Daniel Dobrovodský

Carmelo Di Primo
email: carmelo.diprimo@inserm.fr

Do conformational changes contribute to the surface plasmon resonance signal?

Keywords: Surface plasmon resonance, conformational change, G-quadruplex, refractive index increment, thrombin binding aptamer

Surface plasmon resonance (SPR)-based biosensors are widely used instruments for characterizing molecular interactions. In theory the SPR signal depends only on mass changes for interacting molecules of same chemical nature. Whether conformational changes of interacting molecules also contribute to the SPR signal is still a subject of lively debates. Works have been published claiming that conformational changes were detected but all factors contributing to the SPR signal were not carefully considered, in addition to often using no or improper controls. In the present work we used a very well-characterized oligonucleotide, the thrombin-binding DNA aptamer (TBA), which upon binding of potassium ions folds into a two G-tetrad antiparallel G-quadruplex structure. All terms contributing to the maximal expected SPR response, R max , in particular the refractive index increment, RII, of both partners and the fraction of immobilized TBA target available, c a , were experimentally assessed. The resulting R max was then compared to the maximal experimental SPR response for potassium ions binding to TBA using appropriate controls. Regardless how the RIIs were measured, by SPR or refractometry, and how much TBA available for interacting with potassium ions was considered, the theoretical and the experimental SPR responses never matched, the former being always lower than the latter. Using a straightforward experimental model system and by thoroughly taking into account all contributing factors we therefore conclude that conformational changes can indeed contribute to the measured SPR signal.

Introduction

In the past decades, surface plasmon resonance (SPR)-based biosensors have been firmly established as "gold" standard instruments for analysing molecular interactions. The technique allows for label-free, real-time qualitative monitoring of molecular binding as well as quantitative evaluation of interaction parameters. For these reasons, SPR has found applications in life sciences where it has been used to study the affinity, specificity, and binding kinetics of protein-protein [START_REF] Meyerkord | Protein-protein interactions: Methods and applications: Second edition[END_REF], protein-nucleic acid [START_REF] Majka | Analysis of Protein-DNA Interactions Using Surface Plasmon Resonance[END_REF]Šípová and Homola, 2013), and nucleic acidnucleic acid complexes (Palau et al., 2013;Šípová and Homola, 2013). SPR has also been used to investigate enzymatic reactions (Wang et al., 2008), receptor-drug interactions (Ma et al., 2018), as well as for screening for specific small-molecular ligand (Vo et al., 2019) or aptamers (Dausse et al., 2016) and other purposes (Nguyen et al., 2015).

SPR is based on the measurement of refractive index changes in the proximity of the sensing surface as a result of the immobilization of one molecule, the ligand, onto the surface and its subsequent interaction with its partner, the analyte. This generates the SPR response, which on Biacore instruments is measured in resonance units (RU). In most practical applications, these changes were considered solely as a function of mass increment occurring at the solid-liquid interface of the biochip. However, the SPR signals upon immobilization of the ligand and interaction with the injected analyte depend on multiple factors, according to Eq. 1 (Davis and Wilson, 2000):

where R 1 is the response after immobilization of the ligand, R max is the maximal response at saturating concentration of the analyte, MW 1 and MW 2 are the molecular weights of the ligand and analyte, respectively, RII 1 and RII 2 are the corresponding refractive index increments of the two species, and S refers to the stoichiometry of the interaction, i.e., the number of binding sites of the ligand. With all the terms known, Eq. 1 allows convenient prediction of expected signal response, R max , upon analyte binding. However, not all immobilized target is generally available for analyte binding due to its partial degradation, denaturation, steric effects, or dissociation from the surface, which further influences the maximal obtainable signal of the analyte. For that reason, an availability constant, c a , reflecting the fraction of available target must be included in R max prediction. The final expression is then given by Eq. 2:

Since the beginning of the use of SPR in biomolecular analysis, a question has been raised, whether the binding-associated conformational change in a studied system also contributed to the SPR signal.

Many works have been published on this topic claiming to detect conformational changes of biomolecules using SPR (Salamon et al., 1994;Sota et al., 1998;Boussaad et al., 2000;[START_REF] Mannen | [END_REF]Zako et al., 2001;Flatmark et al., 2001;Gestwicki et al., 2001;May and Russel, 2002;[START_REF] Hsieh | Detection of low-molecular-weight analytes by surface plasmon resonance and receptor conformational changes[END_REF]Geitmann and Danielson, 2004;Wood et al., 2005;Christopeit et al., 2009;Dell'Orco et al., 2010, 2012;Hall et al., 2011;Dejeu et al., 2018Dejeu et al., , 2021)). These claims were usually based on changes in baseline of immobilized proteins upon induction of conformational change by various agents (pH, electric potential, denaturing buffer) (Boussaad et al., 2000;[START_REF] Mannen | [END_REF]May and Russell, 2002;Salamon et al., 1994;Sota et al., 1998;Zako et al., 2001) or on observations of additional SPR signal for interactions of proteins with small molecules or ions, that could not be compensated by the increase of mass alone and thus was attributed to changes in conformation (Christopeit et al., 2009;Dejeu et al., 2021Dejeu et al., , 2018;;Dell'Orco et al., 2012, 2010;Flatmark et al., 2001;Geitmann and Danielson, 2004;Gestwicki et al., 2001;[START_REF] Hsieh | Detection of low-molecular-weight analytes by surface plasmon resonance and receptor conformational changes[END_REF]. However, these results are controversial since the authors failed to consider all factors contributing to the SPR signal, in addition to often using no or improper experimental controls. Although some studies attempted to calculate the expected signal from an equivalent of Eq. 2 (Dejeu et al., 2021(Dejeu et al., , 2018;;Gestwicki et al., 2001;[START_REF] Hsieh | Detection of low-molecular-weight analytes by surface plasmon resonance and receptor conformational changes[END_REF]Wood and Lee, 2005), the authors attributed incorrect values to the ratio of refractive index increments or neglected it entirely. Therefore, the uncompensated signal observed in these studies cannot be unequivocally attributed to a change in conformation.

The present work aimed to properly determine whether conformational changes of biomolecules can be detected using SPR and contribute to the measured response. For this purpose, a very wellcharacterized oligodeoxynucleotide (ODN), the thrombin-binding DNA aptamer (Bock et al., 1992;Macaya et al., 1993;Paborsky et al., 1993;Wang et al., 1993), was chosen that is able, upon interaction with K + ion to fold into a G-quadruplex (G4), a non-canonical compact structure of DNA (Ma et al., 2020). This G4 aptamer was immobilized on a sensor chip through streptavidin-biotin coupling (Fig. 1a). The calculated maximal expected SPR signal, R max , upon potassium binding was predicted from Eq. 2 by careful experimental evaluation of all its terms and using appropriate control ODNs. The theoretical R max was then compared to the maximal experimental SPR signal obtained from the interaction of the ODN with K + inducing folding of the G4 structure. These R max values did not match. After thoroughly considering all known contributing factors, any discrepancy between these two values could be attributed to the conformational change taking place when the ODN folds from a single strand to a G4. Therefore, we conclude that conformational changes can contribute to the SPR signal measured on Biacore instruments and the most widely used SPR biosensors in academic and private laboratories.

Materials and methods

Materials

High purity salt-free thrombin-binding aptamer (TBA; 5'-GGTTGGTGTGGTTGG-3'), a control sequence (TBAcont; 5'-AATTGGTGTAATTGG-3') with or without a biotin tag attached to their 5' end via a triethylene glycol (TEG) spacer as well as complementary strands TBAcomp (5'-CCAACCACACCA-3') and TBAcontcomp (5'-CCAATTACACCA-3') were purchased from Eurofins Genomics (Nantes, France). For the refractometry measurements, the TBA sequence was synthetised at a high synthesis scale in-house using an ÄKTA-O-10 synthesizer (Cytiva, Umea, Sweden). Before use, all ODNs were desalted by running them through Sephadex G-25 spin columns (GE Healthcare, Vélizy-Villacoublay, France). Their concentration was determined in Milli-Q water by absorbance measurement using a NanoDrop One spectrophotometer (Thermo Scientific, Illkirch, France). The stock solutions were stored at -20 °C.

Surface plasmon resonance experiments

All SPR experiments were performed on a Biacore T200 instrument (Cytiva, Uppsala, Sweden) at 25 °C.

