Point Source Extraction with MOPEX

MOPEX (MOsaicking and Point source EXtraction) is a package developed at the Spitzer Science Center for astronomical image processing. We report on the point source extraction capabilities of MOPEX. Point source extraction is implemented as a two step process: point source detection and profile fitting. Non-linear matched filtering of input images can be performed optionally to increase the signal-to-noise ratio and improve detection of faint point sources. Point Response Function (PRF) fitting of point sources produces the final point source list which includes the fluxes and improved positions of the point sources, along with other parameters characterizing the fit. Passive and active deblending allows for successful fitting of confused point sources. Aperture photometry can also be computed for every extracted point source for an unlimited number of aperture sizes. PRF is estimated directly from the input images. Implementation of efficient methods of background and noise estimation, and modified Simplex algorithm contribute to the computational efficiency of MOPEX. The package is implemented as a loosely connected set of perl scripts, where each script runs a number of modules written in C/C++. Input parameter setting is done through namelists, ASCII configuration files. We present applications of point source extraction to the mosaic images taken at 24 and 70 micron with the Multiband Imaging Photometer (MIPS) as part of the Spitzer extragalactic First Look Survey and to a Digital Sky Survey image. Completeness and reliability of point source extraction is computed using simulated data.Comment: 20 pages, 13 Postscript figures, accepted for publication in PAS

Cell size distribution in a random tessellation of space governed by the Kolmogorov-Johnson-Mehl-Avrami model: Grain size distribution in crystallization

The space subdivision in cells resulting from a process of random nucleation and growth is a subject of interest in many scientific fields. In this paper, we deduce the expected value and variance of these distributions while assuming that the space subdivision process is in accordance with the premises of the Kolmogorov-Johnson-Mehl-Avrami model. We have not imposed restrictions on the time dependency of nucleation and growth rates. We have also developed an approximate analytical cell size probability density function. Finally, we have applied our approach to the distributions resulting from solid phase crystallization under isochronal heating conditions

Gravitational Wilson Loop and Large Scale Curvature

In a quantum theory of gravity the gravitational Wilson loop, defined as a suitable quantum average of a parallel transport operator around a large near-planar loop, provides important information about the large-scale curvature properties of the geometry. Here we shows that such properties can be systematically computed in the strong coupling limit of lattice regularized quantum gravity, by performing a local average over rotations, using an assumed near-uniform measure in group space. We then relate the resulting quantum averages to an expected semi-classical form valid for macroscopic observers, which leads to an identification of the gravitational correlation length appearing in the Wilson loop with an observed large-scale curvature. Our results suggest that strongly coupled gravity leads to a positively curved (De Sitter-like) quantum ground state, implying a positive effective cosmological constant at large distances.Comment: 22 pages, 6 figure

Cell Detection with Star-convex Polygons

Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications. Recent successful learning-based approaches include per-pixel cell segmentation with subsequent pixel grouping, or localization of bounding boxes with subsequent shape refinement. In situations of crowded cells, these can be prone to segmentation errors, such as falsely merging bordering cells or suppressing valid cell instances due to the poor approximation with bounding boxes. To overcome these issues, we propose to localize cell nuclei via star-convex polygons, which are a much better shape representation as compared to bounding boxes and thus do not need shape refinement. To that end, we train a convolutional neural network that predicts for every pixel a polygon for the cell instance at that position. We demonstrate the merits of our approach on two synthetic datasets and one challenging dataset of diverse fluorescence microscopy images.Comment: Conference paper at MICCAI 201

З'єднання зварюванням

Метою методичних вказівок «З’єднання зварюванням» є ознайомлен-ня зі способами нероз’ємних з’єднань, вивчення правил зображення та поз-начення швів з’єднань зварюванням на кресленні, набуття навиків виконан-ня, оформлення та читання креслень з’єднань зварюванням, які необхідні для вивчення загально-інженерних та спеціальних технічних дисциплін.Вступ...3 1.Загальні відомості....4 2.Класифікація з’єднаньзварюванням...4 3.Умовне зображення і позначення швів з’єднань, які зварюють....7 4.Спрощене позначення швів з’єднаньякі зварюють...15 5.Оформлення креслень з’єднань зварюванням...23 6.Вказівки до виконання графічних робіт та індивідуальні завдання...24 7.Додаток А...27 8.Додаток Б...28 9.Додаток В...29 10.Використана література...4

