Physiological and proteomic approaches to address heat tolerance during anthesis in rice (Oryza sativa L.)

Episodes of high temperature at anthesis, which in rice is the most sensitive stage to temperature, are expected to occur more frequently in future climates. The morphology of the reproductive organs and pollen number, and changes in anther protein expression, were studied in response to high temperature at anthesis in three rice (Oryza sativa L.) genotypes. Plants were exposed to 6 h of high (38 °C) and control (29 °C) temperature at anthesis and spikelets collected for morphological and proteomic analysis. Moroberekan was the most heat-sensitive genotype (18% spikelet fertility at 38 °C), while IR64 (48%) and N22 (71%) were moderately and highly heat tolerant, respectively. There were significant differences among the genotypes in anther length and width, apical and basal pore lengths, apical pore area, and stigma and pistil length. Temperature also affected some of these traits, increasing anther pore size and reducing stigma length. Nonetheless, variation in the number of pollen on the stigma could not be related to measured morphological traits. Variation in spikelet fertility was highly correlated (r=0.97, n=6) with the proportion of spikelets with ≥20 germinated pollen grains on the stigma. A 2D-gel electrophoresis showed 46 protein spots changing in abundance, of which 13 differentially expressed protein spots were analysed by MS/MALDI-TOF. A cold and a heat shock protein were found significantly up-regulated in N22, and this may have contributed to the greater heat tolerance of N22. The role of differentially expressed proteins and morphology during anther dehiscence and pollination in shaping heat tolerance and susceptibility is discussed.

Adaptive modelling, estimation and fusion from data: a neurofuzzy approach

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

Image fusion based on median filters and SOFM neural networks: a three-step scheme

This paper presents a new image data fusion scheme by combining median filtering with self-organizing feature map (SOFM) neural networks. The scheme consists of three steps: (1) pre-processing of the images, where weighted median filtering removes part of the noise components corrupting the image, (2) pixel clustering for each image using self-organizing feature map neural networks, and (3) fusion of the images obtained in Step (2), which suppresses the residual noise components and thus further improves the image quality. It proves that such a three-step combination offers an impressive effectiveness and performance improvement, which is confirmed by simulations involving three image sensors (each of which has a different noise structure).

From CAD to virtual reality: modelling approaches, data exchange and interactive 3D building design tools

Virtual reality has the potential to improve visualisation of building design and construction, but its implementation in the industry has yet to reach maturity. Present day translation of building data to virtual reality is often unidirectional and unsatisfactory. Three different approaches to the creation of models are identified and described in this paper. Consideration is given to the potential of both advances in computer-aided design and the emerging standards for data exchange to facilitate an integrated use of virtual reality. Commonalities and differences between computer-aided design and virtual reality packages are reviewed, and trials of current system, are described. The trials have been conducted to explore the technical issues related to the integrated use of CAD and virtual environments within the house building sector of the construction industry and to investigate the practical use of the new technology.

A comparison of extreme value theory approaches for determining value at risk

This paper compares a number of different extreme value models for determining the value at risk (VaR) of three LIFFE futures contracts. A semi-nonparametric approach is also proposed, where the tail events are modeled using the generalised Pareto distribution, and normal market conditions are captured by the empirical distribution function. The value at risk estimates from this approach are compared with those of standard nonparametric extreme value tail estimation approaches, with a small sample bias-corrected extreme value approach, and with those calculated from bootstrapping the unconditional density and bootstrapping from a GARCH(1,1) model. The results indicate that, for a holdout sample, the proposed semi-nonparametric extreme value approach yields superior results to other methods, but the small sample tail index technique is also accurate.