L Sancey

V Motto-Ros
email: vincent.motto-ros@univ-lyon1.fr

B Busser

S Kotb

J M Benoit

A Piednoir

F Lux

O Tillement

G Panczer

J Yu

Laser spectrometry for multi-elemental imaging of biological tissues

An increasing interest has arisen in research focused on metallic and organic ions that play crucial roles in both physiological and pathological metabolic processes. Current methods for the observation of trace elements in biological tissues at microscopic spatial resolution often require equipment with high complexity. We demonstrate a novel approach with an all-optical design and multi-elemental scanning imaging, which is unique among methods of elemental detection because of its full compatibility with standard optical microscopy. This approach is based on laser-induced breakdown spectroscopy (LIBS), which allows the elements in a tissue sample to be directly detected and quantified under atmospheric pressure. We successfully applied this method to murine kidneys with 10 mm resolution and a ppm-level detection limit to analyze the renal clearance of nanoparticles. These results offer new insight into the use of laser spectrometry in biomedical applications in the field of label-free elemental mapping of biological tissues.

O ptical microscopy has served as a remarkably effective tool for medical and biological research for more than three centuries, and advanced technologies are pushing back the technical limitations inherent to optical imaging microscopy. Among these advanced technologies, the most significant are those that extend the spatial resolution [START_REF] Garini | From micro to nano: recent advances in high-resolution microscopy[END_REF][START_REF] Hao | From microscopy to nanoscopy via visible light[END_REF][START_REF] Mahou | Multicolor two-photon tissue imaging by wavelength mixing[END_REF][START_REF] Si | Fluorescence imaging beyond the ballistic regime by ultrasound pulse guided digital phase conjugation[END_REF] or improve access for deeper in vivo imaging [START_REF] Flusberg | Fiber-optic fluorescence imaging[END_REF][START_REF] Qian | In vivo tumor targeting and spectroscopic detection with surfaceenhanced Raman nanoparticle tags[END_REF][START_REF] Razansky | Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo[END_REF][START_REF] Wang | Photoacoustic tomography: in vivo imaging from organelles to organs[END_REF] and label-free microscopy techniques based on Raman scattering for molecular imaging [START_REF] Freudiger | Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy[END_REF][START_REF] Freudiger | Stimulated Raman scattering microscopy with a robust fibre laser source[END_REF][START_REF] Itoh | Raman spectroscopy: Molecular discrimination imaging[END_REF][START_REF] Saar | Video-rate molecular imaging in vivo with stimulated Raman scattering[END_REF] . However, the elemental imaging of biological materials still requires equipment of a high level of complexity and vacuum operation conditions, such as synchrotron radiation microanalysis (SXRF) [START_REF] Chao | Soft Xray microscopy at a spatial resolution better than 15 nm[END_REF][START_REF] Pascolo | The interaction of asbestos and iron in lung tissue revealed by synchrotron-based scanning X-ray microscopy[END_REF][START_REF] Wogelius | Trace metals as biomarkers for eumelanin pigment in the fossil record[END_REF][START_REF] Sakdinawat | Nanoscale X-ray imaging[END_REF] , nano secondary ion mass spectrometry (nano-SIMS) [START_REF] Kraft | Imaging lipids with secondary ion mass spectrometry[END_REF] or laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) [START_REF] Austin | Barium distributions in teeth reveal early-life dietary transitions in primates[END_REF][START_REF] Shariatgorji | Mass spectrometry imaging, an emerging technology in neuropsychopharmacology[END_REF][START_REF] Watrous | Imaging mass spectrometry in microbiology[END_REF][START_REF] Zoriy | Zn and C in thin section of rat brain tumor as well as control tissues by laser ablation inductively couples plasma mass spectrometry[END_REF] . Although these approaches offer high performance, the complexity of the required equipment renders the analysis very restrictive and leads to difficulty in applying these methods in research laboratories for routine elementary investigations. This limitation constitutes a major hindrance to the development of emerging biological research regarding endogenous and exogenous metals and metalloproteins, which play central roles in cell metabolism and, by extension, in every form of life. The dysregulation of these substances often leads to disease, and there is an increasing interest in analyzing elemental distributions in biological specimens to gain a better understanding of physiological and pathological processes [START_REF] Zoriy | Zn and C in thin section of rat brain tumor as well as control tissues by laser ablation inductively couples plasma mass spectrometry[END_REF][START_REF] Lei | Tau deficiency induces parkinsonism with dementia by impairing APP-mediated iron export[END_REF] .

In this study, we developed a laser spectrometry method that is compatible with standard microscopy systems for imaging and quantifying elemental distributions in biological organs with ppm-scale sensitivity and a pixel size of up to 10 3 10 mm 2 . This approach uses the concept of a laser-induced plasma to elicit specific optical responses from inorganic elements that are either intrinsically contained in the biological tissue itself or artificially administered in the form of metals. This effective analytical technique, known as laser-induced breakdown spectroscopy (LIBS), is used for various applications [START_REF] Cremers | Handbook of Laser-Induced Breakdown Spectroscopy[END_REF][START_REF] Miziolek | Laser-Induced Breakdown Spectroscopy: Fundamentals and Applications[END_REF] , especially those that require field deployment, such as environmental monitoring [START_REF] Harmon | Applications of laser-induced breakdown spectroscopy for geochemical and environmental analysis: A comprehensive review[END_REF] , biochemical-agent detection [START_REF] Dixon | Feasibility of Detection and Identification of Individual Bioaerosols Using Laser-Induced Breakdown Spectroscopy[END_REF] and space exploration [START_REF] Meslin | Soil diversity and hydration as observed by ChemCam at Gale crater[END_REF][START_REF] Williams | Martian fluvial conglomerates at Gale crater[END_REF] . The advantages of LIBS include its ease of use and speed of operation, as the material sampling, the atomization and the excitation steps occur simultaneously through single laser pulses. Elemental maps can then be obtained, in a pixel-by-pixel manner, by scanning the sample surface over the region of interest. Although LIBS scanning microanalysis has been reported for various types of solid materials [START_REF] Bette | High-speed laser-induced breakdown spectrometry for scanning microanalysis[END_REF][START_REF] Ma | Multi-elemental mapping of a speleothem using laser-induced breakdown spectroscopy[END_REF][START_REF] Menut | Micro-laserinduced breakdown spectroscopy technique: a powerful method for performing quantitative surface mapping on conductive and nonconductive samples[END_REF] , our group was the first to demonstrate its potential use for fresh biological tissues, although with a strict limit on the accessible resolution (100 mm) because of the complexity of mastering the laser ablation of such soft materials [START_REF] Motto-Ros | Mapping of native inorganic elements and injected nanoparticles in a biological organ with laser-induced plasma[END_REF][START_REF] Motto-Ros | Mapping nanoparticles injected into a biological tissue using laser-induced breakdown[END_REF] .

In this paper, we demonstrate a significant step toward multi-elemental LIBS imaging with unprecedented performance in terms of sensitivity and resolution for the analysis of biological tissues. These technological advancements required both a specific microscope configuration and a simple sample preparation (i.e., epoxy embedding). The all-optical tabletop instrumentation we describe here allowed for a study of the renal clearance of theranostic gadolinium-based nanoparticles in rodents and aided in achieving a better understanding of the kinetics of a preclinical nanoparticle in vivo. These results offer new insight into the use of laser spectrometry in biomedical applications in the field of label-free elemental mapping of biological tissues.

Principle

The development of hybrid organic-inorganic nanoparticles has been a subject of strong interest in recent decades for applications such as drug delivery, DNA and protein recognition, and medical diagnosis and therapy [START_REF] Paul | A nanobiohybrid complex of recombinant baculovirus and Tat/ DNA nanoparticles for delivery of Ang-1 transgene in myocardial infarction therapy[END_REF][START_REF] Sahoo | The present and future of nanotechnology in human health care[END_REF][START_REF] Wagner | The emerging nanomedicine landscape[END_REF][START_REF] Wang | Nanoparticle delivery of cancer drugs[END_REF] . Among the particles used for theranostic approaches (i.e., for both diagnosis and therapy), ultra-small gadolinium-based nanoparticles known as AGuIX (with a size of less than 5 nm) were developed for multimodal imaging and radiotherapy guided by magnetic resonance imaging (MRI).38, 39 These particles are composed of a polysiloxane network surrounded by DOTA (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid) derivatives covalently grafted to an inorganic matrix; DOTA is a chelating agent of gadolinium (Gd) and has already been used for clinical purposes (DOTAREMH, Guerbet, France). Because of their heterogeneous nature and potential applications, a characterization of the biological distribution of these nanoparticles in tissues and organs is a crucial step in preclinical evaluation. The biological evaluation of these nanoparticles is challenging because of their small size, which prevents the observation of individual particles using scanning electron microscopy. Moreover, labeling the nanoparticles with an organic fluorophore might affect their physicochemical properties in terms of their charge, size or shape, and thus their global and local distributions as well as their elimination with respect to both kinetics and the organs involved.

In this study, we focused on the renal clearance behavior of these ultra-small nanoparticles. The particles were intravenously administered to mice at the preclinical therapeutic dose, and the kidneys were sampled at various time points after injection. The organs were then embedded in epoxy and subsequently prepared before LIBS imaging using an ultramicrotome (Fig. 1a). Tissues are traditionally embed-ded in epoxy for electron microscopy; this step allows the tissue to be rapidly fixed without any alteration of its biological ultrastructure, and, moreover, it hardens the tissue and facilitates its sectioning [START_REF] Zhai | Immunocytochemistry of renal membrane proteins on epoxy sections[END_REF][START_REF] Glauert | A new embedding medium for electron microscopy[END_REF] . In addition, calibration standards must also be prepared using the same epoxy resin.

In all LIBS experiments, nanosecond ultra-violet (266-nm) laser pulses with a 10-Hz repetition rate were focused by a 153 magnification objective to induce breakdown and sparking of the material (Fig. 1b). The light radiation emitted in the plasma was collected by a deported optical system and analyzed using a Czerny-Turner spectrometer equipped with an intensified charge-coupled device (ICCD) camera. The experiments were performed in ambient atmosphere with argon (Ar) gas flowing through the plasma region to improve the LIBS signal-to-noise ratio (SNR) [START_REF] Motto-Ros | Precise alignment of the collection fiber assisted by real-time plasma imaging in laser-induced breakdown spectroscopy[END_REF] . The use of a computer-controlled xy stage enabled surface scanning along a predefined path such that an emission spectrum was recorded for each sampling position. These spectra collectively constituted a unique signature that was specific to each sample (Fig. 1c). After completion of the 2D raster, elemental images were created for each element of interest by selecting the specific emission lines and displaying their relative abundance in a false-color image, in which an arbitrary color was assigned for each element (Fig. 1d). The relative-abundance images were subsequently transformed into quantitative-abundance images after referring to calibration measurements performed on various standards (Fig. 1e).

In the present study, we focused our attention on 4 elements, 2 from the tissue itself (iron (Fe) and sodium (Na)) and 2 from the nanoparticles (Gd and silicon (Si)), although other elements, including phosphorus (P), magnesium (Mg), copper (Cu), and calcium (Ca), were also detected (Supplementary Fig. 1). In addition, two gold-standard observation techniques, scanning electron microscopy (SEM) and atomic force microscopy (AFM), were used to characterize the size and volume of the ablation craters (Fig. 1f).

Results

LIBS microanalysis performance. One challenging task in LIBS microanalysis is achieving an adequate balance between spatial resolution and detection sensitivity because the material sampling and atomic-emission measurements are both performed using the same excitation source. The image resolution (i.e., the pixel size) is limited to the size of the ablation craters, whereas the sensitivity remains strongly dependent on the amount of vaporized material and the ''excitation'' capability of the laser pulse. Although the use of argon combined with a sample hardener, such as epoxy resin, enables significant improvements in both these aspects compared with fresh tissue; the imaging performance must still be investigated to optimize the experimental parameters.

Therefore, we first determined the optimal laser settings with respect to the laser's pulse energy and focusing properties. Importantly, the laser pulse energy directly affects the SNR and the size of the ablation craters (Fig. 2a). As expected, the crater size increased with increasing laser energy, in contrast to the SNR, which reached a maximum for energies between 0.7 and 1.7 mJ. An excessively low energy was unable to provide sufficient excitation to the plasma, whereas an excessively high energy caused an increase in the level of continuum emission (essentially due to the radiative recombination between electrons and ions as well as Bremsstrahlung emission), representing an additional source of noise. This intermediate energy range was considered to be the nominal operating range for the LIBS microanalysis and offered both the maximum SNR (and therefore the maximum sensitivity) and resolution in the range of 20 mm. Consequently, an 800-mJ pulse energy was selected for subsequent experiments. The influence of the laser's focus position on the sample surface was also investigated for an 800-mJ pulse energy. The crater diameters and the corresponding LIBS intensities were found to be stable for laser focus positions of 625 mm on either side of the sample surface (Fig. 2b). This fairly wide range directly resulted from the use of a low-numerical-aperture (NA) focusing objective (Fig. 1b), which allowed the entire organ to be imaged (surface area of a few tens of mm 2 ) using a simple sample positioning.

In addition, the morphology of the ablation craters was characterized via AFM for various pulse energies, and the amount of mass removed per pulse was also evaluated (Fig. 2c). We found that approximately 0.4 ng of matter was removed from the craters when subjected to an 800-mJ pulse energy. The calibration measurements yielded limit of detection (LoD) estimates for Gd, Si, Na, and Fe of 0.13, 0.06, 0.20, and 0.21 mM, respectively. Therefore, considering the crater volume estimated through the AFM measurements, the absolute LoD values obtained for the elements of interest were in the range of tens of attomoles per pulse. Elemental images of kidney coronal sections recorded with 40-and 20-mm resolution are presented in Fig. 2d and2e, respectively. Both these images, which were obtained by superposing Gd (green) and Na (red) element maps, were acquired in less than one hour and represent approximately 30,000 measurements. It is worth noting that higher resolutions of 10 mm can also be obtained; however, the associated sensitivity is then altered (Supplementary Fig. 2).

Biodistribution of nanoparticles in the kidney. Figures 2d and2e represent the bi-elemental imaging of a coronal section of a murine kidney 24 hours after intravenous (IV) administration of gadoliniumbased nanoparticles. Because Na is a constitutive element of biological tissues, the Na signal was homogeneously distributed, except in regions lacking tissue, as in large collecting ducts and vessels, whereas Gd was primarily detected in the cortex region of the kidney, where the elimination process occurs, and in the outer regions of the collecting ducts (for anatomical information, see Supplementary Fig. 3). The magnified section (Fig. 2e) clearly indicated a high level of Gd in the periphery of the kidney. This region contains the Bowman's capsules, where the initial stages of blood filtration take place, as well as proximal and distal tubules.

Similarly, another kidney sample collected only 4 hours after nanoparticle administration was analyzed. As expected, this sample also reflected the accumulation of nanoparticles in the cortex and near the main collecting ducts, as indicated by the high level of Gd in this region (Fig. 3a). A high spatial correlation was observed between Gd and Si, which are the major components of the nanoparticles, with a correlation factor of r 2 5 0.96 (Fig. 3b). The nanoparticles contain approximately 10 Gd atoms for every 38 Si atoms (SD 1/2 4 Si); thus, the theoretical molar Gd/Si ratio should be close to 1/3.8 for the native particles, as indicated by the dotted line [START_REF] Mignot | A top-down synthesis route to ultrasmall multifunctional gdbased silica nanoparticles for theranostic applications[END_REF] . The results indicated similar renal elimination for both elements 4 hours after administration of the particles. This finding suggests that 4 hours after administration, either the nanoparticles contained in the kidney were preserved before elimination or their byproducts were eliminated in a similar ratio.

A good correlation was also observed between Gd and Fe, with r 2 5 0.80 (Fig. 3c). The Fe signal might be correlated with the presence of blood because hemoglobin contains high levels of Fe. Physiologically, blood vessels are homogeneously distributed in the cortex region of the kidney to allow for filtration and the recycling of elements that are not eliminated in urine. A close proximity between the cells responsible for filtration (Gd signal) and the blood vessels (Fe signal) was apparent in the mapping at 25-mm resolution. As expected, the distributions of Na and Gd differed greatly, as demonstrated by the absence of any correlation between these 2 elements (Fig. 3d). This result can easily be explained by the physiological presence of Na ubiquitously within the biological tissue itself, whereas Gd was associated only with the presence of nanoparticles in precise areas of the kidney related to elimination.

To evaluate the kinetics of nanoparticle elimination, kidneys were collected at various times after nanoparticle administration (ranging from 5 min to 1 week) and were imaged for Gd and Na. The Na signal was homogeneously distributed throughout the entire kidney, independent of the time elapsed since nanoparticle injection (Fig. 4a). It was determined that the nanoparticles rapidly reached the kidneys because the Gd signal could be detected as early as 5 min after the IV injection, in good agreement with previous MRI studies [START_REF] Lux | Ultrasmall rigid particles as multimodal probes for medical applications[END_REF] , indicating that the nanoparticles were distributed throughout the organism and reached the kidney during the first passages of blood through this organ. As with all components filtered by the kidneys, the particles were first observed in close proximity to the cortex region, where the Bowman's capsules are located, in excellent agreement with previous nuclear imaging studies [START_REF] Lux | Ultrasmall rigid particles as multimodal probes for medical applications[END_REF] , before diffusing throughout the entire organ. The elimination process manifested as an increasing Gd signal that reached a maximum at 4 hours after administration, followed by a subsequent decrease. One week after injection, most of the signal had cleared, indicating effective elimination of the particles from the body via urine. The weak Gd signal observed one week after injection could be attributable to particles trapped in the proximal tubules; in this specific region of the kidney, the filtration process may retain specific components for several days before elimination [START_REF] Abdelhalim | The gold nanoparticle size and exposure duration effect on the liver and kidney function of rats: In vivo[END_REF] . These observed kinetics of elimination were in accordance with the ICP measurement performed on the entire contralateral kidney (see Fig. 4b). The LIBS experiments permitted imaging and quantification of the components of the nanoparticles throughout the entire kidney and provided evidence of both their rapid renal uptake and their elimination. The results highlight the appropriate elimination of nanoparticles from the organism, which is a fundamental necessity for the clinical development of such agents. Moreover, selective elimination of the byproducts may be detected based on the quantification of the 2 major elements of the nanoparticles, Gd and Si.

Discussion

We have developed a rapid all-optical, multi-elemental method for biological tissue imaging and quantification. An absolute sensitivity in the range of tens of attomoles per laser pulse was achieved for the quantification of several metallic elements, including Gd, Na, Fe, and Si. The use of epoxy-embedded samples offers better ablation control compared with fresh tissue, thus increasing the accessible resolution. Moreover, this resin enables the quantification of elements by reducing matrix effects and facilitating the preparation of calibration samples. The focusing of 266-nm UV laser pulses using a lownumerical-aperture objective allowed a 40-to 10-mm resolution to be achieved. Better resolutions can be obtained using a higher numerical aperture, which would allow investigations at the subcellular level (,mm), although with an altered sensitivity. The acquisition speed can also be improved by using higher-frequency lasers, which are commercially available. Consequently, the current combination of tissue preparation and a specific LIBS microscope configuration satisfies the requirements for the quantification of elements in most of biomedical applications, especially in terms of resolution and sensitivity. For specific application requiring higher sensitivity, the use of a second laser pulse tuned to excite specifically the element of interest would allow to improve significantly the performances in terms of sensitivity [START_REF] Laville | Laser-induced fluorescence detection of lead atoms in a laserinduced plasma: An experimental analytical optimization study[END_REF] . The major drawback of LIBS imaging is undeniably its invasive nature, although only a very small amount of biological material (,1 ng) is sampled during the measurement. However, material sampling based on laser ablation allows for the acquisition of depth profiles, as well as 3-dimensional images with a resolution depth of a few micrometers.