The interaction experiments with KCl were performed on SAHC200M sensor chips (Xantec Bioanalytics, Düsseldorf, Germany) with a layer of streptavidin immobilized on a carboxy-methyldextran-modified gold surface. The surface of a new sensor chip was first cleaned by three consecutive 30 s injections of a solution of 50 mM NaOH and 1 M NaCl prepared in Milli-Q water to remove any non-covalently associated streptavidin. The biotinylated ODNs were immobilized on flow cells 2 and 4 by injection of 50 nM solutions in HBS-EP+ buffer (10 mM HEPES, pH 7.4, 150 mM NaCl, 3 mM EDTA, 0.05% (v/v) Surfactant P20) until the desired amount of immobilized ODN was reached (ca. 1000 RU). Channels 1 and 3 were left empty and used as a reference. As a running buffer (RB) for the binding experiments, 100 mM HEPES was used with pH adjusted to 7.2 with LiOH. To avoid possible contamination of the buffer solution by K + from the pH electrode a first solution was adjusted to pH 7.2 using the pH-meter to know exactly how much LiOH was required. Then another solution was prepared without using the pH-meter. Solutions of KCl with increasing concentrations in the range from 0.9375 mM to 30 mM in RB were then injected for 30 s at 25 µl min -1 followed by 60 s dissociation. In between injections, the surface was cleaned with RB for 60 s.

The amount of active target available for binding was determined by immobilizing the biotinylated target ODNs on the SAHC200M sensor chip and subsequent injection of the complementary strand in RB at 25 µl min -1 using the single cycle kinetics method (SCK) (Karlsson et al., 2006;Palau and Di Primo, 2012), with 60 s and 400 s contact and dissociation times, respectively. For the regeneration step, 20 mM LiOH in milli-Q water was applied for 60 s. All experiments were performed with duplicate injections and the obtained sensorgrams were double referenced to the blank buffer and to the response of the empty reference channel.

Refractive index determination by SPR was performed by injection of the measured solution over a cleaned, unmodified surface of the SAHC200M sensor chip or the plain gold surface of AU sensor chip (Xantec Bioanalytics, Düsseldorf, Germany) for 30 s at flow rate 25 µl min -1 . The refractive index values were determined from the sensorgrams by reading of the response value 5 s before the end of the injection.

Refractometry

Measurements of refractive index data were carried out at 589 nm using Abbe refractometer DR-A1-Plus (ATAGO, USA) at a controlled temperature of 25 °C. Refractive index of the pure solvent (100 mM HEPES pH 7.2) was subtracted from the value measured for each solution to obtain the change in refractive index change (Δn) corresponding to the dissolved species.

Circular dichroism spectroscopy

Circular dichroism (CD) spectroscopy was used to monitor titration of ODNs with K + ions. CD spectra were recorded on Jasco J-1500 CD spectrophotometer (Jasco, Lisses, France) in a Peltier thermostated holder at 25 °C using 1.0 cm quartz cuvettes in the wavelength range 220 -330 nm with 50 nm min -1 scan speed and 2 nm bandwidth. 5 µM solution of ODN in 100 mM HEPES pH 7.2 was titrated with additions of 100 mM KCl in 100 mM HEPES pH 7.2 with 5 µM of the appropriate ODN to avoid dilution of the ODN in the cuvette. The titration continued until final concentration of 30 mM KCl was reached. The solution was left to equilibrate after each addition for 5 min prior to the measurement. Each spectrum was obtained as an average of three successive accumulations.

Thermal melting experiments

Thermal denaturation and renaturation of TBA and control ODN was carried out using a UV-mc 2 spectrophotometer (SAFAS, Monaco) with Peltier temperature-controlled cuvette holder. The TBA samples prepared at 5 µM strand concentration in 100 mM HEPES pH 7.2 with or without 30 mM KCl were heated to 90 °C for 90 s and subsequently left at room temperature for 10 min to facilitate the formation of G4 before being placed to 1 cm quartz cuvettes and capped by paraffin oil for the measurement. The melting and renaturation curves were recorded from 5 °C to 90 °C at 0.3 °C min -1 .

The structural transition was monitored by measuring the absorbance at 295 nm. The recorded melting curves were referenced to the blank buffer solution as well as to TBA absorbance at control wavelength 320 nm. Melting temperatures (T m ) were determined as the inflection point of the melting curve from its first derivative.

Results

CD-monitored titration of ODNs with K +

In a previous work (Di Primo and Lebars, 2007) it was shown that TBA immobilized on a sensor chip folded as a G4 in the presence of potassium as it was able to recognize the target of the in vitro selection (Bock et al., 1992), thrombin. To confirm again the suitability of TBA as a conformational change model, titration of TBA by K + was performed using CD spectroscopy, at the same buffer conditions and KCl concentration range (0 -30 mM) used in the SPR experiments. The same experiment was performed with TBA carrying biotin-TEG tail which was used for immobilisation of ODNs on a streptavidin coated sensor chip in the SPR experiments, to assess the effect of this covalent tag on the formation of G4. For control experiments, similar K + titrations were performed with a TBAcont (biotinylated and non-biotinylated) sequence. TBAcont was designed to not form G4, due to substitution of two guanines of the TBA sequence with adenines.

A relationship between various G4 conformations and position of bands in CD spectra has been well-established empirically (Karsisiotis et al., 2011) but also rationalised theoretically (Masiero et al., 2010), making it an excellent method for the analysis of G4 structures. TBA displayed a CD spectrum as previously described for this sequence characteristic for antiparallel G4 (Nagatoishi et al., 2007), with a negative band around 270 nm and two positive bands at 245 and 293 nm (Fig. 1). Gradual increase of the latter positive peak for the aptamer without (Fig. 1b) and with biotin-TEG (Fig. 1c) with increasing KCl concentration was used to follow the course of the titration reflecting G4 formation (Fig. 1d).

Significant increase of this signal was observed for TBA already after the first addition of KCl. The signal reached saturation at around 10 mM KCl (Fig. 1b). In the case of TBA with the biotin-TEG tail, the increase was more gradual, with higher concentration of KCl needed for the G4 to be fully folded.

Nevertheless, saturation was reached by the end of the titration. The maximum signal intensity reached at saturation was also lower than for the unmodified TBA (Fig. 1c), possibly indicating lower stability of the modified ODN (Nagatoishi et al., 2007). Additionally, Fig. 1d shows notably larger standard deviation from three experiments for biotin-TEG-TBA, likely as a result of the higher instability of this sample.

The titration data were fitted to a theoretical model to obtain the apparent dissociation equilibrium constant, K D , for K + binding to TBA. Since the concentration of the latter was well below the used concentration range of KCl and the expected K D , Eq. 3 could be used (Jarmoskaite et al., 2020):

where R eq is the obtained response at the equilibrium and R max is the hypothetical maximal response at infinite KCl concentration (Fig. 1d). For TBA carrying the biotin-TEG tail the K D determined from the fit was 1.6 ± 0.3 mM compared to K D of 0.20 ± 0.02 mM for the unmodified TBA, indicating partial loss of affinity for K + ions as a result of the covalent modification.

CD spectra of TBAcont without and with biotin-TEG (Supplementary material, Fig. S1) showed one positive and one negative bands of low intensity, indicating that this ODN is not completely unstructured. However, no conformational transition was observed, as these bands exhibited no response to increasing concentration of KCl. Thus, this sequence was confirmed to be suitable as a non-G4-forming control for TBA.

UV absorbance-monitored thermal melting of TBA

Thermal denaturation/renaturation experiments of TBA monitored by UV-spectroscopy were carried out in order to analyse its stability under the conditions used for the SPR experiments. TBA with and without the biotin-TEG tail, which is going to be used to immobilize it on a streptavidin sensor chip, produced melting profiles in the presence of 30 mM KCl with no hysteresis upon renaturation as expected for a two-state folding reaction (Supplementary material, Fig. S2). No transition was observed in the absence of K + ions, illustrating their crucial role in G4 formation in TBA. From the minimum of first derivatives of the melting curves, T m values were determined as 45.6 ± 1.3 °C for TBA (Supplementary material, Fig. S2a) and 38.4 ± 0.8 °C for biotin-TEG-TBA (Supplementary material, Fig. S2b), indicating destabilisation of the G4 by addition of the biotin-TEG tail. Even though this method of T m determination is known to not be very precise (Mergny and Lacroix, 2009), it is adequate enough for the purpose of this study. Furthermore, the value determined for TBA agrees with the values reported in the literature (Mergny et al., 1998;Saccà et al., 2005).

The fraction of folded G4 strands in the presence of KCl was analysed as described by Mergny et al. (Mergny and Lacroix, 2009). At 25 °C, i.e. at the temperature of the SPR experiments, it was estimated to be 0.99 and 0.95 for TBA and biotin-TEG-TBA, respectively, reflecting the lower stability of the latter species. In spite of its lower stability, essentially all biotin-TEG-TBA molecules are folded under the studied conditions. Without K + , at 25 °C, the biotin-TEG TBA is clearly unfolded (Supplementary material, Fig. S2b). These results validate the use of TBA as a very good model to investigate to what extent conformational changes contribute to the SPR signal.

Determination of refractive index increments

Binding of the free analyte to the immobilized interaction partner (ligand) during an SPR experiment causes change of the refractive index in the proximity of the surface and consequently also change of the resonance angle. Therefore, the refractive index increment, RII, of a given species, defined as δn/δc, i.e. change of refractive index (n) with the concentration (c) of the species, is a crucial parameter to determine the expected maximal response upon binding of the analyte at saturating concentration. This is especially true in systems where the two interaction partners are fairly chemically different, such as in the system presently studied, and the ratio of their RIIs in Eq. 2 may deviate significantly from 1 (Davis and Wilson, 2000;Tumolo et al., 2004).

