Sustainability is becoming more and more a strategic growth driver for numerous companies. In this context transparency on the environmental strengths and weaknesses of products and processes and related opportunities and risks is crucial. Accordingly, the assessment of sustainability aspects is gaining importance for companies and their customers along the value chain. Life cycle-based methodologies as Life Cycle Assessment (LCA) but also other assessment systems are used in decision-making processes, product development and marketing activities. Many companies have a public corporate sustainability policy backed up with commitments in the form of quantitative targets. LCA methodology may be used as a tool supporting the identification of 'hot spots' in the value chain and measuring progress towards sustainability targets. In practice, however, common issues and challenges stand in the way of a full deployment of LCA methods in industry. It is important for companies to find common ground on how to implement these approaches, which data and impact assessments to be used and how results should be interpreted. ISO rules give a good basis for that work, though it is not sufficient for several questions. For exchanging experiences, updating or adopting methods, and generating data the International Sustainability Practitioners Network (ISPN) was created in 2012. The ISPN is an exchange forum for LCA methodology in the context of industry and comprises sustainability experts from a range of different industry sectors. To share experiences from the different activities, examples of good practices of this cross-sectoral initiative and to discuss opportunities for improving sustainability assessments within the companies are introduced. This article highlights challenges and solutions in terms of data availability and uncertainty, streamlining and using standardization processes as well as communication of results with non-LCA-experts.
Connectivity data of the nervous system and subdivisions, such as the brainstem, cerebral cortex and subcortical nuclei, are necessary to understand connectional structures, predict effects of connectional disorders and simulate network dynamics. For that purpose, a database was built and analyzed which comprises all known directed and weighted connections within the rat brainstem. A longterm metastudy of original research publications describing tract tracing results form the foundation of the brainstem connectome (BC) database which can be analyzed directly in the framework neuroVIISAS. The BC database can be accessed directly by connectivity tables, a web-based tool and the framework. Analysis of global and local network properties, a motif analysis, and a community analysis of the brainstem connectome provides insight into its network organization. For example, we found that BC is a scale-free network with a small-world connectivity. The Louvain modularity and weighted stochastic block matching resulted in partially matching of functions and connectivity. BC modeling was performed to demonstrate signal propagation through the somatosensory pathway which is affected in Multiple sclerosis.
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