LIBS elemental analysis of biological tissue yields results that are complementary to those of conventional methods for biological investigations, such as transmission electron microscopy and immunohistochemistry, as well as to those of more complex techniques that offer higher performance under more restrictive conditions (e.g., LA-ICP, nano-SIMS or mXRF). Importantly, our LIBS equipment can also be directly coupled with Raman or fluorescence imaging systems. The increasing interest in metal and organic ion distributions in biological tissues suggests that LIBS technology is likely to become a valuable investigation tool for label-free, multielemental imaging. This approach is highly versatile because almost any element, particularly metals, can be quantified with high sensitivity and because it can easily be used in research laboratories for routine elementary investigations in the fields of nanotechnology, biology, and medicine.

Methods

Sample preparation. These animal experiments were approved by the local ethics committee under agreement #LS-20123-004. All operative procedures related to animal are strictly conformed to the Guidelines of the French Government. Female NMRI nude mice (6-8 weeks, Janvier, Le Genest-Saint-Isle, France) were initially anesthetized with 4% isoflurane, and anesthesia was maintained with ,2% of the same gas. Gadolinium-based nanoparticles (AGuIXH, 8 mmol of Gd) were IV injected into the mice under anesthesia. Kidneys were sampled at various times from 5 minutes to 1 week after injection and were embedded in epoxy, as described elsewhere [START_REF] Rima | Internalization pathways into cancer cells of gadolinium-based radiosensitizing nanoparticles[END_REF] . In brief, after sampling, each kidney was perfused and fixed with 2% glutaraldehyde in 0.1 M sodium cacodylate buffer (pH of 7.4) overnight at room temperature to preserve both the tissue structure and the distribution of the particles. The samples were then rinsed three times for 10 minutes each in 0.1 M sodium cacodylate buffer and postfixed with 1% osmium tetroxide in 0.1 M sodium cacodylate buffer for 1 hour. After washing, the samples were dehydrated in a series of ethanol solutions of increasing concentration, ending with propylene oxide. The samples were then embedded in EPON (151 mixture of diglycidyl ether and dodecenylsuccinic anhydride, density of 1.22 g/L). The sample surfaces were prepared using a microtome. To allow for elemental quantification, standards containing elements of known concentrations were embedded in the same EPON resin. CH 3 COONa, (CH 3 COO) 2 Fe and AGuIXH were used as standards for Na, Fe, Gd, and Si, respectively, at 5 concentrations ranging from 1 to 40 mM. The powders were mixed with the resin (for a minimum of 4 hours), warmed for 2 days (60uC), and finally prepared using a microtome.

Experimental setup and data acquisition. The instrumental setup was based on a homemade optical microscope that combined a LIBS laser injection line, a standard optical-imaging apparatus, and a three-dimensional motorized platform for sample positioning. The LIBS experiment used quadruple Nd:YAG laser pulses of 266 nm, which were vertically focused onto the sample by a high-power 153 magnification objective (LMU-15X-266, Thorlabs) with a numerical aperture of 0.32. The pulse duration was 5 ns and the repetition rate was 10 Hz. During the experiments, the sample could be translated along 3 axes by an xyz motorized stage with a travel range of 50 mm in each direction, a maximum speed of 3 mm/s and a precision of 1 mm. The measurements were performed at room temperature and the rate of the argon gas flowing through the plasma region was set to 1.5 L/min. During the sample scan, trigonometric surface positioning was used to compensate for any flatness anomalies and to accurately control the focalization distance (the objective-to-sample distance). This system was based on the combination of a laser pointer focused obliquely onto the sample and a monitoring camera used to image the laser spot on the sample surface. A beam shutter was used to control the delivery of the laser pulse to the sample such that only one plasma plume was produced for each position in the sample. As shown in Fig. 1b, the light emitted by the plume was collected by a quartz lens (f 5 2 cm) placed approximately 15 mm from the plasma plume and focused onto the entrance of an optical fiber bundle composed of 19 fibers with a 200-mm core diameter, each with a round to keyed linear arrangement. The output of the fiber bundle was connected to a Czerny-Turner spectrometer equipped with a 1200-l/mm grating blazed at 300 nm and an intensified charge-coupled device (ICCD) camera (Shamrock 303 and iStar, Andor Technology). The ICCD camera was synchronized with the Q-switch of the laser and the spectrum acquisition was performed with a delay of 300 ns and a gate of 3 ms. The width of the entrance slit of the spectrometer was set to 30 mm. In this configuration, a spectral measurement range of 30 nm was accessible with a spectral resolution of approximately 0.15 nm. To perform the mapping experiments at the greatest possible speed, the movement of the sample was synchronized with the opening of the beam shutter, and the spatial resolution was set by adjusting the speed of the translation stage. The laser energy was stabilized throughout the experiment by using a servo control loop to improve the long-term stability of the laser output. This loop was achieved by using a power meter and a computer-controlled attenuator (ATT266, Quantum Composers). Homemade software developed in the LabVIEW environment controlled the entire system and allowed for the performance of automated sequences to scan the region of interest of the tissue sample with a specific lateral resolution [START_REF] Motto-Ros | Precise alignment of the collection fiber assisted by real-time plasma imaging in laser-induced breakdown spectroscopy[END_REF] .

Construction of elemental images. An advanced spectrum treatment was developed to perform a rapid intensity extraction for each measurement site and for each species of interest. This algorithm can be applied in real time, during the mapping scan, or afterward. A single emission line was selected for each element of interest, and the algorithm defined a baseline fit using a polynomial function and subtracted it from the emission signal. Emission lines were selected based on two criteria: each selected line was required to be the strongest line in the probed range and to be unaffected by any possible interference from other lines. To retrieve the intensity signals for the elements of interest, the algorithm required less than 5 ms, suggesting that a full map (,30,000 spectra) can be processed in less than 3 minutes. A two-dimensional matrix was then provided for each species. Each cell of this matrix represented the intensity signal from a point on the sample surface for the given element and can then be displayed using a false-color scale to present a visual result in the form of elemental images. The selected emission lines and associated wavelengths are summarized in Supplementary Table I. Different spectrometer ranges could be chosen depending on the elements of interest. To measure the strongest lines associated with Gd ions in the UV-visible region, the central wavelength of the spectrometer was typically set to 333 nm. The spectral range covered in this case, from 315 to 350 nm, also allowed lines originating from Na, Ca, and Cu to be detected. In the case of Fig. 3a, which shows the spatial correlations among Si, Fe, Na, and Gd, two equivalent spectrometers were used in parallel to simultaneously detect all of the elements. All images were then processed using ImageJ software (NIH, Bethesda, MD, www.nih.gov); the images were contrasted (if necessary, and only in a linear manner), slightly smoothed (using a 0.5-pixel Gaussian smoothing), and converted from 16-bit to 8-bit images. Finally, multi-color maps were created by assigning an arbitrary color to each element (8-bit RGB images).

Quantitative measurements and limits of detection. The relative-abundance images were subsequently transformed into quantitative-abundance images after referring to the calibration measurements performed on the standard samples. Four elements were quantified: Gd, Si, Na, and Fe. Calibration measurements for the elements of interest were performed prior to each mapping analysis by recording a sequence of 50 3 50 spectra for each standard. Linear regressions were then obtained by applying the least-squares method to the experimental data. The limit of detection (LoD) values were estimated as 3 times the background noise divided by the slope of the analytical curve. The LoD values determined for the 4 elements in the nominal operating range (energy per pulse of 800 mJ) are presented in Supplementary Table 1 and are expressed in units of both mM and weight ppm. The relative measurement accuracy was estimated between 6 to 8% depending on elements from obtained linear regressions.

Complementary techniques. Ablation craters were observed under various experimental conditions via scanning electron microscopy (SEM) and atomic force microscopy (AFM). SEM observations were performed using an ESEM XL30-FEI microscope equipped with a thermal field emission gun (FEG). SEM images were used to estimate the crater diameters obtained in epoxy resin as a function of laser energy and focus distance (i.e., figure 2a and2b). AFM experiments were performed using an Asylum MFP 3D operating in tapping mode. The z-axis scan sensor could travel up to 15 mm, allowing for the measurement of large samples. For all images, the scan field was 70 3 70 mm 2 , whereas the scan rate was 0.45 Hz, to ensure good regulation of the feedback loop. Crater volumes were determined for various laser energies (i.e., figure 2c) by performing an integration of AFM images in depth.

The concentration of gadolinium in the kidney samples was determined via ICP-MS (inductively coupled plasma-mass spectrometry) using a VarianH 710-ES spectrometer. The samples were dispersed in 5 mL of aqua regia (67% HNO 3 /37% hydrochloric acid (w/w)) for 4 hours at 80uC. Subsequently, the samples were diluted with HNO 3 5% (w/w) matrix to adjust the volume to 10 mL; the samples were then filtered (0.22 mm) and analyzed. For calibration of the ICP-MS, single-element standard solutions were used and prepared from a 1000-ppm Gd standard acquired from SCP ScienceH by successive dilutions with a HNO 3 5% (w/w) matrix. Importantly, in each experiment, the right kidney of each animal was sectioned for LIBS analysis, and the left kidney of the same animal was used for ICP measurement.

Figure 1 |

 1 Figure 1 | General protocol for LIBS imaging. (a), Procedure for epoxy-embedded tissue sampling (drawing created by L. Sancey). (b), Schematic view of the LIBS experiment showing the major components, including the microscope objective, the motorized platform and the detection system. (c), Example of single-shot emission spectra recorded in the three different regions of the kidney indicated in (b) with characteristic emission lines of Gd, Na, Cu, and Ca. (d), Example of relative-abundance images for Gd and Na. (e), Calibration measurements illustrating the correlation between intensities and concentrations for the elements of interest. (f), Wide-angle and zoom images of the sample surface recorded via SEM after completion of the LIBS imaging scan.

