Abhijit Bhattacharyya
email: abhijit9800@gmail.com

Radu Ranta
email: radu.ranta@univ-lorraine.fr

Steven Le Cam

Valérie Louis-Dorr

Louise Tyvaert

Sophie Colnat-Coulbois

Louis Maillard

Ram Bilas Pachori

A multi-channel

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

results indicate the effectiveness of the proposed approach. Conclusion: The proposed method suppresses CS artifacts without affecting the background SEEG signal. Significance: The proposed method can be applied for suppressing both low and high frequency CS artifacts and outperforms current methods from the literature. Index Terms cortical stimulation, SEEG, tunable-Q wavelet transform, subspace correlation approach, blind source separation.

I. INTRODUCTION

The accurate localization of epileptiform discharges is considered to be very crucial in the diagnosis of patients undergoing resective epileptic surgery [START_REF] Kovac | Seizures induced by direct electrical cortical stimulation-Mechanisms and clinical considerations[END_REF]. In this purpose, Stereo-electroencephalography (SEEG), performed using electrode shafts implanted within the brain volume, is considered as the gold standard [START_REF] Gavaret | Source localization of scalp-EEG interictal spikes in posterior cortex epilepsies investigated by HR-EEG and SEEG[END_REF], [START_REF] Koessler | Source localization of ictal epileptic activity investigated by high resolution EEG and validated by SEEG[END_REF]. Apart from this purely clinical purpose, the SEEG signals can be also used to explore the complex brain responses in the presence of provoked electrical stimulations. In particular, the direct cortical stimulation (CS) is a decisive diagnostic measure for localizing epileptogenic zone and eloquent cortex in the pharmacoresistant epilepsy patients who undergo presurgical invasive investigations with depth electrodes [START_REF] Bancaud | Generalized" epileptic seizures elicited by electrical stimulation of the frontal lobe in man[END_REF]. Different stimulation techniques have been discussed, but in principle all of them are based on current injection between two neighboring contacts of an SEEG shaft. The high frequency CS (HFCS) (in the range of 50-55 Hz) has been used in both extraoperative and intraoperative CS [START_REF] Kovac | Seizures induced by direct electrical cortical stimulation-Mechanisms and clinical considerations[END_REF]. On the other hand, low frequency CS (LFCS) (generally at 1 Hz) is recommended for functional mapping of cortical areas. Both LFCS and HFCS are used to localize eloquent and epileptogenic zones.

In most of the cases, no clinical signs are induced by LFCS. However, the LFCS has some possible advantages such as: 1. epileptogenic zone definition (LFCS may induce seizure in the epileptogenic zone especially hippocampus), 2. lower rate of after discharges [START_REF] Zangaladze | The effectiveness of low-frequency stimulation for mapping cortical function[END_REF], 3. motor cortical mapping (sometimes memory function mapping) 4. generates cortico-cortical evoked potentials (CCEP), which indicate direct or indirect interconnections between the recording and stimulated sites [START_REF] Trebaul | Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials[END_REF]. The CCEP have been found useful for clinical study of epileptic networks, for mapping functional networks which include motor control, language networks, intra and inter-lobar connectivity.

From a measurement point of view, the intracerebral SEEG electrodes are roughly similar to surface EEG electrodes (they possess a large surface area, of the order of several mm 2 , thus they record electrical activities of rather large brain sources). The usual macroscopic approximation holds (dipolar current sources and linear instantaneous propagation of the electric field). Consequently, we consider that the recordings are a linear mixing of the CS artifact and of the physiological brain activity, either evoked by the CS or background. Thus, it is of high clinical interest to decouple the stimulation source from the brain sources so that recovered signals can be analyzed later.

There exist several methods for suppression of artifacts generated as a result of electrical stimulation. In [START_REF] Allen | Suppression of deep brain stimulation artifacts from the electroencephalogram by frequency-domain Hampel filtering[END_REF] authors proposed Hampel filter based cancellation of stimulation artifacts.

The Hampel identifier analyzed the frequency spectrum of the signal and replaced the outliers in the frequency domain with interpolated values. Finally, they recovered the clean signal after inverse Fourier transfom. In [START_REF] Hofmanis | Denoising depth EEG signals during DBS using filtering and subspace decomposition[END_REF], stimulation artifacts were suppressed using frequential filtering and a generalized eigenvalue decomposition (GEVD) based method for spatial filtering. In [START_REF] Trebaul | Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials[END_REF], the authors proposed a method for identifying and next canceling stimulation artifacts based on linear regression of the measured signals on simulated models of the CS artifact including models of the tissue-electrode interface.

The previous methods are mostly mono-channel approach for CS artifact suppression. Recently, multicomponent multivariate signal decomposition techniques have gained lot of attention [START_REF] Stanković | Time-frequency decomposition of multivariate multicomponent signals[END_REF], [START_REF] Bhattacharyya | A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform[END_REF]. In particular, empirical mode decomposition (EMD) [START_REF] Huang | The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[END_REF] and synchrosqueezed wavelet transform [START_REF] Daubechies | Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool[END_REF] have been extended for multivariate signals [START_REF] Rehman | Multivariate empirical mode decomposition[END_REF]- [START_REF] Ahrabian | Synchrosqueezing-based time-frequency analysis of multivariate data[END_REF], providing more accurate estimation of components shared among the channels, and proved to be successful in various applications [START_REF] Mandic | Empirical mode decomposition-based time-frequency analysis of multivariate signals: The power of adaptive data analysis[END_REF]- [START_REF] Hu | Adaptive multiscale entropy analysis of multivariate neural data[END_REF].

Another important aspect in the context of CS artifact cancellation is that prior information on the nature of the components to separate is available. Indeed EEG signal exhibits a certain degree of oscillatory nature, as opposed to the transient aspect of the CS artifact to be removed.

A proper tuning of the decomposition bases adapted to the oscillatory nature of the signal can be achieved by the use of tunable-Q wavelet transform (TQWT) [START_REF] Selesnick | Wavelet transform with tunable Q-factor[END_REF], [START_REF] Selesnick | Sparse signal representations using the tunable Q-factor wavelet transform[END_REF], a wavelet transform in which the Q-factor can be easily and continuously tuned. In literature, TQWT has been used extensively for analysis of nonstationary signals [START_REF] Patidar | Segmentation of cardiac sound signals by removing murmurs using constrained tunable-Q wavelet transform[END_REF], [START_REF] Bhattacharyya | Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals[END_REF]. By choosing two different Q-factors adapted to the oscillatory and transient parts of the signal respectively, the sparsity of the decomposition will be increased [START_REF] Selesnick | Sparse signal representations using the tunable Q-factor wavelet transform[END_REF] and provide better separation between these two signal components. In this work, we propose two approaches for suppressing CS artifacts from SEEG signals using a combination of TQWT and different spatial filtering methods. In the first approach, non-linear TQWT based filtering of each individual SEEG channel is applied. The TQWT sparsely represents a CS artifact affected channel as the sum of non-oscillatory transients (mostly CS artifacts) and oscillatory (background EEG) signals using low and high quality factors (Q) simultaneously. Then, we use prior known time-frequency information of the CS artifacts for extracting the propagated CS artifact pattern from the transient signal without presence of any electro-physiological activity. In the second approach, we propose to enhance our proposed TQWT based filtering method by combining it with different spatial filtering methods, inspired either from independent component analysis (e.g., [START_REF] Cichocki | Adaptive Blind Signal and Image Processing Learning Algorithms and Applications[END_REF]) or from subspace analysis techniques [START_REF] Golub | [END_REF]. This has been carried out by applying these spatial filtering techniques to multi-channel SEEG signals in order to isolate and enhance the CS artifacts within few components/sources in the transformed domain. Then, the previously described TQWT based filtering is applied on those few artifact dominated components only, before reconstructing the clean SEEG signals with an inverse spatial filtering operation. In this work, we present the performance of the proposed methods in suppressing both low and high frequency CS artifacts.

The rest of the paper is organized as follows: Section II describes the generation of multichannel simulated SEEG signals and real SEEG signals used in this work. Section III presents the proposed framework of multichannel CS artefact suppression method, Section IV presents the experimental outcome and discusses the effectiveness of the proposed method. Finally, Section V concludes the paper.

II. SIMULATED AND REAL DATASETS

A. Simulated datasets

The performance of the proposed method has been analyzed on synthetically generated realistic SEEG datasets. To this purpose, we have simulated realistic CS, CCEP and background sources, propagating in a realistic, SEEG implanted, head model.

We have used the adult MNI-ICBM152 averaged model, available for download on the web [START_REF] Fonov | Unbiased nonlinear average age-appropriate brain templates from birth to adulthood[END_REF], [START_REF]MNI1552 Head Model[END_REF]. Dipolar sources are placed on a regular 3D grid with 9mm distance between each dipole position, within the segmented gray matter. The resulting 523 dipole positions are next considered as sources of physiological activity. Then, the simulated SEEG electrodes are placed in realistic way following a plausible clinical implantation scheme used in temporal lobe epilepsy context (see Fig. 1). To be more precise, 3 simulated multielectrodes are placed in the left hemisphere, whereas 9 in the right hemisphere. It should be noted that each of these multielectrodes have 7 to 10 sensors inside the brain. The total number of sensors placed inside the brain is 186, 47 sensors being placed in the left hemisphere and 139 in the right. A five compartiment finite element modelling (FEM) is used to compute realistic lead-field (LF) matrix which represents the volume conduction forward model. For a more detailed description of the simulation setup, see [START_REF] Caune | Evaluating dipolar source localization feasibility from intracerebral SEEG recordings[END_REF]. As for every position we can have 3 elementary orthogonal dipoles orientated as, Ax: inion-nasion, Ay: right-left and Az: bottom-up, the final dimension of the LF matrix is 186 × 1569 (number of channels by number of elementary dipoles). The orientation of the CS dipole was set along the corresponding SEEG shaft, as in real clinical situations (recall that the current is injected between two neighboring contacts of a shaft). The time varying amplitude of the CS was generated as a very short duration (1.05 ms) biphasic current pulse at a very high sampling rate:

p(t) =          -A, t ∈ [-(T p + t p ), -t p ] A, t ∈ [t p , T p + t p ] 0, elsewhere (1)
with A = 0.5 -3 mA, T p = 0.5 ms and t p = 0.025 ms.

