Teachers' networks in and out of school

A 'mapping task' was used to explore the networks available to head teachers, school coordinators and local authority staff. Beginning from an ego-centred perspective on networks, we illustrate a number of key analytic categories, including brokerage, formality, and strength and weakness of links with reference to a single UK primary school. We describe how teachers differentiate between the strength of network links and their value, which is characteristically related to their potential impact on classroom practice.

Mathematical modelling of competitive LDL/VLDL binding and uptake by hepatocytes

Elevated levels of low-density-lipoprotein cholesterol (LDL-C) in the plasma are a well-established risk factor for the development of coronary heart disease. Plasma LDL-C levels are in part determined by the rate at which LDL particles are removed from the bloodstream by hepatic uptake. The uptake of LDL by mammalian liver cells occurs mainly via receptor-mediated endocytosis, a process which entails the binding of these particles to specific receptors in specialised areas of the cell surface, the subsequent internalization of the receptor-lipoprotein complex, and ultimately the degradation and release of the ingested lipoproteins' constituent parts. We formulate a mathematical model to study the binding and internalization (endocytosis) of LDL and VLDL particles by hepatocytes in culture. The system of ordinary differential equations, which includes a cholesterol-dependent pit production term representing feedback regulation of surface receptors in response to intracellular cholesterol levels, is analysed using numerical simulations and steady-state analysis. Our numerical results show good agreement with in vitro experimental data describing LDL uptake by cultured hepatocytes following delivery of a single bolus of lipoprotein. Our model is adapted in order to reflect the in vivo situation, in which lipoproteins are continuously delivered to the hepatocyte. In this case, our model suggests that the competition between the LDL and VLDL particles for binding to the pits on the cell surface affects the intracellular cholesterol concentration. In particular, we predict that when there is continuous delivery of low levels of lipoproteins to the cell surface, more VLDL than LDL occupies the pit, since VLDL are better competitors for receptor binding. VLDL have a cholesterol content comparable to LDL particles; however, due to the larger size of VLDL, one pit-bound VLDL particle blocks binding of several LDLs, and there is a resultant drop in the intracellular cholesterol level. When there is continuous delivery of lipoprotein at high levels to the hepatocytes, VLDL particles still out-compete LDL particles for receptor binding, and consequently more VLDL than LDL particles occupy the pit. Although the maximum intracellular cholesterol level is similar for high and low levels of lipoprotein delivery, the maximum is reached more rapidly when the lipoprotein delivery rates are high. The implications of these results for the design of in vitro experiments is discussed.

Seasonal changes in olive fruit characteristics and oil accumulation during ripening process

Olive fruits of three of the most important Spanish and Italian cultivars, 'Picual', `Hojiblanca' and 'Frantoio', were harvested at bi-weekly periods during three crop seasons to study their development and ripening process. Fresh and dry weights and ripening index were determined for fruit, while dry matter, oil and moisture contents were determined in both fruit and pulp (flesh). Fruit growth rate and oil accumulation were calculated. Each olive cultivar showed a different ripening pattern, 'Hojiblanca' being the last one to maturate. Fruit weight increased, decreasing its growth rate from the middle of November. Dry matter and moisture contents decreased during ripening in pulp and fruit, 'Hojiblanca' showing the highest values for both. Oil content, when expressed on a fresh weight basis, increased in all cultivars, although for the last time period showed variations due to climatic conditions. During ripening, oil content on a dry weight basis increased in fruit, but oil biosynthesis in flesh ceased from November. Olive fruits presented lower oil and higher dry matter contents in the year of lowest rainfall. Therefore fruit harvesting should be carried out from the middle of November in order to obtain the highest oil yield and avoid natural fruit drop. (C) 2004 Society of Chemical Industry.