Figure 2 |

 2 Figure 2 | Performance of LIBS microanalysis. (a), LIBS SNR for the 247.8-nm carbon line (black) measured in epoxy resin and the crater sizes measured via SEM (green) as a function of the laser pulse energy, ranging from 100 mJ to 2.5 mJ. (b), LIBS signal intensity (arbitrary units, a. u.) of the 247.8-nm carbon line (blue) and the crater sizes measured via SEM (green) as a function of the laser focus distance. A positive focus distance corresponds to a laser pulse focused below the sample surface (E 5 800 mJ). (c), AFM images of craters for various pulse energies and the corresponding ablated masses. (d), Gadolinium (green) and sodium (red) biodistributions in a coronal murine kidney section, 24 hours after nanoparticle administration (spatial resolution of 40 mm). (e), Magnification of the image presented in (d) in an adjacent section with 20-mm resolution. The Gd and Na concentrations are expressed in units of millimoles per liter (mM). The white arrows indicate regions that are lacking in tissue, corresponding to blood vessels and collecting ducts.

Figure 3 |

 3 Figure 3 | Multi-elemental mapping and quantitative, spatially resolved elemental correlation. (a), Quantitative imaging of Gd, Si, Fe, and Na in a kidney coronal section, 4 hours after nanoparticle administration. The images were recorded with a 25-mm resolution and represent 60,000 pixels. (b), (c), (d), Quantitative and spatially resolved (i.e., point-by-point) correlations between the concentration of Gd and the concentrations of Si (theoretical correlation represented by a dotted blue line), Fe, and Na, respectively, in the region denoted by the white rectangle in (a). The red lines represent the measured correlations.

Figure 4 |

 4 Figure 4 | Distribution of gadolinium-based nanoparticles in the kidney as a function of the time elapsed since administration. (a), Quantitative imaging of Gd and Na in kidney coronal sections. The images were recorded at a 40-mm resolution and represent 30,000 pixels. (b), Agreement between the Gd concentrations measured via LIBS (green) and ICP (yellow).

SCIENTIFIC REPORTS | 4 : 6065 | DOI: 10.1038/srep06065

Acknowledgments

The authors gratefully acknowledge the LABEX iMUST (ANR-10-LABX-0064) and PRIMES (ANR-11-LABX-0063) of Lyon 1 University within the programs "Investissements d'Avenir" (ANR-11-IDEX-0007 and ANR-11-IDEX-0007, respectively), operated by the French National Research Agency (ANR) within the project EMILI, as well as the Lyon Science Transfert, for financial support. We also heartily acknowledge the CTm (A. Rivoire and C. Boule) and F. Pelascini (CRITT Mate ´riaux Alsace) for fruitful discussions and advice regarding epoxy samples and standards.

Author contributions

L.S. and V.M. contributed equally to this work. L.S. prepared all the biological samples. V.M. developed the LIBS system and data software. L.S. and V.M. prepared all figures shown in the manuscript. G.P. and J.Y. contributed to designing the LIBS experiment. L.S., V.M., B.B. and S.K. analyzed the samples, treated the data, and wrote the main manuscript text. J.-M.B. and A.P. performed the AFM investigations, and participated in writing the article. F.L. and O.T. participated in writing the article. All authors reviewed the manuscript.

Additional information

Supplementary information accompanies this paper at http://www.nature.com/ scientificreports Competing financial interests: We declare that the authors have no competing interests as defined by Nature Publishing Group, or other interests that might be perceived to influence the results and discussion reported in this paper.


Safae Dahmani

Loïc Cudennec

Stéphane Louise

Guy Gogniat
email: guy.gogniat@univ-ubs.fr

Using the Spring Physical Model to Extend a Cooperative Caching Protocol for Many-Core Processors

Keywords: Many-cores, Cooperative Caching, Data Sliding, Mass-Spring Physical Model

As the number of embedded cores grows up, the off-chip memory wall becomes an overwhelming bottleneck. As a consequence, it is more and more prevalent to efficiently exploit on-chip data storage. In a previous work, we proposed a data sliding mechanism that allows to store data onto our closest neighborhood, even under heavy stress loads. However, each cache block is allowed to migrate only one time to a neighbor's cache (e.g. 1-Chance Forwarding). In this paper, we propose an extension of our mechanism in order to expand the cooperative caching area. Our work is based on an adaptive physical model, where each cache block is considered as a mass connected to a spring. This technique constrains data migration according to the spring constant and the difference of workloads between cores. This adaptive data sliding approach leads to a balanced spread of data on the chip and therefore improves on-chip storage. On-chip data access has been evaluated using an analytical approach. Results show that the extended data sliding increases the global cache hit rate on the chip, especially in the context of juxtaposed hot spots.

I. INTRODUCTION

Many-cores are processors made of hundreds to thousands cores on a single chip interconnected by a dedicated network. They offer high computing performance -provided the applications take benefit from massively parallel architectures -while drastically reducing the power consumption. They are expected to enter both green HPC and autonomous embedded systems. Today popular examples include Kalray MPPA (256 cores) [START_REF]The kalray mppa 256 manycore processor[END_REF], Tilera Gx (72 cores) [START_REF] Agarwal | Tile processor: Embedded multicore for networking and multimedia[END_REF], Adapteva Epiphany (64 cores) [START_REF]Epiphany-iv 64-core 28nm microprocessor[END_REF], [START_REF] Adapteva Inc | A 1024-core 70 gflop/w floating point manycore microprocessor[END_REF] and Intel Xeon Phi (61 cores) [START_REF] Chrysos | Intel xeon phi coprocessor (codename knights corner)[END_REF]. One big challenge towards efficient programmability of many-cores is the ability to properly feed the computing threads with data. As in large parallel and distributed systems (e.g. computing clusters, grids and clouds), data management is of major importance when it comes to performances.

In this context, the memory wall [START_REF] Mckee | Reflections on the memory wall[END_REF] refers to the fact that the speed of a processor is limited by its data accesses, and no longer by the processing units. This is particularly true for manycores, as a large number of cores solicits memory, even at a low clock frequency. Several manycore architectures have been proposed to address the memory problem. They differ in two main aspects: the data cache organization and the network topology (mesh, torus, hypercube..).

While it is quite common to offer a small high speed L1 cache for both data and instructions attached to each core, the other cache levels are part of the inner chip specificity and the associated programmability choices. Each level makes the chip bigger in terms of silicon surface, power consumption and design complexity. For example, some L2 caches can be attached to each core (and aggregated within a virtual shared L3 cache, as in the Tilera Gx processor), physically shared within a cluster of cores (as for the Kalray MPPA), or even removed from the design (as for the Adapteva Epiphany). For the sake of simplicity, we consider in this paper a chip with a flat organization of cores and memory: each core has a private place to store data and is able to communicate with N neighbors, as well as the external memory. Some class of applications (e.g. adaptive signal processing, computational dataflow,..) can generate hard-to-predict hot-spots on the chip. These hot-spots may be surrounded by a cooler neighborhood that can help locally by sharing some cache slots, in a cooperative behavior. Here, we assume that accessing a data stored by a neighbor is more efficient than fetching it from outside the chip (a dedicated low-latency NoC, versus some external data controller such as a DMA). Cooperative caching (CC) has been used in several contexts, and one implementation has been applied to many-cores in the Elastic Cooperative Caching system (ECC) [START_REF] Herrero | Elastic cooperative caching: an autonomous dynamically adaptive memory hierarchy for chip multiprocessors[END_REF]. This system allocates a shared zone in each private cache that can be used to help the neighborhood.

In a previous work [START_REF] Dahmani | Introducing a data sliding mechanism for cooperative caching in manycore architectures[END_REF], we proposed an extension of the ECC system, called the data sliding mechanism. It allows a core to store a data onto a neighbor, even if this neighbor is stressed by a hot-spot. In order to help, this neighbor will in turn move one of its own data onto a different neighbor. The sliding mechanism stops whenever it reaches a cooler area, or the edge of the chip. However, in order to keep the data close to the owner, the migration can not exceed one hop (1-chance forwarding). This contribution has shown to significantly reduce data eviction.

In this paper, we propose to evaluate the possibility to expand the cooperative caching area (N -chance forwarding). We think that in large many-core architectures, it remains more efficient to access on-chip data, even stored in a few hops, than fetching from the external memory. The NoC should provide small latencies and an aggregated bandwidth, according to its topology. Data are allowed to migrate among the entire chip following some rules. These rules are inspired by the spring physical model, in which the data is an object with a mass (the priority), moving around the cores following their difference of potentials (the cache use rates), and attached to the owner thanks to a spring (in order to stay close to the core).

Starting from the theoretical formula, we propose a lightweight distributed algorithm that calculates the next move if a data needs to be slided. This algorithm has been implemented within an analytical cache simulator that calculates statistics on the network activity, based on a genuine memory access trace. These results show a significant reduction of the cache miss rate in configurations involving close hot-spots. As a counterpart, it also shows an increased traffic activity within the hot-spot neighborhood.