To determine the RIIs of KCl and TBA, refractometry measurements of two independently prepared sets of samples of increasing concentration of each interaction partner were performed.

Additionally, the RII value can be affected by the ionic conditions of the base buffer. To see whether the increasing concentration of K + ions in SPR experiment would have a significant effect on the RII value of TBA, the TBA solutions were prepared both in the presence or absence of 30 mM KCl. RIIs were determined as slopes of concentration dependence of refractive index (Fig. 2) from two independent sets of measurements (Supplementary material, Table S1). It is clear that RIIs of KCl and TBA differ significantly, and thus in Eq. 2 would produce a ratio vastly distant from 1, highlighting the importance of checking this parameter. The results also show that the presence of 30 mM KCl in the solution has a non-negligible effect on the RII of TBA (Fig. 2a), lowering its RII from 0.268 to 0.222.

In order to check for the consistency of the measured RII values, the same samples used for refractometry were also measured using SPR, taking advantage of the T200 instrument, which is actually also a refractometer. The results are shown in Fig. 2b (corresponding sensorgrams shown in the Supplementary material, Fig. S3b,c,d). In the absence of specific binding, the SPR response reflects only the change of refractive index (Δn) at the wavelength used by the instrument (760 nm) near the surface, resulting in sensorgrams with square profile (Supplementary material, Fig. S3). On the Biacore instrument, a response of 1 RU corresponds to a Δn of 1×10 -6 . Samples were injected over the streptavidin coated sensor chip surface used for the SPR binding experiments. Injection of KCl samples with increasing concentration (Supplementary material, Fig. S3b) resulted in RII value of 0.117, in sufficiently good agreement with the refractometry value, 0.122 (Supplementary material, Table S1). The average RII of KCl, 0.120, was further used for R max calculation. On the other hand, the RII values determined by SPR for TBA on the surface of a sensor chip covered with the streptavidin-carrying hydrogel matrix were lower compared to those obtained by refractometry by about 22 %. The presence of 30 mM KCl in the samples of TBA introduces slight curvature in the steady-state portion of the recorded sensorgams (Supplementary material, Fig. S3d) in contrast to the flat sensorgrams of TBA in clean buffer (Fig. S3c). This might suggest the existence of some kind of interaction of TBA with the immobilized layer, facilitated by the K + ions. Nonetheless, the difference in obtained RII value arising from the presence of 30 mM KCl is exactly the same as determined by refractometry (0.046). Clearly, determination of the appropriate RII value for TBA is not straightforward and is further discussed below. Ultimately, for the purpose of this study, all measured values have to be considered. RIIs were determined from the slopes of the fitted straight lines. Data shown are from two independent sets of samples for each analyte.

Determination of active target (c a )

In order to correctly calculate the theoretical R max value (Eq. 2), the fraction of active target, i.e. the immobilized molecules actually available for binding, c a , must be determined. For that purpose, complementary DNA strands were injected to the immobilized TBA or TBAcont. It was assumed, that due to strong affinity, all available target will bind its complementary strand and its SPR response, R max given by Eq. 2, would thus correlate with the fraction available. Since in this case the two interacting molecules, DNA, are of the same type, their RIIs are considered identical (Davis and Wilson, 2000;Di Primo and Lebars, 2007;Tumolo et al., 2004) and therefore their ratio equal to 1.

The stoichiometry of the interaction is also 1 and Eq. 2 can be, therefore, reduced to:

The fraction of active target is then obtained from the ratio of measured R max and the theoretical R max value from Eq. 4.

To investigate how target density influences target availability, three different degrees of surface coverage by target, R 1 , approximately 50, 500 and 1000 RU (exact values shown in Table 1), were produced on individual channels of a streptavidin coated sensor chip. For each R 1 , theoretical R max was calculated. Then, 50, 500, and 5000 nM of complementary strand was injected consecutively using the SCK method (Karlsson et al., 2006;Palau and Di Primo, 2012). This method was developed to avoid repeated exposure to the harsh regeneration conditions that sometimes are needed to disrupt strong interactions, which could conceivably alter the surface and/or the immobilized target. In addition, this method makes the runs shorter compared to the classical multi-cycle kinetics method. The binding experiments were performed in a buffer with no potassium ions so that both TBA and TBAcont could interact with their complementary strand with no interference for the former with G4 formation. Three experiments were performed on the same sensor chip. The recorded sensorgrams are shown in Fig. S4 (Supplementary material) for TBA (Fig. S4a,c,e) and TBAcont (Fig. S4b,d,f). Fitting of the measured data to a Langmuir 1:1 model of interaction produced experimental R max values which are summarised in Table 1.

The results revealed that a surprisingly low fraction of the immobilized molecules was actually available for binding. Discrepancy between the fraction of active molecules of the two studied ODNs was also observed, generally about ½ for TBA and between 1/3 and ½ for TBAcont. The fraction available was also shown to decrease with increasing surface density of target. For TBA, twenty-fold increase between the lowest and the highest tested target density reduced the fraction of active molecules from 0.586 to 0.471. The observed decline was more pronounced for TBAcont, where the active fraction fell from 0.499 to 0.319 under the same conditions.

Additionally, injections of complementary strands were also performed on the sensor chips that were used for the interaction experiments with K + binding to TBA (section 3.5). With a target density R 1 of ca. 1000 RU used for these experiments, the fractions of active target were equal to 0.529 ± 0.059 for TBA and 0.357 ± 0.056 for TBAcont, in good agreement with the values obtained from the experiments detailed above on other sensor chips. The ratio, R max measured to R max theoretical, gives the fraction of active target c a 3.5. Experimental measurement of R max for K + binding to TBA

The experimental maximum SPR signal response, R max , of K + ions binding to TBA was determined from injections of KCl solutions to biotin-TEG-TBA and biotin-TEG-TBAcont immobilized on streptavidin coated sensor chip as described in the experimental section.

The R max corresponding to the specific binding of K + to TBA cannot be obtained directly from the measured data of TBA interacting with K + , because the raw signal carries contributions not only from the specific binding facilitating folding of the G4 but also from the nonspecific loose associations of the cations with the negatively charged sugar-phosphate backbone of the ODN. To resolve this issue, the binding experiments were performed also with TBAcont, where no specific binding was expected, under the assumption that the signal contribution arising from the nonspecific interaction would be the same for the linear TBAcont as for the G4 TBA. Hence, the signal responses recorded for TBAcont were subtracted from those for TBA, producing data purportedly corresponding to the specific binding of K + by TBA.

Representative sensorgrams for the interaction of the ODNs with K + are presented in Fig. 3.

Sensorgrams of TBA (Fig. 3a) clearly exhibit typical binding response with fast dissociation kinetics, arising from the interaction with K + . On the other hand, sensorgrams of TBAcont display almost square profile with only slight curvature in the association phase (Fig. 3b), too fast to be measured by the instrument. Evidently, despite the lack of specific binding, electrostatic interactions of K + with the control strand still induces a considerable signal response, roughly half of the total signal observed for TBA. The SPR signal response was evaluated from the sensorgrams at 5 second before the end of injection (Fig. 3c). As expected from a non-specific interaction signal with the control ODN, TBAcont, exhibited linear response to the increasing KCl concentration in the whole studied range, whereas for TBA, deviations from linearity were observed due to the contribution of specific binding.

Since the amount of immobilized TBA and TBAcont, R 1 , was never exactly the same, the signal of TBAcont was corrected to correspond to the R 1 value of TBA prior to its subtraction from the signal of TBA, in order to accurately compare the two responses. This correction, however, caused only minute changes to the resulting data. Subtraction of signal for TBAcont from signal of TBA produced a saturation curve, which was then fitted to Eq. 3 to acquire the R max value (De Crescenzo et al., 2008) (Fig. 3c). For the average R 1 value of 1033 ± 50 from all performed experiments, the R max determined this way was 30.9 ± 3.5. In addition, K D was also determined from the fit as 3.4 ± 0.9 mM. This value agrees well with the K D acquired from CD titration, 1.6 ± 0.3 mM. Thus, it was shown that immobilization did not significantly alter the affinity of TBA towards K + ions.

Alternatively, full sensorgrams of TBAcont were subtracted from those of TBA. The resulting set of curves was then directly fitted to a 1:1 Langmuir model using BIAevaluation software (Biacore) to assess the R max and the K D (Fig. 3d). The quality of the fits was affected by spikes present in the subtracted sensorgrams. These are simply artefacts due to the serial arrangement of the flow channels carrying TBAcont and TBA, causing slight time shift of the beginning of injection between the two channels. Nonetheless, this approach gave an R max and K D equal to 30.3 ± 3.5 and 3.4 ± 0.9 mM, respectively, in excellent agreement with the values obtained by steady-state analysis (30.9 ± 3.5 and 3.4 ± 0.9 mM).

