to complex representations in descriptive logics such as the Foundational Model of Anatomy2 .

SYNTHESIS MODEL

We are proposing a system focused on information synthesis tasks, supported by a grid-based display, which is inspired from the organisation of items on a TMA slide. This particular viewpoint implies reconsidering the three steps of query formulation, information retrieval and information display, as sketched in Fig. 2.

Fig. 2. Synthesis model -the synthesis process results from the interaction between three entities: user, task and structured documents. It implies several interactions: task

Query formulation is extended from the specification of keywords to include some task specification. Information retrieval is extended from the matching between keywords and document to involve the elaboration of a plan to select and combine the appropriate information. Information display implies the composition of a complex multimedia document instead of a mere list.

The task level

Some general task taxonomies have been devised through information need studies [START_REF] Wilson | Models in information behaviour research[END_REF][START_REF] Choo | Information seeking on the Web -an integrated model of browsing and searching[END_REF]. While they provide an overview of the wide range of tasks to be considered, they give rather few ideas on how to make task expression operational. We focus on information synthesis tasks [START_REF] Blake | Collaborative information synthesis[END_REF]. In all application domains, these tasks may be seen as a finite hierarchy of prototypical tasks. A first attempt at a taxonomy is presented Fig. 3.

Fig. 3. Task taxonomy -synthesis tasks are considered as belonging to three categories which can be further divided into finer grain sub-categories: comparisons provide insight into the structure of the data space, evolutions provide an overview on the correlations between items and distributions provide access to the repartition of the individuals. This hierarchy is represented by solid arrows. Sub-categories can also constructed by the combination of leaf items from different branches represented by dash arrows. For instance, a comparison of individuals towards a reference can be obtained by comparing aberrant individuals towards the barycenter of the group.

Being IR tasks, they imply the usual selection and presentation steps. Being synthesis tasks, they imply an organisational step, to support the critical examination of the selected items. Each task is therefore modelled as the composition of three major sub-tasks (selection, organisation and presentation), which themselves are complex and can be decomposed as subtask trees. These sub-task trees are therefore orthogonal to the previous taxonomy and define the Task Model corresponding to each task. A simplified view of a ''Comparison" Task Model is presented in Fig. 4. Synthesis tasks are further influenced by some application domain knowledge which we call experimental. It is heterogeneous and ill defined; it uses heuristics to drive the practical exploration of data. For instance, the ''Comparison" Task Model integrates various heuristics defining ways to handle missing information: ''Exclusion", which discards items with missing data, or ''Inference", which computes missing values using similar items. The effective heuristics to be applied are selected dynamically, at run-time, according to experimental knowledge.

User archetype and query formulation

Query formulation by users strongly depends on their knowledge of both the IR system and the application domain. We resort to user archetype models, which represent a class of users sharing specific domain knowledge and a common way of working in this domain. The existence of such mental models has been studied among attorneys [START_REF] Sutton | The role of attorney mental models of law in case relevance determinations: an exploratory analysis[END_REF]. In our approach this archetype includes a taxonomy of the domain vocabulary and a view of Experimental Domain. These archetypes are further individualised by preferences.

Including the notion of task alters the nature of the query. It becomes a n-tuple where each tuple plays a different role towards the task. This implies using Query Models such as the one which is schematically presented in Table 1. Formulating a query then consists in selecting a domain, choosing a task of interest to this domain, and finally specialising the corresponding Query Model.

Structured documents

Information visualisation techniques [START_REF] Shneiderman | Inventing discovery tools: combining information visualization with data mining[END_REF] must be used to support the structured presentation of the synthesis result. The usual ordered list is not sufficient. It is replaced by a complex grid-like structure, where each item is considered as situated in the context of other items, with which it may either share some properties, or conversely differ for others. Using a grid allows for a simple and compact visualisation of the search results; it offers a relational overview over the collection at hand, bringing into light major tendencies as well as unexpected phenomenons (regularities, breaks, commonalities or rare events.).

