ANTIBACTERIAL EFFECT AND PHYTOCHEMICAL ANALYSIS OF THE SHOOT SYSTEM OF RUBUS CANESCENS DC. GROWING IN LEBANON

The misuse of antibiotics followed by improved fitness of resistant strains of infectious microorganisms hindered the efficacy of many known antimicrobial agents, and fueled research for the discovery of novel remedies. The current study aims at assessing the antimicrobial activity and understanding the mechanism of action of Rubus canescens DC. growing wild in Lebanon, as well as qualitatively determining its phytochemical profile. The antibacterial activity, MIC, and MBC of the extracts were evaluated by two-fold dilution. Time-kill curves were plotted to assess the bactericidal activity of the R. canescens DC. extracts against the growth of microorganisms, and TEM images were collected to confirm such effect. Overall, the extracts exhibited good antibacterial activity against MRSA and E. coli but not against S. pneumoniae and K. pneumoniae as determined by measuring the inhibition zones in plate-diffusion assays. TEM images of treated microorganisms revealed that the R. canescens DC. extracts induced irreversible deformations and damage to the cell membranes of the microorganisms leading to the leakage of cytoplasmic components and eventual cell death. Analysis of Time-Kill curves indicated that the extracts induced 100% killing of the test microorganisms within 10-18 h at the respective MBC. Finally, qualitative phytochemical analysis was conducted to decipher the active ingredients in the plant extracts.The current study reports the first data on the antimicrobial activity of different parts of R. canescens DC. Such promising data opens new avenues for broader assessment of the pharmacological profile of the scarcely investigated R. canescens DC

Machine à vecteurs de support hyperbolique et ingénierie du noyau

Statistical learning theory is a field of inferential statistics whose foundations were laid by Vapnik at the end of the 1960s. It is considered a subdomain of artificial intelligence. In machine learning, support vector machines (SVM) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. In this thesis, our aim is to propose two new statistical learning problems: one on the conception and evaluation of a multi-class SVM extension and another on the design of a new kernel for support vectors machines. First, we introduced a new kernel machine for multi-class pattern recognition : the hyperbolic support vector machine. Geometrically, it is characterized by the fact that its decision boundaries in the feature space are defined by hyperbolic functions. We then established its main statistical properties. Among these properties we showed that the classes of component functions are uniform Glivenko-Cantelli, this by establishing an upper bound of the Rademacher complexity. Finally, we establish a guaranteed risk for our classifier. Second, we constructed a new kernel based on the Fourier transform of a Gaussian mixture model. We proceed in the following way: first, each class is fragmented into a number of relevant subclasses, then we consider the directions given by the vectors obtained by taking all pairs of subclass centers of the same class. Among these are excluded those allowing to connect two subclasses of two different classes. We can also see this as the search for translation invariance in each class. It successfully on several datasets in the context of machine learning using multiclass support vector machines.La théorie statistique de l’apprentissage est un domaine de la statistique inférentielle dont les fondements ont été posés par Vapnik à la fin des années 60. Il est considéré comme un sous-domaine de l’intelligence artificielle. Dans l’apprentissage automatique, les machines à vecteurs de support (SVM) sont un ensemble de techniques d’apprentissage supervisé destinées à résoudre des problèmes de discrimination et de régression. Dans cette thèse, notre objectif est de proposer deux nouveaux problèmes d’apprentissage statistique: Un portant sur la conception et l’évaluation d’une extension des SVM multiclasses et un autre sur la conception d’un nouveau noyau pour les machines à vecteurs de support. Dans un premier temps, nous avons introduit une nouvelle machine à noyau pour la reconnaissance de modèle multi-classe: la machine à vecteur de support hyperbolique. Géométriquement, il est caractérisé par le fait que ses surfaces de décision dans l’espace de redescription sont définies par des fonctions hyperboliques. Nous avons ensuite établi ses principales propriétés statistiques. Parmi ces propriétés nous avons montré que les classes de fonctions composantes sont des classes de Glivenko-Cantelli uniforme, ceci en établissant un majorant de la complexité de Rademacher. Enfin, nous établissons un risque garanti pour notre classifieur.Dans un second temps, nous avons créer un nouveau noyau s’appuyant sur la transformation de Fourier d’un modèle de mélange gaussien. Nous procédons de la manière suivante: d’abord, chaque classe est fragmentée en un nombre de sous-classes pertinentes, ensuite on considère les directions données par les vecteurs obtenus en prenant toutes les paires de centres de sous-classes d’une même classe. Parmi celles-ci, sont exclues celles permettant de connecter deux sous-classes de deux classes différentes. On peut aussi voir cela comme la recherche d’invariance par translation dans chaque classe. Nous l’avons appliqué avec succès sur plusieurs jeux de données dans le contexte d’un apprentissage automatique utilisant des machines à vecteurs support multi-classes

