Alba Lozano
email: alba.lozano@idaea.csic.es

Alejandro Fernández-Martínez

Carlos Ayora

Devis Di Tommaso

Agnieszka Poulain

Mauro Rovezzi

Carlo Marini

Solid and aqueous speciation of yttrium in passive remediation systems of acid mine drainage

Yttrium belongs to the rare earth elements (REE) together with lanthanides and scandium. REE are commonly used in modern technologies, and their limited supply has made it necessary to look for new alternative resources. Acid mine drainage (AMD) is a potential resource since is moderately enriched in REE. In fact, in passive remediation systems, which are implemented to minimize the environmental impacts of AMD, REE are mainly retained in basaluminite, an aluminum hydroxysulfate precipitate. In this study, the solid and liquid speciation and the local structure of yttrium were studied in high sulfate aqueous solutions, basaluminite standards and samples from remediation columns using synchrotron-based techniques and molecular modeling. Pair distribution function (PDF) analyses and ab initio molecular dynamics density functional theory models of the yttrium sulfate solution show that the YSO 4 + ion pair forms a monodentate inner sphere complex. Extended X-ray absorption fine structure (EXAFS) and PDF analyses show that Y is retained by basaluminite forming a monodentate inner-sphere surface complex at the aluminum hydroxide surface. EXAFS of the column samples show that its signal is represented in more than 75% by the signal of a basaluminite which YSO 4 + is forming an inner-sphere complex. The atomic vision of the REE configuration in AMD environments could help for a deeper research of the REE recovery from waste generated in the AMD remediation systems.

INTRODUCTION

Yttrium belongs to Rare Earth Elements (REE) group, also composed by lanthanides and scandium. REE are necessary for the development of modern technologies, and specifically, yttrium as important applications, for instance, in fluorescent lamps as phosphors, [START_REF] Binnemans | Recycling of rare earths: A critical review[END_REF] and in aircraft industry, used in the thermal barrier coatings for jet engines. [START_REF] Kumar | Low Thermal Conductivity Yttrium Aluminum Garnet Thermal Barrier Coatings Made by the Solution Precursor Plasma Spray: Part II-Planar Pore Formation and CMAS Resistance[END_REF] The REE increasing demand but low worldwide supply has led to consider REE as critical raw materials boosting searches for alternative resources, such as recycling used stocks or identifying new geological sources of these elements. Because the REE concentrations in acid mine drainage (AMD) are from one to two orders of magnitude higher than the average concentrations in natural waters, [START_REF] Noack | Rare Earth Element Distributions and Trends in Natural Waters with a Focus on Groundwater[END_REF] it may be possible to perform secondary REE recovery from precipitates from AMD neutralization in passive remediation systems. The active systems were developed to minimize the environmental impacts of AMD, and they are used worldwide. [START_REF] Evangelou | A Review: Pyrite Oxidation Mechanisms and Acid Mine Drainage Prevention[END_REF][START_REF] Coulton | The design optimisation of active mine water treatment plants[END_REF] However, due to the high water content, sludge storage has substantial operational costs and environmental concerns. [START_REF] Ackman | Sludge disposal from acid mine drainage treatment[END_REF][START_REF] Viadero | Characterization and Dewatering Evaluation of Acid Mine Drainage Sludge from Ammonia Neutralization[END_REF] In contrast, passive remediation systems, which have been developed extensively in recent decades, [START_REF] Hedin | Passive Treatment of Acid Mine Drainage with Limestone[END_REF][START_REF] Younger | Mine Water Hydrology[END_REF][START_REF] Cravotta | Size and performance of anoxic limestone drains to neutralize acidic mine drainage[END_REF][START_REF] Watzlaf | The Passive Treatment of Coal Mine Drainage[END_REF] allowing the AMD neutralization generating lower amounts of solid waste precipitates. Ayora et al. documented nearly complete aqueous REE retention in two laboratory columns simulating a disperse alkaline substrate (DAS), a passive treatment already implemented in the field, for two highly acidic AMD (SW Spain). [START_REF] Ayora | Recovery of Rare Earth Elements and Yttrium from Passive-Remediation Systems of Acid Mine Drainage[END_REF][START_REF] Ayora | Acid Mine Drainage in the Iberian Pyrite Belt: 2. Lessons Learned from Recent Passive Remediation Experiences[END_REF] The REE were scavenged by basaluminite, a mineral precipitated in the columns, which also presented Y enrichment due to the higher yttrium concentration with respect to the rest of REE in the two treated AMD. Basaluminite, an aluminum oxy-hydroxysulfate (Al 4 (SO 4 )(OH) 10 •5H 2 O), precipitates in acidic environments as a consequence of the natural attenuation of the AMD when mixing with more alkaline waters, or due to the induced neutralization of the acid waters, when the solution pH reaches ~4. [START_REF] Bigham | Iron and aluminum hydroxysulfate minerals from acid sulfate waters[END_REF] Basaluminite is considered a nanomineral, with a short-range order, around 1 nm of coherent domain size, which is described as layers of Al-octahedra with structural point defects and with the presence of sulfate groups as outer-sphere complexes between the Al layers. [START_REF] Carrero | The Nanocrystalline Structure of Basaluminite, an Aluminum Hydroxide Sulfate from Acid Mine Drainage[END_REF] Similarly to the REE uptake by basaluminite in DAS treatments, Gammons et al., reported the precipitation of hydrous aluminum oxides accompanied with a decrease in REE concentration from AMD when mixed with natural water. [START_REF] Gammons | Hydrogeochemistry and Rare Earth Element Behavior in a Volcanically Acidified Watershed in Patagonia, Argentina[END_REF] Recently, the scavenge of REE by basaluminite precipitates has been described as a sorption mechanism. [START_REF] Lozano | Sorption of rare earth elements onto basaluminite: the role of sulfate and pH[END_REF] AMD is characterized to contain high loads of dissolved sulfate and the affinity of REE to form aqueous species with sulfate is very high, the MSO 4 + aqueous complex being the more abundant in AMD solutions. [START_REF] Serrano | REE Speciation in Low-Temperature Acidic Waters and the Competitive Effects of Aluminum[END_REF] Sorption of dissolved REE from sulfate-rich waters onto basaluminite is thus described as the sorption of the MSO 4 + aqueous complex via ligand exchange with a surface site of basaluminite, forming a monodentate surface complex with the Al-octahedron as one proton is released. [START_REF] Lozano | Sorption of rare earth elements onto basaluminite: the role of sulfate and pH[END_REF] Here, a structural description of the aqueous YSO 4 + complex and of the local environment of the surface complex formed upon adsorption onto basaluminite are reported.

Most of the previous studies reporting the adsorption mechanisms of REE using X-ray absorption spectroscopy and Extended X-ray Absorption Fine Structure (EXAFS) described the adsorption of free REE ions onto the surface of metal oxides. [19][START_REF] Yang | Investigation of Eu(III) immobilization on γ-Al 2 O 3 surfaces by combining batch technique and EXAFS analyses: Role of contact time and humic acid[END_REF][START_REF] Fan | Sorption of Eu(III) on Attapulgite Studied by Batch, XPS, and EXAFS Techniques[END_REF][START_REF] Dardenne | Identification and Characterization of Sorbed Lutetium Species on 2-Line Ferrihydrite by Sorption Data Modeling , TRLFS and EXAFS[END_REF] However, the AMD environments show a high concentration of dissolved sulfate, which presents high affinity for aqueous REE (M), making that the most abundant species if the MSO 4 + aqueous complex. Thus, sorption models of REE in AMD precipitates might be better explained in terms of MSO 4 + sorption.

The structure of the first hydration shell of lanthanides and yttrium has been well characterized. [START_REF] Allen | Coordination Chemistry of Trivalent Lanthanide and Actinide Ions in Dilute and Concentrated Chloride Solutions FT Magnitude[END_REF][START_REF] Lindqvist-Reis | Hydration of the Yttrium (III) Ion in Aqueous Solution . An X-Ray Diffraction and XAFS Structural Study[END_REF][START_REF] Yaita | Structural Study of Lanthanides(III) in Aqueous Nitrate and Chloride Solutions by EXAFS[END_REF][START_REF] Duvail | Molecular Dynamics Studies of Concentrated Binary Aqueous Solutions of Lanthanide Salts: Structures and Exchange Dynamics[END_REF][START_REF] Kowall | Molecular Dynamics Simulation Study of Lanthanide Ions Ln 3+ in Aqueous Solution Including Water Polarization. Change in Coordination Number from 9 to 8 along the Series[END_REF][START_REF] Ohta | Influence of Multi-Electron Excitation on EXAFS Spectroscopy of Trivalent Rare-Earth Ions and Elucidation of Change in Hydration Number through the Series[END_REF] The coordination number (CN) of the first hydration shell changes from an average of CN ~ 9 oxygen atoms for light REE (LREE La to Pm) to CN ~ 8 oxygen atoms for heavy REE (HREE Tb to Lu), with intermediate values for medium REE (MREE Pm to Gd). [START_REF] Kowall | Molecular Dynamics Simulation Study of Lanthanide Ions Ln 3+ in Aqueous Solution Including Water Polarization. Change in Coordination Number from 9 to 8 along the Series[END_REF] This behavior is explained by the continuous decrease in ionic radii with the atomic number. [START_REF] Kowall | Molecular Dynamics Simulation Study of Lanthanide Ions Ln 3+ in Aqueous Solution Including Water Polarization. Change in Coordination Number from 9 to 8 along the Series[END_REF][START_REF] Rizkalla | Lanthanides and Actinides Hydration and Hydrolysis[END_REF][START_REF] Duvail | A Dynamic Model to Explain Hydration Behaviour along the Lanthanide Series[END_REF] The ionic radius of yttrium is between that those of Ho and Dy, so it is typically considered an HREE with a first hydration shell of CN ~ 8 oxygen atoms. There are several studies describing aqueous complexes of REE with different ligands. REE easily forms inner-sphere complexes with carbonate and phosphate in solution [START_REF] Janicki | Lanthanide carbonates[END_REF][START_REF] Jeanvoine | Electronic structure and bonding of lanthanoid(III) carbonates[END_REF][START_REF] Harris | Gadolinium complex for the catch and release of phosphate from water[END_REF] whereas chloride and nitrate forms inner-sphere complexes at concentrations higher than 10 M. [START_REF] Allen | Coordination Chemistry of Trivalent Lanthanide and Actinide Ions in Dilute and Concentrated Chloride Solutions FT Magnitude[END_REF][START_REF] Yaita | Structural Study of Lanthanides(III) in Aqueous Nitrate and Chloride Solutions by EXAFS[END_REF] In the case of sulfate, no information about the geometry of MSO 4 + complexes has been reported so far despite their strong binding affinity. [START_REF] Serrano | REE Speciation in Low-Temperature Acidic Waters and the Competitive Effects of Aluminum[END_REF] The objective of this study is to elucidate the structure of Y adsorbed onto basaluminite.

