Pablo Arnalte-Mur
email: pablo.arnalte-mur@durham.ac.uk

Antoine Labatie

Nicolas Clerc

Vicent J Martínez

Jean-Luc Starck

Marc Lachièze-Rey

Enn Saar

Silvestre Paredes

Wavelet analysis of baryon acoustic structures in the galaxy distribution

Keywords: large-scale structure of Universe -cosmology, distance scale -galaxies, cluster, general -methods, data analysis -methods, statistical

Context. Baryon Acoustic Oscillations (BAO) are a feature imprinted in the density field by acoustic waves travelling in the plasma of the early universe. Their fixed scale can be used as a standard ruler to study the geometry of the universe. Aims. BAO have been previously detected using correlation functions and power spectra of the galaxy distribution. In this work, we present a new method for the detection of the real-space structures associated with this feature. These baryon acoustic structures are spherical shells with a relatively small density contrast, surrounding high density central regions. Methods. We design a specific wavelet adapted to the search for shells, and exploit the physics of the process by making use of two different mass tracers, introducing a specific statistic to detect the BAO features. We show the effect of the BAO signal in this new statistic when applied to the Λ -Cold Dark Matter (ΛCDM) model, using an analytical approximation to the transfer function.We confirm the reliability and stability of our method by using cosmological N-body simulations from the MareNostrum Institut de Ciències de l'Espai (MICE). Results. We apply our method to the detection of BAO in a galaxy sample drawn from the Sloan Digital Sky Survey (SDSS). We use the 'Main' catalogue to trace the shells, and the Luminous Red Galaxies (LRG) as tracers of the high density central regions. Using this new method, we detect, with a high significance, that the LRGs in our sample are preferentially located close to the centres of shell-like structures in the density field, with characteristics similar to those expected from BAOs. We show that stacking selected shells, we can find their characteristic density profile. Conclusions. We have delineated a new feature of the cosmic web, the BAO shells. As these are real spatial structures, the BAO phenomenon can be studied in detail by examining those shells.

Introduction

Before recombination, the energy of photons is high enough to avoid the formation of neutral hydrogen atoms. This means that baryons and photons are coupled through Compton scattering and electromagnetic interaction between protons and electrons, forming a plasma. In this fluid two phenomena act in opposite directions: gravitational forces tend to compress the plasma around high density regions, while radiation pressure tends to dilute any such over-density. The combination of both in the presence of any initial inhomogeneity give rise to acoustic waves propagating in the baryon-photon plasma. This phenomenon ends abruptly at the epoch of recombination, when the temperature drops enough to allow hydrogen atoms to form, and therefore radiation decouples from the baryons.

Baryon acoustic oscillations (BAO) are therefore due to the propagation of these sound waves in the baryon-photon plasma in the early universe (Peebles & Yu 1970;Hu et al. 1997;Eisenstein & Hu 1998;[START_REF] Bassett | Dark Energy[END_REF]. Any primordial over-density in the early universe produces a spherical acoustic wave in the baryon-photon plasma, travelling outwards: the radiation pressure drags the baryons that are coupled to the photons, and compensates the gravity force that pulls all matter towards the centre. Dark matter, however, is totally decoupled from the photons, and therefore its density at the centre continues growing. About 380, 000 years after the Big Bang, temperature drops so that photons and baryons decouple, and the scale of the baryon shells freezes. After this time, both the central over-density and the shell grow gravitationally, accreting both dark matter and baryons. The result at late times is a large over-density at the position of the original perturbation, surrounded by a faint spherical shell at a fixed co-moving scale (Eisenstein et al. 2007).

The BAO scale is fixed by the sound horizon at decoupling: it is the distance that the expanding acoustic shells can travel before decoupling. It has been accurately measured by the study of the anisotropies in the Cosmic Microwave Background (CMB) to be (Komatsu et al. 2009) r s = 153.3 ± 2.0 Mpc = 110.4 ± 1.4 h -1 Mpc (where we take h = 0.72, Freedman et al. 2001) 1 . Therefore, this scale, once measured, could be used as a standard ruler to measure the Hubble expansion rate with redshift H(z) and the angular diameter distance D A (z) (Cooray et al. 2001;Blake & Glazebrook 2003;Seo & Eisenstein 2005).

The BAO should appear as a series of damping wiggles in the matter power spectrum, with the locations of the peaks and throughs in k-space being a function of r s and other cosmological parameters (Eisenstein & Hu 1998). All the harmonics sum up to the same peak in the galaxy correlation function ξ(r) at the scale r s , and therefore it could seem more appropriate to use this statistic for the detection of the BAO feature on the available galaxy redshift surveys encompassing large volumes of the universe (Sánchez et al. 2008).

The first detection (claiming a ∼ 3σ level) was reported in the analysis of the correlation function (Eisenstein et al. 2005) of the Sloan Digital Sky Survey (SDSS) (York et al. 2000) Luminous Red Galaxies (LRG) sample (Eisenstein et al. 2001), and later in the power spectrum (Cole et al. 2005) of the 2-degree Field Galaxy Redshift Survey (2dFGRS) (Colless et al. 2001). But certainly this is a controversial topic. Cabré & Gaztañaga (2011) are not finding such level of detection using a data set twice as large in volume and in number of galaxies. They do not claim this result to be in contradiction with the standard ΛCDM model, but to be a consequence of insufficient data. One of the arguments in Cabré & Gaztañaga (2011) is the fact that mixing model selection with parameter determination can lead to some confusion in the interpretation of the results and their significance. Different authors are using different criteria to assess the significance of ther BAO detection. For example, when (Eisenstein et al. 2005) affirm that the baryon signature was detected at 3.4 σ (or at 3.0 σ when including only data points between 60 and 180 h -1 Mpc) they are comparing their results of the SDSS-LRG correlation function with the expected for the best-fit pure CDM model and different BAO models. The best BAO detection up to now (Percival et al. 2010) was obtained studying the combined power spectrum of LRG and 'Main ' Strauss et al. (2002) samples of SDSS, together with the 2dFGRS sample, and is at the ∼ 3.6σ level. The authors explicitly state that since this number is obtained comparing to an arbitrary smooth model, the significance cannot be directly compared with the one reported in (Eisenstein et al. 2005). This is a clear example of different authors using different ways to assess the significance of their results that in practice are not comparable. Hütsi (2006) calculated the redshift space power spectrum of the SDSS-LRG sample drawn from the Data Release 4. He concludes that BAO models are favored by 3.3σ over the corresponding models without any oscillatory behavior in the power spectrum. Percival et al. (2007) detected BAOs in the clustering of the combined 2dFGRS and SDSS main galaxy samples, and use their measurements to constrain cosmological models, in particular a given combination of the angular diameter distance D A (z) and the Hubble parameter H(z). Cabré & Gaztañaga (2009a,b) studied the LRGs anisotropic redshift space correlation function 1 h is the Hubble constant in units of 100 km s -1 Mpc -1 ξ(σ, π), where π is the line-of-sight or radial separation and σ is the transverse separation. Moreover, Gaztañaga et al. (2009) have shown how to constrain H(z) using the correlations in the radial direction. Kazin et al. (2010a) found similar results for the correlation measurements and uncertainties, but manifest disagreement in the interpretation of the results regarding the detection of a line-of-sight baryonic acoustic feature.

More recent studies [START_REF] Martínez | [END_REF]Cabré & Gaztañaga 2009a;Sánchez et al. 2009;Kazin et al. 2010b) have confirmed this detection in the last Data Release (DR7, Abazajian et al. 2009) of the SDSS-LRG, containing twice as many galaxies as the original sample, although the observed peak is in these cases wider than that observed in the original detection -an issue that needs further explanation. These measurements of the BAO scale at a low redshift, combined with other cosmological probes, have been used to put stringent constraints on the values of cosmological parameters (Tegmark et al. 2006;Percival et al. 2007;Sánchez et al. 2009;Percival et al. 2010;Reid et al. 2010;Kazin et al. 2010a).

While Bassett & Afshordi (2010) argue that low-level detections may not be sufficient to robustly estimate the cosmological parameters, Cabré & Gaztañaga (2011) show instead that it is still possible -assuming a model-to locate the BAO position with data providing very low significant BAO detection.

It is important, therefore, to find evidence of BAO in the galaxy distribution based on complementary methods. A step further is to search for real structures in the galaxy distribution that are responsible for the BAO feature in these second order statistics. The detection of these structures would be a confirmation of the existence of the baryon acoustic phenomenon. Moreover, if we are able to localize these structures in configuration space, this would allow us to study in more detail the properties of BAO.

In this paper, we introduce a new method for the detection of BAO, which is closely tied to the underlying physics of the process, and apply it to a sample drawn from the SDSS catalogue. This method (described in Section 2) is based on analyzing directly the 3D galaxy distribution using a very specific wavelet function (which we called 'BAOlet'), which is specially well suited to search for BAO features. The method makes use of two different tracers, one to map the overall density field (including the BAO shells), and the other to locate the position of the largest overdensities, which should correspond to centres of the shells. As we study directly the galaxy distribution in configuration space, this method also allows us to identify regions of space where the BAO signal is stronger or fainter. We describe the expected signal in the ΛCDM model in Section 3, using both analytical prediction and a N-body simulation catalogue. We describe the samples used in the case of SDSS in Section 4. In Section 5, we show the results obtained in this case. We also make a test to assess the significance of these results, and explore the implications of this analysis regarding the localization of BAO structures. Finally, we summarize our conclusions and discuss possibilities for future work in Section 6.

The wavelet detection method

The basis of the new BAO detection method is to focus on the positions of massive dark matter haloes, which correspond to the location of large initial perturbations, and to look for the presence of structures resembling the acoustic shells around these. Once we locate the positions of the large over-densities, we need to study the density field to identify the structures corresponding to the acoustic shells around these centres. An appropriate method for identification of structures in continuous fields is wavelet analysis (Martínez et al. 1993;[START_REF] Starck | Astronomical Image and Data Analysis[END_REF][START_REF] Jones | Data Analysis in Cosmology[END_REF]. Wavelet transforms are widely used in many areas, especially in image analysis [START_REF] Mallat | A Wavelet Tour of Signal Processing[END_REF][START_REF] Starck | Sparse Image and Signal Processing[END_REF]. They are specially suited for the analysis of data at different scales, and identification of characteristic patterns or structures. Wavelets have been used in Cosmology for the analysis of the large-scale structure, and of the CMB anisotropies (Martínez et al. 1993;Rauzy et al. 1993;Vielva et al. 2004;Starck et al. 2006;[START_REF] Saar | Data Analysis in Cosmology[END_REF].

Standard wavelet functions like the Mexican hat are, however, not suitable for the detection of shells. Instead, we need a family of wavelets whose shape matches the type of structures we want to find in our data. Therefore, we use a specially designed wavelet (the 'BAOlet'), well adapted to the search of BAO features -shell-like structures around our selected centres. We design this new family of wavelet functions as a transformation of the wide-used B-spline wavelets [START_REF] Saar | Data Analysis in Cosmology[END_REF]). These ψ R,s (x) functions are spherically symmetric, and their radial profiles are defined as

ψ R,s (r) = α R,s 4πr 2 2B 3 2 r -R s -B 3 r -R s , (1)
where R and s are the two parameters that define the scale and width of the BAOlet function, α R,s is the normalization constant defined so that

||ψ R,s || 2 ≡ |ψ R,s (x)| 2 dx = 1 , (2)
and B 3 (x) is the box spline of the third degree, defined by

B 3 (x) = 1 12 |x -2| 3 -4|x -1| 3 + 6|x| 3 -4|x + 1| 3 + |x + 2| 3 .
The BAOlet function is shown in Fig. 1. It can be thought of as a spherical shell of radius R and width s, with zero amplitude at its centre and therefore adapted to the detection of spherical shells of a given radius. This specific choice is motivated by the fact that the integrated profile is the widely-used onedimensional 'B-spline' wavelet function that has a null mean and compact support [-2, 2]. These properties directly translate onto the BAOlet that has also a null mean -a requirement for any wavelet function-if R > 2s, and takes non-zero values only for R -2s ≤ |x| ≤ R + 2s. We describe the density field using the density contrast δ(x), defined as

δ(x) = ρ(x) -ρ 0 ρ 0 ,
where ρ(x) is the density field, and ρ 0 is its mean. Then, given a density contrast map δ(x), properly normalized as in equation (2), we can construct, for each point in the parameter space (R, s), a BAOlet coefficient map as the convolution of our density field with the corresponding wavelet:

W R,s (x) = ℜ 3 ψ R,s (y)δ(y -x)d 3 y . (3)
The BAOlet acts as a matched filter, which is sensitive to data containing shells of different radius and different widths. Its property of zero mean is also of high importance since it makes the statistics derived from the BAOlet coefficients independent of the background level. Indeed, it is obvious that any constant added to the input data would not change the BAOlet coefficients. In comparison, the estimation of such a baseline level is a very delicate aspect of the BAO detection in the two-point correlation function.

