Engineering polymer biomaterial interfaces for promoting cellular morphogenesis

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 143-163).Three-dimensional in vitro tissue and organ cultures have immense promise as models of human pathophysiology and stand to make a significant impact on the process of drug discovery and development. Many existing model systems do not capture the relevant complexity of the native tissue environment, relying on poorly characterized natural extracellular matrices (ECMs) for growth and development. These models are notably limited by the lack of vasculature, a key functional component of most human tissues, enabling oxygen and nutrient exchange, as well as facilitating paracrine signaling with surrounding epithelial cells. Fully-defined and tunable synthetic ECMs that support the generation of vascular network structures in dense tissue environments represent a path towards overcoming the limitations of existing model systems.This thesis focuses on the development and characterization of polymeric biomaterials that can be used to enhance in vitro tissue models through engineering the cell-material interface to guide a particular biological response. A major application focus of this research is to engineer biomaterial tools that would enable vascularization of dense epithelial tissue in vitro. We developed and characterized a poly(ethylene glycol)-based microbead angiogenesis scaffold with tunable physical and biochemical properties, identifying a critical ligand concentration regime on the microbead surface that promotes integrin-mediated endothelial cell attachment and invasion into both a synthetic ECM as well as a tissue aggregate of hepatocarcinoma cells.Furthermore, we investigated a novel hybrid PEG-polypeptide polymer, poly([gamma]-propargyl- L-glutamate) (PPLG) as a hydrogel substrate that can enhance endothelial cell attachment and spreading through modulation of the macromer structure and hydrophobicity properties. This work demonstrates how rational biomaterial design through chemical and structural modifications to polymer scaffolds can control cell fate within an in vitro tissue culture system.by Marianna Sofman.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Biological Engineerin

Online Learning Techniques for Improving Robot Navigation in Unfamiliar Domains

Many mobile robot applications require robots to act safely and intelligently in complex unfamiliarenvironments with little structure and limited or unavailable human supervision. As arobot is forced to operate in an environment that it was not engineered or trained for, various aspectsof its performance will inevitably degrade. Roboticists equip robots with powerful sensorsand data sources to deal with uncertainty, only to discover that the robots are able to make onlyminimal use of this data and still find themselves in trouble. Similarly, roboticists develop andtrain their robots in representative areas, only to discover that they encounter new situations thatare not in their experience base. Small problems resulting in mildly sub-optimal performance areoften tolerable, but major failures resulting in vehicle loss or compromised human safety are not.This thesis presents a series of online algorithms to enable a mobile robot to better deal withuncertainty in unfamiliar domains in order to improve its navigational abilities, better utilizeavailable data and resources and reduce risk to the vehicle. We validate these algorithms throughextensive testing onboard large mobile robot systems and argue how such approaches can increasethe reliability and robustness of mobile robots, bringing them closer to the capabilitiesrequired for many real-world applications.</p

Bandit-Based Online Candidate Selection for Adjustable Autonomy

Abstract In many robot navigation scenarios, the robot is able to choose between some number of operating modes. One such scenario is when a robot must decide how to trade-off online between autonomous and human tele-operation control. When little prior knowledge about the performance of each operator is known, the robot must learn online to model their abilities and be able to take advantage of the strengths of each. We present a bandit-based online candidate selection algorithm that operates in this adjustable autonomy setting and makes choices to optimize overall navigational performance. We justify this technique through such a scenario on logged data and demonstrate how the same technique can be used to optimize the use of high-resolution overhead data when its availability is limited 1.

Anytime online novelty detection for vehicle safeguarding

Novelty detection is often treated as a one-class classification problem: how to segment a data set of examples from everything else that would be considered novel or abnormal. Almost all existing novelty detection techniques, however, suffer from diminished performance when the number of less relevant, redundant or noisy features increases, as often the case with high-dimensional feature spaces. Additionally, many of these algorithms are not suited for online use, a trait that is highly desirable for many robotic applications. We present a novelty detection algorithm that is able to address this sensitivity to high feature dimensionality by utilizing prior class information within the training set. Additionally, our anytime algorithm is well suited for online use when a constantly adjusting environmental model is beneficial. We apply this algorithm to online detection of novel perception system input on an outdoor mobile robot and argue how such abilities could be key in increasing the real-world applications and impact of mobile robotics 1. 1 Most figures in this paper are best viewed in color