GSK-3β is essential for physiological electric field-directed Golgi polarization and optimal electrotaxis

Endogenous electrical fields (EFs) at corneal and skin wounds send a powerful signal that directs cell migration during wound healing. This signal therefore may serve as a fundamental regulator directing cell polarization and migration. Very little is known of the intracellular and molecular mechanisms that mediate EF-induced cell polarization and migration. Here, we report that Chinese hamster ovary (CHO) cells show robust directional polarization and migration in a physiological EF (0.3–1 V/cm) in both dissociated cell culture and monolayer culture. An EF of 0.6 V/cm completely abolished cell migration into wounds in monolayer culture. An EF of higher strength (≥1 V/cm) is an overriding guidance cue for cell migration. Application of EF induced quick phosphorylation of glycogen synthase kinase 3β (GSK-3β) which reached a peak as early as 3 min in an EF. Inhibition of protein kinase C (PKC) significantly reduced EF-induced directedness of cell migration initially (in 1–2 h). Inhibition of GSK-3β completely abolished EF-induced GA polarization and significantly inhibited the directional cell migration, but at a later time (2–3 h in an EF). Those results suggest that GSK-3β is essential for physiological EF-induced Golgi apparatus (GA) polarization and optimal electrotactic cell migration

Meta Modeling for Business Process Improvement

Conducting business process improvement (BPI) initiatives is a topic of high priority for today’s companies. However, performing BPI projects has become challenging. This is due to rapidly changing customer requirements and an increase of inter-organizational business processes, which need to be considered from an end-to-end perspective. In addition, traditional BPI approaches are more and more perceived as overly complex and too resource-consuming in practice. Against this background, the paper proposes a BPI roadmap, which is an approach for systematically performing BPI projects and serves practitioners’ needs for manageable BPI methods. Based on this BPI roadmap, a domain-specific conceptual modeling method (DSMM) has been developed. The DSMM supports the efficient documentation and communication of the results that emerge during the application of the roadmap. Thus, conceptual modeling acts as a means for purposefully codifying the outcomes of a BPI project. Furthermore, a corresponding software prototype has been implemented using a meta modeling platform to assess the technical feasibility of the approach. Finally, the usability of the prototype has been empirically evaluated

Development of temporally refined land-use regression models predicting daily household-level air pollution in a panel study of lung function among asthmatic children

Regulatory monitoring data and land-use regression (LUR) models have been widely used for estimating individual exposure to ambient air pollution in epidemiologic studies. However, LUR models lack fine-scale temporal resolution for predicting acute exposure and regulatory monitoring provides daily concentrations, but fails to capture spatial variability within urban areas. This study coupled LUR models with continuous regulatory monitoring to predict daily ambient nitrogen dioxide (NO2) and particulate matter (PM2.5) at 50 homes in Windsor, Ontario. We compared predicted versus measured daily outdoor concentrations for 5 days in winter and 5 days in summer at each home. We also examined the implications of using modeled versus measured daily pollutant concentrations to predict daily lung function among asthmatic children living in those homes. Mixed effect analysis suggested that temporally refined LUR models explained a greater proportion of the spatial and temporal variance in daily household-level outdoor NO2 measurements compared with daily concentrations based on regulatory monitoring. Temporally refined LUR models captured 40% (summer) and 10% (winter) more of the spatial variance compared with regulatory monitoring data. Ambient PM2.5 showed little spatial variation; therefore, daily PM2.5 models were similar to regulatory monitoring data in the proportion of variance explained. Furthermore, effect estimates for forced expiratory volume in 1 s (FEV1) and peak expiratory flow (PEF) based on modeled pollutant concentrations were consistent with effects based on household-level measurements for NO2 and PM2.5. These results suggest that LUR modeling can be combined with continuous regulatory monitoring data to predict daily household-level exposure to ambient air pollution. Temporally refined LUR models provided a modest improvement in estimating daily household-level NO2 compared with regulatory monitoring data alone, suggesting that this approach could potentially improve exposure estimation for spatially heterogeneous pollutants. These findings have important implications for epidemiologic studies — in particular, for research focused on short-term exposure and health effects