Abstract
In this article, an algorithm to identify LPV State Space models for both continuous-time and discrete-time systems is proposed. The LPV state space system is in the Companion Reachable Canonical Form. The output vector coefficients are linear combinations of a set of a possibly infinite number of nonlinear basis functions dependent on the scheduling signal, the state matrix is either time invariant or a linear combination of a finite number of basis functions of the scheduling signal and the input vector is time invariant. This model structure, although simple, can describe accurately the behaviour of many nonlinear SISO systems by an adequate choice of the scheduling signal. It also partially solves the problems of structural bias caused by inaccurate selection of the basis functions and high variance of the estimates due to over-parameterisation. The use of an infinite number of basis functions in the output vector increases the flexibility to describe complex functions and makes it possible to learn the underlying dependencies of these coefficients from the data. A Least Squares Support Vector Machine (LS-SVM) approach is used to address the infinite dimension of the output coefficients. Since there is a linear dependence of the output on the output vector coefficients and, on the other hand, the LS-SVM solution is a nonlinear function of the state and input matrix coefficients, the LPV system is identified by minimising a quadratic function of the output function in a reduced parameter space; the minimisation of the error is performed by a separable approach where the parameters of the fixed matrices are calculated using a gradient method. The derivatives required by this algorithm are the output of either an LTI or an LPV (in the case of a time-varying SS matrix) system, that need to be simulated at every iteration. The effectiveness of the algorithm is assessed on several simulated examples.

Abstract
The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIREAVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.

Abstract
Deep brain stimulation (DBS) of the internal pallidal segment (GPi: globus pallidus internus) is gold standard treatment for medically intractable dystonia, but detailed knowledge of mechanisms of action is still not available. There is evidence that stimulation of ventral and dorsal GPi produces opposite motor effects. The aim of this study was to analyse connectivity profiles of ventral and dorsal GPi. Probabilistic tractography was initiated from DBS electrode contacts in 8 patients with focal dystonia and connectivity patterns compared. We found a considerable difference in anterior-posterior distribution of fibres along the mesial cortical sensorimotor areas between the ventral and dorsal GPi connectivity. This finding of distinct GPi connectivity profiles further confirms the clinical evidence that the ventral and dorsal GPi belong to different functional and anatomic motor subsystems. Their involvement could play an important role in promoting clinical DBS effects in dystonia.

Abstract
Lung nodule segmentation allows for automatic measurement of the nodule's size or volume which is of utmost importance in lung cancer diagnosis. It is a challenging task since there are many different types of nodules (solid or non-solid, solitary or multiple, etc). A supervised lung nodule segmentation method uses a shape-based, contrast-based and intensity-based feature set to produce three preliminary segmentations and an artificial neural network to obtain a more accurate segmentation. This method was applied to 20 computer tomography studies, all containing nodules. The data has 10 images of solid nodules and 10 images of ground glass opacity nodules, all with ground-truth. The segmentation uses a region growing approach and the volumetric shape index is used for nodule detection and providing a seed point. In the first and second segmentation the probability of each neighbor belonging to the nodule is estimated using the volumetric shape index and the convergence index filter, respectively. The third segmentation is obtained using a feature set region regression method where for each neighbor the probability of belonging to the nodule or not is obtained using k nearest neighbor regression. Then, using a leave-one out method, an artificial neural network uses the three preliminary segmentations as input and is trained to obtain a more accurate segmentation. Results obtained a 12% relative volume error, 88% and 93% Jaccard and Dice coefficient respectively. © 2014, Springer International Publishing Switzerland.

Abstract
The cis-regulatory effects responsible for cancer development have not been as extensively studied as the perturbations of the protein coding genome in tumorigenesis. To better characterize colorectal cancer (CRC) development we conducted an RNA-sequencing experiment of 103 matched tumour and normal colon mucosa samples from Danish CRC patients, 90 of which were germline-genotyped. By investigating allele-specific expression (ASE) we show that the germline genotypes remain important determinants of allelic gene expression in tumours. Using the changes in ASE in matched pairs of samples we discover 71 genes with excess of somatic cis-regulatory effects in CRC, suggesting a cancer driver role. We correlate genotypes and gene expression to identify expression quantitative trait loci (eQTLs) and find 1,693 and 948 eQTLs in normal samples and tumours, respectively. We estimate that 36% of the tumour eQTLs are exclusive to CRC and show that this specificity is partially driven by increased expression of specific transcription factors and changes in methylation patterns. We show that tumour-specific eQTLs are more enriched for low CRC genome-wide association study (GWAS) P values than shared eQTLs, which suggests that some of the GWAS variants are tumour specific regulatory variants. Importantly, tumour-specific eQTL genes also accumulate more somatic mutations when compared to the shared eQTL genes, raising the possibility that they constitute germline-derived cancer regulatory drivers. Collectively the integration of genome and the transcriptome reveals a substantial number of putative somatic and germline cis-regulatory cancer changes that may have a role in tumorigenesis.

Abstract
Chimeric RNAs originating from two or more different genes are known to exist not only in cancer, but also in normal tissues, where they can play a role in human evolution. However, the exact mechanism of their formation is unknown. Here, we use RNA sequencing data from 462 healthy individuals representing 5 human populations to systematically identify and in depth characterize 81 RNA tandem chimeric transcripts, 13 of which are novel. We observe that 6 out of these 81 chimeras have been regarded as cancer-specific. Moreover, we show that a prevalence of long introns at the fusion breakpoint is associated with the chimeric transcripts formation. We also find that tandem RNA chimeras have lower abundances as compared to their partner genes. Finally, by combining our results with genomic data from the same individuals we uncover intronic genetic variants associated with the chimeric RNA formation. Taken together our findings provide an important insight into the chimeric transcripts formation and open new avenues of research into the role of intronic genetic variants in post-transcriptional processing events. © 2014 Greger et al.