726 research outputs found

    Specific heat of the ideal gas obeying the generalized exclusion statistics

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    We calculate the specific heat of the ideal gas obeying the generalized exclusion statistics (GES) in the continuum model and the tight binding model numerically. In the continuum model of 3-d space, the specific heat increases with statistical parameter at low temperature whereas it decreases with statistical parameter at high temperature. We find that the critical temperature normalized by ÎŒf\mu_f (Fermi energy) is 0.290. The specific heat of 2-d space was known to be independent of gg in the continuum model, but it varies with gg drastically in the tight-binding model. From its unique behavior, identification of GES particles will be possible from the specific heat.Comment: 14 pages, 9 figures, to be published in Eur. Phys. J. B, References and figures added, typos corrected, one section removed and two sections merge

    Polycomb group protein complexes exchange rapidly in living Drosophila

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    Fluorescence recovery after photobleaching (FRAP) microscopy was used to determine the kinetic properties of Polycomb group (PcG) proteins in whole living Drosophila organisms (embryos) and tissues (wing imaginal discs and salivary glands). PcG genes are essential genes in higher eukaryotes responsible for the maintenance of the spatially distinct repression of developmentally important regulators such as the homeotic genes. Their absence, as well as overexpression, causes transformations in the axial organization of the body. Although protein complexes have been isolated in vitro, little is known about their stability or exact mechanism of repression in vivo. We determined the translational diffusion constants of PcG proteins, dissociation constants and residence times for complexes in vivo at different developmental stages. In polytene nuclei, the rate constants suggest heterogeneity of the complexes. Computer simulations with new models for spatially distributed protein complexes were performed in systems showing both diffusion and binding equilibria, and the results compared with our experimental data. We were able to determine forward and reverse rate constants for complex formation. Complexes exchanged within a period of 1-10 minutes, more than an order of magnitude faster than the cell cycle time, ruling out models of repression in which access of transcription activators to the chromatin is limited and demonstrating that long-term repression primarily reflects mass-action chemical equilibria

    Using Machine-Learning to Optimize phase contrast in a Low-Cost Cellphone Microscope

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    Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light's phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-printed smartphone microscope for less than 100 \$ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements

    Using Machine-Learning to Optimize phase contrast in a Low-Cost Cellphone Microscope

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    Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light's phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-printed smartphone microscope for less than 100 \$ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements

    Better than a lens -- Increasing the signal-to-noise ratio through pupil splitting

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    Lenses are designed to fulfill Fermats principle such that all light interferes constructively in its focus, guaranteeing its maximum concentration. It can be shown that imaging via an unmodified full pupil yields the maximum transfer strength for all spatial frequencies transferable by the system. Seemingly also the signal-to-noise ratio (SNR) is optimal. The achievable SNR at a given photon budget is critical especially if that budget is strictly limited as in the case of fluorescence microscopy. In this work we propose a general method which achieves a better SNR for high spatial frequency information of an optical imaging system, without the need to capture more photons. This is achieved by splitting the pupil of an incoherent imaging system such that two (or more) sub-images are simultaneously acquired and computationally recombined. We compare the theoretical performance of split pupil imaging to the non-split scenario and implement the splitting using a tilted elliptical mirror placed at the back-focal-plane (BFP) of a fluorescence widefield microscope

    Twin-Photon Confocal Microscopy

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    A recently introduced two-channel confocal microscope with correlated detection promises up to 50% improvement in transverse spatial resolution [Simon, Sergienko, Optics Express {\bf 18}, 9765 (2010)] via the use of photon correlations. Here we achieve similar results in a different manner, introducing a triple-confocal correlated microscope which exploits the correlations present in optical parametric amplifiers. It is based on tight focusing of pump radiation onto a thin sample positioned in front of a nonlinear crystal, followed by coincidence detection of signal and idler photons, each focused onto a pinhole. This approach offers further resolution enhancement in confocal microscopy