MOLYBDENUM VERSUS TUNGSTEN BASED POLYOXOMETALATES FOR HIGHLY EFFECTIVE METHYLENE BLUE REMOVAL

[SiW12O40]4-(SiW12) and [Mo8O26]4- (Mo8) were synthesized and characterized by Fourier Transform Infrared spectroscopy (FTIR). The prepared polyoxometalates were studied as potential adsorbents for the removal of methylene blue (MB) from an aqueous solution. Various operational parameters—contact time, adsorbent dose, initial dye concentration, pH, and temperature— were meticulously assessed by UV/Vis spectrophotometry to study its impact on the adsorption efficacy. An inverse relationship was observed between the percentage dye removal and the initial dye concentration, highlighting the complexes\u27 superior adsorption capabilities under lower dye loads. The studied complexes displayed significantly better efficiency under acidic conditions. Both SiW12 and Mo8 exhibited high percentages of removal for methylene blue within only 10 min for MO8 and 5 min for SiW12. These results not only underline the proficiency of SiW12 and Mo8 as adsorbents, but also position them as promising candidates for fast and effective water purification strategies in the face of escalating environmental pollution challenges

CHEMICAL COMPOSITION, ANTIOXIDANT AND HEMOLYTIC ACTIVITIES OF SAGE (SALVIA FRUTICOSA MILLER) CULTIVATED IN LEBANON

As part of the efforts contributing towards encouraging the cultivation of commercially valuable medicinal and aromatic plants and the conservation of vulnerable wild species suffering from depletion due to destructive unsustainable harvesting from the wild, we set out to assess the in vitro antioxidant activity, decipher the phytochemical profile, and evaluate the hemolytic activity of Salvia fruticosa Miller cultivated at Beirut Arab University herbal garden in Bekaa, Eastern Lebanon. The chemical compositions of the methanolic, aqueous and essential oil extracts were assessed by GC-MS analysis. In addition, the total phenolic, total flavonoid, total carbohydrate and total protein contents were determined for the methanolic and aqueous extracts. The antioxidant activity of all samples was evaluated using the DPPH radical scavenging, β-carotene bleaching, superoxide radical scavenging, reducing power and metal chelating activity assays. The overall analysis of data revealed that the methanolic and aqueous extracts exhibited potent antioxidant activity while the essential oil showed weak activity. Furthermore, strong correlation was found between the antioxidant activities and phytochemicals content. Finally, the cytotoxicity of the essential oil and extracts against human erythrocytes was assessed using the hemolysis assay. The aqueous extract did not show any hemolytic effect within the used concentration range. On the other hand, the methanolic extract showed a weak hemolytic activity, while the essential oil showed high hemolytic activity at the highest concentration used. The collective analysis of the data offered an encouraging evidence for the cultivation of commercially valuable medicinal aromatic plants (MAPs) such as S. fruticosa Miller as a supportive measure for the Lebanese economy

Solvatochromic absorption and fluorescence studies of adenine, thymine and uracil thio-derived acyclonucleosides

Adenine, thymine and uracil thio-derived acyclonucleosides were synthesized and characterized by UV-Vis, FT-IR, 1H and 13C NMR spectroscopic techniques. The photophysical properties of the derivatives were evaluated in solvents with diverse polarities and at various pH values. The solvent dependent absorbance and emission spectral shifts were analysed using physical parameters of the selected solvents. The regression and correlation coefficients were calculated using multiple regression techniques. The fitting coefficients gave an estimate of the contribution of each interaction to the total spectral shift in various solutions. Multiple linear regression studies, Kamlet-Taft equation and stokes shift correlation with orientation polarizability provide valuable information concerning spectroscopic characteristics of the studied molecules

PALLADIUM (II)-CATALYZED SELECTIVE REDUCTION OF 4’-(PHENYLETHYNL)ACETOPHENONE IN THE PRESENCE OF A FORMIC ACID-TRIETHYLAMINE MIXTURE

