Gene expression in human alcoholism: Microarray analysis of frontal cortex

Background: Changes in brain gene expression are thought to be responsible for the tolerance, dependence, and neurotoxicity produced by chronic alcohol abuse, but there has been no large scale study of gene expression in human alcoholism. Methods: RNA was extracted from postmortem samples of superior frontal cortex of alcoholics and nonalcoholics. Relative levels of RNA were determined by array techniques. We used both cDNA and oligonucleotide microarrays to provide coverage of a large number of genes and to allow cross-validation for those genes represented on both types of arrays. Results: Expression levels were determined for over 4000 genes and 163 of these were found to differ by 40% or more between alcoholics and nonalcoholics. Analysis of these changes revealed a selective reprogramming of gene expression in this brain region, particularly for myelin-related genes which were downregulated in the alcoholic samples. In addition, cell cycle genes and several neuronal genes were changed in expression. Conclusions: These gene expression changes suggest a mechanism for the loss of cerebral white matter in alcoholics as well as alterations that may lead to the neurotoxic actions of ethanol.

Verifying metamodel coverage of model transformations

Since the object management group (OMG) commenced its model driven architecture (MDA) initiative, there has been considerable activity proposing and building automatic model transformation systems to help implement the MDA concept. Much less attention has been given to the need to ensure that model transformations generate the intended results. This paper explores one aspect of validation and verification for MDA: coverage of the source and/or target metamodels by a set of model transformations. The paper defines the property of metamodel coverage and some corresponding algorithms. This property helps the user assess which parts of a source (or target) metamodel are referenced by a given model transformation set. Some results are presented from a prototype implementation that is built on the eclipse modeling framework (EMF).

Splines and vector splines

The thrust of this report concerns spline theory and some of the background to spline theory and follows the development in (Wahba, 1991). We also review methods for determining hyper-parameters, such as the smoothing parameter, by Generalised Cross Validation. Splines have an advantage over Gaussian Process based procedures in that we can readily impose atmospherically sensible smoothness constraints and maintain computational efficiency. Vector splines enable us to penalise gradients of vorticity and divergence in wind fields. Two similar techniques are summarised and improvements based on robust error functions and restricted numbers of basis functions given. A final, brief discussion of the application of vector splines to the problem of scatterometer data assimilation highlights the problems of ambiguous solutions.

A generative probabilistic oriented wavelet model for texture segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.

Quantitative assessment of the pharmacotherapy of biopolar disorder and schizophrenia

The work present in this thesis was aimed at assessing the efficacy of lithium in the acute treatment of mania and for the prophylaxis of bipolar disorder, and investigating the value of plasma haloperidol concentration for predicting response to treatment in schizophrenia. The pharmacogenetics of psychotropic drugs is critically appraised to provide insights into interindividual variability in response to pharmacotherapy, In clinical trials of acute mania, a number of measures have been used to assess the severity of illness and its response to treatment. Rating instruments need to be validated in order for a clinical study to provide reliable and meaningful estimates of treatment effects, Eight symptom-rating scales were identified and critically assessed, The Mania Rating Scale (MRS) was the most commonly used for assessing treatment response, The advantage of the MRS is that there is a relatively extensive database of studies based on it and this will no doubt ensure that it remains a gold standard for the foreseeable future. Other useful rating scales are available for measuring mania but further cross-validation and validation against clinically meaningful global changes are required. A total of 658 patients from 12 trials were included in an evaluation of the efficacy of lithium in the treatment of acute mania. Treatment periods ranged from 3 to 4 weeks. Efficacy was estimated using (i) the differences in the reduction in mania severity scores, and (ii) the ratio and difference in improvement response rates. The response rate ratio for lithium against placebo was 1.95 (95% CI 1.17 to 3.23). The mean number needed to treat was 5 (95% CI 3 to 20). Patients were twice as likely to obtain remission with lithium than with chlorpromazine (rate ratio = 1.96, 95% CI 1.02 to 3.77). The mean number needed to treat (NNT) was 4 (95% CI 3 to 9). Neither carbamazepine nor valproate was more effective than lithium. The response rate ratios were 1.01 (95% CI 0.54 to 1.88) for lithium compared to carbarnazepine and 1.22 (95% CI 0.91 to 1.64) for lithium against valproate. Haloperidol was no better than lithium on the basis of improvement based on assessment of global severity. The differences in effects between lithium and risperidone were -2.79 (95% CI -4.22 to -1.36) in favour of risperidone with respect to symptom severity improvement and -0.76 (95% CI -1.11 to -0,41) on the basis of reduction in global severity of disease. Symptom and global severity was at least as well controlIed with lithium as with verapamil. Lithium caused more side-effects than placebo and verapamil, but no more than carbamazepine or valproate. A total of 554 patients from 13 trials were included in the statistical analysis of lithium's efficacy in the prophylaxis of bipolar disorder. The mean follow-up period was 5-34 months. The relapse risk ratio for lithium versus placebo was 0.47 (95% CI 0.26 to 0.86) and the NNT was 3 (95% CI 2 to 7). The relapse risk ratio for lithium versus imipramine was 0.62 (95% CI 0.46 to 0.84) and the NNT was 4 (951% Cl 3 to 7), The combination of lithium and imipramine was no more effective than lithium alone. The risk of relapse was greater with lithium alone than with the lithium-divalproate combination. A risk difference of 0.60 (95% CI 0.21 to 0.99) and an NNT of 2 (95% CI 1 to 5) were obtained. Lithium was as effective as carbamazepine. Based on individual data concerning plasma haloperidol concentration and percent improvement in psychotic symptoms, our results suggest an acceptable concentration range of 11.20-30.30 ng/mL A minimum of 2 weeks should be allowed before evaluating therapeutic response. Monitoring of drug plasma levels seems not to be necessary unless behavioural toxicity or noncompliance is suspected. Pharmacokinetics and pharmacodynamics, which are mainly determined by genetic factors, contribute to interindividual and interethnic variations in clinical response to drugs. These variations are primarily due to differences in drug metabolism. Variability in pharmacokinetics of a number of drugs is associated with oxidation polymorphism. Debrisoquine/sparteine hydroxylase (CYP2D6) and the S-mephenytoin hydroxylase (CYP2C19) are polymorphic P450 enzymes with particular importance in psychopharmacotherapy. The enzymes are responsible for the metabolism of many commonly used antipsychotic and antidepressant drugs. The incidence of poor metabolisers of debrisoquine and S-mephenytoin varies widely among populations. Ethnic variations in polymorphic isoenzymes may, at least in part, explain ethnic differences in response to pharmacotherapy of antipsychotics and antidepressant drugs.

