Computing Interpretable Representations of Cell Morphodynamics

Shape changes (morphodynamics) are one of the principal ways cells interact with their environments and perform key intrinsic behaviours like division. These dynamics arise from a myriad of complex signalling pathways that often organise with emergent simplicity to carry out critical functions including predation, collaboration and migration. A powerful method for analysis can therefore be to quantify this emergent structure, bypassing the low-level complexity. Enormous image datasets are now available to mine. However, it can be difficult to uncover interpretable representations of the global organisation of these heterogeneous dynamic processes. Here, such representations were developed for interpreting morphodynamics in two key areas: mode of action (MoA) comparison for drug discovery (developed using the economically devastating Asian soybean rust crop pathogen) and 3D migration of immune system T cells through extracellular matrices (ECMs). For MoA comparison, population development over a 2D space of shapes (morphospace) was described using two models with condition-dependent parameters: a top-down model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. A variety of landscapes were discovered, describing phenotype transitions during growth, and possible perturbations in the tip growth machinery that cause this variation were identified. For interpreting T cell migration, a new 3D shape descriptor that incorporates key polarisation information was developed, revealing low-dimensionality of shape, and the distinct morphodynamics of run-and-stop modes that emerge at minute timescales were mapped. Periodically oscillating morphodynamics that include retrograde deformation flows were found to underlie active translocation (run mode). Overall, it was found that highly interpretable representations could be uncovered while still leveraging the enormous discovery power of deep learning algorithms. The results show that whole-cell morphodynamics can be a convenient and powerful place to search for structure, with potentially life-saving applications in medicine and biocide discovery as well as immunotherapeutics.Open Acces

Satellite Communications

This study is motivated by the need to give the reader a broad view of the developments, key concepts, and technologies related to information society evolution, with a focus on the wireless communications and geoinformation technologies and their role in the environment. Giving perspective, it aims at assisting people active in the industry, the public sector, and Earth science fields as well, by providing a base for their continued work and thinking

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp