Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Towards automated first-order abduction: the cut-based approach

Traditional abduction imposes as a precondition the restriction that the background information may not derive the goal data. In first-order logic such precondition is, in general, undecidable. To avoid such problem, we present a first-order cut-based abduction method, which has KE-tableaux as its underlying inference system. This inference system allows for the automation of non-analytic proofs in a tableau setting, which permits a generalization of traditional abduction that avoids the undecidable precondition problem. After demonstrating the correctness of the method, we show how this method can be dynamically iterated in a process that leads to the construction of non-analytic first-order proofs and, in some terminating cases, to refutations as well.

Automated analysis of lidocaine and its metabolite in plasma by in-tube solid-phase microextraction coupled with LC-UV for pharmacokinetic study

A sensitive, selective, and reproducible in-tube solid-phase microextraction and liquid chromatographic (in-tube SPME/LC-UV) method for determination of lidocaine and its metabolite monoethylglycinexylidide (MEGX) in human plasma has been developed, validated, and further applied to pharmacokinetic study in pregnant women with gestational diabetes mellitus (GDM) subjected to epidural anesthesia. Important factors in the optimization of in-tube SPME performance are discussed, including the draw/eject sample volume, draw/eject cycle number, draw/eject flow rate, sample pH, and influence of plasma proteins. The limits of quantification of the in-tube SPME/LC method were 50 ng/mL for both metabolite and lidocaine. The interday and intraday precision had coefficients of variation lower than 8%, and accuracy ranged from 95 to 117%. The response of the in-tube SPME/LC method for analytes was linear over a dynamic range from 50 to 5000 ng/mL, with correlation coefficients higher than 0.9976. The developed in-tube SPME/LC method was successfully used to analyze lidocaine and its metabolite in plasma samples from pregnant women with GDM subjected to epidural anesthesia for pharmacokinetic study.

The contribution of HGAL/GCET2 in immunohistological algorithms: a comparative study in 424 cases of nodal diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma can be subclassified into at least two molecular subgroups by gene expression profiling: germinal center B-cell like and activated B-cell like diffuse large B-cell lymphoma. Several immunohistological algorithms have been proposed as surrogates to gene expression profiling at the level of protein expression, but their reliability has been an issue of controversy. Furthermore, the proportion of misclassified cases of germinal center B-cell subgroup by immunohistochemistry, in all reported algorithms, is higher compared with germinal center B-cell cases defined by gene expression profiling. We analyzed 424 cases of nodal diffuse large B-cell lymphoma with the panel of markers included in the three previously described algorithms: Hans, Choi, and Tally. To test whether the sensitivity of detecting germinal center B-cell cases could be improved, the germinal center B-cell marker HGAL/GCET2 was also added to all three algorithms. Our results show that the inclusion of HGAL/GCET2 significantly increased the detection of germinal center B-cell cases in all three algorithms (P<0.001). The proportions of germinal center B-cell cases in the original algorithms were 27%, 34%, and 19% for Hans, Choi, and Tally, respectively. In the modified algorithms, with the inclusion of HGAL/GCET2, the frequencies of germinal center B-cell cases were increased to 38%, 48%, and 35%, respectively. Therefore, HGAL/GCET2 protein expression may function as a marker for germinal center B-cell type diffuse large B-cell lymphoma. Consideration should be given to the inclusion of HGAL/GCET2 analysis in algorithms to better predict the cell of origin. These findings bear further validation, from comparison to gene expression profiles and from clinical/therapeutic data. Modern Pathology (2012) 25, 1439-1445; doi: 10.1038/modpathol.2012.119; published online 29 June 2012

Comparison of 3 modes of automated weaning from mechanical ventilation: A bench study

Purpose: Automated weaning modes are available in some mechanical ventilators, but no studies compared them hitherto. We compared the performance of 3 automated modes under standard and challenging situations. Methods: We used a lung simulator to compare 3 automated modes, adaptive support ventilation (ASV), mandatory rate ventilation (MRV), and Smartcare, in 6 situations, weaning success, weaning failure, weaning success with extreme anxiety, weaning success with Cheyne-Stokes, weaning success with irregular breathing, and weaning failure with ineffective efforts. Results: The 3 modes correctly recognized the situations of weaning success and failure, even when anxiety or irregular breathing were present but incorrectly recognized weaning success with Cheyne-Stokes. MRV incorrectly recognized weaning failure with ineffective efforts. Time to pressure support (PS) stabilization was shorter for ASV (1-2 minutes for all situations) and MRV (1-7 minutes) than for Smartcare (8-78 minutes). ASV had higher rates of PS oscillations per 5 minutes (4-15), compared with Smartcare (0-1) and MRV (0-12), except when extreme anxiety was present. Conclusions: Smartcare, ASV, and MRV were equally able to recognize weaning success and failure, despite the presence of anxiety or irregular breathing but performed incorrectly in the presence of Cheyne-Stokes. PS behavior over the time differs among modes, with ASV showing larger and more frequent PS oscillations over the time. Clinical studies are needed to confirm our results. (C) 2012 Elsevier Inc. All rights reserved.

