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Hard-to-predict bursts of COVID-19 pandemic revealed significance of statistical modeling which would resolve spatio-temporal correlations over geographical areas, for example spread of the infection over a city with census tract granularity. In this manuscript, we provide algorithmic answers to the following two inter-related public health challenges of immense social impact which have not been adequately addressed (1) Inference Challenge assuming that there are N census blocks (nodes) in the city, and given an initial infection at any set of nodes, e.g. any N of possible single node infections, any [Formula: see text] of possible two node infections, etc, what is the probability for a subset of census blocks to become infected by the time the spread of the infection burst is stabilized? (2) Prevention Challenge What is the minimal control action one can take to minimize the infected part of the stabilized state footprint? To answer the challenges, we build a Graphical Model of pandemic of the attractive Ising (pair-wise, binary) type, where each node represents a census tract and each edge factor represents the strength of the pairwise interaction between a pair of nodes, e.g. representing the inter-node travel, road closure and related, and each local bias/field represents the community level of immunization, acceptance of the social distance and mask wearing practice, etc. Resolving the Inference Challenge requires finding the Maximum-A-Posteriory (MAP), i.e. most probable, state of the Ising Model constrained to the set of initially infected nodes. (An infected node is in the [Formula: see text] state and a node which remained safe is in the [Formula: see text] state.) We show that almost all attractive Ising Models on dense graphs result in either of the two possibilities (modes) for the MAP state: either all nodes which were not infected initially became infected, or all the initially uninfected nodes remain uninfected (susceptible). This bi-modal solution of the Inference Challenge allows us to re-state the Prevention Challenge as the following tractable convex programming: for the bare Ising Model with pair-wise and bias factors representing the system without prevention measures, such that the MAP state is fully infected for at least one of the initial infection patterns, find the closest, for example in [Formula: see text], [Formula: see text] or any other convexity-preserving norm, therefore prevention-optimal, set of factors resulting in all the MAP states of the Ising model, with the optimal prevention measures applied, to become safe. We have illustrated efficiency of the scheme on a quasi-realistic model of Seattle. Our experiments have also revealed useful features, such as sparsity of the prevention solution in the case of the [Formula: see text] norm, and also somehow unexpected features, such as localization of the sparse prevention solution at pair-wise links which are NOT these which are most utilized/traveled.
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COVID-19 , Pandemias , COVID-19/epidemiología , COVID-19/prevención & control , Humanos , Modelos Estadísticos , Pandemias/prevención & control , Distanciamiento Físico , Salud PúblicaRESUMEN
Multi-channel wireless networks are increasingly deployed as infrastructure networks, e.g. in metro areas. Network nodes frequently employ directional antennas to improve spatial throughput. In such networks, between two nodes, it is of interest to compute a path with a channel assignment for the links such that the path and link bandwidths are the same. This is achieved when any two consecutive links are assigned different channels, termed as "Channel-Discontinuity-Constraint" (CDC). CDC-paths are also useful in TDMA systems, where, preferably, consecutive links are assigned different time-slots. In the first part of this paper, we develop a t-spanner for CDC-paths using spatial properties; a sub-network containing O(n/θ) links, for any θ > 0, such that CDC-paths increase in cost by at most a factor t = (1-2 sin (θ/2))-2. We propose a novel distributed algorithm to compute the spanner using an expected number of O(n log n) fixed-size messages. In the second part, we present a distributed algorithm to find minimum-cost CDC-paths between two nodes using O(n2) fixed-size messages, by developing an extension of Edmonds' algorithm for minimum-cost perfect matching. In a centralized implementation, our algorithm runs in O(n2) time improving the previous best algorithm which requires O(n3) running time. Moreover, this running time improves to O(n/θ) when used in conjunction with the spanner developed.
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This paper investigates the feasibility of using boats as data mule nodes, carrying medical ultrasound videos from remote and isolated communities in the Amazon region in Brazil, to the main city of that area. The videos will be used by physicians to perform remote analysis and follow-up routine of prenatal examinations of pregnant women. Two open source simulators (the ONE and NS-2) were used to evaluate the results obtained utilizing a CoDPON (continuous displacement plan oriented network). The simulations took into account the connection times between the network nodes (boats) and the number of nodes on each boat route.
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We describe a robust and efficient method for automatically matching and time-aligning electronic slides to videos of corresponding presentations. Matching electronic slides to videos provides new methods for indexing, searching, and browsing videos in distance-learning applications. However, robust automatic matching is challenging due to varied frame composition, slide distortion, camera movement, low-quality video capture, and arbitrary slides sequence. Our fully automatic approach combines image-based matching of slide to video frames with a temporal model for slide changes and camera events. To address these challenges, we begin by extracting scale-invariant feature-transformation (SIFT) keypoints from both slides and video frames, and matching them subject to a consistent projective transformation (homography) by using random sample consensus (RANSAC). We use the initial set of matches to construct a background model and a binary classifier for separating video frames showing slides from those without. We then introduce a new matching scheme for exploiting less distinctive SIFT keypoints that enables us to tackle more difficult images. Finally, we improve upon the matching based on visual information by using estimated matching probabilities as part of a hidden Markov model (HMM) that integrates temporal information and detected camera operations. Detailed quantitative experiments characterize each part of our approach and demonstrate an average accuracy of over 95% in 13 presentation videos.