II. RELATED WORK

The challenge of efficiently using the on-chip cache space is becoming particularly critical in high performance systems. Cache requirements of new emerging high-end applications (Streaming, Imaging, Simulation) remarkably degrade its performance. Among the large literature on data caching, we can highlight these two problems:

• Cache pollution incurred by keeping in cache less frequently used data [START_REF] Seshadri | The evicted-address filter: A unified mechanism to address both cache pollution and thrashing[END_REF], [START_REF] Ding | Srm-buffer: an os buffer management technique to prevent last level cache from thrashing in multicores[END_REF] and • Cache thrashing which is caused by cache access conflicts especially in the LLC (Last Level Cache) [START_REF] Meng | Avoiding cache thrashing due to private data placement in last-level cache for manycore scaling[END_REF], [START_REF] Jaleel | High performance cache replacement using re-reference interval prediction (rrip)[END_REF]. Many-core systems are more affected by vainly off-chip data ejection. Indeed, cache thrashing induced by LLC contention, large workloads and dataflow processing becomes more complicated to be addressed as the number of cores increases. A number of cache management approaches were proposed for multi-core processors and distributed systems in the large. One popular approach is the Cooperative Caching (CC) mechanism that has been proposed for chip multiprocessor systems [START_REF] Rolan | Adaptive set-granular cooperative caching[END_REF], [START_REF] Chang | Cooperative caching for chip multiprocessors[END_REF], [START_REF] Chang | Cooperative cache partitioning for chip multiprocessors[END_REF], several other areas such as mobile networks [START_REF] Yin | Supporting cooperative caching in ad hoc networks[END_REF], [START_REF] Joy | Cache replacement policies for cooperative caching in mobile ad hoc networks[END_REF], [START_REF] Ting | A novel cooperative caching scheme for wireless ad hoc networks: Groupcaching[END_REF] or big data systems [START_REF] Shi | Bwcc: A fs-cache based cooperative caching system for network storage system[END_REF], [START_REF] Holmedahl | Cooperative caching of dynamic content on a distributed web server[END_REF]. The CC mechanism consists in aggregating a group of nodes in order to form a (virtual) unified supercache space. In the context of heavy workloads, available cores provide underused cache space to help other stressed cores.

In a previous work [START_REF] Dahmani | Introducing a data sliding mechanism for cooperative caching in manycore architectures[END_REF], [START_REF] Dahmani | Adaptive cooperative caching for many-cores systems[END_REF], we introduce the data sliding mechanism as a 1-chance forwarding protocol, allowing each core to store its own data in direct neighbors caches.

One of the shortcomings of the closed cooperative caching is that data cannot travel farther in the chip. The direct neighborhood is the only one that can receive evicted blocks in the presence of memory hot spots. Our main contribution, called the mass-spring cache model, is an extended cache cooperative approach that minimizes off-chip data ejection by expanding the sliding area.

In order to mitigate the cache performance degradation in highly stressed context, prior works discussed the possibility to extend data migration radius to N -Hop. Some proposals try to dynamically adjust the aggregate shared cache according to each core activity. This technique allows data spilling to the requesting core closer neighborhood. Several works aimed to optimize data spilling techniques, especially for choosing the right cache resource distribution and replacement policies. A number of works propose some power-aware spilling techniques for different systems, based on selective data migration approach [START_REF] Herrero | Power-efficient spilling techniques for chip multiprocessors[END_REF], [START_REF] Dahlin | Cooperative caching: Using remote client memory to improve file system performance[END_REF], [START_REF] Ting | Improved group-based cooperative caching scheme for mobile ad hoc networks[END_REF]. Another set of proposals try to improve data spilling and the destination selection policies for the N -chance forwarding mechanism. Dominguez-Sal et al. proposed the high reused data aware strategy [START_REF] Dominguez-Sal | Using evolutive summary counters for efficient cooperative caching in search engines[END_REF] that consists on spilling blocks to the destination core where it is going to be reused soon. For all the proposed techniques, the number of allowed hops per block is statically defined. The limitations of such an approach is reached when the stressed zone is important. If no cache space in the defined spilling area is available, cache blocks are evicted off-chip. Another reason why our physical model outperforms the prior ones is that migrated blocks are brought back near the owner once its neighborhood workload is down. Besides, the adaptive aspect of our approach takes into consideration the current load of each eventual host core.

Finally, the Evicted-Address filter proposed by Seshadri et al. [START_REF] Seshadri | The evicted-address filter: a unified mechanism to address both cache pollution and thrashing[END_REF] is a new technique that can predict the reuse behavior of missed cache blocks in order to avoid both pollution and thrashing. This could be used in conjunction with the massspring model to achieve a better sliding scheme.

III. BACKGROUND: DATA COOPERATIVE SLIDING MECHANISM

One of the biggest challenges in highly parallel systems is to manage on-chip memory resources exposed to heterogeneous application workloads. In order to manage efficiently on-chip data storage, many static and dynamic techniques have been proposed, most of them aim to handle either cache partitioning or data migration issues.

The Elastic Cooperative Caching [START_REF] Herrero | Elastic cooperative caching: an autonomous dynamically adaptive memory hierarchy for chip multiprocessors[END_REF] has been presented as an adaptive memory hierarchy. This memory adjusts both local and shared areas according to the amount of data reuse that is available on each core.

Another work called the Adaptive Set-Granular Cooperative caching [START_REF] Rolan | Adaptive set-granular cooperative caching[END_REF] proposed techniques that measure the stress level of each set in a set-associative cache. According to this, the protocol makes a decision on the set that is going to be spilled.

In case of low storage capacity, the presented adaptive mechanisms cannot afford data migration to spread out of the direct cooperative area. But instead, it keeps each block near its owner core. To overcome this, the data sliding mechanism [START_REF] Dahmani | Introducing a data sliding mechanism for cooperative caching in manycore architectures[END_REF] has been proposed. The main motivation of this prior work was to enhance both cache partitioning and data sliding techniques in order to effectively manage highly stressed neighborhoods.

The data sliding mechanism consists in 1-Hop Cooperative Caching. A saturated core is allowed to push the local blocks to its direct neighbors. In order to offer a cache space to host new blocks, saturated neighbors spill their local data to their neighborhood. This process is repeated until the propagation reaches a non-saturated area.

We propose in the current work, a data sliding mechanism with a dynamic sliding radius. This contribution is an intuitive extension of our original work. Thanks to different cache management policies, we expect to reduce the data offchip ejection and to balance the memory workloads across the chip.

The heart of the proposition is not the possibility to move the data from neighbor to neighbor, but the way such a decision is made. We rely on a physical mass-spring model, which is, as far as we know, a unique contribution in the field of large multicore and many-core architectures.

IV. MASS-SPRING MODEL FOR COOPERATIVE CACHING

One important aspect of N -chance forwarding is the migration policy: moving a data too far from its owner may become less efficient than getting it from the outside of the chip. Another consideration is that each move should select the closest, and most available neighbor in terms of cache saturation level. We think that these migration constraints are relevant in the context of the physical mass-spring model.

In our contribution, we propose to apply the mass-spring model to the data cache management. It assumes that each cache block is a mass attached to its owner node with a spring. As in the physical spring model, each block is constrained by the spring constant (K) which defines its degree of freedom. Thus, the sliding radius is defined depending on this constant. The block migration path is chosen according to the memory load of cores. This memory load acts as a potential on each core. When migrating a data, the stressed core compares his potential with its neighbors, and chooses the one with the lowest potential. Migrated blocks will then move between cores, avoiding high potentials. Figure 1 shows a (3 * 4)-core processor in which cores are represented by red bar plots. The height of the plots indicates the potential, that is equivalent to the access rate of the local cache. Selecting the lowest potential gives the neighbor that is much likely willing to help.

The elasticity of the spring model allows owner cores to pull back their remote blocks, whenever the workload is lower or the chip limits are reached. The physical spring A. The physical model theory 1) Approach: Physical phenomena we can encounter everyday are often really good at decreasing local stress points by spreading the stress on the vicinity. They are good at finding an equilibrium that is good compromise on the energy point of view. But if we want a physical analogy, we also want to find a simple one that does not involves too much computation, that can be decided locally with little knowledge from far away points and that is not likely to lead to chaotic behaviors. Although the later point can be hard to prove with the local expression only, at least it would be easy to constrain the model to converge quickly to an equilibrium, at least locally.

If we think about the stress on a cache of a given core as an initial energy, then the most simple physical model would be inspired by the spring physics, since equations are simple and linear. To force a quick flow toward equilibrium and avoid resonant state for local configurations, the idea would be to use a fluid friction, because it is also linear, and choose critical coefficients in order to reach the local equilibrium as fast as possible.

2) Mathematical model: Noting ∆h the stress on the cache, and doing the analogy with a height at which the spring would hang, the mass is pulled away from the center by gravity and because of the slope reaction. The effect can be projected on the x axis, so the reaction that pulls away the mass is proportional to ∆h. The fluid constant would be c and the spring constant k, so:

m d 2 x dt 2 = αmg∆h -c dx dt -kx
We can simplify equation (1) so that:

d 2 x dt 2 + c ′ dx dt + k ′ x = α ′ ∆h (1)
Which is a nice linear equation of the second order. It becomes critical iff c ′2 -4k ′ = 0. If we note a = c ′ /2, then:

(1) ⇒

d 2 x dt 2 + 2a dx dt + a 2 x = b∆h
By using a discretization in time for the model where D i defines the distance from its origin at time t = i (discrete, i ∈ N), we find:

D i = 2AD i-1 -A 2 D i-2 + B∆h (2)
As can be seen, the value at next step only depends on the local value with a history length of 2 in the past, and math are simple products and additions. So this basic model has the required properties of simplicity and locality.

B. Implementation

The implementation of the mass-spring model has been made in a distributed context, keeping in mind that the algorithm should not introduce too much overhead to the global computation. This is why it only relies on a few local information already computed by the regular sliding mechanism, and the simple formula [START_REF] Agarwal | Tile processor: Embedded multicore for networking and multimedia[END_REF].

The main principle is to compute a cumulative distance, tracking each block movement. In the case of a saturated neighborhood, this distance is used to make a decision about the destination core. The corresponding algorithm, executed on each core when it comes to migrate a data, is described as follows:

1) For each of the N neighbors at step i, we compute the difference of potential ∆h, as well as the distance D i , using formula [START_REF] Agarwal | Tile processor: Embedded multicore for networking and multimedia[END_REF].

