Justin Weynand

Aurélie Diman

Michaël Abraham

Lionel Marcélis

Hélène Jamet

Anabelle Decottignies

Jérôme Dejeu

Eric Defrancq

Benjamin Elias

Towards the Development of Photo-Reactive Ruthenium (II) Complexes Targeting Telomeric G-Quadruplex DNA

come

Introduction

DNA is considered as an interesting target for developing novel classes of therapeutic agents. Until recently, the focus has been on double-stranded DNA structures (duplex DNA), in which two sequences of DNA are held together in an antiparallel double-helical architecture through canonical Watson-Crick A/T and G/C base pairing. Targeting duplex DNA has mainly been achieved by using intercalators (i.e., small molecules that interact with DNA through intercalation between two adjacent base pairs) or groove binders (i.e., small molecules that interact with DNA in the minor and/or major groove regions). 1 More recently, targeting alternative DNA architectures, in particular G-quadruplexes (G4s), has been increasingly pursued. G-quadruplexes DNA are secondary DNA structures found in guanine rich sequences. The basic unit is called G-quartet, which corresponds to the coplanar arrangement of four guanines held together by Hoogsteen hydrogen bonds and stabilized by physiologically abundant Na + or K + cations. G-quartets can stack to form G-quadruplexes DNA. G-quadruplexes can adopt a wide variety of topologies according to the number of strands involved in the structure, the strand direction, and variations in loop size and sequence. [2][3][4] Sequencing and bioinformatics analyses of the human genome indicate that it contains as much as 700,000 sequences of potential G-quadruplex structures (PQS). Interestingly, these putative G-quadruplex forming sequences are not distributed randomly in the genome. Indeed a statistically significant enrichment of PQS was found in several relevant domains of the human genome including the telomeric region and promoter regions of a number of genes such as the proto-oncogenes c-Myc, c-Kit, bcl-2, KRAS as well as in viruses. [START_REF] Siddiqui-Jain | Proc. Natl. Acad. Sci[END_REF][START_REF] Métifiot | [END_REF] Strong arguments have been recently provided that argue in favour of the formation of G-quadruplex DNA structures within cells by using G-quadruplex antibodies as well as binding-activated fluorescent G4-targeting ligands. [7][8][9] Several G4-binding regulatory proteins have been identified and G4 formation is now suspected to be involved in numerous pathogenic processes including degenerative disorders, oncogene regulation, and viral infections. Altogether, these data are in agreement with a biologically relevant regulatory role for Gquadruplexes.

In this context, the biological functions of G4s are certainly the most documented for the telomeric region. Human telomeric DNA is made of a repeat of the sequence 5' TTAGGG 3' and it is well admitted that telomeric DNA plays important roles in the development of cancer cells. [10][11] In healthy cells, the telomere shortens after each cell division and when a limit is reached (i.e., Hayflick limit), the cell enters into senescence. Whereas in most cancer cells, the telomere length is maintained, leading to replicative immortality. Two different processes are involved in telomere maintenance: (i) the overexpression of the telomerase enzyme which adds copies of telomeric repeats at the chromosome ends, 12 and (ii) the homologous recombination-based mechanism, termed Alternative Lengthening of telomeres (ALT). [13][14] Telomerase overexpression is observed in almost 85% of cancer, whereas the ALT mechanism is active in 5-10% of the cases (in particular in osteosarcoma and glioblastoma).

Over the past decade, a number of small molecules (called G-quadruplex ligands), displaying varying degrees of affinity and more importantly selectivity (i.e. the ability to interact only with quadruplex DNA and not with duplex DNA), have been designed to target G-quadruplex DNA. [15][16][17][18][19] Most of these molecules have been built from an aromatic core, able to interact with G-quadruplex motifs through stacking, and decorated with substituents (often positively charged) that can interact with G-quadruplex grooves and/or loops for improving the affinity for G4s as well as the selectivity versus duplex DNA. G4 ligands approach is now considered to be a useful molecular tool to enhance and/or promote quadruplex-related biological effects in cells and shows high potential for future therapies. 20 To the best of our knowledge, very few studies have been devoted to the design of photo-reactive probes targeting G4s that could be interesting for phototherapy development. Freccero and Coll. have linked to the wellknown naphthalene diimide (NDI) G4-ligand, a phenol moiety that produced phenoxyl radicals upon irradiation. 21 They have showed the ability of such NDI-phenolate conjugate to kill MCF7 cancer cells after irradiation.

The design of new metal complexes targeting G-quadruplex DNA has gained an intense interest for their potential anticancer properties. [22][23] Indeed, some Pt(II), [24][25][26][27][28] Ni(II), [29][30][31] Ru(II) [32][33][34] or Ir(III) [35][36][37] showed good affinity and selectivity towards G4s. In comparison to organic compounds, metal complexes possess many advantages such as a net positive charge, tunable geometry and, more interestingly, some of them present interesting photochemical properties. In this context, polyazaaromatic ruthenium(II) complexes represent ideal candidates to target genetic material like G4s.

Thanks to their optical properties including a large Stokes shift, good photo-stability, high quantum yield and long-lived luminescence, they have been developed to probe different DNA sequences, 38 such as mismatches, 39 abasic sites 40 or G4s. 32 More recently two ruthenium(II) complexes [Ru(phen) 2 (dph)] 2+ and [{Ru(phen) 2 } 2 (dph)] 4+ based on dph ligand (dph = dipyrazino[2,3-a:2',3'-h]phenazine) have been reported to show a good selectivity towards G4s. 41 However, none of these reported compounds is able to photo-induce oxidative damages under light irradiation. Highly π-deficient ligands such as 1,4,5,8tetrazaphenanthrene (TAP) are known to enhance the photo-oxidizing power of the resulting complexes. 42 Indeed, in the presence of DNA, a photo-induced electron transfer (PET) from a guanine (G) base towards the excited complex has been evidenced for ruthenium(II) complexes bearing at least two TAP ligands. This PET leads to dramatic consequences to the living cells: (i) the formation of adduct between the complex and the guanine or (ii) DNA photo-cleavage (type I photo-oxidation). [43][44][45] A photo-reaction process for bridging two guanine bases of a quadruplex oligonucleotide by the rigid dinuclear [(TAP) 2 Ru(tpac)Ru(TAP) 2 ] 4+ complex has also been reported. 46 In the present study, three new photo-oxidizing ruthenium(II) complexes (Scheme 1) that selectively target G-quadruplex DNA over duplex have been synthesized. They are based on the 2-(4-chlorophenyl)-1H-imidazo [4,5-f] [1,10]phenanthroline (CPIP) ligand, of which some ruthenium(II), 47 platinum(II) 48 or iridium(III) 36 complexes have been previously reported as selective DNA switch.

However, none of them has the ability to induce a photo-electron transfer (PET) with guanine units. For this aim, the TAP moiety has been introduced in our ruthenium(II) complexes through different strategies (Scheme 1): (i) by incorporating two ancillary TAP ligands (complex 3), (ii) by modifying the phenanthroline imidazole ligand with TAP (complex 2), (iii) by combining both strategies (complex 4).

Complex 1 containing phen-based ligands was used as a reference. Steady-state luminescence, circular dichroism (CD), Bio Layer Interferometry (BLI) and Surface Plasmon Resonance (SPR) studies have been performed and demonstrated a good affinity for G-quadruplex DNA as well as selectivity versus duplex DNA. Preliminary biological studies of these complexes have been performed and revealed that two of them showed a remarkable photo-cytotoxicity towards U2OS osteosarcoma cells whereas very low mortality was observed in the dark, using the same photo-drug concentration.

Results and Discussion

Synthesis of complexes 1-4 (Scheme 1).

The planar ligand CPIP (X=CH) was synthesized by condensation of 4-chlorobenzaldehyde with 1,10phenanthroline-5,6-dione in an ammonium media as previously reported in the literature. 36 The ligand CPITAP (X=N) was obtained according to a new protocol developed in our laboratory, using 4chlorobenzaldehyde and 9,10-diamino-1,4,5,8-tetraazaphenanthrene. The reaction was carried out in a refluxing acetic acid/ethanol mixture, during 60 h. Purification by preparative SiO 2 chromatography afforded pure CPITAP ligand, which was characterized by 1 H, 13 C NMR spectroscopy and highresolution mass spectrometry (HRMS) (see the Experimental Section and Figures S1, S2 and S11 in the Supporting Information). The corresponding Ru(II) complexes were synthesized by direct chelation of the N^N ligand onto a Ru(II) precursor bearing either 1,10-phenanthroline (phen, Y=CH) or 1,4,5,8-tetraazaphenanthrene (TAP, Y=N) moieties (Scheme 1). The reactions were carried out in the dark and under argon to avoid photo dechelation and oxidation of the metal centre. Complexes 1-4 have been characterized by 1 H NMR spectroscopy and HRMS analyses (see the Experimental Section and Figures S3-10 and S12-15) as well as UV-vis, cyclic voltammetry (CV) and photochemical studies (vide infra).

Absorption and Luminescence Properties.

The absorption and luminescence data for complexes 1-4 are gathered in Table 1 and in Figures S16-19.

