Reconstructing the Forest of Lineage Trees of Diverse Bacterial Communities Using Bio-inspired Image Analysis

Cell segmentation and tracking allow us to extract a plethora of cell attributes from bacterial time-lapse cell movies, thus promoting computational modeling and simulation of biological processes down to the single-cell level. However, to analyze successfully complex cell movies, imaging multiple interacting bacterial clones as they grow and merge to generate overcrowded bacterial communities with thousands of cells in the field of view, segmentation results should be near perfect to warrant good tracking results. We introduce here a fully automated closed-loop bio-inspired computational strategy that exploits prior knowledge about the expected structure of a colony's lineage tree to locate and correct segmentation errors in analyzed movie frames. We show that this correction strategy is effective, resulting in improved cell tracking and consequently trustworthy deep colony lineage trees. Our image analysis approach has the unique capability to keep tracking cells even after clonal subpopulations merge in the movie. This enables the reconstruction of the complete Forest of Lineage Trees (FLT) representation of evolving multi-clonal bacterial communities. Moreover, the percentage of valid cell trajectories extracted from the image analysis almost doubles after segmentation correction. This plethora of trustworthy data extracted from a complex cell movie analysis enables single-cell analytics as a tool for addressing compelling questions for human health, such as understanding the role of single-cell stochasticity in antibiotics resistance without losing site of the inter-cellular interactions and microenvironment effects that may shape it

Partitioning of Distributed MIMO Systems based on Overhead Considerations

Distributed-Multiple Input Multiple Output (DMIMO) networks is a promising enabler to address the challenges of high traffic demand in future wireless networks. A limiting factor that is directly related to the performance of these systems is the overhead signaling required for distributing data and control information among the network elements. In this paper, the concept of orthogonal partitioning is extended to D-MIMO networks employing joint multi-user beamforming, aiming to maximize the effective sum-rate, i.e., the actual transmitted information data. Furthermore, in order to comply with practical requirements, the overhead subframe size is considered to be constrained. In this context, a novel formulation of constrained orthogonal partitioning is introduced as an elegant Knapsack optimization problem, which allows the derivation of quick and accurate solutions. Several numerical results give insight into the capabilities of D-MIMO networks and the actual sum-rate scaling under overhead constraints.Comment: IEEE Wireless Communications Letter

Social Security Reform with Self-Control Preferences

This paper analyzes a fully funded social security system under the assumption that agents face temptation issues. Agents are required to save through individually managed Personal Security Accounts without, and with mandatory annuitization. When the analysis is restricted to CRRA preferences our results are congruent with the literature in indicating that the complete elimination of social security is among the reform scenarios that maximize welfare. However, when self control preferences are introduced, and as the intensity of self control becomes progressively more severe the "social security elimination" scenario loses ground very rapidly. In fact, in the case of relatively severe temptation the elimination of social security becomes the least desirable alternative. Under the light of the above findings, any reform proposal regarding the social security system should consider departures from standard preferences to preference specifications suitable for dealing with preference reversals.funded social security; unfunded social security; self-control preferences

Self-control Preferences and Taxation: A Quantitative Analysis in a Life Cycle Model

This paper examines the impact of various .fiscal policies, namely, taxes on consumption, lab and capital when agents have self-control preferences. Agents trade in a stochastic overlapping generations economy while facing borrowing constraints. We quantitatively show that modelling choices, such as, liquidity constraints, life-cycle structure and idiosyncratic earnings risks, that were previously considered to be critical in delivering a positive capital income tax, need not be binding in this regard. We argue and quantitatively show that for a sufficiently large measure of individuals having self-control preferences instead of CRRA preferences, or alternatively, for a sufficiently high cost of exercising self control when all individuals are self-control types, the optimal capital income tax is zero. Given there is strong empirical and experimental evidence regarding the existence of self-control problems, our model provides quite an interesting insight: as agents.self-control costs rise, the optimal capital income tax rate will converge to Chamley and Judd value.