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

scenery: Flexible Virtual Reality Visualization on the Java VM

Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. Visualization is often the first step in making sense of data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualizations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools. In this work we introduce scenery, a flexible VR/AR visualization framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features and example applications, such as its use in VR for microscopy, in the biomedical image analysis software Fiji, or for visualizing agent-based simulations.Comment: Added IEEE DOI, version published at VIS 201

A random cell motility gradient downstream of FGF controls elongation of amniote embryos

Vertebrate embryos are characterized by an elongated antero-posterior (AP) body axis, which forms by progressive cell deposition from a posterior growth zone in the embryo. Here, we used tissue ablation in the chicken embryo to demonstrate that the caudal presomitic mesoderm (PSM) has a key role in axis elongation. Using time-lapse microscopy, we analysed the movements of fluorescently labelled cells in the PSM during embryo elongation, which revealed a clear posterior-to-anterior gradient of cell motility and directionality in the PSM. We tracked the movement of the PSM extracellular matrix in parallel with the labelled cells and subtracted the extracellular matrix movement from the global motion of cells. After subtraction, cell motility remained graded but lacked directionality, indicating that the posterior cell movements associated with axis elongation in the PSM are not intrinsic but reflect tissue deformation. The gradient of cell motion along the PSM parallels the fibroblast growth factor (FGF)/mitogen-activated protein kinase (MAPK) gradient1, which has been implicated in the control of cell motility in this tissue2. Both FGF signalling gain- and loss-of-function experiments lead to disruption of the motility gradient and a slowing down of axis elongation. Furthermore, embryos treated with cell movement inhibitors (blebbistatin or RhoK inhibitor), but not cell cycle inhibitors, show a slower axis elongation rate. We propose that the gradient of random cell motility downstream of FGF signalling in the PSM controls posterior elongation in the amniote embryo. Our data indicate that tissue elongation is an emergent property that arises from the collective regulation of graded, random cell motion rather than by the regulation of directionality of individual cellular movements

Curvature-coupling dependence of membrane protein diffusion coefficients

We consider the lateral diffusion of a protein interacting with the curvature of the membrane. The interaction energy is minimized if the particle is at a membrane position with a certain curvature that agrees with the spontaneous curvature of the particle. We employ stochastic simulations that take into account both the thermal fluctuations of the membrane and the diffusive behavior of the particle. In this study we neglect the influence of the particle on the membrane dynamics, thus the membrane dynamics agrees with that of a freely fluctuating membrane. Overall, we find that this curvature-coupling substantially enhances the diffusion coefficient. We compare the ratio of the projected or measured diffusion coefficient and the free intramembrane diffusion coefficient, which is a parameter of the simulations, with analytical results that rely on several approximations. We find that the simulations always lead to a somewhat smaller diffusion coefficient than our analytical approach. A detailed study of the correlations of the forces acting on the particle indicates that the diffusing inclusion tries to follow favorable positions on the membrane, such that forces along the trajectory are on average smaller than they would be for random particle positions.Comment: 16 pages, 8 figure

Parameter identification problems in the modelling of cell motility

We present a novel parameter identification algorithm for the estimation of parameters in models of cell motility using imaging data of migrating cells. Two alternative formulations of the objective functional that measures the difference between the computed and observed data are proposed and the parameter identification problem is formulated as a minimisation problem of nonlinear least squares type. A Levenberg–Marquardt based optimisation method is applied to the solution of the minimisation problem and the details of the implementation are discussed. A number of numerical experiments are presented which illustrate the robustness of the algorithm to parameter identification in the presence of large deformations and noisy data and parameter identification in three dimensional models of cell motility. An application to experimental data is also presented in which we seek to identify parameters in a model for the monopolar growth of fission yeast cells using experimental imaging data. Our numerical tests allow us to compare the method with the two different formulations of the objective functional and we conclude that the results with both objective functionals seem to agree

Tracking Growing Axons by Particle Filtering in 3D+t Fluorescent Two-Photon Microscopy Images

International audienceAnalyzing the behavior of axons in the developing nervous systems is essential for biologists to understand the biological mechanisms underlying how growing axons reach their target cells. The analysis of the motion patterns of growing axons requires detecting axonal tips and tracking their trajectories within complex and large data sets. When performed manually, the tracking task is arduous and time-consuming. To this end, we propose a tracking method, based on the particle filtering technique, to follow the traces of axonal tips that appear as small bright spots in the 3D+t fluorescent two-photon microscopy images exhibiting low signal-to-noise ratios (SNR) and complex background. The proposed tracking method uses multiple dynamic models in the proposal distribution to predict the positions of the growing axons. Furthermore, it incorporates object appearance, motion characteristics of the growing axons, and filament information in the computation of the observation model. The integration of these three sources prevents the tracker from being distracted by other objects that have appearances similar to the tracked objects, resulting in improved accuracy of recovered trajectories. The experimental results obtained from the microscopy images show that the proposed method can successfully estimate trajectories of growing axons, demonstrating its effectiveness even under the presence of noise and complex background

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

<p>Abstract</p> <p>Background</p> <p>Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.</p> <p>Results</p> <p>To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.</p> <p>Conclusions</p> <p>These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.</p

Proteome sequence features carry signatures of the environmental niche of prokaryotes

<p>Abstract</p> <p>Background</p> <p>Prokaryotic environmental adaptations occur at different levels within cells to ensure the preservation of genome integrity, proper protein folding and function as well as membrane fluidity. Although specific composition and structure of cellular components suitable for the variety of extreme conditions has already been postulated, a systematic study describing such adaptations has not yet been performed. We therefore explored whether the environmental niche of a prokaryote could be deduced from the sequence of its proteome. Finally, we aimed at finding the precise differences between proteome sequences of prokaryotes from different environments.</p> <p>Results</p> <p>We analyzed the proteomes of 192 prokaryotes from different habitats. We collected detailed information about the optimal growth conditions of each microorganism. Furthermore, we selected 42 physico-chemical properties of amino acids and computed their values for each proteome. Further, on the same set of features we applied two fundamentally different machine learning methods, Support Vector Machines and Random Forests, to successfully classify between bacteria and archaea, halophiles and non-halophiles, as well as mesophiles, thermophiles and mesothermophiles. Finally, we performed feature selection by using Random Forests.</p> <p>Conclusions</p> <p>To our knowledge, this is the first time that three different classification cases (domain of life, halophilicity and thermophilicity) of proteome adaptation are successfully performed with the same set of 42 features. The characteristic features of a specific adaptation constitute a signature that may help understanding the mechanisms of adaptation to extreme environments.</p