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.

Chronic absence of baroreceptor inputs prevents training-induced cardiovascular adjustments in normotensive and spontaneously hypertensive rats

We investigate whether arterial baroreceptors mediate the training-induced blood pressure fall and resting bradycardia in hypertensive (SHR) and normotensive rats (WKY). Male SHR and WKY rats, submitted to sino-aortic denervation (SAD) or sham surgery (SHAM group), were allocated to training (T; 55% of maximal exercise capacity) or sedentary (S) protocols for 3 months. Rats were instrumented with arterial and venous catheters for haemodynamic measurements at rest (power spectral analysis) and baroreceptor testing. Kidney and skeletal muscles were processed for morphometric analysis of arterioles. Elevated mean arterial pressure (MAP) and heart rate (HR) in SHAM SHRS were accompanied by increased sympathetic variability and arteriolar wall/lumen ratio [+3.4-fold on low-frequency (LF) power and +70%, respectively, versus WKYS, P < 0.05]. Training caused significant HR (similar to 9% in WKY and SHR) and MAP reductions (-8% in the SHR), simultaneously with improvement of baroreceptor reflex control of HR (SHR and WKY), LF reduction (with a positive correlation between LF power and MAP levels in the SHR) and normalization of wall/lumen ratio of the skeletal muscle arterioles (SHR only). In contrast, SAD increased pressure variability in both strains of rats, causing reductions in MAP (-13%) and arteriolar wall/lumen ratio (-35%) only in the SHRS. Training effects were completely blocked by SAD in both strains; in addition, after SAD the resting MAP and HR and the wall/lumen ratio of skeletal muscle arterioles were higher in SHRT versus SHRS and similar to those of SHAM SHRS. The lack of training-induced effects in the chronic absence of baroreceptor inputs strongly suggests that baroreceptor signalling plays a decisive role in driving beneficial training-induced cardiovascular adjustments.

Trigeminal pain and quantitative sensory testing in painful peripheral diabetic neuropathy

Objective: To evaluate patients with Diabetes Mellitus type 2 and painful peripheral neuropathy in order to investigate oral complaints and facial somatosensory findings. Research design and methods: Case-control study; 29 patients (12 women, mean age 57.86 yo) with Diabetes Mellitus type 2 and 31 age-gender-matched controls were evaluated with a standardized protocol for general characteristics, orofacial pain, research diagnostic criteria for temporomandibular disorders, visual analogue scale and McGill Pain questionnaire, and a systematic protocol of quantitative sensory testing for bilateral facial sensitivity at the areas innervated by the trigeminal branches, which included the thermal detection by ThermoSensi 2, tactile evaluation with vonFrey filaments, and superficial pain thresholds with a superficial algometer (Micromar). Statistical analysis was performed with Wilcoxon, chi-square, confidence intervals and Spearman (p < 0.05). Results: Orofacial pain was reported by 55.2% of patients, and the most common descriptor was fatigue (50%); 17.2% had burning mouth. Myofascial temporomandibular disorders were diagnosed in 9(31%) patients. The study group showed higher sensory thresholds of pain at the right maxillary branch (p = 0.017) but sensorial differences were not associated with pain (p = 0.608). Glycemia and HbA(1c) were positively correlated with the quantitative sensory testing results of pain (p < 0.05) and cold (p = 0.044) perceptions. Higher pain thresholds were correlated with higher glycemia and glycated hemoglobin (p = 0.027 and p = 0.026). Conclusions: There was a high prevalence of orofacial pain and burning mouth was the most common complaint. The association of loss of pain sensation and higher glycemia and glycated hemoglobin can be of clinical use for the follow-up of DM complications. (C) 2010 Elsevier Ltd. All rights reserved.

A framework for specification-based testing

Test templates and a test template framework are introduced as useful concepts in specification-based testing. The framework can be defined using any model-based specification notation and used to derive tests from model-based specifications-in this paper, it is demonstrated using the Z notation. The framework formally defines test data sets and their relation to the operations in a specification and to other test data sets, providing structure to the testing process. Flexibility is preserved, so that many testing strategies can be used. Important application areas of the framework are discussed, including refinement of test data, regression testing, and test oracles.