D i = 2 * A * D i-1 -A 2 * D i-2 + B * ∆h
Where D i-1 and D i-2 are the two previous calculated distances taken from the history of data migration. With constant A and B defined by

A = 1 K Where K is the spring constant. And B = g K Where g is the weight constant.
2) We compare the N distances D i together: the greatest one gives the natural direction the mass would have taken in the sandbox. We therefore move the block to the suitable core. If no neighbor is available for help, what can occur when all the neighbors are more stressed than the requester, we choose the nearest destination to the data owner core regarding the number of hops in the network. The neighbor load information are locally stored, using counters called Neighbors Hit Counters (NHC) and described in the data sliding mechanism. These counters are incremented and decremented according to each neighbor request to a data stored in the shared part of the local cache. NHC are therefore continuously updated using a method close to piggybacking, and are available for use when needed, without taking time to ask the neighbors.

V. EXPERIMENTS

In this section, we first compare the mass-spring model to the regular data sliding mechanism that uses a 1-chance forwarding policy.

The second experimental phase compares the mass-spring protocol model with the two following naive models:

• Random-Based protocol: Data is distributed arbitrary through the chip. The destination core is chosen in a random way for each data migration. In this protocol, we do not neither consider the destination memory load nor the data migration distance.

• Load Balancing protocol: The second protocol consists in distributing data in a balanced way on the chip. The destination of the slided data corresponds to the current least loaded core. This technique aims to balance data distribution through the cores by keeping continuously a chip workload global overview that would not be relevant with large chips. The aim of the experiments is to show that the massspring model improves the data availability and proximity in presence of several juxtaposed hot-spots. As a counterpart, the neighbor-to-neighbor communications are increased due to several neighbors accesses. We assume that off-chip memory accesses to satisfy a cache miss is much more costly in terms of latency and power consumption [START_REF] Jouppi | Tradeoffs in two-level on-chip caching[END_REF], than onchip neighbor-to-neighbor requests.

A. Consistency protocol

The mass-spring cache protocol is quite independent from the top level data consistency protocol: it introduces a distributed shared cache among the neighborhood. From the cache coherence point of view, this distributed cache is used the same way as a regular private local cache. Therefore, we use the same data consistency protocol and we measure the cache hit rate, as well as the protocol traffic, with both 1chance forwarding data sliding and N -chance forwarding mass-spring model.

In the following experiments, we refer to the baseline data consistency protocol. This protocol is widely used in multicore processors. One of its popular implementation is the home-based, four-state MESI protocol, standing for Modified, Exclusive, Shared and Invalid. In a home-based protocol, each shared data (or piece of data) is paired with a core (the home-node), that is responsible of keeping trace of the data and its current state. The home-node can be paired using a round-robin distribution over memory addresses. Each time a core accesses a shared data, it has to contact its associated home-node to request clearance. This request to home-node either means that there is a strong concurrency between cores onto this particular data, or that the data is not stored on the chip and has to be fetched from external memory. Therefore, we distinguish small messages that are exchanged between neighbors to retrieve the data, from messages to the home-node that will trigger a costly data transfer from external memory.

B. Experimental setup

In order to evaluate our contribution, we used an analytical simulator that gives a detailed view of the generated onchip traffic. Our in-house simulator collects statistics on exchanged messages between all cores. It also allows to calculate the cache hit rate. The simulator reads a memory trace that contains a sequence of read and write accesses. The trace file is generated by running some synthetic or real applications under the Pinatrace instrumentation tool shipped with the Pin project [START_REF] Luk | Pin: building customized program analysis tools with dynamic instrumentation[END_REF]. We used a modified version of Pinatrace to generate the instructions set with an additional field giving the core id.

For each entry in the memory trace, the simulator calculates the number and the type of messages that are transferred on the network-on-chip. However, this calculation is not time-triggered, which means that except for the implicit precedence relation that is given by the memory access sequence, we do not take into account the possible contentions that can occur on a data or on a protocol role (such as the home-node). Therefore, we are not able to appreciate the real performance of the contribution onto the application. Nonetheless, this approach gives solid clues on the global behavior of the cache protocol, and what we can expect in terms of data transfers within the chip and with the external memory.

As for the network topology, we consider a 2-dimension mesh that interconnects 8 * 8 cores. The network handles transactions through a point-to-point communication routing mode. We can find this configuration in recent platforms such as the Epiphany-IV 64-core microprocessor from Adapteva [START_REF]Epiphany-iv 64-core 28nm microprocessor[END_REF]. We assume each pair of cores is separated by a Manhattan distance that is used to calculate the number of hops.

To compare the 1-HOP data sliding with the mass-spring contribution, we use on-chip traffic metrics (Number of exchanged messages). In the baseline protocol, every cache miss generates a request to the home-node. In the following experimental scenarios, a cache miss systematically triggers a data transfer from the external memory. We also assume that requests to neighbors mean that the block is stored onto the chip.

The cost of each request depends on the number of hops to reach its destination core. According to the Manhattan distance, and because we consider large many-core architectures, an access to the neighborhood is statically less costly than an access to the home-node (if we consider a roundrobin or a random distribution of home-nodes).

Another used metric is the cache hit rate. We define a cache hit as a successful access to the requested data, either it is in the local cache, or in a neighbor cache. Otherwise, we fall back into a cache miss that requires to contact the associated home-node and implies an external memory access cost.

Finally, we use for comparing the mass-spring model with the two naive mechanisms described above the manhattan distance between the requesting core and the accessed data. We then cumulate, for each core, all the distances in order to compare the proximity of data in different scenarios.

C. Scenarios

The used experimental scenarios are based on synthetic applications, that illustrate the behavior of the protocols in presence of juxtaposed hot-spots. All scenarios involve three roles that are mapped onto the chip. The first role frequently accesses a large set of data: 30 different memory addresses. The second role frequently accesses a smaller set of data: 4 different memory addresses that are not included in the first role set of addresses. The third role is the home-node, paired using a random distribution. Figures 2a and2b illustrate the 3 scenarios mapped onto the 8 * 8 chip. The first role appears in dark orange, the second role in soft orange and the homenodes in blue. The basic idea is to generate hot-spots thanks to some close cores that play the first role. These hot-spots are surrounded by cores playing the second role. Some of these saturated cores can also act as home-nodes.

The cache size is set to a value between 4 and 30, which means that there is enough room for all addresses of the second role, but not for the first role. As the sequence of accesses is interpreted by the simulator, all caches in the orange area become full of data. While the second roles are keeping data in their local cache, first role hot-spots ask the neighborhood for help. In turn, this direct neighborhood ask for help according to the data sliding mechanism.

The results of the experiments are displayed as a processor heatmap. For each of the scenarios, a heatmap shows an overview of the protocol traffic and its associated metrics.

The X and Y axises represent a flat view of the processor's mesh array. The grey scale of the heatmap represent the value of the associated metric (the number of messages, the cache hit rate or the cumulative distance). For both datasliding and mass-spring protocols, we use the same grey scale in order to facilitate the comparison.

D. Cache hit rate and on-chip traffic trade-off

As expected, the experiment based on the first scenario shows that the number of cache hits has been enhanced using the mass-spring migration model (115 hits), compared to the 1-hop migration mechanism (88 hits). In figure 3, we then compare the cooperative area traffic between the 1-chance (fig. 3a) and the N -chance (fig. 3b) forwarding strategies. Requests to neighbors traffic show that the 1-Chance sliding mechanism generates less traffic to neighbors (158 messages). Whereas, as the migrated data are most likely to be accessed, frequent accesses to remote data yield to an important neighbor-to-neighbor traffic (264 messages) in case of using the mass-spring sliding model.

Otherwise, the same figure shows that the cooperative neighborhood area is quite larger while using the new sliding mechanism. As the 1-HOP sliding is limited to direct neighborhood, data cannot migrate farther in the chip, thus it is quickly ejected. Whereas, the proposed protocol allows data to look for farther free cache space on the chip which leads to wider cooperative area. However, the spring's stiffness of the model allows to control the data spread over the chip in order to keep it as near as possible from their owner cores.

On one hand, our mechanism proposes a trade-off between caching data farther on the chip and the substantial penalty of ejecting it out of the chip. On the other hand, the mass-spring model promotes cheaper and efficient access to data at the expense of local traffic.

It seems obvious that the final choice for using such a N -chance forwarding policy over a regular cache protocol has to be made according to the intricacies of the chip network topology. Some topologies, even the single 2dimension mesh we consider in these experiments, may be inappropriate: too much communications between neighbors may significantly decrease the application performances. Nonetheless, the mass-spring model should largely benefit from dense mesh networks, if not 3-D stacked, that offer aggregated bandwidth and low latencies. Such network include for example the 6-mesh NoC featured with the Tilera Gx processor.

E. Distance-aware Migration

For the second experimental phase, we used the scenario of 3 hot spots distributed as described in the figure (figure 2b). We initially saturate both of the first and the second roles. Afterwards, we stress the first one twice more than the second within frequently accesses to different sets of data. All the highly stressed first role cores have the same number of accesses (24 accesses). This permits to fairly compare the cores behaviour.

Frequent accesses to remote migrated data generate additional costs. Thus, we analyze through these experiments the access cost using the Cumulative Access Distance metric. This metric is defined as the number of hops corresponding to the global remote accesses performed by each core. The figures below show results using the mass-spring model, compared to the described naive approaches (figure 4).

We deduce from these figures that the Cumulative Access Distance per core is remarkably reduced when using the proposed migration model. On one hand the Random-Based protocol and the Load Balancing protocol solicit randomly storage support from all the on-chip cores, it gives more sliding freedom to data. On another hand, the mass-spring protocol considers distance and destination load in each data migration decision which limits the data sliding zone.

Such naive approaches are efficient when the global on-chip workload is slightly distributed. However, when solicited areas are far from the requesting cores, distance cost is increasingly important. The proposed data sliding strategy with the spring physical model allows to move data back once the neighborhood load is reduced. This leads to enhancing access cost and promoting data storage locality.

