Great cities look small

Great cities connect people; failed cities isolate people. Despite the fundamental importance of physical, face-to-face social-ties in the functioning of cities, these connectivity networks are not explicitly observed in their entirety. Attempts at estimating them often rely on unrealistic over-simplifications such as the assumption of spatial homogeneity. Here we propose a mathematical model of human interactions in terms of a local strategy of maximising the number of beneficial connections attainable under the constraint of limited individual travelling-time budgets. By incorporating census and openly-available online multi-modal transport data, we are able to characterise the connectivity of geometrically and topologically complex cities. Beyond providing a candidate measure of greatness, this model allows one to quantify and assess the impact of transport developments, population growth, and other infrastructure and demographic changes on a city. Supported by validations of GDP and HIV infection rates across United States metropolitan areas, we illustrate the effect of changes in local and city-wide connectivities by considering the economic impact of two contemporary inter- and intra-city transport developments in the United Kingdom: High Speed Rail 2 and London Crossrail. This derivation of the model suggests that the scaling of different urban indicators with population size has an explicitly mechanistic origin.Comment: 19 pages, 8 figure

Accurate Reconstruction of Cell and Particle Tracks from 3D Live Imaging Data

Spatial structures often constrain the 3D movement of cells or particles in vivo, yet this information is obscured when microscopy data are analyzed using standard approaches. Here, we present methods, called unwrapping and Riemannian manifold learning, for mapping particle-tracking data along unseen and irregularly curved surfaces onto appropriate 2D representations. This is conceptually similar to the problem of reconstructing accurate geography from conventional Mercator maps, but our methods do not require prior knowledge of the environments’ physical structure. Unwrapping and Riemannian manifold learning accurately recover the underlying 2D geometry from 3D imaging data without the need for fiducial marks. They outperform standard x-y projections, and unlike standard dimensionality reduction techniques, they also successfully detect both bias and persistence in cell migration modes. We demonstrate these features on simulated data and zebrafish and Drosophila in vivo immune cell trajectory datasets. Software packages that implement unwrapping and Riemannian manifold learning are provided

Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness

Learning meaningful representations of data that can address challenges such as batch effect correction, data integration and counterfactual inference is a central problem in many domains including computational biology. Adopting a Conditional VAE framework, we identify the mathematical principle that unites these challenges: learning a representation that is marginally independent of a condition variable. We therefore propose the Contrastive Mixture of Posteriors (CoMP) method that uses a novel misalignment penalty to enforce this independence. This penalty is defined in terms of mixtures of the variational posteriors themselves, unlike prior work which uses external discrepancy measures such as MMD to ensure independence in latent space. We show that CoMP has attractive theoretical properties compared to previous approaches, especially when there is complex global structure in latent space. We further demonstrate state of the art performance on a number of real-world problems, including the challenging tasks of aligning human tumour samples with cancer cell-lines and performing counterfactual inference on single-cell RNA sequencing data. Incidentally, we find parallels with the fair representation learning literature, and demonstrate CoMP has competitive performance in learning fair yet expressive latent representations

E7(7) formulation of N=2 backgrounds

In this paper we reformulate N=2 supergravity backgrounds arising in type II string theory in terms of quantities transforming under the U-duality group E7(7). In particular we combine the Ramond--Ramond scalar degrees of freedom together with the O(6,6) pure spinors which govern the Neveu-Schwarz sector by considering an extended version of generalised geometry. We give E7(7)-invariant expressions for the Kahler and hyperkahler potentials describing the moduli space of vector and hypermultiplets, demonstrating that both correspond to standard E7(7) coset spaces. We also find E7(7) expressions for the Killing prepotentials defining the scalar potential, and discuss the equations governing N=1 vacua in this formalism.Comment: 40 pages, final version to appear in JHE

Electrically Tunable Excitonic Light Emitting Diodes based on Monolayer WSe2 p-n Junctions