Strategies for genetic model specification in the screening of genome-wide meta-analysis signals for further replication

Background Meta-analysis is increasingly being employed as a screening procedure in large-scale association studies to select promising variants for follow-up studies. However, standard methods for meta-analysis require the assumption of an underlying genetic model, which is typically unknown a priori. This drawback can introduce model misspecifications, causing power to be suboptimal, or the evaluation of multiple genetic models, which augments the number of false-positive associations, ultimately leading to waste of resources with fruitless replication studies. We used simulated meta-analyses of large genetic association studies to investigate naive strategies of genetic model specification to optimize screenings of genome-wide meta-analysis signals for further replication. Methods Different methods, meta-analytical models and strategies were compared in terms of power and type-I error. Simulations were carried out for a binary trait in a wide range of true genetic models, genome-wide thresholds, minor allele frequencies (MAFs), odds ratios and between-study heterogeneity (tau(2)). Results Among the investigated strategies, a simple Bonferroni-corrected approach that fits both multiplicative and recessive models was found to be optimal in most examined scenarios, reducing the likelihood of false discoveries and enhancing power in scenarios with small MAFs either in the presence or in absence of heterogeneity. Nonetheless, this strategy is sensitive to tau(2) whenever the susceptibility allele is common (MAF epsilon 30%), resulting in an increased number of false-positive associations compared with an analysis that considers only the multiplicative model. Conclusion Invoking a simple Bonferroni adjustment and testing for both multiplicative and recessive models is fast and an optimal strategy in large meta-analysis-based screenings. However, care must be taken when examined variants are common, where specification of a multiplicative model alone may be preferable.

Ramp and constant power trials produce equivalent critical power estimates

The standard critical power test protocol on the cycle prescribes a series of trials to exhaustion, each at a different but constant power setting. Recently the protocol has been modified and applied to a series of trials to exhaustion each at a different ramp incremental rate. This study was undertaken to compare critical power and anaerobic work capacity estimates in the same group of subjects when derived from the two protocols. Ten male subjects of mixed athletic ability cycled to exhaustion on eight occasions in randomized order over a 3-wk period. Four trials were performed at differing constant power settings and four trials on differing ramp incremental rates. Both critical power and anaerobic work capacity were estimated for each subject by curve fitting of the ramp model and of three versions of the constant power model. After adjusting for inter-subject variability, no significant differences were detected between critical power estimates or between anaerobic work capacity estimates from any model formulation or from the two protocols. It is concluded that both the ramp and constant power protocols produce equivalent estimates for critical power and anaerobic work capacity.

Laboratory wear testing of polyurethane elastomers

A dry sand-rubber wheel abrasion test was used to investigate the wear behaviour of polyurethanes. The dry sand-rubber wheel abrasion test (DSRW test) is an approved ASTM test designed primarily for testing metals, therefore, in this study the set of test conditions was optimized for use with polyurethane elastomers. The wear performance of polyurethanes was assessed for the range of Shore hardness 85A to 65D, and a correlation was identified between the wear rate and the sample hardness. Polyurethane elastomers can be separated into three classes according to their hardness and wear performance, and each class shows a different dependence on the specimen temperature. This work has implications for use of the DSRW test for the prediction of field performance of polyurethanes. (C) Elsevier Science S.A.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

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.

Lymphocyte proliferation testing in chromium allergic contact dermatitis

Background. Lymphocyte proliferation testing (LPT) has some advantages over patch testing to diagnose allergic contact dermatitis. It is harmless, objective and can be used in clinical situations where patch testing is not recommended. Unfortunately, significant success has only been achieved with nickel. There are few studies on chromium LPT and they were performed with different methods, leading to inconsistent results. Methods. To determine the best parameters for chromium LPT, we tested 20 patients with allergic contact dermatitis to the metal and 20 controls, using various protocols. Results. The best sensitivity and specificity ratios were achieved with 6-day cultures stimulated with a range from 7.5 x 10(-4) to 5 x 10(-3) mol/L of nonfiltered chromium chloride solutions. The sensitivity, specificity and accuracy values found within this range were 65%, 95% and 80%, respectively. Conclusion. Further investigation is necessary to achieve better sensitivity values.