An efficient and straightforward palladium acetylacetonate-catalyzed hydrogen transfer of 4\u27- (phenylethynyl)acetophenone was developed in this study. Formic Acid was found to be the best hydrogen source in this catalytic system in the presence of triethylamine. Excellent conversions and selectivity were obtained in reducing the starting internal aromatic alkyne to either (E)-1-(4- styrylphenyl)ethanone or an interesting cyclic product, 1-(phenanthrene-3-yl)ethenone, over the ketone functional group present. Over-reduction was rarely seen. The reaction conditions were optimized in terms of the choice of the palladium catalyst, temperature, solvent, and the H-donor/base combination. Using this catalytic system, a one-step synthetic pathway of the hindered cyclic ketone was afforded in excellent yields

Radiosensitizing and Hyperthermic Properties of Hyaluronan Conjugated, Dextran-Coated Ferric Oxide Nanoparticles: Implications for Cancer Stem Cell Therapy

Cytotoxicity, radiosensitivity, and hyperthermia sensitivity of hyaluronan-mediated dextran-coated super paramagnetic iron oxide nanoparticles (HA-DESPIONs) were assessed in CD44-expressing head and neck squamous cell carcinoma (HNSCC) cell lines at clinically relevant radiation dose and temperatures. Low-passage HNSCC cells were exposed to HA-DESPIONs and cytotoxicity was assessed using MTT assay. Radiosensitizing properties of graded doses of HA-DESPIONs were assessed in both unsorted and CD44-sorted cells using clonogenic assay in combination with 2 Gy exposure to X-rays. Hyperthermia-induced toxicity was measured at 40°C, 41°C, and 42°C using clonogenic assay. Cell death was assessed 24 hours after treatment using a flow cytometry-based apoptosis analysis. Results showed that HA-DESPIONs were nontoxic at moderate concentrations and did not directly radiosensitize the cell lines. Further, there was no significant difference in the radiosensitivity of CD44high and CD44low cells. However, HA-DESPIONs enhanced the effect of hyperthermia which resulted in reduced cell survival that appeared to be mediated through apoptosis. We demonstrated that HA-DESPIONs are nontoxic and although they do not enhance radiation sensitivity, they did increase the effect of local hyperthermia. These results support further development of drug-attached HA-DESPIONs in combination with radiation for targeting cancer stem cells (CSCs) and the development of an alternating magnetic field approach to activate the HA-DESPIONs attached to CSCs

Machine à vecteurs de support hyperbolique et ingénierie du noyau

Statistical learning theory is a field of inferential statistics whose foundations were laid by Vapnik at the end of the 1960s. It is considered a subdomain of artificial intelligence. In machine learning, support vector machines (SVM) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. In this thesis, our aim is to propose two new statistical learning problems: one on the conception and evaluation of a multi-class SVM extension and another on the design of a new kernel for support vectors machines. First, we introduced a new kernel machine for multi-class pattern recognition : the hyperbolic support vector machine. Geometrically, it is characterized by the fact that its decision boundaries in the feature space are defined by hyperbolic functions. We then established its main statistical properties. Among these properties we showed that the classes of component functions are uniform Glivenko-Cantelli, this by establishing an upper bound of the Rademacher complexity. Finally, we establish a guaranteed risk for our classifier. Second, we constructed a new kernel based on the Fourier transform of a Gaussian mixture model. We proceed in the following way: first, each class is fragmented into a number of relevant subclasses, then we consider the directions given by the vectors obtained by taking all pairs of subclass centers of the same class. Among these are excluded those allowing to connect two subclasses of two different classes. We can also see this as the search for translation invariance in each class. It successfully on several datasets in the context of machine learning using multiclass support vector machines.La théorie statistique de l’apprentissage est un domaine de la statistique inférentielle dont les fondements ont été posés par Vapnik à la fin des années 60. Il est considéré comme un sous-domaine de l’intelligence artificielle. Dans l’apprentissage automatique, les machines à vecteurs de support (SVM) sont un ensemble de techniques d’apprentissage supervisé destinées à résoudre des problèmes de discrimination et de régression. Dans cette thèse, notre objectif est de proposer deux nouveaux problèmes d’apprentissage statistique: Un portant sur la conception et l’évaluation d’une extension des SVM multiclasses et un autre sur la conception d’un nouveau noyau pour les machines à vecteurs de support. Dans un premier temps, nous avons introduit une nouvelle machine à noyau pour la reconnaissance de modèle multi-classe: la machine à vecteur de support hyperbolique. Géométriquement, il est caractérisé par le fait que ses surfaces de décision dans l’espace de redescription sont définies par des fonctions hyperboliques. Nous avons ensuite établi ses principales propriétés statistiques. Parmi ces propriétés nous avons montré que les classes de fonctions composantes sont des classes de Glivenko-Cantelli uniforme, ceci en établissant un majorant de la complexité de Rademacher. Enfin, nous établissons un risque garanti pour notre classifieur.Dans un second temps, nous avons créer un nouveau noyau s’appuyant sur la transformation de Fourier d’un modèle de mélange gaussien. Nous procédons de la manière suivante: d’abord, chaque classe est fragmentée en un nombre de sous-classes pertinentes, ensuite on considère les directions données par les vecteurs obtenus en prenant toutes les paires de centres de sous-classes d’une même classe. Parmi celles-ci, sont exclues celles permettant de connecter deux sous-classes de deux classes différentes. On peut aussi voir cela comme la recherche d’invariance par translation dans chaque classe. Nous l’avons appliqué avec succès sur plusieurs jeux de données dans le contexte d’un apprentissage automatique utilisant des machines à vecteurs support multi-classes

