Lucía Álvarez-Miguel

Inés Álvarez-Miguel

Jose M Martín-Álvarez

Celedonio M Álvarez

Guillaume Rogez

Raúl García-Rodríguez
email: raul.garcia.rodriguez@uva.es

Daniel Miguel

José M Martín-Álvarez

Copper complexes for the promotion of iminopyridine ligands derived from β-alanine and self-aldol additions: relaxivity and cytotoxic properties

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

Introduction

Copper is a bioessential element for humans, and a wide variety of Cu complexes have been demonstrated to be excellent potential candidates for biological and medical applications. [1][2][3][4][5][START_REF] Nyvltova | Encyclopedia of Inorganic and Bioinorganic Chemistry[END_REF][START_REF] Kosman | Encyclopedia of Inorganic and Bioinorganic Chemistry[END_REF][START_REF] Li | [END_REF] Among the plethora of Cu (II) compounds that have been tested as anticancer agents, complexes with Schiff base ligands have emerged as promising candidates. [9][10][11][12][13] Since the pioneering work of Reedijk et al. 14,15 on Schiff base derivatives of pyrimol with copper and other metals, several groups have assessed the DNA cleavage, cytotoxicity and apoptosis induction activities of related families of copper complexes. [16][17][18] Additionally, there is a current interest in the development of paramagnetic complexes to be used as relaxation agents for contrast in magnetic resonance imaging (MRI). [19][20][21] The main focus has been complexes with many unpaired electrons, such as those of Gd(III) (f [START_REF] Kosman | Encyclopedia of Inorganic and Bioinorganic Chemistry[END_REF] ). However, the intrinsic toxicity of Gd 22,23 and most heavy metals has driven interest in complexes of first row transition metals, 19,[24][25][26][27] which are less toxic. Although these complexes exhibit lower relaxivity, they can be tailored for greater target specificity. For example, the well-known histological stain Luxol fast blue (LFB), a copper (II) phthalocyanine, has been tested as an MRI contrast agent. 28 However, some of these complexes have cumbersome syntheses or involve ligands that are difficult to isolate. In particular, the synthesis of polydentate ligands can be multi-step and time-consuming. It is interesting, therefore, to develop systematic procedures for the synthesis and modification of families of complexes whose properties can be tuned by introducing different ligands and substituents. We have been interested in complexes containing pyridine-2carboxaldehyde (pyca) as chelating κ 2 (N, O) ligands, [29][30][31][32][33] which can serve as precursors for the introduction of metal-ligand fragments in biomolecules. [34][35][36] In addition to this, the preparation of the ligands within the metal coordination sphere is also a simple and one-pot procedure, which avoids the need for typical multi-step, time-consuming ligand preparation and purification protocols. In this vein, we have recently found that the Cu(II) complex 1a, in which pyca is coordinated as κ 2 (N, O) to the metal, is able to undergo aldol addition with different ketones under mild and neutral conditions due the enhanced electrophilic character of the aldehyde upon copper coordination (Fig 1A). 37 Complex 1a could not be isolated, but could be prepared in situ. We reasoned that the coordination of 2-pyridine carboxaldehyde (pyca) to copper should also enhance the reactivity towards other nucleophiles such as amines and facilitate Schiff base condensation (Fig 1B). Simple access to complexes of this type is important. In this regard, copper (II) complexes containing Schiff base ligands derived β-alanine 38,39 have shown different various interesting properties, for example, antiradical and antidiabetic activity. 40 Other Schiff base complexes derived from pyca have been used as catalysts for the oxidation of cyclohexane [41][42][43] and alcohols and for nitroaldol additions. 44 In the study presented herein, we explore the ability of 1a and novel isolatable complexes 1b and 2b, in which an 2acetylpyridine (acepy) ligand is coordinated to Cu(II), to promote the addition of amines (Schiff condensation) and other nucleophiles such as alcohols (hemiacetal formation) in the coordination sphere of the metal. We show that acepy copper complexes, unlike pyca complexes, can promote the self-aldol addition due to the chelate coordination of the ligand to copper, which facilitates the formation of the metal enolate (Fig. 1C). We also study the influence of simple chloride ligand substitution on the cytotoxicity and relaxivity of the prepared Schiff base β-ala copper complex.

Results and discussion

Synthesis and structural characterization of acepy copper(II) complexes

We have previously shown that the reaction of CuCl2•2H2O with pyridine-2-carboxaldehyde (pyca) produces the coordination of the pyca ligand to give compound 1a, which could not be isolated. In contrast, the addition of an excess of pyca leads to complex 2a, which could be isolated in high yields (Scheme 1a). 37 Herein, in a similar manner, 2acetylpyridine (acepy) reacts with CuCl2•2H2O in THF to afford compound 1b (see Scheme 1b) Compound 1b precipitates out cleanly in THF and can be isolated in excellent yield (93%). The solid-state structure of 1b reveals that acepy acts as a  2 (N,O) bidentate chelate ligand accompanied by two chloride ligands. A pentacoordinate-type arrangement is formed by the long contact of the Cl ligand of a nearby molecule, thus giving rise to a centrosymmetric dimer with concerted unsymmetrical chloride bridges, as seen in Figure 2 top. Compound 1b reacts with an additional equivalent of acepy in MeOH to afford complex 2b as a green precipitate. This compound can also be obtained by simply reacting CuCl2•2H2O with two equivalents of acepy. In the structure of 2b (Figure 2, below) the copper atom is surrounded by two chloride ligands in a trans arrangement and two acepy ligands coordinated as   N, O) bidentate chelates, thus completing a distorted octahedral six-coordinate environment for the copper atom. The Cu-O distances are longer than expected for a normal bond (2.701(1) in 2b, cf. 2.042(3) in 1b), which can be attributed to Jahn-Teller distortion. The IR spectra of both complexes show a decrease in the ν(CO) (1633 and 1686 cm -1 , respectively for 1b and 2b) with respect to that of free acepy ligand (ν(CO) 1697 cm -1 ), indicating that the carbonyl group is activated for nucleophilic attack by copper coordination. This activation was stronger in 1b, which therefore selected for subsequent studies. Please do not adjust margins Please do not adjust margins

Schiff condensation: metal conjugates with β-alanine

As noted in the introduction, we have recently shown that the copper complex 1a is able to undergo aldol addition with ketones (Scheme 1a). 37 We decided to explore the utility of complexes 1a and novel 1b in other nucleophilic reactions, such as the Schiff condensation reaction. We studied their reactivity towards -alanine (-ala), which is the simplest amino acid, as well as the only naturally occurring -amino acid. These studies would also be useful for assessing the possibilities of these complexes in bioconjugation. Compound 1b reacts with -alanine in methanol to ultimately afford compound 4[-ala], which was isolated from the red reaction crude as a few crystals. The single-crystal X-ray structure of 4[-ala] reveals the unexpected formation of an anionic Cu(II) complex that crystalizes with one molecule of protonated β-alanine, to which it is associated through two hydrogen bonds (Fig 3). The solid-state structure shows the expected formation of the iminopyridine derived from the Schiff condensation between the ketone group of acepy and the NH2 of -ala. The carboxylate group of the iminopyridine is, however, deprotonated and coordinates to Cu(II) through one oxygen; thus, the iminopyridine ligand acts as κ 3 (N,N',O). Two chloride ligands complete the coordination around the copper centre, forming a distorted square pyramidal complex (Addison parameter, = 0.18). 45 It is tentatively proposed that reaction occurs through the formation of iminopyridine complex 3 (Scheme 2a), which presents a pendant free carboxylic terminus (-COOH). The subsequent intramolecular nucleophilic attack of the carboxylic group without the loss of chloride produces the pentacoordinate anion 4, which crystalizes out with the protonated form of -alanine as the counterion, as shown by the structure in Fig 3 . Attempts to improve the yield by forcing the conditions (MeOH/THF reflux, 4 h) were not successful. In all cases, 4[-alaH] was isolated from the red reaction mixture as a few green crystals, indicating that the formation of the Schiff base is not favoured even under strong conditions. In contrast to the acepy complex 1b, complex 1a, which was formed in-situ by mixing equimolar amounts of CuCl2•2H2O and pyca, reacts smoothly at room temperature with one equivalent of -ala in methanol. After 30 min at room temperature, a bright green solution is obtained, from which the corresponding iminopyridine complex 5 (Scheme 2b) could be isolated as a green microcrystalline solid in excellent yield (94%). The fact that 1a is more reactive towards -ala than 1b is not surprising given the higher electrophilicity and lesshindered nature of pyca compared to acepy. Crystals suitable for X-ray analysis were grown from MeOH/H2O. In the structure of 5 (Figure 4, above), the copper atom is bonded to two chlorides and a chelating   N, N')-iminopyridine ligand formed through the Schiff condensation of pyca and -ala. In this case, the carboxylic acid is protonated and is not involved in coordination, unlike in the structure of complex 4[-alaH] (Figure 3). Complex 5 can be viewed at first sight as a squareplanar complex, although in the solid it exhibits two additional interactions with the O and Cl atoms of neighbouring molecules, thus completing a tetragonally elongated octahedron around the copper atom, as could be expected due to the Jahn-Teller effect. Additionally, a water molecule is present in the lattice, connecting the carboxylic O-H and the chlorine atoms of neighbouring molecules (Figure 4, bottom). The deprotonation of compound 5 can be easily accomplished with MeONa to produce the neutral complex 6 (Scheme 2b). The solid state structure of 6 (Figure 5, top) confirms the deprotonation of the carboxylic acid and subsequent intramolecular replacement of chloride by the carboxylate ligand that coordinates Cu(II) through one oxygen, thus, the 2pyridyl-alaninate ligand acts as κ 3 (N,N',O), as observed in the structure of the anionic complex 4. The structure of 6 presents a distorted square pyramidal geometry (= 0.097), which includes a chloride atom and the iminopyridine ligand. The fifth position is occupied by an oxygen atom from the carboxylate, which produces an extended polymeric structure (see Figure 5 bottom). Here, the Cu(II) sits 0.192 Å outside the plane formed by N(1), N(2), O(1) and Cl(1). This journal is © The Royal Society of Chemistry 20xx

