Ulf G. Indahl

Variants of the genes encoding AQP4 and Kir4.1 are associated with subgroups of patients with temporal lobe epilepsy

OBJECTIVE The etiopathogenesis of temporal lobe epilepsy (TLE) and its subgroups - mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) and TLE with antecedent febrile seizures (TLE-FS) - is poorly understood. It has been proposed that the water channel aquaporin-4 (AQP4) and the potassium channel Kir4.1 (KCNJ10 gene) act in concert to regulate extracellular K(+) homeostasis and that functional alterations of these channels influence neuronal excitability. The current study was designed to identify variants of the AQP4 and KCNJ10 genes associated with TLE and subgroups of this condition. MATERIAL AND METHODS We included 218 Norwegian patients with TLE and 181 ethnically matched healthy controls. An association study was established in which all TLE patients were compared with healthy controls. Additionally, subgroups of 56 MTLE-HS patients were compared with 162 TLE patients without HS, and 102 TLE-FS patients were compared with 105 TLE without FS. RESULTS We found eight single SNPs, seven in KCNJ10 and one between KCNJ10 and KCNJ9, associated with TLE-FS (nominal p-values from 0.009 to 0.041). Seven of the SNPs segregate into one large haplotype block expanding from KCNJ10 to KCNJ9, including the region interposed those genes. One haplotype was overrepresented in the TLE-FS cases (nominal p-value 0.014). These results were confirmed by explorative multivariate analysis indicating that a combination of SNPs from KCNJ10, the region between KCNJ10 and KCNJ9, and the AQP4 gene is associated with TLE-FS. For the TLE cohort as a whole, explorative multivariate analysis indicated a combination of SNPs from the KCNJ10 and AQP4 genes in association with TLE. CONCLUSION Variations in the AQP4 and the KCNJ10/KCNJ9 region are likely to be associated with TLE, particularly TLE-FS, supporting the suggestion that perturbations of water and K(+) transport are involved in the etiopathogenesis of TLE.

Evaluation of alternative spectral feature extraction methods of textural images for multivariate modelling

Fast and automatic strategies for extraction of characteristic feature spectra from digital images are investigated. We present a study based on images from confocal laser scanning microscopy (CLSM) of mayonnaise. Based on principal component regression (PCR), six different methods are compared with respect to prediction of external measurements describing the sensory texture of samples. The methods considered are: 1, the magnitude spectrum of the Fourier transform; 2, the autocorrelation spectrum; 3, the autocovariance spectrum; 4, the absolute difference spectrum; 5, the singular value spectrum; 6, the angle measure technique. A technique based on cross‐validated predictions combined with a two‐way ANOVA is suggested to decide eventual differences in prediction ability.

A unified description of classical classification methods for multicollinear data

In this paper a unified description of classification methods in situations with multicollinear data is proposed. It is shown that a number of the well‐established methods can be derived by substituting different modified versions of the covariance matrix into either the classical Bayes method or Fisher’s linear (canonical) discriminant method. A parametric version of this modified covariance matrix is proposed. Each method corresponds to a particular value of the parameters.

Combination of statistical approaches for analysis of 2-DE data gives complementary results

Five methods for finding significant changes in proteome data have been used to analyze a two-dimensional gel electrophoresis data set. We used both univariate (ANOVA) and multivariate (Partial Least Squares with jackknife, Cross Model Validation, Power-PLS and CovProc) methods. The gels were taken from a time-series experiment exploring the changes in metabolic enzymes in bovine muscle at five time-points after slaughter. The data set consisted of 1377 protein spots, and for each analysis, the data set were preprocessed to fit the requirements of the chosen method. The generated results were one list from each analysis method of proteins found to be significantly changed according to the experimental design. Although the number of selected variables varied between the methods, we found that this was dependent on the specific aim of each method. CovProc and P-PLS focused more on getting the minimum necessary subset of proteins to explain properties of the samples. These methods ended up with less selected proteins. There was also a correlation between level of significance and frequency of selection for the selected proteins.

Multivariate feature extraction from textural images of bread

Abstract In order to compute the classical texture measures there is often a need to perform extensive calculations on the images and do a preprocessing in a specialised manner. Some of these texture measures are constructed to estimate specific information. Other texture measures seem to be more global in nature. The techniques presented in this paper define algorithms applied on the raw image without extensive preprocessing. We want to show that mathematical transformations of images on a vectorised form will easily enable the use of multivariate techniques and possibly model several features hidden in the images at the same time. In this paper we will compare five different methods of extracting features from textural images in food by multivariate modelling of the sensory porosity of wheat baguettes. The sample images are recorded from factorial designed baking experiments on wheat baguettes. The multivariate feature extraction methods to be treated are the angle measure technique (AMT), the singular value decomposition (SVD), the autocorrelation and autocovariance functions (ACF) and the so-called size and distance distribution (SDD) method. The methods will be tested on equal basis and the modelling of sensory porosity from extracted features is done using principal component regression (PCR) and partial least square regression (PLS). The difference between the behaviour of the methods will be discussed. The results show that all the methods are suited to extract sensory porosity but the AMT method prove to be the best in this case.

