Increased potency of a novel complement factor 5a receptor antagonist in a rat model of inflammatory bowel disease

We have previously shown that complement factor 5a(C5a) plays a role in the pathogenesis of 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis in rats by using the selective, orally active C5a antagonist AcF-[OP(D-Cha) WR]. This study tested the efficacy and potency of a new C5a antagonist, hydrocinnamate (HC)-[OP(D-Cha) WR], which has limited intestinal lumenal metabolism, in this model of colitis. Analogs of AcF-[OP(D-Cha) WR] were examined for their susceptibility to alimentary metabolism in the rat using intestinal mucosal washings. One metabolically stable analog, HC-[OP(D-Cha)WR], was then evaluated pharmacokinetically and investigated at a range of doses (0.03 - 10 mg/kg/ day p.o.) in the 8-day rat TNBS- colitis model, against the comparator drug AcF-[OP(D-Cha) WR]. Using various amino acid substitutions, it was determined that the AcF moiety of AcF-[OP(D-Cha) WR] was responsible for the metabolic instability of the compound in intestinal mucosal washings. The analog HC-[OP( D-Cha) WR], equiactive in vitro to AcF-[OP(D-Cha) WR], was resistant to intestinal metabolism, but it displayed similar oral bioavailability to AcF-[OP(D-Cha) WR]. However, in the rat TNBS- colitis model, HC-[OP(D-Cha) WR] was effective at reducing mortality, colon edema, colon macroscopic scores, and increasing food consumption and body weights, at 10- to 30- fold lower oral doses than AcF-[OP( D-Cha) WR]. These studies suggest that resistance to intestinal metabolism by HC-[OP(D-Cha) WR] may result in increased local concentrations of the drug in the colon, thus affording efficacy with markedly lower oral doses than AcF-[OP(D-Cha) WR] against TNBS-colitis. This large increase in potency and high efficacy of this compound makes it a potential candidate for clinical development against intestinal diseases such as inflammatory bowel disease.

Binge drinking in university students: A test of the cognitive model

The aim of this study was to test the cognitive model [Addict. Behav. 29 (2004) 159] of binge drinking in university students. In Study 1, 202 participants completed the Drinking Expectancy Questionnaire (DEQ), the Drinking Refusal Self-Efficacy Questionnaire (DRSEQ), and the Khavari Alcohol Test (KAT). The results showed that both alcohol expectancies (AEs) and drinking refusal self-efficacy (DRSE) are needed to discriminate between binge, social, and heavy drinkers. In general, binge drinkers tend to have higher AEs than social drinkers, and have slightly lower DRSE. However, young social and binge drinkers can only be discriminated on the basis of their AEs. One hundred and fourteen students were recruited for the second study, to predict which individuals would engage in binge drinking during a 4-week self-monitoring period. Over 80% of predicted binge drinkers binged at least once during the monitoring period. These two studies confirmed the cognitive model of binge drinking, and thus, hold implications for the prevention of binge drinking among adolescents and young adults. (C) 2004 Elsevier Ltd. All rights reserved.

The complexity of drinking: Interactions between the cognitive and behavioural determinants of alcohol consumption

This study expanded the earlier work conducted by this laboratory ( Hasking, P.A. and Oei, T.P.S. (2002a) . The differential role of alcohol expectancies, drinking refusal self-efficacy and coping resources in predicting alcohol consumption in community and clinical samples. Addiction Research and Theory , 10 , 465-494), by examining the independent and interactive effects of avoidant coping strategies, positive and negative expectancies and self-efficacy, in predicting volume and frequency of alcohol consumption in a sample of community drinkers. Differential relationships were found between the variables when predicting the two consumption measures. Specifically, while self-efficacy, seeking social support for emotional reasons and using drugs or alcohol to cope were independently related to both volume and frequency of drinking, complex interactions with positive and negative alcohol expectancies were also found. These interactions are discussed in terms of the cognitive and behavioural mechanisms thought to underlie drinking behaviour.

Estimation of water retention curve from mercury intrusion porosimetry and van Genuchten model

The water retention curve (WRC) is a hydraulic characteristic of concrete required for advanced modeling of water (and thus solute) transport in variably saturated, heterogeneous concrete. Unfortunately, determination by a direct experimental method (for example, measuring equilibrium moisture levels of large samples stored in constant humidity cells) is a lengthy process, taking over 2 years for large samples. A surrogate approach is presented in which the WRC is conveniently estimated from mercury intrusion porosimetry (MIP) and validated by water sorption isotherms: The well-known Barrett, Joyner and Halenda (BJH) method of estimating the pore size distribution (PSD) from the water sorption isotherm is shown to complement the PSD derived from conventional MIP. This provides a basis for predicting the complete WRC from MIP data alone. The van Genuchten equation is used to model the combined water sorption and MIP results. It is a convenient tool for describing water retention characteristics over the full moisture content range. The van Genuchten parameter estimation based solely on MIP is shown to give a satisfactory approximation to the WRC, with a simple restriction on one. of the parameters.

