Probing expert anticipation with the temporal occlusion paradigm: Experimental investigations of some methodological issues

Two experiments were conducted to examine whether the conclusions drawn regarding the timing of anticipatory information pick-up from temporal occlusion studies are influenced by whether (a) the viewing period is of variable or fixed duration and (b) the task is a laboratory-based one with simple responses or a natural one requiring a coupled, interceptive movement response. Skilled and novice tennis players either made pencil-and-paper predictions of service direction (Experiment 1) or attempted to hit return strokes (Experiment 2) to tennis serves while their vision was temporally occluded in either a traditional progressive mode (where more information was revealed in each subsequent occlusion condition) or a moving window mode (where the visual display was only available for a fixed duration with this window shifted to different phases of the service action). Conclusions regarding the timing of information pick-up were generally consistent across display mode and across task setting lending support to the veracity and generalisability of findings regarding perceptual expertise in existing laboratory-based progressive temporal occlusion studies.

Investigation of an acetate-fed denitrifying microbial community by stable isotope probing, full-cycle rRNA analysis, and fluorescent in situ hybridization-microautoradiography

The acetate-utilizing microbial consortium in a full-scale activated sludge process was investigated without prior enrichment using stable isotope probing (SIP). [C-13]acetate was used in SIP to label the DNA of the denitrifiers. The [C-13]DNA fraction that was extracted was subjected to a full-cycle rRNA analysis. The dominant 16S rRNA gene phylotypes in the C-13 library were closely related to the bacterial families Comamonadaceae and Rhodocyclaceae in the class Betaproteobacteria. Seven oligonucleotide probes for use in fluorescent in situ hybridization (FISH) were designed to specifically target these clones. Application of these probes to the sludge of a continuously fed denitrifying sequencing batch reactor (CFDSBR) operated for 16 days revealed that there was a significant positive correlation between the CFDSBR denitrification rate and the relative abundance of all probe-targeted bacteria in the CFDSBR community. FISH-microautoradiography demonstrated that the DEN581 and DEN124 probe-targeted cells that dominated the CFDSBR were capable of taking Up [C-14] acetate under anoxic conditions. Initially, DEN444 and DEN1454 probe-targeted bacteria also dominated the CFDSBR biomass, but eventually DEN581 and DEN124 probe-targeted bacteria were the dominant bacterial groups. All probe-targeted bacteria assessed in this study were denitrifiers capable of utilizing acetate as a source of carbon. The rapid increase in the number of organisms positively correlated with the immediate increase in denitrification rates observed by plant operators when acetate is used as an external source of carbon to enhance denitrification. We suggest that the impact of bacteria on activated sludge subjected to intermittent acetate supplementation should be assessed prior to the widespread use of acetate in the waste-water industry to enhance denitrification.

Putative glycogen-accumulating organisms belonging to the Alphaproteobacteria identified through rRNA-based stable isotope probing

Deterioration of enhanced biological phosphorus removal (EBPR) has been linked to the proliferation of glycogen-accumulating organisms (GAOs), but few organisms possessing the GAO metabolic phenotype have been identified. An unidentified GAO was highly enriched in a laboratory-scale bioreactor and attempts to identify this organism using conventional 16S rRNA gene cloning had failed. Therefore, rRNA-based stable isotope probing followed by full-cycle rRNA analysis was used to specifically identify the putative GAOs based on their characteristic metabolic phenotype. The study obtained sequences from a group of Alphaproteobacteria not previously shown to possess the GAO phenotype, but 90% identical by 16S rRNA gene analysis to a phylogenetic clade containing cloned sequences from putative GAOs and the isolate Defluvicoccus vanus. Fluorescence in situ hybridization (FISH) probes (DF988 and DF1020) were designed to target the new group and post-FISH chemical staining demonstrated anaerobic-aerobic cycling of polyhydroxyalkanoates, as per the GAO phenotype. The successful use of probes DF988 and DF1020 required the use of unlabelled helper probes which increased probe signal intensity up to 6.6-fold, thus highlighting the utility of helper probes in FISH. The new group constituted 33% of all Bacteria in the lab-scale bioreactor from which they were identified and were also abundant (51 and 55% of Bacteria) in two other similar bioreactors in which phosphorus removal had deteriorated. Unlike the previously identified Defluvicoccus-related organisms, the group identified in this study were also found in two full-scale treatment plants performing EBPR, suggesting that this group may be industrially relevant.