Cash balance management: A comparison between genetic algorithms and particle swarm optimization

This work aimed to apply genetic algorithms (GA) and particle swarm optimization (PSO) in cash balance management using Miller-Orr model, which consists in a stochastic model that does not define a single ideal point for cash balance, but an oscillation range between a lower bound, an ideal balance and an upper bound. Thus, this paper proposes the application of GA and PSO to minimize the Total Cost of cash maintenance, obtaining the parameter of the lower bound of the Miller-Orr model, using for this the assumptions presented in literature. Computational experiments were applied in the development and validation of the models. The results indicated that both the GA and PSO are applicable in determining the cash level from the lower limit, with best results of PSO model, which had not yet been applied in this type of problem.

New Optimization Algorithms for Structural Reliability Analysis

Solution of structural reliability problems by the First Order method require optimization algorithms to find the smallest distance between a limit state function and the origin of standard Gaussian space. The Hassofer-Lind-Rackwitz-Fiessler (HLRF) algorithm, developed specifically for this purpose, has been shown to be efficient but not robust, as it fails to converge for a significant number of problems. On the other hand, recent developments in general (augmented Lagrangian) optimization techniques have not been tested in aplication to structural reliability problems. In the present article, three new optimization algorithms for structural reliability analysis are presented. One algorithm is based on the HLRF, but uses a new differentiable merit function with Wolfe conditions to select step length in linear search. It is shown in the article that, under certain assumptions, the proposed algorithm generates a sequence that converges to the local minimizer of the problem. Two new augmented Lagrangian methods are also presented, which use quadratic penalties to solve nonlinear problems with equality constraints. Performance and robustness of the new algorithms is compared to the classic augmented Lagrangian method, to HLRF and to the improved HLRF (iHLRF) algorithms, in the solution of 25 benchmark problems from the literature. The new proposed HLRF algorithm is shown to be more robust than HLRF or iHLRF, and as efficient as the iHLRF algorithm. The two augmented Lagrangian methods proposed herein are shown to be more robust and more efficient than the classical augmented Lagrangian method.

EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer's disease

Recent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.

Comparison of Visual Acuity and Automated Perimetry Findings in Patients With Neuromyelitis Optica or Multiple Sclerosis After Single or Multiple Attacks of Optic Neuritis

Objective: To review the clinical characteristics of patients with neuromyelitis optica (NMO) and to compare their visual outcome with those of patients with optic neuritis (ON) and multiple sclerosis (MS). Methods: Thirty-three patients with NMO underwent neuro-ophthalmic evaluation, including automated perimetry along with 30 patients with MS. Visual function in both groups was compared overall and specifically for eyes after a single episode of ON. Results: Visual function and average visual field (VF) mean deviation were significantly worse in eyes of patients with NMO. After a single episode of ON, the VF was normal in only 2 of 36 eyes of patients with NMO compared to 17 of 35 eyes with MS (P < 0.001). The statistical analysis indicated that after a single episode of ON, the odds ratio for having NMO was 6.0 (confidence interval [CI]: 1.6-21.9) when VF mean deviation was worse than -20.0 dB while the odds ratio for having MS was 16.0 (CI: 3.6-68.7) when better than -3.0 dB. Conclusion: Visual outcome was significantly worse in NMO than in MS. After a single episode of ON, suspicion of NMO should be raised in the presence of severe residual VF deficit with automated perimetry and lowered in the case of complete VF recovery.

Structure-function Relationships Using the Cirrus Spectral Domain Optical Coherence Tomograph and Standard Automated Perimetry

Purpose: To evaluate the relationship between glaucomatous structural damage assessed by the Cirrus Spectral Domain OCT (SDOCT) and functional loss as measured by standard automated perimetry (SAP). Methods: Four hundred twenty-two eyes (78 healthy, 210 suspects, 134 glaucomatous) of 250 patients were recruited from the longitudinal Diagnostic Innovations in Glaucoma Study and from the African Descent and Glaucoma Evaluation Study. All eyes underwent testing with the Cirrus SDOCT and SAP within a 6-month period. The relationship between parapapillary retinal nerve fiber layer thickness (RNFL) sectors and corresponding topographic SAP locations was evaluated using locally weighted scatterplot smoothing and regression analysis. SAP sensitivity values were evaluated using both linear as well as logarithmic scales. We also tested the fit of a model (Hood) for structure-function relationship in glaucoma. Results: Structure was significantly related to function for all but the nasal thickness sector. The relationship was strongest for superotemporal RNFL thickness and inferonasal sensitivity (R(2) = 0.314, P < 0.001). The Hood model fitted the data relatively well with 88% of the eyes inside the 95% confidence interval predicted by the model. Conclusions: RNFL thinning measured by the Cirrus SDOCT was associated with correspondent visual field loss in glaucoma.