The CS pattern is next passed through a high-pass filter (cutoff frequency 0.15 Hz) and an anti-aliasing filter before down-sampling at 512 Hz (in order to mimic the clinically used acquisition system, see below subsection II-B). We have generated both type of stimulation sessions namely LFCS (pattern frequency at 1 Hz) and HFCS (pattern frequency at 55 Hz).

The detailed description related to time and frequency analysis for this type of model of current stimulation is presented in [START_REF] Hofmanis | Denoising depth EEG signals during DBS using filtering and subspace decomposition[END_REF], [START_REF] Ranta | Assessing human brain impedance using simultaneous surface and intracerebral recordings[END_REF].

b) CCEP source: The CCEP source position is selected as the nearest position to the CS dipole source. The orientation was randomly generated, while the time varying amplitude was simulated by multiplying a slow varying sinusoidal wave with a decaying exponential. Note that the CCEP source is considered only for the LFCS session. For HFCS, we have not considered the CCEP source, as its latency is above the HFCS period. c) Background physiological sources: The remaining 521 positions are responsible for generating background physiological activity. The orientation were random, while the time courses were generated using the nonstationary EEG model developed in [START_REF] Rankine | A nonstationary model of newborn EEG[END_REF] and adapted for SEEG in [START_REF] Caune | EEG/SEEG signal modelling using frequency and fractal analysis[END_REF].

Overall, C = 186 signals of length N were simulated as:

X = LF CS s CS + LF CCEP s CCEP + LF B s B (2)
with sources s CS , s CCEP ∈ R 3×N and s B ∈ R 1563×N , and lead-field matrices LF ∈ R C×Ns , with N s being 3, 3 and 1563 respectively for the three types of sources.

Finally, we have simulated the effects of acquisition instrument such as saturation and jitter.

It should be noted that the electrodes close to the stimulation site face saturation effect due to the fact that amplitudes of the propagated stimulations exceed the acquisition system range. For An example of LFCS recording is presented Fig. 3.

III. METHODS

A. Tunable-Q wavelet transform

The TQWT is implemented with the idea of two channel filter bank structure. Let ζ and κ denote the high-pass and low-pass scale factors, respectively, of the two channel filter bank. The low-pass filter frequency response in TQWT is mathematically defined as:

Λ 0 (Ω) =            1, if |Ω| ≤ (1 -ζ)π, Ψ Ω+(ζ-1)π κ+ζ-1 , if (1 -ζ)π < |Ω| < κπ, 0, if κπ ≤ |Ω| ≤ π. (3)
while the high-pass filter frequency response writes [START_REF] Selesnick | Wavelet transform with tunable Q-factor[END_REF]:

Λ 1 (Ω) =            0, if |Ω| ≤ (1 -ζ)π, Ψ κπ-Ω κ+ζ-1 , if (1 -ζ)π < |Ω| < κπ, 1, if κπ ≤ |Ω| ≤ π, (4)
where Ψ(Ω) denotes Daubechies filter frequency response [START_REF] Selesnick | Wavelet transform with tunable Q-factor[END_REF]. The variables κ and ζ are chosen so that they obey the following conditions

0 < κ < 1; 0 < ζ ≤ 1; κ + ζ > 1 (5)
The quality factor (Q), maximum number of sub-bands (J max ), and redundancy parameter (R) of TQWT can be expressed in terms of low-pass and high-pass scale factors as follows: [START_REF] Selesnick | Wavelet transform with tunable Q-factor[END_REF]:

Q = 2 -ζ ζ ; J max = log(ζN/8) log(1/κ) ; R = ζ 1 -κ , (6)
where analyzed signal length is denoted by N .

The wavelets generated for different Q-factors are shown in Fig. 4. It can be seen that, for Q = 1 the generated wavelet has transient shape without any oscillatory behavior. On the other hand, increasing the value of Q factor increases the number of oscillations in the The redundancy parameter R has the role of localizing the wavelet in time domain without affecting its shape. In the frequency domain, more overlapping is observed between adjacent frequency bands with increased value of R, while Q remains fixed. Thus, it is necessary to put more number of levels to cover the entire frequency range.

However, in real life, sparse representation of some signals may not be possible with a single Q-factor [START_REF] Selesnick | Sparse signal representations using the tunable Q-factor wavelet transform[END_REF]. In such case, the concept of dual Q-factor can be utilized [START_REF] Selesnick | Sparse signal representations using the tunable Q-factor wavelet transform[END_REF]: let the analyzed signal x be represented as x = x 1 + x 2 , and let x 1 and x 2 have sparse representations using transforms Υ 1 and Υ 2 respectively. This sparse representation can be achieved following a basis pursuit approach, using L 1 -norm minimization. The optimization problem is expressed as [START_REF] Selesnick | Sparse signal representations using the tunable Q-factor wavelet transform[END_REF]:

minimize w 1 ,w 2 ||s 1 w 1 || 1 + ||s 2 w 2 || 1 subject to x = Υ * 1 w 1 + Υ * 2 w 2 . (7)
where Υ 1 * and Υ 2 * denote the inverse TQWT operations applied on sparse set of wavelet coefficients w 1 and w 2 , respectively. In eqn [START_REF] Allen | Suppression of deep brain stimulation artifacts from the electroencephalogram by frequency-domain Hampel filtering[END_REF], s 1 and s 2 are the weighting parameters expressed as

s 1 = Φ 1 ||Ψ 1 (k)|| 2 , s 2 = Φ 2 ||Ψ 2 (k)|| 2
with Ψ 1 (k) and Ψ 2 (k) being the wavelets corresponding to the wavelet coefficients w 1 (k) and w 2 (k), respectively. The parameters Φ 1 and Φ 2 are used to calibrate the relative energy of estimated signal components x 1 and x 2 , respectively [START_REF] Selesnick | Sparse signal representations using the tunable Q-factor wavelet transform[END_REF].

The optimization problem of equation ( 7) is solved using an iterative algorithm known as split augmented Lagrangian shrinkage algorithm (SALSA) [START_REF] Afonso | Fast image recovery using variable splitting and constrained optimization[END_REF].

Finally, the signal components x 1 and x 2 can be estimated by inverse TQWT operation as:

x 1 = Υ 1 * w 1 and x 2 = Υ 2 * w 2 (8)
Note that, before the reconstruction of the signal x = x 1 + x 2 [START_REF] Hofmanis | Denoising depth EEG signals during DBS using filtering and subspace decomposition[END_REF], one can also perform nonlinear operations such as thresholding or masking of certain wavelet coefficients. This operation, which can be seen as denoising, can either be implemented directly within the optimization problem by not requiring exact reconstruction [START_REF] Selesnick | A new sparsity-enabled signal separation method based on signal resonance[END_REF] or by using prior knowledge on the timefrequency localization of the components of interest (transients/oscillatory, see also our proposed pipeline, subsection III-C).

B. Spatial filtering

In this context of multichannel recording setup formalized as a linear mixture (2), spatial filtering approaches can complement the monochannel TQWT methodology introduced in the previous section. These approaches are frequently used for EEG artifact canceling (see e.g., [START_REF] Romo-Vázquez | Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling[END_REF]), but also for CS/DBS artifact elimination [START_REF] Hofmanis | Denoising depth EEG signals during DBS using filtering and subspace decomposition[END_REF], as they isolate and enhance the artifacts on one or few components. The signal space is separated in subspaces specific to different types of activities, in our case CS artifacts and physiological sources, and then only the physiological subspace is used to reconstruct the cleaned signal. We briefly recall below some of the most widely used spatial filtering techniques in the EEG artifact canceling context and proposes a new filtering approach, inspired from subspace correlation techniques [START_REF] Golub | [END_REF], [START_REF] Hotelling | Relations between two sets of variates[END_REF].

The first common step is to find a basis of the (complete) signal subspace, usually by whitening.

If X are the recorded signals (N × C, samples by channels), then this basis Z can be found after eigendecomposition of the covariance/scatter matrix R x = X T X = UDU T :

Z = XUD -1 2 (9)
Clearly, right multiplying Z by any orthogonal matrix J yields another basis of the same signal space. The second step is then to find a specific orthogonal matrix J, optimized for a specific criterion. In our case, the aim is to rotate the Z basis such as the artifact and physiological subspaces become as orthogonal as possible between them (i.e., some of the basis vectors/sources/components contain the artifacts, while the complementary basis vectors contain the physiological sources).

1) Blind source separation: Following the hypothesis that the CS artifact pattern is independent of brain sources and projected linearly onto the sensors, any BSS algorithm may be utilized in order to extract the CS sources, without any knowledge of the mixing coefficients. In other words, even if the volume conduction model (LF matrix) is unknown or uncertain, one can still separate between artifacts and physiological activities, if the linear mixture and the independence between them (or multiple decorrelation, see below) hypothesis is respected.

Several families of BSS algorithms were proposed in the literature (see e.g., [START_REF] Cichocki | Adaptive Blind Signal and Image Processing Learning Algorithms and Applications[END_REF], [START_REF] Comon | Handbook of Blind Source Separation[END_REF] for details). The most well known algorithms are based either on Independent Component Analysis (ICA) 1 [36]- [START_REF] Cardoso | High-order contrasts for independent component analysis[END_REF], on second order statistics only (simultaneous diagonalization) [START_REF] Belouchrani | A blind source separation technique using second-order statistics[END_REF], [START_REF] Yeredor | Blind separation of gaussian sources via second-order statistics with asymptotically optimal weigthting[END_REF] or on combinations between them [START_REF] Tichavskỳ | Blind signal separation by combining two ICA algorithms: HOS-based EFICA and time structure-based WASOBI[END_REF], [START_REF] Tichavsky | A Hybrid Technique for Blind Non-Gaussian and Time-Correlated Sources Using a Multicomponent Approach[END_REF].