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

BackgroundDeterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function.ResultsOur results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops.ConclusionsHC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.

Is the brain water channel aquaporin‐4 a pathogenetic factor in idiopathic intracranial hypertension? Results from a combined clinical and genetic study in a Norwegian cohort

Purpose:  Idiopathic intracranial hypertension (IIH) is a condition of increased intracranial pressure of unknown aetiology. Patients with IIH usually suffer from headache and visual disturbances. High intracranial pressure despite normal ventricle size and negative MRI indicate perturbed water flux across cellular membranes, which is provided by the brain water channel aquaporin‐4 (AQP4). IIH could be associated with malfunctioning intracerebral water homeostasis and cerebrospinal fluid (CSF) reabsorption based on functional or regulatory alterations of AQP4.

Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines.

Dynamic contrast enhanced MRI (DCE-MRI) provides insight into the vascular properties of tissue. Pharmacokinetic models may be fitted to DCE-MRI uptake patterns, enabling biologically relevant interpretations. The aim of our study was to determine whether treatment outcome for 81 patients with locally advanced cervical cancer could be predicted from parameters of the Brix pharmacokinetic model derived from pre-chemoradiotherapy DCE-MRI. First-order statistical features of the Brix parameters were used. In addition, texture analysis of Brix parameter maps was done by constructing gray level co-occurrence matrices (GLCM) from the maps. Clinical factors and first- and second-order features were used as explanatory variables for support vector machine (SVM) classification, with treatment outcome as response. Classification models were validated using leave-one-out cross-model validation. A random value permutation test was used to evaluate model significance. Features derived from first-order statistics could not discriminate between cured and relapsed patients (specificity 0%-20%, p-values close to unity). However, second-order GLCM features could significantly predict treatment outcome with accuracies (~70%) similar to the clinical factors tumor volume and stage (69%). The results indicate that the spatial relations within the tumor, quantified by texture features, were more suitable for outcome prediction than first-order features.

Estimation of thalamocortical and intracortical network models from joint thalamic single-electrode and cortical laminar-electrode recordings in the rat barrel system

A new method is presented for extraction of population firing-rate models for both thalamocortical and intracortical signal transfer based on stimulus-evoked data from simultaneous thalamic single-electrode and cortical recordings using linear (laminar) multielectrodes in the rat barrel system. Time-dependent population firing rates for granular (layer 4), supragranular (layer 2/3), and infragranular (layer 5) populations in a barrel column and the thalamic population in the homologous barreloid are extracted from the high-frequency portion (multi-unit activity; MUA) of the recorded extracellular signals. These extracted firing rates are in turn used to identify population firing-rate models formulated as integral equations with exponentially decaying coupling kernels, allowing for straightforward transformation to the more common firing-rate formulation in terms of differential equations. Optimal model structures and model parameters are identified by minimizing the deviation between model firing rates and the experimentally extracted population firing rates. For the thalamocortical transfer, the experimental data favor a model with fast feedforward excitation from thalamus to the layer-4 laminar population combined with a slower inhibitory process due to feedforward and/or recurrent connections and mixed linear-parabolic activation functions. The extracted firing rates of the various cortical laminar populations are found to exhibit strong temporal correlations for the present experimental paradigm, and simple feedforward population firing-rate models combined with linear or mixed linear-parabolic activation function are found to provide excellent fits to the data. The identified thalamocortical and intracortical network models are thus found to be qualitatively very different. While the thalamocortical circuit is optimally stimulated by rapid changes in the thalamic firing rate, the intracortical circuits are low-pass and respond most strongly to slowly varying inputs from the cortical layer-4 population.

The Response of Enterococcus faecalis V583 to Chloramphenicol Treatment.

Many Enterococcus faecalis strains display tolerance or resistance to many antibiotics, but genes that contribute to the resistance cannot be specified. The multiresistant E. faecalis V583, for which the complete genome sequence is available, survives and grows in media containing relatively high levels of chloramphenicol. No specific genes coding for chloramphenicol resistance has been recognized in V583. We used microarrays to identify genes and mechanisms behind the tolerance to chloramphenicol in V583, by comparison of cells treated with subinhibitory concentrations of chloramphenicol and untreated V583 cells. During a time course experiment, more than 600 genes were significantly differentially transcribed. Since chloramphenicol affects protein synthesis in bacteria, many genes involved in protein synthesis, for example, genes for ribosomal proteins, were induced. Genes involved in amino acid biosynthesis, for example, genes for tRNA synthetases and energy metabolism were downregulated, mainly. Among the upregulated genes were EF1732 and EF1733, which code for potential chloramphenicol transporters. Efflux of drug out of the cells may be one mechanism used by V583 to overcome the effect of chloramphenicol.