Structure and activity of lacustrine sediment bacteria involved in nutrient and iron cycles

Knowledge about the bacterial community structure in sediments is essential to better design restoration strategies for eutrophied lakes. In that regard, the aim of this study was to quantify the abundance and activity of bacteria involved in nutrient and iron cycling in sediments from four Azorean lakes with distinct trophic states (Verde, Azul, Furnas and Fogo). Inferred from quantitative PCR, bacteria performing anaerobic ammonia oxidation, were the most abundant in the eutrophic lakes Verde, Azul and Furnas (4.5 % to 16.6 %), followed by nitrifying bacteria (0.8 % to 13.0 %), denitrifying bacteria (0.5 % to 6.8 %), iron-reducing bacteria (0.2 % to 1.4 %), and phosphorus-accumulating organisms (<0.3 %). In contrast, denitrifying bacteria dominated sediments from the oligo-mesotrophic lake Fogo (8.8 %). Activity assays suggested that bacteria performing ammonia oxidation (aerobic and anaerobic), nitrite oxidation, heterothrophic nitrate reduction, iron reduction and biological phosphorus storage/release were present and active in all Azorean lake sediments. The present work also suggested that the activity of denitrifying bacteria might contribute to the release of phosphorus from sediments.The authors are indebted and grateful to the Regional Department of Water Resources and Land Planning (Azores) for the grant (Contrato Excepcionado no. 4/2008/ DROTRH) and its staff (Dina Pacheco), and to Virgilio Cruz and Paulo Antunes (Geosciences Department, University of Azores) for the useful help in sediments' collection, to the technical staff of the Department of Environmental Engineering - DTU for chemical analysis, to Laurent Philippot (INRA - University of Burgundy) for positive controls for DNB, to Richard Glaven and Derek Lovley (Department of Microbiology, University of Massachusetts) for Geobacter strains, to Paul Bodelier, Marzia Milleto and Marion Meima (Netherlands Institute of Ecology, NIOO-KNAW) for SRB clones and to Yunhong Kong and Per Halkjaer Nielsen (Department of Life Sciences, Section of Environmental Engineering, Aalborg University) for PAO clones. The authors also acknowledge the Grant SFRH/BD/25639/2005 from the Foundation for Science and Technology/M.C.T.(Portugal) awarded to G. M. and a Marie Curie Excellence Award (EC FP6) to B.F.S

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Hyper-heuristics: A survey of the state of the art

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed