Broadband Impedance Matching of Antenna Radiators

In the design of any antenna radiator, single or multi-element, a significant amount of time and resources is spent on impedance matching. There are broadly two approaches to impedance matching; the first is the distributed impedance matching approach which leads to modifying the antenna geometry itself by identifying appropriate degrees of freedom within the structure. The second option is the lumped element approach to impedance matching. In this approach instead of modifying the antenna geometry a passive network attempts to equalize the impedance mismatch between the source and the antenna load. This thesis introduces a new technique of impedance matching using lumped circuits (passive, lossless) for electrically small (short) non-resonant dipole/monopole antennas. A closed form upper-bound on the achievable transducer gain (and therefore the reflection coefficient) is derived starting with the Bode-Fano criterion. A 5 element equalizer is proposed which can equalize all dipole/monopole like antennas. Simulation and experimental results confirm our hypothesis. The second contribution of this thesis is in the design of broadband, small size, modular arrays (2, 4, 8 or 16 elements) using the distributed approach to impedance matching. The design of arrays comprising a small number of elements cannot follow the infinite array design paradigm. Instead, the central idea is to find a single optimized radiator (unit cell) which if used to build the 2x1, 4x1, 2x2 arrays, etc. (up to a 4x4 array) will provide at least the 2:1 bandwidth with a VSWR of 2:1 and stable directive gain (not greater than 3 dB variation) in each configuration. Simulation and experimental results for a solution to the 2x1, 4x1 and 2x2 array configurations is presented

Comparative analysis of methods for microbiome study

Microbiome analysis is garnering much interest with benefits including improved treatment options, enhanced capabilities for personalized medicine, greater understanding of the human body, and contributions to ecological study. Data from these communities of bacteria, viruses, and fungi are feature rich, sparse, and have sample sizes not appreciably larger than the feature space, making analysis challenging and necessitating a coordinated approach utilizing multiple techniques alongside domain expertise. This thesis provides an overview and comparative analysis of these methods, with a case study on cirrhosis and hepatic encephalopathy demonstrating a selection of methods. Approaches are considered in a medically motivated context where relationships between microbes in the human body and diseases or conditions are of primary interest, with additional objectives being the identification of how microbes influence each other and how these influences relate to the diseases and conditions being studied. These analysis methods are partitioned into three categories: univariate statistical methods, classifier-based methods, and joint analysis methods. Univariate statistical methods provide results corresponding to how much a single variable or feature differs between groups in the data. Classifier-based approaches can be generalized as those where a classification model with microbe abundance as inputs and disease states as outputs is used, resulting in a predictive model which is then analyzed to learn about the data. The joint analysis category corresponds to techniques which specifically target relationships between microbes and compare those relationships among subpopulations within the data. Despite significant differences between these categories and the individual methods, each has strengths and weaknesses and plays an important role in microbiome analysis

Dynamic Trust-Based Device Legitimacy Assessment Towards Secure IoT Interactions

Establishing trust-based interactions in heterogeneously connected devices appears to be the prominent mechanism in addressing the prevailing concerns of confidence, reliability and privacy relevant in establishing secure interactions among connected devices in the network. Trust-based assessment of device legitimacy is evolving given IoT devices’ dynamic and heterogeneous nature and emerging adversaries. However, computation and application of trust level in establishing secure communications, access control and privacy domain are rarely discussed in the literature. To compute trust, based on the quality of service, direct interactions, and the relationship between devices, we introduce a multi-factor trust computation model that considers the multiple attributes of interactions in an IoT network of heterogeneous devices providing a wide range of data and services. Direct trust is estimated for quality of service considering the response time, reliability, consistency, and integrity attributes of devices. The time decay factor influences the credibility of computed trust over time. The policy-driven mechanism is employed to sift the devices and isolate the malicious ones. Extensive simulations validate the proposed model’s effectiveness using Contiki’s Cooja simulator for IoT networks

Predicting Combinatorial Binding of Transcription Factors to Regulatory Elements in the Human Genome by Association Rule Mining

Cis-acting transcriptional regulatory elements in mammalian genomes typically contain specific combinations of binding sites for various transcription factors. Although some cisregulatory elements have been well studied, the combinations of transcription factors that regulate normal expression levels for the vast majority of the 20,000 genes in the human genome are unknown. We hypothesized that it should be possible to discover transcription factor combinations that regulate gene expression in concert by identifying over-represented combinations of sequence motifs that occur together in the genome. In order to detect combinations of transcription factor binding motifs, we developed a data mining approach based on the use of association rules, which are typically used in market basket analysis. We scored each segment of the genome for the presence or absence of each of 83 transcription factor binding motifs, then used association rule mining algorithms to mine this dataset, thus identifying frequently occurring pairs of distinct motifs within a segment. Results: Support for most pairs of transcription factor binding motifs was highly correlated across different chromosomes although pair significance varied. Known true positive motif pairs showed higher association rule support, confidence, and significance than background. Our subsets of high-confidence, high-significance mined pairs of transcription factors showed enrichment for co-citation in PubMed abstracts relative to all pairs, and the predicted associations were often readily verifiable in the literature. Conclusion: Functional elements in the genome where transcription factors bind to regulate expression in a combinatorial manner are more likely to be predicted by identifying statistically and biologically significant combinations of transcription factor binding motifs than by simply scanning the genome for the occurrence of binding sites for a single transcription factor.NIAAA Alcohol Training GrantNational Science FoundationCellular and Molecular Biolog