ARCHITECTURE AND PROTOTYPE

Architecture

The proposed model has guided the design of an architecture for a synthesis system prototype. Synthesis document generation is performed along a two-stage process by the engine presented in Fig. 5 [START_REF] Bourbeillon | Multimedia data management to assist tissue microarrays design[END_REF]. At the heart of this architecture is the notion of Task Instance, which is built as the result of the query formulation process, and guides the elaboration of the synthesis document.

Fig. 5. Architecture -the synthesis system consists in two main functions: a task instanciation and the execution of this Task Instance. These functions can be decomposed in finer grain operations and imply the manipulation of entities. This Task Instance is generated along several specialisation steps. The user first formulates a Structured Query. He specialises the Query Model corresponding to the task he is interested in (selected in the taxonomy from Fig. 3), using Study Domain and Experimental Domain terms corresponding to his Archetype. This Structured Query is then used to build a Task Instance, based on a generic Task Model, which specifies the main components supporting the successive selection, organisation and presentation steps (as presented in the sub-tasks tree from Fig. 4 for a comparison task).

The Task Instance is then executed. This is performed according to three stages, each corresponding to a major sub-task of the Task Model: Selection (a set of relevant documents is selected), Organisation (the spatial organisation of the selected items is specified) and Presentation (the lay-out of the final synthesis document is specified).

Prototype

A first prototype has been built to experiment the potential of the proposed notions. This prototype is developed in JAVA and relies heavily on XML: ad-hoc schemata accessed through the JAXB library for the domain knowledge, query, generated documents and configuration files ; XFORMS (through the Orbeon Forms1 framework) for the user interface. Each elementary subtask corresponds to a distinct software component which parameters are defined in the query.

Task instance construction phase

The Task Instance construction phase is performed according to the schema presented in Fig. 6. This stage is difficult because several components may be available to solve the same subtask (for example the ''Missing data" sub-task). Heuristics stored as Experimental Domain knowledge should help solve this issue, but they may be insufficient to guarantee the overall relevance of the synthesis document. An additional problem comes from the necessity to fuse potentially heterogeneous and contradictory information extracted from various sources (Query, Preferences, Experimental Domain).

In the prototype both issues have been put aside. We forced the use of one single component for each sub-task. We also defined a precedence order between information sources. Further work will be necessary towards a fully autonomous prototype.

Task instance execution phase

Constructing the synthesis document implies solving each subtask included in the Task Instance according to the schema in Fig. 7. This phase is performed by the Execution Engine. It reads the Task Instance thanks to an Instance Explorer which translates it into a set of Component Headers, which describe the Components' specialisation instructions along with their input/output descriptions. The Engine then resorts to a Scheduler to define the Components' initial running order. Once this order has been established, the Engine starts executing each Component in the specified order. Components are loaded from the Components Library, and then initialised and run according to the instructions of the Header. This process poses two major issues. First of all, dependencies among components are not enough to deduce their running order: the dynamics of the document construction are complex and implicit dependencies play a major role. For instance, it might be required to re-run some components if their previous execution lead to a dead end. Moreover some sub-tasks are both very complex and impossible to decompose into a set of simpler logical sub-tasks. The components we have proposed so far to solve these sub-tasks are still rudimentary and will have to be refined.

EXPERIMENTAL VALIDATION

Validation goals and procedure

The evaluation of the current realisations can be considered along two axes. First of all the proposed system is a piece of software. Most software evaluation metrics have little relevance in the context of a prototype, except the evaluation of the use of the system. This suggests conducting case studies. Moreover the synthesis model is based on IR models. Objective IR measures such as relevance would imply taking into account the lay-out of the synthesis document. They are too complex at this stage. However user studies based on questionnaires can be used to capture the potential users' opinion.

In this section, we will present a twofold experimental validation, based on case studies and a preliminary user study.

Case studies

The preliminary case studies presented here are based on a limited data set so that the results can be manually checked. The cohort consists in 162 patients suffering from colon lesions followed at the Centre Régional de Lutte Contre le Cancer de Montpellier (France) who underwent surgery between 1988 and 2002. The median age of the patients is 66 (range, 24-91). They include 70 female, 90 male and two sex unknown. The localisation of their lesion represents the various areas of the colon, cancer stages are various and even if adenocarcinoma are the majority, other lesion types are represented. Data extracted from their clinical files and the evaluation of several staining measures by a pathologist on corresponding whole section and TMA slides lead to a database including 65 parameters per patient.

