Maroua Ben Haddada

Maria Hübner

Sandra Casale

Dietmar Knopp

Reinhard Niessner

Michele Salmain

Souhir Boujday
email: souhir.boujday@upmc.fr

Maria Huebner

Michèle Salmain

Gold Nanoparticles Assembly on Silicon and Gold Surfaces: Mechanism, Stability and Efficiency in Diclofenac Biosensing

come

ABSTRACT. We investigated the assembly of Gold nanoparticles (AuNPs) on Gold and Silicon sensors with two final objectives: (i) understanding the factors governing the interaction and (ii) building up a nanostructured piezoelectric immunosensor for diclofenac, a small-sized pharmaceutical pollutant. Different surface chemistries were devised to achieve AuNPs assembly on planar substrates. These surface chemistries included amines to immobilize AuNPs via electrostatic interaction, or a mixture of amines and thiols to covalently attach the AuNPs. We also generated PEG-amine terminated surfaces to benefit from the well-known non-biofouling properties of PEG-coated surfaces. The functional substrates and the resulting gold nanoparticle layers were characterized in detail by Surface IR, contact angle measurements and Scanning Electron Microscopy (SEM). The mechanism of adsorption is discussed herein considering the nature of the terminal groups and their charge at the pH of AuNPs adsorption. The coverage and the dispersion of AuNPs were strongly dependent on the anchoring points on the surfaces; the optimal were reached when the attachment layer offered multiple interaction points, in particular, for NH 2 /SH and PEG/NH 2 terminated surfaces, where the percentage of isolated particles was up to 78 %. In addition, PEG-coated surfaces led to a stable AuNPs layer resistant to ultrasounds and to further functionalization of the immobilized nanoparticles. These surfaces were used to engineer quartz crystal microbalance (QCM) biosensors for diclofenac detection. The AuNPs nanostructured substrates significantly enhanced the biosensor sensitivity as compared to planar substrates (up to 6 times higher). This enhancement presages a higher sensitivity in the competitive detection of diclofenac on these systems. More importantly, despite the biorecognition and the drastic regeneration conditions, SEM images show that gold nanoparticles layers are stable and reliable, which paves the way for their use as nanostructured platforms for multiple applications.

INTRODUCTION

The assembly of gold nanoparticles (AuNPs) onto planar substrates has expanded tremendously over the last several decades, particularly in the biosensors field [START_REF] Saha | Gold Nanoparticles in Chemical and Biological Sensing[END_REF][START_REF] Nath | Label-Free Biosensing by Surface Plasmon Resonance of Nanoparticles on Glass: Optimization of Nanoparticle Size[END_REF][START_REF] Sepulveda | LSPR-Based Nanobiosensors[END_REF] . These nano-objects lead to dramatic improvements in the performance and sensitivity of biosensing devices owing to their plasmonic properties and to their efficiency as nanostructuration agents. The benefit from plasmonic properties of planar substrates decorated by gold nanoparticles is dramatic for techniques such as surface enhanced Raman spectroscopy (SERS) now widely employed for biosensing [START_REF] Stewart | Nanostructured Plasmonic Sensors[END_REF][START_REF] Fan | A Review on the Fabrication of Substrates for Surface Enhanced Raman Spectroscopy and Their Applications in Analytical Chemistry[END_REF][START_REF] Felicia | Controlling Gold Nanoparticle Assemblies for Efficient Surface-Enhanced Raman Scattering and Localized Surface Plasmon Resonance Sensors[END_REF] . The interest in these nano-objects for signal enhancement in biosensors based on non optical techniques is more recent, yet it is growing fast. In the particular case of quartz crystal microbalance (QCM) biosensors, nanostructuration of the surface of the quartz crystal electrodes with AuNPs leads to an increase of the accessible surface area and by then the number of binding sites [START_REF] Fonseca | A Nanostructured Piezoelectric Immunosensor for Detection of Human Cardiac Troponin T[END_REF][START_REF] Kong | Molecularly Imprinted Quartz Crystal Microbalance Sensor Based on Poly(O-Aminothiophenol) Membrane and Au Nanoparticles for Ractopamine Determination[END_REF] . For DNA biosensors, nanostructuration by AuNPs results in a better attachment of the oligonucleotides to the surface, up to 10 times higher than on planar substrates, and therefore an increased capacity for nucleic acid detection [START_REF] Cai | Colloid Au-Enhanced DNA Immobilization for the Electrochemical Detection of Sequence-Specific DNA[END_REF][START_REF] Liu | The Enhancement Effect of Gold Nanoparticles as a Surface Modifier on DNA Sensor Sensitivity[END_REF][START_REF] Liu | Surface Modification of Platinum Quartz Crystal Microbalance by Controlled Electroless Deposition of Gold Nanoparticles and Its Enhancing Effect on the Hs-DNA Immobilization[END_REF] . The same trend was observed for antibody-based biosensors; despite the bigger size of these receptors, their density was improved while preserving affinity to their target [START_REF] Chu | Detection of Gliadin in Foods Using a Quartz Crystal Microbalance Biosensor That Incorporates Gold Nanoparticles[END_REF][START_REF] Vidal | Use of Polyclonal Antibodies to Ochratoxin a with a Quartz-Crystal Microbalance for Developing Real-Time Mycotoxin Piezoelectric Immunosensors[END_REF][START_REF] Wang | A Protein a-Based Orientation-Controlled Immobilization Strategy for Antibodies Using Nanometer-Sized Gold Particles and Plasma-Polymerized Film[END_REF] . All the same, antibody fragment based biosensors benefit from AuNPs assembly; higher sensitivity and overall better immunosensing performances were evidenced [START_REF] Wang | Nanogold Particle-Enhanced Oriented Adsorption of Antibody Fragments for Immunosensing Platforms[END_REF][START_REF] Makaraviciute | Antibody Fragment Immobilization on Planar Gold and Gold Nanoparticle Modified Quartz Crystal Microbalance with Dissipation Sensor Surfaces for Immunosensor Applications[END_REF] .

Grafting of metallic particles on substrates is often done a posteriori, starting from colloidal solutions [START_REF] Stewart | Nanostructured Plasmonic Sensors[END_REF] . Commonly synthesized by the so-called "Turkevich Synthesis 17 ," -involving the reduction of Au ions by reducing agents such as citrate -gold nanoparticles are formed through a classical nucleation and growth process terminated by citrate (Na 3 C 6 H 5 O 7 ), which controls the nanoparticle size and morphology by acting as a surface functional layer or a capping agent, and Page 3 of 35

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The Journal of Physical Chemistry preventing inter-particle aggregation through electrostatic repulsion. Prior to AuNPs deposition, surfaces need to be covered with reactive functions, usually amines or thiols, to favour the interaction with AuNPs through electrostatic or covalent bonds. Molecules commonly used at this step are organothiols in the case of gold [START_REF] Morel | Biosensors Elaborated on Gold Nanoparticles, a PM-IRRAS Characterisation of the IgG Binding Efficiency[END_REF][START_REF] Morel | Optimized Immobilization of Gold Nanoparticles on Planar Surfaces through Alkyldithiols and Their Use to Build 3D Biosensors[END_REF] or organosilanes for oxidized silicon and glass surfaces [START_REF] Ben Haddada | Optimizing the Immobilization of Gold Nanoparticles on Functionalized Silicon Surfaces: Amine-Vs Thiol-Terminated Silane[END_REF][START_REF] Aissaoui | Silane Layers on Silicon Surfaces: Mechanism of Interaction, Stability, and Influence on Protein Adsorption[END_REF] . More recently, polymer-based coating was applied to attach AuNP on planar substrates [START_REF] Christau | Brush/Gold Nanoparticle Hybrids: Effect of Grafting Density on the Particle Uptake and Distribution within Weak Polyelectrolyte Brushes[END_REF][START_REF] Christau | Stimuli-Responsive Polyelectrolyte Brushes as a Matrix for the Attachment of Gold Nanoparticles: The Effect of Brush Thickness on Particle Distribution[END_REF] . Poly(ethylene glycol) (PEG) is particularly adapted to biosensing applications by forming smooth and uniform PEG films that inhibit non specific adsorption [START_REF] Mehne | Characterisation of Morphology of Self-Assembled Peg Monolayers: A Comparison of Mixed and Pure Coatings Optimised for Biosensor Applications[END_REF][START_REF] Huebner | Layer-by-Layer Generation of Peg-Based Regenerable Immunosensing Surfaces for Small-Sized Analytes[END_REF] and provide a high affinity to citrate-stabilized gold nanoparticles [START_REF] Diamanti | Tuning Gold Nanoparticle-Poly(2-Hydroxyethyl Methacrylate) Brush Interactions: From Reversible Swelling to Capture and Release[END_REF][START_REF] Meyerbröker | Novel Ultrathin Poly(Ethylene Glycol) Films as Flexible Platform for Biological Applications and Plasmonics[END_REF] . Once AuNPs are immobilized on a substrate, although the citrate capping agent is well-suited as a native functional layer during synthesis, substituting citrate, which is only bound loosely to the nanoparticle surface through weak van der Waals interactions, by a more tightly bound and well-defined capping layer is mandatory for further grafting of biomolecules or target and application in biosensing. For this post-functionalization the organosulfur compounds represent perhaps the most widely practiced route [START_REF] Daniel | Gold Nanoparticles: Assembly, Supramolecular Chemistry, Quantum-Size-Related Properties, and Applications toward Biology, Catalysis, and Nanotechnology[END_REF][START_REF] Brust | Synthesis and Reactions of Functionalized Gold Nanoparticles[END_REF] . To withstand this post-functionalization, AuNP layers should also be stable over time and their interaction with the surface strong enough to ensure that the nanoparticles remain attached during further functionalization and upon utilization. Despite the huge interest in AuNPnanostructured substrates, the assembly of gold nanoparticles on surfaces and their postfunctionalization still rely on an approximate approach since the fundamentals of the chemistry involved in the adsorption process are poorly understood [START_REF] Ben Haddada | Optimizing the Immobilization of Gold Nanoparticles on Functionalized Silicon Surfaces: Amine-Vs Thiol-Terminated Silane[END_REF][START_REF] Pena-Pereira | Immobilization Strategies and Analytical Applications for Metallic and Metal-Oxide Nanomaterials on Surfaces[END_REF][START_REF] Lundgren | Gold-Nanoparticle-Assisted Self-Assembly of Chemical Gradients with Tunable Sub-50 Nm Molecular Domains[END_REF] .

