David Hong

COOPERATION OF A SINGLE LYSINE MUTATION AND A C-TERMINAL DOMAIN IN THE CYTOPLASMIC SEQUESTRATION OF THE P53 PROTEIN

Cytoplasmic sequestration of the p53 tumor suppresser protein has been proposed as a mechanism involved in abolishing p53 function. However, the mechanisms regulating p53 subcellular localization remain unclear. In this report, we analyzed the possible existence of cis-acting sequences involved in intracellular trafficking of the p53 protein. To study p53 trafficking, the jellyfish green fluorescent protein (GFP) was fused to the wild-type or mutated p53 proteins for fast and sensitive analysis of protein localization in human MCF-7 breast cancer, RKO colon cancer, and SAOS-2 sarcoma cells. The wild-type p53/GFP fusion protein was localized in the cytoplasm, the nucleus, or both compartments in a subset of the cells. Mutagenesis analysis demonstrated that a single amino acid mutation of Lys-305 (mt p53) caused cytoplasmic sequestration of the p53 protein in the MCF-7 and RKO cells, whereas the fusion protein was distributed in both the cytoplasm and the nucleus of SAOS-2 cells. In SAOS-2 cells, the mutant p53 was a less efficient inducer of p21/CIP1/WAF1 expression. Cytoplasmic sequestration of the mt p53 was dependent upon the C-terminal region (residues 326–355) of the protein. These results indicated the involvement of cis-acting sequences in the regulation of p53 subcellular localization. Lys-305 is needed for nuclear import of p53 protein, and amino acid residues 326–355 can sequester mt p53 in the cytoplasm.

Accelerating Mobile Applications through Flip-Flop Replication

Mobile devices have less computational power and poorer Internet connections than other computers. Computation offload, in which some portions of an application are migrated to a server, has been proposed as one way to remedy this deficiency. Yet, partition-based offload is challenging because it requires applications to accurately predict whether mobile or remote computation will be faster, and it requires that the computation be large enough to overcome the cost of shipping state to and from the server. Further, offload does not currently benefit network-intensive applications. In this paper, we introduce Tango, a new method for using a remote server to accelerate mobile applications. Tango replicates the application and executes it on both the client and the server. Since either the client or the server execution may be faster during different phases of the application, Tango allows either replica to lead the execution. Tango attempts to reduces user-perceived application latency by predicting which replica will be faster and allowing it to lead execution and display output, leveraging the better network and computation resources of the server when the application can benefit from it. It uses techniques inspired by deterministic replay to keep the two replicas in sync, and it uses flip-flop replication to allow leadership to float between replicas. Tango currently works for several unmodified Android applications. In our results, two computation-heavy applications obtain up to 2-3x speedup, and five network applications obtain from 0 to 2.6x speedup.

Incremental Deployment of SDN in Hybrid Enterprise and ISP Networks

Introducing SDN into an existing network causes both deployment and operational issues. A systematic incremental deployment methodology as well as a hybrid operation model is needed. We present such a system for incremental deployment of hybrid SDN networks consisting of both legacy forwarding devices (i.e., traditional IP routers) and programmable SDN switches. We design the system on a production SDN controller to answer the following questions: which legacy devices to upgrade to SDN, and how legacy and SDN devices can interoperate in a hybrid environment to satisfy a variety of traffic engineering (TE) goals such as load balancing and fast failure recovery. Evaluation on real ISP and enterprise topologies shows that with only 20% devices upgraded to SDN, our system reduces the maximum link usage by an average of 32% compared with pure-legacy networks (shortest path routing), while only requiring an average of 41% of flow table capacity compared with pure-SDN networks.

Tango: Accelerating Mobile Applications through Flip-Flop Replication

Mobile devices have less computational power and poorer Internet connections than other computers. Computation offload [1, 2, 3, 4], in which some portions of an application are migrated to a server, has been proposed as one way to remedy this deficiency. Yet, partitionbased offload is challenging because it requires applications to accurately predict whether mobile or remote computation will be faster, and it requires that the computation be large enough to overcome the cost of shipping state to and from the server. Further, offload does not currently benefit network-intensive applications.

Asymptotic performance of PCA for high-dimensional heteroscedastic data

Principal Component Analysis (PCA) is a classical method for reducing the dimensionality of data by projecting them onto a subspace that captures most of their variation. Effective use of PCA in modern applications requires understanding its performance for data that are both high-dimensional and heteroscedastic. This paper analyzes the statistical performance of PCA in this setting, i.e., for high-dimensional data drawn from a low-dimensional subspace and degraded by heteroscedastic noise. We provide simplified expressions for the asymptotic PCA recovery of the underlying subspace, subspace amplitudes and subspace coefficients; the expressions enable both easy and efficient calculation and reasoning about the performance of PCA. We exploit the structure of these expressions to show that, for a fixed average noise variance, the asymptotic recovery of PCA for heteroscedastic data is always worse than that for homoscedastic data (i.e., for noise variances that are equal across samples). Hence, while average noise variance is often a practically convenient measure for the overall quality of data, it gives an overly optimistic estimate of the performance of PCA for heteroscedastic data.

An Initial Investigation of Protocol Customization

Attacks exploiting design or implementation flaws of particular features in popular protocols are becoming prevalent and have led to severe security impacts on a majority of software systems. Protocol customization as a general approach to specialize a standard protocol holds significant promise in reducing such attack surfaces in common protocols. In this work, we perform an initial investigation of applying protocol customization practices to reduce the attack surface of standard protocols. Our characterization study on 20 medium or high-impact common vulnerability exposures (CVEs) published in recent years indicates that some forms of customization have been supported in existing protocol software, but were implemented with huge manual effort and in an ad-hoc manner. More systematic and automated ways of protocol customization are awaited to generalize common customization practices across protocols. To work towards this goal, we identify key research challenges for the support of systematic and sufficiently automated protocol customization through real-world case study on popular protocol software, and propose an access control framework as a principled solution to unify existing protocol customization practices. We also present a preliminary design of a protocol customization system based on this design principle. Preliminary evaluation results demonstrate that our proposed system supports common customization practices for a majority of real-world protocol vulnerabilities in a systematic way.

Towards a theoretical analysis of PCA for heteroscedastic data

Principal Component Analysis (PCA) is a method for estimating a subspace given noisy samples. It is useful in a variety of problems ranging from dimensionality reduction to anomaly detection and the visualization of high dimensional data. PCA performs well in the presence of moderate noise and even with missing data, but is also sensitive to outliers. PCA is also known to have a phase transition when noise is independent and identically distributed; recovery of the subspace sharply declines at a threshold noise variance. Effective use of PCA requires a rigorous understanding of these behaviors. This paper provides a step towards an analysis of PCA for samples with heteroscedastic noise, that is, samples that have non-uniform noise variances and so are no longer identically distributed. In particular, we provide a simple asymptotic prediction of the recovery of a one-dimensional subspace from noisy heteroscedastic samples. The prediction enables: a) easy and efficient calculation of the asymptotic performance, and b) qualitative reasoning to understand how PCA is impacted by heteroscedasticity (such as outliers).