A twin study of psychometric intelligence and efficiency of information processing

Intelligence (IQ) can be seen as the efficiency of mental processes or cognition, as can basic information processing (IP) tasks like those used in our ongoing Memory, Attention and Problem Solving (MAPS) study. Measures of IQ and IP are correlated and both have a genetic component, so we are studying how the genetic variance in IQ is related to the genetic variance in IP. We measured intelligence with five subscales of the Multidimensional Aptitude Battery (MAB). The IP tasks included four variants of choice reaction time (CRT) and a visual inspection time (IT). The influence of genetic factors on the variances in each of the IQ, IP, and IT tasks was investigated in 250 identical and nonidentical twin pairs aged 16 years. For a subset of 50 pairs we have test–retest data that allow us to estimate the stability of the measures. MX was used for a multivariate genetic analysis that addresses whether the variance in IQ and IP measures is possibly mediated by common genetic factors. Analyses that show the modeled genetic and environmental influences on these measures of cognitive efficiency will be presented and their relevance to ideas on intelligence will be discussed.

Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding

Peptides that induce and recall T-cell responses are called T-cell epitopes. T-cell epitopes may be useful in a subunit vaccine against malaria. Computer models that simulate peptide binding to MHC are useful for selecting candidate T-cell epitopes since they minimize the number of experiments required for their identification. We applied a combination of computational and immunological strategies to select candidate T-cell epitopes. A total of 86 experimental binding assays were performed in three rounds of identification of HLA-All binding peptides from the six preerythrocytic malaria antigens. Thirty-six peptides were experimentally confirmed as binders. We show that the cyclical refinement of the ANN models results in a significant improvement of the efficiency of identifying potential T-cell epitopes. (C) 2001 by Elsevier Science Inc.

A computational analysis of substrate binding strength by phosphorylase kinase and protein kinase A

Protein kinases exhibit various degrees of substrate specificity. The large number of different protein kinases in the eukaryotic proteomes makes it impractical to determine the specificity of each enzyme experimentally. To test if it were possible to discriminate potential substrates from non-substrates by simple computational techniques, we analysed the binding enthalpies of modelled enzyme-substrate complexes and attempted to correlate it with experimental enzyme kinetics measurements. The crystal structures of phosphorylase kinase and cAMP-dependent protein kinase were used to generate models of the enzyme with a series of known peptide substrates and non-substrates, and the approximate enthalpy of binding assessed following energy minimization. We show that the computed enthalpies do not correlate closely with kinetic measurements, but the method can distinguish good substrates from weak substrates and non-substrates. Copyright (C) 2002 John Wiley Sons, Ltd.

Computational immunology: The coming of age

The explosive growth in biotechnology combined with major advancesin information technology has the potential to radically transformimmunology in the postgenomics era. Not only do we now have readyaccess to vast quantities of existing data, but new data with relevanceto immunology are being accumulated at an exponential rate. Resourcesfor computational immunology include biological databases and methodsfor data extraction, comparison, analysis and interpretation. Publiclyaccessible biological databases of relevance to immunologists numberin the hundreds and are growing daily. The ability to efficientlyextract and analyse information from these databases is vital forefficient immunology research. Most importantly, a new generationof computational immunology tools enables modelling of peptide transportby the transporter associated with antigen processing (TAP), modellingof antibody binding sites, identification of allergenic motifs andmodelling of T-cell receptor serial triggering.

Computational tools for the study of allergens

Allergy is a major cause of morbidity worldwide. The number of characterized allergens and related information is increasing rapidly creating demands for advanced information storage, retrieval and analysis. Bioinformatics provides useful tools for analysing allergens and these are complementary to traditional laboratory techniques for the study of allergens. Specific applications include structural analysis of allergens, identification of B- and T-cell epitopes, assessment of allergenicity and cross-reactivity, and genome analysis. In this paper, the most important bioinformatic tools and methods with relevance to the study of allergy have been reviewed.

A comparison of the performance and biological efficiency of new knapsack sprayers and a controlled droplet application (CDA) sprayer for the control of the diamondback moth, Plutella xylostella L. (Lepidoptera : Plutellidae)

Field trials and laboratory bioassays were undertaken to compare the performance and efficacy (mortality of diamondback moth larvae) of insecticides applied to cabbages with three high volume hydraulic knapsack sprayers (NS-16, PB-20 and Selecta 12V) and a controlled droplet application (CDA) sprayer. In field experiments, the high volume knapsack sprayers (application rate 500-600 L ha(-1)) provided better spray coverage on the upper and lower surfaces of inner leaves, the upper surfaces of middle and outer leaves, and greater biological efficacy than the CDA sprayer (application rate 20similar to40 L ha(-1)). The PB-20 provided better spray coverage on the upper surface of middle leaves and both surfaces of outer leaves when compared with the Selecta 12V. However, its biological efficacy in the field was not significantly different from that of the other high volume sprayers. Increasing the application rate from 20 to 40 L ha(-1) for the CDA sprayer significantly increased droplet density but had no impact on test insect mortality. Laboratory evaluations of biological efficacy yielded higher estimates than field evaluations and there was no significant difference between the performance of the PB-20 and the CDA sprayer. Significant positive relationships were detected between insect mortality and droplet density deposited for both the PB-20 and the CDA sprayers.