In this contribution, we investigated the mechanism of assembly and the stability of spherical gold nanoparticles on functionalized silicon and gold substrates. To this purpose, we devised different surface chemistries to establish the key-parameters in the attachment layer that dominate the interaction with gold nanoparticles. Both gold and oxidized silicon substrates were Page 4 of 35

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The Journal of Physical Chemistry modified by thiols and silanes to generate amine-terminated layers. Then, reaction of a thiolterminated carboxylic acid with the grafted amines allowed us to generate mixed thiol/amine terminated layers. In addition, we utilized diamine-PEG cross-linked to acid and epoxy groups on gold and silicon, respectively, to compare the efficiency of these films to the previous ones.

The assembly of gold nanoparticles on these platforms was examined in depth by Scanning Electron Microscopy (SEM), focusing on the coverage and dispersion, and the impact of the attachment layers is discussed. We also investigated the influence of the post-functionalization agent (either an organothiol or a PEG) on the AuNP layers. Finally, the stable and well-dispersed layers are used to build piezoelectric immunosensors for the anti-inflammatory drug diclofenac and their efficiency was compared to planar surfaces. The stability of AuNP layer upon biorecognition and regeneration processes was eventually investigated by SEM. The Journal of Physical Chemistry potential is detailed in reference [START_REF] Huebner | Rapid Analysis of Diclofenac in Freshwater and Wastewater by a Monoclonal Antibody-Based Highly Sensitive Elisa[END_REF] . Milli-Q water (18 MΩ, Millipore, France) was used for the preparation of the solutions and for all washes. Experiments were carried out at room temperature unless otherwise stated.

EXPERIMENTAL SECTION

Materials

Surface chemistry: Silicon wafers cut into 1 x 1 cm pieces were cleaned following a procedure including several washing steps and treatment by hydrochloric acid / methanol 1:1 (v/v) for 1 h.

Reactive hydroxyl groups were generated by shaking wafers in sulfuric acid for 1 h. After washing with water, the substrates were dried under nitrogen flow [START_REF] Cras | Comparison of Chemical Cleaning Methods of Glass in Preparation for Silanization[END_REF] . APTES was grafted by immersing the wafers in a solution of silane in anhydrous toluene (50 mM) and heating at 75°C for 24 h. Samples were washed twice, sonicated for 10 min in anhydrous toluene, dried under nitrogen flow, and heated at 90°C for 2 h [START_REF] Aissaoui | Silane Layers on Silicon Surfaces: Mechanism of Interaction, Stability, and Influence on Protein Adsorption[END_REF] . Surface sulfhydryl functions were generated by exposing the wafers to a solution of MUA (10 mM in EtOH previously activated with NHS (2.4 eq.) and EDC (1.2 eq.)).Grafting of GOPTS was carried out by covering wafers with GOPTS (100 µL) and avoiding exposure to the atmosphere by assembling two substrates face-to-face. After 3 h, the sandwiches were separated and sonicated first in ethanol, then in methanol and again in ethanol for 5 min each. They were dried under nitrogen flow and heated for 5 min at 85°C. Afterwards, the wafers were treated with molten DAPEG 45 (600 µL) and incubated at 98°C for 12 h in the sandwich format. The wafers were separated, sonicated in water for 15 min and dried under nitrogen flow [START_REF] Huebner | Layer-by-Layer Generation of Peg-Based Regenerable Immunosensing Surfaces for Small-Sized Analytes[END_REF] .

Gold-coated sensors were immersed in a freshly prepared solution of CEA (10 mM in water) or MUA (10 mM in EtOH) for 18-24 h. After washing with the same solvent, the CEA-treated surfaces were dipped into a solution of MUA (10 mM in EtOH previously activated with NHS (2.4 eq.) and EDC (1.2 eq.)) for 2 h. The MUA-treated surfaces were exposed to 150 µL of an Page 6 of 35

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The Journal of Physical Chemistry aqueous solution of NHS (2.4 eq.) and EDC (1.2 eq.) for 2 h to activate the carboxyl groups. The surfaces were rinsed with the same solvent and dried under nitrogen flow. DAPEG 45 immobilization was finally performed as described above. All the functional substrates were stored in a dessicator.

Gold nanoparticles preparation and deposition:

Colloidal gold particles were prepared according to the tannic acid method of Slot and Geuze 36 . To produce 100 mL of a 14 nm diameter colloid solution, two stock solutions were prepared: solution A: 1 mL 1% (w/v) HAuCl 4 and 79 mL water; solution B: 4 mL 1% sodium citrate, 0.025 mL 1% tannic acid and 16 mL water. Solutions A and B were heated to 60°C under stirring then mixed. When the solution turned red, the mixture was heated up to 95°C for a few minutes and cooled on ice. Colloidal solutions were kept in amber glassware, stored in the refrigerator at 4°C and used within a month. Modified substrates were dipped into the prepared colloid solution, with no further dilution for 90 min under sonication (Elma, 90 W, 45 kHz), washed twice in water and dried under nitrogen. Surfaces were finally immersed in a solution of CEA or DAPEG 68 (1mM in EtOH) for 2 h to decorate the AuNPs with amino groups. The resulting substrates were stored in a dessicator.

Covalent grafting of Diclofenac: Diclofenac (10 mM in dry DMF) was activated by treatment with NHS (1.2 eq.) and EDC (1.2 eq.) for 150 min at room temperature. Then, the substrates terminated by amino groups were dipped into the solution, shaken over night at room temperature, washed for 15 min in water and dried under nitrogen flow. They were stored in a desiccator.

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The Journal of Physical Chemistry

Binding of anti-diclofenac antibody:

The mixture of PBS and ethanol 9:1 was thoroughly degassed under vacuum (for 15 min) and used as carrier buffer in the flow system (flow rate 25 µL min-1 ). The sequence of injections was carried out as follows: 10-20 min flow of carrier buffer to stabilize the baseline signal; 10 min flow of 1/500 dilution of anti-diclofenac serum in PBS/EtOH; Washing with carrier buffer

The regeneration buffer was a solution of 2 w% sodium dodecyl sulfate (SDS) in water/ethanol (9/1=v/v) at pH=2 (HCl).

Characterization techniques: Transmission Electron Microscopy (TEM) measurements were performed using a JEOL JEM 1011 microscope operating at an accelerating voltage of 100 kV.

The TEM grids were prepared as follows: Typically 1.5 mL of the colloid solution was centrifuged at 11200 g for 10 min to precipitate the particles. The colorless supernatant was discarded. The residue was re-dispersed in a suitable volume of water. 2 µL of this particle suspension was dispensed on a carbon-coated copper grid and dried at room temperature. Gold nanoparticles size distribution was determined using ImageJ. Scanning Electron Microscopy (SEM) of the gold and silicon substrates covered with AuNPs was performed using a FEG SU-70 scanning electron microscope with a low voltage of 1 kV at a distance of 1.9 -2.3 mm; the secondary electron detector "in Lens" was used. Images taken at different scales and in different regions of the samples were recorded. The particle densities were calculated by counting the particles in the SEM images (isolated and aggregates of 2, 3, 4, 5, 6, and > 6 particles) on a representative area of 2.3 -2.5 µm 2 . For Contact Angle measurements static water contact angles were measured at room temperature using the sessile drop method and image analysis of the drop profile. The instrument, which uses a CCD camera and an image analysis processor, was Page 8 of 35

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The Journal of Physical Chemistry purchased from Krüss Optronic GmbH (Hamburg, Germany). The water droplet volume was 1 µL, and the contact angle (Ѳ) was measured 5 s after the drop was deposited on the sample. For each sample, the reported value is the average of the results obtained on 3 droplets and the overall accuracy in the measurements was better than ± 5°. PM-IRRAS spectra were recorded on a commercial NICOLET Nexus FT-IR spectrometer. Details on the experimental set-up are available in reference [START_REF] Boujday | Surface Ir Applied to Rapid and Direct Immunosensing of Environmental Pollutants[END_REF] . Sensor chips were made of borosilicate glass substrates (11 mm × 11 mm), coated successively with a 2.5 ± 1.5 nm thick layer of chromium and a 250 ± 50 nm thick layer of gold (Arrandee, Werther, Germany). They were annealed in a butane flame to ensure a good crystallinity of the topmost layers and dipped in a bath of absolute ethanol during 15 min before adsorption. The Si wafers were analyzed by GA-ATR-IR immediately after their preparation. GA-ATR-IR spectra (256 scans, 4 cm -1 resolution) were recorded on a Tensor 27 spectrometer (Bruker) equipped with a VariGATR accessory (Harrick Scientific, Pleasantville, NY) and incidence angle set at 60°. Quartz Crystal Microbalance with dissipation measurements (QCM-D) were carried out with a 4-channel dissipative quartz crystal microbalance (Q-Sense E4) using AT-cut 5 MHz quartz crystals (QCM sensors, 14 mm diameter) coated with a 3-100 nm layer of gold or a 50 nm SiO 2 film (Lot-oriel) at 22°C. Prior to use, they were cleaned by ethanol and dried under nitrogen flow. Experiments were carried out in flow-through conditions using a peristaltic pump. Data were simultaneously acquired at the fundamental frequency F of 5 MHz

RESULTS AND DISCUSSION

Synthesis and characterization of AuNPs: Citrate-stabilized spherical AuNPs were selected for this work as their synthesis has reached a level of maturity allowing a precise control of their shape, size, and dispersion. The size and dispersion of the gold colloids prepared in this work were estimated from UV-visible spectroscopy and Transmission Electron Microscopy (TEM) analyses. UV-Vis spectra and TEM images are given in the supplementary material section. On the UV-Vis spectrum (Figure S1-a), a narrow resonance plasmon band was present at 520 nm, typical of a particle size around 15 nm [START_REF] Seitz | Preparation and Characterisation of Gold Nanoparticle Assemblies on Silanised Glass Plates[END_REF][START_REF] Link | Size and Temperature Dependence of the Plasmon Absorption of Colloidal Gold Nanoparticles[END_REF] . This was confirmed by TEM images (Figure S1-b), mathematical treatment of the images showed a homogeneous particle size distribution with an average size of 13.5 ± 1 nm. The AuNP concentration in the solution, estimated by two methods (see supplementary section) was 3.6±0.2 nmol L -1 .

