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

Parental origin of the two additional haploid sets of chromosomes in an embryo with tetraploidy

We report on the molecular investigations performed on an embryo with tetraploidy, karyotype 92,XXXY. The embryo was spontaneously aborted after eight weeks of gestation. Molecular analyses were performed in order to determine the parental origin and mode of formation of the two additional haploid sets of chromosomes. Microsatellite markers mapping to pericentromeric chromosome regions were used. Our results show a maternal origin of one additional set of chromosomes most likely due to the incorporation of the polar body of meiosis I and a paternal origin of the second additional set of chromosomes most likely due to dispermy. The karyotype 92,XXXY is rather unusual, indeed the vast majority of cases with tetraploidy have the karyotypes 92,XXXX or 92,XXYY. To the best of our knowledge this is the first case with 92,XXXY for which molecular investigations have been performed

Shift-Symmetric Configurations in Two-Dimensional Cellular Automata: Irreversibility, Insolvability, and Enumeration

The search for symmetry as an unusual yet profoundly appealing phenomenon, and the origin of regular, repeating configuration patterns have long been a central focus of complexity science and physics. To better grasp and understand symmetry of configurations in decentralized toroidal architectures, we employ group-theoretic methods, which allow us to identify and enumerate these inputs, and argue about irreversible system behaviors with undesired effects on many computational problems. The concept of so-called configuration shift-symmetry is applied to two-dimensional cellular automata as an ideal model of computation. Regardless of the transition function, the results show the universal insolvability of crucial distributed tasks, such as leader election, pattern recognition, hashing, and encryption. By using compact enumeration formulas and bounding the number of shift-symmetric configurations for a given lattice size, we efficiently calculate the probability of a configuration being shift-symmetric for a uniform or density-uniform distribution. Further, we devise an algorithm detecting the presence of shift-symmetry in a configuration. Given the resource constraints, the enumeration and probability formulas can directly help to lower the minimal expected error and provide recommendations for system's size and initialization. Besides cellular automata, the shift-symmetry analysis can be used to study the non-linear behavior in various synchronous rule-based systems that include inference engines, Boolean networks, neural networks, and systolic arrays.Comment: 22 pages, 9 figures, 2 appendice

Introducing coherent time control to cavity magnon-polariton modes

By connecting light to magnetism, cavity magnon-polaritons (CMPs) can link quantum computation to spintronics. Consequently, CMP-based information processing devices have emerged over the last years, but have almost exclusively been investigated with single-tone spectroscopy. However, universal computing applications will require a dynamic and on-demand control of the CMP within nanoseconds. Here, we perform fast manipulations of the different CMP modes with independent but coherent pulses to the cavity and magnon system. We change the state of the CMP from the energy exchanging beat mode to its normal modes and further demonstrate two fundamental examples of coherent manipulation. We first evidence dynamic control over the appearance of magnon-Rabi oscillations, i.e., energy exchange, and second, energy extraction by applying an anti-phase drive to the magnon. Our results show a promising approach to control building blocks valuable for a quantum internet and pave the way for future magnon-based quantum computing research

Lack of clustering in low-redshift 21-cm intensity maps cross-correlated with 2dF galaxy densities

We report results from 21-cm intensity maps acquired from the Parkes radio telescope and cross-correlated with galaxy maps from the 2dF galaxy survey. The data span the redshift range $0.057<z<0.098$ and cover approximately 1,300 square degrees over two long fields. Cross correlation is detected at a significance of $5.18\sigma$. The amplitude of the cross-power spectrum is low relative to the expected dark matter power spectrum, assuming a neutral hydrogen (HI) bias and mass density equal to measurements from the ALFALFA survey. The decrement is pronounced and statistically significant at small scales. At $k\sim1.5$ $h \mathrm{Mpc^{-1}}$, the cross power spectrum is more than a factor of 6 lower than expected, with a significance of $14.8\,\sigma$. This decrement indicates either a lack of clustering of neutral hydrogen (HI), a small correlation coefficient between optical galaxies and HI, or some combination of the two. Separating 2dF into red and blue galaxies, we find that red galaxies are much more weakly correlated with HI on $k\sim1.5$ $h \mathrm{Mpc^{-1}}$ scales, suggesting that HI is more associated with blue star-forming galaxies and tends to avoid red galaxies.Comment: 12 pages, 3 figures; fixed typo in meta-data title and paper author