Dynamic iterative ontology learning

The fundamental failure of current approaches to ontology learning is to view it as single pipeline with one or more specific inputs and a single static output. In this paper, we present a novel approach to ontology learning which takes an iterative view of knowledge acquisition for ontologies. Our approach is founded on three open-ended resources: a set of texts, a set of learning patterns and a set of ontological triples, and the system seeks to maintain these in equilibrium. As events occur which disturb this equilibrium, actions are triggered to re-establish a balance between the resources. We present a gold standard based evaluation of the final output of the system, the intermediate output showing the iterative process and a comparison of performance using different seed input. The results are comparable to existing performance in the literature.

Probing molecular changes induced in DNA by reactive oxygen species with monoclonal antibodies

Antibodies reactive with native double stranded DNA are characteristic of the chronic inflammatory disease systemic lupus erythematosus. Native DNA is however, a poor immunogen and the mechanism of anti-DNA antibody production is incompletely understood. Modification of DNA can increase its immunogenicity and in inflammatory disease states reactive oxygen species produced from phagocytic cells have been shown to thus modify DNA. In this study, monoclonal antibodies produced spontaneously by two mice strains with lupus-like disease were used in a competition ELISA to monitor changes to DNA induced by reactive oxygen species. Different procedures for reactive oxygen species generation were found to cause distinct and characteristic changes to DNA involving modifications of base residues, the sugar-phosphate backbone and the gross conformational structure of double-stranded DNA. In view of this, it may be possible to use these antibodies further to probe DNA and infer the source and nature of the reactive oxygen species it has been exposed to, particularly in vivo.

Gaussian processes:iterative sparse approximations

In recent years there has been an increased interest in applying non-parametric methods to real-world problems. Significant research has been devoted to Gaussian processes (GPs) due to their increased flexibility when compared with parametric models. These methods use Bayesian learning, which generally leads to analytically intractable posteriors. This thesis proposes a two-step solution to construct a probabilistic approximation to the posterior. In the first step we adapt the Bayesian online learning to GPs: the final approximation to the posterior is the result of propagating the first and second moments of intermediate posteriors obtained by combining a new example with the previous approximation. The propagation of em functional forms is solved by showing the existence of a parametrisation to posterior moments that uses combinations of the kernel function at the training points, transforming the Bayesian online learning of functions into a parametric formulation. The drawback is the prohibitive quadratic scaling of the number of parameters with the size of the data, making the method inapplicable to large datasets. The second step solves the problem of the exploding parameter size and makes GPs applicable to arbitrarily large datasets. The approximation is based on a measure of distance between two GPs, the KL-divergence between GPs. This second approximation is with a constrained GP in which only a small subset of the whole training dataset is used to represent the GP. This subset is called the em Basis Vector, or BV set and the resulting GP is a sparse approximation to the true posterior. As this sparsity is based on the KL-minimisation, it is probabilistic and independent of the way the posterior approximation from the first step is obtained. We combine the sparse approximation with an extension to the Bayesian online algorithm that allows multiple iterations for each input and thus approximating a batch solution. The resulting sparse learning algorithm is a generic one: for different problems we only change the likelihood. The algorithm is applied to a variety of problems and we examine its performance both on more classical regression and classification tasks and to the data-assimilation and a simple density estimation problems.

Density evolution and power profile optimization for iterative multiuser decoders based on individually optimum multiuser detectors

Iterative multiuser joint decoding based on exact Belief Propagation (BP) is analyzed in the large system limit by means of the replica method. It is shown that performance can be improved by appropriate power assignment to the users. The optimum power assignment can be found by linear programming in most technically relevant cases. The performance of BP iterative multiuser joint decoding is compared to suboptimum approximations based on Interference Cancellation (IC). While IC receivers show a significant loss for equal-power users, they yield performance close to BP under optimum power assignment.

Probing three-way interactions in moderated multiple regression:Development and application of a slope difference test

Researchers often use 3-way interactions in moderated multiple regression analysis to test the joint effect of 3 independent variables on a dependent variable. However, further probing of significant interaction terms varies considerably and is sometimes error prone. The authors developed a significance test for slope differences in 3-way interactions and illustrate its importance for testing psychological hypotheses. Monte Carlo simulations revealed that sample size, magnitude of the slope difference, and data reliability affected test power. Application of the test to published data yielded detection of some slope differences that were undetected by alternative probing techniques and led to changes of results and conclusions. The authors conclude by discussing the test's applicability for psychological research. Copyright 2006 by the American Psychological Association.