D Godefroy

| C Dominici

| H Hardin-Pouzet

| Y Anouar

| S Melik-Parsadaniantz

| W Rostène

| A Reaux-Le Goazigo

William Rostène
email: william.rostene@inserm.fr

Three-dimensional distribution of tyrosine hydroxylase, vasopressin and oxytocin neurones in the transparent postnatal mouse brain

Keywords: 3D neuronal distribution, catecholamine, iDISCO+, neuropeptide, transparent brain

Over the years, advances in immunohistochemistry techniques have been a critical step in detecting and mapping neuromodulatory substances in the central nervous system. The better quality and specificity of primary antibodies, new staining procedures and the spectacular development of imaging technologies have allowed such progress. Very recently, new methods permitting tissue transparency have been successfully used on brain tissues. In the present study, we combined whole-mount immunostaining for tyrosine hydroxylase (TH), oxytocin (OXT) and arginine vasopressin (AVP), with the iDISCO+ clearing method, light-sheet microscopy and semi-automated counting of three-dimensionally-labelled neurones to obtain a (3D) distribution of these neuronal populations in a 5-day postnatal (P5) mouse brain. Segmentation procedure and 3D reconstruction allowed us, with high resolution, to map TH staining of the various catecholaminergic cell groups and their ascending and descending fibre pathways. We show that TH pathways are present in the whole P5 mouse brain, similar to that observed in the adult rat brain. We also provide new information on the postnatal distribution of OXT and AVP immunoreactive cells in the mouse hypothalamus, and show that, compared to AVP neurones, OXT neurones in the supraoptic (SON) and paraventricular (PVN) nuclei are not yet mature in the early postnatal period. 3D semi-automatic quantitative analysis of the PVN reveals that OXT cell bodies are more numerous than AVP neurones, although their immunoreactive soma have a volume half smaller. More AVP nerve fibres compared to OXT were observed in the PVN and the retrochiasmatic area. In conclusion, the results of the present study demonstrate the utility and the potency of imaging large brain tissues with clearing procedures coupled to novel 3D imaging technologies to study, localise and quantify neurotransmitter substances involved in brain and neuroendocrine functions.

| INTRODUCTION

For the past 50 years, immunohistochemical techniques have been recognised to be powerful for the mapping of various neurotransmitter pathways and neuronal cell bodies in the rodent central nervous system (CNS). The development of monoclonal or polyclonal antibodies for various neurotransmitters and neuropeptides enhanced our knowledge of their distribution in various brain regions. In parallel to the immunohistochemical and anterograde or retrograde transport procedures, progress in imaging technologies and computer analyses provided new information concerning neurotransmitter pathways in the CNS. This led to the extensive description of classical neurotransmitters, as well as neuropeptide distributions, in both animal and human brain structures. [START_REF] Björklund | Classical neurotransmitters in the CNS[END_REF][START_REF] Björklund | GABA and neuropeptides in the CNS[END_REF] However, until recently, such tracing approaches relied on tissue sectioning, sometimes followed by serial brain tissue section reconstruction. [START_REF] Simmons | High-resolution paraventricular nucleus serial section model constructed within a traditional rat brain atlas[END_REF] These approaches were time-consuming, yielded incomplete spatial distribution and were carried out on thin sections. During the last decade, light scanning microscopy, digital slide scanners and two-photon imaging technologies have improved the detection of fluorescence immunostaining and cell counting on thick sections. However, such methodologies are still limited by the sample size and by light scattering through thick tissues. [START_REF] Richardson | Clarifying tissue clearing[END_REF] New methods of "clearing samples" have emerged recently to avoid such phenomenon, particularly light scattering. [START_REF] Richardson | Clarifying tissue clearing[END_REF] Approaches were also developed to achieve transparency of intact tissues by refractive index homogenisation. [START_REF] Richardson | Clarifying tissue clearing[END_REF] In conjunction with ultramicroscopy, these techniques allowed successful three-dimensional (3D) visualisation. [START_REF] Dobosz | Multispectral fluorescence ultramicroscopy: three-dimensional visualization and automatic quantification of tumor morphology, drug penetration, and antiangiogenic treatment response[END_REF][START_REF] Dodt | Ultramicroscopy: threedimensional visualization of neuronal networks in the whole mouse brain[END_REF][START_REF] Keller | Light sheet microscopy of living or cleared specimens[END_REF] Subsequently, several chemical improvements of the technique "solvent based clearing" and the development of various protocols termed BABB, 3DISCO, iDISCO, iDISCO+ or uDISCO [START_REF] Dobosz | Multispectral fluorescence ultramicroscopy: three-dimensional visualization and automatic quantification of tumor morphology, drug penetration, and antiangiogenic treatment response[END_REF][START_REF] Erturk | Three-dimensional imaging of solvent-cleared organs using 3DISCO[END_REF][START_REF] Erturk | Imaging cleared intact biological systems at a cellular level by 3DISCO[END_REF][START_REF] Renier | iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging[END_REF][START_REF] Renier | Mapping of brain activity by automated volume analysis of immediate early genes[END_REF][START_REF] Pan | Shrinkage-mediated imaging of entire organs and organisms using uDISCO[END_REF][START_REF] Belle | A simple method for 3D analysis of immunolabeled axonal tracts in a transparent nervous system[END_REF] have been carried out on various tissues. They have provided high-resolution imaging data, which allowed the visualisation of neuroanatomical and neurotransmitter pathways in the brain, trigeminal ganglion or spinal cord of mouse, rat, zebrafish, nonhuman primates or human tissues. [START_REF] Renier | iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging[END_REF][START_REF] Renier | Mapping of brain activity by automated volume analysis of immediate early genes[END_REF][START_REF] Belle | A simple method for 3D analysis of immunolabeled axonal tracts in a transparent nervous system[END_REF][START_REF] Launay | Combined 3DISCO clearing method, retrograde tracer and ultramicroscopy to map corneal neurons in a whole adult mouse trigeminal ganglion[END_REF][START_REF] Liebmann | Three-dimensional study of Alzheimer's disease hallmarks using the iDISCO clearing method[END_REF][START_REF] Belle | Tridimensional visualization and analysis of early human development[END_REF][START_REF] Soderblom | 3D imaging of axons in transparent spinal cords from rodents and nonhuman primates[END_REF] Because extensive studies have been carried out on the distribution of tyrosine hydroxylase (TH), oxytocin (OXT) and arginine vasopressin (AVP) neurones in the rat brain, we have used these neurotransmitter substances to validate the iDISCO+ technique with respect to the mouse brain. We also extended our approach to the generation of 3D maps for these neurotransmitters in the 5-day postnatal (P5) mouse brain by combining whole-mount immunostaining with the iDISCO+ clearing technique and high-resolution ultramicroscopic imaging. These combined methods also permitted a semi-automatic quantitative analysis of the number and volume of specific neuronal AVP and OXT populations in the 3D paraventricular nucleus (PVN).

| MATERIALS AND METHODS

| Animals

Four wild-type adult pregnant female C57BL/6 mice (40 g; Janvier Labs, Le Genest-Saint-Isle, France) were maintained under a 14:10 hours light/dark cycle at 22 ± 1°C and 60 ± 10% relative humidity, with access to food and water available ad lib. All animal procedures were performed in accordance with institutional guidelines for the care and use of experimental animals approved by the European Communities Council Directive 2010/63/UE.

| Sample preparations

Twenty-five male and female P5 mice from multiple litters were anaesthetised with a mixture of ketamine 1000 U (80 mg kg -1 body weight) and xylazine (8 mg kg -1 bodyweight; Virbac, Carros, France) injected i.p., and brains fixed with an intracardiac perfusion of 4% paraformaldehyde in phosphate-buffered saline (PBS). Brains were carefully dissected, post-fixed overnight at 4°C in the same fixative, and randomly assigned to be immunostained for TH, AVP and OXT.

| Brain pretreatment with methanol for TH and OXT immunostaining

Fixed P5 brains were washed in PBS for 1 hour twice, then in 50% methanol (in H 2 O) for 90 minutes, 80% methanol for 90 minutes and 100% methanol for 90 minutes. They were then bleached with 3% H 2 O 2 in 100% methanol overnight at 4°C in the dark without shaking.

Brains were rehydrated in 100% methanol, 80% methanol, 50% methanol, and PBS, allowing 90 minutes for each step before the staining procedures.

| Brain pretreatment without methanol for AVP immunostaining

Fixed brains were washed twice each time in PBS for 1 hour and then used for staining procedures.

| Whole mount staining for TH, OXT and AVP

Pretreated brains were incubated for blocking and permeabilisation in PBSG-T (PBS, 0.2% gelatin and 0.5%Triton-X100) containing thimerosal (0.1 g L -1 ) for 2 days at 37°C under agitation, and then incubated with previously characterised primary antibodies (rabbit TH; Millipore, Guyancourt, France; #AB152, dilution 1:300), [START_REF] Renier | iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging[END_REF] rabbit OXT (immunogen: Cys-Tyr-Ile-Gln-Asn-Cys-Pro-Leu-Gly-NH2; Phoenix Pharma Inc., Strasbourg, France; #G-051-01, dilution 1:250) and rabbit AVP (immunogen: Cys-Tyr-Phe-Gln-Asn-Cys-Pro-Arg-Gly-NH2, disulfide bridge: Cys1-Cys6; Phoenix Pharma Inc.; #G-065-07, dilution 1:500) in PBSG-T containing 0.1% saponin for 7 days at 37°C with gentle shaking. The primary antibodies for neuropeptides did not cross-react with each other, nor did they cross-react with other hypothalamic peptides such as growth hormone peptide, gonadotrophin-releasing hormone, thyrotrophin-releasing hormone, vasoactive intestinal peptide, α-atrial natriuretic peptide and met-enkephalin as provided by the manufacturer. Brains were washed in PBS for 1 day and incubated with a secondary antibody (donkey anti-rabbit Cy3; Jackson ImmunoResearch, Newmarket, UK; #705-165-147, dilution 1:500) and TO-PRO 3 iodide (Invitrogen, Villebon, France; dilution 1:80) in PBSG-T containing 0.1% saponin for 2 days at 37°C in a dark room with gentle shaking. Finally, they were washed in PBS for 36 hours before the clearing procedure.

| Clearing procedure

P5 brains were cleared with the following iDISCO+ protocol. [START_REF] Renier | iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging[END_REF] #108014; Sigma-Aldrich) for at least 3 hours before imaging.

| Light-sheet imaging

Cleared P5 brains were imaged with an Ultramicroscope (LaVision BioTec, Bielefeld, Germany) using imspectorpro (LaVision BioTec). The light sheet was generated by a laser (wavelength 561 nm or 640 nm; Coherent Sapphire Laser; LaVision BioTec) and two cylindrical lenses.