Light-emitting diodes are of importance for lighting, displays, optical interconnects, logic and sensors. Hence the development of new systems that allow improvements in their efficiency, spectral properties, compactness and integrability could have significant ramifications. Monolayer transition metal dichalcogenides have recently emerged as interesting candidates for optoelectronic applications due to their unique optical properties. Electroluminescence has already been observed from monolayer MoS2 devices. However, the electroluminescence efficiency was low and the linewidth broad due both to the poor optical quality of MoS2 and to ineffective contacts. Here, we report electroluminescence from lateral p-n junctions in monolayer WSe2 induced electrostatically using a thin boron nitride support as a dielectric layer with multiple metal gates beneath. This structure allows effective injection of electrons and holes, and combined with the high optical quality of WSe2 it yields bright electroluminescence with 1000 times smaller injection current and 10 times smaller linewidth than in MoS2. Furthermore, by increasing the injection bias we can tune the electroluminescence between regimes of impurity-bound, charged, and neutral excitons. This system has the required ingredients for new kinds of optoelectronic devices such as spin- and valley-polarized light-emitting diodes, on-chip lasers, and two-dimensional electro-optic modulators.Comment: 13 pages main text with 4 figures + 4 pages upplemental material

Wnt-4 regulation by the Wilms' tumour suppressor gene, WT1

The Wilms' tumour suppressor gene, WT1, encodes multiple nuclear protein isoforms, all containing four C-terminal zinc finger motifs. WT1 proteins can both activate and repress putative target genes in vitro, although the in vivo relevance of these putative target genes is often unverified. WT1 mutations can result in Wilms' tumour and the Denys-Drash Syndrome (DDS) of infantile nephropathy, XY pseudohermaphroditism and predisposition to Wilms' tumour. We have established stable transfectants of the mouse mesonephric cell line, M15, which express WT1 harbouring a common DDS point mutation (R394W). A comparison of the expression profiles of M15 and transfectant C2A was performed using Nylon-based arrays. Very few genes showed differential expression. However Wnt-4, a member of the Wnt gene family of secreted glycoproteins, was downregulated in C2A and other similar clones. Doxycycline induction of WT1-A or WT1-D expression in HEK293 stable transfectants also elicited an elevation in Wnt4 expression. Wnt4 is critical for the mesenchyme-to-epithelial transition during kidney development, making it an attractive putative WT1 target. We have mapped human Wnt-4 gene to chromosome 1p35-36, a region of frequent LOH in WT, have characterized the genomic structure of the human Wnt-4 gene and isolated 9 kb of immediate promoter. While several potential WT1 binding sites exist within this promoter, reporter analysis does not strongly support the direct regulation of Wnt4 by WT1. We propose that Wnt-4 regulation by WT1 occurs at a more distant promoter or enhancer site, or is indirect

Systems Analysis of the Dynamic Inflammatory Response to Tissue Damage Reveals Spatiotemporal Properties of the Wound Attractant Gradient

In the acute inflammatory phase following tissue damage, cells of the innate immune system are rapidly recruited to sites of injury by pro-inflammatory mediators released at the wound site. Although advances in live imaging allow us to directly visualize this process in vivo, the precise identity and properties of the primary immune damage attractants remain unclear, as it is currently impossible to directly observe and accurately measure these signals in tissues. Here, we demonstrate that detailed information about the attractant signals can be extracted directly from the in vivo behavior of the responding immune cells. By applying inference-based computational approaches to analyze the in vivo dynamics of the Drosophila inflammatory response, we gain new detailed insight into the spatiotemporal properties of the attractant gradient. In particular, we show that the wound attractant is released by wound margin cells, rather than by the wounded tissue per se, and that it diffuses away from this source at rates far slower than those of previously implicated signals such as H2O2 and ATP, ruling out these fast mediators as the primary chemoattractant. We then predict, and experimentally test, how competing attractant signals might interact in space and time to regulate multi-step cell navigation in the complex environment of a healing wound, revealing a period of receptor desensitization after initial exposure to the damage attractant. Extending our analysis to model much larger wounds, we uncover a dynamic behavioral change in the responding immune cells in vivo that is prognostic of whether a wound will subsequently heal or not