Due to the properties of the wavelet, the coefficient maps W R,s (x) should have a null mean when averaged over all points in the volume considered. Equivalently, if we sampled these maps at N random points uniformly distributed in the volume (x (i) r ), the expected value of the average of the coefficients is zero,

E W R,s (x (i) r ) N = 0 . ( 4
)
This condition holds even in the presence of shell-like structures in the density field. Of course, for such structures the value of W R,s (x c ) (x c is the centre of the shell) is positive, and remains positive in nearby points. For an ideal δ(r -R) density shell the radius of the region around the centre where the wavelet amplitude is positive, is s; the positive signal in this region is compensated by negative amplitudes around |x| = R. However, if we are able to identify the positions of N massive haloes in the same volume (x (i) c ), we can define a new statistic B(R, s) as the mean value of the coefficients W R,s (x) at these positions:

B(R, s) = W R,s (x (i) c ) N . (5)
If there are indeed shell-like structures around the selected density maxima x (i) c , as expected for baryon acoustic structures, we should find positive values of B(R, s) with the maximum of B at the (R, s) values characterizing these shells.

We can obtain further information from the wavelet coefficients W R,s (x), as we have information on the actual dependence of the signal picked up by the BAOlet function on the position. In particular, fixing a set of parameters of interest (R i , s i ), we could use the coefficients W R i ,s i (x c ) to identify which of the selected massive haloes are giving the largest signal for these characteristics of the shells. In the context of BAO, the parameters R i , s i can be chosen a priori using a theoretical model, or a posteriori using the parameters for which the function B(R, s) attains it maximum. In this way, we can localize in configuration space the structures responsible for the largest BAO signal in a given sample.

For our calculation of B(R, s), we sample the (R, s) parameter space on a grid. For each point (R, s), we calculate the coefficient map W R,s (x) as the convolution of the BAOlet with the density field (equation 3). We perform the convolution in Fourier space using a Fast Fourier Transform (FFT) technique. To avoid problems with the FFT, we zero-pad a large region around our density cube. To obtain B(R, s), we sample W R,s (x) at the position of the N selected centres, and calculate the average value (equation 5).

Therefore, to apply this method, we need a way to map the overall density field δ(x), but also to locate the position of massive matter haloes x (i) c . We have to use two different populations of mass tracers, so that they play the appropriate role in the detection algorithm. The idea of using two different tracer sets, one for the small perturbations and another for the high peaks, in a cross-correlation analysis was anticipated by Eisenstein et al. (2007). We implement here a similar idea, but using a wavelet tool directly on the density field. As detailed below, we use galaxies from the 'Main' and LRG samples of SDSS in this case. However, this choice would depend on the kind of data available in each case.

Prediction from ΛCDM

In order to better understand our method, we show here which results we expect according to the ΛCDM model, and the effect of BAO in our new statistic B(R, s). We use for this aim both the analytical approximation to the transfer function of Eisenstein & Hu (1998), and the results from the MareNostrum Institut de Ciències de l'Espai (MICE) simulation (Fosalba et al. 2008).

In the first place, we use the ΛCDM transfer function, which allow us to study directly the effect of the BAO. However, in this case, we must do a series of approximations in order to make a prediction for B(R, s). We want to predict which is the typical result for the wavelet coefficient W R,s at the position of massive matter haloes x c , as a function of R, s. From equation (3), we see that this is equivalent to study the typical density profile around such haloes, δ(yx c ). The ΛCDM transfer function allows us to calculate this profile, provided we know which is the initial perturbation corresponding to the selected haloes. We make here the simple approximation of considering that these initial perturbations are point-like and spherically symmetric, and can thus be simply described by a Dirac delta function in configuration space. This corresponds to a constant value in Fourier space. As the transfer function T (k) describes the relative evolution of the different Fourier modes, the present day radial density profile corresponding to such initial perturbation will we given simply by (Eisenstein et al. 2007)

ρ(r) = C T (r) (6)
where T (r) is the Fourier transform of the transfer function T (k), and C is a normalization constant that depends on the details of the initial perturbation, and on the cosmic growth function D 1 (z).

From equations ( 3) and ( 5), we see that the effect of C will be just to change the normalization of our statistic B(R, s). We used the fitting formulae to the transfer function T (k) from Eisenstein & Hu (1998), and obtained the expected W R,s at a large overdensity using equations ( 6) and (3). In order to highlight the particular signature of BAO, we also calculated W R,s using the 'no wiggle' transfer function formula, in which the BAO have been edited out. We used here the values Ω M = 0.25, Ω Λ = 0.75, Ω b = 0.044, and h = 0.7 for the cosmological parameters, to allow a direct comparison with the MICE simulation. Following Eisenstein & Hu (1998), the sound horizon scale in this case is r s = 109.3 h -1 Mpc. The results for both cases are shown in Fig. 2. In the plot, we mask the region R < 2s, as for these values of the parameters our BAOlet is not compensated (its mean is different from 0). Comparing both panels of the Figure , we see clearly which is the effect of the presence of BAO in our statistic. In the case without BAO W R,s is always negative, and it presents a smooth gradient across the (R, s) plane. This gradient is due to the overall shape of the radial profile (equation (6). However, in the presence of BAO, W R,s shows a prominent peak with positive values. This clearly shows the idea behind the B(R, s) statistic. The BAOlet ψ R,s acts as matched filter with a shape adapted to detect BAO shells. Therefore the positive values in the coefficients W R,s correspond to the cases in which the radial profile is matched by the BAOlet shape. The values at which W R,s attains its absolute maximum, R max = 110 h -1 Mpc and s max = 22 h -1 Mpc, correspond thus to the characteristics of the shell that best matches the observed profile about the selected centres.

In order to test the reliability of the method, and of this ΛCDM prediction, we calculated the B(R, s) for a halo catalogue drawn from the MICE simulations. We used the publicly available halo catalogue from the 'MICE3072' run (Crocce et al. 2010). This particular run contains 2048 3 particles in a box of side 3072 h -1 Mpc, therefore covering a volume of 29 h -3 Gpc 3 . The simulation was run with the GADGET-2 code (Springel 2005), assuming a ΛCDM model with the parameters mentioned above. Haloes in the simulation were selected using a friends-offriends (FoF) algorithm.

We used the resulting halo catalogue at z = 0, which contains a total of 2819031 haloes containing 143 or more particles. This corresponds to haloes with masses ≥ 3.35 × 10 13 h -1 M ⊙ . The halo number density is thus 9.72 × 10 -5 h 3 Mpc -3 . We used the full halo catalogue as a tracer of the overall density field. We then selected as centres for the calculation of B(R, s) in equation ( 5) only the haloes with a mass ≥ 1.76 × 10 14 h -1 M ⊙ . We chose this mass threshold in order to select approximately the 10% most massive haloes in the simulation box. This choice is somewhat arbitrary, but serves for the purpose of testing the BAOlet method and illustrating the expected result.

Fig. 3 shows the BAOlet result B(R, s) for these MICE samples, compared to the theoretical results obtained above from the Eisenstein & Hu (1998) transfer functions. We obtain a result very similar to that of Fig. 2, as B(R, s) shows a clear peak, and attains its absolute maximum for R max = 108 h -1 Mpc, s max = 28 h -1 Mpc. This indicates that our BAOlet method can (Eisenstein & Hu 1998). The normalization is arbitrary. The contours are drawn at steps of 1000 for W R,s < 0 (dotted), W R,s = 0 (solid), and W R,s > 0 (dashed). The map attains a maximum at R = 110 h -1 Mpc, s = 22 h -1 Mpc. be applied to two sets of mass tracers, although the details of the tracers used here are very different from the ones we use later on the SDSS samples. This also confirms the expected effect of the presence of BAO in the B(R, s) function: the presence of a large peak with positive values of B, located approximately at the values of R and s corresponding to the radius and width of the acoustic shells. The fact that we obtain here slightly different values for R max and s max than those predicted above may be due to non-linear evolution effects, which slightly reduce the radius and increase the width of the shells. A similar effect is present in the correlation function (see e.g. Crocce & Scoccimarro 2008). We also used this halo catalogue from MICE to make a qualitative estimation of how different observational effects would affect the BAOlet result. In the first place, we studied the effect of redshift-space distortions. To this end, we calculated the redshift-space positions of all haloes taking into account their peculiar velocities, as output by the simulation, and consider-ing an observer located in one of the vertices of the simulation cube. The result for B(R, s) in this case is shown in the top panel of Fig. 4, where it is compared to the real-space result discussed above. As can be seen from the figure, although small differences appear between the real-and redshift-space results, the main features of the B(R, s) prediction remain the same, with the position of the maximum changing only by ∼ 1 h -1 Mpc.

For the second case, we added the effect of a decreasing radial selection function across the sample. We model this selection as an exponential decay function, such that the final number of haloes used to trace the overall density field is ∼ 20% of the total. In our calculations, we then weight each halo by the inverse of the mean density at its redshift, as we do later for the SDSS data. We do not apply any selection function to the centres. The results for B(R, s) obtained in this case (including also the redshift-space effects) are shown in the bottom panel of Fig. 4. As above, these observational effects do not change significantly the overall behaviour of B(R, s), or the location of the maximum of the peak. Overall, although the MICE catalogue used does not mimic the characteristics of our SDSS samples, we can be confident that neither redshift-space distortions nor a radial selection function (when it is taken into account in the calculation) should bias significantly our results.

SDSS samples used

We used data from two different samples of the latest data release (DR7) of the spectroscopic SDSS. On one side, we used the 'Main' galaxy sample (Strauss et al. 2002) as mass tracers for reconstructing the overall density field δ(x). On the other, we used the LRGs as tracers of the central over-densities, and therefore used them as the selected centres x (i) c to compute B(R, s). Luminous Red Galaxies were selected by the SDSS team using several colour and magnitude cuts to obtain a highly biased sample reaching high redshift (Eisenstein et al. 2001). The galaxies selected in this way are known to reside near the centres of massive dark matter haloes (Zheng et al. 2009) and are thus adequate tracers for the centres of baryon acoustic structures. We applied an extra cut in the K-corrected, evolved, g-band absolute magnitude: -23.2 < M g < -21.2, as in the previous BAO analysis by Eisenstein et al. (2005). This results in an approximately volume-limited sample in the redshift range 0.15 < z < 0.30.

'Main' galaxies in the SDSS constitute a much denser sample, and are therefore more suitable to map small density changes such as BAO shells. We used the 'Main' sample from the Value-Added Galaxy Catalogue (Blanton et al. 2005), which constitutes a magnitude limited sample in the r band, with r < 17.6. We applied an extra simple cut, M r < -20.

For the conversion of angles and redshifts into co-moving distances, we used a fiducial cosmology with the parameters Ω M = 0.25, Ω Λ = 0.75. In all our analysis we use distances in units of h -1 Mpc, so that they do not depend on the specific value of h. We converted the distribution of the 'Main' galaxies into a density field δ(x) binning it into a grid with cubic pixels of 3 h -1 Mpc side. We corrected for the selection effects by weighting each galaxy by the inverse of the average density at its redshift. As explained below, we performed some tests by slightly changing this weighting scheme. Although this weighting may not be optimal, it should not affect significantly our results, given that the wavelet method does not depend on the local background level. We used the density field constructed in this way for the calculation of the BAOlet coefficients following equation (3).

In our calculations, we could only use the region in which these two samples overlap, which corresponds to the redshift Fig. 4. The BAOlet statistic B for the MICE simulation when some observational effects are taken into account. In the top panel, we show the B(R, s) obtained when redshift-space distortions are included in the simulation. In the bottom panel, we show the result when a radial selection function is applied to the halo catalogue. In both cases, the contours are drawn at steps of 5 in B. Solid contours correspond to the results with the observational effects included. The dashed contours correspond to the original real-space result without selection, i.e., they are identical to those in the bottom panel of Fig. 3. limits 0.15 < z < 0.26. To minimize border effects in the B(R, s) calculation, we defined a buffer region of r buff = 175 h -1 Mpc from any of the borders of the 'Main' sample volume. We used as centres only the LRGs in the inner volume. This allows us to use the density field, as traced by the 'Main' sample galaxies, from z > 0.09. In order to minimize angular selection effects and border effects, we use a compact area of the sky where the angular completeness is nearly uniform. This area covers 5511 deg 2 and is defined, in the SDSS survey coordinates [START_REF] Stoughton | [END_REF], by the limits -31.25 • < η < 28.75 • , -54.8 • < λ < 51.8 • . This results in finally using the density field in a volume of 2.2 × 10 8 h -3 Mpc 3 , as traced by N Main = 198342 galaxies. The number of LRGs used as centres (avoiding the buffer region) is N LRG = 1599.