A binocular stereomicroscope (MXV10; Olympus, Tokyo, Japan) with a 2 × objective (MVPLAPO; Olympus) was used at different magnifications (0.63×, 3.2×, 4×, 5×). Brains were placed in a horizontal or coronal orientation in an imaging reservoir made of 100% quartz (LaVision BioTec) filled with DBE and illuminated from the side by the laser light. Images were acquired with a PCO Edge SCMOS CCD Camera (2560 × 2160 pixel size; LaVision BioTec). The step size in Z-orientation between each image was fixed at 2 μm resolution for 1.26 × magnification and 1 μm for 6.4×, 8× and 10× magnifications, respectively.

| Image processing, cell counting and neuronal cell volume determination

Images of the CNS were constructed from a Z-series of ultramicroscope fluorescence images using imaris, version 8.3.0_64 (http:// bitplane.com). Option software tools in imaris were used for 3D reconstruction and segmentation from a juxtaposition of serial two-dimensional images of the region of interest according to the mouse brain atlas of Franklin and Paxinos. [START_REF] Franklin | The mouse brain in stereotaxic coordinates[END_REF] False colours were given to these brain areas with imaris. High-quality 3D images were selected for neuronal cell quantification and cell volume analysis in the PVN.

Cell counting was conducted for three animals per condition in the magnocellular, medial parvocellular and ventral parvocellular subdivisions of the PVN. Each PVN subdivision was identified by its anatomical localisation.

For accurate AVP and OXT expressing cell automatic counting, imaris parameters such as the minimum diameter of the cells (12.0 μm for AVP neurones; 6.5 μm for OXT neurones), as well as the threshold of the fluorescence, were set. After confirming that all nerve cells were detected, possible false positive cells were discarded to ensure that each cell was outlined properly.

For cell volume measurements, the quantification was made in the magnocellular subdivision of the PVN. Briefly, following the elimination of the background (surface grain size: 2.50 μm; diameter of largest sphere: 4.88 μm), the fluorescence thresholding and the minimum number of voxels ("number of voxels" above 1090 to AVP and 900 to OXT) were set, allowing false positives to be discarded and the volume (in μm 3 ) of the selected neuronal cell bodies to be measured on both sides of the PVN.

3D pictures and tiff series were obtained using the optional «animation» tools. Movies were generated with fiji (NIH, Bethesda, MD, USA). Titles and transition additions were performed using movie maker (Microsoft Corp., Redmond, WA, USA).

| Statistical analysis

Data are expressed as the mean ± SEM and analysed with prism, version 7 (GraphPad, San Diego, CA, USA). The difference between AVP and OXT cell volumes was determined using the Mann-Whitney test and that for cell counting using an unpaired t test. P < .05 was considered statistically significant.

| RESULTS

| 3D mapping of TH neurones with the iDISCO+ technique in P5 mouse brain

TH immunostaining was first used to demonstrate the robustness of the iDISCO+ methodology by determining the various catecholaminergic structures and pathways present in the whole P5 postnatal mouse brain. In the present study, we delineated and tracked TH staining in 3D using the original nomenclature A1 to A16 of the various catecholamine-containing cell groups, as initially described by Anden et al. [START_REF] Anden | Mapping out of catecholamine and 5-hydroxytryptamine neurons innervating the telencephalon and diencephalon[END_REF] and Hökfelt et al. [START_REF] Hökfelt | Distributional maps of tyrosine-hydroxylase-immunoreactive neurons in the rat brain[END_REF] Following the whole mount labelling procedure with a highly specific anti-TH antibody and advanced image processing, a pronounced TH immunoreactivity corresponding to the A1-A16 cell groups was detected in a rostrocaudal localisation (Figure 1A), with the most rostral A16 dopamine cells located in the olfactory bulb (white); strong dopamine staining in the striatum and anterior cortical areas (blue) (Figures 1B,C and2A); and catecholaminergic (dopamine, noradrenaline, adrenaline) innervation of the hypothalamus (green) (A11-A15) (Figure 1B,C).

In the mesencephalon, a clear distinction was delineated between the well-known A10 dopamine neurones in the ventral tegmental area (blue) (Figure 1B,C and 2A) corresponding to the ventromedial dopaminergic pathways projecting rostrally to the nucleus accumbens and the frontal cortex, and the nigrostriatal pathway of the A9 dopaminergic neurones (Figure 1B,C,2A). Finally, caudal to the A9 dopamine neurones in the substantia nigra, some cells (A8) dorsal to the medial and lateral lemniscus were detected, as well as strong noradrenergic staining in the raphe nuclei (Figure 1B,C; see also

Supporting information, Movie S1).

Regarding the adrenergic/noradrenergic neurones located in the pons (yellow, Figure 1B) and in the medulla oblongata (pink), the A1-A6 groups of noradrenergic cell bodies were obsevred. More caudally, the two groups of adrenergic/noradrenergic cells were found in the nucleus the solitary tract (A2/C2) and in the ventral medulla/facial nucleus (A1/ C1) (Figure 2; see also Supporting information, Movie S2).

The ascending and descending catecholaminergic pathways innervating the various parts of the brain, particularly the hypothalamus, were visualised using the iDISCO+ technique (Figure 2B; see also Supporting information, Movie S2).

From optical coronal sections, imaris was able to reconstruct, in false colours, the 3D TH innervation in defined structures such as the catecholaminergic system in the hypothalamus (Figure 3; see also Supporting information, Movie S1). Specific TH immunoreactive cells were mainly found in the anteroventral periventricular nucleus, around the third ventricle in the periventricular region, and in the most ventromedial part of the suprachiasmatic nucleus. TH immunoreactive neurones extended caudally to the posterior hypothalamus (A14 neurones in cyan). The A11-A13 dopamine neurones (in green) were visualised in the dorsal hypothalamus/zona incerta, as were the A12 neurones (purple), which constitute the main infundibular dopaminergic neurones in the mediobasal hypothalamus, arcuate nucleus and median eminence (Figure 3; see also Supporting information, Movie S1).

| 3D mapping of AVP and OXT immunoreactive neurones in P5 mouse hypothalamus

By contrast to the rat brain, particularly during the postnatal period, very few studies have investigated the distribution of both neuropeptides AVP and OXT in the mouse brain. Therefore, we used the potent iDISCO+ technique to obtain new information about the distribution of these neuropeptides in the P5 mouse hypothalamus.

AVP and OXT are known to be mainly expressed in magnocellular and parvocellular neurones in the PVN and in the supraoptic nucleus (SON), as well as in a number of scattered hypothalamic and contiguous regions so-called accessory nuclei. [START_REF] Buijs | Ontogeny of vasopressin and oxytocin in the fetal rat: early vasopressinergic innervation of the fetal brain[END_REF][START_REF] Hawthorn | Comparison of the distribution of oxytocin and vasopressin in the rat brain[END_REF][START_REF] Ludwig | Dendritic peptide release and peptide-dependent behaviours[END_REF][START_REF] Sofroniew | Vasopressin, oxytocin and their related neurophysins[END_REF] We confirmed, in P5 brain with the iDISCO+ clearing technique, the well-known distribution of AVP and OXT neurones as described in the adult hypothalamus, the presence of AVP neurones in the suprachiasmatic nucleus and both AVP and OXT immunostaining in accessory nuclei (Figures 4 and5). Bands of immunoreactive cells were also observed ventrally on both sides of the median eminence, extending far anterior through the medial tuberal nucleus to the retrochiasmatic area. These cells appeared to be more

F I G U R E 1

3D images also clearly show the different labelling of AVP and

OXT neuronal cell bodies within the PVN at P5. Indeed, the intensity of AVP immunoreactivity was higher in the magnocellular part of the PVN than in the medial and ventral subdivisions of the parvocellular part of the nucleus (Figure 5A). By contrast, at that age, the fluorescence intensity of OXT immunoreactive neurones was similar in the whole PVN (magno-and parvocellular parts) (Figure 5B; see also Supporting information, Movie S4).

