Mutants with increased sensitivity to caffeine

Mutants with increased sensitivity to caffein

Guanine-requiring mutants

Guanine-requiring mutant

50 Years of the Clean Water Act: Can We Sustain Its Success?

The effectiveness of the Clean Water Act in mandating the abatement of gross pollution by setting technology standards for categories of municipal and industrial point sources is well documented. Still, the CWA has not been modernized to update water quality standards, it has not readily employed the latest science, and the benefits have not been documented nearly well enough. Increasingly insidious attempts to undermine its continued effectiveness have arisen over the past 10–15 years mostly at the state level

Experience with the Applegate-Nelson-Metzenberg method of mutant enrichment in high sorbose medium

Experience with the Applegate-Nelson-Metzenberg method of mutant enrichment in high sorbose mediu

Estimation Of Reference Crop Evapotranspiration Using Fuzzy State Models

Daily evapotranspiration (ET) rates are needed for irrigation scheduling. Owing to the difficulty of obtaining accurate field measurements, ET rates are commonly estimated from weather parameters. A few empirical or semi–empirical methods have been developed for assessing daily reference crop ET, which is converted to actual crop ET using crop coefficients. The FAO Penman–Monteith method, which is now accepted as the standard method for the computation of daily reference ET, is sophisticated. It requires several input parameters, some of which have no actual measurements but are estimated from measured weather parameters. In this study, we examined the suitability of fuzzy logic for estimating daily reference ET with simpler and fewer parameters. Two fuzzy evapotranspiration models, using two or three input parameters, were developed and applied to estimate grass ET. Independent weather parameters from sites representing arid and humid climates were used to test the models. The fuzzy estimated ET values were compared with direct ET measurements from grass–covered weighing lysimeters, and with ET estimations obtained using the FAO Penman–Monteith and the Hargreaves–Samani equations. The estimated ET values from a fuzzy model using three input parameters (Syx = 0.54 mm, r2 = 0.90) were found to be comparable to ET values estimated with the FAO Penman–Monteith equation (Syx = 0.50 mm, r2 = 0.91) and were more accurate than those obtained by the Hargreaves–Samani equation (Syx = 0.66 mm, r2 = 0.53). These results show that fuzzy evapotranspiration models with simpler and fewer input parameters can yield accurate estimation of ET

The Occurrence and Distribution of River Redhorse, Moxostoma carinatum and Greater Redhorse, Moxostoma valenciennesi in the Sandusky River, Ohio

Author Institution: Ohio Environmental Protection Agency, Division of Water Quality Monitoring and AssessmentElectrofishing collections at 10 locations in the middle Sandusky River mainstem between Tiffin and Fremont revealed the presence of previously unknown populations of river redhorse (Moxostoma carinatum) and greater redhorse (Moxostoma valenciennesi). The discovery of these populations expands the Lake Erie drainage distribution of both species which have been either declining in abundance or extirpated in many areas. It is doubtful that these species have recently invaded the middle Sandusky River since barriers to upstream fish movements have been in place in the vicinity of Fremont since the early 1800s. Both species snowed a preference for locations with a moderate to swift current, pool-run-riffle habitat, and a convoluted bedrock channel with a boulder, rubble, and gravel substrate. Sampling locations that were impounded or where the river was predominantly pooled contained comparatively few or no individuals

Optimization Of Fuzzy Evapotranspiration Model Through Neural Training With Input–Output Examples

In a previous study, we demonstrated that fuzzy evapotranspiration (ET) models can achieve accurate estimation of daily ET comparable to the FAO Penman–Monteith equation, and showed the advantages of the fuzzy approach over other methods. The estimation accuracy of the fuzzy models, however, depended on the shape of the membership functions and the control rules built by trial–and–error methods. This paper shows how the trial and error drawback is eliminated with the application of a fuzzy–neural system, which combines the advantages of fuzzy logic (FL) and artificial neural networks (ANN). The strategy consisted of fusing the FL and ANN on a conceptual and structural basis. The neural component provided supervised learning capabilities for optimizing the membership functions and extracting fuzzy rules from a set of input–output examples selected to cover the data hyperspace of the sites evaluated. The model input parameters were solar irradiance, relative humidity, wind speed, and air temperature difference. The optimized model was applied to estimate reference ET using independent climatic data from the sites, and the estimates were compared with direct ET measurements from grass–covered lysimeters and estimations with the FAO Penman–Monteith equation. The model–estimated ET vs. lysimeter–measured ET gave a coefficient of determination (r2) value of 0.88 and a standard error of the estimate (Syx) of 0.48 mm d–1. For the same set of independent data, the FAO Penman–Monteith–estimated ET vs. lysimeter–measured ET gave an r2 value of 0.85 and an Syx value of 0.56 mm d–1. These results show that the optimized fuzzy–neural–model is reasonably accurate, and is comparable to the FAO Penman–Monteith equation. This approach can provide an easy and efficient means of tuning fuzzy ET models

A microbiological assay for host-specific fungal polyketide toxins

Genetic analysis of biosynthetic pathways for fungal secondary metabolites depends on availability of efficient and dependable assays for the end products. Some fungal plant pathogens produce secondary metabolites called host-specific toxins. Until recently, all bioassays for these toxins required use of whole plants or plant parts (Yoder 1981 In: Toxins in Plant Disease, Durbin ed., pp. 45-78). Since host-specific toxins, by definition, affect only plants that are susceptible to the toxin-producing fungus, other plants, animals and microorganisms are not sensitive and therefore cannot be used in bioassays

Soil Characterization Using Textural Features Extracted from GPR Data

Soils can be non-intrusively mapped by observing similar patterns within ground-penetrating radar (GPR) profiles. We observed that the intricate and often indiscernible textural variability found within a complex GPR image possesses important parameters that help delineate regions of similar soil characteristics. Therefore, in this study, we examined the feasibility of using textural features extracted from GPR data to automate soil characterizations. The textural features were matched to a fingerprint database of previous soil classifications of GPR textural features and the corresponding ground truths of soil conditions. Four textural features (energy, contrast, entropy, and homogeneity) were selected for inputs into a neural-network classifier. This classifier was tested and verified using GPR data obtained from two distinctly different field sites. The first data set contained features that indicate the presence or lack of sandstone bedrock in the upper 2 m of a shallow soil profile of fine sandy loan and loam. The second data set contained columnar patterns that correspond to the presence or the lack of vertical preferential-flow paths within a deep loess soil. The classifier automatically grouped each of these data sets into one of the two categories. Comparing the results of classification using extracted textural features to the results obtained by visual interpretation found 93.6% of the sections that lack sandstone bedrock correctly classified in the first set of data, and 90% of the sections that contain pronounced columnar patterns correctly classified in the second set of data. The classified profile sections were mapped using integrated GPR and GPS data to show surface boundaries of different soil categories. These results indicate that extracted textural features can be utilized for automatic characterization of soils using GPR data