In Fig. 5 we show a slice of this survey showing both the 'Main' galaxies and the LRGs. We show how, given the buffer used, the LRGs used as centres are located only in an inner volume of the larger 'Main' sample. To illustrate the idea of the method, we show a zoom around a given LRG galaxy. Even for this single centre, a slight over-density of 'Main' galaxies is seen at the radii of 105-110 h -1 Mpc.

As the structures we look for are huge, with radii about 100 h -1 Mpc, we have to consider the effect of assumed cosmology (different comoving distances) on our result. In order to estimate the distance differences, we compared the distances in our adopted MICE cosmology (Ω M = 0.25, Ω Λ = 0.75) with these in the WMAP 7-year cosmological model (Komatsu et al. 2011), Ω M = 0.271, Ω Λ = 0.729. We fixed the redshift difference δz = 0.07 that corresponds approximately to our shell diameter of 200 h -1 Mpc, and found that this gives distance differences of only a 0.3 and 0.8 per cent at the near and far borders of our sample (the MICE distances are larger than the WMAP7 ones in each case). So, for our nearby volume, the effect is small, and does not affect our results given that the statistical uncertainties are much larger (see next Section). However, this effect will be significant for deep samples.

Results for the SDSS samples

We performed the calculation of B(R, s) for the SDSS in an analogous way to the case of the MICE simulation, using the samples defined in Section 4. Our results are shown in Fig. 6. As above, we mask the region R < 2s. As we are not introducing any border correction when calculating the B(R, s) statistic, we also mask the region corresponding to the values R > r bufs. Values obtained at a those large values of R could contain some spurious signal, as the calculation of W R,s would rely on the density field in regions outside of the survey boundaries.

The resulting B(R, s) map is qualitatively very similar to that expected, either using an analytical ΛCDM model (Fig. 2), or the MICE simulation (Figs. 3 and4). This is an indication that the observed pattern does not originate from spurious features in the SDSS but is closely related to the large scale structure and more specifically the BAO. B(R, s) attains a maximum at R max = 116 h -1 Mpc, s max = 36 h -1 Mpc. This maximum is clearly related to the characteristics of the BAO structures present in our samples. We studied the robustness of this result by changing the weighting scheme applied for the construction of the density map (see Section 4). We did so by capping at different maximum values the possible weights associated to each galaxy, and repeating the calculation of B(R, s) in each case. The results were qualitatively similar, obtaining a peak in B(R, s) in all cases. However, the position of the peak changed in each case, with maximum changes of the order of ±5 h -1 Mpc in R max , and ±10 h -1 Mpc in s max . Therefore, the difference between the position of the peak obtained from the SDSS data and that given by the MICE simulation is not significant. In any case, we can not use the scale and the width of the observed maximum of B(R, s) as direct estimates of the radius or width of the shells, specially given that our analysis of the possible observational biases (Fig. 4) was only qualitative.

In order to assess the significance of the BAO detection with this method, we focused on the value of B(R, s) obtained at the Fig. 6. The BAOlet statistic B calculated for SDSS data as a function of the parameters (R, s). The bottom panel shows the results in the full parameter space considered, where we sampled both R and s at intervals of 1 h -1 Mpc. We mask two areas, at the upper right and left corners, where our results are not reliable (see details in the text). The contours are drawn at steps of 5 for B < 0 (dotted), B = 0 (solid), and B > 0 (dashed). The top two panels show cuts at the arbitrarily chosen values s = 36 h -1 Mpc (top) and s = 20 h -1 Mpc (middle), marked with grey horizontal lines in the 2D panel. In these panels, the blue line is B(R, s), while the green line and the red band show the mean (B MC ) and 1-σ MC interval for the Monte Carlo realizations of random centres. We obtain a clear significant peak at different values of s, with a maximum for R = 116 h -1 Mpc, s = 36 h -1 Mpc.

maximum, B max = B(R max , s max ) = 22.9 ± 3.7 2 . A more thoroughfull analysis would model the B(R, s) statistic in the full parameter space. However, given the large covariances between measurements at different values of (R, s) we do not expect a Fig. 5. The SDSS catalogues used. We show a 6 degree thick slice drawn from the catalogues used in our analysis. The red points are the LRG galaxies, which form a nearly volume-limited sample at z > 0.15. The 'Main' galaxies of the SDSS are depicted in blue. As shown, we use the 'Main' galaxies from z > 0.09, and we use only the LRGs in an inner volume, allowing for a buffer region of r buff = 175 h -1 Mpc from any of the borders of the 'Main' sample volume. For our analysis, we use the samples covering a total area of 5511 deg 2 . The radius of a typical BAO shell is shown as a segment. At the top insets we show two orthogonal slices of width 20 h -1 Mpc, centred on a particular LRG. The BAOlet coefficient map W R,s (x) has a large value at the position of this centre (for R max , s max ), and thus we expect to find a strong BAO signal. The two circles have the radii of 100 and 110 h -1 Mpc respectively. This is a single BAO shell where the over-density can be appreciated by eye at the right scale.

large difference from the simple case we consider. We will assess the probability of finding such a maximum in the case in which there are not baryon acoustic structures present in our sample. We model this null hypothesis by using randomly distributed centres for the calculation of B(R, s) in equation ( 5), instead of LRGs. Even using the W R,s (x) coefficients from the observed density field (traced by SDSS 'Main' galaxies), the expected value of B(R, s) in this case is 0 (see equation 4), and we expect to obtain a significantly higher signal in the data. In this way, we are testing the null hypothesis that, either there are not shell-like structures in the density field traced by the 'Main' sample, or these shell-like structures are not found preferentially around LRG centres. In either case, that would mean that there are not BAO-like structures present in our sample.

To perform the significance test, we generated 10 5 random realizations of a Poisson process, with the mean number of points N LRG , in the same volume as the LRGs considered in the calculation (i.e. taking into account the buffer zone). For a realization j, we use the generated points as our centres x (i) c to compute the B(R, s) statistic following equation (5), using the W R,s (x) coefficients obtained from the data. We can then obtain the mean value B MC (R, s), and the standard deviation σ MC (R, s) of the Monte-Carlo realizations of the centres. We show B MC (R, s) and a band of 1σ MC (R, s) around it in the top panels of Fig. 6.

We now calculate our signal-to-noise ratio at the maximum as S NR max ≡ B max / σ MC (R max , s max ) = 6.60, and assess the probability of finding such a large value of S NR max anywhere in the parameter space for the Monte Carlo realizations. We used S NR max instead of directly using B max because for some regions of parameter space, specially at low s, σ MC (R, s) is extremely large. Therefore, if we used B max , we would need to arbitrarily restrict the parameter space studied, thus introducing a possible a posteriori bias. When using S NR max we sample the full parameter space considered in the calculations (as shown in Fig. 6). We computed the maximum value of S NR for each realization j in the full (R, s) range, S NR MC( j) max . The distribution of the values of S NR MC( j) max is shown in Fig. 7, where it is compared to the value of S NR max obtained in the real data. We found that only one of the realizations gave a value of S NR MC( j) max larger than S NR max . Thus, the probability of obtaining a maximum with such a large S NR in the absence of baryon acoustic structures (our null hypothesis) is p ≃ 10 -5 , equivalent to a ∼ 4.4σ detection in the Gaussian case.

However, we should stress here that the significance found in this work can not be compared directly to other detection levels found in the literature, as it has been stated in the introduction. In particular, we are not comparing our results with an analytical no-BAO model of B(R, s) (such as that shown in the top panel of Fig. 2), since to do so would require the detailed modelling of all the selection effects affecting the two samples used.

As explained in Section 2, we can extract more information about the BAO phenomenon in our samples making further use of the BAOlet coefficient maps W R,s (x). Here, we use the coefficient values at the positions of the LRG, for the parameters R max , s max , which correspond to the characteristics of the BAO shells present in our samples. In this way, the values W max ≡ W R max ,s max are a measure of how strong is the signal com- MC( j) max ). This histogram has a mean of 3.72 and a standard deviation of 0.46. We show as a dashed vertical line the value obtained from the data (using the LRGs as centres), S NR max = 6.60. Only one of the Monte Carlo realizations give a maximum value larger than S NR max .

ing from a BAO shell around a given point, and in particular, a given LRG. Therefore, using W max we can localise in configuration space the regions of the volume covered by our samples where the BAO signal is mostly coming from.

We illustrate this idea in Fig. 8, where we plot a twodimensional projection of the distribution of the LRGs used as centers in our analysis, showing also the value of W max for each of them, following a color gradient. The highest values of W max correspond to the red points in the plot.

In Table 1, we provide the 10 LRGs used as centers with the larger values of W max . The whole catalogue of the N LRG = 1599 LRGs used as our centers, and the value of W max obtained for each of them can be found at the web page http://www.uv.es/martinez. This catalogue could be used to study the relation of the BAO signal at a given LRG to its properties or the environment. It could also be used to make a selection of LRG centres with high signal, and use them to refine the measurements of the BAO characteristics.

As an illustration of this later use, we show a simple way to study the overall properties of the BAO structures, its shape and scale. We select those centres which we know that present a prominent acoustic feature, i.e., those for which W max > 0 This leaves us with N r = 809 centres. In order to improve the signal-to-noise in this illustration for studying the BAO structures, we stacked together the 3D density maps around the N r selected LRGs. In doing so, we kept the line-of-sight direction aligned for all the centres, as this direction will define the possible anisotropies in the distribution. We show a 3D view and a 2D cut of this stacked density map in Fig. 9. Thanks to this selection the characteristic elements of the BAO are amplified: on the one side, a central bump with high density, corresponding to the massive halo traced by the LRG, and on the other side, the shell surrounding it at a scale of ∼ 109 h -1 Mpc, showing a fainter over-density. We also observe the anisotropic nature of these structures. This is a combination of the fact that we have to work in redshift space, and of the redshift-dependent selection function for 'Main' galaxies.

A simpler view can be obtained by calculating the average radial density profile ρ(r) around the N r centres. The resulting profile, shown in Fig. 10, has the same features as the 3D view: a high bump at short scales, and a clear peak at about the acoustic scale, with a maximum at r max = 109.5 ± 3.9 h -1 Mpc. The error in r max was estimated using bootstrap realizations [START_REF] Lupton | Statistics in Theory and Practice[END_REF]. This scale gives the radius of the baryon acoustic shells, and it is therefore a good estimator of the acoustic scale in the sample. We also show in Fig. 10 the radial profiles restricted to different regions of the sphere, to better characterize the anisotropy of the distribution. We define two cones with a width of 45 • with respect to the line-of sight in each direction (we call these 'near' and 'far' regions), and a 'transverse' region covering the belt between the cones. We obtain for each of these regions qualitatively similar results. As expected, we see how the 'near' and 'far' subsamples are more strongly affected by observational effects, such as redshift-space distortions, which are more severe along the line of sight. In contrast, the result for the 'transverse' subsample matches, within the errors, that for the full sphere. It is interesting to note that the value of r max is slightly larger for the 'far' sample than for the 'near' one.

It is worth to emphasize that this approach would be impossible with any statistical BAO detection method used this far, since the spatial localization of the shells is completely lost in the correlation function or in the power spectrum, while the local nature of the wavelet approach has allowed us to identify the positions of the most representative structures in our sample. Moreover, we are measuring the acoustic scale at positions selected for their low contamination from other structures, which is not the case when averaging over the full sample. In this way, we maximize the BAO signal, while minimizing the effect of signals coming from different large-scale structures.

Discussion and conclusions

In summary, we have designed a new method for the detection of baryon acoustic oscillations in the galaxy distribution and for the localization, in configuration space, of the structures responsible for them. This method is based on the use of a specially designed wavelet applied directly on the density field. Our approach also relies on the use of two different tracers: one for the overall density distribution, and the other for the central overdensities of the baryon acoustic structures.

After testing the method with simulations, we applied this method to the detection of baryon acoustic structures in a sample drawn from the SDSS. In this case, we used galaxies from the 'Main' catalogue to trace the overall density field, and galaxies from the LRG catalogue to trace the location of massive dark matter haloes. We clearly detect BAO in the sample providing a confirmation of the detection obtained previously using general two point statistics (the power spectrum and correlation function). In fact, our approach provides an independent method for the detection. Finally, we showed how this method allows us, through the use of W max (x), to localize in configuration space the actual structures responsible for the BAO signal obtained. This is a consequence of using a wavelet acting directly on the density field. We illustrate the utility of this approach by showing the density distribution stacked around a set of centres known to show the BAO feature given their W max value.