The high-quality images allowed us to perform a semi-automatic counting of AVP and OXT immunoreactive cell bodies throughout the entire 3D PVN. Our results show that PVN at P5 contains approximately three times more OXT cells than AVP neurones (1221 vs 409 neuronal cells, respectively). Semi-automatic counting detected 622 ± 11 OXT cells vs 152 ± 6 AVP magnocellular neurones (P < .01; n = 3 each condition), 528 ± 3 vs 145 ± 2 in the medial parvocellular part of the PVN (P < .01; n = 3 each condition) and 71 ± 4 vs 107 ± 2 in the most ventral parvocellular subdivision of the PVN at P5 (P < .01; n = 3 each condition) (Figure 6). In addition, at that age, the volume of the soma of the OXT cells was much smaller than that of AVP neurones (Figure 6; see also Supporting information, Movies S3 and S4). Cell volumes measured on 3D images showed that magnocellular AVP neurones were significantly bigger than the magnocellular OXT neurones in the dorsal part of the PVN : AVP neurones: 1878 ± 181 μm 3 vs OXT neurones: 1172 ± 47 μm 3 (n = 3 each condition; P < .01).

| DISCUSSION

The present study was carried out aiming to demonstrate the utility of the iDISCO+ novel clearing technology with respect to mapping phenotypically-identified neuronal cell bodies and pathways in nontransgenic postnatal mouse brain, in particular in the hypothalamus.

TH staining was first used to map catecholaminergic neuronal cells and nerve fibres, demonstrating that enzymes can be targeted using such techniques in mouse brain. Interestingly, the present study also demonstrates that this technique is not restricted to the detection of high molecular weight proteins because we were also able to map nine amino acids neuropeptides (AVP and OXT) with the iDISCO+ clearing technique described here.

Comparing the precision of the present data with other reports using clarity, it appears that the iDISCO+ approach provides new information on the 3D organisation of immunoreactive neuropeptide distribution and offers new possibilities for studying multiple cell populations without the necessity of using genetically modified animals expressing various fluorescent proteins. [START_REF] Renier | iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging[END_REF][START_REF] Renier | Mapping of brain activity by automated volume analysis of immediate early genes[END_REF][START_REF] Epp | Optimization of CLARITY for clearing whole-brain and other intact organs[END_REF][START_REF] Yeo | Visualisation of Kiss1 neurone distribution using a Kiss1-CRE transgenic mouse[END_REF] The 3DISCO technique, first described by Dodt et al., [START_REF] Dodt | Ultramicroscopy: threedimensional visualization of neuronal networks in the whole mouse brain[END_REF] has recently been extensively improved by means of various solvent solutions that increased the clearing process and decreased light scattering, which hampered the image quality. [START_REF] Erturk | Three-dimensional imaging of solvent-cleared organs using 3DISCO[END_REF][START_REF] Erturk | Imaging cleared intact biological systems at a cellular level by 3DISCO[END_REF][START_REF] Epp | Optimization of CLARITY for clearing whole-brain and other intact organs[END_REF] Thus, in the present study, we were able to decrease the tissue shrinkage and obtain a better clearing of the brains by means of methanol treatment instead of tetrahydrofurane as used previously. [START_REF] Pan | Shrinkage-mediated imaging of entire organs and organisms using uDISCO[END_REF][START_REF] Belle | A simple method for 3D analysis of immunolabeled axonal tracts in a transparent nervous system[END_REF] The clearing procedure did not modify the size of the brain tissue. [START_REF] Renier | iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging[END_REF] However, the various chemical treatments of the clearing procedures could alter antibody specific recognition of the F I G U R E 4 Three-dimensional optical horizontal sections of 5-day postnatal mouse brain stained with an anti-arginine vasopressin (AVP) antibody (A) and an anti-oxytocin (OXT) antibody (C). Similar optical coronal views with an anti-AVP (B) and anti-OXT antibodies (D). Supraoptic nucleus (green), paraventricular nucleus (purple), suprachiasmatic nucleus (blue), accessory neurones (cyan) and mediobasal hypothalamus (yellow). TO-PRO 3 iodide staining For example, we observed that some specific polyclonal antibodies against AVP, previously used on brain sections, did not work properly with the methanol procedure (not shown). To avoid this shortcoming, we have used purified antibodies for which the specificity was previously validated by classical immunohistological procedures.

Another limitation has been reported concerning the presence of more lipids in adult brains compared to young brains, which may hamper the immunostaining process of some antibodies. [START_REF] Renier | iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging[END_REF] Despite these few limitations, the methodology using light-sheet microscopy, as described in the present study, provides a highresolution 3D imaging of intact brains. Segmentation of the staining with false colours allowed us to map, distinguish and identify the different catecholaminergic systems present in/or innervating various brain areas of the P5 mouse. The iDISCO+ approach of imaging large tissues microscopically at different magnifications provides quantitative information on the morphology, number and volume of cells, as well as information about their projections in the brain. [START_REF] Renier | Mapping of brain activity by automated volume analysis of immediate early genes[END_REF][START_REF] Belle | Tridimensional visualization and analysis of early human development[END_REF] The robustness of the technique was demonstrated in the present study when comparing the results obtained with the reference works related to the various types of catecholaminergic cell groups. [START_REF] Hökfelt | Distributional maps of tyrosine-hydroxylase-immunoreactive neurons in the rat brain[END_REF][START_REF] Bjorklund | Dopamine neuron systems in the brain: an update[END_REF][START_REF] Ugrumov | Tyrosine hydroxylase-and/or aromatic L-amino acid decarboxylase-expressing neurons in the rat arcuate nucleus: ontogenesis and functional significance[END_REF][START_REF] Balan | Tyrosine hydroxylase-expressing and/or aromatic L-amino acid decarboxylase-expressing neurons in the mediobasal hypothalamus of perinatal rats: differentiation and sexual dimorphism[END_REF][START_REF] Sawchenko | The organization of noradrenergic pathways from the brainstem to the paraventricular and supraoptic nuclei in the rat[END_REF][START_REF] Ugrumov | Ontogenesis of tyrosine hydroxylase-immunopositive structures in the rat hypothalamus. Fiber pathways and terminal fields[END_REF] Here, each of them (dopaminergic, noradrenergic and adrenergic cells) has been determined independently, and some of their ascending and descending pathways were followed by 3D imaging (Figures 2 and3). Moreover, such catecholaminergic pathways are similar to what is known for the adult rat brain. [START_REF] Björklund | Classical neurotransmitters in the CNS[END_REF][START_REF] Hökfelt | Distributional maps of tyrosine-hydroxylase-immunoreactive neurons in the rat brain[END_REF] Similarly, TH immunoreactive localisation such as that previously shown in the rat brain during ontogenesis was seen in the whole P5 mouse brain and especially in the hypothalamus. [START_REF] Ugrumov | Ontogenesis of tyrosine hydroxylase-immunopositive structures in the rat hypothalamus. Fiber pathways and terminal fields[END_REF][START_REF] Abramova | Dynamical study of tyrosine hydroxylase expression and its correlation with vasopressin turnover in the magnocellular neurons of the supraopticoposthypophysial system under long-term salt loading of adult rats[END_REF][START_REF] Abramova | Vasopressinergic neurons of the supraoptic nucleus in perinatal rats: reaction to osmotic stimulation and its regulation[END_REF][START_REF] Ugrumov | Development of the suprachiasmatic nucleus in rats during ontogenesis: tyrosine hydroxylase immunopositive cell bodies and fibers[END_REF][START_REF] Beltramo | Postnatal development of the suprachiasmatic nucleus in the rat. Morpho-functional characteristics and time course of tyrosine hydroxylase immunopositive fibers[END_REF][START_REF] Wilson | The anti-dopaminergic agent, haloperidol, antagonises the feminising effect of neonatal serotonin on sexually dimorphic hypothalamic nuclei and tyrosine hydroxylase immunoreactive neurones[END_REF] In both the SON and PVN, especially in magnocellular cells, the main TH innervation was represented by noradrenergic pathways originating from the brainstem. [START_REF] Sawchenko | The organization of noradrenergic pathways from the brainstem to the paraventricular and supraoptic nuclei in the rat[END_REF] Even at P5, as in adults, varicosities on axons such as in TH-expressing anteroventral periventricular neurones and periventricular neurones could be observed (see Supporting information, Movie S1). Finally, we confirmed what was reported in the rat hypothalamus during ontogenesis, [START_REF] Ugrumov | Ontogenesis of tyrosine hydroxylase-immunopositive structures in the rat hypothalamus. Fiber pathways and terminal fields[END_REF] and also in the adult, [START_REF] Hökfelt | Distributional maps of tyrosine-hydroxylase-immunoreactive neurons in the rat brain[END_REF] in terms of catecholaminergic neurones giving rise to two different populations localised ventrally, mainly in the arcuate nucleus (A12), and in the zona incerta (A11-A13) in the dorsal hypothalamus (Figure 3). It was observed that dopaminergic neurones present in the adult arcuate nucleus had sizes similar to those found in fetuses, whereas those in the dorsal hypothalamus increased in size, forming ramified processes during ontogenesis. [START_REF] Ugrumov | Ontogenesis of tyrosine hydroxylase-immunopositive structures in the rat hypothalamus. Fiber pathways and terminal fields[END_REF] Observations over the lifespan were not the aim The neurohormones AVP and OXT have been extensively studied by classical immunohistochemical methods, particularly in the rat hypothalamus [START_REF] Ludwig | Dendritic peptide release and peptide-dependent behaviours[END_REF][START_REF] Sofroniew | Vasopressin, oxytocin and their related neurophysins[END_REF][START_REF] Sofroniew | Projections from the parvocellular vasopressin-and neurophysin-containing neurons of the suprachiasmatic nucleus[END_REF][START_REF] Buijs | The development of vasopressin and oxytocin systems in the brain[END_REF] where they are synthetised together with neurophysins in the PVN, SON and accessory nuclei. [START_REF] Hawthorn | Comparison of the distribution of oxytocin and vasopressin in the rat brain[END_REF][START_REF] Ludwig | Dendritic peptide release and peptide-dependent behaviours[END_REF][START_REF] Sofroniew | Vasopressin, oxytocin and their related neurophysins[END_REF][START_REF] Sofroniew | Projections from the parvocellular vasopressin-and neurophysin-containing neurons of the suprachiasmatic nucleus[END_REF][START_REF] Vandesande | Identification of the vasopressin producing and of the oxytocin producing neurons in the hypothalamic magnocellular neurosecretroy system of the rat[END_REF] Less data are available for mice, although this species is used increasingly frequently in neurobiology. [START_REF] Silverman | The ontogenesis of neurophysincontaining neurons in the mouse hypothalamus[END_REF][START_REF] Castel | The neurophysin-containing innervation of the forebrain of the mouse[END_REF][START_REF] Castel | Vasopressinergic innervation of the mouse suprachiasmatic nucleus: an immuno-electron microscopic analysis[END_REF][START_REF] Grinevich | Ontogenesis of oxytocin pathways in the mammalian brain: late maturation and psychosocial disorders[END_REF][START_REF] Rood | Vasopressin innervation of the mouse (Mus musculus) brain and spinal cord[END_REF][START_REF] Otero-Garcia | Distribution of oxytocin and co-localization with arginine vasopressin in the brain of mice[END_REF][START_REF] Ugrumov | Magnocellular vasopressin system in ontogenesis: development and regulation[END_REF] By means of the iDISCO+ approach, we confirmed the neuronal localisation of both peptides in the SON and PVN. Alhough it was previously shown that this technique allows the double-and triple-labelling of neuronal cells, [START_REF] Renier | Mapping of brain activity by automated volume analysis of immediate early genes[END_REF][START_REF] Launay | Combined 3DISCO clearing method, retrograde tracer and ultramicroscopy to map corneal neurons in a whole adult mouse trigeminal ganglion[END_REF][START_REF] Liebmann | Three-dimensional study of Alzheimer's disease hallmarks using the iDISCO clearing method[END_REF][START_REF] Belle | Tridimensional visualization and analysis of early human development[END_REF] we did not conduct double staining of both peptides because primary antibodies were raised in the same species (rabbit). We also visualised the various accessory neurones previously identified in the rat anterior hypothalamus. [START_REF] Ludwig | Dendritic peptide release and peptide-dependent behaviours[END_REF] iDISCO+ clearing revealed that the distribution of OXT and AVP neurones was not homogeneous along the SON at P5, as described previously in the rat. [START_REF] Hou-Yu | Comparative distribution of vasopressin and oxytocin neurons in the rat brain using a double-label procedure[END_REF][START_REF] Callewaere | The chemokine SDF-1/CXCL12 modulates the firing pattern of vasopressin neurons and counteracts induced vasopressin release through CXCR4[END_REF] Both OXT and AVP immunoreactive cell bodies were found in the rostral part of the nucleus, whereas OXT neurones were more numerous in the dorsal part of the SON compared to AVP neurones. Classical immunohistochemical procedures on brain sections previously demonstrated that both peptide pathways not only project to the posterior pituitary, thus showing their well-known neuroendocrine effects, but also to some selective regions outside the hypothalamus, such as the amygdala and the nucleus accumbens. We also describe, for the first time, a dense population of AVP and OXT cell bodies and nerve fibres in the basoventral part of the hypothalamus, extending medially and ventrally, and in a rostral direction from the median eminence (Figure 5C,D; see Supporting information, Movies S3 and S4). Some of these cells may represent peptidergic neurones migrating from accessory nuclei towards SON and PVN.