Local Anisotropy of Fluids using Minkowski Tensors

Statistics of the free volume available to individual particles have previously been studied for simple and complex fluids, granular matter, amorphous solids, and structural glasses. Minkowski tensors provide a set of shape measures that are based on strong mathematical theorems and easily computed for polygonal and polyhedral bodies such as free volume cells (Voronoi cells). They characterize the local structure beyond the two-point correlation function and are suitable to define indices $0\leq \beta_\nu^{a,b}\leq 1$ of local anisotropy. Here, we analyze the statistics of Minkowski tensors for configurations of simple liquid models, including the ideal gas (Poisson point process), the hard disks and hard spheres ensemble, and the Lennard-Jones fluid. We show that Minkowski tensors provide a robust characterization of local anisotropy, which ranges from $\beta_\nu^{a,b}\approx 0.3$ for vapor phases to $\beta_\nu^{a,b}\to 1$ for ordered solids. We find that for fluids, local anisotropy decreases monotonously with increasing free volume and randomness of particle positions. Furthermore, the local anisotropy indices $\beta_\nu^{a,b}$ are sensitive to structural transitions in these simple fluids, as has been previously shown in granular systems for the transition from loose to jammed bead packs

Huisvesting van melkvee: knelpunten uit oogpunt van welzijn

Nederlands melkvee staat een aanzienlijk deel van het jaar op stal. De huisvesting is dan ook van groot belang voor het dierwelzijn. Door middel van beoordeling van welzijn kunnen eventuele knelpunten worden opgespoord

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Ising model on 3D random lattices: A Monte Carlo study

We report single-cluster Monte Carlo simulations of the Ising model on three-dimensional Poissonian random lattices with up to 128,000 approx. 503 sites which are linked together according to the Voronoi/Delaunay prescription. For each lattice size quenched averages are performed over 96 realizations. By using reweighting techniques and finite-size scaling analyses we investigate the critical properties of the model in the close vicinity of the phase transition point. Our random lattice data provide strong evidence that, for the available system sizes, the resulting effective critical exponents are indistinguishable from recent high-precision estimates obtained in Monte Carlo studies of the Ising model and \phi^4 field theory on three-dimensional regular cubic lattices.Comment: 35 pages, LaTex, 8 tables, 8 postscript figure

The Role of TLR4 in the Paclitaxel Effects on Neuronal Growth In Vitro

Paclitaxel (Pac) is an antitumor agent that is widely used for treatment of solid cancers. While being effective as a chemotherapeutic agent, Pac in high doses is neurotoxic, specifically targeting sensory innervations. In view of these toxic effects associated with conventional chemotherapy, decreasing the dose of Pac has been recently suggested as an alternative approach, which might limit neurotoxicity and immunosuppression. However, it remains unclear if low doses of Pac retain its neurotoxic properties or might exhibit unusual effects on neuronal cells. The goal of this study was to analyze the concentration-dependent effect of Pac on isolated and cultured DRG neuronal cells from wild-type and TLR4 knockout mice. Three different morphological parameters were analyzed: the number of neurons which developed neurites, the number of neurites per cell and the total length of neurites per cell. Our data demonstrate that low concentrations of Pac (0.1 nM and 0.5 nM) do not influence the neuronal growth in cultures in both wild type and TLR4 knockout mice. Higher concentrations of Pac (1-100 nM) had a significant effect on DRG neurons from wild type mice, affecting the number of neurons which developed neurites, number of neurites per cell, and the length of neurites. In DRG from TLR4 knockout mice high concentrations of Pac showed a similar effect on the number of neurons which developed neurites and the length of neurites. At the same time, the number of neurites per cell, indicating the process of growth cone initiation, was not affected by high concentrations of Pac. Thus, our data showed that Pac in high concentrations has a significant damaging effect on axonal growth and that this effect is partially mediated through TLR4 pathways. Low doses of Pac are devoid of neuronal toxicity and thus can be safely used in a chemomodulation mode. © 2013 Ustinova et al