Recent works have proposed alternative methods to study the BAO based on wavelets (Xu et al. 2010;Tian et al. 2011) parameters, conceptually similar to ours. They use it to search for a peak in the two point correlation function of the 'Main' SDSS sample, obtaining a detection with a p-value p = 0.002 (equivalent to 3.1σ in the Gaussian case). As in our case, this shows the utility of using the 'Main' sample to reduce the shot noise in the calculation and to obtain significant detections. However, these works apply the wavelet to the measured two point correlation function, instead of directly to the density field. In this way, the use the capabilities of the wavelets to characterize accurately the BAO signal (in terms of radius and width), but they are not able to get any information about the localization of these structures in space.

The use of wavelets directly on the density field isolates valuable information about the baryon acoustic structures that is hidden in the standard two point statistics. In particular it gives us information, through the coefficients W R,s (x), to localize regions in the sampled volume giving the largest or lowest signal. We expect that this new method for studying BAO will be of much use for ongoing or planned surveys, such as the WiggleZ Survey (Drinkwater et al. 2010), the Baryon Oscillation Spectroscopic Survey (BOSS, Eisenstein et al. 2011), or the Physics of the Accelerating Universe (PAU) Survey [START_REF] Benítez | [END_REF]), which will cover a much larger volume than studied here, and will explore higher redshifts.

Fig. 1 .

 1 Fig. 1. The BAOlet function. Here we show a 2D plot (bottom) of the wavelet ψ R,s (x) used in the analysis, as defined by equation (1). The top panel shows a 1D slice along the dashed-dotted axis. The wavelet is plotted here for R = 105 h -1 Mpc, s = 30 h -1 Mpc. The red dot marks the centre of the wavelet. This function has a null mean (provided that R > 2s), and compact support. It takes non-zero values only for R -2s ≤ |x| ≤ R + 2s.

Fig. 2 .

 2 Fig. 2.Values of the BAOlet coefficients W R,s expected at the positions of large initial point-like perturbations, as a function of the BAOlet parameters (R, s). The bottom panel shows the result using a standard ΛCDM transfer function, while the top panel shows the result using a transfer function with the BAO wiggles edited out(Eisenstein & Hu 1998). The normalization is arbitrary. The contours are drawn at steps of 1000 for W R,s < 0 (dotted), W R,s = 0 (solid), and W R,s > 0 (dashed). The map attains a maximum at R = 110 h -1 Mpc, s = 22 h -1 Mpc.

Fig. 3 .

 3 Fig. 3. The BAOlet statistic B calculated for the MICE simulation sample described in the text as a function of the parameters (R, s) (bottom panel). The contours are drawn at steps of 5 for B < 0 (dotted), B = 0 (solid), and B > 0 (dashed). This function attains its maximum for R = 108 h -1 Mpc, s = 28 h -1 Mpc. The top two panels show cuts at the values s = 28 h -1 Mpc (top) and s = 22 h -1 Mpc (middle), marked with grey horizontal lines in the 2D panel. In each case, the solid blue line corresponds to the value obtained from MICE, the dashed red line corresponds to the theoretical expectation from the Eisenstein & Hu (1998) transfer function (bottom panel of Fig.2), and the dotted green line to the theoretical expectation using the 'no wiggle' transfer function (top panel of Fig.2). These theoretical predictions have been re-normalised to get the same value at the maximum in B(R, s).

Fig. 7 .

 7 Fig. 7. Histogram showing the distribution of the maximum S NR values obtained, in the full (R, s) space, for the 10 5 Monte Carlo realizations of Poisson-distributed centres (S NRMC( j) max ). This histogram has a mean of 3.72 and a standard deviation of 0.46. We show as a dashed vertical line the value obtained from the data (using the LRGs as centres), S NR max = 6.60. Only one of the Monte Carlo realizations give a maximum value larger than S NR max .

Table 1 .

 1 . In particular, Tian et al. use a Mexican hat wavelet function with two A two-dimensional projection of the distribution in redshift space of the LRGs used in the analysis (i.e. inside our buffer region). The data showed corresponds to a slice of a width of ≃ 8 • in declination, which contains ∼ 40% of the LRGs. The colour and size of each point corresponds to the value of W max at this LRG position. 20+184733.0 214.44254 18.79250 0.19872 0.00016 517.84 SDSS J121858.41+380813.6 184.74341 38.13714 0.18974 0.00018 436.68 SDSS J112430.27+415557.3 171.12613 41.93260 0.19433 0.00019 419.45 SDSS J112355.53+423816.5 170.98140 42.63793 0.19404 0.00020 414.63 SDSS J112352.72+424542.4 170.96968 42.76178 0.19469 0.00018 414.63 SDSS J122935.13+384636.4 187.39640 38.77680 0.18686 0.00016 413.47 SDSS J112535.99+412608.3 171.39998 41.43564 0.19288 0.00019 401.91 SDSS J104501.94+362944.3 161.25810 36.49566 0.15938 0.00015 399.06 SDSS J140443.31+264439.2 211.18047 26.74424 0.15854 0.00016 396.19 SDSS J142031.28+211700.4 215.13036 21.28346 0.19232 0.00020 395.40 Table containing the basic characteristics (J2000 sky coordinates, redshift and redshift uncertainty) of the 10 LRGs with the larger values of the BAOlet coefficient at the maximum W max . The full table is available at http://www.uv.es/martinez.

		700						396
		650						318
								240
		600
								162
	y (Mpc h -1 )	450 500 550						-72 6 84	W max
		400
								-150
		350
								-228
		300
								-306
		250
		-200	-100	0	100	200	300	-384
				x (Mpc h -1 )
	Fig. 8. SDSS object name	α (deg)	δ (deg)	z	σ z	W max
		SDSS J141746.

This error in B max is obtained from the variance of the coefficients at the N LRG different LRGs. However, our significance test is independent of this error value.

Acknowledgements. This work has been supported by the European Research Council grant SparseAstro (ERC 228261), by the Spanish CONSOLIDER projects AYA2006-14056 and CSD2007-00060, including FEDER contributions, by the Generalitat Valenciana project of excellence PROMETEO/2009/064, and by the Estonian grants SF0060067s08 and ETF8005. P.A.M. was supported by the Spanish Ministerio de Educación through a FPU grant, and by an ERC StG Grant (DEGAS-259586).

We acknowledge the use of data from the MICE simulations, publicly available at http://www.ice.cat/mice. We also acknowledge the use of public data from SDSS. Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS Web Site is http://www.sdss.org/. The SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions. The Participating Institutions are the American Museum of Natural History, Astrophysical Institute Potsdam, University of Basel, University of Cambridge, Case Western Reserve University, University of Chicago, Drexel University, Fermilab, the Institute

Fig. 10. Average radial profiles. We show the radial density profile averaged over the N r centres with W R max ,s max > 0. We plot δ(r) = ρ(r) ρ 0 , where ρ 0 is the average density of the sample. The continuous line with red error band shows the radial profile for the full sphere. We also show the error bands for the radial profile restricted to regions of the sphere, as defined in the text: the 'near' region (green), the 'far' region (blue), and the 'transverse' region (orange). The arrow signals the location of the maximum, r max = 109.5 h -1 Mpc. The error band corresponds to the 1-σ dispersion of 1000 bootstrap realisations. The profiles were estimated using a B 3 kernel of width h = 4 h -1 Mpc in the radial coordinate, but similar results are obtained when using slightly different widths or shapes of the kernel.


Xianran He

Yousong Nie

Min Zhong

Shaolei Li

Xiaolong Li

Yi Guo

Zhenming Liu

Yangguang Gao

Fei Ding

Dan Wen

Yongmin Zhang
email: yongmin.zhang@upmc.fr

New organoselenides (NSAIDs-Se derivatives) as potential anticancer agents: Synthesis, biological evaluation and in silico calculations

Keywords: selenium, selenocyanates, trifluoromethyl selenides, anticancer, in silico calculations

Herein we reported the synthesis of twenty new organoselenium compounds (2a-2j and 3a-3j) based on the hybridization of nonsteroidal antiinflammatory drugs (NSAIDs) skeleton and organoselenium motif (-SeCN and -SeCF 3 ), the anticancer activity was evaluated against four types of cancer cell lines, Caco-2 (human colon adenocarcinoma cells), BGC-823 (human gastric cancer cells), MCF-7 (human breast adenocarcinoma cells), PC-3 (human prostatic cancer cells). Interestingly, the introduction of the -SeCN or -SeCF 3 moiety in corresponding parent NSAIDs results in the significant effect on cancer cell lines. Moreover, the most active compound 3a

showed IC 50 values lower than 5 μM against the four cancer cell lines, particularly to BGC-823 and MCF-7 with IC 50 values of 2.5 and 2.7 μM, respectively. Furthermore, three compounds 3a, 3g and 3i were selected to investigate their ability to induce apoptosis in BGC-823 cells via modulating the expression of anti-apoptotic Bcl-2 protein, pro-inflammatory cytokines (IL-2) and proapoptotic caspase-8 protein. The redox properties of the NSAIDs-Se derivatives prepared herein were conducted by 2, 2-didiphenyl-1-picrylhydrazyl (DPPH), bleomycin dependent DNA damage and glutathione peroxidase (GPx)-like assays. Finally, molecular docking study revealed that an interaction with the active site of thioredoxin reductase 1 (TrxR1) and predicted the anticancer activity of the synthesized candidates. Overall, these results could serve a promising launch point for further design of NSAIDs-Se derivatives as potential anticancer agents.

Introduction

Non-steroidal anti-inflammatory drugs (NSAIDs) are a class of often chemically unrelated compounds commonly used to treat symptoms of inflammatory diseases such as osteoarthritis and rheumatoid arthritis, and are among the most widely used drugs worldwide [1,2]. In the field of cancer research, a large body of evidence from epidemiological and preclinical studies have shown that NSAIDs have used for chemo-preventive agents, especially in colorectal cancer (CRC) and prostate cancer [3][4][5][START_REF] Pruthi | Phase II trial of celecoxib in prostate specific antigen recurrent prostate cancer after definitive radiation therapy or radical prostatectomy[END_REF]. Several modifications, based on NSAIDs scaffolds, have demonstrated stronger cytotoxicity and chemo-preventive than corresponding NSAID alone [7,[START_REF] Williams | Growth inhibition of human colon cancer cells by nitric oxide 2 (NO)-donating aspirin is associated with cyclooxygenase-2 induction and 3 beta-catenin/T-cell factor signaling, nuclear factor-kappaB, and NO synthase 2 4 inhibition: implications for chemoprevention[END_REF]. NSAIDs framework modification has become a structure-based medicinal chemistry strategy to design novel anticancer agents in the past decades [9][10][START_REF] Desai | Synthesis and evaluation of the anti-inflammatory properties of selenium-derivatives of celecoxib[END_REF][START_REF] Desai | Synthesis and antitumor properties of selenocoxib-1 against rat prostate adenocarcinoma cells[END_REF]. Selenium (Se) is an essential trace element that is of importance to human health and disease [START_REF] Alhasan | Selenium Donors at the Junction of Inflammatory Diseases[END_REF]. There are three main categories Se-containing compounds (inorganic, organic and selenoproteins) with potential pharmacological properties, the most developed and studied are the org-Se derivatives [START_REF] Reich | Why nature chose selenium[END_REF]. Different organic selenium compounds with diverse functional groups, including selenocyanates, selenoureas, heterocycles with endocyclic selenium, selenides and diselenides, have been reported to exhibit anticancer activity (Fig 1) [10,[START_REF] Pang | Design, synthesis, and biological evaluation of novel selenium-containing isocombretastatins and phen-statins as antitumor agents[END_REF][START_REF] Yang | Highly stable selenadiazole derivatives. induce bladder cancer cell apoptosis and inhibit cell migration and invasion through the activation of ROS-mediated signaling pathways[END_REF][START_REF] Kunwar | 3,3'-Diselenodipropionic acid, an efficient peroxyl radical scavenger and GPx mimic, protects erythrocytes (RBCs) from AAPH-induced hemolysis[END_REF][START_REF] Wang | Developing selective histone deacetylases (HDACs) inhibitors through ebselen and analogs[END_REF][START_REF] Cierpiał | Organofluorine isoselenocyanate analogues of sulforaphane: synthesis and anticancer activity[END_REF][START_REF] Lagunes | Selenocoumarins as new multitarget antiproliferative agents: Synthesis, biological evaluation and in silico calculations[END_REF][START_REF] He | Synthesis and biological evaluation of organoselenium (NSAIDs-SeCN and SeCF 3 ) derivatives as potential anticancer agents[END_REF]. Although the mechanisms that underlie the potential anticancer activity of seleno compounds are very diverse (including protein modification, cell growth arrest, anti-angiogenic effects, etc) [START_REF] Jariwalla | Differential sensitivity of various human tumour-derived cell types to apoptosis by organic derivatives of selenium[END_REF], the most frequent one is the reduction of oxidative stress through the elimination of free radicals [START_REF] Spallholz | Free radical generation by seleniumcompounds and their prooxidant toxicity[END_REF][START_REF] Storkey | Synthesis and antioxidant capacity of 5-selenopyanose derivatives[END_REF][START_REF] Sands | Key steps and intermediates in the catalytic mechanism for the reduction of peroxides by the antioxidant ebselen[END_REF].