3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation

This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localized) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.Comment: Presented in MICCAI 2017 workshop, DLMIA 2017 (Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Instantiated mixed effects modeling of Alzheimer's disease markers

The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease are of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified “marker signature” that defines a subpopulation of similar cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models

Foreground Subtraction in Intensity Mapping with the SKA

21cm intensity mapping experiments aim to observe the diffuse neutral hydrogen (HI) distribution on large scales which traces the Cosmic structure. The Square Kilometre Array (SKA) will have the capacity to measure the 21cm signal over a large fraction of the sky. However, the redshifted 21cm signal in the respective frequencies is faint compared to the Galactic foregrounds produced by synchrotron and free-free electron emission. In this article, we review selected foreground subtraction methods suggested to effectively separate the 21cm signal from the foregrounds with intensity mapping simulations or data. We simulate an intensity mapping experiment feasible with SKA phase 1 including extragalactic and Galactic foregrounds. We give an example of the residuals of the foreground subtraction with a independent component analysis and show that the angular power spectrum is recovered within the statistical errors on most scales. Additionally, the scale of the Baryon Acoustic Oscillations is shown to be unaffected by foreground subtraction

Erasing the Milky Way: new cleaning technique applied to GBT intensity mapping data

We present the first application of a new foreground removal pipeline to the current leading H I intensity mapping data set, obtained by the Green Bank Telescope (GBT). We study the 15- and 1-h-field data of the GBT observations previously presented in Mausui et al. and Switzer et al., covering about 41 deg2 at 0.6 < z < 1.0, for which cross-correlations may be measured with the galaxy distribution of the WiggleZ Dark Energy Survey. In the presented pipeline, we subtract the Galactic foreground continuum and the point-source contamination using an independent component analysis technique (FASTICA), and develop a Fourier-based optimal estimator to compute the temperature power spectrum of the intensity maps and cross-correlation with the galaxy survey data. We show that FASTICA is a reliable tool to subtract diffuse and point-source emission through the non-Gaussian nature of their probability distributions. The temperature power spectra of the intensity maps are dominated by instrumental noise on small scales which FASTICA, as a conservative subtraction technique of non-Gaussian signals, cannot mitigate. However, we determine similar GBT-WiggleZ cross-correlation measurements to those obtained by the singular value decomposition (SVD) method, and confirm that foreground subtraction with FASTICA is robust against 21 cm signal loss, as seen by the converged amplitude of these cross-correlation measurements. We conclude that SVD and FASTICA are complementary methods to investigate the foregrounds and noise systematics present in intensity mapping data sets

Cellular automaton supercolliders

Gliders in one-dimensional cellular automata are compact groups of non-quiescent and non-ether patterns (ether represents a periodic background) translating along automaton lattice. They are cellular-automaton analogous of localizations or quasi-local collective excitations travelling in a spatially extended non-linear medium. They can be considered as binary strings or symbols travelling along a one-dimensional ring, interacting with each other and changing their states, or symbolic values, as a result of interactions. We analyse what types of interaction occur between gliders travelling on a cellular automaton `cyclotron' and build a catalog of the most common reactions. We demonstrate that collisions between gliders emulate the basic types of interaction that occur between localizations in non-linear media: fusion, elastic collision, and soliton-like collision. Computational outcomes of a swarm of gliders circling on a one-dimensional torus are analysed via implementation of cyclic tag systems