2) Filtering/GEVD: The basic idea proposed in [START_REF] Hofmanis | Denoising depth EEG signals during DBS using filtering and subspace decomposition[END_REF] is the following: applying a low-pass filter on the measured data X (channel by channel) will eliminate most of the CS artifact (very brief and thus large-band) and will provide a second data set X 2 . Next, this second data set can be transformed to

Z 2 = X 2 UD -1 2
with U and D the eigenvectors and eigenvalues of the first data set [START_REF] Stanković | Time-frequency decomposition of multivariate multicomponent signals[END_REF]. Its covariance matrix can be diagonalized by eigendecomposition also, yielding

R z,2 = Z T 2 Z 2 = U 2 D 2 U T 2 .
Finally, the desired rotation matrix will be J = U 2 and the complete spatial filter

V = UD -1 2 U 2 (10)
One can recognize the joint diagonalization of the two data sets covariance matrices R x and R x,2 by GEVD:

   V T R x,2 V = D 2 V T R x V = I
The spatially filtered signals Y = XV will naturally be ordered starting from the common sources of the two data sets (i.e., low frequency physiological sources), while the CS artifact sources will appear among the last components.

Different low-pass filters were tested in the cited publication, with rather small differences (from singular spectrum analysis [START_REF] Elsner | Singular Spectrum Analysis[END_REF] to simple finite impulse response FIR filters). It is important to note though that this approach was only tested on HFCS signals (periodic by nature), for which 1 Involving higher order statistics or information theoretical measures estimated from the signals.

the spectral characteristics are rather different than for LFCS (see spectral analysis in [START_REF] Hofmanis | Denoising depth EEG signals during DBS using filtering and subspace decomposition[END_REF] and [START_REF] Ranta | Assessing human brain impedance using simultaneous surface and intracerebral recordings[END_REF], respectively).

3) Subspace correlation approach (SCA): A third approach for spatially separating source of CS artifacts and physiological activity is based on a somehow similar idea. More precisely, one can construct a second data set by simulating the CS artifact. Different versions of this artifact can be obtained by including the variability due to the characteristics of the acquisition system (possible electrode impedances [START_REF] Trebaul | Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials[END_REF], sampling jitter, delays, . . . ). Let this second data set be Q.

A basis for this dictionary space can be obtained by whitening:

Z q = QU q D -1/2 q
Next, a common subspace between the dictionary Q and the data X can be obtained by rotating the two basis Z and Z q one onto the other, following the approach proposed in [START_REF] Golub | [END_REF]:

1) perform the singular value decomposition (SVD) of

C = Z T Z q C = FΣG T
2) take F as the desired rotation matrix and construct the spatial filter

V = UD -1 2 F (11)
(note that a second spatial filter can be constructed for the dictionary: V q = UqDq -1 2 G;

the two data sets and the two basis do not need to have the same dimension).

One can show that the two basis (signal space and dictionary space)

Y = XV Y q = QV q
are now aligned:

Y T Y q = Σ (12)
The singular values close to 1 in Σ indicate the common subspace basis between the dictionary and the data set [START_REF] Golub | [END_REF]. Consequently, one can reconstruct the artifactual component of the original data set by simply using the common basis vectors (i.e., the artifact sources) from Y and projecting them back onto the signal space, the cleaned version being obtained by subtraction (or by using the complementary basis vectors). It should be noted that SCA yields a spatial filter that optimally decomposes the multichannel artifacted SEEG signal in "sources" close (correlated) to the dictionary (CS artefacts) and a complementary uncorrelated part (it is similar to the iteratively constructed canonical correlation analysis (CCA) [START_REF] Hotelling | Relations between two sets of variates[END_REF]). The objective, and the obtained spatial filter, are thus different from the common spatial patterns (CSP) [START_REF] Park | Classification of motor imagery BCI using multivariate empirical mode decomposition[END_REF], [START_REF] Koles | The quantitative extraction and topographic mapping of the abnormal components in the clinical eeg[END_REF] and its recently introduced version augmented complex CSP [START_REF] Park | Augmented complex common spatial patterns for classification of noncircular EEG from motor imagery tasks[END_REF], which enhance the separability of two groups of signals by maximizing the variance of signals in one group while simultaneously minimizing the variance of signals belonging to the other group.

C. CS artifact suppression pipeline

Both TQWT and spatial filtering are in principle able to separate between CS artifacts and physiological signals. The present section proposes an original methodology for combining the two techniques. The rationale is straightforward: in order to facilitate the wavelet processing (either sparsity enhancement by SALSA or thresholding and masking), one can use spatial filtering as a preprocessing step aiming to separate the transients to be detected on fewer sources, enhancing them thus on these components and reducing the redundancy in the data. Moreover, this spatial preprocessing will in principle also ease the computational burden, as fewer signals will need to be processed by TQWT (only the artifact affected sources instead of potentially all measured channels).

Of course, different parameters need to be defined for our application and adapted depending on the type of stimulation (LFCS or HFCS). They are presented below. 2) Non-linear processing : In real applications, in the process of dual Q-factor decomposition, the separation is not perfect and the extracted "transient" components of the real SEEG signals might also include significant physiological activity. Thus, an additional processing step is required to recover the slow varying wave from the transient component. This is performed with prior known time-frequency information of the CS artifacts.

More precisely, we have analyzed the wavelet coefficients corresponding to low Q-factor component. Let the number of oscillatory levels be J 1 . It should be noted that the wavelet coefficients of first few higher oscillatory levels give rise to CS artifacts. At the same time, the time localization of the CS artifacts is also known to us as they appear periodically (every second or every ∼20 ms for LFCS and HFCS respectively). Thus, we can selectively mask the wavelet coefficients of first L oscillatory levels which lie within specific small time windows (16ms) centered around the CS artifacts using the prior known time localization information.

Note that, in case of HFCS, we did not use the time localization information for masking the CS artifacts, as they appear much more frequently. Thus, for high frequency stimulation, all the wavelet coefficients (corresponding to transient component) which belong from first L higher oscillatory levels were forced to zero.

This masking procedure allows us to extract separately the slow varying part remaining in the transient signal (low-Q component) using an inverse TQWT operation and add it back to the oscillatory background.

The value of L has been fixed to 13 and 4 for signals subject to LFCS and HFCS, respectively, after analyzing the frequency spectrum of TQWT sub-bands of higher oscillatory levels which give rise to the CS artifacts.

In Fig 5, we have presented the single channel real SEEG signal subjected to LFCS, its artifact free version, and the extracted LFCS pattern obtained with TQWT and nonlinear processing.

It can be seen that the shapes of the CCEP responses (confined in ellipse) following the CS artifacts are not distorted and at the same time the artefact pattern is free from any background signal. Thus, the proposed method cancels the LFCS artifacts without distorting the background signal.

Fig. 6 presents the single channel real SEEG signal subjected to HFCS, the clean SEEG signal, and HFCS artifact pattern after applying TQWT and nonlinear processing. It is clear that the corrected signal is almost free from CS artifacts.

3) Multichannel CS artefact suppression: As mentioned, we have used spatial filtering to preprocess the data before TQWT. We have evaluated different flavors of BSS and subspace correlation approaches (SCA).

For the BSS case, we have tested several available algorithms (SOBI, FastICA, InfoMax, For the SCA, we have constructed the dictionary by convolving the CS pattern (1) successively tially at a very high sampling frequency), were down-sampled to the acquisition system sampling rate. In order to increase the variability of the dictionary and to cover possible instrumentation effects, we have simulated sampling jitter and buffering delays of up to 5 samples.

It should be noted that for SCA method the artifact sources (i.e., correlated with the dictionary subspace) appear in order. However, for BSS methods, we have selected the artifact sources as the ones having high correlation coefficients (defined in the next subsection, eqn ( 14)) with the simulated CS pattern. For all the considered spatial filtering methods, the artifact space dimension (number of components/sources with artifacts) were also visually inspected. The overall methodology is presented in Fig. 7.

D. Performance evaluation

For synthetic multichannel SEEG dataset, we know the ground truth signals. Hence, the performance of the proposed CS artifact suppression method is evaluated using the (log) mean square (MSE) error measure, expressed as:

MSE = log 1 C C i=1 1 N N n=1 (x i (n) -x est i (n)) 2 (13)
where C and N denote the number of channels and total number of samples respectively. x i (n) and x est i (n) represent ground truth i th channel signal and its estimate, respectively. However, in case of real SEEG signals, the original clean sources are unknown. Thus, we compute the correlation coefficient between the estimated clean EEG signal with the synthetic CS artifact pattern. Firstly, the correlation coefficient is computed between estimated i th sensor signal x est i (n) and the theoretical CS artifact pattern p(n) (1):

ρ i (m) = COV(x est i (n)p(n -m)) σ x est i (n) σ p (14)
where COV represents covariance operation. Finally, the correlation coefficient index (CCI) is computed as,

CCI = 100 C C i=1 max m |ρ i (m)| (15)
where C is the number of SEEG channels.

IV. RESULTS AND DISCUSSION

In this section we present the performance of the proposed time-frequency-spatial filtering methods in suppressing multichannel LFCS and HFCS artifacts, both on simulated and real signals (see Figs. 2 and3 for examples of LFCS recordings).

The proposed combinations of spatial filtering and TQWT were compared with three existing CS artefact suppression methods namely FIR-GEVD method [8] 2 , frequency domain Hampel filtering method [START_REF] Allen | Suppression of deep brain stimulation artifacts from the electroencephalogram by frequency-domain Hampel filtering[END_REF], and model based artefact correction method [START_REF] Trebaul | Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials[END_REF]. In [START_REF] Allen | Suppression of deep brain stimulation artifacts from the electroencephalogram by frequency-domain Hampel filtering[END_REF], Hampel identifier finds the spectral outliers (harmonics generated due to CS artifacts) in the real and imaginary spectrum and replaces them with interpolated values [START_REF] Allen | Suppression of deep brain stimulation artifacts from the electroencephalogram by frequency-domain Hampel filtering[END_REF]. However as discussed in [START_REF] Allen | Suppression of deep brain stimulation artifacts from the electroencephalogram by frequency-domain Hampel filtering[END_REF], the entire FFT spectrum is not contaminated with CS artefacts (especially the low frequency region of the spectrum). Therefore, we have corrected the outliers in FFT spectrum which appeared above 20 Hz using Hampel filtering while keeping the low frequency bins of the spectrum unaltered. For implementation of model based CS artefact correction method, various artefact shapes have been obtained from the output of the RC circuit (models electrode-tissue interface)

for different RC time constants as described in [START_REF] Trebaul | Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials[END_REF]. However, the results obtained for HFCS artifact correction using model based method are not very satisfactory (note that the method was proposed for LFCS artifact correction) as most of the time the corrected signal contained high artifact residuals. Moreover for HFCS, as the artifacts appear very frequently, it becomes computationally cumbersome to compare every artifact from each channel with a very large dictionary for the best matching. Therefore, we do not present the numerical results of HFCS artifact correction using model based method. We have also compared our combined methodology with a channel by channel TQWT approach, but the quantitative results for this comparison will not be presented, as they are both less convincing and they require a much higher computational burden.

A. Synthetic signals existing FIR-GEVD [START_REF] Hofmanis | Denoising depth EEG signals during DBS using filtering and subspace decomposition[END_REF], Hampel filtering [START_REF] Allen | Suppression of deep brain stimulation artifacts from the electroencephalogram by frequency-domain Hampel filtering[END_REF], and model based methods (for LFCS only) [START_REF] Trebaul | Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials[END_REF].

For LFCS, we have obtained least MSE values using SCA-TQWT method followed by BSS-TQWT (the best performances were obtained for WASOBI [START_REF] Yeredor | Blind separation of gaussian sources via second-order statistics with asymptotically optimal weigthting[END_REF]), Hampel filtering method, model based method, and FIR-GEVD method. For HFCS, WASOBI-TQWT seems to perform slightly 2 In [START_REF] Hofmanis | Denoising depth EEG signals during DBS using filtering and subspace decomposition[END_REF], the results corresponding to different filters were similar. Thus, we have tested the FIR-GEVD approach only.

Concerning the number of artifact sources, we have followed the approach given by [START_REF] Hofmanis | Denoising depth EEG signals during DBS using filtering and subspace decomposition[END_REF], i.e., we have selected the two last components. better than SCA-TQWT, but the results are hardly different and the task of artifacted component selection is more difficult and more subjective for the BSS based methods than for the SCA (for the latter, the components similar to the dictionary appear in the common subspace, while for the BSS, the source order is randomly permuted [START_REF] Cichocki | Adaptive Blind Signal and Image Processing Learning Algorithms and Applications[END_REF]).

B. Real signals

C. Discussion

The proposed pipeline strengthen the capacities of both TQWT and spatial filtering methods to separate between (transient) artifacts and physiological background. The role of each of the approaches can be illustrated by analyzing the multichannel data. For example, Fig. 3 with LFCS (shown in Fig. 3) using SCA-TQWT.

Fig. 16 presents the mean spectrum magnitude of a single session before (shown in black)

and after (shown in red) suppression of CS artifacts subject to low and high frequency CS. As it can be seen that, the harmonics corresponding to CS artifacts are suppressed in the frequency domain while retaining the desired SEEG signal spectrum.

The key point is to (spatially) separate as correctly as possible between artifacts and physiological signals. Both SCA and BSS methods are found useful in separating the artifact sources from the multichannel SEEG signals. But, while BSS is a generic family of methods based on statistical independence (decorrelation) between artifact physiological sources, SCA explicitly optimizes the separation in order to fit the artifact components onto a predefined dictionary. In theory then, the performances should be better, but they are clearly dependent on the dictionary construction (although one can construct, as we did in this paper, a rich dictionary including different possible versions of the artifact, that by linear combination might be able to reproduce the actual recorded ones). On the other hand, although BSS uses less assumptions on the artifacts (no physical model), it uses difficult to verify assumptions (especially the independence between artifacts and physiological sources), which might be even contradicted in practice (as in the CCEP case).

Previous arguments aim to explain the good performances and the robustness of our proposed methods, compared with the literature. Indeed, it has been found that the performance of FIR-GEVD method [START_REF] Hofmanis | Denoising depth EEG signals during DBS using filtering and subspace decomposition[END_REF] is dependent on the decomposition quality and on the number of components selected as artifactual. For example, the results heavily degrade in the presence of sampling jitters, as the CS artifacts spread over more number of components in the transformed domain after GEVD operation. Moreover, some significant background activity (especially high-frequency) might be found in the selected artifact sources after GEVD, and this activity will be lost after reconstruction.

The model based artifact correction method [START_REF] Trebaul | Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials[END_REF] is on the other hand dependent on finding the best match of the real artifact shape in the constructed dictionary, so it depends on the model of the instrumentation. The performance of this method can probably be further improved if one includes in the dictionary better artifact models or even some real artifact shapes (obtained e.g., by averaging or chosen from the output artifact patterns extracted using other methods).

Although our method is also based on modeling the artifact, we allow more variability in the pattern shape, as it does not need to be correlated with a particular element of the dictionary but with some linear combination (i.e., with the dictionary subspace).

Finally, the frequency domain Hampel filtering method is based on the identification of outliers (i.e., high amplitudes) in the real and imaginary part of the spectrum. In the HFCS case, the artifact components are quite easy to identify (they are strong and widely spaced in frequency, with harmonics at avery 50-55Hz, see [START_REF] Hofmanis | Denoising depth EEG signals during DBS using filtering and subspace decomposition[END_REF]), but some of them are aliased in the low frequencies and they will not be seen as outliers. On the other hand, for the LFCS case, the spectrum is much denser in frequency (harmonics spaced by 1Hz) and it also has components in the low frequency part of the spectrum, where physiological activities lie. Detection of outliers is thus more difficult and is unavoidable to miss artifact harmonics in the lower part of the spectrum. The performances of the proposed methods were analyzed using synthetic and real multichannel SEEG signals with low and high frequency artifacts and we compared them with existing mono-channel (Hampel [START_REF] Allen | Suppression of deep brain stimulation artifacts from the electroencephalogram by frequency-domain Hampel filtering[END_REF] or dictionary based [START_REF] Trebaul | Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials[END_REF]) or multichannel methods (GEVD based [START_REF] Hofmanis | Denoising depth EEG signals during DBS using filtering and subspace decomposition[END_REF]).

The experimental results clearly indicate the suitability of the proposed methods in suppressing CS artifacts and their robustness to varying acquisition conditions (jitter, noise, ...). Relatively few parameters need to be tuned, the most critical being the choice of the CS artifact sources after spatial filtering (with SCA based spatial filters easier to tune than BSS ones).

Other CS artifact suppression methods could be proposed, using for example adaptive signal decomposition methods like multivariate EMD (MEMD) and its different versions. Our first

Fig. 1 :

 1 Fig. 1: Plot of depth electrode positions (in green) and considered dipole sources (in red and blue) in the brain volume.

  making the session realistic, we have considered random sampling jitters of maximum 5 samples across all the 186 channels which yields a slight desynchronization of CS artifacts across the channels. This procedure simulates one complete session of multichannel synthetic SEEG signals subjected to CS. We have generated 50 such different stimulation sessions for each of the stimulation type (LFCS and HFCS) by changing the CS source location across the three different mentioned brain locations and considering different SEEG background sources. Fig. 2 presents an example of synthetically generated SEEG signals affected with LFCS artefact. A similar figure can be generated for simulated session subjected to HFCS also.

Fig. 2 :

 2 Fig. 2: Synthetically generated SEEG signals during LFCS with 1 mA biphasic current pattern.

Fig. 3 :

 3 Fig. 3: Real SEEG signals recorded during LFCS with 1 mA biphasic current pattern.

Fig. 4 :

 4 Fig. 4: Plots of wavelets corresponding to different Q factors: (i) Q = 1, R = 3 (ii) Q = 10, R = 3 (iii) Q = 20, R = 3

1 )

 1 TQWT parameters: All along this work, we have fixed Q = 1 to define the wavelet basis corresponding to the CS artifact (transient) component of the SEEG signal, irrespective of the stimulation frequency. As already mentioned, the reason is very similar time frequency characteristics of the generated wavelet and of the CS transient (time support, but also Fourier spectrum). The Q-factor corresponding to oscillatory components of the signal can be chosen depending on the oscillatory nature of the signal. We have tested several values and achieved best performance when the high Q-factor is fixed to 20 and 10 for LFCS and HFCS, respectively.The redundancy parameter (R) of TQWT is set to 3 throughout this work. The number of levels of TQWT corresponding to low and high Q-factor are set to maximum possible values following equation[START_REF] Trebaul | Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials[END_REF].

Fig. 5 :Fig. 6 :

 56 Fig. 5: Plots of (i) monochannel real SEEG signal subjected to LFCS, (iii) artefact free real SEEG signal using TQWT and nonlinear processing (ii) extracted LFCS artefact pattern. The CCEP responses followed by CS artifacts are encircled with ellipses.

Fig. 7 :

 7 Fig. 7: Block diagram of the proposed multichannel channel CS artefact suppression method based on TQWT and spatial filtering

Fig. 8 :

 8 Fig. 8: Plots of (i) monochannel simulated SEEG signal affected by LFCS, (ii)-(iii) physiological background and artefact pattern obtained using classical TQWT (without nonlinear processing), (iv)-(v) physiological background and artefact pattern obtained using model based method [6], (vi)-(vii) physiological background and artefact pattern obtained using FIR-GEVD method [8], (viii)-(ix) physiological background and artefact pattern obtained using Hampel filtering method [7], (x)-(xi) physiological background and artefact pattern obtained using WASOBI-TQWT method, (xii)-(xiii) physiological background and artefact pattern obtained using SCA-TQWT method.

Fig. 9 :

 9 Fig. 9: Plots of (i) monochannel real SEEG signal affected by LFCS, (ii)-(iii) physiological background and artefact pattern obtained using classical TQWT (without nonlinear processing), (iv)-(v) physiological background and artefact pattern obtained using model based method [6], (vi)-(vii) physiological background and artefact pattern obtained using FIR-GEVD method [8], (viii)-(ix) physiological background and artefact pattern obtained using Hampel filtering method [7], (x)-(xi) physiological background and artefact pattern obtained using WASOBI-TQWT method, (xii)-(xiii) physiological background and artefact pattern obtained using SCA-TQWT method.