In the previous study, the modification of NSAID framework with Se functionalities is the novel celecoxib-Se derivatives, which exhibited anti-inflammatory and anti-cancer activity [START_REF] Desai | Synthesis and evaluation of the anti-inflammatory properties of selenium-derivatives of celecoxib[END_REF][START_REF] Desai | Synthesis and antitumor properties of selenocoxib-1 against rat prostate adenocarcinoma cells[END_REF]. Very recently, we have reported the synthesis of a series of novel NSAIDs-Selenium derivatives and screened their anticancer activity by vitro study, the modification of NSAIDs scaffolds with Se functionalities (-SeCN, -Se-Se-, -SeCF 3 ) demonstrated potent inhibition of human tumor cell [START_REF] He | Synthesis and biological evaluation of organoselenium (NSAIDs-SeCN and SeCF 3 ) derivatives as potential anticancer agents[END_REF][START_REF] Liu | Synthesis of NSAIDs-Se derivatives as potent anticancer agents[END_REF][START_REF] Nie | Synthesis and potential anticancer activity of some novel selenocyanates and diselenides[END_REF]. Along with the reports that support the modification of NSAIDs scaffolds with Se functionalities and in continuation of our research program on design and synthesis of new NSAIDs-Se derivatives as potential anticancer agents [START_REF] Hodage | Synthesis, characterization and structures of 2-(3,5-dimethylpyrazol-1-yl) ethylseleno derivatives and their probable glutathione peroxidase (GPx) like activity[END_REF][START_REF] Nascimento | GPx-Like activity of selenides and selenoxides: experimental evidence for the involvement of hydroxy perhydroxy selenane as the active species[END_REF], twenty new NSAIDs-SeCN and NSAIDs-SeCF 3 derivatives were designed by the incorporation of an appropriate Se moiety into various NSAIDs with a general model consist of three essential fragments in their molecular: i) NSAIDs fragment; ii) electron donating group (X = NH, O, Se); iii)functional group bearing the Se atom (Fig 2). Their anticancer activities against the human cancer cell lines Caco-2, BGC-823, MCF-7 and PC-3 in vitro using the MTT assay. Three compounds 3a, 3g and 3i were selected to test the protein expression levels of Bcl-2, IL-8 and caspase-8 biomarkers in BGC-823 cells. Furthermore, the antioxidant potential of the compounds was investigated by employing DPPH, bleomycin-dependent DNA damage and GPx-like assays. Finally, TrxR1 (Thioredoxin Reductase) was selected as docking protein in order to predict the target and anticancer activity of the prepared NSAIDs-Se hybrid compounds.

Results and Discussion

Chemistry

The synthetic route for target compounds (2a-2j and (3a-3j) were prepared as outlined in Scheme 1 according to the procedure described in the literature with some modifications [START_REF] Ficht | Direct carbodiimide-mediated conjugation of carboxylates using pyridinium p-toluenesulfonate and tertiary amines as additives[END_REF]. Compound 1 was obtained by the nucleophilic substitution of -Br atom in 3-bromo-1-propanol by -SeCN, using KSeCN as nucleophilic donor, in acetonitrile as solvent and under a nitrogen atmosphere .

The selenocyanate derivatives 2a-2j were readily obtained by reacting 3-selenocyanatopropan-1-ol with commercially available NSAIDs in the present of DCC and DMAP as condensation agent. The trifluoromethyl selenide derivatives were obtained by conducting corresponding selenocyanate derivative with trimethyl(trifluoromethyl)silane (TMSCF 3 ) in the present of tetrabutylammonium fluoride (TBAF) as catalyst to afford 3a-3j in good yields (yield ≥ 80 %) (Scheme 1) [START_REF] Nikolaienko | Trifluoromethylselenolation of Aryldiazonium Salts: A Mild and Convenient Copper-Catalyzed Procedure for the Introduction of the SeCF 3 Group[END_REF].

The purity of all final compounds was 95% or higher and their chemical structures were characterized using 1 H NMR, 13 C NMR, 19 F NMR and HRMS (ESI).

F H N N H O R = R OH O R O O O R O O SeCF 3 F 3 C H N 2a-2j 3a-3j

Cell viability assay

The synthesized compounds (3a, 3g and 3i) and selected patent NSAIDs Overall, the IC 50 values obtained and summarized in Table 1 shows that all of the tested organoselenium compounds exhibit growth inhibition in all cancer cell lines, while the selected patent NSAIDs (Aspirin, Ibuprofen and Naproxen) are inactive against all cells even in the maximum dose of 50 μM. The IC 50 values obtained for the NSAIDs-Se derivatives 2j, 3b and 3f, showed that introduction of the -SeCN or -SeCF 3 moiety in corresponding parent NSAIDs scaffold result in the significant effect on cancer cell line.

An overview analysis of the IC 50 values obtained and summarized in Table 1 showed that most of the NSAIDs-SeCF 3 derivatives presented better effectiveness than NSAIDs-SeCN derivatives and previous reported NSAIDs-diselenides derivatives against all four cancer cell lines [START_REF] Nie | Synthesis and potential anticancer activity of some novel selenocyanates and diselenides[END_REF]. Furthermore, the most active compounds of these two series are NSAIDs-SeCF 3 derivatives 3a, 3g and 3i. These three compounds show IC 50 values below 10 μM in all of tested cancer cell lines.

Compound 3a emerges the most potent agent with IC 50 values below 5 μM in all cancer cell lines and with remarkable anticancer activity against BGC-823 (2.5 μM)

and MCF-7 (2.7 μM).

Interestingly, among the tested compounds, most of the NSAIDs-SeCF 3 derivatives except 3f and 3h displayed IC 50 values below 10 μM against MCF-7 cells.

The anticancer activity of NSAIDs with trifluoromethyl selenides moiety is better than corresponding NSAIDs with selenocyanates moiety, considering the lipophilicity and electron withdrawing effect. In order to further understand the possibly addressed signaling pathways and obtain hints on the mode(s) of action of the synthesized compounds, we selected the most promising derivatives 3a, 3g and 3i and investigated their ability to induce apoptosis in BGC-823 cells via modulation the expression of anti-apoptotic Bcl-2 protein, pro-inflammatory cytokines (IL-2) and proapoptotic caspase-8 protein. which proved to be effective against prostate and oral carcinoma cells [START_REF] Pinto | Differential effects of naturally occurring and synthetic organoselenium compounds on biomarkers in androgen responsive and androgen independent human prostate carcinoma cells[END_REF][START_REF] Ghose | Enhanced sensitivity of human oral carcinomas to induction of apoptosis by selenium compounds: involvement of mitogen-activated protein kinase and Fas pathways[END_REF].

As shown in

Antioxidant assay

Reactive oxygen species (ROS) is a broad term that encompasses both oxygen free radicals, which have unpaired electrons, such as superoxide, hydroxyl and peroxyl as well as oxidizing agents that are not free radicals such as hydrogen peroxide, hypochlorous acid and ozone [START_REF] Chan | The role of reactive oxygen species in the pathogenesis and treatment of retinal diseases[END_REF]. ROS play essential roles in altering protein structure, thereby changing its function and participate in many pathological processes [START_REF] Gordillo | Revisiting the essential role of oxygen in wound healing[END_REF][START_REF] Martin | Reactive oxygen species as double-edged swords in cellular processes: low-dose cell signaling versus high-dose toxicity[END_REF]. Various human diseases, including different types of cancer, are associated with a disturbed intracellular redox balance and oxidative stress (OS) [37, 38].

Owing to the fact that a number of synthetic organoselenium compounds have been synthesized for their use as redox-modulators in the last few years [START_REF] Rohn | Selenoneine ameliorates peroxide-induced oxidative stress in C. elegans[END_REF][START_REF] Melo | Diphenyl diselenide protects cultured MCF-7 cells against tamoxifen-induced oxidative DNA damage[END_REF][START_REF] Shaaban | Synthesis and biochemical studies of novel organic selenides with increased selectivity for hepatocellular carcinoma and breast adenocarcinoma[END_REF], the antioxidant activity of compounds (3a, 3g, 3i) are further estimated employing different biochemical assays such as DPPH, bleomycin-dependent DNA damage and Gpx-like assays [START_REF] Meriane | Rapid identification of antioxidant compounds of Genista saharae coss. & dur. By combination of DPPH scavenging assay and HPTLC-MS[END_REF][START_REF] Uddin | HPLC-analysis of polyphenolic compounds in gardenia jasminoides and determination of antioxidant activity by using free radical scavenging assays[END_REF].

Radical scavenging capacity (DPPH) assay.

There are various methods which have been developed to provide fast prediction of antioxidant of natural compounds [START_REF] Bunaciu | Recent applications for in vitro antioxidant activity assay[END_REF], however, the DPPH chemical assay is considered to be the rapid tools to evaluate the radical-scavenging activities of nutritional products and organic selenides [START_REF] Tian | Effects of molecular structure on kinetics and dynamics of the trolox equivalent antioxidant capacity assay with ABTS(t*)[END_REF]. The antioxidant activity of a compound is assessed by its ability to decolorize DPPH . radical (purple color in methanol) to DPPHH (colorless) and the corresponding radical-scavenging activity is estimated by the decrease in the absorbance at 517 nm [START_REF] Ibrahim | 1-(2-(2-(2-(1-Aminoethyl)phenyl)diselanyl)phenyl)ethanamine: an amino organoselenium compound with interesting antioxidant profile[END_REF]. Vitamin C was used as a positive control (Table 2). Antioxidant activity was calculated as follows:

% Antioxidant activity = [(control absorbance - sample absorbance) / control absorbance] × 100%
As depicted in Table 2, NSAIDs-SeCF 3 derivatives 3h and 3i were the most active compounds in this assay, demonstrating a good free-radical scavenging activity compared to Vitamin C. The family of NSAIDs-SeCF 3 derivatives is better than the corresponding NSAIDs-SeCN derivatives on this assay except for the compare of 2d and 3d.

Bleomycin DNA damage assay.

Bleomycin (BLM) is a complex of related glycopeptide from Streptomyces verticillus, it inhibits DNA metabolism and is used as an antineoplastic, especially for solid tumors [START_REF] Rose | Base excision repair proteins are required for integrin-mediated suppression of bleomycin-induced DNA breakage in murine lung endothelial cells[END_REF]. The bleomycin-iron DNA damage assay has been routinely used as a preliminary method to test potential of drugs and organic selenium compound [START_REF] Mira | Effect of thiol compounds on bleomycin-induced DNA and chromosome damage in human cells[END_REF][START_REF] Laffon | The Organic selenium compound selenomethionine modulates bleomycin-induced DNA damage and repair in human leukocytes[END_REF]. As shown in Table 2, compounds 3a, 3g and 3i induced DNA degradation significantly more than other tested compounds. Glutathione peroxidase (GPx) is a selenoenzyme that protects cells by catalyzing the reduction of peroxides with the stoichiometric reductant glutathione (GSH) [START_REF] Nascimento | GPx-like activity of selenides and selenoxides: experimental evidence for the involvement of hydroxy perhydroxy selenane as the active species[END_REF][START_REF] Nogueira | Toxicology and pharmacology of selenium: emphasis on synthetic organoselenium compounds[END_REF]. The potential antioxidant activity of all of the NSAIDs-Se derivatives were estimated using NADPH-reductase coupled assay [START_REF] Hodage | Synthesis, characterization and structures of 2-(3,5-dimethylpyrazol-1-yl)ethylseleno derivatives and their probable glutathione peroxidase (GPx) like activity[END_REF]. The GPx activity of the synthesized compounds was estimated by the decrease in absorbance (340 nm) due to the oxidation of NADPH to NADP + . Ebselen was used as the positive control.

As shown in Fig. 4, compounds 2h, 2i, 3b, 3e, 3h and 3i displayed a GPx-like activity better than other derivatives. Compound 3h was the most active derivatives in this assay, up to 3 fold to the GPx mimetic ebselen.

