Ion Collection in Hall Thruster Plumes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77342/1/AIAA-11953-462.pd

PheMaDB: A solution for storage, retrieval, and analysis of high throughput phenotype data

<p>Abstract</p> <p>Background</p> <p>OmniLog™ phenotype microarrays (PMs) have the capability to measure and compare the growth responses of biological samples upon exposure to hundreds of growth conditions such as different metabolites and antibiotics over a time course of hours to days. In order to manage the large amount of data produced from the OmniLog™ instrument, PheMaDB (Phenotype Microarray DataBase), a web-based relational database, was designed. PheMaDB enables efficient storage, retrieval and rapid analysis of the OmniLog™ PM data.</p> <p>Description</p> <p>PheMaDB allows the user to quickly identify records of interest for data analysis by filtering with a hierarchical ordering of Project, Strain, Phenotype, Replicate, and Temperature. PheMaDB then provides various statistical analysis options to identify specific growth pattern characteristics of the experimental strains, such as: outlier analysis, negative controls analysis (signal/background calibration), bar plots, pearson's correlation matrix, growth curve profile search, <it>k</it>-means clustering, and a heat map plot. This web-based database management system allows for both easy data sharing among multiple users and robust tools to phenotype organisms of interest.</p> <p>Conclusions</p> <p>PheMaDB is an open source system standardized for OmniLog™ PM data. PheMaDB could facilitate the banking and sharing of phenotype data. The source code is available for download at <url>http://phemadb.sourceforge.net</url>.</p

PRIDB: a protein–RNA interface database

The Protein–RNA Interface Database (PRIDB) is a comprehensive database of protein–RNA interfaces extracted from complexes in the Protein Data Bank (PDB). It is designed to facilitate detailed analyses of individual protein–RNA complexes and their interfaces, in addition to automated generation of user-defined data sets of protein–RNA interfaces for statistical analyses and machine learning applications. For any chosen PDB complex or list of complexes, PRIDB rapidly displays interfacial amino acids and ribonucleotides within the primary sequences of the interacting protein and RNA chains. PRIDB also identifies ProSite motifs in protein chains and FR3D motifs in RNA chains and provides links to these external databases, as well as to structure files in the PDB. An integrated JMol applet is provided for visualization of interacting atoms and residues in the context of the 3D complex structures. The current version of PRIDB contains structural information regarding 926 protein–RNA complexes available in the PDB (as of 10 October 2010). Atomic- and residue-level contact information for the entire data set can be downloaded in a simple machine-readable format. Also, several non-redundant benchmark data sets of protein–RNA complexes are provided. The PRIDB database is freely available online at http://bindr.gdcb.iastate.edu/PRIDB

From Marginalized to Maximized Opportunities for Diverse Youths With Disabilities: A Position Paper of the Division on Career Development and Transition

Current secondary education and transition practices have created differential education and employment outcomes by gender, race and ethnicity, socioeconomic status, and disability classifications. These differential outcomes result in economic and social marginalization of far too many students with disabilities. Transition education practices need to respond to these differential outcomes and provide targeted, systematic, and long-term opportunities for all students to attain individually and family-determined postschool goals. This position paper recommends an ecological framework for considering the multiple systems that influence transition education and postschool outcomes for diverse youths with disabilities. The authors argue for educators, researchers, and policy makers to attend to social, political, economic, educational, and cultural contexts in developing effective interventions and improving postschool outcomes.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Analysis of interactions between ribosomal proteins and RNA structural motifs

<p>Abstract</p> <p>Background</p> <p>One important goal of structural bioinformatics is to recognize and predict the interactions between protein binding sites and RNA. Recently, a comprehensive analysis of ribosomal proteins and their interactions with rRNA has been done. Interesting results emerged from the comparison of r-proteins within the small subunit in <it>T. thermophilus </it>and <it>E. coli</it>, supporting the idea of a core made by both RNA and proteins, conserved by evolution. Recent work showed also that ribosomal RNA is modularly composed. Motifs are generally single-stranded sequences of consecutive nucleotides (ssRNA) with characteristic folding. The role of these motifs in protein-RNA interactions has been so far only sparsely investigated.</p> <p>Results</p> <p>This work explores the role of RNA structural motifs in the interaction of proteins with ribosomal RNA (rRNA). We analyze composition, local geometries and conformation of interface regions involving motifs such as tetraloops, kink turns and single extruded nucleotides. We construct an interaction map of protein binding sites that allows us to identify the common types of shared 3-D physicochemical binding patterns for tetraloops. Furthermore, we investigate the protein binding pockets that accommodate single extruded nucleotides either involved in kink-turns or in arbitrary RNA strands. This analysis reveals a new structural motif, called <it>tripod</it>.</p> <p>It corresponds to small pockets consisting of three aminoacids arranged at the vertices of an almost equilateral triangle. We developed a search procedure for the recognition of tripods, based on an empirical tripod fingerprint.</p> <p>Conclusion</p> <p>A comparative analysis with the overall RNA surface and interfaces shows that contact surfaces involving RNA motifs have distinctive features that may be useful for the recognition and prediction of interactions.</p

