Visual Collaboration Leader-Follower UAV-Formation for Indoor Exploration.

UAVs operating in a leader-follower formation demand the knowledge of the relative pose between the collaborating members. This necessitates the RF-communication of this information which increases the communication latency and can easily result in lost data packets. In this work, rather than relying on this autopilot data exchange, a visual scheme using passive markers is presented.

Predicting the evolution of Escherichia coli by a data-driven approach.

A tantalizing question in evolutionary biology is whether evolution can be predicted from past experiences. To address this question, we created a coherent compendium of more than 15,000 mutation events for the bacterium Escherichia coli under 178 distinct environmental settings. Compendium analysis provides a comprehensive view of the explored environments, mutation hotspots and mutation co-occurrence.

From data to optimal decision making: a data-driven, probabilistic machine learning approach to decision support for patients with sepsis.

Background: A tantalizing question in medical informatics is how to construct knowledge from heterogeneous datasets, and as an extension, inform clinical decisions. The emergence of large-scale data integration in electronic health records (EHR) presents tremendous opportunities. However, our ability to efficiently extract informed decision support is limited due to the complexity of the clinical states and decision process, missing data and lack of analytical tools to advice based on statistical relationships.

A systems biology analysis of brain microvascular endothelial cell lipotoxicity.

Background: Neurovascular inflammation is associated with a number of neurological diseases including vascular dementia and Alzheimer's disease, which are increasingly important causes of morbidity and mortality around the world. Lipotoxicity is a metabolic disorder that results from accumulation of lipids, particularly fatty acids, in non-adipose tissue leading to cellular dysfunction, lipid droplet formation, and cell death.

Results: Our studies indicate for the first time that the neurovascular circulation also can manifest lipotoxicity, which could have major effects on cognitive function.

An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli.

Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a common framework the various biological processes and their interactions across multiple layers. We trained this methodology on an extensive normalized compendium for the gram-negative bacterium Escherichia coli, which incorporates gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction, and metabolic pathways, as well as growth measurements.

SBROME: a scalable optimization and module matching framework for automated biosystems design.

The development of a scalable framework for biodesign automation is a formidable challenge given the expected increase in part availability and the ever-growing complexity of synthetic circuits. To allow for (a) the use of previously constructed and characterized circuits or modules and (b) the implementation of designs that can scale up to hundreds of nodes, we here propose a divide-and-conquer Synthetic Biology Reusable Optimization Methodology (SBROME). An abstract user-defined circuit is first transformed and matched against a module database that incorporates circuits that have previously been experimentally characterized.