Interestingly, these accessory neurones were more widespread at P5, in contrast to what has previously been reported in the adult rat hypothalamus. [START_REF] Ludwig | Dendritic peptide release and peptide-dependent behaviours[END_REF] Some of these neurones are probably still migrating during this postnatal period to reach their final location in the adult brain. Furthermore, as shown in Figure 5 (see also Supporting information, Movies S3 and S4), the distribution of AVP neurones at P5 is very similar to that described in the adult rat PVN 23 in contrast to OXT neurones. [START_REF] Hawthorn | Comparison of the distribution of oxytocin and vasopressin in the rat brain[END_REF] The OXT cell bodies, although more numerous in the PVN than AVP neurones at P5, were much smaller in volume than the AVP neurones. This could be linked to a delay in the maturation of the OXT system, as occurs in the rat brain, where AVP neurones have been reported to precede the development of OXT neurones that only become mature after birth. [START_REF] Buijs | Ontogeny of vasopressin and oxytocin in the fetal rat: early vasopressinergic innervation of the fetal brain[END_REF][START_REF] Ugrumov | Magnocellular vasopressin system in ontogenesis: development and regulation[END_REF][START_REF] Jing | Ontogeny of the vasopressin and oxytocin RNAs in the mouse hypothalamus[END_REF][START_REF] Trembleau | Vasopressin and oxytocin gene expression in intact rats and under catecholamine deficiency during ontogenesis[END_REF][START_REF] Hyodo | Expression of provasopressin gene during ontogeny in the hypothalamus of developing mice[END_REF] Late maturation of OXT neurones in the mouse was also supported by a progressive maturation of morphological and electrophysiological properties of OXT neurones. [START_REF] Grinevich | Ontogenesis of oxytocin pathways in the mammalian brain: late maturation and psychosocial disorders[END_REF] It was previously reported that neurophysin neurones showed dramatic changes in perikarial size during the mouse development in SON and PVN, with a plateau at P5 until P10, when a huge increase occured to reach adult values. [START_REF] Silverman | The ontogenesis of neurophysincontaining neurons in the mouse hypothalamus[END_REF] The late maturation of OXT vs AVP neurones was also supported by long neuritic processes that can be observed at P5 in the PVN for AVP, whereas only very small processes were noted for OXT neurones (Figure 6 D,H).

The late maturation of OXT neurones compared to AVP-and TH-expressing neurones may have functional significance. Both catecholaminergic and vasopressinergic systems are essential at birth for regulating plasma osmolarity, blood pressure, stress response and various metabolic effects via their release from nerve endings. By contrast, OXT effects following postnatal neurodevelopment need more time to become mature and functional. Such late maturation of hypothalamic OXT neurones may be linked to the acquisition of social skills later on in adulthood. [START_REF] Ludwig | Dendritic peptide release and peptide-dependent behaviours[END_REF][START_REF] Grinevich | Ontogenesis of oxytocin pathways in the mammalian brain: late maturation and psychosocial disorders[END_REF][START_REF] Otero-Garcia | Distribution of oxytocin and co-localization with arginine vasopressin in the brain of mice[END_REF] Finally, the iDISCO+ method allowed us not only to quantify the number of AVP and OXT neuronal cell bodies, but also to measure their volume in a 3D representation. To our knowledge, there is no previous report available that addresses this issue. Quantitative methods in 3D neuronal regionalisation may thus be useful not only for providing information about the development of neurotransmitter/ neuropeptide systems, but also for quantifying the number/volume of

| CONCLUSIONS

The present study describes the potency of the improved clearing procedures coupled to novel image technologies with respect to better studying and anatomically visualising known or still unknown neurotransmitter substances involved in the brain and, more specifically in the present study, in hypothalamic neuroendocrine functions. This opens avenues for new research projects using various transgenic or wild-type mice, addressing important questions related to neuroanatomy and cerebral activity, such as migration, maturation processes and connectomic analysis. [START_REF] Cazemier | Connectomic analysis of brain networks: novel techniques and future directions[END_REF] The findings may be applied when investigating various physiological and pathological situations, including, for example, catecholamine deficiency, sexual differenciation, lactation or dehydration. Such an imaging approach will provide exciting new information on the 3D distribution, morphology and projections of phenotypically-identified cells and pathways for future neuroanatomical and immunohistological studies.

2 3

 23 Five-day postnatal (P5) mouse brain stained with an anti-tyrosine hydroxylase antibody. (A) Three-dimensional horizontal view of the P5 mouse brain. (B) Segmentation and 'surface' rendering used to apply the false colours to the original image of the various anatomical regions with imaris, version 8.3.0: telencephalon (blue), hypothalamus (green), pons (yellow) and medulla (purple). (C) Attribution of false colours and 'volume' rendering using for the main catecholaminergic populations: dopaminergic (blue), various catecholamines (green), noradrenergic (white and orange) and noradrenergic/adrenergic (purple) neurones. Magnification 1.26x. Scale bar = 1000 μm Optical horizontal section (2 μm) of 5-day postnatal mouse brain stained with an anti-tyrosine hydroxylase (TH) antibody and segmentation of the various groups of catecholamine neurones with imaris, version 8.3.0 (A). TH positive neurones in the olfactory bulb (white), dopaminergic neurones in the striatum, substantia nigra (A9) and ventral tegmental area (A10) (in blue); noradrenergic neurones mainly detected in the pons in the locus coeruleus (A6) (yellow) and adrenergic/noradrenergic neurones (purple) in the medulla oblonglata (A1/C1) and in the nucleus of the solitary tract (A2/C2). (B) Optical sagittal section (2 μm) of the mouse brain showing some TH positive fibre pathways (arrow) innervating the hypothalamus (green). The sagittal section in (B) corresponds to the vertical bar in (A). Magnification 1.26×. Scale bars = 1000 μm Five-day postnatal mouse hypothalamus stained with an anti-tyrosine hydroxylase antibody. (A) Optical coronal sections (2 μm) at the level of the anteroventral periventricular nucleus, the periventricular region and the dorsal/mediobasal hypothalamus. Scale bar = 200 μm. (B) Three-dimensional segmentation of the anatomical structures with imaris, version 8.3.0, representing the dopaminergic neurones in the dorsal hypothalamus and zona incerta A11-A13 (green), arcuate nucleus A12 (purple) and in the periventricular A14 region (cyan). TO-PRO 3 iodide staining (glow) was used to label all nuclei. Magnification 6.4×. Scale bar = 300 μm for AVP than OXT and had a different distribution pattern (Figure 5C,D; see also Supporting information, Movies S3 and S4).