Identification of Three Molecular and Functional Subtypes in Canine Hemangiosarcoma through Gene Expression Profiling and Progenitor Cell Characterization

Canine hemangiosarcomas have been ascribed to an endothelial origin based on histologic appearance; however, recent findings suggest that these tumors may arise instead from hematopoietic progenitor cells. To clarify this ontogenetic dilemma, we used genome-wide expression profiling of primary hemangiosarcomas and identified three distinct tumor subtypes associated with angiogenesis (group 1), inflammation (group 2), and adipogenesis (group 3). Based on these findings, we hypothesized that a common progenitor may differentiate into the three tumor subtypes observed in our gene profiling experiment. To investigate this possibility, we cultured hemangiosarcoma cell lines under normal and sphere-forming culture conditions to enrich for tumor cell progenitors. Cells from sphere-forming cultures displayed a robust self-renewal capacity and exhibited genotypic, phenotypic, and functional properties consistent with each of the three molecular subtypes seen in primary tumors, including expression of endothelial progenitor cell (CD133 and CD34) and endothelial cell (CD105, CD146, and αvβ3 integrin) markers, expression of early hematopoietic (CD133, CD117, and CD34) and myeloid (CD115 and CD14) differentiation markers in parallel with increased phagocytic capacity, and acquisition of adipogenic potential. Collectively, these results suggest that canine hemangiosarcomas arise from multipotent progenitors that differentiate into distinct subtypes. Improved understanding of the mechanisms that determine the molecular and phenotypic differentiation of tumor cells in vivo could change paradigms regarding the origin and progression of endothelial sarcomas

Molecular Profiling Reveals Prognostically Significant Subtypes of Canine Lymphoma

We performed genomewide gene expression analysis of 35 samples representing 6 common histologic subtypes of canine lymphoma and bioinformatics analyses to define their molecular characteristics. Three major groups were defined on the basis of gene expression profiles: (1) low-grade T-cell lymphoma, composed entirely by T-zone lymphoma; (2) high-grade T-cell lymphoma, consisting of lymphoblastic T-cell lymphoma and peripheral T-cell lymphoma not otherwise specified; and (3) B-cell lymphoma, consisting of marginal B-cell lymphoma, diffuse large B-cell lymphoma, and Burkitt lymphoma. Interspecies comparative analyses of gene expression profiles also showed that marginal B-cell lymphoma and diffuse large B-cell lymphoma in dogs and humans might represent a continuum of disease with similar drivers. The classification of these diverse tumors into 3 subgroups was prognostically significant, as the groups were directly correlated with event-free survival. Finally, we developed a benchtop diagnostic test based on expression of 4 genes that can robustly classify canine lymphomas into one of these 3 subgroups, enabling a direct clinical application for our results

Discovery pipeline for epigenetically deregulated miRNAs in cancer: integration of primary miRNA transcription

<p>Abstract</p> <p>Background</p> <p>Cancer is commonly associated with widespread disruption of DNA methylation, chromatin modification and miRNA expression. In this study, we established a robust discovery pipeline to identify epigenetically deregulated miRNAs in cancer.</p> <p>Results</p> <p>Using an integrative approach that combines primary transcription, genome-wide DNA methylation and H3K9Ac marks with microRNA (miRNA) expression, we identified miRNA genes that were epigenetically modified in cancer. We find miR-205, miR-21, and miR-196b to be epigenetically repressed, and miR-615 epigenetically activated in prostate cancer cells.</p> <p>Conclusions</p> <p>We show that detecting changes in primary miRNA transcription levels is a valuable method for detection of local epigenetic modifications that are associated with changes in mature miRNA expression.</p