Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Disorders of sex development: challenges for the future

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Theory of a cylindrical Langmuir probe parallel to the magnetic field and its calibration with interferometry

International audienceA theory for data interpretation is presented for a cylindrical Langmuir probe in plasma parallel to the magnetic field direction. The theory is tested in a linear low-temperature plasma device Aline, in a capacitive radio-frequency (RF) discharge. The probe is placed on a 3D manipulator and a position scan is performed. To exclude strong RF perturbations the probe is RF compensated. Using the theory electron densities are obtained from the current at the plasma potential, where no sheath is present. Results are calibrated by line-integrated density measurements of a 26.5 GHz microwave interferometer. Reasonable agreement is observed for probe and interferometer measurements. Furthermore, preceding, more general probe theory is compared to the one developed in the current work and the application limits are discussed

Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models

Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging

Narrow genetic base in forest restoration with holm oak (Quercus ilex L.) in Sicily

In order to empirically assess the effect of actual seed sampling strategy on genetic diversity of holm oak (Quercus ilex) forestations in Sicily, we have analysed the genetic composition of two seedling lots (nursery stock and plantation) and their known natural seed origin stand by means of six nuclear microsatellite loci. Significant reduction in genetic diversity and significant difference in genetic composition of the seedling lots compared to the seed origin stand were detected. The female and the total effective number of parents were quantified by means of maternity assignment of seedlings and temporal changes in allele frequencies. Extremely low effective maternity numbers were estimated (Nfe $\approx$ 2-4) and estimates accounting for both seed and pollen donors gave also low values (Ne $\approx$ 35-50). These values can be explained by an inappropriate forestry seed harvest strategy limited to a small number of spatially close trees

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org

Experimental measurements of the RF sheath thickness with a cylindrical Langmuir probe

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The plasma-wall transition with collisions and an oblique magnetic field: reversal of potential drops at grazing incidences

International audienceThe plasma-wall transition is studied by using 1d3V particle-in-cell (PIC) simulations in the case of a one dimensional plasma bounded by two absorbing walls separated by 200 Debye lengths (λ d). A constant and oblique magnetic field is applied to the system, with an amplitude such that r < λ d < R, where r and R are the electron and ion Larmor radius respectively. Collisions with neutrals are taken into account and modelled by an energy conservative operator, which randomly reorients ion and electron velocities. The plasma-wall transition (PWT) is shown to depend on both the angle of incidence of the magnetic field with respect to the wall θ, and on the ion mean-free-path to Larmor radius ratio, λ ci /R. In the very low collisionality regime (λ ci R) and for a large angle of incidence, the PWT consists in the classical tri-layer structure (Debye sheath / Chodura sheath / Pre-sheath) from the wall towards the center of the plasma. The drops of potential within the different regions are well consistent with already published models. However, when sin θ ≤ R/λ ci or with the ordering λ ci < R , collisions can not be neglected, leading to the disappearance of the Chodura sheath. In these case, a collisional model yields analytic expressions for the potential drop in the quasi-neutral region, and explains, in qualitative and quantitative agreement with the simulation results, its reversal below a critical angle derived in the paper, a regime possibly met in the SOL of tokamaks. It is further shown that the potential drop in the Debye sheath slightly varies with the collision-ality for λ ci R. However, it tends to decrease with λ ci in the high collisionality regime, until the Debye sheath finally vanishes

New Technologies for the Identification of Novel Genetic Markers of Disorders of Sex Development (DSD)

Although the genetic basis of human sexual determination and differentiation has advanced considerably in recent years, the fact remains that in most subjects with disorders of sex development (DSD) the underlying genetic cause is unknown. Where pathogenic mutations have been identified, the phenotype can be highly variable, even within families, suggesting that other genetic variants are influencing the expression of the phenotype. This situation is likely to change, as more powerful and affordable tools become widely available for detailed genetic analyses. Here, we describe recent advances in comparative genomic hybridisation, sequencing by hybridisation and next generation sequencing, and we describe how these technologies will have an impact on our understanding of the genetic causes of DSD