Please do not adjust margins Please do not adjust margins UV-VIS titration studies demonstrated that the reaction is fully reversible (see ESI). The addition of HCl to 6 to generate 5 was followed using UV-VIS, and showed the opening of the alanine chain to form 5 upon addition of HCl to a solution of 6 in methanol. This is particularly interesting, since it indicates that the reversible switching of the coordination mode from κ 3 (N,N′,O) to κ 2 (N,N′) can be triggered by simply manipulating the pH (Scheme 2b). It also demonstrates the robustness of the complex towards acids and bases (HCl, NaOH). These features are particularly interesting for the potential use of these complexes as MRI agents (see later discussion).

Aldol reaction and hemiacetal formation promoted by Cu(II) and Cu(I) reactions

As background for this study, we also explored the reactivity of Cu I Cl with pyca and acepy towards nucleophilic additions. Upon mixing Cu I Cl with pyca in methanol under aerobic conditions (Scheme 3), the solid dissolves quickly to give a colourless solution. However, this solution gradually takes on the green colour characteristic of Cu II . After 15 minutes, the complete precipitation of green 7 is produced. Contrary to our expectations, the solid-state structure of 7 is a dimeric Cu II complex with two coordinated and deprotonated methanol hemiacetal ligands derived from pyca (see Scheme 3 and ESI for the structural characterization). Compound 7 had been previously obtained as the unexpected product of the reaction of 4-iodo-N-(2-pyridylmethylene)-aniline with CuCl. 46 Scheme 3. Synthesis of copper complexes with 2-pyridine carboxaldehyde and iminopyridine-alanine.

A tentative mechanism for the formation of 7 is depicted in Scheme 4. In alcohols, aldehydes are in equilibrium with hemiacetals; 47,[START_REF] Clayden | Organic Chemistry[END_REF] this is also the situation for the pyca ligand in MeOH (see Fig S3-S4/ESI). The presence of the Lewis acid centre Cu(I) would help to promote the nucleophilic attack of MeOH on the aldehyde group of pyca via metal coordination. A soluble Cu(I)/pyca complex could conceivably be formed in the first step, after which the copper would be oxidized to Cu(II) by atmospheric oxygen, providing the basic medium that promotes the deprotonation of the hemiacetal (equation 1): eq 1 Indeed, complex 7 can be also obtained in virtually quantitative yield from CuCl2 via the reaction of complex 1a with sodium methoxide in methanol (see Fig S5/ESI for comparison of the X-ray powder diffraction analysis (XRPD)). It has been reported that the stabilization of the hemiacetal species may be promoted via the coordination of Lewis acids, [START_REF] Barman | [END_REF]50 strong hydrogen bonds 51 and metal ions. 52,53 In this vein, copper (II) is an excellent electron acceptor for nitrogen atoms and hydroxyl groups, 54,55 and could thus allow stabilisation of the otherwise highly reactive deprotonated methanol hemiacetal pyridyl ligand in complex 7 (which is not stable as a free species). This, together with the poor solubility of the final product in MeOH, is likely to be the driving force for the formation of 7. Scheme 4. Mechanism of the synthesis of complex 7.

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Please do not adjust margins Remarkably, the formation of the hemiacetal alkoxide complex 7 does not completely preclude nucleophilic attack by the amine, and complex 7 undergoes Schiff formation with β-ala. Complex 7 is slowly dissolved upon treatment withalanine in methanol to yield a deep green solution from which compound 6 could be isolated as green microcrystals (see Scheme 3). Interestingly, when acepy is reacted with Cu I Cl in methanol, the clean precipitation of complex 8 occurs after 2 h at room temperature, which was isolated in virtually quantitative yield (Scheme 5). The single-crystal X-ray structure of 8 reveals the unexpected formation of an all-Cu(II) tetramer (see Figure 6). The structure shows the formation of a dianionic ligand, which results from a self-aldol addition in which two molecules of acepy react to produce an aldol (β-hydroxy ketone). This ligand coordinates via the N and O atoms. The in-situ formation of two alkoxides favours the bridging of several Cu centres to accommodate the negative charge. This feature, along with the tetradentate nature of the ligand and its relatively rigid structure, prevents its coordination to a single metal and favours the formation of a stable polynuclear complex. A tentative mechanism for the formation of the tetrameric complex 8 is depicted in Scheme 6. The addition of methanol to the ketone would result in the formation of a hemiacetal, as was also observed in the synthesis of 7. In both processes, the coordination to copper and the basic media resulting from the oxidation of Cu(I) to Cu(II) favoured the stabilization of the alkoxide. The key difference between the formation of 7 and 8 was the less nucleophilic character of acepy and the presence of acidic hydrogen atoms, which made self-aldol addition possible. The deprotonation of acepy is facilitated by the coordination of acepy to copper and by the mildly basic media resulting from Cu(I) oxidation. Nucleophilic attack of a second acepy molecule (self-aldol addition) would lead to the formation of a β-alkoxy ketone, in which the negative charge of the alkoxide ligand would be stabilised via metal coordination. The carbonyl group undergoes hemiacetal formation mediated by Cu coordination, and the OH group is deprotonated to bind as a μ2 bridging ligand, which is not surprising based on the previous discussion. Further evidence for this mechanism came from the reaction of Cu II complex 1b in THF and basic media (1 eqv KOH), which results in the formation of 8 in 92% yield. It is interesting to note that prolonged storage of 1b in THF at ambient temperature slowly yielded a few crystals of 8. This indicates that the coordination of acepy to Cu II increases the acidity of the CH3 group in acepy, favouring enolate formation (even in the absence of a base), which ultimately produces 8 via an aldol-type reaction. However, this also indicates that the formation of such an enolate could be partly responsible for the low yield observed for the Schiff reaction of 1b and -ala (see Scheme 2a, above). Aldol addition between two acepy molecules to produce the corresponding neutral β-hydroxy ketone (HL′) ligand was observed by Perlepees et al. under strongly basic conditions. 54 Upon reaction with Cu(ClO4)2•6H2O, a mixture of complexes was formed, including [Cu2Cl2(L′)2](ClO4)2]. The presence of chloro ligands in addition to the original ClO4 in the Cu(II) coordination sphere was proposed to arise via the loss of chloride from CHCl3 (used as a solvent) under such strongly This journal is © The Royal Society of Chemistry 20xx

Please do not adjust margins Please do not adjust margins basic conditions. In a similar vein, Datta et al. 56 prepared a dinuclear dimeric complex, [Cu2(HL)2](ClO4)2, by the oxidation of [Cu I (acepy)2]ClO4 with H2O2 in MeOH, where HL is a monoanionic ligand that results from aldol addition between two acepy molecules and hemiacetal formation. Our results show that the Cu metal precursor has a crucial impact on the association and nuclearity of the final complex. While the presence of weakly coordinating ClO4 ligands appears to promote the formation of dimeric complexes, as observed by Perlepees and Datta for two related ligands, the use of CuCl or CuCl2 both yielded the formation of the tetrameric all-Cu(II) complex 8. In this case, the more strongly coordinating anion (Cl -) results in the formation of a neutral tetrameric complex presenting two dianionic L 2-ligands, in which all the original chloride ligands remain coordinated to copper: two as terminal chloride ligands and two via µ 2 bridging. On a similar note, we have recently shown that the selection of the Cu(II) salt crucially determines the product of the Cu(II)-mediated crossaldol reactions of pyca and different ketones, with double aldol addition occurring when a copper precursor of sufficient Lewis acidity, such as Cu(ClO4)•6H2O, is used. 37