  (glow) was used to label all nuclei. (A, C) Magnification 1.6×, scale bars = 1000 μm. (B, D) Magnification 6.4×. Scale bars = 200 μm

F I G U R E 5

 5 Three-dimensional imaging of 5-day postnatal mouse paraventricular nucleus (A,B) and half anterior hypothalamus (C, D) immunostained with anti-arginine vasopressin (AVP) (A, C) and anti-oxytocin (OXT) (B, D) antibodies. Segmentation of the suprachiasmatic nucleus (blue), supraoptic nucleus (green), paraventricular nucleus (purple), accessory neurones (cyan) and median eminence area (yellow) with imaris, version 8.3.0. (A,B) Magnification 10×, scale bars = 200 μm. (C, D) Magnification 8×, scale bars = 300 μm. 3V, third ventricle study, although such detailed analyses could be performed in future studies using the iDISCO+ method.

F I G U R E 6

 6 Segmentation and semi-automatic counting with imaris, version 8.3.0, of arginine vasopressin (AVP) (A-C) and oxytocin (OXT) (E-G) neurones in the whole paraventricular nucleus (purple) and specifically in its magnocellular (yellow), medial (green) and ventral (blue) parvocellular parts in 5-day postnatal mouse hypothalamus (n = 3 each condition). (D, H) Single 1 μm deep optical coronal sections at the level of the paraventricular nucleus showing the neuritic processes for both AVP (D) and OXT (H) neurones. Magnification 10×. Scale bars = 100 μm such molecules in specific nuclei at different time points under various physiological and/or pathological conditions.

ACKNOWLEDGEMENTS

We thank M. Belle and A. Chedotal for their help with the wholemount immunostaining protocols.

ORCID

W. Rostene

http://orcid.org/0000-0003-0409-5361

Funding information

The present study was supported by the Université Pierre and Marie Curie. Paris VI, the Institut National de la

SUPPORTING INFORMATION

Additional Supporting Information may be found online in the supporting information tab for this article.

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Jorge Martinez-Gil
email: jorgemar@lcc.uma.es

Jose F Aldana-Montes

Semantic Similarity Measurement Using Historical Google Search Patterns

Keywords: Information Integration, Web Intelligence, Semantic Similarity

Computing the similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in the information integration field. The problem is that techniques for textual semantic similarity measurement often fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem by determining the semantic similarity for terms using the knowledge inherent in the search history logs from the Google search engine. To do that, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons. We have shown experimentally that some of these methods correlate well with respect to human judgment when evaluating general purpose benchmark datasets, and significantly outperform existing methods when evaluating datasets containing terms that do not usually appear in dictionaries.

Introduction

Semantic similarity measurement relates to computing the similarity between terms or short text expressions, having the same meaning or related information, but which are not lexicographically similar [START_REF] Li | An approach for Measuring Semantic Similarity between Words Using Multiple Information Sources[END_REF]. This is an important problem in a lot of computer related fields, for instance, in data warehouse integration when creating mappings that link mutually components of data warehouse schemas (semi)automatically [START_REF] Banek | Using Ontologies for Measuring Semantic Similarity in Data Warehouse Schema Matching Process[END_REF] or in the entity resolution field where two given text objects have to be compared [START_REF] Kopcke | Evaluation of entity resolution approaches on real-world match problems[END_REF]. But the problem is that semantic similarity changes over time and across domains [START_REF] Bollegala | Mining for personal name aliases on the web[END_REF]. The traditional approach for solving this problem has consisted of using manually compiled taxonomies such as WordNet [START_REF] Budanitsky | Evaluating WordNet-based Measures of Lexical Semantic Relatedness[END_REF]. The question is that a lot of (sets of) terms (proper nouns, brands, acronyms, new words, and so on) are not covered by these kinds of taxonomies; therefore, similarity measures that are based on this kind of resources cannot be used directly in these tasks. However, we think that the great advances in the web research field have provided new opportunities for developing accurate solutions.

On the other hand, Collective Intelligence (CI) is an active field of research that explores the potential of collaborative work in order to solve complex problems [START_REF] Scarlat | Towards an Increase of Collective Intelligence within Organizations Using Trust and Reputation Models[END_REF]. Scientists from the fields of sociology, mass behavior, and computer science have made important contributions to this field. It is supposed that when a group of individuals collaborate or compete with each other, intelligence or behavior that otherwise did not exist suddenly emerges. We use the name Web Intelligence (WI) when these users use the Web as a means of collaboration. We want to profit from the fact that through their interactions with the web search engines, users provide a rich set of information that can be converted into knowledge reusable for solving problems related with semantic similarity measurement.

To do that, we are going to use Google Trends [START_REF] Choi | Predicting the present with Google Trends[END_REF] which is a web application owned by Google Inc. based on Google Search [START_REF] Brin | The Anatomy of a Large-Scale Hypertextual Web Search Engine[END_REF]. This web application shows how often a particular search-term is entered relative to the total searchvolume across various specific regions, categories, time frames and properties. We are working under the assumption that users are expressing themselves. This expression is in the form of searching for the same concepts from the real world at the same time but represented with different lexicographies. Therefore, the main contributions of this work can be summarized as follows:

-We propose for the first time (to the best of our knowledge) to use historical search patterns from web search engine users to determine the degree of semantic similarity between (sets of) terms. We are especially interested in measuring the similarity between emerging terms or expressions. -We propose and evaluate four algorithmic methods for measuring the semantic similarity between terms using their historical search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons.

The rest of this paper is organized as follows: Section 2 describes the related works that are proposed in the literature currently available. Section 3 describes the key aspects of our contribution, including the different ways of computing the semantic similarity. Section 4 presents a statistical evaluation of our approaches in relation to existing ones. Section 5 presents a discussion based on our results, and finally, Section 6 describes the conclusions and future lines of research.

Related Work

We have not found proposals addressing the problem of semantic similarity measurements using search logs. Only Nandi & Bernstein have proposed a technique which was based on logs from virtual shops for computing similarity between products [START_REF] Nandi | HAMSTER: Using Search Clicklogs for Schema and Taxonomy Matching[END_REF]. However, a number of works have addressed the semantic similarity measurement [START_REF] Hliaoutakis | A Retrieval System for Medical Information Based on Semantic Similarity[END_REF], [START_REF] Patwardhan | Using Measures of Semantic Relatedness for Word Sense Disambiguation[END_REF], [START_REF] Petrakis | X-Similarity: Computing Semantic Similarity between Concepts from Different Ontologies[END_REF], [START_REF] Sanchez | Web-Based Semantic Similarity: An Evaluation in the Biomedical Domain[END_REF], [START_REF] Sanchez | Ontology-driven web-based semantic similarity[END_REF], and the use of WI techniques for solving computational problems [START_REF] Jung | Collective Intelligence for Semantic and Knowledge Grid[END_REF], [START_REF] Scarlat | Towards an Increase of Collective Intelligence within Organizations Using Trust and Reputation Models[END_REF], [START_REF] Jones | Collective Intelligence: It's All in the Numbers[END_REF] separately.

With regards to the first topic, identifying semantic similarities between terms is not only an indicator of mastery of a language, but a key aspect in a lot of computer-related fields too. It should be taken into account that semantic similarity measures can help computers to distinguish one object from another, group them based on the similarity, classify a new object inside the group, predict the behavior of the new object or simplify all the data into reasonable relationships. There are a lot of disciplines where we can benefit from these capabilities [START_REF] Hjorland | Semantics and knowledge organization[END_REF]. Within the most relevant areas is the data warehouse field where applications are characterized by heterogeneous models that have to be analyzed and matched either manually or semi-automatically at design time [START_REF] Fong | A relational-XML data warehouse for data aggregation with SQL and XQuery[END_REF]. The main advantage of matching these models consists of enabling a broader knowledge base for decision-support systems, knowledge discovery and data mining than each of the independent warehouses could offer separately [START_REF] Banek | Automating the Schema Matching Process for Heterogeneous Data Warehouses[END_REF]. There is also possible to avoid model matching by manually copying all data in a centralized warehouse, but this task requires a great cost in terms of resource consumption, and the results are not reusable in other situations. Designing good semantic similarity measures allows us to build a mechanism for automatically query translation (which is a prerequisite for a successful decouple integration) in an efficient, cheap and highly reusable manner.

Several works have been developed over the last few years proposing different ways to measure semantic similarity. Petrakis et al. stated that according to the specific knowledge sources exploited and the way in which they are used, different families of methods can be identified [START_REF] Petrakis | X-Similarity: Computing Semantic Similarity between Concepts from Different Ontologies[END_REF]. These families are:

-Edge Counting Measures: path linking the terms in the taxonomy and of the position of the terms in the taxonomy. -Information Content Measures: measure the difference of information content of the two terms as a function of their probability of occurrence in a corpus. -Feature based Measures: measure the similarity between terms as a function of their properties or based on their relationships to other similar terms. -Hybrid Measures: combine all of the above.

Our proposal does not fit in well enough in any of these families of methods, so that it proposes a new one: Based on WI Measures. However, regarding the use of WI techniques for solving computational problems, we have found many approaches.