Given the development stage of the prototype we focus on two aspects of the approach: -Query formulation: how may a natural language biological problem be expressed at a macroscopic level and translated into a n-tuple representing the Structured Query? -Synthesis document: does this construction, which is visualised as a multimedia document, allow for knowledge inferences and is it really relevant for the user?

Comparison case

Structured query

We decided to analyse whether there are significant differences between tumoral tissues and tissues adjacent to the tumour. Indeed the interface between the non-tumoral and tumoral tissues is not well known. We therefore analysed key molecular elements involved in colon carcinogenesis and compared their expressions in both tissue types:

β-catenin: cellular adhesion molecule, involved at the beginning of tumour progression ; mitosis signals address it to the nucleus where it acts as transcriptional activator for various molecules, among which Cyclin D1;

-Cyclin D1: molecule regulating the cell cycle by contributing to mitosis initiation and cellular proliferation;

-Ki67: widely used marker of cell proliferation, preferentially expressed in the nucleus of active cells;

We focused on the relationship between cellular localisation and expression of the markers in each tissue type. The biological problem therefore is: "comparison of the intracellular localisation of the expression of each marker in tumoral and adjacent tissues from all patients with colon carcinoma".

This query can be formulated as being a kind of "Comparison" task, which goal is to compare the markers' expression, as measured for example by "the percentage of marked cells". The focus of this study (i.e. inclusion criteria), here the organ under examination, is the "colon". Finally, the groups to be compared (i.e. organisation criteria) are selected as being tumoral and adjacent tissues (i.e. Diagnostic for the tissue), under the various possible marker intracellular localisations (i.e. Description for the cell).

This formulation leads to the representation in Fig. 8: the drop-down lists of the query interface allow to go through the domain taxonomy and express the needs of the user. The input system and the underlying representation therefore show a sufficient expression ability to define biological problems as complex as the one addressed here.

Fig. 8. A view of the query interface when filled to formulate the biological problem from the comparison use case: ''Comparison of

the intracellular localisation of the expression of each marker in tumoral and adjacent tissues from all patients with colon carcinoma".

Synthesis document

Some screenshots of a synthesis document are presented in Fig. 9. The documentary grid represents each individual item (TMA spot) as a square, which grey level corresponds to the percentage of marked cells (goal of the comparison), from 0 (white) to 100% (black). These spots are grouped by areas, which are delimited by titles describing their contents. Each spot also serves as a link to various relevant views of associated information: biopsy block the TMA core comes from, patient clinical data... Fig. 9. Synthesis document constructed as a response to the query presented in Fig. 8. Here is displayed the documentary grid. It represents each individual item as a square spot which grey level corresponds to the percentage of marked cells (goal of the comparison), with about a dozen of levels, from 0 (white) to 100% (black). These spots are grouped by areas which are delimited by titles describing their contents. Each spot also serves as a link to other items and gives access to various relevant views on associated information.

Only 46 patients are displayed, because individuals with missing data were excluded by the system. Moreover, for each marker, the staining level depends on the considered tissue (tumoral or adjacent to the tumour) and the intracellular localisation:

β-catenin (left part of the grid): for normal cells (left) the β-catenin localisation is primarily in the membrane, whereas for tumoral cells (right) the staining goes through the whole cell. However we know that, in resting normal cells, β-catenin is expressed in the membrane and then degraded in the cytoplasm. In tumoral cells, mutations and / or other upper biological events in the β-catenin pathway prevent this degradation. The β-catenin then migrates to the nucleus where it activates cyclins transcription.

