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

A semi-supervised large margin algorithm for white matter hyperintensity segmentation

Precise detection and quantification of white matter hyperintensities (WMH) is of great interest in studies of neurodegenerative diseases (NDs). In this work, we propose a novel semi-supervised large margin algorithm for the segmentation of WMH. The proposed algorithm optimizes a kernel based max-margin objective function which aims to maximize the margin averaged over inliers and outliers while exploiting a limited amount of available labelled data. We show that the learning problem can be formulated as a joint framework learning a classifier and a label assignment simultaneously, which can be solved efficiently by an iterative algorithm. We evaluate our method on a database of 280 brain Magnetic Resonance (MR) images from subjects that either suffered from subjective memory complaints or were diagnosed with NDs. The segmented WMH volumes correlate well with the standard clinical measurement (Fazekas score), and both the qualitative visualization results and quantitative correlation scores of the proposed algorithm outperform other well known methods for WMH segmentation

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

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

The Fitness Consequences Of Multiple‐Locus Heterozygosity: The Relationship Between Heterozygosity And Growth Rate In Pitch Pine (Pinus Rigida Mill.)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137487/1/evo05853.pd

Characterization of transport of titanium neutral atoms sputtered in Ar and Ar/N 2 HIPIMS discharges

International audienceIn this work we report on the investigation of the transport behavior of Ti neutral atoms sputtered in a reactive high power impulse magnetron sputtering device used for TiN coating deposition. The time-resolved tunable diode laser induced fluorescence (TR-TDLIF), previously developed to study the transport of tungsten atoms, was improved to measure Ti neutral atom velocity distribution functions. We find that the TR-TDLIF signal has to be fitted using three Gaussian distributions, corresponding to the energetic, thermalized, and quasi-thermalized (atoms with non-zero mean velocity) atom populations. The ability to distinguish populations of atoms and to determine their corresponding deposited flux and energy may be of great interest to control film properties as desired for targeted applications. From the fitting, the vapor transport parameters (flux and energy) are calculated and studied as a function of distance from the target, pressure, and percentage of nitrogen in an Ar/N2 gas mixture. The study focuses on the effect of added nitrogen on the transport of sputtered atoms

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

Experimental and theoretical study of bumped characteristics obtained with cylindrical Langmuir probe in magnetized Helium plasma

Cylindrical Langmuir probe measurements in a Helium plasma were performed and analysed in the presence of a magnetic field. The plasma is generated in the ALINE device, a cylindrical vessel 1 m long and 30 cm in diameter using a direct coupled RF antenna (ν RF = 25 MHz). The density and temperature are of the order of 10 16 m −3 and 1.5 eV, respectively, for 1.2 Pa Helium pressure and 200 W RF power. The axial magnetic field can be set from 0 up to 0.1 T, and the plasma diagnostic is a RF compensated Langmuir probe, which can be tilted with respect to the magnetic field lines. In the presence of a magnetic field, I(V) characteristics look like asymmetrical double probe ones (tanh-shape), which is due to the trapping of charged particles inside a flux tube connected to the probe on one side and to the wall on the other side. At low tilting angle, high magnetic field amplitude, power magnitude and low He pressure, which are the parameters scanned in our study, a bump can appear on the I(V) in the plasma potential range. We then compare different models for deducing plasma parameters from such unusual bumped curves. Finally, using a fluid model, the bump rising on the characteristics can be explained, assuming a density depletion in the flux tube, and emphasizing the role of the perpendicular transport of ions

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).Peer reviewe