For all complexes, the absorption bands in the UV regions were attributed to Ligand Centred (LC) transitions from comparison with literature data while the absorption maxima between 400 and 500 nm were ascribed to Metal to Ligand Charge Transfer (MLCT) transitions as previously shown for similar complexes. [49][50] Emission spectra were measured in acetonitrile and water, at room temperature and at 77 K in EtOH/MeOH mixture (4/1, v/v). Complexes 1-4 display a broad unstructured emission in organic solvent and water. all complexes are able to photosensitize oxygen, as previously reported in the literature for similar compounds. [52][53] It is also noted that luminescence quantum yield of complex 1 is lower in acetonitrile than in water, consistent with data for [Ru(phen) 3 ] 2+ complex. 54 The relatively low luminescence of complex 2 to 4 in water is typical of TAP-based complexes and is generally attributed to increased nonradiative processes, likely due to interaction of the solvent with non-chelating nitrogen atoms (light switch effect). It should be also noted that complex 4 showed a very short excited state lifetime and a very low quantum yield of emission in water subsequently suggesting poor photo-induced damaging properties.

Cl O N N N N N H N Cl N N N N NH 2 NH 2 EtOH, AcOH, 60h + Y N Y N N Y Y N Ru 2 (1) X=Y=CH [Ru(phen) 2 CPIP] 2+ (2) X=N, Y=CH [Ru(phen) 2 CPITAP] 2+ (3) X=CH, Y=N [Ru(TAP) 2 CPIP] 2+ (4) X=Y=N [Ru(TAP) 2 CPITAP] 2+ Y N Y N N Y Y N Ru Cl Cl N X N X N H N Cl 75% (CH 2 OH) 2 , 150°C N X N X N H N Cl A/ B/

Electrochemical Study.

The oxidation and reduction potentials of complexes 1-4 were determined by cyclic voltammetry measurements in dry deoxygenated acetonitrile (Table 2 and Figures S20-23). Based on other similar Ru(II) complexes described in the literature, we can assume that complexes 1-4 display a one-electron oxidation wave. This corresponds to the oxidation of Ru(II) to Ru(III) as the anodic shift observed from 1 to 4 reflects the stabilization of the metal centred HOMO, due to the presence of highly π-deficient ligands (TAP) and consistent with the spectroscopic properties. For the reduction, each complex displayed several one-electron reduction waves, corresponding to the successive addition of electrons onto the ligands. The first reduction wave measured for 1 was attributed to the reduction of phen moieties of CPIP or ancillary ligands. As for complexes 2 to 4, the first reduction wave, anodically shifted with respect to complex 1, corresponds to the reduction of the TAP moiety of either CPITAP or ancillary ligand.

All these data suggest the following photo-physical scheme for this family of complexes: according to literature data and our results, we can safely conclude that the HOMO is metal centred and its relative energy depends on the respective ligands surrounding the metal. The more π-deficient ligands are chelated onto the metal centre, the less reducing the complex will be. As for the LUMO, it is centred on a ligand, confirming the MLCT nature of the excited state. From the cyclic voltammetry measurements and the spectroscopic data, the oxidation and reduction potential of the excited state can be roughly estimated. Not surprisingly, complexes 3 and 4 display a strong photo-oxidizing power (+1.23 and +1.39 V vs Ag/AgCl, respectively). With these results in hands, we have tested whether a photo-induced electron transfer (PET) would occur in the presence of the most reducing building block of DNA Gquadruplex, i.e. a guanine residue (E ox = +1.10V vs Ag/AgCl). 55 S1 andS2).

Luminescence Studies in the Presence of dGMP.

As mentioned in the introduction, G-quadruplex DNA is a guanine rich sequence present in the genome.

Recently, we have reported on a new family of Ru complexes exhibiting a good affinity and selectivity towards G-quadruplex DNA. 41 However, these complexes are not sufficiently oxidizing in their excited state to trigger direct oxidative damages (type I photo-reactivity). Ruthenium(II) complexes bearing at least two TAP (=1,4,5,8-tetraazaphenanthrene) ligands are well known in the literature to photo-react with a guanine moiety upon irradiation. Therefore, the photo-reactivity of our complexes towards dGMP has been investigated by Stern-Volmer steady-state luminescence quenching experiments. As well established in the literature, the luminescence quenching of Ru(II)-TAP complex upon addition of dGMP is due to an electron transfer (ET) from dGMP to the complex at the excited state. According to estimated E * red (Table 2), complexes 1 and 2 should not be enough photo-oxidizing to undergo ET with dGMP (E ox = +1.10V vs Ag/AgCl). As anticipated, no luminescence quenching was observed for those complexes in the presence of dGMP. In contrast, Stern-Volmer plot performed with complex 3 (Figure 1), showed a luminescence quenching in the presence of increasing dGMP concentration, with a high efficiency close to the diffusion limit (quenching rate constant = 2.63 x10 9 M -1 .s -1 ). Based on thermodynamic data, this luminescence quenching can safely be ascribed to a PET from the guanine moiety to the excited state of complex 3 (eq 1).

[

Ru] 2+ * + G → [Ru] 1+ + G •+ (1)
Considering the oxidation power of the excited state of 3 and using the empirical Rehm-Weller equation, it is expected that process (1) will be exergonic by about -0.13 eV. In the case of 4, PET should also be favoured by about -0.29 eV, but the poor luminescence properties of this complex did not allow any luminescence quenching experiments.

Luminescence Studies in the Presence of ODNs.

Due to the ability of complexes 1-3 to emit light in aqueous solvent, monitoring the luminescence upon addition of increased concentrations of oligonucleotides (ODNs) can be achieved. Experiments were performed with human telomeric wtTel23 ( 3' TT(GGGATT) 3 GGG 5' ) and a GC rich hairpin sequence

( 3' (GC) 4 TTTT(GC) 4 5'
). The experiments were carried out in 10 mM HEPES (pH 7.4), 35 mM NaCl, 50 mM KCl buffer in which the desired DNA structures (i.e. duplex or G-quadruplex) were formed (Figure S32). According to the redox properties of the complex, two behaviours can be observed upon addition of ODN: (i) an increase of the luminescence due to protection of the ruthenium(II) probe from the solvent in the hydrophobic environment of ODN or (ii) a decrease of luminescence resulting from a PET with a neighbouring guanine. Figures 2a,b show the enhancement of the luminescence through the addition of increasing amounts of wtTel23 or hairpin ODN for complexes 1 and 2. This behaviour is consistent with (i) the protection of the complex by the ODN that decreases the rate of non-radiative processes, (ii) the fact that no luminescence quenching has been monitored in the presence of guanine moieties (vide supra). In contrast, the luminescence of complex 3 was quenched through the addition of increasing amounts of DNA (Figure 2c), attributed to a PET in agreement with the Stern-Volmer plots.

The above results from steady-state luminescence studies with complexes 1-3 suggested that a strong interaction between DNA and the complexes occurred. We thus decided to further investigate the interactions of the complexes with DNA in order to evaluate whether they display a specific affinity towards G-quadruplex structures over double-stranded DNA.

DNA Binding Analysis.

Different biophysical techniques including FRET-melting, UV-visible spectrophotometry, circular dichroism (CD), NMR and SPR have been developed for studying G-quadruplex DNA/ligand interactions. 17 In our case, the use of UV-visible absorbance was found unsuitable to study the binding affinity. Modifications of the absorption spectra of complexes were indeed detected upon addition of increasing concentrations of G-quadruplex DNA (Figure S24-31) but they were relatively moderate and thus it was tedious to measure a binding affinity. Binding affinities of complexes 1-2 for G-quadruplex and duplex DNA can be also estimated by fitting the variation of luminescence intensity versus the ratio of binding site per complex (Figure 2). Complex 1 showed an apparent dissociation constant (K D ) of 77 µM for duplex DNA and 6.5 µM for G-quadruplex. The difference of binding affinities for duplex and G-quadruplex DNA is more drastic for complex 2 with K D values of 123 µM and 2 µM, respectively.

Only qualitative conclusions can be made about the affinity of complex 3 for wtTel23 G-quadruplex versus GC rich hairpin DNA, since a dynamic quenching process is also taking place, which should alter the luminescence intensity. However, it can be observed that the slope of the curve is steeper with Gquadruplex structure than with duplex DNA, indicating that the binding affinity is certainly stronger for the former one. We thus evaluated the affinities of these complexes towards DNA by means of CD melting assays, Bio Layer Interferometry (BLI) and Surface Plasmon Resonance (SPR).

CD melting assays

The ability of complexes 1-4 to interact with G-quadruplex DNA was first investigated by CD experiments. CD analyses were carried out in 10 mM Tris-HCl, 100 mM NaCl buffer or 100 mM KCl.

As anticipated in 100 mM NaCl containing buffer conditions, wtTel23 folded into an antiparallel topology characterized by two positive peaks at 242 nm and 294 nm, respectively and a negative peak at 262 nm (Figure S33). Upon addition of complexes 2-4, minor change in the ellipticity of wtTel23 was observed suggesting that these complexes do not induce major structural changes of the antiparallel conformation of G-quadruplex (Figure S33). For complex 1, we also observed the appearance of a shoulder at 270 nm that could be explained by a slight modification of the topology induced by this complex (presence of hybrid II-type G4 folding). In 100 mM KCl containing buffer conditions, wtTel23

folded into a hybrid II-type G4 featured by a maximum at 290 nm and a shoulder at 270 nm (Figure S34). Again, minor change in the ellipticity of wtTel23 was observed thus revealing no major structural changes upon binding. In the same way, the duplex structure ( 5' CGT 3 CGT 5 ACGA 3 CG 3' hairpin) was not affected as no change in the CD spectra was observed upon addition of complexes 1-4 (Figure S35). We then performed CD melting assays to evaluate whether complexes 1-4 induced stabilizing or destabilizing effects on the G-quadruplex and hairpin duplex DNA structures. CD melting curves were recorded in the absence or the presence of each complex (at 5:1 complex/DNA ratio) in 100 mM NaCl or 100 mM KCl containing buffers (Table 3 and Figures S36 andS37) in case of wtTel23 and in 100 mM NaCl containing buffer for hairpin duplex (Table 3 and Figure S38). CD melting experiments results clearly showed that complexes 1-3 induced a slight stabilization of wtTel23 whereas all the complexes do not significantly affect the stability of duplex DNA structures. Melting temperature assays thus confirmed the ability of most of the investigated complexes to selectively interact with Gquadruplex DNA versus duplex DNA. It should be mentioned that each complex is a racemic mixture of two enantiomers and that a preferential binding or a different binding mode of each enantiomer could exist. However the separation of both enantiomers represents a new and challenging task. Thus, we are presently unable to comment on whether the changes observed by CD are due to different binding mode of each enantiomer. Because this technique is not the most appropriate for direct measurements of affinity constants, we next performed BLI analysis, which allowed us to determine the thermodynamic parameters for the interaction.

