Metabolomics is a relatively new component in systems biology that focuses on the high-throughput characterization of small molecular metabolites in biological systems. It is widely used in several scientific fields, particularly in that of food. Due to its excellent detection and prediction capacities, metabolomics well suited to analyze such complex matrix. This review emphasizes the most commonly used food metabolomics analytical technologies with a focus on novel approaches that have emerged in recent years, highlighting their suitability for food samples analysis as aided by chemometric data visualization. A comparison is presented among different metabolomics platforms and their prioritization for which metabolite classes in food. Application of metabolomics are presented in the context of food composition analysis, food quality safety, and food traceability. Furthermore, the constraints and limitations of actual metabolomics applications are explored, bringing novel insights into metabolomics use in food science to maximize its application potential in that major industrial sector.

To improve the searching effectiveness of the harmony search (HS) algorithm, an enhanced harmony search algorithm with circular region perturbation (EHS_CRP) is proposed in this paper. In the EHS_CRP algorithm, a global and local dimension selection strategy is designed to accelerate the search speed of the algorithm. A selection learning operator based on the global and local mean level is proposed to improve the balance between exploration and exploitation. Circular region perturbation is employed to avoid the algorithm stagnation and get a better exploration region. To assess performance, the proposed algorithm is compared with 10 state-of-the-art swarm intelligent approaches in a large set of global optimization problems. The simulation results confirm that EHS_CRP has a significant advantage in terms of accuracy, convergence speed, stability and robustness. Moreover, EHS_CRP performs better than other tested methods in engineering design optimization problems. Thus, the EHS_CRP algorithm is a viable and reliable alternative for some difficult and multidimensional real-world problems.

Global concerns towards potentially toxic elements (PTEs) are steadily increasing due to the significant threats that PTEs pose to human health and environmental quality. This calls for immediate, effective and efficient remediation solutions. Earthworms, the 'ecosystem engineers', can modify and improve soil health and enhance plant productivity. Recently, considerable attention has been paid to the potential of earthworms, alone or combined with other soil organisms and/or soil amendments, to remediate PTEs contaminated soils. However, the use of earthworms in the remediation of PTEs contaminated soil (i.e., vermiremediation) has not been thoroughly reviewed to date. Therefore, this review discusses and provides comprehensive insights into the suitability of earthworms as potential candidates for bioremediation of PTEs contaminated soils and mitigating environmental and human health risks. Specifically, we reviewed and discussed: i) the occurrence and abundance of earthworms in PTEs contaminated soils; ii) the influence of PTEs on earthworm communities in contaminated soils; iii) factors affecting earthworm PTEs accumulation and elimination, and iv) the dynamics and fate of PTEs in earthworm amended soils. The technical feasibility, knowledge gaps, and practical challenges have been worked out and critically discussed. Therefore, this review could provide a reference and guidance for bio-restoration of PTEs contaminated soils and shall also help developing innovative and applicable solutions for controlling PTEs bioavailability for the remediation of contaminated soils and the mitigation of the environment and human risks.

The university course timetabling problem (UCTP) is a combinatorial NP-complete problem that has been subject to research since the early 1960’s. Numerous solution techniques have been applied to the timetabling problem ever since. This paper aims at formulating an immune-inspired algorithm, namely the Clonal Selection Algorithm1 (CSA1) and testing its ability in solving the UCTP against the Genetic algorithm (GA). An Immune-Genetic algorithm (IGA) was also created, which combines the crossover operator borrowed from the genetic algorithm with immune-inspired concepts. Also, experimenting with the effects of changing the selection and re-selection schemes of the algorithms motivated the creation of a second
version of CSA1, that is CSA2 and three more versions of IGA: IGA1, IGA2 and IGA3. All the devised algorithms were contrasted in their performances against the GA. Enhancements were applied to the mutation operator of the formulated algorithms by introducing a ‘move factor’. As a means of improving the results attained by the algorithms, local search consisting of three variable neighborhoods was incorporated into each of them. The algorithms were tested over two problem instances, with varying complexities and the results demonstrate the effectiveness of the algorithms in solving the UCTP.

Objectives: Systemic lupus erythematosus (SLE) is an autoimmune disease manifested by self-reactive antibodies due to failure of selection in both B and T lymphocytes leading to immune intolerance accompanied by increased rate of apoptosis and deficiency in the clearance of the apoptotic cells. Micro RNAs regulate posttranscriptional gene expression and have been recently identified to regulate cellular differentiation, establishing immunological tolerance and are involved in the pathogenesis of several diseases. miR-181-a, expressed in haematopoietic cell lineage, has shown to be an important modulator of B and T cell differentiation, maturation and function. This study aims to identify the expression signature of miR-181-a in the peripheral blood of paediatric SLE and its regulatory effect on the consequent expression of its target gene PCAF.