The Impact of Tracking Students in Mathematics on Middle School Student Achievement Outcomes

The purpose of this study was to explore whether and how tracking structures in mathematics courses at the middle school level relate to differences in achievement between white and black students. This study used propensity score matching to compare the achievement outcomes of students enrolled in advanced mathematics classes, with students of comparable ability and background enrolled in grade-level math classes. The study sample was comprised of 1,510 students. Results from the study show that enrollment in an advanced-math course was associated with statistically significant improvement in math achievement for average-ability students. In addition, study results show that increases in student achievement associated with average-ability black student enrollment in advanced-level math courses surpass the increases in math achievement outcomes associated with average-ability white student enrollment in advanced-level math courses. These findings have important equity implications because average-ability black students opt to enroll, or are disproportionately placed, in grade-level math as compared to average-ability white students. The findings suggest that increased enrollment of average-ability black and white students in advanced-level math would lead to a reduction in the racial math achievement gap and to improved math achievement outcomes for both black and white students

Variation in transposable element sequence and activity in the nematode Caenorhabditis elegans

Eukaryotic genomes are replete with transposable elements. The nematode C. elegans will be the first multicellular organism to have its genome completely sequenced. This sequence will allow identification of all the transposon and transposon-related sequences from a single genome. In anticipation of the complete genome sequence I have initiated a series of analyses of sequences from the C. elegans genome database that share significant similarity to known families of transposons. Several members of known transposon families were observed along with a plethora of sequences related to these known transposons. Cladistic analyses were used to describe the relationships among transposons and transposon families. These analyses suggest that transposons in C. elegans may be found in both autonomous and nonautonomous forms. The differences between related element families lies mostly in the length of the inverted repeats and the presence of open reading frames. Differences between sequences within an element family suggest several mechanisms for generating length variation in inverted repeats. Characterization of the consequences of Tc1 insertion requires a means of detecting insertions. I describe reverse genetic methodology for identifying new transposon insertions. To study the regulation of transposon activity I focused on the tissue-specific and developmental regulation of Tc1. I identified sites that are frequent targets for Tc1 insertion. In the most dramatic example, insertion of Tc1 was detected at the same site in the unc-54 gene in nearly every animal screened. This site was previously shown to be a hotspot for germ-line insertion, although at a frequency several orders of magnitude less than the levels now detected. I believe these insertions are somatic events because they increase in frequency during development but are not transmitted to progeny based on both genetic and molecular evidence and because I detect them in animals lacking a germline. Additional sites in unc-54 and src-1, another C. elegans gene, were identified as frequent targets for insertion of Tc1; however, none are hit as frequently as the unc-54 hotspot . Somatic insertion of Tc1 depends on genetic background and may be suppressed early in development

Dynamical entropy in Banach spaces

We introduce a version of Voiculescu-Brown approximation entropy for isometric automorphisms of Banach spaces and develop within this framework the connection between dynamics and the local theory of Banach spaces discovered by Glasner and Weiss. Our fundamental result concerning this contractive approximation entropy, or CA entropy, characterizes the occurrence of positive values both geometrically and topologically. This leads to various applications; for example, we obtain a geometric description of the topological Pinsker factor and show that a C*-algebra is type I if and only if every multiplier inner *-automorphism has zero CA entropy. We also examine the behaviour of CA entropy under various product constructions and determine its value in many examples, including isometric automorphisms of l_p spaces and noncommutative tensor product shifts.Comment: 33 pages; unified approach to last three sections give

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

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

Using Comparative Genomics for Inquiry-Based Learning to Dissect Virulence of Escherichia coli O157:H7 and Yersinia pestis

Genomics and bioinformatics are topics of increasing interest in undergraduate biological science curricula. Many existing exercises focus on gene annotation and analysis of a single genome. In this paper, we present two educational modules designed to enable students to learn and apply fundamental concepts in comparative genomics using examples related to bacterial pathogenesis. Students first examine alignments of genomes of Escherichia coli O157:H7 strains isolated from three food-poisoning outbreaks using the multiple-genome alignment tool Mauve. Students investigate conservation of virulence factors using the Mauve viewer and by browsing annotations available at the A Systematic Annotation Package for Community Analysis of Genomes database. In the second module, students use an alignment of five Yersinia pestis genomes to analyze single-nucleotide polymorphisms of three genes to classify strains into biovar groups. Students are then given sequences of bacterial DNA amplified from the teeth of corpses from the first and second pandemics of the bubonic plague and asked to classify these new samples. Learning-assessment results reveal student improvement in self-efficacy and content knowledge, as well as students’ ability to use BLAST to identify genomic islands and conduct analyses of virulence factors from E. coli O157:H7 or Y. pestis. Each of these educational modules offers educators new ready-to-implement resources for integrating comparative genomic topics into their curricula

Microreact: visualizing and sharing data for genomic epidemiology and phylogeography

Visualization is frequently used to aid our interpretation of complex datasets. Within microbial genomics, visualizing the relationships between multiple genomes as a tree provides a framework onto which associated data (geographical, temporal, phenotypic and epidemiological) are added to generate hypotheses and to explore the dynamics of the system under investigation. Selected static images are then used within publications to highlight the key findings to a wider audience. However, these images are a very inadequate way of exploring and interpreting the richness of the data. There is, therefore, a need for flexible, interactive software that presents the population genomic outputs and associated data in a user-friendly manner for a wide range of end users, from trained bioinformaticians to front-line epidemiologists and health workers. Here, we present Microreact, a web application for the easy visualization of datasets consisting of any combination of trees, geographical, temporal and associated metadata. Data files can be uploaded to Microreact directly via the web browser or by linking to their location (e.g. from Google Drive/Dropbox or via API), and an integrated visualization via trees, maps, timelines and tables provides interactive querying of the data. The visualization can be shared as a permanent web link among collaborators, or embedded within publications to enable readers to explore and download the data. Microreact can act as an end point for any tool or bioinformatic pipeline that ultimately generates a tree, and provides a simple, yet powerful, visualization method that will aid research and discovery and the open sharing of datasets