Complex derivatization of -ala bioconjugates

The β-alanine bioconjugate complex 7, with one halogen in the coordination sphere of the metal, is ideally suited for the study of not only the structural effect of the introduction of a different halogen/pseudohalogen but also the influence of such a change on the magnetic and cytotoxic properties of the complex in a systematic way. Altering the complex by substituting a pseudohalogen could provide an easy way to tune the magnetic and cytotoxic properties of the complex. In addition to this, the pH-dependent behaviour of 7 (see Scheme 2b) could have advantages due to its facile interconversion between the closed and open species in solution, which could favour its interaction with water molecules and improve its response to MRI. The substitution of the chlorine atom in complex 6 for other ligands allows the preparation of complexes with thiocyanate (9), azide (10a) and (10b), and cyanate (11) upon reaction of 6 with the corresponding sodium salt (Scheme 7). The thiocyanate complex 9 presents a distorted square pyramidal geometry with the copper atom displaced 0.117 Å from the basal plane. The coordination of the methanol molecule in the apical position prevents the formation of a polymeric structure such as that in complex 6 (Figure 8). Compound 10 crystallizes in two polymorphs, 10a and 10b, which present different extended structures in the solid state, as confirmed by X-ray diffraction. Although these extended structures are quite different, superposition of the monomer units of each shows that, apart from the obvious difference in the aliphatic carbons of the βalanine residue, the monomeric conformations are quite similar for both structures (Fig 9). As shown in Figure 10, in the polymorph 10a, the copper atom is coordinated to an oxygen of the carboxylate of a neighbouring molecule as found in previous structures, while in 10b there is an additional end-on azide bridge which results in a hexacoordinate (tetragonally elongated octahedron) copper atom, whereas 10a can be considered to be pentacoordinate (distorted square pyramidwith = 0.083). Complex 10b is a kinetic product and can be obtained after the reaction described in the experimental part (and confirmed by XPRD studies). Slow crystallization in MeOH/ether or H2O/MeOH/ether is required to obtain complex 10a (the thermodynamic product), and even in this case, the crystals are accompanied by variable amounts of polymorph 10b. In the case of complex 11, the molecule can be viewed as planar tetracoordinated (Figure 11, above) with the metallic centre displaced 0.169 Å from the basal plane. As in previous cases, in the extended structure, the copper atom attains pentacoordination through the oxygen atom of the carboxylate function of a neighbouring molecule (Figure 11, bottom).

Magnetic Measurements

The magnetic properties of the copper complexes 5, 6, 7, 9, 10b and 11 were investigated, and their magnetic properties correlated well with those the expected from their solid-state structures, which were discussed previously. With the exception of complex 7, which presents very strong antiferromagnetic coupling between the two copper (II) centres via a superexchange mechanism through the oxobridges (ESI Figure S17), the complexes show weak ferromagnetic or weak antiferromagnetic coupling. Table 1 shows that the Curie constants for 5, 6, 9, 10b and 11 are in the range 0.44-0.47 emuKmol -1 , which is in agreement with the expected values for one Cu(II) ion per formula unit. [START_REF] Carlin | Magnetochemistry[END_REF] Compounds 5 and 9 behave as isolated complexes with weak intermolecular antiferromagnetic coupling (see ESI). Compound 6 exhibits intramolecular antiferromagnetic behaviour; its magnetic properties can be descried using a model for regular antiferromagnetic S=1/2 spin chains (see ESI S19). In the case of complex 10b, an interaction that is most probably ferromagnetic is observed, which can be ascribed to the very small Cu-N-Cu angle found in its solid structure (see Figure S20, ESI). Finally, compound 11 presents a slight intramolecular ferromagnetic coupling owing to its anti-anti carboxylate arrangement (see ESI). This kind of behaviour has previously been found in some anti-anti carboxylate complexes. 38,39,[START_REF] Suarez-Varela | [END_REF] Relaxivity measurements Gd 3+ is ideally suited for MRI studies due to its half-filled f 7 electronic configuration. As a result of its seven unpaired electrons, Gd 3+ is an excellent T1 MRI contrast agent that greatly increases the relaxation time of the protons of water. Despite the ubiquity of Gd 3+ in MRI studies, serious concerns about its toxicity have recently motivated the use of other metal complexes. This has stimulated interest in finding new alternatives and other types of reagents. For instance, complexes based on paramagnetic chemical exchange saturation transfer (PARACEST), [START_REF] Ferrauto | Contrast Agents for MRI: Experimental Methods[END_REF][START_REF] Mcmahon | Chemical Exchange Saturation Transfer Imaging: Advances and Applications[END_REF] which contain groups that can exchange protons with water (i.e. OH groups), including Cu(II) complexes, have recently been reported as promising magnetic resonance imaging (MRI) agent. 27 Although less paramagnetic than Gd 3+ , easy available Cu(II) complexes have attracted attention due to their much lower comparative toxicity and cost. Please do not adjust margins

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The relaxivity (r1) of the complexes 5, 6, 9, 10b and 11 bearing an iminopirydine ligand derived from β-ala were measured.

The "open" complex 5 and "closed" complexes 6, 9, 10b and 11, in which different halogen (Cl) or pseudohalogens (N3 NCO, and SCN, respectively) completes the coordination environment of Cu(II), are well suited to evaluate the effect of small structural changes on the relaxivity of the complex. All the complexes were soluble in D2O and in phosphate-buffered saline (PBS), and were found to be stable for months under aerobic conditions. Preliminary measurements were undertaken in a 11.7 T NMR instrument to identify the most promising candidates for more in-depth studies. The relaxivity (r1) of the complexes 5, 6, 9, 10b and 11 was measured in D2O (see ESI, Table S1) at a fixed concentration of 10 mM. The copper (II) chloride complexes 5 and 6 gave the most promising values (r1 values of 0.52 mM -1 s -1 and 0.33 mM -1 s -1 , respectively). The better T1-relaxivity values found for the chloride complexes could be due to the greater lability of this ligand as compared with the pseudohalogens in aqueous media, which might facilitate improved interaction with water molecules. These complexes display relaxivities higher than those measured for LFB 28 and comparable with those reported for other Cu(II) complexes containing N or O-donor ligands that have complicated structures or are more difficult to obtain. [START_REF] Dunbar | [END_REF][62][63] For instance, Reglinski and Spickett et al. reported relaxivities in the range of 0.11-0.42 mM -1 s -1 for N-substituted macrocyclic Cu(II) complexes (9.4 T, 0.1 M NaCl in D2O). As noted in the introduction, Luxol fast blue (LFB), a well-known histological stain, has been tested as an MRI contrast agent, and has a comparatively poorer value of 0.09 mM -1 s -1 (aqueous solution at 4.7 T). 28 Motivated by these results, we performed measurements for 5 and 6 in a 9.4 T pre-clinical MRI system in PBS at concentrations of 0-50 mM (see ESI). The T1-relaxivity values (r1) were calculated by plotting 1/T1 as a function of concentration, which gave values of 0.3798 and 0.2979 mM -1 s - 1 , respectively. The phantom images of complex 5 at different concentrations demonstrating the optical changes associated with relaxivity are shown in Fig. 12. Complex 5 displays brighter T1-weighted MRI images with increasing molar concentration.

Cytotoxicity/cell proliferation assay

A preliminary test of the cytotoxic activity of the iminopyridine complexes derived from β-ala 6 and 10 against Chinese hamster ovary (CHO) and human (HELA) cell lines has been performed. The cell viability was evaluated using the MTT assay (see ESI for details), and Table 2 shows the obtained IC50 values. Both complexes exhibit comparable activity against HELA cells (6: 37.55± 2.35, 10b: 42.74± 3.99 µM). Although these values are relatively high, the activity of complex 10b against CHO cells was relatively good (13.06 M ± 1.4, as compared to 11 M for cisplatin in CHO cell lines). 64,65 The large difference between the activities of the chloride complex 6 and azide complex 10b against CHO cells (Table 2) suggests that otherwise small changes in the coordination sphere of copper can induce dramatic effects in their toxicity against specific cell lines. Importantly, complex 10b presents a degree of selectivity between the studied cell lines (Table 2, entry 2). These results, although preliminary, might help to pave the way for a more general search to prepare tailor-made complexes for specific types of tumours.

Conclusions

Pyca and acepy copper complexes can be easily prepared from inexpensive Cu(I) and Cu(II) salts. These complexes promote aldol addition, acetal formation and Schiff condensation under aerobic and mild conditions, thus providing access to new N,Odonor and N,N donor multidentate pyridyl ligands and the easy synthesis of copper complexes with nuclearity ranging from monomers to tetramers. The method involves the formation of the ligands in the coordination sphere of the metal, and presents several advantages: i) it facilitates the reaction by activating the electrophile via metal coordination. ii) The formation of the ligand in the coordination sphere of copper might lead to the stabilization of otherwise very reactive species, allowing the preparation of novel ligands. iii) It is a simple and one-pot procedure that simplifies the preparation of the complexes by avoiding the need for previous preparation and purification of the ligand, which is usually time-consuming and can involve several steps. This can be seen in the formation of the tetranuclear Cu(II) complex 9, which involves an in situ self-aldol reaction and hemiacetal formation. This reaction also illustrates the ability of copper coordination to promote metal enolate formation under mild conditions by increasing the acidity of the CH3 group protons in acepy. This is distinctly different from the case of the pyca ligand, which does not bear acidic protons. The above features make this procedure particularly attractive for transition metal cluster stabilization, since traditional procedures involve the separate synthesis of the appropriate polydentate ligands, which can involve multiple reaction and purification steps, followed by their later addition to the metal precursor. Deprotonation-protonation of the iminopiridine ligand derived from β-ala results in a change of the coordination mode from κ 2 (N,N′) in 5 to κ 3 (N,N′,O) in 6; this change was demonstrated to be reversible. The chloride ligand in the closed copper

Please do not adjust margins Please do not adjust margins complex 6 can be easily replaced. The solid-state structures of the resulting complexes vary from monomers to different types of coordination polymers; these structural changes have a definite effect on their magnetic properties. Importantly, the simple substitution of a pseudohalogen for a chloride ligand critically changes the cytotoxicity properties of the resulting copper complexes, and can be used to modulate their relaxivities. Among the six Schiff base copper complexes studied, complex 5 presented better relaxivity in water and physiological media than similar derivatives with a more rigid scaffold, and phantom images could be obtained. The replacement of a chloride by N3 in particular had a critical impact on cytotoxicity and selectivity of the Schiff base copper(II) complex towards specific cell lines, thus providing a simple strategy to tune the cytotoxic properties of the ligand.