Comparison of experimental and theoretical R max

In a series of experiments described above, individual terms of Eq. 2 were determined and are summarised in Table 2. Refractive index increment of TBA (RII 1 in Eq. 2) could not be determined unequivocally and, therefore, all relevant applicable values are listed. From the measured values, R max was calculated according to Eq. 2. The calculation was performed for all values of RII 1 of TBA from Table 2. Similarly, since the relevance of the measured value of active target available, c a is questionable (discussed below), the calculation was performed also without including this term.

Results of these calculations are summarised in Table 3. The calculated R max values were compared to the experimental R max determined by SPR (section 3.5. also listed in Table 3). Depending on whether different availability of TBA and the subtracted TBAcont is weighted in or not, the experimental R max was determined as either 16.7 or 30.9, respectively (Table 3), without affecting the K D for potassium binding (Supplementary material, Fig. S5). On the other hand, the calculated R max values range from 1.61 to 5.65 depending on the applied value of RII 1 and c a . Thus, it is evident from Table 3 that in all instances a clear discrepancy exists between the experimentally measured and calculated R max values.

Discussion

Conformational change model

The molecule selected as a conformational change model was the ODN TBA which in the presence of K + ions is able to fold into a antiparallel G4 structure (Macaya et al., 1993;Wang et al., 1993). A G4 nucleic acid was chosen for this purpose due to its relative simplicity and well-defined K + -driven conformational transition, compared to proteins which were used in most publications claiming to detect structural changes using SPR (Boussaad et al., 2000;Flatmark et al., 2001;Geitmann and Danielson, 2004;Hall et al., 2011;[START_REF] Hsieh | Detection of low-molecular-weight analytes by surface plasmon resonance and receptor conformational changes[END_REF][START_REF] Mannen | [END_REF]Salamon et al., 1994;Sota et al., 1998;Zako et al., 2001). Consequently, unwanted effects often encountered with proteins can be avoided, such as aggregation, target heterogeneity, partial denaturation, or secondary binding sites which all can affect the final SPR signal. Specifically, TBA was chosen as one of the best described G4s that also consists of only two G-tetrads binding a single potassium ion between them, so that a simple 1:1 interaction model (Eq. 3) can be used in data analysis. One drawback of using this G4 is its low molecular weight and, more importantly, the low molecular weight of the analyte (K + ), generally expected to produce low SPR response. Low signal can be compensated for by using higher target surface coverage (R 1 ) but only if instruments are sensitive enough to detect low molecular weight compounds. According to the manufacturer, Biacore T200 instruments have no molecular weight detection limit. However, for kinetic analyses high density have been always discouraged as it may negatively affect the interaction (Schasfoort, 2017).

For SPR experiments, TBA biotinylated through TEG linker was used. CD spectroscopy revealed that in conditions used in SPR experiments this covalent conjugate of TBA folds into G4 with increasing concentration of KCl, as expected. Contrarily, CD spectra of G4 non-forming TBAcont displayed no response to added KCl, although presence of clear bands in the spectra indicate nonrandom conformation of this ODN. Even though the biotin-TEG tail lowered the affinity of TBA to K + by about one order of magnitude, the used concentration range of KCl was still suitable to study the interaction. Similarly, the covalent modification was shown to destabilize TBA with a T m value decreasing of about 7 °C. Nonetheless, its thermal stability was still sufficient for the purpose of this study.

Evaluation of R max

With the experimental model validated, the terms of Eq. 2 were collected. Some terms, namely R 1, the amount of TBA immobilized, MW 1 , MW 2 and the stoichiometry of the reaction S, did not require experimental assertion. The amount of immobilized target R 1 is a parameter that can be controlled by regulating concentration and/or injection time of the biotinylated species over streptavidinfunctionalised sensor chip. Since the experiments with varying amount of immobilized target (section 3.4.) revealed only slight negative impact of higher target density on its availability for binding, the highest tested value (≈1000 RU) was selected for the interaction experiment to achieve the highest response. The average R 1 from all experiments used for calculating R max was equal to 1033 RU.

The term MW 2 corresponds to molecular weight of the analyte, i.e. potassium ion (39 g mol -1 ). For the term MW 1 , corresponding to the immobilized target, molecular weight of the whole covalent conjugate biotin-TEG-TBA (5295 g mol -1 ) was considered.

Stoichiometry of the interaction between K + and TBA was considered to be 1:1 (S = 1 in Eq. 2)

based on the single specifically bound ion in the central cavity of the G4. It is important to note, that this model is simplified as it assumes that nonspecific electrostatic interaction of K + with a sugar-phosphate backbone of a G4 would be the same as with a linear ODN of the same length and therefore can be eliminated by subtraction of signal of such control. However, the average number of K + ions loosely associated with a compact globular G4 structure and an unfolded single-strand at any given time is likely not the same, due to different surface charge density/distribution of either form (Gou et al., 2010;[START_REF] Huang | [END_REF]Mimura et al., 2021). Moreover, in the sequence of TBAcont, four guanines of the original TBA sequence were replaced by adenines, possessing smaller dipole moment [START_REF] Burda | Metal interactions with nucleobases, base pairs, and oligomer sequences; computational approach[END_REF]Šponer et al., 1996) which could likewise affect the surrounding ionic environment. Nonetheless, the number of negatively charged phosphates remains the main factor determining the number of cations associated to an ODN. Lastly, based on the results from section 3.6 roughly five more K + ions would have to be bound to TBA than to TBAcont in order to compensate the difference between experimental and calculated R max .

Knowledge of the ratio of RIIs of the interacting molecules is critical for the analysis of SPR signal in systems where the two interaction partners are chemically very different and the ratio is likely to be far from 1 (Davis and Wilson, 2000;Tumolo et al., 2004). In spite of its importance, this parameter is commonly neglected in SPR studies claiming to detect conformational changes of biomolecules upon interaction with small analytes. RIIs were determined experimentally using refractometry as well as SPR. Each of the used methods utilised a different wavelength for refractive index measurement.

However, this is not expected to influence the obtained values significantly. Moreover, in Eq. 2, ratio of the RIIs is used, effectively cancelling any effects resulting from the choice of a method. Although the actual analyte in SPR experiments is the K + ion, RII 2 was determined for KCl, since it is impossible to prepare a solution of the isolated cation. Nonetheless, it should be noted that this approximation might slightly influence the final result.

Unlike for RII 2 , where the values obtained by either method were similar and their average was used, assorted values were acquired for RII 1 of TBA. Firstly, the presence of KCl was shown to cause a non-negligible change of the RII 1 value. It is, however, unclear whether this change stems from the KCl-induced folding of TBA or from the different ionic environment. Regardless of the reason, if the RII of TBA is dependent on KCl concentration and thus changes in the course of the SPR interaction experiment, selecting a suitable RII 1 value to plug into Eq. 2 becomes problematic. Therefore, the R max calculation had to be done for both limit values, 0 and 30 mM KCl. Furthermore, measurement of RII 1 using SPR on streptavidin-coated sensor chip produced different values from refractometric measurements. It is very likely that these differences emerge from interaction of the injected TBA with the complex immobilized layer on the surface of the sensor chip (carboxymethyldextran, streptavidin), especially in the presence of 30 mM KCl as can be seen from the corresponding curves (Supplementary material, Fig. S3d). It is feasible that the value of RII of TBA measured on plain gold surface would more closely approximate the refractometry values, as was seen with KCl (Supplementary material, Fig. S3a -b) and also with other biomolecules (Supplementary material, Fig.

disregarded the difference of RIIs of the two species in comparison of predicted and detected signals.

In our work, all known contributing parameters to SPR signal were considered, which still could compensate maximally for about 18 % of the measured signal. Simultaneously, conformational change of TBA was confirmed by CD to occur under the used conditions. This allows us to attribute the uncompensated SPR signal to refractive index change as a result of conformational change of TBA occurring on the sensor chip surface.

Conclusions

This work set out to deliberately answer the question whether conformational change of a biomolecule immobilized on a sensor chip surface during SPR experiments contributes to the recorded SPR signal. For this purpose, all known contributing parameters to the signal were thoroughly experimentally inspected to assess the theoretical maximum response R max of the model G4-forming oligonucleotide TBA binding K + ion. Various values of refractive index increment of TBA were found in the presence or absence of KCl or depending on the method used for its determination. All collected values were, therefore, considered in R max calculation. Additionally, strikingly low fraction of immobilized TBA on an SPR sensor chip available for interaction with its binding partner was determined, which highlighted the impact this parameter could have on the final SPR signal.

Experimental measurement of R max revealed significant contribution of nonspecific interactions between TBA and K + to the detected SPR response. Experimentally determined R max was shown to be several times higher than the values predicted by calculation after consideration of all known contributing factors, as a result of TBA folding into G4 in the course of the experiment. Therefore, we conclude that conformational changes can contribute to the SPR signal measured on Biacore instruments, the most widely used biosensor to characterize molecular interactions.

was acquired in 2011 with the support of the Conseil Régional d'Aquitaine, the GIS-IBiSA and the Cellule Hôtels à Projets of the CNRS.