-Cyclin D1 (middle): for normal cells (top) Cyclin D1 is mostly located in the cytoplasm, whereas for tumoral cells (bottom) it is also present in the nucleus and the staining is higher. We know Cyclin D1 contributes to mitosis initiation. Higher Cyclin D1 levels imply an increased mitosis rate and higher proliferation level, which fits with tumoral tissues where cells division is fast and anarchic -Bcl2 (top right): staining is low but spread wider among patients in tumoral tissue (right) than in normal tissue (left). Furthermore we know that this molecule inhibits apoptosis. Our findings of increased Bcl2 staining in tumoral cells imply a reduced cellular death rate, which is to be expected, -Ki67 (bottom right): the staining involves higher numbers of nuclei in tumoral tissues (right) than in normal tissue (left). This is to be expected, since tumours have an increased cellular growth pace. The documentary grid of the synthesis document therefore offers an overall picture both simple and fitting with known biological facts. But it is however necessary to confirm these intuitions with a statistical analysis of the data. Therefore a Wilcoxon rank-sum test was used to compare the groups among tumoral and ajacent samples (data not shown). This test showed significant differences between tumoral and adjacent tissues, for all molecules, in all intracellular compartments (|z| close to 0), except membrane β-catenin. This corresponds to the groups which were identified in the synthesis document. The synthesis paradigm can therefore be used as an exploratory method to help in orientating statistical analyses.

Evolution case

Structured query

For colon carcinoma, the pTNM stage of the tumor is a central prognosis element. This classification includes three components, T, N and M, which each represent one dimension of the tumoral invasion: invasion of the different tissue layers for T ; invasion of the lymph nodes for N ; distant invasion through metastasis for M. Therefore, the higher the values of T, N and M, the worse the prognosis for the patient.

The question one can then ask is the independence between the various dimensions of the classification: "is there a relationship between the lymph node invasion and the tissue invasion?" Therefore the second study example will be the "evolution of the number of invaded nodes depending on the number of observed nodes with observation of the T component of the stage for patients with colon carcinoma".

In this particular example, the formulation of the query (see Fig. 10)from the description of the problem seems possible just as it was in the previous case. Fig. 10. A view of the query interface when filled to formulate the biological problem from the evolution use case: ''evolution of the number of invaded nodes depending on the number of observed nodes with observation of the T component of the stage for patients with colon carcinoma".

Synthesis document

A screenshot, corresponding to the documentary grid of the synthesis document, is presented Fig. 11. This grid organises pertinent items according to the two axes defined in the query: the number of observed nodes and the number of positive nodes. For each combination of values along these two axes, the average individual within the set is displayed. This leads to a representation with less displayed individuals (73) than patients in the database (162). Here, the average individual corresponds to the patient with the closest value to the average of the group for the goal of the study (here the T component of the stage). This individual is represented by a square whose background colour is linked to the goal of the study, from light grey (stage 1) to black (stage 4). The number in the square corresponds to the identifier of the individual and provides access to his information sheet. This synthesis document can be used to observe the structure of the data set, for instance to identify aberrant data. Here we observe the number of positive nodes among the observed nodes. There should not be more positive nodes than observed nodes. But it is not the case for patient 3: it looks like a typical data entry error. We also have to consider whether the document helps answering the biological question. To do so, we can compare patients with no node invasion (bottom row) to the others. The output does not show any difference regarding the T component of the stage between the two groups. We can therefore consider the two component T and N of the classification are independent, which is in fact known biological information.

As for the comparison case, the intuition drawn from the synthesis document has to be statistically confirmed. Therefore a Pearson's chi-square test was used to evaluate a potential dependency between node invasion presence and T component of the stage (data not shown). This test showed the two are independent (p-value close to 0). The observation of the synthesis grid allowed to point out known biological facts, which suggests that this kind of evolution tasks could be used to evaluate new hypotheses.

User study

Objectives

We are focused here on the potential users' point of view. Since it is difficult to devise objective metrics, the evaluation is based on answers to a questionnaire and users' comments.

The general goal is to evaluate the usability and efficiency of the system. Usability includes ergonomics, navigability, learning curve, etc. Efficiency can be considered along two axes: a "user" efficiency, which can be related to the user relevance of IR systems, and includes query formulation and result interpretation dimension ; a "system" efficiency, which corresponds to performances measures.

Experimental settings

Given the limited time available to conduct the user studies the size of the user panel was reduced (8 users), which limits the possibility of statistical analyses of the results. However the group included a wide range of profiles, with variable combinations of expertise along two axes: computer literacy and oncology.