Due to its chemical similarities with HREE, this allow us to assume similar structural configuration for this sub-group. Moreover, this element was one of the most concentrated in waste samples allowing to perform X-ray absorption spectroscopy experiments. Since the YSO 4 + aqueous complex is adsorbed onto the mineral, [START_REF] Lozano | Sorption of rare earth elements onto basaluminite: the role of sulfate and pH[END_REF] a previous characterization of the geometry of the aqueous complex has been carried out.

Finally a quantification of Y-species in basaluminite solids precipitated from AMD treatments has been performed. Structural studies were performed using EXAFS and pair distribution function (PDF) analyses of aqueous and solid samples combined with ab initio molecular dynamics (AIMD) simulations of the aqueous YSO 4 + complexes.

Two hypotheses are used to investigate the local structure of the aqueous YSO 4 + ion pair: (1) an outer-sphere complex, with water located between Y 3+ and SO 4

2-

, and (2) an inner sphere complex. In the latter case, two more hypotheses must be considered: (a) a monodentate complex, with one oxygen atom shared between the sulfate and the fist coordination sphere of Y 3+ , and (b) a bidentate complex, with two oxygen atoms shared between the yttrium hydration sphere and the sulfate group.

Once the structure of the aqueous solution is fully described, different hypotheses have been considered to interpret the YSO 4 + surface complexation onto the Aloxyhydroxysulfate: the formation of (1) a monodentate inner sphere complex, (2) a bidentate mononuclear inner-sphere complex, (3) a bidentate binuclear inner sphere complex.

Finally, to determine semi quantitatively the yttrium species in solids from column samples, linear combination fits were performed using the most representative references.

MATERIAL AND METHODS

Synthetic samples (standards)

Different basaluminites doped with Y were prepared for HEXS and EXAFS experiments. To ensure maximum Y incorporation into the solid, sorption and coprecipitation experiments were performed at pH 6, 17 avoiding higher pH values where YCO 3 + aqueous species could be present. [START_REF] Luo | Carbonate complexation of yttrium and the rare earth elements in natural rivers[END_REF] (Fig. S1). The yttrium concentrations of the references are listed in Table S1, and a more comprehensive description of the syntheses is given in the Supporting Information S1.

Waste samples from column treatments

Column samples were collected from two laboratory column experiments of Disperse Alkaline Substrate (DAS), as described in Ayora et al. [START_REF] Ayora | Recovery of Rare Earth Elements and Yttrium from Passive-Remediation Systems of Acid Mine Drainage[END_REF] simulating the remediation systems of the two AMD from the Iberian Pyrite Belt region (SW Spain). The columns adapted from Torres and Auleda to the solid residues. [START_REF] Torres | A Sequential Extraction Procedure for Sediments Affected by Acid Mine Drainage[END_REF] The results showed in Ayora et al. concluded that REE were retained in basaluminite. [START_REF] Cravotta | Size and performance of anoxic limestone drains to neutralize acidic mine drainage[END_REF] The W-MR-C1-4, W-MR-C1-5, W-Alm-C3-8 and W-Alm-C3-9 samples were selected from the depth were maximum Al concentration was extracted from the solids, assuming the most enriched in basaluminite solid slice. These waste samples were used for EXAFS measurements and the Y concentrations for these samples are shown in Table S2. approximations for the exchange and correlation terms were used together with the general dispersion correction termed DFT-D3. [START_REF] Zhang | Comment on 'Generalized Gradient Approximation Made Simple[END_REF][START_REF] Grimme | A Consistent and Accurate Ab Initio Parametrization of Density Functional Dispersion Correction (DFT-D) for the 94 Elements H-Pu[END_REF] The simulations were carried out for 15 ps in the canonical (constant NVT) ensemble to maintain the average temperature at T = 300 K. An in-house Python code using DiffPy-CMI was developed to convert the AIMD trajectories to PDF signals. [START_REF] Juhás | Complex Modeling : A Strategy and Software Program for Combining Multiple Information Sources to Solve Ill Posed Structure and Nanostructure Inverse Problems[END_REF] Details of the MD simulations are described in S4. Fits of the EXAFS spectra of the aqueous solutions were performed using atomistic models from the AIMD simulations as initial structural models for the generation of the scattering paths. The results yielded an average Y-O distance of 2.37 ± 0.02 Å for the Y-sol sample and 2.38 ± 0.01 Å for YSO4-sol (Table 1). The intense peak in the FT function at 2.38 Å for the two aqueous references represents the first solvation shell, and its asymmetry reveals a distribution of Y-O interatomic distances, as reported previously by Lindqvist et al. [START_REF] Lindqvist-Reis | Hydration of the Yttrium (III) Ion in Aqueous Solution . An X-Ray Diffraction and XAFS Structural Study[END_REF] The coordination numbers (CN) were 7.6 ± 1.9 and 7.9 ± 0.9 for Y-sol and YSO4-sol, respectively, which are close to the expected value of 8 for HREE. A second shell is visible in the FT of the YSO4-sol sample. It was fitted with a Y-S distance of 3.50 ± 0.04 Å, similar to that observed in the PDF of the same sample, and a CN = 1. This corresponds to a monodentate coordination, matching the geometry obtained from the AIMD simulations. The coordination number CN = 1 was kept fixed in the EXAFS fitting to increase the stability of the fitting procedure, minimizing the number of parameters and excessive correlations between them. A multiple scattering path (Y-S-O) was included and was found to be relevant and to improve the fit. The fitting parameters matched both the PDF and modeling results and indicated that the YSO 4 + aqueous species forms a monodentate inner-sphere complex. The determined Y-O first shell coordination numbers and interatomic distances are consistent with previous reports describing yttrium coordination shells with N = 8 and Y-O distances of 2.36 Å. [START_REF] Allen | Coordination Chemistry of Trivalent Lanthanide and Actinide Ions in Dilute and Concentrated Chloride Solutions FT Magnitude[END_REF][START_REF] Lindqvist-Reis | Hydration of the Yttrium (III) Ion in Aqueous Solution . An X-Ray Diffraction and XAFS Structural Study[END_REF][START_REF] Ohta | Influence of Multi-Electron Excitation on EXAFS Spectroscopy of Trivalent Rare-Earth Ions and Elucidation of Change in Hydration Number through the Series[END_REF] Likewise aqueous REE carbonate and phosphate complexes, [START_REF] Janicki | Lanthanide carbonates[END_REF][START_REF] Jeanvoine | Electronic structure and bonding of lanthanoid(III) carbonates[END_REF][START_REF] Harris | Gadolinium complex for the catch and release of phosphate from water[END_REF] aqueous Y-SO4 ion-pairs form inner-sphere complexes. This result contrasts with other ligands such as chloride, which hardly forms inner sphere complexes at similar concentrations to those used in this study. [START_REF] Allen | Coordination Chemistry of Trivalent Lanthanide and Actinide Ions in Dilute and Concentrated Chloride Solutions FT Magnitude[END_REF] Only the formation of monodentate complexation between Dy and NO 3 with a distance of 3.57 Å has been reported using MD simulations. 26

Analytical techniques

Measurements

RESULTS AND DISCUSSION

Geometry

Local order of yttrium adsorbed into basaluminite

HEXS measurements were performed on a pure basaluminite sample (B-pure) and on a sample of basaluminite coprecipitated with Y (B-Ycop). The PDFs are shown in Fig. 2 with the differential PDF obtained by subtracting the pure sample from the coprecipitated one. This approach has been previously used to examine the local order of different anions incorporated in poorly crystalline minerals, such as schwertmannite and basaluminite, and the desorption of sulfate from basaluminite when ageing at circumneutral pH values. [START_REF] Carrero | The Nanocrystalline Structure of Basaluminite, an Aluminum Hydroxide Sulfate from Acid Mine Drainage[END_REF][START_REF] Carrero | Arsenate and Selenate Scavenging by Basaluminite: Insights into the Reactivity of Aluminum Phases in Acid Mine Drainage[END_REF] The PDFs of the two samples are similar and show characteristic basaluminite peaks that correspond to the known interatomic distances (S-O bond at ~1.46 Å, Al-O bonds at 1.88, 4, 4.8 and 6 Å and Al-Al bonds at 3 Å). [START_REF] Carrero | The Nanocrystalline Structure of Basaluminite, an Aluminum Hydroxide Sulfate from Acid Mine Drainage[END_REF][START_REF] Carrero | Arsenate and Selenate Scavenging by Basaluminite: Insights into the Reactivity of Aluminum Phases in Acid Mine Drainage[END_REF] The differential PDF shows a main peak at 2.37 Å, which corresponds to the Y-O distance.

