Characterizing the epoch of reionization with the small-scale CMB: Constraints on the optical depth and duration

Patchy reionization leaves a number of imprints on the small-scale cosmic microwave background (CMB) temperature fluctuations, the largest of which is the kinematic Sunyaev-Zel'dovich (kSZ), the Doppler shift of CMB photons scattering off moving electrons in ionized bubbles. It has long been known that in the CMB power spectrum, this imprint of reionization is largely degenerate with the kSZ signal produced by late-time galaxies and clusters, thus limiting our ability to constrain reionization. Following Smith & Ferraro (2017), it is possible to isolate the reionization contribution in a model independent way, by looking at the large scale modulation of the small scale CMB power spectrum. In this paper we extend the formalism to use the full shape information of the small scale power spectrum (rather than just its broadband average), and argue that this is necessary to break the degeneracy between the optical depth τ and parameters setting the duration of reionization. In particular, we show that the next generation of CMB experiments could achieve up to a factor of 3 improvement on the optical depth τ and at the same time, constrain the duration of reionization to ∼25%. This can help tighten the constrains on neutrino masses, which will be limited by our knowledge of τ, and shed light on the physical processes responsible for reionization

Estimation of the basic reproductive number and mean serial interval of a novel pathogen in a small, well-observed discrete population

BACKGROUND:Accurately assessing the transmissibility and serial interval of a novel human pathogen is public health priority so that the timing and required strength of interventions may be determined. Recent theoretical work has focused on making best use of data from the initial exponential phase of growth of incidence in large populations. METHODS:We measured generational transmissibility by the basic reproductive number R0 and the serial interval by its mean Tg. First, we constructed a simulation algorithm for case data arising from a small population of known size with R0 and Tg also known. We then developed an inferential model for the likelihood of these case data as a function of R0 and Tg. The model was designed to capture a) any signal of the serial interval distribution in the initial stochastic phase b) the growth rate of the exponential phase and c) the unique combination of R0 and Tg that generates a specific shape of peak incidence when the susceptible portion of a small population is depleted. FINDINGS:Extensive repeat simulation and parameter estimation revealed no bias in univariate estimates of either R0 and Tg. We were also able to simultaneously estimate both R0 and Tg. However, accurate final estimates could be obtained only much later in the outbreak. In particular, estimates of Tg were considerably less accurate in the bivariate case until the peak of incidence had passed. CONCLUSIONS:The basic reproductive number and mean serial interval can be estimated simultaneously in real time during an outbreak of an emerging pathogen. Repeated application of these methods to small scale outbreaks at the start of an epidemic would permit accurate estimates of key parameters

Expression of proliferation-dependent antigens during cellular ageing of normal and progeroid human fibroblasts

Normal human fibroblasts display a limited lifespan in culture, which is due to a steadily decreasing fraction of cells that are able to proliferate. Using antibodies that react with antigens present in proliferating cells only, in an indirect immunofluorescence assay, we have estimated the fraction of proliferating cells in cultures of normal human fibroblasts. Furthermore, we have estimated the rate of decline in the fraction of proliferating cells during the process of cellular ageing by application of the assay to normal human fibroblasts throughout their lifespan in culture. Werner’s Syndrome is an autosomal recessive disease in which individuals display symptoms of ageing prematurely. Werner’s Syndrome fibroblasts display a reduced lifespan in culture compared with normal human fibroblasts. Like normal human fibroblasts, the growth of Werner’s Syndrome fibroblasts is characterised by a decreasing fraction of cells reacting with the proliferationassociated antibodies throughout their lifespan in culture. However, the rate of loss of proliferating cells in Werner’s Syndrome fibroblasts during the process of cellular ageing is accelerated 5- to 6-fold compared with the rate determined for normal human fibroblasts

Motion and disparity estimation with self adapted evolutionary strategy in 3D video coding

Real world information, obtained by humans is three dimensional (3-D). In experimental user-trials, subjective assessments have clearly demonstrated the increased impact of 3-D pictures compared to conventional flat-picture techniques. It is reasonable, therefore, that we humans want an imaging system that produces pictures that are as natural and real as things we see and experience every day. Three-dimensional imaging and hence, 3-D television (3DTV) are very promising approaches expected to satisfy these desires. Integral imaging, which can capture true 3D color images with only one camera, has been seen as the right technology to offer stress-free viewing to audiences of more than one person. In this paper, we propose a novel approach to use Evolutionary Strategy (ES) for joint motion and disparity estimation to compress 3D integral video sequences. We propose to decompose the integral video sequence down to viewpoint video sequences and jointly exploit motion and disparity redundancies to maximize the compression using a self adapted ES. A half pixel refinement algorithm is then applied by interpolating macro blocks in the previous frame to further improve the video quality. Experimental results demonstrate that the proposed adaptable ES with Half Pixel Joint Motion and Disparity Estimation can up to 1.5 dB objective quality gain without any additional computational cost over our previous algorithm.1Furthermore, the proposed technique get similar objective quality compared to the full search algorithm by reducing the computational cost up to 90%

Vascular complications after liver transplantation: A 5-year experience

During the past 5 years, 104 angiographic studies were performed in 87 patients (45 children and 42 adults) with 92 transplanted livers for evaluation of possible vascular complications. Seventy percent of the studies were abnormal. Hepatic artery thrombosis was the most common complication (seen in 42% of children studied, compared with only 12% of adults) and was a major complication that frequently resulted in graft failure, usually necessitating retransplantation. In six children, reconstitution of the intrahepatic arteries by collaterals was seen. Three survived without retransplant. Arterial stenosis at the anastomosis or in the donor hepatic artery was observed in 11% of patients. Portal vein thrombosis or stenosis occurred in 13% of patients. Two children and one adult with portal vein thrombosis demonstrated hepatopetal collaterals that reconstituted the intrahepatic portal vessels. Uncommon complications included anastomotic and donor hepatic artery pseudoaneurysms, a hepatic artery-dissecting aneurysm, pancreaticoduodenal mycotic aneurysms, hepatic artery-portal vein fistula, biliary-portal vein fistula, hepatic vein occlusion, and inferior vena cava thrombosis

Generalized Shortest Path Kernel on Graphs

We consider the problem of classifying graphs using graph kernels. We define a new graph kernel, called the generalized shortest path kernel, based on the number and length of shortest paths between nodes. For our example classification problem, we consider the task of classifying random graphs from two well-known families, by the number of clusters they contain. We verify empirically that the generalized shortest path kernel outperforms the original shortest path kernel on a number of datasets. We give a theoretical analysis for explaining our experimental results. In particular, we estimate distributions of the expected feature vectors for the shortest path kernel and the generalized shortest path kernel, and we show some evidence explaining why our graph kernel outperforms the shortest path kernel for our graph classification problem.Comment: Short version presented at Discovery Science 2015 in Banf

Learning Temporal Transformations From Time-Lapse Videos

Based on life-long observations of physical, chemical, and biologic phenomena in the natural world, humans can often easily picture in their minds what an object will look like in the future. But, what about computers? In this paper, we learn computational models of object transformations from time-lapse videos. In particular, we explore the use of generative models to create depictions of objects at future times. These models explore several different prediction tasks: generating a future state given a single depiction of an object, generating a future state given two depictions of an object at different times, and generating future states recursively in a recurrent framework. We provide both qualitative and quantitative evaluations of the generated results, and also conduct a human evaluation to compare variations of our models.Comment: ECCV201

Simulation-guided design of serological surveys of the cumulative incidence of influenza infection

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