Anne M. Denton

A Complex Transcription Network Controls the Early Stages of Biofilm Development by Escherichia coli

Historically, researchers have studied bacterial signaling as if it functioned as a set of isolated, linear pathways. More recent studies, however, have demonstrated that many signaling pathways interact and that these interacting pathways should be construed as an intricate network. This network

A clustering approach to improving test case prioritization: An industrial case study

Regression testing is an important activity for controlling the quality of a software product, but it accounts for a large proportion of the costs of software. We believe that an understanding of the underlying relationships in data about software systems, including data correlations and patterns, could provide information that would help improve regression testing techniques. We conjecture that if test cases have common properties, then test cases within the same group may have similar fault detection ability. As an initial approach to investigating the relationships in massive data in software repositories, in this paper, we consider a clustering approach to help improve test case prioritization. We implemented new prioritization techniques that incorporate a clustering approach and utilize code coverage, code complexity, and history data on real faults. To assess our approach, we have designed and conducted empirical studies using an industrial software product, Microsoft Dynamics Ax, which contains real faults. Our results show that test case prioritization that utilizes a clustering approach can improve the effectiveness of test case prioritization techniques.

Kernel-density-based clustering of time series subsequences using a continuous random-walk noise model

Noise levels in time series subsequence data are typically very high, and properties of the noise differ front those of white noise. The proposed algorithm incorporates a continuous random-walk noise model into kernel-density-based clustering. Evaluation is done by testing to what extent the resulting clusters are predictive of the process that generated the time series. It is shown that the new algorithm not only outperforms partitioning techniques that lead to trivial and unsatisfactory results under the given quality measure, but also improves upon other density-based algorithms. The results suggest that the noise elimination properties of kernel-density-based clustering algorithms can be of significant value for the use of clustering in preprocessing of data.

Establishing relationships among patterns in stock market data

Similarities among subsequences are typically regarded as categorical features of sequential data. We introduce an algorithm for capturing the relationships among similar, contiguous subsequences. Two time series are considered to be similar during a time interval if every contiguous subsequence of a predefined length satisfies the given similarity criterion. Our algorithm identifies patterns based on the similarity among sequences, captures the sequence-subsequence relationships among patterns in the form of a directed acyclic graph (DAG), and determines pattern conglomerates that allow the application of additional meta-analyses and mining algorithms. For example, our pattern conglomerates can be used to analyze time information that is lost in categorical representations. We apply our algorithm to stock market data as well as several other time series data sets and show the richness of our pattern conglomerates through qualitative and quantitative evaluations. An exemplary meta-analysis determines timing patterns representing relations between time series intervals and demonstrates the merit of pattern relationships as an extension of time series pattern mining.

Environmental and genetic factors that contribute to Escherichia coli K-12 biofilm formation.

Biofilms are communities of bacteria whose formation on surfaces requires a large portion of the bacteria’s transcriptional network. To identify environmental conditions and transcriptional regulators that contribute to sensing these conditions, we used a high-throughput approach to monitor biofilm biomass produced by an isogenic set of Escherichia coli K-12 strains grown under combinations of environmental conditions. Of the environmental combinations, growth in tryptic soy broth at 37°C supported the most biofilm production. To analyze the complex relationships between the diverse cell-surface organelles, transcriptional regulators, and metabolic enzymes represented by the tested mutant set, we used a novel vector-item pattern-mining algorithm. The algorithm related biofilm amounts to the functional annotations of each mutated protein. The pattern with the best statistical significance was the gene ontology ‘pyruvate catabolic process,’ which is associated with enzymes of acetate metabolism. Phenotype microarray experiments illustrated that carbon sources that are metabolized to acetyl-coenzyme A, acetyl phosphate, and acetate are particularly supportive of biofilm formation. Scanning electron microscopy revealed structural differences between mutants that lack acetate metabolism enzymes and their parent and confirmed the quantitative differences. We conclude that acetate metabolism functions as a metabolic sensor, transmitting changes in environmental conditions to biofilm biomass and structure.

Physical mapping resources for large plant genomes: radiation hybrids for wheat D-genome progenitor Aegilops tauschii

BackgroundDevelopment of a high quality reference sequence is a daunting task in crops like wheat with large (~17Gb), highly repetitive (>80%) and polyploid genome. To achieve complete sequence assembly of such genomes, development of a high quality physical map is a necessary first step. However, due to the lack of recombination in certain regions of the chromosomes, genetic mapping, which uses recombination frequency to map marker loci, alone is not sufficient to develop high quality marker scaffolds for a sequence ready physical map. Radiation hybrid (RH) mapping, which uses radiation induced chromosomal breaks, has proven to be a successful approach for developing marker scaffolds for sequence assembly in animal systems. Here, the development and characterization of a RH panel for the mapping of D-genome of wheat progenitor Aegilops tauschii is reported.ResultsRadiation dosages of 350 and 450 Gy were optimized for seed irradiation of a synthetic hexaploid (AABBDD) wheat with the D-genome of Ae. tauschii accession AL8/78. The surviving plants after irradiation were crossed to durum wheat (AABB), to produce pentaploid RH1s (AABBD), which allows the simultaneous mapping of the whole D-genome. A panel of 1,510 RH1 plants was obtained, of which 592 plants were generated from the mature RH1 seeds, and 918 plants were rescued through embryo culture due to poor germination (<3%) of mature RH1 seeds. This panel showed a homogenous marker loss (2.1%) after screening with SSR markers uniformly covering all the D-genome chromosomes. Different marker systems mostly detected different lines with deletions. Using markers covering known distances, the mapping resolution of this RH panel was estimated to be <140kb. Analysis of only 16 RH lines carrying deletions on chromosome 2D resulted in a physical map with cM/cR ratio of 1:5.2 and 15 distinct bins. Additionally, with this small set of lines, almost all the tested ESTs could be mapped. A set of 399 most informative RH lines with an average deletion frequency of ~10% were identified for developing high density marker scaffolds of the D-genome.ConclusionsThe RH panel reported here is the first developed for any wild ancestor of a major cultivated plant species. The results provided insight into various aspects of RH mapping in plants, including the genetically effective cell number for wheat (for the first time) and the potential implementation of this technique in other plant species. This RH panel will be an invaluable resource for mapping gene based markers, developing a complete marker scaffold for the whole genome sequence assembly, fine mapping of markers and functional characterization of genes and gene networks present on the D-genome.