This peak matches the distance from Y to the first solvation shell obtained from the EXAFS and PDF of the Y 3+ and YSO 4 + aqueous solutions (Fig. 1). Another peak is present at 1.7 Å, which is tentatively assigned to a new Al-O distance of the Aloctahedra, due to probable distortions of the basaluminite. The existence of deformations in the structures of mineral nanoparticles upon oxyanion adsorption has been previously observed. [START_REF] Carrero | Arsenate and Selenate Scavenging by Basaluminite: Insights into the Reactivity of Aluminum Phases in Acid Mine Drainage[END_REF][START_REF] Cruz-Hernández | Influence of As(V) on precipitation and[END_REF] A smaller peak appears at 3.48 Å, which can be tentatively assigned to a Y-S distance, though it is shorter than the Y-S distance reported for the YSO 4 + aqueous complex (Table 2 and Fig. 1A). This peak may also correspond to a Y-Al interatomic distance, which would be consistent with the formation of an inner-sphere complex, as suggested by Lozano et al. [START_REF] Lozano | Sorption of rare earth elements onto basaluminite: the role of sulfate and pH[END_REF] This hypothesis will be tested using different atomistic models for the fitting of the EXAFS data. Other peaks at longer distances are difficult to assign in the absence of a more detailed structural model. The k 3 -weighted EXAFS spectra for the solid references B-YSO4-ads, B-Y-ads, B-Ycop and A-Y-ads (Fig. 3) are indistinguishable and differ from the aqueous Y-sol and YSO4-sol spectra in that a slight oscillation becomes visible at 5-6 Å -1 . In the B-YSO4ads the dominant Y species in solution is the YSO 4 + complex. However, in B-Y-ads, B-Ycop and A-Yads, the dominant Y species is Y 3+ . Still, these four samples show similar spectra. Since the common feature in these three samples is the presence of an Al adsorbent, and this feature agrees with that in the B-YSO4-ads EXAFS spectrum, we hypothesize that the higher frequency observed at 5-6 Å -1 (arrow in Fig. 3) may be related to the presence of an Al backscatterer, rather than to a signal from an close sulfate group. Both Al and S are light atoms with similar atomic numbers (Z = 13 and 16, respectively), which makes their backscattering functions difficult to distinguish via fitting of the EXAFS signal. These observations lead us to assume that the EXAFS measurements have poor sensitivity to the presence of Y-S bonds in this system.

However, a contribution from a sulfate shell for the two first samples cannot be ruled out. The EXAFS fits for B-YSO4-ads for the different models and the calculated parameters are given in Fig. 5 and Table 2, respectively. The first model represents a monodentate inner-sphere surface complex. The coordination number of the shells has been fixed based on information from another sources: The major aqueous species in solution was YSO 4 + , which, as supported by PDF and DFT data, forms a monodentate aqueous complex. Besides, the thermodynamic model of REE sorption on basaluminite indicated monodentate complexation of the YSO 4 + aqueous complex. [START_REF] Lozano | Sorption of rare earth elements onto basaluminite: the role of sulfate and pH[END_REF] 1. This result is consistent with the monodentate inner-sphere complex of Y with one oxygen shared with two surface Al sites (Fig. 4A). The second model, the bidentate binuclear inner-sphere complex configuration, gave poorer results without structural significance and with higher χ ν 2 values (Table 2), so it was discarded. The third model was the bidentate binuclear,: the first shell was fitted with a coordination number of 10 ± 1.31 and a distance of 2.33 Å. The second shell was fitted with a S path yielding an Y-S distance of 3.34 Å with a fix coordination number of N = 1, similar to that for the monodentate hypothesis, and with an Al path with a fixed coordination number of N = 2 giving a distance a bit longer, 3.92 Å.

Overall, the monodentate inner-sphere surface complex gave the best fit results, which was in accordance to the thermodynamic sorption model. Moreover, the bidentate binuclear inner-sphere surface complexes yielded also a good fit with similar χ ν 2 values than the monodentate hypothesis, making this configuration also possible (Fig. 4C). In both fits, the distances between Y-S are similar, 3.33, 3.34 Å, which is also observed in the differential PDF of the B-Ycop reference. From the EXAFS data, the presence of a bidentate binuclear surface complex cannot be ruled out. However, the thermodynamic modeling and the EXAFS data both agree on the monodentate surface complex being the one present at the basaluminite-water interface. The EXAFS of the B-YSO4-ads standard was also fitted with the same three atomistic models presented in Fig. 4 but without sulfate included (Fig S2 ) to analyze the sensitivity of the fit to the presence of sulfate. The Al-O distances obtained with these new fits (Table S3) were almost identical to those calculated when including sulfate shells (Table 2), with the exception in the monodentate inner sphere configuration, which was a bit longer. Thus, the presence of Al is consistent in the two models, with and without sulfate. However, the fits obtained with the sulfate group included showed lower values of the agreement factor (reduced chi-square) (compare Table 2 and Table S3). This discussion highlights the difficulties of the EXAFS technique to discern the presence of low-Z backscatterers, in particular in disordered systems such as the one present here. that showed five water molecules surrounding the metal cations, which was interpreted as a bidentate inner sphere complex. [START_REF] Dardenne | Identification and Characterization of Sorbed Lutetium Species on 2-Line Ferrihydrite by Sorption Data Modeling , TRLFS and EXAFS[END_REF] The similar distance to the second shell found by Dardenne et al. [START_REF] Dardenne | Identification and Characterization of Sorbed Lutetium Species on 2-Line Ferrihydrite by Sorption Data Modeling , TRLFS and EXAFS[END_REF]

Identification of Y species in waste samples from AMD treatment

The k 3 -weighted EXAFS of the column samples (upper part) and standards (lower part)

and their Fourier transform (FT) envelope functions with corrected phase-shifts are shown in Fig. 3. Visual comparisons of the EXAFS spectra of the column samples reveal a strong similarity between them, suggesting similar local order of Y in all samples. In addition, the EXAFS spectra of the column samples are very similar to solid references (B-YSO4-ads, B-Y-ads, B-Y-cop and A-Y-ads). They have a shoulder at ~6.5 Å -1 , indicating a higher frequency which is absent in the aqueous samples (see arrow in Fig. 3).

Based on the PCA analyses (see Chapter S3), the reconstructed spectra indicated that only two components were required to describe the samples (Fig. S4). The two most likely Y species identified by target transformation of the standard spectra, were the B-YSO4-ads and B-Y-ads references (Fig. S5). However, since their EXAFS signals are very similar, LCF of the EXAFS spectra of the waste samples from column treatments was performed using B-YSO4-ads and YSO4-sol (Fig. 3A, upper part) to quantify the proportions of different Y species in the waste samples. The use of these two standards would correspond to YSO 4 + adsorbed forming inner-and outer-sphere complexes, respectively.

The LCF results show that more than 75% of Y adopts a local geometry similar to that of the B-YSO4-ads reference (with the exception of sample W-Alm-C3-8, for which the LCF indicated equal proportion of both YSO4 presented as inner sphere and outer-sphere complexes (Table 3)). The waste samples were selected from the depth where the major proportion of Al precipitated, assuming basaluminite precipitation. However, other minor mineral species may have also been precipitated, such as Feoxyhydroxisulfate, due to the presence of small amount of Fe in the solids. Specifically, samples from Almagrera column (W-Alm-C3-8 and W-Alm-C3-9) presented higher Fe concentration than samples from Monte Romero treatment. REE can be also scavenged by amorphous Fe hydroxides in AMD neutralization environments, 48 so a minor or almost negligible fraction of Y could be retained by the small proportion of ferric oxides precipitated in the selected samples, explaining the lower proportion of B-YSO4ads.

ENVIRONMENTAL IMPLICATIONS

The YSO 4 + aqueous species has been characterized combining PDF analyses of aqueous solutions and AIMD simulations, confirming the formation of an inner-sphere Y-SO 4 ion pair with a monodentate configuration, with an Y-S interatomic distance of 3.5 Å.

Results from thermodynamic sorption model describe REE sorption onto basaluminite via sorption of aqueous REESO 4 + . The use of an atomistic model using this positively charged ion yields the best results for the EXAFS fitting of Y sorbed on basaluminite.

However, the EXAFS technique cannot confirm the presence of YSO 4 + sorbed into basaluminite by itself, due to the low sensitivity to discern between Al and S neighbors.

Rather than this, the EXAFS fitting, together with the PDF, can confirm the strong interaction and the formation of inner-sphere surface complexes of Y onto basaluminite precipitates, via ligand exchange with AlO 6 units of its structure. EXAFS analyses of column waste samples show that most of the Y is retained as the same inner-sphere sorbed species, YSO 4 + , with a low proportion of YSO4+ in outer-sphere configuration.

