Phenotypic diversity amongst strains of Pleurotus sajor-caju: implications for cultivation in and environments

In arid regions, biodiversity and biomass are limited by water availability, and this problem has been compounded by desertification associated with global climate change. The saprotrophic macrofungi that are indigenous to hot subtropical and tropical regions, such as Pleurotus spp., can play key roles in water sequestration, nutrient cycling, human nutrition, and bioremediation of waste materials. We studied 15 strains of Pleurotus sajor-caju, a widespread and phenotypically-diverse species, to establish variability in growth response and primordium development over a range of stress parameters: osmotic potential (-0.5 to -5 MPa), temperature (5-40 degrees C) and pH (2-12). The initiation of primordia precedes basidiome production and therefore represents a key stage in bioremediation strategies and fungi-driven nutrient cycles. Primordia were produced at low pH (4-6), at suboptimal growth temperatures (<or =25 degrees C), and under moderate water stress (-0.5 to -3.5 MPa). Although the growth windows for different strains were similar, their maximum growth rates and the optimum conditions for growth varied. We discuss the phenotypic diversity of Pleurotus strains and discuss their potential for cultivation, bioremediation and ecological regeneration.

Extending BPM Environments of Your Choice with Performance Related Decision Support

What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools process optimizations or a combination Of Such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.