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

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

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

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

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

Sex. Dev.

Campomelic dysplasia (MIM 114290) is a severe malformation syndrome frequently accompanied by male-to-female sex reversal. Causative are mutations within the SOX9 gene on 17q24.3 as well as chromosomal aberrations (translocations, inversions or deletions) in the vicinity of SOX9 . Here, we report on a patient with muscular hypotonia, craniofacial dysmorphism, cleft palate, brachydactyly, malformations of thoracic spine, and gonadal dysgenesis with female external genitalia and müllerian duct derivatives in the presence of a male karyotype. X-ray examination and clinical examinations revealed no signs of campomelia. The combination of molecular cytogenetic analysis and array CGH revealed an unbalanced translocation between one chromosome 7 and one chromosome 17 [46,XY,t(7; 17)(q33;q24).ish t(7; 17) (wcp7+,wcp17+;wcp7+wcp17+)] with a deletion of approximately 4.2 Mb located about 0.5 Mb upstream of SOX9 . STS analysis confirmed the deletion of chromosome 17, which has occurred de novo on the paternal chromosome. The proximal breakpoint on chromosome 17 is localized outside the known breakpoint cluster regions. The deletion on chromosome 17q24 removes several genes. Among these genes PRKAR1A is deleted. Inactivating mutations of PRKAR1A cause Carney complex. To our knowledge, this is the first report of a patient with acampomelic campomelic dysplasia, carrying both a deletion and a translocation

Copy Number Variants in Patients with Severe Oligozoospermia and Sertoli-Cell-Only Syndrome

A genetic origin is estimated in 30% of infertile men with the common phenotypes of oligo- or azoospermia, but the pathogenesis of spermatogenic failure remains frequently obscure. To determine the involvement of Copy Number Variants (CNVs) in the origin of male infertility, patients with idiopathic severe oligozoospermia (N = 89), Sertoli-cell-only syndrome (SCOS, N = 37)) and controls with normozoospermia (N = 100) were analysed by array-CGH using the 244A/400K array sets (Agilent Technologies). The mean number of CNVs and the amount of DNA gain/loss were comparable between all groups. Ten recurring CNVs were only found in patients with severe oligozoospermia, three only in SCOS and one CNV in both groups with spermatogenic failure but not in normozoospermic men. Sex-chromosomal, mostly private CNVs were significantly overrepresented in patients with SCOS. CNVs found several times in all groups were analysed in a case-control design and four additional candidate genes and two regions without known genes were associated with SCOS (P<1×10−3). In conclusion, by applying array-CGH to study male infertility for the first time, we provide a number of candidate genes possibly causing or being risk factors for the men's spermatogenic failure. The recurring, patient-specific and private, sex-chromosomal CNVs as well as those associated with SCOS are candidates for further, larger case-control and re-sequencing studies

Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy

Brains with complex distortion of cerebral anatomy present several challenges to automatic tissue segmentation methods of T1-weighted MR images. First, the very high variability in the morphology of the tissues can be incompatible with the prior knowledge embedded within the algorithms. Second, the availability of MR images of distorted brains is very scarce, so the methods in the literature have not addressed such cases so far. In this work, we present the first evaluation of state-of-the-art automatic tissue segmentation pipelines on T1-weighted images of brains with different severity of congenital or acquired brain distortion. We compare traditional pipelines and a deep learning model, i.e. a 3D U-Net trained on normal-appearing brains. Unsurprisingly, traditional pipelines completely fail to segment the tissues with strong anatomical distortion. Surprisingly, the 3D U-Net provides useful segmentations that can be a valuable starting point for manual refinement by experts/neuroradiologists

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