Deciphering the regulatory network of microRNAs in tuberculosis infected macrophages : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Genetics at Massey University, Albany, New Zealand

Tuberculosis is an infectious disease that is caused by Mycobacterium tuberculosis (Mtb), an intracellular pathogen that uses macrophages as a host for replication. The outcome of the disease depends highly on Mtb’s strategies to circumvent the immune responses of macrophages. MicroRNAs (miRNAs) are small regulatory RNAs that influence gene functions post-transcriptionally. Recent studies indicate that miRNAs have prominent roles in cellular host-pathogen interactions. The aim of this study is to advance our understanding of the regulatory mechanisms that control key miRNAs in mouse M1 macrophages during Mtb infection using network analysis. The study began with a construction of a mouse miRNA-centric regulatory network model by combining a network of miRNA-controlling transcription factors (TFs) with a miRNA target network. The final network places miRNAs at the center of a comprehensive regulatory network of TFs, miRNAs and their targets. This network represents a useful resource for investigating miRNA functions and their control. Subsequently, we populated the network with CAGE-derived expression data for either Mtb-infected mouse M1 macrophages or non-infected controls. We used network analysis to determine key regulatory elements during the infection process. As a result, we identified a core set of TFs and miRNAs, which are likely critical regulatory elements during M1 macrophage host and Mtb interactions. Our results also demonstrate that among the core set of regulatory elements three highly activated miRNAs, mmu-mir-149, mmu-mir-449a, and mmu-mir-449b, work in unison with mmu-mir-155, the top-ranked miRNA. They co-regulate a set of downstream tuberculosis immune response related genes. Four top-ranked TFs, Fosl1, Bhlhe40, Egr1, and Egr2, were identified that they transcriptionally control this group of miRNAs. The TFs and miRNAs, together with their targets constitute a mmu-mir-155 regulatory sub-network. Our results also imply that Bhlhe40 is likely an important TF that modulates the activities of the mmu-mir-155 regulatory sub-network. Bhlhe40 and the mmu-mir-155 regulatory sub-network may be exploited by Mtb to manipulate the host immune defense for advancing survival interests. The findings of this study provide new insights into the host immune regulatory mechanisms of activated macrophages that are essential to control tuberculosis

Searching for Minimum Storage Regenerating Codes

Regenerating codes allow distributed storage systems to recover from the loss of a storage node while transmitting the minimum possible amount of data across the network. We present a systematic computer search for optimal systematic regenerating codes. To search the space of potential codes, we reduce the potential search space in several ways. We impose an additional symmetry condition on codes that we consider. We specify codes in a simple alternative way, using additional recovered coefficients rather than transmission coefficients and place codes into equivalence classes to avoid redundant checking. Our main finding is a few optimal systematic minimum storage regenerating codes for $n=5$ and $k=3$, over several finite fields. No such codes were previously known and the matching of the information theoretic cut-set bound was an open problem

Improvements to context based self-supervised learning

We develop a set of methods to improve on the results of self-supervised learning using context. We start with a baseline of patch based arrangement context learning and go from there. Our methods address some overt problems such as chromatic aberration as well as other potential problems such as spatial skew and mid-level feature neglect. We prevent problems with testing generalization on common self-supervised benchmark tests by using different datasets during our development. The results of our methods combined yield top scores on all standard self-supervised benchmarks, including classification and detection on PASCAL VOC 2007, segmentation on PASCAL VOC 2012, and "linear tests" on the ImageNet and CSAIL Places datasets. We obtain an improvement over our baseline method of between 4.0 to 7.1 percentage points on transfer learning classification tests. We also show results on different standard network architectures to demonstrate generalization as well as portability. All data, models and programs are available at: https://gdo-datasci.llnl.gov/selfsupervised/.Comment: Accepted paper at CVPR 201

Distributed Stochastic Optimization over Time-Varying Noisy Network

This paper is concerned with distributed stochastic multi-agent optimization problem over a class of time-varying network with slowly decreasing communication noise effects. This paper considers the problem in composite optimization setting which is more general in noisy network optimization. It is noteworthy that existing methods for noisy network optimization are Euclidean projection based. We present two related different classes of non-Euclidean methods and investigate their convergence behavior. One is distributed stochastic composite mirror descent type method (DSCMD-N) which provides a more general algorithm framework than former works in this literature. As a counterpart, we also consider a composite dual averaging type method (DSCDA-N) for noisy network optimization. Some main error bounds for DSCMD-N and DSCDA-N are obtained. The trade-off among stepsizes, noise decreasing rates, convergence rates of algorithm is analyzed in detail. To the best of our knowledge, this is the first work to analyze and derive convergence rates of optimization algorithm in noisy network optimization. We show that an optimal rate of $O(1/\sqrt{T})$ in nonsmooth convex optimization can be obtained for proposed methods under appropriate communication noise condition. Moveover, convergence rates in different orders are comprehensively derived in both expectation convergence and high probability convergence sense.Comment: 27 page