Optokinetic stimulation induces illusory movement of both out-of-the-body and on-the-body hand-held visual objects

The coding of body part location may depend upon both visual and proprioceptive information, and allows targets to be localized with respect to the body. The present study investigates the interaction between visual and proprioceptive localization systems under conditions of multisensory conflict induced by optokinetic stimulation (OKS). Healthy subjects were asked to estimate the apparent motion speed of a visual target (LED) that could be located either in the extrapersonal space (visual encoding only, V), or at the same distance, but stuck on the subject's right index finger-tip (visual and proprioceptive encoding, V-P). Additionally, the multisensory condition was performed with the index finger kept in position both passively (V-P passive) and actively (V-P active). Results showed that the visual stimulus was always perceived to move, irrespective of its out- or on-the-body location. Moreover, this apparent motion speed varied consistently with the speed of the moving OKS background in all conditions. Surprisingly, no differences were found between V-P active and V-P passive conditions in the speed of apparent motion. The persistence of the visual illusion during the active posture maintenance reveals a novel condition in which vision totally dominates over proprioceptive information, suggesting that the hand-held visual stimulus was perceived as a purely visual, external object despite its contact with the hand.

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.

Backward elimination model construction for regression and classification using leave-one-out criteria

A fundamental principle in practical nonlinear data modeling is the parsimonious principle of constructing the minimal model that explains the training data well. Leave-one-out (LOO) cross validation is often used to estimate generalization errors by choosing amongst different network architectures (M. Stone, "Cross validatory choice and assessment of statistical predictions", J. R. Stast. Soc., Ser. B, 36, pp. 117-147, 1974). Based upon the minimization of LOO criteria of either the mean squares of LOO errors or the LOO misclassification rate respectively, we present two backward elimination algorithms as model post-processing procedures for regression and classification problems. The proposed backward elimination procedures exploit an orthogonalization procedure to enable the orthogonality between the subspace as spanned by the pruned model and the deleted regressor. Subsequently, it is shown that the LOO criteria used in both algorithms can be calculated via some analytic recursive formula, as derived in this contribution, without actually splitting the estimation data set so as to reduce computational expense. Compared to most other model construction methods, the proposed algorithms are advantageous in several aspects; (i) There are no tuning parameters to be optimized through an extra validation data set; (ii) The procedure is fully automatic without an additional stopping criteria; and (iii) The model structure selection is directly based on model generalization performance. The illustrative examples on regression and classification are used to demonstrate that the proposed algorithms are viable post-processing methods to prune a model to gain extra sparsity and improved generalization.

Reading 'The Truman Show' inside out

This essay explores how The Truman Show, Peter Weir’s film about a television show, deserves more sustained analysis than it has received since its release in 1998. I will argue that The Truman Show problematizes the binary oppositions of cinema/television, disruption/stability, reality/simulation and outside/inside that structure it. The Truman Show proposes that binary oppositions such as outside/inside exist in a mutually implicating relationship. This deconstructionist strategy not only questions the film’s critical position, but also enables a reflection on the very status of film analysis itself.

Delay analysis of enhanced relay-enabled distributed coordination function

This paper analyzes the delay performance of Enhanced relay-enabled Distributed Coordination Function (ErDCF) for wireless ad hoc networks under ideal condition and in the presence of transmission errors. Relays are nodes capable of supporting high data rates for other low data rate nodes. In ideal channel ErDCF achieves higher throughput and reduced energy consumption compared to IEEE 802.11 Distributed Coordination Function (DCF). This gain is still maintained in the presence of errors. It is also expected of relays to reduce the delay. However, the impact on the delay behavior of ErDCF under transmission errors is not known. In this work, we have presented the impact of transmission errors on delay. It turns out that under transmission errors of sufficient magnitude to increase dropped packets, packet delay is reduced. This is due to increase in the probability of failure. As a result the packet drop time increases, thus reflecting the throughput degradation.

Long-term effect of volcanic forcing on ocean heat content

Explosive volcanic eruptions cause episodic negative radiative forcing of the climate system. Using coupled atmosphere-ocean general circulation models (AOGCMs) subjected to historical forcing since the late nineteenth century, previous authors have shown that each large volcanic eruption is associated with a sudden drop in ocean heat content and sea-level from which the subsequent recovery is slow. Here we show that this effect may be an artefact of experimental design, caused by the AOGCMs not having been spun up to a steady state with volcanic forcing before the historical integrations begin. Because volcanic forcing has a long-term negative average, a cooling tendency is thus imposed on the ocean in the historical simulation. We recommend that an extra experiment be carried out in parallel to the historical simulation, with constant time-mean historical volcanic forcing, in order to correct for this effect and avoid misinterpretation of ocean heat content changes