Bio Layer Interferometry studies

BLI is a label-free method for measurements of affinity constants, which allows the determination of the kinetics parameters of the interaction (such as SPR) and has been used to study biomolecular interactions between large biomolecules like protein-membrane interactions. 56 Until recently, we employed SPR analysis method based on the use of a Template Assembled Synthetic G-Quadruplex (TASQ) that allows precise control of G-quadruplex topology through assembly of constrained structures on a template. [57][58][59][60][61] We have adapted this SPR method for BLI. Different G-quadruplex Each evaluated complex showed K D values in the micromolar range for G-quadruplex topologies A, B

and C (Table 4 and Figures S39-42). These values fall within the range of those reported for related ruthenium(II) complexes interacting with G-quadruplexes. It is noticed that the substitution of carbon atoms by nitrogen atoms on the ligand (i.e. two Phen ligands in 1 replaced by two TAP ligands in 3 or CPIP ligand in 1 replaced by CPITAP ligand in 2) only weakly affects the interaction with Gquadruplex. More interestingly, each ruthenium(II) complex showed a higher affinity for G-quadruplex structures than for duplex DNA. Indeed it was impossible to measure a K D for interaction with duplex system D within the concentration range used in this study (i.e. from 5 to 40 µM) that suggests K D values higher than 1 mM for duplex DNA D bound to each complex. The good selectivity was further confirmed by SPR analysis by using systems B and D. We obtained K D values for the G-quadruplex system B of the same order of magnitude as for BLI analysis whereas none of the complex showed an affinity with duplex DNA D within the used concentration range (Figure S43 and Table S3). It was also noticed that the affinity of complexes 1-4 is higher for G-quadruplex structures B and C, which contain TTA loops in contrast with parallel-stranded quadruplex A. This is in agreement with interactions of the complexes with G-quadruplexes through mixed π-stacking over the guanine tetrad and further interactions with loops and grooves (see modelling part). To obtain further information of the affinity of complexes 1-4 for G-quadruplex, the association (k on ) and dissociation (k off ) constants of the interaction were determined (Table 4) and again revealed the relatively low influence of the replacement of carbon by nitrogen atoms in complexes 1-4. Indeed for a given G-quadruplex structure, k on and k off differ only slightly. As illustrated in Figure 4, complexes 1-4 display association and dissociation rates that are similar to those of BRACO, MMQ1 and TMPyP4. In particular the fact that our complexes showed similar affinity than BRACO-19, which has demonstrated anticancer activity through the stabilization of Gquadruplex at the telomere, 62 prompted us to study their photo-toxicity.

Molecular Modelling.

To obtain further insights into the interactions with G-quadruplex DNA, molecular docking calculations were carried out. The most interesting complex 3 (due to its photophysical properties) was docked to the human telomeric DNA structure (PDB entry 1KF1, wtTel23 in parallel conformation). From analysis of the best-ranked docked positions, two binding modes were obtained (Figure 5). The first one consisted in a π-stacked positioning of 3 over the guanine tetrad while the second one showed an insertion of the complex into the TTA loop of G-quadruplex via the CPIP ligand. The initial calculations have been performed with the Δ-isomer of complex 3 to afford the two best-ranked positions in Figure 5.

However, similar docked positions have been obtained with the other Λ enantiomer (see Figure S44 in the supporting information). Molecular mechanics dynamic simulations were then carried out in order to assess the stability of those two docking positions. For both of them, the complexes remained tightly bound in their relative positions (either in interaction with the TTA loop, or π-stacked) for up to 20 ns, thus emphasizing on the strong affinity displayed by 3 towards G-quadruplex DNA. These results are consistent with BLI experiments. Indeed, the affinity of complex 3 with structure A (i.e. without loop) is lower than with both structures with loop (K D of 20 µM for A while K D is around 2 µM for B and C) that confirms the importance of the second docked position (i.e. loop interactions).

Cell Penetration.

Thanks to the ability of complex 1 to emit light in aqueous solvent, this one was studied by confocal microscopy to investigate the penetration into U2OS osteosarcoma cells. [START_REF]reactivity (complexes 3 and 4 undergo PET with guanine residues which quenches the excited state). Their luminescence in the cells is thus too weak to be detected by confocal microscopy[END_REF] Incubation was carried out during 24 h at 20 µM. As shown in Figure 6, complex 1 showed an efficient penetration in the cell, including the nucleus.

Photo-Cytotoxicity.

Preliminary photo-cytotoxicity studies were performed with complexes 1-4 on U2OS osteosarcoma cells. Figure 7 depicts the percentage of metabolically active cells after incubation of the cells with 10 µM of complexes 1-4 for 24 h and subsequent irradiation for 30 minutes (orange bars). Non-irradiated controls were also performed (blue bars). Interestingly, non-irradiated cells displayed very low rates of mortality whereas irradiation of the cells was associated with a dramatic decrease in survival. Indeed, 100% of mortality was obtained at 10 µM for complexes 1 and 3, while complex 2 was slightly less efficient with 70% of mortality. As anticipated, complex 4 showed a very weak photo-toxicity in comparison with non-irradiated control cells. The strong photo-toxicity of complex 3 was confirmed by microscopic observations of the cells revealed by the tetrazolium salt-based metabolic assay that showed massive cells death upon irradiation (dead cells are clearly recognized by the change in shape, see Figure S45). A possible explanation of the photo-toxicity of 1-3 is that the internalized ruthenium complexes should photo-react with the biological material through type I photo-reaction (i.e. photo-electron transfer) or type II photo-reaction (singlet oxygen photosensitization), both mechanisms of photoreaction being likely to induce DNA damages, ultimately leading to cell death. Contrariwise, the low photocytotoxicity of 4 more likely originates from its short excited-state lifetime, resulting in a poor photodamaging ability.

Conclusion

A series of new ruthenium(II) complexes have been designed to target and photo-react with Gquadruplex DNA through the incorporation of CPIP ligand (or similar). As anticipated, the photophysical properties of the complexes are consistent with MLCT transitions and a metal centred HOMO. Consequently, these complexes are able to react with DNA through type II photoreaction (i.e.

formation of singlet oxygen) or through photo-induced charged transfer (PET). All four designed complexes 1-4 displayed a good affinity for G-quadruplex DNA and selectivity versus duplex DNA.

Docking based studies and molecular dynamic simulations revealed that this affinity is due to a π-π stacking above the tetrad and an interaction with the TTA loop.

Strikingly both complexes 1 and 3 were associated with dramatic photo-cytotoxic effect as 100% of mortality was obtained upon irradiation of U2OS osteosarcoma cells whereas very low mortality was observed in the dark, using the same drug concentration. These studies will be completed in the future to know whether this photo-cytotoxic effect is mainly due to a type II photoreaction (i.e. singlet oxygen photosensitization) or type I photoreaction (i.e. photo-induced charged transfer (PIET). Further experiments are currently performed to investigate whether telomeric DNA damages are induced into cells by complexes 1 and 3. Interestingly, this photo-cytotoxicity should not involve the inhibition of telomerase activity through the stabilization of G-quadruplex as U2OS osteosarcoma cells do not express the telomerase enzyme. To the best of our knowledge, this would be the first example of high photo-cytotoxicity based on the use of metal complexes targeting telomeric DNA, through a mechanism, which does not involve the inhibition of telomerase. Therefore the photo-cytotoxicity of these two complexes will be also evaluated towards either telomerase-expressing cancer cells or normal non-immortalized cells.

Experimental Section

Material and methods.

[Ru(phen) 2 Cl 2 ], [START_REF] Jacquet | [END_REF] [Ru(TAP) 2 Cl 2 ], 42 1,10-phenanthroline-5,6-dione, 65 9,10-diamino-1,4,5,8tetraazaphenanthrene 66 and 2-(4-chlorophenyl)-1H-imidazo[4,5-f][1,10]phenanthroline(CPIP) 48 were synthesized according to previously described literature protocols. The oligonucleotides wtTel23

( 3' TT(GGGATT) 3 GGG 5' ), GC rich hairpin duplex ( 3' (GC) 4 TTTT(GC) 4

) and hairpin duplex sequence acid acetic (3 mL) was added and the mixture was heated at 110°C for 60h. After cooling, acid acetic was evaporated under vacuum. The crude dark green solid was then purified over preparative SiO 2 chromatography (CHCl 3 /EtOH 99/1) to afford pure CPITAP ligand as a yellow powder (62 mg, 75%). Cary Eclipse instrument for complex 1 and 3; and on a FluoroLog3 FL3-22 from Jobin Yvon for complex 2. The titration was performed from the highest DNA concentration (10 µM) and progressively decreased it whereas the complex concentration (5 µM) was kept constant. The fitting equations are described in the Supporting Information.

CD experiments.

Prior to CD analysis, the oligonucleotides were annealed by heating the sample at 95°C for 5 min in the buffer conditions and cooling it overnight to room temperature. Analyses were recorded on a Jasco J-810 spectro-polarimeter using 1 cm length quartz cuvette. Spectra were recorded in a range of 5 from 25°C to 90°C with a wavelength range of 220 to 330 nm. For each temperature, the spectrum was an average of three scans with a 0.5 s response time, a 1 nm data pitch, a 4 nm bandwidth and a 200 nm min -1 scanning speed. For CD melting experiments, the ellipticity was recorded at 290 and 252 nm for wtTel23 and duplex hairpin, respectively. Melting temperatures were obtained using Boltzmann fit on Origin software. Each curve fit was only accepted with a r value >0.99.

Bio layer interferometry (BLI).

BLI sensors coated with streptavidin (SA sensors) were purchased from Forte Bio (PALL). Prior to use, they were immerged 10 minutes in buffer before functionalization to dissolve the sucrose layer. Then Computational studies.

The docking experiments were performed with complex 3 which geometry was first optimized at the B3LYP/6-31g* level using the Gaussian09 software. Then, the AutoDock 4.0 software package was used on the crystal structure of parallel quadruplexes from human telomeric DNA (PDB entry: 1KF1).

A grid of 80x80x80 points with a spacing of 0.5 Å between the grid points was used, non-polar hydrogens were merged and Gasteiger-Hückel charges were added on both the complex and the Gquadruplex. The parameters for the Ru atom were set on r=2.96 Å, q=+2.0 and the van der Waals well depth was 0.056 kcal.mol -1 . The docking calculations were obtained through a genetic algorithm search generating one hundred docked structures. A default protocol was applied with an initial population of 150 randomly placed individuals, a maximum number of 2.5x10 5 energy evaluations, a maximum number of 2.7x10 4 generations, a mutation rate of 0.02, and a crossover rate of 0.8. Results differing by less than 2 Å in positional root-mean-square-deviation (RMSD) were clustered together and represented the result with the most favourable free binding energy. Regarding the molecular mechanics dynamic simulation, the AMBER 12 software package was chosen and applied on the two best-ranked positions obtained from the docking calculations. The complex was broken down into its different ligands and their specific parameters were generated via the ANTECHAMBER module and the GAFF force field whereas charges were calculated through a RESP fitting of HF/6-31g* level calculations. The docking structures were solvated in a TIP3P water box which dimension was set to be at least 10 Å larger than the solute in every direction. Sodium cations were added until global charge was neutral and long-range electrostatic interactions were computated using the particle-mesh Ewald method with a cut-off value of

Scheme 1 .

 1 Scheme 1. Synthesis of A/ CPITAP ligand and B/ Ru II complexes 1-4.

Figure 1 .

 1 Figure 1. Luminescence quenching of complex 3 in the presence of dGMP (a) Emission spectra of complex 3

Figure 2 .

 2 Figure 2. Luminescence titration of complexes 1-3 by DNA. Luminescence titrations of complexes 1, 2 and 3 (figures (a), (b) and (c), respectively) were carried out in 10 mM HEPES (pH 7.4), 35 mM NaCl, 50 mM KCl buffer by increasing proportions of ds-DNA (GC rich hairpin duplex 3' (GC) 4 TTTT(GC) 4 5' , base pairs equivalents per Ru complex) or G-quadruplex DNA (wtTel23, 3' TT(GGGATT) 3 GGG 5' , G-quartet equivalents per Ru complex). Solid lines obtained by a modified McGhee-von Hippel fitting process (see the SI) to evaluate the binding affinities. Excitation was performed at λ = 450 nm.

58 Figure 3 .

 583 Figure 3. G-quadruplex systems A-C and duplex control D used for Bio Layer Interferometry studies. (A) parallel-stranded quadruplex (intermolecular like G-quadruplex); (B) intra quadruplex (intramolecular like Gquadruplex); (C) antiparallel human telomeric sequence and (D) duplex (hairpin).

Table 4 .a

 4 Data for the interaction of complexes 1-4 with DNA structures A-D from BLI analyses. Equilibrium dissociation constants deduced from the kinetic rate constants. b Due to very low binding of the different complexes with hairpin DNA the kinetic of the interactions could not be determined (nd) in the studied range of concentrations. This has been confirmed by SPR analysis (FigureS43). Running buffer: 10 mM HEPES pH 7.4, 35 mM NaCl, 50 mM KCl.

For

  the most biologically relevant G-quadruplex structure C, k on and k off values were also compared with those of well-known compounds that interact with G-quadruplex (i.e. pyridostatine PDS, Phen-DC3, BRACO-19, MMQ1 and TMPyP4).61

Figure 4 .

 4 Figure 4. Isoaffinity plot and kinetic characterization for G-quadruplex structure C. For PhenDC 3 , BRACO-19, MMQ 1 and TMPyP 4 (in red) the analyses were performed by SPR; 61 for ruthenium(II) complexes (in green) from BLI analysis. K D (parallel diagonal lines), k on (association kinetic constant, y axis), k off (dissociation kinetic constant, x axis).

Figure 5 .

 5 Figure 5. Molecular modelling for the interactions of complex 3 (Δ-isomer) with G-quadruplex DNA. Docking calculations for interaction of complex 3 with G-quadruplex human telomeric DNA structure (PDB entry 1KF1).(A) in π-stacked positioning over the guanine tetrad, (B) interaction with TTA loop.

Figure 6 .

 6 Figure 6. Cell penetration study. Fluorescence microscopy images of U2OS cells after incubation with 20 µM of complex 1 for 24 h, DMEM buffer. (A) Ru(II) complex in green (B) the nucleus in red, stained by Draq5 (C) Bright-field and (D) merged image. Scale 14 m.

Figure 7 .

 7 Figure 7. Cell viability studies. Percentage of viable U2OS cells after incubation with complexes 1-4 for 24 h in the dark followed by 30 min of irradiation (orange bar chart) or not (blue bar chart). The tetrazolium salt-based WST-1 assay was performed 24 h after irradiation. Values were normalized to untreated and non-irradiated U2OS cells. Error bars indicate SD.

5 ' 13 C

 513 CGT 3 CGT 5 ACGA 3 CG 3' were prepared by standard automated solid phase oligonucleotide synthesis on a 3400 DNA synthesizer. After purification by RP-HPLC, they were thoroughly desalted by size exclusion chromatography (SEC). All solvents and reagents for the synthesis were of reagent grade and were used without any further purification. All solvents for the spectroscopic and electrochemical measurements were of spectroscopic grade. Water was purified with a Millipore Milli-Q system.1 H and NMR experiments were performed in CDCl 3 or CD 3 CN on a Bruker AC-300 Avance II (300 MHz) or on a Bruker AM-500 (500 MHz) at 20°C. The chemical shifts (given in ppm) are measured vs the residual peak of the solvent as the internal standard. High-resolution mass spectrometry (HRMS) spectra were recorded on a Q-Extractive orbitrap from ThermoFisher using reserpine as internal standard.Samples were ionized by electrospray ionization (ESI; capillary temperature = 320°C, vaporizer temperature = 320°C, sheath gas flow rate = 5 mL/min). Synthesis. 2-(4-chlorophenyl)-1H-imidazo[4,5-f]pyrazino[2,3-h]quinoxaline (CPITAP). A solution of 9,10diamino-1,4,5,8-tetraazaphenanthrene (53 mg, 0.250 mmol), 2-(4-chlorophenyl)-1H-imidazo[4,5-f][1,10] phenanthroline (35 mg, 0.250 mmol) and EtOH (2.5 mL) were heated at reflux for 24h. Then

  the sensors were dipped for 15 minutes in DNA containing solutions (biotinylated systems A-D) at 100 nM and rinsed in buffer solution (10 mM HEPES pH 7.4, 35 mM NaCl, 50 mM KCl and 0.5% v/v surfactant P 20 ) for 10 minutes. The functionalized sensors were next dipped in different ruthenium complex containing solution at different concentrations (see the Supporting Information) for 2 minutes interspersed by a rinsing step in the buffer solution during 4 minutes. Reference sensors without DNA immobilization were used to subtract the non-specific adsorption on the SA layer. The sensorgrams were fitted using a heterogeneous model (see sensorgrams in FiguresS39-S42). The reported values are the means of representative independent experiments, and the errors provided are standard deviations from the mean. Each experiment was repeated at least two times.

10 Å.

 10 After minimization and heating, an MD simulation was run at 300 K for 20 ns with time step set at 1 fs. acknowledged for their support. We also thank Dr L. Troian-Gautier for the scientific advice in the synthesis of CPITAP, Dr L. Bonnat and Dr T. Lavergne for providing biomolecular systems A-D, and Professor F. Loiseau for the help in the measurements of the luminescence lifetimes. M.-C. Eloy is thanked for the help about confocal microscopy experiments.

Table 1 .

 1 Absorption and luminescence data for complexes 1

-4.

