Search

As wireless cyber-physical systems (WCPS) are increasingly being deployed in mission-critical applications, it becomes imperative that we consider application QoS requirements in in-network processing (INP). In this dissertation, we explore the potentials of two INP methods, packet packing and network coding, on improving network performance while satisfying application QoS requirements. We find that not only can these two techniques increase the energy efficiency, reliability, and throughput of WCPS while satisfying QoS requirements of applications in a relatively static environment, but also they can provide low cost proactive protection against transient node failures in a more dynamic wireless environment. We first study the problem of jointly optimizing packet packing and the timeliness of data delivery. We identify the conditions under which the problem is strong NP-hard, and we find that the problem complexity heavily depends on aggregation constraints instead of network and traffic properties. For cases when the problem is NP-hard, we show that there is no polynomial-time approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. We design a distributed, online protocol "tPack" that schedules packet transmissions to maximize the local utility of packet packing at each node. We evaluate the properties of tPack in NetEye testbed. We find that jointly optimizing data delivery timeliness and packet packing and considering real-world aggregation constraints significantly improve network performance. We then work on the problem of minimizing the transmission cost of network coding based routing in sensor networks. We propose the first mathematical framework so far as we know on how to theoretically compute the expected transmission cost of NC-based routing in terms of expected number of transmission. Based on this framework, we design a polynomial-time greedy algorithm for forwarder set selection and prove its optimality on transmission cost minimization. We designed EENCR, an energy-efficient NC-based routing protocol that implement our forwarder set selection algorithm to minimize the overall transmission cost. Through comparative study on EENCR and other state-of-the-art routing protocols, we show that EENCR significantly outperforms CTP, MORE and CodeOR in delivery reliability, delivery cost and network goodput. Furthermore, we study the 1+1 proactive protection problem using network coding. We show that even under a simplified setting, finding two node-disjoint routing braids with minimal total cost is NP-hard. We then design a heuristic algorithm to construct two node-disjoint braids with a transmission cost upper bounded by two shortest node-disjoint paths. And we design ProNCP, a proactive NC-based protection protocol using similar design philosophy as in EENCR. We evaluate the performance of ProNCP under various transient network failure scenarios. Experiment results show that ProNCP is resilient to various network failure scenarios and provides a state performance in terms of reliability, delivery cost and goodput. Our findings in this dissertation explore the challenges, benefits and solutions in designing real-time, efficient, resilient and QoS-guaranteed wireless cyber-physical systems, and our solutions shed lights for future research on related topics. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Bacterial peptidoglycan (PG) is an exoskeleton structure that maintains cell shape and protects cells from lysis. Peptidoglycan is essential in bacteria but is not found in mammalian cells. Therefore, it is the target for several classes of antibiotics, including the beta-lactam family. Beta-lactams target the transpeptidase (TP) domain of penicillin-binding proteins (PBPs), the enzymes responsible for the final step of peptidoglycan biosynthesis. Mutations of transpeptidases in Staphylococcus aureus (S. aureus) have been implicated in beta-lactam resistance in the clinic (commonly known as MRSA infections). Despite the fact that PBPs are important antibiotic targets, there have been no direct assays to monitor their enzymatic activity, primarily due to inaccessibility to appropriate substrates. The PG precursor, Lipid II, is required to study transpeptidase activity. Lipid II contains a glycopeptide attached to a pyrophosphate lipid containing 55 carbons. It has poor physical properties and is present in low abundance in bacteria. This thesis describes the reconstitution of peptidoglycan assembly in Staphylococcus aureus by the essential Class A PBP, PBP2. This was enabled by several key advances, which are also described. The first advance is the discovery that PBP4, a low-molecular weight PBP in S. aureus, can use a Lipid II analogue as a transpeptidation substrate. It can incorporate biotin-D-Lys (BDL) and other non-canonical D-amino acids into the terminal position of the stem peptide in Lipid II. BDL labeling of Lipid II with S. aureus PBP4 has enabled the second advance: the direct detection of native Lipid II extracted from bacteria. This has facilitated elucidation of cellular mechanisms of antibiotics that target cell wall biosynthesis. The third advance is a general strategy to accumulate and isolate native Lipid II in bacteria in useful quantities. Access to substantial quantities of native S. aureus Lipid II has enabled reconstitution of PBP2 transpeptidase activity, as well as characterization of several beta-lactam antibiotics by monitoring enzymatic inhibition. In sum, this work establishes important tools for studying enzymatic mechanisms of bacterial transpeptidases and for characterizing inhibitors that target bacterial peptidoglycan biosynthesis., Chemical Biology