Surface functionalization of Au and Si substrates:

Six different surface chemistries were devised to generate a layer of AuNPs on gold and silicon planar substrates as schematized in Figure 1. Adhesion layers either included amines to ensure a strong electrostatic interaction with citrate-stabilized AuNPs [START_REF] Grabar | Preparation and Characterization of Au Colloid Monolayers[END_REF] or amines and thiols to covalently bind AuNPs through S-Au bonds. In addition, adhesion layers including NH 2 -terminated poly(ethylene) glycol) were built up to benefit from the well-known anti-biofouling properties of PEG-coated surfaces and prevent non specific adsorption during the immunosensing process [START_REF] Meyerbröker | Novel Ultrathin Poly(Ethylene Glycol) Films as Flexible Platform for Biological Applications and Plasmonics[END_REF] . We used a diamine-PEG (DAPEG 45 ) with a molecular weight of 2,000 Da, since this medium size PEG has been previously shown to provide a homogeneous cover on Si wafers [START_REF] Mehne | Characterisation of Morphology of Self-Assembled Peg Monolayers: A Comparison of Mixed and Pure Coatings Optimised for Biosensor Applications[END_REF][START_REF] Diamanti | Tuning Gold Nanoparticle-Poly(2-Hydroxyethyl Methacrylate) Brush Interactions: From Reversible Swelling to Capture and Release[END_REF][START_REF] Onses | Tunable Assembly of Gold Nanoparticles on Nanopatterned Poly(Ethylene Glycol) Brushes[END_REF] . Amine-terminated surfaces were generated on gold and silicon using CEA and APTES, respectively. Mixed amine/thiol terminated layers were obtained by reacting MUA on the previous amine-terminated layers. MUA reacts with approx. 24 % of amine groups leading to layers with SH/NH 2 ratio ~1/4 [START_REF] Ben Haddada | Optimizing the Immobilization of Gold Nanoparticles on Functionalized Silicon Surfaces: Amine-Vs Thiol-Terminated Silane[END_REF] . Amine-terminated PEG layers were built up by conjugating DAPEG to carboxy-decorated gold and epoxy-decorated silicon substrates that had been obtained by self-assembling of MUA and GOPTS, respectively (see Figure 1). The terminal carboxylic acid groups of MUA SAM on gold were converted into NHS esters by reaction with EDC and NHS prior to DAPEG grafting according to a previously published procedure [START_REF] Huebner | Layer-by-Layer Generation of Peg-Based Regenerable Immunosensing Surfaces for Small-Sized Analytes[END_REF] . The Terminal group (NH 2 , NH 2 /SH, or PEG) as depicted in Figure 1.

Prior to AuNPs immobilization, the functionalized substrates were characterized by contact angle measurements and surface IR. Table 1 summarizes the static water contact angle values measured for these substrates.

Table 1 Static water contact angle measurements

The initial clean (oxidized) silicon surface was very hydrophilic as expected by the strong oxidizing conditions applied to the samples before analysis to generate a high density of surface hydroxyl groups [START_REF] Huebner | Layer-by-Layer Generation of Peg-Based Regenerable Immunosensing Surfaces for Small-Sized Analytes[END_REF] . Silanization with APTES resulted in a very large increase of the contact angle from 15° to 83° due to the presence of more or less ordered aminopropyl chains [START_REF] Aissaoui | Silane Layers on Silicon Surfaces: Mechanism of Interaction, Stability, and Influence on Protein Adsorption[END_REF][START_REF] Kanan | Method to Double the Surface Concentration and Control the Orientation of Adsorbed (3-Aminopropyl)Dimethylethoxysilane on Silica Powders and Glass Slides[END_REF] .

Further covalent binding of MUA resulted in a drop of the contact angle to 40°. This increase of hydrophilic character may result from a better packing of the organic layer owing to Van der Waals forces between the longer hydrocarbon chains of MUA [START_REF] Vericat | Self-Assembled Monolayers of Alkanethiols on Au(111): Surface Structures, Defects and Dynamics[END_REF] . Silanization with GOPTS also led to an increase of contact angle from 15 to 51° as a result of organic group presence on the surface. Treatment with DAPEG slightly decreased the contact angle to 43° in agreement with the known hydrophilic character of PEG derivatives [START_REF] Diamanti | Tuning Gold Nanoparticle-Poly(2-Hydroxyethyl Methacrylate) Brush Interactions: From Reversible Swelling to Capture and Release[END_REF] . The same trend was observed for gold surfaces, i.e. an increase of the contact angle upon CEA or MUA grafting [START_REF] Vallee | Bsa Adsorption on Aliphatic and Aromatic Acid Sams: Investigating the Effect of Residual Surface Charge and Sublayer Nature[END_REF] and a decrease after DAPEG grafting consistent with the higher hydrophilic character expected for PEG-covered surfaces. The contact angle measured for Au-PEG was roughly the same as the one measured for Si-PEG, ~ 40°. Surface IR, either in ATR mode for silicon substrates, or PM-IRRAS mode for gold substrates, was used to characterize the molecular composition of the thin films deposited on both surfaces.

The recorded spectra are shown in the supplementary information section (Figures S2, 3, and4) together with the detailed band attributions (table S2). Briefly, for both substrates the characteristic bands expected upon surface modification were observed. On Au-NH 2 and Si-NH 2 the primary amine bands were present in the range 1660-1640 cm -1 the ammonium group bands around 1480 and 1571 cm -1 . Upon MUA grafting the intensity of these bands was lower and amide bands appeared giving evidence of the successful conjugation of activated acid to surface amine groups. All the same, successful grafting of DAPEG on Au-MUA and Si-GOPTS substrates was evidenced on both systems by the presence in the IR spectra of an intense band at 1110 cm -1 , assigned to the intense C-O-C stretching mode of the PEG chains [START_REF] Huebner | Layer-by-Layer Generation of Peg-Based Regenerable Immunosensing Surfaces for Small-Sized Analytes[END_REF][START_REF] Harder | Molecular Conformation in Oligo(Ethylene Glycol)-Terminated Self-Assembled Monolayers on Gold and Silver Surfaces Determines Their Ability to Resist Protein Adsorption[END_REF] .

Gold nanoparticles immobilization on functionalized surfaces:

The AuNPs layer attachment to the surface must be strong enough to prevent desorption/release during the successive steps of biosensor elaboration, test and regeneration. Therefore, ultrasounds were applied during AuNP deposition, to remove loosely bound nanoparticles. In addition, we have previously observed that applying ultrasounds during deposition of AuNPs on Si surfaces prevented the formation of aggregates [START_REF] Ben Haddada | Optimizing the Immobilization of Gold Nanoparticles on Functionalized Silicon Surfaces: Amine-Vs Thiol-Terminated Silane[END_REF] .

Scanning electron microscopy was used to estimate the coverage and dispersion of AuNP on the functionalized surfaces. SEM images and graphical estimation of AuNPs density and dispersion are shown in Figure 2. The fractional coverage was calculated by dividing the area covered by the 2D projection of the spherical AuNPs on the surface by the total area of the surface [START_REF] Nath | Label-Free Biosensing by Surface Plasmon Resonance of Nanoparticles on Glass: Optimization of Nanoparticle Size[END_REF] , results are shown in table 2.

The coverage and dispersion of the nanoparticles layers were highly dependent on the structure Their absence is ascribable to the application of ultrasounds. Indeed, when the deposition was repeated under gentle stirring, SEM images (Supplementary information section, Figure S5)

showed a small quantity (29 particles/µm²) of gold nanoparticles on the surface, which supports the hypothesis of weak interactions between AuNPs and Au-NH 2 surface. Conversely, a relatively high density of gold nanoparticles (510±15 x NP/µm 2 ) was observed on Si-NH 2 substrates (Figure 2-a). This difference observed between Si and Au can be understood by considering the difference in amine coverage on the two substrates: while CEA forms a monolayer of more or less ordered thiols on gold [START_REF] Bedford | An Experimental and Theoretical Approach to Investigate the Effect of Chain Length on Aminothiol Adsorption and Assembly on Gold[END_REF] , APTES condensation on silica-like substrates leads to a low amine coverage [START_REF] Aissaoui | Silane Layers on Silicon Surfaces: Mechanism of Interaction, Stability, and Influence on Protein Adsorption[END_REF] . Therefore on Au-NH 2 substrates only amine groups are present whereas on Si-NH 2 both silanols (Si-OH) and NH 2 groups are present. Moreover, at the acidic pH of AuNPs solutions (pH=5.4), amino groups on Au-NH 2 are mostly positively charged, (pK a (CEA) =8.3 [START_REF] Riauba | A Study of Cysteamine Ionization in Solution by Raman Spectroscopy and Theoretical Modeling[END_REF] ), therefore, their interaction with AuNPs can be envisioned mainly through electrostatic bonds with negatively charged citrate ligands. Whereas on APTES modified surfaces, pK a =7.6 [START_REF] Bhat | Tuning the Number Density of Nanoparticles by Multivariant Tailoring of Attachment Points on Flat Substrates[END_REF] , and though ammonium species dominate, they coexist with silanol groups that are negatively charged at this pH (silica PZC=2 [START_REF] Iler | The Chemistry of Silica[END_REF] ). Silanolates may be involved in AuNP interaction with the surface as they can replace citrate ligands and directly interact with gold.