Pattern-based time-series subsequence clustering using radial distribution functions

Clustering of time series subsequence data commonly produces results that are unspecific to the data set. This paper introduces a clustering algorithm, that creates clusters exclusively from those subsequences that occur more frequently in a data set than would be expected by random chance. As such, it partially adopts a pattern mining perspective into clustering. When subsequences are being labeled based on such clusters, they may remain without label. In fact, if the clustering was done on an unrelated time series it is expected that the subsequences should not receive a label. We show that pattern-based clusters are indeed specific to the data set for 7 out of 10 real-world sets we tested, and for window-lengths up to 128 time points. While kernel-density-based clustering can be used to find clusters with similar properties for window sizes of 8–16 time points, its performance degrades fast for increasing window sizes.

Active-Optical Sensors Using Red NDVI Compared to Red Edge NDVI for Prediction of Corn Grain Yield in North Dakota, U.S.A.

Active-optical sensor readings from an N non-limiting area standard established within a farm field are used to predict yield in the standard. Lower yield predictions from sensor readings obtained from other parts of the field outside of the N non-limiting standard area indicate a need for supplemental N. Active-optical sensor algorithms for predicting corn (Zea mays, L.) yield to direct in-season nitrogen (N) fertilization in corn utilize red NDVI (normalized differential vegetative index). Use of red edge NDVI might improve corn yield prediction at later growth stages when corn leaves cover the inter-row space resulting in “saturation” of red NDVI readings. The purpose of this study was to determine whether the use of red edge NDVI in two active-optical sensors (GreenSeeker™ and Holland Scientific Crop Circle™) improved corn yield prediction. Nitrogen rate experiments were established at 15 sites in North Dakota (ND). Sensor readings were conducted at V6 and V12 corn. Red NDVI and red edge NDVI were similar in the relationship of readings with yield at V6. At V12, the red edge NDVI was superior to the red NDVI in most comparisons, indicating that it would be most useful in developing late-season N application algorithms.

Use of corn height measured with an acoustic sensor improves yield estimation with ground based active optical sensors

An acoustic height sensor could be used with the INSEY value to improve corn in-season N management.The height sensor was especially useful at earlier and later growth stages depending on the site.Normalizing height aided the relationships with active optical sensor INSEY when combining sites. Corn height measured manually has shown promising results in improving the relationship between active-optical (AO) sensor readings and crop yield. Manual measurement of corn height is not practical in US commercial corn production, so an alternative automatic method must be found in order to capture the benefit of including canopy height into in-season yield estimates and from there into in-season nitrogen (N) fertilizer applications. One existing alternative to measure canopy height is an acoustic height sensor. A commercial acoustic height sensor was utilized in these experiments at two corn growth stages (V6 and V12) along with AO sensors. Eight corn N rate sites in North Dakota, USA, were used to compare the acoustic height sensor as a practical alternative to manual height measurements as an additional parameter to increase the relationship between AO sensor readings and corn yield. Six N treatments, 0, 45, 90, 134, 179, and 224kgha-1, were applied before planting in a randomized complete block experimental design with four replications. Height measurement using the acoustic sensor provided an improved yield relationship compared to manual height at all locations. The level of improvement of the relationship between AO readings multiplied by acoustic sensor readings and yield was greater at V6 growth stage compared to the V12 growth stage. At V12, corn height measured manually and with the acoustic sensor multiplied by AO readings provided similar improvement to the relationship with yield compared to relating AO readings alone with yield at most locations. The acoustic height sensor may be useful in increasing the usefulness of AO sensor corn yield prediction algorithms for use in on-the-go in-season N application to corn particularly if the sensor height is normalized within site before combining multiple locations.

Clustering sequences by overlap

A clustering algorithm is introduced that combines the strengths of clustering and motif finding techniques. Clusters are identified based on unambiguously defined sequence sections as in motif finding algorithms. The definition of similarity within clusters allows transitive matches and, thereby, enables the discovery of remote homologies that cannot be found through motif-finding algorithms. Directed Acyclic Graph (DAG) structures are constructed that link short clusters to the longer ones. We compare the clustering results to the corresponding domains in the InterPro database. A second comparison shows that annotations based on our domains are inherently more consistent than those based on InterPro domains.