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Using primary cell culture to screen for changes in neuronal morphology requires specialized analysis software. We developed NeuronMetrics for semi-automated, quantitative analysis of two-dimensional (2D) images of fluorescently labeled cultured neurons. It skeletonizes the neuron image using two complementary image-processing techniques, capturing fine terminal neurites with high fidelity. An algorithm was devised to span wide gaps in the skeleton. NeuronMetrics uses a novel strategy based on geometric features called faces to extract a branch number estimate from complex arbors with numerous neurite-to-neurite contacts, without creating a precise, contact-free representation of the neurite arbor. It estimates total neurite length, branch number, primary neurite number, territory (the area of the convex polygon bounding the skeleton and cell body), and Polarity Index (a measure of neuronal polarity). These parameters provide fundamental information about the size and shape of neurite arbors, which are critical factors for neuronal function. NeuronMetrics streamlines optional manual tasks such as removing noise, isolating the largest primary neurite, and correcting length for self-fasciculating neurites. Numeric data are output in a single text file, readily imported into other applications for further analysis. Written as modules for ImageJ, NeuronMetrics provides practical analysis tools that are easy to use and support batch processing. Depending on the need for manual intervention, processing time for a batch of approximately 60 2D images is 1.0-2.5 h, from a folder of images to a table of numeric data. NeuronMetrics' output accelerates the quantitative detection of mutations and chemical compounds that alter neurite morphology in vitro, and will contribute to the use of cultured neurons for drug discovery.
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Diagnóstico por Imagen , Neuritas/ultraestructura , Neuronas/ultraestructura , Programas Informáticos , Animales , Automatización , Células Cultivadas , Sistema Nervioso Central/citología , Sistema Nervioso Central/ultraestructura , Drosophila , Colorantes Fluorescentes , Microscopía , Programas Informáticos/normas , Factores de TiempoRESUMEN
Subtle cellular phenotypes in the CNS may evade detection by routine histopathology. Here, we demonstrate the value of primary culture for revealing genetically determined neuronal phenotypes at high resolution. Gamma neurons of Drosophila melanogaster mushroom bodies (MBs) are remodeled during metamorphosis under the control of the steroid hormone 20-hydroxyecdysone (20E). In vitro, wild-type gamma neurons retain characteristic morphogenetic features, notably a single axon-like dominant primary process and an arbor of short dendrite-like processes, as determined with microtubule-polarity markers. We found three distinct genetically determined phenotypes of cultured neurons from grossly normal brains, suggesting that subtle in vivo attributes are unmasked and amplified in vitro. First, the neurite outgrowth response to 20E is sexually dimorphic, being much greater in female than in male gamma neurons. Second, the gamma neuron-specific "naked runt" phenotype results from transgenic insertion of an MB-specific promoter. Third, the recessive, pan-neuronal "filagree" phenotype maps to singed, which encodes the actin-bundling protein fascin. Fascin deficiency does not impair the 20E response, but neurites fail to maintain their normal, nearly straight trajectory, instead forming curls and hooks. This is accompanied by abnormally distributed filamentous actin. This is the first demonstration of fascin function in neuronal morphogenesis. Our findings, along with the regulation of human Fascin1 (OMIM 602689) by CREB (cAMP response element-binding protein) binding protein, suggest FSCN1 as a candidate gene for developmental brain disorders. We developed an automated method of computing neurite curvature and classifying neurons based on curvature phenotype. This will facilitate detection of genetic and pharmacological modifiers of neuronal defects resulting from fascin deficiency.
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Encéfalo/fisiología , Proteínas Portadoras/fisiología , Drosophila melanogaster/fisiología , Proteínas de Microfilamentos/fisiología , Neuritas/fisiología , Neuritas/ultraestructura , Neuronas/fisiología , Actinas/metabolismo , Animales , Axones/fisiología , Encéfalo/citología , Proteínas Portadoras/genética , Polaridad Celular/fisiología , Células Cultivadas , Proteínas de Unión al ADN/fisiología , Dendritas/fisiología , Proteínas de Drosophila , Ecdisterona/farmacología , Femenino , Masculino , Proteínas de Microfilamentos/genética , Cuerpos Pedunculados/ultraestructura , Mutación/fisiología , Neuronas/efectos de los fármacos , Neuronas/metabolismo , Fenotipo , Caracteres Sexuales , Distribución Tisular , Factores de Transcripción/fisiologíaRESUMEN
In proteomics, two-dimensional gel electrophoresis (2-DE) is a separation technique for proteins. The resulting protein spots can be identified either by using picking robots and subsequent mass spectrometry or by visual cross inspection of a new gel image with an already analyzed master gel. Difficulties especially arise from inherent noise and irregular geometric distortions in 2-DE images. Aiming at the automated analysis of large series of 2-DE images, or at the even more difficult interlaboratory gel comparisons, the bottleneck is to solve the two most basic algorithmic problems with high quality: Identifying protein spots and computing a matching between two images. For the development of the analysis software CAROl at Freie Universität Berlin, we have reconsidered these two problems and obtained new solutions which rely on methods from computational geometry. Their novelties are: 1. Spot detection is also possible for complex regions formed by several "merged" (usually saturated) spots; 2. User-defined landmarks are not necessary for the matching. Furthermore, images for comparison are allowed to represent different parts of the entire protein pattern, which only partially "overlap." The implementation is done in a client server architecture to allow queries via the internet. We also discuss and point at related theoretical questions in computational geometry.