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Bioengineered hydrogels have been explored in cell and tissue engineering applications to support cell growth and modulate its behavior. A rationally designed scaffold should allow for encapsulated cells to survive, adhere, proliferate, remodel the niche, and can be used for controlled delivery of biomolecules. Here we report a microarray of composite bioadhesive microgels with modular dimensions, tunable mechanical properties and bulk modified adhesive biomolecule composition. Composite bioadhesive microgels of maleimide functionalized polyethylene glycol (PEG-MAL) with interpenetrating network (IPN) of gelatin ionically cross-linked with silicate nanoparticles were engineered by integrating microfabrication with Michael-type addition chemistry and ionic gelation. By encapsulating clinically relevant anchorage-dependent cervical cancer cells and suspension leukemia cells as cell culture models in these composite microgels, we demonstrate enhanced cell spreading, survival, and metabolic activity compared to control gels. The composite bioadhesive hydrogels represent a platform that could be used to study independent effect of stiffness and adhesive ligand density on cell survival and function. We envision that such microarrays of cell adhesive microenvironments, which do not require harsh chemical and UV crosslinking conditions, will provide a more efficacious cell culture platform that can be used to study cell behavior and survival, function as building blocks to fabricate 3D tissue structures, cell delivery systems, and high throughput drug screening devices.

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Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a nonparametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications.

We present a verification study of three simulation techniques for fluid-particle flows, including an Euler-Lagrange approach (EL) inspired by Jackson's seminal work on fluidized particles, a quadrature-based moment method based on the anisotropic Gaussian closure (AG), and the traditional two-fluid model. We perform simulations of two problems: particles in frozen homogeneous isotropic turbulence (HIT) and cluster-induced turbulence (CIT). For verification, we evaluate various techniques for extracting statistics from EL and study the convergence properties of the three methods under grid refinement. The convergence is found to depend on the simulation method and on the problem, with CIT simulations posing fewer difficulties than HIT. Specifically, EL converges under refinement for both HIT and CIT, but statistics exhibit dependence on the postprocessing parameters. For CIT, AG produces similar results to EL. For HIT, converging both TFM and AG poses challenges. Overall, extracting converged, parameter-independent Eulerian statistics remains a challenge for all methods. V C 2017 American Institute of Chemical Engineers AIChE J, 63: [5396][5397][5398][5399][5400][5401][5402][5403][5404][5405][5406][5407][5408][5409][5410][5411][5412] 2017 In this section, the governing equations of the fluid and particle phases are briefly presented. The behavior of fluid phase Figure 4. St 5 5 particle fields at t 5 1 for TFM, AG, EL (left to right) and increasing resolution (top to bottom) forHIT case. a p shown for EE methods and particle locations for EL method.Statistics are captured using the various density estimation techniques shown in the legend. Fix and Var refer to kernel density estimation using fixed filter width and variable filter width, respectively. In the case of Fix, the number following refers to the filter width, in multiples of particle diameter, h/d p , and in the case of Var, the number refers to the number of particles sampled, N p .Spectra are shown for increasing grid resolution. For EL, Fix 10 was used for density estimation. Darker lines refer to higher resolutions. For TFM and AG, the black dashed lines are from the highest resolution EL simulation for comparison.Particle volume-fraction variance (left) and granular temperature (right). K refers to grid resolution, D, for EE methods and filter width, h, for EL.