		λ Abs (ε) a		λ Em	b		Φ Em	c	τ (ns) d
	Complex	CH 3 CN	CH 3 CN	H 2 O	77K	CH 3 CN	H 2 O	CH 3 CN	H 2 O
	1	460	597	603	570	0.009	0.069	115	572
		(1.61)					(0.058)	(0.14)	(380)	(1315)
	2	488	671	704	612	0.011	0.0059	376	162
		(0.59)					(0.082)	(0.007)	(692)	(162)
	3	471	623	641	598	0.037	0.017	686	629
		(1.11)					(0.138)	(0.011)	(1620)	(786)
	4	451	586	600	565	0.01	0.0006	80	6
		(1.28)					(0.02)	(0.0005)	(78)	(6)
	a λ in nm for the most bathochromic transition in MeCN (extinction coefficient, ε x 10 4 M -1 cm -1 ). b λ in nm at RT
	in MeCN and H 2 O and at 77K in EtOH/MeOH (4/1, v/v). c Quantum yield of emission measured by comparison
	with the reference [Ru(bpy) 3 ] 2+ , under air and under argon (in brackets), excitation at 450 nm, errors are estimated
	to 10%. 51 d Luminescence lifetime (after irradiation at λ = 400 nm) measured under air and under argon (in
				brackets), errors are estimated to 5%.
	The positive solvatochromism from acetonitrile to water and the hypsochromic shift of the emission
	band at 77K suggest a more polar excited state than that of the ground state, in agreement with the
	occurrence of a charge transfer (CT) upon irradiation, i.e. Metal to Ligand Charge Transfer (MLCT).
	The excited state energy decreases from 1 to 2, in agreement with the stabilization of the LUMO
	localized on the imidazophenanthroline ligand, i.e. going from CPIP to the more electron-withdrawing
	CPITAP. A similar observation can be made for complexes 2 to 4, for which the increased number of -
	deficient ligands results in the stabilization of the metal-centred HOMO, leading to a more energetic
	transition. Comparison of luminescence lifetimes, under air and under argon allows us to conclude that

Table 2 .

 2 Electrochemical data of complexes 1-4.

	Complex E ox 1/2	E * ox	a	E red 1/2 E * red	a
	1	+ 1.37 -0.74	-1.30	+ 0.81
	2	+ 1.63 -0.29	-0.77	+ 1.15
	3	+ 1.83 -0.18	-0.78	+ 1.23
	4	> 2	>-0.16 -0.77	+ 1.39

Data were measured at room temperature in MeCN with 0.1M Bu 4 NClO 4 as the supporting electrolyte (V vs Ag/AgCl), complexes concentration = 0.8 mM, a Excited state potentials estimated from equations and . The energy of the excited state, E 0-0 , is estimated by Franck-Condon line-shape analysis of the emission spectrum at 298 K in CH 3 CN (see SI, Table

Table 3 .

 3 Melting temperatures (T m ) of wtTel23 and duplex hairpin in the absence or presence of ligands (5 equiv).WtTel23 sequence 3' TT(GGGATT) 3 GGG 5' was first annealed by heating at 95°C for 5 min in Tris-HCl buffer (10 mM, pH 7.04) with 100 mM NaCl or 100 mM KCl (in brackets) and cooled down overnight to room temperature.Oligonucleotides concentration = 2.5 M. Hairpin sequence 5' CGT 3 CGT 5 ACGA 3 CG 3' was first annealed by heating at 95°C for 5 min in Tris-HCl buffer (10 mM, pH 7.04) with 100 mM NaCl and cooled down overnight to room temperature. The ellipticity was recorded at 290 and 252 nm for wtTel23 and duplex hairpin, respectively.

		T m ( o C) (±1)
	Complex	wtTel23	hairpin
	No ligand	52.9 (61.9)	68.7
	1	58.5 (76.0)	58.0
	2	58.0 (64.0)	67.6
	3	57.3 (67.0)	68.1
	4	52.9 (61.9)	67.2

  Cl 2 ] (20 mg, 0.037 mmol) and CPIP (15 mg, 0.045 mmol) were dissolved in ethylene glycol(3 mL) and heated at 120°C for 20h in the dark and under argon. After cooling and addition of aqueous solution of NH 4 PF 6 , a solid was formed. The latter was washed 3 times with water, EtOH and Et 2 O to afford the final pure product as an orange powder (36 mg, 90%). 6H). HR-MS Calcd for C 41 H 25 N 10 ClRu (2 -2PF 6 ): 394.05086 Da, found 394.05131 Da.[Ru(TAP) 2 CPIP].2PF 6 3. [Ru(TAP) 2 Cl 2 ] (20 mg, 0.037 mmol) and CPIP (15 mg, 0.045 mmol) were dissolved in ethylene glycol (3 mL) and heated at 120°C for 20h in the dark and under argon. After cooling and addition of aqueous solution of NH 4 PF 6 , a solid was formed. The latter was washed 3 times with water, EtOH and Et 2 O to afford the final pure product as an orange powder (34 mg, 83%). Cl 2 ] (20 mg, 0.037 mmol) and CPITAP (15 mg, 0.044 mmol) were dissolved in ethylene glycol (3 mL) and heated at 120°C for 20h in the dark and under argon. After cooling and addition of aqueous solution of NH 4 PF 6 , a solid was formed. The latter was washed 3 times with water, EtOH and Et 2 O to afford the crude product. Purification over preparative SiO 2 Quantum yield were obtained using [Ru(bpy) 3 ] 2+ as a reference.51 Luminescence lifetime measurements were performed after irradiation at λ = 400 nm obtained by the second harmonic of a Titanium:Sapphire laser (picosecond Tsunami laser spectra physics 3950-M1BB+39868-03 pulse picker doubler) at a 80 kHz repetition rate. The Fluotime 200 from AMS technologies was used for the decay acquisition. It consists of a GaAs microchannel plate photomultiplier tube (Hamamatsu model R3809U-50) followed by a time-correlated single photon counting system from Picoquant (PicoHarp300). The ultimate time resolution of the system is close to 30 ps. Luminescence decays were analysed with FLUOFIT software available from Picoquant.

	1 H-NMR (300 MHz, CDCl 3 ): δ (ppm) 9.13 (d, 2H, J=1.8 Hz), 9.06 (d, 2H, J=2.0 Hz), 8.24 (d, 2H, J=8.6 Hz), 7.52 (d, 2H, J=8.6 Hz). 13 C-NMR (75 MHz, CDCl 3 ): δ (ppm) 151.71, 151.70, 145.73, 145.72, 143.79, 139.52, 137.6, 129.63, 129.62, 128.13. HR-MS Calcd for C 17 H 10 N 6 Cl: 333.06500 Da, found 333.06489 Da. NMR (500 MHz, CD 3 CN): δ (ppm) 9.05 (d, 1H, J = 8.2 Hz), 8.89 (d, 1H, J = 8.4 Hz), 8.59 (dd, 4H, J = 8.3 Hz, J = 1.2 Hz), 8.28 (d, 2H, J = 8.7 Hz), 8.25 (s, 4H), 8.07 (dd, 2H, J = 10.5 Hz, J = 4.8 Hz), 8.01 [Ru(TAP) 2 CPITAP].2PF 6 4. [Ru(TAP) 2 chromatography (CH 3 CN/H 2 O/KNO 3(sat) 7/2/1, v/v/v) gave the final product as a red powder (10 mg, [Ru(phen) 2 CPIP].2PF 6 1. [Ru(phen) 2 1 H-7.69-7.62 (m, 1 H-NMR (500 MHz, CD 3 CN): δ (ppm) 9.01-8.94 (m, 6H), 8.62 (s, 4H), 8.27 (d, 2H, J = 8.6 Hz), 8.24 (d, 2H, J = 2.8 Hz), 8.19 (d, 2H, J = 2.7 Hz), 8.04 (d, 2H, J = 4.3 Hz), 7.73 (dd, 2H, J = 8.3 Hz, J = 5.3 Hz), 7.66 (d, 2H, J = 8.6 Hz). HR-MS Calcd for C 39 H 23 N 12 ClF 6 PRu (3 -1PF 6 ): 941.053699 Da, found 941.054467 Da. (ITC503S) instrument. Electrochemical studies.
	(d, 2H, J = 5.4 Hz), 7.96 (d, 2H, J = 5.2 Hz), 7.67-7.59 (m, 8H). HR-MS Calcd for C 43 H 27 N 8 ClF 6 PRu (1 25%). 1 H-NMR (500MHz, CD 3 CN): δ (ppm) 9.01-8.98 (m, 4H), 8.97 (d, 2H, J = 2.8 Hz), 8.64 (s, 4H),
	-1PF 6 ): 931.07595 Da, found 931.07688 Da. 8.32 (d, 2H, J = 8.6 Hz), 8.29 (s, 2H), 8.24 (d, 2H, J = 2.7 Hz), 8.15 (d, 2H, J = 2.8 Hz), 7.66 (d, 2H, J =
	[Ru(phen) 2 CPITAP].2PF 6 2. [Ru(phen) 2 Cl 2 ] (20 mg, 0.037 mmol) and CPITAP (15 mg, 0.044 mmol) 8.5 Hz). HR-MS Calcd for C 37 H 21 N 14 ClRu (4 -2PF 6 ): 396.04136 Da, found 396.04171 Da.
	were dissolved in ethylene glycol (3 mL) and heated at 120°C for 20h in the dark and under argon. After Absorption and luminescence studies. Luminescence titration.
	cooling and addition of aqueous solution of NH 4 PF 6 , a solid was formed. The latter was washed 3 times UV-vis absorption spectra were recorded on a Shimadzu UV-1700. The concentration of the complexes dGMP titration experiment of complexes 1-3 were recorded on a Varian Cary Eclipse instrument. A
	with water, EtOH and Et 2 O to afford the crude product. Purification over preparative SiO 2 was 50 M. Room temperature luminescence spectra were recorded on a Varian Cary Eclipse solution of dGMP (5 mM) was progressively added to a solution of complex (50 µM) in 50 mM Tris-
	chromatography (CH 3 CN/H 2 O/NH 4 Cl (sat) 4/4/1, v/v/v) gave the final product as a red powder (18 mg, instrument. Luminescence intensity at 77 K was recorded on a FluoroLog3 FL3-22 from Jobin Yvon HCl buffer, pH = 7.4. Luminescence titration with ODN (GC rich hairpin or wtTel23 G-quadruplex
	64%). 1 H NMR (500 MHz, CD 3 CN): δ (ppm) 8.86 (d, 2H, J = 2.9 Hz), 8.65 (d, 4H, J = 8.3 Hz), 8.31 (d, equipped with an 18 V 450 W Xenon Short Arc lamp and an R928P photomultiplier, using an Oxford DNA) spectra were recorded in 10 mM HEPES, 35 mM NaCl, 50 mM KCl (pH=7.4) buffer on a Varian
	2H, J = 8.8 Hz), 8.28 (s, 4H), 8.13 (d, 2H, J = 5.0 Hz), 8.08 (d, 2H J = 2.9 Hz), 8.00 (d, 2H, J = 4.8 Hz), Instrument Optistat DN nitrogen cryostat controlled by an Oxford Intelligent Temperature Controller

