XRate: a fast prototyping, training and annotation tool for phylo-grammars

BACKGROUND: Recent years have seen the emergence of genome annotation methods based on the phylo-grammar, a probabilistic model combining continuous-time Markov chains and stochastic grammars. Previously, phylo-grammars have required considerable effort to implement, limiting their adoption by computational biologists. RESULTS: We have developed an open source software tool, xrate, for working with reversible, irreversible or parametric substitution models combined with stochastic context-free grammars. xrate efficiently estimates maximum-likelihood parameters and phylogenetic trees using a novel "phylo-EM" algorithm that we describe. The grammar is specified in an external configuration file, allowing users to design new grammars, estimate rate parameters from training data and annotate multiple sequence alignments without the need to recompile code from source. We have used xrate to measure codon substitution rates and predict protein and RNA secondary structures. CONCLUSION: Our results demonstrate that xrate estimates biologically meaningful rates and makes predictions whose accuracy is comparable to that of more specialized tools

The future for beef

Beef is a fast-growing, multi -billion dollar industry today in the United States. And the outlook for tomorrow is most favorable. Beef consumption has increased by 26 pounds per capita during the past 15 years, hitting an all-time high of 90 pounds per capita in 1962. During the same period quality has improved, and now. beef commands even wider consumer acceptance than in the mid-1940\u27s. New technology has lowered production and marketing costs. But even a strong, healthy industry must be sensitive to change--and take advantage of new opportunities for growth and improvement. The beef industry is no exception.https://lib.dr.iastate.edu/card_reports/1016/thumbnail.jp

Insights from the genome of the biotrophic fungal plant pathogen Ustilago maydis

Ustilago maydis is a ubiquitous pathogen of maize and a well-established model organism for the study of plant-microbe interactions. This basidiomycete fungus does not use aggressive virulence strategies to kill its host. U. maydis belongs to the group of biotrophic parasites (the smuts) that depend on living tissue for proliferation and development. Here we report the genome sequence for a member of this economically important group of biotrophic fungi. The 20.5-million-base U. maydis genome assembly contains 6,902 predicted protein-encoding genes and lacks pathogenicity signatures found in the genomes of aggressive pathogenic fungi, for example a battery of cell-wall-degrading enzymes. However, we detected unexpected genomic features responsible for the pathogenicity of this organism. Specifically, we found 12 clusters of genes encoding small secreted proteins with unknown function. A significant fraction of these genes exists in small gene families. Expression analysis showed that most of the genes contained in these clusters are regulated together and induced in infected tissue. Deletion of individual clusters altered the virulence of U. maydis in five cases, ranging from a complete lack of symptoms to hypervirulence. Despite years of research into the mechanism of pathogenicity in U. maydis, no 'true' virulence factors had been previously identified. Thus, the discovery of the secreted protein gene clusters and the functional demonstration of their decisive role in the infection process illuminate previously unknown mechanisms of pathogenicity operating in biotrophic fungi. Genomic analysis is, similarly, likely to open up new avenues for the discovery of virulence determinants in other pathogens. ©2006 Nature Publishing Group.J.K., M. B. and R.K. thank G. Sawers and U. Kämper for critical reading of the manuscript. The genome sequencing of Ustilago maydis strain 521 is part of the fungal genome initiative and was funded by National Human Genome Research Institute (USA) and BayerCropScience AG (Germany). F.B. was supported by a grant from the National Institutes of Health (USA). J.K. and R.K. thank the German Ministry of Education and Science (BMBF) for financing the DNA array setup and the Max Planck Society for their support of the manual genome annotation. F.B. was supported by a grant from the National Institutes of Health, B.J.S. was supported by the Natural Sciences and Engineering Research Council of Canada and the Canada Foundation for Innovation, J.W.K. received funding from the Natural Sciences and Engineering Research Council of Canada, J.R.-H. received funding from CONACYT, México, A.M.-M. was supported by a fellowship from the Humboldt Foundation, and L.M. was supported by an EU grant. Author Contributions All authors were involved in planning and executing the genome sequencing project. B.W.B., J.G., L.-J.M., E.W.M., D.D., C.M.W., J.B., S.Y., D.B.J., S.C., C.N., E.K., G.F., P.H.S., I.H.-H., M. Vaupel, H.V., T.S., J.M., D.P., C.S., A.G., F.C. and V. Vysotskaia contributed to the three independent sequencing projects; M.M., G.M., U.G., D.H., M.O. and H.-W.M. were responsible for gene model refinement, database design and database maintenance; G.M., J. Kämper, R.K., G.S., M. Feldbrügge, J.S., C.W.B., U.F., M.B., B.S., B.J.S., M.J.C., E.C.H.H., S.M., F.B., J.W.K., K.J.B., J. Klose, S.E.G., S.J.K., M.H.P., H.A.B.W., R.deV., H.J.D., J.R.-H., C.G.R.-P., L.O.-C., M.McC., K.S., J.P.-M., J.I.I., W.H., P.G., P.S.-A., M. Farman, J.E.S., R.S., J.M.G.-P., J.C.K., W.L. and D.H. were involved in functional annotation and interpretation; T.B., O.M., L.M., A.M.-M., D.G., K.M., N.R., V. Vincon, M. VraneŠ, M.S. and O.L. performed experiments. J. Kämper, R.K. and M.B. wrote and edited the paper with input from L.-J.M., J.G., F.B., J.W.K., B.J.S. and S.E.G. Individual contributions of authors can be found as Supplementary Notes

Identification and Establishment of Social and Sociomathematical Norms Associated with Mathematically Productive Discourse