Figure 1 .

 1 Figure 1. (a) Schematic diagram of the experimental design, TBA G4 structure was made from https://www.rcsb.org/structure/148D, 10.2210/pdb148D/pdb (Schultze et al., 1994); CD spectra of the titration of 5 µM TBA (b) and biotin-TEG-TBA (c) with KCl in 100 mM HEPES buffer pH 7.2; arrows indicate increasing concentrations of KCl. (d) Fitted titration curves evaluated at 293 nm; error bars indicate standard deviation from three independent experiments.

Figure 2 .

 2 Figure 2. Dependencies of refractive index change on concentration of KCl (black) and TBA in the presence (red) or absence (blue) of 30 mM KCl measured at 25 °C in 100 mM HEPES pH 7.2 using refractometry (a) or SPR (b). All SPR measurements were performed on streptavidin-coated surface.

Figure 3 .

 3 Figure 3. SPR analysis of potassium ions binding to TBA. KCl samples prepared in 100 mM HEPES buffer, pH 7.2, were injected at increasing concentrations over approximately 1000 RU of TBA (a) or TBAcont (b). Dependence of the SPR signal of the interaction of KCl (c) with TBA (blue) and TBAcont (red) at 25 s after injection and fitted saturation curve obtained by subtraction of TBAcont data from TBA ones (black). Sensorgrams obtained by subtraction of sensorgrams of TBAcont from those of TBA (d) fitted to a 1:1 Langmuir model of interaction in BIAevaluation Software (Biacore).

Table 1 .

 1 Theoretical and measured R max for given densities, R 1 , of immobilized TBA and TBAcont interacting with their respective complementary strands.

	ODN	R 1 (RU)	R max (RU)	R max (RU)	Ratio a
			theoretical	measured
	TBA	47	31.2	18.3 ± 0.2	0.586 ± 0.007
		522	347.9	180.4 ± 2.2	0.518 ± 0.006
		1000	666.5	313.9 ± 2.9	0.471 ± 0.004
	TBAcont	49	33.5	16.7 ± 0.8	0.499 ± 0.025
		506	344.4	148.9 ± 2.6	0.432 ± 0.007
		1001	680.8	217.5 ± 3.4	0.319 ± 0.005
	a

Table 2 .

 2 Measured values of individual terms of Eq. 2.

	R 1 a		RII 1 (ml g-1)		RII 2	c a	b
	(RU)	SPR		Refractometry	(ml g -1 )
		KCl 30 mM	no KCl	KCl 30 mM	no KCl
	1033	0.164	0.210	0.222	0.268	0.120	0.471
	a Indexes 1 and 2 refer to TBA (5295 g mol -1 ) and K + (39 g mol -1 ), respectively. b Fraction of the active target. The
	stoichiometry S (not shown) of K + binding to TBA is 1

Table 3 .

 3 R max calculated from Eq. 2 for all various determined RII values of TBA (RII 1 ) and experimental R max .

	RII 1 (ml g -1 )	Calculated R max (RU)
		c a not considered	c a considered
	0.164	5.56	2.62
	0.210	4.36	2.05
	0.222	4.12	1.94
	0.268	3.41	1.61
		Experimental R max (RU)
		raw data	corrected data
		30.9	16.7

Acknowledgments

CD is very grateful to Dr. Jean-Louis Mergny for suggesting DD to come to the IECB and to Dr. Miroslav Fojta from the Institute of Biophysics of the Czech Academy of Sciences for allowing DD to stay in Pessac. This work has been supported by the SYMBIT project reg. no. CZ.02.1.01/0.0/0.0/15_003/0000477 financed from the ERDF and by the INSERM U1212 -CNRS UMR 5320 ARNA laboratory. We gratefully thank Dr. Mikayel Aznauryan for careful reading of the manuscript. We thank Alysson Duchalet for performing preliminary experiments and Laetitia Minder from the Structural Biophysico-Chemistry plateform of the European Institute of Chemistry and Biology (IECB, UAR 3033 -US001) for technical assistance with the Biacore T200. The instrument

Abbreviations

c a : Fraction of active target S6-S7, Table S2). This discrepancy between methods further complicates the issue of selecting the accurate value for RII 1 . On one hand, direct refractometry of samples in solution should be considered the benchmark method for determination of refractive indexes. On the other hand, streptavidin-coated sensor chips were used for the actual SPR interaction experiments of interest, so it can be argued that the value acquired by SPR on the same surface is more suitable for the present application. Ultimately, the choice of the most accurate value, albeit noteworthy, was not critical for the objective of this study.

All obtained values were considered in the R max calculation and, as is evident from Table 3, neither one fundamentally impacts the final outcome of the calculation.

The fraction of immobilized target available for K + binding (c a ) determined by injecting the complementary strands to both TBA and TBAcont was unexpectedly low. The marked difference in this value for the two studied ODNs was also surprising. One possible reason for the observed low availability may be steric hindrance from neighbouring strands caused by high target density (≈ 1000 RU). However, twentyfold decrease in surface density of TBA caused only marginal increase in its availability. For TBAcont, this increase was slightly larger, possibly reflecting bigger contribution of the steric effect to availability of this ODN. Another reason for the low response could be target loss between immobilization and the experiment caused by degradation of the DNA. The possibility of target heterogeneity was excluded by quality control of the sample by gel electrophoresis and also due to uniformly oriented immobilisation of target through streptavidin-biotin coupling.

Overall, this result points out the importance of target availability determination not only for the purpose of this study but for the use of the SPR technique in general, in particular and obviously for stoichiometry measurements. Although the c a parameter is needed to calculate the R max value, we were operating under the assumption that the availability of TBA for K + was the same as for its complementary strand. However, this assumption may likely be untrue due to the vastly different chemical nature of the two species. It is feasible that the much smaller cation would suffer from less steric interference and thus be able to access more of the immobilized TBA strands. Due to this debatable relevancy of the measured c a value, R max calculations were performed also without taking it into account.

About one half of the raw SPR signal of K + binding to TBA was attributed to nonspecific electrostatic interactions. The remaining signal was used to determine experimental value of R max .

Comparison of this value and the R max calculated from the formerly discussed parameters revealed a significant discrepancy which persisted regardless of the RII 1 value applied or whether target availability was considered or not. Even though the determined c a might be unrealistically low as discussed above, even consideration of full availability still produced calculated R max 3 to 9 times lower than experimental. Substantial difference in predicted and measured signal response upon binding of a single ion was previously detected and attributed to conformational change of the immobilized target protein by Gestwicki et al. (Gestwicki et al., 2001), who however, completely


M Zeineddine

F Boumediene

A Al-Hajje

P Salameh

A Helme

M G Thor

B Yamout

Barriers to Access Multiple Sclerosis Disease-Modifying Therapies in Middle East and North Africa: A Regional Survey-Based Study

published or not. The documents may come

Methods

• This is a descriptive, survey-based study whereby we extracted data collected between October 2019 and April 2020 for countries in the MENA region by the MSIF through their Atlas of MS online survey.

Results

• 16 out of 19 countries in the MENA region were included in this study: Algeria, Egypt, Iraq, Lebanon, Libya, Morocco, Palestine Sudan, Syria, Tunisia, Yemen, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates (UAE).

• Regulatory Approval of DMTs in the MENA region

M

  . Zeineddine 1 , F. Boumediene 1 , A. Al-Hajje 2 , P. Salameh 2 , A. Helme 3 , M.G. Thor 3 , B. Yamout 4 1 Limoges University, Institut De Neurologie Tropicale, Limoges, France, 2 Lebanese University, Beirut, Lebanon, 3 Multiple Sclerosis International Federation, London, United Kingdom, 4 Harley Street Medical Center, Neurology Institute, Abu Dhabi, United Arab Emirates Background • Multiple Sclerosis International Federation (MSIF) Atlas recorded an increase in the prevalence of multiple sclerosis (MS) in Middle-Eastern countries from 24 per 100,000 to 33 per 100,000 between 2013 and 2020. • Moreover, epidemiological studies from the Middle East and North Africa (MENA) region showed a rising MS prevalence during the first two decades of the 21 st Century. • Despite shifting of the MENA region from low to moderately-high risk zone for MS, the management of the disease still varies markedly between different countries based on disease-modifying therapies (DMTs) availability, accessibility, and reimbursement. • In general, introduction of novel DMTs in most MENA countries is usually delayed, as drug approval can take several months to years once approved for use by the Food and Drug Administration (FDA) or European Medicines Agency (EMA), depending on the country. Objective In order to gain better understanding of treatment accessibility and explore potential common areas of need in our MENA region, we carried an indepth analysis of the Arab region Atlas of MS data, compiled by the MSIF, focusing on accessibility to originators and off-label DMTs in each country and identifying barriers to MS treatment.

