Phytochemical and In Silico ADME/Tox Analysis of Eruca sativa Extract with Antioxidant,Antibacterial and Anticancer Potential against Caco-2 and HCT-116 Colorectal Carcinoma Cell Lines

Eruca sativa Mill. (E. sativa) leaves recently grabbed the attention of scientific communities around the world due to its potent bioactivity. Therefore, the present study investigates the metabolite profiling of the ethanolic crude extract of E. sativa leaves using high resolution-liquid chromatography-mass spectrometry (HR-LC/MS), including antibacterial, antioxidant and anticancer potential against human colorectal carcinoma cell lines. In addition, computer-aided analysis was performed for determining the pharmacokinetic properties and toxicity prediction of the identified compounds. Our results show that E. sativa contains several bioactive compounds, such as vitamins, fatty acids, alkaloids, flavonoids, terpenoids and phenols. Furthermore, the antibacterial assay of E. sativa extract showed inhibitory effects of the tested pathogenic bacterial strains. Moreover, the antioxidant activity of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydrogen peroxide (H₂O₂) were found to be IC₅₀ = 66.16 μg/mL and 76.05 μg/mL, respectively. E. sativa also showed promising anticancer activity against both the colorectal cancer cells HCT-116 (IC₅₀ = 64.91 μg/mL) and Caco-2 (IC₅₀ = 83.98 μg/mL) in a dose/time dependent manner. The phytoconstituents identified showed promising pharmacokinetics properties, representing a valuable source for drug or nutraceutical development. These investigations will lead to the further exploration as well as development of E. sativa-based nutraceutical products.     

Isolated singularities of polyharmonic inequalities

We study nonnegative classical solutions u of the polyharmonic inequality

     

A Banking Platform to Leverage Data Driven Marketing with Machine Learning

Payment data is one of the most valuable assets that retail banks can leverage as the major competitive advantage with respect to new entrants such as Fintech companies or giant internet companies. In marketing, the value behind data relates to the power of encoding customer preferences: the better you know your customer, the better your marketing strategy. In this paper, we present a B2B2C lead generation application based on payment transaction data within the online banking system. In this approach, the bank is an intermediary between its private customers and merchants. The bank uses its competence in Machine Learning driven marketing to build a lead generation application that helps merchants run data driven campaigns through the banking channels to reach retail customers. The bank’s retail customers trade the utility hidden in its payment transaction data for special offers and discounts offered by merchants. During the entire process banks protects the privacy of the retail customer.     

Acoustic detection and classification of river boats

We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces false alarms rate. The proposed algorithm is a generic solution for process control that is based on a learning phase (training) followed by an automatic real time detection while minimizing the false alarms rate.     

On the nuclear modification factor at RHIC and LHC

We show that pQCD factorization incorporated with pre-hadronization energy-loss effect naturally leads to flatness of the nuclear modification factor RAA$R_{\mathit{AA}}$ for produced hadrons at high transverse momentum pT$p_T$. We consider two possible scenarios for the pre-hadronization: In scenario 1, the produced gluon propagates through dense QCD medium and loses energy. In scenario 2, all gluons first decay to quark–antiquark pairs and then each pair loses energy as propagating through the medium. We show that the estimates of the energy-loss in these two different models lead to very close values and is able to explain the suppression of high-pT$p_T$ hadrons in nucleus–nucleus collisions at RHIC. We show that the onset of the flatness of RAA$R_{\mathit{AA}}$ for the produced hadron in central collisions at midrapidity is about pT≈15$p_T\approx 15$ and 25 GeV at RHIC and the LHC energies, respectively. We show that the smallness (RAA<0.5$R_{\mathit{AA}}<0.5$ ) and the high-pT$p_T$ flatness of RAA$R_{\mathit{AA}}$ obtained from the kT$k_T$ factorization supplemented with the Balitsky–Kovchegov (BK) equation is rather generic and it does not strongly depend on the details of the BK solutions. We show that energy-loss effect reduces the nuclear modification factor obtained from the kT$k_T$ factorization about 30–50% at moderate pT$p_T$.     

