Nanoparticle assisted photothermal deformation of individual neuronal organelles and cells.

Stimulation of the localized surface plasmon of metallic nanoparticles has been shown to be an effective mechanism to induce photothermal damage in biological tissues. However, few studies have focused on single cell or subcellular ablation. Our results show that, upon incubation, gold nanostars are internalized by neurons of acute mouse cerebellar brain slices, clustering inside or close to the nucleus. By stimulating the nanostars' surface plasmon using a femtosecond laser, we show deformation of single nuclei and single cells. Given its precision and extremely localized effect, this is a promising technique for photothermal therapy in areas sensitive to collateral thermal damage such as the nervous system.

Emergent properties during dorsal closure in Drosophila morphogenesis.

Dorsal closure is an essential stage of Drosophila development that is a model system for research in morphogenesis and biological physics. Dorsal closure involves an orchestrated interplay between gene expression and cell activities that produce shape changes, exert forces and mediate tissue dynamics. We investigate the dynamics of dorsal closure based on confocal microscopic measurements of cell shortening in living embryos. During the mid-stages of dorsal closure we find that there are fluctuations in the width of the leading edge cells but the time-averaged analysis of measurements indicate that there is essentially no net shortening of cells in the bulk of the leading edge, that contraction predominantly occurs at the canthi as part of the process for zipping together the two leading edges of epidermis and that the rate constant for zipping correlates with the rate of movement of the leading edges. We characterize emergent properties that regulate dorsal closure, i.e., a velocity governor and the coordination and synchronization of tissue dynamics.

Upregulation of forces and morphogenic asymmetries in dorsal closure during Drosophila development.

Tissue dynamics during dorsal closure, a stage of Drosophila development, provide a model system for cell sheet morphogenesis and wound healing. Dorsal closure is characterized by complex cell sheet movements, driven by multiple tissue specific forces, which are coordinated in space, synchronized in time, and resilient to UV-laser perturbations. The mechanisms responsible for these attributes are not fully understood. We measured spatial, kinematic, and dynamic antero-posterior asymmetries to biophysically characterize both resiliency to laser perturbations and failure of closure in mutant embryos and compared them to natural asymmetries in unperturbed, wild-type closure. We quantified and mathematically modeled two processes that are upregulated to provide resiliency--contractility of the amnioserosa and formation of a seam between advancing epidermal sheets, i.e., zipping. Both processes are spatially removed from the laser-targeted site, indicating they are not a local response to laser-induced wounding and suggesting mechanosensitive and/or chemosensitive mechanisms for upregulation. In mutant embryos, tissue junctions initially fail at the anterior end indicating inhomogeneous mechanical stresses attributable to head involution, another developmental process that occurs concomitant with the end stages of closure. Asymmetries in these mutants are reversed compared to wild-type, and inhomogeneous stresses may cause asymmetries in wild-type closure.

Determination of physical properties of bitumens by use of near-infrared spectroscopy with neural networks. Joint modelling of linear and non-linear parameters.

The fact that bitumens behave as non-Newtonian fluids results in non-linear relationships between their near-infrared (NIR) spectra and the physico-chemical properties that define their consistency (viz. penetration and viscosity). Determining such properties using linear calibration techniques [e.g. partial least-squares regression (PLSR)] entails the previous transformation of the original variables by use of non-linear functions and employing the transformed variables to construct the models. Other properties of bitumens such as density and composition exhibit linear relationships with their NIR spectra. Artificial neural networks (ANNs) enable modelling of systems with a non-linear property-spectrum relationship; also, they allow one to determine several properties of a sample with a single model, so they are effective alternatives to linear calibration methods. In this work, the ability of ANNs simultaneously to determine both linear and non-linear parameters for bitumens without the need previously to transform the original variables was assessed. Based on the results, ANNs allow the simultaneous determination of several linear and non-linear physical properties typical of bitumens.