The description of the local structure of yttrium sorbed onto the basaluminite surface provided here complements the atomic configuration studies of other trace metals, such as As and Se oxyanions. [START_REF] Carrero | Arsenate and Selenate Scavenging by Basaluminite: Insights into the Reactivity of Aluminum Phases in Acid Mine Drainage[END_REF] The chemical similarity between yttrium and other HREE (from Tb to Lu) suggests that similar environments could be present for the other elements of the same group. This fact has important environmental consequences, as the HREE would be strongly sorbed, via covalent bonds, which could result in their longterm immobilization at least until the host phase is dissolved or re-precipitated. A key question emerges about the long-term stability of the complex, particularly with an increase in the solution pH. The structural evolution of pure basaluminite with increasing pH showed its recrystallization onto boehmite, releasing sulfate into the solution. [START_REF] Lozano | Local Structure and Ageing of Basaluminite at Different pH Values and Sulphate Concentrations[END_REF] Previous studies on poorly crystalline Fe phases from AMD such as schwertmannite have shown a release of adsorbed toxic elements during aging due to recrystallization processes. 49 The occurrence of such processes in REE-doped basaluminite could result in the remobilization of REEs and of other potentially

( 9 .

 9 6 cm inner diameter and 39 cm in height) consisted in a permeable mixture of grained limestone and wood shavings at a 1:1 weighed ratio. Samples W-MR-C1-4 and W-MR-C1-5 belonged to columns which treated AMD from Monte Romero mine and W-Alm-C3-8 and W-Alm-C3-9 samples belonged to columns which treated leaching of Almagrera mine tailing dam. Y concentration in different AMDs at the inlet ranged from 9 to 42 ppm, and sulfate concentrations 3.5 and 11.7 mg/L. Physical-chemical parameters and water samples were collected every two weeks. Once the treatment finished after 18 weeks, the solid was divided in 2 cm thick slices and dried. The partitioning of Al, Fe and REE was studied by performing a sequential extraction

  of the pH values of the initial and final solutions were made with a Crison ® glass electrode calibrated with buffer solutions of pH 2, 4 and 7. The Al and S concentrations were measured by inductively coupled plasma optical emission spectroscopy (ICP-OES), and the Y concentration was measured by inductively coupled plasma mass spectrometry (ICP-MS). The aqueous speciation and saturation index of the solid phases were calculated with the PHREEQC code[START_REF] Parkhurst | User´s guide to PhreeqC (version 2.18) A computer program for speciation, and inverse geochemical calculations[END_REF] using the Donnee Thermoddem_V1.10 database[START_REF] Blanc | Thermoddem: A Geochemical Database Focused on Low Temperature Water/Rock Interactions and Waste Materials[END_REF] provided by the Bureau de Recherches Géologiques et Minières (BRGM). Details of the analytical procedures and the database for the REE speciation are described in the Supporting Information S2.The HEXS measurements were performed at the ID31 beamline at the European Synchrotron Radiation Facility (ESRF). The samples were loaded into 1.5 or 2 mm Kapton capillaries, and the HEXS patterns were obtained using a Pilatus3 X CdTe 2M detector. The data correction and generation of structure factors and pair distribution functions were performed with the PDFgetX3 software.[START_REF] Juhás | PDFgetX3: a rapid and highly automatable program for processing powder diffraction data into total scattering pair distribution functions[END_REF] PDF for aqueous solutions were obtained by subtracting the scattering signal of pure water from the total scattering signal of YSO4-sol sample and Y-sol sample, respectively. The differential pair distribution functions (d-PDF) for B-Ycop sample were obtained by subtracting the PDF signal of a pure synthetic basaluminite from the PDF of B-Ycop sample. The EXAFS measurements of the samples B-Yads, B-Ycop, A-Yads, Y-sol and YSO4sol were performed at the Y K-edge (17.038 keV) at the FAME beamline, 39 BM30B at the ESRF. The EXAFS measurements of the B-YSO4 sample was performed at the Y K-edge (17.038 keV) at CLAESS beamline (ALBA synchrotron). Atomistic models of YSO 4 + and Y 3+ complexes adsorbed on a basaluminite surface with different initial configurations were employed in the data analysis to extract the Y local order parameters, including the neighbor distances, R, coordination number, N, and Debye-Waller factors, σ 2 . The proportion of Y species present in each waste sample from column treatments was obtained by linear combination fitting (LCF) of the EXAFS spectra using the most representative reference compounds as the basis set. The experimental setup and the details of the data analysis are given in the Supporting Information S3. The Ab Initio Molecular Dynamics (AIMD) simulations of an aqueous solution of yttrium(III) sulfate were carried out on a Y 3+ -SO 4 2--OH -system embedded in a box of 61 water molecules. The simulations were conducted with the electronic structure code CP2K/Quickstep code, version 5.1. 40 CP2K implements density functional theory (DFT) based on a hybrid Gaussian plane wave. The revPBE generalized gradient

of the aqueous complex YSO 4 +

 4 The experimental (YSO4-sol, Y-sol) and theoretical (YSO4-calc) PDFs of the Ycontaining aqueous solutions, 0.1 M YNO 3 and YSO 4 , are shown in Fig.1. Background subtraction of the aqueous samples was performed using a deionized water background, which implies that the experimental PDFs shown here are in reality differential PDFs (water substracted). The background scale factor was adjusted to minimize the O-O correlation (~2.8 Å) in the PDFs. All spectra show an intense peak at 2.37 Å, which corresponds to the Y-O interatomic distances to oxygen in the first coordination shell.The experimental PDF of the YSO4-sol sample shows a small and sharp peak at 1.45 Å that corresponds unequivocally to the S-O interatomic distance in sulfate, and another smaller peak at ~3.6 Å, which is absent in Y-sol sample. The DFT-based PDFs obtained from AIMD simulations of the YSO 4 + aqueous complex also reproduced this latter peak. Analyses of the AIMD trajectories show that the 3.6 Å distance between Y and S is consistent with the formation of a monodentate inner sphere complex (one shared oxygen) between the coordination shells of the sulfur and yttrium.

Figure 1 .

 1 Figure 1. (A) Top: Experimental PDFs of YSO4-sol and Y-sol samples. Bottom: Simulated (AIMD) PDF (YSO4-calc) and partial PDFs of an YSO 4 + aqueous complex.

Figure 2 .

 2 Figure 2. PDFs of basaluminite coprecipitated in the presence of Y (B-Ycop), pure

Figure 3 .

 3 Figure 3. k 3 -weighted EXAFS (A) and FT amplitude functions (B) for four waste

Figure 4 .

 4 Figure 4. Atomistic representations of the three models of YSO4 aqueous complex

  With this information the fit was performed assuming S and Al coordination numbers equal to 1 and 2, respectively, as it is shown in Fig.4A. Besides, two oxygen paths were considered due to the Y-O length variation improving the fit. Thus, the fit yielded a first coordination shell with a Y-O distance range between 2.32 and 2.47 ± 0.02 Å with a fix coordination number of 8. The second shell was fitted yielding a Y-Al distance at 3.52 Å with a fixed coordination number of N = 2, and an Y-S distance at 3.33 with a fixed number of N =

Figure 5 .

 5 Figure 5. (A) The k 3 -weighted EXAFS spectra at the Y K-edge of the basaluminite with

  adsorbed toxic elements. The extent to which these processes can occur in natural waters requires further investigation.Further studies on the potential uptake of LREEs by basaluminite are necessary to confirm and/or compare different structural mechanisms to those of their HREE counterpart, as an step to develop separation methods for REEs. These investigations will help to enhance a selective recovery of REE from AMD wastes and thus to generate an economic value of the waste generated during the neutralization of AMDaffected waters.Associated informationSupporting information. The supporting information contains 1) five chapters with detailed description of synthesis of reference materials, analytical procedures and aqueous speciation calculations, synchrotron experiments and data analyses, molecular dynamics simulations and the Phyton code used to generate PDF from AIMD simulations; 2) three 3 tables describing samples used for EXAFS measurements and the modeling parameters from EXAFS fitting; and 3) four figures illustrating fitting of EXAFS spectra.Materials and Molecular Modelling Hub for computational resources, which is partially funded by EPSRC (EP/P020194/1). This research utilised Queen Mary's Apocrita HPC facility, supported by QMUL Research-IT. The authors would like to thank the ESRF for in-house beamtime allocation at ID31 and the SOLEIL review committee for beamtime allocation at BM30B-FAME on the French quota, proposal n. 30-02-1124.

Table 1 .

 1 Modeling parameters of the Y K-edge EXAFS spectra of the 0.1 M YSO 4 aqueous solution (the error is expressed in the parentheses after the last digit).

	Sample Neighbor path	N	σ 2	ΔE 0	R (Å)	Var.	χ ν 2
	Y sol	1 st shell Y-O	7(2) 0.005(3) -2(3) 2.37(2)	4	354
	Y-SO4 sol 1 st shell Y-O 1 7.9(9) 0.007(2)		2.38(1)	8	498
		2 nd shell Y-S	1 fixed 0.003(6)	-1(1)	3.50(4)
		3 rd shell Y-S-O 1 fixed 0.015(19)		3.18(10)

Table 2 .

 2 Results of the EXAFS fits for B-YSO4 reference. Model 1: monodentate inner sphere. Model 2: bidentate mononuclear inner sphere. Model 3: bidentate binuclear inner sphere. Var. indicates independent variables. The best model is indicated with a star (*). The error is expressed in the parentheses after the last digit.