Hyperbolic Support Vector Machine and Kernel design

La théorie statistique de l’apprentissage est un domaine de la statistique inférentielle dont les fondements ont été posés par Vapnik à la fin des années 60. Il est considéré comme un sous-domaine de l’intelligence artificielle. Dans l’apprentissage automatique, les machines à vecteurs de support (SVM) sont un ensemble de techniques d’apprentissage supervisé destinées à résoudre des problèmes de discrimination et de régression. Dans cette thèse, notre objectif est de proposer deux nouveaux problèmes d’aprentissagestatistique: Un portant sur la conception et l’évaluation d’une extension des SVM multiclasses et un autre sur la conception d’un nouveau noyau pour les machines à vecteurs de support. Dans un premier temps, nous avons introduit une nouvelle machine à noyau pour la reconnaissance de modèle multi-classe: la machine à vecteur de support hyperbolique. Géometriquement, il est caractérisé par le fait que ses surfaces de décision dans l’espace de redescription sont définies par des fonctions hyperboliques. Nous avons ensuite établi ses principales propriétés statistiques. Parmi ces propriétés nous avons montré que les classes de fonctions composantes sont des classes de Glivenko-Cantelli uniforme, ceci en établissant un majorant de la complexité de Rademacher. Enfin, nous établissons un risque garanti pour notre classifieur.Dans un second temps, nous avons créer un nouveau noyau s’appuyant sur la transformation de Fourier d’un modèle de mélange gaussien. Nous procédons de la manière suivante: d’abord, chaque classe est fragmentée en un nombre de sous-classes pertinentes, ensuite on considère les directions données par les vecteurs obtenus en prenant toutes les paires de centres de sous-classes d’une même classe. Parmi celles-ci, sont exclues celles permettant de connecter deux sous-classes de deux classes différentes. On peut aussi voir cela comme la recherche d’invariance par translation dans chaque classe. Nous l’avons appliqué avec succès sur plusieurs jeux de données dans le contexte d’un apprentissage automatique utilisant des machines à vecteurs support multi-classes.Statistical learning theory is a field of inferential statistics whose foundations were laid by Vapnik at the end of the 1960s. It is considered a subdomain of artificial intelligence. In machine learning, support vector machines (SVM) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. In this thesis, our aim is to propose two new statistical learning problems : one on the conception and evaluation of a multi-class SVM extension and another on the design of a new kernel for support vectors machines. First, we introduced a new kernel machine for multi-class pattern recognition : the hyperbolic support vector machine. Geometrically, it is characterized by the fact that its decision boundaries in the feature space are defined by hyperbolic functions. We then established its main statistical properties. Among these properties we showed that the classes of component functions are uniform Glivenko-Cantelli, this by establishing an upper bound of the Rademacher complexity. Finally, we establish a guaranteed risk for our classifier. Second, we constructed a new kernel based on the Fourier transform of a Gaussian mixture model. We proceed in the following way: first, each class is fragmented into a number of relevant subclasses, then we consider the directions given by the vectors obtained by taking all pairs of subclass centers of the same class. Among these are excluded those allowing to connect two subclasses of two different classes. We can also see this as the search for translation invariance in each class. It successfully on several datasets in the context of machine learning using multiclass support vector machines