Catalytic enantioselective conjugate addition with Grignard reagents

In this Account, recent advances in catalytic asymmetric conjugate addition of Grignard reagents are discussed. Synthetic methodology to perform highly enantioselective Cu-catalyzed conjugate addition of Grignard reagents to cyclic enones with ee's up to 96% was reported in 2004 from our laboratories. Excellent levels of stereocontrol were achieved with Cu(I) halides, alkylmagnesium bromides, and commercially available chiral ferrocenyl diphosphines. Studies carried out during the last 2 years demonstrated that these Cu-catalysts are very effective for the enantioselective conjugate addition of Grignard reagents to acyclic enones, alpha,beta-unsaturated esters, and thioesters. On the basis of this methodology, a diastereo- and enantioselective iterative route to deoxypropionate units was developed and applied to the synthesis of natural products. Finally, we summarize our recently conducted mechanistic investigations and the application of this catalytic system to the enantioselective SN2' substitution reactions of allylic bromides with Grignard reagents.     

The memory recall paradigm: Straightening out the historical record.

Traces the inadvertent distortion of historical detail committed by various authors in the dissemination of memory recall results from A. D. de Groot's (1946 [1965]) work with chess masters. The misrepresentation of these details not only has not been corrected but seems to be spreading. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The use of a microcomputer in the classification of grape brandies by pattern recognition

German and French grape brandies have been differentiated by means of a binary pattern recognition technique, using the concentrations of 2,4,5-trimethyl-1,3-dioxolane, ethyl butanoate, ethyl octanoate, and benzaldehyde as characteristic variables.     

Gap filling strategies for 3-D-FBP reconstructions of High-Resolution Research Tomograph scans

The High-Resolution Research Tomograph (HRRT) is a dedicated human brain positron emission tomography scanner. Currently available iterative reconstruction algorithms show bias due to nonnegativity constraints. Consequently, implementation of 3-D filtered backprojection (3-D-FBP) is of interest. To apply 3-D-FBP all missing data including those due to gaps between detector heads need to be estimated. The aim of this study was to evaluate various gap filling strategies for 3-D-FBP reconstructions of HRRT data, such as linear and bilinear interpolation or constraint Fourier space gap filling (confosp). Furthermore, missing planes were estimated using segment 0 image data only (noniterative) or by using reconstructed images based on all previous segments (iterative method). Use of bilinear interpolation showed worst correspondence between reconstructed and true activity concentration, especially for small structures. Moreover, phantom data indicated that use of linear interpolation resulted in artifacts in planes located near the edge of the field-of-view. Use of confosp did not show these artifacts. Iterative estimations of the missing planes for $vert {hbox {segments}}vert > 0$ improved image quality at the cost of more computation time. Therefore, use of confosp for filling sinogram gaps with both iterative and noniterative estimation of missing planes are recommended for quantitative 3-D-FBP of HRRT studies.      

Predicting runoff-induced pesticide input in agricultural sub-catchment surface waters: linking catchment variables and contamination

An urgent need exists for applicable methods to predict areas at risk of pesticide contamination within agricultural catchments. As such, an attempt was made to predict and validate contamination in nine separate sub-catchments of the Lourens River, South Africa, through use of a geographic information system (GIS)-based runoff model, which incorporates geographical catchment variables and physicochemical characteristics of applied pesticides. We compared the results of the prediction with measured contamination in water and suspended sediment samples collected during runoff conditions in tributaries discharging these sub-catchments. The most common insecticides applied and detected in the catchment over a 3-year sampling period were azinphos-methyl (AZP), chlorpyrifos (CPF) and endosulfan (END). AZP was predominantly found in water samples, while CPF and END were detected at higher levels in the suspended particle samples. We found positive (p<0.002) correlations between the predicted average loss and the concentrations of the three insecticides both in water and suspended sediments (r between 0.87 and 0.94). Two sites in the sub-catchment were identified as posing the greatest risk to the Lourens River mainstream. It is assumed that lack of buffer strips, presence of erosion rills and high slopes are the main variables responsible for the high contamination at these sites. We conclude that this approach to predict runoff-related surface water contamination may serve as a powerful tool for risk assessment and management in South African orchard areas.     