	Model	Neighbor	path	N	σ 2	ΔE 0	R	Var.	χ ν 2
	1*	1 st shell	Y-O 1	6 fix	0.007(1) -3(2) 2.32(1)	9	338
			Y-O 2	2 fix	0.004(8) -3(2) 2.47(2)
		2 nd shell	Y-S	1 fix	0.008(5) -3(2) 3.33(4)
			Y-Al	2 fix	0.012(5) -3(2) 3.52(5)
	2	1 st shell	Y-O	10 (1)	0.012(2) -6(2) 2.33 (1)	8	402
		2 nd shell	Y-S	1 fix	0.009(5) -6(2) 3.88 (5)
			Y-Al	1 fix	0.006(3) -6(2) 3.42 (3)
	3	1 st shell	Y-O	10(1)	0.013(1) -4(1) 2.33(1)	8	334
		2 nd shell	Y-S	1 fix	0.008(3) -4(1) 3.34(3)
			Y-Al	2 fix	0.011(5) -4(1) 3.92(4)

  These results are consistent with those presented by Rabung et al. where Gd and Lu were shown to adsorb onto γ-Al 2 O 3 to form inner sphere complexes.47 Lu, which is considered a HREE such as Y, was shown to form a monodentate surface complex with a first shell of 7 atoms and a Lu-O distance of 2.28 Å. The longer Y-O distance found here for Y is consistent with its larger Y ionic radius. However, these authors could not detect any Lu-Al bonds in their EXAFS data. Other sorption studies of Eu onto γ-Al 2 O 3 showed the formation of both monodentate and bidentate binuclear complexes.[START_REF] Yang | Investigation of Eu(III) immobilization on γ-Al 2 O 3 surfaces by combining batch technique and EXAFS analyses: Role of contact time and humic acid[END_REF] In contrast to this study and to the results of Rabung et al. on Al oxides, EXAFS studies of lanthanides sorbed onto ferric oxides have shown a predominance of bidentate innersphere complexes.47 Lu adsorbed onto ferrihydrite at pH ≥ 5.5 yielded an Lu-O

	coordination number of 7 at a distance of 2.30 Å, and a second coordination shell was
	identified with a Lu-Fe interatomic distance at 3.38 Å. These EXAFS results were
	complemented by time-resolved laser fluorescence spectroscopy (TRLF) measurements

  and by us in these two different systems can be explained by the smaller size of Lu in comparison to Y, which is compensated for by the longer Fe-O bond distance in comparison to the Al-O bond, making possible a bidentate mononuclear sphere at 3.4 Å in Fe oxides and Al oxides. Another study by Estes et al.reported Eu K-edge EXAFS data of Eu(III) sorbed onto hematite.19 They observed a decrease in the coordination number of the Eu-O bond from 8 to 5 upon adsorption accompanied by a smaller Eu-Fe bond distance at 3 Å. This result was interpreted via molecular simulations as being due to the formation of an inner sphere bidentate mononuclear surface complex. Such a decrease in the coordination number was not observed in this study; the CN of the Y-O correlation was maintained at ~8 ± 1 for both the aqueous and surface complexes.

Table 3 .

 3 Percentages of Y species in the column samples obtained from LCF of the EXAFS spectra. R-factor and χ 2 are defined in Chapter S3 of the SI.

	Standard	C1-4	C1-5	C3-8	C3-9
	B-YSO4ads	0.719 ± 0.036	0.867 ± 0.038 0.495 ± 0.059 0.746 ± 0.061
	YSO4-solution	0.281 ± 0.069	0.133 ± 0.071 0.505 ± 0.084 0.254 ± 0.085
	R-factor	0.14	0.14	0.29	0.30
	χ2	133	154	371	392

Acknowledgements

This work was funded by the European EIT 'Morerecovery' and the Spanish SCYRE (CGL2016-468 78783-C2-R)] projects. A.L. was also funded by a FPI grant (BES-2014-069978) and the "Make Our Planet Great Again" short-stay program. A.F.-M. acknowledges a grant from Labex OSUG@2020 (investissements d'avenir, ANR10 LABX56) for financial support of this research. A.L. thanks J. Bellés, M. Cabañas and R. Bartrolí (IDAEA-CSIC) for their analytical assistance. D.D.T. is grateful to the UK


Johanne Bakalara

Thomas Guyet

Olivier Dameron

André Happe

Emmanuel Oger

An extension of chronicles temporal model with taxonomies -Application to epidemiological studies

Keywords: Temporal query, Medico-administrative databases, Sequences of events, Chronicles, Semantic Web

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Pharmaco-epidemiology (PE) studies the conditions and consequences of health products, i.e. drugs or medical devices usage at the population scale in real situations using methodologies developed in general epidemiology.

Modern PE relies on administrative databases to perform such studies on care trajectories, i.e. on patient-centered sequences of drugs deliveries, medical procedures and hospitalizations. The use of medico-administrative databases (MADB) is useful in PE studies, since data are readily available and cover a large population.

The problem with MADB is the semantic gap between raw data and the epidemiological question. On the one side, epidemiologists are looking for medical events. For instance, they would like to identify patients suffering from venous thromboembolism (VTE). On the other side, raw data are related to reimbursements of medical acts or drug deliveries. There is no exploitable diagnosis available in administrative databases and no clinical results related to medical acts or exams.

The challenge for epidemiologists is to define phenotypes of medical events [START_REF] Hong | Developing a FHIRbased EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries[END_REF], i.e. a combination of information available in the database that reveals an occurrence of a medical event. For instance, a patient having a lower limbs doppler ultrasonography exam and few days after a delivery of anticoagulant drugs for 3 to 6 or 12 months is probably suffering from VTE. As MADB record medical exam and drugs deliveries, the above description may be used as a proxy of VTE.

The Semantic Web offers a relevant framework for representing complex data patterns and linking them with domain knowledge. Semantic Web data language (e.g. RDF) is suitable to represent structured data of MADB [START_REF] Rivault | queryMed: Semantic web functions for linking phar-macological and medical knowledge to data[END_REF]. Moreover, linking raw data to standard medical taxonomies is interesting to enrich the description of cares with formalized expert knowledge (for instance, ICD-10 1 for diagnosis or ATC 2 for drugs). Once care trajectories have been represented in standard Semantic Web format, SPARQL query engines can be used to enumerate all situations that match a query. A query can be seen as a phenotype. However, if the expressiveness of SPARQL is interesting to specify complex care trajectories as a phenotype, the drawback is their com-putation time. In the following, we assume the reader to be familiar with RDF and SPARQL, but a thorough introduction to semantic web can be found in [START_REF] Hitzler | Foundations of semantic web technologies[END_REF]. The example of VTE illustrates that such query may be a complex arrangement of cares in a patient care trajectory. These arrangement involve temporal relations between events (quantitative delays). Thus, we are interested in specifying complex temporal patterns that may occur in care trajectories. Taking into account numerical filters in metric temporal constraints is not efficient in SPARQL queries, and we can not expect to achieve reasonable computational efficiency on large MADB.

This article addresses the problem of enumerating the occurrences of a complex temporal pattern in a dataset of care trajectories.

We focus on a class of temporal patterns called chronicles and propose a template of SPARQL query that is both expressive and efficient. A chronicle is an expressive temporal pattern. It is defined as a set of events linked with temporal constraints. Despite its lack of taxonomy handling, this temporal model is suitable to represent complex temporal care pathways. One of its interests is its efficiently to be recognized in a sequence of events [START_REF] Dousson | Chronicle recognition improvement using temporal focusing and hierarchization[END_REF].

Our contribution is threefold: (i) we show how chronicles can be encoded as SPARQL queries to enumerate all their occurrences in a sequence of events represented in RDF; (ii) we propose HYCOR, an hybrid method combining the expressiveness of Semantic Web and the efficiency of a pattern occurrence enumeration algorithm; (iii) we evaluate HYCOR on a real case study of enumerating VTE events in the French MADB.

Related Work

In this section, we review some approaches to enumerate occurrences of temporal patterns in sequences of events and their connection to Semantic Web.

Temporal databases and querying tools (Snodgrass and llsoo Ahn, 1986) address a part of the problem by extending the notion of database to timestamped data. They cover data representation problems but also specific querying language problems. This family encompasses the temporal extension of relational databases (e.g. TSQL) but also Semantic Web approaches which combine query language (SPARQL) extended to temporal data with Allen's relations [START_REF] Wang | Timely YAGO: harvesting, querying, and visualizing temporal knowledge from wikipedia[END_REF]. These approaches defines relative temporal constraints between intervals which are not relevant for the MADB query problems [START_REF] Pacaci | A semantic transformation methodology for the secondary use of observational healthcare data in postmarketing safety studies[END_REF]. [START_REF] Rivault | queryMed: Semantic web functions for linking phar-macological and medical knowledge to data[END_REF] used RDF to represent care trajectories and shown that querying care trajectories can be achieved with the SPARQL query language. Semantic Web is a relevant approach for our problem: it does not explicitly address the problem of timed queries, but it is relevant to deal with data representation and taxonomies querying. RDF also enables ontology management with OWL based on the Description Logic (DL) [START_REF] Baader | The description logic handbook: Theory, implementation and applications[END_REF] allowing ontology-mediated query answering (OMQA) [START_REF] Bienvenu | Ontology-mediated query answering: harnessing knowledge to get more from data[END_REF]. For instance, O'Connor et al. [START_REF] O'connor | Knowledge-data integration for temporal reasoning in a clinical trial system[END_REF] developed a tool based on OWL for research data management with a temporal reasoning in a clinical trial system. This aspect could be added to the presented method. Some approaches proposed to extend RDF/SPARQL with temporal queries in a generic way. For instance, Zhang et al. [START_REF] Zhang | Temporal data representation and querying based on RDF[END_REF] propose SPARQL[t] and EP-SPARQL (Anicic et al., 2011a) which is a SPARQL extension of event processing. Finally, ON-TOP is an ontology-based data access framework that has been extended for temporal data [START_REF] Kalayci | Ontologybased access to temporal data with ontop: A framework proposal[END_REF]. However, these generic tools turn out to lack practical efficiency. This calls for investigating more algorithmic solutions.

