334 research outputs found
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No full textFirst published: 17 July 2023Genomic-scale datasets, sophisticated analytical techniques, and conceptual advances have disproportionately failed to resolve species boundaries in some groups relative to others. To understand the processes that underlie taxonomic intractability, we dissect the speciation history of an Australian lizard clade that arguably represents a “worst-case” scenario for species delimitation within vertebrates: the Ctenotus inornatus species group, a clade beset with decoupled genetic and phenotypic breaks, uncertain geographic ranges, and parallelism in purportedly diagnostic morphological characters. We sampled hundreds of localities to generate a genomic perspective on population divergence, structure, and admixture. Our results revealed rampant paraphyly of nominate taxa in the group, with lineages that are either morphologically cryptic or polytypic. Isolation-by-distance patterns reflect spatially continuous differentiation among certain pairs of putative species, yet genetic and geographic distances are decoupled in other pairs. Comparisons of mitochondrial and nuclear gene trees, tests of nuclear introgression, and historical demographic modelling identified gene flow between divergent candidate species. Levels of admixture are decoupled from phylogenetic relatedness; gene flow is often higher between sympatric species than between parapatric populations of the same species. Such idiosyncratic patterns of introgression contribute to species boundaries that are fuzzy while also varying in fuzziness. Our results suggest that “taxonomic disaster zones” like the C. inornatus species group result from spatial variation in the porosity of species boundaries and the resulting patterns of genetic and phenotypic variation. This study raises questions about the origin and persistence of hybridizing species and highlights the unique insights provided by taxa that have long eluded straightforward taxonomic categorization.Ivan Prates, Mark N. Hutchinson, Sonal Singhal, Craig Moritz, Daniel L. Rabosk
Understanding the importance of side information in graph matching problem
No full textGraph matching algorithms rely on the availability of seed vertex pairs as side information to deanonymize users across networks. Although such algorithms work well in practice, there are other types of side information available which are potentially useful to an attacker. In this thesis, we consider the problem of matching two correlated graphs when an attacker has access to side information either in the form of community labels or an imperfect initial matching. First, we propose a naive graph matching algorithm by introducing the community degree vectors which harness the information from community labels in an e cient manner. Next, we analyze the basic percolation algorithm for graphs with community structure. Finally, we propose a novel percolation algorithm with two thresholds which uses an imperfect matching as input to match correlated graphs. We also analyze these algorithms and provide theoretical guarantees for matching graphs generated using the Stochastic Block Model.
We evaluate the proposed algorithms on synthetic as well as real world datasets using various experiments. The experimental results demonstrate the importance of communities as side information especially when the number of seeds is small and the networks are weakly correlated. These results motivate the study of other types of potential side information available to the attacker. Such studies could assist in devising mechanisms to counter the effects of side information in network deanonymization.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-12-01The student, Kushagra Singhal, accepted the attached license on 2016-11-22 at 11:10.The student, Kushagra Singhal, submitted this Thesis for approval on 2016-11-22 at 11:16.This Thesis was approved for publication on 2016-11-22 at 12:00.DSpace SAF Submission Ingestion Package generated from Vireo submission #10224 on 2017-02-28 at 14:36:15Made available in DSpace on 2017-03-01T16:36:46Z (GMT). No. of bitstreams: 2
SINGHAL-THESIS-2016.pdf: 390320 bytes, checksum: 96d12f05add1e7756426924faa9c6f2d (MD5)
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Previous issue date: 2016-11-22Embargo set by: Seth Robbins for item 98583
Lift date: 2019-03-01T16:37:19Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 98583 on 2019-03-02T10:15:33Z
A new framework of optimizing keyword weights in text categorization and record querying
No full textIn text mining research, the Vector Space Model (VSM) has been commonly used to represent text documents as a vector where each component is associated with a particular word in the documents. Assigning appropriate keyword weights in VSM has been critical in Information Retrieval (IR) and Text Categorization (TC).
Traditionally keyword weighting processes are unsupervised; that is, the knowledge of document's category is not leveraged to label the documents. Typically, each keyword weight is assigned using the term frequency -- inverse document frequency (TFIDF) measure. Although the TFIDF measure has been proven effective in several text mining problems, it might not give the optimal classification power for IR and TC. In this thesis, we propose a new optimization framework to find the best keyword weights based on the proposed inter-class and intra-class similarity concept.