Fig. 8

 8 Fig. 8 illustrates the results on an example of simulated signals. For readability, only one channel is presented. One can notice that the classical TQWT identify as transient the CCEP, as well as other short-duration events, while FIR-GEVD and Hampel filtering leaves a significant part of background activity in the transient components. The model based method fails to remove the CS artifact completely and significant residues of the artifacts still remain in the corrected signal. Our proposed spatial-filtering-TQWT pipeline yields the best results (for this example, MSE values are equal to 3.56, 2.79, 2.92, 2.53, 1.43, and 1.05, respectively for TQWT only, model based method, FIR-GEVD, Hampel filtering, WASOBI-TQWT, and SCA-TQWT).

  presents a multichannel SEEG signal subjected to LFCS, whereas Fig. 10 presents the first 15 components after rotation of the multi-channel SEEG signals onto the dictionary using SCA. Fig. 11 presents the obtained clean SEEG signals after applying SCA-TQWT based filtering on the multi-channel SEEG signals (shown in Fig. 3) affected with LFCS artefact. For HFCS, we have presented the multichannel artefacted SEEG signals and the separated 15 sources (using WASOBI) in Figs. 12 and 13, respectively. Fig. 14 shows the recovered clean SEEG signals after applying WASOBI-TQWT based filtering on the multi-channel SEEG signals (shown in Fig. 12) affected with HFCS artefact. Analyzing these figures, it appears clearly that it is more convenient to suppress the artifacts in those few components (sources) which mostly represent the CS. Fig. 15 graphically presents the variation of MSE and CCI values with respect to number of cleaned components for both LFCS and HFCS in synthetic and real EEG signals. It can be observed from these figures that maximum number of six cleaned components provided consistent results (least MSE and CCI values) across all data sets.

Fig. 10 :Fig. 11 :

 1011 Fig. 10: Plot of first 15 SCA components of the real SEEG signal (shown in Fig. 3 ) affected with LFCS.

Fig. 12 :

 12 Fig. 12: Real SEEG signals recorded during HFCS with 1 mA biphasic current pattern.

Fig. 13 :

 13 Fig. 13: Plot of 15 WASOBI components of the real SEEG signal (shown in Fig. 12) affected with HFCS.

Fig. 14 :

 14 Fig. 14: Plot of obtained artefact free version of the real multi-channel SEEG signal affected with HFCS (shown in Fig. 12) using WASOBI-TQWT.

Fig. 15 :

 15 Fig. 15: Plots of the variations of MSE and CCI values with respect to number of cleaned components of synthetic and real multichannel SEEG signals.

Fig. 16 :

 16 Fig.16: Plots of the mean spectrum magnitude of a single session before (black) and after (red) applying WASOBI-TQWT method (i) for LFCS (ii) for HFCS. Spectral peaks corresponding to artifacts are absent after processing.

  Table I presents the mean and standard deviation (std) of MSE values (for 50 different sessions) obtained using different methods on synthetic multichannel SEEG signals. More precisely, we compare two spatial filtering -TQWT based methods (SCA-TQWT and BSS-TQWT) with

TABLE I :

 I Obtained MSE values corresponding to proposed methods and existing methods in suppressing CS artifacts of synthetic SEEG signals (mean ± std).

	Stimulation	Raw	SCA-	WASOBI-	FIR-	Hampel	Model based
	type	SEEG	TQWT	TQWT	GEVD [8] filtering [7]	method [6]
	LFCS	3.52±0.05 1.49±0.24 1.83±0.22 3.17±0.19	2.51±0.12	2.87±0.05
	HFCS	3.22±0.11 2.12±0.12 2.11±0.13 2.99±0.15	2.47±0.12	NR

TABLE II :

 II Obtained CCI values for proposed methods and existing methods in suppressing CS artifacts of real SEEG signals.(mean ± std).

	Stimulation	Raw	SCA-	WASOBI-	FIR-	Hampel	Model based
	type	SEEG	TQWT	TQWT	GEVD [8] filtering [7]	method [6]
	LFCS	3.39±1.85 0.16±0.06 0.34±0.12 0.93±0.51	0.84±0.35	1.07±0.75
	HFCS	3.78±2.49 0.30±0.19 0.60±0.41 1.27±1.07	1.18±1.02	NR

  Table II presents the computed mean and std of the CCI values (over 50 different real sessions of stimulations) for the artifact affected SEEG signals, artifact corrected SEEG signals using our proposed methods, existing model based method (for LFCS only), Hampel filtering method, and FIR-GEVD method. As expected, the highest CCI values correspond to the artifacted SEEG signal for both LFCS and HFCS artifacts. For these real signals, both LFCS and HFCS, the smallest CCI values are obtained using SCA-TQWT method, followed by WASOBI-TQWT

	method and existing methods.
	Fig. 9 presents a monochannel real SEEG signal with LFCS artefact, the extracted physio-
	logical background signals and LFCS artefact patterns using classical TQWT, existing model
	based method [6], FIR-GEVD [8], Hampel filtering method [7], proposed WASOBI-TQWT, and
	SCA-TQWT methods. It can be noticed that the proposed methods extract the CS artifact pattern
	without any physiological background and are more effective to suppress the CS artifacts in real
	SEEG signals (for this example, CCI values are equal to 0.10, 1.32, 1.25, 1.06, 0.48, and 0.36
	respectively for TQWT only, model based method, FIR-GEVD, Hampel filtering, WASOBI-
	TQWT, and SCA-TQWT).

ACKNOWLEDGEMENT This work was carried out during the visit of first author at the Université de Lorraine, CNRS, CRAN, F-54000 Nancy, France under Raman-Charpak fellowship 2017. The authors would like to thank Indo-French Centre for the Promotion of Advanced Research (IFCPAR) for their support during this work.


Dr Sahar S M El Souda
email: saharchem@yahoo.com

Nehal Ibrahim

Pierre Pério

Hany Ibrahim

Christel Hénocq-Pigasse

Christine Roques

Françoise Nepveu

Pierre Perio

Christel Pigasse-Hénocq

Powerful Antioxidant and Pro-Oxidant Properties of Cassia roxburghii DC. Leaves Cultivated in Egypt in Relation to Their Anti-Infectious Activities

Keywords: Antimicrobial, antiplasmodial, Cassia marginata Roxb, radical scavenging d

come   L'archive ouverte pluridisciplinaire

Leaf extracts of Cassia roxburghii DC., prepared in petroleum ether, chloroform, ethyl acetate, butanol, and methanol/water (70:30, v/v), were evaluated as antioxidant, pro-oxidant, anti-infectious, and cytotoxic agents. The major metabolite of each extract was identified by chromatographic and spectroscopic means. The redox properties were assessed with a battery of assays, which revealed that the ethyl acetate extract demonstrated an interesting scavenging activity of DPPH and superoxide radicals and an ascorbic acid-like pro-oxidant activity. All the tested extracts showed moderate antiplasmodial activity against a chloroquine-resistant strain of Plasmodium falciparum, by possible disruption of parasite fine redox balance. Cytotoxicity was evaluated against a human breast cancer cell line. The antimicrobial activities of the extracts were estimated against representative bacterial strains ( Staphylococcus aureus, Enterococcus hirae, Pseudomonas aeruginosa, Escherichia coli) and fungal species ( Candida albicans, Aspergillus niger). The ethylacetate extract possessed the highest redox properties and

INTRODUCTION

Plants belonging to Cassia species are extensively used in traditional medicine in various parts of the world against a wide range of ailments [START_REF] Mazumder | Cassia: A wonder gift to medical sciences[END_REF]. Various species of Cassia are reported to have antiviral [START_REF] Anubhuti | Antiviral activity of Cassia fistula against IBR virus[END_REF], anti-inflammatory [START_REF] Doshi | Antiinflammatory potential of Cassia auriculata flowers[END_REF], larvicidal [START_REF] Govindarajan | Larvicidal and ovicidal activity of Cassia fistula Linn. leaf extract against filarial and malarial vector mosquitoes[END_REF], antiprotozoal [START_REF] Sartorelli | Isolation of antileishmanial sterol from the fruits of Cassia fistula using bioguided fractionation[END_REF], antimalarial [START_REF] Mbatchi | In vitro antiplasmodial activity of 18 plants used in Congo Brazzaville traditional medicine[END_REF][START_REF] Zirihi | In vitro antiplasmodial activity and cytotoxicity of 33 West African plants used for treatment of malaria[END_REF], hepatoprotective [START_REF] Pradeep | Effect of Cassia fistula Linn. leaf extract on diethylnitrosamine induced hepatic injury in rats[END_REF], and anticancer [START_REF] Villaseñor | Bioactivity studies on Cassia alata Linn. leaf extracts[END_REF] properties.

Cassia roxburghii DC. (Caesalpiniaceae), also known as C. marginata Roxb. [START_REF] Wiersema | World Economic Plants: A Standard Reference[END_REF], is a small deciduous tree native to Sri Lanka, southern India, and Egypt, where it is used in folk medicine to treat various liver disorders. The in vivo hepatoprotective effect of Cassia roxburghii seed have been reported [START_REF] Arulkumaran | Cassia roxburghii seeds protect liver against toxic effects of ethanol and carbontetrachloride in rats[END_REF]. Skin diseases such as ringworm infections and the wound healing potential of Cassia roxburghii seed and leaf extracts have been reported [START_REF] Reddy | Evaluation of in vivo wound healing potential of Cassia roxburghii leaves extracts[END_REF]. It has also been used as a cathartic agent [START_REF] Mansoor | Pharmacological evaluation of Cassia roxburghii as cathartic agent[END_REF]. Phytochemical studies have demonstrated the presence of alkaloids, sterols, anthraquinones, glycosides, tannins, and flavonoids in C. marginata [START_REF] Chauhan | Two flavonoid triglycosides from Cassia marginata[END_REF][START_REF] Mazumder | Cassia: A wonder gift to medical sciences[END_REF][START_REF] Reddy | Chemical examination of pods of Cassia roxb[END_REF]. In spite of this phytochemical richness, little is known about the redox capacity and bioactive properties of C. marginata.