Experimental section

Materials and general methods.

Solvents, ligands and other reagents were purchased from commercial suppliers and used without purification unless otherwise stated. Kieselguhr (diatomaceous earth, Merck) was used for filtration. IR spectra in solid were recorded with a Frontier Perkin Elmer Spectrum RX I FT-IR instrument. XRPD measurements (X-Ray Powder Diffraction) were performed in the Laboratory of Instrumental Techniques of the University of Valladolid (L.T.I., www.laboratoriotecnicasinstrumentales.es) using a Bruker Discover D8. The XRPD patterns all isolated compounds were coincident with those predicted by the software package MERCURY from the X-ray single crystal analysis (see SI), thus confirming the identity of the bulk products. Magnetic measurements were performed using a Quantum Design SQUID-VSM magnetometer. Magnetization measurements at different fields at a given temperature confirmed the absence of ferromagnetic impurities. Data were corrected for the sample holder, and their diamagnetism was estimated from the Pascal constants. The static susceptibility measurements were performed in the temperature range 1.8-300 K with an applied field of 5000 Oe. Chinese hamster ovary (CHO) and HELA cells were maintained in Dulbeco's modified Eagle's medium (DMEM) with 10% fetal bovine serum, penicillin-streptomycin (100 U/ml each) and 2 mM Lglutamine at 37 • C in a 5% CO2 humidified atmosphere. Cells were grown as a monolayer to a confluence of up to 80% in a 35 mm Petri dish. After trypsinization, the cells were counted with a hemocytometer and seeded at a density of 1.5  10 4 cells per 0.1 ml per well in 96-well microplates prior to cell viability measurements. Synthesis of complex 1b. To a green solution of CuCl2•2H2O (0.170 g, 1 mmol) in tetrahydrofuran (15 mL)

Synthesis of [CuCl(py-2-C(H)=NCH2CH2COO)] (6). a)

To a bright green suspension of 5 (0.312g, 1 mmol) prepared as described above was added NaOMe (0.054 g, 1 mmol) in methanol. After 30 min of stirring, the solution became dark green and was filtered using kieselghur. The solution was concentrated, and the crystalline solid was isolated by precipitation with ether. b) To a solution of -alanine (0.178 g, 2 mmol) in methanol was added complex 7 (1 mmol, 0.474g) and the mixture was refluxed with stirring for 30 min to give a dark green solution of 6. After filtration to remove traces of undissolved 7, the solution was evaporated slowly to give green microcrystals of 6. Yield 0.454 g, 82%. Anal. Calcd. for C9H9Cl1Cu1N2O2: C 39. Please do not adjust margins Please do not adjust margins 0.5 mmol) was deposited in the bottom of a vial (2 cm diameter, 10 cm high), and was covered by a layer of MeOH (5 mL). A second layer consisting of a solution of pyca (0.053 g, 0.5 mmol) in Et2O (5 mL) was deposited on top of the MeOH layer. The vial was left open to air. After several days of slow diffusion, a crop of suitable crystals had grown on the wall of the vial. b) To a green solution of CuCl2•2H2O (0.170 g, 1 mmol), in methanol was added pyca (0.107 g, 1 mmol) and NaOMe (0.192 g, 3 mmol) with stirring. After 15 min, the expected green precipitate appeared. The precipitate and the crystals were confirmed to be identical by powder diffraction and IR in solid phase. (See text and Electronic Supplementary Information)

Synthesis of complex 8. To a suspension of CuCl (0.099 g, 1 mmol) in methanol was added acepy (0.121 g, 1 mmol). After 2 hours, a dark green microcrystalline precipitate appeared. The precipitate was filtered with a frit and washed with methanol. Yield 0.215 g (91.5%). Anal. Calcd. C30H32Cl4Cu4N4O6: C 38.31 H 3.43 N 5.96. Found: C 38.42 H 3.39 N 5.81.

Synthesis of complex 9.

To a solution of 6 (0.276 g, 1 mmol) in MeOH (15 mL) was added KSCN (0.098 g, 1 mmol) and the mixture was stirred for 4 h. After this time, a green precipitate appeared and was collected by filtration. Yield 0.245 g, 82%. Anal. Calcd. C11H13Cu1N3O3S1: C 39.93, H 3.96, N 12.70. Found C 39.36, H 3.16, N 19.09. A crystal suitable for X-ray determination was obtained by slow diffusion of methanol/Et2O into a solution of 9 in H2O.

Synthesis of complex 10a. To a solution of 6 (0.276 g, 1 mmol) in MeOH (15 mL) was added NaN3 (0.065 g, 1 mmol), and the mixture was stirred for 24 h. The green precipitate was isolated by filtration. XPRD showed that the bulk precipitate consisted of a mixture of polymorphs 10a (ca. 30%) and 10b (ca. 70%). Single crystals suitable for X-ray analysis were obtained as follows: a solution of 6 (0.276 g, 1 mmol) in methanol was deposited at the bottom of a vial (2 cm diameter and 10 cm high) and was carefully covered by another layer consisting of NaN3 (0.065 g, 1 mmol) in MeOH. After 24 hours of slow diffusion, green single crystals of 10a and 10b were separated and picked by hand under the microscope.

Synthesis of complex 10b. To a solution of 6 (0.276 g, 1 mmol) in MeOH (15 mL) was added NaN3 (0.065 g, 1 mmol) and the mixture was stirred for 1 h. The green precipitate was isolated by filtration. Yield 0.223 g, 79%. Anal. Calcd. for C9H9Cu1N5O2: C 38.23, H 3.21, N 24.77. Found C 38.11, H 2.99, N 24.29. A crystal suitable for X-ray determination was obtained by slow diffusion of methanol/Et2O into a solution of 10b in H2O.

Synthesis of [Cu(NCO)(py-2-C(H)=NCH2CH2COO)] (11).

To a solution of 6 (0.276 g, 1 mmol) in MeOH (15 mL) was added NaNCO (0.065 g, 1 mmol), and the mixture was stirred for 4 h. The solution became green-bluish, and a precipitate appeared. The precipitate was isolated by filtration. Yield 0.172 g, 61%. Diffraction data were collected using an Oxford Diffraction Supernova diffractometer equipped with an Atlas CCD area detector and a four-circle kappa goniometer. For data collection, Mo or Cu micro-focus sources with multilayer optics were used. Data integration, scaling and empirical absorption correction were carried out using the CrysAlis Program package. [START_REF]CrysAlisPro-Data Collection and Integration Software[END_REF] The structure was solved using direct methods and refined by Full-Matrix-Least-Squares against F 2 with SHELX 67 in OLEX2. 68 The non-hydrogen atoms were refined anisotropically, and hydrogen atoms were placed at idealised positions and refined using the riding model. Graphics were made with OLEX2 and MERCURY. 69 Crystal data, particular details and CCDC reference numbers are given in the Supporting Information Table S3.

Figure 1 .

 1 Figure 1. A) Reactivity of 1a in the promotion of the cross-aldol reaction. 37 B) Template formation of Schiff bases by reaction of amines (this work). C) Reactivity of acepy complexes facilitating different nucleophilic processes (this work).

Scheme 1 .

 1 Scheme 1. a) Synthesis of copper pyca complexes 1a and 2a. b) Synthesis of coper acepy complexes 1b and 2b.

Fig. 2 (

 2 Fig. 2 (Top) Perspective view of compound 1b showing the atom numbering. Selected bond distances (Å) and angles (°): Cu(1A)-N(1A) 2.012(3), Cu(1A)-O(1A), 2.042(3), Cu(1A)-Cl(1A) 2.253(1), Cu(1A)-Cl(2A) 2.216 (1), Cu(1A)-Cl(1) 2.701(1), Cl(1A)-Cu(1).Cl(1) 92.41(4), Cl(2A)-Cu(1)-Cl(1) 100.76(4), Cl(2A)-Cu(1)-Cl(1A) 94.55(5), N(1)-Cu(1)-O(1) 78.97(11). (Bottom) Perspective view of compound 2b showing the additional interactions. Selected bond distances (Å) and angles (°): Cu(1)-Cl(1) 2.315(1), Cu(1)-N(1) 1.989(2), Cu(1)-O(1) 2.380(2), Cl(1)-Cu(1)-O(1) 88.74(5), N(1)-Cu(1)-Cl(1) 90.24(6), N(1)-Cu(1)-O(1) 75.25(7).