Docking Studies

The interaction mode between our organoselenium compounds and Mammalian

TrxR1 protein, which is closely related to the anticancer activity of compounds, need to be further explained by docking studies. TrxR1 consists of four monimers which have the FAD and NAD binding domains at the N-terminal and the dimerization interface domain at the flexible C-terminal side [START_REF] Gromer | Mutational studies confirm。 the catalytic triad in the human selenoenzyme thioredoxin reductase predicted by molecular modeling[END_REF][START_REF] Brandt | The functional role of selenocysteine (Sec) in the catalysis mechanism of large thioredoxin reductases: proposition of a swapping catalytic triad including a Sec-His-Glu state[END_REF][START_REF] Sandalova | Threedimensional structure of a mammalian thioredoxin reductase: implications for mechanism and evolution of a selenocysteine-dependent enzyme[END_REF]. In the insufficiency of human 3D structure complexes cocreytallized of human TrxR1 with inhibitors, flexible docking was considered to be a practical method according to the literature [START_REF] Shaaban | Combinatorial synthesis, in silico, molecular and biochemical studies of tetrazole-derived organic selenides with increased selectivity against hepatocellular carcinoma[END_REF]. With good antioxidant activity, compounds 3a, 3g and 3i were docked into the TrxR1 protein (PDB id: 1H6V) using Flexible Docking Protocol as reported in the literature [START_REF] Shaaban | Combinatorial synthesis, in silico, molecular and biochemical studies of tetrazole-derived organic selenides with increased selectivity against hepatocellular carcinoma[END_REF]. All three compounds showed acceptable docking results (Table 3-5 are reported in the supporting information). It is thought that the distance between the selenium atom and Cys497/Cys498 is closely related to the accessibility of cysteine thiol attacking the selenide. Therefore, for each structure, the selection of the best pose of the docking results is related to the value of binding energy, while the distance would also be focused. Among the three compounds, Pose 3 of 3a showed a better docking results with the relatively good value of -CDOCKER energy (30.184 kcal/mol).

Meanwhile, the distance between the selenium atom and Cys498 was only 4.388 Å (Table 3, Pose 3). This good result may be related to the key hydrogen bond interaction between the Fluorine on benzene group and His472 (2.11 Å). In addition, 3a also formed two hydrogen bonds, which are the hydrogen bond between -SeCF 3 group and His472 (2.97 Å) and the hydrogen bond between the oxygens of ester groups and Cys475 (2.62 Å) (Figure 5). For compound 3g, the interactions shown in pose 3 are not only the hydrogen bonds, but also a π-π stacking between the benzene ring and Phe406 (Figure 6). However, the distance between the selenium atom and Cys497/Cys498 is far than 3a (Table 4). For 3i, although there are multiple hydrogen bonds near the carbonyl group, the long distance between the selenium atom and Cys497/Cys498 may be related to the long linear structure of the whole compound (Figure 7, Table 5). This structure makes it difficult for molecule to penetrate into the pocket as a whole, thus affecting the interaction between molecule and protein.

Conclusions

In summary, twenty new organoselenium compounds were synthesized and

Experimental procedures

Procedure for the synthesis of compound 1

To a solution of 3-bromopropan-1-amine hydrobromide (3g, 13.7 mmol) in anhydrous acetonitrile (40 mL) was added KSeCN (1.97 g, 13.7mmol). The mixture was stirred at 80°C for 24 hours. Then the mixture was cooled to 25°C and filtered.

The filter cake was washed with acetonitrile (5mL×2) and dried under vacuum to obtain the brown solid 3.1g (Yield = 91%). The isolated solid was used without purification for further reactions.

General procedure for the synthesis of compounds (2a-2j)

To a solution of patent NSAIDs (1.0 eq) in DCM (5 mL) and DMF (5 mL) was added EDCI (1.2 eq.), HOBT (1.2 eq.) and TEA (3.0 eq.). The mixture was stirred at 25℃ for 30 minutes. Then 2-selenocyanatoethanamine hydrobromide (1.2 eq) or 2-selenocyanatopropanamine hydrobromide (1.2 eq.) was added into the mixture. The mixture was stirred at 25℃ for 16 hrs. TLC showed the reaction was complete. The mixture was diluted with H 2 O (20 mL), the aqueous layer was extracted with DCM (15 mL×2), the combined organic layer was washed with brine (20 mL×2), dried over Na 2 SO 4 , filtered and the filtrate was concentrated under reduced pressure. The residue was purified by column chromatography on silica gel, eluting with dichloromethane /methanol solution to obtain the desire compound.

4.2.2.1.3-selenocyanatopropyl 2-(2-fluoro-[1,1'-biphenyl]-4-yl)propanoate (2a).

Yield: 78 %. White solid. Mp: 103-105°C. 1

3-selenocyanatopropyl (Z)-2-(5-fluoro-2-methyl-1-(4-(methylsulfinyl)benzylidene)-1H-inden-3-yl)acetate

General procedure for the synthesis of compounds 3a-3j

To a solution of compound 2a-2j (300 mg, 1.0eq.) in THF (10ml) was added TBAF (1 eq.) and TMSCF 3 (10 eq.). The mixture was stirred at 25℃ for 2 hours. TLC showed the reaction was completed. The mixture was concentrated under reduced pressure. The desire compound was purified by column chromatography on silica gel.

4.2.3.1.3-((trifluoromethyl)selanyl)propyl 2-(2-fluoro-[1,1'-biphenyl]-4-yl)propanoate

(3a). Yield: 80 %. White solid. Mp: 113-115°C. 1 H NMR (400 MHz, CDCl 3 ): δ 1.54 (d, 3H, J = 8.00 Hz, -CH 3 ), 2.09-2.12 (m, 2H, -CH 2 ), 2.90-2.93 (m, 2H, -CH 2 ), 3.75 (q, 1H, J = 8.00 Hz, -CH), 4.20-4.22 (m, 2H, -CH 2 ), 7.13 (t, 2H, J = 8.00 Hz, ArH), 7.37-7.39 (m, 2H, ArH), 7.44 (t, 2H, J = 8.00 Hz, ArH), 7.53(d, 2H, J = 8.00 Hz, ArH). 13

3-((trifluoromethyl)selanyl)propyl 2-(4-isobutylphenyl)propanoate (3b).

Yield: 82%. Yellow solid. Mp: 102-104°C. 1 H NMR (400 MHz, CDCl 3 ): δ 0.89 (d, 6H, J = 8.00 Hz, 2-CH 3 ), 1.49 (d, 3H, J = 8.00Hz, -CH 3 ), 1.84 (q, 1H, J = 8.00Hz, -CH), 2.04-2.07 (m, 2H, -CH 2 ), 2.44 (d, 2H, J = 8.00Hz, -CH 2 ), 2.78-2.83 (m, 2H, -CH 2 ), 3.68 (q, 1H, J = 8.00Hz, -CH), 4.12-4.21 (m, 2H, -CH 2 ), 7.09 (d, 2H, ArH), 7.18 (d, 2H, ArH). 13 units/mL of penicillin and streptomycin (Thermo Fisher Scientific, shanghai, China) at 37 °C and 5% CO 2 in a humidified atmosphere. Cells were passaged at preconfluent densities, using a solution containing 0.05% trypsin and 0.5 mM EDTA.

Human cancer cell lines Caco-2, BGC-823, MCF-7 and PC-3 used in this work were obtained from the American Type Culture Collection (ATCC, Manassas, VA).

All the tested NSAIDs-Se derivatives were evaluated in vitro for their antitumor activity against four cancer cell lines by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay according to the method as described before [START_REF] Arafa | OSU-CG5, a novel energy restriction mimetic agent, targets human colorectal cancer cells in vitro[END_REF][START_REF] Omar | Sensitization of hepatocellular carcinoma cells to Apo2l/TRAIL by a novel Akt/NF-B signalling Inhibitor[END_REF]. Exponentially growing cells were harvested and plated in 96-well plates at a concentration of 1×104 cells / well.

After 24 h incubation at 37 °C under a humidified 5% CO 2 to allow cell attachment, the cells in the wells were respectively treated with target compounds at various concentrations for 24 h, 48 h and 72 h. The concentration of DMSO was always kept below 1.25%, which was found to be non-toxic to the cells. Three hours prior to experiment termination, MTT solution (20 μL of 5.0 mg/mL solution) was added to each well and incubated at 37°C. At the termination time point, the medium/MTT mixtures were removed, and the formazan crystals formed by the mitochondrial dehydrogenase activity of vital cells were dissolved in 100 μL of DMSO per well.

The optical densities were measured at 570 nm using a 96-well multiscanner (Dynex Technologies, MRX Revelation; Chantilly, VA, USA).

Detection of Bcl-2, IL-2 and caspase-8 protein expression levels

Bcl-2, IL-2 and capase-8 levels were evaluated in BGC-823 cells treated with the corresponding IC 50 s of each compound and incubated for 48 h and compared with their levels in control untreated BGC-823 cell line. The cells were harvested by applying trypsin and lysed by freezing with liquid nitrogen and then thawing with gentle mixing and the total proteins were isolated. Protein levels of the anti-apoptotic marker Bcl-2 were then measured using enzymelinked immunosorbent assay (ELISA) according to the manufacturers' instructions (Merck, USA). Enzyme-linked immunosorbent assay was used for quantitative detection of IL-2 and caspase-8

(Platinum ELISA). The reaction product was detected at 450 nm using enzyme-linked immunosorbent assay (Platinum ELISA; Merck) according to the instructions of the manufacturer.

DPPH free radical scavenging activity

DPPH free radical scavenging activity of corresponding compounds was measured according to the method as previous reported with little optimization [59].

Briefly, 20 mL of test samples at different concentrations was mixed with 180 mL of or DPPH solution for 30 min in the dark. Then, the change in absorbance at 517 nm for DPPH was measured on a microplate reader. Ascorbic acid (vitamin C) and ebselen were used as a positive control, DMSO was used as a negative control.

Bleomycin-dependent DNA damage

The reaction mixture contained DNA (0.5 mg/mL), bleomycin sulfate (0.05 mg/mL), MgCl 2 (5 mM), FeCl 3 (50 mM), and tested compound in a conc. of 0.1 mg/mL. L-ascorbic acid was used as positive control. The mixture was incubated at 37°C for 1h. The reaction was terminated by addition of 0.05 mL EDTA (0.1 M).

The color was developed by adding 0.5 mL TBA (1% w/v) and 0.5 mL HCl (25% v/v), followed by heating at 80°C for 30 minutes. After cooling in ice water, the extent of DNA damage was measured by increase in absorbance at 532 nm [START_REF] El-Gazzar | Design and synthesis of azolopyrimidoquinolines, pyrimidoquinazolines as anti-oxidant, anti-inflammatory and analgesic activities[END_REF]. Under the conformation method FAST, every ligand were generated 25 conformations with the value of 20 kcal in the energy threshold. With all other parameters as default, three ligands were docked into protein structure in the Flexible Docking Protocol. For each poses, the distance between the compound's selenium atom and the sulfur atom of either Cys497 or Cys498 was calculated by the distance monitor in the Discovery Studio. For each ligand, average -CDocker energy and average selenium-sulfur distance were calculated. The hydrogen bond interaction and π-π stacking between the compounds and protein were analyzed.

Statistical analysis

Data were given as mean ± SD of three independent experiments, graphs and curve fitting were using origin Version 8.0 (OriginLab Corporation, Northampton, USA). P value less than 0.05 was considered statistically significant.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Organic selenium compounds previously reported with anticancer activity

(

  Aspirin, Ibuprofen and Naproxen) were evaluated for their anticancer activity towards human tumor cell lines: Caco-2 (human epithelial colorectal adenocarcinoma cell line), BGC-823 (human gastric cancer cell line), MCF-7 (human breast adenocarcinoma cell line) and PC-3 (human prostate cancer cell line). In vitro evaluation of anticancer activity was determined by the MTT assay. 5-Fluorouracil was used as positive control because it is commonly used in adjuvant and palliative cancer chemotherapy.

Fig 3 ,

 3 all the three compounds were able to downregulate the expression of Bcl-2 and upregulate the expression of IL-2 and Caspase-3 in BGC-823 cells compared with untreated cells. Interestingly, compound 1g downregulate over 50% the expression levels of Bcl-2 compared to untreated cells. Further more, compounds 1g and 1h modulate the Caspase-8 level at most 1.5 fold increase in expression when compared to the untreated control cells. From these results, it's likely that organic selenocyanates may induced apoptosis to inhibit tumor cells growth, and in line with the first selenocyanate (1,4-phenylenebis(methylene)selenocyanate)

Fig. 3 .

 3 Fig. 3. Protein expression levels of Bcl-2, IL-2 and caspase-8 in BGC-823 cells after 48 h incubation with compounds 3a, 3g and 3i at their respective IC 50 s compared to untreated cells.

Fig. 4 .

 4 Fig. 4. GPx-like activity assay of NSAID-Se hybrid compounds in μM. Min -1 .

Fig. 5 .

