From isolated subgroups to generic permutation representations

Let $G$ be a countable group, $\operatorname{Sub}(G)$ the (compact, metric) space of all subgroups of $G$ with the Chabauty topology and $\operatorname{Is}(G) \subset \operatorname{Sub}(G)$ the collection of isolated points. We denote by $X!$ the (Polish) group of all permutations of a countable set $X$. Then the following properties are equivalent: (i) $\operatorname{Is}(G)$ is dense in $\operatorname{Sub}(G)$, (ii) $G$ admits a "generic permutation representation". Namely there exists some $\tau^* \in \operatorname{Hom}(G,X!)$ such that the collection of permutation representations $\{\phi \in \operatorname{Hom}(G,X!) \ | \ \phi {\text{is permutation isomorphic to}} \tau^*\}$ is co-meager in $\operatorname{Hom}(G,X!)$. We call groups satisfying these properties solitary. Examples of solitary groups include finitely generated LERF groups and groups with countably many subgroups.Comment: 21 page

Reordering contigs of draft genomes using the Mauve Aligner

Summary: Mauve Contig Mover provides a new method for proposing the relative order of contigs that make up a draft genome based on comparison to a complete or draft reference genome. A novel application of the Mauve aligner and viewer provides an automated reordering algorithm coupled with a powerful drill-down display allowing detailed exploration of results

Analyzing the dose-dependence of the Saccharomyces cerevisiae global transcriptional response to methyl methanesulfonate and ionizing radiation

BACKGROUND: One of the most crucial tasks for a cell to ensure its long term survival is preserving the integrity of its genetic heritage via maintenance of DNA structure and sequence. While the DNA damage response in the yeast Saccharomyces cerevisiae, a model eukaryotic organism, has been extensively studied, much remains to be elucidated about how the organism senses and responds to different types and doses of DNA damage. We have measured the global transcriptional response of S. cerevisiae to multiple doses of two representative DNA damaging agents, methyl methanesulfonate (MMS) and gamma radiation. RESULTS: Hierarchical clustering of genes with a statistically significant change in transcription illustrated the differences in the cellular responses to MMS and gamma radiation. Overall, MMS produced a larger transcriptional response than gamma radiation, and many of the genes modulated in response to MMS are involved in protein and translational regulation. Several clusters of coregulated genes whose responses varied with DNA damaging agent dose were identified. Perhaps the most interesting cluster contained four genes exhibiting biphasic induction in response to MMS dose. All of the genes (DUN1, RNR2, RNR4, and HUG1) are involved in the Mec1p kinase pathway known to respond to MMS, presumably due to stalled DNA replication forks. The biphasic responses of these genes suggest that the pathway is induced at lower levels as MMS dose increases. The genes in this cluster with a threefold or greater transcriptional response to gamma radiation all showed an increased induction with increasing gamma radiation dosage. CONCLUSION: Analyzing genome-wide transcriptional changes to multiple doses of external stresses enabled the identification of cellular responses that are modulated by magnitude of the stress, providing insights into how a cell deals with genotoxicity

Logarithmically Slow Expansion of Hot Bubbles in Gases

We report logarithmically slow expansion of hot bubbles in gases in the process of cooling. A model problem first solved, when the temperature has compact support. Then temperature profile decaying exponentially at large distances is considered. The periphery of the bubble is shown to remain essentially static ("glassy") in the process of cooling until it is taken over by a logarithmically slowly expanding "core". An analytical solution to the problem is obtained by matched asymptotic expansion. This problem gives an example of how logarithmic corrections enter dynamic scaling.Comment: 4 pages, 1 figur

