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.

Mixtures of g-priors in Generalized Linear Models

Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of g-priors to Generalized Linear Models (GLMs) have been proposed in the literature; however, the choice of prior distribution of g and resulting properties for inference have received considerably less attention. In this paper, we extend mixtures of g-priors to GLMs by assigning the truncated Compound Confluent Hypergeometric (tCCH) distribution to 1/(1+g) and illustrate how this prior distribution encompasses several special cases of mixtures of g-priors in the literature, such as the Hyper-g, truncated Gamma, Beta-prime, and the Robust prior. Under an integrated Laplace approximation to the likelihood, the posterior distribution of 1/(1+g) is in turn a tCCH distribution, and approximate marginal likelihoods are thus available analytically. We discuss the local geometric properties of the g-prior in GLMs and show that specific choices of the hyper-parameters satisfy the various desiderata for model selection proposed by Bayarri et al, such as asymptotic model selection consistency, information consistency, intrinsic consistency, and measurement invariance. We also illustrate inference using these priors and contrast them to others in the literature via simulation and real examples.

Bioengineered Approaches to Prevent Hypertrophic Scar Contraction

Burn injuries in the United States account for over one million hospital admissions per year, with treatment estimated at four billion dollars. Of severe burn patients, 30-90% will develop hypertrophic scars (HSc). Current burn therapies rely upon the use of bioengineered skin equivalents (BSEs), which assist in wound healing but do not prevent HSc. HSc contraction occurs of 6-18 months and results in the formation of a fixed, inelastic skin deformity, with 60% of cases occurring across a joint. HSc contraction is characterized by abnormally high presence of contractile myofibroblasts which normally apoptose at the completion of the proliferative phase of wound healing. Additionally, clinical observation suggests that the likelihood of HSc is increased in injuries with a prolonged immune response. Given the pathogenesis of HSc, we hypothesize that BSEs should be designed with two key anti-scarring characterizes: (1) 3D architecture and surface chemistry to mitigate the inflammatory microenvironment and decrease myofibroblast transition; and (2) using materials which persist in the wound bed throughout the remodeling phase of repair. We employed electrospinning and 3D printing to generate scaffolds with well-controlled degradation rate, surface coatings, and 3D architecture to explore our hypothesis through four aims.

In the first aim, we evaluate the impact of elastomeric, randomly-oriented biostable polyurethane (PU) scaffold on HSc-related outcomes. In unwounded skin, native collagen is arranged randomly, elastin fibers are abundant, and myofibroblasts are absent. Conversely, in scar contractures, collagen is arranged in linear arrays and elastin fibers are few, while myofibroblast density is high. Randomly oriented collagen fibers native to the uninjured dermis encourage random cell alignment through contact guidance and do not transmit as much force as aligned collagen fibers. However, the linear ECM serves as a system for mechanotransduction between cells in a feed-forward mechanism, which perpetuates ECM remodeling and myofibroblast contraction. The electrospinning process allowed us to create scaffolds with randomly-oriented fibers that promote random collagen deposition and decrease myofibroblast formation. Compared to an in vitro HSc contraction model, fibroblast-seeded PU scaffolds significantly decreased matrix and myofibroblast formation. In a murine HSc model, collagen coated PU (ccPU) scaffolds significantly reduced HSc contraction as compared to untreated control wounds and wounds treated with the clinical standard of care. The data from this study suggest that electrospun ccPU scaffolds meet the requirements to mitigate HSc contraction including: reduction of in vitro HSc related outcomes, diminished scar stiffness, and reduced scar contraction. While clinical dogma suggests treating severe burn patients with rapidly biodegrading skin equivalents, these data suggest that a more long-term scaffold may possess merit in reducing HSc.

In the second aim, we further investigate the impact of scaffold longevity on HSc contraction by studying a degradable, elastomeric, randomly oriented, electrospun micro-fibrous scaffold fabricated from the copolymer poly(l-lactide-co-ε-caprolactone) (PLCL). PLCL scaffolds displayed appropriate elastomeric and tensile characteristics for implantation beneath a human skin graft. In vitro analysis using normal human dermal fibroblasts (NHDF) demonstrated that PLCL scaffolds decreased myofibroblast formation as compared to an in vitro HSc contraction model. Using our murine HSc contraction model, we found that HSc contraction was significantly greater in animals treated with standard of care, Integra, as compared to those treated with collagen coated-PLCL (ccPLCL) scaffolds at d 56 following implantation. Finally, wounds treated with ccPLCL were significantly less stiff than control wounds at d 56 in vivo. Together, these data further solidify our hypothesis that scaffolds which persist throughout the remodeling phase of repair represent a clinically translatable method to prevent HSc contraction.