Automated system of seedling image analysis (SVIS) and electrical conductivity to assess sun hemp seed vigor

The development of new procedures for quickly obtaining accurate information on the physiological potential of seed lots is essential for developing quality control programs for the seed industry. In this study, the effectiveness of an automated system of seedling image analysis (Seed Vigor Imaging System - SVIS) in determining the physiological potential of sun hemp seeds and its relationship with electrical conductivity tests, were evaluated. SVIS evaluations were performed three and four days after sowing and data on the vigor index and the length and uniformity of seedling growth were collected. The electrical conductivity test was made on 50 seed replicates placed in containers with 75 mL of deionised water at 25 ºC and readings were taken after 1, 2, 4, 8 and 16 hours of imbibition. Electrical conductivity measurements at 4 or 8 hours and the use of the SVIS on 3-day old seedlings can effectively detect differences in vigor between different sun hemp seed lots.

Adaptação cultural e confiabilidade para o Brasil do Automated Telephone Disease Management: resultados preliminares

OBJETIVOS: Traduzir, adaptar culturalmente para o Brasil o ATDM Satisfaction Scales e avaliar a confiabilidade da versão adaptada em adultos brasileiros com DM. MÉTODOS: Estudo metodológico, cujo processo de adaptação cultural incluiu: tradução, comitê de juízes, retrotradução, análise semântica e pré-teste. Este estudo incluiu uma amostra de 39 adultos brasileiros com DM cadastrados em um programa educativo do interior paulista. RESULTADOS: A versão adaptada do instrumento mostrou boa aceitação com fácil compreensão dos itens pelos participantes, com confiabilidade variando entre 0,30 e 0,43. CONCLUSÃO: Após a análise das propriedades psicométricas e finalização do processo de validação no País, o instrumento poderá ser utilizado por pesquisadores brasileiros, possibilitando ser comparado com outras culturas.

Reliability algorithms applied to reinforced concrete structures durability assessment

This paper addresses the analysis of probabilistic corrosion time initiation in reinforced concrete structures exposed to ions chloride penetration. Structural durability is an important criterion which must be evaluated in every type of structure, especially when these structures are constructed in aggressive atmospheres. Considering reinforced concrete members, chloride diffusion process is widely used to evaluate the durability. Therefore, at modelling this phenomenon, corrosion of reinforcements can be better estimated and prevented. These processes begin when a threshold level of chlorides concentration is reached at the steel bars of reinforcements. Despite the robustness of several models proposed in the literature, deterministic approaches fail to predict accurately the corrosion time initiation due to the inherently randomness observed in this process. In this regard, the durability can be more realistically represented using probabilistic approaches. A probabilistic analysis of ions chloride penetration is presented in this paper. The ions chloride penetration is simulated using the Fick's second law of diffusion. This law represents the chloride diffusion process, considering time dependent effects. The probability of failure is calculated using Monte Carlo simulation and the First Order Reliability Method (FORM) with a direct coupling approach. Some examples are considered in order to study these phenomena and a simplified method is proposed to determine optimal values for concrete cover.

Effects of low-salinity and high-turbidity waters one mpirical ocean colour algorithms: an example for Southwestern Atlantic waters

The spectral reflectance of the sea surface recorded using ocean colour satellite sensors has been used to estimate chlorophyll-a concentrations for decades. However, in bio-optically complex coastal waters, these estimates are compromised by the presence of several other coloured components besides chlorophyll, especially in regions affected by low-salinity waters. The present work aims to (a) describe the influence of the freshwater plume from the La Plata River on the variability of in situ remote sensing reflectance and (b) evaluate the performance of operational ocean colour chlorophyll algorithms applied to Southwestern Atlantic waters, which receive a remarkable seasonal contribution from La Plata River discharges. Data from three oceanographic cruises are used, in addition to a historical regional bio-optical dataset. Deviations found between measured and estimated concentrations of chlorophyll-a are examined in relation to surface water salinity and turbidity gradients to investigate the source of errors in satellite estimates of pigment concentrations. We observed significant seasonal variability in surface reflectance properties that are strongly driven by La Plata River plume dynamics and arise from the presence of high levels of inorganic suspended solids and coloured dissolved materials. As expected, existing operational algorithms overestimate the concentration of chlorophyll-a, especially in waters of low salinity (S<33.5) and high turbidity (Rrs(670)>0.0012 sr−1). Additionally, an updated version of the regional algorithm is presented, which clearly improves the chlorophyll estimation in those types of coastal environment. In general, the techniques presented here allow us to directly distinguish the bio-optical types of waters to be considered in algorithm studies by the ocean colour community.