nifH diversity associated with Montastraea cavernosa identified using an optimized primer protocol

The diversity of nitrogen fixing bacteria in any system must be identified to in order to fully understand their ecological role. PCR is commonly used to investigate bacterial diversity. To capture the full diversity PCR primers must bind to and amplify all targeted DNA sequences. For this study I analyzed published universal nifH primers\u27 ability to capture the full diversity of nitrogen fixing bacteria. Based on this work I developed a new protocol for capturing the full diversity of nifH sequences. Using this optimized protocol I investigated community differences in nitrogen fixing bacteria between orange and brown color morphs of the Caribbean coral Montastraea cavernosa among three geographic locations. Whole community analysis revealed no difference between morphs or location. However, specific groups of proteobacteria and cyanobacteria differed in abundance between the morphs, indicating specific bacterial groups are responsible for differences previously observed in fixation between color morphs

Assessing 16S rRNA Marker-Gene Survey Measurement Process Using Mixtures of Environmental Samples

Microbial communities play a fundamental role in environmental and human health. Targeted sequencing of the 16S rRNA gene, 16S rRNA marker-gene surveys, is used to measure and thus characterize these communities. The 16S rRNA marker- gene survey measurement process includes a number of molecular laboratory and computational steps. A rigorous measurement assessment framework can evaluate measurement method performance, in turn improving the validity of marker-gene survey study conclusions. In this dissertation, I present a novel framework and mixture dataset for assessing 16S rRNA marker-gene survey bioinformatic methods. Additionally, I developed software to facilitate working with 16S rRNA reference sequence databases and 16S rRNA marker-gene survey feature data. Computational steps, collectively referred to as bioinformatic pipelines, combine multiple algorithms to convert raw sequence data into a count table, which is subsequently used to test biological hypotheses. Algorithm choice and parameters can significantly impact pipeline results. The assessment framework and software developed for this dissertation improve upon existing assessment methods and can be used to evaluate new computational methods and optimize existing pipelines. Furthermore, the assessment framework presented here can be applied to other microbial community measurement methods such as shotgun metagenomics

A case of recurrent epilepsy-associated rosette-forming glioneuronal tumor with anaplastic transformation in the absence of therapy.

Rosette-forming glioneuronal tumor (RGNT) most commonly occurs adjacent to the fourth ventricle and therefore rarely presents with epilepsy. Recent reports describe RGNT occurrence in other anatomical locations with considerable morphologic and genetic overlap with the epilepsy-associated dysembryoplastic neuroepithelial tumor (DNET). Examples of RGNT or DNET with anaplastic change are rare, and typically occur in the setting of radiation treatment. We present the case of a 5-year-old girl with seizures, who underwent near total resection of a cystic temporal lobe lesion. Pathology showed morphologic and immunohistochemical features of RGNT, albeit with focally overlapping DNET-like patterns. Resections of residual or recurrent tumor were performed 1 year and 5 years after the initial resection, but no adjuvant radiation or chemotherapy was given. Ten years after the initial resection, surveillance imaging identified new and enhancing nodules, leading to another gross total resection. This specimen showed areas similar to the original tumor, but also high-grade foci with oligodendroglial morphology, increased cellularity, palisading necrosis, microvascular proliferation, and up to 13 mitotic figures per 10 high power fields. Ancillary studies the status by sequencing showed wild-type of the isocitrate dehydrogenase 1 (IDH1), IDH2, and human histone 3.3 (H3F3A) genes, and BRAF studies were negative for mutation or rearrangement. Fluorescence in situ hybridization (FISH) showed codeletion of 1p and 19q limited to the high-grade regions. By immunohistochemistry there was loss of nuclear alpha-thalassemia mental retardation syndrome, X-linked (ATRX) expression only in the high-grade region. Next-generation sequencing showed an fibroblast growth factor receptor receptor 1 (FGFR1) kinase domain internal tandem duplication in three resection specimens. ATRX mutation in the high-grade tumor was confirmed by sequencing which showed a frameshift mutation (p.R1427fs), while the apparent 1p/19q-codeletion by FISH was due to loss of chromosome arm 1p and only partial loss of 19q. Exceptional features of this case include the temporal lobe location, 1p/19q loss by FISH without true whole-arm codeletion, and anaplastic transformation associated with ATRX mutation without radiation or chemotherapy