Visual information is conveyed from the retina to the brain through axons of retinal ganglion cells (RGCs). There are >20 different subtypes of RGCs, each of which detects specific features. Classification of RGC subtypes is thus essential for us to understand how visual information is processed and delivered to the brain. Here I reported my efforts in classifying different subtypes of RGCs, using morphological, physiological and molecular criteria. A combination of these criteria allowed me to successfully identify subtypes from alpha RGCs, Foxp2-positive RGCs (F-RGCs) and RGCs labeled in a transgenic mouse line W3. First, I presented studies of classifying subtypes of alpha RGCs. Cell attached recording followed by morphology reconstruction revealed four subtypes of alpha-like RGCs: Off-sustained, Off-transient, On-sustained, On-transient subtypes, each of which has distinct morphological properties. In addition, we found osteopontin (OPN) as a molecular marker for all alpha RGCs. Following this discovery, we studied the role of OPN in alpha RGCs, Analysis showed that alpha RGCs preferentially survive and regenerate compared with other RGCs, leading us to test whether OPN can promote axon regeneration. Indeed, by combining OPN with growth factors, we were able to promote axon regenerations of RGCs. Second, I presented work in classifying subtypes of F-RGCs, which are recognized by expressing a transcription factor, Foxp2. Combinatory expression of Foxp2 with other transcriptional factors divides F-RGCs into four subtypes, which form two pairs differing in their dendritic field sizes. Cell attached recording showed that one pair, F-minion and F-minioff RGCs, are direction-selective, while the other pair, F-midion and F-midioff RGCs, are not. Thus, we identified four new subtypes of RGCs labeled by transcriptional factor Foxp2. Third, I described initial efforts in classifying subtypes of RGCs labeled in the transgenic mouse line W3. W3 RGCs can be separated into two group based on their expression levels of fluorescent proteins, with the dimly labeled RGCs (W3D) remained uncharacterized. Initial analysis showed W3D RGCs include at least five subtypes of RGCs, which are different in their structures and physiological properties. Lastly, I described my work in developing a molecular tool for mapping electrical synaptic connections from genetically defined neurons or neuronal subtypes, making use of a dipeptide transporter, Pept2. Cells expressing Pept2 (in a Cre-dependent way) take up a gap junction permeable fluorescent dipeptide, which then diffuses and labels the coupled cells. We tested this method in cultured cells and validated it in mouse retina using AAV carrying Cre-dependent Pept2. I applied this method to one subtype of RGCs, J-RGCs, to label their coupling partners., Biology, Molecular and Cellular

During the last two decades, genotyping technology has advanced rapidly, which enabled the tremendous success of genome-wide association studies (GWAS) in the search of disease susceptibility loci (DSLs). However, only a small fraction of the overall predicted heritability can be explained by the DSLs discovered. One possible explanation for this ”missing heritability” phenomenon is that many causal variants are rare. The recent development of high-throughput next-generation sequencing (NGS) technology provides the instrument to look closely at these rare variants with precision and efficiency. However, new approaches for both the storage and analysis of sequencing data are in imminent needs. In this thesis, we introduce three methods that could be utilized in the management and analysis of sequencing data. In Chapter 1, we propose a novel and simple algorithm for compressing sequencing data that leverages on the scarcity of rare variant data, which enables the storage and analysis of sequencing data efficiently in current hardware environment. We also provide a C++ implementation that supports direct and parallel loading of the compressed format without requiring extra time for decompression. Chapter 2 and 3 focus on the association analysis of sequencing data in population-based design. In Chapter 2, we present a statistical methodology that allows the identification of genetic outliers to obtain a genetically homogeneous subpopulation, which reduces the false positives due to population substructure. Our approach is computationally efficient that can be applied to all the genetic loci in the data and does not require pruning of variants in linkage disequilibrium (LD). In Chapter 3, we propose a general analysis framework in which thousands of genetic loci can be tested simultaneously for association with complex phenotypes. The approach is built on spatial-clustering methodology, assuming that genetic loci that are associated with the target phenotype cluster in certain genomic regions. In contrast to standard methodology for multi-loci analysis, which has focused on the dimension reduction of data, the proposed approach profits from the availability of large numbers of genetic loci. Thus it will be especially relevant for whole-genome sequencing studies which commonly record several thousand loci per gene.