How to Train a Criminal: Making a Fully Autonomous Vehicles Safe for Humans Note

The tireless advancement of computing and sensor technology has brought the dawn of the age of autonomous vehicles. This emerging revolution in transportation has the potential to save thousands of lives every year and untold sums in economic losses related to traffic collisions. There remain significant challenges technologically, morally, and legally before society will begin to reap these benefits. Like many technology revolutions the change will be gradual, and one of the most notable factors for autonomous vehicles is the continued presence of human drivers on the road Navigating the physical world is extraordinarily complex on its own and is compounded by human behavior. Human drivers do not obey all traffic laws all the time and regularly exhibit driving behavior that is not necessarily consistent with the goals of safe or efficient transportation. Thus, for at least a period of time, autonomous vehicles will find it exceptionally difficult-if not impossible-to coexist on the road if they attempt to rigidly adhere to the laws and regulations codified by the states and federal government. This Note advocates that autonomous vehicles be developed to strategically break the rules of the road so they blend more easily into the existing ecosystem of human drivers. The benefit of adopting this position will ameliorate the headwinds facing the adoption of autonomous vehicles by society. Autonomous vehicles, if given the chance, could usher in an era of unprecedented safety for road-based transportation

Engineering Polymeric Scaffolds for Studying Neural Tissue Development, Pathology, and Repair

Digital light processing (DLP) 3D printing involves projecting a near-UV or visible light via an LED light source onto a digital micromirror device (DMD), which is the same device that is used in light projectors. I used this light-based bioprinting technique throughout my PhD work to develop hydrogel scaffolds to address pressing questions and challenges in in vitro neural tissue engineering and in vivo regenerative medicine applications.In Chapter 1, an overview of the development of biomaterials suited for light-based 3D-printing modalities with an emphasis on bioprinting applications is presented. The chemical mechanisms that govern photopolymerization are discussed, and the application of natural, synthetic, and composite biomaterials as 3D-printed hydrogels are highlighted. Since the quality of a 3D-printed construct is highly dependent on both the material properties and processing technique, the theoretical and practical aspects governing light-based 3D printing are also discussed. In Chapter 2, the development of a 3D printed in vitro optic nerve formation model is described. Hereditary and age-related optic nerve diseases as well as injuries can lead to vision loss and impairment which greatly reduces a person’s quality of life. In development, the optic nerve is derived from axonal projections of retinal ganglion cells in the retina. There is an unmet need in ophthalmology for a complete visual circuit in vitro model to study formation, neurodegeneration, and function of the optic nerve. We have taken the first step to create the visual circuit in vitro by developing a 3D-printed microgroove hydrogel platform for guiding axonal outgrowth from iPSC-derived retinal ganglion cell (RGC) spheroids. In Chapter 3, the development of a 3D-printed conduit for functional recovery after a peripheral nerve injury is described. Many studies have explored different materials and active cues to guide neural regeneration, with some success. However, none have demonstrated a comparable or better functional recovery than the gold standard autograft. Autografts remain insufficient for full recovery from a large injury to the sciatic nerve or brachial plexus nerves. We have designed a 3D-printed hydrogel multi-microchannel conduits with and without orthogonal micropores to guide axonal outgrowth and external vascular integration, respectively, that perform as well as the gold standard autograft therapy

High-fidelity 3D Printing using Flashing Photopolymerization.

