University of Guelph

About: University of Guelph is a education organization based out in Guelph, Ontario, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 26542 authors who have published 50553 publications receiving 1715255 citations. The organization is also known as: U of G & Guelph University.

A framework for human microbiome research

Abstract: A variety of microbial communities and their genes (microbiome) exist throughout the human body, playing fundamental roles in human health and disease. The NIH funded Human Microbiome Project (HMP) Consortium has established a population-scale framework which catalyzed significant development of metagenomic protocols resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 to 18 body sites up to three times, which to date, have generated 5,177 microbial taxonomic profiles from 16S rRNA genes and over 3.5 Tb of metagenomic sequence. In parallel, approximately 800 human-associated reference genomes have been sequenced. Collectively, these data represent the largest resource to date describing the abundance and variety of the human microbiome, while providing a platform for current and future studies.

Identification of Birds through DNA Barcodes

Abstract: Short DNA sequences from a standardized region of the genome provide a DNA barcode for identifying species. Compiling a public library of DNA barcodes linked to named specimens could provide a new master key for identifying species, one whose power will rise with increased taxon coverage and with faster, cheaper sequencing. Recent work suggests that sequence diversity in a 648-bp region of the mitochondrial gene, cytochrome c oxidase I (COI), might serve as a DNA barcode for the identification of animal species. This study tested the effectiveness of a COI barcode in discriminating bird species, one of the largest and best-studied vertebrate groups. We determined COI barcodes for 260 species of North American birds and found that distinguishing species was generally straightforward. All species had a different COI barcode(s), and the differences between closely related species were, on average, 18 times higher than the differences within species. Our results identified four probable new species of North American birds, suggesting that a global survey will lead to the recognition of many additional bird species. The finding of large COI sequence differences between, as compared to small differences within, species confirms the effectiveness of COI barcodes for the identification of bird species. This result plus those from other groups of animals imply that a standard screening threshold of sequence difference (10× average intraspecific difference) could speed the discovery of new animal species. The growing evidence for the effectiveness of DNA barcodes as a basis for species identification supports an international exercise that has recently begun to assemble a comprehensive library of COI sequences linked to named specimens.

Hybridization using cytoplasmic male sterility, cytoplasmic herbicide tolerance, and herbicide tolerance from nuclear genes

Abstract: The process of the present invention provides a convenient route for producing a predetermined hybrid variety of a crop which is capable of undergoing both self-pollination and cross-pollination Cytoplasmic male sterile plants which also exhibit cytoplasmic herbicide tolerance (ie, to a Type A herbicide) and tolerance to a different herbicide attributable solely to nuclear genes (ie, to a Type B herbicide) are the key plants for use in the present process The maintainer and restorer plants exhibit tolerance to different herbicides (ie, to either a Type A herbicide or a Type B herbicide) The economical bulk planting of the parent plants is made possible during each step of the process For instance, cytoplasmic male sterile plants, plants resulting from the self-pollination of a maintainer, and restorer plants can be grown in a substantially random population, with the self-pollinated maintainer plants being destroyed by an appropriate herbicide prior to pollination, and the self-pollinated restorer plants being destroyed by an appropriate herbicide immediately following pollination or in the subsequent generation The process of the present invention is applicable to grain crops, forage crops, seed-propagated fruits, seed-propagated ornamentals, and industrial species In a particularly preferred embodiment a predetermined variety of Brassica napus (ie, rape or improved forms thereof known as canola) is formed which is the product of cross-pollination

TRY - a global database of plant traits

Abstract: Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.

Using LISREL for Structural Equation Modeling: A Researcher′s Guide

Abstract: Structural equation modeling (SEM) is one of the most rapidly growing analytic techniques in use today. Proponents of the approach have virtually declared the advent of a statistical revolution, while skeptics worry about the widespread misuse of complex and often poorly understood analytic methods. Despite the growing interest in and use of structural equation models, few individuals using these techniques have benefitted from any formal training. Indeed, most graduate programs provide no courses on SEM. Individuals interested in acquiring skills in this technique must eider attend expensive training seminars or plow through technical books and manuals on their own.The two new books under renew are therefore timely. Both are valuable, but differ in important ways. Kevin Kelloway's book is directed at the researcher with little knowledge of structural equation modeling and is intricately linked to one of the more popular structural equation modeling programs, LISREL. For researchers keen to begin analyzing data quickly, this book is an invaluable resource that will speed one's introduction to SEM.On the other hand, the volume written by Rex Kline represents one of the most comprehensive of available introductions to the application, execution, and interpretation of this technique. The book is written for both students and researchers who do not have extensive quantitative background. It is especially attentive to quantitative issues common to most structural equation applications.Kelloway's book is designed for the researcher unfamiliar with structural equation modeling and structural equation software. Chapter 1 provides a brief overview of the book and differentiates among historical concepts such as path analysis and latent variable model. Although the focus of the book is on using LISREL, the book offers two of the most clearly written and concise introductory chapters on SEM that I have ever read. They provide an ideal introduction to the relevant basic concepts of the technique.The theory behind the basic steps of structural equation modeling is reviewed in Chapter 2 and includes model specification, identification, estimation, testing fit, and respecification. The author emphasizes the importance of specifying the model. Indeed, this is the fundamental step in SEM that allows researchers to test hypotheses about the relation among a number of variables, and that makes structural equation modeling an inherently confirmatory technique. How a model is specified influences other issues such as identification and testing fit. Currently, there are over 20 indices of fit computed by most programs. Chapter 3 provides an overview of three general classes of fit indices, namely those assessing absolute fit, comparative fit, and parsimonious fit. Absolute fit indices assess the ability of the specified model to reproduce accurately the manner in which observed variables actually covary. Comparative fit indices assess the ability of the proposed model to account for the observed data relative to a less complex restricted model. Parsimonious fit indices recognize that better fit is usually achieved simply by increasing the number of parameters estimated. Parsimonious fit indices compensate by evaluating the benefit achieved, given the cost of estimating additional parameters.Chapter 4, the most technical chapter of the book, explains the various algebraic components and matrices required in fitting a structural equation model. Although no understanding of the algebraic components associated with fitting a structural equation model is needed to run the most recent versions of LISREL, EQS, and AMOS, this overview is useful. Indeed, the author has prudently avoided directing the book towards "point-and-click" users. This chapter provides sufficient information to novice users to appreciate the complexity of fitting a structural equation model without discouraging them.Chapters 5, 6, and 7 are devoted to the three most common applications of SEM, namely confirmatory factor analysis, observed variable path analysis, and latent variable path analysis. …