 5 Fig. 5. The pose 3 of 3a. Three interactions are shown: hydrogen bonding between the Fluorine on benzene group and His472 (2.11 Å); hydrogen bonding between the Fluorine of -SeCF3 group and His472 (2.97 Å) and hydrogen bonding between the oxygens of ester groups and Cys475 (2.62 Å).

Fig. 6 .

 6 Fig. 6. The pose 3 of 3g. Three interactions are shown: hydrogen bonding between the Fluorine of -CF3 group and LEU409 (2.57 Å); hydrogen bonding between the oxygens of ester groups and Trp407 (2.78 Å) and π-π stacking between the benzene ring and Phe406.

Fig. 7 .

 7 Fig. 7. The pose 4 of 3i. Three interactions are shown: hydrogen bonding between the Fluorine of -SeCF3 group and Ser404 (2.80 Å); hydrogen bonding between the two hydrogens on carbonyl group α postion and Ile492 (2.54 Å, 2.64 Å) or Gln494 (2.90 Å); hydrogen bonding between the oxygens of ester groups and Phe406 (2.46 Å).

  abbreviations were used to explain the multiplicities: s = singlet, d = doublet, t = triplet, q = quartet, and m = multiplet. Chemical shifts (δ) are reported in parts per million (ppm) downfield from TMS and the coupling constants (J) are expressed in Hertz(Hz). High-resolution MS were performed on a SCIEX,TripleTOF 5600+, operating in ionization mode.

(

  2h). Yield: 82%. White solid. Mp: 91-93°C.1 H NMR (400 MHz, CDCl 3 ): δ 2.21-2.26 (m, 2H, -CH 2 ), 2.22 (s, 3H, -CH 3 ), 2.82 (s, 3H, -CH 3 ), 3.00 (t, 2H, J = 8.00 Hz, -CH 2 ), 4.26 (t, 2H, J = 8.00 Hz, -CH 2 ), 6.56-6.61(m, 1H, ArH), 6.87 (d, 1H, J = 8.00 Hz, ArH), 7.15-7.18 (m, 2H, ArH), 7.67(d, 2H, J = 8.00 Hz, ArH), 7.72 (d, 2H, J = 8.00 Hz, ArH). 13 C NMR (100 MHz, CDCl 3 ): δ 10.7, 25.7, 29.7, 31.8, 43.9, 63.2, 101.2, 105.9 (d, J = 24 Hz), 110.9 (d, J = 23 Hz), 123.8, 123.9, 128.6 (d, J = 2.0 Hz), 129.5 (d, J = 3.0 Hz), 130.3, 131.4 (d, J = 3.0 Hz), 138.4, 139.5, 141.5, 145.5, 146.5 (d, J = 9.0 Hz), 163.3 (d, J = 245.0 Hz), 170.

  C NMR (100 MHz, CDCl 3 ): δ 18.2, 21.8, 29.4, 45.0, 63.6, 115.2 (d, J = 24.0 Hz), 122.5 (q, J = 329.0 Hz, -SeCF 3 ), 123.4 (d, J = 3.0 Hz), 127.7, 128.0 (d, J = 13.0 Hz), 128.5, 128.9 (d, J = 5.0 Hz), 130.9 (d, J = 4.0 Hz), 135.4, 141.5 (d, J = 7.0Hz), 159.7 (d, J = 247.0 Hz), 173.8. 19 F NMR (CDCl 3 , 376 MHz): δ -34.3 (s, -SeCF 3 ), -117.5 (s, F). HRMS calcd. For C 19 H 18 F 4 O 2 Se [M+H] + : 435.0486, found 435.0462 [M+H] + .

4. 7 .

 7 Glutathione peroxidase-like activityGPx kit (Biodiagnostic, Egypt) was used for the determination of GPx according to Paglia et al[START_REF] Giles | Targeting oxidative stress-related diseases: organochalcogen catalysts as redox sensitizers[END_REF]. The reaction mixture contained 1ml assay buffer (50mM phosphate buffer containing 0.1% Triton X-100) and 0.1ml NADPH reagent (24 mmol Glutathione, 12 unit Glutathione reductase and 4.8 mmol NADPH) and 0.01ml (41 mM) tested compounds and the reaction was started by the addition of H 2 O 2 (0.8 mM). The contents were mixed well and the absorbances were recorded at 340 nm over a period of 3 min against deionized water. The change of absorbance per minute (A340 nm/min) was estimated using ebselen (41mM) as positive control. The values represented in Fig 3 are expressed after background correction for the reaction with H 2 O 2 and GSH. In case of colored compounds, their activities were estimated after subtracting their own absorbances at the used wave length. 4.8. Molecular Modeling 4.8.1 Protein and Ligand Preparation Prepared by Protein Preparation Wizard in Maestro 11.5 (Schrödinger, LLC, New York, NY, 2019.), the Mammalian TrxR1 protein (PDB ID: 1H6V) was obtainedfrom Protein Data Bank. The other subunits were deleted and only one monomer F was retained. Next, subunits F was assigned in sequence, hydrogen was added, ionization and tautomerism were adjusted, hydrogen bond distribution was optimized, water was removed, and structure was minimized. The LigPrep utility inMaestro 11.5 was used to perform ligand preparation applying OPLS2005 force field. Generation of tautomers and possible ionization states was mediated by Epik utility, followed by minimization of the resulting 3D comformations.4.8.2 Ligand DockingThe docking task was completed on Discovery Studio Client 3.1. and the binding site of TrxR1 was defined as a docking sphere with dimensions X: 27.757, Y: 6.510, Z: 33.698 and a radius of 15 Å. Before using Flexible Docking Protocol, TrxR1 protein was typed in CHARMm field force. 10 protein conformations were generated with a maximum alteration of 8 residues.

Table 1

 1 Cytotoxic activity expressed by IC 50 of NSAIDs-Se hybrid compounds (2a-2j and 3a-3j) on different cancer cell lines.

	Compound			IC 50 (μM) a
		Caco2	BGC-823	MCF-7	PC-3
	Aspirin b	>50	>50	>50	>50
	Ibuprofen b	>50	>50	>50	>50
	Naproxen b	>50	>50	>50	>50
	2a	27.5±3.1	29.4±3.3	22.4±2.1	19.7±1.8
	2b	14.5±1.3	24.5±2.3	19.5±1.7	22.5±3.4
	2c	32.4±3.5	35.5±3.4	29.3±1.9	21.8±1.6
	2d	17.2±1.4	22.1±1.9	17.4±2.1	33.2±3.3
	2e	11.5±1.1	21.4±2.3	14.4±1.3	31.4±3.0
	2f	21.5±2.4	17.3±2.3	32.8±3.1	22±1.7
	2g	8.4±0.8	13.7±1.2	14.2±1.1	7.5±1.3
	2h	28.6±2.5	17.5±1.8	31.3±3.2	22.3±2.1
	2i	19.7±2.0	12.6±1.4	8.3±0.7	12.6±1.5
	2j	14.5±1.8	17.3±2.3	8.9±0.8	11.2±2.3
	3a	4.5±0.6	2.5±0.4	2.7±0.2	3.3±0.3
	3b	9.5±0.6	14.3±1.5	9.9±0.7	10.4±2.0
	3c	10.5±1.1	7.3±0.5	9.3±0.7	7.8±0.7
	3d	13.3±1.6	19.6±2.1	8.5±1.3	24.5±2.3
	3e	10.4±1.3	18.5±1.7	8.7±0.7	19.7±1.9
	3f	16.3±1.4	10.8±0.8	12.4±0.4	18.4±1.7
	3g	3.5±1.8	2.7±1.8	4.2±1.8	5.8±1.8
	3h	16.4±2.2	14.4±1.6	19.6±2.4	11.6±0.7
	3i	9.5±1.1	4.8±0.3	6.5±1.8	8.8±1.3
	3j	11.3±1.5	8.2±0.7	7.7±0.6	10.4±0.9
	5-Fu c	7.8±3.1	15.4±1.8	12.3±2.2	9.5±1.1
	a IC 50 values (±SD) of % cell viability determined by the MTT assay of three
	repititions
	b Patent NSAIDs
	c Standard benchmark compound.
	2.3. Detection of Bcl-2, IL-2 and caspase-8 protein expression levels in BGC-823
	cells.

Table 2

 2 Redox modulation activity of NSAID-Se hybrid compounds.

	Compd.	DPPH		Bleomycin-dependent DNA damage
	No.	assay		assay
		Inhibition	Fold	Absorbance
		%
	Vitamin C 96.4±1.3	1	297±2.83
	2a	17.2±1.4	0.2	86.5±0.54
	2b	31.2±2.8	0.3	60.3±0.43
	2c	44.3±36	0.4	72.4±0.33
	2d	29.6±2.7	0.3	95.6±1.82
	2e	30.4±1.4	0.3	69.4±0.42
	2f	24.6±1.3	0.2	81.6±0.48
	2g	51.5±1.2	0.5	76.1±0.39
	2h	45.7±4.3	0.5	91.3±1.63
	2i	57.1±4.3	0.6	67.6±1.83
	2j	27.3±3.1	0.3	78.3±1.17
	3a	73.5±4.1	0.8	119.4±1.78
	3b	48.5±2.8	0.5	95.7±2.27
	3c	36.6±2.2	0.4	62.6±1.18
	3d	23.3±1.2	0.3	77.6±1.40
	3e	41.4±2.2	0.4	86.4±1.21
	3f	37.0±1.0	0.4	91.4±1.13
	3g	68.6±2.6	0.7	114.8±2.32
	3h	44.9±2.3	0.5	73.7±1.12

  CDCl 3 ): δ 18.2, 25.7, 29.7, 45.2, 55.4, 62.7, 101.3, 105.6, 119.3, 126.0, 126.1, 127.3, 128.9, 129.2, 133.7, 135.4, 157.8, 174.5. HRMS calcd. For C 18 H 19 NO 3 Se[M+H] + : 378.0608, found 378.0596 [M+H] + .

	H NMR (400 MHz, CDCl 3 ): δ 1.54 (d, 3H, J = 8.00 Hz, -CH 3 ), 2.20-2.23 (m, 2H, -CH 2 ), 2.94-2.99 (m, 2H, -CH 2 ), 3.76 (q, 1H, J = 8.00 Hz, -CH), 4.23-4.24 (m, 2H, -CH 2 ), 7.09-7.15 (m, 2H, ArH), 7.37-7.43 (m, 4H, ArH), 7.46-7.54 (m, 2H, ArH). White solid. Mp: 97-99°C. 1 H NMR (400 MHz, CDCl 3 ): δ 0.89 (d, 6H, J = 8.00Hz, 2-CH 3 ), 1.49 (d, 3H, J = 8.00Hz, -CH 3 ), 1.84 (q, 1H, J = 8.00Hz, -CH), 2.14-2.17 (m, 2H, -CH 2 ), 2.45 (d, 2H, J = 8.00Hz, -CH 2 ), 2.78-2.88 (m, 2H, -CH 2 ), 3.69 (q, 1H, J = 8.00 Hz, -CH), 4.12-4.27 (m, 2H, -CH 2 ), 7.10 (d, 2H, ArH), 7.18 (d, 2H, ArH). 13 C NMR (100 MHz, CDCl 3 ): δ 18.1, 22.4, 25.7, 29.7, 30.2, 45.0, 45.1, 62.5, 127.1, 129.5, 137.6, 140.9, 174.6. HRMS calcd. For C 17 H 23 NO 2 Se [M+Na] + : 376.0792, found 376.0770 [M+Na] + . 4.2.2.3.3-selenocyanatopropyl2-(1-(4-chlorobenzoyl)-5-methoxy-2-methyl-1H-indol-3 -yl)acetate (2c). Yield: 78 %. White solid. Mp: 110-112°C. 1 H NMR (400 MHz, (2g). Yield: 77%. White solid. Mp: 121-123°C. 1 H NMR (400 MHz, CDCl 3 ): δ 2.38-2.45 (m, 2H, -CH 2 ), 3.19-3.23 (m, 2H, -CH 2 ), 4.46-4.49 (m, 2H, -CH 2 ), 6.82 (t, 1H, J = 8.00Hz, ArH), 7.28-7.49 (m, 6H, ArH), 7.96 (d, 1H, J = 1.0Hz, ArH), 9.54 (s, 1H, -NH). 13 C NMR (100 MHz, CDCl 3 ): δ 26.0, 30.1, 62.9, 101.1, 112.3, 114.3, 118.2 (q, J = 4.0 Hz), 118.3, 119.8 (q, J = 4.0 Hz), 123.9 (q, J = 271.0 Hz), 124.9, 130.0, 131.6, 131.9 (q, J = 32.0 Hz), 134.7, 141.4, 147.1, 168.1. HRMS calcd. For C 18 H 15 F 3 N 2 O 2 Se[M+H] + : 429.0329, found 429.0318 [M+H] + . CDCl 4.2.2.7. 3-selenocyanatopropyl 2-((3-(trifluoromethyl)phenyl)amino)benzoate 4.2.2.8.