Therefore, these cooperative interactions on Si-NH 2 would explain the huge difference observed with Au-NH 2 . Note that despite this cooperative interaction, the coverage observed on Si-NH 2 layers was the lowest of all the silicon substrates under study (Figure 2 and Table 2). After conjugation of MUA to the amine-terminated surfaces, the Si-NH 2 /SH substrates exhibited a higher coverage of AuNPs (Figure 2-b). For Au-NH 2 /SH the improvement could not be seen on Figure 2-e, but again when the deposition was done without applying ultrasounds (Figure S5, Supplementary section) AuNPs coverage was clearly improved compared to on Au-NH 2 . This result corroborates the efficiency of cooperative interactions towards AuNPs immobilization, here through Sulfur/Gold and ammonium/citrate interactions. On Si-NH 2 /SH, the fractional coverage in AuNPs reached 0.20 (see Table 2). However, the increase in AuNPs coverage on Si-NH 2 /SH substrates slightly worsen their dispersion, the percentage of isolated particles was lowered to 62% and few aggregates were observed (Figure 2-b andg).

The best dispersion of AuNPs and the highest surface coverage were observed for the PEGfunctionalized substrates, Si-PEG and Au-PEG (Figure 2-c andf). Both systems showed 78% of isolated particles and fractional coverage of 0.14 -0.18 (Table 2). This difference with the previous substrates may result from the swelling properties of PEG films that make them capable of capturing citrate-stabilized AuNPs [START_REF] Meyerbröker | Novel Ultrathin Poly(Ethylene Glycol) Films as Flexible Platform for Biological Applications and Plasmonics[END_REF] and trapping them by displacement of loosely bound citrate ligands and creation of multidentate interactions between the PEG chains and the Au atoms of the AuNPs [START_REF] Diamanti | Tuning Gold Nanoparticle-Poly(2-Hydroxyethyl Methacrylate) Brush Interactions: From Reversible Swelling to Capture and Release[END_REF] . This effect cooperates with the terminal amino group of the PEG chains that acts by electrostatic interaction with citrate ligands to stabilize the nanoparticle layers.

Surprisingly, despite the thick layer of PEG (1.7 nm [START_REF] Huebner | Layer-by-Layer Generation of Peg-Based Regenerable Immunosensing Surfaces for Small-Sized Analytes[END_REF] ), the substrates and/or the organic underlayers influence the coverage, as the value is 24% lower on Au-PEG than on Si-PEG.

Nevertheless, though all the coverage reported in Table 2 may look low, one must keep in mind that they in the same range than the data reported in the literature where the deposition and washing conditions were less drastic, (0.15 -0.25) [START_REF] Morel | Optimized Immobilization of Gold Nanoparticles on Planar Surfaces through Alkyldithiols and Their Use to Build 3D Biosensors[END_REF][START_REF] Grabar | Preparation and Characterization of Au Colloid Monolayers[END_REF][START_REF] Nath | Label-Free Biosensing by Surface Plasmon Resonance of Nanoparticles on Glass: Optimization of Nanoparticle Size[END_REF] . This limit is determined by the citrate ligands co-adsorbed with AuNPs that maintain an electrostatic repulsion between adsorbed To summarize this part, offering multiple anchoring points and cooperative interactions improved the coverage and dispersion of gold nanoparticles at the surface of gold and silicon planar substrates. These systems also show a resistance to leaching upon sonication which should make them suitable for further functionalization. The PEG layers provided an excellent dispersion on both substrates (78%). In what follows, we investigate for Si-PEG and Au-PEG substrates the stability of the AuNP layers upon post-functionalization, diclofenac biosensing and under the drastic regeneration conditions.

Post-functionalization of gold nanoparticle layers:

The use of the AuNPs-modified platforms for biosensing requires the exchange of the surrounding citrate ligands by reactive functions that will be used to further construct the biosensing layer. Mastering this step is important because the AuNPs layer is mobile and ligand exchange may lead to particle aggregation or leaching from the support [START_REF] Sato | Nucleation and Growth of Nano-Gold Colloidal Lattices[END_REF][START_REF] Enders | Situ Surface-Enhanced Infrared Absorption Spectroscopy for the Analysis of the Adsorption and Desorption Process of Au Nanoparticles on the SiO2/Si Surface[END_REF] . Two reagents were utilized for post-functionalization: CEA and PEG 68 , both introduce primary amine groups for further anchoring of diclofenac via its carboxylic acid function (see molecular structure in Figure 5). These post-functionalization treatments were applied to AuNP arrays deposited on PEG-coated silicon and gold substrates. The density and dispersion of AuNPs was then estimated by SEM. Representative images and their mathematical treatment are shown in Figure 3. As expected, post-functionalization modified the AuNP dispersion and coverage. On both substrates, the overall coverage was slightly lowered. desorption of particles (up to 30% with CEA) as well as aggregation. This behavior may be explained by the fact that, since the fractional coverage of AuNP on Si-PEG was higher than 0.15 established as the upper limit determined by Grabar et al. [START_REF] Grabar | Preparation and Characterization of Au Colloid Monolayers[END_REF] for 15-nm diameter citrate-capped AuNPs, the exceeding AuNPs were probably more loosely bound and/or mobile so that further treatment with a weak ligand like DAPEG favored aggregation whereas further treatment with a stronger adsorbate like CEA led to both aggregation and desorption. Similar desorption was previously reported on APTES-coated silicon upon treatment with CEA and explained by the positive charge of nanoparticles upon citrate ions replacement by CEA molecules that led to repulsion from the positively charged APTES-coated surface [START_REF] Enders | Reversible Adsorption of Au Nanoparticles on Sio2/Si: An in Situ Atr-Ir Study[END_REF][START_REF] Enders | Situ Surface-Enhanced Infrared Absorption Spectroscopy for the Analysis of the Adsorption and Desorption Process of Au Nanoparticles on the SiO2/Si Surface[END_REF] . Conversely, postfunctionalization of Au-PEG-AuNP by CEA or DAPEG did only marginally affect the overall particle density. This is in line with the fractional coverage being lower than 0.15, and therefore the probable absence of loosely bound particles on this surface. Furthermore, particle aggregation was only induced by CEA. In this situation, repulsion due to negative charges of citrates was no longer effective and aggregates formed [START_REF] Sato | Single Electron Transistor Using a Molecularly Linked Gold Colloidal Particle Chain[END_REF] . On the whole, post-treatment on Au-PEG-AuNP by DAPEG allowed maintaining a high density of NP as well as a good dispersion.

The nanostructured Si-PEG-AuNP and Au-PEG-AuNP substrates post-functionalized with DAPEG were further selected to build up a piezoelectric immunosensor for the antiinflammatory drug diclofenac.

Anti-diclofenac antibody recognition, specificity and reversibility: Diclofenac was covalently bound through its carboxylic acid function to the PEG amino groups of the nanostructured gold and silicon sensors and recognition of anti-diclofenac antibody was investigated by QCM-D as depicted in Figure 4. QCM was also used to assess the specificity of recognition towards non specific anti-mouse IgG (Supplementary section, Figure S6) as well as the reversibility of the hapten / antibody interaction (Supplementary section, Figure S7). In addition, to determine whether nanostructuration benefits to target recognition, the same experiment was run on flat silicon and gold substrates modified by GOPTS-PEG and MUA-PEG, respectively (Figure 4).

Figure 4 Frequency and dissipation shifts monitored during capture of anti-diclofenac antibody and washing with carrier buffer with the 4 different immunosensors Injection of non-specific antibody had no influence on the resonance frequency (Figure S6).

Conversely, injection of anti-diclofenac antibody resulted in an immediate decrease of the resonance frequency of the 4 sensors confirming the recognition of the specific anti-diclofenac antibody by the capture layer comprising diclofenac (Figure 4). The magnitude of the shift (∆F) as well as the rate of frequency change d∆F/dt were highly dependent on the nature of the sensing layer. On all systems, applying a regeneration buffer (pH=2) led to the release of the captured antibodies and return to the initial frequency (Figure S7).

