Data model organism

After intakes have been estimated, they arc organized by population, as appropriate. Then, tlie sources of uncertainty (e.g., variability in analytical data, modeling results, parameter assumptions) and their effect on tlie exposure estimates are evaluated and sunuiumzed. Tliis information on uncertainty is important to site decision-makers who must evaluate tlie results of the e.xposure... 

The criteria for inclusion of studies in this summary have been as follows (1) use of marine macroalgae as model organisms, and (2) explicit tests of any of the described defense theories (ODM, CNBM, or GDBM) either through observations or through manipulative experiments. We have not included studies that only measure variation in secondary metabolites in different seaweed parts or after different treatments, unless they have specifically mentioned any of the models or hypotheses derived from the models in the introductory part of the paper. Therefore, the summary presented here does not represent an exhaustive review of all data from algal studies that are relevant for defense theories and algal chemical defenses. 

An important and recently reported issue, namely slow sorption/desorption rates, their causes at the intra-particle level of various solid phases, and how these phenomena relate to contaminant transport, bio availability, and remediation, is also discussed and evaluated. A case study showing the environmental impact of solid waste materials which are mainly complex organic mixtures and/or their reuse/recycling as highway construction and repair materials is presented and evaluated from the point of view of sorption/desorption behavior and data modeling. 

The data modeled are from gas chromatograms obtained for Aroclors 1242, 1248, 1254 and 1260. The unknown samples are from the anaysis of used transformer oil obtained from a waste dump in New Jersey. The concentration of individual isomers in selected Aroclor and transformer oil samples are given in Appendix I. The data are organized in a matrix in which the first four data entries for each sample in row 1 of the data array (Table 2, Apendix I) designate the composition of the sample. For standards, these four variables represent the fractional parts of Aroclor 1242, 1248, 1254, or 1260, respectively, that were combined. Results from the analysis of transformer oil (samples 21-23) are of unknown fractional composition and variables 1 through 4 are null entries. In the examples that follow data from samples analyzed (Table 1, Appendix I) were used in part or in total to illustrate the PLS method. 

There are thousands of references and several source books on liquid-liquid equilibrium (LLE) data for organic systems in the literature [1]. However, the available data may be incomplete or unreliable, which makes experimental work necessary. Different thermodynamic models can then be used for interpolation and extrapolation. 

Unifying disparate information sources into a single data model is an intrinsically difficult process. Even when the same kinds of information are collected in different places, it is done so for different reasons, which are reflected in data organization. A common example is extraction of information from scientific literature, where the goal is to make a collection of a particular kind of data. Since the origins of the data are research papers, such efforts often result in document databases where the article is the primary entity and the content is represented by various attributes (e.g., lists of referenced molecules). However, a document-oriented database is rarely suitable for the specific requirements of most scientific investigations. Such efforts often invert ... 

Fraser MR Gass GR, Simoneit BRT, Rasmussen RA, Air quality model evaluation data for organics 4. G2 to G3g non-aromatic hydrocarbons. Environ Sci Technol 31 2356-2367, 1997. 

The utility of SCFs for PTC was demonstrated for several model organic reactions - the nucleophilic displacement of benzyl chloride with bromide ion (26) and cyanide ion (27), which were chosen as model reversible and irreversible Sn2 reactions. The next two reactions reported were the alkylation and cycloalkylation of phenylacetonitrile (28,29). Catalyst solubility in the SCF was very limited, yet the rate of reaction increased linearly with the amount of catalyst present. Figure 5 shows data for the cyanide displacement of benzyl bromide, and the data followed pseudo-first order, irreversible kinetics. The catalyst amounts ranged from 0.06 (solubility limit) to 10% of the limiting reactant, benzyl chloride. 

Fraser, M. P D. Grosjean, E. Grosjean, R. A. Rasmussen, and G. R. Cass, Air Quality Model Evaluation Data for Organics. 1. Bulk Chemical Composition and Gas Particle Distribution Factors, Environ. Sci. Technol., 30, 1731-1743 (1996). 

To support these functions, we have organized our data model as a set of concentric circles. Our data model is inspired from the Snort relational database schema, the IDMEF message format (Curry et al., 2004), and the M2D2 model (Morin et al., 2002). We participated in deploying these tools and developing these models, so they naturally were used as a starting point for our development. However, we believe that event and contextual information are not equivalent and this is not obvious in the three models cited before. Hence, we choose to provide a different representation shown in Figure 2. 

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