Predicting functional capacity outcome 12 months after hospitalized injury

Background: There is a recognized need to move from mortality to morbidity outcome predictions following traumatic injury. However, there are few morbidity outcome prediction scoring methods and these fail to incorporate important comorbidities or cofactors. This study aims to develop and evaluate a method that includes such variables. Methods: This was a consecutive case series registered in the Queensland Trauma Registry that consented to a prospective 12-month telephone conducted follow-up study. A multivariable statistical model was developed relating Trauma Registry data to trichotomized 12-month post-injury outcome (categories: no limitations, minor limitations and major limitations). Cross-validation techniques using successive single hold-out samples were then conducted to evaluate the model's predictive capabilities. Results: In total, 619 participated, with 337 (54%) experiencing no limitations, 101 (16%) experiencing minor limitations and 181 (29%) experiencing major limitations 12 months after injury. The final parsimonious multivariable statistical model included whether the injury was in the lower extremity body region, injury severity, age, length of hospital stay, pulse at admission and whether the participant was admitted to an intensive care unit. This model explained 21% of the variability in post-injury outcome. Predictively, 64% of those with no limitations, 18% of those with minor limitations and 37% of those with major limitations were correctly identified. Conclusion: Although carefully developed, this statistical model lacks the predictive power necessary for its use as a basis of a useful prognostic tool. Further research is required to identify variables other than those routinely used in the Trauma Registry to develop a model with the necessary predictive utility.

Application of edge-to-edge matching model to understand the in-plane texture of TiSi2 (C49) thin films on (001)(Si) surface

The edge-to-edge matching model, which was originally developed for predicting crystallographic features in diffusional phase transformations in solids, has been used to understand the formation of in-plane textures in TiSi2 (C49) thin films on Si single crystal (001)si surface. The model predicts all the four previously reported orientation relationships between C49 and Si substrate based on the actual atom matching across the interface and the basic crystallographic data only. The model has strong potential to be used to develop new thin film materials. (c) 2006 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Extreme learning machine for predicting HLA-peptide binding

Machine learning techniques have been recognized as powerful tools for learning from data. One of the most popular learning techniques, the Back-Propagation (BP) Artificial Neural Networks, can be used as a computer model to predict peptides binding to the Human Leukocyte Antigens (HLA). The major advantage of computational screening is that it reduces the number of wet-lab experiments that need to be performed, significantly reducing the cost and time. A recently developed method, Extreme Learning Machine (ELM), which has superior properties over BP has been investigated to accomplish such tasks. In our work, we found that the ELM is as good as, if not better than, the BP in term of time complexity, accuracy deviations across experiments, and most importantly - prevention from over-fitting for prediction of peptide binding to HLA.

Polyethism and comparability of termite choice assays in a model system using Microcerotermes turneri (Termitidae : Termitinae): Implications for standardised testing techniques

Traditional measures of termite food preference assess consequences of foraging behavior such as wood consumption, aggregation and/or termite survivorship. Although studies have been done to investigate the specifics of foraging behavior this is not generally integrated into choice assay experiments. Here choice assays were conducted with small isolated (orphaned) groups of workers and compared with choice assays involving foragers from whole nests (non-orphaned) in the laboratory. Aggregation to two different wood types was used as a measure of preference. Specific worker caste and instars participating in initial exploration were compared between assay methods, with samples of termites taken from nest carton material and sites where termites were feeding. Aggregation results differ between choice assay techniques. Castes and instars responsible for initial exploration, as determined in whole nest trials, were not commonly found exploring in isolated group trials, nor were they numerous in termites taken from active feeding sites. Consequently the use of small groups of M. turneri worker termites extracted from active feeding sites may not be appropriate for use in choice assays.

Predicting population dynamics of weed biological control agents: science or gazing into crystal balls?

Various factors can influence the population dynamics of phytophages post introduction, of which climate is fundamental. Here we present an approach, using a mechanistic modelling package (CLIMEX), that at least enables one to make predictions of likely dynamics based on climate alone. As biological control programs will have minimal funding for basic work (particularly on population dynamics), we show how predictions can be made using a species geographical distribution, relative abundance across its range, seasonal phenology and laboratory rearing data. Many of these data sets are more likely to be available than long-term population data, and some can be incorporated into the exploratory phase of a biocontrol program. Although models are likely to be more robust the more information is available, useful models can be developed using information on species distribution alone. The fitted model estimates a species average response to climate, and can be used to predict likely geographical distribution if introduced, where the agent is likely to be more abundant (i.e. good locations) and more importantly for interpretation of release success, the likely variation in abundance over time due to intra- and inter-year climate variability. The latter will be useful in predicting both the seasonal and long-term impacts of the potential biocontrol agent on the target weed. We believe this tool may not only aid in the agent selection process, but also in the design of release strategies, and for interpretation of post-introduction dynamics and impacts. More importantly we are making testable predictions. If biological control is to become more of a science making and testing such hypothesis will be a key component.

