Ligament-derived matrix stimulates a ligamentous phenotype in human adipose-derived stem cells.

Human adipose stem cells (hASCs) can differentiate into a variety of phenotypes. Native extracellular matrix (e.g., demineralized bone matrix or small intestinal submucosa) can influence the growth and differentiation of stem cells. The hypothesis of this study was that a novel ligament-derived matrix (LDM) would enhance expression of a ligamentous phenotype in hASCs compared to collagen gel alone. LDM prepared using phosphate-buffered saline or 0.1% peracetic acid was mixed with collagen gel (COL) and was evaluated for its ability to induce proliferation, differentiation, and extracellular matrix synthesis in hASCs over 28 days in culture at different seeding densities (0, 0.25 x 10(6), 1 x 10(6), or 2 x 10(6) hASC/mL). Biochemical and gene expression data were analyzed using analysis of variance. Fisher's least significant difference test was used to determine differences between treatments following analysis of variance. hASCs in either LDM or COL demonstrated changes in gene expression consistent with ligament development. hASCs cultured with LDM demonstrated more dsDNA content, sulfated-glycosaminoglycan accumulation, and type I and III collagen synthesis, and released more sulfated-glycosaminoglycan and collagen into the medium compared to hASCs in COL (p

Interstitial engraftment of adipose-derived stem cells into an acellular dermal matrix results in improved inward angiogenesis and tissue incorporation.

Acellular dermal matrices (ADM) are commonly used in reconstructive procedures and rely on host cell invasion to become incorporated into host tissues. We investigated different approaches to adipose-derived stem cells (ASCs) engraftment into ADM to enhance this process. Lewis rat adipose-derived stem cells were isolated and grafted (3.0 × 10(5) cells) to porcine ADM disks (1.5 mm thick × 6 mm diameter) using either passive onlay or interstitial injection seeding techniques. Following incubation, seeding efficiency and seeded cell viability were measured in vitro. In addition, Eighteen Lewis rats underwent subcutaneous placement of ADM disk either as control or seeded with PKH67 labeled ASCs. ADM disks were seeded with ASCs using either onlay or injection techniques. On day 7 and or 14, ADM disks were harvested and analyzed for host cell infiltration. Onlay and injection techniques resulted in unique seeding patterns; however cell seeding efficiency and cell viability were similar. In-vivo studies showed significantly increased host cell infiltration towards the ASCs foci following injection seeding in comparison to control group (p < 0.05). Moreover, regional endothelial cell invasion was significantly greater in ASCs injected grafts in comparison to onlay seeding (p < 0.05). ADM can successfully be engrafted with ASCs. Interstitial engraftment of ASCs into ADM via injection enhances regional infiltration of host cells and angiogenesis, whereas onlay seeding showed relatively broad and superficial cell infiltration. These findings may be applied to improve the incorporation of avascular engineered constructs.

Cumulon: Simplified Matrix-Based Data Analytics in the Cloud

Cumulon is a system aimed at simplifying the development and deployment of statistical analysis of big data in public clouds. Cumulon allows users to program in their familiar language of matrices and linear algebra, without worrying about how to map data and computation to specific hardware and cloud software platforms. Given user-specified requirements in terms of time, monetary cost, and risk tolerance, Cumulon automatically makes intelligent decisions on implementation alternatives, execution parameters, as well as hardware provisioning and configuration settings -- such as what type of machines and how many of them to acquire. Cumulon also supports clouds with auction-based markets: it effectively utilizes computing resources whose availability varies according to market conditions, and suggests best bidding strategies for them. Cumulon explores two alternative approaches toward supporting such markets, with different trade-offs between system and optimization complexity. Experimental study is conducted to show the efficiency of Cumulon's execution engine, as well as the optimizer's effectiveness in finding the optimal plan in the vast plan space.