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

On the extent of genetic variation for transpiration efficiency in sorghum

A glasshouse study examined 49 diverse sorghum lines for variation in transpiration efficiency. Three of the 49 lines grown were Sorghum spp, native to Australia; one was the major weed Johnson grass (Sorghum halepense), and the remaining 45 lines were cultivars of Sorghum bicolor. All plants were grown under non-limiting water and nutrient conditions using a semi-automatic pot watering system designed to facilitate accurate measurement of water use. Plants were harvested 56-58 days after sowing and dry weights of plant parts were determined. Transpiration efficiency differed significantly among cultivars. The 3 Australian native sorghums had much lower transpiration efficiency than the other 46 cultivars, which ranged from 7.7 to 6.0 g/kg. For the 46 diverse cultivars, the ratio of range in transpiration efficiency to its l.s.d. was 2.0, which was similar to that found among more adapted cultivars in a previous study. This is a significant finding as it suggests that there is likely to be little pay-off from pursuing screening of unadapted material for increased variation in transpiration efficiency. It is necessary, however, also to examine absolute levels of transpiration efficiency to determine whether increased levels have been found. The cultivar with greatest transpiration efficiency in this study (IS9710) had a value 9% greater (P < 0.05) than the accepted standard for adapted sorghum cultivars. The potential impact of such an increase in transpiration efficiency warrants continued effort to capture it. Transpiration efficiency has been related theoretically and experimentally to the degree of carbon isotope discrimination in leaf tissue in sorghum, which thus offers a relatively simple selection index. In this study, the variation in transpiration efficiency was not related simply to carbon isotope discrimination. Significant associations of transpiration efficiency with ash content and indices of photosynthetic capacity were found. However, the associations were not strong. These results suggest that a simple screening technique could not be based on any of the measures or indices analysed in this study. A better understanding of the physiological basis of the observed genetic differences in transpiration efficiency may assist in developing reliable selection indices. It was concluded that the potential value of the improvement in transpiration efficiency over the accepted standard and the degree of genetic variation found warrant further study on this subject. It was suggested that screening for genetic variation under water-limiting conditions may provide useful insights and should be pursued.

Effect of specific leaf nitrogen on radiation use efficiency and growth of sunflower

Specific leaf nitrogen (SLN, g/m(2)) is known to affect radiation use efficiency (RUE, g/MJ) in different crops, However, this association and importance have not been well established over a range of different nitrogen regimes for held-grown sunflower (Helianthus annuus L.). An experiment was conducted to investigate different combinations and rates of applied nitrogen on SLN, RUE, and growth of sunflower, A fully irrigated crop was sown on an alluvial-prairie soil (Fluventic Haplustoll) and treated with five combinations of applied nitrogen, Greater nitrogen increased biomass, grain number, and yield, but did not affect harvest index energy-corrected for oil (0.4) or canopy extinction coefficient (0.88), Decreases in biomass accumulation under low nitrogen treatments were associated,vith reductions in leaf area index (LAI) and light interception, When SLN and RUE were examined together, both were less in the anthesis to physiological maturity period, but relatively stable between bud visible and anthesis, However, the effects of canopy SLN on RUE were confounded by high SLN in the top of the canopy and the crop maintaining SLN by reducing LAI, Measurements of leaf CO2 assimilation and theoretical analyses of RUE supported that RUE was related to SLN, The major effect of nitrogen on early growth of sunflower was mediated by leaf area and the distribution of SLN in the canopy rather than direct effects of canopy SLN on RUE alone. Greater responses of RUE to SLN are more evident later in growth, and may be related to the demand of nitrogen by the grain.

Effect of radiation environment on radiation use efficiency and growth of sunflower

The level of incident radiation and the proportion of radiation that is diffuse affects radiation use efficiency (RUE) in crops, However, the degree of this effect, and its importance to growth and yield of sunflower (Helianthus annuus L.) have not been established. A field experiment was conducted to investigate the effects of radiation environment on RUE, growth, and yield of sunflower. A fully irrigated crop was sown on an alluvial-prairie soil (Fluventic Haplustoll) and was exposed to three distinct radiation environments. In two treatments, the level of incident radiation was reduced by 14 and 20% by suspending tao different types of polyethylene plastic films well above the crop. In addition to the reductions in incident radiation, the proportion of radiation that was diffuse was increased by about 14% in these treatments. Lower incident radiation and increased proportion of diffuse radiation had no effect on total biomass, phenology, leaf area, and the canopy light extinction coefficient (k = 0.89). However, yield was reduced in shaded treatments due to smaller grain size and lower harvest index. Although crop RUE measured over the entire crop cycle (1.25 g/MJ) did not differ significantly among treatments, there was a trend where RUE compensated for less intercepted incident radiation. Theoretical derivations of the response of RUE to different levels of incident radiation supported this finding. Shaded sunflower crops have the ability to produce biomass similar to unshaded crops by increasing RUE, but have lower harvest indices.

Testing market efficiency: Evidence from the NFL sports betting market

This article examines the efficiency of the National Football League (NFL) betting market. The standard ordinary least squares (OLS) regression methodology is replaced by a probit model. This circumvents potential econometric problems, and allows us to implement more sophisticated betting strategies where bets are placed only when there is a relatively high probability of success. In-sample tests indicate that probit-based betting strategies generate statistically significant profits. Whereas the profitability of a number of these betting strategies is confirmed by out-of-sample testing, there is some inconsistency among the remaining out-of-sample predictions. Our results also suggest that widely documented inefficiencies in this market tend to dissipate over time.