Abstract
Chaos Game Representation (CGR) of DNA sequences has been used for visual representation as well as alignment-free comparisons. CGR is considered to be of great value as the images obtained from parts of a genome present the same structure as those obtained for the whole genome. However, the robustness of the CGR method to compare DNA sequences obtained in a variety of scenarios is not yet fully demonstrated. This paper addresses this issue by presenting a method to evaluate the potential of CGR to distinguish various classes in a DNA dataset. Two indices are proposed for this purpose - a rejection rate and an overlapping rate. The method was applied to 4 datasets, with between 31 to 400 classes each. Nearly 430 million pairs of DNA sequences were compared using the CGR. © 2018, Springer Nature Switzerland AG.

Abstract
Doctoral programmes are facing several challenges in modern societies. The societal role of the University, funded by the state, requires it to: a) increase the offer and admission of third cycle students; b) to reach industry/companies expectations; c) to ensure reasonable employability prospects for the PhD candidates. With the current demography, most candidates can only find a job in industry/companies. Therefore, significant pressure is being put on doctoral programmes to include transferable skills in their curriculum. This paper presents a course "Fit for Industry?" aiming at filling this need. The course design methodology is presented in detail. It includes: a) the involvement of industry since its inception; b) the joint identification of a small number of key competencies to be addressed; c) the inclusion of assessment and feedback mechanisms in its design; d) an immersive and international dimension. It was found that the course had a profound impact on the candidates' perceptions of industry and valued by industry participants. Other stakeholders, such as PhD supervisors, also had a positive perception. The paper concludes with recommendations for those willing to replicate the course locally.

Abstract
The aim of this study was to investigate the effect chitosan (Ch) porous 3D scaffolds embedded with resolvin D1 (RvD1), an endogenous pro-resolving lipid mediator, on bone tissue healing. These scaffolds previous developed by us have demonstrated to have immunomodulatory properties namely in the modulation of the macrophage inflammatory phenotypic profile in an in vivo model of inflammation. Herein, results obtained in an in vivo rat femoral defect model demonstrated that two months after Ch+RvD1 scaffolds implantation, an increase in new bone formation, in bone trabecular thickness, and in collagen type I and Coll I/Coll III ratio were observed. These results suggest that Ch scaffolds embedded with RvD1 were able to lead to the formation of new bone with improvement of trabecular thickness. This study shows that the presence of RvD1 in the acute phase of the inflammatory response to the implanted biomaterial had a positive role in the subsequent bone tissue repair, thus demonstrating the importance of innovative approaches for the control of immune responses to biomedical implants in the design of advanced strategies for regenerative medicine. (c) 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 1626-1633, 2018.

Abstract

Abstract
This paper looks as initial results of research into the validity of application of selected priority rules in the development of a levelled production plan. The development of a levelled production plan is the key stage of the authors' own methodology of levelling production to mitigate the adverse impact of variable demand. A levelled production plan permits to maximize effects, defined as being able to deliver diverse products in a timely manner and at the same time reduce stocks and optimize efficient use of manufacturing resources. Application of an appropriate priority rule in the development of a levelled production plan is the key factor determining the effectiveness of the developed methodology. Initial research has been conducted for twenty automatically generated task sets. One hundred manufacturing schedules have been developed in total-five schedules for each set, according to the selected priority rules (shortest task time, longest task time, shortest processing time, longest processing time, first in first out). The schedules have been assessed in terms of meeting the selected key criteria for the objectives of levelled production.

Abstract
Machine Learning (ML) progressed significantly in the last decade, evolving the computer-based learning/prediction paradigm to a much more effective class of models known as Deep learning (DL). Since then, hyperspectral data processing relying on DL approaches is getting more popular, competing with the traditional classification techniques. In this paper, a valid ML/DL-based works applied to hyperspectral data processing is reviewed in order to get an insight regarding the approaches available for the effective meaning extraction from this type of data. Next, a general DL-based methodology focusing on hyperspectral data processing to provide farmers and winemakers effective tools for earlier threat detection is proposed.