Role of Macrophages and RhoA Pathway in Atherosclerosis

The development, progression, or stabilization of the atherosclerotic plaque depends on the pro-inflammatory and anti-inflammatory macrophages. The influx of the macrophages and the regulation of macrophage phenotype, inflammatory or anti-inflammatory, are controlled by the small GTPase RhoA and its downstream effectors. Therefore, macrophages and the components of the RhoA pathway are attractive targets for anti-atherosclerotic therapies, which would inhibit macrophage influx and inflammatory phenotype, maintain an anti-inflammatory environment, and promote tissue remodeling and repair. Here, we discuss the recent findings on the role of macrophages and RhoA pathway in the atherosclerotic plaque formation and resolution and the novel therapeutic approaches.     

Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.     

Effect of extraction methods on kinetic,chemical composition and antibacterial activities of Tunisian Thymus vulgaris. L. essential oil

The present study aims to compare two innovative extraction techniques: microwave-assisted hydrodistillation (MAHD) and solvent-free microwave extraction (SFME) through traditional extraction techniques: hydrodistillation (HD) and steam distillation (SD) for their efficiency in the extraction of the volatile compounds from Tunisian Thymus vulgaris leaves; the kinetic, yield, composition and antibacterial activities of the essential oil were assessed in vitro. Results show that the essential oils extracted by microwaves were quantitatively (yield) similar to those obtained through the conventional methods, but qualitatively, essential oils analysed by gas chromatography–mass spectrometry (GC–MS) presented 17, 11, 11 and 8 compounds obtained through SFME, MAHD, SD and HD, respectively, mostly consisting of carvacrol (89.24–41.17%), followed by γ-terpinene (11.37–1.37%) and para-cymene (27.95–2.05%). The essential oils were screened for antibacterial activity against 5 microorganisms. All essential oils obtained by studied extraction methods showed the same resistance against Gram (?) and Gram (+) bacteria. The SFME method gave the best results: rapid kinetic of extraction (30 min vs. 35 min for MAHD, 120 min for SD, and 180 min for HD), less energy saving and cleanest process.     

Coupled heat and moisture transfer in multi-layer building materials

A dynamic mathematical model for simulating the coupled heat and moisture migration through multi-layer porous building materials was proposed. Vapor content and temperature were chosen as the principal driving potentials. The discretization of the governing equations was done by the finite difference approach. A new experimental set-up was also developed in this study. The evolution of transient temperature and moisture distributions inside specimens were measured. The method for determining the temperature gradient coefficient was also presented. The moisture diffusion coefficient, temperature gradient coefficient, sorption–desorption isotherms were experimentally evaluated for some building materials (sandstone and lime-cement mortar). The model was validated by comparing with the experimental data with good agreement. Another advantage of the method lies in the fact that the required transport properties for predicting the non-isothermal moisture flow only contain the vapor diffusion coefficient and temperature gradient coefficient. They are relatively simple, and can be easily determined.     

Bacterial-based additives for the production of artificial snow: What are the risks to human health?

For around two decades, artificial snow has been used by numerous winter sports resorts to ensure good snow cover at low altitude areas or more generally, to lengthen the skiing season. Biological additives derived from certain bacteria are regularly used to make artificial snow. However, the use of these additives has raised doubts concerning the potential impact on human health and the environment. In this context, the French health authorities have requested the French Agency for Environmental and Occupational Health Safety (Afsset) to assess the health risks resulting from the use of such additives. The health risk assessment was based on a review of the scientific literature, supplemented by professional consultations and expertise. Biological or chemical hazards from additives derived from the ice nucleation active bacterium Pseudomonas syringae were characterised. Potential health hazards to humans were considered in terms of infectious, toxic and allergenic capacities with respect to human populations liable to be exposed and the means of possible exposure. Taking into account these data, a qualitative risk assessment was carried out, according to four exposure scenarios, involving the different populations exposed, and the conditions and routes of exposure. It was concluded that certain health risks can exist for specific categories of professional workers (mainly snowmakers during additive mixing and dilution tank cleaning steps, with risks estimated to be negligible to low if workers comply with safety precautions). P. syringae does not present any pathogenic capacity to humans and that the level of its endotoxins found in artificial snow do not represent a danger beyond that of exposure to P. syringae endotoxins naturally present in snow. However, the risk of possible allergy in some particularly sensitive individuals cannot be excluded. Another important conclusion of this study concerns use of poor microbiological water quality to make artificial snow.     

Nonlinear state feedback design for continuous polynomial systems

The objective of this paper is to simplify the complexity of practical implementation of the input-state feedback linearization technique for the control of input-affine systems. A polynomial approach which makes use of the Taylor series expansion and the Kronecker product is developed. Our work aims to address the problem of synthesizing a polynomial control via a nonlinear analytical coordinates transformation. To check the effectiveness of the investigated approach, we consider the control problem of a series DC motor. A comparative study with the input-state feedback linearization control is developed.     

A Strategy for Controlling Groundwater Depletion in the Sa'dah Plain,Yemen

Over-exploitation of the groundwater resources is the major problem leading to groundwater depletion in the Sa'dah Plain, one of the major semi-arid highland basins of Yemen. Groundwater-irrigated agriculture is the chief economic activity in the Plain. Consequently, depletion places socioeconomic development in jeopardy. Owing to the lack of institutional arrangements and management instruments, government intervention is not likely to alleviate the crisis. One non-governmental approach that takes advantage of the existing local sociopolitical structure and customary law would be to adopt an annual abstraction quota. Approaching the crisis at a grass-roots level and relying on the conformity of the local citizens with customary law are the main characteristics of this strategy, the optimum objective of which is sustainable utilization of water resources.     

Generating a Condensed Representation for Association Rules

Association rule extraction from operational datasets often produces several tens of thousands, and even millions, of association rules. Moreover, many of these rules are redundant and thus useless. Using a semantic based on the closure of the Galois connection, we define a condensed representation for association rules. This representation is characterized by frequent closed itemsets and their generators. It contains the non-redundant association rules having minimal antecedent and maximal consequent, called min-max association rules. We think that these rules are the most relevant since they are the most general non-redundant association rules. Furthermore, this representation is a basis, i.e., a generating set for all association rules, their supports and their confidences, and all of them can be retrieved needless accessing the data. We introduce algorithms for extracting this basis and for reconstructing all association rules. Results of experiments carried out on real datasets show the usefulness of this approach. In order to generate this basis when an algorithm for extracting frequent itemsets—such as Apriori for instance—is used, we also present an algorithm for deriving frequent closed itemsets and their generators from frequent itemsets without using the dataset.     

Volume-based large dynamic graph analysis supported by evolution provenance

We present an approach for the visualization and interactive analysis of dynamic graphs that contain a large number of time steps. A specific focus is put on the support of analyzing temporal aspects in the data. Central to our approach is a static, volumetric representation of the dynamic graph based on the concept of space-time cubes that we create by stacking the adjacency matrices of all time steps. The use of GPU-accelerated volume rendering techniques allows us to render this representation interactively. We identified four classes of analytics methods as being important for the analysis of large and complex graph data, which we discuss in detail: data views, aggregation and filtering, comparison, and evolution provenance. Implementations of the respective methods are presented in an integrated application, enabling interactive exploration and analysis of large graphs. We demonstrate the applicability, usefulness, and scalability of our approach by presenting two examples for analyzing dynamic graphs. Furthermore, we let visualization experts evaluate our analytics approach.