The aim of this study was to evaluate the redox properties of Cassia roxburghii leaves. The in vitro anti-infectious activity of leaf extracts was evaluated against representative Gram-positive and Gram-negative bacterial strains (S. aureus CIP 4.83, E. hirae CIP 5855, P. aeruginosa CIP 82118, E. coli CIP 53126), fungal species (C. albicans IP 48.72, A. niger IP 1431.83), and a protozoan (Plasmodium falciparum) in relation to the redox capacity of the extracts. Cytotoxicity against a human breast cancer cell line (MCF-7) is also reported.

MATERIALS AND METHODS

Plant Material

Cassia marginata Roxb (C. roxburghii DC.) leaves were collected in December 2006 from the Orman Botanical Garden, Giza, Egypt. Plant samples were authenticated by the taxonomist of the Orman Botanical Garden. A voucher specimen No. C-5-63 was kept in the Herbarium of Orman Garden.

Extraction and Isolation

The shade dried powdered leaves of C. roxburghii (250 g) were defatted with petroleum ether. The crude petroleum ether extract (I), 17 g, was chromatographed on a flash column of silica gel G, eluted with petroleum ether (60-80) with gradient increase of polarity with ethyl acetate (0.5, 1, 2, 3, and 5%), allowing the isolation of emodin from fraction eluted with 2% ethyl acetate. The defatted powder was then extracted with methanol/water (70:30, v/v) and evaporated under vacuum yielding the total defatted extract (V), 60 g, which was further fractionated with chloroform (II), 6 g; ethyl acetate (III), 15 g; and butanol (IV), 12 g. Part of the total extract (V) was kept for biological study. The chloroform fraction (II) was applied to a silica gel column, and eluted with n-hexane/ethyl acetate with increasing polarity, resulting in the isolation of aloe-emodin. The ethyl acetate fraction (III) was subjected to reversed phase liquid chromatography on Sephadex LH20 to give quercitrin. The butanol fraction (IV) was fractionated on polyamide to yield three fractions that were purified by paper chromatography (15% AcOH), and then on a Sephadex LH20 sub-column to obtain emodin glucoside and aloe-emodin glucoside. Pure compounds were chromatographed with two-dimensional TLC on cellulose using butanol:acetic acid:water, 4:1:5 (BAW) and 15% acetic acid as solvent systems.

Chemicals

Dimethyformamide (DMF), dimethylsulfoxide (DMSO), acetonitrile (ACN), ethanol, linoleic acid, xanthine, xanthine oxidase, superoxide dismutase, ascorbic acid, α-tocopherol (vitamin E), Trolox (6-hydroxy-2,5,7,8tetramethylchroman-2-carboxylic acid), Tween 20, β-carotene, H 2 O 2 , FeCl 3 , diethylenetriamine pentacetate (DTPA), 5,5-dimethyl-1-pyrroline N -oxide (DMPO), and 2,2-diphenyl-1-picrylhydrazyl (DPPH • ) were purchased from Sigma (Sigma-Aldrich, France). Polyamide 0.05-0.16 mm (Roth) was used for column chromatography. TLC analysis was carried out using cellulose and silica gel 60 F254 plates (Merck).

Instruments

UV absorption spectra were recorded on UV-visible double beam UWD-3500 spectrophotometer (Labomed, Inc.). Mass spectrometry analyses were performed on a Finnigan MAT 112 electron impact ionization at 70 eV. 1 H and 13 C NMR spectra were done on a Varian Mercury VX 300 NMR spectrometer at 300 MHz and 75 MHz, respectively, using DMSO-d 6 or CDCl 3 as solvents. The chemical shifts were reported in ppm values using tetramethylsilane (TMS) as the internal standard. Chromatograms were visualized under UV light at λ reagent. Electron paramagnetic resonance (EPR) spectra were obtained at X-band on a Bruker EMX-8/2.7 spectrometer (Bruker, Wissembourg, France) (9.86 GHz) equipped with a high-sensitivity cavity (4119/HS 0205) and a gaussmeter (Bruker). A flat quartz cell FZK160-5 × 0.3 mm (Magnettech, Berlin, Germany) was used for analysis. EPR data processing and spectrum computer simulation were performed using WINEPR and SIMFONIA software (Bruker). Typical scanning parameters were: scan rate, 1.2 G/s; scan number, 1; modulation amplitude, 1 G; modulation frequency, 100 kHz; microwave power, 20 mW; sweep width, 100 G; sweep time, 83.88 s; time constant, 40.96 ms; magnetic field, 3450-3580 G.

Major Chemical Components in Tested Fractions

Emodin was obtained from the petroleum ether fraction (I). The structural identification was carried out by UV, MS, and NMR spectroscopies. The results match with the previously reported data of emodin [START_REF] Liu | Preparative isolation and purification of hydroxyanthraquinones cinnamic acid from the Chinese medicinal herb Rheum officinale Baill. by high-speed counter-current chromatography[END_REF].

Aloe-emodin was obtained from the chloroform extract (II). The anthraquinone was isolated as yellowish needles from methanol, mp 220-222 (s,1H,), 0.81 (d, J = 6.0 Hz, 3H,-CH 3 ). 13 Two pure anthraquinone glycosides from the butanol extract (IV) were chromatographed with two-dimensional TLC on cellulose (BAW/15% acetic acid as solvent systems). The two anthraquinone glycosides had R f values of 0.34 and 0.43 in BAW and 0.70 and 0.62 in 15% acetic acid, respectively. The two compounds were identified by chemical tests (positive Borntrager's test and positive Molish's test), acid hydrolysis with HCl (2 N), and co-chromatography with authentic standards (BAW 4:1:5 as the solvent system). This enabled glucose to be identified as the sugar moiety and emodin and aloe-emodin as the aglycone. ESI mass [M+2H] + 596. The two compounds were identified as emodin diglucoside and aloe-emodin diglucoside.

Redox Properties SIMPLE PRO-OXIDANT ASSAY

Extract (100 mg/L) and DMPO (50 mM) were diluted in DMSO/HEPES buffer, pH = 7.4, (10/90) solution. EPR spectra were recorded (accumulation of 10 scans) every 15 minutes during 1 h. Ascorbic acid (100 μM) and artemisinin (100 μM) were used as positive and negative controls, respectively.

FE 3+ /H 2 O 2 PRO-OXIDANT ASSAY
HEPES buffered solutions (50 mM, pH = 7.4) containing 250 μM FeCl 3 , 250 μM hydrogen peroxide, 100 mg/L of extract in DMF (100 μL), and 50 mM DMPO were incubated for 2 min at room temperature prior to EPR measurements (accumulation of 10 scans). Ascorbic acid (100 μM) and artemisinin (100 μM) were used as positive and negative controls respectively. FE 2+ PRO-OXIDANT ASSAY Solutions containing 8.3 mM Fe(NH 4 ) 2 (SO 4 ) 2 , 2.2 mg/mL of extract in DMF, and 167 mM DMPO were incubated for 10 min at room temperature prior to EPR spectra recording (accumulation of 10 scans). As a positive control, artemisinin (6.67 mM) replaced the extract [START_REF] Meshnick | Iron-dependent free radical generation from the antimalarial agent artemisinin (Qinghaosu)[END_REF].

DPPH Assay

The antioxidant activity was evaluated using the stable 2,2-diphenyl-2picrylhydrazyl nitrogen-centered free radical (DPPH • ). DPPH • in ethanol (300 μM, 100 μL) was added to 200 μL of the test compounds at different concentrations in the appropriate solvent (MeOH, EtOH, MeOH/H 2 O 2:1, v/v). Each mixture was then mixed thoroughly and the absorbance was recorded every 5 min for 30 min, using a microplate reader (UVMax, Molecular Devices). The decrease in DPPH • absorbance (at λ 530 nm) was monitored. DPPH • solution (100 μL) in the respective solvent (200 μL) served as the blank. All tests were performed in triplicate. The radical scavenging activity of the samples (antioxidant activity) was expressed in terms of IC 50 (concentration in mg/L required for a 50% decrease in DPPH • absorbance).

A plot of absorbance vs. concentration was made to establish the standard curve and to calculate the IC 50 . Ascorbic acid and Trolox (water soluble form of vitamin E) were used as positive controls and vitamin E acetate was used as the negative control.

Beta-Carotene Bleaching Assay

The antioxidant activity of extracts was determined according to the βcarotene bleaching method [START_REF] Marco | A rapid method for evaluation of antioxidants[END_REF][START_REF] Mille | A simplified method for the evaluation of antioxidants[END_REF][START_REF] Taga | Chia seeds as a source of natural lipid antioxidants[END_REF] with the following modifications: A solution of β-carotene was prepared by dissolving 2 mg of β-carotene in 1 mL of chloroform. 500 μL of this solution was then transferred into a round-bottom rotary flask containing 15 mg of linoleic acid and 200 mg of Tween 20. After removing the chloroform under a nitrogen stream, 50 mL of aerated distilled water was added to the flask with manual shaking. Aliquots of 5 mL of this prepared emulsion were transferred into tubes containing 200 μL of extracts or α tocopherol (50 mg/L), which was used as positive control. The blank consisted of 0.2 mL of the respective solvent without the extract. Zero time absorbance was recorded at λ 470 nm as quickly as possible. The samples were then subjected to thermal autoxidation at 50 • C in a temperature-controlled eight-cell holder. Subsequent absorbance readings were recorded at regular time intervals until the color of the β-carotene in the control sample had disappeared (25 min). The extent of inhibition of βcarotene bleaching is related to the concentration of antioxidant compounds. All samples were assessed in triplicate. Antioxidant activity (AA) was calculated as percentage inhibition of β-carotene bleaching relative to the blank using the following equation:

AA = [1 -(A i -A t )/(A i -A t )] × 100
where A i is the measured absorbance value of the sample at zero time; A t is the measured absorbance value of the sample after incubation (25 min) at 50 • C; A i is the measured absorbance value of the blank at zero time, and A t is the measured absorbance value of the blank after incubation (25 min) at 50 • C.