ArrayPlex: distributed, interactive and programmatic access to genome sequence, annotation, ontology, and analytical toolsets

ArrayPlex is a software package that centrally provides a large number of flexible toolsets useful for functional genomics

Chd1 co-localizes with early transcription elongation factors independently of H3K36 methylation and releases stalled RNA polymerase II at introns

BACKGROUND: Chromatin consists of ordered nucleosomal arrays that are controlled by highly conserved adenosine triphosphate (ATP)-dependent chromatin remodeling complexes. One such remodeler, chromodomain helicase DNA binding protein 1 (Chd1), is believed to play an integral role in nucleosomal organization, as the loss of Chd1 is known to disrupt chromatin. However, the specificity and basis for the functional and physical localization of Chd1 on chromatin remains largely unknown. RESULTS: Using genome-wide approaches, we found that the loss of Chd1 significantly disrupted nucleosome arrays within the gene bodies of highly transcribed genes. We also found that Chd1 is physically recruited to gene bodies, and that its occupancy specifically corresponds to that of the early elongating form of RNA polymerase, RNAPII Ser 5-P. Conversely, RNAPII Ser 5-P occupancy was affected by the loss of Chd1, suggesting that Chd1 is associated with early transcription elongation. Surprisingly, the occupancy of RNAPII Ser 5-P was affected by the loss of Chd1 specifically at intron-containing genes. Nucleosome turnover was also affected at these sites in the absence of Chd1. We also found that deletion of the histone methyltransferase for H3K36 (SET2) did not affect either Chd1 occupancy or nucleosome organization genome-wide. CONCLUSIONS: Chd1 is specifically recruited onto the gene bodies of highly transcribed genes in an elongation-dependent but H3K36me3-independent manner. Chd1 co-localizes with the early elongating form of RNA polymerase, and affects the occupancy of RNAPII only at genes containing introns, suggesting a role in relieving splicing-related pausing of RNAPII. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1756-8935-7-32) contains supplementary material, which is available to authorized users

The Longhorn Array Database (LAD): An Open-Source, MIAME compliant implementation of the Stanford Microarray Database (SMD)

BACKGROUND: The power of microarray analysis can be realized only if data is systematically archived and linked to biological annotations as well as analysis algorithms. DESCRIPTION: The Longhorn Array Database (LAD) is a MIAME compliant microarray database that operates on PostgreSQL and Linux. It is a fully open source version of the Stanford Microarray Database (SMD), one of the largest microarray databases. LAD is available at CONCLUSIONS: Our development of LAD provides a simple, free, open, reliable and proven solution for storage and analysis of two-color microarray data

Quantitative gene expression assessment identifies appropriate cell line models for individual cervical cancer pathways

<p>Abstract</p> <p>Background</p> <p>Cell lines have been used to study cancer for decades, but truly quantitative assessment of their performance as models is often lacking. We used gene expression profiling to quantitatively assess the gene expression of nine cell line models of cervical cancer.</p> <p>Results</p> <p>We find a wide variation in the extent to which different cell culture models mimic late-stage invasive cervical cancer biopsies. The lowest agreement was from monolayer HeLa cells, a common cervical cancer model; the highest agreement was from primary epithelial cells, C4-I, and C4-II cell lines. In addition, HeLa and SiHa cell lines cultured in an organotypic environment increased their correlation to cervical cancer significantly. We also find wide variation in agreement when we considered how well individual biological pathways model cervical cancer. Cell lines with an anti-correlation to cervical cancer were also identified and should be avoided.</p> <p>Conclusion</p> <p>Using gene expression profiling and quantitative analysis, we have characterized nine cell lines with respect to how well they serve as models of cervical cancer. Applying this method to individual pathways, we identified the appropriateness of particular cell lines for studying specific pathways in cervical cancer. This study will allow researchers to choose a cell line with the highest correlation to cervical cancer at a pathway level. This method is applicable to other cancers and could be used to identify the appropriate cell line and growth condition to employ when studying other cancers.</p

Wide-ranging functions of E2F4 in transcriptional activation and repression revealed by genome-wide analysis

The E2F family of transcription factors has important roles in cell cycle progression. E2F4 is an E2F family member that has been proposed to be primarily a repressor of transcription, but the scope of its binding activity and functions in transcriptional regulation is not fully known. We used ChIP sequencing (ChIP-seq) to identify around 16 000 E2F4 binding sites which potentially regulate 7346 downstream target genes with wide-ranging functions in DNA repair, cell cycle regulation, apoptosis, and other processes. While half of all E2F4 binding sites (56%) occurred near transcription start sites (TSSs), ∼20% of sites occurred more than 20 kb away from any annotated TSS. These distal sites showed histone modifications suggesting that E2F4 may function as a long-range regulator, which we confirmed by functional experimental assays on a subset. Overexpression of E2F4 and its transcriptional cofactors of the retinoblastoma (Rb) family and its binding partner DP-1 revealed that E2F4 acts as an activator as well as a repressor. E2F4 binding sites also occurred near regulatory elements for miRNAs such as let-7a and mir-17, suggestive of regulation of miRNAs by E2F4. Taken together, our genome-wide analysis provided evidence of versatile roles of E2F4 and insights into its functions