-Aggregate information that consists of creating lists of items generated in the aggregate by your users [START_REF] Dhurandhar | Improving predictions using aggregate information[END_REF]. Some examples are a Top List of items bought, or a Top Search Items or a List of Recent Items. -Ratings, reviews, and recommendations that consists of understanding how collective information from users can influence others [START_REF] Hu | Manipulation of online reviews: An analysis of ratings, readability, and sentiments[END_REF]. -User-generated content like blogs, wikis or message boards that consist of extracting some kind of intelligence from contributions by users [START_REF] Liu | A Survey of Opinion Mining and Sentiment Analysis[END_REF]. Now we propose using a kind of WI technique for trying to determine the semantic similarity between terms that consists of comparing the historical web search logs from the users. The rest of this paper consists of explaining, evaluating, and discussing the semantic similarity measurement of terms using historical search patterns from the Google search engine.

Finally, in order to compare our approaches with the existing ones; we are considering techniques which are based on dictionaries. We have chosen the Path Length algorithm [START_REF] Pedersen | WordNet::Similarity -Measuring the Relatedness of Concepts[END_REF] which is a simple edge counting technique. The score is inversely proportional to the number of nodes along the shortest path between the definitions. The shortest possible path occurs when the two definitions are the same, in which case the length is 1. Thus, the maximum score is 1. Another approach proposed by Lesk [22] which consists of finding overlaps in the definitions of the two terms. The score is the sum of the squares of the overlap lengths. The Leacock and Chodorow algorithm [START_REF] Leacock | Using Corpus Statistics and WordNet Relations for Sense Identification[END_REF] which takes into account the depth of the taxonomy in which the definitions are found. An Information Content (IC) measure proposed by Resnik [START_REF] Resnik | Using Information Content to Evaluate Semantic Similarity in a Taxonomy[END_REF] and which computes common information between concepts a and b is represented by the IC of their most specific common ancestor subsuming both concepts found in the taxonomy to which they belong. Finally, the Vector Pairs technique [START_REF] Banerjee | Extended Gloss Overlaps as a Measure of Semantic Relatedness[END_REF] which is a Feature based measure which works by comparing the co-occurrence vectors from the WordNet definitions of concepts.

Contribution

Web searching is the process of typing freeform text, either words or small phrases, in order to look for websites, photos, articles, bookmarks, blog entries, videos, and more. People may search things on the Web in order to find information of interest related to a given topic. In a globalized world, our assumption is that large sets of people will search for the same things at the same time but probably from different parts of the world and using different lexicographies. We want to take advantage of this in order to detect similarities between terms and short text expressions. Although our proposal also works with longer text statements, we are going to focus on short expressions only.

The problem which we are addressing consists of trying to measure the semantic similarity between two given (sets of) terms a and b. Semantic similarity is a concept that extends beyond synonymy and is often called semantic relatedness in the literature. According to Bollegala et al.; a certain degree of semantic similarity can be observed not only between synonyms (e.g. lift and elevator), but also between meronyms (e.g. car and wheel), hyponyms (leopard and cat), related words (e.g. blood and hospital) as well as between antonyms (e.g. day and night) [START_REF] Bollegala | Measuring semantic similarity between words using web search engines[END_REF]. To do this, we are going to work with time series. The reason is that Google stores the user queries in the form of time series in order to offer or exploit this information in an efficient manner in the future.

According to the Australian Bureau of Statistics1 , a time series is a collection of observations of well-defined data items obtained through repeated measurements over time. For example, measuring the value of retail sales each month of the year would comprise a time series. This is because sales revenue is well defined, and consistently measured at equally spaced intervals. In this way, data which is collected irregularly or only once are not time series.

The similarity problem in time series consists of being two sequences of real numbers representing the measurements of a real variable at equal time intervals defining and computing its similarity. However, this is not a trivial task, because even between different people, the notion of similarity varies. However, it is possible to offer a minimal notion of what is a similarity measure from a mathematical point of view: Definition 1 (Similarity measure). A similarity measure sm is a function sm : µ 1 × µ 2 → R that associates the similarity of two input terms µ 1 and µ 2 to a similarity score sc ∈ in the range [0, 1].

A similarity score of 0 stands for complete inequality and 1 for equality of the input terms µ 1 and µ 2 .

In this paper, we refer to the expression semantic similarity in order to express that we are comparing the meaning of terms instead of comparing their associated lexicography. For example, the terms card and car are quite similar from a lexicographical point of view but do not share the same meaning at all. We are just interested in the real world concept that they represent.

Before beginning to discuss our proposal it is necessary to take into account that in this work we have worked under the assumption that Google has not suffered any transient malfunction when taking measurements of the user searches, so that the morphology of the search patterns is only due to user searches on the Web. Once the problem is clear, the first, and perhaps most intuitive solution, could consist of viewing each sequence as a point in ndimensional Euclidean space, and define similarity between the two sequences, this solution would be easy to compute but there is an important problem because there are no actual scales used in the graphics due to the normalized results and, therefore it is not clear what the exact or absolute numbers are.

In order to avoid this kind of problem, we propose using four different ways to define and compute the semantic similarity: Co-occurrence of Terms in Search Patterns, Computing the Relationships between Search Patterns, Outlier Coincidence on Search Patterns, and Forecasting comparisons. The great advantage of our proposal is that any of proposed methods take into account the scale of the results, but other kinds of characteristics like frequent co-occurrences, correlations, anomalies, or future trends respectively. Moreover, it should be taken into account that for the rest of this work, we are going to evaluate our four approaches using two benchmark datasets:

-Miller & Charles benchmark dataset which is a dataset of term pairs rated by a group of 38 human beings [START_REF] Miller | Contextual Correlates of Semantic Similarity[END_REF]. Term pairs are rated on a scale from 0 (no similarity) to 4 (complete similarity). Miller & Charles ratings has been considered as the traditional benchmark dataset to evaluate solutions that involve semantic similarity measures [START_REF] Bollegala | Measuring semantic similarity between words using web search engines[END_REF].

-Another new dataset that we will name Martinez & Aldana which is a dataset rated by a group of 20 people belonging to several countries, indicating a value of 0 for not similar terms and 1 for totally similar terms. This dataset is specially designed to evaluate terms that are not frequently included in dictionaries but which are used by people daily. In this way, we will be able to determine the most appropriate algorithm for comparing the semantic similarity of emerging words. This could be useful in very dynamic domains like medicine, finance, technology, and so on.

The comparison between these two benchmark datasets and our results is made using the Pearson's Correlation Coefficient, which is a statistical measure for the comparison of two matrices of numeric values. Therefore the results can be in the interval [-1, 1], where -1 represents the worst case (totally different values) and 1 represents the best case (totally equivalent values). Note that all tables, except those for the Miller & Charles ratings, are normalized into values in [0, 1] range for ease of comparison. Pearson's correlation coefficient is invariant against a linear transformation [START_REF] Bollegala | Measuring semantic similarity between words using web search engines[END_REF]. As a general rule, for all the table below the two first columns represent each of the term of the pair to be studied, the third column presents the results from the benchmark dataset, and finally the fourth column represents the value returned by our algorithm.

Co-occurrence of Terms in Search Patterns

The first algorithmic method that we propose consists of measuring how often two terms appear in the same query. Co-occurrence of terms in a given corpus is usually used as an indicator of semantic similarity in the literature [START_REF] Bollegala | Measuring semantic similarity between words using web search engines[END_REF], [START_REF] Cilibrasi | The Google Similarity Distance[END_REF], [START_REF] Sanchez | Web-Based Semantic Similarity: An Evaluation in the Biomedical Domain[END_REF]. We propose adapting this paradigm for our purposes. To do that, we are going to compute the joint probability p(a, b) so that a user query may contain both the search term a and the search term b over the time. Figure 1 shows a example for the co-occurrence of the terms car and automobile along the time. As can be seen, the terms car and automobile appear together 6 years and the search log is 6 years old, so the resulting score is 6 divided by 6, thus 1. Therefore, we have evidence of their semantic similarity.

The method that we propose to measure the similarity using the notion of co-occurrence consists of using the following formula: Fig. 1 Search pattern containing both terms car and automobile. User queries have included both terms at the same time frequently so that there is evidence that the both terms represent the same object

We think that the proposed formula is appropriate because it computes a score according to the fact that the terms never appear together or appear together every year. In this way a similarity score of 0 stands for complete inequality and 1 for equality of the input terms. Table 1 shows us the results obtained using this method. The problem is that there are terms that are not semantically similar but are searched together frequently, for instance: coast and forest, or coast and hill in this dataset. However, our technique provides good results most cases, therefore, the correlation of this technique with respect to human judgment is moderate and could be useful in such cases where a dictionary or thesaurus do not exist.

Table 2 shows us the results obtained using the study of co-occurrence over the specific benchmark. The problem is that there are terms that are not semantically similar but are searched together frequently, for instance the terms sustainable and renewable or slumdog and underprivileged. However, the global score is fine what confirm us that it could be used for identifying similarities when dictionaries or other kinds of external resources do not exist.

Correlation between Search Patterns

The correlation between two variables is the degree to which there is a relationship between them [START_REF] Aitken | Statistical mathematics[END_REF]. Correlation is usually expressed as a coefficient which Fig. 2 Historical search log for the terms Furnace and Stove. According to Pearson coefficient, similarity between these temporal series is high which shows us that maybe the two words represent a quite similar object measures the strength of a relationship between the variables. We propose using two measures of correlation: Pearson and Spearman.

The first measure of correlation that we propose, i.e. Pearson correlation coefficient, is closely related to the Euclidean distance over a normalized vector space. Using this measure means that we are interested in the shape of the time series instead of their quantitative values. The philosophy behind this technique can be appreciated in Figure 2, where the terms furnace and stove present almost exactly the same shape and, therefore, semantic similarity between them is supposed to be very high. The Pearson correlation coefficient can be computed as follows:

ρ X,Y = cov(X, Y ) σ X σ Y = E[(X -µ X )(Y -µ Y )] σ X σ Y (2)
Table 3 shows us the results for the general purpose benchmark dataset. As can be seen, some term pairs present negative correlation, i.e. one of them presents an ascendant pattern while the other presents a descendant one, so the final quality of the method is going to be decreased. Therefore, negative correlations worsen the final score.

