An HPLC method for simultaneous determination of residual benomyl and MBC on apple foliage without cleanup

A simple High Performance Liquid Chromatograph (HPLC) method has been developed to identify benamyl (methyl 1- (butylcarbamoyl)-2-benzimidazole carbamate) and MBC (methyl 2-benzimidazole carbamat~ residues on apple leaves without cleanup. Sample leaves are freeze dried in a Mason jar and residues are then extracted by tumbling them in chloroform containing 5,000 microgram per milliliter of n-propyl isocyanate (PIC) at 10 C. To the extract, n-butyl isocyanate (BIC) was added at 5,000 microgram per milliliter and 20 microliter of this mixture injected onto the HPLC system. Separation is accomplished by the use of a Brownlee LiChrosorb silica gel column with a guard column and' operated with a mixed mobile phase consisting of chloroform and hexane (4:1) saturated with water. MBC, a degradation product of benomyl is identified if present as methyl l-(npropyl carbamoyl)-2-benzimidazole carbamate (MBC-n-PIC). Both benomyl and MBC-n-PIC can be detected with aKUltraviolet (UV) detector (280nm) at a concentration as low as 0.2 microgram per milliliter in apple leaves. The fate of benomyl on apple foliage after spray application of benomyl (Ben late 50 per cent wettable powder) was investigated by the method thus described. Benomyl quickly dissipated during the first 3-7 days, but the dissipatio'n sltowed down thereafter. In contrast, the concentration of MBC in leaves gradually increased after repeated applications of Benlate.

Simultaneous extraction of order parameters and orientational distribution fuctions from 31[superscript]P NMR spectra of magnetically partially oriented phospholipid bilayers

Order parameter profiles extracted from the NMR spectra of model membranes are a valuable source of information about their structure and molecular motions. To al1alyze powder spectra the de-Pake-ing (numerical deconvolution) ~echnique can be used, but it assumes a random (spherical) dist.ribution of orientations in the sample. Multilamellar vesicles are known to deform and orient in the strong magnetic fields of NMR magnets, producing non-spherical orientation distributions. A recently developed technique for simultaneously extracting the anisotropies of the system as well as the orientation distributions is applied to the analysis of partially magnetically oriented 31p NMR spectra of phospholipids. A mixture of synthetic lipids, POPE and POPG, is analyzed to measure distortion of multilamellar vesicles in a magnetic field. In the analysis three models describing the shape of the distorted vesicles are examined. Ellipsoids of rotation with a semiaxis ratio of about 1.14 are found to provide a good approximation of the shape of the distorted vesicles. This is in reasonable agreement with published experimental work. All three models yield clearly non-spherical orientational distributions, as well as a precise measure of the anisotropy of the chemical shift. Noise in the experimental data prevented the analysis from concluding which of the three models is the best approximation. A discretization scheme for finding stability in the algorithm is outlined

The application of gas liquid chromatography to the simultaneous analysis of sugars and sugar phosphates in biological samples /|nby David J. LeBlanc. -- 260 St. Catharines [Ont. : s. n.],

A study was undertaken' to determine the applicability of gas liquid chromatography to the simultaneous analysis of sugars and sugar phosphates from biological samples. A new method of silylation involving dimethylsulfoxide, hexamethyldisilazane, trimethylchlorosilane and cyclohexane (1:0.2:0.1:1) which rapidly silylated sugars and sugar phosphates was developed. Subsequent chromatography on a 5% SE-52 column gave good resolution of the sugar and sugar phosphate samples. Sugar phosphates decomposed during chromatography and were lost at the 7 x 10-3 ~mole level. Acidic ethanol extraction of yeast samples revealed background contamination from the yeast sample, the culture medium and the silylation reagents which would further limit the level of detection obtainable with the glc for sugars in biological samples to the 3 x 10-4 ~mole level.

"Re-huminization": the role of human-animal similarity in predicting prejudice towards immigrants and non-human animals

Research implies that there ~ay be an association between attitudes toward margil1alized human outgroups and non-human animals. Very few studies, however, have specifically tested this relation empirically. The general purpose of the present research was to determine if such a relation exists and if perceptions of human-animal similarity avail as a common predictor of both types of attitudes. Ideological orientations associated with prejudiced attitudes (Social Dominance Orientation, Right-Wing Authoritarianism, and Universal Orientation) were also examined as individual differences in predicting perceptions of human-animal similarity. As predicted, people who endorsed prejudiced attitudes toward human outgroups (Study 1) and immigrants in particular (Studies 2 and 3), were more likely to endorse prejudiced attitudes toward non-human animals. In Study 2, perceptions that humans are superior (versus similar) to other animals directly predicted higher levels of prejudice toward non-human animals, whereas the effect of human superiority beliefs on immigrant prejudice was mediated by dehumanization. In other words, greater perceptions of humans as superior (versus similar) to other animals "allowed for" greater dehumanization of immigrants, which in turn resulted in heightened immigrant prejudice. Furthermore, people higher in Social Dominance Orientation or Right-Wing Authoritarianism were particularly likely to perceive humans as superior (versus similar) to other animals, whereas people characterized by a greater Universal Orientation were more likely to perceive humans and non-human animals as similar. Study 3 examined whether inducing perceptions of human-animal similarity through experimental manipulation would lead to more favourable attitudes toward non-human animals and immigrants. Participants were randomly assigned to read one of four 11 editorials designed to highlight either the similarities or differences between humans and other animals (i.e., animals are similar to humans; humans are similar to animals;~~nimals are inferior to humans; humans are superior to animals) or to a neutral control condition. Encouragingly, when animals were described as similar to humans, prejudice towards non-human animals and immigrants was significantly lower, and to some extent this finding was also true for people naturally high in prejudice (i.e., high in Social Dominance Orientation or Right-Wing Authoritarianism). Inducing perceptions that nonhuman animals are similar to humans was particularly effective at reducing the tendency to dehumanize immigrants ("re-humanization"), lowering feelings of personal threat regarding one's animal-nature, and at increasing inclusive intergroup representations and empathy, all of which uniquely accounted for the significant decreases in prejudiced attitudes. Implications for research, theory and prejudice interventions are considered.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.