We also analyzed the AuNPs layers by SEM to establish the possible effect of immunorecognition and regeneration on their dispersion and coverage. The SEM images in Figure 5 show that the gold nanoparticles layer was remarkably stable after the biorecognition step and even after the drastic regeneration conditions at acidic pH. No change was recorded upon anti-diclofenac binding to the surface. Upon regeneration, the drastic conditions applied led to a decrease in nanoparticle dispersion, yet, only small aggregates were formed and less than 15% of AuNP formed aggregates larger than 4 units. The regeneration process needs to be optimized to further limit its impact on AuNP dispersion. The Journal of Physical Chemistry since dissipation shifts were lower that 1x10 -6 during antibody capture (Table 3). The QCM responses in terms of ∆F and d∆F/dt were significantly larger for the 2 nanostructured sensor chips in agreement with the increase of specific surface brought by the layer of AuNPs. Clearly, the two flat sensors had lower antibody binding capacity than the nanostructured ones. Before washing with buffer, the increase was over 120% for both substrates, i.e. more than 6 times higher than on flat surfaces. After washing, the input of nanostruturation was still important, 32 and 48% for silicon and gold, respectively. The origin of this enhancement may be geometrical as the nanostructured sensors display a higher specific area than the flat ones. Knowing the surface coverage in AuNPs and their mean size, the increase of accessible surface area can be readily estimated. We have done these calculations depending on the exposed height of AuNPs (i.e. how the AuNPs are actually embedded in the organic film). The increase in area ranged from 17 to 33% for the Si sensor (with 1073 particles/µm 2 ) and from 13 to 26% for the Au sensor (with 833 particles /µm 2 ). Alternatively, the surface topology of nanostructured sensors may also favor the accessibility of the immobilized diclofenac ligand towards its antibody further enhancing the geometric benefit of nanostructuration. The Journal of Physical Chemistry despite a difference in the total density of AuNPs greater than 20% for silicon substrates. This result corroborates the assumption that increasing the surface area is the one and only reason of the improved performances of nanostructured immunosensors and abounds and towards a greater contribution granted by the new surface topology and the increased accessibility of the target.

This result thus also proves that, at this stage, the influence of the substrate is limited which paves the way for a generalization of this surface chemistry to other substrates by expecting the same performances.

To conclude this part, AuNPs layers led to a significant enhancement in the biosensor sensitivity.

This enhancement presages a higher sensitivity in the competitive detection of diclofenac on these systems. More importantly, despite the biorecognition and the drastic regeneration conditions, gold nanoparticle layers were stable and reliable, which paves the way for their use as nanostructured platforms for various applications.

CONCLUSION

Different surface chemistries were devised to generate layers of gold nanoparticles (AuNPs) on gold and silicon planar substrates. Adhesion layers either included alkyl chains or poly(ethylene glycol) (PEG) and were terminated by amines or mixed amine and thiol groups. The assembly of gold nanoparticles on the functionalized substrates was studied in depth by Scanning Electron

Microscopy. The highest coverage and dispersion were reached when gold nanoparticles were deposited on adhesion layers offering multiple anchoring points and cooperative interactions.

These systems showed a resistance to leaching upon sonication which made them suitable for further functionalization. Adhesion layers including PEG motifs provided an excellent coverage and the best dispersion on both Si and Au substrates (78%). These layers were post-

:

  Sodium citrate, gold(III) chloride trihydrate, 3-glycidyloxypropyltrimethoxysilane (GOPTS), tannic acid, N-ethyl-N'-(3-(dimethylamino)propyl)carbodiimide hydrochloride (EDC), cysteamine.HCl (CEA), diclofenac sodium salt, phosphate-buffered saline (PBS) pH= 7.4, poly(ethylene glycol) bis(amine) DAPEG 68 (MW= 3000 Da) , N-hydroxysuccinimide (NHS), 11-mercaptoundecanoic acid (MUA), (3-aminopropyl)triethoxysilane (APTES) (99%), silicon wafers <111> and anti-mouse IgG (Fc specific) antibody produced in goat were purchased from Sigma-Aldrich. Poly(ethylene glycol) bis(amine) DAPEG 45 (MW = 2000 Da) was obtained fromHuntsman (Rotterdam, Netherlands). Sodium dodecyl sulfate (SDS) was purchased from Fisher Scientific. The rabbit polyclonal anti-diclofenac serum was prepared in-house[START_REF] Deng | Residue Analysis of the Pharmaceutical Diclofenac in Different Water Types Using Elisa and Gc-Ms[END_REF] , its analytical

(N = 1 )

 1 and several overtone frequencies(15, 25, 35, 45, and 55 MHz, i.e. N = 3, 5, 7, 9 and 11). Two physical parameters are discussed: the resonance frequency and the dissipation. The frequency change can be correlated to the mass of the adsorbed layer using the Sauerbrey equation 38 : ∆F = -N ∆m / C f , where C f (= 17.7 ng cm -2 •Hz -1 at F = 5 MHz) is the masssensitivity constant, and N is the overtone number.

Figure 1

 1 Figure 1 Surface functionalization strategies devised for immobilization of AuNPs on gold and

  resulting surfaces are named according to the following nomenclature: Substrate (Au or Si) -

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 35 Paragon Plus EnvironmentThe Journal of Physical Chemistry of the organic adhesion film. On Au-NH 2 surfaces (Figure2-d), AuNPs were barely detectable.

Page 14 of 35 ACSFigure 2

 352 Figure 2 SEM images taken after AuNPs immobilization on the functionalized surfaces (a-f) and

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 35 Paragon Plus EnvironmentThe Journal of Physical Chemistry citrate-coated AuNPs and prevent the formation of close-packed monolayer41, 52-53 .

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 35

Figure 3

 3 Figure 3 SEM images (a-d) and AuNP densities (e) for Si-PEG-AuNP post-functionalized by

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 35

Figure 5

 5 Figure 5 Reversible immunorecognition scheme and SEM images and data of Au-PEG gold

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 35 Paragon Plus EnvironmentThe Journal of Physical Chemistry functionalized using either PEG-diamine or cysteamine to exchange the surrounding citrate ligands by reactive functions that will be used to further bind the bioreceptors. This step slightly decreased the coverage in gold nanoparticles, particularly on silicon substrates, but the use of PEG-diamine better preserved the dispersion of gold nanoparticles. The resulting layers were successfully used to build up a piezoelectric immunosensor for the anti-inflammatory drug diclofenac. They were sensitive and specific to diclofenac antibody. Compared to planar surfaces, they showed a significant enhancement in the biosensor sensitivity, up to 6 times the signal recorded for planar substrates. More importantly, despite the biorecognition and the drastic regeneration conditions, QCM sensors covered with gold nanoparticle layers were stable and reusable with a preserved coverage and dispersion, which paves the way for their use as nanostructured platforms for various applications.SUPPORTING INFORMATIONGold nanoparticles characterizations. IR characterization of functionalized surfaces. Gold nanoparticles deposition. QCM data.

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					Page
		Si		Au
	Clean	< 10°		46±2°
	1 st layer	APTES 83±2°	GOPTS 51±1°	CEA 51±2°	MUA 59±2°
	2 nd layer	APTES+MUA 40±4°	PEG 43±2°	CEA+MUA 48±1°	PEG 40±4°

Table 2 .

 2 AuNPs dispersion and coverage calculated from SEM images

	Sample	Isolated AuNPs / %	Number of particles / µm2	Fractional coverage
	Si-NH 2	77	510 ± 20	0.08
	Si-NH 2 /SH	62	1324 ± 40	0.20
	Si-PEG	78	1186 ± 40	0.18
	Au-PEG	78	905 ± 30	0.14

Table 3

 3 QCM data for flat and AuNPs nanostructured surfaces

			Gold		Silicon
		Flat	Nanostructured	Flat	Nanostructured
	-∆F 5 (Hz)	11.8	17.5± 1	12.5	16.5 ± 0.5
	∆m/A (ng/cm²)	208	310 ± 18	222	293 ± 9
	Γ(exp) (pmol.cm -2 )*	1.4	2	1.5	1.95
	Increase (%) before washing	---	128	---	124
	Increase after washing (%)	---	48	---	32
	-d∆F/dt (Hz.s -1 )	0.016	0.077	0.019	0.088

Furthermore, the nanostructured surfaces on a silicon or gold substrates gave very similar responses in QCM. The amount of anti-diclofenac antibodies recognizing the target differs little

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ACKNOWLEDGEMENTS

We would like to thank the DIM Analytics and Region Ile-de-France for M. Ben Haddada PhD scholarship. This work was also co-financially supported by ANR (Agence Nationale de la Recherche) and DFG (Deutsche Forschungsgemeinschaft), ANR-DFG program, project NArBioS, Grant no: ANR-11-INTB-1013.

ACS Paragon Plus Environment

The Journal of Physical Chemistry

ACS


Gevorg Poghosyan

Ioannis Pefkianakis

Pascal Le Guyadec

Vassilis Christophides

International Workshop on Big Data and Data Mining Challenges on IoT and Pervasive Systems (BigD2M 2016) Mining usage patterns in residential intranet of things

Keywords: intranet of things, home device analytics, multi-dimensional association rules

Ubiquitous smart technologies gradually transform modern homes into Intranet of Things, where a multitude of connected devices allow for novel home automation services (e.g., energy or bandwidth savings, comfort enhancement, etc.). Optimizing and enriching the Quality of Experience (QoE) of residential users emerges as a critical differentiator for Internet and Communication Service providers (ISPs and CSPs, respectively) and heavily relies on the analysis of various kinds of data (connectivity, performance, usage) gathered from home networks. In this paper, we are interested in new Machine-to-Machine data analysis techniques that go beyond binary association rule mining for traditional market basket analysis considered by previous works, to analyze individual device logs of home gateways. Based on multidimensional patterns mining framework, we extract complex device co-usage patterns of 201 residential broadband users of an ISP, subscribed to a triple-play service. Such fine-grained device usage patterns provide valuable insights for emerging use cases such as an adaptive usage of home devices, and also "things" recommendation.

Introduction

With the rapid growth of smart technologies, modern homes are gradually transformed into Intranet of Things. A variety of devices (smartphones, IPTVs, network extenders, cameras, utility meters, etc.) get connected via wireless or wired home networks to offer multiple (not always integrated) services (e.g., remote surveillance, home automation). Connectivity of things as well as broadband access in homes are supported by residential gateways capable of monitoring the operations and the performance of the devices connected to them.

