We present a rapid, reproducible and sensitive neurotoxicity testing platform that combines the benefits of neurite outgrowth analysis with cell patterning. This approach involves patterning neuronal cells within a hexagonal array to standardize the distance between neighbouring cellular nodes, and thereby standardize the length of the neurite interconnections. This feature coupled with defined assay coordinates provides a streamlined display for rapid and sensitive analysis. We have termed this the network formation assay (NFA). To demonstrate the assay we have used a novel cell patterning technique involving thin film poly(dimethylsiloxane) (PDMS) microcontact printing. Differentiated human SH-SY5Y neuroblastoma cells colonized the array with high efficiency, reliably producing pattern occupancies above 70%. The neuronal array surface supported neurite outgrowth, resulting in the formation of an interconnected neuronal network. Exposure to acrylamide, a neurotoxic reference compound, inhibited network formation. A dose-response curve from the NFA was used to determine a 20% network inhibition (NI(20)) value of 260 microM. This concentration was approximately 10-fold lower than the value produced by a routine cell viability assay, and demonstrates that the NFA can distinguish network formation inhibitory effects from gross cytotoxic effects. Inhibition of the mitogen-activated protein kinase (MAPK) ERK1/2 and phosphoinositide-3-kinase (PI-3K) signaling pathways also produced a dose-dependent reduction in network formation at non-cytotoxic concentrations. To further refine the assay a simulation was developed to manage the impact of pattern occupancy variations on network formation probability. Together these developments and demonstrations highlight the potential of the NFA to meet the demands of high-throughput applications in neurotoxicology and neurodevelopmental biology.

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As the amount of data gathered by monitoring systems increases, using computational tools to analyze it becomes a necessity. Machine learning algorithms can be used in both regression and classification problems, providing useful insights while avoiding the bias and proneness to errors of humans. In this paper, a specific kind of decision tree algorithm, called conditional inference tree, is used to extract relevant knowledge from data that pertains to electrical motors. The model is chosen due to its flexibility, strong statistical foundation, as well as great capabilities to generalize and cope with problems in the data. The obtained knowledge is organized in a structured way and then analyzed in the context of health condition monitoring. The final results illustrate how the approach can be used to gain insight into the system and present the results in an understandable, user-friendly manner.

In the process industries where multiple products have to be produced in the batch mode, the optimal assignment of the operations to the available resources and their sequencing can contribute considerably to economic success. Among the several methods proposed to model and solve batch scheduling problems, techniques based on a reachability analysis of timed automata (TA) models have gained attention recently. The appeal of the approach is the modular, intuitive, and straightforward graphical modeling of complex scheduling problems, and an efficient solution technique based upon reachability algorithms. In this contribution, we present an introduction to the TA-based approach to scheduling and specifically address the problem of batch scheduling with sequence-dependent setup and changeover times. In the TA-based approach, the resources, recipes, and additional timing constraints are modeled independently as sets of (priced) timed automata. The sets of individual automata are synchronized by means of synchronization labels and are composed by parallel composition to form a global automaton. A cost-optimal symbolic reachability analysis is performed on the composed automaton to derive schedules with the objective of minimizing makespan. The TA models of the recipes are extended here to include setup times as well as sequence-dependent changeovers. The performance of the approach to model and to solve real-world scheduling problems with sequence-dependent changeovers is demonstrated for two different case studies. A comparative study on the TA-based approach with various MILP formulations is performed on a famous case study from the literature, and the results are discussed.

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