Nanomotors have been extensively explored for various applications in nanomedicine, especially in cargo transportation. Motile properties enable them to deliver pharmaceutical ingredients more efficiently to the targeted site. However, it still remains a challenge to design motor systems that are therapeutically active and can also be effectively traced when taken up by cells. Here, we designed a nanomotor with integrated fluorescence and therapeutic potential based on biodegradable polymersomes equipped with aggregation-induced emission (AIE) agents. The AIE segments provided the polymersomes with autofluorescence, facilitating the visualization of cell uptake. Furthermore, the membrane structure enabled the reshaping of the AIE polymersomes into asymmetric, peanut-shaped polymersomes. Upon laser irradiation, these peanut polymersomes not only displayed fluorescence, but also produced reactive oxygen species (ROS). Because of their specific shape, the ROS gradient induced motility in these particles. As ROS is also used for cancer cell treatment, the peanut polymersomes not only acted as delivery vehicles but also as therapeutic agents. As an integrated platform, these peanut polymersomes therefore represent an interesting delivery system with biomedical potential.

In this paper, we present, to our knowledge, the first known I/O efficient solutions for computing the k-bisimulation partition of a massive directed graph, and performing maintenance of such a partition upon updates to the underlying graph. Ubiquitous in the theory and application of graph data, bisimulation is a robust notion of node equivalence which intuitively groups together nodes in a graph which share fundamental structural features. k-bisimulation is the standard variant of bisimulation where the topological features of nodes are only considered within a local neighborhood of radius $k\geqslant 0$. The I/O cost of our partition construction algorithm is bounded by $O(k\cdot \mathit{sort}(|\et|) + k\cdot scan(|\nt|) + \mathit{sort}(|\nt|))$, while our maintenance algorithms are bounded by $O(k\cdot \mathit{sort}(|\et|) + k\cdot \mathit{sort}(|\nt|))$. The space complexity bounds are $O(|\nt|+|\et|)$ and $O(k\cdot|\nt|+k\cdot|\et|)$, resp. Here, $|\et|$ and $|\nt|$ are the number of disk pages occupied by the input graph's edge set and node set, resp., and $\mathit{sort}(n)$ and $\mathit{scan}(n)$ are the cost of sorting and scanning, resp., a file occupying $n$ pages in external memory. Empirical analysis on a variety of massive real-world and synthetic graph datasets shows that our algorithms perform efficiently in practice, scaling gracefully as graphs grow in size.