We have developed a prototype three-channel microfluidic chip that is capable of generating a linear concentration gradient within a microfluidic channel and is useful in the study of bacterial chemotaxis. The linear chemical gradient is established by diffusing a chemical through a porous membrane located in the side wall of the channel and can be established without through-flow in the channel where cells reside. As a result, movement of the cells in the center channel is caused solely by the cells chemotactic response and not by variations in fluid flow. The advantages of this microfluidic chemical linear gradient generator are (i) its ability to produce a static chemical gradient, (ii) its rapid implementation, and (iii) its potential for highly parallel sample processing. Using this device, wildtype Escherichia coli strain RP437 was observed to move towards an attractant (e.g., l-asparate) and away from a repellent (e.g., glycerol) while derivatives of RP437 that were incapable of motility or chemotaxis showed no bias of the bacteria's distribution. Additionally, the degree of chemotaxis could be easily quantified using this assay in conjunction with fluorescence imaging techniques, allowing for estimation of the chemotactic partition coefficient (CPC) and the chemotactic migration coefficient (CMC). Finally, using this approach we demonstrate that E. coli deficient in autoinducer-2-mediated quorum sensing respond to the chemoattractant l-aspartate in a manner that is indistinguishable from wildtype cells suggesting that chemotaxis is insulated from this mode of cell-cell communication.
Accurate traffic flow prediction is the precondition for many applications in Intelligent Transportation Systems, such as traffic control and route guidance. Traditional data driven traffic flow prediction models tend to ignore traffic self-features (e.g., periodicities), and commonly suffer from the shifts brought by various complex factors (e.g., weather and holidays). These would reduce the precision and robustness of the prediction models. To tackle this problem, in this paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts network-scale traffic flow with multiple spatiotemporal features and external factors (weather and holidays). Specifically, we classify traffic self-features into temporal continuity as short-term feature, daily periodicity and weekly periodicity as long-term features, then map them to three two-dimensional spaces, which each one is composed of time and space, represented by two-dimensional matrices. The high-level spatiotemporal features learned by CNNs from the matrices with different time lags are further fused with external factors by a logistic regression layer to derive the final prediction. Experimental results indicate that the MF-CNN model considering multi-features improves the predictive performance compared to five baseline models, and achieves the trade-off between accuracy and efficiency.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.