A robust and reliable method for detecting signals of interest in multiexponential decays

The concept of rejecting the null hypothesis for definitively detecting a signal was extended to relaxation spectrum space for multiexponential reconstruction. The novel test was applied to the problem of detecting the myelin signal, which is believed to have a time constant below 40ms, in T2 decays from MRI's of the human brain. It was demonstrated that the test allowed the detection of a signal in a relaxation spectrum using only the information in the data, thus avoiding any potentially unreliable prior information. The test was implemented both explicitly and implicitly for simulated T2 measurements. For the explicit implementation, the null hypothesis was that a relaxation spectrum existed that had no signal below 40ms and that was consistent with the T2 decay. The confidence level by which the null hypothesis could be rejected gave the confidence level that there was signal below the 40ms time constant. The explicit implementation assessed the test's performance with and without prior information where the prior information was the nonnegative relaxation spectrum assumption. The test was also implemented implicitly with a data conserving multiexponential reconstruction algorithm that used left invertible matrices and that has been published previously. The implicit and explicit implementations demonstrated similar characteristics in detecting the myelin signal in both the simulated and experimental T2 decays, providing additional evidence to support the close link between the two tests. [Full abstract in paper]Comment: 23 pages with 8 figure

Symmetric vs asymmetric protection levels in SDC methods for tabular data

The final publication is available at link.springer.comProtection levels on sensitive cells—which are key parameters of any statistical disclosure control method for tabular data—are related to the difficulty of any attacker to recompute a good estimation of the true cell values. Those protection levels are two numbers (one for the lower protection, the other for the upper protection) imposing a safety interval around the cell value, that is, no attacker should be able to recompute an estimate within such safety interval. In the symmetric case the lower and upper protection levels are equal; otherwise they are referred as asymmetric protection levels. In this work we empirically study the effect of symmetry in protection levels for three protection methods: cell suppression problem (CSP), controlled tabular adjustment (CTA), and interval protection (IP). Since CSP and CTA are mixed integer linear optimization problems, it is seen that the symmetry (or not) of protection levels affect to the CPU time needed to compute a solution. For IP, a linear optimization problem, it is observed that the symmetry heavily affects to the quality of the solution provided rather than to the solution time.Peer ReviewedPostprint (author's final draft

Feasibility and dominance rules in the electromagnetism-like algorithm for constrained global optimization

This paper presents the use of a constraint-handling technique, known as feasibility and dominance rules, in a electromagnetismlike (ELM) mechanism for solving constrained global optimization problems. Since the original ELM algorithm is specifically designed for solving bound constrained problems, only the inequality and equality constraints violation together with the objective function value are used to select points and to progress towards feasibility and optimality. Numerical experiments are presented, including a comparison with other methods recently reported in the literature

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

<p>Abstract</p> <p>Background</p> <p>The binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC.</p> <p>Results</p> <p>We have developed the Regularized Thermodynamic Average, or RTA, method for predicting the affinities of peptides binding to class II MHC. RTA accounts for all possible peptide binding conformations using a thermodynamic average and includes a parameter constraint for regularization to improve accuracy on novel data. RTA was shown to achieve higher accuracy, as measured by AUC, than SMM-align on the same data for all 17 MHC allotypes examined. RTA also gave the highest accuracy on all but three allotypes when compared with results from 9 different prediction methods applied to the same data. In addition, the method correctly predicted the peptide binding register of 17 out of 18 peptide-MHC complexes. Finally, we found that suboptimal peptide binding registers, which are often ignored in other prediction methods, made significant contributions of at least 50% of the total binding energy for approximately 20% of the peptides.</p> <p>Conclusions</p> <p>The RTA method accurately predicts peptide binding affinities to class II MHC and accounts for multiple peptide binding registers while reducing overfitting through regularization. The method has potential applications in vaccine design and in understanding autoimmune disorders. A web server implementing the RTA prediction method is available at <url>http://bordnerlab.org/RTA/</url>.</p

MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes

abstract: Background The binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable. Results We fit MultiRTA models for both HLA-DR and HLA-DP using large experimental binding data sets. The performance in predicting binding affinities for novel MHC allotypes, not in the training set, was tested in two different ways. First, we performed leave-one-allele-out cross-validation, in which predictions are made for one allotype using a model fit to binding data for the remaining MHC allotypes. Comparison of the HLA-DR results with those of two other prediction methods applied to the same data sets showed that MultiRTA achieved performance comparable to NetMHCIIpan and better than the earlier TEPITOPE method. We also directly tested model transferability by making leave-one-allele-out predictions for additional experimentally characterized sets of overlapping peptide epitopes binding to multiple MHC allotypes. In addition, we determined the applicability of prediction methods like MultiRTA to other MHC allotypes by examining the degree of MHC variation accounted for in the training set. An examination of predictions for the promiscuous binding CLIP peptide revealed variations in binding affinity among alleles as well as potentially distinct binding registers for HLA-DR and HLA-DP. Finally, we analyzed the optimal MultiRTA parameters to discover the most important peptide residues for promiscuous and allele-specific binding to HLA-DR and HLA-DP allotypes. Conclusions The MultiRTA method yields competitive performance but with a significantly simpler and physically interpretable model compared with previous prediction methods. A MultiRTA prediction webserver is available at http://bordnerlab.org/MultiRTA.The electronic version of this article is the complete one and can be found online at: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-48

Design of a Pilot SOFC System for the Combined Production of Hydrogen and Electricity under Refueling Station Requirements

The objective of the current work is to support the design of a pilot hydrogen and electricity producing plant that uses natural gas (or biomethane) as raw material, as a transition option towards a 100% renewable transportation system. The plant, with a solid oxide fuel cell (SOFC) as principal technology, is intended to be the main unit of an electric vehicle station. The refueling station has to work at different operation periods characterized by the hydrogen demand and the electricity needed for supply and self-consumption. The same set of heat exchangers has to satisfy the heating and cooling needs of the different operation periods. In order to optimize the operating variables of the pilot plant and to provide the best heat exchanger network, the applied methodology follows a systematic procedure for multi-objective, i.e. maximum plant efficiency and minimum number of heat exchanger matches, and multi-period optimization. The solving strategy combines process flow modeling in steady state, superstructure-based mathematical programming and the use of an evolutionary-based algorithm for optimization. The results show that the plant can reach a daily weighted efficiency exceeding 60%, up to 80% when considering heat utilization