Figure 1 .

 1 Figure 1. Percentage of MENA countries with regulatory approval for originator DMTs. Green = High efficacy; Yellow = Moderate efficacy; Red = Low efficacy


et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction of the 2nd Phase of the Integrated Hydrologic Model Intercomparison Project

Stefan Kollet, Reed Maxwell, Cécile Dagès, Emmanuel Mouche, Claude Mugler, Claudio Paniconi, Young-Jin Park, Mario Putti, Chaopeng Shen, Simon Stisen, et al. Three test cases were defined and compared that are available online at www.hpsc-terrsys.de including a tilted vcatchment case; a case called superslab based on multiple slab-heterogeneities in the hydraulic conductivity along a hillslope; and the Borden site case, based on a published field experiment. The goal of this phase is to further interrogate the coupling of surface-subsurface flow implemented in various integrated hydrologic models; and to understand and quantify the impact of differences in the conceptual and technical implementations on the simulation results, which may constitute an additional source of uncertainty. The focus has been broadened considerably including e.g. saturated and unsaturated subsurface storages, saturated surface area, ponded surface storage in addition to discharge, and pressure/saturation profiles and cross-sections. Here, first results are presented and discussed demonstrating the conceptual and technical challenges in implementing essentially the same governing equations describing highly non-linear moisture redistribution processes and surface-groundwater interactions.

To cite this version:

  Research Centre Jülich, Germany, (2) Centre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABCJ, (3) Department of Geology and Geological Engineering, Colorado School of Mines, Golden, USA, (4) Laboratoire d'Etude des Interactions Sol Agrosysteme Hydrosysteme, INRA Montpellier, France, (5) Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France, (6) INRS-ETE, Université du Québec, Québec, Canada, (7) Earth and Environmental Sciences, University of Waterloo, Waterloo, Canada, (8) Dipartimento di Metodi e Modelli Matematici per le Scienze Applicate, Universita' di Padova, Padova, Italy, (9) Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, USA, (10) Remote sensing and hydrology, Geological Survey of Denmark and Greenland, Denmark, (11) Meteorological Institute, Bonn University, Bonn, Germany The 2nd Phase of the Integrated Hydrologic Model Intercomparison Project commenced in June 2013 with a workshop at Bonn University funded by the German Science Foundation and US National Science Foundation.

	Introduction of the 2nd Phase of the Integrated Hydrologic Model
	Intercomparison Project
	Stefan Kollet (1,2), Reed Maxwell (3), Cecile Dages (4), Emmanuel Mouche (5), Claude Mugler (5), Claudio
	Paniconi (6), Young-Jin Park (7), Mario Putti (8), Chaopeng Shen (9), Simon Stisen (10), Edward Sudicky (7),
	Mauro Sulis (11), and Xinye Ji (9)
	(1) IBG-3,

Stefan Kollet, Reed Maxwell, Cécile Dagès, Emmanuel Mouche, Claude Mugler, et al.. Introduction of the 2nd Phase of the Integrated Hydrologic Model Intercomparison Project. European Geosciences Union General Assembly, Apr 2015, Vienna (Austria), Austria. hal-04092963


C Lupan

O Lupan

M.-I Terasa

J Drewes

O Polonskyi

F Faupel

R Adelung

S Hansen

B Viana

Th Pauporté

H2 gas sensing properties of a Pd/ZnO:Eu nanosensor

Keywords: ZnO:Eu, nanosensor, hydrogen, Pd-functionalized

Hydrogen is considered fuel for the future, but its properties make it dangerous to use without protection from leaks and explosions, meaning that hydrogen gas sensors are necessary for safe use and storage of this important gas. Gas sensors based on semiconducting material like ZnO have been studied intensively, especially multiple methods of improving their parameters with doping, functionalization, etc. In this work, ZnO was doped with Eu during electrodeposition (ranging from 2 µM to 22 µM) and functionalized with Pd nanoparticles on its surface. The effects of doping and functionalization were studied, observing an improvement in response value to 100 ppm hydrogen gas up to ~3965 at 150 °C of Pd-functionalized ZnO:Eu nanosensor compared ~150-200 to nonfunctionalized ZnO:Eu with similar doping concentration. The obtained results on a single Pdfunctionalized ZnO:Eu nanowire-based nanosensor can be used for further improvement of synthesis parameters that can lead to the production of low-cost and highly efficient ZnO:Eu Pd-functionalized miniaturized gas sensors, selective to H2 gas, even at room temperature, for personal, industrial, safety and environmental use.

INTRODUCTION

Nowadays there is an increased focus on smaller devices, energy efficiency, reliability, and other parameters. Miniaturized solid-state sensors are suitable devices that can tick all the requirements, because of their superior properties and portability [START_REF] Aliofkhazraei | Recent Developments in Miniaturization of Sensor Technologies and Their Applications[END_REF] . Finding perfect material that can have desirable properties is a challenge, because sometimes sensors based on a single material don't show desired results. Improving properties can be done by various methods, such as doping, metal Please cite this paper as : C. Lupan, O. Lupan, M.-I. Terasa, J. Drewes, O. Polonskyi, F. Faupel, R. Adelung, S. Hansen, B. Viana, Th. Pauporté, H2 gas sensing properties of a Pd/ZnO:Eu nanosensor. Proc. SPIE, 12422 (2023) 124220P. https://doi.org/10.1117/12.2651116 loading, functionalization, etc. Sensors based on semiconductor materials such as metal oxides are attractive due to low cost, simplicity and variety in application fields [START_REF] Wang | Metal Oxide Gas Sensors: Sensitivity and Influencing Factors[END_REF] . ZnO is one of the most widely used semiconductor material for gas sensors, and doping with Eu was reported before to improve its properties [START_REF] Lupan | Eu-Doped ZnO Nanowire Arrays Grown by Electrodeposition[END_REF][START_REF] Lupan | Pd-Functionalized ZnO:Eu Columnar Films for Room-Temperature Hydrogen Gas Sensing: A Combined Experimental and Computational Approach[END_REF][START_REF] Lupan | Nanosensors Based on a Single ZnO:Eu Nanowire for Hydrogen Gas Sensing[END_REF] , obtaining high response value, selectivity and lower operating temperature. But, there is also a need for improvement in device parameters, and functionalization with noble materials is one way to obtain that performance. Pd/ PdO are considered effective catalysts for gas sensing devices, in combination with other metal oxides, lowering operating temperature of devices and increasing response [START_REF] Lupan | Pd-Functionalized ZnO:Eu Columnar Films for Room-Temperature Hydrogen Gas Sensing: A Combined Experimental and Computational Approach[END_REF][START_REF] Veziroglu | Nanoparticles Decorated TiO2 Film with Enhanced Photocatalytic and Self-Cleaning Properties[END_REF] .

In this work, single ZnO:Eu nanowire were integrated in the devices and then functionalized with Pd nanodots/nanoparticles (Pd/ZnO:Eu, 4 μM) on its surface, thus possess an order higher response and selectivity to H2 gas at 150 C. The maximum gas response value was experimentally determined to be ~3965 for functionalized Pd/ZnO:Eu nanowires grown in an electrochemical bath containing 4 μM Eu ions, then functionalized with noble metals. Such nano-microscale devices can be used for detecting hydrogen leakage in hydrogen cars or else. Obtained results clarify the importance of noble metal nanoparticles on oxide surface due to functionalization of nanomaterial in the fabrication of higher-sensitive and selective gas sensors at room temperature with reduced power consumption. The developed materials and sensor structures would provide a particular advantage in the fields of applied physics studies, safety and industrial uses.

MATHERIAL SYNTHESIS AND DEVICE STRUCTURES FABRICATION

ZnO:Eu nanowire-networks were grown onto Fluor-Tin Oxide films on glass substrates using an electrochemical deposition method, with ZnCl2 and KCl as precursors and different quantities of EuCl3 for doping (noted as Eu4 for 4 µM EuCl3), as described in Ref. [START_REF] Lupan | Nanosensors Based on a Single ZnO:Eu Nanowire for Hydrogen Gas Sensing[END_REF] . Using focused electron/ion beam instrument (FEI, Helios Nanolab), single ZnO:Eu nanowire was integrated into a device with two prepatterned electrodes, Si/SiO2 and quartz with Cr/Au pads as contact electrodes, as previously reported in Refs [START_REF] Lupan | Nanosensors Based on a Single ZnO:Eu Nanowire for Hydrogen Gas Sensing[END_REF][START_REF] Lupan | Versatile Growth of Freestanding Orthorhombic α-Molybdenum Trioxide Nano-and Microstructures by Rapid Thermal Processing for Gas Nanosensors[END_REF] . Afterwards surface functionalization with Pd nanoparticles was done, using a method described in Ref. [START_REF] Veziroglu | Nanoparticles Decorated TiO2 Film with Enhanced Photocatalytic and Self-Cleaning Properties[END_REF] , where a gas aggregation source (GAS) based on magnetron sputtering was used to prepare Pd nanoparticles in a ultra-high vacuum chamber. In order to determine the chemical composition of europium doped ZnO (ZnO:Eu) with different Eu doping concentrations and Pd nanoparticles presence on surface were investigated by X-ray photoelectron spectroscopy (XPS, Omicron Nano-Technology GmbH, Al-anode, 240W). All recorded spectra were charged referenced by calibrating the C-1s line of advantageous carbon at 285.0 eV [START_REF] Moulder | Handbook of Photoelectron Spectroscopy[END_REF] . For the analysis of XPS spectra the software CasaXPS (version 2.3.16) was used. Gas sensing studies were performed as reported before in Ref. [START_REF] Lupan | Nanosensors Based on a Single ZnO:Eu Nanowire for Hydrogen Gas Sensing[END_REF] .

