Antimicrobial Activity and Synergy Investigation of Hypericum scabrum Essential Oil with Antifungal Drugs

The chemical composition of Lebanese Hypericum scabrum essential oil (EO) was analyzed by gas chromatography (GC) and gas chromatography-mass spectrometry (GG-MS). Its antimicrobial activity was evaluated by determining its minimal inhibitory concentrations (MICs) against a Gram-negative and a Gram-positive bacterium, one yeast, and five dermatophytes. H. scabrum EO was most active on filamentous fungi (MIC values of 32–64 µg/mL). Synergy within the oil was investigated by testing each of the following major components on Trichophyton rubrum: α-pinene, limonene, myrcene, β-pinene and nonane, as well as a reconstructed EO. The antifungal activity of the natural oil could not be reached, meaning that its activity might be due, in part, to minor constituent(s). The interactions between H. scabrum EO and commercially available antifungals were assessed by the checkerboard test. A synergistic effect was revealed in the combination of the EO with amphotericin B.     

A microstructure-based model for time-dependent mechanics of multi-layered soft tissues and its application to intervertebral disc annulus

Meccanica - In recent experimental studies an unusual time-dependent transversal behavior of the annulus fibrosus of the intervertebral disc mainly caused by the coupling between mechanics and...     

The Dual Hilbert–Samuel Function of a Maximal Cohen-Macaulay Module

Let R be a Cohen–Macaulay local ring with a canonical module ω _R . Let I be an 𝔪-primary ideal of R and M, a maximal Cohen–Macaulay R-module. We call the function n??(Hom _R (M, ω _R /I ⁿ⁺¹ω _R )) the dual Hilbert–Samuel function of M with respect to I . By a result of Theodorescu, this function is of polynomial type. We study its first two normalized coefficients. In particular, we analyze the case when R is Gorenstein.     

Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages

This paper deals with the problem of inference in distributed systems where the probability model is stored in a distributed fashion. Graphical models provide powerful tools for modeling this kind of problems. Inspired by the box particle filter which combines interval analysis with particle filtering to solve temporal inference problems, this paper introduces a belief propagation-like message-passing algorithm that uses bounded error methods to solve the inference problem defined on an arbitrary graphical model. We show the theoretic derivation of the novel algorithm and we test its performance on the problem of calibration in wireless sensor networks. That is the positioning of a number of randomly deployed sensors, according to some reference defined by a set of anchor nodes for which the positions are known a priori. The new algorithm, while achieving a better or similar performance, offers impressive reduction of the information circulating in the network and the needed computation times.