In Vitro Evaluation of Antibacterial and Anticoagulant Activities of Harmala Alkaloids Roots

Peganum harmala L. is a wild herbal plant rich in active compounds, and is among the most used plants in local medicine. The purpose of this study is to contribute to the evaluation of some biological activities of alkaloids extract of P. harmala roots. Using the classical method of extraction of β-carboline alkaloids, the yield of alkaloids in the roots was estimated at 2.34 %. The thin layer chromatography (TLC) method identified the two components Harmine and Hrmaline in the total alkaloid extract of the roots. We evaluated the antibacterial activity of harmala alkaloids in roots against three referenced bacterial strains, and this using the agar medium diffusion method, all extracts showed significant activity against all tested bacterial strains, the largest inhibition zone diameter of 20 mm was recorded with Staphylococcus aureus at a concentration of 50 mg/ml of crude alkaloids. The anticoagulant activity of alkaloid extracts was also examined using the prothrombin time (PT) and partial activation thromboplastin time (APTT) tests. The clotting times obtained in normal plasma indicate that they have good activity in both coagulation pathways compared with sodium heparin, especially in the prolongation of blood clotting in the intrinsic pathway

Cribado fitoquímico y evaluación de la actividad antimicrobiana del extractometanólico de Ficus carica

La higuera (Ficus caricaLinn.) fue apreciada como alimento y por sus propiedades medicinales, crece en la región mediterránea y se adapta bien a las condiciones de Argelia. El uso de recursos naturales para tratar y curar enfermedades es un método antiguo y aún muy extendido. El objetivo deeste trabajo fue evaluar la actividad antibacteriana que existe a través de extractos metanólicosde hojas de higuera cultivadas en el medio argelino. El ensayo antibacteriano se llevó a cabo mediante el método de difusión en disco para medir el diámetro de la zona de inhibición en la placa de agar Müller-Hinton contra cuatro cepas de bacterias seleccionadas Staphylococcus aureus(Gram positivas) y Pseudomonas aeruginosa,Escherichia coli,Klebsiella pneumonia(Gram negativas), además de la detección de algunos compuestos activos se llevó a cabo mediante cribado fisicoquímico. El resultado obtenido mostró que los extractos de F. caricarevelaron la presencia de flavonoides, saponinas, taninos, alcaloides. La presencia de metabolitos secundarios elaborados en estos extractos es la causa del potencial antimicrobiano observado. En consecuencia, todos los extractos exhibieron el efecto bactericida hacia las bacterias ensayadas, mientras que el extracto crudo de metanol fue más activo contra las bacterias Gram positivas que contra las Gram negativas. En este estudio se destacó el potencial de desarrollo de antibióticos alternativos derivados del extracto metanólico de hojas de higueraFig tree (FicuscaricaLinn.) was appreciated as food and for its medicinal properties, it grows in Mediterranean region, and it is admirably adaptedto the conditions of Algeria. The use of natural resources to treat and cure diseases is an old and still widespread method.Theobjective of this work was to evaluate the antibacterial activitythat existsthrough methanolic extractsof fig leaves grown in the Algerian environment.Antibacterial assay was carried out via disc diffusion method to measure the diameter of the zone of inhibition on the Müller-Hinton agar plateagainst four selected bacteria strains Staphylococcus aureus (Gram positive) and Pseudomonas aeruginosa, Escherichia coli,klebsiella pneumonia(Gram negative), in addition to the detection of some active compounds was carried out by phychemical screening.The result obtained showed that F.caricaextracts revealed the presence offlavonoids, saponins, tannin, alkaloids. The presence of secondary metabolites made in these extracts is the cause of the observed antimicrobial potential. Consequently, all extracts exhibited the bactericidal effect towards the bacteria tested, while the crude extract of methanol was active against Gram positive bacteria more than Gramnegative bacteria.In this study, the potential for development of alternative antibiotics derived from the methalonic extract of fig leaves was highlighte

A Framework for Statistically-Sound Customer Segment Search Authors' Copy

International audienceWe develop S4, a Statistically-Sound Segment Search framework that combines principled data partitioning and sound statistical testing to verify common hypotheses in retail data and return interpretable customer data segments. Our framework accommodates one-sample, two-sample, and multiple-sample testing, to provide various aggregations and comparisons of customer transactions. To control the proportion of false discoveries in multiple hypothesis testing, we enforce an FDR-controlling procedure and formulate a unified optimization problem that returns customer data segments that satisfy the test for a given significance level, maximize coverage of the input data, and are within a risk capital. We develop a greedy algorithm to explore different data partitions and test multiple hypotheses in a sound manner. Our extensive experiments on four retail data sets examine the interaction between significance, risk and coverage, and demonstrate the expressivity, usefulness, and scalability of S4 in practice

QeNoBi: A System for QuErying and miNing BehavIoral Patterns Authors' Copy

International audienceWe demonstrate QeNoBi, a system for mining and querying customer behavioral patterns. QeNoBi combines an interactive visual interface, on-demand mining, and efficient topk processing, to provide the exploration of customer behavior over time. QeNoBi relies on two distinct data models: a customercentric graph that represents customers with similar purchasing behaviors and is annotated with a change algebra to reflect their behavior evolution, and product-centric time series that reflect the evolution of customer purchases over time. Users can query both representations along three dimensions: shape (the sketched trend of the behavior), scope (the set of customers/products of interest), and time granularity. QeNoBi provides a holistic behavior exploration capability by allowing users to seamlessly switch between customer-centric and product-centric views in a coordinated manner, thereby catering to various needs. A demonstration of QeNoBi is available at https://bit.ly/2HlcO3S

Cytokinins and ethylene stimulate indole alkaloid accumulation in cell suspension cultures of Catharanthus roseus by two distinct mechanisms

peer reviewedThe interactions between cytokinins and ethylene on alkaloid accumulation in a periwinkle cell line have been examined. It was found that (a) either exogenously-applied cytokinins or ethylene (the latter through ethephon degradation) greatly enhanced ajmalicine accumulation in cells subcultured in a 2,4-dichlorophenoxyacetic acid-free medium; (b) the enhancing effect of cytokinin was not mediated by enhancement of endogenous ethylene production contrary to what is found in some plant models, (c) the responses to exogenous cytokinin and ethylene were additive and showed a different pattern of expression. It may be concluded that cytokinin and ethylene can up-regulate the alkaloid production in a periwinkle cells through independent pathways when added exogenously to the cultures. (C) 1998 Elsevier Science Ireland Ltd. All rights reserved

COVID-19 detection from Xray and CT scans using transfer learning

Abstract Since the novel coronavirus SARS-CoV-2 outbreak, intensive research has been conducted to find suitable tools for diagnosis and identifying infected people in order to take appropriate action. Chest imaging plays a significant role in this phase where CT and Xrays scans have proven to be effective in detecting COVID-19 within the lungs. In this research, we propose deep learning models using Transfer learning to detect COVID-19. Both X-ray and CT scans were considered to evaluate the proposed methods

An ML-Powered Human Behavior Management System

International audienceOur work aims to develop novel technologies for building an efficient data infrastructure as a backbone for a human behavior management system. Our infrastructure aims at facilitating behavior modeling, discovery, and exploitation, leading to two major outcomes: a behavior data management back-end and a high-level behavior specification API that supports mining, indexing and search, and AI-powered algorithms that provide the ability to extract insights on human behavior and to leverage data to advance human capital. We discuss the role of ML in populating and maintaining the back-end, and in exploiting it for human interest