Several temporal models have been highlighted in literature, one of the most promising is the Complex Event Processing (CEP) [START_REF] Giatrakos | Complex event recognition in the big data era[END_REF] which aims at processing a stream of event logs with patterns. CEP processes these logs to detect or to locate complex events (or patterns) defined by the user. These models emphasis on the effectiveness of processing and the expressivity of patterns. Some expressive formalisms, e.g. ETALIS [START_REF] Anicic | ETALIS: Rule-based reasoning in event processing[END_REF] or logic-based event recognition [START_REF] Giatrakos | Complex event recognition in the big data era[END_REF] propose very expressive representations of complex events, including reasoning techniques (encompassing ontologies).

While the complex event descriptions of ETALIS are based on Allen's logic, temporal constraint networks [START_REF] Cabalar | Temporal constraint networks in action[END_REF] and Chronicles [START_REF] Dousson | Chronicle recognition improvement using temporal focusing and hierarchization[END_REF] propose complex event descriptions with more permissive temporal constraints. These temporal models are also interesting for their graphical representation, but are restricted to patterns which do not involve taxonomies. It has been mainly used to discover patterns in biomedical data [START_REF] Dauxais | Discriminant chronicles mining[END_REF][START_REF] Sahuguède | An ordered chronicle discovery algorithm[END_REF] or in logs of industrial processes [START_REF] Sellami | Frequent chronicle mining: Application on predictive maintenance[END_REF].

In this work, we adopt a longitudinal view of a MADB. Each patient is represented by a sequence of timestamped cares, so called events. We first introduce the definition of event and taxonomy of event labels. Then, we introduce the notion of temporal sequence of events, or sequence for short.

Formally, an event is a pair (e,t) where e is an event label and t ∈ N is a timestamp (in days). In the following, (E, ≤ E ) denotes the totally ordered set of events. Usually, labels of medical events are related to taxonomies such as ATC 2 for drugs or ICD-10 1 for diseases.

Definition 1 (Event taxonomy). An event taxonomy is an ordered set of equivalence relations

(R i ) i∈[n] ,
where n is the number of levels of the taxonomy, such that: ∀(i, j), i < j, ∀(e, e ) ∈ E, eR j e =⇒ eR i e . (1)

We denote by c j i the i-th equivalent class at level j induced by the taxonomy. By definition, we have that e ∈ E ⇔ ∃!i, c 0 i = e. C denotes the set of all equivalent classes.

An event label e ∈ E is a c ∈ C, denoted e c, iff e is in the equivalent class of c. By extension, c ∈ C is a c ∈ C, denoted c c iff e c ⇒ e c for all e ∈ E. And then (E, ≤ E , ) denotes a set of ordered event labels equipped by a taxonomy relation.

In the Table 1, events are represented by ATC codes. Each ATC code is a class in the ATC taxonomy. For example, the ATC code A01AA01 is a subclass of A (A01AA01 A).

Let us now introduce the formal definition of a temporal sequence of events.

Definition 2 (Sequence). A sequence s is a finite list of events (e 1 ,t 1 ), (e 2 ,t 2 ), • • • , (e n ,t n ) where e i is an event label which is a taxonomy class. Events in a sequence are ordered by their timestamps and then their label

: i ≤ j ⇔ t i < t j ∨(t i = t j ∧e i ≤ E e j ), ∀i, j ∈ {1, . . . , n}.
A dataset of sequences is a finite unordered set of sequences, S = {s 1 , . . . , s m }. Tab. 1 illustrates six sequences where each event is a drug delivery where event labels are issued from the ATC taxonomy.

Chronicle Occurrences Enumeration

In this section, we propose an extended definition of chronicles [START_REF] Dauxais | Discriminant chronicles mining[END_REF] and Le [START_REF] Dousson | Chronicle recognition improvement using temporal focusing and hierarchization[END_REF][START_REF] Sahuguède | An ordered chronicle discovery algorithm[END_REF] with events belonging to taxonomy classes. Then, we define formally a chronicle occurrence in a sequence and the enumeration of all chronicle occurrences in a sequence.

; Dousson A01,1 B01A,2 [ -1 , 3 ] C,3 [ -3 , 5 ] [-2,2] C,4 [1,3]
A chronicle is a set of events and a set of temporal constraints between pairs of events [START_REF] Dousson | Chronicle recognition improvement using temporal focusing and hierarchization[END_REF]. In our applied context, chronicle enables to represent a phenotype. The enumeration of chronicles occurrences aims at localizing where this medical pattern occurs in a patient care trajectory. This paper proposes a chronicle extension where event may have label belonging to the equivalence class of an event label.

Definition 3 (Chronicle). A chronicle C is a pair (E, T ) where • E is an ordered set of events {(c 1 , 1), • • • , (c m , m)}
, where for all i ∈ {1, . . . , m}, c i ∈ E is an event label. i designates the index of the i-th event index.

• T is a set of temporal constraints, i.e. expressions of the form

(c j , j)[t -,t + ](c k , k) such that -(c j , j), (c k , k) ∈ E, -t -,t + ∈ R ∪ {+∞, -∞} and -For all (c j , j), (c k , k) ∈ E, j < k, c j c k =⇒ ∃(c j , j)[t -,t + ](c k , k) ∈ T s.t. [t -,t + ] ⊆ [1, +∞[ (2)
The chronicle size is m (number of events).

A temporal constraint (c j , j)[t -,t + ](c k , k) enforces an event (c k , k) to occur with a temporal delay in between t -and t + from an occurrence of (c j , j). Note that several events can have the same label. A chronicle event may also have its label belonging to the equivalent class of another event label. In these cases, Eq. 2 enforces event occurrences to be ordered by their index.

The Fig. 1 illustrates graphically the following 4sized chronicle C = (E, T ): id Sequence s 1 (A01AA01, 1), (B01AA01, 3), (A01AB14, 4), (C01AA01, 5), (C02AC01, 6), (D01AA01, 7) s 2 (B01AA01, 2), (D01AA01, 4), (A01AA01, 5), (C01AA01, 7) s 3 (A03AA01, 1), (B01AA01, 4), (C01AA01, 5), (B01AA01, 6), (C01AA01, 8), (D01AA01, 9) s 4 (B01AA01, 4), (A01AB14, 6), (N01AA01, 8), (C01AA01, 9) s 5 (B01AA01, 1), (A01AA01, 3), (C01AA01, 4) s 6 (C01AA01, 4), (B01AA01, 5), (A01AA01, 6), (C01AA01, 7), (D01AA01, 10)

• E = {(A01, 1), (B01A, 2), (C, 3), (C, 4)} • T = {(A01, 1)[-1, 3](B01A, 2) , (A01, 1)[-3, 5](C, 3) , (B01A, 2)[-2, 2](C, 3) , (C, 3)[1, 3](C, 4)}
where event labels belong to the ATC taxonomy. Notice that temporal constraints may have negative values. The temporal constraint (A01, 1)[-3, 5](C, 3) states that an event with label in the equivalence class of A01 must occur from 3 days before occurrence of a C to 5 days after this occurrence. Thus, the chronicle Fig. 1 means: "An event A01 is followed by an event B01A within a delay of [-1, 3] units of time (ut). The later is followed by an event C within a delay of [-2, 2] ut. In addition the delay between this event C and the event labeled A01 must be in [-3, 5] ut. Finally, C event is followed by an another event C within a delay of [1, 3] ut".

In the following, we introduce the definition of a chronicle occurrence in a sequence. Then, one can be interested in two different tasks: enumerating all occurrences of a chronicle in a sequence (chronicle enumeration), or deciding whether a chronicle occurs at least once in the sequence (chronicle recognition). In the following, we focus on the chronicle enumeration task. Definition 4 (Chronicle occurrence). Let s = (e 1 ,t 1 ), (e 2 ,t 2 ), . . . , (e n ,t n ) be a sequence of length n and

C = (E = {(c 1 , 1), . . . , (c m , m)}, T ) be a chronicle of size m over a set of labels (E, ≤ E , ). An occurrence of C in s is a subsequence of s of length m, denoted s = (e ε 1 ,t ε 1 ), . . . , (c ε m ,t ε m ) , where (ε i ) i=1..m are indices of an event in s and s.t. 1. e ε i c i 2. t ε j -t ε i ∈ [t -,t + ] whenever (c i , i)[t -,t + ](c j , j) ∈ T . (ε i ) i=1..m
describes s a subsequence of s. The first condition ensures that the i-th event label of s is a subclass of c i . The second condition ensures that temporal constraints are satisfied. Note that Eq. 2 enforces to have a strict order between events c k , c j whenever c k c j . Thus, all ε i , i ∈ [1, n] are distinct. The chronicle of Fig. 1 occurs in sequences s 1 and s 6 of the dataset in Table 1. For instance, {(A01AA01, 1), (B01AA01, 3), (C01AA01, 5), (C02AC01, 6)} is an occurrence of C in s 1 . This occurrence is the subsequence of s 1 with indices 1, 2, 4, 5 . The chronicle does not occur in s 2 neither in s 4 because of unsatisfied temporal constraints. It does not occur in s 5 as there is only one event with a type of class C in the sequence and the chronicle requires two different events. It does not occur in s 3 because there is not an event in the subgroup of A01.