Cyclic voltammetry was carried out in a one-compartment cell, using a glassy carbon disk working electrode (approximate area = 0.03 cm 2 ), a platinum wire counter electrode, and an Ag/AgCl reference electrode. The potential of the working electrode is controlled by an Autolab PGSTAT 100 potentiostat through a PC interface. The cyclic voltammograms were recorded with a sweep rate of 100 mV s -1 , in dried acetonitrile (Sigma-Aldrich, HPLC grade). The concentration of the complexes was 8.10 -4 mol/L, with 0.1 mol/L tetrabutylammonium perchlorate as supporting electrolyte. Before each measurement, the samples were purged by nitrogen. Redox potentials were controlled by comparison with ferrocene, added at the end of the measurement.

Acknowledgements

J.W., M.A., L.M. and B.E. gratefully acknowledge the "Fonds National pour la Recherche Scientifique" (F.R.S.-FNRS), the "Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture" (F.R.I.A.), the "Région Wallonne", the "Université catholique de Louvain" and the "Prix Pierre et Colette Bauchau" for financial support. This work was supported by the "Agence National de la Recherche" (ANR-12-BSV8-0008-04 and ANR-16-CE11-0006-01), Labex ARCANE (ANR-11-LABX-0003-01) and the Région Auvergne-Rhône-Alpes. The NanoBio-ICMG platforms (FR 2607) are

Confocal laser scanning microscopy. U2OS cells were grown at 37 °C in a humidified atmosphere with 5 % CO 2 in DMEM medium (Westburg) containing 10% foetal bovine serum (FBS) (Gibco) and 1% penicillin/streptomycin (Westburg). 20 000 cells were seeded onto coated microscope slide and incubated with 20 µM of complex 1 for 24 h in the dark. After incubation, the medium containing the complex was removed, and fresh medium was added to the cells. The cells were rinsed in pre-warmed PBS, fixed in 4 % paraformaldehyde (VWR) for 10 min, labelled with Draq5 (eBioscience) following the instructions of the manufacturer. A confocal laser scanning microscopy system (Zeiss LSM 710) was used to acquire the images. Pictures were processed with Zen software.

Photocytotoxicity experiments.

U2OS cells were cultured in 96-well plates for 24h in DMEM (Westburg) containing 10% fetal bovine serum (FBS) (Gibco) and 1% penicillin/streptomycin (Westburg) to reach a density of 10 000 cells/well. The medium was removed and fresh one containing 10 µM of the complexes was added. After 24 h of incubation at 37 °C in the dark, cells were rinsed twice with PBS to remove non-internalized complexes.

Illumination was performed during 30 minutes with blue LED (LED strip IP68 60 LED/m from Prolumia, 405 nm at 15.7 W/m 2 ). The distance between the light source and the culture plate was of 10 cm. Before illumination, cultures were rinsed with PBS and illuminated in PBS to avoid absorption by coloured culture medium. Plates serving as a dark control were protected from illumination with alumina foil. Illuminated and control cultures were put back immediately to the incubator at 37 °C in a humidified environment and cultured in fresh culture medium for an additional 24 h. The cell viability was measured 1 day post-irradiation using 10 µl/well of WST-1 reagent (Sigma-Aldrich) following the manufacturer's instructions. The ratio of the optical density at λ = 450 nm under each set of conditions relative to that of control cells (non-transfected and non-irradiated, 100% viability) was used to determine a relative viability. The measurements were performed twelve times.

ASSOCIATE CONTENT

Copies of NMR and MS spectra, absorption and luminescence spectra, cyclic voltammograms, CD, BLI and SPR sensorgrams and cells microscopy.

AUTHOR INFORMATION

Corresponding Authors * Benjamin.Elias@uclouvain.be * Eric.Defrancq@univ-grenoble-alpes.fr ORCID Benjamin Elias: /0000-0001-5037-3313 Eric Defrancq: /0000-0002-3911-6241

Notes

The authors declare no competing financial interest.


Guifen Chen
email: chinaguifchen@163.com

Jian Lu

Ying Meng

Dongxue Wang

Research on Evaluation of Soil Fertility Resource Space Based on Regional Hotspots and Clustering Method

Keywords: soil fertility resource, soil nutrient, regional hotspots and clustering method, GIS

Based on each unit of Nong'an County,With ArcGis software platform, the establishment of a part of the total nitrogen in the soil attribute data Nong'an topsoil fertility resources, phosphorus, potassium and organic matter and effective nutrient database,Global Clustering and outlier analysis and hot spot analysis of the autocorrelation method, the county topsoil fertility resources were analyzed and evaluated. The results for farmland protection and optimal layout policy to provide a reference for the theory and methods, but also on the implementation of the agricultural work provides precise basis for decision making.

Introduction

Topsoil soil resources refers to as agricultural production in a most basic means of production, cultivated land is the basic resources for human survival and development, and the natural elements in agricultural production has shown the potential production capacity, to promote farmland protection and scientific determination of cultivated land spatial distribution pattern and variation characteristics and other aspects has important significance.Topsoil soil resources is a concept of cultivated land quality in the agricultural workers, soil fertility, crops grow well, high yield; poor soil fertility, is crop growth is not good, low yield. Criteria for evaluation of soil resources is the most direct should is the fertility of the soil, and measure indicators of soil fertility levels tend to use the organic matter content, the level of soil nutrient and soil physical properties.

Therefore, the attribute data of soil nitrogen nutrient, available phosphorus, available potassium and organic matter were analysis and research, in order to provide the basis for the implementation of Xiang area tilth soil resource function zoning and differential control and precise farmland operation.

In the first law of geography, it is pointed out that all things are related, and that the things which are far from far away have higher relativity. In other words, the

Getis-Ord Gi * (Hot Spot Analysis)

Hot spot analysis of each element of the data set can be calculated Getis-Ord Gi * statistic (called G-i-asterisk). By z score and p-values obtained may know the location of clustering feature high or low values occur in space. Working way:

view adjacent elements of the environment in every element. High value of the element is often easy to attract attention, but may not have a statistically significant hot spots. To become a hot demand significant statistical significance, the elements should have a high value, and is surrounded by other factors also have a high value.

Elements of a partial sum of its neighboring elements will be compared with the sum of all the elements; when there is a big difference when the sum of the partial sum of the partial expected and that they can not be the result of randomly generated, it will produce a significant statistical significance of the z-score. Its expression is as follows:

Getis-Ord local statistics can be expressed as:

1 ] ) ( [ 2 , 1 2 , 1 , 1 , 1 *             n w w n S w X x w G j i n j j i n j j i n j j j i n j i (1)
Wherein j x
is the attribute value of the element j , j i w , is the space between the right elements i and j weight, n is equal to the total number of elements, and:

n x X j n j    1 (2) 2 2 1 ) ( X n x S j n j     (3) * i
G is z score statistics, there is no need for further calculations.

For each feature dataset returned Gi * statistic is a z-score. For positive z having a statistically significant score, the higher the z score, high values (hot spots) clustering more closely. For statistically significant negative z score on, z lower the score, the low (cold spots) clustering more closely.

Moran's I (clustering and outlier analysis)

Moran's simultaneously according to feature locations and feature values to measure spatial autocorrelation. Under a given set of elements and attributes related to the case, the expression pattern was clustering mode, the discrete mode or random mode. By computing Moran's I index value, z score and p-values for the significance index were evaluated. p value is based on the known distribution curve derived from the area of approximation (by the test statistic restrictions). Its expression is as follows: Spatial autocorrelation Moran's I statistic can be expressed as:

Wherein i z is a property of its elements i mean ) ( X x i  deviation, j i w , is
space right elements i and j weight, n is equal to the total number of elements, 0 S is the weight of all polymeric space right:

     n j j i n i w S 1 , 1 0 (2)
Statistics of I z -score calculated according to the following form:

] [ ] [ I V I E I z I   (3) Among them: ) 1 /( 1 ] [    n I E (4) 2 2 ] [ ] [ ] [ I E I E I V   (5)
If the data set values tend to occur on spatial clustering (high values gathered in the vicinity of other high value; low gathered in the vicinity of other low value), the Moran's I index will be positive. If the high value of the exclusion of other high-value, low and tend to close, the index will be negative. If the positive and negative values of the cross product cross product offset value, the index will be close to zero. Since the molecules are normalized by the variance, so the value of the index will fall within the range of -1.0 to +1.0.

