Background Eukaryotes are ubiquitous in natural environments such as soil and freshwater. Little is known of their presence in drinking water distribution systems (DWDSs) or of the environmental conditions that affect their activity and survival. Methods Eukaryotes were characterized by Illumina high-throughput sequencing targeting 18S rRNA gene (DNA) that estimates the total community and the 18S rRNA gene transcript (RNA) that is more representative of the active part of the community. DWDS cold water ( N = 124), hot water ( N = 40), and biofilm ( N = 16) samples were collected from four cities in Finland. The sampled DWDSs were from two waterworks A–B with non-disinfected, recharged groundwater as source water and from three waterworks utilizing chlorinated water (two DWDSs of surface waterworks C–D and one of ground waterworks E). In each DWDS, samples were collected from three locations during four seasons of 1 year. Results A beta-diversity analysis revealed that the main driver shaping the eukaryotic communities was the DWDS (A–E) ( R = 0.73, P < 0.001, ANOSIM). The kingdoms Chloroplastida (green plants and algae), Metazoa (animals: rotifers, nematodes), Fungi (e.g., Cryptomycota ), Alveolata (ciliates, dinoflagellates), and Stramenopiles (algae Ochrophyta ) were well represented and active—judging based on the rRNA gene transcripts—depending on the surrounding conditions. The unchlorinated cold water of systems (A–B) contained a higher estimated total number of taxa (Chao1, average 380–480) than chlorinated cold water in systems C–E (Chao1 ≤ 210). Within each DWDS, unique eukaryotic communities were identified at different locations as was the case also for cold water, hot water, and biofilms. A season did not have a consistent impact on the eukaryotic community among DWDSs. Conclusions This study comprehensively characterized the eukaryotic community members within the DWDS of well-maintained ground and surface waterworks providing good quality water. The study gives an indication that each DWDS houses a unique eukaryotic community, mainly dependent on the raw water source and water treatment processes in place at the corresponding waterworks. In particular, disinfection as well as hot water temperature seemed to represent a strong selection pressure that controlled the number of active eukaryotic species. Electronic supplementary material The online version of this article (10.1186/s40168-019-0715-5) contains supplementary material, which is available to authorized users.

Background and Purpose:In every organization, project management raises many different decision-making problems, a large proportion of which can be efficiently solved using specific decision-making support systems. Yet such kinds of problems are always a challenge since there is no time-efficient or computationally efficient algorithm to solve them as a result of their complexity. In this study, we consider the problem of optimal financial investment. In our solution, we take into account the following organizational resource and project characteristics: profits, costs and risks. Design/Methodology/Approach: The decision-making problem is reduced to a multi-criteria 0-1 knapsack problem. This implies that we need to find a non-dominated set of alternative solutions, which are a trade-off between maximizing incomes and minimizing risks. At the same time, alternatives must satisfy constraints. This leads to a constrained two-criterion optimization problem in the Boolean space. To cope with the peculiarities and high complexity of the problem, evolution-based algorithms with an island meta-heuristic are applied as an alternative to conventional techniques. Results: The problem in hand was reduced to a two-criterion unconstrained extreme problem and solved with different evolution-based multi-objective optimization heuristics. Next, we applied a proposed meta-heuristic combining the particular algorithms and causing their interaction in a cooperative and collaborative way. The obtained results showed that the island heuristic outperformed the original ones based on the values of a specific metric, thus showing the representativeness of Pareto front approximations. Having more representative approximations, decision-makers have more alternative project portfolios corresponding to different risk and profit estimations. Since these criteria are conflicting, when choosing an alternative with an estimated high profit, decision-makers follow a strategy with an estimated high risk and vice versa. Conclusion: In the present paper, the project portfolio decision-making problem was reduced to a 0-1 knapsack constrained multi-objective optimization problem. The algorithm investigation confirms that the use of the island meta-heuristic significantly improves the performance of genetic algorithms, thereby providing an efficient tool for Financial Responsibility Centres Management.Keywords: 0-1 multi-objective constrained knapsack problem; project management portfolio problem; multi-objective evolution-based optimization algorithms; collaborative and cooperative meta-heuristics

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