Semantic Web for Chronicle Recognition

Semantic Web is a framework designed to represent, share and manipulate structured data. The keystones of Semantic Web are (i) formal data representations, such as the RDF language, and (ii) query languages, such as SPARQL. Semantic Web is particularly suitable to represent taxonomies.

Semantic Web is suitable to represent sequences with label events belonging to taxonomies and to encode chronicle enumeration with SPARQL. So, we propose to represent sequences in RDF and to encode a chronicle enumeration in SPARQL. We propose two approaches for chronicle enumeration: the first approach fully uses Semantic Web technologies; the second approach is an hybrid tool combining SPARQL query and a dedicated algorithm.

Sequence Representation in RDF

Sequences are represented in a RDF-Graph (Fig. 2). We remind that our concrete objective is to query a dataset of sequences, where each patient care trajectory is represented as a sequence. ?evt4 seq:evtDate ?date4 . ?evt4 pseq:evtlabel ?atc4 . ?atc4 rdfs:subClassOf* atc:C . FILTER ( ?date2 -?date1 >= -1) FILTER ( ?date2 -?date1 <= 3) FILTER ( ?date3 -?date1 >= -3) FILTER ( ?date3 -?date1 <= 5) FILTER ( ?date3 -?date2 >= -2) FILTER ( ?date3 -?date2 <= 2) FILTER ( ?date4 -?date3 >= 1) FILTER ( ?date4 -?date3 <= 3) } In RDF, each event (e j ,t j ) in a sequence s i is encoded by three tuples:

• seq:sequencei seq:hasEvent seq:sequenceievtj denotes existence of an event e j in sequence s i

• seq:sequenceievtj seq:eventLabel atc:lk denotes event e j has the label l k . Note atc referees to the ATC taxonomy 2 where l k is a leaf-class

• seq:sequenceievtj seq:eventDate '1'ˆˆxsd:integer denotes event e j has a date t j equal to 1. Fig. 1 illustrates the representation in RDF by providing the representation of sequence s 5 (see Table 1).

SPARQL for chronicle occurrences enumeration

This section presents the chronicle recognition task with SPARQL. SPARQL queries RDF sequences where all sequences are in the same RDF named Graph.

Figure 3 gives the SPARQL query for the chronicle in Fig. 1. The query has three types of variables:

• ?sequence denotes an identifier of a sequence • ?evtj corresponds the j-th element of the chronicle set.

• ?datej is the date of the j-th element of an occurrence (t ε j with notation of Definition 4).

The taxonomy of event labels are handled by rdfs:subClassOf* pattern operator. This operator is equivalent to the operator defined in Def. 1. Temporal constraints are expressed in FILTER clauses. For instance, the temporal constraint (C, 3)[1, 3](C, 4) is translated in a couple of constraints between ?date4 and ?date3.

SPARQL is expressive enough for enumerating chronicle occurrences. However the enumeration of chronicle occurrences is a very computational task. A SPARQL query can not compete with dedicated enumeration algorithms as its solver strategy is not optimised for this task (see experiments in Sect. 6). Therefore, we propose an hybrid approach to benefit from the best of both fields: efficiency of dedicated approaches and expressiveness of Semantic Web.

HYCOR for chronicle recognition

HYCOR (Hybrid-Chronicle Occurrences Recognition) combines SPARQL and a specific algorithm to enumerate efficiently occurrences of a chronicle. Fig. 5 illustrates the HYCOR process.

First (left box of Fig. 5), a SPARQL query yields flattened sequences . A flattened sequence contains only the sequence events that belong to the equivalent class of at least one event label of E, i.e. the chronicle events (see Def. 3). Such a query for chronicle of Fig. 1 Input:

C = (E = {(c 1 , 1), . . . , (c m , m)}, T ), s = (e 1 ,
π ← {[t,t], [-∞, ∞], . . . , [-∞, ∞]}; // propagate chronicle constraints 5 foreach (c 1 , 1)[t -,t + ](c p , p) ∈ T do 6 π p = [max(t 1 ,t + t -), min(t + t + ,t n )];
7 occs ← occs∪ RECENUMERATE(π,1,C ,s); 8 return occs occur in s. These intervals are called admissible positions. The algorithm goes through the set of events (e i ,t i ) ∈ s and propagates the temporal constraints of the chronicle to narrow position intervals until intervals are only single position. Thus admissible position designates a subsequence of s, i.e., an occurrence of the chronicle. Algorithm 1 makes recursive calls to Algorithm 2. The later assumes that the k -1 first events have been located in s. This means that the k first intervals of the admissible intervals π are singleton intervals. The recursive call looks for event c k in the admissible positions of s for k-th event (lines 5-6). If found, it is a candidate for further refinements and temporal constraints of the chronicle are propagated. The constraint (c k , k)[t -,t + ](c p , p) is a constraint from (c k , k) event to the event c p at position p. It is used to possibly narrow the admissible positions of event p (line 11). In case the new positions are inconsistent (line 12) then this candidate occurrence can not satisfy the temporal constraints and Algorithm 2: RECENUMERATE(π, k, C , s).

Input: π: admissible positions, k: recursion level,

C = (E = {(c 1 , 1), . . . , (c m , m)}, T )), s = (e 1 ,t 1 ) . . . (e n ,t n ) Output: occs: a set of occurrences of C in s 1 occs ← / 0 // Set of occurrences 2 if k = m + 1 then
// An occurrence has been found is discarded (satis f iable is set to f alse). If all constraints are satisfied, the recursive call attempts to refine further these positions (line 16). Note that only forward constraints are propagated. Indeed, backward constraints (i.e. constraint to event at position lower than k in the set) have already been taken into account in parent calls.

3 occ ← {(e k i ,t k i ) ∈ s | c i = e k i , π i = t k i , i = 1.
Let us illustrate the algorithm on a simple example.

Let

s = (B, 2) (C, 3) (A, 5) (B, 6) (C, 7) (C, 9) (C, 10) and C = ({(A, 1), (B, 2), (C, 3)}, {(A, 1)[-2, 2](B, 2), (A, 1)[-3, 5](C, 3), (B, 2)[-1, 3](C, 3)}).

Processing of event A

• generates a single tuple of admissible positions π

= ([5, 5], [-∞, ∞], [-∞, ∞]) • constraints propagation: -(A, 1)[-2, 2](B, 2): π = ([5, 5], [3, 7], [-∞, ∞]) -(A, 1)[-3, 5](C, 3): π = ([5, 5], [3, 7], [2, 10]) 2. Processing of event B • narrows positions with occurrences: (B, 2) is invalid (2 / ∈ [3, 7]
), but (B, 6) satisfies the admissible positions [3, 7] so the admissible positions can be updated

(π = ([5, 5], [6, 6], [2, 10])) • constraints propagation: -(B, 2)[-1, 3](C, 3): π = ([5, 5], [6, 6], [2, 10] ∩ [5, 9]) = ([5, 5], [6, 6], [5, 9

Experiments

In this section, we compare execution times of SPARQL and HYCOR on synthetic datasets. All experiments have been executed with a TDB-graph format for RDF and Jena-Fuseki as SPARQL engine. The HYCOR algorithm is implemented in Python. The computer has 16Go RAM and an SSD.

Synthetic datasets generation and plan of experiments

Several synthetic datasets have been generated. Each dataset contains a set of sequences where event labels are randomly chosen at the lowest level of ATC taxonomy. The ATC taxonomy contains 1 900 classes.

In addition, occurrences of ten 15-sized chronicles 3 are embedded in the dataset. For each chronicle, a constraint is generated for each pair of events without inconsistency between the temporal constraints.

3 A n-sized-chronicle denotes a chronicle of size n.

The synthetic dataset generation process ensures that each chronicle occurs in about 20% of the sequences. Chronicles contain event labels from several levels of ATC following this probability: 1 15 level 1 (ex: N), 2 15 level 2 (ex: N02), 3 15 level 3 (ex: N02B), 3 15 level 4 (ex: N02BE), 6 15 level 5 (ex: N02BE01). We introduce the notation D ns,ne to denote a synthetic dataset with ns sequences and ne care events per sequence (all sequences have the same number of events). For the following experiments, 25 synthetic datasets have been generated where ns ∈ {1 000, 5 000, 10 000, 15 000, 20 000} and ne ∈ {100, 200, 300, 400, 500}. Each dataset is encoded in RDF. The ATC taxonomy is attached to the dataset.

Experiments and Results

The following experiments evaluate the impact of two main parameters on execution times of SPARQL and HYCOR: the size of the dataset (number of sequences and number of events per sequence) and the chronicle size.

Fig. 6 compares the execution times of SPARQL and HYCOR with respect to the length of the sequences. It shows that HYCOR is at least one order of magnitude more efficient than pure SPARQL. 6 shows that SPARQL does not scale up for datasets containing more than 50 000 sequences. The HYCOR SPARQL query language does not have the same limitation. Indeed, the pure SPARQL query uses filters to deal with temporal constraints on each admissible event while SPARQL HYCOR query only uses values to find admissible event. So, the use of filters on a large scale of admissible events seems to be inefficient in SPARQL for this kind of use.

We also evaluate the part of HYCOR execution times spent by the SPARQL mapping and the chronicle enumeration algorithm. On average, the SPARQL query execution represents 85% ± 3.47 of the total execution time.