In this paper, we are interested in analyzing device usage logs in order to support emerging use cases in smart homes such as adaptive usage of home devices 1 and "things" recommendation 1 . Such use cases fall within the wider area of human-cognizant Machine-to-Machine communication aiming to predict user needs and complete tasks without users initiating the action or interfering with the service. While it is not a new concept, according to Gartner cognizant computing is a natural evolution of a world driven not by devices but collections of applications and services that span across multiple devices in which human intervention becomes as little as possible by analyzing past human habits.

To realize this vision, we are interested in co-usage patterns featuring spatio-temporal information regarding the context under which devices have been actually used in homes. For example, a network extender which is currently turned off, could be turned on at a certain day period (e.g., evening) when it has been observed to be highly used along with other devices (e.g., a laptop or a tablet). Alternatively, the identification of frequent co-usage of particular devices at a home (say iPhone with media player), could be used by a "things" recommender to advertise the same set of devices at another home (say another iPhone user could be interested in a media player).

We advocate frequent pattern and association rule mining techniques since we believe that they are more easily understood by both end-users (for raising awareness regarding device energy or bandwidth consumption) and developers (for programming if this then that scripts of home automation), than the potentially more accurate but opaque Machine Learning techniques (e.g., classification). Traditional market basket analysis has been recently revised for extracting associations between users' interactions (e.g., communication and entertainment services) and context (e.g., time periods) captured by mobile devices [START_REF] Ma | A habit mining approach for discovering similar mobile users[END_REF][START_REF] Li | Bp-growth: Searching strategies for efficient behavior pattern mining[END_REF] , frequent co-occurring mobile context events (e.g., a user listens to music during workdays, while driving) [START_REF] Srinivasan | Mobileminer: Mining your frequent behavior patterns on your phone[END_REF] or frequent co-usage patterns of different appliances under various contexts [START_REF] Rollins | Using rule mining to understand appliance energy consumption patterns[END_REF] . Unlike these works, we extract n-ary (vs. binary) patterns from device logs involving attributes of at least three distinct entities: Device, Context, and Activity. An extra Gateway dimension is also considered when extracting recurring patterns across homes. Rather than decomposing our analysis problem into several binary ones (Context × Activity, Context × Device, etc.), we leverage recent advances in constraint-based algorithms [START_REF] Nguyen | Multidimensional association rules in boolean tensors[END_REF][START_REF] Cerf | Data-Peeler: Constraint-based Closed Pattern Mining in n-ary Relations[END_REF] for mining arbitrary n-ary relations. Our main contributions are: 1) We analyze a new dataset collected from home gateways, subscribers of a large European ISP (Section 2). It is a high variety dataset with device connectivity, performance and usage data (mainly numerical but also categorical) produced in high velocity (per 30 seconds) under normal service operation by an important number of gateways (201) on which a large number (2828) of fixed (e.g., desktops, laptops) and portable (e.g., tablets, smartphones) devices are connected, as well are also IPTVs and phones.

2) We introduce a discrete representation of gateway logs that is flexible enough to capture device activities spanning multiple contexts or vice versa (Section 2). We enable an on-demand generation of device usage logs that combines usage evidence from multiple raw data logs (unlike unique transactions in market-basket analysis), while controls the spatial and temporal log resolution.

3) We extract frequent n-ary patterns and rules from device logs within or across houses (Section 4). These patterns uncover that devices are more frequently co-used at low traffic data rates, close to the gateway, during evenings. These co-usage patterns are significantly different across homes and independent of the number of devices.

Our plans for future work are presented in Section 5.

Dataset Description

We analyze more than 21 million measurement reports collected over a 3-month period (February -April, 2014) from the home gateways of 201 residential broadband subscribers of a large European ISP, geographically distributed over 10 cities. Each gateway has an ADSL2+ modem or fiber WAN access link, 4 Ethernet ports, an 802.11b/g/n WiFi access point, FXO POTS ports to connect phones. An IPTV connects to the gateway through an Ethernet set-top box (STB) device. The 2/3 of our deployment gateways use fiber and the rest ADSL access technology.

The gateway reports for each device: a) the MAC address, b) the connection interface (Ethernet or WiFi), c) device names given by users (e.g., "Kelitas-iPad"). It also periodically reports every 30 seconds the traffic data rate in Kbps at both downlink (from the gateway to the device) and uplink directions and the signal strength (RSSI) for each wireless device associated with it. For each connected Ethernet device, gateways report every 1 minute the timestamped traffic counters in bytes for both downlink and uplink directions. Finally, the start time, duration, and direction (incoming or outgoing) of phone calls are reported, along with a flag indicating whether they have been answered or not. A device is defined by its MAC address -e.g., a laptop with WiFi and Ethernet interfaces appears as two devices. Out of the 2828 distinct devices, 69.5% are WiFi and 30.5% are Ethernet. The number of WiFi and Ethernet devices per home varies from 3 to 50. Roughly 80% of the homes has more than 8 devices, as can be seen in Figure 1 (dashed lines). WiFi devices typically outnumber the Ethernet devices (typically at least a STB), and vary between 0 and 46 per home. Among the connected devices, there are also WiFi extenders connected to the gateway via Ethernet or WiFi. Although we can identify the devices behind an extender, we cannot specify their physical interface; e.g., all the devices of a WiFi extender which connects through Ethernet to the gateway, will appear as Ethernet devices. This justifies the high number of Ethernet devices that appear in some homes (up to 40 Ethernet devices in a single home).

In our analysis we distinguish between resident devices (owned and operated regularly by home residents), and guest devices, which are occasionally used and typically belong to visitors. Devices which are connected for 7 days are labelled as guest, and the devices connected for more than 7 days are labelled as resident. Resident devices also include those permanently hosted in a home, such as desktop computers, regardless of their connection days. Figure 1 (solid lines) shows the distribution of the number of resident devices connected at each gateway (varies from 2 to 28). In the rest of the paper we focus only on resident devices.

Core Entities in Home Intranet of Things

To represent device usage logs in residential Intranet of Things we rely on 4 types of entities shown in Table 1. Gateways are described by their identifier (Gid) and the broadband access type (Access ∈ { f iber, adsl}). Devices are described by their globally unique identifiers (Did), the gateway (Gid) and the physical interface (Port ∈ {wlan, eth, phone}) to which they are connected to, as well as, their MAC address (Mac). The need for globally unique Did stems from the fact that, devices may move across the homes, so the same MAC address may appear in more than one home. We also consider the device Kind and Vendor derived mainly by the MAC addresses. Table 2 describes the different device types observed in our dataset. Our gateways do not report any information regarding the device For all the devices with the exception of STBs, whose MAC addresses have been provided by our ISP, we have used a simple heuristic classification based on their MAC addresses and device names [START_REF] Poghosyan | Device analytics in home networks[END_REF] . We have assessed the high accuracy of classification results against a ground truth collected by surveys from 49 homes of our deployment.

(a) Gateway. The Context and Activity of Table 1 capture information related to the actual device usage. Context records the contiguous time intervals (Begin and End timestamps) a particular device has been reported to be connected or disconnected in (S tate). Other contextual information could be considered depending on the device type and the scope of analysis. For example, for WiFi devices the Quality of the received signal strength is an indicator of the device proximity to the gateway. In order to map RSSI to wireless link speed and quality, we use the thresholds presented in [START_REF] Pefkianakis | Characterizing home wireless performance: The gateway view[END_REF] . Period∈ {night; morning; a f ternoon; evening} or Weekday∈ {workday; weekend} represents useful temporal context that can be easily derived from the session timestamps and included to the device usage logs. Note that each device could operate only under one context within the corresponding time-interval recorded in the table Context.

Activity refers to the traffic rate of connected devices during contiguous time intervals (Begin and End timestamps). We discretize the cumulative traffic rates (downlink and uplink) generated by a data device into different Levels capturing general classes of applications that could run on devices [START_REF]What speeds do I need for Skype[END_REF] , as shown in Table 3. Note that, although the instantaneous peak traffic of the above applications may exceed their data rate bin, our gateways report an average rate over a 30-second or 1-minute period, which falls into the above bins. The activity levels for STBs and IP phones vary from the ones specified in Table 3. We consider the STBs to be idle, when no content is being watched. During idle activity, there still can be some traffic (< 500 kbps) from STB firmware updates or from users browsing the menu. The activity level is high when a user is watching TV (rate 500 kbps). Phone's activity level is considered idle when there are no successful phone calls, and high when there are active calls. Also note that each device could exhibit only one activity within the corresponding time-interval. The adopted discretization aims to reduce the inherent data noise and scarceness in order to improve the statistical significance and interpretability of extracted patterns. The proposed data representation, can flexibly capture complex usage logs in Intranets of Things where a device activity may span multiple contexts or vice versa. For example, a user may switch from checking email to video watching (activity changes) on her tablet, while she is close to the gateway (context remains the same). Or, a user may move from living room to the garden (signal quality and the context change), while she is watching a video (activity remains the same). More precisely, the usage logs of a device are generated from the recorded Context and Activity sessions using the following join condition: (C.Begin < A.End ∧ A.Begin < C.End). Since each device activity can be tracked only within a context, we need only to check for time intervals of contexts that overlap those of activities. The granularity of temporal aggregations clearly affects the density of generated usage logs, and is guided by the objectives of our analysis.