Superoxide Assay

The superoxide radical was generated by reaction of the xanthine-xanthine oxidase system. The extract sample was dissolved in dimethyl sulfoxide (DMSO). 0.5 mM xanthine (80 μL), DMSO, or extract solution (final volume 32 μL), 1 M DMPO (32 μL), appropriate volume of HEPES buffer containing 0.1 mM DTPA, and xanthine oxidase (20 units/mL) (10 μL) were mixed in a test tube to give a total volume of 400 μL. The mixture was quickly transferred to a flat quartz cell. The EPR spectra of the DMPO-OOH spin adducts were recorded 90 s after xanthine oxidase addition. The radical scavenging activity of the samples was expressed in terms of percent inhibition of DMPO-OOH spin adduct according to the following formula:

% Inhibition = [(I blank -I test )/I blank ] × 100)
where I blank is the intensity of the first peak of the blank spectrum, I test is the intensity of the first peak of the test sample spectrum. Ascorbic acid was used as positive control.

Antimicrobial Assays

The extracts were dissolved in water/DMSO to obtain an initial concentration of 1000 μg/mL. The resulting solutions were then diluted in microtiter plates in culture medium: trypcase soja (Biomérieux, Craponne, France) for bacteria and Sabouraud (Biomérieux) for fungi and yeast. Gram-positive strains S. aureus CIP 4.83 and E. hirae CIP 58.55 and Gram-negative strains, P. aeruginosa CIP 82118 and E. coli CIP 53126 were used for the antimicrobial assays. For the antifungal assay cultures,a yeast, C. albicans IP 48.72, and mold, A. niger IP 1431.83, were employed. Strains were obtained from the collection of the Pasteur Institute (Paris, France). Microbial suspensions were prepared in sterile distilled water to obtain final inoculums of 10 6 cells/mL and 10 5 spores/mL for bacteria and fungi, respectively. Minimal inhibitory concentrations (MICs) and minimal germicidal concentrations (MBC or MFC) were determined after incubation of the bacterial strains at 37 • C for 24 h and the fungal strains at 30 or 22.5 • C for 24 h (36 h for A. niger) in the presence of serial dilutions of the test compounds. The MIC was defined as the concentration of compound at which no macroscopic sign of cellular growth was detected in comparison to the control without antimicrobial compound. The MBC/MFC were determined by subcultivating on corresponding agar plates after incubation of the bacterial strains at 37 • C for 24 h and fungal strains at 30 or 22.5 • C for 48 h. MBC/MFC was defined as the concentration of compound at which no macroscopic sign of cellular growth was detected in comparison to the control without antimicrobial compound upon subculturing. All the experiments were carried out in duplicate at each concentration. In order to ensure that DMSO per se did not interfere with the antimicrobial activity evaluation of the products under assay, a control test was also performed containing inoculated broth supplemented with only DMSO at the same dilutions used in our experiments.

Antiplasmodial Assay

Antiplasmodial activity was tested according to the protocol previously reported [START_REF] Nepveu | Synthesis and antiplasmodial activity of new indolone N -oxide derivatives[END_REF]. RPMI 1640 medium [BioWhittaker, Cambrex (cat no. BE12-702F), Belgium] containing L-glutamine (BioWhittaker, cat no. BE17-605E), 25 mM HEPES (BioWhittaker, cat no. 17-737F), and 10% human serum (Etablissement Français du Sang, EFS, Toulouse, France), was used to cultivate P. falciparum (P.f.). Human red blood cells (RBCs; group O±) were obtained from EFS. They were extensively washed with RPMI medium to remove plasma and leucocytes. Leucocyte-free erythrocytes were stored at 50% hematocrit for a maximum period of 21 days. P.f. asexual blood stage parasites were propagated by incubation at 37 • C in P.f. culture media at 3-5% hematocrit in a controlled atmosphere (5% CO 2 , 100% relative humidity). Parasitized RBCs were maintained in 25 cm 2 culture flasks (TPP, Switzerland, ref 90025). The reference drugs chloroquine (CQ) and artesunate were obtained from Sigma (ref C6628) and Cambrex, respectively. CQ was dissolved in culture medium and artesunate in ethanol (stock solutions: 10 mg/mL) and stored at -20 • C prior to testing. For extract assays, serial extract dilutions were made in P.f. culture media and added to 96-well (TPP) culture plates. All samples were tested in triplicate. Plasmodiuminfected RBCs were distributed at 2% parasitaemia (2% hematocrit) in 96-well microtiter plates with different drug concentrations and incubated for 48 h at 37 • C and 5% of CO 2 . [ 3 H]-Hypoxanthine (Perkin-Elmer) was added 24 h after the beginning of the incubation. At the end of incubation (48 h), microtiter plates were frozen and thawed, and each well was harvested onto a glass-fiber filter paper. The quantity of incorporated [ 3 H]-hypoxanthine was determined with a beta-counter (1450-Microbeta Trilux, Wallac-PerkinElmer). Growth-inhibition percentages were plotted as a semi-logarithmic function of drug concentration. The IC 50 values were determined by linear regression analysis on the linear segments of the curves. Assays were repeated three times. Controls were carried out to assess the background (negative control) and parasite growth (positive control).

Cytotoxicity Assay

The cytotoxicity was tested according to the protocol previously reported [START_REF] Nepveu | Synthesis and antiplasmodial activity of new indolone N -oxide derivatives[END_REF]. Cytotoxicity was estimated on human breast cancer cells (MCF7) cultured in the same conditions as those used for P. falciparum, except that 10% human serum was replaced by 10% fetal calf serum (Cambrex). After trypsinization, cells were distributed in 96-well plates at 2 × 10 4 cells/well in 100 μL of culture medium added to 100 μL of the same medium containing the test samples at various concentrations (the final concentrations in the wells were 1, 10, and 100 μg/mL). Cell growth was estimated by a colorimetric assay based on sodium 2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide inner salt (XTT; Sigma, Saint Quentin Fallavier, France) reduction. After 48 h of contact between cells and test compounds the culture medium was replaced by 50 μl of a XTT in water solution (0.5 mg/mL), and cells were incubated for 180 min. XTT was converted into a formazan product, detected at λ 450 nm. IC 50 values were determined graphically from dose-response curves (the positive control being doxorubicin; Sigma). Experiments were performed twice in triplicate.

RESULTS

The main flavonoid or anthranoids present in each extract after purification and identification were emodin, aloe-emodin, and quercetin-3-O-αrhamnpyranoside in petroleum ether (I), chloroform (II), and ethyl acetate (III) extracts, respectively, and emodin diglucoside and aloemodin diglucoside in butanol extract (IV). The four extracts, along with the defatted methanol/water (total extract) (V) of C. roxburghii DC. leaf, were evaluated for their redox molecular pool with different pro-oxidant and antioxidant assays.

To explore whether the extracts possess artemisinin-like and/or an ascorbic acid-like pro-oxidant activities or not, the pro-oxidant capacity of Cassia roxburghii DC. leaf extracts was evaluated using EPR spectroscopy according to three different assays. The results are summarized in Table 1. The ethyl acetate extract (III) demonstrated an ascorbic acid-like pro-oxidant activity.

The antioxidant properties were assessed using DPPH, β-carotenebleaching, and superoxide assays (Table 2). The antioxidant activity of the extracts was expressed as IC 50 and percentage inhibition. The ethyl acetate extract (III) demonstrated the highest antioxidant properties in the DPPH and superoxide radical scavenging assays.

The antimicrobial potential of Cassia roxburghii DC. leaf extracts was evaluated on selected bacterial and fungal strains. Table 3 demonstrates the antimicrobial properties of the extracts. The majority of the extracts exhibited antibacterial activity against Gram-positive bacteria, with MIC values around 125 μg/mL. Interestingly, the Gram-negative strain P. aeruginosa and the yeast C. albicans were more susceptible to the majority of the extracts, with MIC values of 62.5 μg/mL.

Cassia roxburghii DC. leaf extracts were assessed for their antiplasmodial activity against a chloroquine-resistant strain of P. falciparum. The cytotoxicity of the extracts was also evaluated on a human breast cancer cell line (MCF-7). Selectivity index was calculated as the ratio of CC 50 value in MCF-7 cells to IC 50 value in the CQ-resistant P.f . strain FcB1. The results are shown in Table 4.

DISCUSSION

This study allowed identification of two anthraquinones, emodin and aloe-emodin, and two anthraquinone glycosides, emodin diglucoside and aloe-emodin diglucoside, besides quercitrin from C. roxburghii leaves, which have been previously reported in roots and stem bark [START_REF] Pradeep | Effect of Cassia fistula Linn. leaf extract on diethylnitrosamine induced hepatic injury in rats[END_REF] and in other Cassia species such as C. fistula, C. nigricans, and C. tora [START_REF] Ayo | Cytotoxicity and antimicrobial studies of 1,6,8-trihydroxy-3-methyl-anthraquinone (emodin) isolated from the leaves of Cassia nigricans Vahl[END_REF][START_REF] Maity | Metabolism of 1,8-dihydroxy 3-hydroxy methyl anthraquinone (aloe-emodin) isolated from the leaves of Cassia tora in Albino rats[END_REF]. The redox properties of the five extracts were evaluated with six assays, two based on UV-visible absorbance measurements and four on radical species detection by EPR spectroscopy. These different assays were used to compare the redox molecular content of the extracts by analyzing two facets of the molecular redox properties: the pro-oxidant and the antioxidant capacities. Some natural products are pro-oxidant and cannot be antioxidant (e.g., artemisinin). Other compounds may undergo redox cycles and present both properties (pro-oxidant and antioxidant) depending on conditions, such as quinones, pseudo-quinones, and their hydrogen-conjugates (i.e., ascorbic acid).

Table 1 compares the pro-oxidant capacities of the extracts. None of the extracts may produce free radicals by direct oxidation in solution as ascorbic acid does. In the presence of Fe 3+ and H 2 O 2 , all the extracts generated free radicals as ascorbic acid does while artemisinin cannot. These results are in agreement with the presence of quinoid or pseudo-quinoid compounds, some of which were identified in this study. In the presence of Fe 2+ , extracts II-V generate radical species in amounts comparable to those generated by ascorbic acid and much less than that generated by artemisinin.

