Determining the HI content of galaxies via intensity mapping cross-correlations

We propose an innovative method for measuring the neutral hydrogen (HI) content of an optically-selected spectroscopic sample of galaxies through cross-correlation with HI intensity mapping measurements. We show that the HI-galaxy cross-power spectrum contains an additive shot noise term which scales with the average HI brightness temperature of the optically-selected galaxies, allowing constraints to be placed on the average HI mass per galaxy. This approach can estimate the HI content of populations too faint to directly observe through their 21cm emission over a wide range of redshifts. This cross-correlation, as a function of optical luminosity or colour, can be used to derive HI-scaling relations. We demonstrate that this signal will be detectable by cross-correlating upcoming Australian SKA Pathfinder (ASKAP) observations with existing optically-selected samples. We also use semi-analytic simulations to verify that the HI mass can be successfully recovered by our technique in the range M_HI > 10^8 M_solar, in a manner independent of the underlying power spectrum shape. We conclude that this method is a powerful tool to study galaxy evolution, which only requires a single intensity mapping dataset to infer complementary HI gas information from existing optical and infra-red observations.Comment: 8 pages, 4 figures, submitted to MNRA

Intensity mapping cross-correlations II: HI halo models including shot noise

HI intensity mapping data traces the large-scale structure matter distribution using the integrated emission of neutral hydrogen gas (HI). The cross-correlation of the intensity maps with optical galaxy surveys can mitigate foreground and systematic effects, but has been shown to significantly depend on galaxy evolution parameters of the HI and the optical sample. Previously, we have shown that the shot noise of the cross-correlation scales with the HI content of the optical samples, such that the shot noise estimation infers the average HI masses of these samples. In this article, we present an adaptive framework for the cross-correlation of HI intensity maps with galaxy samples using our implementation of the halo model formalism (Murray et al 2018, in prep) which utilises the halo occupation distribution of galaxies to predict their power spectra. We compare two HI population models, tracing the spatial halo and the galaxy distribution respectively, and present their auto- and cross-power spectra with an associated galaxy sample. We find that the choice of the HI model and the distribution of the HI within the galaxy sample have minor significance for the shape of the auto- and cross-correlations, but highly impact the measured shot noise amplitude of the estimators, a finding we confirm with simulations. We demonstrate parameter estimation of the HI halo occupation models and advocate this framework for the interpretation of future experimental data, with the prospect of determining the HI masses of optical galaxy samples via the cross-correlation shot noise.Comment: 15 pages, 8 figures, 3 tables. Comments welcom

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

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

Initial maternal meiotic I error leading to the formation of a maternal i(2q) and a paternal i(2p) in a healthy male

We report on the investigation of the parental origin and mode of formation of the two isochromosomes, i(2p) and i(2q), detected in a healthy adult male. Conventional cytogenetic analysis revealed the proband’s lack of structurally normal chromosomes 2, these being replaced by an i(2p) and an i(2q). Investigation of the parental origin of the isochromosomes revealed a paternal origin of the i(2p) chromosome and a maternal origin of the i(2q) chromosome. Thus, the formation of both isochromosomes, or at least of the paternal i(2p), appears to have occurred postzygotically. Interestingly, whilst a paternal isodisomy was observed for the entire 2p, maternal heterodisomy was detected for two segments of 2q, separated by a segment showing isodisomy. The results are indicative of an initial error (non-disjunction or i(2q) formation) concerning the maternal chromosomes 2 during meiosis I, which likely favored the subsequent mitotic recombination event resulting in the presence of two isochromosomes. To the best of our knowledge this is the first case of an initial meiotic error, followed by postzygotic trisomy rescue through the formation of isochromosomes, resulting in a normal phenotype. A prenatal detection, by cytogenetic and molecular analysis, of such chromosome abnormality would have led to the incorrect conclusion of a most likely poor prognosis for the fetus

On the Parity Problem in One-Dimensional Cellular Automata

We consider the parity problem in one-dimensional, binary, circular cellular automata: if the initial configuration contains an odd number of 1s, the lattice should converge to all 1s; otherwise, it should converge to all 0s. It is easy to see that the problem is ill-defined for even-sized lattices (which, by definition, would never be able to converge to 1). We then consider only odd lattices. We are interested in determining the minimal neighbourhood that allows the problem to be solvable for any initial configuration. On the one hand, we show that radius 2 is not sufficient, proving that there exists no radius 2 rule that can possibly solve the parity problem from arbitrary initial configurations. On the other hand, we design a radius 4 rule that converges correctly for any initial configuration and we formally prove its correctness. Whether or not there exists a radius 3 rule that solves the parity problem remains an open problem.Comment: In Proceedings AUTOMATA&JAC 2012, arXiv:1208.249

Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion

Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regionsof- interest and anatomical landmarks are key to many clinical studies. However, due to the time consuming nature of manually generating these annotations, they tend to be scarce and limited to small subsets of data. This work explores a novel framework to propagate voxel-wise annotations between morphologically dissimilar images by diffusing and mapping the available examples through intermediate steps. A spatially-variant graph structure connecting morphologically similar subjects is introduced over a database of images, enabling the gradual diffusion of information to all the subjects, even in the presence of large-scale morphological variability. We illustrate the utility of the proposed framework on two example applications: brain parcellation using categorical labels and tissue segmentation using probabilistic features. The application of the proposed method to categorical label fusion showed highly statistically significant improvements when compared to state-of-the-art methodologies. Significant improvements were also observed when applying the proposed framework to probabilistic tissue segmentation of both synthetic and real data, mainly in the presence of large morphological variability

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

Impact of foregrounds on Hi intensity mapping cross-correlations with optical surveys

The future of precision cosmology could benefit from cross-correlations between intensity maps of unresolved neutral hydrogen (HI) and more conventional optical galaxy surveys. A major challenge that needs to be overcome is removing the 21cm foreground emission that contaminates the cosmological HI signal. Using N-body simulations we simulate HI intensity maps and optical catalogues which share the same underlying cosmology. Adding simulated foreground contamination and using state-of-the-art reconstruction techniques we investigate the impacts that 21cm foregrounds and other systematics have on these cross-correlations. We find that the impact a FASTICA 21cm foreground clean has on the cross-correlations with spectroscopic optical surveys with well-constrained redshifts is minimal. However, problems arise when photometric surveys are considered: we find that a redshift uncertainty {\sigma}_z {\geq} 0.04 causes significant degradation in the cross power spectrum signal. We diagnose the main root of these problems, which relates to arbitrary amplitude changes along the line-of-sight in the intensity maps caused by the foreground clean and suggest solutions which should be applicable to real data. These solutions involve a reconstruction of the line-of-sight temperature means using the available overlapping optical data along with an artificial extension to the HI data through redshift to address edge effects. We then put these solutions through a further test in a mock experiment that uses a clustering-based redshift estimation technique to constrain the photometric redshifts of the optical sample. We find that with our suggested reconstruction, cross-correlations can be utilized to make an accurate prediction of the optical redshift distribution.Comment: Version 2 - accepted for publication on 5th July 2019 in Monthly Notices of the Royal Astronomical Society Main Journa