The optimal keyword weight can be viewed as the feature space projection where documents from the same category are best clustered together and separated from other categories. Subsequently, the category average (centroid) classification is employed to categorize text documents. The proposed approach is tested on two practical applications: record query and text categorization. The record query application is slightly different from traditional IR problems as the goal is to find correlated (duplicate and master) text records. This problem was initiated by a telecommunication company where service engineers attempt to look for associations of the current defect problem in previously recorded problems in the database. Extensive experiments demonstrate that the proposed framework significantly improves the classification accuracy and provides balanced performance as measured on all text categories when compared to the standard TFIDF search. The text categorization application is tested on the Reuters news data set which is a gold-standard benchmark data set. The results show that our framework improves performance for the two applications considered, namely Information Retrieval and Text Categorization.M.S.Includes bibliographical references (p. 80-83)
Evaluation of UML based wireless network virtualization
No full textVirtualization of wireless networks is recognized to be a difficult problem due to the fact that radios interact with their neighbors at various layers of the protocol stack, making strict isolation of virtual networks ("or slices") quite challenging. The goal of virtualization is to support concurrent experiments, both long-running services as well as short-term experiments on shared wireless network. In a wireless network, the radio resources that can be shared and hence virtualized are in time, space and frequency. Efforts have been going on to modify the ORBIT control structure to accommodate different forms of virtualization including VMAC, SDMA, FDMA and TDMA. Among different possible wireless virtualization techniques, this work is focused on allowing a node to run more than one experiment simultaneously using different frequencies i.e. Frequency Division Multiplexing (FDM). Each node in the ORBIT test bed is provided with two physical wireless cards. FDMA virtualization is achieved by running two concurrent User Level Operating Systems (ULOS) on each node and providing each operating system access to a radio card. Thus an experimental end user would view a single node as two virtual nodes, each equipped with one wireless card.
Experimental results are provided to compare the performance of a virtualized radio node with the non virtualized one for basic point-to-point experiments using TCP and UDP. Bounds on performance metrics of throughput, delay and jitter are determined and cross-coupling effects between two virtualized experiments are examined. We also look at transient behavior associated with sudden changes in traffic on one of the virtual networks. Finally, the uncertainty in performance measurements for a few typical usage scenarios is investigated, leading to guidelines for use of virtualized radio nodes for simultaneous ORBIT experiments.M.S.Includes bibliographical references (p. 44-45)
Data from: De novo transcriptomic analyses for non-model organisms: an evaluation of methods across a multi-species data set
No full textHigh-throughput sequencing (HTS) is revolutionizing biological research by enabling scientists to quickly and cheaply query variation at a genomic scale. Despite the increasing ease of obtaining such data, using these data effectively still poses notable challenges, especially for those working with organisms without a high-quality reference genome. For every stage of analysis – from assembly to annotation to variant discovery – researchers have to distinguish technical artefacts from the biological realities of their data before they can make inference. In this work, I explore these challenges by generating a large de novo comparative transcriptomic data set data for a clade of lizards and constructing a pipeline to analyse these data. Then, using a combination of novel metrics and an externally validated variant data set, I test the efficacy of my approach, identify areas of improvement, and propose ways to minimize these errors. I find that with careful data curation, HTS can be a powerful tool for generating genomic data for non-model organisms
transcriptomic analyses for non‐model organisms: an evaluation of methods across a multi‐species data set
No full textdataset for Bio 440 Mod 2 - featureCounts
No full textdata from Wu et al 2019: https://www.pnas.org/content/117/45/2833
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A Fine Scale Analysis of a Tropical Suture Zone
No full textIn my dissertation, I use a comparative approach to exploit an outstanding natural experiment, a suture zone in the rainforest of northeast Australia, to answer questions about speciation and hybridization. The suture zone consists of twenty identified contacts between phylogeographic lineages, mostly within morphologically defined species. Although the contacts in the zone likely formed concurrently in response to Holocene expansion from glacial refugia, the lineage-pairs meeting in the contacts exhibit a wide range of genetic divergences. This natural variation enables analysis of the outcomes of secondary contact at different stages of the divergence process. Importantly, although most studies of speciation focus on lineages that show marked phenotypic divergence, I focus on morphologically cryptic lineages, which, though common in nature, have been understudied in this regard. Through my dissertation, I consider contact zones between six lineage-pairs within four morphologically-defined skink species, Carlia rubrigularis, Lampropholis coggeri, Saproscincus basiliscus, and S. lewisi.Through this work, I find support for the reality of cryptic species and argue that the presence of cryptic species can suggest a wider plurality of speciation models than we typically consider (Chapter 1). Indeed, by combining multilocus methods and dense sampling, I find that reproductive isolation between phylogeographic lineages scales tightly with divergence (Chapter 2). These results support the widespread, yet previously unsubstantiated, notion that phylogeographic structure of increasing depth represents a continuum towards complete speciation, even in the absence of overt ecologically-driven divergent selection. I extend these results by looking at introgression across the genome, finding that genome-wide selection, driven by selection against hybrids, structures introgression patterns much more strongly than locus-specific selection histories (Chapter 3). By analyzing the sole lineage-pair in this system that exhibits genealogical discordance, I suggest that geographic stability across time is key to driving divergence (Chapter 4). Further, through combining a fine-scale investigation of a single contact zone with simulations and a meta-analysis, I argue that selection against hybrids, in the form of intrinsic genetic incompatibilities, maintains species boundaries at these contact zones (Chapter 5). Finally, as my work is enabled by emerging genomic technologies for non-model organisms, I summarize my genomics approach, and its associated benefits and challenges, as applied to transcriptome data from these lineages (Chapter 6)
RNAseq data for Module 4
No full text<p>RNAseq data from here: https://www.ncbi.nlm.nih.gov/sra/SRR1171978</p>
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