Altogether, these results show that the extracts do not contain peroxidelike molecules and contain pseudo-quinones or quinone-like molecules that generate free radical species when introduced in a redox cycle. The highest ascorbic acid-like pro-oxidant activity was observed in extract III. It must be noted that extract I showed no activity in two pro-oxidant assays.

By examining the results in Table 2, which compares the antioxidant properties, it appears that extract III is the most active. It demonstrates the highest activity in the DPPH and superoxide assays. In the β-carotene bleaching assay, extract V was slightly more active, which, since this assay discriminates hydrophilic and lipophilic antioxidants, indicates a higher content of lipophilic redox molecules. Extract I was the least active. Emodin, aloe-emodin, and quercitrin (the major components of extract I, II, and III, respectively) have been reported to exhibit free radical scavenging and antioxidant roles [START_REF] Wagner | Quercitrin, a glycoside form of quercetin, prevents lipid peroxidation in vitro[END_REF][START_REF] Yen | Antioxidant activity of anthraquinones and anthrone[END_REF]. Certainly, these compounds are responsible for the interesting antioxidant properties of C. roxburghii leaves, but not alone. The isolated pure compounds showed less antioxidant activity than the tested extracts in the DPPH assay (unpublished results). Other compounds may exert synergistic action.

Altogether, the pro-oxidant/antioxidant capacity of the five extracts shows that extract III has the highest redox molecular content, which can be related to the antiplasmodial properties. All the tested extracts showed moderate antiplasmodial activity against a chloroquine-resistant strain of P. falciparum with IC 50 values ranging from 17.25 to 28.5 μg/mL. The ethyl acetate extract (III) gave the highest antiplasmodial activity. The selectivity indices were calculated after the evaluation of cytotoxicity on a human breast cancer cell line. The most selective was ethyl acetate extract.

These results suggest a relationship between the redox capacity and the antiplasmodial activity of the extracts. The redox properties may be responsible, at least in part, for the observed antiplasmodial activity of C. roxburghii leaves. Oxidative stress has been reported to be a key clinical and pathobiochemical factor as well as an effective therapeutic approach in malaria. Plasmodium creates a fine balance of redox interactions in parasitized red blood cells [START_REF] Becker | Oxidative stress in malaria parasite-infected erythrocytes: host-parasite interactions[END_REF]. Disruption of this redox equilibrium by Cassia redox active extracts may be unfavorable for parasite development.

Extracts I, IV, and V possessed antimicrobial activity with MIC values ranging from 62.5 to 125 μg/mL. Of special interest is the antibacterial activity against the Gram-negative strain P. aeruginosa, which was more susceptible to these extracts (MIC = 62.5 μg/mL) than the Gram-positive strains used in the assay. The extracts exhibited better antifungal properties against C. albicans than A. niger (Table 3). This antimicrobial activity can support the traditional use of the plant in skin infections and in wounds.

The antimicrobial activity of C. roxburghii leaf extracts did not correlate with their redox properties. This may suggest that the redox status is not as critical for microbial growth as it is for plasmodium.

CONCLUSION

Cassia roxburghii leaf extracts possess interesting redox capacity (antioxidant and ascorbic acid-like pro-oxidant activities), moderate antiplasmodial properties, and antimicrobial (antibacterial and antifungal) effects. A direct relation between the interesting redox power and the antiplasmodial properties deserves further evaluation of the role of C. roxburghii leaf extracts in combating oxidative stress induced pathogenesis. The antimicrobial properties described do not correlate with these redox properties, suggesting that redox interactions are not the major factor in the antimicrobial activity.

  C NMR (DMSO-d 6 , 75 MHz): δ (ppm) 156.9 (C-2), 134.6 (C-3), 178.2 (C-4), 161.8 (C-5), 99.3 (C-6), 165.1 (C-7), 94.1 (C-8), 157.7 (C-9), 104.4 (C-10), 121.6 (C-1 ), 115.9 (C-2 ), 145.7 (C-3 ), 149.0 (C-4 ), 116.1 (C-5 ), 121.2 (C-6 ), 102.3 (C-1 ), 70.8 (C-2 ), 71.0 (C-3 ), 71.7 (C-4 ),70.5 (C-5 ), 18.0 (C-6 ). The MS, 1 H NMR, and 13 C NMR data are in agreement with those of quercetin-3-O-α-rhamnpyranoside (9).

a

  MIC: minimum inhibitory concentration (μg/mL).b MBC: minimum bactericidal concentration (μg/mL). c MFC: minimum fungicidal concentration (μg/mL).

  = 1.5 Hz, 1H, H-2 ), 7.24 (d, J = 8.4 Hz, 1H, H-6 ), 6.85 (d, J = 8.4 Hz, 1H, H-5 ), 6.36 (br.s,1H, H-8), 6.19 (br.s, 1H, H-6), 5.25

	• C. EIMS, m/z: 270 [M + ]; 1 H NMR (CDCl 3 ,300 MHz): δ (ppm) 12.04
	(s, 1H, OH-8), 11.97 (s, 1H, OH-1), 7.76 (dd, J = 0.76 and 7.52Hz,1H, H-5),
	7.60 (d, J = 8.1 Hz, 1H, H-6), 7.59 (d, J = 0.4 Hz, 1H, H-4), 7.23 (dd, J =
	0.74 and 8.4 Hz, 1H, H-7), 7.04 (d, J = 0.4 Hz, 1H, H-2), 4.50 (s, 2H, H-3).
	Quercitrin (quercetin 3-O-α-rhamnoside) was obtained from the ethyl
	acetate extract (III). The structural identification of quercetin-3-O-α-
	rhamnopyranoside was carried out by UV, MS, and NMR spectroscopy. UV
	λ max (MeOH): 265, 298sh, 351 nm; (+NaOMe): 271, 326sh, 398 nm; (+AlCl 3 ):
	275, 304, 330, 422 nm; (+AlCl 3 /+HCl): 269, 300sh, 353, 398 nm; (+NaOAc):
	269, 317, 362 nm; (+NaOAc /H 3 BO 3 ): 259, 299, 378 nm. EIMS, m/z: 448 [M
	+ ], 302 [M + -rhamnose unit]. 1 H NMR (DMSO-d 6 , 300 MHz): δ (ppm) 12.60
	(s, 1H, 5-OH), 7.29 (d,J

TABLE 1

 1 Pro-oxidant capacity of Cassia roxburghii DC leaf extracts I1: intensity of the first peak of the EPR signal. b aH = 1.8 Gauss; g = 2.0051. c aN = 14.7 Gauss; aH = 14.5 Gauss; g = 2.0058. d aN = 15.2 Gauss; aH = 21.1 Gauss; g = 2.0053.

		Simple pro-oxidant	Fe 3+ /H 2 O 2 pro-oxidant	Fe 2+ pro-oxidant assay
	Extract		assay	assay I 1 ± SD (RSD %)	I 1 ± SD (RSD %)
	I	Inactive	I 1 = 7317 ± 327 (4 %)	Inactive
				quadruplet c
	II	Inactive	I 1 = 6767 ± 510 (8 %)	I 1 = 517 ± 74 (14 %)
				quadruplet c	sextuplet d
	III	Inactive	I 1 = 9967 ± 332 (3 %)	I 1 = 1857 ± 23 (1 %)
				quadruplet c	sextuplet d
	IV	Inactive	I 1 = 8867 ± 527 (6 %)	I 1 = 1648 ± 139 (8 %)
				quadruplet c	sextuplet d
	V	Inactive	I 1 = 8667 ± 330 (4 %)	I 1 = 2057 ± 29 (1 %)
				quadruplet c	sextuplet d
	artemisinin Inactive	Inactive	I 1 = 6133 ± 464 (8 %)
					sextuplet d
	ascorbic acid I 1	a = 1534	I 1 = 13137	I 1 = 783 ± 73 (9 %)
			doublet b (ascorbyl	doublet b (ascorbyl	doublet b (ascorbyl
			radical)	radical)	radical)

a

TABLE 2

 2 Antioxidant Capacity of Cassia roxburghii DC. Leaf Extracts Concentration required for a 50% decrease in DPPH • absorbance. b % inhibition of the β-carotene bleaching using a 100 mg/L extract solution. c % inhibition of the EPR signal intensity of the reference superoxide radical using a 50 mg/L extract solution. % inhibition of the EPR signal intensity of the reference superoxide radical using a 10 mg/L ascorbic acid solution.

					β-Carotene
		DPPH assay	bleaching assay	Superoxide assay
					Percentage	Percentage
					inhibition b	inhibition c
	Extract	Solvent	IC 50	a (mg/L)	± SD	± SD
	I	EtOH	173.81 ± 4.94	21.39 ± 0.58%	Inactive
	II	EtOH	90.19 ± 2.02	28.50 ± 3.29%	30.08 ± 2.49%
	III	EtOH	18.36 ± 0.09	33.85 ± 6.31%	75.00 ± 1.16%
	IV	ACN/H 2 O	88.00 ± 3.30	34.82 ± 0.97%	53.03 ± 0.62%
	V	A C N /H 2 O	69.05 ± 0.74	39.72 ± 2.77%	59.38 ± 2.77%
	Trolox	EtOH	8.36 ± 0.05	76.35 ± 0.39%	ND d
	Vitamin C	ACN/H 2 O	3.81 ± 0.08	Inactive	100% e
	a

d Not determined. e

TABLE 3

 3 Antibacterial and Antifungal Activity of Cassia roxburghii DC. Leaf Extracts (all concentrations expressed in

	μg/mL)

TABLE 4

 4 Antiplasmodial Activity and Cytotoxicity of Cassia roxburghii DC. Leaf Extracts IC 50 ± SD (μg/mL) CC 50 ± SD (μg/mL) a CC 50 (μg/mL): drug concentration needed to cause 50% decrease of the cellular viability.

	Selectivity

a b Ref.

[START_REF] Nepveu | Synthesis and antiplasmodial activity of new indolone N -oxide derivatives[END_REF]

.

ACKNOWLEDGEMENTS

The authors thank Terase Labib for authentification of plant samples, and Eliane Pelissou and Severine Maurel for technical assistance.

FUNDING

Thanks are due to the French Centre for Culture and for Cooperation and French Embassy in Cairo, Egypt, for awarding a fellowship to S. S. M. El Souda.