Probing Endothelial Mechanics with a Twist
Regulation of endothelial cell-cell junctions is critical for maintaining tissue homeostasis. Dysfunctional regulation leads to increased vascular permeability associated with pathological conditions such as acute lung injury and asthma. These conditions are associated with altered tissue mechanics. Importantly, the primary endothelial cell-cell adhesion receptors are linked to the actin cytoskeleton, which plays a role in propagating contractile forces in cells. A major gap in current knowledge of the impact of endothelial contractile force on cell-cell junctions derives from the limited ability of current technologies to integrate both biophysical and molecular information. Specialized techniques identified spatially and temporally distinct changes in contractile force and molecular signaling. Using magnetic twisting cytometry (MTC), external mechanical forces were applied through specific cell-cell adhesion receptors expressed on endothelial cells. These experiments demonstrated force-actuated mechanical stiffening (active generation of tension) of these cells. Fluorescence imaging characterizing morphological and subcellular changes correlated with the mechanical information. Understanding the role of mechanical force in the regulation of endothelial cell-cell junctions could reveal novel therapeutic targets for pathologies associated with vascular leakage, which are critically needed for patients who do not respond to current treatment options.
High-Resolution MR Neuroimaging: Model, Algorithm and Applications
The capability of Magnetic Resonance Imaging (MRI) to simultaneously provide structural, functional, and metabolic information makes it a powerful tool to study how human brain works, how it is influenced by external interventions, and what goes wrong when it is injured or diseased. However, significant challenges remain in obtaining better trade-offs between the signal-to-noise ratio, spatial resolution and imaging speed for MR-based neuroimaging. In this talk, I will present our recent attempts to address these challenges, from a signal processing perspective. I will describe our proposed mathematical framework that exploits underlying signal characteristics to recover high quality MR brain images from noisy or limited measurements. Results from denoising for improving resolution of quantitative diffusion MRI and sparse sampling for accelerating functional MRI data acquisition will be shown, demonstrating the practical utility of the proposed method.
DNA Sensing using Graphene Nanopores
Inexpensive and fast methods to sequence the genome of individuals using nanopore tech- nology revolutionizes modern medicine. The thickness of the membrane used to make the nanopore presents a fundamental limitation to the physical dimension that can be resolved. Typical solid-state membranes are too thick and usually fail to recognize single nucleotides on a DNA strand. Graphene is a sub-nanometer membrane, comprising of carbon atoms arranged in a honeycomb lattice, with remarkable electronic and mechanical properties. The thickness of a graphene membrane (3 A) is comparable to the vertical stacking distance between base pairs in the DNA (3.5 A) making graphene an ideal candidate for DNA sequencing. Resolving at the atomic level electric field-driven DNA translocation through graphene nanopores is crucial to guide the design of graphene-based sequencing devices. Molecular dynamics simulations, in principle, can achieve such resolution and are employed to investigate the effects of applied voltage, DNA conformation and sequence as well as pore charge on the translocation characteristics of DNA. We demonstrate that such simulations yield current characteristics consistent with recent measurements and suggest that under suitable bias conditions A-T and G-C base pairs can be discriminated using graphene nanopores.