Analysis of Device Co-usage

The analysis of usage logs in residential Intranet of Things involves different types of entities (e.g., Gateways, Devices, Context and Activity), which may be described by several attributes depending on the scope of the analysis. The more we zoom into device usage scenarios, the more attributes need to be considered. Rather than reducing the complexity of the n-ary usage logs analysis into a frequent binary pattern and association rule mining problem by converting n-ary to binary logs (e.g., Context × Activity, Context × Device) [START_REF] Cao | An effective approach for mining mobile user habits[END_REF][START_REF] Ma | A habit mining approach for discovering similar mobile users[END_REF][START_REF] Nath | Ace: Exploiting correlation for energy-efficient and continuous context sensing[END_REF][START_REF] Ong | Exploring sequential and association rule mining for pattern-based energy demand characterization[END_REF][START_REF] Srinivasan | Mobileminer: Mining your frequent behavior patterns on your phone[END_REF][START_REF] Rollins | Using rule mining to understand appliance energy consumption patterns[END_REF] , we rely on recent advances in data mining over n-ary relations (e.g., Device × Context × Activity) [START_REF] Nguyen | Multidimensional association rules in boolean tensors[END_REF][START_REF] Cerf | Data-Peeler: Constraint-based Closed Pattern Mining in n-ary Relations[END_REF] . Our choice is motivated by the need to employ the same general purpose mining algorithms for serving different analytical use cases.

In general, n-ary usage logs are defined as subsets of the Cartesian Product of a number of finite and disjoint attribute domains: U ⊆ D 1 × ... × D n . Unlike extracting frequent co-occurring elements from the same domain (D i ), in our setting we are mining frequent sets of n-tuples (D 1 × ... × D n ) in U (called n-sets). More formally, we are interested in extracting patterns under the form of closed n-sets 7 : H = [X 1 , . . . , X n ] such that X i ⊆ D i . H is called a closed n-set iff (a) all elements of each set X i are in relation with all the other elements of the other sets in U, and (b) X i sets cannot be enlarged without violating condition (a). A notable characteristic of the Data-Peeler algorithm [START_REF] Cerf | Data-Peeler: Constraint-based Closed Pattern Mining in n-ary Relations[END_REF] we employ for extracting closed n-sets is that, it enables us to consider user-defined (anti-monotonic) constraints on the relevancy of the mined collections of n-sets. We may ask for patterns with a minimal number of elements in some domains (e.g. with at least 2 devices) and/or patterns covering at least a given number of tuples in U. N-ary association rules are based on the notion of association which essentially boils down to closed n-sets defined on subsets of the original attribute domains. The frequency (support) of an association is the subset of the support domain used to count the association occurrences on the remaining domains on which it is defined. For formal definitions of a rule and frequency readers are referred to [START_REF] Nguyen | Multidimensional association rules in boolean tensors[END_REF] .

We first study device usage patterns across all the homes of our deployment, which are needed in order to support "things" recommendation use cases. We seek to identify, what types of devices are co-used more frequently and at what time periods in day? Our analysis requires to extract n-ary associations from device usage logs defined over four domains: Gateway, Day, Device, Period. Since we are interested in frequent daily patterns across gateways, Gateway and Day are the support domains. For example, the support s({ f i , po } × { a f , ev }) reflects the number of days across all gateways, that fixed ('fi') and portable ('po') devices in a home were co-connected at both afternoon ('af') and evening ('ev'). Any subset of this n-set (e.g., s({ po } × { a f , ev })) has support greater or equal than its superset.

We next investigate, the activity level exhibited by connected devices. We first differentiate idle, from low, medium, high activity levels. Then we construct the usage logs including all instances when devices in a home are idle during a certain time period. A connected device is considered to be idle during a time period, if all the gateway reports during this period show the device to be in idle activity level. Two (or more) devices are co-idle at a certain time period if the above condition holds, and they overlap with the period under analysis. Figure 2 illustrates the heat map of the support of co-idle devices at different periods, e.g., the support s({ f i , po } × { a f , ev }) is the number of days across all the gateways, that fixed and portable devices were idle, at both afternoon and evening. For illustration purposes, we sort the heat map cells based on decreasing support order and omit the patterns with very low support ( 0.003).

Our analysis shows the devices with the longest idle times to be the IP phones and TVs, whose maximum support is 0.78 and 0.41, respectively. This behavior implies that TVs and phones are frequently connected, but rarely used. Portables (support ranges from 0.004 and 0.1) show longer idle times than fixed devices (support ranges from almost zero to 0.02). Network equipment shows relatively long idle times with supports between 0.02 and 0.08. Finally, game consoles are rarely observed idle (supports from 0.003 to 0.008) compared with the other device kinds. Figure 2 shows that for all devices apart from game consoles, the highest supports in decreasing order appear at night, morning, afternoon and evening. This implies that devices are typically inactive during nights and more active during evenings. On the contrary, the game consoles present the highest supports in reverse order (evening, afternoon, morning, night). This suggests that when residents in our deployment homes play video games, they usually don't use other devices. We also observe that network equipment is co-idle with portable devices, which is expected since the traffic typically routes through the networking device to the gateway. The Quality dimension, which solely applies to wireless devices, was not considered so far, because 91% of our gateway reports for wireless devices are classified in the high quality category. The remaining 8.5% and 0.5% of records are classified in the medium and low categories, respectively.

We next perform an in-depth analysis of individual homes aiming to support home-automation use cases. We seek to identify, what types of devices are co-used more frequently, at what time periods in day and what is their activity level? We select as case study a home with 17 resident and 10 guest devices of various kinds and vendors (see Table 4) that in their majority use WiFi to connect to the gateway. The number of days that a device appears to be connected varies from 15 to the total 89 days of interest. Figure 3, illustrates the heat map plot of the support of co-idle devices at different time periods, where the support domain is the Day. As expected, Phone (d4) and STB (d3) have the longest idle times. Besides these devices, the peripheral d1 and the data device d5 exhibit the longest and the shortest idle times, respectively. Interestingly, we do not observe any strong correlation between the number of connected days and the idle times. For example, the peripheral d1, is connected less days than the portables d0 and d2. For all the devices, the highest supports in decreasing order appear at night, morning, afternoon and evening, as shown in Figure 3.

We consider two (or more) devices to be co-active with activity level x at a certain time period, if they have overlapping activity intervals at level x, during this period. In the heat map of Figure 4(a) we present the co-active devices along with their activity levels, in decreasing support of high activity level. The support domain is the Day, and we filter out co-active devices with less than 4 days. In the sequel, we focus on high, medium activity, in the evenings. As we can observe in Figure 4(a), almost all co-active device sets include STB (d3) and phone (d4). The highest support is s({ d16 , d3 } × { hi }) = 0.51. This means that, the STB and the media player show high co-activity level, for 45 out of the 89 days of monitoring period. The large co-activity supports for phone and STB is justified from the fact that, we define only idle and high activity levels for those devices. Apart from these frequently used devices, a media player and a fixed device (['d16', 'd12']), exhibit a high activity level with 4 days of support.

Figure 4(b) depicts device co-usage at low activity, in decreasing support (support domain is again the Day). Due to space restrictions, we present only the 15 highest support device sets. Our results show two key differences compared with the high activity level patterns of Figure 4(a). First the supports are overall higher, with the peak support s({ d2 , d9 } × { lo }) = 0.94 (83 days). This implies that co-used devices operate more at low than high activity level, in our case study home. Second, the device sets do not include STB and phone because low activity does not apply to them. Device sets include portable, fixed, media player devices and their combinations. We finally observe co-active devices at different activity levels with the peak support to be 65 days (s({ d16 , d12 } × { low , medium }) = 0.73).

Fig. 1 :

 1 Fig. 1: Number of devices per home.

Table 1 :

 1 Gateways, Devices, Usage Context and Activity sessions.

					(b) Device.					(c) Context.				(d) Activity.
	Gid Access	Did Gid	Mac	Port	Kind	Vendor	Did	Begin	End	State	Quality	Did	Begin	End	Activity
	g1	fiber	d1	g4 00:22:3a:*...*	eth	tv	Cisco	d30 5/3/2014, 23:30 6/3/2014, 00:10	connected	high	d30 5/3/2014, 23:30 6/3/2014, 00:00	high
	g2	adsl	d3	g54		phone	phone		d30 6/3/2014, 00:10 6/3/2014, 00:20	connected	medium	d30 6/3/2014, 00:00 6/3/2014, 00:30 medium
									d30 6/3/2014, 00:20 6/3/2014 00:40	connected	low	d30 6/3/2014, 00:30 6/3/2014, 00:40	idle
	g10	adsl	d30	g2	9c:e6:35:*...*	wlan portable Apple	d30 6/3/2014, 00:40 6/3/2014, 07:00 disconnected
														d31 6/3/2014, 06:30 6/3/2014, 07:00	low
									d31 6/3/2014, 06:30 6/3/2014, 07:00	connected

Table 2 :

 2 Overview of home device kinds.

	Activity Level	Application	Traffic Rate (kbps)
	idle	mail client sync	< 7
	< 15 kbps	Skype/Viber text chat	< 4
		Skype voice/video call	2×(24 to 300)
	low	online radio	32 to 320
	[15, 500) kbps	YouTube (240p, 360p)	300 to 500
		web surfing (FB, news)	< 500
	medium [500, 2000) kbps	YouTube (360p, 480p, 720p) Skype video call HQ file transfer, cloud sync	500 to 2000 2×(400 to 2000) < 2000
	high 2000 kbps	YouTube HD (720p+) Skype HD, conf. video call torrents	2000 to 6000 2000 to 8000 2000

Table 3 :

 3 Activity levels and corresponding applications.

Table 4 :

 4 Overview of devices in our case study home.

	Device ID	Interface	Device Kind	Vendor	Number of Days Connected
	d0	WiFi	portable	AMPAK Technology	69
	d1	Ethernet	peripheral	Fujitsu	46
	d2	WiFi	portable (iPhone)	Apple	89
	d3	Ethernet	IPTV		87
	d4		IP phone		89
	d5	WiFi	data	Unknown	32
	d6	WiFi	portable (iPhone)	Apple	68
	d7	WiFi	portable	Samsung	74
	d8	WiFi	portable	Samsung	56
	d9	WiFi	portable	Sony	89
	d10	WiFi	data	Azurewave Technologies	15
	d11	WiFi	portable	Samsung	50
	d12	WiFi	fixed	Hon Hai Precision	88
	d13	WiFi	fixed	Intel	81
	d14	WiFi	fixed (laptop)	GVC Corporation	68
	d15	WiFi	fixed	Hon Hai Precision	46
	d16	WiFi	media player	Liteon Technology	89

Acknowledgements

This work was partially funded by the European ICT FP7 User Centric Networking project (grant no. 611001).

