Glucocorticoid receptors in human preadipocytes: regional and gender differences

Glucocorticoid excess causes visceral obesity and its accompanying insulin resistance, dyslipidemia and hypertension. Glucocorticoids enhance preadipocyte (PA) differentiation and increase their aromatase activity (oestrogen production) and there is regional variability in these PA processes. Therefore, we studied human PAs for the presence of, and any regional or gender differences in, glucocorticoid receptors (GRs). Confluent subcultured human subcutaneous (Sc) and visceral (Vis) PAs from both genders contained GRs as assessed by GR gene expression and specific glucocorticoid (dexamethasone) binding. The dissociation constant was similar to that of other human cells and there was no difference between Sc and Vis sites or between males and females. There was significantly less GR mRNA in Vis PAs compared with Sc PAs in females (P=0.008) but not in males. There was less glucocorticoid binding in Vis compared with Sc PAs in females, measured by maximal binding capacity (P=0.035) or single saturating dose glucocorticoid binding (Bssd) (P=0.019). There was no regional difference in specific glucocorticoid binding in males. There was a gender difference with fewer GRs in Vis PAs in females compared with males measured by Bssd (P=0.006). In summary, GRs are present in human PAs. There is a lower GR density in Vis compared with Sc PAs in females, and females have fewer GRs in Vis PAs compared with males. These differences are likely to affect regional aromatase activity and to contribute to the smaller visceral fat mass in females compared with males.

Context-free and context-sensitive dynamics in recurrent neural networks

Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languages. The dynamics of such networks is usually based on damped oscillation around fixed points in state space and requires that the dynamical components are arranged in certain ways. It is shown that qualitatively similar dynamics with similar constraints hold for a(n)b(n)c(n), a context-sensitive language. The additional difficulty with a(n)b(n)c(n), compared with the context-free language a(n)b(n), consists of 'counting up' and 'counting down' letters simultaneously. The network solution is to oscillate in two principal dimensions, one for counting up and one for counting down. This study focuses on the dynamics employed by the sequential cascaded network, in contrast to the simple recurrent network, and the use of backpropagation through time. Found solutions generalize well beyond training data, however, learning is not reliable. The contribution of this study lies in demonstrating how the dynamics in recurrent neural networks that process context-free languages can also be employed in processing some context-sensitive languages (traditionally thought of as requiring additional computation resources). This continuity of mechanism between language classes contributes to our understanding of neural networks in modelling language learning and processing.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Visuospatial processing and the function of prefrontal-parietal networks in autism spectrum disorders: a functional MRI study

Objective: Individuals with autism spectrum disorders typically have normal visuospatial abilities but impaired executive functioning, particularly in abilities related to working memory and attention. The aim of this study was to elucidate the functioning of frontoparietal networks underlying spatial working memory processes during mental rotation in persons with autism spectrum disorders. Method: Seven adolescent males with normal IQ with an autism spectrum disorder and nine age- and IQ-matched male comparison subjects underwent functional magnetic resonance imaging scans while performing a mental rotation task. Results: The autism spectrum disorders group showed less activation in lateral and medial premotor cortex, dorsolateral prefrontal cortex, anterior cingulate gyrus, and caudate nucleus. Conclusions: The finding of less activation in prefrontal regions but not in parietal regions supports a model of dysfunction of frontostriatal networks in autism spectrum disorders.

Forecasting regional crop production using SOI phases: an example for the Australian peanut industry

Using peanuts as an example, a generic methodology is presented to forward-estimate regional crop production and associated climatic risks based on phases of the Southern Oscillation Index (SOI). Yield fluctuations caused by a highly variable rainfall environment are of concern to peanut processing and marketing bodies. The industry could profitably use forecasts of likely production to adjust their operations strategically. Significant, physically based lag-relationships exist between an index of ocean/atmosphere El Nino/Southern Oscillation phenomenon and future rainfall in Australia and elsewhere. Combining knowledge of SOI phases in November and December with output from a dynamic simulation model allows the derivation of yield probability distributions based on historic rainfall data. This information is available shortly after planting a crop and at least 3-5 months prior to harvest. The study shows that in years when the November-December SOI phase is positive there is an 80% chance of exceeding average district yields. Conversely, in years when the November-December SOI phase is either negative or rapidly falling there is only a 5% chance of exceeding average district yields, but a 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production. The study shows that simulation models can enhance SOI signals contained in rainfall distributions by discriminating between useful and damaging rainfall events. The methodology can be applied to other industries and regions.

Retinal neurons: cell types and coupled networks

Retinal neurons with distinct dendritic morphologies are likely to comprise different cell types, subject to three important caveats. First, it is necessary to avoid creating “artificial” cell types based on arbitrary criteria—for example, the presence of two or three primary dendrites. Second, it is essential to take into account changes in morphology with retinal eccentricity and cell density. Third, the retina contains imperfections like any natural system and a significant number of retinal neurons display aberrant morphologies or make aberrant connections that are not typical of the population as a whole. Many types of retinal ganglion cells show diverse patterns of tracer coupling, with the simplest pattern represented by the homologous coupling shown by On-Off direction-selective (DS) ganglion cells in the rabbit retina. Neighboring DS ganglion cells with a common preferred direction have regularly spaced somata and territorial dendritic fields, whereas DS ganglion cells with different preferred directions may have closely spaced somata and overlapping dendritic fields.