The tests were conducted according to a defined test scenario:

-Query formulation and result interpretation for the two biological problems from the case study, -Query formulation and result interpretation for user defined biological problems, -Query reformulation leading to a change of focus on the comparison case. The associated questionnaire included 28 sentences for which the user had to provide an approbation level from 1 (disagree) to 5 (completely agree). These were organised according to three categories: "Generalities" covering usability issues, "Query formulation" and "Results interpretation". The observations of the user behaviour was conducted by an experimentalist who evaluated the time spent on each screen, measured the computing time and recorded the remarks spoken aloud by the users.

Results

Usability

The "Generalities" section of the questionnaire is declined along several axes: ease of use, conviviality, ease of learning, navigability and overall satisfaction. The size of the panel being small, no definitive conclusion can be drawn, but in general the users seemed interested in the prototype they tested. The only restrictions are minor and have to be expected from a prototype: some ergonomics flaws or limited contextual help, etc.

In the context of an unusual display such as ours the ease of learning dimension is of particular interest. Indeed it is closely linked with the adaptation time, which is essential for the adoption of a new technology. It has been evaluated by measuring the time spent to formulate queries (see Fig. 12) and interpret results (data not shown). These measures show a general decrease of the time spent over the various queries, except for the open studies. This can be explained by the time required to devise a new biological problem, whereas the problem is already defined for the other queries. There seems to be a significant effect of learning on the ease of use of the tool for both query formulation and synthesis document interpretation.

Query formulation and synthesis document interpretation

The "Query formulation" section of the questionnaire is declined along several axes: adequacy of the query to the task at hand, actual formulation, completeness, expressiveness, extensibility and navigability. The results seem to indicate an overall satisfaction of the users. The only limits relate to the completeness and expressiveness. Regarding the completeness, users expressed a need for other tasks. The expressiveness of the query is limited by the lack of a boolean OR and the impossibility to build composed concepts ("the staining intensity for the marker Ki67" for instance). This should be solved by future developments.

The "Results interpretation" section of the questionnaire explores also several dimensions: intuitiveness, informativeness, utility, suggestiveness and navigability. The results are here again positive. The grid representation is considered as relevant, sensible and as bringing new information. The intuitiveness is the less positive axis: users have to get used to the representation as our analysis of the learning process has shown. Users also showed their enthusiasm through the amount of suggestions they have made, either to facilitate the interpretation of the synthesis document (adding more colour-coded elements, allow for a joint view of several documents, add exportation tools to ease reuse in publications) or to extend the document (add a display of simple descriptive statistics and links to data analysis tools, improve the interaction with the grid through zoom/focus or selections as basis for a new study).

System performances

As part of the development of a prototype, an evaluation of the performances allows to point out possible flaws, in the perspective of developing a complete production system. This evaluation was conducted in an informal manner through rough estimates evaluated during the user study on a Pentium IV dual-core 1.83GHz with 1Go RAM. Table 5 presents the results. For most items these measures indicate delays within a few seconds range which is compatible with an interactive system. However two items pose problems: computation of the synthesis document and display of reformulation forms. Both issues have been analysed and solutions individuated.

CONCLUSION

In this paper we took an interest in defining a task oriented IR model, which led us to explore the notion of synthesis task. The construction of a prototype based on this model allowed for a first validation of the task-oriented IR concepts applied to the biomedical domain. The current prototype is still in its first development stages, which leaves numerous opportunities for future work.

First of all the prototype has only been tested on a limited data set, so that the generated synthesis documents could be easily checked. However the synthesis concept aims at helping scientists to get a grasp on large volumes of data. Scale-up experiments have to be conducted to assess the usability of the system on larger data sets, regarding both the number of records and number of parameters.

Secondly the prototype has its deficiencies, including the small number of available components and task models, some limits to the query expressiveness and interactions with the synthesis documents, and a few performance flaws. The first step towards improving the prototype will be to solve these issues. The second step will be to expand on the existing system. For instance we are studying the inclusion of statistical analysis tools in the context of a synthesis document, through the call to R functions. We are also currently planning on adding a new task to explore the distribution of a data set.