For some time, the mathematics education community has sought to involve students more actively in classroom mathematical discourse, but realizing this goal has been problematic. This study has two goals: the first is to better characterize mathematically productive discourse by identifying social and sociomathematical norms that accompany it. Mathematically productive discourse is defined as discourse which holds mathematics as the authority, focuses on sense-making, and strives to create mathematical coherency. The second goal of the study is to identify strategies that teachers can use to establish these norms in their classroom.An in-depth case study was performed of a 5th grade classroom where mathematically productive discourse regularly occurs. Twenty-five observations were performed over the course of the 2014-2015 school year, as well as twelve interviews with the teacher. Observations were video-recorded and interviews were audio-recorded. The data were analyzed using principles of grounded theory including open coding, axial coding, and the constant comparative method.One social norm, active listening, and four sociomathematical norms, coherency, justification, computational strategies, and multiple perspectives, were identified in the classroom. To establish these norms, the teacher employed four “in-the-moment” strategies: direct prompts, normative comments, highlighting positive examples, and modeling. However, the teacher also had a more comprehensive vision for the progression of her class’s mathematical development over the first two months of the school year. This was reflected in the way that she established a conducive classroom environment, systematically taught her students mathematical skills and practices that built upon each other, and in her concept-focused use of mathematical tasks.The results of this study offer insight into how mathematical discourse, tasks, and practices should be conceptualized. They also underscore the importance of the teacher’s content knowledge in enabling these norms to emerge

Connecting new knowledge to old: Uncovering hidden premises in mathematical explanations

The article focuses on the attitude of students towards mathematical explanations. It includes the views of president of the Australian Association of Mathematics Teachers Peter Sullivan on Australian Curriculum Mathematics. It also includes information on students\u27 reasoning on mathematical connected explanation to their prior knowledge

A comparison of the university mathematics learning environment with its high school equivalent

In light of rising college student debt, many states now offer multiple options for students to earn college credit while still in high school. Concurrent enrollment programs, which allow qualified high school teachers to teach college credit-bearing classes in the high school, are one such option. Because concurrent enrollment classes teach college-level material at college-level rigor, they offer an ideal way to compare the secondary and tertiary learning environments across identical academic expectations. This study sought to compare the university mathematics environment with its concurrent enrollment counterpart. The WIHIC was found to be valid and reliable for the university population. The comparison of 242 students in university classrooms with 260 students in concurrent enrollment classrooms revealed a statistical difference, with the concurrent enrollment setting scoring higher in Involvement, Teacher Support, and Student Cohesion and the university setting scoring higher in Task Orientation. This implies that earning college credit in a secondary setting is a viable, and possibly even preferable, alternative to earning it in a university setting. We examine the discrepancy in scores—particularly the large discrepancy in Task Orientation—and discuss the benefit of the flipped classroom as one path to improving the university learning environment

SCOR: Structural classification of RNA, Version 2.0

SCOR (http://scor.lbl.gov), the Structural Classification of RNA, is a database designed to provide a comprehensive perspective and understanding of RNA motif three-dimensional structure,function, tertiary interactions, and their relationships.SCOR 2.0 represents a major expansion and introduces a wholly new classification system. The new version represents the classification as a Directed Acyclic Graph (DAG), which allows a classification node to have multiple parents, in contrast to the strictly hierarchical classification used in SCOR 1.2. SCOR 2.0 supports three types of query terms in the updated search engine: PDB or NDB identifier, nucleotide sequence, and keyword. We also provide parseable XML files for all information. This new release contains 511 RNA entries from the PDB as of 15 May 2003. A total of 5,880 secondary structural elements are classified; 2,104 hairpin loops and 3,776 internal loops. RNA motifs reported in the literature, such as "Kink turn" and "GNRA loops," are now incorporated into the structural classification along with definitions and descriptions

SCOR: Structural classification of RNA, Version 2.0

SCOR (http://scor.lbl.gov), the Structural Classification of RNA, is a database designed to provide a comprehensive perspective and understanding of RNA motif three-dimensional structure, function, tertiary interactions, and their relationships. SCOR 2.0 represents a major expansion and introduces a wholly new classification system. The new version represents the classification as a Directed Acyclic Graph (DAG), which allows a classification node to have multiple parents, in contrast to the strictly hierarchical classification used in SCOR 1.2. SCOR 2.0 supports three types of query terms in the updated search engine: PDB or NDB identifier, nucleotide sequence, and keyword. We also provide parseable XML files for all information. This new release contains 511RNA entries from the PDB as of 15 May 2003. A total of 5,880 secondary structural elements are classified; 2,104 hairpin loops and 3,776 internal loops. RNA motifs reported in the literature, such as ''Kinkturn'' and ''GNRA loops,'' are now incorporated into the structural classification along with definitions and descriptions

SCOR: Structural classification of RNA, Version 2.0

SCOR (http://scor.lbl.gov), the Structural Classification of RNA, is a database designed to provide a comprehensive perspective and understanding of RNA motif three-dimensional structure,function, tertiary interactions, and their relationships.SCOR 2.0 represents a major expansion and introduces a wholly new classification system. The new version represents the classification as a Directed Acyclic Graph (DAG), which allows a classification node to have multiple parents, in contrast to the strictly hierarchical classification used in SCOR 1.2. SCOR 2.0 supports three types of query terms in the updated search engine: PDB or NDB identifier, nucleotide sequence, and keyword. We also provide parseable XML files for all information. This new release contains 511 RNA entries from the PDB as of 15 May 2003. A total of 5,880 secondary structural elements are classified; 2,104 hairpin loops and 3,776 internal loops. RNA motifs reported in the literature, such as "Kink turn" and "GNRA loops," are now incorporated into the structural classification along with definitions and descriptions