VI. CONCLUSION AND PERSPECTIVES

As the number of cores is expected to increase in manycore architectures, the data management becomes prevalent and shares some similarities with large distributed infrastructures. Cooperative caching can be used to virtually extend the private caches, reducing data evictions and cache misses. In this paper, we have presented an extension of the sliding mechanism that allows a data to migrate from neighbor to neighbor. The decisions are made following the spring physical model, that offers relevant constraints to move data to the less saturated core and to stay close to the owner. It is also possible to set physical parameters to describe data priority and the cooperative area size. This model is implemented using local core information and a simple formula that could even be set into the hardware.

Analytical results show that in presence of multiple juxtaposed hot-spots, this approach decreases the number of cache misses compared to the regular cooperative datasliding protocol. This enhancement has to be put into perspective with the growing traffic that can be observed in the neighborhood. Further experiments should determine if this local traffic is worth to generate in comparison to the costly external accesses.

This work can be expanded with a multi-spring model. In this model, a data is attached thanks to a spring to each core wherein a thread is accessing in shared mode. This should dynamically constrain data migration to equally serve all accesses. Another perspective concerns the setting of the spring protocol parameters: the data mass and the spring constant can be statically or dynamically adapted, depending on the application performance requirements or the current chip status. Some off-line decisions, based on operational research, should be taken by the compiler in order to tune the spring protocol for each shared data and each computing step in the application.

ACKNOWLEDGMENT

We would like to thank Jean-Thomas Acquaviva for the help he provided with the cache validation simulator, and the modified pinatrace tool during the validation phase.

Figure 1 :

 1 Figure 1: Applying the spring-mass physical model to a 12core processor.

  (a) B: four hot-spots forming a square, and the sparse homenodes. (b) C: Three random hot-spots, and the sparse home-nodes.

Figure 2 :

 2 Figure 2: The used scenarios mapped onto a 8 * 8 chip: dark orange is for hot-spots, soft orange is for stressed neighbors and blue is for home-nodes.

  (a) Original data sliding protocol (b) Mass-Spring protocol

Figure 3 :

 3 Figure 3: Neighbors message traffic

Figure 4 :

 4 Figure 4: Data Access Distance


José Marcelo A P Cestari
email: jose.cestari@pucpr.br

Eduardo Rocha Loures
email: eduardo.loures@pucpr.br

Eduardo A P Santos

Yongxin Liao
email: yongxin.liao@pucpr.br

Hervé Panetto
email: herve.panetto@univ-lorraine.fr

Mario Lezoche
email: mario.lezoche@univ-lorraine.fr

Eduardo De Freitas

R Loures

An overview of attributes characterization for interoperability assessment from the public administration perspective

Keywords: Interoperability Assessment, Public Administration, Attributes Extraction, e-Government. Points of Interest definition

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Introduction

Organizations are facing a competitive marketplace and they must develop partnerships and work in an integrated way with competitors and stakeholders. As one of the important factor of this collaboration, interoperability [START_REF]A Compilation of IEEE Standard computer glossaries, IEEE Std[END_REF][START_REF] Carney | Topics in Interoperability: System-of-Systems Evolution[END_REF] is the ability of two or more systems (or components) of: sharing specific information, using them with a specific semantics and creating, with that information, a specific mission in a precise context. Interoperability takes into account dimensions such as concerns, barriers, degrees of maturity and assessment, and consists of more than technical aspects [START_REF] Pardo | Improving Government Interoperability: A Capability Assessment Framework for Government Managers[END_REF]. Interoperability assessment (IA) is the method that defines some formal metrics to quantify and qualify the level of interoperability between heterogeneous systems from one or more organizations [START_REF] Yahia | Formal measures for semantic interoperability assessment in cooperative enterprise information systems[END_REF]. With the increasing use of ICT (Information and Communication Technologies) in government institutions, the concept of eGovernment emerges in the late 1990s [START_REF] Camargo | Cidade e informática, contatos e interações: explorando manifestações de urbanização virtual[END_REF]. The term eGovernment, e-gov, eGov and similar are an abbreviation of "electronic government" and refers to the use of ICT to support the government business, providing or enhancing public services or managing internal government operations [START_REF] Novakouski | Interoperability in the e-Government Context[END_REF]. Assembling the concepts of interoperability and eGovernment, an eGovernment interoperability domain arises, in a broad sense, as the ability of constituencies (or public agencies) to work together [START_REF]Government Interoperability: Overview[END_REF].

The literature analysis, presented in [START_REF] Cestari | A Research Strategy for Public Administration Interoperability Assessment[END_REF], reveals that there are many works regarding interoperability models and government initiatives of eGov models and frameworks. Nevertheless, since 2000 it was not possible to identify specific models, frameworks or assessment procedures regarding the evaluation of government entities not related to (almost entirely) technical issues and also it was not found in the literature review papers describing the rules (rationale and procedures) regarding the knowledge discovering steps [START_REF] Ray | A critical survey of selected government interoperability frameworks[END_REF][START_REF] Solli-Sather | A framework for analyzing interoperability in electronic government[END_REF][START_REF] Gottschalk | Maturity levels for interoperability in digital government[END_REF].

The desire to map the concerns and identify the barriers (other than the technical ones) is a recent interest in the literature [START_REF] Zutshi | The business interoperability quotient measurement model[END_REF]. This new approach, focused on semantics, opens an important issue regarding the proper collection of attributes related to interoperability of these other dimensions related to public administration (e.g. processes, services, governance, legal and political). Obtaining, identifying and organizing relevant knowledge are important steps to the IA considering also the models and frameworks established in Enterprise Interoperability Assessment (EIA) in the private industrial sector, as well as the work done in eGov.

An exploratory approach into the literature sources will allow the identification of important dimensions (concerns, barriers and attributes) for the evaluation of the ability to interoperate in the governmental entities. There is need of a specific rationale for this broader knowledge extraction of organizational behavior into the public domain. In fact, to characterize the qualification attributes and the IA in their different visions other than the technological, such attributes and guidelines should present the structure, the quality and the completeness of observation in the IA performed by a method that ensures these requirements.

It is important to contextualize that this paper is part of a wider research scope, represented in Figure 1 and more specifically related to the process A2 and A3. The IDEF0 diagram [START_REF]Integration Definition for Function Modeling (IDEF0)[END_REF] identifies basic steps (which are contained in phases and may contain activities), proposing an attributes' and guidelines' structure collection and composition as well as the assessment method definition. The results of A0 and A1 processes can be summarized as (i) definition of a database of related papers, (ii) analysis regarding the world publication and authoring, helping the justification of the research and mapping the distribution of the subject, (iii) domain definition and (iv) identification of gaps regarding the existence models and related works. Based on the information gathered from A0 and A1, the next step (A2) relates to the identification of preliminary set of attributes and guidelines and in A3, in conjunction with expert assessment, a fact-oriented transformation (FOT) method is applied to evaluate and gather new (if any) attributes from other knowledge sources and types of databases. Those identified objects can be considered as a set of best practices, requirements or desired interoperability characteristics within public entities. The organization, meth-ods and tools regarding the process A2 is exposed in Figure 2, which also provides an overview of the structure and main goals of this paper.

Fig. 1. Full research methodological steps

Considering all the presented aspects, this paper exposes the rationale for the extraction of attributes, as well as a set of preliminary attributes themselves in order to help the IA of public administration entities. Figure 2 shows a general structure of the components adopted as basis for the extraction process, taking into account that the solution space helps to provide information in order to solve issues related to the problem space.

Fig. 2. Structure of the attributes extraction and its application for EIA

After that, a join of the aspects leads to reflections associated to measurement selection, culminating in the generation of a set of attributes and its use into the EIA applications and domain. In addition, a proposal of ranking mechanism (for the attributes) based on AHP [START_REF] Saaty | The analytic hierarchy process-what it is and how it is used[END_REF] is presented as one of the next steps of the research.

Methodology

According to [START_REF] Nonaka | A dynamic theory of organizational knowledge creation[END_REF], knowledge is a justified belief that increases an entity's capacity for taking effective action. In [START_REF] Swan | Knowledge management and innovation: networks and networking[END_REF] the authors defines knowledge as experience, facts, rules, assertions and concepts about their subject areas that are crucial to the business (customers, markets, processes, regulations). There are two basic dimensions of knowledge: tacit and explicit [START_REF] Nonaka | A dynamic theory of organizational knowledge creation[END_REF], which can be combined to an ontological dimension. In complement, knowledge management is a process of identifying, capturing and leveraging collective knowledge to help the organization compete [START_REF] Krogh | Care in knowledge Creation[END_REF]. Knowledge can be represented using different formal forms, among others, semantic nets, rules, ontologies, mind maps and conceptual maps. A general model of the information retrieval process is exposed in Figure 3, based in [START_REF] Keeling | Development of Systematic Knowledge Management for Public Health: A Public Health Law Ontology[END_REF].

Fig. 3. Overview of the attributes extraction steps

In the initial steps, information regarding the problems of the domain and the concepts involved were processed with conceptual queries in order to filter and select an initial documents database with the execution of conceptual searches. In summary, considering the existence of a database collection, the steps to extract the attributes are: (i) concept identification; (ii) keyword analysis and word count as support mechanisms; (iii) tagging words to identify groups of knowledge and (iv) matching process to search for relations among the words found and root concepts of the research. These steps are described in the subsections bellow.

Concept identification

As the goal is to extract attributes from the literature (knowledge) database, it is important to define and characterize attributes as in: (i) A quality or characteristic given to a person, group, or some other thing. An abstraction belonging to or characteristic of an entity [START_REF]Vocabulary Dictionary[END_REF]; (ii) A quality or feature of a person or thing, especially one that is an important part of its nature [20]; (iii) A quality or feature regarded as a characteristic or inherent part of someone or something [21] and (iv) Regarding class diagrams, an attribute represents a data definition for an instance of a classifier. An attribute describes a range of values for that data definition and may describe the structure and value of an instance of a class [START_REF]Object Management Group[END_REF].