Teleworking practice in small and medium-sized firms: Management style and worker autonomy

In an empirical study of teleworking practices amongst small and medium-sized enterprises (SMEs) in West London, organisational factors such as management attitudes, worker autonomy and employment flexibility were found to be more critical than technological provision in facilitating successful implementation. Consequently, we argue that telework in most SMEs appears as a marginal activity performed mainly by managers and specialist mobile workers

Identification of Ventricular Tachycardia Using Intracardiac Electrograms: A Comparison of Unipolar Versus Bipolar Waveform Analysis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75535/1/j.1540-8159.1991.tb04091.x.pd

Intraventricular Electrogram Analysis for Ventricular Tachycardia Detection: Statistical Validation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72298/1/j.1540-8159.1990.tb06860.x.pd

Computing and applying atomic regulons to understand gene expression and regulation

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb.2016.01819/full#supplementary-materialUnderstanding gene function and regulation is essential for the interpretation prediction and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets Atomic Regulons ARs represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here we describe an approach for inferring ARs that leverages large-scale expression data sets gene context and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness CLR analysis finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms we computed ARs for Shewanella oneidensis Pseudomonas aeruginosa Thermus thermophilus and Staphylococcus aureus each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.JF acknowledges funding from [SFRH/BD/70824/2010] of the FCT (Portuguese Foundation for Science and Technology) PhD program. CH and PW were supported by the National Science Foundation under grant number EFRI-MIKS-1137089. RT was supported by the Genomic Science Program (GSP), Office of Biological and Environmental Research (OBER), U.S. Department of Energy(DOE),and his work is a contribution of the Pacific North west National Laboratory (PNNL) Foundational Scientific Focus Area. This work was partially supported by an award from the National Science Foundation to MD, AB, NT, and RO (NSFABI-0850546). This work was also supported by the United States National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Service [Contract No. HHSN272201400027C]

A framework for assessing 16S rRNA marker-gene survey data analysis methods using mixtures.

There are a variety of bioinformatic pipelines and downstream analysis methods for analyzing 16S rRNA marker-gene surveys. However, appropriate assessment datasets and metrics are needed as there is limited guidance to decide between available analysis methods. Mixtures of environmental samples are useful for assessing analysis methods as one can evaluate methods based on calculated expected values using unmixed sample measurements and the mixture design. Previous studies have used mixtures of environmental samples to assess other sequencing methods such as RNAseq. But no studies have used mixtures of environmental to assess 16S rRNA sequencing. We developed a framework for assessing 16S rRNA sequencing analysis methods which utilizes a novel two-sample titration mixture dataset and metrics to evaluate qualitative and quantitative characteristics of count tables. Our qualitative assessment evaluates feature presence/absence exploiting features only present in unmixed samples or titrations by testing if random sampling can account for their observed relative abundance. Our quantitative assessment evaluates feature relative and differential abundance by comparing observed and expected values. We demonstrated the framework by evaluating count tables generated with three commonly used bioinformatic pipelines: (i) DADA2 a sequence inference method, (ii) Mothur a de novo clustering method, and (iii) QIIME an open-reference clustering method. The qualitative assessment results indicated that the majority of Mothur and QIIME features only present in unmixed samples or titrations were accounted for by random sampling alone, but this was not the case for DADA2 features. Combined with count table sparsity (proportion of zero-valued cells in a count table), these results indicate DADA2 has a higher false-negative rate whereas Mothur and QIIME have higher false-positive rates. The quantitative assessment results indicated the observed relative abundance and differential abundance values were consistent with expected values for all three pipelines. We developed a novel framework for assessing 16S rRNA marker-gene survey methods and demonstrated the framework by evaluating count tables generated with three bioinformatic pipelines. This framework is a valuable community resource for assessing 16S rRNA marker-gene survey bioinformatic methods and will help scientists identify appropriate analysis methods for their marker-gene surveys.https://doi.org/10.1186/s40168-020-00812-