Photopolymerization-based 3D printing has emerged as a promising technique to fabricate 3D structures. However, during the printing process, polymerized materials such as hydrogels often become highly light-scattering, thus perturbing incident light distribution and thereby deteriorating the final print resolution. To overcome this scattering-induced resolution deterioration, we developed a novel method termed flashing photopolymerization (FPP). Our FPP approach is informed by the fundamental kinetics of photopolymerization reactions, where light exposure is delivered in millisecond-scale flashes, as opposed to continuous light exposure. During the period of flash exposure, the prepolymer material negligibly scatters light. The material then polymerizes and opacifies in absence of light, therefore the exposure pattern is not perturbed by scattering. Compared to the conventional use of a continuous wave (CW) light source, the FPP fabrication resolution is improved. FPP also shows little dependency on the exposure, thus minimizing trial-and-error type optimization. Using FPP, we demonstrate its use in generating high-fidelity 3D printed constructs

X-Ray Characterization of Mesophases of Human Telomeric G-Quadruplexes and Other DNA Analogues

Observed in the folds of guanine-rich oligonucleotides, non-canonical G-quadruplex structures are based on G-quartets formed by hydrogen bonding and cation-coordination of guanosines. In dilute 5′-guanosine monophosphate (GMP) solutions, G-quartets form by the self-assembly of four GMP nucleotides. We use x-ray diffraction to characterize the columnar liquid-crystalline mesophases in concentrated solutions of various model G-quadruplexes. We then probe the transitions between mesophases by varying the PEG solution osmotic pressure, thus mimicking in vivo molecular crowding conditions. Using the GMP-quadruplex, built by the stacking of G-quartets with no covalent linking between them, as the baseline, we report the liquid-crystalline phase behaviors of two other related G-quadruplexes: (i) the intramolecular parallel-stranded G-quadruplex formed by the 22-mer four-repeat human telomeric sequence AG3(TTAG3)3 and (ii) the intermolecular parallel-stranded G-quadruplex formed by the TG4T oligonucleotides. Finally, we compare the mesophases of the G-quadruplexes, under PEG-induced crowding conditions, with the corresponding mesophases of the canonical duplex and triplex DNA analogues

Adipocytes and metabolism: Contributions to multiple myeloma

Obesity contributes to many cancers, including breast cancer and multiple myeloma, two cancers that often colonize the bone marrow (BM). Obesity often causes metabolic disease, but at the cellular level, there is uncertainty regarding how these shifts affect cellular phenotypes. Evidence is building that different types of fuel affect tumor cell metabolism, mitochondrial function, and signaling pathways differently, but tumor cells are also flexible and adapt to less-than ideal metabolic conditions, suggesting that single-pronged attacks on tumor metabolism may not be efficacious enough to be effective clinically. In this review, we describe the newest research at the pre-clinical level on how tumor metabolic pathways and energy sources affect cancer cells, with a special focus on multiple myeloma (MM). We also describe the known forward-feedback loops between bone marrow adipocytes (BMAds) and local tumor cells that support tumor growth. We describe how metabolic targets and transcription factors related to fatty acid (FA) oxidation, FA biosynthesis, glycolysis, oxidative phosphorylation (OXPHOS), and other pathways hold great promise as new vulnerabilities in myeloma cells. Specifically, we describe the importance of the acetyl-CoA synthetase (ACSS) and the acyl-CoA synthetase long chain (ACSL) families, which are both involved in FA metabolism. We also describe new data on the importance of lactate metabolism and lactate transporters in supporting the growth of tumor cells in a hypoxic BM microenvironment. We highlight new data showing the dependency of myeloma cells on the mitochondrial pyruvate carrier (MPC), which transports pyruvate to the mitochondria to fuel the tricarboxylic acid (TCA) cycle and electron transport chain (ETC), boosting OXPHOS. Inhibiting the MPC affects myeloma cell mitochondrial metabolism and growth, and synergizes with proteosome inhibitors in killing myeloma cells. We also describe how metabolic signaling pathways intersect established survival and proliferation pathways; for example, the fatty acid binding proteins (FABPs) affect MYC signaling and support growth, survival, and metabolism of myeloma cells. Our goal is to review the current the field so that novel, metabolic-focused therapeutic interventions and treatments can be imagined, developed and tested to decrease the burden of MM and related cancers

Compensating the cell-induced light scattering effect in light-based bioprinting using deep learning

