PubMed · 5582598
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L E Morehouse. 1967. Heart-paced ergometry.. https://pubmed.ncbi.nlm.nih.gov/5582598/
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BACKGROUND AND AIMS: The early growth rate of seedlings in the exponential phase is an important eco- physiological trait in crop/weed competition models based on assessments of relative weed green area. An understanding of the role of various plant traits in determining early growth rate may also be useful for identifying contrasting weed strategies for establishment before canopy closure. METHODS: The response of seedling relative growth rate (RGR) to the environment was measured in outdoor sand beds in the autumn and the spring for 18 temperate annual weed species and two crops. Seedling growth was modelled using thermal time and effective day-degrees (combining the effect of temperature and radiation). The contribution of various plant traits in determining variability in RGR was investigated using regression analysis. KEY RESULTS: The effective day-degree model was more effective for describing early weed growth than thermal time. Variability in RGR measured in the autumn was largely determined by differences between the species in net assimilation rate (NAR), whereas in the spring leaf area ratio (LAR) played a larger part. There were differences between the broadleaf and grass species in the relative contribution of NAR and LAR to RGR in both seasons. RGR in the spring was negatively correlated with initial seedling size. CONCLUSIONS: The parameters derived in this study can be used to calibrate empirical models of crop yield loss based on relative weed green area to different growing seasons and assessment dates. The grass weeds, which tended to have large seeds, had a higher investment in roots in the seedling stage, potentially making them more competitive later in the season when resources become limiting.
Cell-based assays are widely used in high-throughput screening to determine the effects of toxicants and drugs on their biological targets. To enable a functional genomics modeling of gene-environment interactions, quantitative assays are required both for gene expression and for the phenotypic responses to environmental challenge. To address this need, we describe an automated high-throughput methodology that provides phenotypic profiling of the cellular responses to environmental stress in Saccharomyces cerevisiae. Standardized assay conditions enable the use of a single metric value to quantify yeast microculture growth curves. This assay format allows precise control of both genetic and environmental determinants of the cellular responses to oxidative stress, a common mechanism of environmental insult. These yeast-cell-based assays are validated with hydrogen peroxide, a simple direct-acting oxidant. Phenotypic profiling of the oxidative stress response of a yap1 mutant strain demonstrates the mechanistic analysis of genetic susceptibility to oxidative stress. As a proof of concept for analysis of more complex gene-environment interactions, we describe a combinatorial assay design for phenotypic profiling of the cellular responses to tert-butyl hydroperoxide, a complex oxidant that is actively metabolized by its target cells. Thus, the yeast microculture assay format supports comprehensive applications in toxicogenomics.
Representations of scientific knowledge must reflect the dynamic nature of knowledge construction and the evolving networks of relations between scientific concepts. In this article, we describe initial work toward dynamic, visual methods and tools that support the construction, communication, revision, and application of scientific knowledge. Specifically, we focus on tools to capture and explore the concepts that underlie collaborative science activities, with examples drawn from the domain of human-environment interaction. These tools help individual researchers describe the process of knowledge construction while enabling teams of collaborators to synthesize common concepts. Our visualization approach links geographic visualization techniques with concept-mapping tools and allows the knowledge structures that result to be shared through a Web portal that helps scientists work collectively to advance their understanding. Our integration of geovisualization and knowledge representation methods emphasizes the process through which abstract concepts can be contextualized by the data, methods, people, and perspectives that produced them. This contextualization is a critical component of a knowledge structure, without which much of the meaning that guides the sharing of concepts is lost. By using the tools we describe here, human-environment scientists are given a visual means to build concepts from data (individually and collectively) and to connect these concepts to each other at appropriate levels of abstraction.