13

C NMR (100 MHz, CDCl 3 ): δ 18.2, 25.7, 29.8, 45.0, 63.0, 101.1, 115.2 (d, J = 23.0 Hz), 123.5 (d, J = 3.0 Hz), 127.8, 127.9 (d, J= 14.0 Hz), 129.0, 128.9 (d, J = 2.0 Hz), 130.9 (d, J = 4.0 Hz), 135.3 (d, J = 2.0 Hz), 141.4 (d, J = 7.0Hz), 159.5 (d, J = 247.0 Hz), 173.8. HRMS calcd. For C 19 H 18 FNO 2 Se [M+Na] + : 414.0385, found 414.0365 [M+Na] + .

4.2.2.2. 3-selenocyanatopropyl 2-(4-isobutylphenyl)propanoate (2b

). Yield: 82 %. 3 ): δ 2.19-2.25 (m, 2H, -CH 2 ), 2.40 (s, 3H, -CH 3 ), 2.92-3.00 (m, 2H, -CH 2 ), 3.69 (s, 2H, -CH 2 ), 3.84 (s, 3H, -CH 3 ), 4.24-4.26 (m, 2H, -CH 2 ), 6.66 (d, 1H, J = 4.00 Hz, ArH), 6.86(d, 1H, J = 8.00 Hz, ArH), 6.93(s, 1H, ArH), 7.48 (d, 2H, J = 8.00 Hz, 13 C NMR (100 MHz,

  1. HRMS calcd. For C 24 H 22 FNO 3 SSe[M+H] + : 504.0548, found 504.0528 [M+H] + . J = 8.00Hz, -CH), 4.21-4.25 (m, 2H, -CH 2 ), 7.43-7.54 (m, 4H, ArH), 7.61(t, 1H, J = 8.00 Hz, ArH), 7.67 (d, 1H, J = 8.00 Hz, ArH), 7.76(s, 1H, ArH), 7.80 (d, 2H, J = 8.00 Hz, ArH). 13 C NMR (100 MHz, CDCl 3 ): δ 18.3, 25.7, 29.7, 45.4, 63.0, 101.2, 128.4, 128.6, 129.0, 129.2, 130.1, 131.4, 132.7, 137.4, 138.1, 140.7, 173.8, 196.4. HRMS calcd. For C 20 H 19 NO 3 Se[M+H] + : 402.0608, found 402.0588 [M+H] + . ArH). 13 C NMR (100 MHz, CDCl 3 ): δ 21.1, 25.8, 30.0, 63.1, 101.3, 122.8, 123.9, 126.1, 131.5, 134.3, 150.7, 164.3, 169.8. HRMS calcd. For C 24 H 23 FN 2 O 2 SSe[M+Na] + : 349.9908, found 349.9896 [M+Na] + .

	4.2.2.9. 3-selenocyanatopropyl 2-(3-benzoylphenyl)propanoate (2i). Yield: 85%. White solid. Mp: 96-98°C. 1 H NMR (400 MHz, CDCl 3 ): δ 1.55 (d, 3H, J = 8.00Hz, -CH 3 ), 2.18-2.21 (m, 2H, -CH 2 ), 2.94-2.98 (m, 2H, -CH 2 ), 3.82 (q, 117-118°C. 1 H NMR (400 MHz, CDCl 3 ): δ 2.38 (s, 3H, -CH 3 ), 2.33-2.40 (m, 2H, -CH 2 ), 3.11-3.17 (m, 2H, -CH 2 ), 4.44-4.46 (m, 2H, -CH 2 ), 7.12 (d, 1H, J = 8.00Hz, ArH), 7.33 (t, 1H, J = 8.00Hz, ArH), 7.58 (t, 1H, J = 8.00Hz, ArH), 7.99 (d, 1H, J = 1H, 4.2.2.10. 3-selenocyanatopropyl 2-acetoxybenzoate (2j). Yield: 90%. White solid. Mp: 8.00Hz,

  C NMR (100 MHz, CDCl 3 ): δ 18.2, 21.8, 22.4, 29.4, 30.2, 45.0, 45.1, 63.1, 122.5 (q, J = 328.0 Hz, -SeCF 3 ), 127.1, 129.4, 137.6, 140.7, 174.6. 19 F NMR (CDCl 3 , 376 MHz): δ -34.3 (s, -SeCF 3 ). HRMS calcd. For C 17 H 23 F 3 O 2 Se [M+H] + : 397.0893, found 397.0883 [M+H] + .

	4.2.3.3.	3-((trifluoromethyl)selanyl)propyl
	2-(1-(4-chlorobenzoyl)-5-methoxy-2-methyl-1H-indol-3-yl)acetate (3c). Yield: 80%.

White solid. Mp: 131-133°C.

1

H NMR (400 MHz, CDCl 3 ): δ 2.09-2.15 (m, 2H, -CH

  J = 328.0 Hz, -SeCF 3 ), 124.0 (q, J = 270 Hz, -CF 3 ), 124.7, 129.9, 131.6, 131.8 (q, J = 32.0 Hz), 134.5, 141.5, 147.0, 168.2.19 F NMR (CDCl 3 , 376 MHz): δ -34.2 (s, -SeCF 3 ), -62.8(s, -CF 3 ). HRMS calcd. For C 18 H 15 F 6 NO 2 Se [M+H] + :

	2.35 (s, 3H, -CH 3 ), 3.07-3.10 (m, 2H, -CH 2 ), 4.38-4.41 (m, 2H, -CH 2 ), 7.11 (d, 1H, J = 8.00 Hz, ArH), 7.30-7.57 (m, 1H, ArH), 7.58-7.60 (m, 1H, ArH), 7.99 (d, 1H, J = 8.00 Hz, ArH). 13 C NMR (100 MHz, CDCl 3 ): δ 21.0, 22.0, 29.6, 63.7, 122.6 (q, J = 122.6 (q, 472.0250, found 472.0233 [M+H] + . 328 Hz, -SeCF 3 ), 123.0, 123.9, 131.6, 134.1, 150.8, 164.3, 169.7. 19 F NMR (CDCl 3 ,
	376 MHz): δ -34.2 (s, -SeCF 3 ). HRMS calcd. For C 13 H 13 F 3 O 4 Se [M+H] + : 392.9829,
	4.2.3.8. found 392.9827 [M+H] + .	3-((trifluoromethyl)selanyl)propyl
	(Z)-2-(5-fluoro-2-methyl-1-(4-(methylsulfinyl)benzylidene)-1H-inden-3-yl)acetate
	(3h). Yield: 80%. White solid. Mp: 116-118°C. 1 H NMR (400 MHz, CDCl 3 ): δ 4.3. Cell lines and culture conditions
	2.10-2.13 (m, 2H, -CH 2 ), 2.21 (s, 3H, -CH 3 ), 2.82 (s, 3H, -CH 3 ), 2.94 (t, 2H, J = 8.00 Four human cancer cell lines Caco-2, BGC-823, MCF-7 and PC-3 cells were
	Hz, -CH 2 ), 3.58 (s, 2H, -CH 2 ), 4.22 (t, 2H, J = 8.00 Hz, -CH 2 ), 6.55-6.60(m, 1H, ArH), maintained in RPMI 1640 medium with 10% fetal bovine serum (FBS) and 100
	6.87 (d, 1H, J = 8.00 Hz, ArH), 7.14-7.18 (m, 2H, ArH), 7.67 (d, 2H, J = 8.00 Hz,
	ArH), 7.72 (d, 2H, J = 8.00 Hz, ArH). 13 C NMR (100 MHz, CDCl 3 ): δ 10.5, 21.8,
	29.3, 31.8, 43.9, 63.7, 105.9 (d, J = 23 Hz), 110.4 (d, J = 23 Hz), 122.5 (q, J = 328 Hz,
	-SeCF 3 ), 123.7 (d, J = 9.0 Hz), 123.8, 128.4 (d, J = 2.0 Hz), 129.5 (d, J = 3.0Hz),
	130.3, 131.5 (d, J = 2.0Hz), 138.3, 139.6, 141.6, 145.5, 146.5 (d, J = 9.0 Hz), 163.3 (d,
	ArH), 6.94 (s, 1H, ArH), 7.48 (d, 2H, J = 8.00 Hz, ArH), 7.66 (d, 2H, J = 8.00Hz, J = 245 Hz), 170.0. 19 F NMR (CDCl 3 , 376 MHz): δ -34.3 (s, -SeCF 3 ), -112.8(s, -F).
	ArH). 13 C NMR (100 MHz, CDCl 3 ): δ 13.3, 21.9, 29.4, 30.3, 55.7, 63.7, 101.3, 111.6, HRMS calcd. For C 24 H 22 F 4 O 3 SSe [M+Na] + : 569.0289, found 569.0263 [M+Na] + .
	112.3, 115.0, 122.5 (q, J = 329 Hz, -SeCF 3 ), 129.2, 130.5, 130.8, 131.2, 133.9, 136.0,
	139.3, 156.1, 168.3, 170.7. 19 F NMR (CDCl 3 , 376 MHz): δ -34.3 (s, -SeCF 3 ). HRMS 4.2.3.9. 3-((trifluoromethyl)selanyl)propyl 2-(3-benzoylphenyl)propanoate (3i). Yield:
	calcd. For C 23 H 21 ClF 3 NO 4 Se [M+H] + : 548.0354, found 508.0305 [M+H] + . 85%. White solid. Mp: 87-89°C. 1 H NMR (400 MHz, CDCl 3 ): δ 1.55 (d, 3H, J =
	8.00Hz, -CH 3 ), 2.05-2.12(m, 2H, -CH 2 ), 2.87-2.89 (m, 2H, -CH 2 ), 3.82 (q, 1H, J =
	4.2.3.4. 3-((trifluoromethyl)selanyl)propyl 2-((2,3-dimethylphenyl)amino)benzoate 8.00Hz, -CH), 4.19-4.21 (m, 2H, -CH 2 ), 7.43-7.59 (m, 4H, ArH), 7.60 (t, 1H, J = 8.00
	(3d). Yield: 82%. White solid. Mp: 116-118°C. 1 H NMR (400 MHz, CDCl 3 ): δ 2.18 Hz, ArH), 7.67 (d, 1H, J = 8.00 Hz, ArH), 7.76 (s, 1H, ArH), 7.79 (d, 2H, J = 8.00 Hz,
	(s, 3H, -CH 3 ), 2.28-2.31 (m, 2H, -CH 2 ), 2.33 (s, 3H, -CH 3 ), 3.13-3.17 (m, 2H, -CH 2 ), ArH). 13 C NMR (100 MHz, CDCl 3 ): δ 18.3, 21.8, 29.4, 45.4, 63.5, 122.5 (q, J = 329.0
	4.41-4.44 (m, 2H, -CH 2 ), 6.67 (t, 1H, J = 8.00 Hz, ArH), 6.75 (d, 1H, J = 8.00 Hz, Hz, -SeCF 3 ), 128.4, 128.6, 129.1, 129.2, 130.1, 131.4, 132.6, 137.4, 138.0, 140.7,
	ArH), 7.02 (d, 1H, J = 8.00 Hz, ArH), 7.10-7.15 (m, 2H, ArH), 7.23-7.27 (m, 1H, 174.0, 195.5. 19 F NMR (CDCl 3 , 376 MHz): δ -34.2 (s, -SeCF 3 ). HRMS calcd. For
	ArH), 7.93 (d, 1H, J = 8.00 Hz, ArH), 9.23 (s, 1H, -NH). 13 C NMR (100 MHz, C 20 H 19 F 3 O 3 Se [M+H] + : 445.0530, found 445.0491 [M+H] + .
	CDCl 3 ): δ 14.0, 20.7, 22.2, 29.7, 63.1, 110.4, 113.8, 116.1, 122.6 (q, J = 328.0 Hz,
	-SeCF 3 ), 123.2, 126.0, 126.9, 131.3, 132.5, 134.4, 138.3, 138.6, 149.7, 168.5. 19 F 4.2.3.10. 3-((trifluoromethyl)selanyl)propyl 2-acetoxybenzoate (3j). Yield: 80%.
	White solid. Mp: 104-106°C. 1 H NMR (400 MHz, CDCl 3 ): δ 2.23-2.28 (m, 2H, -CH 2) ,

, 2H, -CH 2 ), 6.67 (d, 1H, J = 4.00 Hz, ArH), 6.85 (d, 1H, J = 8.00Hz,

Acknowledgments

This investigation was made possible through the financial support of National Natural Science Foundation of China (Grant No: 21302065) and Shenzhen Fushan Biological Technology Co., Ltd. China.