Evaluation of Resistance Pattern to Fenpyroximateand Pyridaben in Tetranychus Urticae collectedfrom Ggreenhouses and Apple Orchards using Lethal Concentration-slope Relationship

This study aimed to monitor the present and future developments of the resistance of Tetranychus urticae Koch to fenpyroximate and pyridaben, using the relationship of the LC₅₀ and slope of the concentration-mortality line in a probit model, for the provision of reliable resistance management tactics. Tetranychus urticae populations were collected from 16 commercial greenhouses, where various crops were cultivated, as well as from 10 apple orchards throughout Korea. The resistance to fenpyroximate and pyridaben of each population was estimated by calculating the median lethal concentration (LC₅₀), resistance ratio (RR) and slope of the concentration-mortality regression. Most of the greenhouse populations exhibited moderate levels of resistance, whereas the apple orchard populations showed only low levels, indicating that T. urticae populations in greenhouses were more strongly selected than those in apple orchards. Four population groups were established based on either the habitats (greenhouse and apple orchard) or acaricides (fenpyroximate and pyridaben). To test the hypothesis, “the slope is greatest at low and high levels of resistance,” the slopes were regressed as a function of the LC₅₀, and fitted to a polynomial regression. The polynomial regression model explained this relationship well for the four population groups (p < 0.05), indicating that the development of resistance toward fenpyroximate or pyridaben was consistent with the gradient. A laboratory selection study agreed with the results from both acaricide field populations. These results suggest that the gradient was a good indicator of the susceptibility of T. urticae to genetic variations, which was related to the LC₅₀. The application of these findings is also discussed in relation to the resistance management of T. urticae.     

Floral-Dip Transformation of Flax (Linum usitatissimum) to Generate Transgenic Progenies with a High Transformation Rate

Agrobacterium-mediated plant transformation via floral-dip is a widely used technique in the field of plant transformation and has been reported to be successful for many plant species. However, flax (Linum usitatissimum) transformation by floral-dip has not been reported. The goal of this protocol is to establish that Agrobacterium and the floral-dip method can be used to generate transgenic flax. We show that this technique is simple, inexpensive, efficient, and more importantly, gives a higher transformation rate than the current available methods of flax transformation.In summary, inflorescences of flax were dipped in a solution of Agrobacterium carrying a binary vector plasmid (T-DNA fragment plus the Linum Insertion Sequence, LIS-1) for 1 - 2 min. The plants were laid flat on their side for 24 hr. Then, plants were maintained under normal growth conditions until the next treatment. The process of dipping was repeated 2 - 3 times, with approximately 10 - 14 day intervals between dipping. The T1 seeds were collected and germinated on soil. After approximately two weeks, treated progenies were tested by direct PCR; 2 - 3 leaves were used per plant plus the appropriate T-DNA primers. Positive transformants were selected and grown to maturity. The transformation rate was unexpectedly high, with 50 - 60% of the seeds from treated plants being positive transformants. This is a higher transformation rate than those reported for Arabidopsis thaliana and other plant species, using floral-dip transformation. It is also the highest, which has been reported so far, for flax transformation using other methods for transformation.     

The Use of Multiple Outcomes Analysis in Neurotoxicity: An Example from the New Zealand Study of Offspring of Mothers Exposed to Methyl Mercury

Neurotoxicity has become an important area in the study of the risk to children of noncarcinogens. Many of the tests used to evaluate neurotoxic effects in neurotoxic studies are psychometric tests to assess cognitive, motor, and perceptual performance in individuals in order to determine the presence of psychological problems, impaired educational achievement, or neurological abnormalities. The underlying assumption of these procedures are that a test performance deficit is indicative of a neurological disability, and that such disability derives from the prenatal exposure to an offending substance. The usual analytic paradigm for discerning environmental impacts is to analyze the scores of a particular subtest in a battery taken separately as the dependent variable with regression analysis, which adjusts for cultural background, educational level, and socioeconomic status. The impact of this analysis is to determine the correlation of each of the psychometric test results to exposure. We show a statistical method to simultaneously analyze the multiple outcomes of many tests using General Estimating Equations (GEE) to determine if a correlation exists between the global measure of all these tests and exposure adjusted for covariates of interest and the correlation among the dependent psychometric variables. We show the application of this method to the results of children whose mothers were exposed to mercury during pregnancy.     

Maximum Likelihood Neural Network Prediction Models

Neural networks have received much attention in recent years mostly by non-statisticians. The purpose of this paper is to incorporate neural networks in a non-linear regression model and obtain maximum likelihood estimates of the network parameters using a standard Newton-Raphson algorithm. We use maximum likelihood estimators instead of the usual back-propagation technique and compare the neural network predictions with predictions of quadratic regression models and with non-parametric nearest neighbor predictions. These comparisons are made using data generated from a variety of functions. Because of the number of parameters involved, neural network models can easily over-fit the data, hence validation of results is crucial.     

Highly efficient labeling of DNA polymerases by a binary system of photoaffinity reagents

A binary system of photoaffinity reagents was proposed earlier for highly efficient labeling of DNA polymerases by 5"-[³²P]DNA primers. In the present study we demonstrate the feasibility of this approach to increase the efficiency of DNA polymerase labeling. A photoactive 2,3,5,6-tetrafluoro-4-azidobenzoyl (FAB) group was incorporated at the 3"-end of 5"-[³²P]DNA primers synthesized by DNA polymerase or Tte in the presence of one of the dTTP analogs—FAB-4-dUTP, FAB-9-dUTP, or FAB-4-ddUTP. The reaction mixture was irradiated by light with wavelength of 334-365 nm (direct labeling) or 365-450 nm in the presence of photosensitizer, one of dTTP analogs containing a pyrene moiety, Pyr-6-dUTP or Pyr-8-dUTP. In the case of the binary system of photoaffinity reagents, a FAB group is activated by energy transfer from sensitizer localized in the dNTP-binding site of DNA polymerase in the triple complex, comprised by reagent, DNA polymerase, and Pyr-6(8)-dUTP. Direct activation of the FAB group under these conditions is negligible. The most efficient photolabeling of DNA polymerases was observed with a primer containing a FAB-4-dUMP group at the 3"-end, and Pyr-6-dUTP as a photosensitizer. Using 10-fold molar excess of photoreagent to DNA polymerase , the labeling efficiency was shown to achieve 60%, which is 2-fold higher than the efficiency of the direct DNA polymerase labeling under harsher conditions (334-365 nm).     

Dynamics of a mechanistically derived stoichiometric producer-grazer model

One of the simplest predator-prey models that tracks the quantity and the quality of prey is the one proposed by [I. Loladze, Y. Kuang, and J.J. Elser, Stoichiometry in producer-grazer systems: Linking energy flow with element cycling, Bull. Math. Biol. 62 (2000) pp. 1137–1162.] (LKE model). In it, the ratio of two essential chemical elements, carbon to phosphorus, C:P, represents prey quality. However, that model does not explicitly track P neither in the prey nor in the media that supports the prey. Here, we extend the LKE model by mechanistically deriving and accounting for P in both the prey and the media. Bifurcation diagrams and simulations show that our model behaves similarly to the LKE model. However, in the intermediate range of the carrying capacity, especially near the homoclinic bifurcation point for the carrying capacity, quantitative behaviour of our model is different. We analyze positive invariant region and stability of boundary steady states. We show that as the uptake rate of P by producer becomes infinite, LKE models become the limiting case of our model. Furthermore, our model can be readily extended to multiple producers and consumers.