The Interaction of Canine Plasminogen with Streptococcus pyogenes Enolase: They Bind to One Another but What Is the Nature of the Structures Involved?

For years it has been clear that plasminogen from different sources and enolase from different sources interact strongly. What is less clear is the nature of the structures required for them to interact. This work examines the interaction between canine plasminogen (dPgn) and Streptococcus pyogenes enolase (Str enolase) using analytical ultracentrifugation (AUC), surface plasmon resonance (SPR), fluorescence polarization, dynamic light scattering (DLS), isothermal titration calorimetry (ITC), and simple pull-down reactions. Overall, our data indicate that a non-native structure of the octameric Str enolase (monomers or multimers) is an important determinant of its surface-mediated interaction with host plasminogen. Interestingly, a non-native structure of plasminogen is capable of interacting with native enolase. As far as we can tell, the native structures resist forming stable mixed complexes

A Framework for Exploring Functional Variability in Olfactory Receptor Genes

BACKGROUND: Olfactory receptors (ORs) are the largest gene family in mammalian genomes. Since nearly all OR genes are orphan receptors, inference of functional similarity or differences between odorant receptors typically relies on sequence comparisons. Based on the alignment of entire coding region sequence, OR genes are classified into families and subfamilies, a classification that is believed to be a proxy for OR gene functional variability. However, the assumption that overall protein sequence diversity is a good proxy for functional properties is untested. METHODOLOGY: Here, we propose an alternative sequence-based approach to infer the similarities and differences in OR binding capacity. Our approach is based on similarities and differences in the predicted binding pockets of OR genes, rather than on the entire OR coding region. CONCLUSIONS: Interestingly, our approach yields markedly different results compared to the analysis based on the entire OR coding-regions. While neither approach can be tested at this time, the discrepancy between the two calls into question the assumption that the current classification reliably reflects OR gene functional variability

Closed-Loop supply chains in circular economy business models

With the emergence of the circular economy (CE) approach into business models, there is need for deeper understanding of resource loops activities and how current supply chains can support the development of emerging CE business models. However, there is still limited research addressing the conceptualization of closed loops in the supply chain literature. This work addresses this research gap and proposes a typology for closed loops that is independent from the type of product under concern. Our findings suggest that there are two types of closed-loop supply chains in circular business models. Further work is envisaged to understand how companies can effectively develop their closed-loop supply chains as part of their transformation towards a more circular business model

Sustainable Business Model Ideation and Development of Early Ideas for Sustainable Business Models: Analyzing a New Tool Facilitating the Ideation Process

This chapter presents an early ideation tool, the Impact Canvas® (IC), that has been specifically designed to involve different kinds of stakeholders in the early stages of the business and research ideation process. The authors discuss how a tool can support the ideation process and how the IC tool has been designed to incorporate different elements for the development of sustainable and impactful ideas. The usefulness of the tool when cooperating in a multidisciplinary team is described. The authors report feedback from users of the tool that supports the perception of the user-friendliness and usefulness of the tool. The chapter concludes with a description of how the IC tool is being further developed to support a more multidisciplinary approach to research and business ideation.acceptedVersionPeer reviewe

Exploring the relationship between data science and circular economy: an enhanced CRISP-DM process model

To date, data science and analytics have received much attention from organizations seeking to explore how to use their massive volumes of data to create value and accelerate the adoption of Circular Economy (CE) concepts. The correct utilization of analytics with circular strategies may enable a step change that goes beyond incremental efficiency gains towards a more sustainable and circular economy. However, the adoption of such smart circular strategies by the industry is lagging, and few studies have detailed how to operationalize this potential at scale. Motivated by this, this study seeks to address how organizations can better structure their data understanding and preparation to align with overall business and CE goals. Therefore, based on the literature and a case study the relationship between data science and the CE is explored, and a generic process model is proposed. The proposed process model extends the Cross Industry Standard Process for Data Mining (CRISP-DM) with an additional phase of data validation and integrates the concept of analytic profiles. We demonstrate its application for the case study of a manufacturing company seeking to implement the smart circular strategy - predictive maintenance