Although the grammar structure is a specific body of knowledge (and not the focus of this research), it is important to mention that attributes are nouns and/or adjectives that modify that noun (e.g. collaboration (noun), collaborationist (adjective)), and guidelines are suggestions to show how to behave and usually derives action from its statements [START_REF]Vocabulary Dictionary[END_REF]. Guidelines can contain (or suggests) attributes and they (guidelines) will be used (along with the attributes themselves) to assess the interoperability of the entities, with a similar approach exposed in [START_REF] Deschamps | Proposal for the systematization of enterprise engineering contributions: guidelines for enterprise engineering initiatives[END_REF]. Figure 4 illustrates the relation among important concepts for the research, which is useful in the process of attributes extraction and the idea is to expose the components that are connected (somehow) primarily with an attribute and secondly with a guideline. Considering the database of files regarding the domain (150 documents), a keyword analysis and counting was made as a first attempted to derive some attributes. The rationale adopted was that if a document deals with such important issues of the government interoperability aspects, there is a chance that qualification words (attributes) could be cited in the keywords of the paper.

Fig. 4. Concept model of attributes

The keywords are not only "one word", but may be also a group of words separated by commas or other symbols. The MaxQDA software [24] was used to count (and organize) the keywords of the documents and the results are represented in Table 1. The highest appearance are the words "interoperability" (55 times) and "egovernment" (53 times) and this was expected because of the rules adopted during the literature review (these two words were one of the most important composing the search strings) [START_REF] Cestari | A Research Strategy for Public Administration Interoperability Assessment[END_REF]. Nevertheless, as the idea is not have a sort of a "sample" approach, the quantity and distribution of the keywords are not as important as the meaning in terms of creating knowledge [START_REF] Nonaka | A dynamic theory of organizational knowledge creation[END_REF]. However, after the analysis of all keywords retrieved, it was detected that most of them are related to the paper itself (as expected) but in general ways. That is, the keywords tends to show generic aspects of the paper, containing words of global meanings, in order to introduce to the reader some "tags". Because of this, it was not possible to find words that could be used (directly or indirectly) to compose the preliminary set of attributes.

As the keyword analysis did not bring information about possible attributes, it was defined that the extraction of the attributes would follow a process based on a word counting and Natural Language processing analysis. According to [START_REF] Pierce | An introduction to information theory: symbols, signals & noise by John Robinson Pierce[END_REF], an average English word has from 4.5 to 7 characters and, using the MaxQDA software [24], the whole documents database were scanned searching for words of six characters length. In a first round, 44,111 words were found and, after a second round of execution (with the elimination of authors names, symbols, irrelevant words and words that appeared less than 10 times) a total of 21,644 words were selected. The quantitative analysis by itself does not solve the problem of finding attributes, so it was necessary investigate some of the semantic meaning of this large group of words. That is, as a next step, try to discover the lexical linguistic category (e.g., verbs, nouns, adverbs) of the words, searching specially for adjectives (considering the relations in Figure 4).

Conceptual tags, matching process and results

As an attribute may define (or derives to/from) adjectives and also because of the large number of existent words (21,644), a Natural Language Processing Software was used in order to detected the adjectives in the word's database. The software adopted was the Stanford POS Tagger [START_REF]Stanford POS Tagger software[END_REF], maintained by the Stanford Natural Language Processing Group and built based on the research of a log-linear part-of-speech taggers described in [START_REF] Toutanova | Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network[END_REF]. A Part-Of-Speech Tagger (POS Tagger) is a software that reads text in some language and assigns parts of speech to each word (and other token), such as noun, verb and adjective. The tool was also used in [START_REF] Keeling | Development of Systematic Knowledge Management for Public Health: A Public Health Law Ontology[END_REF] within the natural language processing approach. The list of 21,644 words were processed with the Stanford POS Tagger and the results were 3,739 words tagged as adjectives.

At this point, considering that the set of words identified as adjectives could contain a subset of attributes, there is a need to search for a connection among each of the identified words with the concept words that represents the research subject domain (interoperability within public administrations/government). This process was done using a lexical matching functionality of MaxQDA tool and the results are the following preliminary list of attributes: adaptability, collaboration, commitment, communication (semantic), conflicts, cooperation, economical, efficiency, historical, integration, loyalty, politic, process, responsibility, senior management support, sociological, standardization.

2.3

Conceptualization process (with Fact-Oriented Transformation)

Figure 5 presents an approach related to the process A3 (Figure 1), aimed to evaluate (expert view) and gather new information from other types of knowledge sources and databases (structured or semi-structured data) in order to corroborate and improve/refine the obtained set of attributes.. The first step is a reverse engineering method through which a model from the application or schema level can be derived (Step 1). Then, the resulted model is enriched through a step of Application Knowledge Injection (AKI) where the model is automatically compared and matched with existing domain and application ontology (Step 2). The model is corrected through a Knowledge Extraction and Mapping Validation in a harmonizing step (Step 3). In fact, the model is examined with the help of a domain expert or an end-user, who is a qualified person to describe the context of the peculiar domain and to put in evidence the contextual knowledge. The next step (Step 4) is a FOT through the application of a set of patterns rules for transforming the enriched conceptual model to a Fact-Oriented Model (FOM) with its finest-grained semantic atoms completely displayed [START_REF] Lezoche | Conceptualisation approach for cooperative information systems interoperability[END_REF]. The Step 5 represents a structural optimization through the application of the Formal Concept Analysis methodology. The output of the entire conceptualization process is a set of formal objects (attributes) inserted in an optimized structure derived from the data analysis and ready to be conceptually analyzed and tagged. The use of attributes and guidelines on an AHP structure

The AHP method, proposed in [START_REF] Saaty | The analytic hierarchy process-what it is and how it is used[END_REF], is one of the widely accepted and frequently used mathematic analysis method that supports multi-criteria decision-making. The priority assessment in AHP is based on mathematical analysis of pairwise comparisons defined on hierarchical tree structure, in which, the priority scales between each two related terms are relying on the judgments of domain experts. A significant number of research projects employing AHP method has been proposed in several domains [START_REF] Vaidya | Analytic hierarchy process: An overview of applications[END_REF], however, as highlighted in [START_REF] Liao | A Novel Approach for Ontological Representation of Analytic Hierarchy Process[END_REF], little work has been done in specifying and formalizing the important concepts together with their relationships from the AHP method itself. In [START_REF] Liao | A Novel Approach for Ontological Representation of Analytic Hierarchy Process[END_REF] the authors propose an ontological representation of AHP method to deal with the lack of a deep conceptual understanding on its objects (attributes and reasoning) as well as the lack of flexibility and reusability. This conceptual model (Figure 6) acts in this research as a reference model to facilitate the concept mapping between the domain ontology (obtained in previous knowledge discovering and extraction process) and the IA method.

The importance of having an AHP ontology (Figure 6) merged with a concept model of the attributes (Figure 4) is that, for EIA purposes, each attribute is considered a criterion. Such connection helps the prioritization and evaluation of attributes, made by practitioners and academy, by adopting the AHP technique as a tool to compare the criteria. In addition, during the assessment activities, as each criterion is being compared with others, it is possible to have an adherence degree related to the achievement of the attributes (and guidelines).

The proposed attributes will be firstly evaluated by domain experts and then validated in a selected organization. Figure 7 overviews the evaluation and validation scenario, as a zoom in process A4 (Figure 1). The preliminary attributes (and guidelines) are inputs to expert evaluation process in order to refine those objects. The vali-dation process is divided in two modes: design and execution mode. In the design mode, a decision hierarchy is firstly created based on refined attributes and the AHP method.

Fig. 6. Ontological representation of AHP

Then, an algorithm will process this hierarchy and produce corresponding questionnaires for criteria assessment and alternative assessment. In the execution mode, practitioners and experts related to the entities will be invited to answer these questionnaires. Finally, based on the assessment of criteria and the alternatives (with respect to each criterion), this validation process will generate the ranked results used as input for the A5 step (Figure 1).

Fig. 7. Overview of the evaluation and validation scenario

Regarding the planning of the assessment and the assessment itself (Figure 1, A6 and A8), the AHP can provide a structure to assess two aspects of EI: potentiality and compatibility [START_REF] Guédria | A Maturity Model Assessing Interoperability Potential[END_REF]. Potentiality can be analyzed as an internal characteristic that reflects the readiness (adherence to attributes) and preparation to interoperate with a possible future partner. Compatibility can also be analyzed regarding internal characteristics, but comparing them between two (or more) known public entities willing to interoperate. In this research, the compatibility approach intends to evaluate which entity, from a group of alternatives (Figure 8, GE B...Z) will better interoperate with a specified entity (Figure 8, GE A). A potential approach implies the ranking of the attributes in order to create ranges that are related to some maturity level. That is, according to the ranking of the group of attributes of an entity A, the entity is associated to a certain maturity level, with a potential degree for future interoperability actions (Figure 8, ML-1, ML-2 and ML-N).

Conclusions

The problem of obtaining, identifying and organizing relevant knowledge is an important issue to the IA in public administration. Considering the models and frameworks established in EIA (in private and public sector), it was detected a gap in the literature regarding the extraction of measurements attributes and criteria. This paper is part of the result of a wider research scope (Figure 1) and presented a rationale of an attributes' extraction from a literature database in order to help the definition and composition of guidelines regarding the IA of public entities. The presented rationale follow steps related to identification of concepts, word count and word analysis, resulting in a set of 17 preliminary attributes and a wide structured and formalized knowledge formal concepts set. The paper also presents an approach of how to adopt a decision-making technique (AHP) in order to organize the attributes (during a validation phase) and assess the interoperability (during an assessment phase).

Fig. 5 .

 5 Fig. 5. Entire conceptualization process overview for concept identification

Fig. 8 .

 8 Fig. 8. Potential and compatibility EIA into AHP structure

Table 1 .

 1 Quantitative overview of keywords.

	# of appearances	# of words	# of appearances	# of words
	1	178	7	2
	2	21	8	1
	3	6	10	1
	4	4	55	1
	6	1	53	1

Availability analysis

Delphi AHP AHP FOT