Experiments now focus on the HYCOR evaluation. Fig. 7 illustrates the impact of the number of events per sequence on the execution times. We observe that time increases linearly with the number of events and with the number of sequences. Outliers and variance of the computing time can be explained by the variability of the number of events occurrences in the sequence that influence the time execution of the Algorithm 2. The more an event occurs in a sequence, the more candidate occurrences, therefore the longer the time spent in the algorithm. Nonetheless, we can notice in the hardest condition: D 20 000,500 , enumeration of a chronicle with 15 events takes in average less than a minute. Fig. 8 presents execution time of HYCOR wrt chronicle size. HYCOR is run on a unique dataset (ns = 10 000 and ne = 100) and seven sets of 10 chronicles with sizes 2 to 14. We observe execution time linearly increases with the chronicle size.

HYCOR outperforms pure-SPARQL for the chronicle enumeration task. Its execution time increases with the chronicle sizes and the dataset size, but it still offers impressive results for large datasets.

7 Use case on the SNDS to find patients with thromboembolism

Our use case proposes to find patients diagnosed with venous thromboembolism (VTE) in the SNDS. The SNDS is the french national health insurance database, which covers most of the french population (above 65 million inhabitants). The advantage of this database is to gather information about most the reimbursed medical events, from drug deliveries to nurse home cares, specialist consultations, etc. The range of medical events that are recorded in the database makes it suitable to conduct a wide variety of health studies [START_REF] Tuppin | Value of a national administrative database to guide public decisions: From the système national d'information interrégimes de l'assurance maladie (sniiram) to the système national des données de santé (snds) in france[END_REF]. However, SNDS has been designed for administrative purposes (care reimbursements) and does not contain detailed medical information such as medical reports, laboratory results or diagnosis. This use case uses a geographical-based SNDS subset (the north western French Brittany population) which contains 377 359 individuals. For the use case application, rdf:type have been added in the RDF event triples to speed up access to event labels. We used five different types: DrugDeliveries (e.g., drug deliveries from pharmacy), Cares (e.g., nurse assistance, domestic assistance), Medical acts (e.g., radiology, surgery), Biologies (e.g., blood withdrawal), Hospitalisations.

VTE is identified by epidemiologists in SNDS when a patient matches the following description:

In clinical practice facing a suspicion of VTE physicians first prescribe anticoagulant and then confirm or not the diagnosis through specific medical acts: e.g. Doppler ultrasonography or CT scan. Patients with suspected PE are often hospitalized whereas patients with suspected DVT are managed on an ambulatory basis. If the suspicion is confirmed, anticoagulant deliveries continues for 3 to 12 months (once per month) or sometimes longer duration. Hence, the diagnosis (through medical act) is preceded or followed by anticoagulant initiation within a time window of at most 0 to 7 days, keeping in mind that PE suspicion leads to hospitalisation during which medical acts to confirm the diagnosis are performed and then anticoagulant delivery is observed only after the patient comes back home. Fig. 9 illustrates two chronicles defining the phenotype of "patients with VTE". Each chronicle specifies temporal constraints (within 7 days and one anticoagulant delivery per month) but also takes into account some details on the anti-coagulant class and on the medical acts on ambulatory or in hospital. Anticoagulant are identified with the ATC code B01. We also added to the knowledge base a class named ccam:thrombose which is defined as an union of 36 different codes of medical acts issued of the CCAM taxonomy 4 . Furthermore, some VTE are identified in hospital through ICD-10 codes 1 , they are gathered in a class called cim:diagthrombose.

For this experiment, we load RDF graphs, we query them with both chronicles, one by one, and evaluate computing time. Note that we do not have the ground truth. Thus, we are not interested in the accuracy of the chronicles to identify true VTE. In this experiments, we compare the computational efficiency to enumerate the same set of VTE occurrences.

The first result is that the corresponding pure-SPARQL query does not finish the enumeration within a day of computation. This is due to the scale up limitation shown in Sect. 5.2. HYCOR finds 2 686 patients having VTE in 105.06s. The first chronicle finds 2 568 patients in 56.21s (of which 52.86s in algorithm execution), the second chronicle finds 118 patients in 48.85s (of which 48.46s in algorithm execution). Use case time execution is even faster than expected by experiments on synthetic data. The real patient sequences length is about 100 events in average. So if we refer to Fig. 6, on the right, the expected execution time is about 11min41s. We explain this difference by the lower size of the chronicles in our use case query.

4 CCAM : common classification of medical acts used by the french social security

Conclusion

In this article, we extended the model of chronicle with taxonomies to enumerate complex temporal patterns in sequences. This problem is motivated by the need for phenotyping patients in medicoadministrative databases. We proposed HYCOR, an hybrid approach that combines the expressiveness of SPARQL and the efficiency of a dedicated algorithm. The results show that HYCOR is one order of magnitude faster than pure SPARQL queries on both synthetic and real dataset. As a perspective, chronicles should be extended with negation to denote the absence of event. Furthermore, it could be interesting to compare the efficiency of different SPARQL engines.

Figure 1 :

 1 Figure 1: Chronicle example with 4 events (vertices) and 4 temporal constraints (edges with temporal intervals). Vertex labels give the event label (ATC codes).

Figure 3 :

 3 Figure 3: Example of a SPARQL query for chronicle enumeration

Figure 4 :Figure 5 :

 45 Figure 4: Illustration of SPARQL query to map sequences on the set of events of chronicle in Fig. 1.Second (right box of Fig.5), HYCOR applies Algorithm 1 to enumerate chronicle occurrences in the flattened sequences.The algorithm's principle is to refine progressively intervals in which a chronicle event (c i , i) ∈ E may

]) 3 .

 3 Processing of event C • narrows intervals with occurrences: (C, 3) and (C, 10) are invalid, but (C, 7) and (C, 9) are valid, then the both subsequences where the chronicle occurs are obtained by updating the admissible positions (([5, 5], [6, 6], [7, 7]) and ([5, 5], [6, 6], [9, 9])).

Figure 6 :Figure 7 :

 67 Figure 6: Execution times (in seconds) of SPARQL and HYCOR wrt sequences length on 10 000 sequences (on the left) and wrt number of sequences (with length 100).

Figure 8 :

 8 Figure 8: Execution time (in seconds) wrt chronicle size (ns = 10 000 and ne = 100)

Figure 9 :

 9 Figure 9: Chronicles for representing VTE phenotype.

Table 1 :

 1 Example of a dataset of six sequences (longitudinal view on six patients). Each sequence is made of drug deliveries events (couples of label and timestamp). Labels are ATC codes, i.e. the code of a delivered drug in the ATC toxonomy.

seq:seq5 seq:hasEvent seq:seq5evt0 . seq:seq5evt0 seq:evtLabel atc:B01AA01

; seq:evtDate '1'ˆˆxsd:integer . seq:

seq5 seq:hasEvent seq:seq5evt1 . seq:seq5evt1 seq:evtLabel atc:A01AA01 ;

  Figure 2: Example of sequence representation in RDF graph (see sequence s 5 in Table1).

	seq:evtDate '3'ˆˆxsd:integer .
	seq:seq5 seq:hasEvent seq:seq5evt2 .
	seq:seq5evt2 seq:evtLabel atc:C01AA01 ;
	seq:evtDate '4'ˆˆxsd:integer .
	SELECT DISTINCT * WHERE{
	?sequence patdb:hasEvent ?evt1 .
	?evt1 seq:evtDate ?date1 .
	?evt1 seq:evtlabel ?atc1 .
	?atc1 rdfs:subClassOf* atc:A01 .
	?sequence patdb:hasEvent ?evt2 .
	?evt2 seq:evtDate ?date2 .
	?evt2 seq:evtlabel ?atc2 .
	?atc2 rdfs:subClassOf* atc:B01A .
	?sequence patdb:hasEvent ?evt3 .
	?evt3 seq:evtDate ?date3 .
	?evt3 seq:evtlabel ?atc3 .
	?atc3 rdfs:subClassOf* atc:C .
	?sequence patdb:hasEvent ?evt4 .

  is on 4:

	SELECT DISTINCT ?seq ?date ?label where{
	values ?label { atc:A01 atc:B01A atc:C }
	GRAPH patdb:onto { ?l rdfs:subClassOf* ?label }.
	GRAPH ?seq{
	?event patdb:drugDelivered ?l.

?event patdb:deliveryDate ?date.}}

  t 1 ) . . . (e n ,t n ) Output: occs: a set of occurrences of C in s

	1 occs ← / 0 // Set of occurrences
	2 foreach (e,t) ∈ s do
	3	if e = c 1 then
		// create a set of admissible
		positions π of size m
	4

  .m};

	4	return {occ}
	5 foreach (e,t) ∈ s s.t. t ∈ π k do
	6	if e = c k then
		// create a copy of
		admissible positions π
	7	π ← π;
	8	πk ← [t,t];
		// propagate chronicle
		constraints
	14	break;
	15	if satis f iable then
		// Recursive call
	16	occs ← occs∪
		RECENUMERATE( π, k + 1, C , s);
	17 return occs

9

satis f iable ← true;

10 foreach (c k , k)[t -,t + ](c, p) ∈ T do 11 πp ← [max( πp ,t + t -),

min( π+ p ,t + t + )]; 12 if πp > π+ p then 13 satis f iable ← f alse;

ICD-10: International Classification of Diseases, 10th Revision. http://bioportal.bioontology.org/ontologies/ ICD10

ATC: Anatomical Therapeutic Chemical. https:// bioportal.bioontology.org/ontologies/ATC