Predicting the solvent accessibility of transmembrane residues from protein sequence

In this study, we propose a novel method to predict the solvent accessible surface areas of transmembrane residues. For both transmembrane alpha-helix and beta-barrel residues, the correlation coefficients between the predicted and observed accessible surface areas are around 0.65. On the basis of predicted accessible surface areas, residues exposed to the lipid environment or buried inside a protein can be identified by using certain cutoff thresholds. We have extensively examined our approach based on different definitions of accessible surface areas and a variety of sets of control parameters. Given that experimentally determining the structures of membrane proteins is very difficult and membrane proteins are actually abundant in nature, our approach is useful for theoretically modeling membrane protein tertiary structures, particularly for modeling the assembly of transmembrane domains. This approach can be used to annotate the membrane proteins in proteomes to provide extra structural and functional information.

Investigation of a model of weight restricting behaviour amongst adolescent girls

Objective: This study examined the validity of a model predicting weight restricting behaviour both cross-sectionally and longitudinally. Method: Participants comprised 1207 girls aged from 12 to 14 years. The girls completed self-report questionnaires at three time points over 1-year intervals. Results: The cross-sectional results suggested that weight preoccupation and body dissatisfaction directly predicted weight restricting behaviour. In addition, upset induced by teasing, depressive symptoms, BMI and negative attributional style demonstrated indirect effects on weight restricting behaviour through their effects on body dissatisfaction and/or weight preoccupation. Longitudinally however, only weight restricting behaviour and body dissatisfaction were significant in the prediction of weight restricting behaviour. Discussion: The implications of the results are discussed, together with suggestions for future research. Copyright (c) 2006 John Wiley & Sons, Ltd and Eating Disorders Association.

Measuring and predicting the dynamics of linear monodisperse entangled polymers in rapid flow through an abrupt contraction. A small angle neutron scattering study

Small-angle neutron scattering measurements on a series of monodisperse linear entangled polystyrene melts in nonlinear flow through an abrupt 4:1 contraction have been made. Clear signatures of melt deformation and subsequent relaxation can be observed in the scattering patterns, which were taken along the centerline. These data are compared with the predictions of a recently derived molecular theory. Two levels of molecular theory are used: a detailed equation describing the evolution of molecular structure over all length scales relevant to the scattering data and a simplified version of the model, which is suitable for finite element computations. The velocity field for the complex melt flow is computed using the simplified model and scattering predictions are made by feeding these flow histories into the detailed model. The modeling quantitatively captures the full scattering intensity patterns over a broad range of data with independent variation of position within the contraction geometry, bulk flow rate and melt molecular weight. The study provides a strong, quantitative validation of current theoretical ideas concerning the microscopic dynamics of entangled polymers which builds upon existing comparisons with nonlinear mechanical stress data. Furthermore, we are able to confirm the appreciable length scale dependence of relaxation in polymer melts and highlight some wider implications of this phenomenon.

Energy consumption, economic growth and prices: A reassessment using panel VECM for developed and developing countries

This paper reinvestigates the energy consumption-GDP growth nexus in a panel error correction model using data on 20 net energy importers and exporters from 1971 to 2002. Among the energy exporters, there was bidirectional causality between economic growth and energy consumption in the developed countries in both the short and long run, while in the developing countries energy consumption stimulates growth only in the short run. The former result is also found for energy importers and the latter result exists only for the developed countries within this category. In addition, compared to the developing countries, the developed countries' elasticity response in terms of economic growth from an increase in energy consumption is larger although its income elasticity is lower and less than unitary. Lastly. the implications for energy policy calling for a more holistic approach are discussed. (c) 2006 Elsevier Ltd. All rights reserved.

Predicting structural disruption of proteins caused by crossover

We present a machine learning model that predicts a structural disruption score from a protein’s primary structure. SCHEMA was introduced by Frances Arnold and colleagues as a method for determining putative recombination sites of a protein on the basis of the full (PDB) description of its structure. The present method provides an alternative to SCHEMA that is able to determine the same score from sequence data only. Circumventing the need for resolving the full structure enables the exploration of yet unresolved and even hypothetical sequences for protein design efforts. Deriving the SCHEMA score from a primary structure is achieved using a two step approach: first predicting a secondary structure from the sequence and then predicting the SCHEMA score from the predicted secondary structure. The correlation coefficient for the prediction is 0.88 and indicates the feasibility of replacing SCHEMA with little loss of precision. ©2005 IEEE

Predicting peroxisomal proteins

PTS1 proteins are peroxisomal matrix proteins that have a well conserved targeting motif at the C-terminal end. However, this motif is present in many non peroxisomal proteins as well, thus predicting peroxisomal proteins involves differentiating fake PTS1 signals from actual ones. In this paper we report on the development of an SVM classifier with a separately trained logistic output function. The model uses an input window containing 12 consecutive residues at the C-terminus and the amino acid composition of the full sequence. The final model gives a Matthews Correlation Coefficient of 0.77, representing an increase of 54% compared with the well-known PeroxiP predictor. We test the model by applying it to several proteomes of eukaryotes for which there is no evidence of a peroxisome, producing a false positive rate of 0.088%.