We observe the activity with the peak supports to be 0.9, 0.67, 0.51, 0.4 for evening, afternoon, morning and night. Although, there are common co-activity patterns across periods, residents co-use different devices at different times, which calls for different home profiling among time periods.

Figure 5, illustrates the n-sets of our case study home using hypergraph diagram of rules with support and confidence of at least 0.5. We focus only on the temporal device co-usage, without considering the activity level while always-connected IPTV and phone devices are excluded from our analysis. Each vertex represents a set of devices that appear either in the head or the body of an extracted rule. A hyper-edge represents all possible temporal periods for which devices in n-sets have been observed co-active. Note that hyper-edges are undirected since the confidences of the extracted rules which have the set of devices represented by a vertex as head or body, are similar. The hypergraph of our case study, includes a media player (d16) and two portable devices (iPhone d2, Sony d9). Rules' supports range from 0.5 to 0.63 and the confidences from 0.5 to 0.78. More precisely, the activity of the media player at a certain time period is correlated (with confidence from 0.5 to 0.78) with the iPhone usage at another period (and vice versa), for evening, morning and afternoon periods. The rule with the highest confidence (0.78) shows that if the media player is active in the afternoon, then the iPhone will be used in the evening (d16, a f ternoon → d2, evening). The largest hyper-edge represents the co-activity of the media player, Sony portable (with confidence from 0.6 to 0.74) and iPhone, Sony portable (with confidence from 0.62 to 0.73), during afternoon and evening.

We have performed the analysis on a Python and MongoDB 2.4 implementation on a Dell Latitude E5430. For our case study home the log generation from one day and three months of raw records took correspondingly ∼30 seconds and ∼2.5 hours. The rule mining, implemented in C++ [START_REF] Nguyen | Multidimensional association rules in boolean tensors[END_REF] , took less than 5 seconds for all three-dimensional logs.

Summary

In this paper, we have explored n-ary association rules for mining device usage patterns in residential Intranet of Things. These patterns provide valuable insights to uncover daily practices of residents without employing intrusive home sensors. Several plans for future work are foreseen. First, our gateways currently report only network-level feedback. We could enhance the dimensions of our analytics with application-level gateway feedback, or reports from other types of devices as home sensors. Second, our proposed use cases mainly apply to devices connected to the gateway. We foresee a unified Intranet of Things architecture where "things" can talk also to each other, thus enabling applications over an even more diverse set of devices.


Aravind Venkatesan
email: aravind.venkatesan@lirmm.fr

Nordine El Hassouni
email: nordine.el_hassouni@cirad.fr

Florian Phillipe
email: fphilippe@versailles.inra.fr

Cyril Pommier
email: cyril.pommier@versailles.inra.fr

Hadi Quesneville
email: hadi.quesneville@versailles.inra.fr

Manuel Ruiz
email: manuel.ruiz@cirad.fr

Pierre Larmande
email: pierre.larmande@ird.fr

Exposing French agronomic resources as Linked Open Data

Keywords: Data integration, Data interoperability, Knowledge management, Linked Data, RDF, Bioinformatics application, Agronomic research

The advancements in empirical technologies has generated vast amounts of heterogeneous data. This situation has created a need to integrate the data to understand the system of interest in its entirety. Therefore, information systems play a crucial role in managing these data, enabling the biologists in the extraction of new knowledge. The plant bioinformatics node of the Institut Français de Bioinformatique (IFB) maintains public information systems that houses domain specific data. Currently, efforts are being taken to expose the IFB plant bioinformatics resources as Linked Open Data, utilising domain specific ontologies and metadata. Here, we present the overview and the initial results of the project.

Introduction

Agronomy is an overarching field that encompasses various research areas such as genetics, plant molecular biology, and agro-ecology. The last several decades has seen the successful implementation of high-throughput technologies that have revolutionised research in agronomy. These technological advancements have resulted in a number of initiatives been taken to systematically store and share information over the web such as Gramene [START_REF] Monaco | Gramene 2013: Comparative plant genomics resources[END_REF], TAIR (Lamesch et al., 2012), OryzaBase [START_REF] Kurata | Oryzabase. An integrated biological and genome information database for rice[END_REF], Plant Reactome [START_REF] Croft | The Reactome pathway knowledgebase[END_REF], GnpIS [START_REF] Steinbach | GnpIS: an information system to integrate genetic and genomic data from plants and fungi[END_REF] and the South Green bioinformatics platform (http://www.southgreen.fr), to name a few. However, using these resources comprehensively, taking advantage of the associated cross-disciplinary research opportunities poses a major challenge to both domain scientists and information technologists. Effective data integration and management allows a broader perspective across many disciplines, than is possible from one or a series of individual studies. A solution for the data integration challenges is offered by the Semantic Web (SW) technologies [START_REF] Berners-Lee | Publishing on the semantic web[END_REF]. The objective of the current effort is to develop RDF knowledge bases that integrates existing domain specific ontologies and data from the respective regional portals, promoting data interoperability between the resources. To this end, we have developed the Agronomic Linked Data knowledge base (www.agrold.org) that is representative of the data housed in the southern region portal of France, the SouthGreen Bioinformatics platform (SG) (http://www.southgreen.fr/).

Semantification of the IFB plant bioinformatics nodes

Institut Français de Bioinformatique (IFB) is a French national node (http://www.elixir-europe.org/about/elixir-france) that is focused on providing integrated services for the life science community. The IFB platform provides access to databases, tools and services that covers three main domains namely, microbial, plant and health sciences. The IFB IT infrastructure is linked to six regional bioinformatics centers, the ReNaBi (French Bioinformatics Platforms Network), representing various regions of the French territory (ReNaBi-NE, North-East; PRABI, Rhône-Alpes region; ReNaBi-GS, Great South; ReNaBi-SO, South-West; ReNaBi-GO, Great West and APLIBIO, Paris area). These six regional centers are consists of regional bioinformatics platforms (PFs). Taken together, IFB will represent France in the ELIXIR European infrastructure initiative. To this end, the plant bioinformatics PFs maintain public data repositories that ranges from 'omics' to genetic data (genetic markers, maps and phenotypes) for various crop species.

Currently, the plant-centric PFs are working towards exposing their resources as linked data. The objective of the current effort is to develop RDF knowledge base that integrates existing domain specific ontologies and data from the respective PFs. This will promote interoperability between the databases. In the initial phase, two representative PFs are involved in this semantification process, namely:

a) The Unité de Recherche Génomique--Info (URGI) platform (https://urgi.versailles.inra.fr/) associated with the Institut National de la Recherche Agronomique (INRA), dedicated to maintain curated information on plants and crop parasite. The platform is part of the APLIBIO ReNaBi and plays a key role in the Wheat Initiative (http://wheatis.org/).

b) The South Green Bioinformatics platform (SG) part of the ReNaBi GS mainly associated with Centre de coopération internationale en recherche agronomique pour le développement (CIRAD) and Institut de recherche pour le développement (IRD) among other regional institutes. SG provides tools and databases dedicated for genomic resource analysis of southern and Mediterranean plants.

AgroLD for SG resources

Currently, SG consists of 12 databases covering various plant species such as Banana, Cocoa, Maize and Rice. AgroLD is being developed in phases to expose all of these databases as Linked Data. Currently, Phase I of AgroLD includes data from:

1. TropGeneDB [START_REF] Hamelin | TropGeneDB, the multi-tropical crop information system updated and extended[END_REF], a database that hosts genetic, molecular and phenotypic information on tropical crop species. 2. OryGenesDB [START_REF] Droc | OryGenesDB: a database for rice reverse genetics[END_REF], a database that serves as a repository on functional genomics for rice. 3. Oryza Tag Line [START_REF] Larmande | Oryza Tag Line, a phenotypic mutant database for the Génoplante rice insertion line library[END_REF], a database that contains sequence information (Flanking Sequence Tags) that are based on molecular categorisation of mutagen insertion sites for rice. 4. GreenPhylDB [START_REF] Conte | GreenPhylDB: A database for plant comparative genomics[END_REF]), provides sequence homology information for the members of kingdom plantae.

Additionally, domain specific ontologies, ontology annotations, proteomics and genomics information from a variety of publically available data sources have been integrated, this includes Gene Ontology, Plant Ontology, UniprotKB, Gramene (Gene, ontology annotation, gene, Quantitative Trait Loci (QTL) and Metabolic Pathway information) [START_REF] Monaco | Gramene 2013: Comparative plant genomics resources[END_REF]. The objective of this is to provide the critical mass required to implement real world use cases. Currently, AgroLD includes data pertaining to selected species namely, Oryza species (O.sativa, O.barthii, O.brachyantha, O. glaberimma and O.meridionalis), Arabidopsis thaliana, Sorghum bicolor, Zea mays and Triticum species (T.aestivum and T. uraruta). In the subsequent phases information pertaining to other species and SG databases will be considered. The AgroLD effort will be further extended set-up RDF knowledge bases to host data from other regional portals.