RESULTS AND DISCUSSIONS

In Figure 1a In Figure 3b the presented dynamic response to 100 ppm hydrogen gas at 125 °C is presented. To the test repeatability, hydrogen gas was applied two times, observing a similar response value of ~3250. The response time represents the time needed for the nanosensor to reach 90% of maximum current after applying hydrogen gas. The recovery time represents the time needed by the nanosensor to reach 10% of the initial current after stopping the gas exposure [START_REF] Lupan | Nanosensors Based on a Single ZnO:Eu Nanowire for Hydrogen Gas Sensing[END_REF] . Response and recovery times from dynamic response at 125 °C were determined, observing a response time of ~1.3 s and a recovery time of ~1.6 s. By comparing the response value of Pd-functionalized ZnO:Eu nanowire nanosensor and nonfunctionalized ones with similar doping concentration, it was observed that the response value is much higher for the Pd-functionalized one with 4 µm Eu, than the ones with 3 and 5 µm Eu. Higher response was observed only for non-functionalized ZnO:Eu with 10 µm Eu, showing that we can use Pd nanoparticles to improve gas response even for lower doping concentration of Eu [START_REF] Lupan | Pd-Functionalized ZnO:Eu Columnar Films for Room-Temperature Hydrogen Gas Sensing: A Combined Experimental and Computational Approach[END_REF][START_REF] Lupan | Nanosensors Based on a Single ZnO:Eu Nanowire for Hydrogen Gas Sensing[END_REF] .

Pd is known as a hydrogen sensitizer. The higher response at temperatures above 100 °C for Pdfunctionalized ZnO:Eu nanosensor under hydrogen gas exposure, can be explained by the reduction of PdO nanoparticles to Pd, as in the following reaction 4 : 𝑃𝑑𝑂 + 𝐻 2(𝑔) → 𝑃𝑑 + 𝐻 2 𝑂 (𝑔) (2) During hydrogen gas exposure at temperatures higher than 100 °C, hydrogen molecules dissolve into the octahedral sites of metallic Pd, leading to formation of hybrid form of Pd (PdHxx), which has lower function than ZnO:Eu nanomaterial [START_REF] Lupan | Pd-Functionalized ZnO:Eu Columnar Films for Room-Temperature Hydrogen Gas Sensing: A Combined Experimental and Computational Approach[END_REF] .

Please cite this paper as

CONCLUSIONS

In this work, we studied the influence of surface functionalizing ZnO:Eu nanosensors with Pd nanoparticles on morphological, chemical, electronic and sensing properties. From SEM investigations it was found, that the Pd nanodots on the ZnO:Eu nanowire have a diameter of ~12 nm. The XPS scans of ZnO:Eu and Pd-functionalized ZnO:Eu, confirmed the presence of Pd on the surface of the functionalized sensor. By testing the functionalized nanosensor to a series of gases at different operating temperatures, a high selectivity to hydrogen gas and a high response of ~3965 at 150 °C for 100 ppm hydrogen gas was observed. The nanosensor showed very fast response and recovery times of ~1.3 s and ~1.6 s, respectively. By comparing single nanowire-based ZnO:Eu and Pd-functionalized ZnO:Eu nanosensors with similar doping concentration of Eu nanowire, it was observed that the Pd-functionalized nanosensor has a much higher response for a lower doping concentration. These results can be used for further improvement of single nanowire ZnO:Eu nanosensors by the control of the synthesis parameters such as optimal doping concentration and Pd functionalization for better sensing properties. Obtained results prove the importance of nanowire oxide for improvement of hydrogen sensing characteristics to fabricate higher-performance sensors.

  SEM images of ZnO:Eu nanosensor are presented, which show a nanowire, functionalized with Pd nanoparticles. A length of ~2.2 µm and radius of ~100 nm was measured for nanowire. In Figure 1b a SEM image with a higher magnification of the ZnO:Eu nanosensor is presented, where Pd nanodots are observed. The diameter of the dots is ~12 nm and they are Please cite this paper as : C. Lupan, O. Lupan, M.-I. Terasa, J. Drewes, O. Polonskyi, F. Faupel, R. Adelung, S. Hansen, B. Viana, Th. Pauporté, H2 gas sensing properties of a Pd/ZnO:Eu nanosensor. Proc. SPIE, 12422 (2023) 124220P. https://doi.org/10.1117/12.2651116 uniformly distributed on the surface. The density of nanodots was controlled by the working parameters of the GAS.

Figure 1 .Figure 2 .

 12 Figure 1. SEM images of Pd nanoparticles-functionalized ZnO:Eu nanosensor: (a) scale 200 nm; and (b) at lower scale of 20 nm showing palladium nanodots on nanowire surface.A high-resolution XPS scan of Pd was performed and is shown in Figure2. For metallic Pd typically peak positions between 335.1 eV and 335.6 eV are reported, in contrast for Pd 2+ in PdO the peak position of the Pd-3d5/2 line is commonly reported to lie in an interval between 335.6 eV and 337.1 eV[START_REF] Rumble | The NIST X-ray Photoelectron Spectroscopy Database[END_REF][START_REF] Lupan | PdO/PdO2 Functionalized ZnO:::Pd Films for Lower Operating Temperature H2 Gas Sensing[END_REF] . Accordingly, the peak position of the Pd-3d5/2 line around 336.8 eV as observed in the Pd NP decorated ZnO sample is a strong indication for the presence of PdO rather than metallic Pd 10 . These results confirm deposited Pd nanoparticles on the surface.

Figure 3 .IgasIair ( 1 )

 31 Figure 3. (a) Gas response to a series of gases of Pd nanoparticles-functionalized ZnO:Eu nanowire at different operating temperature. (b) Dynamic response to 100 ppm hydrogen gas for Pd nanoparticles-functionalized ZnO:Eu nanowire measured at 125 °C. Gas response (S) was determined using ratio of current measured during gas exposure (Igas) and in air (Iair): 𝑆 = Igas Iair (1)

Figure 4 .

 4 Figure 4. Comparison of gas response values for 100 ppm hydrogen gas at 150 °C operating temperature for ZnO:Eu nanosensors with different doping concentrations and nanosensor with ZnO:Eu Pd-functionalized on surface

  : C. Lupan, O. Lupan, M.-I. Terasa, J. Drewes, O. Polonskyi, F. Faupel, R. Adelung, S. Hansen, B. Viana, Th. Pauporté, H2 gas sensing properties of a Pd/ZnO:Eu nanosensor. Proc. SPIE, 12422 (2023) 124220P. https://doi.org/10.1117/12.2651116

Commenté [TP1]:? What do you mean?

Acknowledgments

C. Lupan gratefully acknowledges Kiel University, Functional Nanomaterials, Germany and PSL Université, Chimie-ParisTech IRCP, CNRS, Paris, France for internship positions. We acknowledge funding within the project "SuSiBaBy" -SulfurSilicon Batteries by the EUSH and EFRE in SH (LPW-E/3.1.1/1801). German Research Foundation (DFG-Deutsche Forschungsgemeinschaft) under the Project-ID 434434223-SFB 1461, Project-ID 286471992-SFB 1261 project A2, GRK 2154 project P4 and AD 183/16-1. Dr. Lupan gratefully acknowledges CNRS Council for support as expert scientist at IRCP Chimie ParisTech, Paris. This work was supported by the ANCD Grant No. 20.80009.5007.09 at Technical University of Moldova.


Shihong Ren
email: shihong.ren@univ-st-etienne.fr

Michel Buffa
email: buffa@univ-cotedazur.fr

Gerwin Schalk
email: gerwin.schalk@shanda.com

Chrissy Chen

Laurent Pottier
email: laurent.pottier@univ-st-etienne.fr

Yang Yu
email: yuyang@shcmusic.edu.cn

Reflex-in: Generate Music on the Web with Real-time Brain Wave

The music generated from the algorithm -mapping brain wave signal to musical events -aims to produce a form of furniture music that is relaxing and meditative, possibly therapeutic. This effect can be further enhanced through binaural beats or other forms of auditory stimulation, also known as "digital drugs," which can be enabled through the user interface. The system represents a potential avenue for the development of closed-loop brain-computer interfaces by using the listener's own brain waves as the source of musical stimuli, which can be used for therapeutic or medical purposes.