The evolution of metabolic networks of E. coli

<p>Abstract</p> <p>Background</p> <p>Despite the availability of numerous complete genome sequences from <it>E. coli </it>strains, published genome-scale metabolic models exist only for two commensal <it>E. coli </it>strains. These models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for <it>E. coli </it>strains could enhance these efforts.</p> <p>Results</p> <p>We used the genomic information from 16 <it>E. coli </it>strains to generate an <it>E. coli </it>pangenome metabolic network by evaluating their collective 76,990 ORFs. Each of these ORFs was assigned to one of 17,647 ortholog groups including ORFs associated with reactions in the most recent metabolic model for <it>E. coli </it>K-12. For orthologous groups that contain an ORF already represented in the MG1655 model, the gene to protein to reaction associations represented in this model could then be easily propagated to other <it>E. coli </it>strain models. All remaining orthologous groups were evaluated to see if new metabolic reactions could be added to generate a pangenome-scale metabolic model (iEco1712_pan). The pangenome model included reactions from a metabolic model update for <it>E. coli </it>K-12 MG1655 (iEco1339_MG1655) and enabled development of five additional strain-specific genome-scale metabolic models. These additional models include a second K-12 strain (iEco1335_W3110) and four pathogenic strains (two enterohemorrhagic <it>E. coli </it>O157:H7 and two uropathogens). When compared to the <it>E. coli </it>K-12 models, the metabolic models for the enterohemorrhagic (iEco1344_EDL933 and iEco1345_Sakai) and uropathogenic strains (iEco1288_CFT073 and iEco1301_UTI89) contained numerous lineage-specific gene and reaction differences. All six <it>E. coli </it>models were evaluated by comparing model predictions to carbon source utilization measurements under aerobic and anaerobic conditions, and to batch growth profiles in minimal media with 0.2% (w/v) glucose. An ancestral genome-scale metabolic model based on conserved ortholog groups in all 16 <it>E. coli </it>genomes was also constructed, reflecting the conserved ancestral core of <it>E. coli </it>metabolism (iEco1053_core). Comparative analysis of all six strain-specific <it>E. coli </it>models revealed that some of the pathogenic <it>E. coli </it>strains possess reactions in their metabolic networks enabling higher biomass yields on glucose. Finally the lineage-specific metabolic traits were compared to the ancestral core model predictions to derive new insight into the evolution of metabolism within this species.</p> <p>Conclusion</p> <p>Our findings demonstrate that a pangenome-scale metabolic model can be used to rapidly construct additional <it>E. coli </it>strain-specific models, and that quantitative models of different strains of <it>E. coli </it>can accurately predict strain-specific phenotypes. Such pangenome and strain-specific models can be further used to engineer metabolic phenotypes of interest, such as designing new industrial <it>E. coli </it>strains.</p

A comparative analysis to forecast apartment burglaries in Vienna, Austria, based on repeat and near repeat victimization

In this paper, we introduce two methods to forecast apartment burglaries that are based on repeat and near repeat victimization. While the first approach, the “heuristic method” generates buffer areas around each new apartment burglary, the second approach concentrates on forecasting near repeat chain links. These near repeat chain links are events that follow a near repeat pair of an originating and (near) repeat event that is close in space and in time. We name this approach the “near repeat chain method”. This research analyzes apartment burglaries from November 2013 to November 2016 in Vienna, Austria. The overall research goal is to investigate whether the near repeat chain method shows better prediction efficiencies (using a capture rate and the prediction accuracy index) while producing fewer prediction areas. Results show that the near repeat chain method proves to be the more efficient compared to the heuristic method for all bandwidth combinations analyzed in this research.DK W 1237-N23(VLID)286105

Gene Ontology annotation highlights shared and divergent pathogenic strategies of type III effector proteins deployed by the plant pathogen Pseudomonas syringae pv tomato DC3000 and animal pathogenic Escherichia coli strains

Genome-informed identification and characterization of Type III effector repertoires in various bacterial strains and species is revealing important insights into the critical roles that these proteins play in the pathogenic strategies of diverse bacteria. However, non-systematic discipline-specific approaches to their annotation impede analysis of the accumulating wealth of data and inhibit easy communication of findings among researchers working on different experimental systems. The development of Gene Ontology (GO) terms to capture biological processes occurring during the interaction between organisms creates a common language that facilitates cross-genome analyses. The application of these terms to annotate type III effector genes in different bacterial species – the plant pathogen Pseudomonas syringae pv tomato DC3000 and animal pathogenic strains of Escherichia coli – illustrates how GO can effectively describe fundamental similarities and differences among different gene products deployed as part of diverse pathogenic strategies. In depth descriptions of the GO annotations for P. syringae pv tomato DC3000 effector AvrPtoB and the E. coli effector Tir are described, with special emphasis given to GO capability for capturing information about interacting proteins and taxa. GO-highlighted similarities in biological process and molecular function for effectors from additional pathosystems are also discussed

Genomic Mining for Aspergillus Natural Products

SummaryThe genus Aspergillus is renowned for its ability to produce a myriad of bioactive secondary metabolites. Although the propensity of biosynthetic genes to form contiguous clusters greatly facilitates assignment of putative secondary metabolite genes in the completed Aspergillus genomes, such analysis cannot predict gene expression and, ultimately, product formation. To circumvent this deficiency, we have examined Aspergillus nidulans microarrays for expressed secondary metabolite gene clusters by using the transcriptional regulator LaeA. Deletion or overexpression of laeA clearly identified numerous secondary metabolite clusters. A gene deletion in one of the clusters eliminated the production of the antitumor compound terrequinone A, a metabolite not described, from A. nidulans. In this paper, we highlight that LaeA-based genome mining helps decipher the secondary metabolome of Aspergilli and provides an unparalleled view to assess secondary metabolism gene regulation

Thinking strategically about assessment

Drawing upon the literature on strategy formulation in organisations, this paper argues for a focus on strategy as process. It relates this to the need to think strategically about assessment, a need engendered by resource pressures, developments in learning and the demands of external stakeholders. It is argued that in practice assessment strategies are often formed at the level of practice, but that this produces contradiction and confusion at higher levels. Such tensions cannot be managed away, but they can be reflected on and mitigated. The paper suggests a framework for the construction of assessment strategies at different levels of an institution. However, the main conclusion is that the process of constructing such strategies should be an opportunity for learning and reflection, rather than one of compliance

Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens

<p>Abstract</p> <p>Background</p> <p>The Enteropathogen Resource Integration Center (ERIC; <url>http://www.ericbrc.org</url>) has a goal of providing bioinformatics support for the scientific community researching enteropathogenic bacteria such as <it>Escherichia coli </it>and <it>Salmonella </it>spp. Rapid and accurate identification of experimental conclusions from the scientific literature is critical to support research in this field. Natural Language Processing (NLP), and in particular Information Extraction (IE) technology, can be a significant aid to this process.</p> <p>Description</p> <p>We have trained a powerful, state-of-the-art IE technology on a corpus of abstracts from the microbial literature in PubMed to automatically identify and categorize biologically relevant entities and predicative relations. These relations include: Genes/Gene Products and their Roles; Gene Mutations and the resulting Phenotypes; and Organisms and their associated Pathogenicity. Evaluations on blind datasets show an F-measure average of greater than 90% for entities (genes, operons, etc.) and over 70% for relations (gene/gene product to role, etc). This IE capability, combined with text indexing and relational database technologies, constitute the core of our recently deployed text mining application.</p> <p>Conclusion</p> <p>Our Text Mining application is available online on the ERIC website <url>http://www.ericbrc.org/portal/eric/articles</url>. The information retrieval interface displays a list of recently published enteropathogen literature abstracts, and also provides a search interface to execute custom queries by keyword, date range, etc. Upon selection, processed abstracts and the entities and relations extracted from them are retrieved from a relational database and marked up to highlight the entities and relations. The abstract also provides links from extracted genes and gene products to the ERIC Annotations database, thus providing access to comprehensive genomic annotations and adding value to both the text-mining and annotations systems.</p