A Neural Model for Context-dependent Sequence Learning

A novel neural network model is described that implements context-dependent learning of complex sequences. The model utilises leaky integrate-and-fire neurons to extract timing information from its input and modifies its weights using a learning rule with synaptic noise. Learning and recall phases are seamlessly integrated so that the network can gradually shift from learning to predicting its input. Experimental results using data from the real-world problem domain demonstrate that the use of context has three important benefits: (a) it prevents catastrophic interference during learning of multiple overlapping sequences, (b) it enables the completion of sequences from missing or noisy patterns, and (c) it provides a mechanism to selectively explore the space of learned sequences during free recall.     

Effect of inter-image spectral variation on land cover separability in heterogeneous areas

Supervised classification is a popular approach for deriving land cover data from satellite imagery, but the collection of suitable training data of large areas is expensive. Signature extension has been proposed as a method of limiting the number of training areas. Signature extension is particularly difficult in large, heterogeneous areas where the spectral characteristics of land cover classes are highly variable.

The quantification of spectral separability can be used to determine to what extent a set of training areas collected in a small area can be extended to classify a larger area. This article investigates the changes in spectral separability of land cover classes in an increasing geographical area. A highly heterogeneous study area, containing nine different vegetation biomes, was chosen. Separability analyses were carried out on four Landsat-8 scenes that were sequentially mosaicked. The effect of multi-seasonal imagery on separability was also investigated. The results show that the mean spectral separability did not change when the geographical area was increased. We conclude that supervised classification with a small subset of training data should be possible in the chosen study area, since there is high separability between the classes. Some classes, however, require multi-temporal imagery as input.     

Feature clustering and ranking for selecting stable features from high dimensional remotely sensed data

ABSTRACT

High dimensional remote sensing data sets typically contain redundancy amongst the features. Traditional approaches to feature selection are prone to instability and selection of sub-optimal features in these circumstances. They can also be computationally expensive, especially when dealing with very large remote sensing data sets. This article presents an efficient, deterministic feature ranking method that is robust to redundancy. Affinity propagation is used to group correlated features into clusters. A relevance criterion is evaluated for each feature. Clusters are then ranked based on the median of the relevance values of their constituent features. The most relevant individual features can then be selected automatically from the best clusters. Other criteria, such as computation time or measurement cost, can optionally be considered interactively when making this selection. The proposed feature selection method is compared to competing filter approach methods on a number of remote sensing data sets containing feature redundancy. Mutual information and naive Bayes relevance criteria were evaluated in conjunction with the feature selection methods. Using the proposed method it was shown that the stability of selected features improved under different data samplings, while similar or better classification accuracies were achieved compared to competing methods.     

Radiometric homogenisation of aerial images by calibrating with satellite data

The use of very high resolution (VHR) aerial imagery for quantitative remote sensing has been limited by unwanted radiometric variation over temporal and spatial extents. In this paper we propose a simple yet effective technique for the radiometric homogenisation of the digital numbers of aerial images. The technique requires a collocated and concurrent, well-calibrated satellite image as surface reflectance reference to which the aerial images are calibrated. The bands of the reference satellite sensor should be spectrally similar to those of the aerial sensor. Using radiative transfer theory, we show that a spatially varying local linear model can be used to approximate the relationship between the surface reflectance of the reference image and the digital numbers of the aerial images. The model parameters for each satellite pixel location are estimated using least squares regression inside a small sliding window. The technique was applied to a set of aerial images captured over multiple days with an Intergraph Digital Mapping Camera (DMC) system. A near-concurrent Moderate Resolution Imaging Spectroradiometer (MODIS) nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance image was used as the reflectance reference dataset. The resulting DMC mosaic was compared to a near-concurrent Satellite Pour l’Observation de la Terre (SPOT) 5 reflectance image of a portion of the same area, omitting the blue channel from the DMC mosaic due to its absence in the SPOT 5 data. The mean absolute reflectance difference was found to be 3.43% and the mean coefficient of determination (R²) over the bands was 0.84. The technique allows the production of seamless mosaics corrected for coarse scale atmospheric and BRDF effects and does not require the manual acquisition (or provision) of ground reflectance references. The accuracy of corrections is limited by the resolution of the reference image, which is generally significantly coarser than VHR imagery. The method cannot correct for small scale BRDF or other variations not captured at the reference resolution. Nevertheless, results show a significant improvement in homogeneity and correlation with SPOT 5 reflectance.