Table 4 shows us the results for the specific benchmark dataset. As in the Miller & Charles benchmark dataset, some term pairs present negative correlation, i.e. one of them presents an ascendant pattern whist the other presents a descendant one, so the final quality of the method is not good.

The second measure that we propose using is the Spearman correlation coefficient which assesses how well the relationship between two variables can be described using a monotonic function. If there are no repeated data values, a perfect Spearman correlation occurs when each of the variables is a perfect monotone function of the other [START_REF] Aitken | Statistical mathematics[END_REF]. This is the formula to compute it:

ρ X,Y = 1 - 6 d 2 i n(n 2 -1) (3)
After using this correlation coefficient for our experiments, we have determined that is not useful for our purposes, because no correlation was detected (a value near to zero). We have discovered that an increment in the web searches for a term does not suppose an increment on the web searches for a synonym, so this kind of correlation is not good for trying to determine the semantic similarity between terms using historical search logs and therefore is not going to be considered further in the paper.

Outlier Coincidence on Search Patterns

There is no rigid mathematical definition of what constitutes an outlier. Grubbs said that "An outlying observation, or outlier, is one that appears to deviate markedly from other members of the sample in which it occurs" [START_REF] Grubbs | Procedures for Detecting Outlying Observations in Samples[END_REF]. So our proposal consists of looking for elements of a time series that distinctly stands out from the rest of the series. Outliers can have many causes. Once we have discarded a Google malfunction, we have to assume that outliers in search patterns occur due to historical events, and that users search for information related to this historical event at the same time but maybe using different lexicographies.

Figure 3 shows us a screenshot form Google Trends where the time series representing the terms gem and jewel can be seen. There is a common outlying observation in the year 2007. We do not know the reason, but this information is not necessary for our purpose. We only look for overlapping outliers in order to determine the similarity between search patterns, and therefore, their associated terms.

Various indicators are used to identify outliers. For our purposes we are going to use the proposal of Rousseeuw and Leroy that affirm that an outlier is an observation which has a value that is more than 2.5 standard deviations from the mean [START_REF] Rousseeuw | Robust Regression and Outlier Detection[END_REF].

Table 5 shows us the results obtained by this method using the Miller & Charles benchmark dataset. The obtained correlation for this benchmark dataset is low, because only terms which have suffered a search boom in their search histories can be identified as similar.

Table 6 shows us the results obtained by this method using the Martinez & Aldana benchmark dataset. The obtained correlation for the this benchmark datasets is low, because only terms which which present outliers can be compared, thus, it cannot be outlier coincidence if there is not outliers in the historical search pattern.

Forecasting Comparison

Our forecasting comparison method consists of comparing the prediction of the (sets of) terms for the months following. There are many methods for time series forecasting, but the problem is that people's behavior cannot be predicted, or at least, can be notably influenced by complex or random causes. For example, it is possible to predict searches related to holidays every summer, but it is not possible to predict searches related to cars, because it is a kind of non-stationary good. Anyway, we wish to obtain a quantitative result for the quality of this method in order to compare it with the other ones since we can extract positive hints.

To do that, we propose training a neural network in order to predict the results for the searches. We can establish the similarity between two terms on basis of the similarity between these predictions. We have chosen a forecasting based on neural networks and discarded such techniques as moving average or exponential smoothing. Moving average uses past observations weighted equally, while exponential smoothing assigns exponentially decreasing weights as the observation get older. The reason for our choice is that neural networks have been widely used successfully as time series forecasters for real situations [START_REF] Patuwo | Forecasting with artificial neural networks: The state of the art[END_REF]. In order to train the Neural Network we have chosen the following parameters:

-For neurons count: an input layer of 12, a hidden layer of 12 and an output layer of 1 -For the learning: a learning rate of 0.05, a Momentum of 0.5 and Max Iteration of 10000 -For the activation function: bipolar sigmoid -The period of time chosen is 6 months In order to compare the predictions we have chosen the Pearson correlation coefficient because in our previous experiment we have shown that is better than the Spearman coefficient. Table 7 shows us the results obtained for the the Miller & Charles benchmark dataset once again.

Table 8 shows us the results obtained for the Martinez-Aldana once again. The final score obtained is not particularly good due to the partial negative correlations for some term pairs.

Evaluation

In order to evaluate the considered approaches, we adopt the Pearson correlation coefficient [START_REF] Egghe | The relation between Pearson's correlation coefficient r and Salton's cosine measure[END_REF] as a measure of the strength of the relation between human ratings of similarity and computational values. However, Pirro stated that to have a deeper interpretation of the results is also necessary to evaluate the significance of this relation [START_REF] Pirro | A semantic similarity metric combining features and intrinsic information content[END_REF]. To do this, we are going to use the p-value technique, which shows how unlikely a given correlation coefficient, r, will occur given no relation in the population [START_REF] Pirro | A semantic similarity metric combining features and intrinsic information content[END_REF]. Note that the smaller the p-level, the more significant the relation. Moreover, the larger the correlation value the stronger the relation. The p-value for Pearson's correlation coefficient is based on the test statistics defined as follows:

s = r • √ n -2 √ 1 -n 2 (4)
where r is the correlation coefficient and n is the number of pairs of data. When the p-value is less than 0.05, then we can say the obtained value is 10 Results for the statistical study concerning to the benchmark dataset which contains terms that do not appear in dictionaries very frequently statistically significant. We have obtained that, for our benchmark datasets, all values above 0.25 are statistically significant. Before to explain the obtained results it is necessary to state that all results have been obtained from data collected before 22nd may 2011. Results from third party approaches have been obtained by the tool offered by Pedersen2 .

Table 9 shows the results for the general purpose benchmark dataset, i.e. Miller & Charles. Existing techniques are better than most of our approaches. However, Outlier and Co-occurrence techniques present a moderate accuracy. The rest of approaches do not seem to be as good as most of the techniques based on synonym dictionaries when identifying the semantic similarity for well known terms. The reason is that knowledge represented in a dictionary is considered to be really good, and therefore, it is not possible for artificial techniques to surpass it.

Table 10 shows the results for the specific purpose benchmark dataset, i.e. Martinez & Aldana. Our approaches present, in general, a better quality than those currently in existence. It is the case for the co-occurrence techniques which significantly beat all others. Moreover, we have experimentally confirmed our hypothesis related to the fact that using historical search patterns could be more beneficial when the terms to be analyzed are not covered by dictionaries.

Discussion

Search trends in users web search data have traditionally been shown to very useful when providing models of real world phenomena. Now, we have proposed another way to reuse these search patterns. We have propose comparing search patterns in order to determine the semantic similarity between their associated terms. Despite the results that we have obtained, there are several problems related to the use of historical search patterns for determining the semantic similarity between text expressions:

1. Terms typed by the users that can have multiple meanings based on their context 2. Users use multiple terms to look for both singular and plurals 3. Many of these results rely on the careful choice of queries that prior knowledge suggests should correspond with the phenomenon On the other hand, our proposal has a number of additional advantages with respect to other existing techniques. It is not time consuming since it do not imply that large corpora should be parsed. We have shown that it correlates well with respect to human judgment (even better than some other preexisting measures). Moreover, our work could be considered as seminal for new research lines:

-The time series representing the historical search pattern for a given term could be used as a kind of semantic fingerprint, thus, some kind of data which identifies a term on the Web. If two semantic fingerprints are similar, it could be supposed that the terms could represent a similar real world entity.

-The results of this work are also applicable to study the stability of ontology mappings. This means that it is possible to establish semantic correspondences between any kind of ontologies according to time constraints.

Conclusions

In this paper, we have proposed a novel idea for determining the semantic similarity between (sets of) terms which consists of using the knowledge inherent in the historical search logs from the Google search engine.

To validate our hypothesis, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence in search patterns, and d) forecasting comparisons.

We have shown experimentally that the method which studies the cooccurrence of terms in the search patterns correlates well with respect to human judgment when evaluating general purpose benchmark datasets, and significantly outperform existing methods when evaluating datasets containing terms that do not usually appear in dictionaries. Moreover, we have found than the other three additional methods seem to be better than most of the existing ones when dealing with this special kind of emerging terms.

As future work, we want to keep working towards applying new time series comparison algorithms so that we can determine which are the best approaches for solving this problem and implement them in real information systems where the automatic computation of semantic similarity between terms may be necessary. Moreover, we want to analyze the possibility to smartly combine our algorithmic methods in order to determine if two terms are or no semantically similar.

Bio

Dr. Jorge Martinez-Gil was a senior researcher in the Department of Computer Languages and Computing Sciences at the University of Malaga (Spain) at the time of this work was done. His main research interests are related with the interoperability in the World Wide Web. In fact, his PhD thesis has addressed the ontology meta-matching and reverse ontology matching problems. Dr. Martinez-Gil has published several papers in prestigious journals like SIGMOD Record, Knowledge and Information Systems, Knowledge Engineering Review, Online Information Review, Journal of Computer Science & Technology, Journal of Universal Computer Science, and so on. Moreover, he is a reviewer for conferences and journals related to the Data and Knowledge Engineering field.

Prof. Dr. José F. Aldana-Montes is currently a professor in the Department of Languages of Computing Sciences at the Higher Computing School from the University of Malaga (Spain) and Head of Khaos Research, a group for researching about semantic aspects of databases. Dr. Aldana-Montes has more than 20 years of experience in research about several aspects of databases, semistructured data and semantic technologies and its application to such fields as bioinformatics or tourism. He is author of several relevant papers in top bioinformatic journals and conferences. Related to teaching, he has been teaching theoretical and practical aspects of databases at all possible university levels: from undergraduate courses to PhD.

