Smartphone-based, rapid, wide-field fundus photography for diagnosis of pediatric retinal diseases

PurposeAn important, unmet clinical need is for cost-effective, reliable, easy-to-use, and portable retinal photography to evaluate preventable causes of vision loss in children. This study presents the feasibility of a novel smartphone-based retinal imaging device tailored to imaging the pediatric fundus.MethodsSeveral modifications for children were made to our previous device, including a child-friendly 3D printed housing of animals, attention-grabbing targets, enhanced image stitching, and video-recording capabilities. Retinal photographs were obtained in children undergoing routine dilated eye examination. Experienced masked retina-specialist graders determined photograph quality and made diagnoses based on the images, which were compared to the treating clinician's diagnosis.ResultsDilated fundus photographs were acquired in 43 patients with a mean age of 6.7 years. The diagnoses included retinoblastoma, Coats' disease, commotio retinae, and optic nerve hypoplasia, among others. Mean time to acquire five standard photographs totaling 90-degree field of vision was 2.3 ± 1.1 minutes. Patients rated their experience of image acquisition favorably, with a Likert score of 4.6 ± 0.8 out of 5. There was 96% agreement between image-based diagnosis and the treating clinician's diagnosis.ConclusionsWe report a handheld smartphone-based device with modifications tailored for wide-field fundus photography in pediatric patients that can rapidly acquire fundus photos while being well-tolerated.Translational relevanceAdvances in handheld smartphone-based fundus photography devices decrease the technical barrier for image acquisition in children and may potentially increase access to ophthalmic care in communities with limited resources

A Smartphone-Based Tool for Rapid, Portable, and Automated Wide-Field Retinal Imaging.

Purpose:High-quality, wide-field retinal imaging is a valuable method for screening preventable, vision-threatening diseases of the retina. Smartphone-based retinal cameras hold promise for increasing access to retinal imaging, but variable image quality and restricted field of view can limit their utility. We developed and clinically tested a smartphone-based system that addresses these challenges with automation-assisted imaging. Methods:The system was designed to improve smartphone retinal imaging by combining automated fixation guidance, photomontage, and multicolored illumination with optimized optics, user-tested ergonomics, and touch-screen interface. System performance was evaluated from images of ophthalmic patients taken by nonophthalmic personnel. Two masked ophthalmologists evaluated images for abnormalities and disease severity. Results:The system automatically generated 100° retinal photomontages from five overlapping images in under 1 minute at full resolution (52.3 pixels per retinal degree) fully on-phone, revealing numerous retinal abnormalities. Feasibility of the system for diabetic retinopathy (DR) screening using the retinal photomontages was performed in 71 diabetics by masked graders. DR grade matched perfectly with dilated clinical examination in 55.1% of eyes and within 1 severity level for 85.2% of eyes. For referral-warranted DR, average sensitivity was 93.3% and specificity 56.8%. Conclusions:Automation-assisted imaging produced high-quality, wide-field retinal images that demonstrate the potential of smartphone-based retinal cameras to be used for retinal disease screening. Translational Relevance:Enhancement of smartphone-based retinal imaging through automation and software intelligence holds great promise for increasing the accessibility of retinal screening

Constitutively active Notch4 receptor elicits brain arteriovenous malformations through enlargement of capillary-like vessels

Arteriovenous (AV) malformation (AVM) is a devastating condition characterized by focal lesions of enlarged, tangled vessels that shunt blood from arteries directly to veins. AVMs can form anywhere in the body and can cause debilitating ischemia and life-threatening hemorrhagic stroke. The mechanisms that underlie AVM formation remain poorly understood. Here, we examined the cellular and hemodynamic changes at the earliest stages of brain AVM formation by time-lapse two-photon imaging through cranial windows of mice expressing constitutively active Notch4 (Notch4*). AVMs arose from enlargement of preexisting microvessels with capillary diameter and blood flow and no smooth muscle cell coverage. AV shunting began promptly after Notch4* expression in endothelial cells (ECs), accompanied by increased individual EC areas, rather than increased EC number or proliferation. Alterations in Notch signaling in ECs of all vessels, but not arteries alone, affected AVM formation, suggesting that Notch functions in the microvasculature and/or veins to induce AVM. Increased Notch signaling interfered with the normal biological control of hemodynamics, permitting a positive feedback loop of increasing blood flow and vessel diameter and driving focal AVM growth from AV connections with higher blood velocity at the expense of adjacent AV connections with lower velocity. Endothelial expression of constitutively active Notch1 also led to brain AVMs in mice. Our data shed light on cellular and hemodynamic mechanisms underlying AVM pathogenesis elicited by increased Notch signaling in the endothelium.American Heart Association (Grant 0715062Y)Tobacco-Related Disease Research Program (Predoctoral Fellowship 18DT-0009