Fig. 3

 3 Fig. 3 Perspective view of compound 4[-alaH] showing the atom numbering. Selected bond distances (Å) and angles (°): Cu(1)-Cl(1) 2.249 (1), Cu(1)-Cl(2) 2.585(1), Cu(1)-N(1) 1.997(2), Cu(1)-N(2) 1.987(2), Cu(1)-O(1) 1.962(2), Cl(2)-H(90) 2.224, O(2)-H(92) 1,894, Cl(1)-Cu(1)-Cl(2) 104.72(3), N(1)-Cu(1)-Cl(1) 95.82(7), N(1)-Cu(1)-Cl(2) 91.98(7), N(2)-Cu(1)-Cl(1) 159.47(8), N(2)-Cu(1)-N(1) 80.46(9), O(1)-Cu(1)-N(2) 92.07(8).

Fig. 4 (

 4 Fig. 4 (Top) Perspective view of compound 5 showing the atom numbering. Selected bond distances (Å) and angles (°): Cu(1)-Cl(1) 2.268(1), Cu(1)-Cl(2) 2.258(1), Cu(1)-N(1) 2.029(2), Cu(1)-N(2) 2.030(2), Cl(1)-Cu(1)-Cl(2) 92.88(3), Cl(1)-Cu(1)-N(2) 94.71(7), Cl(2)-Cu(1)-N(1) 92.74(7), N(1)-Cu(1)-N(2) 80.36(9). (Bottom) Perspective view of compound 5 showing the additional interactions. Selected bond distances (Å) and angles (°): Cu(1)-O(1A) 2.652(3), Cu(1)-Cl(1A) 2.981(1), O(2)-O(90) 2.616(4), O(90)-Cl(2A) 3.204(4), O(90)-Cl(1A) 3.241(3), O(2)-H(2)-O(90) 175.1(2), O(90)-H(90A)-Cl(2A) 168.5(2), O(90)-H(90B)-Cl(1A) 165.7(2).

Fig. 5 (

 5 Fig. 5 (Top) Perspective view of the "monomer" of compound 6 showing the atom numbering. Selected bond distances (Å) and angles (°): Cu(1)-Cl(1) 2.254(1), Cu(1)-N(1) 2.040(2), Cu(1)-N(2) 1.983(2), Cu(1)-O(1) 1.937(2), Cl(1)-Cu(1)-O(1) 91.62(6), Cl(1)-Cu(1)-N(1) 94.26(7), N(1)-Cu(1)-N(2) 80.30(9), N(2)-Cu(1)-O(1) 92.13(8). (Bottom) Extended polymeric structure of compound 6 in the solid phase. The copper atoms attain pentacoordination through long Cu-O(2) bonds of 2.303 Å (cf. Cu-O(1) 1.937 Å).

Scheme 5 .

 5 Scheme 5. Synthesis of complex 8 from CuCl.

Fig. 6

 6 Fig. 6 Structure of 8 showing the atom numbering. Selected bond lengths (Å) and angles (°): Cu(2)-Cl(2) 2.217(2), Cu(2)-Cl(3) 2.274(3), Cu(2)-N(2) 1.960(8), Cu(2)-O(1) 1.920(5), Cu(1)-Cl(3) 2.587(3), Cu(1)-O(1) 1.922(5), Cu(1)-O(2) 1.942(5), Cu(1)-O(2A) 1.948(5), Cu(1)-Cu(1A) 2.853(2), Cu(1)-Cu(2) 3.107(1), N(2)-Cu(2)-O(1) 101.4(3), O(1)-Cu(2)-Cl(3) 88.95(18), O(1)-Cu(1)-Cl(3) 80.25(17), O(2)-Cu(1)-O(2A) 82.4(2).

Scheme 6 .

 6 Scheme 6. Suggested pathway for the formation of 8.

Scheme 7 .

 7 Scheme 7. Derivatization of 6 to obtain complexes 9-11.

Fig. 8

 8 Fig. 8 Perspective view of compound 9 showing the atom numbering. Selected bond distances (Å) and angles (°): Cu(1)-N(1) 2.013(3), Cu(1)-N(2) 1.969(3), Cu(1)-N(3) 1.955(3), Cu(1)-O(1) 1.925(2), Cu(1)-O(3) 2.288(2), N(3)-Cu(1)-N(1) 95.19(11), N(1)-Cu(1)-N(2) 81.07(11), N(2)-Cu(1)-O(1) 92.50(10), N(3)-Cu(1)-O(1) 90.74(10).  = 0.08.

Figure 9 .

 9 Figure 9. (Top) Perspective view of the "monomer" of compound 10b showing the atom numbering. Selected bond distances (Å) and angles (°): Cu(1)-N(1) 2.025(3), Cu(1)-N(2) 1.995(3), Cu(1)-N(3) 1.977(3), Cu(1)-O(1) 1.938(3), N(3)-Cu(1)-N(1) 92.26(15), N(1)-Cu(1)-N(2) 80.25(14), N(2)-Cu(1)-O(1) 95.08(13), N(3)-Cu(1)-O(1) 93.16(13). (Bottom) Overlay (MERCURY) of the conformations of the monomers in the structures of the two azide complexes 10a (blue) and 10b (red).

Fig. 10 (

 10 Fig. 10 (Left) Extended polymeric structure of compound 10a in the solid phase. The copper atoms attain pentacoordination through bonding with the oxygen of the carboxylate (Cu(1)-O′ is 2.255 Å). It can be seen as a zig-zag polymer. (Right) Extended polymeric structure of compound 10b in the solid phase. The copper atoms attain hexacoordination through alternating coordination to the oxygen of the carboxylate (Cu(1)-O′ is 2.496 Å) and the N atom of the azide ligand (Cu(1)-N' is 2.715 Å). It can be seen as a chain of asymmetric azide-bridged dimers with Cu(1)-Cu(2) 3.487 Å, Cu(1)-N(1)-Cu(2) 94.71

Figure 11 .

 11 Figure 11. (Top) Perspective view of the "monomer" of compound 11 showing the atom numbering. Selected bond distances (Å) and angles (°): Cu(1)-N(1) 2.033(2), Cu(1)-N(2) 1.978(2), Cu(1)-N(3) 1.979(3), Cu(1)-O(1) 1.938(2), N(3)-Cu(1)-N(1) 94.47(10), N(1)-Cu(1)-N(2) 80.35(9), N(2)-Cu(1)-O(1) 92.40(9), N(3)-Cu(1)-O(1) 91.13(10). (Bottom) Extended polymeric structure of compound 8 in the solid phase. The copper atoms attain pentacoordination through long Cu-O(2) bonds of 2.264 Å (cf. Cu-O(1) 1.937 Å) in a fashion similar to compounds 5 and 7a.  = 0.037

Figure 12 .

 12 Figure 12. Selected T1-weighted phantom images of complex 5 at different concentrations (0-50 mM).

  Anal. Calcd. for C10H9Cu1N3O3: C 42.48, H 3.21, N 14.86. Found C 42.48, H 3.11, N 14.59. A crystal suitable for X-ray determination was obtained by slow diffusion of methanol/Et2O into a solution of 11 in H2O.

Table 1 .

 1 Curie constant and Weiss temperature for compounds 5

, 6, 9, 10b and 11.

	5	6	9	10b	11
	C (emuKmol -1 ) 0.45(1)	0.44(1) 0.45(1) 0.47(1) 0.48(1)
	 (K)

-0.11(1) -0.13(1) 0.00(5) -1.07(5) 0.27

(3)

This journal is © The Royal Society of Chemistry 20xx

Table 2 .

 2 IC50 values in M

	Complex	HELA	CHO
	6	37.55 ± 2.35	42.74± 3.99
	10b	40.05 ±2.18	13.06 ±1.41

  After 30 min, a bright green precipitate appeared. The solid was isolated by filtration and washed again with methanol. Yield 0.292 g, 94%. Anal. Calcd. for C9H10Cl2Cu1N2O2: C 34.58, H 3.22, N 8.96. Found C 34.57, H 3.11, N 8.90. A crystal suitable for X-ray determination was obtained by slow diffusion of MeOH/Et2O into a solution of 5 in H2O.

	by slow evaporation of a solution of CuCl2•2H2O with acepy in
	MeOH/ether
	Synthesis of complex 2b. a) To a solution of 1b in
	methanol (0.128 g, 0.5 mmol) was added acepy (0.061 g, 0.5
	mmol) with stirring. After 30 min, a dark green precipitate
	appeared which was collected by filtration in a fritted funnel.
	b) To a green solution of CuCl2•2H2O (0.170 g, 1 mmol) in
	methanol (15 mL) was added 2-acetylpyridine (0.242 g, 2
	mmol) with stirring. After 30 min, a dark green precipitate
	appeared. The solid was isolated by filtration with a fritted
	funnel. Yield 0.355 g, 94%. Anal. Calcd. for C14H14Cu2Cl2N2O2: C
	44.64, H 3.75, N 7.44. Found C 44.51, H 3.70, N 7.35. A crystal
	suitable for X-ray determination was obtained by slow
	evaporation of a solution of CuCl2•2H2O with acepy in
	MeOH/H2O.
	Synthesis of complex 4[-alaH]. To a green solution of 1b
	in methanol (0.128 g, 0.5 mmol) was added -alanine (0.045 g,
	0.5 mmol) with stirring. The solution turned a red-brown
	colour. After several days, it was possible to obtain a few
	crystals of the complex 4[-alaH].
	[CuCl2(py-2-C(H)=NCH2CH2COOH)] (5). To a green solution
	of CuCl2•2H2O (0.170 g, 1 mmol) in methanol was added
	pyridine-2-carboxaldehyde (pyca) (0.107 g, 1 mmol) and -
	alanine (0.089 g, 1 mmol) with stirring.
	was added 2-
	acetylpyridine (0.121 g, 1 mmol) with stirring. After 30 min, a
	green precipitate appeared. The solid was isolated by filtration
	with a fritted funnel. Yield 0.238 g, 93%. Anal. Calcd. for C7H7
	Cl2Cu1N1O1: C 32.90, H 2.76, N 5.48. Found C 32.70, H 2.55, N
	5.41. A crystal suitable for X-ray determination was obtained