Ground and Electronic Excited States from Pairing Matrix Fluctuation and Particle-Particle Random Phase Approximation

The accurate description of ground and electronic excited states is an important and challenging topic in quantum chemistry. The pairing matrix fluctuation, as a counterpart of the density fluctuation, is applied to this topic. From the pairing matrix fluctuation, the exact electron correlation energy as well as two electron addition/removal energies can be extracted. Therefore, both ground state and excited states energies can be obtained and they are in principle exact with a complete knowledge of the pairing matrix fluctuation. In practice, considering the exact pairing matrix fluctuation is unknown, we adopt its simple approximation --- the particle-particle random phase approximation (pp-RPA) --- for ground and excited states calculations. The algorithms for accelerating the pp-RPA calculation, including spin separation, spin adaptation, as well as an iterative Davidson method, are developed. For ground states correlation descriptions, the results obtained from pp-RPA are usually comparable to and can be more accurate than those from traditional particle-hole random phase approximation (ph-RPA). For excited states, the pp-RPA is able to describe double, Rydberg, and charge transfer excitations, which are challenging for conventional time-dependent density functional theory (TDDFT). Although the pp-RPA intrinsically cannot describe those excitations excited from the orbitals below the highest occupied molecular orbital (HOMO), its performances on those single excitations that can be captured are comparable to TDDFT. The pp-RPA for excitation calculation is further applied to challenging diradical problems and is used to unveil the nature of the ground and electronic excited states of higher acenes. The pp-RPA and the corresponding Tamm-Dancoff approximation (pp-TDA) are also applied to conical intersections, an important concept in nonadiabatic dynamics. Their good description of the double-cone feature of conical intersections is in sharp contrast to the failure of TDDFT. All in all, the pairing matrix fluctuation opens up new channel of thinking for quantum chemistry, and the pp-RPA is a promising method in describing ground and electronic excited states.

Distributed Feature Selection in Large n and Large p Regression Problems

Fitting statistical models is computationally challenging when the sample size or the dimension of the dataset is huge. An attractive approach for down-scaling the problem size is to first partition the dataset into subsets and then fit using distributed algorithms. The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space), and the challenge arise in defining an algorithm with low communication, theoretical guarantees and excellent practical performance in general settings. For sample space partitioning, I propose a MEdian Selection Subset AGgregation Estimator ({\em message}) algorithm for solving these issues. The algorithm applies feature selection in parallel for each subset using regularized regression or Bayesian variable selection method, calculates the `median' feature inclusion index, estimates coefficients for the selected features in parallel for each subset, and then averages these estimates. The algorithm is simple, involves very minimal communication, scales efficiently in sample size, and has theoretical guarantees. I provide extensive experiments to show excellent performance in feature selection, estimation, prediction, and computation time relative to usual competitors.

While sample space partitioning is useful in handling datasets with large sample size, feature space partitioning is more effective when the data dimension is high. Existing methods for partitioning features, however, are either vulnerable to high correlations or inefficient in reducing the model dimension. In the thesis, I propose a new embarrassingly parallel framework named {\em DECO} for distributed variable selection and parameter estimation. In {\em DECO}, variables are first partitioned and allocated to m distributed workers. The decorrelated subset data within each worker are then fitted via any algorithm designed for high-dimensional problems. We show that by incorporating the decorrelation step, DECO can achieve consistent variable selection and parameter estimation on each subset with (almost) no assumptions. In addition, the convergence rate is nearly minimax optimal for both sparse and weakly sparse models and does NOT depend on the partition number m. Extensive numerical experiments are provided to illustrate the performance of the new framework.

For datasets with both large sample sizes and high dimensionality, I propose a new "divided-and-conquer" framework {\em DEME} (DECO-message) by leveraging both the {\em DECO} and the {\em message} algorithm. The new framework first partitions the dataset in the sample space into row cubes using {\em message} and then partition the feature space of the cubes using {\em DECO}. This procedure is equivalent to partitioning the original data matrix into multiple small blocks, each with a feasible size that can be stored and fitted in a computer in parallel. The results are then synthezied via the {\em DECO} and {\em message} algorithm in a reverse order to produce the final output. The whole framework is extremely scalable.