In the third aim, we attempt to optimize cell-scaffold interactions by employing an anti-inflammatory coating on electrospun PLCL scaffolds. The anti-inflammatory sub-epidermal glycosaminoglycan, hyaluronic acid (HA) was used as a coating material for PLCL scaffolds to encourage a regenerative healing phenotype. To minimize local inflammation, an anti-TNFα monoclonal antibody (mAB) was conjugated to the HA backbone prior to PLCL coating. ELISA analysis confirmed mAB activity following conjugation to HA (HA+mAB), and following adsorption of HA+mAB to the PLCL backbone [(HA+mAB)PLCL]. Alican blue staining demonstrated thorough HA coating of PLCL scaffolds using pressure-driven adsorption. In vitro studies demonstrated that treatment with (HA+mAB)PLCL prevented downstream inflammatory events in mouse macrophages treated with soluble TNFα. In vivo studies using our murine HSc contraction model suggested positive impact of HA coating, which was partiall impeded by the inclusion of the TNFα mAB. Further characterization of the inflammatory microenvironment of our murine model is required prior to conclusions regarding the potential for anti-TNFα therapeutics for HSc. Together, our data demonstrate the development of a complex anti-inflammatory coating for PLCL scaffolds, and the potential impact of altering the ECM coating material on HSc contraction.

In the fourth aim, we investigate how scaffold design, specifically pore dimensions, can influence myofibroblast interactions and subsequent formation of OB-cadherin positive adherens junctions in vitro. We collaborated with Wake Forest University to produce 3D printed (3DP) scaffolds with well-controlled pore sizes we hypothesized that decreasing pore size would mitigate intra-cellular communication via OB-cadherin-positive adherens junctions. PU was 3D printed via pressure extrusion in basket-weave design with feature diameter of ~70 µm and pore sizes of 50, 100, or 150 µm. Tensile elastic moduli of 3DP scaffolds were similar to Integra; however, flexural moduli of 3DP were significantly greater than Integra. 3DP scaffolds demonstrated ~50% porosity. 24 h and 5 d western blot data demonstrated significant increases in OB-cadherin expression in 100 µm pores relative to 50 µm pores, suggesting that pore size may play a role in regulating cell-cell communication. To analyze the impact of pore size in these scaffolds on scarring in vivo, scaffolds were implanted beneath skin graft in a murine HSc model. While flexural stiffness resulted in graft necrosis by d 14, cellular and blood vessel integration into scaffolds was evident, suggesting potential for this design if employed in a less stiff material. In this study, we demonstrate for the first time that pore size alone impacts OB-cadherin protein expression in vitro, suggesting that pore size may play a role on adherens junction formation affiliated with the fibroblast-to-myofibroblast transition. Overall, this work introduces a new bioengineered scaffold design to both study the mechanism behind HSc and prevent the clinical burden of this contractile disease.

Together, these studies inform the field of critical design parameters in scaffold design for the prevention of HSc contraction. We propose that scaffold 3D architectural design, surface chemistry, and longevity can be employed as key design parameters during the development of next generation, low-cost scaffolds to mitigate post-burn hypertrophic scar contraction. The lessening of post-burn scarring and scar contraction would improve clinical practice by reducing medical expenditures, increasing patient survival, and dramatically improving quality of life for millions of patients worldwide.

Predicting Transcript Production Rates in Yeast With Sparse Linear Models

To provide biological insights into transcriptional regulation, a couple of groups have recently presented models relating the promoter DNA-bound transcription factors (TFs) to downstream gene’s mean transcript level or transcript production rates over time. However, transcript production is dynamic in response to changes of TF concentrations over time. Also, TFs are not the only factors binding to promoters; other DNA binding factors (DBFs) bind as well, especially nucleosomes, resulting in competition between DBFs for binding at same genomic location. Additionally, not only TFs, but also some other elements regulate transcription. Within core promoter, various regulatory elements influence RNAPII recruitment, PIC formation, RNAPII searching for TSS, and RNAPII initiating transcription. Moreover, it is proposed that downstream from TSS, nucleosomes resist RNAPII elongation.

Here, we provide a machine learning framework to predict transcript production rates from DNA sequences. We applied this framework in the S. cerevisiae yeast for two scenarios: a) to predict the dynamic transcript production rate during the cell cycle for native promoters; b) to predict the mean transcript production rate over time for synthetic promoters. As far as we know, our framework is the first successful attempt to have a model that can predict dynamic transcript production rates from DNA sequences only: with cell cycle data set, we got Pearson correlation coefficient Cp = 0.751 and coefficient of determination r2 = 0.564 on test set for predicting dynamic transcript production rate over time. Also, for DREAM6 Gene Promoter Expression Prediction challenge, our fitted model outperformed all participant teams, best of all teams, and a model combining best team’s k-mer based sequence features and another paper’s biologically mechanistic features, in terms of all scoring metrics.

Moreover, our framework shows its capability of identifying generalizable fea- tures by interpreting the highly predictive models, and thereby provide support for associated hypothesized mechanisms about transcriptional regulation. With the learned sparse linear models, we got results supporting the following biological insights: a) TFs govern the probability of RNAPII recruitment and initiation possibly through interactions with PIC components and transcription cofactors; b) the core promoter amplifies the transcript production probably by influencing PIC formation, RNAPII recruitment, DNA melting, RNAPII searching for and selecting TSS, releasing RNAPII from general transcription factors, and thereby initiation; c) there is strong transcriptional synergy between TFs and core promoter elements; d) the regulatory elements within core promoter region are more than TATA box and nucleosome free region, suggesting the existence of still unidentified TAF-dependent and cofactor-dependent core promoter elements in yeast S. cerevisiae; e) nucleosome occupancy is helpful for representing +1 and -1 nucleosomes’ regulatory roles on transcription.