Moreover some stages of the process itself are quite complex ; for instance, the organisation of relevant items on the documentary grid is a problem which has elements in common with the classic Operational Research "bin packing" problem. However it uses imbricated boxes of variable sizes, which makes its resolution very difficult. Future works around these complex algorithms have to be considered.

Our prototype also lacks a system to evaluate the quality of the synthesis document, with regards to the structured query. It should be a situational relevance evaluation, which goes beyond classic IR measures. The definition of such a quality measure, taking into account the position of items on the grid, is still in its first stages.

Fig. 1 .

 1 Fig. 1. TMA technology -patients are selected according to the biological question addressed and corresponding core samples to extract are organised in a TMA design plan.

Fig. 4 .

 4 Fig. 4. Simplified Task Model -a ''Comparison" task is decomposed in a sub-tasks tree. Each rectangular node represents a subtask category which is decomposed at the next level until leaves are reached. These leaves represent elementary sub-tasks.

Fig. 6 .

 6 Fig. 6. Software architecture for the elaboration of the Task Instance.Central to this stage is the Model Manager. This piece of software goes through the Task Model. It designs the Task Instance by integrating information from the Structured Query and User Preferences along with Experimental Domain knowledge. Its main objective is the selection of components and the fusion of information, to instantiate the task in a manner which will lead to the most relevant synthesis document.This stage is difficult because several components may be available to solve the same subtask (for example the ''Missing data" sub-task). Heuristics stored as Experimental Domain knowledge should help solve this issue, but they may be insufficient to guarantee the overall relevance of the synthesis document. An additional problem comes from the necessity to fuse potentially heterogeneous and contradictory information extracted from various sources (Query, Preferences, Experimental Domain).In the prototype both issues have been put aside. We forced the use of one single component for each sub-task. We also defined a precedence order between information sources. Further work will be necessary towards a fully autonomous prototype.

Fig. 7 .

 7 Fig. 7.Software architecture for the execution of the Task Instance.

Fig. 11 .

 11 Fig.11. Synthesis document for the evolution example whose query is introduced in Fig.10. For each combination of values along these two axes for which items exist the average individual within the set is chosen. This individual is represented by a square whose background colour is linked to the goal of the study (here the T component of the stage), from light grey (stage 1) to black (stage 4). The number in the square correspond to the identifier of the average individual and provides access to his information sheet.

Fig. 12 .

 12 Fig. 12. Time spent for query formulations by the various users depending on the study.

Table 1 .

 1 Example of a Query Model for a comparison task.

	Model Item	Description
	Generalities
	Task	Task Category, e.g. Comparison
	Title	Short description of the synthesis goal
	Description	More precise description of the study
	Domain	Application domain the query is related to
	Needs
	Goal	Target item, e.g. item to compare
	Inclusion Criteria	Criteria guiding the selection of items (classical IR terms)
	Organisation Criteria	Criteria guiding the organisation of the items to handle, e.g., criteria defining
		the composition of the groups to compare
	Ordering Criteria	Criteria guiding the organisation of the items inside each group
	Experimental constraints
	Language	Language to use for the display
	Colour	Colour scheme to use inside the synthesis document to represent the values
		of the variables
	Selection criteria application	Approach to use in the application of the selection criteria, either strict (exact
	method	match) or approximate (similar values)
	Missing data management	Approach to use to manage items with missing data, either exclude the items
		or infer missing data from similar items

Table 2 .

 2 Results of the performances evaluation.

	Item

http://trec.nist.gov/.

http://www.ncbi.nlm.nih.gov/pubmed/

http://www.geneontology.org/.

http://sig.biostr.washington.edu/projects/fm/.

http://www.orbeon.com/.

Acknowledgments.

J. Bourbeillon is funded by the Minstère de l'Education Nationale, de la Recherche et des Technologies (French Ministry of Research and Technology). Part of this research was also supported by the "Bio-Informatique" inter-EPST program and "La Ligue Contre le Cancer", Savoie committee. The authors would like to thank Dr. Joëlle Simony-Lafontaine who conducted the anatomopathological evaluations of samples, all team members who participated in the user study and the anonymous reviewers whose comments contributed to the improvement of this paper.