  14, H 3.29, N 10.14. Found C 38.55, H 3.28, N 9.78. A crystal suitable for X-ray determination was obtained by slow diffusion of Et2O into a solution of 6 in MeOH. Upon stirring, the solid CuCl first dissolved to give a pale green solution, and after 30 min, a bright green precipitate of 7 was produced. The solid was collected in a frit, washed with MeOH, and dried by suction. Yield 0.225 g, 95%. Anal. Calcd. for C14H16Cl2Cu2N2O4: C 35.45, H 3.40, N 5.91. Found C 34.84, H 3.38, N 5.57. Compound 7 was too insoluble to be recrystallized. A crystal suitable for Xray determination was prepared as follows: solid CuCl (0.044 g, This journal is © The Royal Society of Chemistry 20xx

	Synthesis of complex 7. 46 a) To a solution of pyridine-2-
	carboxaldehyde (pyca) (0.107 g, 1 mmol) in MeOH (10 mL) in a
	50 ml wide-neck round bottom flask open to air was added
	CuCl (0.099 g, 1 mmol).

Scheme 2. Synthesis of copper complexes with 2-pyridine carboxaldehyde and iminopyridine-alanine.

Acknowledgements

We thank the Spanish Ministry of Science, Innovation and Universities (MCIU) for funding (project numbers PGC2018-096880-A-I00 and PGC2018-099470-B-I00, MCIU/AEI/FEDER, UE). R.G.-R. acknowledges the Spanish MINECOAEI and the European Union (ESF) for a Ramon y Cajal contract (RYC-2015-19035).

Notes and references

Conflicts of interest

There are no conflicts to declare.


Joseph Razik

Hervé Glotin

Maia Hoeberechts

Yann Doh

Sébastien Paris

Sparse coding for efficient bioacoustic data mining: Preliminary application to analysis of whale songs

Bioacoustic monitoring, such as surveys of animal populations and migration, needs efficient data mining methods to extract information from large datasets covering multi-year and multi-location recordings. Usually, the study of the humpback whake songs is based on the classification of sound units, notably to extract the song theme of the singers, which might signify the geographic origin and the year of the song. Most of these analyses are currently done with expert intervention, but the volume of recordings drive the need for automated methods for sound unit classification.

This paper introduces a method for sparse coding of bioacoustic recordings in order to efficiently compress and automatically extract patterns in data. Moreover, this paper proposes that sparse coding of the song at different time scales supports the distinction of stable song components versus those which evolve year to year. It is shown that shorter codes are more stable, occurring with similar frequency across two consecutive years, while the occurrence of longer units varies across years as expected based on the prior manual analysis. We conclude by exploring further possibilities of the application of this method for biopopulation analysis.

I. INTRODUCTION

Different kinds of vocalizations (moans and screams) are emitted by Humpback whales [START_REF] Schevill | Underwater sounds of cetaceans[END_REF], reported as songs by Roger and Katy Payne [START_REF] Anonymous | Singing whales[END_REF], [START_REF] Winn | Sounds of the humpback whale[END_REF]. These songs are predominant in the breeding zone but have also been recorded during migration and occasionally in the feeding area [START_REF] Clapham | Humpback whale songs as indicators of migration routes[END_REF], [START_REF] Clark | Acoustic monitoring on a humpback whale (megaptera novaeangliae) feeding ground shows continual singing into late spring[END_REF].

These sounds are emitted by male individuals [START_REF] Glockner | Determining the sex of humpback whales (megaptera novaeangliae) in their natural environment[END_REF]. Different hypotheses about these songs is that they possibly play a role in female attraction [START_REF] Winn | The song of the humpback whale megaptera novaeangliae in the west indies[END_REF], [START_REF] Herman | The communication systems of cetaceans[END_REF], [START_REF] Tyack | Interactions between singing hawaiian humpback whales and conspecifics nearby[END_REF], [START_REF] Medrano | Sex identification of humpback whales, megaptera novaeangliae, on the wintering grounds of the pacific ocean[END_REF] and/or for strong interactions between males as territorial defense or challenges [START_REF] Darling | Migrations, abundance and behavior of hawaiian humpback whales (megaptera novaeangliae)[END_REF], [START_REF] Cholewiak | Evaluating the role of song in the humpback whale (megaptera novaeangliae) breeding system with respect to intra-sexual interactions[END_REF]. Noad et al. [START_REF] Noad | Cultural revolution in whale songs[END_REF] highlighted song copying between males from the Australian East coast and those from the Australian West coast.

Songs are cyclic and composed of a structured and continuous sequence of sounds that can be repeated several times without interruption. These short continuous sounds between 2 silences are called sound units [START_REF] Payne | Songs of humpback whales[END_REF]. The complex structure of these songs are based on successive specific sound units forming a sequence, several sequences forming a phrase, and several phrases forming a theme-song [START_REF] Payne | Songs of humpback whales[END_REF].

Current challenging objectives in the analysis of humpback whale songs include:

1) detection of the different kinds of vocalizations;

2) automatic classification of the sound units;

3) extraction of phrases of the songs; 4) localization of individuals and characterization of interaction of the singers.

Objectives 1. and 2. are particularly challenging because:

• there is a large diversity of the sound units (moans, growls, sets of pulses, cries and trumpet sounds). Sound units' features vary both in the time domain and in the frequency domain. The main frequency is from 100 Hz up to 20 kHz and the source levels could be more than 170 dB re 1 u Pa at 1 m (see examples in Fig. 1); • singers emit sounds simultaneously;

• varied underwater ambient noise is present. For objectives 1. and 2., researchers proposed new approaches based on temporal and spectral features [START_REF] Suzuki | Information entropy of humpback whale songs[END_REF]. Some variations in these features were reported and could contain part of the information needed for detection and classification [START_REF] Au | The temporal characteristics of humpback whale songs[END_REF].

Methods used to analyze human speech have been applied to Humpback whale calls since they present several similarities including the presence of voiced and unvoiced type vocalizations, as defined in Mercado and Kuh [START_REF] Mercado | Classification of humpback whale vocalizations using a self-organizing neural network[END_REF]. Humpback whale calls have been analyzed using linear prediction coding (LPC) [START_REF] Mercado | Classification of humpback whale vocalizations using a self-organizing neural network[END_REF], energy content in specific time windows [START_REF] Rickwood | Methods for automatically analyzing humpback song units[END_REF], spectrographic analysis [START_REF] Suzuki | Information entropy of humpback whale songs[END_REF], Mel-Frequency Cepstral Coefficients (MFCCs) [START_REF] Helweg | Geographic and temporal variation in songs of humpback whales[END_REF], [START_REF] Mazhar | An analysis of humpback whale songs for individual classification[END_REF], [START_REF] Picot | Automatic prosodic clustering of humpback whales song[END_REF], [START_REF] Glotin | New automatic classification for humpback whale songs[END_REF], affinity propagation [START_REF] Glotin | New automatic classification for humpback whale songs[END_REF], K-means [START_REF] Pace | Subunit definition and analysis for humpback whale classification[END_REF], and classified with self-organizing maps (SOM) [START_REF] Mercado | Classification of humpback whale vocalizations using a self-organizing neural network[END_REF], [START_REF] Suzuki | Information entropy of humpback whale songs[END_REF], Hidden Markov Models (HMM) [START_REF] Pace | Classification of humpback whale (megaptera novaeangliae) calls using hidden markov models[END_REF], the sliding window match length (SWML) entropy estimation [START_REF] Suzuki | Information entropy of humpback whale songs[END_REF] and neural networks. The great variety of methods used by researchers to analyze Humpback whale vocalizations reflects the great diversity of the features of these sounds [START_REF] Harris | Automatic speech processing methods for bioacoustics signal analysis: a case study of cross-disciplinary acoustic research[END_REF].

The main drawback of most of these methods is that the number of sound units seems unknown, maybe unlimited, because songs are changing during the season, from one year to another and in the different breeding areas [START_REF] Payne | Humpback whale (megaptera novaeangliae) songs as an indicator of "stocks[END_REF], [START_REF] Helweg | Comparison of songs of humpback whales (megaptera novaeangliae) recorded in japan, hawaii, and mexico during the winter of 1989[END_REF], [START_REF] Cerchio | Temporal and geographical variation in songs of humpback whales, megaptera novaeangliae: Synchronous change in hawaiian and mexican breeding assemblages[END_REF].

To better analyze these songs and to improve performance of classification, we recently introduced the new concept of subunit [START_REF] Glotin | New automatic classification for humpback whale songs[END_REF], [START_REF] Pace | Characterisation of sound subunits for humpback whale song analysis[END_REF], [START_REF] Pace | Classification of humpback whale (megaptera novaeangliae) calls using hidden markov models[END_REF]. We suggest that one or more than one subunits are present in one sound unit. The interest of this approach is to show that a number of subunits could be used for characterizing the sound units, meaning that a sound unit could be built from a combination of these subunits.

This paper proposes for the first time a fully automatic sparse coding of humpback whale songs, to determine their stable components versus their evolving ones, at different time scales. We also propose a definition of code complexity which separates the song components from the background sea noise. We then explore the method's applicability to analyze the relative contribution sound units vs. subunits to song decomposition and evolution.

II. MATERIALS AND METHODS

A. Humpback whale recordings

For this paper we focus on recordings of humpback whales from Sainte Marie Channel (Madagascar). The recordings vary in length from several years to months or days, as they were collected by different collaborators (Megaptera, LAM, Cesigma). The datasets used were collected in 2008 and 2009. The frequency sampling is between 16 kHz and 44.1 kHz with 16 bit sample encoding. Fig. 1 shows a spectrogram of a typical sample of a Madagascar song from our corpus. The duration for the different recording sites is presented in Tab. I.

The hydrophone used for the recordings is a ColmarItalia GP280 (omni-directional, [5Hz, 90kHz], sensitivity -170 dB re 1 V/u Pa, see datasheet on www.colmaritalia.it). The hydrophone was deployed from a motor boat (motor off), positioned ≈100 m in front of the singers at depth 20 m (the water column depth was between 40 m and 50 m).

In order to normalize the data according to other recording parameters, we have down sampled all the sound files to 16 kHz sampling frequency, 16 bits.

B. Cepstral representation

The first step in the analysis is to characterize the recorded songs by Mel-Frequency Cepstral Coefficients (MFCC) [START_REF] Davis | Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences[END_REF], [START_REF] Rabiner | Fundamentals of Speech Recognition[END_REF], [START_REF] Pace | Subunit definition and analysis for humpback whale classification[END_REF]. The use of the cepstral scale is motivated by the fact that mammals perceive frequency on a logarithmic scale along the cochlea [START_REF] Greenwood | Auditory masking and the critical band[END_REF], [START_REF]Critical bandwidth and the frequency coordinates of the basilar membrane[END_REF]. In our approach, we apply a method developed for human speech analysis to humpback whale vocalizations [START_REF] Pace | Characterisation of sound subunits for humpback whale song analysis[END_REF]. Rather than directly duplicating the method, we demonstrate its application for analyzing the harmonic parts of each sound unit or subunit of whale songs. We then build codebooks at different time scales and explore their properties in characterizing song evolution.

We compute the 12 first static Mel-Frequency Cepstral Coefficients (MFCC), M 1 , M 2 , . . . , M 12 . To these 12 coefficients M 1 , M 2 , . . . , M 12 we add a M 0 coefficient that captures the energy of the signal, thus yielding 13-dimensional MFCC vectors. These coefficients are computed with a 512 point Fast Fourier Transform (32 ms), with a window length of 250 ms and a frameshift of 10 ms.

On these resulting vectors, Cepstral Mean Subtraction (CMS) and variance normalization were applied. The extraction of these parameters is done with the SPro toolkit [START_REF] Gravier | Spro: a free speech signal processing toolkit[END_REF].

As songs' patterns are longer than a 10 ms scale, we form super-vectors by concatenating MFCC vectors. These supervectors form the dictionary words in the sparse analysis (see Section II-C). We consider words of length 250 ms, 500 ms, 1 s, 2 s, and 4 s, which are formed by concatenating 25, 50, 100, 200 and 400 MFCC vectors respectively. In order to be sure to capture sound units, the vectors are concatenated with a 50% overlap; for example, the MFCC vectors we manipulate for 500 ms scale are 650-dimensional vectors (13 × 50 component), one every 250 ms.

C. Dictionary and sparse coding

In order to support efficient bioacoustic data mining, the large MFCC vectors are encoded by a learned dictionary and a sparse code. The sparse code identifies how dictionary words are recombined to produce a reconstructed representation of the original vectors. This dictionary was learned on the union of the MFCC representations of the original humpback whale song datasets. In this section we explain the details of the sparse coding method.

In order to obtain a global robust representation of the signal X = [x 1 , . . . , x N ] ∈ R n×N , each MFCC vector x i (n = 650 in the case of 500 ms) are first linearly encoded as the vector

c i ∈ R k such that x i ≈ Dc i where D [d 1 , . . . , d k ] ∈ R n×k
is a preliminary trained dictionary with the constraint d j 2 = 1. In a first attempt to solve this linear problem, c i can be the solution of the Ordinary Least Square (OLS) problem:

l OLS (c i |x i ; D) min c i ∈R k 1 2 x i -Dc i 2 2
(1)

OLS formulation can be extended to include a regularization term avoiding data overfitting. Thus, we obtain the ridge regression (RID) formulation:

l RID (c i |x i ; D) min c i ∈R k 1 2 x i -Dc i 2 2 + β c i 2 2
(2)

This problem can be analytically solved and then c i = (D T D + βI k ) -1 D T x i . In order to decrease reconstruction error and to have a sparse solution, this problem can then be reformulated as a constrained Quadratic Problem (QP):

l QP (c i |x i ; D) min c i ∈R k 1 2 x i -Dc i 2 2 s.t. c i 1 = 1 (3)
To solve this problem, we can use a QP solver involving high combinational computation to find the solution. Under the RIP assumption [START_REF] Tibshirani | Regression shrinkage and selection via the lasso[END_REF], a greedy approach can be used efficiently to solve 3. Finally, the sparse code (SC) is defined by:

l SC (c i |x i ; D) min c i ∈R k 1 2 x i -Dc i 2 2 + λ c i 1 , ( 4
)
where λ is a regularization parameter which controls the level of sparsity of the sparse code c i . This problem is also known as basis pursuit [START_REF] Chen | Atomic decomposition by basis pursuit[END_REF] or Lasso [START_REF] Tibshirani | Regression shrinkage and selection via the lasso[END_REF] problem. To solve this problem, we can use the popular Least Angle Regression (LARS) algorithm.

The dual part of the training of the dictionary D and the computation of the projection sparse codes C = [c i , . . . , c N ] supports the reconstruction of the MFCC vectors. For an MFCC vector x ∈ R n and the associated sparse code vector c ∈ R k , the reconstructed MFCC vector x is the linear combination of the dictionary codebook vector d i according to c i values of the sparse code c. More formally, x is given by the following equation:

x = D.c = k i=1 d i .c i (5)

D. Dictionary size

An important aspect of the encoding is the choice of an appropriate size for the dictionary. The goal of sparse coding is to create an encoding of the larger dataset which maintains structure in the data to facilitate analysis, but at the same time reduces the size of the encoding to permit efficient computation.

One drawback of sparse coding is that the size of the dictionary has to be fixed manually and this size should not over complete the expected number of classes after clustering. In this experiment, we learned three dictionaries with K = 16, K = 32 and K = 1024 words respectively. The K = 32 dictionary is used for the analyses in presented in III, as it was empirically determined to support the best discriminative representation of the full vectors.

E. Relationship between MFCC signal and sparse code

In this section we present an approach to verifying that a given sparse code is representative of its original set of MFCC vectors. Each MFCC vector corresponds one sparse code vector. We expect that patterns appearing with MFCC auto-correlation to still appear with the sparse code vector auto-correlation. In Fig. 2, we present an example for the dictionary with K = 1024 words. The figure shows the 2009 recording auto-correlation with MFCC vectors on the top and sparse codes vectors on the bottom over a subset of 400 randomly chosen samples. We can note that if there is information in the MFCC space, this information also appears in the sparse code space. This is seen in the figure as the corresponding structure in the auto-correlations.

F. Codebook complexity estimation

The estimation of complexity of time-frequency plane can include moment-based measures such as time-bandwidth product and the Shannon and Renyi entropies [START_REF] Flandrin | Time-frequency complexity and information[END_REF]. In order to analyze the dictionary we generate, we extend the time-frequency complexity definition to cepstral pattern complexity, based on the principle that a concentration of energy in the timefrequency plane will also generate an energy concentration in the cepstral plane. We investigate a quantitative measure of complexity inspired from existing work [START_REF] Flandrin | Time-frequency complexity and information[END_REF]. This measure is closely related to the assumption that signals of high complexity (and therefore high information content) must be constructed from a large numbers of elementary components. We thus define the complexity measure of the sparse vectors d i of the dictionary D as the Shannon entropy:

H(d i ) = - t,j p(d i (t, M j )). log(p(d i (t, M j ))), (6)
where p(d i (t, M j )) is the estimate of the energy distribution at time t for the cepstral coefficient M j . The codebook for dictionary size K = 32, time scale 250 ms, sorted by their complexity measure is shown in Fig. 3.

G. Divergence measure of the codes

Information Theoretic methods support the analysis of structure and the organization within a communication system [START_REF] Shannon | A mathematical theory of communication[END_REF].

As our goal is to analyze differences between whale songs (communication system) over different years, we propose to use an information theoretic measure to estimate the song divergence.