Genotypic and environmentally derived variation in the cell wall composition of Miscanthus in relation to its use as a biomass feedstock

Fifteen Miscanthus genotypes grown in five locations across Europe were analysed to investigate the influence of genetic and environmental factors on cell wall composition. Chemometric techniques combining near infrared reflectance spectroscopy (NIRS) and conventional chemical analyses were used to construct calibration models for determination of acid detergent lignin (ADL), acid detergent fibre (ADF), and neutral detergent fibre (NDF) from sample spectra. Results generated were subsequently converted to lignin, cellulose and hemicellulose content and used to assess the genetic and environmental variation in cell wall composition of Miscanthus and to identify genotypes which display quality traits suitable for exploitation in a range of energy conversion systems. The NIRS calibration models developed were found to predict concentrations with a good degree of accuracy based on the coefficient of determination (R2), standard error of calibration (SEC), and standard error of cross-validation (SECV) values. Across all sites mean lignin, cellulose and hemicellulose values in the winter harvest ranged from 76–115 g kg-1, 412–529 g kg-1, and 235–338 g kg-1 respectively. Overall, of the 15 genotypes Miscanthus x giganteus and Miscanthus sacchariflorus contained higher lignin and cellulose concentrations in the winter harvest. The degree of observed genotypic variation in cell wall composition indicates good potential for plant breeding and matching feedstocks to be optimised to different energy conversion processes.

Relaxation procedures for an iterative MFS algorithm for the stable reconstruction of elastic fields from Cauchy data in two-dimensional isotropic linear elasticity

We investigate two numerical procedures for the Cauchy problem in linear elasticity, involving the relaxation of either the given boundary displacements (Dirichlet data) or the prescribed boundary tractions (Neumann data) on the over-specified boundary, in the alternating iterative algorithm of Kozlov et al. (1991). The two mixed direct (well-posed) problems associated with each iteration are solved using the method of fundamental solutions (MFS), in conjunction with the Tikhonov regularization method, while the optimal value of the regularization parameter is chosen via the generalized cross-validation (GCV) criterion. An efficient regularizing stopping criterion which ceases the iterative procedure at the point where the accumulation of noise becomes dominant and the errors in predicting the exact solutions increase, is also presented. The MFS-based iterative algorithms with relaxation are tested for Cauchy problems for isotropic linear elastic materials in various geometries to confirm the numerical convergence, stability, accuracy and computational efficiency of the proposed method.

Predicting class II MHC-Peptide binding:a kernel based approach using similarity scores

Background - Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel. Results - The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP [1], MCHBN [2], and MHCBench [3]. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 AROC for the MHCBench data sets (up from 0.756), and an average of 0.96 AROC for multiple alleles of the MHCPEP database. Conclusion - The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems.

Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties

Subunit vaccine discovery is an accepted clinical priority. The empirical approach is time- and labor-consuming and can often end in failure. Rational information-driven approaches can overcome these limitations in a fast and efficient manner. However, informatics solutions require reliable algorithms for antigen identification. All known algorithms use sequence similarity to identify antigens. However, antigenicity may be encoded subtly in a sequence and may not be directly identifiable by sequence alignment. We propose a new alignment-independent method for antigen recognition based on the principal chemical properties of protein amino acid sequences. The method is tested by cross-validation on a training set of bacterial antigens and external validation on a test set of known antigens. The prediction accuracy is 83% for the cross-validation and 80% for the external test set. Our approach is accurate and robust, and provides a potent tool for the in silico discovery of medically relevant subunit vaccines.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.

