Effects of Therapeutic Targeting of Cancer Associated Fibroblasts on Extracellular Matrix Remodeling in an Engineered Tumor Stroma Model

The tumor microenvironment (TME) is a complex combination of stromal cells and extracellular matrix. The cancer-associated fibroblast (CAF) plays an integral role in remodeling the TME and promoting tumor aggression. In this study, we present a 3D microfluidic model of the tumor stroma, which consists of a single straight micro-channel filled with CAF-embedded or acellular collagen gels. Using this platform, we can quantify both fiber alignment and hydraulic permeability within the collagen matrix. These results provide an enhanced understanding of the CAF's influence on the TME, and this knowledge is critical to the development of more effective cancer treatments. First, this study shows that genetic silencing of phosphatase and tensin homolog (PTEN) in CAFs causes a significant decrease in hydraulic permeability. Moreover, this change occurs without physical reorientation of the collagen fibers, thereby suggesting that PTEN deleted CAFs may be secreting molecules – hypothesized to be hyaluronan – into the TME to cause this observed effect. Using our microsystem as a drug screening platform, we also (1) identify the application of hyaluronidase and the inhibition of p-AKT as promising methods for mitigating the adverse effects of PTEN deletion and (2) show that GDC-0449 undesirably decreases the hydraulic permeability of the TME. Finally, we have also utilized our microsystems to measure the properties of various acellular ECM gel compositions and compared these results to our CAF data. Our findings suggest that HA supplementation to collagen gels does not have an equivalent effect on matrix architecture as CAF-secreted HA. Overall, this study demonstrates the utility of our microfluidic model for studying the TME and provides key insights for developing more effective cancer treatments.The College of EngineeringPelotonia Undergraduate Fellowship ProgramA one-year embargo was granted for this item.Academic Major: Biomedical Engineerin

Learning Representations of Emotional Speech with Deep Convolutional Generative Adversarial Networks

Automatically assessing emotional valence in human speech has historically been a difficult task for machine learning algorithms. The subtle changes in the voice of the speaker that are indicative of positive or negative emotional states are often "overshadowed" by voice characteristics relating to emotional intensity or emotional activation. In this work we explore a representation learning approach that automatically derives discriminative representations of emotional speech. In particular, we investigate two machine learning strategies to improve classifier performance: (1) utilization of unlabeled data using a deep convolutional generative adversarial network (DCGAN), and (2) multitask learning. Within our extensive experiments we leverage a multitask annotated emotional corpus as well as a large unlabeled meeting corpus (around 100 hours). Our speaker-independent classification experiments show that in particular the use of unlabeled data in our investigations improves performance of the classifiers and both fully supervised baseline approaches are outperformed considerably. We improve the classification of emotional valence on a discrete 5-point scale to 43.88% and on a 3-point scale to 49.80%, which is competitive to state-of-the-art performance

Hierarchical relational models for document networks

We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each document are its words, that is, discrete observations taken from a fixed vocabulary. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efficient inference and estimation algorithms based on variational methods that take advantage of sparsity and scale with the number of links. We evaluate the predictive performance of the RTM for large networks of scientific abstracts, web documents, and geographically tagged news.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS309 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Taxes and Growth in a Financially Underdeveloped Country: Evidence from the Chilean Investment Boom

This paper argues that taxation of retained profits is particularly distortionary in an economy with good growth prospects and poorly developed financial markets because it primarily reduces the investment of financially constrained firms, investment that has marginal product greater than the after-tax market real interest rate. Contrarily, taxes on distributed profits or capital gains primarily reduce the investment of financially unconstrained firms. Chile experienced a banking crisis over the period from 1982 to 1986 and in 1984 reduced its tax rate on retained profits from 50 percent to 10 percent. We show that, consistent with our theory, there was a large increase in aggregate investment after the reform which was entirely funded by an increase in retained profits. Further, we show that investment grew by more in industries that depend more on external financing, according to the Rajan and Zingales (1998) measure. Finally, we present some weak evidence from comparisons of investment rates across firms for several different measures of their likelihood of being financially constrained.

Long Term Evolution of Magnetic Turbulence in Relativistic Collisionless Shocks: Electron-Positron Plasmas

We study the long term evolution of magnetic fields generated by a collisionless relativistic $e^+e^-$ shock which is initially unmagnetized. Our 2D particle-in-cell numerical simulations show that downstream of such a Weibel-mediated shock, particle distributions are close to isotropic, relativistic Maxwellians, and the magnetic turbulence is highly intermittent spatially, with the non-propagating magnetic fields forming relatively isolated regions with transverse dimension $\sim 10-20$ skin depths. These structures decay in amplitude, with little sign of downstream merging. The fields start with magnetic energy density $\sim (0.1-0.2)$ of the upstream kinetic energy within the shock transition, but rapid downstream decay drives the fields to much smaller values, below $10^{-3}$ of equipartition after $10^3$ skin depths. In an attempt to construct a theory that follows field decay to these smaller values, we explore the hypothesis that the observed damping is a variant of Landau damping in an unmagnetized plasma. The model is based on the small value of the downstream magnetic energy density, which suggests that particle orbits are only weakly perturbed from straight line motion, if the turbulence is homogeneous. Using linear kinetic theory applied to electromagnetic fields in an isotropic, relativistic Maxwellian plasma, we find a simple analytic form for the damping rates, $\gamma_k$, in two and three dimensions for small amplitude, subluminous electromagnetic fields. We find that magnetic energy does damp due to phase mixing of current carrying particles as $(\omega_p t)^{-q}$ with $q \sim 1$. (abridged)Comment: 10 pages, 6 figures, accepted to ApJ; Downsampled version for arXiv. Full resolution figures available at http://astro.berkeley.edu/~pchang/full_res_weibel.pd

The First Crusade: The Forgotten Realities

In the Middle Ages, Europe saw a great amassing of thousands of lords, knights, and ordinary people for an extraordinary expedition into the Holy Land. This event was called the First Crusade. The First Crusade was one of the more successful crusades, however, this fact is overshadowed by the negatives of the crusades. My paper explores the reasons for how the crusaders were able to be victorious in the First Crusade

CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos

Temporal action localization is an important yet challenging problem. Given a long, untrimmed video consisting of multiple action instances and complex background contents, we need not only to recognize their action categories, but also to localize the start time and end time of each instance. Many state-of-the-art systems use segment-level classifiers to select and rank proposal segments of pre-determined boundaries. However, a desirable model should move beyond segment-level and make dense predictions at a fine granularity in time to determine precise temporal boundaries. To this end, we design a novel Convolutional-De-Convolutional (CDC) network that places CDC filters on top of 3D ConvNets, which have been shown to be effective for abstracting action semantics but reduce the temporal length of the input data. The proposed CDC filter performs the required temporal upsampling and spatial downsampling operations simultaneously to predict actions at the frame-level granularity. It is unique in jointly modeling action semantics in space-time and fine-grained temporal dynamics. We train the CDC network in an end-to-end manner efficiently. Our model not only achieves superior performance in detecting actions in every frame, but also significantly boosts the precision of localizing temporal boundaries. Finally, the CDC network demonstrates a very high efficiency with the ability to process 500 frames per second on a single GPU server. We will update the camera-ready version and publish the source codes online soon.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201