Transition metal-metal oxide hybrids as versatile materials for hydrogen storage

Metal atom located on metal oxide (MMO) is a promising material with various applications such as hydrogen storage. As one of the metal oxides, niobium oxide (NbO) presents fascinating properties that make it a possibly applicable in MMOs. Here, we investigated the feasibility of transition metal-NbO hybrids as MMO materials for application in the hydrogen storage technology. In this respect, the hydrogen adsorption of transition metals (Fe, Ni, Cu, Pd, Ag, and Pt) decorated on the NbO nanocluster has been explored using density functional theory calculations. We found that the adsorption energy of the H₂ molecule on the NbO adsorbent is remarkably increased by locating the transition metals on the NbO metal oxide. Our results reveal that the transition metals decorated on the NbO nanocluster can act as active sites for hydrogen adsorption. Among the studied transition metals, Pt shows the highest hydrogen capacity up to 6.52 wt%.     

Iron Precursor Decomposition in the Magnesium Combustion Flame: A New Approach for the Synthesis of Particulate Metal Oxide Nanocomposites

Powders of Fe–Mg–O nanocomposite particles have been grown using a novel chemical vapor synthesis approach that employs the decomposition of a metalorganic precursor inside the metal combustion flame. After annealing in controlled gas atmospheres composition distribution functions, structure and phase stability of the obtained magnesiowüstite nanoparticles are measured with a combination of techniques such as inductively coupled plasma‐optical emission spectroscopy, energy dispersive X‐ray spectroscopy, X‐ray diffraction, and scanning and transmission electron microscopy. Complementary Mössbauer spectroscopy measurements reveal that depending on Fe loading and temperature of annealing either metastable and superparamagnetic solid solutions of Fe³⁺ ions in periclase (MgO) or phase separated mixtures of MgO and ferrimagnetic magnesioferrite (MgFe₂O₄) nanoparticles can be obtained. The described combustion technique represents a novel concept for the production of mixed metal oxide nanoparticles. Adressing the impact of selected annealing protocols, this study underlines the great potential of vapor phase grown non‐equilibrium solids, where thermal processing provides means to trigger phase separation and, concomitantly, the emergence of new magnetic properties.     

ZnO thin film characterization by X-ray reflectivity optimization using genetic algorithm and Fourier transformation

Zinc oxide (ZnO) thin film was fabricated by sol-gel spin coating method on glass substrate. X-ray reflectivity (XRR) and its optimization have been used for characterization and extracting physical parameters of the film. Genetic algorithm (GA) has been applied for this optimization process. The model independent information was needed to establish data analyzing process for X-ray reflectivity before optimization process. Independent information was exploited from Fourier transform of Fresnel reflectivity normalized X-ray reflectivity. This Fourier transformation (Auto Correlation Function) yields thickness of each coated layer on substrate. This information is a keynote for constructing optimization process. Specular X-ray reflectivity optimization yields structural parameters such as thickness, roughness of surface and interface and electron density profile of the film. Acceptable agreement exists between results obtained from Fourier transformation and X-ray reflectivity fitting.     

On the cover time of random walks on graphs

This article deals with random walks on arbitrary graphs. We consider the cover time of finite graphs. That is, we study the expected time needed for a random walk on a finite graph to visit every vertex at least once. We establish an upper bound ofO(n ²) for the expectation of the cover time for regular (or nearly regular) graphs. We prove a lower bound of (n logn) for the expected cover time for trees. We present examples showing all our bounds to be tight.Mike Saks was supported by NSF-DMS87-03541 and by AFOSR-0271. Jeff Kahn was supported by MCS-83-01867 and by AFOSR-0271.     

Reductions of points on elliptic curves

Let E be an elliptic curve defined over \mathbbQ{\mathbb{Q}}. Let Γ be a subgroup of rank r of the group of rational points E(\mathbbQ){E(\mathbb{Q})} of E. For any prime p of good reduction, let [`(G)]{\bar{\Gamma}} be the reduction of Γ modulo p. Under certain standard assumptions, we prove that for almost all primes p (i.e. for a set of primes of density one), we have