BackgroundThe molecular mechanisms that determine the organism's response to a variety of doses and modalities of stress factors are not well understood.ResultsWe studied effects of ionizing radiation (144, 360 and 864 Gy), entomopathogenic fungus (10 and 100 CFU), starvation (16 h), and cold shock (+4, 0 and -4°C) on an organism's viability indicators (survival and locomotor activity) and transcriptome changes in the Drosophila melanogaster model. All stress factors but cold shock resulted in a decrease of lifespan proportional to the dose of treatment. However, stress-factors affected locomotor activity without correlation with lifespan. Our data revealed both significant similarities and differences in differential gene expression and the activity of biological processes under the influence of stress factors.ConclusionsStudied doses of stress treatments deleteriously affect the organism's viability and lead to different changes of both general and specific cellular stress response mechanisms.

A structured collection of tools for engineering resilience and a research approach to improve the resilience of a power grid are described in this paper. The collection is organized by a two-dimensional array formed from typologies of power grid components and business processes. These two dimensions provide physical and operational outlooks, respectively, for a power grid. The approach for resilience research is based on building a simulation model of a power grid which utilizes a resilience assessment equation to assess baseline resilience to a hazards’ profile, then iteratively selects a subset of tools from the collection, and introduces these as interventions in the power grid simulation model. Calculating the difference in resilience associated with each subset supports multicriteria decision-making to find the most convenient subset of interventions for a power grid and hazards’ profile. Resilience is an emergent quality of a power grid system, and therefore resilience research and interventions must be system-driven. This paper outlines further research required prior to the practical application of this approach.

Scientific modelling is a prime means to generate understanding and provide much-needed information to support public decision-making in the fluid area of sustainability. A growing, diverse sustainability modelling literature, however, does not readily lend itself to standard validation procedures, which are typically rooted in the positivist principles of empirical verification and predictive success. Yet, to be useful to decision-makers, models, including their outputs and the processes through which they are established must be, and must be seen to be “valid.” This study explores what model validity means in a problem space with increasingly interlinked and fast-moving challenges. We examine validation perspectives through ontological, epistemic, and methodological lenses, for a range of modelling approaches that can be considered as “complexity-compatible.” The worldview taken in complexity-compatible modelling departs from the more standard modelling assumptions of complete objectivity and full predictability. Drawing on different insights from complexity science, systems thinking, economics, and mathematics, we suggest a ten-dimensional framework for progressing on model validity when investigating sustainability concerns. As such, we develop a widened view of the meaning of model validity for sustainability. It includes (i) acknowledging that several facets of validation are critical for the successful modelling of the sustainability of complex systems; (ii) tackling the thorny issues of uncertainty, subjectivity, and unpredictability; (iii) exploring the realism of model assumptions and mechanisms; (iv) embracing the role of stakeholder engagement and scrutiny throughout the modelling process; and (v) considering model purpose when assessing model validity. We wish to widen the debate on the meaning of model validity in a constructive way. We conclude that consideration of all these elements is necessary to enable sustainability models to support, more effectively, decision-making for complex interdependent systems.