In order to get a diachronic analysis, i.e. to determine which code is more or less used from one year to one other, we compute the Kullback-Liebler distance [START_REF] Kullback | On information and sufficiency[END_REF] over song components as represented in the sparse coding. We interpret a difference in the average of the Kullback-Leibler distance for a song encoding subset between 2008 and 2009 recordings as an evolution of the song, assuming that higher this distance is, the more the songs evolved from one year to the other.

Therefore, the song distance is defined as follows. Let be A di (resp. B di ) be the discrete probability distribution over R bins r = {1, . . . , R}, of the 2008 C sparse codes for the sparse vector d i (resp. 2009). Then the distance for the sparse vector d i is: The difference between highest and lowest complex codes is significant.

dist KL (A di , B di ) = R r=1 (Ad r i -Bd r i ). log 2 (Ad r i /Bd r i ) (7)
Finally, the final song distance is the average of the dist KL of target code subsets.

III. RESULTS

The results in this section are computed over a sparse code and corresponding K = 32 word dictionary. As explained in II-B, the MFCC input vectors are composed of concatenations of 13 coefficient vectors to form super-vectors with length of 250 ms, 500 ms, 1 s, 2 s to 4 s.

A. Analysis of song code complexity

Fig. 4 shows a graph of the sorted complexity (as defined in Section II-F) of the quefrequency words of the learned dictionary. We hypothesize an interpretation of the complex code words, which exhibit energy variation in time and frequency, as encodings of whale song subunits and of the less complex words, which exhibit more uniformity, as components of sea noise.

The next section will compare the evolution between 2008 and 2009 of the songs with reference to the sparse coding.

B. Analysis of song evolution

Using the formula given in Section II-G, we compute the Kullback-Liebler (KL) distance between the 2008 and 2009 code words, resulting in 32 distances for each set of supervectors of increasing length (250 ms, 500 ms, 1 s, 2 s, 4 s). The histogram of the distribution of these distances grouped into bins of average distance is shown in Fig. 5. It can be seen from the distribution that short-duration (250 ms) representations are more stable across years than longer ones.

In order to determine whether the evolution can be attributed to changes in the more or less complex code words, we compute the KL distance between 2008 and 2009 for the 2 most complex codes, versus the 2 least complex code words, for all lengths. The divergence analysis (Fig. 6) illustrates that the simplest code words show far less variation across years than the most complex code words. Furthermore, the complex code words are similar for short durations (the KL distance is low for 250 ms), but differ at longer durations with the largest variability at the time scale of 1 s. We can interpret this result to means that the code of 1 s scale are vary year by year, and may be composed of stable codes on a shorter time scale which exhibit less variation. This result is compatible with the subunit concept [START_REF] Pace | Subunit definition and analysis for humpback whale classification[END_REF], which postulates that evolving whale songs are composed from shorter stable song elements. Our results suggest that the subunit could be coded at the 250 ms time scale, while the units would be coded at the 1 second scale. The longer time scale (2 and 4 seconds) are less diverging, possibly due to the fact that this time scale is relative to global song structure that may vary less than the unit level. Note that the distance computation directly on the raw MFCC yields, as expected, to insignificant difference in distance, whatever the size or the year of the units. This effect is known as the "dimensionality curse effect" which makes the KL-distance metric inefficient in high-dimensional spaces. Indeed, the dimensionality of the MFCC vectors is 650 (13x50), and according to [START_REF] Beyer | When is "nearest neighbor" meaningful?[END_REF], any simple distance computation of any pair of vectors in such a high dimensional space results will result in a similar distance.

IV. DISCUSSION

We presented an unsupervised dictionary learning algorithm for generating a proto-lexicon of the songs of Humpback whales at different time scales. These dictionaries are used as the basis for a sparse encoding of the original datasets. These representations are more generic and efficient than obtained in our previous method [START_REF] Pace | Subunit definition and analysis for humpback whale classification[END_REF], [START_REF] Doh | Décomposition parcimonieuse des chants de cétacés pour leur suivi[END_REF].

We show in this paper the utility of a sparse representation of complex bioacoustic patterns for efficient data mining. We presented the hypothesis that the long and short time duration sequences might represent varying sound units and stable subunits of whale songs, respectively. In order to support this hypothesis, in future work we intend to further analyze the structure of the representation of the sparse coding with respect to the original acoustic dataset. Two ideas for this analysis are presented here, computed on a dictionary with K = 16. Fig. 7 shows the relative contributions of code words to the encoding of a song segment from the 2008 dataset. We see clearly the variation of the code word activity. For example at 1.5 s, words 2 and 3 contribute simultaneously to generate a complex pattern, whereas at 2 s, word 1 is more active than any other word. This type of analysis is suggested as a means of characterizing the song structures.

To determine whether the code words actually represent meaningful sound subunits, we could consider a pairwise analysis of the probability of the occurrence of bigrams in the encoded representation. As an example, again presented on the dictionary with K = 16, we computed the probability of occurrence each consecutive word (sparse vector) pair for the 2008 and 2009 datasets. The probability of the ordered occurrence for the pair (w 1 , w 2 ) (bigram), is written as P (w 1 , w 2 ). In a random system, we would not expect to see patterns across years in the probabilities.

We then computed the log ratio of these probabilities from 2008 encoded dataset and the encoded 2009 dataset at the 250 ms time scale, which is shown in Fig. 8.

The results show that there are differences in bigram occurrence across years. For example we see that pair (6, 7) is more frequent in 2008, while pair (6, 2) is less frequent in 2008. This could be interpreted as song evolution (encoded as order of words) from one year to another, while subunits (encoded as words) remain stable.

The sparse coding technique shows promise as a means to provide objective insight into evolution of song structure, although further development is needed to support strong conclusions about the interpretation of the encoding.

This paper focuses on the year-to-year variability of the songs structure and do not discuss the possible influence of other factors on the songs structure, such as populations, habitat, weather conditions . . . Current research works are conducted in our team on these topics using our original sparse coding framework.

V. CONCLUSION

This paper demonstrates the promise of the sparse coding method to learn features in an unsupervised manner for humpback whale songs and to support their analysis. The sparse dictionary that is presented in this paper is automatically learned from the recordings, and captures variations in sound units and subunits with a limited number of elements. This approach is suitable for analyzing signals with which contain both stable and variable features. The acoustic datasets processed in this work exhibit those characteristics, where the variability is found among singers and between years, but stable features are present for example, in the ambient noise.

In summary, we presented: 1) An unsupervised method for encoding a large and variable bioacoustic dataset into a dictionary; 2) a method which establishes criteria for sparse coding to limit the number of the elements of this dictionary; 3) using the Lasso algorithm, the sparse coding distinguishes the salient features of the signal from the noise components; 4) an interpretation of the approach in characterizing evolving sound units and stable subunits; 5) an analysis of year to year variation of the dataset. The results establish sparse coding as a promising method for analyzing humpback whale songs.

This paper lends new support for the concept of units versus component subunits in humpback whale songs. We show that the shortest units (subunits) are the most stable, occurring with similar frequency across two consecutive years, while the longest units exhibit more variation from one year to one other. 250 ms segments appear to be an appropriate length for encoding stable features of whale songs, possibly corresponding to subunits.

In future work, a systematic information theoretic analysis will be used to characterize the evolution of sound units. The approach will be applied to multiple geographic locations across multiple years to further explore population differences and song evolution.

Another potential application would be to model the vocal identity of individual whales, which could provide a basis for singer authentication or dialect identification. We also intend to explore the method's applicability to analyzing sounds from other species beyond humpback whales.

Fig. 1 .

 1 Fig.1. Spectrogram of Madagascar song segments (from[START_REF] Pace | Classification of humpback whale (megaptera novaeangliae) calls using hidden markov models[END_REF]).

Fig. 2 .

 2 Fig. 2. Representation of the auto-correlation matrix between MFCC vectors (a), and sparse code vectors (b). Results obtained on the 4 s encoding of the corpus with a dictionary of 1024 vectors.

Fig. 3 .

 3 Fig. 3. The codebook composed of 32 codes, sorted by degree of complexity (from top left to bottom right), computed at the time scale of 250 ms, learned from the union of 2008 and 2009 song sets.

Fig. 4 .

 4 Fig. 4. Complexity values of the 32 codes (sorted) of the codebook illustrated previously (time scale 250 ms learned on 2008 union 2009). The difference between highest and lowest complex codes is significant.

Fig. 5 .Fig. 6 .

 56 Fig. 5. Histogram of the KL distance (2008, 2009) computed over the 32 sparse vectors of the dictionary.

Fig. 7 .

 7 Fig. 7. Contributions of four code words to the encoding of a 2008 song segment for the dictionary size K = 16.

Fig. 8 .

 8 Fig. 8. Log ratio of the probabilities of sparse vector pairs from the 2008 dataset versus the 2009 dataset. This is illustrated for K = 16, scale 250 ms.

TABLE I DURATION

 I OF AVAILABLE DATA ACCORDING TO DATASET.

ACKNOWLEDGMENT

The authors would like to thank PACA region which supported the Phd program of Y. Doh. We also thank Cetamada NGO (www.cetamada.org) and LAM and Cesigma for helping to record the files. We also thank Fondation Total for the support of our project BAOBAB, along with Institut Universitaire de France, and MASTODONS MI CNRS SABIOD Scaled Acoustic Biodiversity project (http://sabiod.org).