CCS CONCEPTS

ABOUT THE WORK

Generative music from physiological sensors or EEG devices (brain wave) has been implemented since the middle of the 20th century. [START_REF] Lucier | Music for a Solo Performer, live performance[END_REF][START_REF] Rosenboom | The Performing Brain[END_REF] Controlling a human-computer interface using these signals is often more challenging than using gestures or physical objects due to the need for greater precision and the presence of measurement noise. The difficulty and complexity of the music creation process has hindered its development in recent decades.

However, this type of music may have potential benefit for human health. Its impact has been explored through various perspectives, including the famous "Mozart effect." [START_REF] Rauscher | Music and spatial task performance[END_REF] By using physiological measurements to assess changes in symptoms of illnesses, emotions, and sleep, researchers are able to conduct more precise experiments on the ways music can impact human health.

EEG signal is one of the main measurement tools for sleep and brain related diseases. Analysis of brain waves enables the direct identification of different sleep cycles, making it easier to conduct research on sleep quality. This property facilitates the quantitative research on sleep quality and makes the effect of auditory stimulation or music on sleep measurable in real time. [START_REF] Hong-Viet | Auditory Closed-Loop Stimulation of the Sleep Slow Oscillation Enhances Memory[END_REF] Based on this context, our work aims to use Web technologies to transform smart devices into a brain-controlled music box. Our previous researches focused on web-based DSP techniques, sound design systems, visual programming languages for real-time audio processing and interactive music [START_REF] Ren | From Diagram to Code: a Web-based Interactive Graph Editor for Faust DSP Design and Code Generation[END_REF][START_REF] Ren | Build WebAudio and JavaScript Web Applications using JSPatcher: A Web-based Visual Programming Editor[END_REF][START_REF] Ren | Making Computer Music on the Web with JSPatcher[END_REF], and led to the design and implementation of a "patcher" web application named JSPatcher [START_REF] Ren | JSPatcher, a Visual Programming Environment for Building High-Performance Web Audio Applications[END_REF], a high-level visual language similar to the one of Max/MSP [START_REF] Miller Puckette | The patcher[END_REF][START_REF] Puckette | Max/msp[END_REF], that enables users to visually, interactively, construct audio graphs using the Web Audio API. For this new project, we also rely on JSPatcher. Users can graphically design and execute DSP algorithms using domain-specific languages (DSL) for audio processing such as Faust or Gen. These algorithms are executed in a dedicated high-priority thread called an AudioWorklet. This application can also be utilized to design interactive programs and shareable digital artworks online, incorporating other JavaScript language features, Web APIs, web-based audio plugins, or external JavaScript modules.

Typical BCI software such as BCI2000 [START_REF] Schalk | BCI2000: a general-purpose brain-computer interface (BCI) system[END_REF] or OpenBCI1 can be used for transferring brain wave data through the UDP network protocol with the OSC format. We first forward the data through WebSocket protocol, possibly with a local server, then capture it in the browser. The peaks in the brain wave signal are then used to trigger the web-based synthesizer built in JSPatcher, which has access to the WebSocket API for processing the incoming brain wave signal.

To process the signal, a patcher is created with a web browser using the JSPatcher web application2 . When a patcher is hosted on an online server, a URL can be used to prompt JSPatcher to open the patcher program in run-time mode, displaying user interface elements and providing a usable application for end-users, as opposed to the "editing mode. "

The generated web application3 can be used to adjust the rhythm, harmony, and timbre of the sound. These adjustments can be performed manually (using GUI elements like buttons, knobs, and sliders, etc.) or automatically over time.

The synthesizer part is created from Faust DSP. Faust is a functional, synchronous, domain-specific programming language designed for real-time audio signal processing and synthesis [START_REF] Orlarey | FAUST : an Efficient Functional Approach to DSP Programming[END_REF]. Thanks to the Emscripten transpiler and the WebAssembly format, the Faust compiler is available as a JavaScript module named faustwasm [START_REF] Shihong Ren | Modernized Toolchains to Create JSPatcher Objects and WebAudioModules from Faust Code[END_REF] which can compile Faust code to a fully functional WebAudio AudioWorklet node, directly in the browser. The main Faust DSP in this work is a a 16-voice additive synthesizers with envelopes, frequency modulators and amplitude modulators, Parameters affecting timbres can be controlled from the user interface.

A real-time WebGL visualization is displayed as the background of the user interface. The graphics are generated using the three.js library 4 and are based on the brain wave and the music generated. Two real-time signal analyzers are used for reporting the peak values of the brain wave signal and the audio signal. The values are used by the WebGL shaders to modify the position of the particles rendered as a real-time 2-dimensional waveform visualization.

Reflex-in is an original creation experience, implementing a set of standard web technologies. It shows that it is possible to develop original audio/musical applications with the web platform. It has mainly been designed as an artwork, while we also discussed its potential therapeutic applications. This work is a prototype for people who need to hear a real-time musical feedback from their brain wave in order to eventually improve the sleep quality. It will be tested soon with neuroscientists to assess its effect with different music parameters on a closed-loop BCI system. This work is commissioned by Shanghai Conservatory of Music and Shanghai Key Laboratory for Music Acoustics.

Figure 1 :

 1 Figure 1: Diagram of the proposed medical use of Reflex-in

•

  Human-centered computing → Interaction design; • Information systems → Web applications.

	KEYWORDS
	music, brain wave, web audio, visual programming language
	ACM Reference Format:
	Shihong Ren, Michel Buffa, Gerwin Schalk, Laurent Pottier, and Yang Yu.
	2023. Reflex-in: Generate Music on the Web with Real-time Brain Wave. In
	Proceedings of The Web Conference 2023 (Conference WWW '23). ACM, New
	York, NY, USA, 3 pages. https://doi.org/XXXXXXX.XXXXXXX

Conference WWW '23, April 30-May 4, 2023, Austin TX, USA Ren, et al.

OpenBCI homepage: https://openbci.com/

We propose to present this artwork at the conference, using a large display, ideally large TV with touch screen. For the demonstration/exhibition of the artwork We can simulate real-time brain wave by replaying EEG recordings on the computer.

We call a "patcher" a program written online using the JSPatcher web application. JSPatcher can run a patcher in "editing mode" that allows editing the program, or in "run-time mode" that shows the patcher like a web application.

A live demo can be found at https://jspat.shren.site/dist/?projectZip=https://static. shren.site/reflex-in/project.zip&file=main.jspat&runtime=1

Three.js Homepage: https://threejs.org/


Fadil Boodoo

Carole Delenne

Renaud Hostache

Julien Freychet

Accurate streamflow forecasting can minimize negative impacts of hydrological events Two models were compared for daily streamflow forecasting:

• Traditional conceptual rainfall-runoff (hydrological) model • Artificial neural network (ANN) model

Introduction

5)

Models evaluated using Nash-Sutcliffe Efficiency (NSE) score

𝑁𝑆𝐸 = 1 - ∑ !"# $ (𝑄 % ! -𝑄 & ! ) '
∑ !"# $ (𝑄 % ! -𝑄 % ) ' Where 𝑄 % is the mean discharges, 𝑄 & is modeled discharge. 𝑄 % ! is observed discharge at time t.

If a model has no estimation error, NSE equals 1; if its error equals the variance of the observed data, NSE equals 0.

Methodology

Results

Results

Conclusion

Comparison of a conceptual rainfall-runoff model with an artificial neural network model for streamflow prediction 2) The conceptual hydrological model called Superflex is used as a reference.

4)

The models are : 2)

•
3) The Severn River basin was chosen for this study.

• The River Severn is Great Britain's longest river, spanning 220 miles with an average flow rate of 107 m3/s. It is the most voluminous river in England and Wales. • The flow was measured at Saxon Lode.

• The data were obtained from the ERA5 database 4

Methodology 1 )

 1 The adopted ANN model is a long short-term memory (LSTM) architecture with two hidden layers, each with 256 neurons. It was computed using Neuralhydrology library1

Figure 1 :

 1 Figure 1 : Structure of the LSTM neuron. We can see the different gates inside of an LSTM neuron 2

  Trained between 1 and 13 years dataset from 2004 to 2017 • Validated on a 2-year dataset from 2018 to 2020 • The Severn catchment in the United Kingdom was used as a test case The same inputs meteorological forcings were used for both models : Total precipitation, Daily maximum and minimum temperatures, Daylight duration, Mean surface downward short-wave radiation flux, and Vapor pressure.

Figure 2 : 3 Figure 5 :

 235 Figure 2 : Structural diagram of the Flex TM model 3

Figure 6 : 6 3)

 66 Figure 6 : Comparison of simulated outflow (in black) and observed outflow (in red) over the validation period for SuperFlex. 6

Figure 4 :

 4 Figure 4 : NSE score as a function of the number of training years for LSTM (in blue) and Superflex (in green)

Figure 3 :

 3 Figure3: The River Severn study area5