Digital light processing (DLP)-based three-dimensional (3D) printing technology has the advantages of speed and precision comparing with other 3D printing technologies like extrusion-based 3D printing. Therefore, it is a promising biomaterial fabrication technique for tissue engineering and regenerative medicine. When printing cell-laden biomaterials, one challenge of DLP-based bioprinting is the light scattering effect of the cells in the bioink, and therefore induce unpredictable effects on the photopolymerization process. In consequence, the DLP-based bioprinting requires extra trial-and-error efforts for parameters optimization for each specific printable structure to compensate the scattering effects induced by cells, which is often difficult and time-consuming for a machine operator. Such trial-and-error style optimization for each different structure is also very wasteful for those expensive biomaterials and cell lines. Here, we use machine learning to learn from a few trial sample printings and automatically provide printer the optimal parameters to compensate the cell-induced scattering effects. We employ a deep learning method with a learning-based data augmentation which only requires a small amount of training data. After learning from the data, the algorithm can automatically generate the printer parameters to compensate the scattering effects. Our method shows strong improvement in the intra-layer printing resolution for bioprinting, which can be further extended to solve the light scattering problems in multilayer 3D bioprinting processes

3D Printing of a Biocompatible Double Network Elastomer with Digital Control of Mechanical Properties

The majority of 3D-printed biodegradable biomaterials are brittle, limiting their potential application to compliant tissues. Poly (glycerol sebacate) acrylate (PGSA) is a synthetic biodegradable and biocompatible elastomer, compatible with light-based 3D printing. In this work we employed digital-light-processing (DLP)-based 3D printing to create a complex PGSA network structure. Nature-inspired double network (DN) structures with two geometrically interconnected segments with different mechanical properties were printed from the same material in a single shot. Such capability has not been demonstrated by any other fabrication technique. The biocompatibility of PGSA after 3D printing was confirmed via cell-viability analysis. We used a finite element analysis (FEA) model to predict the failure of the DN structure under uniaxial tension. FEA confirmed the soft segments act as sacrificial elements while the hard segments retain structural integrity. The simulation demonstrated that the DN design absorbs 100% more energy before rupture than the network structure made by single exposure condition (SN), doubling the toughness of the overall structure. Using the FEA-informed design, a new DN structure was printed and the FEA predicted tensile test results agreed with tensile testing of the printed structure. This work demonstrated how geometrically-optimized material design can be easily and rapidly achieved by using DLP-based 3D printing, where well-defined patterns of different stiffnesses can be simultaneously formed using the same elastic biomaterial, and overall mechanical properties can be specifically optimized for different biomedical applications

3D printing of a biocompatible double network elastomer with digital control of mechanical properties.

The majority of 3D-printed biodegradable biomaterials are brittle, limiting their potential application to compliant tissues. Poly (glycerol sebacate) acrylate (PGSA) is a synthetic biodegradable and biocompatible elastomer, compatible with light-based 3D printing. In this work we employed digital-light-processing (DLP)-based 3D printing to create a complex PGSA network structure. Nature-inspired double network (DN) structures with two geometrically interconnected segments with different mechanical properties were printed from the same material in a single shot. Such capability has not been demonstrated by any other fabrication technique. The biocompatibility of PGSA after 3D printing was confirmed via cell-viability analysis. We used a finite element analysis (FEA) model to predict the failure of the DN structure under uniaxial tension. FEA confirmed the soft segments act as sacrificial elements while the hard segments retain structural integrity. The simulation demonstrated that the DN design absorbs 100% more energy before rupture than the network structure made by single exposure condition (SN), doubling the toughness of the overall structure. Using the FEA-informed design, a new DN structure was printed and the FEA predicted tensile test results agreed with tensile testing of the printed structure. This work demonstrated how geometrically-optimized material design can be easily and rapidly achieved by using DLP-based 3D printing, where well-defined patterns of different stiffnesses can be simultaneously formed using the same elastic biomaterial, and overall mechanical properties can be specifically optimized for different biomedical applications