    Defining a Superlens Operating Regime for Imaging Fluorescent Molecules

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    It has been shown that thin metal-based films can at certain frequencies act as planar near-field lenses for certain polarization components. A desirable property of such “lenses” is that they can also enhance and focus some large transverse spatial frequency components which contain sub-diffraction limit details. Over the last decade there has been much work in optimizing designs to reduce effects (such as material losses and surface roughness) that are detrimental to image reconstruction. One design that can reduce some of these undesirable effects, and which has received a fair amount of attention recently, is the stacked metal-dielectric superlens. Here we theoretically explore the imaging ability of such a design for the specific purpose of imaging a fluorescent dye (the common bio-marker GFP) in the vicinity of the superlens surface. Our calculations take into consideration the interaction (damping) of an oscillating electric dipole with the metallic layers in the superlens. We also assume a Gaussian frequency distribution spectrum for the dipole. We treat the metallic-alloy and dielectric-alloy layers separately using an appropriate effective medium theory. The transmission properties are evaluated via Transfer matrix (-matrix) calculations that were performed in the MatLab and MathCad environments. Our study shows that it is in principle possible to image fluorescent molecules using a simple bilayer planar superlens. We find that optimal parameters for such a superlens occur when the peak dipole emission-frequency is slightly offset from the Surface Plasmon resonance frequency of the metal-dielectric interfaces. The best resolution is obtained when the fluorescent molecules are not too close ( nm) or too far ( nm) from the superlens surface. The realization and application of a superlens with the specified design is possible using current nanofabrication techniques. When combined with e.g. a sub-wavelength grating structure (such as in the far-field superlens design previously proposed [1]) or a fast near-field scanning probe, it could provide a means for fast fluorescent imaging with sub-diffraction limit resolution

    One-shot phase-recovery using a cellphone RGB camera on a Jamin-Lebedeff microscope

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    Jamin-Lebedeff (JL) polarization interference microscopy is a classical method for determining the change in the optical path of transparent tissues. Whilst a differential interference contrast (DIC) microscopy interferes an image with itself shifted by half a point spread function, the shear between the object and reference image in a JL-microscope is about half the field of view. The optical path difference (OPD) between the sample and reference region (assumed to be empty) is encoded into a color by white-light interference. From a color-table, the Michel-Levy chart, the OPD can be deduced. In cytology JL-imaging can be used as a way to determine the OPD which closely corresponds to the dry mass per area of cells in a single image. Like in other interference microscopy methods (e.g. holography), we present a phase retrieval method relying on single-shot measurements only, thus allowing real-time quantitative phase measurements. This is achieved by adding several customized 3D-printed parts (e.g. rotational polarization-filter holders) and a modern cellphone with an RGB-camera to the Jamin-Lebedeff setup, thus bringing an old microscope back to life. The algorithm is calibrated using a reference image of a known phase object (e.g. optical fiber). A gradient-descent based inverse problem generates an inverse look-up-table (LUT) which is used to convert the measured RGB signal of a phase-sample into an OPD. To account for possible ambiguities in the phase-map or phase-unwrapping artifacts we introduce a total-variation based regularization. We present results from fixed and living biological samples as well as reference samples for comparison

    Optical photon reassignment microscopy (OPRA)

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    To enhance the resolution of a confocal laser scanning microscope the additional information of a pinhole plane image taken at every excitation scan position can be used (Sheppard 1988). This photon reassignment principle is based on the fact that the most probable position of an emitter is at half way between the nominal focus of the excitation laser and the position corresponding to the (off centre) detection position. Therefore, by reassigning the detected photons to this place, an image with enhanced detection efficiency and resolution is obtained. Here we present optical photon reassignment microscopy (OPRA) which realizes this concept in an all-optical way obviating the need for image-processing. With the help of an additional intermediate optical beam expansion between descanning and a further rescanning of the detected light, an image with the advantages of photon reassignment can be acquired. However, just as in computational photon reassignment, a loss in confocal sectioning performance is caused by working with relatively open pinholes. The OPRA system shares properties such as flexibility and ease of use with a confocal laser scanning microscope, and is therefore expected to be of use for future biomedical routine research

    simpleISM—A straight forward guide to upgrade from confocal to ISM

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    Resolution in a confocal laser scanning microscopes (CLSM) can be improved if the pinhole is closed. But closing the pinhole will deteriorate the signal to noise ratio (SNR). A simple technique to improve the SNR while keeping the resolution same by upgrading the system to an image scanning microscope. In this paper, we explain in detail, based on an Olympus Fluoview 300 system, how a scanning microscope can be upgraded into an image scanning microscope (ISM) using a simple camera-based detector and an Arduino Due providing a galvo driving and camera synchronization signals. We could confirm a resolution improvement as well as superconcentration and made the interesting observation of a reduced influence of laser fluctuations
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