Table 1

 1 Results for the study of the co-occurrence using the Miller & Charles dataset n. years terms co -occur n. years registered in the log

			Miller-Charles Co-occurrence
	rooster	voyage	0.080	0.000
	noon	string	0.080	0.000
	glass	magician	0.110	0.000
	cord	smile	0.130	0.000
	coast	forest	0.420	0.625
	lad	wizard	0.420	0.000
	monk	slave	0.550	0.000
	forest	graveyard	0.840	0.000
	coast	hill	0.870	0.750
	food	rooster	0.890	0.000
	monk	oracle	1.100	0.000
	car	journey	1.160	0.750
	brother	lad	1.660	0.000
	crane	implement	1.680	0.000
	brother	monk	2.820	0.000
	implement	tool	2.950	0.000
	bird	crane	2.970	0.625
	bird	cock	3.050	0.000
	food	fruit	3.080	1.000
	furnace	stove	3.110	0.875
	midday	noon	3.420	0.000
	magician	wizard	3.500	0.125
	asylum	madhouse	3.610	0.000
	coast	shore	3.700	0.750
	boy	lad	3.760	0.250
	journey	voyage	3.840	0.375
	gem	jewel	3.840	0.500
	automobile	car	3.920	1.000
	Score		1.000	0.364

Table 2

 2 Results for the study of the co-occurrence using the Martinez & Aldana dataset

			Martinez-Aldana Co-occurrence
	peak oil	apocalypse	0.056	0.000
	bobo	bohemian	0.185	0.000
	windmills	offshore	0.278	0.000
	copyleft	copyright	0.283	0.000
	whalewatching	birdwatching	0.310	0.000
	tweet	snippet	0.314	0.000
	subprime	risky business	0.336	0.000
	imo	in my opinion	0.376	0.000
	buzzword	neologism	0.383	0.000
	quantitave easing	money flood	0.410	0.000
	glamping	luxury camping	0.463	0.000
	slumdog	underprivileged	0.482	0.500
	i18n	internationalization	0.518	0.000
	vuvuzela	soccer horn	0.523	0.125
	pda	computer	0.526	1.000
	sustainable	renewable	0.536	0.625
	sudoku	number place	0.538	0.000
	terabyte	gigabyte	0.573	0.625
	ceo	chief executive officer	0.603	0.375
	tanorexia	tanning addiction	0.608	0.000
	the big apple	New York	0.641	0.500
	asap	as soon as possible	0.661	0.000
	qwerty	keyboard	0.676	1.000
	thx	thanks	0.784	0.375
	vlog	video blog	0.788	0.000
	wifi	wireless network	0.900	1.000
	hi-tech	high technology	0.903	0.000
	app	application	0.915	1.000
	Score		1.000	0.523

Table 3

 3 Results for the Pearson's correlation using the Miller & Charles dataset

			Miller-Charles Pearson
	rooster	voyage	0.080	-0.060
	noon	string	0.080	0.338
	glass	magician	0.110	0.405
	cord	smile	0.130	-0.007
	coast	forest	0.420	0.863
	lad	wizard	0.420	0.449
	monk	slave	0.550	0.423
	forest	graveyard	0.840	0.057
	coast	hill	0.870	0.539
	food	rooster	0.890	0.128
	monk	oracle	1.100	0.234
	car	journey	1.160	-0.417
	brother	lad	1.660	0.101
	crane	implement	1.680	0.785
	brother	monk	2.820	0.121
	implement	tool	2.950	0.771
	bird	crane	2.970	0.610
	bird	cock	3.05	0.507
	food	fruit	3.080	0.286
	furnace	stove	3.110	0.728
	midday	noon	3.420	0.026
	magician	wizard	3.500	0.622
	asylum	madhouse	3.610	0.149
	coast	shore	3.700	0.183
	boy	lad	3.760	0.090
	journey	voyage	3.840	0.438
	gem	jewel	3.840	0.155
	automobile	car	3.920	0.840
	Score		1.000	0.163

Table 4

 4 Results for the Pearson's correlation using the Martinez & Aldana dataset

			Martinez-Aldana Pearson
	peak oil	apocalypse	0.056	-0.100
	bobo	bohemian	0.185	-0.147
	windmills	offshore	0.278	0.779
	copyleft	copyright	0.283	-0.127
	whalewatching	birdwatching	0.310	-0.090
	tweet	snippet	0.314	0.159
	subprime	risky business	0.336	0.000
	imo	in my opinion	0.376	0.831
	buzzword	neologism	0.383	0.459
	quantitave easing	money flood	0.410	0.165
	glamping	luxury camping	0.463	0.000
	slumdog	underprivileged	0.482	-0.010
	i18n	internationalization	0.518	0.966
	vuvuzela	soccer horn	0.523	0.828
	pda	computer	0.526	0.900
	sustainable	renewable	0.536	0.640
	sudoku	number place	0.538	-0.220
	terabyte	gigabyte	0.573	-0.060
	ceo	chief executive officer	0.603	0.163
	tanorexia	tanning addiction	0.608	0.000
	the big apple	New York	0.641	0.200
	asap	as soon as possible	0.661	0.455
	qwerty	keyboard	0.676	0.124
	thx	thanks	0.784	-0.272
	vlog	video blog	0.788	0.838
	wifi	wireless network	0.900	-0.659
	hi-tech	high technology	0.903	0.867
	app	application	0.915	0.473
	Score		1.000	0.106

Table 6

 6 Results from outlier coincidence using the Martinez & Aldana dataset

Table 7

 7 Results from search forecasting comparison using the Miller & Charles dataset

			Miller-Charles Forecast
	rooster	voyage	0.080	0.661
	noon	string	0.080	0.108
	glass	magician	0.110	0.235
	cord	smile	0.130	0.176
	coast	forest	0.420	0.703
	lad	wizard	0.420	0.647
	monk	slave	0.550	0.971
	forest	graveyard	0.840	0.355
	coast	hill	0.870	0.218
	food	rooster	0.890	0.770
	monk	oracle	1.100	0.877
	car	journey	1.160	0.478
	brother	lad	1.660	0.707
	crane	implement	1.680	0.083
	brother	monk	2.820	0.154
	implement	tool	2.950	0.797
	bird	crane	2.970	0.315
	bird	cock	3.050	0.893
	food	fruit	3.080	0.229
	furnace	stove	3.110	0.876
	midday	noon	3.420	0.932
	magician	wizard	3.500	0.595
	asylum	madhouse	3.610	0.140
	coast	shore	3.700	0.631
	boy	lad	3.760	0.369
	journey	voyage	3.840	0.690
	gem	jewel	3.840	0.940
	automobile	car	3.920	0.689
	Score		1.000	0.193

Table 8

 8 Results from search forecasting comparison using the Martinez & Aldana dataset

			Martinez-Aldana Forecast
	peak oil	apocalypse	0.056	0.359
	bobo	bohemian	0.185	0.671
	windmills	offshore	0.278	-0.731
	copyleft	copyright	0.283	-0.352
	whalewatching	birdwatching	0.310	0.626
	tweet	snippet	0.314	0.010
	subprime	risky business	0.336	-0.011
	imo	in my opinion	0.376	-0.136
	buzzword	neologism	0.383	0.924
	quantitave easing	money flood	0.410	0.548
	glamping	luxury camping	0.463	0.166
	slumdog	underprivileged	0.482	-0.701
	i18n	internationalization	0.518	-0.401
	vuvuzela	soccer horn	0.523	-0.374
	pda	computer	0.526	0.964
	sustainable	renewable	0.536	0.869
	sudoku	number place	0.538	0.137
	terabyte	gigabyte	0.573	0.896
	ceo	chief executive officer	0.603	-0.396
	tanorexia	tanning addiction	0.608	0.267
	the big apple	New York	0.641	-0.830
	asap	as soon as possible	0.661	0.711
	qwerty	keyboard	0.676	0.879
	thx	thanks	0.784	0.760
	vlog	video blog	0.788	0.752
	wifi	wireless network	0.900	0.204
	hi-tech	high technology	0.903	-0.117
	app	application	0.915	-0.322
	Score		1.000	0.027

Table 9

 9 Results for the statistical study concerning to the general purpose benchmark dataset

	Algorithm	Score
	Resnik	0.814
	Leacock	0.782
	Path length	0.749
	Vector pairs	0.597
	Outlier	0.372
	Co-ocurr.	0.364
	Lesk	0.348
	Prediction	0.193
	Pearson	0.163
	Algorithm	Score
	Co-ocurr.	0.523
	Vector pairs	0.207
	Pearson	0.106
	Lesk	0.079
	Path length	0.061
	Prediction	0.027
	Outlier	0.007
	Leacock	0.005
	Resnik	-0.016
	Table

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Acknowledgments

We would like to to thank in advance the reviewers for their time and consideration. We thank Lisa Huckfield for proofreading this manuscript. This work has been funded by Spanish Ministry of Innovation and Science through: ICARIA: From Semantic Web to Systems Biology, Project Code: TIN2008-04844 and by the Regional Government of Andalucia through: Pilot Project for Training and Developing Applied Systems Biology Technology, Project Code: P07-TIC-02978.

So we have seen that the major problem for this technique is that not all terms present outliers. It cannot be outlier coincidence if outliers do not exist. Therefore, our method does not fit well enough to all situations. However, score shows us that this kind of technique could be very useful in situations where outlier exists, e.g. sustainable and renewable, i18n and internationalization, and so on.