The Whole is Greater than the Sum of the Parts: Optimizing the Joint Science Return from LSST, Euclid and WFIRST

The focus of this report is on the opportunities enabled by the combination of LSST, Euclid and WFIRST, the optical surveys that will be an essential part of the next decade's astronomy. The sum of these surveys has the potential to be significantly greater than the contributions of the individual parts. As is detailed in this report, the combination of these surveys should give us multi-wavelength high-resolution images of galaxies and broadband data covering much of the stellar energy spectrum. These stellar and galactic data have the potential of yielding new insights into topics ranging from the formation history of the Milky Way to the mass of the neutrino. However, enabling the astronomy community to fully exploit this multi-instrument data set is a challenging technical task: for much of the science, we will need to combine the photometry across multiple wavelengths with varying spectral and spatial resolution. We identify some of the key science enabled by the combined surveys and the key technical challenges in achieving the synergies.Comment: Whitepaper developed at June 2014 U. Penn Workshop; 28 pages, 3 figure

Transfer Functions for Protein Signal Transduction: Application to a Model of Striatal Neural Plasticity

We present a novel formulation for biochemical reaction networks in the context of signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of 'source' species, which receive input signals. Signals are transmitted to all other species in the system (the 'target' species) with a specific delay and transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and recalled to build discrete dynamical models. By separating reaction time and concentration we can greatly simplify the model, circumventing typical problems of complex dynamical systems. The transfer function transformation can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant insight, while remaining an executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modules that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. We also found that overall interconnectedness depends on the magnitude of input, with high connectivity at low input and less connectivity at moderate to high input. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation.Comment: 13 pages, 5 tables, 15 figure

Bayesian Cluster Finder: Clusters in the CFHTLS Archive Research Survey

The detection of galaxy clusters in present and future surveys enables measuring mass-to-light ratios, clustering properties, galaxy cluster abundances and therefore, constraining cosmological parameters. We present a new technique for detecting galaxy clusters, which is based on the Matched Filter Algorithm from a Bayesian point of view. The method is able to determine the position, redshift and richness of the cluster through the maximization of a filter depending on galaxy luminosity, density and photometric redshift combined with a galaxy cluster prior that accounts for color-magnitude relations and BCG-redshift relation. We tested the algorithm through realistic mock galaxy catalogs, revealing that the detections are 100% complete and 80% pure for clusters up to z $$20 (Abell Richness $\sim$0, M$\sim4\times10^{14} M_{\odot}$). The completeness and purity remains approximately the same if we do not include the prior information, implying that this method is able to detect galaxy cluster with and without a well defined red sequence. We applied the algorithm to the CFHTLS Archive Research Survey (CARS) data, recovering similar detections as previously published using the same or deeper data plus additional clusters which appear to be real.Comment: Accepted for publication in MNRAS; 17 pages, 38 figure

Using ILP to Identify Pathway Activation Patterns in Systems Biology

We show a logical aggregation method that, combined with propositionalization methods, can construct novel structured biological features from gene expression data. We do this to gain understanding of pathway mechanisms, for instance, those associated with a particular disease. We illustrate this method on the task of distinguishing between two types of lung cancer; Squamous Cell Carcinoma (SCC) and Adenocarcinoma (AC). We identify pathway activation patterns in pathways previously implicated in the development of cancers. Our method identified a model with comparable predictive performance to the winning algorithm of a recent challenge, while providing biologically relevant explanations that may be useful to a biologist