Modeling the peptide-T cell receptor interaction by the comparative molecular similarity indices analysis-soft independent modeling of class analogy technique

A set of 38 epitopes and 183 non-epitopes, which bind to alleles of the HLA-A3 supertype, was subjected to a combination of comparative molecular similarity indices analysis (CoMSIA) and soft independent modeling of class analogy (SIMCA). During the process of T cell recognition, T cell receptors (TCR) interact with the central section of the bound nonamer peptide; thus only positions 4−8 were considered in the study. The derived model distinguished 82% of the epitopes and 73% of the non-epitopes after cross-validation in five groups. The overall preference from the model is for polar amino acids with high electron density and the ability to form hydrogen bonds. These so-called “aggressive” amino acids are flanked by small-sized residues, which enable such residues to protrude from the binding cleft and take an active role in TCR-mediated T cell recognition. Combinations of “aggressive” and “passive” amino acids in the middle part of epitopes constitute a putative TCR binding motif

AllerTOP v.2 - a server for in silico prediction of allergens

Allergy is an overreaction by the immune system to a previously encountered, ordinarily harmless substance - typically proteins - resulting in skin rash, swelling of mucous membranes, sneezing or wheezing, or other abnormal conditions. The use of modified proteins is increasingly widespread: their presence in food, commercial products, such as washing powder, and medical therapeutics and diagnostics, makes predicting and identifying potential allergens a crucial societal issue. The prediction of allergens has been explored widely using bioinformatics, with many tools being developed in the last decade; many of these are freely available online. Here, we report a set of novel models for allergen prediction utilizing amino acid E-descriptors, auto- and cross-covariance transformation, and several machine learning methods for classification, including logistic regression (LR), decision tree (DT), naïve Bayes (NB), random forest (RF), multilayer perceptron (MLP) and k nearest neighbours (kNN). The best performing method was kNN with 85.3% accuracy at 5-fold cross-validation. The resulting model has been implemented in a revised version of the AllerTOP server (http://www.ddg-pharmfac.net/AllerTOP). © Springer-Verlag 2014.

A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to be analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of the input space and the sample size n are usually the main ingredients in the characterization of data bigness. The specific statistical machine learning technique used to handle a particular big data set will depend on which category it falls in within the bigness taxonomy. Large p small n data sets for instance require a different set of tools from the large n small p variety. Among other tools, we discuss Preprocessing, Standardization, Imputation, Projection, Regularization, Penalization, Compression, Reduction, Selection, Kernelization, Hybridization, Parallelization, Aggregation, Randomization, Replication, Sequentialization. Indeed, it is important to emphasize right away that the so-called no free lunch theorem applies here, in the sense that there is no universally superior method that outperforms all other methods on all categories of bigness. It is also important to stress the fact that simplicity in the sense of Ockham’s razor non-plurality principle of parsimony tends to reign supreme when it comes to massive data. We conclude with a comparison of the predictive performance of some of the most commonly used methods on a few data sets.

Optimised phase disposition pulse-width modulation strategy for hybrid-clamped multilevel inverters using switching state sequences

This study describes an optimised modulation strategy based on switching state sequences for the hybrid-clamped multilevel converter. Two key control variables defined as 'phase shift angle' and 'switching state change' for a five-level hybrid-clamped inverter are proposed to improve all switches' operation, and by changing their values, different control methods can be obtained for modulation optimisation purposes. Two example methods can solve the voltage imbalance problem of the dc-link capacitors and furthermore avoid two switches' simultaneous switching transitions and improve the inverter's performance as compared with the traditional phase disposition pulse-width modulation strategy. A 6 kW prototype inverter is developed and a range of simulation and experiments are carried out for validation. It is found that simulation and experimental results are in a good agreement and the proposed modulation strategy is verified in terms of low-order harmonic reduction.

Service productivity:what stops service firms from measuring it?

Productivity measurement poses a challenge for service organizations. Conventional management wisdom holds that this challenge is rooted in the difficulty of accurately quantifying service inputs and outputs. Few service firms have adequate service productivity measurement (SPM) systems in place and implementing such systems may involve organizational transformation. Combining field interviews and literature-based insights, the authors develop a conceptual model of antecedents of SPM in service firms and test it using data from 276 service firms. Results indicate that one out of five antecedents affects the choice to use SPM, namely, the degree of service standardization. In addition, all five hypothesized antecedents and one additional antecedent (perceived appropriateness of the current SPM) predict the degree of SPM usage. In particular, the degree of SPM is positively influenced by the degree of service standardization, service customization, investments in service productivity gains, and the appropriateness of current service productivity measures. In turn, customer integration and the perceived difficulty of measuring service productivity negatively affect SPM. The fact that customer integration impedes actual measurement of service productivity is a surprising finding, given that customer integration is widely seen as a means to increase service productivity. The authors conclude with implications for service organizations and directions for research.