Recent advances in 3D printing of biomaterials.

3D Printing promises to produce complex biomedical devices according to computer design using patient-specific anatomical data. Since its initial use as pre-surgical visualization models and tooling molds, 3D Printing has slowly evolved to create one-of-a-kind devices, implants, scaffolds for tissue engineering, diagnostic platforms, and drug delivery systems. Fueled by the recent explosion in public interest and access to affordable printers, there is renewed interest to combine stem cells with custom 3D scaffolds for personalized regenerative medicine. Before 3D Printing can be used routinely for the regeneration of complex tissues (e.g. bone, cartilage, muscles, vessels, nerves in the craniomaxillofacial complex), and complex organs with intricate 3D microarchitecture (e.g. liver, lymphoid organs), several technological limitations must be addressed. In this review, the major materials and technology advances within the last five years for each of the common 3D Printing technologies (Three Dimensional Printing, Fused Deposition Modeling, Selective Laser Sintering, Stereolithography, and 3D Plotting/Direct-Write/Bioprinting) are described. Examples are highlighted to illustrate progress of each technology in tissue engineering, and key limitations are identified to motivate future research and advance this fascinating field of advanced manufacturing

GMC Collisions As Triggers of Star Formation. IV. The Role of Ambipolar Diffusion

We investigate the role of ambipolar diffusion (AD) in collisions between magnetized giant molecular clouds (GMCs), which may be an important mechanism for triggering star cluster formation. Three dimensional simulations of GMC collisions are performed using a version of the Enzo magnetohydrodynamics code that has been extended to include AD. The resistivities are calculated using the 31-species chemical model of Wu et al. (2015). We find that in the weak-field, $10\:{\rm \mu G}$ case, AD has only a modest effect on the dynamical evolution during the collision. However, for the stronger-field, $30\:{\rm \mu G}$ case involving near-critical clouds, AD results in formation of dense cores in regions where collapse is otherwise inhibited. The overall efficiency of formation of cores with $n_{\rm H}\geq10^{6}\:{\rm cm}^{-3}$ in these simulations is increases from about 0.2% to 2% once AD is included, comparable to observed values in star-forming GMCs. The gas around these cores typically has relatively slow infall at speeds that are a modest fraction of the free-fall speed.Comment: 15 pages, 15 figures, Accepted to Ap

GMC Collisions As Triggers of Star Formation. IV. The Role of Ambipolar Diffusion

We investigate the role of ambipolar diffusion (AD) in collisions between magnetized giant molecular clouds (GMCs), which may be an important mechanism for triggering star cluster formation. Three dimensional simulations of GMC collisions are performed using a version of the Enzo magnetohydrodynamics code that has been extended to include AD. The resistivities are calculated using the 31-species chemical model of Wu et al. (2015). We find that in the weak-field, $10\:{\rm \mu G}$ case, AD has only a modest effect on the dynamical evolution during the collision. However, for the stronger-field, $30\:{\rm \mu G}$ case involving near-critical clouds, AD results in formation of dense cores in regions where collapse is otherwise inhibited. The overall efficiency of formation of cores with $n_{\rm H}\geq10^{6}\:{\rm cm}^{-3}$ in these simulations is increases from about 0.2% to 2% once AD is included, comparable to observed values in star-forming GMCs. The gas around these cores typically has relatively slow infall at speeds that are a modest fraction of the free-fall speed.Comment: 15 pages, 15 figures, Accepted to Ap

Stellar Wakes from Dark Matter Subhalos

We propose a novel method utilizing stellar kinematic data to detect low-mass substructure in the Milky Way's dark matter halo. By probing characteristic wakes that a passing dark matter subhalo leaves in the phase space distribution of ambient halo stars, we estimate sensitivities down to subhalo masses $\sim 10^7\,M_\odot$ or below. The detection of such subhalos would have implications for dark-matter and cosmological models that predict modifications to the halo-mass function at low halo masses. We develop an analytic formalism for describing the perturbed stellar phase-space distributions, and we demonstrate through simulations the ability to detect subhalos using the phase-space model and a likelihood framework. Our method complements existing methods for low-mass subhalo searches, such as searches for gaps in stellar streams, in that we can localize the positions and velocities of the subhalos today.Comment: 6 + 3 pages, 1 + 2 figures, code available at: https://github.com/bsafdi/stellarWake

Translational aspects of cardiac cell therapy.

Cell therapy has been intensely studied for over a decade as a potential treatment for ischaemic heart disease. While initial trials using skeletal myoblasts, bone marrow cells and peripheral blood stem cells showed promise in improving cardiac function, benefits were found to be short-lived likely related to limited survival and engraftment of the delivered cells. The discovery of putative cardiac 'progenitor' cells as well as the creation of induced pluripotent stem cells has led to the delivery of cells potentially capable of electromechanical integration into existing tissue. An alternative strategy involving either direct reprogramming of endogenous cardiac fibroblasts or stimulation of resident cardiomyocytes to regenerate new myocytes can potentially overcome the limitations of exogenous cell delivery. Complimentary approaches utilizing combination cell therapy and bioengineering techniques may be necessary to provide the proper milieu for clinically significant regeneration. Clinical trials employing bone marrow cells, mesenchymal stem cells and cardiac progenitor cells have demonstrated safety of catheter based cell delivery, with suggestion of limited improvement in ventricular function and reduction in infarct size. Ongoing trials are investigating potential benefits to outcome such as morbidity and mortality. These and future trials will clarify the optimal cell types and delivery conditions for therapeutic effect

Estimation and uncertainty of reversible Markov models

Reversibility is a key concept in Markov models and Master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model relies heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is therefore crucial to the successful application of the previously developed theory. In this work we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta- stable features of the observed process during posterior inference. All algorithms here are implemented in the PyEMMA software - http://pyemma.org - as of version 2.0