Spatial Autocorrelation of the Two Algorithms Similarities and

Differences

Spatial autocorrelation statistic is a measure of geographic data (geographic data) of a fundamental nature: the degree of interdependence of the data in such a position with other positions between. Moran's I and Getis-Ord Gi * is an important method of calculating spatial autocorrelation of two. These two approaches can be effective on the characteristics of spatial distribution of elements in space phenomena were analyzed. Getis-Ord Gi * similarities and differences and Moran's I are as follows:

Algorithmically principle: common is the use of two algorithms are the average of all the properties of the overall assessment of the data analysis for identifying local spatial autocorrelation phenomenon. The difference is, Moran's I for lower weights attention; 1 represents upright element having contain the same or similar low-high property values approaching element, the element is part of the cluster, negative 1 represents a near containing different values element, the element is an abnormal value, while Getis-Ord Gi * weights sensitivity higher than the sensitivity of Moran's I; this algorithm is the sum total of the partial sum is compared with the sum of the partial when expected great differences that can not be randomly generated when the result is, will produce z having a significant score.

Results: the common denominator is two algorithms will compute p-values and z-score. The difference is, Moran's I would explicitly given the high value of the spatial concentration (HH), whispered spatial concentration value (LL), low high spatial concentration value (LH) and the level of spatial concentration value (HL) result Fourth, through this four things that can tell us whether there is a gathering space or outliers, and Getis-Ord Gi * only compute p-values and z-score.

Results and Analysis

Use Lahai and Xin Nong countryside of Nong'an County, regional hot spots mainly distributed in Nong'an Fu Longquan ,Bao Jia, He Long countryside near; by Fig. 3 (b) shows that P Moran 's I analysis graph was mainly manifested as high value of spatial agglomeration, distribution situation and hotspot analysis of regional hot spots are generally the same, shows that P content rich in Fu Longquan , San Gang, Bao Jia of Nong'an country and around the township P equally rich.

Hot spots, clustering and outlier analysis of nutrient K

Conclusions

Through the analysis of the difference of each township Nong'an county domain resources space unit soil fertility, results show that:

(1) Nong'an County topsoil fertility resource attribute of soil total nitrogen, available phosphorus, available potassium and organic matter and nutrient data has accumulated to a certain extent, based on Getis-Ord Gi* on soil N, P, K and organic matter spatial hot spots were analyzed. Research and analysis shows that the N nutrient and organic matter regional hot spots distribution similar, hot spots are mainly distributed in Nong'an County in the south central part of the township area, and the freezing point of water is mainly distributed in Nong'an County, the northern part of the township area;

(2) using Moran's I distinguish Nong'an County topsoil soil resources of soil property n, P, K and organic matter on nutrient data of space agglomeration and discrete spatial regions, the research result shows that the, different types of cultivated land quality index showed a strong spatial correlation, namely nutrient data high value or low value of Xiang area in space showed significant concentration of the state, and the aggregation degree is higher;

(3) Getis-Ord Gi* and Moran 's I in the plough layer soil resource analysis based on, for the protection of arable land partitioning provides new ideas, the cultivated land quality in rural area of differential protection and fine management has a certain reference value.

  geographical data are related to each other due to the influence of spatial interaction and spatial diffusion. Space correlation analysis originated in the mid-19th century biometric study has now become one of the basic methods of Theoretical Geography, mainly used to analyze the spatial data statistical distribution. The analysis results are often dependent on the spatial distribution of data and different to traditional econometrics. The analysis method is realized by spatial autocorrelation index, this paper uses Gi* Getis-Ord (hot analysis) and Morans' I (clustering and outlier analysis) to analyze the spatial autocorrelation of cultivated land quality With the aid of GIS analysis technology, using the above two methods of spatial analysis, Nong'an County topsoil fertility resources part attribute data of soil total nitrogen, available phosphorus, available potassium and organic matter spatial hot spots and clustering and abnormal value are analyzed and provide significant test, the conclusion of the two methods for implementation of Xiang area tilth soil resource function zoning and differential control and precise farmland operation provides decision-making basis.

Fig. 1 .

 1 Fig. 1. The soil fertility level map of Nong'an 2.1 Research Methods Spatial autocorrelation is a geographic feature attributes in a certain area of space characterization. With the help of GIS analysis techniques using Getis-Ord Gi * (Hot Spot Analysis) and Moran's I (cluster analysis and outliers) in two spatial autocorrelation analysis of Nong'an part tilth soil fertility resource attribute data TN ( N), phosphorus (P), available potassium (K) and organic matter of space clustering and hot spots and outliers are verified and the corresponding significance test, validation and test results show that: the two methods of analysis as a result, the job can accurately implement township gamut topsoil fertility resource function zoning and differential control farmland and provide a basis for decision making.

Fig. 2 .

 2 Fig. 2. Autocorrelation analysis space of nutrient N Fig.2 (a) shows, the cold spot region nutrients N mainly Yang Shulin countryside near of Nong'an, hot spots mainly in the field of agricultural villages County

3 . 2 Fig. 3 .

 323 Fig. 3. Autocorrelation analysis space of nutrient P From Fig. 3 (a) visible, P nutrient cold areas mainly distributed in Yang Shulin, Ha

Fig. 4 .Fig. 5 .

 45 Fig. 4. Autocorrelation analysis space of nutrient K Fig. 4 (a) visible, K cold areas mainly distributed in Yongan, Yang Shu Lin, Ha Lahai countryside of Nong'an County, regional hot spots mainly distributed in Nong'an County Central Rural Areas; by Fig. 4 (b) shows that high value space agglomeration appear in Nong'an County in Central rural area shows that in Nong'an County Central K content rich, around the country K content in the same rich, high and low values of the space agglomeration appear in Nong'an County Xin Nong countryside shows that in around the Xin Nong countryside domain K content and new agricultural township, compare less.

2 Materials and Methods 2.1 The Basic Situation of the Study Area

  hectares, seed coverage reached more than 98%. The soil in Nongan county is divided into 10 soil groups, 20 subgroups, 50 soil genera and 111 species of soil, black soil, chernozem soil zonality. The local soil has meadow soil, alluvial soil, saline alkali soil, swamp soil and sand soil. According to the survey results of soil fertility level, the soil fertility level map of Nong'an GIS technology produced as shown in Fig.1 based on.

	reached 366 thousand
	The Nongan County, Jilin Province, Nong'an county at Songliao plain, in the
	central part of Jilin Province, belonging to Changchun City, located 60 kilometers
	northwest of Changchun City, The latitude 43 degrees 54 ' to 44 degrees 56 ' ,
	Longitude 124 degrees 32 ' to 125 degrees 45 ', Second Songhua River, Yitong
	River, Xinkai River, Yinma River, passing through Weng Kehe. South and
	Changchun suburban neighborhood, Dehui City, bordering the East, northeast and
	Fuyu, across the river, north, Qian Gorlos Mongol Autonomous County, west of
	Changling, Gongzhuling City in the southwest and border. County 114.7
	kilometers long from north to south, east-west width of 97.7 kilometers, the total
	area of 5400 square kilometers. In 2013, Nong'an County grain planting area

Guifen Chen(1956-),College of Information Technology,Jilin Agricultural University, Changchun, Jilin, China

Acknowledgments

This work was funded by the China Spark Program.2015GA66004."Integration and demonstration of corn precise operation technology based on Internet of things"


Guifen Chen
email: guifchen@163.com

Dongxue Wang

Shan Zhao

Siwei Fu

Research on GA-RBF optimization algorithm in the prediction of yield loss of maize diseases and pests

Keywords: Genetic algorithm, RBF neural network, fusion algorithm, maize diseases and pests, yield loss predict 2 GA-RBF fusion

In view of the high complexity and nonlinearity of crop pests and diseases, using the traditional BP network and RBF network model method to predict is pretty difficult. And the prediction accuracy is low. Also the effect is not ideal when the sample size is small and the noise is more,therefore,this article presents a fusion optimization algorithm based on genetic-algorithm (GA) and radial basis function neural network (RBF).By unified coding the data center of RBF neural network and its corresponding expansion constant and weight,strengthened the cooperation between the hidden and output layer,furthermore using the functional characteristics of global search using genetic algorithm to obtain the optimal model of yield loss,finally predict on yield loss of maize diseases and pests. By making simulation test data of the National 863 project demonstration area -13 village, Gong' peng town in Jilin province Yu'shu County,the experimental results show that:After using the GA to optimize the RBF in the network's structure and approximation has obvious improvement and enhancemen,can effectively reflect the fluctuation characteristics of maize diseases and pests,has been widely application prospect in the agricultural field.

Introduction

One of the main grain crops in our country is maize. It is also the main growing crop in the northeast region. Maize production plays an important role in food production as a whole.Crop diseases and pests is one of the important factors affecting crop yields. Therefore, plant diseases and insect pests of agricultural yield loss forecasting has become an important issue in the study of the economic development and its research and analytical prediction has a strong practical significance.

In view of the high complexity and nonlinearity of crop pests and diseases,using the traditional BP network and RBF network model method to predict is difficult. And the prediction accuracy is low. Also in solving small sample size and more noise effect is not ideal.

