Functional properties of cell-seeded three-dimensionally woven poly(epsilon-caprolactone) scaffolds for cartilage tissue engineering.

Articular cartilage possesses complex mechanical properties that provide healthy joints the ability to bear repeated loads and maintain smooth articulating surfaces over an entire lifetime. In this study, we utilized a fiber-reinforced composite scaffold designed to mimic the anisotropic, nonlinear, and viscoelastic biomechanical characteristics of native cartilage as the basis for developing functional tissue-engineered constructs. Three-dimensionally woven poly(epsilon-caprolactone) (PCL) scaffolds were encapsulated with a fibrin hydrogel, seeded with human adipose-derived stem cells, and cultured for 28 days in chondrogenic culture conditions. Biomechanical testing showed that PCL-based constructs exhibited baseline compressive and shear properties similar to those of native cartilage and maintained these properties throughout the culture period, while supporting the synthesis of a collagen-rich extracellular matrix. Further, constructs displayed an equilibrium coefficient of friction similar to that of native articular cartilage (mu(eq) approximately 0.1-0.3) over the prescribed culture period. Our findings show that three-dimensionally woven PCL-fibrin composite scaffolds can be produced with cartilage-like mechanical properties, and that these engineered properties can be maintained in culture while seeded stem cells regenerate a new, functional tissue construct.

Imaging through turbulence using compressive coherence sensing.

Previous studies have shown that the isoplanatic distortion due to turbulence and the image of a remote object may be jointly estimated from the 4D mutual intensity across an aperture. This Letter shows that decompressive inference on a 2D slice of the 4D mutual intensity, as measured by a rotational shear interferometer, is sufficient for estimation of sparse objects imaged through turbulence. The 2D slice is processed using an iterative algorithm that alternates between estimating the sparse objects and estimating the turbulence-induced phase screen. This approach may enable new systems that infer object properties through turbulence without exhaustive sampling of coherence functions.

Nanotopography-induced changes in focal adhesions, cytoskeletal organization, and mechanical properties of human mesenchymal stem cells.

The growth of stem cells can be modulated by physical factors such as extracellular matrix nanotopography. We hypothesize that nanotopography modulates cell behavior by changing the integrin clustering and focal adhesion (FA) assembly, leading to changes in cytoskeletal organization and cell mechanical properties. Human mesenchymal stem cells (hMSCs) cultured on 350 nm gratings of tissue-culture polystyrene (TCPS) and polydimethylsiloxane (PDMS) showed decreased expression of integrin subunits alpha2, alpha , alpha V, beta2, beta 3 and beta 4 compared to the unpatterned controls. On gratings, the elongated hMSCs exhibited an aligned actin cytoskeleton, while on unpatterned controls, spreading cells showed a random but denser actin cytoskeleton network. Expression of cytoskeleton and FA components was also altered by the nanotopography as reflected in the mechanical properties measured by atomic force microscopy (AFM) indentation. On the rigid TCPS, hMSCs on gratings exhibited lower instantaneous and equilibrium Young's moduli and apparent viscosity. On the softer PDMS, the effects of nanotopography were not significant. However, hMSCs cultured on PDMS showed lower cell mechanical properties than those on TCPS, regardless of topography. These suggest that both nanotopography and substrate stiffness could be important in determining mechanical properties, while nanotopography may be more dominant in determining the organization of the cytoskeleton and FAs.

Coding Strategies and Implementations of Compressive Sensing

This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others.

This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system.

Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity.

Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or information from a noisy environment. Using engineering efforts to accomplish the same task usually requires multiple detectors, advanced computational algorithms, or artificial intelligence systems. Compressive acoustic sensing incorporates acoustic metamaterials in compressive sensing theory to emulate the abilities of sound localization and selective attention. This research investigates and optimizes the sensing capacity and the spatial sensitivity of the acoustic sensor. The well-modeled acoustic sensor allows localizing multiple speakers in both stationary and dynamic auditory scene; and distinguishing mixed conversations from independent sources with high audio recognition rate.