A Method For Eclipsing Component Identification In Large Photometric Datasets

We describe an automated method for assigning the most likely physical parameters to the components of an eclipsing binary (EB), using only its photometric light curve and combined color. In traditional methods (e.g. WD and EBOP) one attempts to optimize a multi-parameter model over many iterations, so as to minimize the chi-squared value. We suggest an alternative method, where one selects pairs of coeval stars from a set of theoretical stellar models, and compares their simulated light curves and combined colors with the observations. This approach greatly reduces the EB parameter-space over which one needs to search, and allows one to determine the components' masses, radii and absolute magnitudes, without spectroscopic data. We have implemented this method in an automated program using published theoretical isochrones and limb-darkening coefficients. Since it is easy to automate, this method lends itself to systematic analyses of datasets consisting of photometric time series of large numbers of stars, such as those produced by OGLE, MACHO, TrES, HAT, and many others surveys.Comment: 6 pages, 5 figures. To appear in the conference proceedings of "Close Binaries in the 21st Century: New Opportunities and Challenges", Syros, Greece, 27-30 June, 200

Diagnosing idiopathic learning disability: a cost-effectiveness analysis of microarray technology in the National Health Service of the United Kingdom

Array based comparative genomic hybridisation (aCGH) is a powerful technique for detecting clinically relevant genome imbalance and can offer 40 to > 1000 times the resolution of karyotyping. Indeed, idiopathic learning disability (ILD) studies suggest that a genome-wide aCGH approach makes 10–15% more diagnoses involving genome imbalance than karyotyping. Despite this, aCGH has yet to be implemented as a routine NHS service. One significant obstacle is the perception that the technology is prohibitively expensive for most standard NHS clinical cytogenetics laboratories. To address this, we investigated the cost-effectiveness of aCGH versus standard cytogenetic analysis for diagnosing idiopathic learning disability (ILD) in the NHS. Cost data from four participating genetics centres were collected and analysed. In a single test comparison, the average cost of aCGH was £442 and the average cost of karyotyping was £117 with array costs contributing most to the cost difference. This difference was not a key barrier when the context of follow up diagnostic tests was considered. Indeed, in a hypothetical cohort of 100 ILD children, aCGH was found to cost less per diagnosis (£3,118) than a karyotyping and multi-telomere FISH approach (£4,957). We conclude that testing for genomic imbalances in ILD using microarray technology is likely to be cost-effective because long-term savings can be made regardless of a positive (diagnosis) or negative result. Earlier diagnoses save costs of additional diagnostic tests. Negative results are cost-effective in minimising follow-up test choice. The use of aCGH in routine clinical practice warrants serious consideration by healthcare providers

Dynamical mean-field theory of the small polaron

A dynamical mean-field theory of the small polaron problem is presented, which becomes exact in the limit of infinite dimensions. The ground state properties and the one-electron spectral function are obtained for a single electron interacting with Einstein phonons by a mapping of the lattice problem onto a polaronic impurity model. The one-electron propagator of the impurity model is calculated through a continued fraction expansion (CFE), both at zero and finite temperature, for any electron-phonon coupling and phonon energy. In contrast to the ground state properties such as the effective polaron mass, which have a smooth behaviour, spectral properties exhibit a sharp qualitative change at low enough phonon frequency: beyond a critical coupling, one energy gap and then more and more open in the density of states at low energy, while the high energy part of the spectrum is broad and can be explained by a strong coupling adiabatic approximation. As a consequence narrow and coherent low-energy subbands coexist with an incoherent featureless structure at high energy. The subbands denote the formation of quasiparticle polaron states. Also, divergencies of the self-energy may occur in the gaps. At finite temperature such effect triggers an important damping and broadening of the polaron subbands. On the other hand, in the large phonon frequency regime such a separation of energy scales does not exist and the spectrum has always a multipeaked structure.Comment: 21 Pages Latex, 19 PostScript figure