To solve the above problem, this article proposes an optimization algorithm based on genetic algorithm (GA) and radial basis function neural network (RBF) fusion to predict yield loss of maize diseases and pests. Build GA-RBF model to predict effects of the occurrence dynamics of plant diseases and pests on maize yield, growers can make timely disaster prevention and cost-effectively reduce the extent of the disaster, increase maize yield.

Fig. 1. Genetic algorithm flowchart

GA is a kind of optimization to the problem space encoding, with guided random search optimization method and its implied parallelism and robustness in nature is the symbol of genetic algorithm is different from the traditional optimization search method.

Radial basis function(RBF)

RBF neural network structure and multilayer forward networks are similar, it is a three layer to the network, input,hidden and the output layers. From input to hidden,the transformation is nonlinear, and from hidden to output transformation is linear.

A typical three layer topology is shown in Figure 2.

Ye s

Group fitness evaluation (1) =(x 1 ,x 2 ,…,x n )is network input vector; c i =(c i1 ,c i2 ,…,c in )is the center vector of NO.i neuron in the hidden layer, is a vector with same dimension to the X; is the width of NO.i basis function;Next,M is the number of neurons in hidden layer; represents between x and c i vector Euclidean norm;The output layer is a linear layer, is the linear weighted sum of the hidden layer output.Complete following nonlinear mapping. X ∈ R n is input vector, is R+→R nonlinear function.

RBF can solve the nonlinear problems, it has good generalization ability, for the problems with complex function relation of functional approximation, its precision is higher. But this method just accelerate the convergence speed in a certain extent.Therefore, this paper proposes an optimization algorithm based on genetic algorithm (GA) and radial basis function neural network (RBF), which can solve this problem better.

w 2 w 1 x 2 x 1 y x m 1  Center X 1 2  Center X 2 p  Center Xp  Output layer m i c x x i i T , , 2 , 1 ), 2 exp( ) ( 2 2 i             n i i i n c X w w x 1 0 ) ( ) ( f  2.3

GA-RBF

As mentioned before,neural network technology has penetrated into many fields,but there are still some problems that can not be solved.Because of the GA can converge to the global optimal solution and robustness is strong.Combine GA and RBF can not only play the ability of neural network-generalization , also can make RBF has fastconvergence and better learning-ability.

The three layers of RBF have different function.While designing the network, the most critical step is hidden layer data center Ci, the propagation constant and the value of between hidden and output the size of the weights wi choice, in order to make RBF neural network has the best approximation effect,it needs select the most accurate parameter values.

The basic problems to be solved when using GA to optimize the parameters of RBF are:

(1)Genetic encoding corresponding to RBF In this algorithm, the width of the RBF network is the same as the center, which is obtained by the genetic algorithm.The code includes the binary method and the real number coding method In this article, we use a real coding method, each individual is expressed by a letter of the alphabet, and include its range, not only can improve the accuracy, but also to find the best value in a larger range (2)Create initial group In this paper, the population size is set to 20。According to the range of the input vector, the initial group is randomly generated in the range, the center of the initial basis function is generated, which is the initial cluster center.

(3)construct the fitness function The size of individual fitness in genetic algorithm to determine the probability of the individual to be inherited to the next generation of individuals.Fitness is the only information that guides the search of genetic algorithm,its good or bad is a measure of the merits of the key algorithm.The fitness function formula is as follows:

) ( 1 ) ( F Y E Y  (3)
Among them,E (Y) is the sum of the error of the network output value and the expected value of all training samples for neural network training.This selection of fitness function can directly translate the differences in performance of each individual.

(4) genetic operator a.Select operation:In this paper,the method of proportion selection were adopted.

b.Crossover operation:The crossover rate is the core of the genetic algorithm.Through the cross, the search ability of the algorithm can be improved. c.Mutation operation:Mutation operation through determining locus for all individuals at random to change the gene mutation probability values,the purpose is to strengthen the regional search ability of genetic algorithm, and increase the various kind of the group.

Simulation experiment and result analysis

Experimental materials

The research area is the farm field of the village of the town of 13 village, Gong' peng town in Jilin province Yu'shu County,is a plain area of Changbai Mountain foreground,black calcium soil,major crops are corn and soybeans, etc.It is the most important commodity grain bases in Jilin province.During the period of rain and heat, sunshine is longer, which is beneficial to the growth of maize crops.Part of data in the experimental area is shown in Table 1.

Simulation results analysis

Simulation parameter setting

By using the GA and neural network toolbox provided by Matlab, the initial population of genetic algorithm is 20, and the maximum generation is 1000.Assignment of genetic operators:

(1) Selection rate (generation gap):After repeated calculation or test view,when the ratio is 0.8, the selection method is more appropriate.Principle of the method is to calculate the fitness value of each parent individualeach and sum all the parent individual fitness value first,then calculate the ratio of each fitness.The calculation formula is as follows:

(2) Crossover rate:The crossover rate is generally between 0.5~1.0,because the crossover rate is too small to make the search process stalled.In this article, the crossover rate is 0.7.

m i F F i i  3 , 2 , 1 , p    (4)
(3) Mutation rate:If there is no mutation operation in genetic algorithm, it will not generate new genes,but if the mutation rate is too large, it will make the genetic algorithm into a random search,the general mutation rate is between 0.01~0.1.In this paper, the variation rate of 0.02 and 0.05 were compared.

According to the sample data,setting RBF neural network input layer neurons for two,namely Diseased plant rate and Pest rate;Output layer neuron for one,namely maize yield;Using iterative method for design of RBF network,for each iteration a neuron is added,the iteration will stop until the error sum of squares down to the target error or number of neurons reached the maximum value.Finally, a network model with 5 hidden layer neurons is obtained.

Experimental process and results analysis

(1)Forecast error comparison After several simulation experiments,when the number of training samples increases, compared with the traditional RBF network, the GA-RBF fusion optimization algorithm is improved in the convergence speed and the accuracy of pproximation,and the global search ability of the network is better.In order to verify and let the error between predicted and actual reflect more clearly.So,in this article, the error curve were outputed by the matlab GA and RBF neural network toolbox.

The error curve is shown in Figure 3.

Fig.3 . Forecast error results as compared

Through the analysis of the error curve, it is further explained that the GA-RBF fusion algorithm is more accurate than the traditional single RBF neural network algorithm.

(2)Determine prediction accuracy If there is no mutation operation in genetic algorithm, it will not generate new genes,but if the mutation rate is too large, it will make the genetic algorithm into a random search,select the appropriate mutation rate is one of the key points in the optimization operation of GA.Therefore,in this article, uses the crossover rate as 0.7 and the mutation rate as 0.02 and 0.05 to do the simulation predictions.0.02 mutation rate forecast results were shown in Figure 4,0.05 were shown in Figure 5. Through the simulation calculation,when the mutation rate was 0.05, the prediction accuracy was higher than the 0.02.Therefore,the final prediction was made with the mutation rate of 0.05.

Rrom Figure 4,we can observe that the prediction relative error of GA-RBF fusion algorithm is smaller than that predicted by RBF neural network only,which was used to predict the yield loss of maize diseases and pests,and also the prediction accuracy has been improved.

Conclusion

For maize pests of yield loss prediction of a strong complexity and nonlinear characteristics,an optimization algorithm based on GA and RBF neural network fusion is presented,it is used to predict yield loss of maize diseases and pests in this paper.Research indicates:

(1)The RBF model based on GA is optimized, which can predict the yield loss of maize diseases and pests effectively.After the test and analysis of the forecast results,it is proved that the relative error of GA-RBF fusion algorithm in the prediction of yield loss of maize diseases and pests is lower than that of the traditional RBF neural network.

(2)By calculating and analyzing: after using RBF neural network to learning training sample data, the prediction error is 8.8%; And after GA -RBF fusion algorithm of prediction error is 6.4%, compared with before optimization error was reduced by 2.4%.Thus, after the optimization of the parameters of the GA-RBF fusion algorithm, maize yield loss prediction accuracy has been improved.

(3)Through many simulation experiments,when the number of training samples increases, compared with the traditional RBF network, the GA-RBF fusion optimization algorithm is improved in the convergence speed and the accuracy of the neural network approximation,and the global search ability of the network is better.and also achieved good effect in forecasting the yield loss of maize diseases and pests.

Fig. 2 .

 2 Fig.2. Genetic algorithm flowchart

Fig. 4 .Fig. 5 .

 45 Fig.4 . yield loss prediction of maize diseases and pests(Mutation rate 0.02)

Table 1 .

 1 FontPartial Sample data in experimental area

	Block area Diseased plant rate (%)	Pest rate (%)	Yield (kg / HA)
	A1	1.82	0.00	4756.98
	A2	3.51	0.00	5272.62
	A3	1.85	3.70	4800.49
	A4	1.85	3.70	4271.18
	E1	3.57	5.36	6232.88
	E2	2.00	4.00	5665.62
	E3	5.66	1.89	5210.89
	F1	3.51	5.26	5928.16
	F2	1.96	7.84	4682.38
	F3	1.85	1.85	5138.17
	H1	1.72	3.45	4664.02
	H2	0.00	5.36	4808.12
	L8	1.69	5.08	4673.46
	L9	6.56	3.28	3969.84

3.2 Partial Matlab code

This article uses Matlab neural network and genetic algorithm toolbox to train the neural network,it can directly train and simulate the network by using the function provided by it.

Part of the Matlab code is as follows:

net=newrb(train_x,train_y,0.01,1,5,1);

Acknowledgment

This work was funded by the China Spark Program.2015GA66004."Integration and demonstration of corn precise operation technology based on Internet of things".