2,677 research outputs found
Chlorinated organic contaminants in breast milk of New Zealand women.
Breast milk samples from 38 women in New Zealand were analyzed for organochlorine pesticides, polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins (PCDDs), and polychlorinated dibenzofurans (PCDFs) as part of a World Health Organization collaborative study of breast-milk contaminants. The women were recruited from two urban areas (Auckland and Christchurch) and two rural areas (Northland and North Canterbury) in the North and South Islands of New Zealand. The best predictor of contaminant concentrations in breast milk was found to be the age of the mother. Regional differences were found for hexachlorobenzene, dieldrin, and pp-DDE, reflecting historical use patterns. Urban-rural differences were found for several PCBs, PCDDs, and PCDFs when contaminant concentrations were calculated on a whole-milk basis. However, these differences could be attributed to variation in breast-milk fat concentrations between urban and rural mothers. Urban mothers had about 50% more breast-milk fat than rural mothers. Evidence suggests that breast-milk consumption by babies is regulated by caloric intake. Almost all of the caloric content of milk is in the fat fraction. This suggests that breast-milk contaminant levels calculated on a whole-milk basis do not necessarily reflect the relative levels of exposure of infants to these contaminants. However, the factors that influence breast-milk fat concentration deserve further study
Liber
In this paper a cognitive model for visual attention is introduced. The cognitive model is part of the design of a software agent that supports a naval warfare officer in its task to compile a tactical picture of the situation in the field. An executable formal specification of the cognitive model is given and a case study is described in which the model is used to simulate a human subject's attention. The foundation of the model is based on formal specification of representation relations for attentional states, specifying their intended meaning. The model has been automatically verified against these relations. © 2006 IEEE
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Development of Antipsychotic Medications with Novel Mechanisms of Action Based on Computational Modeling of Hippocampal Neuropathology
A large number of cellular level abnormalities have been identified in the hippocampus of schizophrenic subjects. Nonetheless, it remains uncertain how these pathologies interact at a system level to create clinical symptoms, and this has hindered the development of more effective antipsychotic medications. Using a 72-processor supercomputer, we created a tissue level hippocampal simulation, featuring multicompartmental neuron models with multiple ion channel subtypes and synaptic channels with realistic temporal dynamics. As an index of the schizophrenic phenotype, we used the specific inability of the model to attune to 40 Hz (gamma band) stimulation, a well-characterized abnormality in schizophrenia. We examined several possible combinations of putatively schizophrenogenic cellular lesions by systematically varying model parameters representing NMDA channel function, dendritic spine density, and GABA system integrity, conducting 910 trials in total. Two discrete “clusters” of neuropathological changes were identified. The most robust was characterized by co-occurring modest reductions in NMDA system function (-30%) and dendritic spine density (-30%). Another set of lesions had greater NMDA hypofunction along with low level GABA system dysregulation. To the schizophrenic model, we applied the effects of 1,500 virtual medications, which were implemented by varying five model parameters, independently, in a graded manner; the effects of known drugs were also applied. The simulation accurately distinguished agents that are known to lack clinical efficacy, and identified novel mechanisms (e.g., decrease in AMPA conductance decay time constant, increase in projection strength of calretinin-positive interneurons) and combinations of mechanisms that could re-equilibrate model behavior. These findings shed light on the mechanistic links between schizophrenic neuropathology and the gamma band oscillatory abnormalities observed in the illness. As such, they generate specific falsifiable hypotheses, which can guide postmortem and other laboratory research. Significantly, this work also suggests specific non-obvious targets for potential pharmacologic agents
Three boundary conditions for computing the fixed-point property in binary mixture data
The notion of “mixtures” has become pervasive in behavioral and cognitive sciences, due to the success of dual-process theories of cognition. However, providing support for such dual-process theories is not trivial, as it crucially requires properties in the data that are specific to mixture of cognitive processes. In theory, one such property could be the fixed-point property of binary mixture data, applied–for instance- to response times. In that case, the fixed-point property entails that response time distributions obtained in an experiment in which the mixture proportion is manipulated would have a common density point. In the current article, we discuss the application of the fixed-point property and identify three boundary conditions under which the fixed-point property will not be interpretable. In Boundary condition 1, a finding in support of the fixed-point will be mute because of a lack of difference between conditions. Boundary condition 2 refers to the case in which the extreme conditions are so different that a mixture may display bimodality. In this case, a mixture hypothesis is clearly supported, yet the fixed-point may not be found. In Boundary condition 3 the fixed-point may also not be present, yet a mixture might still exist but is occluded due to additional changes in behavior. Finding the fixed-property provides strong support for a dual-process account, yet the boundary conditions that we identify should be considered before making inferences about underlying psychological processes
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